Datasets:

ml6team

/

the-stack-smol-python

like 2

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # Copyright 2020- IBM Inc. All rights reserved # SPDX-License-Identifier: Apache2.0 # """ """ from abc import ABC, abstractproperty, abstractmethod class AbstractType(ABC): @abstractproperty def length(self): pass @abstractmethod def __call__(self): pass def _get_chunk(self): return self.locator.content(self.length)

ambitus/cbexplorer

cbexplorer/types/AbstractType.py

Python

""" Copyright (C) 2018-2019 Intel Corporation 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 openvino.inference_engine as ie from ..infer_raw_results import InferRawResults from ..aggregated_statistics import AggregatedStatistics class CollectResultsCallback: def __init__( self, network: ie.IENetwork, exec_network: ie.ExecutableNetwork, collect_resuls: bool = True, collect_layers: set = None, collect_aggregated_statistics: bool = True, iterations_count: int = 1, dataset_size: int = 1): if not network: raise ValueError("network is not specified") if not exec_network: raise ValueError("exec_network is not specified") self._network = network self._exec_network = exec_network self._aggregated_statistics = None self._iterations_count = iterations_count self._dataset_size = dataset_size self._collect_results = collect_resuls self._collect_layers = collect_layers self._collect_aggregated_statistics = collect_aggregated_statistics self._infer_raw_results = InferRawResults() if collect_resuls else None self._latencies = list() def callback(self, value, latency = None): if self._collect_aggregated_statistics: if not self._aggregated_statistics: self._aggregated_statistics = AggregatedStatistics( iterations_count = self._iterations_count, dataset_size = self._dataset_size) self._aggregated_statistics.add(self._network, self._exec_network, value) if self._collect_results: if self._collect_layers: collect_value = dict() for layer_name in value: if layer_name in self._collect_layers: collect_value[layer_name] = value[layer_name] self._infer_raw_results.add(collect_value) else: self._infer_raw_results.add(value) if latency: self._latencies.append(latency) @property def aggregated_statistics(self) -> AggregatedStatistics: return self._aggregated_statistics @property def infer_raw_result(self) -> InferRawResults: return self._infer_raw_results @property def latencies(self) -> list: return self._latencies def release(self): if self._aggregated_statistics: self._aggregated_statistics.release() if self._infer_raw_results: self._infer_raw_results.release() def get_accuracy_drop(self): return None

ChinHuatAng/dldt

tools/calibration/process_dataset_callbacks/collect_results_callback.py

Python

from django.shortcuts import render from wiki.models import Page from django.views.generic.list import ListView from django.views.generic.detail import DetailView from django.shortcuts import get_object_or_404,render class PageList(ListView): """ This view grabs all the pages out of the database returns a list of each unique wiki page for the user to access on the website through 'list.html' """ model = Page def get(self, request): """ Returns a list of wiki pages. """ pages = Page.objects.all() context = {'pages': pages} return render(request, 'list.html', context=context) class PageDetailView(DetailView): """ This view returns a page for a unique wiki using it's slug as an identifier or a 404 message if the page does not exist """ model = Page def get(self, request, slug): wiki = get_object_or_404(Page, slug=slug) return render(request, 'page.html', {'wiki': wiki}) def post(self, request, slug): pass

ebonnecab/makewiki

wiki/views.py

Python

import policy import traceback import logging import monitoring import itertools from .policy_registry import GetConfig def ApplyPolicies(g): config = GetConfig() enabled = config.get('enabled', True) if enabled is not None and not enabled: return monitoring_db = monitoring.GetDatabase('spinbot') logging.info('Processing issues, repos') for i in itertools.chain(*[g.issues(), g.repos()]): for p in policy.Policies(): if p.applies(i): err = None try: p.apply(g, i) except Exception as _err: logging.warn('Failure applying {} to {}: {}'.format( p, i, traceback.format_exc() )) err = _err monitoring_db.write('issues_handled', { 'value': 1 }, tags={ 'policy': p.id, 'error': err })

pchinmay/spinnaker

spinbot/policy/executor.py

Python

"""Polynomial model class used by agents for building stuff. """ from torch import nn, optim import torch import torch.nn.functional as F from stock_trading_backend.agent.model import Model class NNModel(nn.Module): """Torch neural network model. """ def __init__(self, num_inputs, num_hidden_layers, num_inner_features): """Initializer for linear model. Args: num_inputs: the dimension of input data. num_hidden_layers: the number of hidden layers. num_inner_features: the number of features in the hidden layers """ super(NNModel, self).__init__() self.input_layer = nn.Linear(num_inputs, num_inner_features) hidden_layers = [] for _ in range(num_hidden_layers): hidden_layers.append(nn.Linear(num_inner_features, num_inner_features)) hidden_layers.append(nn.ReLU()) self.hidden_layers = nn.Sequential(*hidden_layers) self.output_layer = nn.Linear(num_inner_features, 1) def forward(self, input_tensor): """Forward pass on the neural network model. Args: input_tensor: the input tensor. Returns: Tensor with model results. """ output = F.relu(self.input_layer(input_tensor)) output = self.hidden_layers(output) output = self.output_layer(output) return output class NeuralNetworkModel(Model): """Neural netowrk model class. """ name = "neural_network_model" def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100): """Initializer for model class. Args: learning_rate: the learning rate of the model. num_hidden_layers: number of hidden layers in the network. num_inner_features: number of features in the hidden layers. """ super(NeuralNetworkModel, self).__init__() self.model = None self.optimizer = None self.criterion = nn.MSELoss() self.learning_rate = learning_rate self.num_hidden_layers = num_hidden_layers self.num_inner_features = num_inner_features self.id_str = "{}_{}_{}_{}".format(self.name, learning_rate, num_hidden_layers, num_inner_features) def _init_model(self, num_inputs): """Initializes internal linear model. Args: num_inputs: number of inputs that model will have. """ self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features) self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) def _predict(self, state_action_tensor): """Use provided information to make a prediction. Args: state_action_tensor: pytorch tensor with state-action values. Returns: Predicted values for observation-action tensors. """ if self.model is None: self._init_model(state_action_tensor.shape[1]) return self.model(state_action_tensor).detach().reshape(-1) def _train(self, state_action_tensor, expected_values_tensor): """Train the model for 1 epoch. Args: state_action_tensor: pytorch tensor with state-action expected_values. expected_values: pytorch tensor with expected values for each state-action. Returns: The loss before trainig. """ if self.model is None: self._init_model(state_action_tensor.shape[1]) self.optimizer.zero_grad() output = self.model(state_action_tensor) loss = self.criterion(output, expected_values_tensor) loss_value = loss.data.item() loss.backward() self.optimizer.step() return loss_value

iryzhkov/stock-trading-backend

stock_trading_backend/agent/neural_network_model.py

Python

#!/usr/bin/python # Copyright (c) 2020, 2021 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_waas_access_rules_facts short_description: Fetches details about one or multiple AccessRules resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple AccessRules resources in Oracle Cloud Infrastructure - Gets the currently configured access rules for the Web Application Firewall configuration of a specified WAAS policy. The order of the access rules is important. The rules will be checked in the order they are specified and the first matching rule will be used. version_added: "2.9.0" author: Oracle (@oracle) options: waas_policy_id: description: - The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the WAAS policy. type: str required: true extends_documentation_fragment: [ oracle.oci.oracle, oracle.oci.oracle_name_option ] """ EXAMPLES = """ - name: List access_rules oci_waas_access_rules_facts: # required waas_policy_id: "ocid1.waaspolicy.oc1..xxxxxxEXAMPLExxxxxx" """ RETURN = """ access_rules: description: - List of AccessRules resources returned: on success type: complex contains: name: description: - The unique name of the access rule. returned: on success type: str sample: name_example criteria: description: - The list of access rule criteria. The rule would be applied only for the requests that matched all the listed conditions. returned: on success type: complex contains: condition: description: - "The criteria the access rule and JavaScript Challenge uses to determine if action should be taken on a request. - **URL_IS:** Matches if the concatenation of request URL path and query is identical to the contents of the `value` field. URL must start with a `/`. - **URL_IS_NOT:** Matches if the concatenation of request URL path and query is not identical to the contents of the `value` field. URL must start with a `/`. - **URL_STARTS_WITH:** Matches if the concatenation of request URL path and query starts with the contents of the `value` field. URL must start with a `/`. - **URL_PART_ENDS_WITH:** Matches if the concatenation of request URL path and query ends with the contents of the `value` field. - **URL_PART_CONTAINS:** Matches if the concatenation of request URL path and query contains the contents of the `value` field. - **URL_REGEX:** Matches if the concatenation of request URL path and query is described by the regular expression in the value field. The value must be a valid regular expression recognized by the PCRE library in Nginx (https://www.pcre.org). - **URL_DOES_NOT_MATCH_REGEX:** Matches if the concatenation of request URL path and query is not described by the regular expression in the `value` field. The value must be a valid regular expression recognized by the PCRE library in Nginx (https://www.pcre.org). - **URL_DOES_NOT_START_WITH:** Matches if the concatenation of request URL path and query does not start with the contents of the `value` field. - **URL_PART_DOES_NOT_CONTAIN:** Matches if the concatenation of request URL path and query does not contain the contents of the `value` field. - **URL_PART_DOES_NOT_END_WITH:** Matches if the concatenation of request URL path and query does not end with the contents of the `value` field. - **IP_IS:** Matches if the request originates from one of the IP addresses contained in the defined address list. The `value` in this case is string with one or multiple IPs or CIDR notations separated by new line symbol \\\\n *Example:* \\"1.1.1.1\\\\n1.1.1.2\\\\n1.2.2.1/30\\" - **IP_IS_NOT:** Matches if the request does not originate from any of the IP addresses contained in the defined address list. The `value` in this case is string with one or multiple IPs or CIDR notations separated by new line symbol \\\\n *Example:* \\"1.1.1.1\\\\n1.1.1.2\\\\n1.2.2.1/30\\" - **IP_IN_LIST:** Matches if the request originates from one of the IP addresses contained in the referenced address list. The `value` in this case is OCID of the address list. - **IP_NOT_IN_LIST:** Matches if the request does not originate from any IP address contained in the referenced address list. The `value` field in this case is OCID of the address list. - **HTTP_HEADER_CONTAINS:** The HTTP_HEADER_CONTAINS criteria is defined using a compound value separated by a colon: a header field name and a header field value. `host:test.example.com` is an example of a criteria value where `host` is the header field name and `test.example.com` is the header field value. A request matches when the header field name is a case insensitive match and the header field value is a case insensitive, substring match. *Example:* With a criteria value of `host:test.example.com`, where `host` is the name of the field and `test.example.com` is the value of the host field, a request with the header values, `Host: www.test.example.com` will match, where as a request with header values of `host: www.example.com` or `host: test.sub.example.com` will not match. - **HTTP_METHOD_IS:** Matches if the request method is identical to one of the values listed in field. The `value` in this case is string with one or multiple HTTP methods separated by new line symbol \\\\n The list of available methods: `GET`, `HEAD`, `POST`, `PUT`, `DELETE`, `CONNECT`, `OPTIONS`, `TRACE`, `PATCH`" - "*Example:* \\"GET\\\\nPOST\\"" - "- **HTTP_METHOD_IS_NOT:** Matches if the request is not identical to any of the contents of the `value` field. The `value` in this case is string with one or multiple HTTP methods separated by new line symbol \\\\n The list of available methods: `GET`, `HEAD`, `POST`, `PUT`, `DELETE`, `CONNECT`, `OPTIONS`, `TRACE`, `PATCH`" - "*Example:* \\"GET\\\\nPOST\\"" - "- **COUNTRY_IS:** Matches if the request originates from one of countries in the `value` field. The `value` in this case is string with one or multiple countries separated by new line symbol \\\\n Country codes are in ISO 3166-1 alpha-2 format. For a list of codes, see L(ISO's website,https://www.iso.org/obp/ui/#search/code/). *Example:* \\"AL\\\\nDZ\\\\nAM\\" - **COUNTRY_IS_NOT:** Matches if the request does not originate from any of countries in the `value` field. The `value` in this case is string with one or multiple countries separated by new line symbol \\\\n Country codes are in ISO 3166-1 alpha-2 format. For a list of codes, see L(ISO's website,https://www.iso.org/obp/ui/#search/code/). *Example:* \\"AL\\\\nDZ\\\\nAM\\" - **USER_AGENT_IS:** Matches if the requesting user agent is identical to the contents of the `value` field. *Example:* `Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:35.0) Gecko/20100101 Firefox/35.0` - **USER_AGENT_IS_NOT:** Matches if the requesting user agent is not identical to the contents of the `value` field. *Example:* `Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:35.0) Gecko/20100101 Firefox/35.0`" returned: on success type: str sample: URL_IS value: description: - The criteria value. returned: on success type: str sample: value_example is_case_sensitive: description: - When enabled, the condition will be matched with case-sensitive rules. returned: on success type: bool sample: true action: description: - The action to take when the access criteria are met for a rule. If unspecified, defaults to `ALLOW`. - "- **ALLOW:** Takes no action, just logs the request." - "- **DETECT:** Takes no action, but creates an alert for the request." - "- **BLOCK:** Blocks the request by returning specified response code or showing error page." - "- **BYPASS:** Bypasses some or all challenges." - "- **REDIRECT:** Redirects the request to the specified URL. These fields are required when `REDIRECT` is selected: `redirectUrl`, `redirectResponseCode`." - "- **SHOW_CAPTCHA:** Show a CAPTCHA Challenge page instead of the requested page." - Regardless of action, no further rules are processed once a rule is matched. returned: on success type: str sample: ALLOW block_action: description: - The method used to block requests if `action` is set to `BLOCK` and the access criteria are met. If unspecified, defaults to `SET_RESPONSE_CODE`. returned: on success type: str sample: SET_RESPONSE_CODE block_response_code: description: - "The response status code to return when `action` is set to `BLOCK`, `blockAction` is set to `SET_RESPONSE_CODE`, and the access criteria are met. If unspecified, defaults to `403`. The list of available response codes: `200`, `201`, `202`, `204`, `206`, `300`, `301`, `302`, `303`, `304`, `307`, `400`, `401`, `403`, `404`, `405`, `408`, `409`, `411`, `412`, `413`, `414`, `415`, `416`, `422`, `444`, `494`, `495`, `496`, `497`, `499`, `500`, `501`, `502`, `503`, `504`, `507`." returned: on success type: int sample: 56 block_error_page_message: description: - The message to show on the error page when `action` is set to `BLOCK`, `blockAction` is set to `SHOW_ERROR_PAGE`, and the access criteria are met. If unspecified, defaults to 'Access to the website is blocked.' returned: on success type: str sample: block_error_page_message_example block_error_page_code: description: - The error code to show on the error page when `action` is set to `BLOCK`, `blockAction` is set to `SHOW_ERROR_PAGE`, and the access criteria are met. If unspecified, defaults to 'Access rules'. returned: on success type: str sample: block_error_page_code_example block_error_page_description: description: - The description text to show on the error page when `action` is set to `BLOCK`, `blockAction` is set to `SHOW_ERROR_PAGE`, and the access criteria are met. If unspecified, defaults to 'Access blocked by website owner. Please contact support.' returned: on success type: str sample: block_error_page_description_example bypass_challenges: description: - The list of challenges to bypass when `action` is set to `BYPASS`. If unspecified or empty, all challenges are bypassed. - "- **JS_CHALLENGE:** Bypasses JavaScript Challenge." - "- **DEVICE_FINGERPRINT_CHALLENGE:** Bypasses Device Fingerprint Challenge." - "- **HUMAN_INTERACTION_CHALLENGE:** Bypasses Human Interaction Challenge." - "- **CAPTCHA:** Bypasses CAPTCHA Challenge." returned: on success type: list sample: [] redirect_url: description: - The target to which the request should be redirected, represented as a URI reference. Required when `action` is `REDIRECT`. returned: on success type: str sample: redirect_url_example redirect_response_code: description: - The response status code to return when `action` is set to `REDIRECT`. - "- **MOVED_PERMANENTLY:** Used for designating the permanent movement of a page (numerical code - 301)." - "- **FOUND:** Used for designating the temporary movement of a page (numerical code - 302)." returned: on success type: str sample: MOVED_PERMANENTLY captcha_title: description: - The title used when showing a CAPTCHA challenge when `action` is set to `SHOW_CAPTCHA` and the request is challenged. returned: on success type: str sample: captcha_title_example captcha_header: description: - The text to show in the header when showing a CAPTCHA challenge when `action` is set to `SHOW_CAPTCHA` and the request is challenged. returned: on success type: str sample: captcha_header_example captcha_footer: description: - The text to show in the footer when showing a CAPTCHA challenge when `action` is set to `SHOW_CAPTCHA` and the request is challenged. returned: on success type: str sample: captcha_footer_example captcha_submit_label: description: - The text to show on the label of the CAPTCHA challenge submit button when `action` is set to `SHOW_CAPTCHA` and the request is challenged. returned: on success type: str sample: captcha_submit_label_example response_header_manipulation: description: - An object that represents an action to apply to an HTTP response headers if all rule criteria will be matched regardless of `action` value. returned: on success type: complex contains: action: description: - "" returned: on success type: str sample: EXTEND_HTTP_RESPONSE_HEADER header: description: - A header field name that conforms to RFC 7230. - "Example: `example_header_name`" returned: on success type: str sample: header_example value: description: - A header field value that conforms to RFC 7230. - "Example: `example_value`" returned: on success type: str sample: value_example sample: [{ "name": "name_example", "criteria": [{ "condition": "URL_IS", "value": "value_example", "is_case_sensitive": true }], "action": "ALLOW", "block_action": "SET_RESPONSE_CODE", "block_response_code": 56, "block_error_page_message": "block_error_page_message_example", "block_error_page_code": "block_error_page_code_example", "block_error_page_description": "block_error_page_description_example", "bypass_challenges": [], "redirect_url": "redirect_url_example", "redirect_response_code": "MOVED_PERMANENTLY", "captcha_title": "captcha_title_example", "captcha_header": "captcha_header_example", "captcha_footer": "captcha_footer_example", "captcha_submit_label": "captcha_submit_label_example", "response_header_manipulation": [{ "action": "EXTEND_HTTP_RESPONSE_HEADER", "header": "header_example", "value": "value_example" }] }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.waas import WaasClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class AccessRulesFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: list""" def get_required_params_for_list(self): return [ "waas_policy_id", ] def list_resources(self): optional_list_method_params = [ "name", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_access_rules, waas_policy_id=self.module.params.get("waas_policy_id"), **optional_kwargs ) AccessRulesFactsHelperCustom = get_custom_class("AccessRulesFactsHelperCustom") class ResourceFactsHelper(AccessRulesFactsHelperCustom, AccessRulesFactsHelperGen): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict(waas_policy_id=dict(type="str", required=True), name=dict(type="str"),) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="access_rules", service_client_class=WaasClient, namespace="waas", ) result = [] if resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(access_rules=result) if __name__ == "__main__": main()

oracle/oci-ansible-collection

plugins/modules/oci_waas_access_rules_facts.py

Python

import enum import warnings from optuna import exceptions from optuna import logging from optuna import type_checking if type_checking.TYPE_CHECKING: from datetime import datetime # NOQA from typing import Any # NOQA from typing import Dict # NOQA from typing import Optional # NOQA from optuna.distributions import BaseDistribution # NOQA class TrialState(enum.Enum): """State of a :class:`~optuna.trial.Trial`. Attributes: RUNNING: The :class:`~optuna.trial.Trial` is running. COMPLETE: The :class:`~optuna.trial.Trial` has been finished without any error. PRUNED: The :class:`~optuna.trial.Trial` has been pruned with :class:`~optuna.exceptions.TrialPruned`. FAIL: The :class:`~optuna.trial.Trial` has failed due to an uncaught error. """ RUNNING = 0 COMPLETE = 1 PRUNED = 2 FAIL = 3 WAITING = 4 def __repr__(self): # type: () -> str return str(self) def is_finished(self): # type: () -> bool return self != TrialState.RUNNING and self != TrialState.WAITING class StudyDirection(enum.Enum): """Direction of a :class:`~optuna.study.Study`. Attributes: NOT_SET: Direction has not been set. MINIMIZE: :class:`~optuna.study.Study` minimizes the objective function. MAXIMIZE: :class:`~optuna.study.Study` maximizes the objective function. """ NOT_SET = 0 MINIMIZE = 1 MAXIMIZE = 2 class FrozenTrial(object): """Status and results of a :class:`~optuna.trial.Trial`. Attributes: number: Unique and consecutive number of :class:`~optuna.trial.Trial` for each :class:`~optuna.study.Study`. Note that this field uses zero-based numbering. state: :class:`TrialState` of the :class:`~optuna.trial.Trial`. value: Objective value of the :class:`~optuna.trial.Trial`. datetime_start: Datetime where the :class:`~optuna.trial.Trial` started. datetime_complete: Datetime where the :class:`~optuna.trial.Trial` finished. params: Dictionary that contains suggested parameters. distributions: Dictionary that contains the distributions of :attr:`params`. user_attrs: Dictionary that contains the attributes of the :class:`~optuna.trial.Trial` set with :func:`optuna.trial.Trial.set_user_attr`. intermediate_values: Intermediate objective values set with :func:`optuna.trial.Trial.report`. """ def __init__( self, number, # type: int state, # type: TrialState value, # type: Optional[float] datetime_start, # type: Optional[datetime] datetime_complete, # type: Optional[datetime] params, # type: Dict[str, Any] distributions, # type: Dict[str, BaseDistribution] user_attrs, # type: Dict[str, Any] system_attrs, # type: Dict[str, Any] intermediate_values, # type: Dict[int, float] trial_id, # type: int ): # type: (...) -> None self.number = number self.state = state self.value = value self.datetime_start = datetime_start self.datetime_complete = datetime_complete self.params = params self.user_attrs = user_attrs self.system_attrs = system_attrs self.intermediate_values = intermediate_values self._distributions = distributions self._trial_id = trial_id # Ordered list of fields required for `__repr__`, `__hash__` and dataframe creation. # TODO(hvy): Remove this list in Python 3.6 as the order of `self.__dict__` is preserved. _ordered_fields = [ 'number', 'value', 'datetime_start', 'datetime_complete', 'params', '_distributions', 'user_attrs', 'system_attrs', 'intermediate_values', '_trial_id', 'state', ] def __eq__(self, other): # type: (Any) -> bool if not isinstance(other, FrozenTrial): return NotImplemented return other.__dict__ == self.__dict__ def __lt__(self, other): # type: (Any) -> bool if not isinstance(other, FrozenTrial): return NotImplemented return self.number < other.number def __le__(self, other): # type: (Any) -> bool if not isinstance(other, FrozenTrial): return NotImplemented return self.number <= other.number def __hash__(self): # type: () -> int return hash(tuple(getattr(self, field) for field in self._ordered_fields)) def __repr__(self): # type: () -> str return ('{cls}({kwargs})'.format( cls=self.__class__.__name__, kwargs=', '.join('{field}={value}'.format( field=field if not field.startswith('_') else field[1:], value=repr(getattr(self, field))) for field in self._ordered_fields))) def _validate(self): # type: () -> None if self.datetime_start is None: raise ValueError('`datetime_start` is supposed to be set.') if self.state.is_finished(): if self.datetime_complete is None: raise ValueError('`datetime_complete` is supposed to be set for a finished trial.') else: if self.datetime_complete is not None: raise ValueError( '`datetime_complete` is supposed to not be set for a finished trial.') if self.state == TrialState.COMPLETE and self.value is None: raise ValueError('`value` is supposed to be set for a complete trial.') if set(self.params.keys()) != set(self.distributions.keys()): raise ValueError('Inconsistent parameters {} and distributions {}.'.format( set(self.params.keys()), set(self.distributions.keys()))) for param_name, param_value in self.params.items(): distribution = self.distributions[param_name] param_value_in_internal_repr = distribution.to_internal_repr(param_value) if not distribution._contains(param_value_in_internal_repr): raise ValueError( "The value {} of parameter '{}' isn't contained in the distribution {}.". format(param_value, param_name, distribution)) @property def distributions(self): # type: () -> Dict[str, BaseDistribution] """Return the distributions for this trial. Returns: The distributions. """ return self._distributions @distributions.setter def distributions(self, value): # type: (Dict[str, BaseDistribution]) -> None """Set the distributions for this trial. Args: value: The distributions. """ self._distributions = value @property def trial_id(self): # type: () -> int """Return the trial ID. .. deprecated:: 0.19.0 The direct use of this attribute is deprecated and it is recommended that you use :attr:`~optuna.trial.FrozenTrial.number` instead. Returns: The trial ID. """ warnings.warn( 'The use of `FrozenTrial.trial_id` is deprecated. ' 'Please use `FrozenTrial.number` instead.', DeprecationWarning) logger = logging.get_logger(__name__) logger.warning( 'The use of `FrozenTrial.trial_id` is deprecated. ' 'Please use `FrozenTrial.number` instead.') return self._trial_id @property def last_step(self): # type: () -> Optional[int] if len(self.intermediate_values) == 0: return None else: return max(self.intermediate_values.keys()) class StudySummary(object): """Basic attributes and aggregated results of a :class:`~optuna.study.Study`. See also :func:`optuna.study.get_all_study_summaries`. Attributes: study_name: Name of the :class:`~optuna.study.Study`. direction: :class:`StudyDirection` of the :class:`~optuna.study.Study`. best_trial: :class:`FrozenTrial` with best objective value in the :class:`~optuna.study.Study`. user_attrs: Dictionary that contains the attributes of the :class:`~optuna.study.Study` set with :func:`optuna.study.Study.set_user_attr`. system_attrs: Dictionary that contains the attributes of the :class:`~optuna.study.Study` internally set by Optuna. n_trials: The number of trials ran in the :class:`~optuna.study.Study`. datetime_start: Datetime where the :class:`~optuna.study.Study` started. """ def __init__( self, study_name, # type: str direction, # type: StudyDirection best_trial, # type: Optional[FrozenTrial] user_attrs, # type: Dict[str, Any] system_attrs, # type: Dict[str, Any] n_trials, # type: int datetime_start, # type: Optional[datetime] study_id, # type: int ): # type: (...) -> None self.study_name = study_name self.direction = direction self.best_trial = best_trial self.user_attrs = user_attrs self.system_attrs = system_attrs self.n_trials = n_trials self.datetime_start = datetime_start self._study_id = study_id def __eq__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return other.__dict__ == self.__dict__ def __lt__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return self._study_id < other._study_id def __le__(self, other): # type: (Any) -> bool if not isinstance(other, StudySummary): return NotImplemented return self._study_id <= other._study_id @property def study_id(self): # type: () -> int """Return the study ID. .. deprecated:: 0.20.0 The direct use of this attribute is deprecated and it is recommended that you use :attr:`~optuna.structs.StudySummary.study_name` instead. Returns: The study ID. """ message = 'The use of `StudySummary.study_id` is deprecated. ' \ 'Please use `StudySummary.study_name` instead.' warnings.warn(message, DeprecationWarning) logger = logging.get_logger(__name__) logger.warning(message) return self._study_id class TrialPruned(exceptions.TrialPruned): """Exception for pruned trials. .. deprecated:: 0.19.0 This class was moved to :mod:`~optuna.exceptions`. Please use :class:`~optuna.exceptions.TrialPruned` instead. """ def __init__(self, *args, **kwargs): # type: (Any, Any) -> None message = 'The use of `optuna.structs.TrialPruned` is deprecated. ' \ 'Please use `optuna.exceptions.TrialPruned` instead.' warnings.warn(message, DeprecationWarning) logger = logging.get_logger(__name__) logger.warning(message)

VladSkripniuk/optuna

optuna/structs.py

Python

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from .. import utilities, tables class UsagePlan(pulumi.CustomResource): api_stages: pulumi.Output[list] """ The associated API stages of the usage plan. * `api_id` (`str`) - API Id of the associated API stage in a usage plan. * `stage` (`str`) - API stage name of the associated API stage in a usage plan. """ arn: pulumi.Output[str] """ Amazon Resource Name (ARN) """ description: pulumi.Output[str] """ The description of a usage plan. """ name: pulumi.Output[str] """ The name of the usage plan. """ product_code: pulumi.Output[str] """ The AWS Markeplace product identifier to associate with the usage plan as a SaaS product on AWS Marketplace. """ quota_settings: pulumi.Output[dict] """ The quota settings of the usage plan. * `limit` (`float`) - The maximum number of requests that can be made in a given time period. * `offset` (`float`) - The number of requests subtracted from the given limit in the initial time period. * `period` (`str`) - The time period in which the limit applies. Valid values are "DAY", "WEEK" or "MONTH". """ tags: pulumi.Output[dict] """ Key-value map of resource tags """ throttle_settings: pulumi.Output[dict] """ The throttling limits of the usage plan. * `burstLimit` (`float`) - The API request burst limit, the maximum rate limit over a time ranging from one to a few seconds, depending upon whether the underlying token bucket is at its full capacity. * `rate_limit` (`float`) - The API request steady-state rate limit. """ def __init__(__self__, resource_name, opts=None, api_stages=None, description=None, name=None, product_code=None, quota_settings=None, tags=None, throttle_settings=None, __props__=None, __name__=None, __opts__=None): """ Provides an API Gateway Usage Plan. ## Example Usage ```python import pulumi import pulumi_aws as aws myapi = aws.apigateway.RestApi("myapi") dev = aws.apigateway.Deployment("dev", rest_api=myapi.id, stage_name="dev") prod = aws.apigateway.Deployment("prod", rest_api=myapi.id, stage_name="prod") my_usage_plan = aws.apigateway.UsagePlan("myUsagePlan", api_stages=[ { "api_id": myapi.id, "stage": dev.stage_name, }, { "api_id": myapi.id, "stage": prod.stage_name, }, ], description="my description", product_code="MYCODE", quota_settings={ "limit": 20, "offset": 2, "period": "WEEK", }, throttle_settings={ "burstLimit": 5, "rate_limit": 10, }) ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[list] api_stages: The associated API stages of the usage plan. :param pulumi.Input[str] description: The description of a usage plan. :param pulumi.Input[str] name: The name of the usage plan. :param pulumi.Input[str] product_code: The AWS Markeplace product identifier to associate with the usage plan as a SaaS product on AWS Marketplace. :param pulumi.Input[dict] quota_settings: The quota settings of the usage plan. :param pulumi.Input[dict] tags: Key-value map of resource tags :param pulumi.Input[dict] throttle_settings: The throttling limits of the usage plan. The **api_stages** object supports the following: * `api_id` (`pulumi.Input[str]`) - API Id of the associated API stage in a usage plan. * `stage` (`pulumi.Input[str]`) - API stage name of the associated API stage in a usage plan. The **quota_settings** object supports the following: * `limit` (`pulumi.Input[float]`) - The maximum number of requests that can be made in a given time period. * `offset` (`pulumi.Input[float]`) - The number of requests subtracted from the given limit in the initial time period. * `period` (`pulumi.Input[str]`) - The time period in which the limit applies. Valid values are "DAY", "WEEK" or "MONTH". The **throttle_settings** object supports the following: * `burstLimit` (`pulumi.Input[float]`) - The API request burst limit, the maximum rate limit over a time ranging from one to a few seconds, depending upon whether the underlying token bucket is at its full capacity. * `rate_limit` (`pulumi.Input[float]`) - The API request steady-state rate limit. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['api_stages'] = api_stages __props__['description'] = description __props__['name'] = name __props__['product_code'] = product_code __props__['quota_settings'] = quota_settings __props__['tags'] = tags __props__['throttle_settings'] = throttle_settings __props__['arn'] = None super(UsagePlan, __self__).__init__( 'aws:apigateway/usagePlan:UsagePlan', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, api_stages=None, arn=None, description=None, name=None, product_code=None, quota_settings=None, tags=None, throttle_settings=None): """ Get an existing UsagePlan resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[list] api_stages: The associated API stages of the usage plan. :param pulumi.Input[str] arn: Amazon Resource Name (ARN) :param pulumi.Input[str] description: The description of a usage plan. :param pulumi.Input[str] name: The name of the usage plan. :param pulumi.Input[str] product_code: The AWS Markeplace product identifier to associate with the usage plan as a SaaS product on AWS Marketplace. :param pulumi.Input[dict] quota_settings: The quota settings of the usage plan. :param pulumi.Input[dict] tags: Key-value map of resource tags :param pulumi.Input[dict] throttle_settings: The throttling limits of the usage plan. The **api_stages** object supports the following: * `api_id` (`pulumi.Input[str]`) - API Id of the associated API stage in a usage plan. * `stage` (`pulumi.Input[str]`) - API stage name of the associated API stage in a usage plan. The **quota_settings** object supports the following: * `limit` (`pulumi.Input[float]`) - The maximum number of requests that can be made in a given time period. * `offset` (`pulumi.Input[float]`) - The number of requests subtracted from the given limit in the initial time period. * `period` (`pulumi.Input[str]`) - The time period in which the limit applies. Valid values are "DAY", "WEEK" or "MONTH". The **throttle_settings** object supports the following: * `burstLimit` (`pulumi.Input[float]`) - The API request burst limit, the maximum rate limit over a time ranging from one to a few seconds, depending upon whether the underlying token bucket is at its full capacity. * `rate_limit` (`pulumi.Input[float]`) - The API request steady-state rate limit. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["api_stages"] = api_stages __props__["arn"] = arn __props__["description"] = description __props__["name"] = name __props__["product_code"] = product_code __props__["quota_settings"] = quota_settings __props__["tags"] = tags __props__["throttle_settings"] = throttle_settings return UsagePlan(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

JakeGinnivan/pulumi-aws

sdk/python/pulumi_aws/apigateway/usage_plan.py

Python

# -*- coding: utf-8 -*- """Tests for NullTask plugin.""" import unittest from pomito.plugins.task import nulltask, TaskPlugin class NullTaskTests(unittest.TestCase): """Tests for NullTask.""" def setUp(self): self.task = nulltask.NullTask(None) def test_nulltask_is_a_task_plugin(self): assert issubclass(nulltask.NullTask, TaskPlugin) def test_nulltask_initialize_should_not_throw(self): self.task.initialize() def test_nulltask_get_tasks_returns_empty_list(self): assert len(self.task.get_tasks()) == 0 def test_nulltask_get_tasks_by_filter_returns_empty_list(self): assert len(self.task.get_tasks_by_filter("")) == 0 def test_nulltask_get_task_by_id_returns_none(self): assert self.task.get_task_by_id(1) is None

codito/pomito

tests/plugins/task/test_nulltask.py

Python

#!/usr/bin/python # Copyright (c) 2017, 2020 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_network_ip_sec_connection_device_status_facts short_description: Fetches details about a IpSecConnectionDeviceStatus resource in Oracle Cloud Infrastructure description: - Fetches details about a IpSecConnectionDeviceStatus resource in Oracle Cloud Infrastructure - Deprecated. To get the tunnel status, instead use L(GetIPSecConnectionTunnel,https://docs.cloud.oracle.com/en-us/iaas/api/#/en/iaas/20160918/IPSecConnectionTunnel/GetIPSecConnectionTunnel). version_added: "2.9" author: Oracle (@oracle) options: ipsc_id: description: - The OCID of the IPSec connection. type: str aliases: ["id"] required: true extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: Get a specific ip_sec_connection_device_status oci_network_ip_sec_connection_device_status_facts: ipsc_id: ocid1.ipsc.oc1..xxxxxxEXAMPLExxxxxx """ RETURN = """ ip_sec_connection_device_status: description: - IpSecConnectionDeviceStatus resource returned: on success type: complex contains: compartment_id: description: - The OCID of the compartment containing the IPSec connection. returned: on success type: string sample: ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx id: description: - The IPSec connection's Oracle ID (OCID). returned: on success type: string sample: ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx time_created: description: - The date and time the IPSec connection was created, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339). - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z tunnels: description: - Two L(TunnelStatus,https://docs.cloud.oracle.com/en-us/iaas/api/#/en/iaas/20160918/TunnelStatus/) objects. returned: on success type: complex contains: ip_address: description: - The IP address of Oracle's VPN headend. - "Example: `203.0.113.50`" returned: on success type: string sample: 203.0.113.50 lifecycle_state: description: - The tunnel's current state. returned: on success type: string sample: UP time_created: description: - The date and time the IPSec connection was created, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339). - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z time_state_modified: description: - When the state of the tunnel last changed, in the format defined by L(RFC3339,https://tools.ietf.org/html/rfc3339). - "Example: `2016-08-25T21:10:29.600Z`" returned: on success type: string sample: 2016-08-25T21:10:29.600Z sample: { "compartment_id": "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx", "id": "ocid1.resource.oc1..xxxxxxEXAMPLExxxxxx", "time_created": "2016-08-25T21:10:29.600Z", "tunnels": [{ "ip_address": "203.0.113.50", "lifecycle_state": "UP", "time_created": "2016-08-25T21:10:29.600Z", "time_state_modified": "2016-08-25T21:10:29.600Z" }] } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.core import VirtualNetworkClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class IpSecConnectionDeviceStatusFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: get""" def get_required_params_for_get(self): return [ "ipsc_id", ] def get_resource(self): return oci_common_utils.call_with_backoff( self.client.get_ip_sec_connection_device_status, ipsc_id=self.module.params.get("ipsc_id"), ) IpSecConnectionDeviceStatusFactsHelperCustom = get_custom_class( "IpSecConnectionDeviceStatusFactsHelperCustom" ) class ResourceFactsHelper( IpSecConnectionDeviceStatusFactsHelperCustom, IpSecConnectionDeviceStatusFactsHelperGen, ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update(dict(ipsc_id=dict(aliases=["id"], type="str", required=True),)) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="ip_sec_connection_device_status", service_client_class=VirtualNetworkClient, namespace="core", ) result = [] if resource_facts_helper.is_get(): result = resource_facts_helper.get() elif resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(ip_sec_connection_device_status=result) if __name__ == "__main__": main()

A7rMtWE57x/oci-ansible-collection

plugins/modules/oci_network_ip_sec_connection_device_status_facts.py

Python

from plotly_study.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType import copy as _copy class Font(_BaseTraceHierarchyType): # color # ----- @property def color(self): """ The 'color' property is a color and may be specified as: - A hex string (e.g. '#ff0000') - An rgb/rgba string (e.g. 'rgb(255,0,0)') - An hsl/hsla string (e.g. 'hsl(0,100%,50%)') - An hsv/hsva string (e.g. 'hsv(0,100%,100%)') - A named CSS color: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgrey, darkgreen, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, grey, green, greenyellow, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgrey, lightgreen, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, red, rosybrown, royalblue, rebeccapurple, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen - A list or array of any of the above Returns ------- str|numpy.ndarray """ return self["color"] @color.setter def color(self, val): self["color"] = val # colorsrc # -------- @property def colorsrc(self): """ Sets the source reference on plot.ly for color . The 'colorsrc' property must be specified as a string or as a plotly_study.grid_objs.Column object Returns ------- str """ return self["colorsrc"] @colorsrc.setter def colorsrc(self, val): self["colorsrc"] = val # family # ------ @property def family(self): """ HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on- premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". The 'family' property is a string and must be specified as: - A non-empty string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self["family"] @family.setter def family(self, val): self["family"] = val # familysrc # --------- @property def familysrc(self): """ Sets the source reference on plot.ly for family . The 'familysrc' property must be specified as a string or as a plotly_study.grid_objs.Column object Returns ------- str """ return self["familysrc"] @familysrc.setter def familysrc(self, val): self["familysrc"] = val # size # ---- @property def size(self): """ The 'size' property is a number and may be specified as: - An int or float in the interval [1, inf] - A tuple, list, or one-dimensional numpy array of the above Returns ------- int|float|numpy.ndarray """ return self["size"] @size.setter def size(self, val): self["size"] = val # sizesrc # ------- @property def sizesrc(self): """ Sets the source reference on plot.ly for size . The 'sizesrc' property must be specified as a string or as a plotly_study.grid_objs.Column object Returns ------- str """ return self["sizesrc"] @sizesrc.setter def sizesrc(self, val): self["sizesrc"] = val # property parent name # -------------------- @property def _parent_path_str(self): return "streamtube.hoverlabel" # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . """ def __init__( self, arg=None, color=None, colorsrc=None, family=None, familysrc=None, size=None, sizesrc=None, **kwargs ): """ Construct a new Font object Sets the font used in hover labels. Parameters ---------- arg dict of properties compatible with this constructor or an instance of plotly_study.graph_objs.streamtube.hoverlabel.Font color colorsrc Sets the source reference on plot.ly for color . family HTML font family - the typeface that will be applied by the web browser. The web browser will only be able to apply a font if it is available on the system which it operates. Provide multiple font families, separated by commas, to indicate the preference in which to apply fonts if they aren't available on the system. The plotly service (at https://plot.ly or on-premise) generates images on a server, where only a select number of fonts are installed and supported. These include "Arial", "Balto", "Courier New", "Droid Sans",, "Droid Serif", "Droid Sans Mono", "Gravitas One", "Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow", "Raleway", "Times New Roman". familysrc Sets the source reference on plot.ly for family . size sizesrc Sets the source reference on plot.ly for size . Returns ------- Font """ super(Font, self).__init__("font") # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly_study.graph_objs.streamtube.hoverlabel.Font constructor must be a dict or an instance of plotly_study.graph_objs.streamtube.hoverlabel.Font""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) # Import validators # ----------------- from plotly_study.validators.streamtube.hoverlabel import font as v_font # Initialize validators # --------------------- self._validators["color"] = v_font.ColorValidator() self._validators["colorsrc"] = v_font.ColorsrcValidator() self._validators["family"] = v_font.FamilyValidator() self._validators["familysrc"] = v_font.FamilysrcValidator() self._validators["size"] = v_font.SizeValidator() self._validators["sizesrc"] = v_font.SizesrcValidator() # Populate data dict with properties # ---------------------------------- _v = arg.pop("color", None) self["color"] = color if color is not None else _v _v = arg.pop("colorsrc", None) self["colorsrc"] = colorsrc if colorsrc is not None else _v _v = arg.pop("family", None) self["family"] = family if family is not None else _v _v = arg.pop("familysrc", None) self["familysrc"] = familysrc if familysrc is not None else _v _v = arg.pop("size", None) self["size"] = size if size is not None else _v _v = arg.pop("sizesrc", None) self["sizesrc"] = sizesrc if sizesrc is not None else _v # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False __all__ = ["Font"]

lucasiscovici/plotly_py

plotly_study/graph_objs/streamtube/hoverlabel/__init__.py

Python

class MGDHCPSettings(object): def __init__(self, session): super(MGDHCPSettings, self).__init__() self._session = session def getNetworkCellularGatewaySettingsDhcp(self, networkId: str): """ **List common DHCP settings of MGs** https://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp - networkId (string) """ metadata = { 'tags': ['MG DHCP settings'], 'operation': 'getNetworkCellularGatewaySettingsDhcp', } resource = f'/networks/{networkId}/cellularGateway/settings/dhcp' return self._session.get(metadata, resource) def updateNetworkCellularGatewaySettingsDhcp(self, networkId: str, **kwargs): """ **Update common DHCP settings of MGs** https://developer.cisco.com/meraki/api/#!update-network-cellular-gateway-settings-dhcp - networkId (string) - dhcpLeaseTime (string): DHCP Lease time for all MG of the network. It can be '30 minutes', '1 hour', '4 hours', '12 hours', '1 day' or '1 week'. - dnsNameservers (string): DNS name servers mode for all MG of the network. It can take 4 different values: 'upstream_dns', 'google_dns', 'opendns', 'custom'. - dnsCustomNameservers (array): list of fixed IP representing the the DNS Name servers when the mode is 'custom' """ kwargs.update(locals()) metadata = { 'tags': ['MG DHCP settings'], 'operation': 'updateNetworkCellularGatewaySettingsDhcp', } resource = f'/networks/{networkId}/cellularGateway/settings/dhcp' body_params = ['dhcpLeaseTime', 'dnsNameservers', 'dnsCustomNameservers'] payload = {k: v for (k, v) in kwargs.items() if k in body_params} return self._session.put(metadata, resource, payload)

NoFliesOnYou/dashboard-api-python

meraki/api/mg_dhcp_settings.py

Python

"""Config Port Stats message tests.""" from pyof.v0x04.controller2switch.common import PortStats from tests.test_struct import TestStruct class TestPortStats(TestStruct): """Config Port Stats message tests.""" @classmethod def setUpClass(cls): """Configure raw file and its object in parent class (TestDump).""" super().setUpClass() super().set_raw_dump_file('v0x04', 'ofpt_port_stats') super().set_raw_dump_object(PortStats) super().set_minimum_size(112)

smythtech/python-openflow-legacy

build/lib/tests/v0x04/test_controller2switch/test_port_stats.py

Python

"""Report routes.""" import os from urllib import parse import bottle import requests from pymongo.database import Database from database import sessions from database.datamodels import latest_datamodel from database.measurements import recent_measurements_by_metric_uuid from database.reports import insert_new_report, latest_reports from initialization.report import import_json_report from model.actions import copy_report from model.data import ReportData from model.transformations import hide_credentials, summarize_report from server_utilities.functions import report_date_time, uuid from server_utilities.type import ReportId @bottle.post("/api/v3/report/import") def post_report_import(database: Database): """Import a preconfigured report into the database.""" report = dict(bottle.request.json) result = import_json_report(database, report) result["new_report_uuid"] = report["report_uuid"] return result @bottle.post("/api/v3/report/new") def post_report_new(database: Database): """Add a new report.""" report_uuid = uuid() user = sessions.user(database) report = dict( report_uuid=report_uuid, title="New report", subjects={}, delta=dict(uuids=[report_uuid], email=user["email"], description=f"{user['user']} created a new report.")) result = insert_new_report(database, report) result["new_report_uuid"] = report_uuid return result @bottle.post("/api/v3/report/<report_uuid>/copy") def post_report_copy(report_uuid: ReportId, database: Database): """Copy a report.""" data_model = latest_datamodel(database) reports = latest_reports(database) data = ReportData(data_model, reports, report_uuid) report_copy = copy_report(data.report, data.datamodel) user = sessions.user(database) report_copy["delta"] = dict( uuids=[report_uuid, report_copy["report_uuid"]], email=user["email"], description=f"{user['user']} copied the report '{data.report_name}'.") result = insert_new_report(database, report_copy) result["new_report_uuid"] = report_copy["report_uuid"] return result @bottle.get("/api/v3/report/<report_uuid>/pdf") def export_report_as_pdf(report_uuid: ReportId): """Download the report as pdf.""" renderer_host = os.environ.get("RENDERER_HOST", "renderer") renderer_port = os.environ.get("RENDERER_PORT", "9000") render_url = f"http://{renderer_host}:{renderer_port}/api/render" proxy_host = os.environ.get("PROXY_HOST", "www") proxy_port = os.environ.get("PROXY_PORT", "80") query_string = f"?{bottle.request.query_string}" if bottle.request.query_string else "" report_url = parse.quote(f"http://{proxy_host}:{proxy_port}/{report_uuid}{query_string}") margins = "&".join([f"pdf.margin.{side}=25" for side in ("top", "bottom", "left", "right")]) # Set pdf scale to 70% or otherwise the dashboard falls off the page options = f"emulateScreenMedia=false&goto.timeout=60000&pdf.scale=0.7&{margins}" response = requests.get(f"{render_url}?url={report_url}&{options}") response.raise_for_status() bottle.response.content_type = "application/pdf" return response.content @bottle.delete("/api/v3/report/<report_uuid>") def delete_report(report_uuid: ReportId, database: Database): """Delete a report.""" data_model = latest_datamodel(database) reports = latest_reports(database) data = ReportData(data_model, reports, report_uuid) data.report["deleted"] = "true" user = sessions.user(database) data.report["delta"] = dict( uuids=[report_uuid], email=user["email"], description=f"{user['user']} deleted the report '{data.report_name}'.") return insert_new_report(database, data.report) @bottle.post("/api/v3/report/<report_uuid>/attribute/<report_attribute>") def post_report_attribute(report_uuid: ReportId, report_attribute: str, database: Database): """Set a report attribute.""" data_model = latest_datamodel(database) reports = latest_reports(database) data = ReportData(data_model, reports, report_uuid) value = dict(bottle.request.json)[report_attribute] old_value = data.report.get(report_attribute) or "" data.report[report_attribute] = value value_change_description = "" if report_attribute == "layout" else f" from '{old_value}' to '{value}'" user = sessions.user(database) data.report["delta"] = dict( uuids=[report_uuid], email=user["email"], description=f"{user['user']} changed the {report_attribute} of report '{data.report_name}'" f"{value_change_description}.") return insert_new_report(database, data.report) @bottle.get("/api/v3/tagreport/<tag>") def get_tag_report(tag: str, database: Database): """Get a report with all metrics that have the specified tag.""" date_time = report_date_time() reports = latest_reports(database, date_time) data_model = latest_datamodel(database, date_time) subjects = _get_subjects_and_metrics_by_tag(data_model, reports, tag) tag_report = dict( title=f'Report for tag "{tag}"', subtitle="Note: tag reports are read-only", report_uuid=f"tag-{tag}", timestamp=date_time, subjects=subjects) hide_credentials(data_model, tag_report) summarize_report(tag_report, recent_measurements_by_metric_uuid(database, date_time), data_model) return tag_report def _get_subjects_and_metrics_by_tag(data_model, reports, tag: str): """Return all subjects and metrics that have the tag.""" subjects = {} for report in reports: for subject_uuid, subject in list(report.get("subjects", {}).items()): for metric_uuid, metric in list(subject.get("metrics", {}).items()): if tag not in metric.get("tags", []): del subject["metrics"][metric_uuid] if subject.get("metrics", {}): subject_name = subject.get("name") or data_model["subjects"][subject["type"]]["name"] subject["name"] = report["title"] + " / " + subject_name subjects[subject_uuid] = subject return subjects

Gamer1120/quality-time

components/server/src/routes/report.py

Python

# Copyright 2019 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 # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Various ops for augmentation.""" import math import tensorflow as tf from tensorflow_addons import image as tfa_image # Default replace value REPLACE_VALUE = 128 def blend(image1, image2, factor): """Blend image1 and image2 using 'factor'. A value of factor 0.0 means only image1 is used. A value of 1.0 means only image2 is used. A value between 0.0 and 1.0 means we linearly interpolate the pixel values between the two images. A value greater than 1.0 "extrapolates" the difference between the two pixel values, and we clip the results to values between 0 and 255. Args: image1: An image Tensor. image2: An image Tensor. factor: A floating point value above 0.0. Returns: A blended image Tensor. """ image1 = tf.cast(image1, tf.float32) image2 = tf.cast(image2, tf.float32) return tf.saturate_cast(image1 + factor * (image2 - image1), tf.uint8) def wrap(image): """Returns 'image' with an extra channel set to all 1s.""" shape = tf.shape(image) extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype) extended = tf.concat([image, extended_channel], 2) return extended def unwrap(image): """Unwraps an image produced by wrap. Where there is a 0 in the last channel for every spatial position, the rest of the three channels in that spatial dimension are grayed (set to 128). Operations like translate and shear on a wrapped Tensor will leave 0s in empty locations. Some transformations look at the intensity of values to do preprocessing, and we want these empty pixels to assume the 'average' value, rather than pure black. Args: image: A 3D Image Tensor with 4 channels. Returns: image: A 3D image Tensor with 3 channels. """ image_shape = tf.shape(image) # Flatten the spatial dimensions. flattened_image = tf.reshape(image, [-1, image_shape[2]]) # Find all pixels where the last channel is zero. alpha_channel = tf.expand_dims(flattened_image[:, image_shape[2] - 1], 1) replace = tf.constant([REPLACE_VALUE, REPLACE_VALUE, REPLACE_VALUE, 1], image.dtype) # Where they are zero, fill them in with 'replace'. flattened_image = tf.where( tf.equal(alpha_channel, 0), tf.ones_like(flattened_image, dtype=image.dtype) * replace, flattened_image) image = tf.reshape(flattened_image, image_shape) image = tf.slice(image, [0, 0, 0], [image_shape[0], image_shape[1], image_shape[2] - 1]) return image def solarize(image, threshold=128): # For each pixel in the image, select the pixel # if the value is less than the threshold. # Otherwise, subtract 255 from the pixel. threshold = tf.saturate_cast(threshold, image.dtype) return tf.where(image < threshold, image, 255 - image) def solarize_add(image, addition=0, threshold=128): # For each pixel in the image less than threshold # we add 'addition' amount to it and then clip the # pixel value to be between 0 and 255. The value # of 'addition' is between -128 and 128 threshold = tf.saturate_cast(threshold, image.dtype) added_im = tf.cast(image, tf.int32) + tf.cast(addition, tf.int32) added_im = tf.saturate_cast(added_im, tf.uint8) return tf.where(image < threshold, added_im, image) def invert(image): """Inverts the image pixels.""" return 255 - tf.convert_to_tensor(image) def invert_blend(image, factor): """Implements blend of invert with original image.""" return blend(invert(image), image, factor) def color(image, factor): """Equivalent of PIL Color.""" degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image)) return blend(degenerate, image, factor) def contrast(image, factor): """Equivalent of PIL Contrast.""" grayscale_im = tf.image.rgb_to_grayscale(image) mean = tf.reduce_mean(tf.cast(grayscale_im, tf.float32)) mean = tf.saturate_cast(mean + 0.5, tf.uint8) degenerate = tf.ones_like(grayscale_im, dtype=tf.uint8) * mean degenerate = tf.image.grayscale_to_rgb(degenerate) return blend(degenerate, image, factor) def brightness(image, factor): """Equivalent of PIL Brightness.""" degenerate = tf.zeros_like(image) return blend(degenerate, image, factor) def posterize(image, bits): """Equivalent of PIL Posterize.""" shift = tf.cast(8 - bits, image.dtype) return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift) def rotate(image, degrees): """Equivalent of PIL Rotation.""" # Convert from degrees to radians degrees_to_radians = math.pi / 180.0 radians = degrees * degrees_to_radians # In practice, we should randomize the rotation degrees by flipping # it negatively half the time, but that's done on 'degrees' outside # of the function. image = tfa_image.transform_ops.rotate(wrap(image), radians) return unwrap(image) def translate_x(image, pixels): """Equivalent of PIL Translate in X dimension.""" image = tfa_image.translate_ops.translate(wrap(image), [-pixels, 0]) return unwrap(image) def translate_y(image, pixels): """Equivalent of PIL Translate in Y dimension.""" image = tfa_image.translate_ops.translate(wrap(image), [0, -pixels]) return unwrap(image) def shear_x(image, level): """Equivalent of PIL Shearing in X dimension.""" # Shear parallel to x axis is a projective transform # with a matrix form of: # [1 level # 0 1] image = tfa_image.transform_ops.transform( wrap(image), [1., level, 0., 0., 1., 0., 0., 0.]) return unwrap(image) def shear_y(image, level): """Equivalent of PIL Shearing in Y dimension.""" # Shear parallel to y axis is a projective transform # with a matrix form of: # [1 0 # level 1] image = tfa_image.transform_ops.transform( wrap(image), [1., 0., 0., level, 1., 0., 0., 0.]) return unwrap(image) def autocontrast(image): """Implements Autocontrast function from PIL using TF ops.""" def scale_channel(channel): """Scale the 2D image using the autocontrast rule.""" # A possibly cheaper version can be done using cumsum/unique_with_counts # over the histogram values, rather than iterating over the entire image. # to compute mins and maxes. lo = tf.cast(tf.reduce_min(channel), tf.float32) hi = tf.cast(tf.reduce_max(channel), tf.float32) # Scale the image, making the lowest value 0 and the highest value 255. def scale_values(im): scale = 255.0 / (hi - lo) offset = -lo * scale im = tf.cast(im, tf.float32) * scale + offset return tf.saturate_cast(im, tf.uint8) result = tf.cond(hi > lo, lambda: scale_values(channel), lambda: channel) return result # Assumes RGB for now. Scales each channel independently # and then stacks the result. s1 = scale_channel(image[:, :, 0]) s2 = scale_channel(image[:, :, 1]) s3 = scale_channel(image[:, :, 2]) image = tf.stack([s1, s2, s3], 2) return image def autocontrast_blend(image, factor): """Implements blend of autocontrast with original image.""" return blend(autocontrast(image), image, factor) def sharpness(image, factor): """Implements Sharpness function from PIL using TF ops.""" orig_im = image image = tf.cast(image, tf.float32) # Make image 4D for conv operation image = tf.expand_dims(image, 0) # SMOOTH PIL Kernel kernel = tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13. # Tile across channel dimension kernel = tf.tile(kernel, [1, 1, 3, 1]) strides = [1, 1, 1, 1] degenerate = tf.nn.depthwise_conv2d( image, kernel, strides, padding='VALID', dilations=[1, 1]) degenerate = tf.squeeze(tf.saturate_cast(degenerate, tf.uint8), [0]) # For the borders of the resulting image, fill in the values of the # original image. mask = tf.ones_like(degenerate) padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]]) padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]]) result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_im) # Blend the final result return blend(result, orig_im, factor) def equalize(image): """Implements Equalize function from PIL using TF ops.""" def scale_channel(im, c): """Scale the data in the channel to implement equalize.""" im = tf.cast(im[:, :, c], tf.int32) # Compute the histogram of the image channel. histo = tf.histogram_fixed_width(im, [0, 255], nbins=256) # For the purposes of computing the step, filter out the nonzeros. nonzero = tf.where(tf.not_equal(histo, 0)) nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1]) step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255 def build_lut(histo, step): # Compute the cumulative sum, shifting by step // 2 # and then normalization by step. lut = (tf.cumsum(histo) + (step // 2)) // step # Shift lut, prepending with 0. lut = tf.concat([[0], lut[:-1]], 0) # Clip the counts to be in range. This is done # in the C code for image.point. return tf.clip_by_value(lut, 0, 255) # If step is zero, return the original image. Otherwise, build # lut from the full histogram and step and then index from it. result = tf.cond( tf.equal(step, 0), lambda: im, lambda: tf.gather(build_lut(histo, step), im)) return tf.cast(result, tf.uint8) # Assumes RGB for now. Scales each channel independently # and then stacks the result. s1 = scale_channel(image, 0) s2 = scale_channel(image, 1) s3 = scale_channel(image, 2) image = tf.stack([s1, s2, s3], 2) return image def equalize_blend(image, factor): """Implements blend of equalize with original image.""" return blend(equalize(image), image, factor) def _convolve_image_with_kernel(image, kernel): num_channels = tf.shape(image)[-1] kernel = tf.tile(kernel, [1, 1, num_channels, 1]) image = tf.expand_dims(image, axis=0) convolved_im = tf.nn.depthwise_conv2d( tf.cast(image, tf.float32), kernel, strides=[1, 1, 1, 1], padding='SAME') # adding 0.5 for future rounding, same as in PIL: # https://github.com/python-pillow/Pillow/blob/555e305a60d7fcefd1ad4aa6c8fd879e2f474192/src/libImaging/Filter.c#L101 # pylint: disable=line-too-long convolved_im = convolved_im + 0.5 return tf.squeeze(convolved_im, axis=0) def blur(image, factor): """Blur with the same kernel as ImageFilter.BLUR.""" # See https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py # pylint: disable=line-too-long # class BLUR(BuiltinFilter): # name = "Blur" # # fmt: off # filterargs = (5, 5), 16, 0, ( # 1, 1, 1, 1, 1, # 1, 0, 0, 0, 1, # 1, 0, 0, 0, 1, # 1, 0, 0, 0, 1, # 1, 1, 1, 1, 1, # ) # # fmt: on # # filterargs are following: # (kernel_size_x, kernel_size_y), divisor, offset, kernel # blur_kernel = tf.constant( [[1., 1., 1., 1., 1.], [1., 0., 0., 0., 1.], [1., 0., 0., 0., 1.], [1., 0., 0., 0., 1.], [1., 1., 1., 1., 1.]], dtype=tf.float32, shape=[5, 5, 1, 1]) / 16.0 blurred_im = _convolve_image_with_kernel(image, blur_kernel) return blend(image, blurred_im, factor) def smooth(image, factor): """Smooth with the same kernel as ImageFilter.SMOOTH.""" # See https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py # pylint: disable=line-too-long # class SMOOTH(BuiltinFilter): # name = "Smooth" # # fmt: off # filterargs = (3, 3), 13, 0, ( # 1, 1, 1, # 1, 5, 1, # 1, 1, 1, # ) # # fmt: on # # filterargs are following: # (kernel_size_x, kernel_size_y), divisor, offset, kernel # smooth_kernel = tf.constant([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0 smoothed_im = _convolve_image_with_kernel(image, smooth_kernel) return blend(image, smoothed_im, factor) def rescale(image, level): """Rescales image and enlarged cornet.""" # See tf.image.ResizeMethod for full list size = image.shape[:2] scale = level * 0.25 scale_height = tf.cast(scale * size[0], tf.int32) scale_width = tf.cast(scale * size[1], tf.int32) cropped_image = tf.image.crop_to_bounding_box( image, offset_height=scale_height, offset_width=scale_width, target_height=size[0] - scale_height, target_width=size[1] - scale_width) rescaled = tf.image.resize(cropped_image, size, tf.image.ResizeMethod.BICUBIC) return tf.saturate_cast(rescaled, tf.uint8) NAME_TO_FUNC = { 'Identity': tf.identity, 'AutoContrast': autocontrast, 'AutoContrastBlend': autocontrast_blend, 'Equalize': equalize, 'EqualizeBlend': equalize_blend, 'Invert': invert, 'InvertBlend': invert_blend, 'Rotate': rotate, 'Posterize': posterize, 'Solarize': solarize, 'SolarizeAdd': solarize_add, 'Color': color, 'Contrast': contrast, 'Brightness': brightness, 'Sharpness': sharpness, 'ShearX': shear_x, 'ShearY': shear_y, 'TranslateX': translate_x, 'TranslateY': translate_y, 'Blur': blur, 'Smooth': smooth, 'Rescale': rescale, }

google-research/crest

third_party/augment_ops.py

Python

from harry import get_harry_most_common_word def test_get_harry_most_common_word(): top_word = get_harry_most_common_word() assert type(top_word) == tuple assert top_word[0] == 'dursley' assert top_word[1] == 45

alex-vegan/100daysofcode-with-python-course

days/day101/Bite 18. Find the most common word/test_harry.py

Python

""" Scrape quotes, books and authors from ``Good Reads`` website. """ import bs4 from .utils import * def get_author_name(soup): """Get the author's name from its main page. Args: soup (bs4.element.Tag): connection to the author page. Returns: string: name of the author. Examples:: >>> from scrapereads import connect >>> url = 'https://www.goodreads.com/author/show/1077326' >>> soup = connect(url) >>> get_author_name(soup) J.K. Rowling """ author_h1 = soup.find('h1', attrs={'class': 'authorName'}) return author_h1.find('span').text def get_author_desc(soup): """Get the author description / biography. Args: soup (bs4.element.Tag): connection to the author page. Returns: str: long description of the author. Examples:: >>> from scrapereads import connect >>> url = 'https://www.goodreads.com/author/show/1077326' >>> soup = connect(url) >>> get_author_desc(soup) See also: Robert Galbraith Although she writes under the pen name J.K. Rowling, pronounced like rolling, her name when her first Harry Potter book was published was simply Joanne Rowling. ... """ author_info_desc = soup.find('div', attrs={'class': 'aboutAuthorInfo'}) author_info_long = author_info_desc.findAll('span')[-1] long_desc = "" for sentence in author_info_long.children: if isinstance(sentence, bs4.element.Tag): if sentence.name == 'br': long_desc += '\n' else: long_desc += sentence.text else: long_desc += sentence long_desc = long_desc.replace('’', "'") return long_desc def get_author_info(soup): """Get all information from an author (genres, influences, website etc.). Args: soup (bs4.element.Tag): author page connection. Returns: dict """ container = soup.find('div', attrs={'class': 'rightContainer'}) author_info = {} data_div = container.find('br', attrs={'class': 'clear'}) while data_div: if data_div.name: data_class = data_div.get('class')[0] # Information section is finished if data_class == 'aboutAuthorInfo': break # Key elements elif data_class == 'dataTitle': key = data_div.text.strip() author_info[key] = [] # Born section if data_div.text == 'Born': data_div = data_div.next_sibling author_info[key].append(data_div.strip()) # Influences section elif data_div.text == 'Influences': data_div = data_div.next_sibling.next_sibling data_items = data_div.findAll('span')[-1].findAll('a') for data_a in data_items: author_info[key].append(data_a.text.strip()) # Member since section elif data_div.text == 'Member Since': data_div = data_div.next_sibling.next_sibling author_info[key].append(data_div.text.strip()) # Genre, website and other sections else: data_items = data_div.findAll('a') for data_a in data_items: author_info[key].append(data_a.text.strip()) data_div = data_div.next_sibling author_info.update({'Description': get_author_desc(soup)}) return author_info def scrape_quotes_container(soup): """Get the quote container from a quote page. Args: soup (bs4.element.Tag): connection to the quote page. Returns: bs4.element.Tag """ return soup.findAll('div', attrs={'class': 'quotes'}) def scrape_quotes(soup): """Retrieve all ``<div>`` quote element from a quote page. Args: soup (bs4.element.Tag): connection to the quote page. Returns: yield bs4.element.Tag """ for container_div in scrape_quotes_container(soup): quote_div = container_div.find('div', attrs={'class': 'quote'}) while quote_div: if quote_div.name == 'div' and quote_div.get('class') and 'quote' in quote_div.get('class'): yield quote_div quote_div = quote_div.next_sibling def get_quote_text(quote_div): """Get the text from a ``<div>`` quote element. Args: quote_div (bs4.element.Tag): ``<div>`` quote element to extract the text. Returns: string """ quote_text = '' text_iterator = quote_div.find('div', attrs={'class': 'quoteText'}).children for text in text_iterator: if text.name == 'br': quote_text += '\n' elif not text.name: quote_text += text.strip() quote_text = process_quote_text(quote_text) return quote_text def scrape_quote_tags(quote_div): """Scrape tags from a ``<div>`` quote element. Args: quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page. Returns: yield ``<a>`` tags """ tags_container = quote_div.find('div', attrs={'class': 'greyText smallText left'}) if tags_container: for tag in tags_container.children: if tag.name == 'a': yield tag return None def get_quote_book(quote_div): """Get the reference (book) from a ``<div>`` quote element. Args: quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page. Returns: bs4.element.Tag """ quote_details = quote_div.find('div', attrs={'class': 'quoteText'}) return quote_details.find('a', attrs={'class': 'authorOrTitle'}) def get_quote_author_name(quote_div): """Get the author's name from a ``<div>`` quote element. Args: quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page. Returns: string """ quote_text = quote_div.find('div', attrs={'class': 'quoteText '}) author_name = quote_text.find('span', attrs={'class': 'authorOrTitle'}).text return remove_punctuation(author_name).title() def get_quote_likes(quote_div): """Get the likes ``<a>`` tag from a ``<div>`` quote element. Args: quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page. Returns: bs4.element.Tag: ``<a>`` tag for likes. """ quote_footer = quote_div.find('div', attrs={'class': 'quoteFooter'}) return quote_footer.find('a', attrs={'class': 'smallText'}) # TODO: deprecate this def get_quote_name_id(quote_div): """Get the name and id of a ``<div>`` quote element. Args: quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page. Returns: tuple: id and name. """ quote_href = get_quote_likes(quote_div).get('href') quote_id = quote_href.split('/')[-1].split('-')[0] quote_name = '-'.join(quote_href.split('/')[-1].split('-')[1:]) return quote_id, quote_name def scrape_author_books(soup): """Retrieve books from an author's page. Args: soup (bs4.element.Tag): connection to an author books page. Returns: yield bs4.element.Tag: ``<tr>`` element. """ table_tr = soup.find('tr') while table_tr: if table_tr.name == 'tr': yield table_tr table_tr = table_tr.next_sibling def get_author_book_title(book_tr): """Get the book title ``<a>`` element from a table ``<tr>`` element from an author page. Args: book_tr (bs4.element.Tag): ``<tr>`` book element. Returns: bs4.element.Tag: book title ``<a>`` element. Examples:: >>> for book_tr in scrape_author_books(soup): ... book_title = get_author_book_title(book_tr) ... print(book_title.text.strip(), book_title.get('href')) The Bell Jar /book/show/6514.The_Bell_Jar Ariel /book/show/395090.Ariel The Collected Poems /book/show/31426.The_Collected_Poems The Unabridged Journals of Sylvia Plath /book/show/11623.The_Unabridged_Journals_of_Sylvia_Plath """ return book_tr.find('a', attrs={'class': 'bookTitle'}) def get_author_book_author(book_tr): """Get the author ``<a>`` element from a table ``<tr>`` element. Args: book_tr (bs4.element.Tag): ``<tr>`` book element. Returns: bs4.element.Tag: author name ``<a>`` element. Examples:: >>> for book_tr in scrape_author_books(soup): ... book_author = get_author_book_author(book_tr) ... print(book_author.text, book_author.get('href')) Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath """ return book_tr.find('a', attrs={'class': 'authorName'}) def get_author_book_ratings(book_tr): """Get the ratings ``<span>`` element from a table ``<tr>`` element from an author page. Args: book_tr (bs4.element.Tag): ``<tr>`` book element. Returns: bs4.element.Tag: ratings ``<span>`` element. Examples:: >>> for book_tr in scrape_author_books(soup): ... ratings_span = get_author_book_ratings(book_tr) ... print(ratings_span.contents[-1]) 4.55 avg rating — 2,414 ratings 3.77 avg rating — 1,689 ratings 4.28 avg rating — 892 ratings 4.54 avg rating — 490 ratings ... """ return book_tr.find('span', attrs={'class': 'minirating'}) def get_author_book_edition(book_tr): """Get the edition ``<a>`` element from a table ``<tr>`` element from an author page. Args: book_tr (bs4.element.Tag): ``<tr>`` book element. Returns: bs4.element.Tag: book edition ``<a>`` element. Examples:: >>> for book_tr in scrape_author_books(soup): ... book_edition = get_author_book_edition(book_tr) ... if book_edition: ... print(book_edition.text, book_edition.get('href')) ... print() 493 editions /work/editions/1385044-the-bell-jar 80 editions /work/editions/1185316-ariel 30 editions /work/editions/1003095-the-collected-poems 45 editions /work/editions/3094683-the-unabridged-journals-of-sylvia-plath ... """ book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'}) return book_details.find('a', attrs={'class': 'greyText'}) def get_author_book_date(book_tr): """Get the published date from a table ``<tr>`` element from an author page. Args: book_tr (bs4.element.Tag): ``<tr>`` book element. Returns: int: date of publication Examples:: >>> for book_tr in scrape_author_books(soup): ... book_date = get_author_book_date(book_tr) ... print(book_date) None None 1958 2009 ... """ book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'}) book_publish = book_details.contents[-1].replace('—', '').replace('\n', '') book_date = book_publish.replace('published', '').strip() book_date = eval(book_date) if book_date != '' else None return book_date def get_book_quote_page(soup): """Find the ``<a>`` element pointing to the quote page of a book. Args: soup (bs4.element.Tag): Returns: """ quote_div = soup.findAll('div', attrs={'class': ' clearFloats bigBox'}) if quote_div: return quote_div[-1].find('a') return None

arthurdjn/scrape-goodreads

scrapereads/scrape.py

Python

from pyspark.sql import Column, DataFrame, SparkSession, functions from pyspark.sql.functions import * from py4j.java_collections import MapConverter from delta.tables import * import shutil import threading tableName = "tbltestpython" # Enable SQL/DML commands and Metastore tables for the current spark session. # We need to set the following configs spark = SparkSession.builder \ .appName("quickstart_sql") \ .master("local[*]") \ .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \ .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \ .getOrCreate() # Clear any previous runs spark.sql("DROP TABLE IF EXISTS " + tableName) spark.sql("DROP TABLE IF EXISTS newData") try: # Create a table print("############# Creating a table ###############") spark.sql("CREATE TABLE %s(id LONG) USING delta" % tableName) spark.sql("INSERT INTO %s VALUES 0, 1, 2, 3, 4" % tableName) # Read the table print("############ Reading the table ###############") spark.sql("SELECT * FROM %s" % tableName).show() # Upsert (merge) new data print("########### Upsert new data #############") spark.sql("CREATE TABLE newData(id LONG) USING parquet") spark.sql("INSERT INTO newData VALUES 3, 4, 5, 6") spark.sql('''MERGE INTO {0} USING newData ON {0}.id = newData.id WHEN MATCHED THEN UPDATE SET {0}.id = newData.id WHEN NOT MATCHED THEN INSERT * '''.format(tableName)) spark.sql("SELECT * FROM %s" % tableName).show() # Update table data print("########## Overwrite the table ###########") spark.sql("INSERT OVERWRITE %s select * FROM (VALUES 5, 6, 7, 8, 9) x (id)" % tableName) spark.sql("SELECT * FROM %s" % tableName).show() # Update every even value by adding 100 to it print("########### Update to the table(add 100 to every even value) ##############") spark.sql("UPDATE {0} SET id = (id + 100) WHERE (id % 2 == 0)".format(tableName)) spark.sql("SELECT * FROM %s" % tableName).show() # Delete every even value print("######### Delete every even value ##############") spark.sql("DELETE FROM {0} WHERE (id % 2 == 0)".format(tableName)) spark.sql("SELECT * FROM %s" % tableName).show() # Read old version of data using time travel print("######## Read old data using time travel ############") df = spark.read.format("delta").option("versionAsOf", 0).table(tableName) df.show() finally: # cleanup spark.sql("DROP TABLE " + tableName) spark.sql("DROP TABLE IF EXISTS newData") spark.stop()

Kimahriman/delta

examples/python/quickstart_sql.py

Python

import numpy as np from sigman.analyzer import InvalidArgumentError procedure_type = 'points' description = ( """Procedure calculate time of B point from equation: RB = 1.233RZ-0.0032RZ^2-31.59 where RZ - time between R and dz/dt max [ms] RB - time between R and B Equation was proposed by D.L. Lozano in paper "Where to B in dZ/dt" (2007) """) author = 'mzylinski' arguments = { } default_arguments = { } output_type = 'B' required_waves = ['Signal'] required_points = [ 'R','dzdtmax'] def procedure(waves, points, begin_time, end_time, settings): wave = waves['Signal'] R = points['R'] dzdtmax = points['dzdtmax'] r_x = [] r_y = [] for i in range(0,len(R)-1): data = wave.data_slice(R.data_x[i], R.data_x[i+1]) RZ = (dzdtmax.data_x[i] - R.data_x[i])/wave.sample_length RB = 1.233*RZ -0.0032*(RZ*RZ)-31.59 t = int(round(RB)) if (t<0): t = 0 r_y.append(data[t]) r_x.append(R.data_x[i] + t*wave.sample_length) return r_x, r_y def interpret_arguments(waves, points, arguments): output_arguments = {} for key, item in arguments.items(): try: output_arguments[key] = float(item) except: raise InvalidArgumentError("{} is invalid.".format(arguments[key])) return output_arguments def execute(waves, points, begin_time, end_time, arguments): arguments = interpret_arguments(waves, points, arguments) return procedure(waves, points, begin_time, end_time, arguments)

k-cybulski/sigman-project

procedures/points_B_ICG_Lozaano_Equation.py

Python

"""Welcome to MLToolset, a package to simplify machine learning research! Author: Ryan Eloff Contact: [email protected] Date: May 2018 """ from . import data from . import nearest_neighbour from . import neural_blocks from . import siamese from . import training from . import utils from ._globals import TF_FLOAT from ._globals import TF_INT from ._globals import NP_FLOAT from ._globals import NP_INT

rpeloff/multimodal-one-shot-learning

src/mltoolset/__init__.py

Python

""" This file implements the signature scheme from "Unique Ring Signatures: A Practical Construction" by Matthew Franklin and Haibin Zhang """ import sys import math from random import randint import hashlib from libsig.AbstractRingSignatureScheme import AbstractRingSignatureScheme #from AbstractRingSignatureScheme import AbstractRingSignatureScheme #from libsig import primes # ----------- HELPER FUNCTIONS ----------- # function to find divisors in order to find generators def find_divisors(x): """ This is the "function to find divisors in order to find generators" module. This DocTest verifies that the module is correctly calculating all divisors of a number x. >>> find_divisors(10) [1, 2, 5, 10] >>> find_divisors(112) [1, 2, 4, 7, 8, 14, 16, 28, 56, 112] """ divisors = [ i for i in range(1,x+1) if x % i == 0] return divisors # function to find random generator of G def find_generator(p): ''' The order of any element in a group can be divided by p-1. Step 1: Calculate all Divisors. Step 2: Test for a random element e of G wether e to the power of a Divisor is 1. if neither is one but e to the power of p-1, a generator is found. ''' # Init # Generate element which is tested for generator characteristics. # Saved in list to prevent checking the same element twice. testGen = randint(1,p) listTested = [] listTested.append(testGen) # Step 1. divisors = find_divisors(p) # try for all random numbers # Caution: this leads to a truly random generator but is not very efficient. while len(listTested) < p-1: # only test each possible generator once if testGen in listTested: # Step 2. for div in divisors: testPotency = math.pow(testGen,div) % (p+1) if testPotency == 1.0 and div != divisors[-1]: # element does not have the same order like the group, # therefore try next element break elif testPotency == 1.0 and div == divisors[-1]: # generator is found return testGen # try new element testGen = randint(1,p) listTested.append(testGen) def list_to_string(input_list): ''' convert a list into a concatenated string of all its elements ''' result = ''.join(map(str,input_list)) return result # ----------- HELPER FUNCTIONS END ----------- class UniqueRingSignature(AbstractRingSignatureScheme): ''' | output: pp = (lamdba, q, G, H, H2) with, | q is prime, | g is generator of G, | G is multiplicative Group with prime order q, | H1 and H2 are two Hash functions H1: {0,1}* -> G, | (as well as H2: {0,1}* -> Zq which is the same). ''' # set prime p (Sophie-Germain and therefore save) #q = 53 q = 59 # find random generator of G g = find_generator(q-1) # hash functions with desired range and the usage of secure hashes h1 = lambda x: int(hashlib.sha256(str(x).encode()).hexdigest(),16)%(UniqueRingSignature.q) # this way to share the information should be improved h2 = lambda x: int(hashlib.sha512(str(x).encode()).hexdigest(),16)%(UniqueRingSignature.q) # list of public keys Rp = list() @staticmethod def keygen(verbose=False): #print("---- KeyGen Started ---- \n") r = randint(1,UniqueRingSignature.q) # x = g**r % q x = pow(UniqueRingSignature.g, r,UniqueRingSignature.q) # y = g**x y = pow(UniqueRingSignature.g, x, UniqueRingSignature.q) if verbose == True: print("KeyGen Config: public key y=" + str(y) + ", private key x=" + str(x) + "\n") print("---- KeyGen Completed ---- \n") # Caution! I know, keygen should NOT return the private key, but this is needed to "play" through a whole signature - validation process return x,y @staticmethod def ringsign(x, pubkey, message,verbose=False): ''' input: x is the privkey from user i, | all public keys: pubkeys, | the message output: (R,m, (H(mR)^xi), c1,t1,...,cn,tn), | R: all the pubkeys concatenated, | cj,tj: random number within Zq ''' # calculate R = pk1,pk2,..,pkn R = list_to_string(pubkey) g = UniqueRingSignature.g q = UniqueRingSignature.q h1 = UniqueRingSignature.h1 h2 = UniqueRingSignature.h2 # message + pubkeys concatenated mR = message + str(R) C = list() T = list() A = list() B = list() ri = -1 # simulation step # for i in pubkey: # Step 1: # a = 0 b = 0 c = 0 t = 0 if pow(g,x,q) != i: c, t = randint(1,q), randint(1,q) a = (pow(g, t) * pow(int(i), c)) % q b = (pow(h1(mR), t) * pow(pow(h1(mR),x),c)) % q else: # Step 2: # ri = randint(1, q) a = pow(g, ri, q) b = pow(h1(mR), ri, q) # insert to allocate place c = -1 t = -1 A.append(a) B.append(b) C.append(c) T.append(t) # for end # Step 3: # cj = 0 # list count from 0 ab = ''.join('{}{}'.format(*t) for t in zip(A,B)) usernr = 0 for i in range(len(pubkey)): if pubkey[i] != (pow(g,x,q)): cj = (cj + C[i]) % q else: usernr = i ci = h2(message + R + ab) - (cj % (q-1)) # update ci, this was initialized with -1 C[usernr] = ci ti = ((ri - (C[usernr]*x)) % (q-1)) if ti < 0: ti = (q-1) + ti # update ti, this was initialized with -1 T[usernr] = ti # Step 4: # # concatenate ct: c1,t1,c2,t2,...,cn,tn ct = ','.join('{},{}'.format(*t) for t in zip(C,T)) # returning result result = R + ","+message+","+str(pow(h1(mR),x, q))+"," + ct if verbose == True: print("RingSign Result: "+ result) print("---- RingSign Completed ---- \n") return result @staticmethod def verify(R, message, signature,verbose=False): ''' Input: the public keys R | the message | the signature computed with ringsign Output: whether the message was signed by R or not ''' g = UniqueRingSignature.g q = UniqueRingSignature.q h1 = UniqueRingSignature.h1 h2 = UniqueRingSignature.h2 # parse the signature parsed = signature.split(",") tt = int(parsed[2]) cjs = list() tjs = list() for i in range(0,int(((len(parsed))/2)-1)): cjs.append(int(parsed[3+2*i])) tjs.append(int(parsed[4+2*i])) #print(str(cjs)+" "+str(tjs) + " "+ str(tt)) # check signature # sum of all cjs # =? # self.pp['h2'](message + R + gyh1) mR = list_to_string(R) val1 = sum(cjs) % q # for all users in R: # g**tj * yj ** cj , h1(m||R)**tj * tt**cj gyh1 = "" for i in range(len(tjs)): if tjs[i] < 0: tjs[i] = (q-1) + tjs[i] if cjs[i] < 0: cjs[i] = (q-1) + cjs[i] gy = (pow(g,(tjs[i]),q) * (pow((R[i]),(cjs[i]),q))) % q h = (pow(int(h1(message + mR)), int(tjs[i])) * pow(tt,int(cjs[i]))) % q gyh1 = gyh1 + str( gy) + str( h) val2 = str(h2(message + list_to_string(R) + gyh1)) if int(val1) == int(val2): if verbose == True: print("Signature is valid!\n") print("Common Result: " + str(val1)) print("---- Validation Completed ---- \n") return True else: if verbose == True: print("Signature is not valid!\n") print(str(val1) + " != " + str(val2)) print("---- Validation Completed ---- \n") return False def local_test(verbose=True): # verbose output print(verbose) # user 1 will signate and validate later, # therefore his private key is saved for test purposes privKey1,pubkey = UniqueRingSignature.keygen(verbose) UniqueRingSignature.Rp.append(pubkey) a,pubkey = UniqueRingSignature.keygen(verbose) UniqueRingSignature.Rp.append(pubkey) # usernr start from 0 # ringsign(self, privkey, usernr, pubkeys, message) ring = UniqueRingSignature.ringsign(privKey1, UniqueRingSignature.Rp, "asdf", verbose) if verbose: print("Result of Signature Validation:") # verify(pubkeys, message, signature): UniqueRingSignature.verify(UniqueRingSignature.Rp, "asdf", ring, verbose) if __name__ == '__main__': # doctest start import doctest doctest.testmod() if len(sys.argv) > 1: verbose = False if sys.argv[1] == "True": verbose = True # run a local test local_test(verbose)

vs-uulm/libsig_pets

libsig/FZZ_unique_ring_signature.py

Python

import crcmod from selfdrive.car.hyundai.values import CAR, CHECKSUM hyundai_checksum = crcmod.mkCrcFun(0x11D, initCrc=0xFD, rev=False, xorOut=0xdf) def create_lkas11(packer, car_fingerprint, bus, apply_steer, steer_req, cnt, enabled, lkas11, hud_alert, lane_visible, left_lane_depart, right_lane_depart, keep_stock=False): values = { "CF_Lkas_Bca_R": lkas11["CF_Lkas_Bca_R"] if keep_stock else 3, #"CF_Lkas_LdwsSysState": 3 if steer_req else lane_visible, "CF_Lkas_LdwsSysState": 3 if enabled else 1, "CF_Lkas_SysWarning": hud_alert, #"CF_Lkas_LdwsLHWarning": lkas11["CF_Lkas_LdwsLHWarning"], #"CF_Lkas_LdwsRHWarning": lkas11["CF_Lkas_LdwsRHWarning"], "CF_Lkas_LdwsLHWarning": left_lane_depart, "CF_Lkas_LdwsRHWarning": right_lane_depart, "CF_Lkas_HbaLamp": lkas11["CF_Lkas_HbaLamp"] if keep_stock else 0, "CF_Lkas_FcwBasReq": lkas11["CF_Lkas_FcwBasReq"] if keep_stock else 0, "CR_Lkas_StrToqReq": apply_steer, "CF_Lkas_ActToi": steer_req, "CF_Lkas_ToiFlt": 0, "CF_Lkas_HbaSysState": lkas11["CF_Lkas_HbaSysState"] if keep_stock else 1, "CF_Lkas_FcwOpt": lkas11["CF_Lkas_FcwOpt"] if keep_stock else 0, "CF_Lkas_HbaOpt": lkas11["CF_Lkas_HbaOpt"] if keep_stock else 3, "CF_Lkas_MsgCount": cnt, "CF_Lkas_FcwSysState": lkas11["CF_Lkas_FcwSysState"] if keep_stock else 0, "CF_Lkas_FcwCollisionWarning": lkas11["CF_Lkas_FcwCollisionWarning"] if keep_stock else 0, "CF_Lkas_FusionState": lkas11["CF_Lkas_FusionState"] if keep_stock else 0, "CF_Lkas_Chksum": 0, "CF_Lkas_FcwOpt_USM": lkas11["CF_Lkas_FcwOpt_USM"] if keep_stock else 2, "CF_Lkas_LdwsOpt_USM": lkas11["CF_Lkas_LdwsOpt_USM"] if keep_stock else 3, } if car_fingerprint == CAR.GENESIS: values["CF_Lkas_Bca_R"] = 2 values["CF_Lkas_HbaSysState"] = lkas11["CF_Lkas_HbaSysState"] if keep_stock else 0 values["CF_Lkas_HbaOpt"] = lkas11["CF_Lkas_HbaOpt"] if keep_stock else 1 values["CF_Lkas_FcwOpt_USM"] = lkas11["CF_Lkas_FcwOpt_USM"] if keep_stock else 2 values["CF_Lkas_LdwsOpt_USM"] = lkas11["CF_Lkas_LdwsOpt_USM"] if keep_stock else 0 if car_fingerprint == CAR.KIA_OPTIMA: values["CF_Lkas_Bca_R"] = 0 values["CF_Lkas_HbaOpt"] = lkas11["CF_Lkas_HbaOpt"] if keep_stock else 1 values["CF_Lkas_FcwOpt_USM"] = lkas11["CF_Lkas_FcwOpt_USM"] if keep_stock else 0 if car_fingerprint == CAR.KIA_CARDENZA: ######################################################## #values["CF_Lkas_Bca_R"] = int(left_lane) + (int(right_lane) << 1) #values["CF_Lkas_FcwOpt_USM"] = 2 if enabled else 1 # FcwOpt_USM 5 = Orange blinking car + lanes # FcwOpt_USM 4 = Orange car + lanes # FcwOpt_USM 3 = Green blinking car + lanes # FcwOpt_USM 2 = Green car + lanes # FcwOpt_USM 1 = White car + lanes # FcwOpt_USM 0 = No car + lanes #values["CF_Lkas_SysWarning"] = 4 if sys_warning else 0 # SysWarning 4 = keep hands on wheel # SysWarning 5 = keep hands on wheel (red) # SysWarning 6 = keep hands on wheel (red) + beep # Note: the warning is hidden while the blinkers are on #values["CF_Lkas_LdwsOpt_USM"] = 2 ######################################################## values["CF_Lkas_Bca_R"] = 0 values["CF_Lkas_FcwOpt_USM"] = 1 values["CF_Lkas_LdwsOpt_USM"] = 3 dat = packer.make_can_msg("LKAS11", 0, values)[2] if car_fingerprint in CHECKSUM["crc8"]: # CRC Checksum as seen on 2019 Hyundai Santa Fe dat = dat[:6] + dat[7:8] checksum = hyundai_checksum(dat) elif car_fingerprint in CHECKSUM["6B"]: # Checksum of first 6 Bytes, as seen on 2018 Kia Sorento checksum = sum(dat[:6]) % 256 else: # Checksum of first 6 Bytes and last Byte as seen on 2018 Kia Stinger checksum = (sum(dat[:6]) + dat[7]) % 256 values["CF_Lkas_Chksum"] = checksum return packer.make_can_msg("LKAS11", bus, values) def create_clu11(packer, bus, clu11, button, speed, cnt): values = { "CF_Clu_CruiseSwState": button, "CF_Clu_CruiseSwMain": clu11["CF_Clu_CruiseSwMain"], "CF_Clu_SldMainSW": clu11["CF_Clu_SldMainSW"], "CF_Clu_ParityBit1": clu11["CF_Clu_ParityBit1"], "CF_Clu_VanzDecimal": clu11["CF_Clu_VanzDecimal"], "CF_Clu_Vanz": speed, "CF_Clu_SPEED_UNIT": clu11["CF_Clu_SPEED_UNIT"], "CF_Clu_DetentOut": clu11["CF_Clu_DetentOut"], "CF_Clu_RheostatLevel": clu11["CF_Clu_RheostatLevel"], "CF_Clu_CluInfo": clu11["CF_Clu_CluInfo"], "CF_Clu_AmpInfo": clu11["CF_Clu_AmpInfo"], "CF_Clu_AliveCnt1": cnt, } return packer.make_can_msg("CLU11", bus, values) def create_scc12(packer, apply_accel, enabled, cnt, scc12): values = { "CF_VSM_Prefill": scc12["CF_VSM_Prefill"], "CF_VSM_DecCmdAct": scc12["CF_VSM_DecCmdAct"], "CF_VSM_HBACmd": scc12["CF_VSM_HBACmd"], "CF_VSM_Warn": scc12["CF_VSM_Warn"], "CF_VSM_Stat": scc12["CF_VSM_Stat"], "CF_VSM_BeltCmd": scc12["CF_VSM_BeltCmd"], "ACCFailInfo": scc12["ACCFailInfo"], "ACCMode": scc12["ACCMode"], "StopReq": scc12["StopReq"], "CR_VSM_DecCmd": scc12["CR_VSM_DecCmd"], "aReqMax": apply_accel if enabled and scc12["ACCMode"] == 1 else scc12["aReqMax"], "TakeOverReq": scc12["TakeOverReq"], "PreFill": scc12["PreFill"], "aReqMin": apply_accel if enabled and scc12["ACCMode"] == 1 else scc12["aReqMin"], "CF_VSM_ConfMode": scc12["CF_VSM_ConfMode"], "AEB_Failinfo": scc12["AEB_Failinfo"], "AEB_Status": scc12["AEB_Status"], "AEB_CmdAct": scc12["AEB_CmdAct"], "AEB_StopReq": scc12["AEB_StopReq"], "CR_VSM_Alive": cnt, "CR_VSM_ChkSum": 0, } dat = packer.make_can_msg("SCC12", 0, values)[2] values["CR_VSM_ChkSum"] = 16 - sum([sum(divmod(i, 16)) for i in dat]) % 16 return packer.make_can_msg("SCC12", 0, values) def create_mdps12(packer, car_fingerprint, cnt, mdps12): values = { "CR_Mdps_StrColTq": mdps12["CR_Mdps_StrColTq"], "CF_Mdps_Def": mdps12["CF_Mdps_Def"], "CF_Mdps_ToiActive": 0, "CF_Mdps_ToiUnavail": 1, "CF_Mdps_MsgCount2": cnt, "CF_Mdps_Chksum2": 0, "CF_Mdps_ToiFlt": mdps12["CF_Mdps_ToiFlt"], "CF_Mdps_SErr": mdps12["CF_Mdps_SErr"], "CR_Mdps_StrTq": mdps12["CR_Mdps_StrTq"], "CF_Mdps_FailStat": mdps12["CF_Mdps_FailStat"], "CR_Mdps_OutTq": mdps12["CR_Mdps_OutTq"], } dat = packer.make_can_msg("MDPS12", 2, values)[2] checksum = sum(dat) % 256 values["CF_Mdps_Chksum2"] = checksum return packer.make_can_msg("MDPS12", 2, values)

zzune/openpilot

selfdrive/car/hyundai/hyundaican.py

Python

import torch from facenet_pytorch import MTCNN, InceptionResnetV1 from torchvision import transforms from Configs import Global_Config IMAGE_SIZE = 220 mtcnn = MTCNN( image_size=IMAGE_SIZE, margin=0, min_face_size=20, thresholds=[0.6, 0.7, 0.7], factor=0.709, post_process=True, device=Global_Config.device ) to_pil = transforms.ToPILImage(mode='RGB') crop_transform = transforms.Compose([transforms.Resize(IMAGE_SIZE), transforms.CenterCrop(IMAGE_SIZE)]) resnet = InceptionResnetV1(pretrained='vggface2', classify=False).eval().to(Global_Config.device) class ID_Encoder(torch.nn.Module): def __init__(self): super(ID_Encoder, self).__init__() def crop_tensor_according_to_bboxes(self, images, bboxes): cropped_batch = [] for idx, image in enumerate(images): try: cropped_image = crop_transform(image[:, int(bboxes[idx][0][1]):int(bboxes[idx][0][3]), int(bboxes[idx][0][0]):int(bboxes[idx][0][2])].unsqueeze(0)) except: cropped_image = crop_transform(image.unsqueeze(0)) cropped_batch.append(cropped_image) return torch.cat(cropped_batch, dim=0) def preprocess_images_to_id_encoder(self, images): bboxes = [mtcnn.detect(to_pil(image))[0] for image in images] cropped_images = self.crop_tensor_according_to_bboxes(images, bboxes) return cropped_images def forward(self, images): cropped_images = self.preprocess_images_to_id_encoder(images) img_embeddings = resnet(cropped_images) return img_embeddings

YuGong123/ID-disentanglement-Pytorch

Models/Encoders/ID_Encoder.py

Python

""" Current-flow betweenness centrality measures for subsets of nodes. """ # Copyright (C) 2010-2011 by # Aric Hagberg <[email protected]> # Dan Schult <[email protected]> # Pieter Swart <[email protected]> # All rights reserved. # BSD license. __author__ = """Aric Hagberg ([email protected])""" __all__ = ['current_flow_betweenness_centrality_subset', 'edge_current_flow_betweenness_centrality_subset'] import itertools import networkx as nx from networkx.algorithms.centrality.flow_matrix import * def current_flow_betweenness_centrality_subset(G,sources,targets, normalized=True, weight='weight', dtype=float, solver='lu'): r"""Compute current-flow betweenness centrality for subsets of nodes. Current-flow betweenness centrality uses an electrical current model for information spreading in contrast to betweenness centrality which uses shortest paths. Current-flow betweenness centrality is also known as random-walk betweenness centrality [2]_. Parameters ---------- G : graph A NetworkX graph sources: list of nodes Nodes to use as sources for current targets: list of nodes Nodes to use as sinks for current normalized : bool, optional (default=True) If True the betweenness values are normalized by b=b/(n-1)(n-2) where n is the number of nodes in G. weight : string or None, optional (default='weight') Key for edge data used as the edge weight. If None, then use 1 as each edge weight. dtype: data type (float) Default data type for internal matrices. Set to np.float32 for lower memory consumption. solver: string (default='lu') Type of linear solver to use for computing the flow matrix. Options are "full" (uses most memory), "lu" (recommended), and "cg" (uses least memory). Returns ------- nodes : dictionary Dictionary of nodes with betweenness centrality as the value. See Also -------- approximate_current_flow_betweenness_centrality betweenness_centrality edge_betweenness_centrality edge_current_flow_betweenness_centrality Notes ----- Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)` time [1]_, where `I(n-1)` is the time needed to compute the inverse Laplacian. For a full matrix this is `O(n^3)` but using sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the Laplacian matrix condition number. The space required is `O(nw) where `w` is the width of the sparse Laplacian matrix. Worse case is `w=n` for `O(n^2)`. If the edges have a 'weight' attribute they will be used as weights in this algorithm. Unspecified weights are set to 1. References ---------- .. [1] Centrality Measures Based on Current Flow. Ulrik Brandes and Daniel Fleischer, Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05). LNCS 3404, pp. 533-544. Springer-Verlag, 2005. http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf .. [2] A measure of betweenness centrality based on random walks, M. E. J. Newman, Social Networks 27, 39-54 (2005). """ from networkx.utils import reverse_cuthill_mckee_ordering try: import numpy as np except ImportError: raise ImportError('current_flow_betweenness_centrality requires NumPy ', 'http://scipy.org/') try: import scipy except ImportError: raise ImportError('current_flow_betweenness_centrality requires SciPy ', 'http://scipy.org/') if G.is_directed(): raise nx.NetworkXError('current_flow_betweenness_centrality() ', 'not defined for digraphs.') if not nx.is_connected(G): raise nx.NetworkXError("Graph not connected.") n = G.number_of_nodes() ordering = list(reverse_cuthill_mckee_ordering(G)) # make a copy with integer labels according to rcm ordering # this could be done without a copy if we really wanted to mapping=dict(zip(ordering,range(n))) H = nx.relabel_nodes(G,mapping) betweenness = dict.fromkeys(H,0.0) # b[v]=0 for v in H for row,(s,t) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver): for ss in sources: i=mapping[ss] for tt in targets: j=mapping[tt] betweenness[s]+=0.5*np.abs(row[i]-row[j]) betweenness[t]+=0.5*np.abs(row[i]-row[j]) if normalized: nb=(n-1.0)*(n-2.0) # normalization factor else: nb=2.0 for v in H: betweenness[v]=betweenness[v]/nb+1.0/(2-n) return dict((ordering[k],v) for k,v in betweenness.items()) def edge_current_flow_betweenness_centrality_subset(G, sources, targets, normalized=True, weight='weight', dtype=float, solver='lu'): """Compute current-flow betweenness centrality for edges using subsets of nodes. Current-flow betweenness centrality uses an electrical current model for information spreading in contrast to betweenness centrality which uses shortest paths. Current-flow betweenness centrality is also known as random-walk betweenness centrality [2]_. Parameters ---------- G : graph A NetworkX graph sources: list of nodes Nodes to use as sources for current targets: list of nodes Nodes to use as sinks for current normalized : bool, optional (default=True) If True the betweenness values are normalized by b=b/(n-1)(n-2) where n is the number of nodes in G. weight : string or None, optional (default='weight') Key for edge data used as the edge weight. If None, then use 1 as each edge weight. dtype: data type (float) Default data type for internal matrices. Set to np.float32 for lower memory consumption. solver: string (default='lu') Type of linear solver to use for computing the flow matrix. Options are "full" (uses most memory), "lu" (recommended), and "cg" (uses least memory). Returns ------- nodes : dictionary Dictionary of edge tuples with betweenness centrality as the value. See Also -------- betweenness_centrality edge_betweenness_centrality current_flow_betweenness_centrality Notes ----- Current-flow betweenness can be computed in `O(I(n-1)+mn \log n)` time [1]_, where `I(n-1)` is the time needed to compute the inverse Laplacian. For a full matrix this is `O(n^3)` but using sparse methods you can achieve `O(nm{\sqrt k})` where `k` is the Laplacian matrix condition number. The space required is `O(nw) where `w` is the width of the sparse Laplacian matrix. Worse case is `w=n` for `O(n^2)`. If the edges have a 'weight' attribute they will be used as weights in this algorithm. Unspecified weights are set to 1. References ---------- .. [1] Centrality Measures Based on Current Flow. Ulrik Brandes and Daniel Fleischer, Proc. 22nd Symp. Theoretical Aspects of Computer Science (STACS '05). LNCS 3404, pp. 533-544. Springer-Verlag, 2005. http://www.inf.uni-konstanz.de/algo/publications/bf-cmbcf-05.pdf .. [2] A measure of betweenness centrality based on random walks, M. E. J. Newman, Social Networks 27, 39-54 (2005). """ from networkx.utils import reverse_cuthill_mckee_ordering try: import numpy as np except ImportError: raise ImportError('current_flow_betweenness_centrality requires NumPy ', 'http://scipy.org/') try: import scipy except ImportError: raise ImportError('current_flow_betweenness_centrality requires SciPy ', 'http://scipy.org/') if G.is_directed(): raise nx.NetworkXError('edge_current_flow_betweenness_centrality ', 'not defined for digraphs.') if not nx.is_connected(G): raise nx.NetworkXError("Graph not connected.") n = G.number_of_nodes() ordering = list(reverse_cuthill_mckee_ordering(G)) # make a copy with integer labels according to rcm ordering # this could be done without a copy if we really wanted to mapping=dict(zip(ordering,range(n))) H = nx.relabel_nodes(G,mapping) betweenness=(dict.fromkeys(H.edges(),0.0)) if normalized: nb=(n-1.0)*(n-2.0) # normalization factor else: nb=2.0 for row,(e) in flow_matrix_row(H, weight=weight, dtype=dtype, solver=solver): for ss in sources: i=mapping[ss] for tt in targets: j=mapping[tt] betweenness[e]+=0.5*np.abs(row[i]-row[j]) betweenness[e]/=nb return dict(((ordering[s],ordering[t]),v) for (s,t),v in betweenness.items()) # fixture for nose tests def setup_module(module): from nose import SkipTest try: import numpy import scipy except: raise SkipTest("NumPy not available")

AllenDowney/networkx

networkx/algorithms/centrality/current_flow_betweenness_subset.py

Python

# Generated by Django 3.1.4 on 2021-09-28 13:49 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('store', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='payment', name='paystack_response', ), ]

Joetib/jshop

apps/store/migrations/0002_remove_payment_paystack_response.py

Python

#!/usr/bin/python3 import functools from copy import deepcopy from .grammar import BASE_NODE_TYPES class NodeBase: """Represents a node within the solidity AST. Attributes: depth: Number of nodes between this node and the SourceUnit offset: Absolute source offsets as a (start, stop) tuple contract_id: Contract ID as given by the standard compiler JSON fields: List of attributes for this node """ def __init__(self, ast, parent): self.depth = parent.depth + 1 if parent is not None else 0 self._parent = parent self._children = set() src = [int(i) for i in ast["src"].split(":")] self.offset = (src[0], src[0] + src[1]) self.contract_id = src[2] self.fields = sorted(ast.keys()) for key, value in ast.items(): if isinstance(value, dict) and value.get("nodeType") == "Block": value = value["statements"] elif key == "body" and not value: value = [] if isinstance(value, dict): item = node_class_factory(value, self) if isinstance(item, NodeBase): self._children.add(item) setattr(self, key, item) elif isinstance(value, list): items = [node_class_factory(i, self) for i in value] setattr(self, key, items) self._children.update(i for i in items if isinstance(i, NodeBase)) else: setattr(self, key, value) def __hash__(self): return hash(f"{self.nodeType}{self.depth}{self.offset}") def __repr__(self): repr_str = f"<{self.nodeType}" if hasattr(self, "nodes"): repr_str += " iterable" if hasattr(self, "type"): if isinstance(self.type, str): repr_str += f" {self.type}" else: repr_str += f" {self.type._display()}" if self._display(): repr_str += f" '{self._display()}'" else: repr_str += " object" return f"{repr_str}>" def _display(self): if hasattr(self, "name") and hasattr(self, "value"): return f"{self.name} = {self.value}" for attr in ("name", "value", "absolutePath"): if hasattr(self, attr): return f"{getattr(self, attr)}" return "" def children( self, depth=None, include_self=False, include_parents=True, include_children=True, required_offset=None, offset_limits=None, filters=None, exclude_filter=None, ): """Get childen nodes of this node. Arguments: depth: Number of levels of children to traverse. 0 returns only this node. include_self: Includes this node in the results. include_parents: Includes nodes that match in the results, when they also have child nodes that match. include_children: If True, as soon as a match is found it's children will not be included in the search. required_offset: Only match nodes with a source offset that contains this offset. offset_limits: Only match nodes when their source offset is contained inside this source offset. filters: Dictionary of {attribute: value} that children must match. Can also be given as a list of dicts, children that match one of the dicts will be returned. exclude_filter: Dictionary of {attribute:value} that children cannot match. Returns: List of node objects.""" if filters is None: filters = {} if exclude_filter is None: exclude_filter = {} if isinstance(filters, dict): filters = [filters] filter_fn = functools.partial( _check_filters, required_offset, offset_limits, filters, exclude_filter ) find_fn = functools.partial(_find_children, filter_fn, include_parents, include_children) result = find_fn(find_fn, depth, self) if include_self or not result or result[0] != self: return result return result[1:] def parents(self, depth=-1, filters=None): """Get parent nodes of this node. Arguments: depth: Depth limit. If given as a negative value, it will be subtracted from this object's depth. filters: Dictionary of {attribute: value} that parents must match. Returns: list of nodes""" if filters and not isinstance(filters, dict): raise TypeError("Filters must be a dict") if depth < 0: depth = self.depth + depth if depth >= self.depth or depth < 0: raise IndexError("Given depth exceeds node depth") node_list = [] parent = self while True: parent = parent._parent if not filters or _check_filter(parent, filters, {}): node_list.append(parent) if parent.depth == depth: return node_list def parent(self, depth=-1, filters=None): """Get a parent node of this node. Arguments: depth: Depth limit. If given as a negative value, it will be subtracted from this object's depth. The parent at this exact depth is returned. filters: Dictionary of {attribute: value} that the parent must match. If a filter value is given, will return the first parent that meets the filters up to the given depth. If none is found, returns None. If no filter is given, returns the parent at the given depth.""" if filters and not isinstance(filters, dict): raise TypeError("Filters must be a dict") if depth < 0: depth = self.depth + depth if depth >= self.depth or depth < 0: raise IndexError("Given depth exceeds node depth") parent = self while parent.depth > depth: parent = parent._parent if parent.depth == depth and not filters: return parent if filters and _check_filter(parent, filters, {}): return parent return None def is_child_of(self, node): """Checks if this object is a child of the given node object.""" if node.depth >= self.depth: return False return self.parent(node.depth) == node def is_parent_of(self, node): """Checks if this object is a parent of the given node object.""" if node.depth <= self.depth: return False return node.parent(self.depth) == self def get(self, key, default=None): """ Gets an attribute from this node, if that attribute exists. Arguments: key: Field name to return. May contain decimals to return a value from a child node. default: Default value to return. Returns: Field value if it exists. Default value if not. """ if key is None: raise TypeError("Cannot match against None") obj = self for k in key.split("."): if isinstance(obj, dict): obj = obj.get(k) else: obj = getattr(obj, k, None) return obj or default class IterableNodeBase(NodeBase): def __getitem__(self, key): if isinstance(key, str): try: return next(i for i in self.nodes if getattr(i, "name", None) == key) except StopIteration: raise KeyError(key) return self.nodes[key] def __iter__(self): return iter(self.nodes) def __len__(self): return len(self.nodes) def __contains__(self, obj): return obj in self.nodes def node_class_factory(ast, parent): ast = deepcopy(ast) if not isinstance(ast, dict) or "nodeType" not in ast: return ast if "body" in ast: ast["nodes"] = ast.pop("body") base_class = IterableNodeBase if "nodes" in ast else NodeBase base_type = next((k for k, v in BASE_NODE_TYPES.items() if ast["nodeType"] in v), None) if base_type: ast["baseNodeType"] = base_type return type(ast["nodeType"], (base_class,), {})(ast, parent) def _check_filters(required_offset, offset_limits, filters, exclude, node): if required_offset and not is_inside_offset(required_offset, node.offset): return False if offset_limits and not is_inside_offset(node.offset, offset_limits): return False for f in filters: if _check_filter(node, f, exclude): return True return False def _check_filter(node, filters, exclude): for key, value in filters.items(): if node.get(key) != value: return False for key, value in exclude.items(): if node.get(key) == value: return False return True def _find_children(filter_fn, include_parents, include_children, find_fn, depth, node): if depth is not None: depth -= 1 if depth < 0: return [node] if filter_fn(node) else [] if not include_children and filter_fn(node): return [node] node_list = [] for child in node._children: node_list.extend(find_fn(find_fn, depth, child)) if (include_parents or not node_list) and filter_fn(node): node_list.insert(0, node) return node_list def is_inside_offset(inner, outer): """Checks if the first offset is contained in the second offset Args: inner: inner offset tuple outer: outer offset tuple Returns: bool""" return outer[0] <= inner[0] <= inner[1] <= outer[1]

danhper/py-solc-ast

solcast/nodes.py

Python

#!/usr/bin/env python # -*- coding: utf-8 -*- from multiprocessing import Pool import requests PROCESS_POOL_SIZE = 10 REQUESTS = 10000 BASE_URL = "http://localhost:8888" RESOURCE_NAME = "resource" def f(process_number): resource_name = RESOURCE_NAME raw_body = '{"title": "%i", "lifetime": 300, "wait": 20}' % process_number r = requests.post("%s/locks/%s" % (BASE_URL, resource_name), data=raw_body) if r.status_code != 201: raise Exception("bad status code %i from post request" % r.status_code) lock_url = r.headers['Location'] r = requests.delete(lock_url) if r.status_code != 204: raise Exception("bad status code %i from delete request" % r.status_code) if __name__ == '__main__': pool = Pool(processes=PROCESS_POOL_SIZE) pool.map(f, range(0, REQUESTS))

thefab/restful-distributed-lock-manager

tests/bomb1.py

Python

# Copyright (c) 1996-2015 PSERC. All rights reserved. # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. """Splits the gencost variable into two pieces if costs are given for Qg. """ from sys import stderr from numpy import array, arange def pqcost(gencost, ng, on=None): """Splits the gencost variable into two pieces if costs are given for Qg. Checks whether C{gencost} has cost information for reactive power generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng} rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves C{qcost} empty. Also does some error checking. If C{on} is specified (list of indices of generators which are on line) it only returns the rows corresponding to these generators. @author: Ray Zimmerman (PSERC Cornell) """ if on is None: on = arange(ng) if gencost.shape[0] == ng: pcost = gencost[on, :] qcost = array([]) elif gencost.shape[0] == 2 * ng: pcost = gencost[on, :] qcost = gencost[on + ng, :] else: stderr.write('pqcost: gencost has wrong number of rows\n') return pcost, qcost

AdrienGougeon/pandapower

pandapower/pypower/pqcost.py

Python

""" Full assembly of the parts to form the complete network """ import torch.nn.functional as F from .unet_parts import * from .channels import C class UNet3D(nn.Module): def __init__(self, n_channels, n_classes, bilinear=True, apply_sigmoid_to_output=False): super(UNet3D, self).__init__() self.n_channels = n_channels self.n_classes = n_classes self.bilinear = bilinear self.inc = DoubleConv3D(n_channels, C[0]) self.down1 = Down(C[0], C[1]) self.down2 = Down(C[1], C[2]) self.down3 = Down(C[2], C[3]) factor = 2 if bilinear else 1 self.down4 = Down(C[3], C[4] // factor) # switch do Double CONV if stick do 8x spatial down self.up1 = Up(C[4], C[3] // factor, bilinear) self.up2 = Up(C[3], C[2] // factor, bilinear) self.up3 = Up(C[2], C[1] // factor, bilinear) self.up4 = Up(C[1], C[0], bilinear) self.outc = OutConv(C[0], n_classes) if apply_sigmoid_to_output is False else OutConv(C[0], n_classes, sigmoid=True) def forward(self, x): x1 = self.inc(x) x2 = self.down1(x1) x3 = self.down2(x2) x4 = self.down3(x3) x5 = self.down4(x4) x = self.up1(x5, x4) x = self.up2(x, x3) x = self.up3(x, x2) x = self.up4(x, x1) logits = self.outc(x) return logits

mistermoutan/ModelsGenesis

pytorch/unet_3d/unet_model.py

Python

# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from oslo_log import versionutils from oslo_policy import policy from neutron.conf.policies import base DEPRECATED_REASON = ( "The security group API now supports system scope and default roles.") SG_COLLECTION_PATH = '/security-groups' SG_RESOURCE_PATH = '/security-groups/{id}' RULE_COLLECTION_PATH = '/security-group-rules' RULE_RESOURCE_PATH = '/security-group-rules/{id}' RULE_ADMIN_OR_SG_OWNER = 'rule:admin_or_sg_owner' RULE_ADMIN_OWNER_OR_SG_OWNER = 'rule:admin_owner_or_sg_owner' rules = [ policy.RuleDefault( name='admin_or_sg_owner', check_str=base.policy_or( 'rule:context_is_admin', 'tenant_id:%(security_group:tenant_id)s'), description='Rule for admin or security group owner access'), policy.RuleDefault( name='admin_owner_or_sg_owner', check_str=base.policy_or( 'rule:owner', RULE_ADMIN_OR_SG_OWNER), description=('Rule for resource owner, ' 'admin or security group owner access')), # TODO(amotoki): admin_or_owner is the right rule? # Does an empty string make more sense for create_security_group? policy.DocumentedRuleDefault( name='create_security_group', check_str=base.SYSTEM_ADMIN_OR_PROJECT_MEMBER, scope_types=['system', 'project'], description='Create a security group', operations=[ { 'method': 'POST', 'path': SG_COLLECTION_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='create_security_group', check_str=base.RULE_ADMIN_OR_OWNER, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), policy.DocumentedRuleDefault( name='get_security_group', check_str=base.SYSTEM_OR_PROJECT_READER, scope_types=['system', 'project'], description='Get a security group', operations=[ { 'method': 'GET', 'path': SG_COLLECTION_PATH, }, { 'method': 'GET', 'path': SG_RESOURCE_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='get_security_group', check_str=base.RULE_ANY, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), policy.DocumentedRuleDefault( name='update_security_group', check_str=base.SYSTEM_ADMIN_OR_PROJECT_MEMBER, scope_types=['system', 'project'], description='Update a security group', operations=[ { 'method': 'PUT', 'path': SG_RESOURCE_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='update_security_group', check_str=base.RULE_ADMIN_OR_OWNER, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), policy.DocumentedRuleDefault( name='delete_security_group', check_str=base.SYSTEM_ADMIN_OR_PROJECT_MEMBER, scope_types=['system', 'project'], description='Delete a security group', operations=[ { 'method': 'DELETE', 'path': SG_RESOURCE_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='delete_security_group', check_str=base.RULE_ADMIN_OR_OWNER, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), # TODO(amotoki): admin_or_owner is the right rule? # Does an empty string make more sense for create_security_group_rule? policy.DocumentedRuleDefault( name='create_security_group_rule', check_str=base.SYSTEM_ADMIN_OR_PROJECT_MEMBER, scope_types=['system', 'project'], description='Create a security group rule', operations=[ { 'method': 'POST', 'path': RULE_COLLECTION_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='create_security_group_rule', check_str=base.RULE_ADMIN_OR_OWNER, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), policy.DocumentedRuleDefault( name='get_security_group_rule', check_str=base.policy_or( base.SYSTEM_OR_PROJECT_READER, base.RULE_SG_OWNER), scope_types=['system', 'project'], description='Get a security group rule', operations=[ { 'method': 'GET', 'path': RULE_COLLECTION_PATH, }, { 'method': 'GET', 'path': RULE_RESOURCE_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='get_security_group_rule', check_str=RULE_ADMIN_OWNER_OR_SG_OWNER, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), policy.DocumentedRuleDefault( name='delete_security_group_rule', check_str=base.SYSTEM_ADMIN_OR_PROJECT_MEMBER, scope_types=['system', 'project'], description='Delete a security group rule', operations=[ { 'method': 'DELETE', 'path': RULE_RESOURCE_PATH, }, ], deprecated_rule=policy.DeprecatedRule( name='delete_security_group_rule', check_str=base.RULE_ADMIN_OR_OWNER, deprecated_reason=DEPRECATED_REASON, deprecated_since=versionutils.deprecated.WALLABY) ), ] def list_rules(): return rules

AurelienLourot/neutron

neutron/conf/policies/security_group.py

Python

import re find_image_scheme = re.compile(r'(?P<image_construction><img\b[^>]*src="(?P<image_url>[^"]+?)"[^>]*?\/>)') # find_link_around_image_scheme = re.compile(r"<a\b[^>]*>(.*?)<img\b(.*?)<\/a>") def move_image_to_attachment(content, attachment_object): # collect images from the post body intext_image_list = re.findall(find_image_scheme, content) if intext_image_list: # delete images form text content = re.sub(find_image_scheme, r"", content) # insert link to image into attachments attachment_object += [{ "type": "Document", "mediaType": "image/jpeg", "url": image[1], "name": "null" } for image in intext_image_list] return content

autogestion/pubgate-rssbot

rssbot/utils.py

Python

import numpy as np import matplotlib.pyplot as plt import gym import random # hyper parameters # test 1 # alpha = 0.5 # gamma = 0.95 # epsilon = 0.1 epsilon = 0.1 alpha = 0.1 gamma = 0.1 def update_sarsa_table(sarsa, state, action, reward, next_state, next_action, alpha, gamma): ''' update sarsa state-action pair value, main difference from q learning is that it uses epsilon greedy policy return action ''' next_max = sarsa[next_state,next_action] # corresponding action-state value to current action # print(f'current status is: {type(q[pre_state,action])},{type(alpha)},{type(reward)},{type(gamma)},{type(next_max)}') sarsa[state,action] = sarsa[state,action] + alpha * (reward + gamma * next_max - sarsa[state,action]) def epsilon_greedy_policy_sarsa(env, state, sarsa, epsilon): ''' epsilon greedy policy for q learning to generate actions ''' if random.uniform(0,1) < epsilon: return env.action_space.sample() else: return np.argmax(sarsa[state]) def epsilon_greedy_policy(env, state, q, epsilon): ''' epsilon greedy policy for q learning to generate actions ''' if random.uniform(0,1) < epsilon: return env.action_space.sample() else: return np.argmax(q[state]) def update_q_table(q, pre_state, action, reward, next_state, alpha, gamma): ''' ''' next_max = np.max(q[next_state]) # max state-action value for next state # print(f'current status is: {type(q[pre_state,action])},{type(alpha)},{type(reward)},{type(gamma)},{type(next_max)}') q[pre_state,action] = q[pre_state,action] + alpha * (reward + gamma * next_max - q[pre_state,action]) #-----------------------q learning------------------------------------------- env = gym.make("Taxi-v3") # initialize q table q = np.zeros((env.observation_space.n, env.action_space.n)) q_pre = np.zeros((env.observation_space.n, env.action_space.n)) # to check convergence when training reward_record = [] error_record = [] # loop for each episode: for episode in range(5000): r = 0 state = env.reset() while True:# loop for each step of episode # choose A from S using policy derived from Q(e.g, epsilon greedy policy) action = epsilon_greedy_policy(env,state,q,epsilon) # take action A, observe R, S' next_state, reward, done, _ = env.step(action) # update Q(S,A) update_q_table(q,state,action,reward,next_state,alpha,gamma) # S<--S' state = next_state r += reward if done: break reward_record.append(r) error = 0 for i in range(q.shape[0]): error = error + np.sum(np.abs(q[i]-q_pre[i])) # print(f'{np.abs(q[i]-q_pre[i])},{np.sum(np.abs(q[i]-q_pre[i]))}') error_record.append(error) q_pre = np.copy(q) if episode%100 == 0: print(f'{episode}th episode: {r}, {error}') #close game env env.close() #plot diagram # plt.plot(list(range(5000)),reward_record) # plt.show() # plt.plot(list(range(5000)),error_record) # plt.show() #double q learning env = gym.make("Taxi-v3") # initialize q table q1 = np.zeros((env.observation_space.n, env.action_space.n)) q2 = np.zeros((env.observation_space.n, env.action_space.n)) q1_pre = np.zeros((env.observation_space.n, env.action_space.n)) # to check convergence when training q2_pre = np.zeros((env.observation_space.n, env.action_space.n)) # to check convergence when training # reward and error record d_reward_record = [] d_error_record = [] # loop for each episode: for episode in range(5000): r = 0 state = env.reset() while True:# loop for each step of episode # choose A from S using policy derived from Q1+Q2(e.g, epsilon greedy policy) action = epsilon_greedy_policy(env,state,q1+q2,epsilon) # take action A, observe R, S' next_state, reward, done, _ = env.step(action) # with 0.5 probability: if random.uniform(0,1) < 0.5: update_q_table(q1,state,action,reward,next_state,alpha,gamma) else: update_q_table(q2,state,action,reward,next_state,alpha,gamma) # S<--S' state = next_state r += reward if done: break d_reward_record.append(r) error = 0 for i in range(q.shape[0]): error = error + 0.5 * np.sum(np.abs(q1[i]-q1_pre[i])) + 0.5 * np.sum(np.abs(q2[i]-q2_pre[i])) # print(f'{np.abs(q[i]-q_pre[i])},{np.sum(np.abs(q[i]-q_pre[i]))}') d_error_record.append(error) q1_pre = np.copy(q1) q2_pre = np.copy(q2) if episode%100 == 0: print(f'{episode}th episode: {r}, {error}') #close game env env.close() #plot diagram plt.plot(list(range(5000)),reward_record,label='q learning') plt.plot(list(range(5000)),d_reward_record,label='double q learning') plt.legend() plt.show() plt.plot(list(range(5000)),error_record,label='q learning') plt.plot(list(range(5000)),d_error_record, label='double q learning') plt.legend() plt.show()

hadleyhzy34/reinforcement_learning

TD/double_q_learning.py

Python

""" Meta Data Extension for Python-Markdown ======================================= This extension adds Meta Data handling to markdown. See <https://Python-Markdown.github.io/extensions/meta_data> for documentation. Original code Copyright 2007-2008 [Waylan Limberg](http://achinghead.com). All changes Copyright 2008-2014 The Python Markdown Project License: [BSD](http://www.opensource.org/licenses/bsd-license.php) """ from __future__ import absolute_import from __future__ import unicode_literals from . import Extension from ..preprocessors import Preprocessor import re import logging log = logging.getLogger('MARKDOWN') # Global Vars META_RE = re.compile(r'^[ ]{0,3}(?P<key>[A-Za-z0-9_-]+):\s*(?P<value>.*)') META_MORE_RE = re.compile(r'^[ ]{4,}(?P<value>.*)') BEGIN_RE = re.compile(r'^-{3}(\s.*)?') END_RE = re.compile(r'^(-{3}|\.{3})(\s.*)?') class MetaExtension (Extension): """ Meta-Data extension for Python-Markdown. """ def extendMarkdown(self, md, md_globals): """ Add MetaPreprocessor to Markdown instance. """ md.preprocessors.add("meta", MetaPreprocessor(md), ">normalize_whitespace") class MetaPreprocessor(Preprocessor): """ Get Meta-Data. """ def run(self, lines): """ Parse Meta-Data and store in Markdown.Meta. """ meta = {} key = None if lines and BEGIN_RE.match(lines[0]): lines.pop(0) while lines: line = lines.pop(0) m1 = META_RE.match(line) if line.strip() == '' or END_RE.match(line): break # blank line or end of YAML header - done if m1: key = m1.group('key').lower().strip() value = m1.group('value').strip() try: meta[key].append(value) except KeyError: meta[key] = [value] else: m2 = META_MORE_RE.match(line) if m2 and key: # Add another line to existing key meta[key].append(m2.group('value').strip()) else: lines.insert(0, line) break # no meta data - done self.markdown.Meta = meta return lines def makeExtension(*args, **kwargs): return MetaExtension(*args, **kwargs)

AzDan/Sac-Portal

venv/lib/python3.6/site-packages/markdown/extensions/meta.py

Python

from datetime import datetime, timedelta from typing import List, Optional from django.conf import settings from django.core.cache import cache from django.utils.translation import ugettext as _ from celery.schedules import crontab from celery.task import periodic_task, task from celery.utils.log import get_task_logger from dimagi.utils.couch import CriticalSection from corehq.apps.domain.models import Domain from corehq.apps.domain_migration_flags.api import any_migrations_in_progress from corehq.form_processor.interfaces.dbaccessors import FormAccessors from corehq.motech.repeaters.dbaccessors import ( get_couch_repeat_record_ids_by_payload_id, get_sql_repeat_records_by_payload_id, iter_repeat_record_ids_by_repeater, ) from corehq.motech.repeaters.models import SQLRepeatRecord from corehq.sql_db.util import get_db_aliases_for_partitioned_query from corehq.toggles import CASE_DEDUPE, DISABLE_CASE_UPDATE_RULE_SCHEDULED_TASK from corehq.util.celery_utils import no_result_task from corehq.util.decorators import serial_task from .deduplication import reset_deduplicate_rule, backfill_deduplicate_rule from .interfaces import FormManagementMode from .models import ( AUTO_UPDATE_XMLNS, AutomaticUpdateRule, CaseDuplicate, CaseRuleSubmission, DomainCaseRuleRun, ) from .utils import ( add_cases_to_case_group, archive_or_restore_forms, iter_cases_and_run_rules, operate_on_payloads, run_rules_for_case, ) logger = get_task_logger('data_interfaces') ONE_HOUR = 60 * 60 def _get_upload_progress_tracker(upload_id): def _progress_tracker(current, total): cache.set(upload_id, { 'inProgress': True, 'current': current, 'total': total, }, ONE_HOUR) return _progress_tracker @no_result_task(queue='case_rule_queue', acks_late=True, soft_time_limit=15 * settings.CELERY_TASK_SOFT_TIME_LIMIT) def reset_and_backfill_deduplicate_rule_task(domain, rule_id): if not CASE_DEDUPE.enabled(domain): return try: rule = AutomaticUpdateRule.objects.get( id=rule_id, domain=domain, workflow=AutomaticUpdateRule.WORKFLOW_DEDUPLICATE, active=True, deleted=False, ) except AutomaticUpdateRule.DoesNotExist: return AutomaticUpdateRule.clear_caches(rule.domain, AutomaticUpdateRule.WORKFLOW_DEDUPLICATE) reset_deduplicate_rule(rule) backfill_deduplicate_rule(domain, rule) @task(queue='background_queue') def delete_duplicates_for_cases(case_ids): CaseDuplicate.bulk_remove_unique_cases(case_ids) CaseDuplicate.remove_duplicates_for_case_ids(case_ids) @task(serializer='pickle', ignore_result=True) def bulk_upload_cases_to_group(upload_id, domain, case_group_id, cases): results = add_cases_to_case_group( domain, case_group_id, cases, progress_tracker=_get_upload_progress_tracker(upload_id) ) cache.set(upload_id, results, ONE_HOUR) @task(serializer='pickle') def bulk_form_management_async(archive_or_restore, domain, couch_user, form_ids): task = bulk_form_management_async mode = FormManagementMode(archive_or_restore, validate=True) if not form_ids: return {'messages': {'errors': [_('No Forms are supplied')]}} response = archive_or_restore_forms(domain, couch_user.user_id, couch_user.username, form_ids, mode, task) return response @periodic_task(serializer='pickle', run_every=crontab(hour='*', minute=0), queue=settings.CELERY_PERIODIC_QUEUE, ignore_result=True ) def run_case_update_rules(now=None): domains = (AutomaticUpdateRule .objects .filter(active=True, deleted=False, workflow=AutomaticUpdateRule.WORKFLOW_CASE_UPDATE) .values_list('domain', flat=True) .distinct() .order_by('domain')) hour_to_run = now.hour if now else datetime.utcnow().hour for domain in domains: if not any_migrations_in_progress(domain) and not DISABLE_CASE_UPDATE_RULE_SCHEDULED_TASK.enabled(domain): domain_obj = Domain.get_by_name(domain) if domain_obj.auto_case_update_hour is None: domain_hour = settings.RULE_UPDATE_HOUR else: domain_hour = domain_obj.auto_case_update_hour if hour_to_run == domain_hour: run_case_update_rules_for_domain.delay(domain, now) @task(serializer='pickle', queue='case_rule_queue') def run_case_update_rules_for_domain(domain, now=None): now = now or datetime.utcnow() domain_rules = AutomaticUpdateRule.by_domain(domain, AutomaticUpdateRule.WORKFLOW_CASE_UPDATE) all_rule_case_types = set(domain_rules.values_list('case_type', flat=True)) for case_type in all_rule_case_types: run_record = DomainCaseRuleRun.objects.create( domain=domain, started_on=datetime.utcnow(), status=DomainCaseRuleRun.STATUS_RUNNING, case_type=case_type ) for db in get_db_aliases_for_partitioned_query(): run_case_update_rules_for_domain_and_db.delay(domain, now, run_record.pk, case_type, db=db) @serial_task( '{domain}-{case_type}-{db}', timeout=36 * 60 * 60, max_retries=0, queue='case_rule_queue', ) def run_case_update_rules_for_domain_and_db(domain, now, run_id, case_type, db=None): all_rules = AutomaticUpdateRule.by_domain(domain, AutomaticUpdateRule.WORKFLOW_CASE_UPDATE) rules = list(all_rules.filter(case_type=case_type)) boundary_date = AutomaticUpdateRule.get_boundary_date(rules, now) iterator = AutomaticUpdateRule.iter_cases(domain, case_type, boundary_date, db=db) run = iter_cases_and_run_rules(domain, iterator, rules, now, run_id, case_type, db) if run.status == DomainCaseRuleRun.STATUS_FINISHED: for rule in rules: AutomaticUpdateRule.objects.filter(pk=rule.pk).update(last_run=now) @task(serializer='pickle', queue='background_queue', acks_late=True, ignore_result=True) def run_case_update_rules_on_save(case): key = 'case-update-on-save-case-{case}'.format(case=case.case_id) with CriticalSection([key]): update_case = True if case.xform_ids: last_form = FormAccessors(case.domain).get_form(case.xform_ids[-1]) update_case = last_form.xmlns != AUTO_UPDATE_XMLNS if update_case: rules = AutomaticUpdateRule.by_domain(case.domain, AutomaticUpdateRule.WORKFLOW_CASE_UPDATE).filter(case_type=case.type) now = datetime.utcnow() run_rules_for_case(case, rules, now) @periodic_task(run_every=crontab(hour=0, minute=0), queue='case_rule_queue', ignore_result=True) def delete_old_rule_submission_logs(): start = datetime.utcnow() max_age = start - timedelta(days=90) CaseRuleSubmission.objects.filter(created_on__lt=max_age).delete() @task(serializer='pickle') def task_operate_on_payloads( record_ids: List[str], domain: str, action, # type: Literal['resend', 'cancel', 'requeue'] # 3.8+ use_sql: bool, ): return operate_on_payloads(record_ids, domain, action, use_sql, task=task_operate_on_payloads) @task(serializer='pickle') def task_generate_ids_and_operate_on_payloads( payload_id: Optional[str], repeater_id: Optional[str], domain: str, action, # type: Literal['resend', 'cancel', 'requeue'] # 3.8+ use_sql: bool, ) -> dict: repeat_record_ids = _get_repeat_record_ids(payload_id, repeater_id, domain, use_sql) return operate_on_payloads(repeat_record_ids, domain, action, use_sql, task=task_generate_ids_and_operate_on_payloads) def _get_repeat_record_ids( payload_id: Optional[str], repeater_id: Optional[str], domain: str, use_sql: bool, ) -> List[str]: if not payload_id and not repeater_id: return [] if payload_id: if use_sql: records = get_sql_repeat_records_by_payload_id(domain, payload_id) return [r.id for r in records] else: return get_couch_repeat_record_ids_by_payload_id(domain, payload_id) else: if use_sql: queryset = SQLRepeatRecord.objects.filter( domain=domain, repeater__repeater_id=repeater_id, ) return [r['id'] for r in queryset.values('id')] else: return list(iter_repeat_record_ids_by_repeater(domain, repeater_id))

akashkj/commcare-hq

corehq/apps/data_interfaces/tasks.py

Python

#!/usr/bin/env python2 # -*- coding: utf-8 -*- from hermes_python.hermes import Hermes INTENT_HOW_ARE_YOU = "mikpan:how_are_you" INTENT_GOOD = "bezzam:feeling_good" INTENT_BAD = "bezzam:feeling_bad" INTENT_ALRIGHT = "bezzam:feeling_alright" INTENT_FILTER_FEELING = [INTENT_GOOD, INTENT_BAD, INTENT_ALRIGHT] def main(): with Hermes("localhost:1883") as h: h.subscribe_intent(INTENT_HOW_ARE_YOU, how_are_you_callback) \ .subscribe_intent(INTENT_GOOD, feeling_good_callback) \ .subscribe_intent(INTENT_BAD, feeling_bad_callback) \ .subscribe_intent(INTENT_ALRIGHT, feeling_alright_callback) \ .start() def how_are_you_callback(hermes, intent_message): session_id = intent_message.session_id response = "I'm doing great. How about you?" hermes.publish_continue_session(session_id, response, INTENT_FILTER_FEELING) def feeling_good_callback(hermes, intent_message): session_id = intent_message.session_id response = "That's awesome! I'm happy to hear that." hermes.publish_end_session(session_id, response) def feeling_bad_callback(hermes, intent_message): session_id = intent_message.session_id response = "Sorry to hear that. I hope you feel better soon." hermes.publish_end_session(session_id, response) def feeling_alright_callback(hermes, intent_message): session_id = intent_message.session_id response = "That's cool." hermes.publish_end_session(session_id, response) if __name__ == "__main__": main()

mikpan/amld19-snips-workshop

V2_action-how-are-you.py

Python

"""Python 3.9.5""" import cv2 import HandTrackingModule as htm def thumbIncrementCheck(lmList: list[list[int]]) -> int: """Checks whether your thumb is up or not. No matter what hand you use. returns 1 if thumb is up else 0""" count = 0 t_x = lmList[4][1] p_x = lmList[17][1] if t_x > p_x: # If true: RIGHT hand if lmList[4][1] >= lmList[2][1]: count += 1 else: # ELse: LEFT hand if lmList[4][1] <= lmList[2][1]: count += 1 return count def textOutput(count, cc) -> str: """Returns an appropriate text output depending on `count` and `cc`.""" text = "NOTHING" if (count, cc) == (2, 2): text = "SCISSOR" elif count == 0: text = "ROCK" elif count == 5: text = "PAPER" else: pass return text def main(): # cap = cv2.VideoCapture(0) # opens the camera detector = htm.HandDetector() while True: success, img = cv2.imread("/home/laughinglouds/Pictures/Webcam/2021-04-13-133250.jpg") img = detector.findHands(img) lmlist = detector.findPosition(img, draw=True) # If a hand is not detected value will be 0 # else non-zero (21) hand_exists = len(lmlist) tipIDs = [4, 8, 12, 16, 20] # Represents fingertips dipIDs = [2, 7, 11, 15, 19] # Represents landmarks below the tips count = 0 # keeps count of how many fingers are up cc = 0 # for later checking if `Scissor` or not if hand_exists: # Looping for the five fingers for i in range(0, 5): if i == 0: count += thumbIncrementCheck(lmlist) else: # 8: Index finger # 12: Middle finger if (lmlist[tipIDs[i]][2] < lmlist[dipIDs[i]][2]) and ( tipIDs[i] in (8, 12) # if either index or middle ): count += 1 cc += 1 elif lmlist[tipIDs[i]][2] < lmlist[dipIDs[i]][2]: count += 1 # print(cc) else: count = -1 txt = textOutput(count, cc) # (10, 140) is coordinate of txt on the screen cv2.putText(img, str(txt), (10, 140), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3) cv2.imshow("Image", img) # close key isn't working for me # os: linux mint 20.1 if cv2.waitKey(1) & 0xFF == ord("q"): break if __name__ == "__main__": main()

laughingclouds/dt-mst-project

forOutput.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ """Basic neural network layers.""" from ..block import Block, HybridBlock from ..utils import _indent class Sequential(Block): """Stacks `Block`s sequentially. Example:: net = nn.Sequential() # use net's name_scope to give child Blocks appropriate names. with net.name_scope(): net.add(nn.Dense(10, activation='relu')) net.add(nn.Dense(20)) """ def __init__(self, prefix=None, params=None): super(Sequential, self).__init__(prefix=prefix, params=params) def add(self, block): """Adds block on top of the stack.""" self.register_child(block) def forward(self, x): for block in self._children: x = block(x) return x def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, i): return self._children[i] def __len__(self): return len(self._children) class HybridSequential(HybridBlock): """Stacks `HybridBlock`s sequentially. Example:: net = nn.Sequential() # use net's name_scope to give child Blocks appropriate names. with net.name_scope(): net.add(nn.Dense(10, activation='relu')) net.add(nn.Dense(20)) """ def __init__(self, prefix=None, params=None): super(HybridSequential, self).__init__(prefix=prefix, params=params) def add(self, block): """Adds block on top of the stack.""" self.register_child(block) def hybrid_forward(self, F, x): for block in self._children: x = block(x) return x def __repr__(self): s = '{name}(\n{modstr}\n)' modstr = '\n'.join([' ({key}): {block}'.format(key=key, block=_indent(block.__repr__(), 2)) for key, block in enumerate(self._children) if isinstance(block, Block)]) return s.format(name=self.__class__.__name__, modstr=modstr) def __getitem__(self, i): return self._children[i] def __len__(self): return len(self._children) class Dense(HybridBlock): """Just your regular densely-connected NN layer. `Dense` implements the operation: `output = activation(dot(input, weight) + bias)` where `activation` is the element-wise activation function passed as the `activation` argument, `weight` is a weights matrix created by the layer, and `bias` is a bias vector created by the layer (only applicable if `use_bias` is `True`). Note: the input must be a tensor with rank 2. Use `flatten` to convert it to rank 2 manually if necessary. Parameters ---------- units : int Dimensionality of the output space. activation : str Activation function to use. See help on `Activation` layer. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). use_bias : bool Whether the layer uses a bias vector. weight_initializer : str or `Initializer` Initializer for the `kernel` weights matrix. bias_initializer: str or `Initializer` Initializer for the bias vector. in_units : int, optional Size of the input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_units` will be inferred from the shape of input data. prefix : str or None See document of `Block`. params : ParameterDict or None See document of `Block`. Input shape: A 2D input with shape `(batch_size, in_units)`. Output shape: The output would have shape `(batch_size, units)`. """ def __init__(self, units, activation=None, use_bias=True, weight_initializer=None, bias_initializer='zeros', in_units=0, **kwargs): super(Dense, self).__init__(**kwargs) with self.name_scope(): self._units = units self._in_units = in_units self.weight = self.params.get('weight', shape=(units, in_units), init=weight_initializer, allow_deferred_init=True) if use_bias: self.bias = self.params.get('bias', shape=(units,), init=bias_initializer, allow_deferred_init=True) else: self.bias = None if activation is not None: self.act = Activation(activation, prefix=activation+'_') else: self.act = None def hybrid_forward(self, F, x, weight, bias=None): if bias is None: act = F.FullyConnected(x, weight, no_bias=True, num_hidden=self._units, name='fwd') else: act = F.FullyConnected(x, weight, bias, num_hidden=self._units, name='fwd') if self.act is not None: act = self.act(act) return act def __repr__(self): s = '{name}({layout}, {act})' return s.format(name=self.__class__.__name__, act=self.act if self.act else 'linear', layout='{0} -> {1}'.format(self._in_units, self._units) if self._in_units else self._units) class Activation(HybridBlock): """Applies an activation function to input. Parameters ---------- activation : str Name of activation function to use. See :func:`~mxnet.ndarray.Activation` for available choices. Input shape: Arbitrary. Output shape: Same shape as input. """ def __init__(self, activation, **kwargs): self._act_type = activation super(Activation, self).__init__(**kwargs) def _alias(self): return self._act_type def hybrid_forward(self, F, x): return F.Activation(x, act_type=self._act_type, name='fwd') def __repr__(self): s = '{name}({_act_type})' return s.format(name=self.__class__.__name__, **self.__dict__) class Dropout(HybridBlock): """Applies Dropout to the input. Dropout consists in randomly setting a fraction `rate` of input units to 0 at each update during training time, which helps prevent overfitting. Parameters ---------- rate : float Fraction of the input units to drop. Must be a number between 0 and 1. Input shape: Arbitrary. Output shape: Same shape as input. References ---------- `Dropout: A Simple Way to Prevent Neural Networks from Overfitting <http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf>`_ """ def __init__(self, rate, **kwargs): super(Dropout, self).__init__(**kwargs) self._rate = rate def hybrid_forward(self, F, x): return F.Dropout(x, p=self._rate, name='fwd') def __repr__(self): s = '{name}(p = {_rate})' return s.format(name=self.__class__.__name__, **self.__dict__) class BatchNorm(HybridBlock): """Batch normalization layer (Ioffe and Szegedy, 2014). Normalizes the input at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1. Parameters ---------- axis : int, default 1 The axis that should be normalized. This is typically the channels (C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`, set `axis=1` in `BatchNorm`. If `layout='NHWC'`, then set `axis=3`. momentum: float, default 0.9 Momentum for the moving average. epsilon: float, default 1e-5 Small float added to variance to avoid dividing by zero. center: bool, default True If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: bool, default True If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling will be done by the next layer. beta_initializer: str or `Initializer`, default 'zeros' Initializer for the beta weight. gamma_initializer: str or `Initializer`, default 'ones' Initializer for the gamma weight. moving_mean_initializer: str or `Initializer`, default 'zeros' Initializer for the moving mean. moving_variance_initializer: str or `Initializer`, default 'ones' Initializer for the moving variance. in_channels : int, default 0 Number of channels (feature maps) in input data. If not specified, initialization will be deferred to the first time `forward` is called and `in_channels` will be inferred from the shape of input data. Input shape: Arbitrary. Output shape: Same shape as input. """ def __init__(self, axis=1, momentum=0.9, epsilon=1e-5, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', running_mean_initializer='zeros', running_variance_initializer='ones', in_channels=0, **kwargs): super(BatchNorm, self).__init__(**kwargs) self._kwargs = {'axis': axis, 'eps': epsilon, 'momentum': momentum, 'fix_gamma': not scale} if in_channels != 0: self.in_channels = in_channels self.gamma = self.params.get('gamma', grad_req='write' if scale else 'null', shape=(in_channels,), init=gamma_initializer, allow_deferred_init=True, differentiable=scale) self.beta = self.params.get('beta', grad_req='write' if center else 'null', shape=(in_channels,), init=beta_initializer, allow_deferred_init=True, differentiable=center) self.running_mean = self.params.get('running_mean', grad_req='null', shape=(in_channels,), init=running_mean_initializer, allow_deferred_init=True, differentiable=False) self.running_var = self.params.get('running_var', grad_req='null', shape=(in_channels,), init=running_variance_initializer, allow_deferred_init=True, differentiable=False) def hybrid_forward(self, F, x, gamma, beta, running_mean, running_var): return F.BatchNorm(x, gamma, beta, running_mean, running_var, name='fwd', **self._kwargs) def __repr__(self): s = '{name}({content}' if hasattr(self, 'in_channels'): s += ', in_channels={0}'.format(self.in_channels) s += ')' return s.format(name=self.__class__.__name__, content=', '.join(['='.join([k, v.__repr__()]) for k, v in self._kwargs.items()])) class LeakyReLU(HybridBlock): """Leaky version of a Rectified Linear Unit. It allows a small gradient when the unit is not active:: `f(x) = alpha * x for x < 0`, `f(x) = x for x >= 0`. Parameters ---------- alpha : float slope coefficient for the negative half axis. Must be >= 0. Input shape: Arbitrary. Output shape: Same shape as input. """ def __init__(self, alpha, **kwargs): super(LeakyReLU, self).__init__(**kwargs) self._alpha = alpha def hybrid_forward(self, F, x): return F.LeakyReLU(x, act_type='leaky', slope=self._alpha, name='fwd') def __repr__(self): s = '{name}({alpha})' return s.format(name=self.__class__.__name__, alpha=self._alpha) class Embedding(HybridBlock): """Turns non-negative integers (indexes/tokens) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]] Parameters ---------- input_dim : int Size of the vocabulary, i.e. maximum integer index + 1. output_dim : int Dimension of the dense embedding. dtype : str or np.dtype, default 'float32' Data type of output embeddings. weight_initializer : Initializer Initializer for the `embeddings` matrix. Input shape: 2D tensor with shape: `(N, M)`. Output shape: 3D tensor with shape: `(N, M, output_dim)`. """ def __init__(self, input_dim, output_dim, dtype='float32', weight_initializer=None, **kwargs): super(Embedding, self).__init__(**kwargs) self._kwargs = {'input_dim': input_dim, 'output_dim': output_dim, 'dtype': dtype} self.weight = self.params.get('weight', shape=(input_dim, output_dim), init=weight_initializer, allow_deferred_init=True) def hybrid_forward(self, F, x, weight): return F.Embedding(x, weight, name='fwd', **self._kwargs) def __repr__(self): s = '{block_name}({input_dim} -> {output_dim}, {dtype})' return s.format(block_name=self.__class__.__name__, **self._kwargs) class Flatten(HybridBlock): """Flattens the input to two dimensional. Input shape: Arbitrary shape `(N, a, b, c, ...)` Output shape: 2D tensor with shape: `(N, a*b*c...)` """ def __init__(self, **kwargs): super(Flatten, self).__init__(**kwargs) def hybrid_forward(self, F, x): return x.reshape((0, -1)) def __repr__(self): return self.__class__.__name__

IIMarch/mxnet

python/mxnet/gluon/nn/basic_layers.py

Python

import numpy as np np.random.seed(0) from bokeh.io import curdoc from bokeh.layouts import widgetbox, row, column from bokeh.models import ColumnDataSource, Select, Slider from bokeh.plotting import figure from bokeh.palettes import Spectral6 from sklearn import cluster, datasets from sklearn.neighbors import kneighbors_graph from sklearn.preprocessing import StandardScaler # define some helper functions def clustering(X, algorithm, n_clusters): # normalize dataset for easier parameter selection X = StandardScaler().fit_transform(X) # estimate bandwidth for mean shift bandwidth = cluster.estimate_bandwidth(X, quantile=0.3) # connectivity matrix for structured Ward connectivity = kneighbors_graph(X, n_neighbors=10, include_self=False) # make connectivity symmetric connectivity = 0.5 * (connectivity + connectivity.T) # Generate the new colors: if algorithm=='MiniBatchKMeans': model = cluster.MiniBatchKMeans(n_clusters=n_clusters) elif algorithm=='Birch': model = cluster.Birch(n_clusters=n_clusters) elif algorithm=='DBSCAN': model = cluster.DBSCAN(eps=.2) elif algorithm=='AffinityPropagation': model = cluster.AffinityPropagation(damping=.9, preference=-200) elif algorithm=='MeanShift': model = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True) elif algorithm=='SpectralClustering': model = cluster.SpectralClustering(n_clusters=n_clusters, eigen_solver='arpack', affinity="nearest_neighbors") elif algorithm=='Ward': model = cluster.AgglomerativeClustering(n_clusters=n_clusters, linkage='ward', connectivity=connectivity) elif algorithm=='AgglomerativeClustering': model = cluster.AgglomerativeClustering(linkage="average", affinity="cityblock", n_clusters=n_clusters, connectivity=connectivity) model.fit(X) if hasattr(model, 'labels_'): y_pred = model.labels_.astype(np.int) else: y_pred = model.predict(X) return X, y_pred def get_dataset(dataset, n_samples): if dataset == 'Noisy Circles': return datasets.make_circles(n_samples=n_samples, factor=0.5, noise=0.05) elif dataset == 'Noisy Moons': return datasets.make_moons(n_samples=n_samples, noise=0.05) elif dataset == 'Blobs': return datasets.make_blobs(n_samples=n_samples, random_state=8) elif dataset == "No Structure": return np.random.rand(n_samples, 2), None # set up initial data n_samples = 1500 n_clusters = 2 algorithm = 'MiniBatchKMeans' dataset = 'Noisy Circles' X, y = get_dataset(dataset, n_samples) X, y_pred = clustering(X, algorithm, n_clusters) spectral = np.hstack([Spectral6] * 20) colors = [spectral[i] for i in y] # set up plot (styling in theme.yaml) plot = figure(toolbar_location=None, title=algorithm) source = ColumnDataSource(data=dict(x=X[:, 0], y=X[:, 1], colors=colors)) plot.circle('x', 'y', fill_color='colors', line_color=None, source=source) # set up widgets clustering_algorithms= [ 'MiniBatchKMeans', 'AffinityPropagation', 'MeanShift', 'SpectralClustering', 'Ward', 'AgglomerativeClustering', 'DBSCAN', 'Birch' ] datasets_names = [ 'Noisy Circles', 'Noisy Moons', 'Blobs', 'No Structure' ] algorithm_select = Select(value='MiniBatchKMeans', title='Select algorithm:', width=200, options=clustering_algorithms) dataset_select = Select(value='Noisy Circles', title='Select dataset:', width=200, options=datasets_names) samples_slider = Slider(title="Number of samples", value=1500.0, start=1000.0, end=3000.0, step=100, width=400) clusters_slider = Slider(title="Number of clusters", value=2.0, start=2.0, end=10.0, step=1, width=400) # set up callbacks def update_algorithm_or_clusters(attrname, old, new): global X algorithm = algorithm_select.value n_clusters = int(clusters_slider.value) X, y_pred = clustering(X, algorithm, n_clusters) colors = [spectral[i] for i in y_pred] source.data['colors'] = colors source.data['x'] = X[:, 0] source.data['y'] = X[:, 1] plot.title.text = algorithm def update_samples_or_dataset(attrname, old, new): global X, y dataset = dataset_select.value algorithm = algorithm_select.value n_clusters = int(clusters_slider.value) n_samples = int(samples_slider.value) X, y = get_dataset(dataset, n_samples) X, y_pred = clustering(X, algorithm, n_clusters) colors = [spectral[i] for i in y_pred] source.data['x'] = X[:, 0] source.data['y'] = X[:, 1] source.data['colors'] = colors algorithm_select.on_change('value', update_algorithm_or_clusters) clusters_slider.on_change('value', update_algorithm_or_clusters) dataset_select.on_change('value', update_samples_or_dataset) samples_slider.on_change('value', update_samples_or_dataset) # set up layout selects = row(dataset_select, algorithm_select, width=420) inputs = column(selects, widgetbox(samples_slider, clusters_slider)) # add to document curdoc().add_root(row(inputs, plot)) curdoc().title = "Clustering"

SiggyF/bokeh

examples/app/clustering/main.py

Python

import csv source_file = "Resources/budget_data.csv" output_file = "Resources/budget_data_analysis.txt" #initialize months counter, total income, decrease and increase in revenue amounts number_of_months = 0 # to track the total number of months income_total = 0 #variable to hold total income as we iterate through the csv previous_income = 0 #variable to hold previously eveluated value from csv greatest_profit_increase = ["",0] #list to hold the greatest profit increase, inaitialized to lowest value 0 greatest_loss_decrease = ["",1000000000000] #list to hold the greatest loss decrease, inaitialized to highest value change_in_pl = [] #list to hold change in profit/loss as we iterate through the csv change_in_income = 0 #print (revenue_decrease) with open(source_file) as budget_data: csv_reader = csv.DictReader(budget_data) for row in csv_reader: number_of_months = number_of_months + 1 #print(row["Profit/Losses"]) income_total = income_total + int(row["Profit/Losses"]) #print(row) #trace the changes in amount change_in_income = int(row["Profit/Losses"]) - previous_income #print(change_in_income) #reinitiate the value to the record we completed evaluating previous_income = int(row["Profit/Losses"]) #print(previous_income) #greatest increase if(change_in_income > greatest_profit_increase[1]): greatest_profit_increase[0] = row["Date"] greatest_profit_increase[1] = change_in_income #greatest decrease if(change_in_income < greatest_loss_decrease[1]): greatest_loss_decrease[0] = row["Date"] greatest_loss_decrease[1] = change_in_income #append to the change_in_pl for sum calculations #print(int(row['Profit/Losses'])) change_in_pl.append(int(row['Profit/Losses'])) #calculate net profit or loss net_profit = sum(change_in_pl) #print(net_profit) print() print('Financial Anlysis') print('--------------------------') print("Total Months: " + str(number_of_months)) print("Total Income: " + "$" + str(net_profit)) print("Greatest Increase in Profits: " + str(greatest_profit_increase[0]) + " $" + str(greatest_profit_increase[1])) print("Greatest Decrease in Profits: " + str(greatest_loss_decrease[0]) + " $" + str(greatest_loss_decrease[1])) #write outup to text file with open(output_file,"w") as results: results.write("Total Months: " + str(number_of_months)) results.write("\n") results.write("Total Income: " + "$" + str(net_profit)) results.write("\n") results.write("Greatest Increase in Profits: " + str(greatest_profit_increase[0]) + " $" + str(greatest_profit_increase[1])) results.write("\n") results.write("Greatest Decrease in Profits: " + str(greatest_loss_decrease[0]) + " $" + str(greatest_loss_decrease[1]))

abelgk/python-challenge

PyBank/main.py

Python

# Copyright (C) 2010 Google Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following disclaimer # in the documentation and/or other materials provided with the # distribution. # * Neither the name of Google Inc. nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import logging import os import subprocess import sys import time import urllib2 import xml.dom.minidom from webkitpy.common.net.file_uploader import FileUploader try: import json except ImportError: # python 2.5 compatibility import webkitpy.thirdparty.simplejson as json # A JSON results generator for generic tests. # FIXME: move this code out of the layout_package directory. _log = logging.getLogger(__name__) _JSON_PREFIX = "ADD_RESULTS(" _JSON_SUFFIX = ");" def has_json_wrapper(string): return string.startswith(_JSON_PREFIX) and string.endswith(_JSON_SUFFIX) def strip_json_wrapper(json_content): # FIXME: Kill this code once the server returns json instead of jsonp. if has_json_wrapper(json_content): return json_content[len(_JSON_PREFIX):len(json_content) - len(_JSON_SUFFIX)] return json_content def load_json(filesystem, file_path): content = filesystem.read_text_file(file_path) content = strip_json_wrapper(content) return json.loads(content) def write_json(filesystem, json_object, file_path, callback=None): # Specify separators in order to get compact encoding. json_string = json.dumps(json_object, separators=(',', ':')) if callback: json_string = callback + "(" + json_string + ");" filesystem.write_text_file(file_path, json_string) def convert_trie_to_flat_paths(trie, prefix=None): """Converts the directory structure in the given trie to flat paths, prepending a prefix to each.""" result = {} for name, data in trie.iteritems(): if prefix: name = prefix + "/" + name if len(data) and not "results" in data: result.update(convert_trie_to_flat_paths(data, name)) else: result[name] = data return result def add_path_to_trie(path, value, trie): """Inserts a single flat directory path and associated value into a directory trie structure.""" if not "/" in path: trie[path] = value return directory, slash, rest = path.partition("/") if not directory in trie: trie[directory] = {} add_path_to_trie(rest, value, trie[directory]) def test_timings_trie(port, individual_test_timings): """Breaks a test name into chunks by directory and puts the test time as a value in the lowest part, e.g. foo/bar/baz.html: 1ms foo/bar/baz1.html: 3ms becomes foo: { bar: { baz.html: 1, baz1.html: 3 } } """ trie = {} for test_result in individual_test_timings: test = test_result.test_name add_path_to_trie(test, int(1000 * test_result.test_run_time), trie) return trie # FIXME: We already have a TestResult class in test_results.py class TestResult(object): """A simple class that represents a single test result.""" # Test modifier constants. (NONE, FAILS, FLAKY, DISABLED) = range(4) def __init__(self, test, failed=False, elapsed_time=0): self.test_name = test self.failed = failed self.test_run_time = elapsed_time test_name = test try: test_name = test.split('.')[1] except IndexError: _log.warn("Invalid test name: %s.", test) pass if test_name.startswith('FAILS_'): self.modifier = self.FAILS elif test_name.startswith('FLAKY_'): self.modifier = self.FLAKY elif test_name.startswith('DISABLED_'): self.modifier = self.DISABLED else: self.modifier = self.NONE def fixable(self): return self.failed or self.modifier == self.DISABLED class JSONResultsGeneratorBase(object): """A JSON results generator for generic tests.""" MAX_NUMBER_OF_BUILD_RESULTS_TO_LOG = 750 # Min time (seconds) that will be added to the JSON. MIN_TIME = 1 # Note that in non-chromium tests those chars are used to indicate # test modifiers (FAILS, FLAKY, etc) but not actual test results. PASS_RESULT = "P" SKIP_RESULT = "X" FAIL_RESULT = "F" FLAKY_RESULT = "L" NO_DATA_RESULT = "N" MODIFIER_TO_CHAR = {TestResult.NONE: PASS_RESULT, TestResult.DISABLED: SKIP_RESULT, TestResult.FAILS: FAIL_RESULT, TestResult.FLAKY: FLAKY_RESULT} VERSION = 4 VERSION_KEY = "version" RESULTS = "results" TIMES = "times" BUILD_NUMBERS = "buildNumbers" TIME = "secondsSinceEpoch" TESTS = "tests" FIXABLE_COUNT = "fixableCount" FIXABLE = "fixableCounts" ALL_FIXABLE_COUNT = "allFixableCount" RESULTS_FILENAME = "results.json" TIMES_MS_FILENAME = "times_ms.json" INCREMENTAL_RESULTS_FILENAME = "incremental_results.json" URL_FOR_TEST_LIST_JSON = "http://%s/testfile?builder=%s&name=%s&testlistjson=1&testtype=%s&master=%s" # FIXME: Remove generate_incremental_results once the reference to it in # http://src.chromium.org/viewvc/chrome/trunk/tools/build/scripts/slave/gtest_slave_utils.py # has been removed. def __init__(self, port, builder_name, build_name, build_number, results_file_base_path, builder_base_url, test_results_map, svn_repositories=None, test_results_server=None, test_type="", master_name="", generate_incremental_results=None): """Modifies the results.json file. Grabs it off the archive directory if it is not found locally. Args port: port-specific wrapper builder_name: the builder name (e.g. Webkit). build_name: the build name (e.g. webkit-rel). build_number: the build number. results_file_base_path: Absolute path to the directory containing the results json file. builder_base_url: the URL where we have the archived test results. If this is None no archived results will be retrieved. test_results_map: A dictionary that maps test_name to TestResult. svn_repositories: A (json_field_name, svn_path) pair for SVN repositories that tests rely on. The SVN revision will be included in the JSON with the given json_field_name. test_results_server: server that hosts test results json. test_type: test type string (e.g. 'layout-tests'). master_name: the name of the buildbot master. """ self._port = port self._filesystem = port._filesystem self._builder_name = builder_name self._build_name = build_name self._build_number = build_number self._builder_base_url = builder_base_url self._results_directory = results_file_base_path self._test_results_map = test_results_map self._test_results = test_results_map.values() self._svn_repositories = svn_repositories if not self._svn_repositories: self._svn_repositories = {} self._test_results_server = test_results_server self._test_type = test_type self._master_name = master_name self._archived_results = None def generate_json_output(self): json_object = self.get_json() if json_object: file_path = self._filesystem.join(self._results_directory, self.INCREMENTAL_RESULTS_FILENAME) write_json(self._filesystem, json_object, file_path) def generate_times_ms_file(self): # FIXME: rename to generate_times_ms_file. This needs to be coordinated with # changing the calls to this on the chromium build slaves. times = test_timings_trie(self._port, self._test_results_map.values()) file_path = self._filesystem.join(self._results_directory, self.TIMES_MS_FILENAME) write_json(self._filesystem, times, file_path) def get_json(self): """Gets the results for the results.json file.""" results_json = {} if not results_json: results_json, error = self._get_archived_json_results() if error: # If there was an error don't write a results.json # file at all as it would lose all the information on the # bot. _log.error("Archive directory is inaccessible. Not " "modifying or clobbering the results.json " "file: " + str(error)) return None builder_name = self._builder_name if results_json and builder_name not in results_json: _log.debug("Builder name (%s) is not in the results.json file." % builder_name) self._convert_json_to_current_version(results_json) if builder_name not in results_json: results_json[builder_name] = ( self._create_results_for_builder_json()) results_for_builder = results_json[builder_name] self._insert_generic_metadata(results_for_builder) self._insert_failure_summaries(results_for_builder) # Update the all failing tests with result type and time. tests = results_for_builder[self.TESTS] all_failing_tests = self._get_failed_test_names() all_failing_tests.update(convert_trie_to_flat_paths(tests)) for test in all_failing_tests: self._insert_test_time_and_result(test, tests) return results_json def set_archived_results(self, archived_results): self._archived_results = archived_results def upload_json_files(self, json_files): """Uploads the given json_files to the test_results_server (if the test_results_server is given).""" if not self._test_results_server: return if not self._master_name: _log.error("--test-results-server was set, but --master-name was not. Not uploading JSON files.") return _log.info("Uploading JSON files for builder: %s", self._builder_name) attrs = [("builder", self._builder_name), ("testtype", self._test_type), ("master", self._master_name)] files = [(file, self._filesystem.join(self._results_directory, file)) for file in json_files] url = "http://%s/testfile/upload" % self._test_results_server uploader = FileUploader(url) try: # Set uploading timeout in case appengine server is having problem. # 120 seconds are more than enough to upload test results. uploader.upload(attrs, files, 120) except Exception, err: _log.error("Upload failed: %s" % err) return _log.info("JSON files uploaded.") def _get_test_timing(self, test_name): """Returns test timing data (elapsed time) in second for the given test_name.""" if test_name in self._test_results_map: # Floor for now to get time in seconds. return int(self._test_results_map[test_name].test_run_time) return 0 def _get_failed_test_names(self): """Returns a set of failed test names.""" return set([r.test_name for r in self._test_results if r.failed]) def _get_modifier_char(self, test_name): """Returns a single char (e.g. SKIP_RESULT, FAIL_RESULT, PASS_RESULT, NO_DATA_RESULT, etc) that indicates the test modifier for the given test_name. """ if test_name not in self._test_results_map: return self.__class__.NO_DATA_RESULT test_result = self._test_results_map[test_name] if test_result.modifier in self.MODIFIER_TO_CHAR.keys(): return self.MODIFIER_TO_CHAR[test_result.modifier] return self.__class__.PASS_RESULT def _get_result_char(self, test_name): """Returns a single char (e.g. SKIP_RESULT, FAIL_RESULT, PASS_RESULT, NO_DATA_RESULT, etc) that indicates the test result for the given test_name. """ if test_name not in self._test_results_map: return self.__class__.NO_DATA_RESULT test_result = self._test_results_map[test_name] if test_result.modifier == TestResult.DISABLED: return self.__class__.SKIP_RESULT if test_result.failed: return self.__class__.FAIL_RESULT return self.__class__.PASS_RESULT # FIXME: Callers should use scm.py instead. # FIXME: Identify and fix the run-time errors that were observed on Windows # chromium buildbot when we had updated this code to use scm.py once before. def _get_svn_revision(self, in_directory): """Returns the svn revision for the given directory. Args: in_directory: The directory where svn is to be run. """ if self._filesystem.exists(self._filesystem.join(in_directory, '.svn')): # Note: Not thread safe: http://bugs.python.org/issue2320 output = subprocess.Popen(["svn", "info", "--xml"], cwd=in_directory, shell=(sys.platform == 'win32'), stdout=subprocess.PIPE).communicate()[0] try: dom = xml.dom.minidom.parseString(output) return dom.getElementsByTagName('entry')[0].getAttribute( 'revision') except xml.parsers.expat.ExpatError: return "" return "" def _get_archived_json_results(self): """Download JSON file that only contains test name list from test-results server. This is for generating incremental JSON so the file generated has info for tests that failed before but pass or are skipped from current run. Returns (archived_results, error) tuple where error is None if results were successfully read. """ results_json = {} old_results = None error = None if not self._test_results_server: return {}, None results_file_url = (self.URL_FOR_TEST_LIST_JSON % (urllib2.quote(self._test_results_server), urllib2.quote(self._builder_name), self.RESULTS_FILENAME, urllib2.quote(self._test_type), urllib2.quote(self._master_name))) try: # FIXME: We should talk to the network via a Host object. results_file = urllib2.urlopen(results_file_url) info = results_file.info() old_results = results_file.read() except urllib2.HTTPError, http_error: # A non-4xx status code means the bot is hosed for some reason # and we can't grab the results.json file off of it. if (http_error.code < 400 and http_error.code >= 500): error = http_error except urllib2.URLError, url_error: error = url_error if old_results: # Strip the prefix and suffix so we can get the actual JSON object. old_results = strip_json_wrapper(old_results) try: results_json = json.loads(old_results) except: _log.debug("results.json was not valid JSON. Clobbering.") # The JSON file is not valid JSON. Just clobber the results. results_json = {} else: _log.debug('Old JSON results do not exist. Starting fresh.') results_json = {} return results_json, error def _insert_failure_summaries(self, results_for_builder): """Inserts aggregate pass/failure statistics into the JSON. This method reads self._test_results and generates FIXABLE, FIXABLE_COUNT and ALL_FIXABLE_COUNT entries. Args: results_for_builder: Dictionary containing the test results for a single builder. """ # Insert the number of tests that failed or skipped. fixable_count = len([r for r in self._test_results if r.fixable()]) self._insert_item_into_raw_list(results_for_builder, fixable_count, self.FIXABLE_COUNT) # Create a test modifiers (FAILS, FLAKY etc) summary dictionary. entry = {} for test_name in self._test_results_map.iterkeys(): result_char = self._get_modifier_char(test_name) entry[result_char] = entry.get(result_char, 0) + 1 # Insert the pass/skip/failure summary dictionary. self._insert_item_into_raw_list(results_for_builder, entry, self.FIXABLE) # Insert the number of all the tests that are supposed to pass. all_test_count = len(self._test_results) self._insert_item_into_raw_list(results_for_builder, all_test_count, self.ALL_FIXABLE_COUNT) def _insert_item_into_raw_list(self, results_for_builder, item, key): """Inserts the item into the list with the given key in the results for this builder. Creates the list if no such list exists. Args: results_for_builder: Dictionary containing the test results for a single builder. item: Number or string to insert into the list. key: Key in results_for_builder for the list to insert into. """ if key in results_for_builder: raw_list = results_for_builder[key] else: raw_list = [] raw_list.insert(0, item) raw_list = raw_list[:self.MAX_NUMBER_OF_BUILD_RESULTS_TO_LOG] results_for_builder[key] = raw_list def _insert_item_run_length_encoded(self, item, encoded_results): """Inserts the item into the run-length encoded results. Args: item: String or number to insert. encoded_results: run-length encoded results. An array of arrays, e.g. [[3,'A'],[1,'Q']] encodes AAAQ. """ if len(encoded_results) and item == encoded_results[0][1]: num_results = encoded_results[0][0] if num_results <= self.MAX_NUMBER_OF_BUILD_RESULTS_TO_LOG: encoded_results[0][0] = num_results + 1 else: # Use a list instead of a class for the run-length encoding since # we want the serialized form to be concise. encoded_results.insert(0, [1, item]) def _insert_generic_metadata(self, results_for_builder): """ Inserts generic metadata (such as version number, current time etc) into the JSON. Args: results_for_builder: Dictionary containing the test results for a single builder. """ self._insert_item_into_raw_list(results_for_builder, self._build_number, self.BUILD_NUMBERS) # Include SVN revisions for the given repositories. for (name, path) in self._svn_repositories: self._insert_item_into_raw_list(results_for_builder, self._get_svn_revision(path), name + 'Revision') self._insert_item_into_raw_list(results_for_builder, int(time.time()), self.TIME) def _insert_test_time_and_result(self, test_name, tests): """ Insert a test item with its results to the given tests dictionary. Args: tests: Dictionary containing test result entries. """ result = self._get_result_char(test_name) time = self._get_test_timing(test_name) this_test = tests for segment in test_name.split("/"): if segment not in this_test: this_test[segment] = {} this_test = this_test[segment] if not len(this_test): self._populate_results_and_times_json(this_test) if self.RESULTS in this_test: self._insert_item_run_length_encoded(result, this_test[self.RESULTS]) else: this_test[self.RESULTS] = [[1, result]] if self.TIMES in this_test: self._insert_item_run_length_encoded(time, this_test[self.TIMES]) else: this_test[self.TIMES] = [[1, time]] def _convert_json_to_current_version(self, results_json): """If the JSON does not match the current version, converts it to the current version and adds in the new version number. """ if self.VERSION_KEY in results_json: archive_version = results_json[self.VERSION_KEY] if archive_version == self.VERSION: return else: archive_version = 3 # version 3->4 if archive_version == 3: num_results = len(results_json.values()) for builder, results in results_json.iteritems(): self._convert_tests_to_trie(results) results_json[self.VERSION_KEY] = self.VERSION def _convert_tests_to_trie(self, results): if not self.TESTS in results: return test_results = results[self.TESTS] test_results_trie = {} for test in test_results.iterkeys(): single_test_result = test_results[test] add_path_to_trie(test, single_test_result, test_results_trie) results[self.TESTS] = test_results_trie def _populate_results_and_times_json(self, results_and_times): results_and_times[self.RESULTS] = [] results_and_times[self.TIMES] = [] return results_and_times def _create_results_for_builder_json(self): results_for_builder = {} results_for_builder[self.TESTS] = {} return results_for_builder def _remove_items_over_max_number_of_builds(self, encoded_list): """Removes items from the run-length encoded list after the final item that exceeds the max number of builds to track. Args: encoded_results: run-length encoded results. An array of arrays, e.g. [[3,'A'],[1,'Q']] encodes AAAQ. """ num_builds = 0 index = 0 for result in encoded_list: num_builds = num_builds + result[0] index = index + 1 if num_builds > self.MAX_NUMBER_OF_BUILD_RESULTS_TO_LOG: return encoded_list[:index] return encoded_list def _normalize_results_json(self, test, test_name, tests): """ Prune tests where all runs pass or tests that no longer exist and truncate all results to maxNumberOfBuilds. Args: test: ResultsAndTimes object for this test. test_name: Name of the test. tests: The JSON object with all the test results for this builder. """ test[self.RESULTS] = self._remove_items_over_max_number_of_builds( test[self.RESULTS]) test[self.TIMES] = self._remove_items_over_max_number_of_builds( test[self.TIMES]) is_all_pass = self._is_results_all_of_type(test[self.RESULTS], self.PASS_RESULT) is_all_no_data = self._is_results_all_of_type(test[self.RESULTS], self.NO_DATA_RESULT) max_time = max([time[1] for time in test[self.TIMES]]) # Remove all passes/no-data from the results to reduce noise and # filesize. If a test passes every run, but takes > MIN_TIME to run, # don't throw away the data. if is_all_no_data or (is_all_pass and max_time <= self.MIN_TIME): del tests[test_name] def _is_results_all_of_type(self, results, type): """Returns whether all the results are of the given type (e.g. all passes).""" return len(results) == 1 and results[0][1] == type # Left here not to break anything. class JSONResultsGenerator(JSONResultsGeneratorBase): pass

JavaScriptTesting/LJS

WebKit/Tools/Scripts/webkitpy/layout_tests/layout_package/json_results_generator.py

Python

from io import BytesIO import pytest from app import app def test_otter(): with open('./images/otter.jpeg', 'rb') as img: img_string = BytesIO(img.read()) response = app.test_client().post('/predict', data={'file': (img_string, 'otter.jpeg')}, content_type="multipart/form-data") assert response.json['class_name'] == 'otter'

tadashi0713/circleci-demo-pytorch-api

tests/test_otter.py

Python

################################################################################ # # MIT License # # Copyright (c) 2020 Advanced Micro Devices, Inc. # # 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. # ################################################################################ from __future__ import print_function import argparse import sys, os, shutil from python import * OUT_DIR='out' def igemm_flatten(args, config_content): asm_target = os.path.join(args.dir, os.path.splitext(os.path.basename(args.config_file))[0] + '.s') emitter = mc_emit_to_file_t(asm_target) sec_root = config_content.get_section('codegen')[0] arch = amdgpu_arch_config_t({ 'arch' : amdgpu_string_to_arch( sec_root['arch'] ), 'data_type' : AMDGPU_PRECISION_FP32, 'code_object' : amdgpu_string_to_codeobj( sec_root['code_object']) }) # create mc mc = mc_asm_printer_t(emitter, arch) mc_set_current(mc) tunable_dicts = [sec.to_dict() for sec in config_content if sec.get_name().startswith('igemm_')] for td in tunable_dicts: td['arch'] = sec_root['arch'] # append arch to each section codegen_driver_t(mc, tunable_dicts)(split_kernel = args.split_kernel) # os.chmod(asm_target, 0x777) def igemm_out_tunable_param(output_file, config_content): sec_root = config_content.get_section('codegen')[0] list_emitter = mc_emit_to_file_t(output_file) list_emitter.open() tunable_dicts = [sec.to_dict() for sec in config_content if sec.get_name().startswith('igemm_')] for td in tunable_dicts: td['arch'] = sec_root['arch'] # append arch to each section td_item = igemm_gtc_tunable_parameter_t(td) list_emitter.emit(td_item.output()) list_emitter.close() def igemm_check_fp16_configs(config_content): tunable_dicts = [sec.to_dict() for sec in config_content if sec.get_name().startswith('igemm_')] for td in tunable_dicts: if "fp16" in td['precision']: return True return False def igemm_check_int8_configs(config_content): tunable_dicts = [sec.to_dict() for sec in config_content if sec.get_name().startswith('igemm_')] for td in tunable_dicts: if "int8" in td['precision']: return True return False def igemm_check_bf16_configs(config_content): tunable_dicts = [sec.to_dict() for sec in config_content if sec.get_name().startswith('igemm_')] for td in tunable_dicts: if "bf16" in td['precision']: return True return False if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("config_file", help="config file as input") parser.add_argument("-d", "--dir", help="directory of output files", default = OUT_DIR) parser.add_argument("-output", nargs='?', const='tunable_parameter_list.txt', help="output tunable parameter list") parser.add_argument("-s", "--split_kernel", action="store_true") args = parser.parse_args() config_parser = config_parser_t(args.config_file) #print(os.getcwd()) config_content = config_parser() #config_content.dump() #print(args.output) if args.output: igemm_out_tunable_param(args.output, config_content) arch = config_content.get_section('codegen')[0]['arch'] code_object = config_content.get_section('codegen')[0]['code_object'] has_fp16_config = igemm_check_fp16_configs(config_content) has_int8_config = igemm_check_int8_configs(config_content) has_bf16_config = igemm_check_bf16_configs(config_content) if config_content.get_section('codegen')[0]['mode'] in ('flat', 'flatten'): if os.path.exists(args.dir): shutil.rmtree(args.dir) os.mkdir(args.dir) cxxflags = [] if args.split_kernel: cxxflags += ["-DIGEMM_SPLIT_KERNEL"] host_driver(cxxflags=cxxflags, arch=arch, config_file=args.config_file, out_dir=args.dir, has_fp16_config=has_fp16_config, has_int8_config=has_int8_config, has_bf16_config=has_bf16_config) igemm_flatten(args, config_content) if config_content.get_section('codegen')[0]['mode'] in ('seq', 'sequencer'): # config_content.dump() # igemm_sequence(args, config_content) if os.path.exists(args.dir): shutil.rmtree(args.dir) os.mkdir(args.dir) sequence_driver(arch=arch, code_object=code_object, config_content=config_content, out_dir=args.dir )

ROCmSoftwarePlatform/iGEMMgen

igemm_codegen.py

Python

from __future__ import unicode_literals from boto.ec2.instancetype import InstanceType from moto.core.responses import BaseResponse from moto.core.utils import camelcase_to_underscores from moto.ec2.utils import instance_ids_from_querystring, filters_from_querystring, \ dict_from_querystring, optional_from_querystring class InstanceResponse(BaseResponse): def describe_instances(self): filter_dict = filters_from_querystring(self.querystring) instance_ids = instance_ids_from_querystring(self.querystring) if instance_ids: reservations = self.ec2_backend.get_reservations_by_instance_ids( instance_ids, filters=filter_dict) else: reservations = self.ec2_backend.all_reservations( make_copy=True, filters=filter_dict) template = self.response_template(EC2_DESCRIBE_INSTANCES) return template.render(reservations=reservations) def run_instances(self): min_count = int(self.querystring.get('MinCount', ['1'])[0]) image_id = self.querystring.get('ImageId')[0] user_data = self.querystring.get('UserData') security_group_names = self._get_multi_param('SecurityGroup') security_group_ids = self._get_multi_param('SecurityGroupId') nics = dict_from_querystring("NetworkInterface", self.querystring) instance_type = self.querystring.get("InstanceType", ["m1.small"])[0] placement = self.querystring.get( "Placement.AvailabilityZone", [None])[0] subnet_id = self.querystring.get("SubnetId", [None])[0] private_ip = self.querystring.get("PrivateIpAddress", [None])[0] associate_public_ip = self.querystring.get( "AssociatePublicIpAddress", [None])[0] key_name = self.querystring.get("KeyName", [None])[0] if self.is_not_dryrun('RunInstance'): new_reservation = self.ec2_backend.add_instances( image_id, min_count, user_data, security_group_names, instance_type=instance_type, placement=placement, subnet_id=subnet_id, key_name=key_name, security_group_ids=security_group_ids, nics=nics, private_ip=private_ip, associate_public_ip=associate_public_ip) template = self.response_template(EC2_RUN_INSTANCES) return template.render(reservation=new_reservation) def terminate_instances(self): instance_ids = instance_ids_from_querystring(self.querystring) if self.is_not_dryrun('TerminateInstance'): instances = self.ec2_backend.terminate_instances(instance_ids) template = self.response_template(EC2_TERMINATE_INSTANCES) return template.render(instances=instances) def reboot_instances(self): instance_ids = instance_ids_from_querystring(self.querystring) if self.is_not_dryrun('RebootInstance'): instances = self.ec2_backend.reboot_instances(instance_ids) template = self.response_template(EC2_REBOOT_INSTANCES) return template.render(instances=instances) def stop_instances(self): instance_ids = instance_ids_from_querystring(self.querystring) if self.is_not_dryrun('StopInstance'): instances = self.ec2_backend.stop_instances(instance_ids) template = self.response_template(EC2_STOP_INSTANCES) return template.render(instances=instances) def start_instances(self): instance_ids = instance_ids_from_querystring(self.querystring) if self.is_not_dryrun('StartInstance'): instances = self.ec2_backend.start_instances(instance_ids) template = self.response_template(EC2_START_INSTANCES) return template.render(instances=instances) def describe_instance_status(self): instance_ids = instance_ids_from_querystring(self.querystring) include_all_instances = optional_from_querystring('IncludeAllInstances', self.querystring) == 'true' if instance_ids: instances = self.ec2_backend.get_multi_instances_by_id( instance_ids) elif include_all_instances: instances = self.ec2_backend.all_instances() else: instances = self.ec2_backend.all_running_instances() template = self.response_template(EC2_INSTANCE_STATUS) return template.render(instances=instances) def describe_instance_types(self): instance_types = [InstanceType( name='t1.micro', cores=1, memory=644874240, disk=0)] template = self.response_template(EC2_DESCRIBE_INSTANCE_TYPES) return template.render(instance_types=instance_types) def describe_instance_attribute(self): # TODO this and modify below should raise IncorrectInstanceState if # instance not in stopped state attribute = self.querystring.get("Attribute")[0] key = camelcase_to_underscores(attribute) instance_ids = instance_ids_from_querystring(self.querystring) instance_id = instance_ids[0] instance, value = self.ec2_backend.describe_instance_attribute( instance_id, key) if key == "group_set": template = self.response_template( EC2_DESCRIBE_INSTANCE_GROUPSET_ATTRIBUTE) else: template = self.response_template(EC2_DESCRIBE_INSTANCE_ATTRIBUTE) return template.render(instance=instance, attribute=attribute, value=value) def modify_instance_attribute(self): handlers = [self._dot_value_instance_attribute_handler, self._block_device_mapping_handler, self._security_grp_instance_attribute_handler] for handler in handlers: success = handler() if success: return success msg = "This specific call to ModifyInstanceAttribute has not been" \ " implemented in Moto yet. Feel free to open an issue at" \ " https://github.com/spulec/moto/issues" raise NotImplementedError(msg) def _block_device_mapping_handler(self): """ Handles requests which are generated by code similar to: instance.modify_attribute('blockDeviceMapping', {'/dev/sda1': True}) The querystring contains information similar to: BlockDeviceMapping.1.Ebs.DeleteOnTermination : ['true'] BlockDeviceMapping.1.DeviceName : ['/dev/sda1'] For now we only support the "BlockDeviceMapping.1.Ebs.DeleteOnTermination" configuration, but it should be trivial to add anything else. """ mapping_counter = 1 mapping_device_name_fmt = 'BlockDeviceMapping.%s.DeviceName' mapping_del_on_term_fmt = 'BlockDeviceMapping.%s.Ebs.DeleteOnTermination' while True: mapping_device_name = mapping_device_name_fmt % mapping_counter if mapping_device_name not in self.querystring.keys(): break mapping_del_on_term = mapping_del_on_term_fmt % mapping_counter del_on_term_value_str = self.querystring[mapping_del_on_term][0] del_on_term_value = True if 'true' == del_on_term_value_str else False device_name_value = self.querystring[mapping_device_name][0] instance_ids = instance_ids_from_querystring(self.querystring) instance_id = instance_ids[0] instance = self.ec2_backend.get_instance(instance_id) if self.is_not_dryrun('ModifyInstanceAttribute'): block_device_type = instance.block_device_mapping[ device_name_value] block_device_type.delete_on_termination = del_on_term_value # +1 for the next device mapping_counter += 1 if mapping_counter > 1: return EC2_MODIFY_INSTANCE_ATTRIBUTE def _dot_value_instance_attribute_handler(self): attribute_key = None for key, value in self.querystring.items(): if '.Value' in key: attribute_key = key break if not attribute_key: return if self.is_not_dryrun('Modify' + attribute_key.split(".")[0]): value = self.querystring.get(attribute_key)[0] normalized_attribute = camelcase_to_underscores( attribute_key.split(".")[0]) instance_ids = instance_ids_from_querystring(self.querystring) instance_id = instance_ids[0] self.ec2_backend.modify_instance_attribute( instance_id, normalized_attribute, value) return EC2_MODIFY_INSTANCE_ATTRIBUTE def _security_grp_instance_attribute_handler(self): new_security_grp_list = [] for key, value in self.querystring.items(): if 'GroupId.' in key: new_security_grp_list.append(self.querystring.get(key)[0]) instance_ids = instance_ids_from_querystring(self.querystring) instance_id = instance_ids[0] if self.is_not_dryrun('ModifyInstanceSecurityGroups'): self.ec2_backend.modify_instance_security_groups( instance_id, new_security_grp_list) return EC2_MODIFY_INSTANCE_ATTRIBUTE EC2_RUN_INSTANCES = """<RunInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <reservationId>{{ reservation.id }}</reservationId> <ownerId>123456789012</ownerId> <groupSet> <item> <groupId>sg-245f6a01</groupId> <groupName>default</groupName> </item> </groupSet> <instancesSet> {% for instance in reservation.instances %} <item> <instanceId>{{ instance.id }}</instanceId> <imageId>{{ instance.image_id }}</imageId> <instanceState> <code>0</code> <name>pending</name> </instanceState> <privateDnsName>{{ instance.private_dns }}</privateDnsName> <publicDnsName>{{ instance.public_dns }}</publicDnsName> <dnsName>{{ instance.public_dns }}</dnsName> <reason/> <keyName>{{ instance.key_name }}</keyName> <amiLaunchIndex>0</amiLaunchIndex> <instanceType>{{ instance.instance_type }}</instanceType> <launchTime>{{ instance.launch_time }}</launchTime> <placement> <availabilityZone>{{ instance.placement}}</availabilityZone> <groupName/> <tenancy>default</tenancy> </placement> <monitoring> <state>enabled</state> </monitoring> {% if instance.nics %} {% if instance.nics[0].subnet %} <subnetId>{{ instance.nics[0].subnet.id }}</subnetId> <vpcId>{{ instance.nics[0].subnet.vpc_id }}</vpcId> {% endif %} <privateIpAddress>{{ instance.private_ip }}</privateIpAddress> {% if instance.public_ip %} <ipAddress>{{ instance.public_ip }}</ipAddress> {% endif %} {% else %} <subnetId>{{ instance.subnet_id }}</subnetId> {% endif %} <sourceDestCheck>{{ instance.source_dest_check }}</sourceDestCheck> <groupSet> {% for group in instance.dynamic_group_list %} <item> <groupId>{{ group.id }}</groupId> <groupName>{{ group.name }}</groupName> </item> {% endfor %} </groupSet> {% if instance.platform %} <platform>{{ instance.platform }}</platform> {% endif %} <virtualizationType>{{ instance.virtualization_type }}</virtualizationType> <architecture>{{ instance.architecture }}</architecture> <kernelId>{{ instance.kernel }}</kernelId> <clientToken/> <hypervisor>xen</hypervisor> <ebsOptimized>false</ebsOptimized> <networkInterfaceSet> {% for nic in instance.nics.values() %} <item> <networkInterfaceId>{{ nic.id }}</networkInterfaceId> {% if nic.subnet %} <subnetId>{{ nic.subnet.id }}</subnetId> <vpcId>{{ nic.subnet.vpc_id }}</vpcId> {% endif %} <description>Primary network interface</description> <ownerId>123456789012</ownerId> <status>in-use</status> <macAddress>1b:2b:3c:4d:5e:6f</macAddress> <privateIpAddress>{{ nic.private_ip_address }}</privateIpAddress> <sourceDestCheck>{{ instance.source_dest_check }}</sourceDestCheck> <groupSet> {% for group in nic.group_set %} <item> <groupId>{{ group.id }}</groupId> <groupName>{{ group.name }}</groupName> </item> {% endfor %} </groupSet> <attachment> <attachmentId>{{ nic.attachment_id }}</attachmentId> <deviceIndex>{{ nic.device_index }}</deviceIndex> <status>attached</status> <attachTime>2015-01-01T00:00:00Z</attachTime> <deleteOnTermination>true</deleteOnTermination> </attachment> {% if nic.public_ip %} <association> <publicIp>{{ nic.public_ip }}</publicIp> <ipOwnerId>123456789012</ipOwnerId> </association> {% endif %} <privateIpAddressesSet> <item> <privateIpAddress>{{ nic.private_ip_address }}</privateIpAddress> <primary>true</primary> {% if nic.public_ip %} <association> <publicIp>{{ nic.public_ip }}</publicIp> <ipOwnerId>123456789012</ipOwnerId> </association> {% endif %} </item> </privateIpAddressesSet> </item> {% endfor %} </networkInterfaceSet> </item> {% endfor %} </instancesSet> </RunInstancesResponse>""" EC2_DESCRIBE_INSTANCES = """<DescribeInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>fdcdcab1-ae5c-489e-9c33-4637c5dda355</requestId> <reservationSet> {% for reservation in reservations %} <item> <reservationId>{{ reservation.id }}</reservationId> <ownerId>123456789012</ownerId> <groupSet> {% for group in reservation.dynamic_group_list %} <item> {% if group.id %} <groupId>{{ group.id }}</groupId> <groupName>{{ group.name }}</groupName> {% else %} <groupId>{{ group }}</groupId> {% endif %} </item> {% endfor %} </groupSet> <instancesSet> {% for instance in reservation.instances %} <item> <instanceId>{{ instance.id }}</instanceId> <imageId>{{ instance.image_id }}</imageId> <instanceState> <code>{{ instance._state.code }}</code> <name>{{ instance._state.name }}</name> </instanceState> <privateDnsName>{{ instance.private_dns }}</privateDnsName> <publicDnsName>{{ instance.public_dns }}</publicDnsName> <dnsName>{{ instance.public_dns }}</dnsName> <reason>{{ instance._reason }}</reason> <keyName>{{ instance.key_name }}</keyName> <amiLaunchIndex>0</amiLaunchIndex> <productCodes/> <instanceType>{{ instance.instance_type }}</instanceType> <launchTime>{{ instance.launch_time }}</launchTime> <placement> <availabilityZone>{{ instance.placement }}</availabilityZone> <groupName/> <tenancy>default</tenancy> </placement> {% if instance.platform %} <platform>{{ instance.platform }}</platform> {% endif %} <monitoring> <state>disabled</state> </monitoring> {% if instance.nics %} {% if instance.nics[0].subnet %} <subnetId>{{ instance.nics[0].subnet.id }}</subnetId> <vpcId>{{ instance.nics[0].subnet.vpc_id }}</vpcId> {% endif %} <privateIpAddress>{{ instance.private_ip }}</privateIpAddress> {% if instance.nics[0].public_ip %} <ipAddress>{{ instance.nics[0].public_ip }}</ipAddress> {% endif %} {% endif %} <sourceDestCheck>{{ instance.source_dest_check }}</sourceDestCheck> <groupSet> {% for group in instance.dynamic_group_list %} <item> {% if group.id %} <groupId>{{ group.id }}</groupId> <groupName>{{ group.name }}</groupName> {% else %} <groupId>{{ group }}</groupId> {% endif %} </item> {% endfor %} </groupSet> <stateReason> <code>{{ instance._state_reason.code }}</code> <message>{{ instance._state_reason.message }}</message> </stateReason> <architecture>{{ instance.architecture }}</architecture> <kernelId>{{ instance.kernel }}</kernelId> <rootDeviceType>ebs</rootDeviceType> <rootDeviceName>/dev/sda1</rootDeviceName> <blockDeviceMapping> {% for device_name,deviceobject in instance.get_block_device_mapping %} <item> <deviceName>{{ device_name }}</deviceName> <ebs> <volumeId>{{ deviceobject.volume_id }}</volumeId> <status>{{ deviceobject.status }}</status> <attachTime>{{ deviceobject.attach_time }}</attachTime> <deleteOnTermination>{{ deviceobject.delete_on_termination }}</deleteOnTermination> <size>{{deviceobject.size}}</size> </ebs> </item> {% endfor %} </blockDeviceMapping> <virtualizationType>{{ instance.virtualization_type }}</virtualizationType> <clientToken>ABCDE1234567890123</clientToken> <tagSet> {% for tag in instance.get_tags() %} <item> <resourceId>{{ tag.resource_id }}</resourceId> <resourceType>{{ tag.resource_type }}</resourceType> <key>{{ tag.key }}</key> <value>{{ tag.value }}</value> </item> {% endfor %} </tagSet> <hypervisor>xen</hypervisor> <networkInterfaceSet> {% for nic in instance.nics.values() %} <item> <networkInterfaceId>{{ nic.id }}</networkInterfaceId> {% if nic.subnet %} <subnetId>{{ nic.subnet.id }}</subnetId> <vpcId>{{ nic.subnet.vpc_id }}</vpcId> {% endif %} <description>Primary network interface</description> <ownerId>123456789012</ownerId> <status>in-use</status> <macAddress>1b:2b:3c:4d:5e:6f</macAddress> <privateIpAddress>{{ nic.private_ip_address }}</privateIpAddress> <sourceDestCheck>{{ instance.source_dest_check }}</sourceDestCheck> <groupSet> {% for group in nic.group_set %} <item> {% if group.id %} <groupId>{{ group.id }}</groupId> <groupName>{{ group.name }}</groupName> {% else %} <groupId>{{ group }}</groupId> {% endif %} </item> {% endfor %} </groupSet> <attachment> <attachmentId>{{ nic.attachment_id }}</attachmentId> <deviceIndex>{{ nic.device_index }}</deviceIndex> <status>attached</status> <attachTime>2015-01-01T00:00:00Z</attachTime> <deleteOnTermination>true</deleteOnTermination> </attachment> {% if nic.public_ip %} <association> <publicIp>{{ nic.public_ip }}</publicIp> <ipOwnerId>123456789012</ipOwnerId> </association> {% endif %} <privateIpAddressesSet> <item> <privateIpAddress>{{ nic.private_ip_address }}</privateIpAddress> <primary>true</primary> {% if nic.public_ip %} <association> <publicIp>{{ nic.public_ip }}</publicIp> <ipOwnerId>123456789012</ipOwnerId> </association> {% endif %} </item> </privateIpAddressesSet> </item> {% endfor %} </networkInterfaceSet> </item> {% endfor %} </instancesSet> </item> {% endfor %} </reservationSet> </DescribeInstancesResponse>""" EC2_TERMINATE_INSTANCES = """ <TerminateInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <instancesSet> {% for instance in instances %} <item> <instanceId>{{ instance.id }}</instanceId> <previousState> <code>16</code> <name>running</name> </previousState> <currentState> <code>32</code> <name>shutting-down</name> </currentState> </item> {% endfor %} </instancesSet> </TerminateInstancesResponse>""" EC2_STOP_INSTANCES = """ <StopInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <instancesSet> {% for instance in instances %} <item> <instanceId>{{ instance.id }}</instanceId> <previousState> <code>16</code> <name>running</name> </previousState> <currentState> <code>64</code> <name>stopping</name> </currentState> </item> {% endfor %} </instancesSet> </StopInstancesResponse>""" EC2_START_INSTANCES = """ <StartInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <instancesSet> {% for instance in instances %} <item> <instanceId>{{ instance.id }}</instanceId> <previousState> <code>16</code> <name>running</name> </previousState> <currentState> <code>0</code> <name>pending</name> </currentState> </item> {% endfor %} </instancesSet> </StartInstancesResponse>""" EC2_REBOOT_INSTANCES = """<RebootInstancesResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <return>true</return> </RebootInstancesResponse>""" EC2_DESCRIBE_INSTANCE_ATTRIBUTE = """<DescribeInstanceAttributeResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <instanceId>{{ instance.id }}</instanceId> <{{ attribute }}> <value>{{ value }}</value> </{{ attribute }}> </DescribeInstanceAttributeResponse>""" EC2_DESCRIBE_INSTANCE_GROUPSET_ATTRIBUTE = """<DescribeInstanceAttributeResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <instanceId>{{ instance.id }}</instanceId> <{{ attribute }}> {% for sg_id in value %} <item> <groupId>{{ sg_id }}</groupId> </item> {% endfor %} </{{ attribute }}> </DescribeInstanceAttributeResponse>""" EC2_MODIFY_INSTANCE_ATTRIBUTE = """<ModifyInstanceAttributeResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <return>true</return> </ModifyInstanceAttributeResponse>""" EC2_INSTANCE_STATUS = """<?xml version="1.0" encoding="UTF-8"?> <DescribeInstanceStatusResponse xmlns="http://ec2.amazonaws.com/doc/2013-10-15/"> <requestId>59dbff89-35bd-4eac-99ed-be587EXAMPLE</requestId> <instanceStatusSet> {% for instance in instances %} <item> <instanceId>{{ instance.id }}</instanceId> <availabilityZone>{{ instance.placement }}</availabilityZone> <instanceState> <code>{{ instance.state_code }}</code> <name>{{ instance.state }}</name> </instanceState> {% if instance.state_code == 16 %} <systemStatus> <status>ok</status> <details> <item> <name>reachability</name> <status>passed</status> </item> </details> </systemStatus> <instanceStatus> <status>ok</status> <details> <item> <name>reachability</name> <status>passed</status> </item> </details> </instanceStatus> {% else %} <systemStatus> <status>not-applicable</status> </systemStatus> <instanceStatus> <status>not-applicable</status> </instanceStatus> {% endif %} </item> {% endfor %} </instanceStatusSet> </DescribeInstanceStatusResponse>""" EC2_DESCRIBE_INSTANCE_TYPES = """<?xml version="1.0" encoding="UTF-8"?> <DescribeInstanceTypesResponse xmlns="http://api.outscale.com/wsdl/fcuext/2014-04-15/"> <requestId>f8b86168-d034-4e65-b48d-3b84c78e64af</requestId> <instanceTypeSet> {% for instance_type in instance_types %} <item> <name>{{ instance_type.name }}</name> <vcpu>{{ instance_type.cores }}</vcpu> <memory>{{ instance_type.memory }}</memory> <storageSize>{{ instance_type.disk }}</storageSize> <storageCount>{{ instance_type.storageCount }}</storageCount> <maxIpAddresses>{{ instance_type.maxIpAddresses }}</maxIpAddresses> <ebsOptimizedAvailable>{{ instance_type.ebsOptimizedAvailable }}</ebsOptimizedAvailable> </item> {% endfor %} </instanceTypeSet> </DescribeInstanceTypesResponse>"""

adtsys-cloud/moto-aws-mock

moto/ec2/responses/instances.py

Python

import urllib.parse from functools import partial, wraps from pathlib import Path from drfs import config from drfs.util import prepend_scheme, remove_scheme def get_fs(path, opts=None, rtype="instance"): """Helper to infer filesystem correctly. Gets filesystem options from settings and updates them with given `opts`. Parameters ---------- path: str Path for which we want to infer filesystem. opts: dict Kwargs that will be passed to inferred filesystem instance. rtype: str Either 'instance' (default) or 'class'. """ from drfs.filesystems import FILESYSTEMS try: protocol = path.scheme except AttributeError: protocol = _get_protocol(path) try: cls = FILESYSTEMS[protocol] if rtype == "class": return cls except KeyError: raise KeyError( f"No filesystem for protocol {protocol}. Try " f"installing it. Available protocols are: " f"{set(FILESYSTEMS.keys())}" ) config_scheme_key = protocol if protocol else "file" opts_ = config["fs_opts"][config_scheme_key].get(dict).copy() # type: dict if opts is not None: opts_.update(opts) opts_ = _fix_opts_abfs(cls, path, opts_) return cls(**opts_) def _get_protocol(path): if "://" in str(path): protocol = urllib.parse.urlparse(str(path)).scheme else: # most likely a windows path, basically if in doubt assume local protocol = "" return protocol def _fix_opts_abfs(cls, path, opts: dict): try: from drfs.filesystems.azure_blob import AzureBlobFileSystem, extract_abfs_parts except ImportError: AzureBlobFileSystem = extract_abfs_parts = None if ( AzureBlobFileSystem is not None and cls is AzureBlobFileSystem and "account_name" not in opts ): opts = opts.copy() opts["account_name"] = extract_abfs_parts(path)[0] return opts def allow_pathlib(func): """Allow methods to receive pathlib.Path objects. Parameters ---------- func: callable function to decorate must have the following signature self, path, *args, **kwargs Returns ------- wrapper: callable """ @wraps(func) def wrapper(self, path, *args, **kwargs): # Can only be used if path is passed as first argument right # after self from drfs.path import asstr p = asstr(path) return func(self, p, *args, **kwargs) return wrapper def return_pathlib(func): @wraps(func) def wrapper(self, path, *args, **kwargs): from drfs.path import aspath res = func(self, path, *args, **kwargs) as_path = aspath(res) return as_path return wrapper def return_schemes(func): """Make sure method returns full path with scheme.""" @wraps(func) def wrapper(self, path, *args, **kwargs): res = func(self, path, *args, **kwargs) try: res = list(map(partial(prepend_scheme, self.scheme), res)) except TypeError: res = prepend_scheme(self.scheme, res) return res return wrapper def maybe_remove_scheme(func): """Remove scheme from args and kwargs in case underlying fs does not support it.""" @wraps(func) def wrapper(self, path, *args, **kwargs): if not self.supports_scheme: path = remove_scheme(path, raise_=False) args = [remove_scheme(a, raise_=False) for a in args] kwargs = { k: remove_scheme(v, raise_=False) if isinstance(v, (Path, str)) else v for k, v in kwargs.items() } return func(self, path, *args, **kwargs) return wrapper

datarevenue-berlin/drfs

drfs/filesystems/util.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import io import json import textwrap import unittest from contextlib import redirect_stdout from airflow.cli import cli_parser from airflow.cli.commands import plugins_command from airflow.hooks.base import BaseHook from airflow.listeners.listener import get_listener_manager from airflow.plugins_manager import AirflowPlugin from tests.plugins.test_plugin import AirflowTestPlugin as ComplexAirflowPlugin from tests.test_utils.mock_plugins import mock_plugin_manager class PluginHook(BaseHook): pass class TestPlugin(AirflowPlugin): name = "test-plugin-cli" hooks = [PluginHook] class TestPluginsCommand(unittest.TestCase): @classmethod def setUpClass(cls): cls.parser = cli_parser.get_parser() @mock_plugin_manager(plugins=[]) def test_should_display_no_plugins(self): with redirect_stdout(io.StringIO()) as temp_stdout: plugins_command.dump_plugins(self.parser.parse_args(['plugins', '--output=json'])) stdout = temp_stdout.getvalue() assert 'No plugins loaded' in stdout @mock_plugin_manager(plugins=[ComplexAirflowPlugin]) def test_should_display_one_plugins(self): with redirect_stdout(io.StringIO()) as temp_stdout: plugins_command.dump_plugins(self.parser.parse_args(['plugins', '--output=json'])) stdout = temp_stdout.getvalue() print(stdout) info = json.loads(stdout) assert info == [ { 'name': 'test_plugin', 'macros': ['tests.plugins.test_plugin.plugin_macro'], 'executors': ['tests.plugins.test_plugin.PluginExecutor'], 'flask_blueprints': [ "<flask.blueprints.Blueprint: name='test_plugin' import_name='tests.plugins.test_plugin'>" ], 'appbuilder_views': [ { 'name': 'Test View', 'category': 'Test Plugin', 'view': 'tests.plugins.test_plugin.PluginTestAppBuilderBaseView', } ], 'global_operator_extra_links': [ '<tests.test_utils.mock_operators.AirflowLink object>', '<tests.test_utils.mock_operators.GithubLink object>', ], 'timetables': ['tests.plugins.test_plugin.CustomCronDataIntervalTimetable'], 'operator_extra_links': [ '<tests.test_utils.mock_operators.GoogleLink object>', '<tests.test_utils.mock_operators.AirflowLink2 object>', '<tests.test_utils.mock_operators.CustomOpLink object>', '<tests.test_utils.mock_operators.CustomBaseIndexOpLink object>', ], 'hooks': ['tests.plugins.test_plugin.PluginHook'], 'listeners': ['tests.listeners.empty_listener'], 'source': None, 'appbuilder_menu_items': [ {'name': 'Google', 'href': 'https://www.google.com', 'category': 'Search'}, { 'name': 'apache', 'href': 'https://www.apache.org/', 'label': 'The Apache Software Foundation', }, ], 'ti_deps': ['<TIDep(CustomTestTriggerRule)>'], } ] get_listener_manager().clear() @mock_plugin_manager(plugins=[TestPlugin]) def test_should_display_one_plugins_as_table(self): with redirect_stdout(io.StringIO()) as temp_stdout: plugins_command.dump_plugins(self.parser.parse_args(['plugins', '--output=table'])) stdout = temp_stdout.getvalue() # Remove leading spaces stdout = "\n".join(line.rstrip(" ") for line in stdout.splitlines()) # Assert that only columns with values are displayed expected_output = textwrap.dedent( """\ name | hooks ================+=================================================== test-plugin-cli | tests.cli.commands.test_plugins_command.PluginHook """ ) self.assertEqual(stdout, expected_output)

AMS-Kepler/airflow

tests/cli/commands/test_plugins_command.py

Python

"""Conversion tool from SQD to FIF. RawKIT class is adapted from Denis Engemann et al.'s mne_bti2fiff.py. """ # Authors: Teon Brooks <[email protected]> # Joan Massich <[email protected]> # Christian Brodbeck <[email protected]> # # License: BSD (3-clause) from collections import defaultdict, OrderedDict from math import sin, cos from os import SEEK_CUR, path as op from struct import unpack import numpy as np from scipy import linalg from ..pick import pick_types from ...utils import (verbose, logger, warn, fill_doc, _check_option, _stamp_to_dt) from ...transforms import apply_trans, als_ras_trans from ..base import BaseRaw from ..utils import _mult_cal_one from ...epochs import BaseEpochs from ..constants import FIFF from ..meas_info import _empty_info from .constants import KIT, LEGACY_AMP_PARAMS from .coreg import read_mrk from ...event import read_events from .._digitization import _set_dig_kit def _call_digitization(info, mrk, elp, hsp, kit_info): # Use values from kit_info only if all others are None if mrk is None and elp is None and hsp is None: mrk = kit_info.get('mrk', None) elp = kit_info.get('elp', None) hsp = kit_info.get('hsp', None) # prepare mrk if isinstance(mrk, list): mrk = [read_mrk(marker) if isinstance(marker, str) else marker for marker in mrk] mrk = np.mean(mrk, axis=0) # setup digitization if mrk is not None and elp is not None and hsp is not None: dig_points, dev_head_t = _set_dig_kit( mrk, elp, hsp, kit_info['eeg_dig']) info['dig'] = dig_points info['dev_head_t'] = dev_head_t elif mrk is not None or elp is not None or hsp is not None: raise ValueError("mrk, elp and hsp need to be provided as a group " "(all or none)") return info class UnsupportedKITFormat(ValueError): """Our reader is not guaranteed to work with old files.""" def __init__(self, sqd_version, *args, **kwargs): # noqa: D102 self.sqd_version = sqd_version ValueError.__init__(self, *args, **kwargs) @fill_doc class RawKIT(BaseRaw): """Raw object from KIT SQD file. Parameters ---------- input_fname : str Path to the sqd file. mrk : None | str | array_like, shape (5, 3) | list of str or array_like Marker points representing the location of the marker coils with respect to the MEG Sensors, or path to a marker file. If list, all of the markers will be averaged together. elp : None | str | array_like, shape (8, 3) Digitizer points representing the location of the fiducials and the marker coils with respect to the digitized head shape, or path to a file containing these points. hsp : None | str | array, shape (n_points, 3) Digitizer head shape points, or path to head shape file. If more than 10,000 points are in the head shape, they are automatically decimated. stim : list of int | '<' | '>' | None Channel-value correspondence when converting KIT trigger channels to a Neuromag-style stim channel. For '<', the largest values are assigned to the first channel (default). For '>', the largest values are assigned to the last channel. Can also be specified as a list of trigger channel indexes. If None, no synthesized channel is generated. slope : '+' | '-' How to interpret values on KIT trigger channels when synthesizing a Neuromag-style stim channel. With '+', a positive slope (low-to-high) is interpreted as an event. With '-', a negative slope (high-to-low) is interpreted as an event. stimthresh : float The threshold level for accepting voltage changes in KIT trigger channels as a trigger event. If None, stim must also be set to None. %(preload)s stim_code : 'binary' | 'channel' How to decode trigger values from stim channels. 'binary' read stim channel events as binary code, 'channel' encodes channel number. allow_unknown_format : bool Force reading old data that is not officially supported. Alternatively, read and re-save the data with the KIT MEG Laboratory application. %(standardize_names)s %(verbose)s Notes ----- ``elp`` and ``hsp`` are usually the exported text files (*.txt) from the Polhemus FastScan system. hsp refers to the headshape surface points. elp refers to the points in head-space that corresponds to the HPI points. Currently, '*.elp' and '*.hsp' files are NOT supported. See Also -------- mne.io.Raw : Documentation of attribute and methods. """ @verbose def __init__(self, input_fname, mrk=None, elp=None, hsp=None, stim='>', slope='-', stimthresh=1, preload=False, stim_code='binary', allow_unknown_format=False, standardize_names=None, verbose=None): # noqa: D102 logger.info('Extracting SQD Parameters from %s...' % input_fname) input_fname = op.abspath(input_fname) self.preload = False logger.info('Creating Raw.info structure...') info, kit_info = get_kit_info( input_fname, allow_unknown_format, standardize_names) kit_info['slope'] = slope kit_info['stimthresh'] = stimthresh if kit_info['acq_type'] != KIT.CONTINUOUS: raise TypeError('SQD file contains epochs, not raw data. Wrong ' 'reader.') logger.info('Creating Info structure...') last_samps = [kit_info['n_samples'] - 1] self._raw_extras = [kit_info] self._set_stimchannels(info, stim, stim_code) super(RawKIT, self).__init__( info, preload, last_samps=last_samps, filenames=[input_fname], raw_extras=self._raw_extras, verbose=verbose) self.info = _call_digitization( info=self.info, mrk=mrk, elp=elp, hsp=hsp, kit_info=kit_info) logger.info('Ready.') def read_stim_ch(self, buffer_size=1e5): """Read events from data. Parameter --------- buffer_size : int The size of chunk to by which the data are scanned. Returns ------- events : array, [samples] The event vector (1 x samples). """ buffer_size = int(buffer_size) start = int(self.first_samp) stop = int(self.last_samp + 1) pick = pick_types(self.info, meg=False, ref_meg=False, stim=True, exclude=[]) stim_ch = np.empty((1, stop), dtype=np.int64) for b_start in range(start, stop, buffer_size): b_stop = b_start + buffer_size x = self[pick, b_start:b_stop][0] stim_ch[:, b_start:b_start + x.shape[1]] = x return stim_ch def _set_stimchannels(self, info, stim, stim_code): """Specify how the trigger channel is synthesized from analog channels. Has to be done before loading data. For a RawKIT instance that has been created with preload=True, this method will raise a NotImplementedError. Parameters ---------- info : instance of MeasInfo The measurement info. stim : list of int | '<' | '>' Can be submitted as list of trigger channels. If a list is not specified, the default triggers extracted from misc channels will be used with specified directionality. '<' means that largest values assigned to the first channel in sequence. '>' means the largest trigger assigned to the last channel in sequence. stim_code : 'binary' | 'channel' How to decode trigger values from stim channels. 'binary' read stim channel events as binary code, 'channel' encodes channel number. """ if self.preload: raise NotImplementedError("Can't change stim channel after " "loading data") _check_option('stim_code', stim_code, ['binary', 'channel']) if stim is not None: if isinstance(stim, str): picks = _default_stim_chs(info) if stim == '<': stim = picks[::-1] elif stim == '>': stim = picks else: raise ValueError("stim needs to be list of int, '>' or " "'<', not %r" % str(stim)) else: stim = np.asarray(stim, int) if stim.max() >= self._raw_extras[0]['nchan']: raise ValueError( 'Got stim=%s, but sqd file only has %i channels' % (stim, self._raw_extras[0]['nchan'])) # modify info nchan = self._raw_extras[0]['nchan'] + 1 info['chs'].append(dict( cal=KIT.CALIB_FACTOR, logno=nchan, scanno=nchan, range=1.0, unit=FIFF.FIFF_UNIT_NONE, unit_mul=FIFF.FIFF_UNITM_NONE, ch_name='STI 014', coil_type=FIFF.FIFFV_COIL_NONE, loc=np.full(12, np.nan), kind=FIFF.FIFFV_STIM_CH, coord_frame=FIFF.FIFFV_COORD_UNKNOWN)) info._update_redundant() self._raw_extras[0]['stim'] = stim self._raw_extras[0]['stim_code'] = stim_code def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a chunk of raw data.""" sqd = self._raw_extras[fi] nchan = sqd['nchan'] data_left = (stop - start) * nchan conv_factor = sqd['conv_factor'] n_bytes = sqd['dtype'].itemsize assert n_bytes in (2, 4) # Read up to 100 MB of data at a time. blk_size = min(data_left, (100000000 // n_bytes // nchan) * nchan) with open(self._filenames[fi], 'rb', buffering=0) as fid: # extract data pointer = start * nchan * n_bytes fid.seek(sqd['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'] + pointer) stim = sqd['stim'] for blk_start in np.arange(0, data_left, blk_size) // nchan: blk_size = min(blk_size, data_left - blk_start * nchan) block = np.fromfile(fid, dtype=sqd['dtype'], count=blk_size) block = block.reshape(nchan, -1, order='F').astype(float) blk_stop = blk_start + block.shape[1] data_view = data[:, blk_start:blk_stop] block *= conv_factor # Create a synthetic stim channel if stim is not None: stim_ch = _make_stim_channel( block[stim, :], sqd['slope'], sqd['stimthresh'], sqd['stim_code'], stim) block = np.vstack((block, stim_ch)) _mult_cal_one(data_view, block, idx, cals, mult) # cals are all unity, so can be ignored def _default_stim_chs(info): """Return default stim channels for SQD files.""" return pick_types(info, meg=False, ref_meg=False, misc=True, exclude=[])[:8] def _make_stim_channel(trigger_chs, slope, threshold, stim_code, trigger_values): """Create synthetic stim channel from multiple trigger channels.""" if slope == '+': trig_chs_bin = trigger_chs > threshold elif slope == '-': trig_chs_bin = trigger_chs < threshold else: raise ValueError("slope needs to be '+' or '-'") # trigger value if stim_code == 'binary': trigger_values = 2 ** np.arange(len(trigger_chs)) elif stim_code != 'channel': raise ValueError("stim_code must be 'binary' or 'channel', got %s" % repr(stim_code)) trig_chs = trig_chs_bin * trigger_values[:, np.newaxis] return np.array(trig_chs.sum(axis=0), ndmin=2) class EpochsKIT(BaseEpochs): """Epochs Array object from KIT SQD file. Parameters ---------- input_fname : str Path to the sqd file. events : str | array, shape (n_events, 3) Path to events file. If array, it is the events typically returned by the read_events function. If some events don't match the events of interest as specified by event_id,they will be marked as 'IGNORED' in the drop log. event_id : int | list of int | dict | None The id of the event to consider. If dict, the keys can later be used to access associated events. Example: dict(auditory=1, visual=3). If int, a dict will be created with the id as string. If a list, all events with the IDs specified in the list are used. If None, all events will be used with and a dict is created with string integer names corresponding to the event id integers. tmin : float Start time before event. baseline : None or tuple of length 2 (default (None, 0)) The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between "a (s)" and "b (s)". If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal to (None, None) all the time interval is used. The baseline (a, b) includes both endpoints, i.e. all timepoints t such that a <= t <= b. reject : dict | None Rejection parameters based on peak-to-peak amplitude. Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'. If reject is None then no rejection is done. Example:: reject = dict(grad=4000e-13, # T / m (gradiometers) mag=4e-12, # T (magnetometers) eeg=40e-6, # V (EEG channels) eog=250e-6 # V (EOG channels) ) flat : dict | None Rejection parameters based on flatness of signal. Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg', and values are floats that set the minimum acceptable peak-to-peak amplitude. If flat is None then no rejection is done. reject_tmin : scalar | None Start of the time window used to reject epochs (with the default None, the window will start with tmin). reject_tmax : scalar | None End of the time window used to reject epochs (with the default None, the window will end with tmax). mrk : None | str | array_like, shape = (5, 3) | list of str or array_like Marker points representing the location of the marker coils with respect to the MEG Sensors, or path to a marker file. If list, all of the markers will be averaged together. elp : None | str | array_like, shape = (8, 3) Digitizer points representing the location of the fiducials and the marker coils with respect to the digitized head shape, or path to a file containing these points. hsp : None | str | array, shape = (n_points, 3) Digitizer head shape points, or path to head shape file. If more than 10`000 points are in the head shape, they are automatically decimated. allow_unknown_format : bool Force reading old data that is not officially supported. Alternatively, read and re-save the data with the KIT MEG Laboratory application. %(standardize_names)s %(verbose)s Notes ----- ``elp`` and ``hsp`` are usually the exported text files (*.txt) from the Polhemus FastScan system. hsp refers to the headshape surface points. elp refers to the points in head-space that corresponds to the HPI points. Currently, '*.elp' and '*.hsp' files are NOT supported. See Also -------- mne.Epochs : Documentation of attribute and methods. """ @verbose def __init__(self, input_fname, events, event_id=None, tmin=0, baseline=None, reject=None, flat=None, reject_tmin=None, reject_tmax=None, mrk=None, elp=None, hsp=None, allow_unknown_format=False, standardize_names=None, verbose=None): # noqa: D102 if isinstance(events, str): events = read_events(events) logger.info('Extracting KIT Parameters from %s...' % input_fname) input_fname = op.abspath(input_fname) self.info, kit_info = get_kit_info( input_fname, allow_unknown_format, standardize_names) kit_info.update(filename=input_fname) self._raw_extras = [kit_info] self._filenames = [] if len(events) != self._raw_extras[0]['n_epochs']: raise ValueError('Event list does not match number of epochs.') if self._raw_extras[0]['acq_type'] == KIT.EPOCHS: self._raw_extras[0]['data_length'] = KIT.INT else: raise TypeError('SQD file contains raw data, not epochs or ' 'average. Wrong reader.') if event_id is None: # convert to int to make typing-checks happy event_id = {str(e): int(e) for e in np.unique(events[:, 2])} for key, val in event_id.items(): if val not in events[:, 2]: raise ValueError('No matching events found for %s ' '(event id %i)' % (key, val)) data = self._read_kit_data() assert data.shape == (self._raw_extras[0]['n_epochs'], self.info['nchan'], self._raw_extras[0]['frame_length']) tmax = ((data.shape[2] - 1) / self.info['sfreq']) + tmin super(EpochsKIT, self).__init__( self.info, data, events, event_id, tmin, tmax, baseline, reject=reject, flat=flat, reject_tmin=reject_tmin, reject_tmax=reject_tmax, filename=input_fname, verbose=verbose) self.info = _call_digitization( info=self.info, mrk=mrk, elp=elp, hsp=hsp, kit_info=kit_info) logger.info('Ready.') def _read_kit_data(self): """Read epochs data. Returns ------- data : array, [channels x samples] the data matrix (channels x samples). times : array, [samples] returns the time values corresponding to the samples. """ info = self._raw_extras[0] epoch_length = info['frame_length'] n_epochs = info['n_epochs'] n_samples = info['n_samples'] filename = info['filename'] dtype = info['dtype'] nchan = info['nchan'] with open(filename, 'rb', buffering=0) as fid: fid.seek(info['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset']) count = n_samples * nchan data = np.fromfile(fid, dtype=dtype, count=count) data = data.reshape((n_samples, nchan)).T data = data * info['conv_factor'] data = data.reshape((nchan, n_epochs, epoch_length)) data = data.transpose((1, 0, 2)) return data def _read_dir(fid): return dict(offset=np.fromfile(fid, np.uint32, 1)[0], size=np.fromfile(fid, np.int32, 1)[0], max_count=np.fromfile(fid, np.int32, 1)[0], count=np.fromfile(fid, np.int32, 1)[0]) @verbose def get_kit_info(rawfile, allow_unknown_format, standardize_names=None, verbose=None): """Extract all the information from the sqd/con file. Parameters ---------- rawfile : str KIT file to be read. allow_unknown_format : bool Force reading old data that is not officially supported. Alternatively, read and re-save the data with the KIT MEG Laboratory application. %(standardize_names)s %(verbose)s Returns ------- info : instance of Info An Info for the instance. sqd : dict A dict containing all the sqd parameter settings. """ sqd = dict() sqd['rawfile'] = rawfile unsupported_format = False sqd['dirs'] = dirs = list() with open(rawfile, 'rb', buffering=0) as fid: # buffering=0 for np bug # # directories (0) # dirs.append(_read_dir(fid)) dirs.extend(_read_dir(fid) for _ in range(dirs[0]['count'] - 1)) assert len(dirs) == dirs[KIT.DIR_INDEX_DIR]['count'] # # system (1) # fid.seek(dirs[KIT.DIR_INDEX_SYSTEM]['offset']) # check file format version version, revision = unpack('2i', fid.read(2 * KIT.INT)) if version < 2 or (version == 2 and revision < 3): version_string = "V%iR%03i" % (version, revision) if allow_unknown_format: unsupported_format = True logger.warning("Force loading KIT format %s", version_string) else: raise UnsupportedKITFormat( version_string, "SQD file format %s is not officially supported. " "Set allow_unknown_format=True to load it anyways." % (version_string,)) sysid = unpack('i', fid.read(KIT.INT))[0] # basic info system_name = unpack('128s', fid.read(128))[0].decode() # model name model_name = unpack('128s', fid.read(128))[0].decode() # channels sqd['nchan'] = channel_count = unpack('i', fid.read(KIT.INT))[0] comment = unpack('256s', fid.read(256))[0].decode() create_time, last_modified_time = unpack('2i', fid.read(2 * KIT.INT)) fid.seek(KIT.INT * 3, SEEK_CUR) # reserved dewar_style = unpack('i', fid.read(KIT.INT))[0] fid.seek(KIT.INT * 3, SEEK_CUR) # spare fll_type = unpack('i', fid.read(KIT.INT))[0] fid.seek(KIT.INT * 3, SEEK_CUR) # spare trigger_type = unpack('i', fid.read(KIT.INT))[0] fid.seek(KIT.INT * 3, SEEK_CUR) # spare adboard_type = unpack('i', fid.read(KIT.INT))[0] fid.seek(KIT.INT * 29, SEEK_CUR) # reserved if version < 2 or (version == 2 and revision <= 3): adc_range = float(unpack('i', fid.read(KIT.INT))[0]) else: adc_range = unpack('d', fid.read(KIT.DOUBLE))[0] adc_polarity, adc_allocated, adc_stored = unpack('3i', fid.read(3 * KIT.INT)) system_name = system_name.replace('\x00', '') system_name = system_name.strip().replace('\n', '/') model_name = model_name.replace('\x00', '') model_name = model_name.strip().replace('\n', '/') full_version = f'V{version:d}R{revision:03d}' logger.debug("SQD file basic information:") logger.debug("Meg160 version = %s", full_version) logger.debug("System ID = %i", sysid) logger.debug("System name = %s", system_name) logger.debug("Model name = %s", model_name) logger.debug("Channel count = %i", channel_count) logger.debug("Comment = %s", comment) logger.debug("Dewar style = %i", dewar_style) logger.debug("FLL type = %i", fll_type) logger.debug("Trigger type = %i", trigger_type) logger.debug("A/D board type = %i", adboard_type) logger.debug("ADC range = +/-%s[V]", adc_range / 2.) logger.debug("ADC allocate = %i[bit]", adc_allocated) logger.debug("ADC bit = %i[bit]", adc_stored) # MGH description: 'acquisition (megacq) VectorView system at NMR-MGH' description = \ f'{system_name} ({sysid}) {full_version} {model_name}' sqd['dtype'] = np.dtype(getattr(np, f'int{adc_allocated}')) # check that we can read this file if fll_type not in KIT.FLL_SETTINGS: fll_types = sorted(KIT.FLL_SETTINGS.keys()) use_fll_type = fll_types[ np.searchsorted(fll_types, fll_type) - 1] warn('Unknown site filter settings (FLL) for system ' '"%s" model "%s" (ID %s), will assume FLL %d->%d, check ' 'your data for correctness, including channel scales and ' 'filter settings!' % (system_name, model_name, sysid, fll_type, use_fll_type)) fll_type = use_fll_type # # channel information (4) # chan_dir = dirs[KIT.DIR_INDEX_CHANNELS] chan_offset, chan_size = chan_dir['offset'], chan_dir['size'] sqd['channels'] = channels = [] exg_gains = list() for i in range(channel_count): fid.seek(chan_offset + chan_size * i) channel_type, = unpack('i', fid.read(KIT.INT)) # System 52 mislabeled reference channels as NULL. This was fixed # in system 53; not sure about 51... if sysid == 52 and i < 160 and channel_type == KIT.CHANNEL_NULL: channel_type = KIT.CHANNEL_MAGNETOMETER_REFERENCE if channel_type in KIT.CHANNELS_MEG: if channel_type not in KIT.CH_TO_FIFF_COIL: raise NotImplementedError( "KIT channel type %i can not be read. Please contact " "the mne-python developers." % channel_type) channels.append({ 'type': channel_type, # (x, y, z, theta, phi) for all MEG channels. Some channel # types have additional information which we're not using. 'loc': np.fromfile(fid, dtype='d', count=5), }) if channel_type in KIT.CHANNEL_NAME_NCHAR: fid.seek(16, SEEK_CUR) # misc fields channels[-1]['name'] = _read_name(fid, channel_type) elif channel_type in KIT.CHANNELS_MISC: channel_no, = unpack('i', fid.read(KIT.INT)) fid.seek(4, SEEK_CUR) name = _read_name(fid, channel_type) channels.append({ 'type': channel_type, 'no': channel_no, 'name': name, }) if channel_type in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG): offset = 6 if channel_type == KIT.CHANNEL_EEG else 8 fid.seek(offset, SEEK_CUR) exg_gains.append(np.fromfile(fid, 'd', 1)[0]) elif channel_type == KIT.CHANNEL_NULL: channels.append({'type': channel_type}) else: raise IOError("Unknown KIT channel type: %i" % channel_type) exg_gains = np.array(exg_gains) # # Channel sensitivity information: (5) # # only sensor channels requires gain. the additional misc channels # (trigger channels, audio and voice channels) are passed # through unaffected fid.seek(dirs[KIT.DIR_INDEX_CALIBRATION]['offset']) # (offset [Volt], gain [Tesla/Volt]) for each channel sensitivity = np.fromfile(fid, dtype='d', count=channel_count * 2) sensitivity.shape = (channel_count, 2) channel_offset, channel_gain = sensitivity.T assert (channel_offset == 0).all() # otherwise we have a problem # # amplifier gain (7) # fid.seek(dirs[KIT.DIR_INDEX_AMP_FILTER]['offset']) amp_data = unpack('i', fid.read(KIT.INT))[0] if fll_type >= 100: # Kapper Type # gain: mask bit gain1 = (amp_data & 0x00007000) >> 12 gain2 = (amp_data & 0x70000000) >> 28 gain3 = (amp_data & 0x07000000) >> 24 amp_gain = (KIT.GAINS[gain1] * KIT.GAINS[gain2] * KIT.GAINS[gain3]) # filter settings hpf = (amp_data & 0x00000700) >> 8 lpf = (amp_data & 0x00070000) >> 16 bef = (amp_data & 0x00000003) >> 0 else: # Hanger Type # gain input_gain = (amp_data & 0x1800) >> 11 output_gain = (amp_data & 0x0007) >> 0 amp_gain = KIT.GAINS[input_gain] * KIT.GAINS[output_gain] # filter settings hpf = (amp_data & 0x007) >> 4 lpf = (amp_data & 0x0700) >> 8 bef = (amp_data & 0xc000) >> 14 hpf_options, lpf_options, bef_options = KIT.FLL_SETTINGS[fll_type] sqd['highpass'] = KIT.HPFS[hpf_options][hpf] sqd['lowpass'] = KIT.LPFS[lpf_options][lpf] sqd['notch'] = KIT.BEFS[bef_options][bef] # # Acquisition Parameters (8) # fid.seek(dirs[KIT.DIR_INDEX_ACQ_COND]['offset']) sqd['acq_type'], = acq_type, = unpack('i', fid.read(KIT.INT)) sqd['sfreq'], = unpack('d', fid.read(KIT.DOUBLE)) if acq_type == KIT.CONTINUOUS: # samples_count, = unpack('i', fid.read(KIT.INT)) fid.seek(KIT.INT, SEEK_CUR) sqd['n_samples'], = unpack('i', fid.read(KIT.INT)) elif acq_type == KIT.EVOKED or acq_type == KIT.EPOCHS: sqd['frame_length'], = unpack('i', fid.read(KIT.INT)) sqd['pretrigger_length'], = unpack('i', fid.read(KIT.INT)) sqd['average_count'], = unpack('i', fid.read(KIT.INT)) sqd['n_epochs'], = unpack('i', fid.read(KIT.INT)) if acq_type == KIT.EVOKED: sqd['n_samples'] = sqd['frame_length'] else: sqd['n_samples'] = sqd['frame_length'] * sqd['n_epochs'] else: raise IOError("Invalid acquisition type: %i. Your file is neither " "continuous nor epoched data." % (acq_type,)) # # digitization information (12 and 26) # dig_dir = dirs[KIT.DIR_INDEX_DIG_POINTS] cor_dir = dirs[KIT.DIR_INDEX_COREG] dig = dict() hsp = list() if dig_dir['count'] > 0 and cor_dir['count'] > 0: # directories (0) fid.seek(dig_dir['offset']) for _ in range(dig_dir['count']): name = _read_name(fid, n=8).strip() # Sometimes there are mismatches (e.g., AFz vs AFZ) between # the channel name and its digitized, name, so let's be case # insensitive. It will also prevent collisions with HSP name = name.lower() rr = np.fromfile(fid, 'd', 3) if name: assert name not in dig dig[name] = rr else: hsp.append(rr) # nasion, lpa, rpa, HPI in native space elp = [dig.pop(key) for key in ( 'fidnz', 'fidt9', 'fidt10', 'hpi_1', 'hpi_2', 'hpi_3', 'hpi_4')] if 'hpi_5' in dig and dig['hpi_5'].any(): elp.append(dig.pop('hpi_5')) elp = np.array(elp) hsp = np.array(hsp, float).reshape(-1, 3) assert elp.shape in ((7, 3), (8, 3)) # coregistration fid.seek(cor_dir['offset']) mrk = np.zeros((elp.shape[0] - 3, 3)) for _ in range(cor_dir['count']): done = np.fromfile(fid, np.int32, 1)[0] fid.seek(16 * KIT.DOUBLE + # meg_to_mri 16 * KIT.DOUBLE, # mri_to_meg SEEK_CUR) marker_count = np.fromfile(fid, np.int32, 1)[0] if not done: continue assert marker_count >= len(mrk) for mi in range(len(mrk)): mri_type, meg_type, mri_done, meg_done = \ np.fromfile(fid, np.int32, 4) assert meg_done fid.seek(3 * KIT.DOUBLE, SEEK_CUR) # mri_pos mrk[mi] = np.fromfile(fid, 'd', 3) fid.seek(256, SEEK_CUR) # marker_file (char) sqd.update(hsp=hsp, elp=elp, mrk=mrk) all_names = set(ch.get('name', '') for ch in channels) if standardize_names is None and all_names.difference({'', 'EEG'}): standardize_names = True warn('standardize_names defaults to True in 0.21 but will change ' 'to False in 0.22', DeprecationWarning) # precompute conversion factor for reading data if unsupported_format: if sysid not in LEGACY_AMP_PARAMS: raise IOError("Legacy parameters for system ID %i unavailable" % (sysid,)) adc_range, adc_stored = LEGACY_AMP_PARAMS[sysid] is_meg = np.array([ch['type'] in KIT.CHANNELS_MEG for ch in channels]) ad_to_volt = adc_range / (2 ** adc_stored) ad_to_tesla = ad_to_volt / amp_gain * channel_gain conv_factor = np.where(is_meg, ad_to_tesla, ad_to_volt) # XXX this is a bit of a hack. Should probably do this more cleanly at # some point... the 2 ** (adc_stored - 14) was emperically determined using # the test files with known amplitudes. The conv_factors need to be # replaced by these values otherwise we're off by a factor off 5000.0 # for the EEG data. is_exg = [ch['type'] in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG) for ch in channels] exg_gains /= 2 ** (adc_stored - 14) conv_factor[is_exg] = exg_gains sqd['conv_factor'] = conv_factor[:, np.newaxis] # Create raw.info dict for raw fif object with SQD data info = _empty_info(float(sqd['sfreq'])) info.update(meas_date=_stamp_to_dt((create_time, 0)), lowpass=sqd['lowpass'], highpass=sqd['highpass'], kit_system_id=sysid, description=description) # Creates a list of dicts of meg channels for raw.info logger.info('Setting channel info structure...') info['chs'] = fiff_channels = [] channel_index = defaultdict(lambda: 0) sqd['eeg_dig'] = OrderedDict() for idx, ch in enumerate(channels, 1): if ch['type'] in KIT.CHANNELS_MEG: ch_name = ch.get('name', '') if ch_name == '' or standardize_names: ch_name = 'MEG %03d' % idx # create three orthogonal vector # ch_angles[0]: theta, ch_angles[1]: phi theta, phi = np.radians(ch['loc'][3:]) x = sin(theta) * cos(phi) y = sin(theta) * sin(phi) z = cos(theta) vec_z = np.array([x, y, z]) vec_z /= linalg.norm(vec_z) vec_x = np.zeros(vec_z.size, dtype=np.float64) if vec_z[1] < vec_z[2]: if vec_z[0] < vec_z[1]: vec_x[0] = 1.0 else: vec_x[1] = 1.0 elif vec_z[0] < vec_z[2]: vec_x[0] = 1.0 else: vec_x[2] = 1.0 vec_x -= np.sum(vec_x * vec_z) * vec_z vec_x /= linalg.norm(vec_x) vec_y = np.cross(vec_z, vec_x) # transform to Neuromag like coordinate space vecs = np.vstack((ch['loc'][:3], vec_x, vec_y, vec_z)) vecs = apply_trans(als_ras_trans, vecs) unit = FIFF.FIFF_UNIT_T loc = vecs.ravel() else: ch_type_label = KIT.CH_LABEL[ch['type']] channel_index[ch_type_label] += 1 ch_type_index = channel_index[ch_type_label] ch_name = ch.get('name', '') eeg_name = ch_name.lower() # some files have all EEG labeled as EEG if ch_name in ('', 'EEG') or standardize_names: ch_name = '%s %03i' % (ch_type_label, ch_type_index) unit = FIFF.FIFF_UNIT_V loc = np.zeros(12) if eeg_name and eeg_name in dig: loc[:3] = sqd['eeg_dig'][eeg_name] = dig[eeg_name] fiff_channels.append(dict( cal=KIT.CALIB_FACTOR, logno=idx, scanno=idx, range=KIT.RANGE, unit=unit, unit_mul=KIT.UNIT_MUL, ch_name=ch_name, coord_frame=FIFF.FIFFV_COORD_DEVICE, coil_type=KIT.CH_TO_FIFF_COIL[ch['type']], kind=KIT.CH_TO_FIFF_KIND[ch['type']], loc=loc)) info._update_redundant() return info, sqd def _read_name(fid, ch_type=None, n=None): n = n if ch_type is None else KIT.CHANNEL_NAME_NCHAR[ch_type] return fid.read(n).split(b'\x00')[0].decode('utf-8') @fill_doc def read_raw_kit(input_fname, mrk=None, elp=None, hsp=None, stim='>', slope='-', stimthresh=1, preload=False, stim_code='binary', allow_unknown_format=False, standardize_names=None, verbose=None): """Reader function for Ricoh/KIT conversion to FIF. Parameters ---------- input_fname : str Path to the sqd file. mrk : None | str | array_like, shape (5, 3) | list of str or array_like Marker points representing the location of the marker coils with respect to the MEG Sensors, or path to a marker file. If list, all of the markers will be averaged together. elp : None | str | array_like, shape (8, 3) Digitizer points representing the location of the fiducials and the marker coils with respect to the digitized head shape, or path to a file containing these points. hsp : None | str | array, shape (n_points, 3) Digitizer head shape points, or path to head shape file. If more than 10,000 points are in the head shape, they are automatically decimated. stim : list of int | '<' | '>' Channel-value correspondence when converting KIT trigger channels to a Neuromag-style stim channel. For '<', the largest values are assigned to the first channel (default). For '>', the largest values are assigned to the last channel. Can also be specified as a list of trigger channel indexes. slope : '+' | '-' How to interpret values on KIT trigger channels when synthesizing a Neuromag-style stim channel. With '+', a positive slope (low-to-high) is interpreted as an event. With '-', a negative slope (high-to-low) is interpreted as an event. stimthresh : float The threshold level for accepting voltage changes in KIT trigger channels as a trigger event. %(preload)s stim_code : 'binary' | 'channel' How to decode trigger values from stim channels. 'binary' read stim channel events as binary code, 'channel' encodes channel number. allow_unknown_format : bool Force reading old data that is not officially supported. Alternatively, read and re-save the data with the KIT MEG Laboratory application. %(standardize_names)s %(verbose)s Returns ------- raw : instance of RawKIT A Raw object containing KIT data. See Also -------- mne.io.Raw : Documentation of attribute and methods. Notes ----- If mrk, hsp or elp are array_like inputs, then the numbers in xyz coordinates should be in units of meters. """ return RawKIT(input_fname=input_fname, mrk=mrk, elp=elp, hsp=hsp, stim=stim, slope=slope, stimthresh=stimthresh, preload=preload, stim_code=stim_code, allow_unknown_format=allow_unknown_format, standardize_names=standardize_names, verbose=verbose) @fill_doc def read_epochs_kit(input_fname, events, event_id=None, mrk=None, elp=None, hsp=None, allow_unknown_format=False, standardize_names=None, verbose=None): """Reader function for Ricoh/KIT epochs files. Parameters ---------- input_fname : str Path to the sqd file. events : array, shape (n_events, 3) The events typically returned by the read_events function. If some events don't match the events of interest as specified by event_id, they will be marked as 'IGNORED' in the drop log. event_id : int | list of int | dict | None The id of the event to consider. If dict, the keys can later be used to access associated events. Example: dict(auditory=1, visual=3). If int, a dict will be created with the id as string. If a list, all events with the IDs specified in the list are used. If None, all events will be used with and a dict is created with string integer names corresponding to the event id integers. mrk : None | str | array_like, shape (5, 3) | list of str or array_like Marker points representing the location of the marker coils with respect to the MEG Sensors, or path to a marker file. If list, all of the markers will be averaged together. elp : None | str | array_like, shape (8, 3) Digitizer points representing the location of the fiducials and the marker coils with respect to the digitized head shape, or path to a file containing these points. hsp : None | str | array, shape (n_points, 3) Digitizer head shape points, or path to head shape file. If more than 10,000 points are in the head shape, they are automatically decimated. allow_unknown_format : bool Force reading old data that is not officially supported. Alternatively, read and re-save the data with the KIT MEG Laboratory application. %(standardize_names)s %(verbose)s Returns ------- epochs : instance of Epochs The epochs. Notes ----- .. versionadded:: 0.9.0 """ epochs = EpochsKIT(input_fname=input_fname, events=events, event_id=event_id, mrk=mrk, elp=elp, hsp=hsp, allow_unknown_format=allow_unknown_format, standardize_names=standardize_names, verbose=verbose) return epochs

alexisicte/aviate

venv/lib/python3.8/site-packages/mne/io/kit/kit.py

Python

#!/usr/bin/python3 # --- 001 > U5W2P1_Task6_w1 def solution( n ): if(n > 2 and n < 7 ): return True; else: return False; if __name__ == "__main__": print('----------start------------') n = 10 print(solution( n )) print('------------end------------')

MingjunGeng/Code-Knowledge

src/CodeLearn/plaintextCode/BloomTech/BTU5W1/U5W1P2_Task6_w1.py

Python

from typing import Optional, Dict from tabulate import tabulate import pandas as pd from mdrsl.utils.value_collection import ValueCollector class MIDSObjectiveFunctionStatistics: def __init__(self): self.last_f0: Optional[int] = None self.last_f1: Optional[int] = None self.last_f2: Optional[int] = None self.last_f3: Optional[int] = None self.last_f4: Optional[int] = None self.last_f5: Optional[int] = None self.last_f6: Optional[int] = None self.last_f7: Optional[int] = None self.last_f_total: Optional[int] = None self.value_collectors = dict( f0=ValueCollector(), f1=ValueCollector(), f2=ValueCollector(), f3=ValueCollector(), f4=ValueCollector(), f5=ValueCollector(), f6=ValueCollector(), f_total=ValueCollector() ) def add_values(self, f0, f1, f2, f3, f4, f5, f6, f_total): self.last_f0 = f0 self.last_f1 = f1 self.last_f2 = f2 self.last_f3 = f3 self.last_f4 = f4 self.last_f5 = f5 self.last_f6 = f6 self.last_f_total = f_total self.value_collectors['f0'].add_value(f0) self.value_collectors['f1'].add_value(f1) self.value_collectors['f2'].add_value(f2) self.value_collectors['f3'].add_value(f3) self.value_collectors['f4'].add_value(f4) self.value_collectors['f5'].add_value(f5) self.value_collectors['f6'].add_value(f6) self.value_collectors['f_total'].add_value(f_total) def values_to_pandas_dataframe(self) -> Optional[pd.DataFrame]: if ValueCollector.collect_values: columns = ['type', 'value'] data = [] for function_name, value_collector in self.value_collectors.items(): for value in value_collector.values: data.append([function_name, value]) df = pd.DataFrame(data=data, columns=columns) return df else: return None def values_to_pandas_dataframe2(self) -> Optional[pd.DataFrame]: if ValueCollector.collect_values: columns = ['call_index', 'type', 'value'] data = [] for function_name, value_collector in self.value_collectors.items(): for call_index, value in enumerate(value_collector.values): data.append([call_index, function_name, value]) df = pd.DataFrame(data=data, columns=columns) return df else: return None def get_last_f_values(self) -> Dict[str, float]: return dict( f0=self.last_f0, f1=self.last_f1, f2=self.last_f2, f3=self.last_f3, f4=self.last_f4, f5=self.last_f5, f6=self.last_f6, f_total=self.last_f_total) def __str__(self): table_str = tabulate( [ ['count', self.value_collectors['f0'].count, self.value_collectors['f1'].count, self.value_collectors['f2'].count, self.value_collectors['f3'].count, self.value_collectors['f4'].count, self.value_collectors['f5'].count, self.value_collectors['f6'].count, self.value_collectors['f_total'].count ], ['sum', self.value_collectors['f0'].sum, self.value_collectors['f1'].sum, self.value_collectors['f2'].sum, self.value_collectors['f3'].sum, self.value_collectors['f4'].sum, self.value_collectors['f5'].sum, self.value_collectors['f6'].sum, self.value_collectors['f_total'].sum ], ['min', self.value_collectors['f0'].min, self.value_collectors['f1'].min, self.value_collectors['f2'].min, self.value_collectors['f3'].min, self.value_collectors['f4'].min, self.value_collectors['f5'].min, self.value_collectors['f6'].min, self.value_collectors['f_total'].min ], ['avg', self.value_collectors['f0'].get_avg(), self.value_collectors['f1'].get_avg(), self.value_collectors['f2'].get_avg(), self.value_collectors['f3'].get_avg(), self.value_collectors['f4'].get_avg(), self.value_collectors['f5'].get_avg(), self.value_collectors['f6'].get_avg(), self.value_collectors['f_total'].get_avg() ], ['max', self.value_collectors['f0'].max, self.value_collectors['f1'].max, self.value_collectors['f2'].max, self.value_collectors['f3'].max, self.value_collectors['f4'].max, self.value_collectors['f5'].max, self.value_collectors['f6'].max, self.value_collectors['f_total'].max ], ['last_val', self.last_f0, self.last_f1, self.last_f2, self.last_f3, self.last_f4, self.last_f5, self.last_f6, self.last_f_total ] ], headers=['type', 'f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f_total'] ) return table_str if __name__ == '__main__': vc = ValueCollector() vc.add_value(1) vc.add_value(2) vc.add_value(3) print(vc)

joschout/Multi-Directional-Rule-Set-Learning

mdrsl/rule_models/mids/objective_function/mids_objective_function_statistics.py

Python

# uncompyle6 version 3.7.4 # Python bytecode 3.7 (3394) # Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)] # Embedded file name: T:\InGame\Gameplay\Scripts\Server\objects\gardening\gardening_commands.py # Compiled at: 2017-11-18 00:09:10 # Size of source mod 2**32: 1465 bytes from objects.components import types from objects.components.types import GARDENING_COMPONENT from objects.gardening.gardening_component_fruit import GardeningFruitComponent import services, sims4.commands @sims4.commands.Command('gardening.cleanup_gardening_objects') def cleanup_gardening_objects(_connection=None): for obj in services.object_manager().get_all_objects_with_component_gen(GARDENING_COMPONENT): gardening_component = obj.get_component(types.GARDENING_COMPONENT) if not isinstance(gardening_component, GardeningFruitComponent): continue if obj.parent is None: obj.is_in_inventory() or obj.is_on_active_lot() or sims4.commands.output('Destroyed object {} on open street was found without a parent at position {}, parent_type {}.'.format(obj, obj.position, obj.parent_type), _connection) obj.destroy(source=obj, cause='Fruit/Flower with no parent on open street') sims4.commands.output('Gardening cleanup complete', _connection) return True

velocist/TS4CheatsInfo

Scripts/simulation/objects/gardening/gardening_commands.py

Python

# coding: utf-8 # /*########################################################################## # # Copyright (c) 2015-2016 European Synchrotron Radiation Facility # # 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. # # ###########################################################################*/ from __future__ import absolute_import __authors__ = ["D. Naudet"] __license__ = "MIT" __date__ = "15/09/2016" from ...io.XsocsH5 import ScanPositions from .ProjectItem import ProjectItem from .ProjectDef import ItemClassDef @ItemClassDef('ScanPositionsItem') class ScanPositionsItem(ProjectItem): def _createItem(self): with self.xsocsH5 as h5f: entries = h5f.entries() entry = entries[0] scan_positions = h5f.scan_positions(entry) pathTpl = self.path + '/' + '{0}' with self: itemPath = pathTpl.format('pos_0') self._set_array_data(itemPath, scan_positions.pos_0) itemPath = pathTpl.format('pos_1') self._set_array_data(itemPath, scan_positions.pos_1) itemPath = pathTpl.format('motor_0') self._set_scalar_data(itemPath, scan_positions.motor_0) itemPath = pathTpl.format('motor_1') self._set_scalar_data(itemPath, scan_positions.motor_1) itemPath = pathTpl.format('n_0') self._set_scalar_data(itemPath, scan_positions.shape[0]) itemPath = pathTpl.format('n_1') self._set_scalar_data(itemPath, scan_positions.shape[1]) def positions(self): pathTpl = self.path + '/' + '{0}' with self: itemPath = pathTpl.format('pos_0') pos_0 = self._get_array_data(itemPath) itemPath = pathTpl.format('pos_1') pos_1 = self._get_array_data(itemPath) itemPath = pathTpl.format('motor_0') motor_0 = self._get_scalar_data(itemPath) itemPath = pathTpl.format('motor_1') motor_1 = self._get_scalar_data(itemPath) itemPath = pathTpl.format('n_0') n_0 = self._get_scalar_data(itemPath) itemPath = pathTpl.format('n_1') n_1 = self._get_scalar_data(itemPath) return ScanPositions(motor_0=motor_0, pos_0=pos_0, motor_1=motor_1, pos_1=pos_1, shape=(n_0, n_1))

omserta/xsocs

xsocs/gui/project/ScanPositionsItem.py

Python

#!/usr/bin/env python # -*- coding:utf-8 -*- # @Time : 2022/2/9 12:09 下午 # @Author: zhoumengjie # @File : tabledrawer.py import numpy as np import pandas as pd from matplotlib import pyplot as plt from matplotlib.font_manager import FontProperties def draw_table(columns_head:[], cell_vals=[]): # 设置字体及负数 plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False # 画布 fig, ax = plt.subplots(figsize=(10, 4), dpi=100) # 数据 data = [ [100, 200, 300, -100, 350], [-120, 290, -90, 450, 150] ] # 列与行 columns = ('一', '二', '三', '四', '五') rows = ['A', 'B'] # 作图参数 index = np.arange(len(columns)) - 0.1 bar_width = 0.4 # 设置颜色 colors = ['turquoise', 'coral'] # 柱状图 bar1 = plt.bar(index, data[0], bar_width, color=colors[0], edgecolor='grey') bar2 = plt.bar(index + bar_width, data[1], bar_width, color=colors[1], edgecolor='grey') # 设置标题 ax.set_title('收益情况', fontsize=16, y=1.1, x=0.44) ax.set_ylabel('元', fontsize=12, color='black', alpha=0.7, rotation=360) ax.set_ylim(-150, 500) # 显示数据标签 # ax.bar_label(bar1, label_type='edge') # ax.bar_label(bar2, label_type='edge') # x,y刻度不显示 ax.tick_params(axis=u'both', which=u'both', length=0) plt.xticks([]) table = plt.table(cellText=data, rowLabels=rows, rowColours=colors, colLabels=columns, cellLoc='center', loc='bottom', bbox=[0, -0.4, 1, 0.24]) cellDict = table.get_celld() for i in range(0, len(columns)): cellDict[(0, i)].set_height(0.6) for j in range(1, len(rows) + 1): cellDict[(j, i)].set_height(0.4) cellDict[(1, -1)].set_height(0.4) cellDict[(2, -1)].set_height(0.4) table.auto_set_font_size(False) table.set_fontsize(10) for key, cell in table.get_celld().items(): cell.set_linewidth(0.6) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['bottom'].set_visible(False) ax.spines['left'].set_visible(False) name = ['', ''] ax.legend(name, handlelength=0.7, labelspacing=0.6, bbox_to_anchor=(-0.1, -0.23), loc='upper left', frameon=False) plt.show() if __name__ == '__main__': # draw_table(['A', 'B'], [['中国', '必胜'], ['你好', '谢谢']]) # print(4800 / 1100 / 1000) data = { 'linux': [1.2, 2.2, 3.1, '中国', 2.0, 1.0, 2.1, 3.5, 4.0, 2.0, ], 'linuxmi': [5.2, 6.7, 7.9, 8.3, 1.2, 5.7, 6.1, 7.2, 8.3, '-', ], } df = pd.DataFrame(data) fig, ax = plt.subplots(figsize=(3, 3)) ax.axis('off') ax.axis('tight') ax.table(cellText=df.values, colLabels=df.columns, bbox=[0, 0, 1, 1], ) # plt.savefig('xx.png') plt.show()

vandyzhou/wxcloudrun-django

wxcloudrun/common/tabledrawer.py

Python

import os from setuptools import find_packages, setup this = os.path.dirname(os.path.realpath(__file__)) def read(name): with open(os.path.join(this, name)) as f: return f.read() setup( name='pyramid_pages', version='0.0.5', url='http://github.com/uralbash/pyramid_pages/', author='Svintsov Dmitry', author_email='[email protected]', packages=find_packages(), include_package_data=True, zip_safe=False, test_suite="nose.collector", license="MIT", description='Tree pages for pyramid', long_description=read('README.rst'), install_requires=read('requirements.txt'), tests_require=read('requirements.txt') + read('requirements-test.txt'), classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python', "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Framework :: Pyramid ", "Topic :: Internet", "Topic :: Database", ], )

ITCase/ps_pages

setup.py

Python

from .TapChanger import TapChanger class RatioTapChanger(TapChanger): ''' A tap changer that changes the voltage ratio impacting the voltage magnitude but not the phase angle across the transformer. :tculControlMode: Specifies the regulation control mode (voltage or reactive) of the RatioTapChanger. Default: None :stepVoltageIncrement: Tap step increment, in per cent of nominal voltage, per step position. Default: 0.0 :RatioTapChangerTable: The ratio tap changer of this tap ratio table. Default: None :TransformerEnd: Ratio tap changer associated with this transformer end. Default: None ''' cgmesProfile = TapChanger.cgmesProfile possibleProfileList = {'class': [cgmesProfile.EQ.value, cgmesProfile.SSH.value, ], 'tculControlMode': [cgmesProfile.EQ.value, ], 'stepVoltageIncrement': [cgmesProfile.EQ.value, ], 'RatioTapChangerTable': [cgmesProfile.EQ.value, ], 'TransformerEnd': [cgmesProfile.EQ.value, ], } serializationProfile = {} __doc__ += '\n Documentation of parent class TapChanger: \n' + TapChanger.__doc__ def __init__(self, tculControlMode = None, stepVoltageIncrement = 0.0, RatioTapChangerTable = None, TransformerEnd = None, *args, **kw_args): super().__init__(*args, **kw_args) self.tculControlMode = tculControlMode self.stepVoltageIncrement = stepVoltageIncrement self.RatioTapChangerTable = RatioTapChangerTable self.TransformerEnd = TransformerEnd def __str__(self): str = 'class=RatioTapChanger\n' attributes = self.__dict__ for key in attributes.keys(): str = str + key + '={}\n'.format(attributes[key]) return str

CIM-IEC/CIMpy

cimpy/cgmes_v2_4_15/RatioTapChanger.py

Python

import torch import math from torch import nn, Tensor from torch.nn import functional as F from semseg.models.backbones import * from semseg.models.modules.common import ConvModule class SpatialPath(nn.Module): def __init__(self, c1, c2) -> None: super().__init__() ch = 64 self.conv_7x7 = ConvModule(c1, ch, 7, 2, 3) self.conv_3x3_1 = ConvModule(ch, ch, 3, 2, 1) self.conv_3x3_2 = ConvModule(ch, ch, 3, 2, 1) self.conv_1x1 = ConvModule(ch, c2, 1, 1, 0) def forward(self, x): x = self.conv_7x7(x) x = self.conv_3x3_1(x) x = self.conv_3x3_2(x) return self.conv_1x1(x) class ContextPath(nn.Module): def __init__(self, backbone: nn.Module) -> None: super().__init__() self.backbone = backbone c3, c4 = self.backbone.channels[-2:] self.arm16 = AttentionRefinmentModule(c3, 128) self.arm32 = AttentionRefinmentModule(c4, 128) self.global_context = nn.Sequential( nn.AdaptiveAvgPool2d(1), ConvModule(c4, 128, 1, 1, 0) ) self.up16 = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True) self.up32 = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True) self.refine16 = ConvModule(128, 128, 3, 1, 1) self.refine32 = ConvModule(128, 128, 3, 1, 1) def forward(self, x): _, _, down16, down32 = self.backbone(x) # 4x256x64x128, 4x512x32x64 arm_down16 = self.arm16(down16) # 4x128x64x128 arm_down32 = self.arm32(down32) # 4x128x32x64 global_down32 = self.global_context(down32) # 4x128x1x1 global_down32 = F.interpolate(global_down32, size=down32.size()[2:], mode='bilinear', align_corners=True) # 4x128x32x64 arm_down32 = arm_down32 + global_down32 # 4x128x32x64 arm_down32 = self.up32(arm_down32) # 4x128x64x128 arm_down32 = self.refine32(arm_down32) # 4x128x64x128 arm_down16 = arm_down16 + arm_down32 # 4x128x64x128 arm_down16 = self.up16(arm_down16) # 4x128x128x256 arm_down16 = self.refine16(arm_down16) # 4x128x128x256 return arm_down16, arm_down32 class AttentionRefinmentModule(nn.Module): def __init__(self, c1, c2) -> None: super().__init__() self.conv_3x3 = ConvModule(c1, c2, 3, 1, 1) self.attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(c2, c2, 1, bias=False), nn.BatchNorm2d(c2), nn.Sigmoid() ) def forward(self, x): fm = self.conv_3x3(x) fm_se = self.attention(fm) return fm * fm_se class FeatureFusionModule(nn.Module): def __init__(self, c1, c2, reduction=1) -> None: super().__init__() self.conv_1x1 = ConvModule(c1, c2, 1, 1, 0) self.attention = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(c2, c2 // reduction, 1, bias=False), nn.ReLU(True), nn.Conv2d(c2 // reduction, c2, 1, bias=False), nn.Sigmoid() ) def forward(self, x1, x2): fm = torch.cat([x1, x2], dim=1) fm = self.conv_1x1(fm) fm_se = self.attention(fm) return fm + fm * fm_se class Head(nn.Module): def __init__(self, c1, n_classes, upscale_factor, is_aux=False) -> None: super().__init__() ch = 256 if is_aux else 64 c2 = n_classes * upscale_factor * upscale_factor self.conv_3x3 = ConvModule(c1, ch, 3, 1, 1) self.conv_1x1 = nn.Conv2d(ch, c2, 1, 1, 0) self.upscale = nn.PixelShuffle(upscale_factor) def forward(self, x): x = self.conv_1x1(self.conv_3x3(x)) return self.upscale(x) class BiSeNetv1(nn.Module): def __init__(self, backbone: str = 'ResNet-18', num_classes: int = 19) -> None: super().__init__() backbone, variant = backbone.split('-') self.context_path = ContextPath(eval(backbone)(variant)) self.spatial_path = SpatialPath(3, 128) self.ffm = FeatureFusionModule(256, 256) self.output_head = Head(256, num_classes, upscale_factor=8, is_aux=False) self.context16_head = Head(128, num_classes, upscale_factor=8, is_aux=True) self.context32_head = Head(128, num_classes, upscale_factor=16, is_aux=True) self.apply(self._init_weights) def _init_weights(self, m: nn.Module) -> None: if isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out // m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) def init_pretrained(self, pretrained: str = None) -> None: if pretrained: self.context_path.backbone.load_state_dict(torch.load(pretrained, map_location='cpu'), strict=False) def forward(self, x): # 4x3x1024x2048 spatial_out = self.spatial_path(x) # 4x128x128x256 context16, context32 = self.context_path(x) # 4x128x128x256, 4x128x64x128 fm_fuse = self.ffm(spatial_out, context16) # 4x256x128x256 output = self.output_head(fm_fuse) # 4xn_classesx1024x2048 if self.training: context_out16 = self.context16_head(context16) # 4xn_classesx1024x2048 context_out32 = self.context32_head(context32) # 4xn_classesx1024x2048 return output, context_out16, context_out32 return output if __name__ == '__main__': model = BiSeNetv1('MobileNetV2-1.0', 19) # model.init_pretrained('checkpoints/backbones/resnet/resnet18.pth') model.eval() image = torch.randn(1, 3, 224, 224) output = model(image) print(output.shape)

Apexsf/test

semseg/models/bisenetv1.py

Python

# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*- # ex: set sts=4 ts=4 sw=4 et: # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the datalad package for the # copyright and license terms. # # ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## import collections from collections.abc import Callable import re import builtins import time import logging import shutil import os import sys import tempfile from tempfile import NamedTemporaryFile import platform import gc import glob import gzip import stat import string import warnings import os.path as op from copy import copy as shallow_copy from contextlib import contextmanager from functools import ( lru_cache, wraps, ) from time import sleep import inspect from itertools import tee # this import is required because other modules import opj from here. from os.path import join as opj from os.path import ( abspath, basename, commonprefix, curdir, dirname, exists, expanduser, expandvars, isabs, isdir, islink, lexists, normpath, pardir, relpath, sep, split, splitdrive ) import posixpath from shlex import ( quote as shlex_quote, split as shlex_split, ) # from datalad.dochelpers import get_docstring_split from datalad.consts import TIMESTAMP_FMT from datalad.support.exceptions import CapturedException unicode_srctypes = str, bytes lgr = logging.getLogger("datalad.utils") lgr.log(5, "Importing datalad.utils") # # Some useful variables # platform_system = platform.system().lower() on_windows = platform_system == 'windows' on_osx = platform_system == 'darwin' on_linux = platform_system == 'linux' on_msys_tainted_paths = on_windows \ and 'MSYS_NO_PATHCONV' not in os.environ \ and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING') # Takes ~200msec, so should not be called at import time @lru_cache() # output should not change through life time of datalad process def get_linux_distribution(): """Compatibility wrapper for {platform,distro}.linux_distribution(). """ if hasattr(platform, "linux_distribution"): # Use deprecated (but faster) method if it's available. with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=DeprecationWarning) result = platform.linux_distribution() else: import distro # We require this for Python 3.8 and above. result = distro.linux_distribution(full_distribution_name=False) return result # Those weren't used for any critical decision making, thus we just set them to None # Use get_linux_distribution() directly where needed linux_distribution_name = linux_distribution_release = None # Maximal length of cmdline string # Query the system and use hardcoded "knowledge" if None # probably getconf ARG_MAX might not be available # The last one would be the most conservative/Windows CMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767 try: CMD_MAX_ARG = os.sysconf('SC_ARG_MAX') assert CMD_MAX_ARG > 0 if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6: # workaround for some kind of a bug which comes up with python 3.4 # see https://github.com/datalad/datalad/issues/3150 # or on older CentOS with conda and python as new as 3.9 # see https://github.com/datalad/datalad/issues/5943 # TODO: let Yarik know that the world is a paradise now whenever 1e6 # is not large enough CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED) except Exception as exc: # ATM (20181005) SC_ARG_MAX available only on POSIX systems # so exception would be thrown e.g. on Windows, or # somehow during Debian build for nd14.04 it is coming up with -1: # https://github.com/datalad/datalad/issues/3015 CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED lgr.debug( "Failed to query or got useless SC_ARG_MAX sysconf, " "will use hardcoded value: %s", exc) # Even with all careful computations we do, due to necessity to account for # environment and what not, we still could not figure out "exact" way to # estimate it, but it was shown that 300k safety margin on linux was sufficient. # https://github.com/datalad/datalad/pull/2977#issuecomment-436264710 # 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50% # of the length for "safety margin". We might probably still blow due to # env vars, unicode, etc... so any hard limit imho is not a proper solution CMD_MAX_ARG = int(0.5 * CMD_MAX_ARG) lgr.debug( "Maximal length of cmdline string (adjusted for safety margin): %d", CMD_MAX_ARG) # # Little helpers # # `getargspec` has been deprecated in Python 3. ArgSpecFake = collections.namedtuple( "ArgSpecFake", ["args", "varargs", "keywords", "defaults"]) def getargspec(func, *, include_kwonlyargs=False): """Compat shim for getargspec deprecated in python 3. The main difference from inspect.getargspec (and inspect.getfullargspec for that matter) is that by using inspect.signature we are providing correct args/defaults for functools.wraps'ed functions. `include_kwonlyargs` option was added to centralize getting all args, even the ones which are kwonly (follow the ``*,``). For internal use and not advised for use in 3rd party code. Please use inspect.signature directly. """ # We use signature, and not getfullargspec, because only signature properly # "passes" args from a functools.wraps decorated function. # Note: getfullargspec works Ok on wrapt-decorated functions f_sign = inspect.signature(func) # Loop through parameters and compose argspec args4 = [[], None, None, {}] # Collect all kwonlyargs into a dedicated dict - name: default kwonlyargs = {} # shortcuts args, defaults = args4[0], args4[3] P = inspect.Parameter for p_name, p in f_sign.parameters.items(): if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD): assert not kwonlyargs # yoh: must not come after kwonlyarg args.append(p_name) if p.default is not P.empty: defaults[p_name] = p.default elif p.kind == P.VAR_POSITIONAL: args4[1] = p_name elif p.kind == P.VAR_KEYWORD: args4[2] = p_name elif p.kind == P.KEYWORD_ONLY: assert p.default is not P.empty kwonlyargs[p_name] = p.default if kwonlyargs: if not include_kwonlyargs: raise ValueError( 'Function has keyword-only parameters or annotations, either use ' 'inspect.signature() API which can support them, or provide include_kwonlyargs=True ' 'to this function' ) else: args.extend(list(kwonlyargs)) defaults.update(kwonlyargs) # harmonize defaults to how original getargspec returned them -- just a tuple args4[3] = None if not defaults else tuple(defaults.values()) return ArgSpecFake(*args4) def any_re_search(regexes, value): """Return if any of regexes (list or str) searches successfully for value""" for regex in ensure_tuple_or_list(regexes): if re.search(regex, value): return True return False def not_supported_on_windows(msg=None): """A little helper to be invoked to consistently fail whenever functionality is not supported (yet) on Windows """ if on_windows: raise NotImplementedError("This functionality is not yet implemented for Windows OS" + (": %s" % msg if msg else "")) def get_home_envvars(new_home): """Return dict with env variables to be adjusted for a new HOME Only variables found in current os.environ are adjusted. Parameters ---------- new_home: str or Path New home path, in native to OS "schema" """ new_home = str(new_home) out = {'HOME': new_home} if on_windows: # requires special handling, since it has a number of relevant variables # and also Python changed its behavior and started to respect USERPROFILE only # since python 3.8: https://bugs.python.org/issue36264 out['USERPROFILE'] = new_home out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home) return {v: val for v, val in out.items() if v in os.environ} def shortened_repr(value, l=30): try: if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__): value_repr = repr(value) if not value_repr.startswith('<') and len(value_repr) > l: value_repr = "<<%s++%d chars++%s>>" % ( value_repr[:l - 16], len(value_repr) - (l - 16 + 4), value_repr[-4:] ) elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x': raise ValueError("I hate those useless long reprs") else: raise ValueError("gimme class") except Exception as e: value_repr = "<%s>" % value.__class__.__name__.split('.')[-1] return value_repr def __auto_repr__(obj): attr_names = tuple() if hasattr(obj, '__dict__'): attr_names += tuple(obj.__dict__.keys()) if hasattr(obj, '__slots__'): attr_names += tuple(obj.__slots__) items = [] for attr in sorted(set(attr_names)): if attr.startswith('_'): continue value = getattr(obj, attr) # TODO: should we add this feature to minimize some talktative reprs # such as of URL? #if value is None: # continue items.append("%s=%s" % (attr, shortened_repr(value))) return "%s(%s)" % (obj.__class__.__name__, ', '.join(items)) def auto_repr(cls): """Decorator for a class to assign it an automagic quick and dirty __repr__ It uses public class attributes to prepare repr of a class Original idea: http://stackoverflow.com/a/27799004/1265472 """ cls.__repr__ = __auto_repr__ return cls def _is_stream_tty(stream): try: # TODO: check on windows if hasattr check would work correctly and # add value: return stream.isatty() except ValueError as exc: # Who knows why it is a ValueError, but let's try to be specific # If there is a problem with I/O - non-interactive, otherwise reraise if "I/O" in str(exc): return False raise def is_interactive(): """Return True if all in/outs are open and tty. Note that in a somewhat abnormal case where e.g. stdin is explicitly closed, and any operation on it would raise a `ValueError("I/O operation on closed file")` exception, this function would just return False, since the session cannot be used interactively. """ return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr)) def get_ipython_shell(): """Detect if running within IPython and returns its `ip` (shell) object Returns None if not under ipython (no `get_ipython` function) """ try: return get_ipython() except NameError: return None def md5sum(filename): """Compute an MD5 sum for the given file """ from datalad.support.digests import Digester return Digester(digests=['md5'])(filename)['md5'] # unused in -core def sorted_files(path): """Return a (sorted) list of files under path """ return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files] for r, d, files in os.walk(path) if not '.git' in r], [])) _encoded_dirsep = r'\\' if on_windows else r'/' _VCS_REGEX = r'%s\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % ( _encoded_dirsep, _encoded_dirsep) _DATALAD_REGEX = r'%s\.(?:datalad)(?:%s|$)' % ( _encoded_dirsep, _encoded_dirsep) def find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False): """Generator to find files matching regex Parameters ---------- regex: basestring exclude: basestring, optional Matches to exclude exclude_vcs: If True, excludes commonly known VCS subdirectories. If string, used as regex to exclude those files (regex: `%r`) exclude_datalad: If True, excludes files known to be datalad meta-data files (e.g. under .datalad/ subdirectory) (regex: `%r`) topdir: basestring, optional Directory where to search dirs: bool, optional Whether to match directories as well as files """ for dirpath, dirnames, filenames in os.walk(topdir): names = (dirnames + filenames) if dirs else filenames # TODO: might want to uniformize on windows to use '/' paths = (op.join(dirpath, name) for name in names) for path in filter(re.compile(regex).search, paths): path = path.rstrip(sep) if exclude and re.search(exclude, path): continue if exclude_vcs and re.search(_VCS_REGEX, path): continue if exclude_datalad and re.search(_DATALAD_REGEX, path): continue yield path find_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX) def expandpath(path, force_absolute=True): """Expand all variables and user handles in a path. By default return an absolute path """ path = expandvars(expanduser(path)) if force_absolute: path = abspath(path) return path def posix_relpath(path, start=None): """Behave like os.path.relpath, but always return POSIX paths... on any platform.""" # join POSIX style return posixpath.join( # split and relpath native style # python2.7 ntpath implementation of relpath cannot handle start=None *split( relpath(path, start=start if start is not None else ''))) def is_explicit_path(path): """Return whether a path explicitly points to a location Any absolute path, or relative path starting with either '../' or './' is assumed to indicate a location on the filesystem. Any other path format is not considered explicit.""" path = expandpath(path, force_absolute=False) return isabs(path) \ or path.startswith(os.curdir + os.sep) \ or path.startswith(os.pardir + os.sep) # handle this dance once, and import pathlib from here # in all other places from pathlib import ( Path, PurePath, PurePosixPath, ) def rotree(path, ro=True, chmod_files=True): """To make tree read-only or writable Parameters ---------- path : string Path to the tree/directory to chmod ro : bool, optional Whether to make it R/O (default) or RW chmod_files : bool, optional Whether to operate also on files (not just directories) """ if ro: chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE) else: chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD) for root, dirs, files in os.walk(path, followlinks=False): if chmod_files: for f in files: fullf = op.join(root, f) # might be the "broken" symlink which would fail to stat etc if exists(fullf): chmod(fullf) chmod(root) def rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs): """To remove git-annex .git it is needed to make all files and directories writable again first Parameters ---------- path: Path or str Path to remove chmod_files : string or bool, optional Whether to make files writable also before removal. Usually it is just a matter of directories to have write permissions. If 'auto' it would chmod files on windows by default children_only : bool, optional If set, all files and subdirectories would be removed while the path itself (must be a directory) would be preserved `*args` : `**kwargs` : Passed into shutil.rmtree call """ # Give W permissions back only to directories, no need to bother with files if chmod_files == 'auto': chmod_files = on_windows # TODO: yoh thinks that if we could quickly check our Flyweight for # repos if any of them is under the path, and could call .precommit # on those to possibly stop batched processes etc, we did not have # to do it on case by case # Check for open files assert_no_open_files(path) # TODO the whole thing should be reimplemented with pathlib, but for now # at least accept Path path = str(path) if children_only: if not isdir(path): raise ValueError("Can remove children only of directories") for p in os.listdir(path): rmtree(op.join(path, p)) return if not (islink(path) or not isdir(path)): rotree(path, ro=False, chmod_files=chmod_files) if on_windows: # shutil fails to remove paths that exceed 260 characters on Windows machines # that did not enable long path support. A workaround to remove long paths # anyway is to preprend \\?\ to the path. # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces path = r'\\?\ '.strip() + path _rmtree(path, *args, **kwargs) else: # just remove the symlink unlink(path) def rmdir(path, *args, **kwargs): """os.rmdir with our optional checking for open files""" assert_no_open_files(path) os.rmdir(path) def get_open_files(path, log_open=False): """Get open files under a path Note: This function is very slow on Windows. Parameters ---------- path : str File or directory to check for open files under log_open : bool or int If set - logger level to use Returns ------- dict path : pid """ # Original idea: https://stackoverflow.com/a/11115521/1265472 import psutil files = {} # since the ones returned by psutil would not be aware of symlinks in the # path we should also get realpath for path # do absolute() in addition to always get an absolute path # even with non-existing paths on windows path = str(Path(path).resolve().absolute()) for proc in psutil.process_iter(): try: open_paths = [p.path for p in proc.open_files()] + [proc.cwd()] for p in open_paths: # note: could be done more efficiently so we do not # renormalize path over and over again etc if path_startswith(p, path): files[p] = proc # Catch a race condition where a process ends # before we can examine its files except psutil.NoSuchProcess: pass except psutil.AccessDenied: pass if files and log_open: lgr.log(log_open, "Open files under %s: %s", path, files) return files _assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES') if _assert_no_open_files_cfg: def assert_no_open_files(path): files = get_open_files(path, log_open=40) if _assert_no_open_files_cfg == 'assert': assert not files, "Got following files still open: %s" % ','.join(files) elif files: if _assert_no_open_files_cfg == 'pdb': import pdb pdb.set_trace() elif _assert_no_open_files_cfg == 'epdb': import epdb epdb.serve() pass # otherwise we would just issue that error message in the log else: def assert_no_open_files(*args, **kwargs): pass def rmtemp(f, *args, **kwargs): """Wrapper to centralize removing of temp files so we could keep them around It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP environment variable is defined """ if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'): if not os.path.lexists(f): lgr.debug("Path %s does not exist, so can't be removed", f) return lgr.log(5, "Removing temp file: %s", f) # Can also be a directory if isdir(f): rmtree(f, *args, **kwargs) else: unlink(f) else: lgr.info("Keeping temp file: %s", f) def file_basename(name, return_ext=False): """ Strips up to 2 extensions of length up to 4 characters and starting with alpha not a digit, so we could get rid of .tar.gz etc """ bname = basename(name) fbname = re.sub(r'(\.[a-zA-Z_]\S{1,4}){0,2}$', '', bname) if return_ext: return fbname, bname[len(fbname) + 1:] else: return fbname # unused in -core def escape_filename(filename): """Surround filename in "" and escape " in the filename """ filename = filename.replace('"', r'\"').replace('`', r'\`') filename = '"%s"' % filename return filename # unused in -core def encode_filename(filename): """Encode unicode filename """ if isinstance(filename, str): return filename.encode(sys.getfilesystemencoding()) else: return filename # unused in -core def decode_input(s): """Given input string/bytes, decode according to stdin codepage (or UTF-8) if not defined If fails -- issue warning and decode allowing for errors being replaced """ if isinstance(s, str): return s else: encoding = sys.stdin.encoding or 'UTF-8' try: return s.decode(encoding) except UnicodeDecodeError as exc: lgr.warning( "Failed to decode input string using %s encoding. " "Decoding allowing for errors", encoding) return s.decode(encoding, errors='replace') # unused in -core if on_windows: def lmtime(filepath, mtime): """Set mtime for files. On Windows a merely adapter to os.utime """ os.utime(filepath, (time.time(), mtime)) else: def lmtime(filepath, mtime): """Set mtime for files, while not de-referencing symlinks. To overcome absence of os.lutime Works only on linux and OSX ATM """ from .cmd import WitlessRunner # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS] smtime = time.strftime("%Y%m%d%H%M.%S", time.localtime(mtime)) lgr.log(3, "Setting mtime for %s to %s == %s", filepath, mtime, smtime) WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath]) filepath = Path(filepath) rfilepath = filepath.resolve() if filepath.is_symlink() and rfilepath.exists(): # trust no one - adjust also of the target file # since it seemed like downloading under OSX (was it using curl?) # didn't bother with timestamps lgr.log(3, "File is a symlink to %s Setting mtime for it to %s", rfilepath, mtime) os.utime(str(rfilepath), (time.time(), mtime)) # doesn't work on OSX # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath]) def ensure_tuple_or_list(obj): """Given an object, wrap into a tuple if not list or tuple """ if isinstance(obj, (list, tuple)): return obj return (obj,) def ensure_iter(s, cls, copy=False, iterate=True): """Given not a list, would place it into a list. If None - empty list is returned Parameters ---------- s: list or anything cls: class Which iterable class to ensure copy: bool, optional If correct iterable is passed, it would generate its shallow copy iterate: bool, optional If it is not a list, but something iterable (but not a str) iterate over it. """ if isinstance(s, cls): return s if not copy else shallow_copy(s) elif isinstance(s, str): return cls((s,)) elif iterate and hasattr(s, '__iter__'): return cls(s) elif s is None: return cls() else: return cls((s,)) def ensure_list(s, copy=False, iterate=True): """Given not a list, would place it into a list. If None - empty list is returned Parameters ---------- s: list or anything copy: bool, optional If list is passed, it would generate a shallow copy of the list iterate: bool, optional If it is not a list, but something iterable (but not a str) iterate over it. """ return ensure_iter(s, list, copy=copy, iterate=iterate) def ensure_list_from_str(s, sep='\n'): """Given a multiline string convert it to a list of return None if empty Parameters ---------- s: str or list """ if not s: return None if isinstance(s, list): return s return s.split(sep) def ensure_dict_from_str(s, **kwargs): """Given a multiline string with key=value items convert it to a dictionary Parameters ---------- s: str or dict Returns None if input s is empty """ if not s: return None if isinstance(s, dict): return s out = {} for value_str in ensure_list_from_str(s, **kwargs): if '=' not in value_str: raise ValueError("{} is not in key=value format".format(repr(value_str))) k, v = value_str.split('=', 1) if k in out: err = "key {} was already defined in {}, but new value {} was provided".format(k, out, v) raise ValueError(err) out[k] = v return out def ensure_bytes(s, encoding='utf-8'): """Convert/encode unicode string to bytes. If `s` isn't a string, return it as is. Parameters ---------- encoding: str, optional Encoding to use. "utf-8" is the default """ if not isinstance(s, str): return s return s.encode(encoding) def ensure_unicode(s, encoding=None, confidence=None): """Convert/decode bytestring to unicode. If `s` isn't a bytestring, return it as is. Parameters ---------- encoding: str, optional Encoding to use. If None, "utf-8" is tried, and then if not a valid UTF-8, encoding will be guessed confidence: float, optional A value between 0 and 1, so if guessing of encoding is of lower than specified confidence, ValueError is raised """ if not isinstance(s, bytes): return s if encoding is None: # Figure out encoding, defaulting to 'utf-8' which is our common # target in contemporary digital society try: return s.decode('utf-8') except UnicodeDecodeError as exc: lgr.debug("Failed to decode a string as utf-8: %s", CapturedException(exc)) # And now we could try to guess from chardet import detect enc = detect(s) denc = enc.get('encoding', None) if denc: denc_confidence = enc.get('confidence', 0) if confidence is not None and denc_confidence < confidence: raise ValueError( "Failed to auto-detect encoding with high enough " "confidence. Highest confidence was %s for %s" % (denc_confidence, denc) ) lgr.log(5, "Auto-detected encoding to be %s", denc) return s.decode(denc) else: raise ValueError( "Could not decode value as utf-8, or to guess its encoding: %s" % repr(s) ) else: return s.decode(encoding) def ensure_bool(s): """Convert value into boolean following convention for strings to recognize on,True,yes as True, off,False,no as False """ if isinstance(s, str): if s.isdigit(): return bool(int(s)) sl = s.lower() if sl in {'y', 'yes', 'true', 'on'}: return True elif sl in {'n', 'no', 'false', 'off'}: return False else: raise ValueError("Do not know how to treat %r as a boolean" % s) return bool(s) def as_unicode(val, cast_types=object): """Given an arbitrary value, would try to obtain unicode value of it For unicode it would return original value, for python2 str or python3 bytes it would use ensure_unicode, for None - an empty (unicode) string, and for any other type (see `cast_types`) - would apply the unicode constructor. If value is not an instance of `cast_types`, TypeError is thrown Parameters ---------- cast_types: type Which types to cast to unicode by providing to constructor """ if val is None: return u'' elif isinstance(val, str): return val elif isinstance(val, unicode_srctypes): return ensure_unicode(val) elif isinstance(val, cast_types): return str(val) else: raise TypeError( "Value %r is not of any of known or provided %s types" % (val, cast_types)) def unique(seq, key=None, reverse=False): """Given a sequence return a list only with unique elements while maintaining order This is the fastest solution. See https://www.peterbe.com/plog/uniqifiers-benchmark and http://stackoverflow.com/a/480227/1265472 for more information. Enhancement -- added ability to compare for uniqueness using a key function Parameters ---------- seq: Sequence to analyze key: callable, optional Function to call on each element so we could decide not on a full element, but on its member etc reverse: bool, optional If True, uniqueness checked in the reverse order, so that the later ones will take the order """ seen = set() seen_add = seen.add trans = reversed if reverse else lambda x: x if not key: out = [x for x in trans(seq) if not (x in seen or seen_add(x))] else: # OPT: could be optimized, since key is called twice, but for our cases # should be just as fine out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))] return out[::-1] if reverse else out def all_same(items): """Quick check if all items are the same. Identical to a check like len(set(items)) == 1 but should be more efficient while working on generators, since would return False as soon as any difference detected thus possibly avoiding unnecessary evaluations """ first = True first_item = None for item in items: if first: first = False first_item = item else: if item != first_item: return False # So we return False if was empty return not first def map_items(func, v): """A helper to apply `func` to all elements (keys and values) within dict No type checking of values passed to func is done, so `func` should be resilient to values which it should not handle Initial usecase - apply_recursive(url_fragment, ensure_unicode) """ # map all elements within item return v.__class__( item.__class__(map(func, item)) for item in v.items() ) def partition(items, predicate=bool): """Partition `items` by `predicate`. Parameters ---------- items : iterable predicate : callable A function that will be mapped over each element in `items`. The elements will partitioned based on whether the return value is false or true. Returns ------- A tuple with two generators, the first for 'false' items and the second for 'true' ones. Notes ----- Taken from Peter Otten's snippet posted at https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html """ a, b = tee((predicate(item), item) for item in items) return ((item for pred, item in a if not pred), (item for pred, item in b if pred)) def generate_chunks(container, size): """Given a container, generate chunks from it with size up to `size` """ # There could be a "smarter" solution but I think this would suffice assert size > 0, "Size should be non-0 positive" while container: yield container[:size] container = container[size:] def generate_file_chunks(files, cmd=None): """Given a list of files, generate chunks of them to avoid exceeding cmdline length Parameters ---------- files: list of str cmd: str or list of str, optional Command to account for as well """ files = ensure_list(files) cmd = ensure_list(cmd) maxl = max(map(len, files)) if files else 0 chunk_size = max( 1, # should at least be 1. If blows then - not our fault (CMD_MAX_ARG - sum((len(x) + 3) for x in cmd) - 4 # for '--' below ) // (maxl + 3) # +3 for possible quotes and a space ) # TODO: additional treatment for "too many arguments"? although # as https://github.com/datalad/datalad/issues/1883#issuecomment # -436272758 # shows there seems to be no hardcoded limit on # of arguments, # but may be we decide to go for smth like follow to be on safe side # chunk_size = min(10240 - len(cmd), chunk_size) file_chunks = generate_chunks(files, chunk_size) return file_chunks # # Generators helpers # def saved_generator(gen): """Given a generator returns two generators, where 2nd one just replays So the first one would be going through the generated items and 2nd one would be yielding saved items """ saved = [] def gen1(): for x in gen: # iterating over original generator saved.append(x) yield x def gen2(): for x in saved: # yielding saved entries yield x return gen1(), gen2() # # Decorators # # Originally better_wraps was created to provide `wrapt`-based, instead of # `functools.wraps` implementation to preserve the correct signature of the # decorated function. By using inspect.signature in our getargspec, which # works fine on `functools.wraps`ed functions, we mediated this necessity. better_wraps = wraps # Borrowed from pandas # Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team # License: BSD-3 def optional_args(decorator): """allows a decorator to take optional positional and keyword arguments. Assumes that taking a single, callable, positional argument means that it is decorating a function, i.e. something like this:: @my_decorator def function(): pass Calls decorator with decorator(f, `*args`, `**kwargs`)""" @better_wraps(decorator) def wrapper(*args, **kwargs): def dec(f): return decorator(f, *args, **kwargs) is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable) if is_decorating: f = args[0] args = [] return dec(f) else: return dec return wrapper # TODO: just provide decorators for tempfile.mk* functions. This is ugly! def get_tempfile_kwargs(tkwargs=None, prefix="", wrapped=None): """Updates kwargs to be passed to tempfile. calls depending on env vars """ if tkwargs is None: tkwargs_ = {} else: # operate on a copy of tkwargs to avoid any side-effects tkwargs_ = tkwargs.copy() # TODO: don't remember why I had this one originally # if len(targs)<2 and \ if 'prefix' not in tkwargs_: tkwargs_['prefix'] = '_'.join( ['datalad_temp'] + ([prefix] if prefix else []) + ([''] if (on_windows or not wrapped) else [wrapped.__name__])) directory = os.environ.get('TMPDIR') if directory and 'dir' not in tkwargs_: tkwargs_['dir'] = directory return tkwargs_ @optional_args def line_profile(func): """Q&D helper to line profile the function and spit out stats """ import line_profiler prof = line_profiler.LineProfiler() @wraps(func) def _wrap_line_profile(*args, **kwargs): try: pfunc = prof(func) return pfunc(*args, **kwargs) finally: prof.print_stats() return _wrap_line_profile # unused in -core @optional_args def collect_method_callstats(func): """Figure out methods which call the method repeatedly on the same instance Use case(s): - .repo is expensive since does all kinds of checks. - .config is expensive transitively since it calls .repo each time TODO: - fancy one could look through the stack for the same id(self) to see if that location is already in memo. That would hint to the cases where object is not passed into underlying functions, causing them to redo the same work over and over again - ATM might flood with all "1 lines" calls which are not that informative. The underlying possibly suboptimal use might be coming from their callers. It might or not relate to the previous TODO """ from collections import defaultdict import traceback from time import time memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count # gross timing times = [] toppath = dirname(__file__) + sep @wraps(func) def _wrap_collect_method_callstats(*args, **kwargs): try: self = args[0] stack = traceback.extract_stack() caller = stack[-2] stack_sig = \ "{relpath}:{s.name}".format( s=caller, relpath=relpath(caller.filename, toppath)) sig = (id(self), stack_sig) # we will count based on id(self) + wherefrom memo[sig][caller.lineno] += 1 t0 = time() return func(*args, **kwargs) finally: times.append(time() - t0) pass def print_stats(): print("The cost of property {}:".format(func.__name__)) if not memo: print("None since no calls") return # total count counts = {k: sum(v.values()) for k,v in memo.items()} total = sum(counts.values()) ids = {self_id for (self_id, _) in memo} print(" Total: {} calls from {} objects with {} contexts taking {:.2f} sec" .format(total, len(ids), len(memo), sum(times))) # now we need to sort by value for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True): print(" {} {}: {} from {} lines" .format(self_id, caller, count, len(memo[(self_id, caller)]))) # Upon total exit we print the stats import atexit atexit.register(print_stats) return _wrap_collect_method_callstats # Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe def never_fail(f): """Assure that function never fails -- all exceptions are caught Returns `None` if function fails internally. """ @wraps(f) def wrapped_func(*args, **kwargs): try: return f(*args, **kwargs) except Exception as e: lgr.warning( "DataLad internal failure while running %s: %r. " "Please report at https://github.com/datalad/datalad/issues" % (f, e) ) if os.environ.get('DATALAD_ALLOW_FAIL', False): return f else: return wrapped_func # # Context Managers # # unused in -core @contextmanager def nothing_cm(): """Just a dummy cm to programmically switch context managers""" yield @contextmanager def swallow_outputs(): """Context manager to help consuming both stdout and stderr, and print() stdout is available as cm.out and stderr as cm.err whenever cm is the yielded context manager. Internally uses temporary files to guarantee absent side-effects of swallowing into StringIO which lacks .fileno. print mocking is necessary for some uses where sys.stdout was already bound to original sys.stdout, thus mocking it later had no effect. Overriding print function had desired effect """ class StringIOAdapter(object): """Little adapter to help getting out/err values """ def __init__(self): kw = get_tempfile_kwargs({}, prefix="outputs") self._out = NamedTemporaryFile(delete=False, mode='w', **kw) self._err = NamedTemporaryFile(delete=False, mode='w', **kw) def _read(self, h): with open(h.name) as f: return f.read() @property def out(self): if not self._out.closed: self._out.flush() return self._read(self._out) @property def err(self): if not self._err.closed: self._err.flush() return self._read(self._err) @property def handles(self): return self._out, self._err def cleanup(self): self._out.close() self._err.close() out_name = self._out.name err_name = self._err.name from datalad import cfg if cfg.getbool('datalad.log', 'outputs', default=False) \ and lgr.getEffectiveLevel() <= logging.DEBUG: for s, sname in ((self.out, 'stdout'), (self.err, 'stderr')): if s: pref = os.linesep + "| " lgr.debug("Swallowed %s:%s%s", sname, pref, s.replace(os.linesep, pref)) else: lgr.debug("Nothing was swallowed for %s", sname) del self._out del self._err gc.collect() rmtemp(out_name) rmtemp(err_name) def fake_print(*args, **kwargs): sep = kwargs.pop('sep', ' ') end = kwargs.pop('end', '\n') file = kwargs.pop('file', sys.stdout) if file in (oldout, olderr, sys.stdout, sys.stderr): # we mock try: sys.stdout.write(sep.join(args) + end) except UnicodeEncodeError as exc: lgr.error( "Failed to write to mocked stdout, got %s, continue as it " "didn't happen", exc) else: # must be some other file one -- leave it alone oldprint(*args, sep=sep, end=end, file=file) from .ui import ui # preserve -- they could have been mocked already oldprint = getattr(builtins, 'print') oldout, olderr = sys.stdout, sys.stderr olduiout = ui.out adapter = StringIOAdapter() try: sys.stdout, sys.stderr = adapter.handles ui.out = adapter.handles[0] setattr(builtins, 'print', fake_print) yield adapter finally: sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout setattr(builtins, 'print', oldprint) adapter.cleanup() @contextmanager def swallow_logs(new_level=None, file_=None, name='datalad'): """Context manager to consume all logs. """ lgr = logging.getLogger(name) # Keep old settings old_level = lgr.level old_handlers = lgr.handlers # Let's log everything into a string # TODO: generalize with the one for swallow_outputs class StringIOAdapter(object): """Little adapter to help getting out values And to stay consistent with how swallow_outputs behaves """ def __init__(self): if file_ is None: kw = get_tempfile_kwargs({}, prefix="logs") self._out = NamedTemporaryFile(mode='a', delete=False, **kw) else: out_file = file_ # PY3 requires clearly one or another. race condition possible self._out = open(out_file, 'a') self._final_out = None def _read(self, h): with open(h.name) as f: return f.read() @property def out(self): if self._final_out is not None: # we closed and cleaned up already return self._final_out else: self._out.flush() return self._read(self._out) @property def lines(self): return self.out.split('\n') @property def handle(self): return self._out def cleanup(self): # store for access while object exists self._final_out = self.out self._out.close() out_name = self._out.name del self._out gc.collect() if not file_: rmtemp(out_name) def assert_logged(self, msg=None, level=None, regex=True, **kwargs): """Provide assertion on whether a msg was logged at a given level If neither `msg` nor `level` provided, checks if anything was logged at all. Parameters ---------- msg: str, optional Message (as a regular expression, if `regex`) to be searched. If no msg provided, checks if anything was logged at a given level. level: str, optional String representing the level to be logged regex: bool, optional If False, regular `assert_in` is used **kwargs: str, optional Passed to `assert_re_in` or `assert_in` """ from datalad.tests.utils import assert_re_in from datalad.tests.utils import assert_in if regex: match = r'\[%s\] ' % level if level else r"\[\S+\] " else: match = '[%s] ' % level if level else '' if msg: match += msg if match: (assert_re_in if regex else assert_in)(match, self.out, **kwargs) else: assert not kwargs, "no kwargs to be passed anywhere" assert self.out, "Nothing was logged!?" adapter = StringIOAdapter() # TODO: it does store messages but without any formatting, i.e. even without # date/time prefix etc. IMHO it should preserve formatting in case if file_ is # set swallow_handler = logging.StreamHandler(adapter.handle) # we want to log levelname so we could test against it swallow_handler.setFormatter( logging.Formatter('[%(levelname)s] %(message)s')) swallow_handler.filters = sum([h.filters for h in old_handlers], []) lgr.handlers = [swallow_handler] if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them! lgr.handlers += old_handlers if isinstance(new_level, str): new_level = getattr(logging, new_level) if new_level is not None: lgr.setLevel(new_level) try: yield adapter # TODO: if file_ and there was an exception -- most probably worth logging it? # although ideally it should be the next log outside added to that file_ ... oh well finally: lgr.handlers = old_handlers lgr.setLevel(old_level) adapter.cleanup() # TODO: May be melt in with swallow_logs at some point: @contextmanager def disable_logger(logger=None): """context manager to temporarily disable logging This is to provide one of swallow_logs' purposes without unnecessarily creating temp files (see gh-1865) Parameters ---------- logger: Logger Logger whose handlers will be ordered to not log anything. Default: datalad's topmost Logger ('datalad') """ class NullFilter(logging.Filter): """Filter class to reject all records """ def filter(self, record): return 0 if logger is None: # default: all of datalad's logging: logger = logging.getLogger('datalad') filter_ = NullFilter(logger.name) [h.addFilter(filter_) for h in logger.handlers] try: yield logger finally: [h.removeFilter(filter_) for h in logger.handlers] # # Additional handlers # _sys_excepthook = sys.excepthook # Just in case we ever need original one def setup_exceptionhook(ipython=False): """Overloads default sys.excepthook with our exceptionhook handler. If interactive, our exceptionhook handler will invoke pdb.post_mortem; if not interactive, then invokes default handler. """ def _datalad_pdb_excepthook(type, value, tb): import traceback traceback.print_exception(type, value, tb) print() if is_interactive(): import pdb pdb.post_mortem(tb) if ipython: from IPython.core import ultratb sys.excepthook = ultratb.FormattedTB(mode='Verbose', # color_scheme='Linux', call_pdb=is_interactive()) else: sys.excepthook = _datalad_pdb_excepthook def ensure_dir(*args): """Make sure directory exists. Joins the list of arguments to an os-specific path to the desired directory and creates it, if it not exists yet. """ dirname = op.join(*args) if not exists(dirname): os.makedirs(dirname) return dirname def updated(d, update): """Return a copy of the input with the 'update' Primarily for updating dictionaries """ d = d.copy() d.update(update) return d _pwd_mode = None def _switch_to_getcwd(msg, *args): global _pwd_mode _pwd_mode = 'cwd' lgr.debug( msg + ". From now on will be returning os.getcwd(). Directory" " symlinks in the paths will be resolved", *args ) # TODO: we might want to mitigate by going through all flywheighted # repos and tuning up their .paths to be resolved? def getpwd(): """Try to return a CWD without dereferencing possible symlinks This function will try to use PWD environment variable to provide a current working directory, possibly with some directories along the path being symlinks to other directories. Unfortunately, PWD is used/set only by the shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify it, thus `os.getcwd()` returns path with links dereferenced. While returning current working directory based on PWD env variable we verify that the directory is the same as `os.getcwd()` after resolving all symlinks. If that verification fails, we fall back to always use `os.getcwd()`. Initial decision to either use PWD env variable or os.getcwd() is done upon the first call of this function. """ global _pwd_mode if _pwd_mode is None: # we need to decide! try: pwd = os.environ['PWD'] if on_windows and pwd and pwd.startswith('/'): # It should be a path from MSYS. # - it might start with a drive letter or not # - it seems to be "illegal" to have a single letter directories # under / path, i.e. if created - they aren't found # - 'ln -s' does not fail to create a "symlink" but it just # copies! # so we are not likely to need original PWD purpose on # those systems # Verdict: _pwd_mode = 'cwd' else: _pwd_mode = 'PWD' except KeyError: _pwd_mode = 'cwd' if _pwd_mode == 'cwd': return os.getcwd() elif _pwd_mode == 'PWD': try: cwd = os.getcwd() except OSError as exc: if "o such file" in str(exc): # directory was removed but we promised to be robust and # still report the path we might know since we are still in PWD # mode cwd = None else: raise try: pwd = os.environ['PWD'] # do absolute() in addition to always get an absolute path # even with non-existing paths on windows pwd_real = str(Path(pwd).resolve().absolute()) # This logic would fail to catch the case where chdir did happen # to the directory where current PWD is pointing to, e.g. # $> ls -ld $PWD # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp// # hopa:~/.tmp/tmp # $> python -c 'import os; os.chdir("/tmp"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())' # ('/home/yoh/.tmp/tmp', '/tmp') # but I guess that should not be too harmful if cwd is not None and pwd_real != cwd: _switch_to_getcwd( "realpath of PWD=%s is %s whenever os.getcwd()=%s", pwd, pwd_real, cwd ) return cwd return pwd except KeyError: _switch_to_getcwd("PWD env variable is no longer available") return cwd # Must not happen, but may be someone # evil purges PWD from environ? else: raise RuntimeError( "Must have not got here. " "pwd_mode must be either cwd or PWD. And it is now %r" % (_pwd_mode,) ) class chpwd(object): """Wrapper around os.chdir which also adjusts environ['PWD'] The reason is that otherwise PWD is simply inherited from the shell and we have no ability to assess directory path without dereferencing symlinks. If used as a context manager it allows to temporarily change directory to the given path """ def __init__(self, path, mkdir=False, logsuffix=''): if path: pwd = getpwd() self._prev_pwd = pwd else: self._prev_pwd = None return if not isabs(path): path = normpath(op.join(pwd, path)) if not os.path.exists(path) and mkdir: self._mkdir = True os.mkdir(path) else: self._mkdir = False lgr.debug("chdir %r -> %r %s", self._prev_pwd, path, logsuffix) os.chdir(path) # for grep people -- ok, to chdir here! os.environ['PWD'] = str(path) def __enter__(self): # nothing more to do really, chdir was in the constructor pass def __exit__(self, exc_type, exc_val, exc_tb): if self._prev_pwd: # Need to use self.__class__ so this instance, if the entire # thing mocked during the test, still would use correct chpwd self.__class__(self._prev_pwd, logsuffix="(coming back)") def dlabspath(path, norm=False): """Symlinks-in-the-cwd aware abspath os.path.abspath relies on os.getcwd() which would not know about symlinks in the path TODO: we might want to norm=True by default to match behavior of os .path.abspath? """ if not isabs(path): # if not absolute -- relative to pwd path = op.join(getpwd(), path) return normpath(path) if norm else path def with_pathsep(path): """Little helper to guarantee that path ends with /""" return path + sep if not path.endswith(sep) else path def get_path_prefix(path, pwd=None): """Get path prefix (for current directory) Returns relative path to the topdir, if we are under topdir, and if not absolute path to topdir. If `pwd` is not specified - current directory assumed """ pwd = pwd or getpwd() path = dlabspath(path) path_ = with_pathsep(path) pwd_ = with_pathsep(pwd) common = commonprefix((path_, pwd_)) if common.endswith(sep) and common in {path_, pwd_}: # we are in subdir or above the path = use relative path location_prefix = relpath(path, pwd) # if benign "here" - cut off if location_prefix in (curdir, curdir + sep): location_prefix = '' return location_prefix else: # just return absolute path return path def _get_normalized_paths(path, prefix): if isabs(path) != isabs(prefix): raise ValueError("Both paths must either be absolute or relative. " "Got %r and %r" % (path, prefix)) path = with_pathsep(path) prefix = with_pathsep(prefix) return path, prefix def path_startswith(path, prefix): """Return True if path starts with prefix path Parameters ---------- path: str prefix: str """ path, prefix = _get_normalized_paths(path, prefix) return path.startswith(prefix) def path_is_subpath(path, prefix): """Return True if path is a subpath of prefix It will return False if path == prefix. Parameters ---------- path: str prefix: str """ path, prefix = _get_normalized_paths(path, prefix) return (len(prefix) < len(path)) and path.startswith(prefix) def knows_annex(path): """Returns whether at a given path there is information about an annex It is just a thin wrapper around GitRepo.is_with_annex() classmethod which also checks for `path` to exist first. This includes actually present annexes, but also uninitialized ones, or even the presence of a remote annex branch. """ from os.path import exists if not exists(path): lgr.debug("No annex: test path {0} doesn't exist".format(path)) return False from datalad.support.gitrepo import GitRepo return GitRepo(path, init=False, create=False).is_with_annex() @contextmanager def make_tempfile(content=None, wrapped=None, **tkwargs): """Helper class to provide a temporary file name and remove it at the end (context manager) Parameters ---------- mkdir : bool, optional (default: False) If True, temporary directory created using tempfile.mkdtemp() content : str or bytes, optional Content to be stored in the file created wrapped : function, optional If set, function name used to prefix temporary file name `**tkwargs`: All other arguments are passed into the call to tempfile.mk{,d}temp(), and resultant temporary filename is passed as the first argument into the function t. If no 'prefix' argument is provided, it will be constructed using module and function names ('.' replaced with '_'). To change the used directory without providing keyword argument 'dir' set DATALAD_TESTS_TEMP_DIR. Examples -------- >>> from os.path import exists >>> from datalad.utils import make_tempfile >>> with make_tempfile() as fname: ... k = open(fname, 'w').write('silly test') >>> assert not exists(fname) # was removed >>> with make_tempfile(content="blah") as fname: ... assert open(fname).read() == "blah" """ if tkwargs.get('mkdir', None) and content is not None: raise ValueError("mkdir=True while providing content makes no sense") tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped) # if DATALAD_TESTS_TEMP_DIR is set, use that as directory, # let mktemp handle it otherwise. However, an explicitly provided # dir=... will override this. mkdir = tkwargs_.pop('mkdir', False) filename = {False: tempfile.mktemp, True: tempfile.mkdtemp}[mkdir](**tkwargs_) # MIH: not clear to me why we need to perform this (possibly expensive) # resolve. It was already part of the original implementation # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f filename = Path(filename).resolve() if content: (filename.write_bytes if isinstance(content, bytes) else filename.write_text)(content) # TODO globbing below can also be done with pathlib filename = str(filename) if __debug__: lgr.debug( 'Created temporary %s named %s', 'directory' if mkdir else 'file', filename) try: yield filename finally: # glob here for all files with the same name (-suffix) # would be useful whenever we requested .img filename, # and function creates .hdr as well # MIH: this is undocumented behavior, and undesired in the general # case. it should be made conditional and explicit lsuffix = len(tkwargs_.get('suffix', '')) filename_ = lsuffix and filename[:-lsuffix] or filename filenames = glob.glob(filename_ + '*') if len(filename_) < 3 or len(filenames) > 5: # For paranoid yoh who stepped into this already ones ;-) lgr.warning("It is unlikely that it was intended to remove all" " files matching %r. Skipping" % filename_) return for f in filenames: try: rmtemp(f) except OSError: # pragma: no cover pass def _path_(*p): """Given a path in POSIX" notation, regenerate one in native to the env one""" if on_windows: return op.join(*map(lambda x: op.join(*x.split('/')), p)) else: # Assume that all others as POSIX compliant so nothing to be done return op.join(*p) def get_timestamp_suffix(time_=None, prefix='-'): """Return a time stamp (full date and time up to second) primarily to be used for generation of log files names """ args = [] if time_ is not None: if isinstance(time_, int): time_ = time.gmtime(time_) args.append(time_) return time.strftime(prefix + TIMESTAMP_FMT, *args) # unused in -core def get_logfilename(dspath, cmd='datalad'): """Return a filename to use for logging under a dataset/repository directory would be created if doesn't exist, but dspath must exist and be a directory """ assert(exists(dspath)) assert(isdir(dspath)) ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix()) def get_trace(edges, start, end, trace=None): """Return the trace/path to reach a node in a tree. Parameters ---------- edges : sequence(2-tuple) The tree given by a sequence of edges (parent, child) tuples. The nodes can be identified by any value and data type that supports the '==' operation. start : Identifier of the start node. Must be present as a value in the parent location of an edge tuple in order to be found. end : Identifier of the target/end node. Must be present as a value in the child location of an edge tuple in order to be found. trace : list Mostly useful for recursive calls, and used internally. Returns ------- None or list Returns a list with the trace to the target (the starts and the target are not included in the trace, hence if start and end are directly connected an empty list is returned), or None when no trace to the target can be found, or start and end are identical. """ # the term trace is used to avoid confusion with a path in the sense # of a filesystem path, but the analogy fits and nodes can be paths if trace is None: trace = [] if not edges: raise ValueError("no edges given") for cand in edges: cand_super, cand_sub = cand if cand_sub in trace: # only DAGs, skip any cyclic traces continue if trace and cand_super != trace[-1]: # only consider edges that lead off the end of the trace continue if not trace and cand_super != start: # we got nothing yet, and this edges is not matching the start continue if cand_sub == end: return trace # dive into potential subnodes cand_trace = get_trace( edges, start, end, trace + [cand_sub]) if cand_trace: return cand_trace return None def get_dataset_root(path): """Return the root of an existent dataset containing a given path The root path is returned in the same absolute or relative form as the input argument. If no associated dataset exists, or the input path doesn't exist, None is returned. If `path` is a symlink or something other than a directory, its the root dataset containing its parent directory will be reported. If none can be found, at a symlink at `path` is pointing to a dataset, `path` itself will be reported as the root. Parameters ---------- path : Path-like Returns ------- str or None """ path = str(path) suffix = '.git' altered = None if islink(path) or not isdir(path): altered = path path = dirname(path) apath = abspath(path) # while we can still go up while split(apath)[1]: if exists(op.join(path, suffix)): return path # new test path in the format we got it path = normpath(op.join(path, os.pardir)) # no luck, next round apath = abspath(path) # if we applied dirname() at the top, we give it another go with # the actual path, if it was itself a symlink, it could be the # top-level dataset itself if altered and exists(op.join(altered, suffix)): return altered return None # ATM used in datalad_crawler extension, so do not remove yet def try_multiple(ntrials, exception, base, f, *args, **kwargs): """Call f multiple times making exponentially growing delay between the calls""" for trial in range(1, ntrials+1): try: return f(*args, **kwargs) except exception as exc: if trial == ntrials: raise # just reraise on the last trial t = base ** trial lgr.warning("Caught %s on trial #%d. Sleeping %f and retrying", CapturedException(exc), trial, t) sleep(t) @optional_args def try_multiple_dec( f, ntrials=None, duration=0.1, exceptions=None, increment_type=None, exceptions_filter=None, logger=None, ): """Decorator to try function multiple times. Main purpose is to decorate functions dealing with removal of files/directories and which might need a few seconds to work correctly on Windows which takes its time to release files/directories. Parameters ---------- ntrials: int, optional duration: float, optional Seconds to sleep before retrying. increment_type: {None, 'exponential'} Note that if it is exponential, duration should typically be > 1.0 so it grows with higher power exceptions: Exception or tuple of Exceptions, optional Exception or a tuple of multiple exceptions, on which to retry exceptions_filter: callable, optional If provided, this function will be called with a caught exception instance. If function returns True - we will re-try, if False - exception will be re-raised without retrying. logger: callable, optional Logger to log upon failure. If not provided, will use stock logger at the level of 5 (heavy debug). """ if not exceptions: exceptions = (OSError, WindowsError, PermissionError) \ if on_windows else OSError if not ntrials: # Life goes fast on proper systems, no need to delay it much ntrials = 100 if on_windows else 10 if logger is None: def logger(*args, **kwargs): return lgr.log(5, *args, **kwargs) assert increment_type in {None, 'exponential'} @wraps(f) def _wrap_try_multiple_dec(*args, **kwargs): t = duration for trial in range(ntrials): try: return f(*args, **kwargs) except exceptions as exc: if exceptions_filter and not exceptions_filter(exc): raise if trial < ntrials - 1: if increment_type == 'exponential': t = duration ** (trial + 1) logger( "Caught %s on trial #%d. Sleeping %f and retrying", CapturedException(exc), trial, t) sleep(t) else: raise return _wrap_try_multiple_dec @try_multiple_dec def unlink(f): """'Robust' unlink. Would try multiple times On windows boxes there is evidence for a latency of more than a second until a file is considered no longer "in-use". WindowsError is not known on Linux, and if IOError or any other exception is thrown then if except statement has WindowsError in it -- NameError also see gh-2533 """ # Check for open files assert_no_open_files(f) return os.unlink(f) @try_multiple_dec def _rmtree(*args, **kwargs): """Just a helper to decorate shutil.rmtree. rmtree defined above does more and ideally should not itself be decorated since a recursive definition and does checks for open files inside etc - might be too runtime expensive """ return shutil.rmtree(*args, **kwargs) def slash_join(base, extension): """Join two strings with a '/', avoiding duplicate slashes If any of the strings is None the other is returned as is. """ if extension is None: return base if base is None: return extension return '/'.join( (base.rstrip('/'), extension.lstrip('/'))) # # IO Helpers # # unused in -core def open_r_encdetect(fname, readahead=1000): """Return a file object in read mode with auto-detected encoding This is helpful when dealing with files of unknown encoding. Parameters ---------- readahead: int, optional How many bytes to read for guessing the encoding type. If negative - full file will be read """ from chardet import detect import io # read some bytes from the file with open(fname, 'rb') as f: head = f.read(readahead) enc = detect(head) denc = enc.get('encoding', None) lgr.debug("Auto-detected encoding %s for file %s (confidence: %s)", denc, fname, enc.get('confidence', 'unknown')) return io.open(fname, encoding=denc) def read_file(fname, decode=True): """A helper to read file passing content via ensure_unicode Parameters ---------- decode: bool, optional if False, no ensure_unicode and file content returned as bytes """ with open(fname, 'rb') as f: content = f.read() return ensure_unicode(content) if decode else content def read_csv_lines(fname, dialect=None, readahead=16384, **kwargs): """A generator of dict records from a CSV/TSV Automatically guesses the encoding for each record to convert to UTF-8 Parameters ---------- fname: str Filename dialect: str, optional Dialect to specify to csv.reader. If not specified -- guessed from the file, if fails to guess, "excel-tab" is assumed readahead: int, optional How many bytes to read from the file to guess the type **kwargs Passed to `csv.reader` """ import csv if dialect is None: with open(fname) as tsvfile: # add robustness, use a sniffer try: dialect = csv.Sniffer().sniff(tsvfile.read(readahead)) except Exception as exc: lgr.warning( 'Could not determine file-format, assuming TSV: %s', CapturedException(exc) ) dialect = 'excel-tab' kw = dict(encoding='utf-8') with open(fname, 'r', **kw) as tsvfile: # csv.py doesn't do Unicode; encode temporarily as UTF-8: csv_reader = csv.reader( tsvfile, dialect=dialect, **kwargs ) header = None for row in csv_reader: # decode UTF-8 back to Unicode, cell by cell: row_unicode = map(ensure_unicode, row) if header is None: header = list(row_unicode) else: yield dict(zip(header, row_unicode)) def import_modules(modnames, pkg, msg="Failed to import {module}", log=lgr.debug): """Helper to import a list of modules without failing if N/A Parameters ---------- modnames: list of str List of module names to import pkg: str Package under which to import msg: str, optional Message template for .format() to log at DEBUG level if import fails. Keys {module} and {package} will be provided and ': {exception}' appended log: callable, optional Logger call to use for logging messages """ from importlib import import_module _globals = globals() mods_loaded = [] if pkg and not pkg in sys.modules: # with python 3.5.1 (ok with 3.5.5) somehow kept running into # Failed to import dlsub1: Parent module 'dltestm1' not loaded # while running the test. Preloading pkg resolved the issue import_module(pkg) for modname in modnames: try: _globals[modname] = mod = import_module( '.{}'.format(modname), pkg) mods_loaded.append(mod) except Exception as exc: from datalad.support.exceptions import CapturedException ce = CapturedException(exc) log((msg + ': {exception}').format( module=modname, package=pkg, exception=ce.message)) return mods_loaded def import_module_from_file(modpath, pkg=None, log=lgr.debug): """Import provided module given a path TODO: - RF/make use of it in pipeline.py which has similar logic - join with import_modules above? Parameters ---------- pkg: module, optional If provided, and modpath is under pkg.__path__, relative import will be used """ assert(modpath.endswith('.py')) # for now just for .py files log("Importing %s" % modpath) modname = basename(modpath)[:-3] relmodpath = None if pkg: for pkgpath in pkg.__path__: if path_is_subpath(modpath, pkgpath): # for now relying on having .py extension -- assertion above relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.') break try: if relmodpath: from importlib import import_module mod = import_module(relmodpath, pkg.__name__) else: dirname_ = dirname(modpath) try: sys.path.insert(0, dirname_) mod = __import__(modname, level=0) finally: if dirname_ in sys.path: sys.path.pop(sys.path.index(dirname_)) else: log("Expected path %s to be within sys.path, but it was gone!" % dirname_) except Exception as e: raise RuntimeError( "Failed to import module from %s" % modpath) from e return mod def get_encoding_info(): """Return a dictionary with various encoding/locale information""" import sys, locale from collections import OrderedDict return OrderedDict([ ('default', sys.getdefaultencoding()), ('filesystem', sys.getfilesystemencoding()), ('locale.prefered', locale.getpreferredencoding()), ]) def get_envvars_info(): from collections import OrderedDict envs = [] for var, val in os.environ.items(): if ( var.startswith('PYTHON') or var.startswith('LC_') or var.startswith('GIT_') or var in ('LANG', 'LANGUAGE', 'PATH') ): envs.append((var, val)) return OrderedDict(envs) # This class is modified from Snakemake (v5.1.4) class SequenceFormatter(string.Formatter): """string.Formatter subclass with special behavior for sequences. This class delegates formatting of individual elements to another formatter object. Non-list objects are formatted by calling the delegate formatter's "format_field" method. List-like objects (list, tuple, set, frozenset) are formatted by formatting each element of the list according to the specified format spec using the delegate formatter and then joining the resulting strings with a separator (space by default). """ def __init__(self, separator=" ", element_formatter=string.Formatter(), *args, **kwargs): self.separator = separator self.element_formatter = element_formatter def format_element(self, elem, format_spec): """Format a single element For sequences, this is called once for each element in a sequence. For anything else, it is called on the entire object. It is intended to be overridden in subclases. """ return self.element_formatter.format_field(elem, format_spec) def format_field(self, value, format_spec): if isinstance(value, (list, tuple, set, frozenset)): return self.separator.join(self.format_element(v, format_spec) for v in value) else: return self.format_element(value, format_spec) # TODO: eventually we might want to make use of attr module class File(object): """Helper for a file entry in the create_tree/@with_tree It allows to define additional settings for entries """ def __init__(self, name, executable=False): """ Parameters ---------- name : str Name of the file executable: bool, optional Make it executable """ self.name = name self.executable = executable def __str__(self): return self.name def create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True): """Given an archive `name`, create under `path` with specified `load` tree """ from datalad.support.archives import compress_files dirname = file_basename(name) full_dirname = op.join(path, dirname) os.makedirs(full_dirname) create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir) # create archive if archives_leading_dir: compress_files([dirname], name, path=path, overwrite=overwrite) else: compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))), op.join(pardir, name), path=op.join(path, dirname), overwrite=overwrite) # remove original tree rmtree(full_dirname) def create_tree(path, tree, archives_leading_dir=True, remove_existing=False): """Given a list of tuples (name, load) create such a tree if load is a tuple itself -- that would create either a subtree or an archive with that content and place it into the tree if name ends with .tar.gz """ lgr.log(5, "Creating a tree under %s", path) if not exists(path): os.makedirs(path) if isinstance(tree, dict): tree = tree.items() for file_, load in tree: if isinstance(file_, File): executable = file_.executable name = file_.name else: executable = False name = file_ full_name = op.join(path, name) if remove_existing and lexists(full_name): rmtree(full_name, chmod_files=True) if isinstance(load, (tuple, list, dict)): if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'): create_tree_archive( path, name, load, archives_leading_dir=archives_leading_dir) else: create_tree( full_name, load, archives_leading_dir=archives_leading_dir, remove_existing=remove_existing) else: open_func = open if full_name.endswith('.gz'): open_func = gzip.open elif full_name.split('.')[-1] in ('xz', 'lzma'): import lzma open_func = lzma.open with open_func(full_name, "wb") as f: f.write(ensure_bytes(load, 'utf-8')) if executable: os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC) def get_suggestions_msg(values, known, sep="\n "): """Return a formatted string with suggestions for values given the known ones """ import difflib suggestions = [] for value in ensure_list(values): # might not want to do it if we change presentation below suggestions += difflib.get_close_matches(value, known) suggestions = unique(suggestions) msg = "Did you mean any of these?" if suggestions: if '\n' in sep: # if separator includes new line - we add entire separator right away msg += sep else: msg += ' ' return msg + "%s\n" % sep.join(suggestions) return '' def bytes2human(n, format='%(value).1f %(symbol)sB'): """ Convert n bytes into a human readable string based on format. symbols can be either "customary", "customary_ext", "iec" or "iec_ext", see: http://goo.gl/kTQMs >>> from datalad.utils import bytes2human >>> bytes2human(1) '1.0 B' >>> bytes2human(1024) '1.0 KB' >>> bytes2human(1048576) '1.0 MB' >>> bytes2human(1099511627776127398123789121) '909.5 YB' >>> bytes2human(10000, "%(value).1f %(symbol)s/sec") '9.8 K/sec' >>> # precision can be adjusted by playing with %f operator >>> bytes2human(10000, format="%(value).5f %(symbol)s") '9.76562 K' Taken from: http://goo.gl/kTQMs and subsequently simplified Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com> License: MIT """ n = int(n) if n < 0: raise ValueError("n < 0") symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y') prefix = {} for i, s in enumerate(symbols[1:]): prefix[s] = 1 << (i + 1) * 10 for symbol in reversed(symbols[1:]): if n >= prefix[symbol]: value = float(n) / prefix[symbol] return format % locals() return format % dict(symbol=symbols[0], value=n) def quote_cmdlinearg(arg): """Perform platform-appropriate argument quoting""" # https://stackoverflow.com/a/15262019 return '"{}"'.format( arg.replace('"', '""') ) if on_windows else shlex_quote(arg) def guard_for_format(arg): """Replace { and } with {{ and }} To be used in cases if arg is not expected to have provided by user .format() placeholders, but 'arg' might become a part of a composite passed to .format(), e.g. via 'Run' """ return arg.replace('{', '{{').replace('}', '}}') def join_cmdline(args): """Join command line args into a string using quote_cmdlinearg """ return ' '.join(map(quote_cmdlinearg, args)) def split_cmdline(s): """Perform platform-appropriate command line splitting. Identical to `shlex.split()` on non-windows platforms. Modified from https://stackoverflow.com/a/35900070 """ if not on_windows: return shlex_split(s) # the rest is for windows RE_CMD_LEX = r'''"((?:""|\\["\\]|[^"])*)"?()|(\\\\(?=\\*")|\\")|(&&?|\|\|?|\d?>|[<])|([^\s"&|<>]+)|(\s+)|(.)''' args = [] accu = None # collects pieces of one arg for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s): if word: pass # most frequent elif esc: word = esc[1] elif white or pipe: if accu is not None: args.append(accu) if pipe: args.append(pipe) accu = None continue elif fail: raise ValueError("invalid or incomplete shell string") elif qs: word = qs.replace('\\"', '"').replace('\\\\', '\\') if platform == 0: word = word.replace('""', '"') else: word = qss # may be even empty; must be last accu = (accu or '') + word if accu is not None: args.append(accu) return args def get_wrapped_class(wrapped): """Determine the command class a wrapped __call__ belongs to""" mod = sys.modules[wrapped.__module__] command_class_name = wrapped.__qualname__.split('.')[-2] _func_class = mod.__dict__[command_class_name] lgr.debug("Determined class of decorated function: %s", _func_class) return _func_class def _make_assure_kludge(fn): old_name = fn.__name__.replace("ensure", "assure") @wraps(fn) def compat_fn(*args, **kwargs): warnings.warn( "{} is deprecated and will be removed in a future release. " "Use {} instead." .format(old_name, fn.__name__), DeprecationWarning) return fn(*args, **kwargs) compat_fn.__doc__ = ("Note: This function is deprecated. Use {} instead." .format(fn.__name__)) return compat_fn assure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list) assure_iter = _make_assure_kludge(ensure_iter) assure_list = _make_assure_kludge(ensure_list) assure_list_from_str = _make_assure_kludge(ensure_list_from_str) assure_dict_from_str = _make_assure_kludge(ensure_dict_from_str) assure_bytes = _make_assure_kludge(ensure_bytes) assure_unicode = _make_assure_kludge(ensure_unicode) assure_bool = _make_assure_kludge(ensure_bool) assure_dir = _make_assure_kludge(ensure_dir) lgr.log(5, "Done importing datalad.utils") def check_symlink_capability(path, target): """helper similar to datalad.tests.utils.has_symlink_capability However, for use in a datalad command context, we shouldn't assume to be able to write to tmpfile and also not import a whole lot from datalad's test machinery. Finally, we want to know, whether we can create a symlink at a specific location, not just somewhere. Therefore use arbitrary path to test-build a symlink and delete afterwards. Suitable location can therefore be determined by high lever code. Parameters ---------- path: Path target: Path Returns ------- bool """ try: target.touch() path.symlink_to(target) return True except Exception: return False finally: if path.exists(): path.unlink() if target.exists(): target.unlink()

AKSoo/datalad

datalad/utils.py

Python

""" ORY Keto A cloud native access control server providing best-practice patterns (RBAC, ABAC, ACL, AWS IAM Policies, Kubernetes Roles, ...) via REST APIs. # noqa: E501 The version of the OpenAPI document: v0.0.0 Contact: [email protected] Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 import nulltype # noqa: F401 from ory_keto_client.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) def lazy_import(): from ory_keto_client.model.delete_ory_access_control_policy_internal_server_error_body import DeleteOryAccessControlPolicyInternalServerErrorBody globals()['DeleteOryAccessControlPolicyInternalServerErrorBody'] = DeleteOryAccessControlPolicyInternalServerErrorBody class DeleteOryAccessControlPolicyInternalServerError(ModelNormal): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. attribute_map (dict): The key is attribute name and the value is json key in definition. discriminator_value_class_map (dict): A dict to go from the discriminator variable value to the discriminator class name. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { } validations = { } additional_properties_type = None _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ lazy_import() return { 'payload': (DeleteOryAccessControlPolicyInternalServerErrorBody,), # noqa: E501 } @cached_property def discriminator(): return None attribute_map = { 'payload': 'Payload', # noqa: E501 } _composed_schemas = {} required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): # noqa: E501 """DeleteOryAccessControlPolicyInternalServerError - a model defined in OpenAPI Keyword Args: _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) payload (DeleteOryAccessControlPolicyInternalServerErrorBody): [optional] # noqa: E501 """ _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _path_to_item = kwargs.pop('_path_to_item', ()) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) for var_name, var_value in kwargs.items(): if var_name not in self.attribute_map and \ self._configuration is not None and \ self._configuration.discard_unknown_keys and \ self.additional_properties_type is None: # discard variable. continue setattr(self, var_name, var_value)

Stackwalkerllc/sdk

clients/keto/python/ory_keto_client/model/delete_ory_access_control_policy_internal_server_error.py

Python

from io import BytesIO from uniborg import util from telethon import types from telethon.errors import PhotoInvalidDimensionsError from telethon.tl.functions.messages import SendMediaRequest @borg.on(util.admin_cmd(r"^\.i$")) async def on_file_to_photo(event): await event.delete() target = await event.get_reply_message() try: image = target.media.document except AttributeError: return if not image.mime_type.startswith('image/'): return # This isn't an image if image.mime_type == 'image/webp': return # Telegram doesn't let you directly send stickers as photos if image.size > 10 * 1024 * 1024: return # We'd get PhotoSaveFileInvalidError otherwise file = await borg.download_media(target, file=BytesIO()) file.seek(0) img = await borg.upload_file(file) img.name = 'image.png' try: await borg(SendMediaRequest( peer=await event.get_input_chat(), media=types.InputMediaUploadedPhoto(img), message=target.message, entities=target.entities, reply_to_msg_id=target.id )) except PhotoInvalidDimensionsError: return

anandvfc/UniBorg

stdplugins/file to img.py

Python

#!/bin/python import json import re import sys from datetime import datetime import dateutil.parser from dateutil.tz import tzutc from six.moves import range from mtools.util.pattern import json2pattern class DateTimeEncoder(json.JSONEncoder): """Custom datetime encoder for json output.""" def default(self, obj): if isinstance(obj, datetime): return obj.isoformat() return json.JSONEncoder.default(self, obj) class LogEvent(object): """ Extract information from log line and store properties/variables. line_str: the original line string split_tokens: a list of string tokens after splitting line_str using whitespace as split points datetime: a datetime object for the logevent. For logfiles created with version 2.4+, it also contains micro-seconds duration: the duration of a timed operation in ms thread: the thread name (e.g. "conn1234") as string operation: insert, update, remove, query, command, getmore, None namespace: the namespace of the operation, or None command: the type of command, if the operation was a "command" pattern: the query pattern for queries, updates, counts, etc ... Certain operations also add the number of affected/scanned documents. If applicable, the following variables are also set, otherwise the default is None: nscanned, ntoreturn, nreturned, ninserted, nupdated For performance reason, all fields are evaluated lazily upon first request. """ # datetime handler for json encoding dthandler = lambda obj: obj.isoformat() if isinstance(obj, datetime) else None weekdays = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] log_operations = ['query', 'insert', 'update', 'remove', 'getmore', 'command'] log_levels = ['D', 'F', 'E', 'W', 'I', 'U'] log_components = ['-', 'ACCESS', 'COMMAND', 'CONTROL', 'GEO', 'INDEX', 'NETWORK', 'QUERY', 'REPL', 'SHARDING', 'STORAGE', 'JOURNAL', 'WRITE', 'TOTAL'] def __init__(self, doc_or_str): self._year_rollover = False if isinstance(doc_or_str, bytes): doc_or_str = doc_or_str.decode("utf-8") if isinstance(doc_or_str, str) or (sys.version_info.major == 2 and isinstance(doc_or_str, unicode)): # create from string, remove line breaks at end of _line_str self.from_string = True self._line_str = doc_or_str.rstrip() self._profile_doc = None self._reset() else: self.from_string = False self._profile_doc = doc_or_str # docs don't need to be parsed lazily, they are fast self._parse_document() def _reset(self): self._split_tokens_calculated = False self._split_tokens = None self._duration_calculated = False self._duration = None self._datetime_calculated = False self._datetime = None self._datetime_nextpos = None self._datetime_format = None self._datetime_str = '' self._thread_calculated = False self._thread = None self._operation_calculated = False self._operation = None self._namespace = None self._pattern = None self._sort_pattern = None self._command_calculated = False self._command = None self._counters_calculated = False # TODO: refactor from the legacy names to modern # (eg: nscanned => keysExamined). Currently _extract_counters() # maps newer property names into legacy equivalents for # broader log file support. self._nscanned = None # keysExamined self._nscannedObjects = None # docsExamined self._ntoreturn = None self._nupdated = None # nModified self._nreturned = None # nReturned or nMatched (updates) self._ninserted = None # nInserted self._ndeleted = None # nDeleted self._numYields = None self._planSummary = None self._writeConflicts = None self._r = None self._w = None self._conn = None self._level_calculated = False self._level = None self._component = None self.merge_marker_str = '' def set_line_str(self, line_str): """ Set line_str. Line_str is only writeable if LogEvent was created from a string, not from a system.profile documents. """ if not self.from_string: raise ValueError("can't set line_str for LogEvent created from " "system.profile documents.") if line_str != self._line_str: self._line_str = line_str.rstrip() self._reset() def get_line_str(self): """Return line_str depending on source, logfile or system.profile.""" if self.from_string: return ' '.join([s for s in [self.merge_marker_str, self._datetime_str, self._line_str] if s]) else: return ' '.join([s for s in [self._datetime_str, self._line_str] if s]) line_str = property(get_line_str, set_line_str) @property def split_tokens(self): """Split string into tokens (lazy).""" if not self._split_tokens_calculated: # split into items (whitespace split) self._split_tokens = self._line_str.split() self._split_tokens_calculated = True return self._split_tokens @property def duration(self): """Calculate duration if available (lazy).""" if not self._duration_calculated: self._duration_calculated = True # split_tokens = self.split_tokens line_str = self.line_str if (line_str and line_str.endswith('ms') and 'Scheduled new oplog query' not in line_str): try: # find duration from end space_pos = line_str.rfind(" ") if space_pos == -1: return self._duration = int(line_str[line_str.rfind(" ") + 1:-2].replace(',', '')) except ValueError: self._duration = None elif "flushing" in self.line_str: matchobj = re.search(r'flushing mmaps took (\d+)ms', self.line_str) if matchobj: self._duration = int(matchobj.group(1)) return self._duration @property def datetime(self): """Extract datetime if available (lazy).""" if not self._datetime_calculated: self._datetime_calculated = True # if no datetime after 10 tokens, break to avoid parsing # very long lines split_tokens = self.split_tokens[:10] for offs in range(len(split_tokens)): dt = self._match_datetime_pattern(split_tokens[offs:offs + 4]) if dt: self._datetime = dt self._datetime_nextpos = offs if self._datetime_format.startswith("iso8601"): self._datetime_nextpos += 1 else: self._datetime_nextpos += 4 # separate datetime str and linestr self._line_str = (' '.join(self.split_tokens [self._datetime_nextpos:])) if self.level: self._datetime_nextpos += 2 self._reformat_timestamp(self._datetime_format) break return self._datetime @property def datetime_format(self): if not self._datetime_calculated: _ = self.datetime return self._datetime_format @property def datetime_nextpos(self): if self._datetime_nextpos is None and not self._datetime_calculated: _ = self.datetime return self._datetime_nextpos def set_datetime_hint(self, format, nextpos, rollover): self._datetime_format = format self._datetime_nextpos = nextpos self._year_rollover = rollover # Fast check if timestamp format changed. # If it has, trigger datetime evaluation. if format.startswith('ctime'): if (len(self.split_tokens) < 4 or self.split_tokens[self._datetime_nextpos - 4] not in self.weekdays): _ = self.datetime return False return True else: if len(self.split_tokens) == 0: # empty line, no need to parse datetime self._datetime_calculated = True return False try: if not (self.split_tokens[self._datetime_nextpos - 1][0] .isdigit()): # not the timestamp format that was hinted _ = self.datetime return False except Exception: pass return True def _match_datetime_pattern(self, tokens): """ Match the datetime pattern at the beginning of the token list. There are several formats that this method needs to understand and distinguish between (see MongoDB's SERVER-7965): ctime-pre2.4 Wed Dec 31 19:00:00 ctime Wed Dec 31 19:00:00.000 iso8601-utc 1970-01-01T00:00:00.000Z iso8601-local 1969-12-31T19:00:00.000+0500 """ # first check: less than 4 tokens can't be ctime assume_iso8601_format = len(tokens) < 4 # check for ctime-pre-2.4 or ctime format if not assume_iso8601_format: weekday, month, day, time = tokens[:4] if (len(tokens) < 4 or (weekday not in self.weekdays) or (month not in self.months) or not day.isdigit()): assume_iso8601_format = True if assume_iso8601_format: # sanity check, because the dateutil parser could interpret # any numbers as a valid date if not re.match(r'\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}.\d{3}', tokens[0]): return None # convinced that this is a ISO-8601 format, the dateutil parser # will do the rest dt = dateutil.parser.parse(tokens[0]) self._datetime_format = "iso8601-utc" \ if tokens[0].endswith('Z') else "iso8601-local" else: # assume current year unless self.year_rollover # is set (from LogFile) year = datetime.now().year dt = dateutil.parser.parse(' '.join(tokens[: 4]), default=datetime(year, 1, 1)) if dt.tzinfo is None: dt = dt.replace(tzinfo=tzutc()) if self._year_rollover and dt > self._year_rollover: dt = dt.replace(year=year - 1) self._datetime_format = "ctime" \ if '.' in tokens[3] else "ctime-pre2.4" return dt @property def thread(self): """Extract thread name if available (lazy).""" if not self._thread_calculated: self._thread_calculated = True split_tokens = self.split_tokens if not self.datetime_nextpos: return None if len(split_tokens) <= self.datetime_nextpos: return None connection_token = split_tokens[self.datetime_nextpos] match = re.match(r'^\[([^\]]*)\]$', connection_token) if match: self._thread = match.group(1) if self._thread is not None: if self._thread in ['initandlisten', 'mongosMain']: if len(split_tokens) >= 5 and split_tokens[-5][0] == '#': self._conn = 'conn' + split_tokens[-5][1:] elif self._thread.startswith('conn'): self._conn = self._thread return self._thread @property def conn(self): r""" Extract conn name if available (lazy). This value is None for all lines except the log lines related to connections, that is lines matching '\[conn[0-9]+\]' or '\[(initandlisten|mongosMain)\] .* connection accepted from'. """ self.thread return self._conn @property def operation(self): """ Extract operation if available (lazy). Operations: query, insert, update, remove, getmore, command """ if not self._operation_calculated: self._operation_calculated = True self._extract_operation_and_namespace() return self._operation @property def namespace(self): """Extract namespace if available (lazy).""" if not self._operation_calculated: self._operation_calculated = True self._extract_operation_and_namespace() return self._namespace def _extract_operation_and_namespace(self): """ Helper method to extract both operation and namespace from a logevent. It doesn't make sense to only extract one as they appear back to back in the token list. """ split_tokens = self.split_tokens if not self._datetime_nextpos: # force evaluation of thread to get access to datetime_offset and # to protect from changes due to line truncation. _ = self.thread if not self._datetime_nextpos or (len(split_tokens) <= self._datetime_nextpos + 2): return op = split_tokens[self._datetime_nextpos + 1].lower() if op == 'warning:': # check if this log line got truncated if ("warning: log line attempted" in self._line_str and "over max size" in self._line_str): self._datetime_nextpos = split_tokens.index('...') op = split_tokens[self._datetime_nextpos + 1] else: # unknown warning, bail out return if op in self.log_operations: self._operation = op self._namespace = split_tokens[self._datetime_nextpos + 2] @property def pattern(self): """Extract query pattern from operations.""" if not self._pattern: # trigger evaluation of operation if (self.operation in ['query', 'getmore', 'update', 'remove'] or self.command in ['count', 'findandmodify']): self._pattern = self._find_pattern('query: ') elif self.command == 'find': self._pattern = self._find_pattern('filter: ') return self._pattern @property def sort_pattern(self): """Extract query pattern from operations.""" if not self._sort_pattern: # trigger evaluation of operation if self.operation in ['query', 'getmore']: self._sort_pattern = self._find_pattern('orderby: ') return self._sort_pattern @property def command(self): """Extract query pattern from operations.""" if not self._command_calculated: self._command_calculated = True if self.operation == 'command': try: command_idx = self.split_tokens.index('command:') command = self.split_tokens[command_idx + 1] if command == '{': # workaround for <= 2.2 log files, # where command was not listed separately command = self.split_tokens[command_idx + 2][:-1] self._command = command.lower() except ValueError: pass return self._command @property def nscanned(self): """Extract nscanned or keysExamined counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._nscanned @property def nscannedObjects(self): """ Extract counters if available (lazy). Looks for nscannedObjects or docsExamined. """ if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._nscannedObjects @property def ntoreturn(self): """Extract ntoreturn counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._ntoreturn @property def writeConflicts(self): """Extract ntoreturn counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._writeConflicts @property def nreturned(self): """ Extract counters if available (lazy). Looks for nreturned, nReturned, or nMatched counter. """ if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._nreturned @property def ninserted(self): """Extract ninserted or nInserted counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._ninserted @property def ndeleted(self): """Extract ndeleted or nDeleted counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._ndeleted @property def nupdated(self): """Extract nupdated or nModified counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._nupdated @property def numYields(self): """Extract numYields counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._numYields @property def planSummary(self): """Extract numYields counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._planSummary @property def r(self): """Extract read lock (r) counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._r @property def w(self): """Extract write lock (w) counter if available (lazy).""" if not self._counters_calculated: self._counters_calculated = True self._extract_counters() return self._w def _extract_counters(self): """Extract counters like nscanned and nreturned from the logevent.""" # extract counters (if present) counters = ['nscanned', 'nscannedObjects', 'ntoreturn', 'nreturned', 'ninserted', 'nupdated', 'ndeleted', 'r', 'w', 'numYields', 'planSummary', 'writeConflicts', 'keyUpdates'] # TODO: refactor mtools to use current counter names throughout # Transitionary hack: mapping of current names into prior equivalents counter_equiv = { 'docsExamined': 'nscannedObjects', 'keysExamined': 'nscanned', 'nDeleted': 'ndeleted', 'nInserted': 'ninserted', 'nMatched': 'nreturned', 'nModified': 'nupdated' } counters.extend(counter_equiv.keys()) split_tokens = self.split_tokens # trigger operation evaluation to get access to offset if self.operation: for t, token in enumerate(split_tokens[self.datetime_nextpos + 2:]): for counter in counters: if token.startswith('%s:' % counter): try: # Remap counter to standard name, if applicable counter = counter_equiv.get(counter, counter) vars(self)['_' + counter] = int((token.split(':') [-1]).replace(',', '')) except ValueError: # see if this is a pre-2.5.2 numYields with space # in between (e.g. "numYields: 2") # https://jira.mongodb.org/browse/SERVER-10101 if (counter == 'numYields' and token.startswith('numYields')): try: self._numYields = int((split_tokens[t + 1 + self.datetime_nextpos + 2]).replace(',', '')) except ValueError: pass if (counter == 'planSummary' and token.startswith('planSummary')): try: self._planSummary = split_tokens[t + 1 + self.datetime_nextpos + 2] except ValueError: pass # token not parsable, skip break @property def level(self): """Extract log level if available (lazy).""" if not self._level_calculated: self._level_calculated = True self._extract_level() return self._level @property def component(self): """Extract log component if available (lazy).""" self.level return self._component def _extract_level(self): """Extract level and component if available (lazy).""" if self._level is None: split_tokens = self.split_tokens if not split_tokens: self._level = False self._component = False return x = (self.log_levels.index(split_tokens[1]) if split_tokens[1] in self.log_levels else None) if x is not None: self._level = split_tokens[1] self._component = split_tokens[2] else: self._level = False self._component = False def parse_all(self): """ Trigger extraction of all information. These values are usually evaluated lazily. """ tokens = self.split_tokens duration = self.duration datetime = self.datetime thread = self.thread operation = self.operation namespace = self.namespace pattern = self.pattern nscanned = self.nscanned nscannedObjects = self.nscannedObjects ntoreturn = self.ntoreturn nreturned = self.nreturned ninserted = self.ninserted ndeleted = self.ndeleted nupdated = self.nupdated numYields = self.numYields w = self.w r = self.r def _find_pattern(self, trigger): # get start of json query pattern start_idx = self.line_str.rfind(trigger) if start_idx == -1: # no query pattern found return None stop_idx = 0 brace_counter = 0 search_str = self.line_str[start_idx + len(trigger):] for match in re.finditer(r'{|}', search_str): stop_idx = match.start() if search_str[stop_idx] == '{': brace_counter += 1 else: brace_counter -= 1 if brace_counter == 0: break search_str = search_str[:stop_idx + 1].strip() if search_str: return json2pattern(search_str) else: return None def _reformat_timestamp(self, format, force=False): if format not in ['ctime', 'ctime-pre2.4', 'iso8601-utc', 'iso8601-local']: raise ValueError('invalid datetime format %s, choose from ctime, ' 'ctime-pre2.4, iso8601-utc, iso8601-local.') if ((self.datetime_format is None or (self.datetime_format == format and self._datetime_str != '')) and not force): return elif self.datetime is None: return elif format.startswith('ctime'): dt_string = (self.weekdays[self.datetime.weekday()] + ' ' + self.datetime.strftime("%b %d %H:%M:%S")) # remove zero-padding from day number tokens = dt_string.split(' ') if tokens[2].startswith('0'): tokens[2] = tokens[2].replace('0', ' ', 1) dt_string = ' '.join(tokens) if format == 'ctime': dt_string += '.' + str(int(self.datetime.microsecond / 1000)).zfill(3) elif format == 'iso8601-local': dt_string = self.datetime.isoformat() if self.datetime.utcoffset() is None: dt_string += '+00:00' ms_str = str(int(self.datetime.microsecond / 1000)).zfill(3)[:3] # change isoformat string to have 3 digit milliseconds and no : # in offset dt_string = re.sub(r'(\.\d+)?([+-])(\d\d):(\d\d)', '.%s\\2\\3\\4' % ms_str, dt_string, count=1) elif format == 'iso8601-utc': if self.datetime.utcoffset(): dt_string = self.datetime.astimezone(tzutc()).strftime("%Y-%m-" "%dT%H:" "%M:%S") else: dt_string = self.datetime.strftime("%Y-%m-%dT%H:%M:%S") dt_string += '.' + str(int(self.datetime.microsecond / 1000)).zfill(3)[:3] + 'Z' # set new string and format self._datetime_str = dt_string self._datetime_format = format def __str__(self): """Default string conversion for LogEvent object is its line_str.""" return str(self.line_str) def to_dict(self, labels=None): """Convert LogEvent object to a dictionary.""" output = {} if labels is None: labels = ['line_str', 'split_tokens', 'datetime', 'operation', 'thread', 'namespace', 'nscanned', 'ntoreturn', 'nreturned', 'ninserted', 'nupdated', 'ndeleted', 'duration', 'r', 'w', 'numYields'] for label in labels: value = getattr(self, label, None) if value is not None: output[label] = value return output def to_json(self, labels=None): """Convert LogEvent object to valid JSON.""" output = self.to_dict(labels) return json.dumps(output, cls=DateTimeEncoder, ensure_ascii=False) def _parse_document(self): """Parse system.profile doc, copy all values to member variables.""" self._reset() doc = self._profile_doc self._split_tokens_calculated = True self._split_tokens = None self._duration_calculated = True self._duration = doc[u'millis'] self._datetime_calculated = True self._datetime = doc[u'ts'] if self._datetime.tzinfo is None: self._datetime = self._datetime.replace(tzinfo=tzutc()) self._datetime_format = None self._reformat_timestamp('ctime', force=True) self._thread_calculated = True self._thread = doc['thread'] self._operation_calculated = True self._operation = doc[u'op'] self._namespace = doc[u'ns'] self._command_calculated = True if self.operation == 'command': self._command = doc[u'command'].keys()[0] # query pattern for system.profile events, all three cases. # See SERVER-13245 if 'query' in doc: if 'query' in doc['query'] and isinstance(doc['query']['query'], dict): self._pattern = str(doc['query']['query']).replace("'", '"') elif '$query' in doc['query']: self._pattern = str(doc['query']['$query']).replace("'", '"') else: self._pattern = str(doc['query']).replace("'", '"') # sort pattern if ('orderby' in doc['query'] and isinstance(doc['query']['orderby'], dict)): self._sort_pattern = str(doc['query'] ['orderby']).replace("'", '"') elif '$orderby' in doc['query']: self._sort_pattern = str(doc['query'] ['$orderby']).replace("'", '"') else: self._sort_pattern = None self._counters_calculated = True self._nscanned = doc[u'nscanned'] if 'nscanned' in doc else None self._ntoreturn = doc[u'ntoreturn'] if 'ntoreturn' in doc else None self._nupdated = doc[u'nupdated'] if 'nupdated' in doc else None self._nreturned = doc[u'nreturned'] if 'nreturned' in doc else None self._ninserted = doc[u'ninserted'] if 'ninserted' in doc else None self._ndeleted = doc[u'ndeleted'] if 'ndeleted' in doc else None self._numYields = doc[u'numYield'] if 'numYield' in doc else None if u'lockStats' in doc: self._r = doc[u'lockStats'][u'timeLockedMicros'][u'r'] self._w = doc[u'lockStats'][u'timeLockedMicros'][u'w'] self._r_acquiring = doc[u'lockStats']['timeAcquiringMicros'][u'r'] self._w_acquiring = doc[u'lockStats']['timeAcquiringMicros'][u'w'] locks = 'w:%i' % self.w if self.w is not None else 'r:%i' % self.r elif u'locks' in doc: locks = json.dumps(doc[u'locks']) else: locks = '' # build a fake line_str payload = '' if 'query' in doc: payload += ('query: %s' % str(doc[u'query']) .replace("u'", "'").replace("'", '"')) if 'command' in doc: payload += ('command: %s' % str(doc[u'command']) .replace("u'", "'").replace("'", '"')) if 'updateobj' in doc: payload += (' update: %s' % str(doc[u'updateobj']) .replace("u'", "'").replace("'", '"')) scanned = 'nscanned:%i' % self._nscanned if 'nscanned' in doc else '' yields = 'numYields:%i' % self._numYields if 'numYield' in doc else '' duration = '%ims' % self.duration if self.duration is not None else '' self._line_str = ("[{thread}] {operation} {namespace} {payload} " "{scanned} {yields} locks(micros) {locks} " "{duration}".format(datetime=self.datetime, thread=self.thread, operation=self.operation, namespace=self.namespace, payload=payload, scanned=scanned, yields=yields, locks=locks, duration=duration))

sindbach/mtools

mtools/util/logevent.py

Python

import subprocess import sys from distutils.version import LooseVersion from re import fullmatch def get_shell_version(): try: for line in ( subprocess.check_output(["gnome-shell", "--version"]).decode().splitlines() ): m = fullmatch(r"GNOME Shell (?P<version>[0-9.]+)", line) if m: return m.group("version") except BaseException: print("Warning, cannot retrieve current Gnome Shell version", file=sys.stderr) def version_comparator(a, b): if a == b: return 0 if a is None: return 1 if b is None: return -1 a, b = LooseVersion(str(a)), LooseVersion(str(b)) if a < b: return 1 if a > b: return -1 return 0

essembeh/gnome-extensions-cli

src/gnome_extensions_cli/utils.py

Python

import functools import warnings def deprecated_alias(**aliases): def deco(f): @functools.wraps(f) def wrapper(*args, **kwargs): rename_kwargs(f.__name__, kwargs, aliases) return f(*args, **kwargs) return wrapper return deco def rename_kwargs(func_name, kwargs, aliases): # noqa for alias, new in aliases.items(): if alias in kwargs: if new in kwargs: raise TypeError("{} received both {} and {}".format(func_name, alias, new)) warnings.warn("{} is deprecated; use {}".format(alias, new), DeprecationWarning, 3) if alias == "device": if kwargs[alias].__contains__("cuda"): kwargs.pop(alias) kwargs[new] = 1 elif kwargs[alias].__contains__("cpu"): kwargs.pop(alias) kwargs[new] = 0 else: kwargs[new] = kwargs.pop(alias) elif alias == "multi_gpu": kwargs.pop(alias) else: kwargs[new] = kwargs.pop(alias)

ahmed-dj/bio-transformers

biotransformers/utils/deprecated.py

Python

# -*- coding: utf-8 -*- """ Calculation of cumulant expressions for non-linear response functions of the third order for a multilevel three band system. """ from quantarhei.symbolic.cumulant import Ugde, Uedg, Uged, Uegd #, ExpdV from quantarhei.symbolic.cumulant import gg #, g1, g2 from quantarhei.symbolic.cumulant import CumulantExpr from quantarhei.symbolic.abc import a, b, f, tau, tau1, tau2, tau3, c, d #, e, t, T, tau, x, y from quantarhei.symbolic.abc import t1, t2, t3 from quantarhei.symbolic.lang import python_code from quantarhei.symbolic.lang import fortran_code import time def evaluate_cumulant(cum, positive_times = [], leading_index=None, lang = "Python", arrays=None): """ """ t0 = time.time() A = cum.rewrite(gg) expr = CumulantExpr(A) expr = expr.evaluate() t1 = time.time() for tt in positive_times: expr = CumulantExpr(expr)._make_positive(tt) t2 = time.time() #a = leading_index[0] if leading_index is not None: D = expr._leading_index(leading_index) expr = D._getExpr() t3 = time.time() if lang == "Fortran": ss = fortran_code(expr.__str__()) elif lang == "Python": ss = python_code(expr.__str__(),arrays=arrays) else: raise Exception("Unknown language") print(t1-t0) print(t2-t1) print(t3-t2) return ss def R1g(): """ """ A = Ugde(b,t1)*Uedg(b,t1+t2)*Ugde(a,t1+t2+t3) return evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"]) def R2g(): """ """ A = Uedg(a,t1+t2)*Ugde(b,t1+t2+t3)*Uedg(b,t1) return evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"]) def R3g(): """ """ A = Uedg(a,t1)*Ugde(b,t1+t2+t3)*Uedg(b,t1+t2) return evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"]) def R4g(): """ """ A = Ugde(b,t1+t2+t3)*Uedg(b,t1+t2)*Ugde(a,t1) return evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"]) def R1fs(): """ """ A = (Uedg(a,t1+t2+t3)*Ugde(f,t1+t2+t3)*Uedg(f,t1+t2) *Ugde(b,t1+t2)*Uedg(b,t1)) return evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"]) def R2fs(): """ """ A = (Ugde(b,t1)*Uedg(b,t1+t2+t3)*Ugde(f,t1+t2+t3) *Uedg(f,t1+t2)*Ugde(a,t1+t2)) return evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"]) def print_R1gt(): """ """ A = Ugde(b,t3) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) B = Ugde(a,t1) print(evaluate_cumulant(B, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def print_R2gt(): """ """ A = Ugde(b,t3) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) B = Uedg(a,t1) print(evaluate_cumulant(B, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def print_R1fst(): """ """ A = Uedg(b,t3)*Ugde(f,t3) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) B = Uedg(a,t1) print(evaluate_cumulant(B, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def print_R2fst(): """ """ A = Uedg(b,t3)*Ugde(f,t3) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) B = Ugde(a,t1) print(evaluate_cumulant(B, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def print_trans_R2g(): """ """ A = (Uedg(a,t1+tau)*Ugde(b,t1+tau)*Uedg(b,t1+t2)*Ugde(b,t1+t2+t3) *Uedg(b,t1+tau)*Ugde(a,t1+tau)*Uedg(a,t1)) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def print_trans_R2g_alt(): """ """ #A = (Uedg(a,t1+tau)*Ugde(b,t1+tau)*Uedg(b,t1+t2)*Ugde(b,t1+t2+t3) # *Uedg(b,t1+tau)*Ugde(a,t1+tau)*Uedg(a,t1)) A = (Uged(a,t1)*Uedg(a,tau1)*Ugde(b,tau1)*Uedg(b,t2)*Ugde(b,t2+t3)*Uedg(b,tau1)*Ugde(a,tau1)) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def print_trans_R2g_alt2(): """ """ #A = (Uedg(a,t1+tau)*Ugde(b,t1+tau)*Uedg(b,t1+t2)*Ugde(b,t1+t2+t3) # *Uedg(b,t1+tau)*Ugde(a,t1+tau)*Uedg(a,t1)) #A = (Uged(a,t1)*Uedg(a,tau1)*Ugde(b,tau1)*Uedg(b,t2)*Ugde(b,t2+t3)*Uedg(b,tau1)*Ugde(a,tau1)) A = (Uged(a,t1+tau1)*Uedg(b,t2-tau1)*Ugde(b,t2+t3-tau1)*Uegd(a,tau1)) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def generate_nth_order_R2g(states_tuple, times_tuple): order = len(states_tuple) if order != len(times_tuple): raise Exception("Wrong tuple/list length") # starting state a = states_tuple[0] # final state (can be the same as starting) b = states_tuple[len(states_tuple)-1] # final time (must be t2) tt = times_tuple[len(times_tuple)-1] AL = Uged(a,t1) Amid = Uedg(b,tt)*Ugde(b,t3+tt) filL = 1 filR = 1 for k in range(len(times_tuple)-1): tau = times_tuple[k] s1 = states_tuple[k] s2 = states_tuple[k+1] filL = filL*Uedg(s1,tau)*Ugde(s2,tau) filR = Uedg(s2,tau)*Ugde(s1,tau)*filR A = AL*filL*Amid*filR print(A) print(evaluate_cumulant(A, positive_times=(t1, tt, t3), leading_index=a, arrays=["gg"])) def test(): A = Uged(a,t1+t2)*Ugde(d,t3)*Uegd(a,t2) print(evaluate_cumulant(A, positive_times=(t1, t2, t3), leading_index=a, arrays=["gg"])) def oneex_twoex(): A = Uedg(f,t1)*Ugde(a,t1) print(evaluate_cumulant(A, positive_times=(t1,), leading_index=a, arrays="gg")) # ============================================================================= # print("R1g:") # st_R1g = "numpy.exp("+R1g()+")" # print(st_R1g) # # print("") # print("R2g:") # print(R2g()) # # print("") # print("R3g:") # print(R3g()) # # print("") # print("R4g:") # print(R4g()) # # print("") # print("R1fs:") # print(R1fs()) # # print("") # print("R2fs:") # print(R2fs()) # # print("") # print("R1gt") # print_R1gt() # # print("") # print("R2gt") # print_R2gt() # # print("") # print("R1fst") # print_R1fst() # # print("") # print("R2fst") # print_R2fst() # # ============================================================================= #print("") #print("Trans_R2g") #print_trans_R2g() # #print("") #print("Trans_R2g_alt") #print_trans_R2g_alt() # #print("") #print("Trans_R2g_alt2") #print_trans_R2g_alt2() #print("***") #states = (a, c, b) #(a,c,b) #times = (tau1, tau2, t2) # (tau1,tau2,t2) #generate_nth_order_R2g(states, times) # #print("===") #A = Uged(a,t1)*Uedg(a,tau1)*Ugde(c,tau1)*Uedg(c,tau2)*Ugde(b,tau2)*Uedg(b,t2)*Ugde(b,t2 + t3)*Uedg(b,tau2)*Ugde(c,tau2)*Uedg(c,tau1)*Ugde(a,tau1) # #print(evaluate_cumulant(A, positive_times=(t1, t2, t3), # leading_index=a, arrays=["gg"])) #print("***") #states = (a,b,c, d) #(a,c,b) #times = (tau1, tau2, tau3, t2) # (tau1,tau2,t2) #states = (a,c,b) #times = (tau1,tau2,t2) #generate_nth_order_R2g(states, times) #test() oneex_twoex()

MichalPt/quantarhei

examples/symbolic/test_symbolic_8.py

Python

# This example is inspired by https://github.com/dasguptar/treelstm.pytorch import argparse, cPickle, math, os, random import logging logging.basicConfig(level=logging.INFO) import numpy as np from tqdm import tqdm import mxnet as mx from mxnet import gluon from mxnet.gluon import nn from mxnet import autograd as ag from tree_lstm import SimilarityTreeLSTM from dataset import Vocab, SICKDataIter parser = argparse.ArgumentParser(description='TreeLSTM for Sentence Similarity on Dependency Trees') parser.add_argument('--data', default='data/sick/', help='path to raw dataset. required when preprocessed dataset is not available.') parser.add_argument('--word_embed', default='data/glove/glove.840B.300d.txt', help='directory with word embeddings. required when preprocessed dataset is not available.') parser.add_argument('--batch_size', type=int, default=25, help='training batch size per device (CPU/GPU).') parser.add_argument('--epochs', default=50, type=int, help='number of total epochs to run') parser.add_argument('--lr', default=0.02, type=float, help='initial learning rate') parser.add_argument('--wd', default=0.0001, type=float, help='weight decay factor') parser.add_argument('--optimizer', default='adagrad', help='optimizer (default: adagrad)') parser.add_argument('--seed', default=123, type=int, help='random seed (default: 123)') parser.add_argument('--use-gpu', action='store_true', help='whether to use GPU.') opt = parser.parse_args() logging.info(opt) context = [mx.gpu(0) if opt.use_gpu else mx.cpu()] rnn_hidden_size, sim_hidden_size, num_classes = 150, 50, 5 optimizer = opt.optimizer.lower() mx.random.seed(opt.seed) np.random.seed(opt.seed) random.seed(opt.seed) batch_size = opt.batch_size # read dataset if os.path.exists('dataset.cPickle'): with open('dataset.cPickle', 'rb') as f: train_iter, dev_iter, test_iter, vocab = cPickle.load(f) else: root_dir = opt.data segments = ['train', 'dev', 'test'] token_files = [os.path.join(root_dir, seg, '%s.toks'%tok) for tok in ['a', 'b'] for seg in segments] vocab = Vocab(filepaths=token_files, embedpath=opt.word_embed) train_iter, dev_iter, test_iter = [SICKDataIter(os.path.join(root_dir, segment), vocab, num_classes) for segment in segments] with open('dataset.cPickle', 'wb') as f: cPickle.dump([train_iter, dev_iter, test_iter, vocab], f) logging.info('==> SICK vocabulary size : %d ' % vocab.size) logging.info('==> Size of train data : %d ' % len(train_iter)) logging.info('==> Size of dev data : %d ' % len(dev_iter)) logging.info('==> Size of test data : %d ' % len(test_iter)) # get network net = SimilarityTreeLSTM(sim_hidden_size, rnn_hidden_size, vocab.size, vocab.embed.shape[1], num_classes) # use pearson correlation and mean-square error for evaluation metric = mx.metric.create(['pearsonr', 'mse']) def to_target(x): target = np.zeros((1, num_classes)) ceil = int(math.ceil(x)) floor = int(math.floor(x)) if ceil==floor: target[0][floor-1] = 1 else: target[0][floor-1] = ceil - x target[0][ceil-1] = x - floor return mx.nd.array(target) def to_score(x): levels = mx.nd.arange(1, 6, ctx=x.context) return [mx.nd.sum(levels*mx.nd.exp(x), axis=1).reshape((-1,1))] # when evaluating in validation mode, check and see if pearson-r is improved # if so, checkpoint and run evaluation on test dataset def test(ctx, data_iter, best, mode='validation', num_iter=-1): data_iter.reset() batches = len(data_iter) data_iter.set_context(ctx[0]) preds = [] labels = [mx.nd.array(data_iter.labels, ctx=ctx[0]).reshape((-1,1))] for _ in tqdm(range(batches), desc='Testing in {} mode'.format(mode)): l_tree, l_sent, r_tree, r_sent, label = data_iter.next() z = net(mx.nd, l_sent, r_sent, l_tree, r_tree) preds.append(z) preds = to_score(mx.nd.concat(*preds, dim=0)) metric.update(preds, labels) names, values = metric.get() metric.reset() for name, acc in zip(names, values): logging.info(mode+' acc: %s=%f'%(name, acc)) if name == 'pearsonr': test_r = acc if mode == 'validation' and num_iter >= 0: if test_r >= best: best = test_r logging.info('New optimum found: {}. Checkpointing.'.format(best)) net.collect_params().save('childsum_tree_lstm_{}.params'.format(num_iter)) test(ctx, test_iter, -1, 'test') return best def train(epoch, ctx, train_data, dev_data): # initialization with context if isinstance(ctx, mx.Context): ctx = [ctx] net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx[0]) net.embed.weight.set_data(vocab.embed.as_in_context(ctx[0])) train_data.set_context(ctx[0]) dev_data.set_context(ctx[0]) # set up trainer for optimizing the network. trainer = gluon.Trainer(net.collect_params(), optimizer, {'learning_rate': opt.lr, 'wd': opt.wd}) best_r = -1 Loss = gluon.loss.KLDivLoss() for i in range(epoch): train_data.reset() num_batches = len(train_data) # collect predictions and labels for evaluation metrics preds = [] labels = [mx.nd.array(train_data.labels, ctx=ctx[0]).reshape((-1,1))] for j in tqdm(range(num_batches), desc='Training epoch {}'.format(i)): # get next batch l_tree, l_sent, r_tree, r_sent, label = train_data.next() # use autograd to record the forward calculation with ag.record(): # forward calculation. the output is log probability z = net(mx.nd, l_sent, r_sent, l_tree, r_tree) # calculate loss loss = Loss(z, to_target(label).as_in_context(ctx[0])) # backward calculation for gradients. loss.backward() preds.append(z) # update weight after every batch_size samples if (j+1) % batch_size == 0: trainer.step(batch_size) # translate log-probability to scores, and evaluate preds = to_score(mx.nd.concat(*preds, dim=0)) metric.update(preds, labels) names, values = metric.get() metric.reset() for name, acc in zip(names, values): logging.info('training acc at epoch %d: %s=%f'%(i, name, acc)) best_r = test(ctx, dev_data, best_r, num_iter=i) train(opt.epochs, context, train_iter, dev_iter)

ChidanandKumarKS/mxnet

example/gluon/tree_lstm/main.py

Python

import time from threading import Thread, Condition class StingySpendy: money = 100 cv = Condition() def stingy(self): for i in range(1000000): self.cv.acquire() self.money += 10 self.cv.notify() self.cv.release() print("Stingy Done") def spendy(self): for i in range(500000): self.cv.acquire() while self.money < 20: self.cv.wait() self.money -= 20 if self.money < 0: print("Money in bank", self.money) self.cv.release() print("Spendy Done") ss = StingySpendy() Thread(target=ss.stingy, args=()).start() Thread(target=ss.spendy, args=()).start() time.sleep(5) print("Money in the end", ss.money)

ajvill/multithreadinginpython

condition_variables/stingy_spendy_cond_variable.py

Python

import os import glob import sys from typing import Optional, List, Union from .utils.utils import calc_mean_score, save_json, image_dir_to_json, image_file_to_json from .handlers.model_builder import Nima from deepinsight_iqa.common.utility import thread_safe_singleton, set_gpu_limit from deepinsight_iqa.data_pipeline.nima_gen.nima_datagen import NimaDataGenerator as TestDataGenerator import tensorflow as tf import six import logging logger = logging.getLogger(__name__) @six.add_metaclass(thread_safe_singleton) class Prediction: def __init__(self, weights_file: str, base_model_name: str): """ Invoke a predict method of this class to predict image quality using nima model """ try: # set_gpu_limit() self.nima = Nima(base_model_name, weights=None) self.nima.build() self.nima.nima_model.load_weights(weights_file) except Exception as e: print("Unable to load NIMA weights", str(e)) sys.exit(1) def predict( self, image_source: str, predictions_file: Optional[str] = None, img_format: str = 'jpg' ) -> List: # load samples if os.path.isfile(image_source): image_dir, samples = image_file_to_json(image_source) else: image_dir = image_source samples = image_dir_to_json(image_source, img_type='jpg') # initialize data generator n_classes = 10 batch_size = 64 samples = [] sample = {"imgage_id": "img_1"} samples.append(sample) data_generator = TestDataGenerator( samples, image_dir, batch_size, n_classes, self.nima.preprocessing_function(), img_format=img_format ) # get predictions predictions = self.nima.nima_model.predict_generator( data_generator, workers=1, use_multiprocessing=False, verbose=1) # calc mean scores and add to samples for i, sample in enumerate(samples): sample['mean_score_prediction'] = calc_mean_score(predictions[i]) # print(json.dumps(samples, indent=2)) if predictions_file is not None: save_json(samples, predictions_file) return samples

sandyz1000/deepinsight-iqa

deepinsight_iqa/nima/predict.py

Python

import os class MockRequests: def __init__(self): return def get(self, source): source_no_http = source.replace("http://","") test_website_path = f"{os.path.dirname(os.path.abspath(__file__))}/test_data/test_website/{source_no_http}" with open(test_website_path,'r') as website_file: return MockData(website_file.read()) class MockData: def __init__(self,text): self.text = text

OtGabaldon/multiSourceWordMap

tests/mock_requests.py

Python

# qubit number=3 # total number=60 import numpy as np from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ from qiskit.visualization import plot_histogram from typing import * from pprint import pprint from math import log2 from collections import Counter from qiskit.test.mock import FakeVigo, FakeYorktown kernel = 'circuit/bernstein' def bitwise_xor(s: str, t: str) -> str: length = len(s) res = [] for i in range(length): res.append(str(int(s[i]) ^ int(t[i]))) return ''.join(res[::-1]) def bitwise_dot(s: str, t: str) -> str: length = len(s) res = 0 for i in range(length): res += int(s[i]) * int(t[i]) return str(res % 2) def build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit: # implement the oracle O_f # NOTE: use multi_control_toffoli_gate ('noancilla' mode) # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate controls = QuantumRegister(n, "ofc") target = QuantumRegister(1, "oft") oracle = QuantumCircuit(controls, target, name="Of") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) oracle.mct(controls, target[0], None, mode='noancilla') for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.barrier() # oracle.draw('mpl', filename=(kernel + '-oracle.png')) return oracle def build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit: # implement the Bernstein-Vazirani circuit zero = np.binary_repr(0, n) b = f(zero) # initial n + 1 bits input_qubit = QuantumRegister(n+1, "qc") classicals = ClassicalRegister(n, "qm") prog = QuantumCircuit(input_qubit, classicals) # inverse last one (can be omitted if using O_f^\pm) prog.x(input_qubit[n]) # circuit begin prog.h(input_qubit[1]) # number=1 prog.h(input_qubit[2]) # number=38 prog.cz(input_qubit[0],input_qubit[2]) # number=39 prog.h(input_qubit[2]) # number=40 prog.cx(input_qubit[0],input_qubit[2]) # number=31 prog.h(input_qubit[2]) # number=42 prog.cz(input_qubit[0],input_qubit[2]) # number=43 prog.h(input_qubit[2]) # number=44 prog.h(input_qubit[2]) # number=48 prog.cz(input_qubit[0],input_qubit[2]) # number=49 prog.h(input_qubit[2]) # number=50 prog.cx(input_qubit[0],input_qubit[2]) # number=54 prog.x(input_qubit[2]) # number=55 prog.h(input_qubit[2]) # number=57 prog.cz(input_qubit[0],input_qubit[2]) # number=58 prog.h(input_qubit[2]) # number=59 prog.cx(input_qubit[0],input_qubit[2]) # number=47 prog.cx(input_qubit[0],input_qubit[2]) # number=37 prog.h(input_qubit[2]) # number=51 prog.cz(input_qubit[0],input_qubit[2]) # number=52 prog.h(input_qubit[2]) # number=53 prog.h(input_qubit[2]) # number=25 prog.cz(input_qubit[0],input_qubit[2]) # number=26 prog.h(input_qubit[2]) # number=27 prog.h(input_qubit[1]) # number=7 prog.cz(input_qubit[2],input_qubit[1]) # number=8 prog.rx(0.17592918860102857,input_qubit[2]) # number=34 prog.rx(-0.3989822670059037,input_qubit[1]) # number=30 prog.h(input_qubit[1]) # number=9 prog.h(input_qubit[1]) # number=18 prog.cz(input_qubit[2],input_qubit[1]) # number=19 prog.h(input_qubit[1]) # number=20 prog.y(input_qubit[1]) # number=14 prog.h(input_qubit[1]) # number=22 prog.cz(input_qubit[2],input_qubit[1]) # number=23 prog.h(input_qubit[1]) # number=24 prog.z(input_qubit[2]) # number=3 prog.z(input_qubit[1]) # number=41 prog.x(input_qubit[1]) # number=17 prog.y(input_qubit[2]) # number=5 prog.x(input_qubit[2]) # number=21 # apply H to get superposition for i in range(n): prog.h(input_qubit[i]) prog.h(input_qubit[n]) prog.barrier() # apply oracle O_f oracle = build_oracle(n, f) prog.append( oracle.to_gate(), [input_qubit[i] for i in range(n)] + [input_qubit[n]]) # apply H back (QFT on Z_2^n) for i in range(n): prog.h(input_qubit[i]) prog.barrier() # measure return prog def get_statevector(prog: QuantumCircuit) -> Any: state_backend = Aer.get_backend('statevector_simulator') statevec = execute(prog, state_backend).result() quantum_state = statevec.get_statevector() qubits = round(log2(len(quantum_state))) quantum_state = { "|" + np.binary_repr(i, qubits) + ">": quantum_state[i] for i in range(2 ** qubits) } return quantum_state def evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any: # Q: which backend should we use? # get state vector quantum_state = get_statevector(prog) # get simulate results # provider = IBMQ.load_account() # backend = provider.get_backend(backend_str) # qobj = compile(prog, backend, shots) # job = backend.run(qobj) # job.result() backend = Aer.get_backend(backend_str) # transpile/schedule -> assemble -> backend.run results = execute(prog, backend, shots=shots).result() counts = results.get_counts() a = Counter(counts).most_common(1)[0][0][::-1] return { "measurements": counts, # "state": statevec, "quantum_state": quantum_state, "a": a, "b": b } def bernstein_test_1(rep: str): """011 . x + 1""" a = "011" b = "1" return bitwise_xor(bitwise_dot(a, rep), b) def bernstein_test_2(rep: str): """000 . x + 0""" a = "000" b = "0" return bitwise_xor(bitwise_dot(a, rep), b) def bernstein_test_3(rep: str): """111 . x + 1""" a = "111" b = "1" return bitwise_xor(bitwise_dot(a, rep), b) if __name__ == "__main__": n = 2 a = "11" b = "1" f = lambda rep: \ bitwise_xor(bitwise_dot(a, rep), b) prog = build_circuit(n, f) sample_shot =4000 writefile = open("../data/startQiskit_QC292.csv", "w") # prog.draw('mpl', filename=(kernel + '.png')) IBMQ.load_account() provider = IBMQ.get_provider(hub='ibm-q') provider.backends() backend = provider.get_backend("ibmq_5_yorktown") circuit1 = transpile(prog, FakeYorktown()) circuit1.h(qubit=2) circuit1.x(qubit=3) circuit1.measure_all() info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts() print(info, file=writefile) print("results end", file=writefile) print(circuit1.depth(), file=writefile) print(circuit1, file=writefile) writefile.close()

UCLA-SEAL/QDiff

data/p3BR/R2/benchmark/startQiskit_QC292.py

Python

import socket, threading, sys, traceback, os, tkinter from ui import Ui_MainWindow from PyQt5.QtCore import * from PyQt5.QtGui import * from PyQt5 import QtCore, QtGui, QtWidgets from tkinter import * from PIL import Image, ImageTk from tkinter import messagebox, Tk from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True from RtpPacket import RtpPacket RECV_SIZE = 20480 + 14 HIGHT = 500 CACHE_FILE_NAME = "cache-" CACHE_FILE_EXT = ".jpg" class Client: INIT = 0 READY = 1 PLAYING = 2 state = INIT SETUP = 0 PLAY = 1 PAUSE = 2 TEARDOWN = 3 FASTER = 4 SLOWER = 5 # Initiation.. def __init__(self, serveraddr, serverport, rtpport, filename): self.page_main = Ui_MainWindow() self.state == self.READY self.serverAddr = serveraddr self.serverPort = int(serverport) self.rtpPort = int(rtpport) self.fileName = filename self.rtspSeq = 0 self.sessionId = 0 self.requestSent = -1 self.teardownAcked = 0 self.connectToServer() self.frameNbr = 0 self.createWidgets() def createWidgets(self): app = QtWidgets.QApplication(sys.argv) page_tmp = QtWidgets.QMainWindow() self.page_main.setupUi(page_tmp) page_tmp.show() self.page_main.btn_setup.clicked.connect(lambda: self.setupMovie()) self.page_main.btn_play.clicked.connect(lambda: self.playMovie()) self.page_main.btn_pause.clicked.connect(lambda: self.pauseMovie()) self.page_main.btn_teardown.clicked.connect(lambda: self.exitClient()) self.page_main.btn_faster.clicked.connect(lambda: self.fasterMovie()) self.page_main.btn_slower.clicked.connect(lambda: self.slowerMovie()) sys.exit(app.exec_()) def fasterMovie(self): """Let movie faster.""" if self.state == self.PLAYING or self.state == self.READY: self.sendRtspRequest(self.FASTER) def slowerMovie(self): """Let movie slower.""" if self.state == self.PLAYING or self.state == self.READY: self.sendRtspRequest(self.SLOWER) def setupMovie(self): """Setup init.""" if self.state == self.INIT: self.sendRtspRequest(self.SETUP) def exitClient(self): """Teardown the client.""" self.sendRtspRequest(self.TEARDOWN) sys.exit(0) # Close the gui window print(os.remove(CACHE_FILE_NAME + str(self.sessionId) + CACHE_FILE_EXT)) # Delete the cache image from video def pauseMovie(self): """Pause movie.""" if self.state == self.PLAYING: self.sendRtspRequest(self.PAUSE) def playMovie(self): """Play movie.""" if self.state == self.READY: # Create a new thread to listen for RTP packets threading.Thread(target=self.listenRtp).start() self.playEvent = threading.Event() self.playEvent.clear() self.sendRtspRequest(self.PLAY) def listenRtp(self): """Listen for RTP packets.""" while 1: try: cachename = CACHE_FILE_NAME + str(self.sessionId) + CACHE_FILE_EXT file = open(cachename, "wb+") while 1: data = self.rtpSocket.recv(RECV_SIZE) if data: rtpPacket = RtpPacket() rtpPacket.decode(data) # self.cutFrameList.append(rtpPacket.getPayload()) currFrameNbr = rtpPacket.seqNum() file.write(rtpPacket.getPayload()) print("Current Seq Num: " + str(currFrameNbr)) if currFrameNbr > self.frameNbr and rtpPacket.getIfEnd(): # Discard the late packet self.frameNbr = currFrameNbr self.updateMovie(cachename) file.close() break except: # Stop listening upon requesting PAUSE or TEARDOWN if self.playEvent.isSet(): break print('Frame receiving failed!') # Upon receiving ACK for TEARDOWN request, # close the RTP socket if self.teardownAcked == 1: self.rtpSocket.shutdown(socket.SHUT_RDWR) self.rtpSocket.close() break def writeFrame(self): """Write the received frame to a temp image file. Return the image file.""" cachename = CACHE_FILE_NAME + str(self.sessionId) + CACHE_FILE_EXT file = open(cachename, "wb") for item in self.cutFrameList: file.write(item) file.close() return cachename def updateMovie(self, imageFile): """Update the image file as video frame in the GUI.""" pixmap = QtGui.QPixmap(imageFile) self.page_main.label_display.setPixmap(pixmap) self.page_main.label_display.setScaledContents(True) def connectToServer(self): """Connect to the Server. Start a new RTSP/TCP session.""" self.rtspSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: self.rtspSocket.connect((self.serverAddr, self.serverPort)) except: # tkMessageBox.showwarning('Connection Failed', 'Connection to \'%s\' failed.' %self.serverAddr) messagebox.showwarning('Connection Failed', 'Connection to \'%s\' failed.' %self.serverAddr) def sendRtspRequest(self, requestCode): """Send RTSP request to the server.""" # Setup if requestCode == self.SETUP and self.state == self.INIT: threading.Thread(target=self.recvRtspReply).start() # Update RTSP sequence number. self.rtspSeq += 1 # Write the RTSP request to be sent. request = 'SETUP ' + self.fileName + ' RTSP/1.0\nCSeq: ' + str(self.rtspSeq) + '\nTransport: RTP/UDP; client_port= ' + str(self.rtpPort) # Keep track of the sent request. self.requestSent = self.SETUP # Play elif requestCode == self.PLAY and self.state == self.READY: self.rtspSeq += 1 request = 'PLAY ' + self.fileName + ' RTSP/1.0\nCSeq: ' + str(self.rtspSeq) + '\nSession: ' + str(self.sessionId) self.requestSent = self.PLAY # Pause elif requestCode == self.PAUSE and self.state == self.PLAYING: self.rtspSeq += 1 request = 'PAUSE ' + self.fileName + ' RTSP/1.0\nCSeq: ' + str(self.rtspSeq) + '\nSession: ' + str(self.sessionId) self.requestSent = self.PAUSE # Teardown elif requestCode == self.TEARDOWN and not self.state == self.INIT: self.rtspSeq += 1 request = 'TEARDOWN ' + self.fileName + ' RTSP/1.0\nCSeq: ' + str(self.rtspSeq) + '\nSession: ' + str(self.sessionId) self.requestSent = self.TEARDOWN # Faster elif requestCode == self.FASTER and (self.state == self.PLAYING or self.state == self.READY): self.rtspSeq += 1 request = 'FASTER ' + self.fileName + ' RTSP/1.0\nCSeq: ' + str(self.rtspSeq) + '\nSession: ' + str(self.sessionId) # Slower elif requestCode == self.SLOWER and (self.state == self.PLAYING or self.state == self.READY): self.rtspSeq += 1 request = 'SLOWER ' + self.fileName + ' RTSP/1.0\nCSeq: ' + str(self.rtspSeq) + '\nSession: ' + str(self.sessionId) else: return # Send the RTSP request using rtspSocket. self.rtspSocket.send(request.encode()) print('\nData sent:\n' + request) def recvRtspReply(self): """Receive RTSP reply from the server.""" while True: reply = self.rtspSocket.recv(1024) if reply: self.parseRtspReply(reply.decode("utf-8")) # Close the RTSP socket upon requesting Teardown if self.requestSent == self.TEARDOWN: self.rtspSocket.shutdown(socket.SHUT_RDWR) self.rtspSocket.close() break def parseRtspReply(self, data): """Parse the RTSP reply from the server.""" lines = str(data).split('\n') seqNum = int(lines[1].split(' ')[1]) # Process only if the server reply's sequence number is the same as the request's if seqNum == self.rtspSeq: session = int(lines[2].split(' ')[1]) # New RTSP session ID if self.sessionId == 0: self.sessionId = session # Process only if the session ID is the same if self.sessionId == session: if int(lines[0].split(' ')[1]) == 200: if self.requestSent == self.SETUP: # Update RTSP state. self.state = self.READY # Open RTP port. self.openRtpPort() elif self.requestSent == self.PLAY: self.state = self.PLAYING elif self.requestSent == self.PAUSE: self.state = self.READY # The play thread exits. A new thread is created on resume. self.playEvent.set() elif self.requestSent == self.TEARDOWN: self.state = self.INIT # Flag the teardownAcked to close the socket. self.teardownAcked = 1 def openRtpPort(self): """Open RTP socket binded to a specified port.""" # Create a new datagram socket to receive RTP packets from the server self.rtpSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # Set the timeout value of the socket to 0.5sec self.rtpSocket.settimeout(0.5) try: # Bind the socket to the address using the RTP port given by the client user self.rtpSocket.bind(("", self.rtpPort)) except: messagebox.showwarning('Unable to Bind', 'Unable to bind PORT=%d' %self.rtpPort) def handler(self): """Handler on explicitly closing the GUI window.""" self.pauseMovie() if messagebox.askokcancel("Quit?", "Are you sure you want to quit?"): self.exitClient() else: # When the user presses cancel, resume playing. self.playMovie() if __name__ == "__main__": try: # serverAddr = sys.argv[1] # serverPort = sys.argv[2] # rtpPort = sys.argv[3] # fileName = sys.argv[4] serverAddr = sys.argv[1] serverPort = sys.argv[4] rtpPort = sys.argv[3] fileName = sys.argv[2] except: print ("[Usage: ClientLauncher.py Server_name Server_port RTP_port Video_file]\n") # root = tkinter.Tk() client = Client(serverAddr, serverPort, rtpPort, fileName) # client.master.title('RTP Client') # root.mainloop()

Aiemu/CourseCN-Proj-RTP

Task2/Client_dev.py

Python

""" core app configuration """ import os environment = os.getenv('LAMBTASTIC_ENV', 'development') if environment == 'testing': from .testing import * elif environment == 'production': from .production import * else: from .development import *

ppold/lambtastic

settings/__init__.py

Python

# -*- coding: utf-8 -*- TIME_OUT = 60 EXCEPT_FILE = ['test.py','login.py','mix.py'] class Api(object): login = "/api/users/login" user_info="/api/users/info" signin = "/api/users/sign/signIn" map = "/api/RedEnvelope/updateUserMap" find_redbag = "/api/RedEnvelope/findReds" get_redbag = "/api/redUser/getRed" test= "/api/sys/testJson"

weigun/StressTest

config.py

Python

""" Copyright 2020 Inmanta 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. Contact: [email protected] """ import os import common from inmanta.loader import SourceInfo from inmanta.module import Project def test_collect_python_requirements(tmpdir): # Create project common.makeproject(tmpdir, "test-project", deps=[("mod1", ""), ("mod2", "")], imports=["mod1", "mod2"]) project_dir = os.path.join(tmpdir, "test-project") libs_dir = os.path.join(project_dir, "libs") # Create mod1 common.makemodule(libs_dir, "mod1", project=False) mod1 = os.path.join(libs_dir, "mod1") mod1_req_txt = """iplib@git+https://github.com/bartv/python3-iplib pytest\ >=\ 1.5 iplib>=0.0.1 """ common.add_file(mod1, "requirements.txt", mod1_req_txt, msg="initial commit") # Create mod2 common.makemodule(libs_dir, "mod2", project=False) mod2 = os.path.join(libs_dir, "mod2") mod2_req_txt = """# A comment dummy-yummy # A comment # Another comment """ common.add_file(mod2, "requirements.txt", mod2_req_txt, msg="initial commit") project = Project(project_dir, venv_path=os.path.join(project_dir, ".env")) Project.set(project) project.load_module("mod1", allow_v1=True) project.load_module("mod2", allow_v1=True) reqs = project.collect_python_requirements() expected_reqs = ["iplib@git+https://github.com/bartv/python3-iplib", "pytest>=1.5", "iplib>=0.0.1", "dummy-yummy"] assert sorted(reqs) == sorted(expected_reqs) def test_requirements_from_source_info(tmpdir): """Test the code path used by the exporter""" common.makeproject(tmpdir, "test-project", deps=[("mod1", "")], imports=["mod1"]) project_dir = os.path.join(tmpdir, "test-project") libs_dir = os.path.join(project_dir, "libs") common.makemodule(libs_dir, "mod1", project=False) mod1 = os.path.join(libs_dir, "mod1") mod1_req_txt = """# I'm a comment pytest\ >=\ 1.5 iplib>=0.0.1 """ common.add_file(mod1, "requirements.txt", mod1_req_txt, msg="initial commit") project = Project(project_dir, venv_path=os.path.join(project_dir, ".env")) Project.set(project) project.load_module("mod1", allow_v1=True) requirements = SourceInfo(mod1, "inmanta_plugins.mod1").requires assert sorted(requirements) == sorted(["pytest>=1.5", "iplib>=0.0.1"]) # This would fail if the comments weren't filtered out project.virtualenv.install_from_list(requirements)

inmanta/inmanta-core

tests/moduletool/test_python_dependencies.py

Python

from __future__ import division import fa import sys import os from fa import chunker if __name__ == "__main__": from sys import stderr import argparse parser = argparse.ArgumentParser(description=( "Create a set of synthetic genomes consisting " "of subgroups per tax level. Some kmers are unique, " "some are shared, and this provides a case where we can test" " the efficacy and behavior of our bitmap method.")) parser.add_argument("-n", "--num-nucleotides-per-leaf", type=int, default=13000) parser.add_argument("-N", "--num-nucs-shared-per-subgroup", type=int, default=2000) parser.add_argument("-l", "--num-nucs-shared-per-level", type=int, default=8000) parser.add_argument("-d", "--tree-depth", type=int, default=4) parser.add_argument("-s", "--split-size", type=int, default=3, help=("Number of subgroups for " "each parent node.")) parser.add_argument("--parent-map", "-p", help="Path to which to write synthetic taxonomy.", default="nodes.dmp") parser.add_argument("-S", "--subgroup-size", type=int, default=3, help="Number of genomes for each subgroup") parser.add_argument("-o", "--outdir", default=".", type=str) parser.add_argument("--name-id-map", "-m", default="synth_nameidmap.txt") args = parser.parse_args() # Variables/settings for constructing synthetic genome # and accessory files. mult_per_layer = args.split_size * args.subgroup_size depth = args.tree_depth nleaves = mult_per_layer ** (depth - 1) leaf_seqs = [fa.SeqId(fa.gen_seq(args.num_nucleotides_per_leaf), i) for i in range(nleaves)] nleaf_seq = len(leaf_seqs) outdir = args.outdir if not os.path.isdir(outdir): if os.path.isfile(outdir): raise Exception("Path set for outdir ('%s') is a" " file... Nah, dawg." % outdir) os.mkdir(outdir) outdir = outdir + '/' # Append slash name_id_map = outdir + args.name_id_map parent_map = outdir + args.parent_map # Variables for constructing the parent_map dictionary. pcmap = {} used_seqids = set(i.taxid() for i in leaf_seqs) ctax = max(used_seqids) + 1 last_layer = [] for i in range(1, depth): nchunks = nleaf_seq // (mult_per_layer ** i) chunk_size = nleaf_seq // nchunks assert nleaf_seq % chunk_size == 0 for seqsetid, seqset in enumerate(chunker(leaf_seqs, chunk_size)): print("seqset len: %i" % len(seqset), file=stderr) add = fa.gen_seq(args.num_nucs_shared_per_level) for seq in seqset: seq.seq += add seq.subsets[i] = seqsetid for sssid, seqsubset in enumerate(chunker(seqset, args.subgroup_size)): # print("seqsubset len: %i" % len(seqsubset), file=stderr) add = fa.gen_seq(args.num_nucs_shared_per_subgroup) for seq in seqset: seq.seq += add seq.subgroups[i] = seqsetid if i == 1: # or it not last_layer # Add leaf node to parent connections for seq in seqset: pcmap[seq.taxid()] = ctax + seqsetid if i > 1: # Add higher nodes to parent connections if i == depth - 1: pcmap.update((el, 1) for el in last_layer) break # This leaves the loop on the last layer in the tree # because the root is 1 by construction else: # pcmap.update((tax, i + ctax) for tax in # last_layer[i:i+mult_per_layer] for # i in range(mult_per_layer)) for i in range(mult_per_layer): for tax in last_layer[i:i + mult_per_layer]: pcmap[tax] = i + ctax last_layer = [ctax + i for i in range(nchunks)] used_seqids.update(last_layer) ctax = max(used_seqids) + 1 del used_seqids del ctax del last_layer {seq.write(outdir + seq.filename()) for seq in leaf_seqs} print("[1/3] Successfully created synthetic genomes.", file=stderr) filenames = [outdir + seq.filename() for seq in leaf_seqs] fa.write_nameid_map(name_id_map, filenames) print("[2/3] Successfully wrote nameidmap to %s." % name_id_map, file=stderr) fa.write_parent_map(parent_map, pcmap) print("[3/3] Successfully wrote child->parent map.", file=stderr) stderr.write("Genomes: %s\n" % ', '.join(filenames)) stderr.write("Nameidmap: %s\n" % name_id_map) stderr.write("Taxonomy: %s\n" % parent_map)

dnbaker/bonsai

sim/main.py

Python

# Copyright 2019 The TensorFlow Probability 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. # ============================================================================ """Tests generating test combinations.""" from collections import OrderedDict # Dependency imports from tensorflow_probability.python.internal import test_combinations from tensorflow_probability.python.internal import test_util class TestingCombinationsTest(test_util.TestCase): def test_combine(self): self.assertEqual([{ "a": 1, "b": 2 }, { "a": 1, "b": 3 }, { "a": 2, "b": 2 }, { "a": 2, "b": 3 }], test_combinations.combine(a=[1, 2], b=[2, 3])) def test_arguments_sorted(self): self.assertEqual([ OrderedDict([("aa", 1), ("ab", 2)]), OrderedDict([("aa", 1), ("ab", 3)]), OrderedDict([("aa", 2), ("ab", 2)]), OrderedDict([("aa", 2), ("ab", 3)]) ], test_combinations.combine(ab=[2, 3], aa=[1, 2])) def test_combine_single_parameter(self): self.assertEqual([{ "a": 1, "b": 2 }, { "a": 2, "b": 2 }], test_combinations.combine(a=[1, 2], b=2)) def test_add(self): self.assertEqual( [{ "a": 1 }, { "a": 2 }, { "b": 2 }, { "b": 3 }], (test_combinations.combine(a=[1, 2]) + test_combinations.combine(b=[2, 3]))) @test_combinations.generate( test_combinations.combine(a=[1, 0], b=[2, 3], c=[1])) class CombineTheTestSuite(test_util.TestCase): def test_add_things(self, a, b, c): self.assertLessEqual(3, a + b + c) self.assertLessEqual(a + b + c, 5) def test_add_things_one_more(self, a, b, c): self.assertLessEqual(3, a + b + c) self.assertLessEqual(a + b + c, 5) def not_a_test(self, a=0, b=0, c=0): del a, b, c self.fail() def _test_but_private(self, a=0, b=0, c=0): del a, b, c self.fail() # Check that nothing funny happens to a non-callable that starts with "_test". test_member = 0 if __name__ == "__main__": test_util.main()

AI-App/TensorFlow-Probability

tensorflow_probability/python/internal/test_combinations_test.py

Python

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import subprocess import os from celery import Celery from airflow.config_templates.default_celery import DEFAULT_CELERY_CONFIG from airflow.exceptions import AirflowException from airflow import configuration from xTool.utils.log.logging_mixin import LoggingMixin from xTool.utils.module_loading import import_string from xTool.executors.celery_executor import CeleryExecutor ''' To start the celery worker, run the command: airflow worker ''' # 获得配置文件的路径，并导入celery默认配置 if configuration.conf.has_option('celery', 'celery_config_options'): celery_configuration = import_string( configuration.conf.get('celery', 'celery_config_options') ) else: celery_configuration = DEFAULT_CELERY_CONFIG # 创建一个celery客户端 celery_app_name = configuration.conf.get('celery', 'CELERY_APP_NAME') app = Celery( celery_app_name, config_source=celery_configuration) @app.task def execute_command(command): """airflow worker 执行shell命令 .""" log = LoggingMixin().log log.info("Executing command in Celery: %s", command) env = os.environ.copy() try: # celery worker 收到消息后，执行消息中的shell命令 subprocess.check_call(command, shell=True, stderr=subprocess.STDOUT, close_fds=True, env=env) except subprocess.CalledProcessError as e: log.exception('execute_command encountered a CalledProcessError') log.error(e.output) raise AirflowException('Celery command failed')

fengzhongzhu1621/XAirflow

airflow/executors/celery_executor.py

Python

from django.conf.urls import url from . import views app_name = 'reports' urlpatterns = [ # url(r'^graph/', views.graph, name='graph'), url(r'^graph/', views.statistics, name='graph'), url(r'^csv_export/', views.csv_export, name='csv_export'), ]

peachman05/Pwcrew

reports/urls.py

Python

"""Preview mixins for Zinnia views""" from django.http import Http404 from django.utils.translation import ugettext as _ class EntryPreviewMixin(object): """ Mixin implementing the preview of Entries. """ def get_object(self, queryset=None): """ If the status of the entry is not PUBLISHED, a preview is requested, so we check if the user has the 'zinnia.can_view_all' permission or if it's an author of the entry. """ obj = super(EntryPreviewMixin, self).get_object(queryset) if obj.is_visible: return obj if (self.request.user.has_perm('zinnia.can_view_all') or self.request.user.pk in [ author.pk for author in obj.authors.all()]): return obj raise Http404(_('No entry found matching the query'))

Admoroux/django-blog-zinnia

zinnia/views/mixins/entry_preview.py

Python

from libs import reaction as reactioncommand class Reaction(reactioncommand.AdminReactionAddCommand): '''Retries a text command **Usage** React to the message you want to re-run with the retry emoji (The emoji is server-defined; ask your fellow server members for the correct emoji)''' def matches(self, reaction, user): return user == reaction.message.author def action(self, reaction, user, client): yield from client.on_message(reaction.message)

IdeaBot/dev-addons

retry.py

Python

#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = "Marc-Olivier Buob, Maxime Raynal" __maintainer__ = "Marc-Olivier Buob, Maxime Raynal" __email__ = "{marc-olivier.buob,maxime.raynal}@nokia.com" __copyright__ = "Copyright (C) 2020, Nokia" __license__ = "BSD-3" from collections import defaultdict from pybgl.graph import Graph from pybgl.incidence_automaton import ( IncidenceAutomaton, finals, initial, remove_vertex, vertices ) from pybgl.depth_first_search import depth_first_search_graph from pybgl.property_map import make_assoc_property_map from pybgl.reverse import reverse_graph def find_reachable_vertices(g: Graph, sources: set) -> set: """ Returns the set of vertices of a graph which are reachable from a set of source vertices. Args: g: Graph, an instance of `Graph` sources: set, a set of integers representing the source vertices Returns: The set of vertices that are reachable from the source vertices """ map_vcolor = defaultdict(int) pmap_vcolor = make_assoc_property_map(map_vcolor) depth_first_search_graph(g, sources, pmap_vcolor=pmap_vcolor) return set(map_vcolor.keys()) def prune_incidence_automaton(g: IncidenceAutomaton): """ Prunes the vertices of an IncidenceAutomaton that cannot be reached from the intial state, or that cannot reach a final state. Args: g: IncidenceAutomaton, an instance of IncidenceAutomaton """ to_keep = find_reachable_vertices(g, {initial(g)}) reverse_graph(g) to_keep &= find_reachable_vertices(g, finals(g)) reverse_graph(g) to_remove = set(vertices(g)) - to_keep for q in to_remove: remove_vertex(q, g)

nokia/PyBGL

pybgl/prune_incidence_automaton.py

Python

"""Module containing examples of report builder functions and classes.""" from collections import OrderedDict import numpy as np def example_fn_build_report(report, pvarray): """Example function that builds a report when used in the :py:class:`~pvfactors.engine.PVEngine` with full mode simulations. Here it will be a dictionary with lists of calculated values. Parameters ---------- report : dict Initially ``None``, this will be passed and updated by the function pvarray : PV array object PV array with updated calculation values Returns ------- report : dict Report updated with newly calculated values """ # Initialize the report if report is None: list_keys = ['qinc_front', 'qinc_back', 'iso_front', 'iso_back'] report = OrderedDict({key: [] for key in list_keys}) # Add elements to the report if pvarray is not None: pvrow = pvarray.pvrows[1] # use center pvrow report['qinc_front'].append( pvrow.front.get_param_weighted('qinc')) report['qinc_back'].append( pvrow.back.get_param_weighted('qinc')) report['iso_front'].append( pvrow.front.get_param_weighted('isotropic')) report['iso_back'].append( pvrow.back.get_param_weighted('isotropic')) else: # No calculation was performed, because sun was down report['qinc_front'].append(np.nan) report['qinc_back'].append(np.nan) report['iso_front'].append(np.nan) report['iso_back'].append(np.nan) return report class ExampleReportBuilder(object): """A class is required to build reports when running calculations with multiprocessing because of python constraints""" @staticmethod def build(report, pvarray): """Method that will build the simulation report. Here we're using the previously defined :py:function:`~pvfactors.report.example_fn_build_report`. Parameters ---------- report : dict Initially ``None``, this will be passed and updated by the function pvarray : PV array object PV array with updated calculation values Returns ------- report : dict Report updated with newly calculated values """ return example_fn_build_report(report, pvarray) @staticmethod def merge(reports): """Method used to merge multiple reports together. Here it simply concatenates the lists of values saved in the different reports. Parameters ---------- reports : list of dict List of reports that need to be concatenated together Returns ------- report : dict Final report with all concatenated values """ report = reports[0] # Merge only if more than 1 report if len(reports) > 1: keys_report = list(reports[0].keys()) for other_report in reports[1:]: for key in keys_report: report[key] += other_report[key] return report

tcapelle/pvfactors

pvfactors/report.py

Python

from mmcv.runner import load_checkpoint as mmcv_load_checkpoint from mmcv.runner.checkpoint import load_url_dist import urllib mmskeleton_model_urls = { 'st_gcn/kinetics-skeleton': "https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/st-gcn/st_gcn.kinetics-6fa43f73.pth", 'st_gcn/ntu-xsub': "https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/st-gcn/st_gcn.ntu-xsub-300b57d4.pth", 'st_gcn/ntu-xview': "https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/st-gcn/st_gcn.ntu-xview-9ba67746.pth", 'mmdet/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e': 'https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/mmdet/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e_20190408-0e50669c.pth', 'pose_estimation/pose_hrnet_w32_256x192': 'https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmskeleton/models/pose_estimation/pose_hrnet_w32_256x192-76ea353b.pth', 'mmdet/cascade_rcnn_r50_fpn_20e': 'https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth', } # yapf: disable def load_checkpoint(model, filename, *args, **kwargs): try: filename = get_mmskeleton_url(filename) return mmcv_load_checkpoint(model, filename, *args, **kwargs) except (urllib.error.HTTPError, urllib.error.URLError) as e: raise Exception(url_error_message.format(filename)) from e def get_mmskeleton_url(filename): if filename.startswith('mmskeleton://'): model_name = filename[13:] model_url = (mmskeleton_model_urls[model_name]) return model_url return filename def cache_checkpoint(filename): try: filename = get_mmskeleton_url(filename) load_url_dist(get_mmskeleton_url(filename)) except (urllib.error.HTTPError, urllib.error.URLError) as e: raise Exception(url_error_message.format(filename)) from e url_error_message = """ ================================================== MMSkeleton fail to load checkpoint from url: {} Please check your network connection. Or manually download checkpoints according to the instructor: https://github.com/open-mmlab/mmskeleton/blob/master/doc/MODEL_ZOO.md """

GlenGGG/DR-GCN

mmskeleton/utils/checkpoint.py

Python

# -*- coding: utf-8 -*- """ Created on Mon Apr 27 17:38:25 2020 @author: Wu Yichen """ from PIL import Image import os import os.path import errno import numpy as np import sys import pickle import torch.utils.data as data from torchvision.datasets.utils import download_url, check_integrity import torch import torch.nn.functional as F from torch.autograd import Variable as V import wideresnet as wrn import torchvision.transforms as transforms def uniform_mix_C(mixing_ratio, num_classes): ''' returns a linear interpolation of a uniform matrix and an identity matrix ''' return mixing_ratio * np.full((num_classes, num_classes), 1 / num_classes) + \ (1 - mixing_ratio) * np.eye(num_classes) def flip_labels_C(corruption_prob, num_classes, seed=1): ''' returns a matrix with (1 - corruption_prob) on the diagonals, and corruption_prob concentrated in only one other entry for each row ''' np.random.seed(seed) C = np.eye(num_classes) * (1 - corruption_prob) row_indices = np.arange(num_classes) for i in range(num_classes): C[i][np.random.choice(row_indices[row_indices != i])] = corruption_prob return C def flip_labels_C_two(corruption_prob, num_classes, seed=1): ''' returns a matrix with (1 - corruption_prob) on the diagonals, and corruption_prob concentrated in only one other entry for each row ''' np.random.seed(seed) C = np.eye(num_classes) * (1 - corruption_prob) row_indices = np.arange(num_classes) for i in range(num_classes): C[i][np.random.choice(row_indices[row_indices != i], 2, replace=False)] = corruption_prob / 2 return C class CIFAR10(data.Dataset): base_folder = 'cifar-10-batches-py' url = "http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" filename = "cifar-10-python.tar.gz" tgz_md5 = 'c58f30108f718f92721af3b95e74349a' train_list = [ ['data_batch_1', 'c99cafc152244af753f735de768cd75f'], ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'], ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'], ['data_batch_4', '634d18415352ddfa80567beed471001a'], ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'], ] test_list = [ ['test_batch', '40351d587109b95175f43aff81a1287e'], ] def __init__(self, root='', train=True, meta=True, num_meta=1000, corruption_prob=0, corruption_type='unif', transform=None, target_transform=None, download=False, seed=1): self.count = 0 self.root = root self.transform = transform self.target_transform = target_transform self.train = train # training set or test set self.meta = meta self.corruption_prob = corruption_prob self.num_meta = num_meta if download: self.download() if not self._check_integrity(): raise RuntimeError('Dataset not found or corrupted.' + ' You can use download=True to download it') # now load the picked numpy arrays if self.train: self.train_data = [] self.train_labels = [] self.train_coarse_labels = [] self.train_labels_true = [] self.soft_labels = [] for fentry in self.train_list: f = fentry[0] file = os.path.join(root, self.base_folder, f) fo = open(file, 'rb') if sys.version_info[0] == 2: entry = pickle.load(fo) else: entry = pickle.load(fo, encoding='latin1') self.train_data.append(entry['data']) if 'labels' in entry: self.train_labels += entry['labels'] self.train_labels_true += entry['labels'] img_num_list = [int(self.num_meta/10)] * 10 num_classes = 10 else: self.train_labels += entry['fine_labels'] self.train_labels_true += entry['fine_labels'] self.train_coarse_labels += entry['coarse_labels'] img_num_list = [int(self.num_meta/100)] * 100 num_classes = 100 fo.close() self.train_data = np.concatenate(self.train_data) self.train_data = self.train_data.reshape((50000, 3, 32, 32)) self.train_data = self.train_data.transpose((0, 2, 3, 1)) # convert to HWC data_list_val = {} for j in range(num_classes): data_list_val[j] = [i for i, label in enumerate(self.train_labels) if label == j] idx_to_meta = [] idx_to_train = [] print(img_num_list) for cls_idx, img_id_list in data_list_val.items(): np.random.shuffle(img_id_list) img_num = img_num_list[int(cls_idx)] idx_to_meta.extend(img_id_list[:img_num]) idx_to_train.extend(img_id_list[img_num:]) if meta is True: self.train_data = self.train_data[idx_to_meta] self.train_labels = list(np.array(self.train_labels)[idx_to_meta]) else: self.train_data = self.train_data[idx_to_train] self.train_labels = list(np.array(self.train_labels)[idx_to_train]) self.train_labels_true = list(np.array(self.train_labels_true)[idx_to_train]) self.soft_labels = list(np.zeros((len(self.train_data),num_classes),dtype=np.float32)) self.prediction = np.zeros((len(self.train_data),10,num_classes),dtype=np.float32) clean_labels = self.train_labels np.save('clean_labels.npy', clean_labels) if corruption_type == 'unif': C = uniform_mix_C(self.corruption_prob, num_classes) print(C) self.C = C elif corruption_type == 'flip': C = flip_labels_C(self.corruption_prob, num_classes) print(C) self.C = C elif corruption_type == 'flip2': C = flip_labels_C_two(self.corruption_prob, num_classes) print(C) self.C = C elif corruption_type == 'hierarchical': assert num_classes == 100, 'You must use CIFAR-100 with the hierarchical corruption.' coarse_fine = [] for i in range(20): coarse_fine.append(set()) for i in range(len(self.train_labels)): coarse_fine[self.train_coarse_labels[i]].add(self.train_labels[i]) for i in range(20): coarse_fine[i] = list(coarse_fine[i]) C = np.eye(num_classes) * (1 - corruption_prob) for i in range(20): tmp = np.copy(coarse_fine[i]) for j in range(len(tmp)): tmp2 = np.delete(np.copy(tmp), j) C[tmp[j], tmp2] += corruption_prob * 1/len(tmp2) self.C = C print(C) elif corruption_type == 'clabels': net = wrn.WideResNet(40, num_classes, 2, dropRate=0.3).cuda() model_name = './cifar{}_labeler'.format(num_classes) net.load_state_dict(torch.load(model_name)) net.eval() else: assert False, "Invalid corruption type '{}' given. Must be in {'unif', 'flip', 'hierarchical'}".format(corruption_type) np.random.seed(seed) if corruption_type == 'clabels': mean = [x / 255 for x in [125.3, 123.0, 113.9]] std = [x / 255 for x in [63.0, 62.1, 66.7]] test_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize(mean, std)]) # obtain sampling probabilities sampling_probs = [] print('Starting labeling') for i in range((len(self.train_labels) // 64) + 1): current = self.train_data[i*64:(i+1)*64] current = [Image.fromarray(current[i]) for i in range(len(current))] current = torch.cat([test_transform(current[i]).unsqueeze(0) for i in range(len(current))], dim=0) data = V(current).cuda() logits = net(data) smax = F.softmax(logits / 5) # temperature of 1 sampling_probs.append(smax.data.cpu().numpy()) sampling_probs = np.concatenate(sampling_probs, 0) print('Finished labeling 1') new_labeling_correct = 0 argmax_labeling_correct = 0 for i in range(len(self.train_labels)): old_label = self.train_labels[i] new_label = np.random.choice(num_classes, p=sampling_probs[i]) self.train_labels[i] = new_label if old_label == new_label: new_labeling_correct += 1 if old_label == np.argmax(sampling_probs[i]): argmax_labeling_correct += 1 print('Finished labeling 2') print('New labeling accuracy:', new_labeling_correct / len(self.train_labels)) print('Argmax labeling accuracy:', argmax_labeling_correct / len(self.train_labels)) else: for i in range(len(self.train_labels)): self.train_labels_true[i] = self.train_labels[i] for i in range(len(self.train_labels)): self.train_labels[i] = np.random.choice(num_classes, p=C[self.train_labels[i]]) print('train',len(self.train_labels)) print('type',type(self.train_labels)) self.corruption_matrix = C noise_labels = self.train_labels np.save('noise_labels.npy', noise_labels) else: f = self.test_list[0][0] file = os.path.join(root, self.base_folder, f) fo = open(file, 'rb') if sys.version_info[0] == 2: entry = pickle.load(fo) else: entry = pickle.load(fo, encoding='latin1') self.test_data = entry['data'] if 'labels' in entry: self.test_labels = entry['labels'] else: self.test_labels = entry['fine_labels'] fo.close() self.test_data = self.test_data.reshape((10000, 3, 32, 32)) self.test_data = self.test_data.transpose((0, 2, 3, 1)) # convert to HWC def label_update(self, results): self.count += 1 # While updating the noisy label y_i by the probability s, we used the average output probability of the network of the past 10 epochs as s. idx = (self.count - 1) % 10#10 #10 self.prediction[:, idx] = results #self.prediction[:] =results #print(self.prediction) if self.count == 79: #79 self.soft_labels = self.prediction.mean(axis=1) #print(self.soft_labels.shape) #print(self.soft_labels) #self.soft_labels = list(np.argmax(self.soft_labels, axis=1).astype(np.int64)) if self.count > 79: self.soft_labels = results #self.soft_labels = list(np.argmax(self.soft_labels, axis=1).astype(np.int64)) def __getitem__(self, index): if self.train: if self.meta: #print(self.train_labels[index]) img, target, target_true= self.train_data[index], self.train_labels[index],self.train_labels_true[index] else: img, target, target_true= self.train_data[index], self.train_labels[index],self.train_labels_true[index] soft_labels = self.soft_labels[index] else: img, target = self.test_data[index], self.test_labels[index] # doing this so that it is consistent with all other datasets # to return a PIL Image img = Image.fromarray(img) if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) if self.train : if self.meta: return img, target else: return img,target,target_true,soft_labels,index else: return img, target def __len__(self): if self.train: if self.meta is True: return self.num_meta else: return 50000 - self.num_meta else: return 10000 def _check_integrity(self): root = self.root for fentry in (self.train_list + self.test_list): filename, md5 = fentry[0], fentry[1] fpath = os.path.join(root, self.base_folder, filename) if not check_integrity(fpath, md5): return False return True def download(self): import tarfile if self._check_integrity(): print('Files already downloaded and verified') return root = self.root download_url(self.url, root, self.filename, self.tgz_md5) # extract file cwd = os.getcwd() tar = tarfile.open(os.path.join(root, self.filename), "r:gz") os.chdir(root) tar.extractall() tar.close() os.chdir(cwd) class CIFAR100(CIFAR10): base_folder = 'cifar-100-python' url = "http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz" filename = "cifar-100-python.tar.gz" tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85' train_list = [ ['train', '16019d7e3df5f24257cddd939b257f8d'], ] test_list = [ ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'], ]

WuYichen-97/Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector

dataloader.py

Python

import os import torch from typing import List from dqc.utils.datastruct import CGTOBasis __all__ = ["loadbasis"] _dtype = torch.double _device = torch.device("cpu") def loadbasis(cmd: str, dtype: torch.dtype = _dtype, device: torch.device = _device, requires_grad: bool = False) -> \ List[CGTOBasis]: """ Load basis from a file and return the list of CGTOBasis. Arguments --------- cmd: str This can be a file path where the basis is stored or a string in format ``"atomz:basis"``, e.g. ``"1:6-311++G**"``. dtype: torch.dtype Tensor data type for ``alphas`` and ``coeffs`` of the GTO basis device: torch.device Tensor device for ``alphas`` and ``coeffs`` requires_grad: bool If ``True``, the ``alphas`` and ``coeffs`` tensors become differentiable Returns ------- list of CGTOBasis List of GTO basis loaded from the given file """ res = [] if not os.path.exists(cmd): file = _get_basis_file(cmd) else: file = cmd # read the content with open(file, "r") as f: lines = f.read().split("\n") # skip the header while True: line = lines.pop(0) if line == "": continue if line.startswith("!"): continue break # now it is at the orbital description while len(lines) > 0: line = lines.pop(0) if line.startswith("**"): break desc = line.split() nlines = int(desc[1]) if nlines == 0: raise RuntimeError("Zero line on basis %s" % file) # read the exponents and the coefficients alphas = [] coeffsT = [] for i in range(nlines): alphacoeff = [_read_float(f) for f in lines.pop(0).split()] alphas.append(alphacoeff[0]) coeffsT.append(alphacoeff[1:]) # coeffsT: list with shape (nbasis, ncontr) # coeffs: list with shape (ncontr, nbasis) coeffs = list(zip(*coeffsT)) ncoeffs = len(coeffs) angmoms = _expand_angmoms(desc[0], ncoeffs) # convert to tensor alpha = torch.tensor(alphas, dtype=dtype, device=device, requires_grad=requires_grad) for i in range(ncoeffs): coeff = torch.tensor(coeffs[i], dtype=dtype, device=device, requires_grad=requires_grad) basis = CGTOBasis(angmom=angmoms[i], alphas=alpha, coeffs=coeff) basis.wfnormalize_() res.append(basis) return res def _read_float(s: str) -> float: s = s.replace("D", "E") return float(s) def _get_basis_file(cmd: str) -> str: # parse the string command, check if the basis has already been downloaded # (download if not), and return the file name # parse to get the atomz and the basisname atomz_str, raw_basisname = cmd.split(":") raw_basisname = raw_basisname.strip() atomz = int(atomz_str) # get the path to the database basisname = _normalize_basisname(raw_basisname) thisdir = os.path.dirname(os.path.realpath(__file__)) fname = "%02d.gaussian94" % atomz fdir = os.path.join(thisdir, ".database", basisname) fpath = os.path.join(fdir, fname) # if the file does not exist, download it if not os.path.exists(fpath): print("The %s basis for atomz %d does not exist, but we will download it" % (raw_basisname, atomz)) if not os.path.exists(fdir): os.makedirs(fdir) _download_basis(fpath, atomz, raw_basisname) return fpath def _normalize_basisname(basisname: str) -> str: b = basisname.lower() b = b.replace("+", "p") b = b.replace("*", "s") b = b.replace("(", "_") b = b.replace(")", "_") b = b.replace(",", "_") return b def _download_basis(fname: str, atomz: int, basisname: str) -> None: import basis_set_exchange as bse s = bse.get_basis(basisname, elements=[atomz], fmt="gaussian94") with open(fname, "w") as f: f.write(s) print("Downloaded to %s" % fname) def _expand_angmoms(s: str, n: int) -> List[int]: # convert the angular momentum characters into angmom and returns a list # of n integer containing the angular momentums if len(s) == n: pass elif n % len(s) == 0: s = s * (n // len(s)) else: raise RuntimeError("Do not know how to read orbital %s with %d coefficient columns" % (s, n)) s = s.lower() spdfmap = { "s": 0, "p": 1, "d": 2, "f": 3, "g": 4, "h": 5, "i": 6, } angmoms = [spdfmap[c] for c in s] return angmoms

Jaikinator/dqc

dqc/api/loadbasis.py

Python

import datetime import threading import contextlib import pyotp import qrcode from errbot import BotPlugin, botcmd, arg_botcmd, cmdfilter # OTP expires every hour _OTP_EXPIRE = datetime.timedelta(hours=1) _BASE_TIME = datetime.datetime(year=datetime.MINYEAR, month=1, day=1) class otp(BotPlugin): ''' Implement One Time Passwords for command filtering. ''' # lock protects storage lock = threading.Lock() def activate(self): super(otp, self).activate() # Set the data directory for the plugin self.DATA_DIR = '{0}/ '.format(self.bot_config.BOT_DATA_DIR) if 'commands' not in self: self['commands'] = set() if 'secrets' not in self: self['secrets'] = dict() @contextlib.contextmanager def stored(self, key): ''' This is a convenience tool to make plugin storage easier. ''' value = self[key] try: yield value finally: self[key] = value def get_configuration_template(self): return dict( provision_via_chat=False, max_retries=10 ) def build_qrcode(self, user, url): '''Internal method used to build the QRCode image for token provisioning.''' prefix = self.DATA_DIR qrcode.make(url).save('{0}{1}-qrcode.png'.format(prefix, user), format='png') def get_identity(self, message): '''Wrapper to make sure the correct identity object is used.''' try: return message.frm.aclattr except AttributeError: return message.frm.person @botcmd(admin_only=True) def otp_delete_all(self, message, args): ''' WARNING: This command removes ALL OTP entries. ''' self['commands'] = set() self['secrets'] = dict() return 'Removed **all** OTP tokens and command filters.' @arg_botcmd('cmd', type=str, admin_only=True, template='otp_add_command') def otp_add_command(self, message, cmd=None): ''' Add a command to OTP command filtering. ''' with self.lock: with self.stored('commands') as commands: commands.add(cmd) return dict(command=cmd) #return 'Added {0} to OTP filtered commands.'.format(cmd) @arg_botcmd('cmd', type=str, admin_only=True, template='otp_remove_command') def otp_remove_command(self, message, cmd=None): ''' Remove a command from OTP command filtering. ''' with self.lock: with self.stored('commands') as commands: if cmd not in commands: return dict(err=True, command=cmd) commands.remove(cmd) return dict(err=False, command=cmd) @botcmd(admin_only=True, template='otp_commands') def otp_commands(self, message, args): ''' List the commands that are filtered by OTP. ''' return dict(commands=self['commands']) @arg_botcmd('user', type=str, admin_only=True, template='otp_secret_create') def otp_secret_create(self, message, user=None): ''' Send a new secret for a user. ''' secret = pyotp.random_base32() with self.lock: with self.stored('secrets') as secrets: secrets[user] = (secret, 0, _BASE_TIME) totp = pyotp.TOTP(secret) url = totp.provisioning_uri(user) self.build_qrcode(user, url) if self.config: if self.config.get('provision_via_chat'): f = open('{0}{1}-qrcode.png'.format(self.DATA_DIR, user), 'rb') self.send_stream_request(self.build_identifier(user), f, name='OTP-secret.png') self.send_templated(self.build_identifier(user), 'otp_secret_create_pm', dict(url=url)) return dict(chat_enrollment=True, user=user) return dict(chat_enrollment=False, user=user) @arg_botcmd('otp', type=int, template='otp_auth') def otp_auth(self, message, otp=None): ''' Authenticate with OTP to the bot to pass OTP filtering. ''' # OTP shouldn't be done in a group chat channel. if message.is_group: return dict(group_chat=True) identity = self.get_identity(message) if identity not in self['secrets']: return dict(not_enrolled=True) secret, attempts, _ = self['secrets'][identity] totp = pyotp.TOTP(secret) if totp.verify(otp): with self.lock: with self.stored('secrets') as secrets: secret, _, _ = secrets[identity] secrets[identity] = (secret, 0, datetime.datetime.now()) return dict(success=True) else: # Increase the number of attempts, or burn secret with self.lock: with self.stored('secrets') as secrets: secret, attempts, ts = secrets[identity] if attempts > self.config.get('max_retries'): secret = '' secrets[identity] = (secret, attempts+1, ts) return dict(success=False) @cmdfilter def otp_filter(self, message, command, args, dry_run): ''' Filter commands to determine if user has recently validated with OTP. ''' with self.lock: if command in self['commands']: self.log.info('{0} is protected by OTP. Processing.'.format(command)) identity = self.get_identity(message) secrets = self['secrets'] if identity not in secrets: # Command is filtered, user doesn't have an OTP token self.send_templated(message.frm, 'otp_filter', dict(not_enrolled=True)) return None, None, None _, _, lastotp = secrets[identity] if datetime.datetime.now() - lastotp > _OTP_EXPIRE: self.log.info('{0} has not authenticated with OTP since expire'.format(identity)) self.send_templated(message.frm, 'otp_filter', dict(auth_required=True)) return None, None, None self.log.info('OTP ok, permit command.') return message, command, args

hosom/jarvis

plugins/otp/otp.py

Python

""" WSGI config for kweetservice project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "kweetservice.settings") application = get_wsgi_application()

teunw/JEA6-Kweeter

kweetservice/kweetservice/wsgi.py

Python

lengths = {0: 0, 1: 1} def sequenceLength(n: int) -> int: global lengths if n not in lengths: if n % 2 == 0: lengths[n] = sequenceLength(n//2) + 1 else: lengths[n] = sequenceLength(3 * n + 1) + 1 return lengths[n] def solution(n: int = 1000000) -> int: result = 0 maxLength = 0 for i in range(n): counter = sequenceLength(i) if counter > maxLength: result = i maxLength = counter return result print(solution())

gashev/algorithms

project-euler/14/solution.py

Python

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. 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. """ TF 2.0 ConvBERT model.""" import tensorflow as tf from ...activations_tf import get_tf_activation from ...file_utils import ( MULTIPLE_CHOICE_DUMMY_INPUTS, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, ) from ...modeling_tf_outputs import ( TFBaseModelOutput, TFMaskedLMOutput, TFMultipleChoiceModelOutput, TFQuestionAnsweringModelOutput, TFSequenceClassifierOutput, TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( TFMaskedLanguageModelingLoss, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, TFSequenceClassificationLoss, TFSequenceSummary, TFTokenClassificationLoss, get_initializer, input_processing, keras_serializable, shape_list, ) from ...utils import logging from .configuration_convbert import ConvBertConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "YituTech/conv-bert-base" _CONFIG_FOR_DOC = "ConvBertConfig" _TOKENIZER_FOR_DOC = "ConvBertTokenizer" TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "YituTech/conv-bert-base", "YituTech/conv-bert-medium-small", "YituTech/conv-bert-small", # See all ConvBERT models at https://huggingface.co/models?filter=convbert ] # Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings with Albert->ConvBert class TFConvBertEmbeddings(tf.keras.layers.Layer): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config: ConvBertConfig, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.type_vocab_size = config.type_vocab_size self.embedding_size = config.embedding_size self.max_position_embeddings = config.max_position_embeddings self.initializer_range = config.initializer_range self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) def build(self, input_shape: tf.TensorShape): with tf.name_scope("word_embeddings"): self.weight = self.add_weight( name="weight", shape=[self.vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("token_type_embeddings"): self.token_type_embeddings = self.add_weight( name="embeddings", shape=[self.type_vocab_size, self.embedding_size], initializer=get_initializer(self.initializer_range), ) with tf.name_scope("position_embeddings"): self.position_embeddings = self.add_weight( name="embeddings", shape=[self.max_position_embeddings, self.embedding_size], initializer=get_initializer(self.initializer_range), ) super().build(input_shape) # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call def call( self, input_ids: tf.Tensor = None, position_ids: tf.Tensor = None, token_type_ids: tf.Tensor = None, inputs_embeds: tf.Tensor = None, past_key_values_length=0, training: bool = False, ) -> tf.Tensor: """ Applies embedding based on inputs tensor. Returns: final_embeddings (`tf.Tensor`): output embedding tensor. """ if input_ids is None and inputs_embeds is None: raise ValueError("Need to provide either `input_ids` or `input_embeds`.") if input_ids is not None: inputs_embeds = tf.gather(params=self.weight, indices=input_ids) input_shape = shape_list(inputs_embeds)[:-1] if token_type_ids is None: token_type_ids = tf.fill(dims=input_shape, value=0) if position_ids is None: position_ids = tf.expand_dims( tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0 ) position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) final_embeddings = inputs_embeds + position_embeds + token_type_embeds final_embeddings = self.LayerNorm(inputs=final_embeddings) final_embeddings = self.dropout(inputs=final_embeddings, training=training) return final_embeddings class TFConvBertSelfAttention(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " f"heads ({config.num_attention_heads})" ) new_num_attention_heads = int(config.num_attention_heads / config.head_ratio) if new_num_attention_heads < 1: self.head_ratio = config.num_attention_heads num_attention_heads = 1 else: num_attention_heads = new_num_attention_heads self.head_ratio = config.head_ratio self.num_attention_heads = num_attention_heads self.conv_kernel_size = config.conv_kernel_size assert ( config.hidden_size % self.num_attention_heads == 0 ), "hidden_size should be divisible by num_attention_heads" self.attention_head_size = config.hidden_size // config.num_attention_heads self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" ) self.value = tf.keras.layers.Dense( self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.key_conv_attn_layer = tf.keras.layers.SeparableConv1D( self.all_head_size, self.conv_kernel_size, padding="same", activation=None, depthwise_initializer=get_initializer(1 / self.conv_kernel_size), pointwise_initializer=get_initializer(config.initializer_range), name="key_conv_attn_layer", ) self.conv_kernel_layer = tf.keras.layers.Dense( self.num_attention_heads * self.conv_kernel_size, activation=None, name="conv_kernel_layer", kernel_initializer=get_initializer(config.initializer_range), ) self.conv_out_layer = tf.keras.layers.Dense( self.all_head_size, activation=None, name="conv_out_layer", kernel_initializer=get_initializer(config.initializer_range), ) self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x, batch_size): # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size)) return tf.transpose(x, perm=[0, 2, 1, 3]) def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False): batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) mixed_key_conv_attn_layer = self.key_conv_attn_layer(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) conv_attn_layer = tf.multiply(mixed_key_conv_attn_layer, mixed_query_layer) conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer) conv_kernel_layer = tf.reshape(conv_kernel_layer, [-1, self.conv_kernel_size, 1]) conv_kernel_layer = tf.nn.softmax(conv_kernel_layer, axis=1) paddings = tf.constant( [ [ 0, 0, ], [int((self.conv_kernel_size - 1) / 2), int((self.conv_kernel_size - 1) / 2)], [0, 0], ] ) conv_out_layer = self.conv_out_layer(hidden_states) conv_out_layer = tf.reshape(conv_out_layer, [batch_size, -1, self.all_head_size]) conv_out_layer = tf.pad(conv_out_layer, paddings, "CONSTANT") unfold_conv_out_layer = tf.stack( [ tf.slice(conv_out_layer, [0, i, 0], [batch_size, shape_list(mixed_query_layer)[1], self.all_head_size]) for i in range(self.conv_kernel_size) ], axis=-1, ) conv_out_layer = tf.reshape(unfold_conv_out_layer, [-1, self.attention_head_size, self.conv_kernel_size]) conv_out_layer = tf.matmul(conv_out_layer, conv_kernel_layer) conv_out_layer = tf.reshape(conv_out_layer, [-1, self.all_head_size]) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = tf.matmul( query_layer, key_layer, transpose_b=True ) # (batch size, num_heads, seq_len_q, seq_len_k) dk = tf.cast(shape_list(key_layer)[-1], attention_scores.dtype) # scale attention_scores attention_scores = attention_scores / tf.math.sqrt(dk) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in TFBertModel call() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = tf.nn.softmax(attention_scores, axis=-1) # 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.dropout(attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask value_layer = tf.reshape( mixed_value_layer, [batch_size, -1, self.num_attention_heads, self.attention_head_size] ) value_layer = tf.transpose(value_layer, [0, 2, 1, 3]) context_layer = tf.matmul(attention_probs, value_layer) context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) conv_out = tf.reshape(conv_out_layer, [batch_size, -1, self.num_attention_heads, self.attention_head_size]) context_layer = tf.concat([context_layer, conv_out], 2) context_layer = tf.reshape( context_layer, (batch_size, -1, self.head_ratio * self.all_head_size) ) # (batch_size, seq_len_q, all_head_size) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class TFConvBertSelfOutput(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class TFConvBertAttention(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.self_attention = TFConvBertSelfAttention(config, name="self") self.dense_output = TFConvBertSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call(self, input_tensor, attention_mask, head_mask, output_attentions, training=False): self_outputs = self.self_attention( input_tensor, attention_mask, head_mask, output_attentions, training=training ) attention_output = self.dense_output(self_outputs[0], input_tensor, training=training) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class GroupedLinearLayer(tf.keras.layers.Layer): def __init__(self, input_size, output_size, num_groups, kernel_initializer, **kwargs): super().__init__(**kwargs) self.input_size = input_size self.output_size = output_size self.num_groups = num_groups self.kernel_initializer = kernel_initializer self.group_in_dim = self.input_size // self.num_groups self.group_out_dim = self.output_size // self.num_groups def build(self, input_shape): self.kernel = self.add_weight( "kernel", shape=[self.group_out_dim, self.group_in_dim, self.num_groups], initializer=self.kernel_initializer, trainable=True, ) self.bias = self.add_weight( "bias", shape=[self.output_size], initializer=self.kernel_initializer, dtype=self.dtype, trainable=True ) def call(self, hidden_states): batch_size = shape_list(hidden_states)[0] x = tf.transpose(tf.reshape(hidden_states, [-1, self.num_groups, self.group_in_dim]), [1, 0, 2]) x = tf.matmul(x, tf.transpose(self.kernel, [2, 1, 0])) x = tf.transpose(x, [1, 0, 2]) x = tf.reshape(x, [batch_size, -1, self.output_size]) x = tf.nn.bias_add(value=x, bias=self.bias) return x class TFConvBertIntermediate(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.num_groups == 1: self.dense = tf.keras.layers.Dense( config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) else: self.dense = GroupedLinearLayer( config.hidden_size, config.intermediate_size, num_groups=config.num_groups, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class TFConvBertOutput(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) if config.num_groups == 1: self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) else: self.dense = GroupedLinearLayer( config.intermediate_size, config.hidden_size, num_groups=config.num_groups, kernel_initializer=get_initializer(config.initializer_range), name="dense", ) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob) def call(self, hidden_states, input_tensor, training=False): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states, training=training) hidden_states = self.LayerNorm(hidden_states + input_tensor) return hidden_states class TFConvBertLayer(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.attention = TFConvBertAttention(config, name="attention") self.intermediate = TFConvBertIntermediate(config, name="intermediate") self.bert_output = TFConvBertOutput(config, name="output") def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False): attention_outputs = self.attention( hidden_states, attention_mask, head_mask, output_attentions, training=training ) attention_output = attention_outputs[0] intermediate_output = self.intermediate(attention_output) layer_output = self.bert_output(intermediate_output, attention_output, training=training) outputs = (layer_output,) + attention_outputs[1:] # add attentions if we output them return outputs class TFConvBertEncoder(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.layer = [TFConvBertLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] def call( self, hidden_states, attention_mask, head_mask, output_attentions, output_hidden_states, return_dict, training=False, ): all_hidden_states = () if output_hidden_states else None all_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_outputs = layer_module( hidden_states, attention_mask, head_mask[i], output_attentions, training=training ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) # Add last layer if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class TFConvBertPredictionHeadTransform(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.transform_act_fn = get_tf_activation(config.hidden_act) else: self.transform_act_fn = config.hidden_act self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") def call(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states @keras_serializable class TFConvBertMainLayer(tf.keras.layers.Layer): config_class = ConvBertConfig def __init__(self, config, **kwargs): super().__init__(**kwargs) self.embeddings = TFConvBertEmbeddings(config, name="embeddings") if config.embedding_size != config.hidden_size: self.embeddings_project = tf.keras.layers.Dense(config.hidden_size, name="embeddings_project") self.encoder = TFConvBertEncoder(config, name="encoder") self.config = config def get_input_embeddings(self): return self.embeddings def set_input_embeddings(self, value): self.embeddings.weight = value self.embeddings.vocab_size = value.shape[0] def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError def get_extended_attention_mask(self, attention_mask, input_shape, dtype): if attention_mask is None: attention_mask = tf.fill(input_shape, 1) # We create a 3D attention mask from a 2D tensor mask. # Sizes are [batch_size, 1, 1, to_seq_length] # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] # this attention mask is more simple than the triangular masking of causal attention # used in OpenAI GPT, we just need to prepare the broadcast dimension here. extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[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. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. extended_attention_mask = tf.cast(extended_attention_mask, dtype) extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def get_head_mask(self, head_mask): if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers return head_mask def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif inputs["input_ids"] is not None: input_shape = shape_list(inputs["input_ids"]) elif inputs["inputs_embeds"] is not None: input_shape = shape_list(inputs["inputs_embeds"])[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") if inputs["attention_mask"] is None: inputs["attention_mask"] = tf.fill(input_shape, 1) if inputs["token_type_ids"] is None: inputs["token_type_ids"] = tf.fill(input_shape, 0) hidden_states = self.embeddings( inputs["input_ids"], inputs["position_ids"], inputs["token_type_ids"], inputs["inputs_embeds"], training=inputs["training"], ) extended_attention_mask = self.get_extended_attention_mask( inputs["attention_mask"], input_shape, hidden_states.dtype ) inputs["head_mask"] = self.get_head_mask(inputs["head_mask"]) if hasattr(self, "embeddings_project"): hidden_states = self.embeddings_project(hidden_states, training=inputs["training"]) hidden_states = self.encoder( hidden_states, extended_attention_mask, inputs["head_mask"], inputs["output_attentions"], inputs["output_hidden_states"], inputs["return_dict"], training=inputs["training"], ) return hidden_states class TFConvBertPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ConvBertConfig base_model_prefix = "convbert" CONVBERT_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. <Tip> TF 2.0 models accepts two formats as inputs: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional arguments. This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the tensors in the first argument of the model call function: `model(inputs)`. If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument : - a single Tensor with `input_ids` only and nothing else: `model(inputs_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` </Tip> Args: config ([`ConvBertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ CONVBERT_INPUTS_DOCSTRING = r""" Args: input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`ConvBertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`Numpy array` or `tf.Tensor` of shape `({0})`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False`): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare ConvBERT Model transformer outputting raw hidden-states without any specific head on top.", CONVBERT_START_DOCSTRING, ) class TFConvBertModel(TFConvBertPreTrainedModel): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.convbert = TFConvBertMainLayer(config, name="convbert") @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFBaseModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, training=False, **kwargs, ): inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, kwargs_call=kwargs, ) outputs = self.convbert( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) return outputs def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns) class TFConvBertMaskedLMHead(tf.keras.layers.Layer): def __init__(self, config, input_embeddings, **kwargs): super().__init__(**kwargs) self.vocab_size = config.vocab_size self.embedding_size = config.embedding_size self.input_embeddings = input_embeddings def build(self, input_shape): self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") super().build(input_shape) def get_output_embeddings(self): return self.input_embeddings def set_output_embeddings(self, value): self.input_embeddings.weight = value self.input_embeddings.vocab_size = shape_list(value)[0] def get_bias(self): return {"bias": self.bias} def set_bias(self, value): self.bias = value["bias"] self.vocab_size = shape_list(value["bias"])[0] def call(self, hidden_states): seq_length = shape_list(tensor=hidden_states)[1] hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size]) hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size]) hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) return hidden_states class TFConvBertGeneratorPredictions(tf.keras.layers.Layer): def __init__(self, config, **kwargs): super().__init__(**kwargs) self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") self.dense = tf.keras.layers.Dense(config.embedding_size, name="dense") def call(self, generator_hidden_states, training=False): hidden_states = self.dense(generator_hidden_states) hidden_states = get_tf_activation("gelu")(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states @add_start_docstrings("""ConvBERT Model with a `language modeling` head on top.""", CONVBERT_START_DOCSTRING) class TFConvBertForMaskedLM(TFConvBertPreTrainedModel, TFMaskedLanguageModelingLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, **kwargs) self.vocab_size = config.vocab_size self.convbert = TFConvBertMainLayer(config, name="convbert") self.generator_predictions = TFConvBertGeneratorPredictions(config, name="generator_predictions") if isinstance(config.hidden_act, str): self.activation = get_tf_activation(config.hidden_act) else: self.activation = config.hidden_act self.generator_lm_head = TFConvBertMaskedLMHead(config, self.convbert.embeddings, name="generator_lm_head") def get_lm_head(self): return self.generator_lm_head def get_prefix_bias_name(self): return self.name + "/" + self.generator_lm_head.name @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs, ): r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs, ) generator_hidden_states = self.convbert( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) generator_sequence_output = generator_hidden_states[0] prediction_scores = self.generator_predictions(generator_sequence_output, training=inputs["training"]) prediction_scores = self.generator_lm_head(prediction_scores, training=inputs["training"]) loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores) if not inputs["return_dict"]: output = (prediction_scores,) + generator_hidden_states[1:] return ((loss,) + output) if loss is not None else output return TFMaskedLMOutput( loss=loss, logits=prediction_scores, hidden_states=generator_hidden_states.hidden_states, attentions=generator_hidden_states.attentions, ) # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns) class TFConvBertClassificationHead(tf.keras.layers.Layer): """Head for sentence-level classification tasks.""" def __init__(self, config, **kwargs): super().__init__(**kwargs) self.dense = tf.keras.layers.Dense( config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = tf.keras.layers.Dropout(classifier_dropout) self.out_proj = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" ) self.config = config def call(self, hidden_states, **kwargs): x = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = get_tf_activation(self.config.hidden_act)(x) x = self.dropout(x) x = self.out_proj(x) return x @add_start_docstrings( """ ConvBERT Model transformer with a sequence classification/regression head on top e.g., for GLUE tasks. """, CONVBERT_START_DOCSTRING, ) class TFConvBertForSequenceClassification(TFConvBertPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convbert = TFConvBertMainLayer(config, name="convbert") self.classifier = TFConvBertClassificationHead(config, name="classifier") @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs, ): r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs, ) outputs = self.convbert( inputs["input_ids"], attention_mask=inputs["attention_mask"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) logits = self.classifier(outputs[0], training=inputs["training"]) loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits) if not inputs["return_dict"]: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ ConvBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """, CONVBERT_START_DOCSTRING, ) class TFConvBertForMultipleChoice(TFConvBertPreTrainedModel, TFMultipleChoiceLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.convbert = TFConvBertMainLayer(config, name="convbert") self.sequence_summary = TFSequenceSummary( config, initializer_range=config.initializer_range, name="sequence_summary" ) self.classifier = tf.keras.layers.Dense( 1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) @property def dummy_inputs(self): """ Dummy inputs to build the network. Returns: tf.Tensor with dummy inputs """ return {"input_ids": tf.convert_to_tensor(MULTIPLE_CHOICE_DUMMY_INPUTS)} @add_start_docstrings_to_model_forward( CONVBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFMultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs, ): r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs, ) if inputs["input_ids"] is not None: num_choices = shape_list(inputs["input_ids"])[1] seq_length = shape_list(inputs["input_ids"])[2] else: num_choices = shape_list(inputs["inputs_embeds"])[1] seq_length = shape_list(inputs["inputs_embeds"])[2] flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None flat_attention_mask = ( tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None ) flat_token_type_ids = ( tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None ) flat_position_ids = ( tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None ) flat_inputs_embeds = ( tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3])) if inputs["inputs_embeds"] is not None else None ) outputs = self.convbert( flat_input_ids, flat_attention_mask, flat_token_type_ids, flat_position_ids, inputs["head_mask"], flat_inputs_embeds, inputs["output_attentions"], inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) logits = self.sequence_summary(outputs[0], training=inputs["training"]) logits = self.classifier(logits) reshaped_logits = tf.reshape(logits, (-1, num_choices)) loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits) if not inputs["return_dict"]: output = (reshaped_logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFMultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @tf.function( input_signature=[ { "input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"), "attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"), "token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"), } ] ) def serving(self, inputs): output = self.call(inputs) return self.serving_output(output) def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ ConvBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """, CONVBERT_START_DOCSTRING, ) class TFConvBertForTokenClassification(TFConvBertPreTrainedModel, TFTokenClassificationLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convbert = TFConvBertMainLayer(config, name="convbert") classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = tf.keras.layers.Dropout(classifier_dropout) self.classifier = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" ) @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs, ): r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs, ) outputs = self.convbert( inputs["input_ids"], attention_mask=inputs["attention_mask"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output, training=inputs["training"]) logits = self.classifier(sequence_output) loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits) if not inputs["return_dict"]: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFTokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns) @add_start_docstrings( """ ConvBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, CONVBERT_START_DOCSTRING, ) class TFConvBertForQuestionAnswering(TFConvBertPreTrainedModel, TFQuestionAnsweringLoss): def __init__(self, config, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.convbert = TFConvBertMainLayer(config, name="convbert") self.qa_outputs = tf.keras.layers.Dense( config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" ) @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFQuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC, ) def call( self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, start_positions=None, end_positions=None, training=False, **kwargs, ): r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. """ inputs = input_processing( func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, start_positions=start_positions, end_positions=end_positions, training=training, kwargs_call=kwargs, ) outputs = self.convbert( inputs["input_ids"], attention_mask=inputs["attention_mask"], token_type_ids=inputs["token_type_ids"], position_ids=inputs["position_ids"], head_mask=inputs["head_mask"], inputs_embeds=inputs["inputs_embeds"], output_attentions=inputs["output_attentions"], output_hidden_states=inputs["output_hidden_states"], return_dict=inputs["return_dict"], training=inputs["training"], ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = tf.split(logits, 2, axis=-1) start_logits = tf.squeeze(start_logits, axis=-1) end_logits = tf.squeeze(end_logits, axis=-1) loss = None if inputs["start_positions"] is not None and inputs["end_positions"] is not None: labels = {"start_position": inputs["start_positions"]} labels["end_position"] = inputs["end_positions"] loss = self.compute_loss(labels, (start_logits, end_logits)) if not inputs["return_dict"]: output = (start_logits, end_logits) + outputs[1:] return ((loss,) + output) if loss is not None else output return TFQuestionAnsweringModelOutput( loss=loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def serving_output(self, output): hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None return TFQuestionAnsweringModelOutput( start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns )

AK391/transformers

src/transformers/models/convbert/modeling_tf_convbert.py

Python

""" Third party api wrappers""" import os import json import nexmo import africastalking username = os.getenv('africastalking_username') api_key = os.getenv('africastalking_api_key') africastalking.initialize(username, api_key) sms = africastalking.SMS class ProvidersWrapper: """ Class with all the thirdy party helper functions""" def send_message(number, message): client = nexmo.Client(key=os.getenv('nexmokey'), secret=os.getenv('nexmosecret')) response = client.send_message({ 'from': 'Nexmo', 'to': number, 'text': message, }) if response["messages"][0]["status"] != "0": response = sms.send(message, ['+' + number]) return response

kwanj-k/sibsco

providers.py

Python

import time import cv2 import numpy as np from collections import defaultdict class Tracker(object): def __init__(self, pLK=None): if pLK is None: # default LK param pLK = self.pLK0() self.lk_ = cv2.SparsePyrLKOpticalFlow_create( **pLK) self.tmp_ = defaultdict(lambda:None) def pLK0(self): """ Default LK Params. """ return dict( winSize = (12,6), maxLevel = 4, # == effective winsize up to 32*(2**4) = 512x256 crit= (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 100, 0.03), flags = 0, minEigThreshold = 1e-3 # TODO : disable eig? ) def __call__(self, img1, img2, pt1, pt2=None, thresh=2.0, return_msk=False ): """ Arguments: img1(np.ndarray) : previous image. (color/mono) (HxWx?) img2(np.ndarray) : current image (color/mono) (HxWx?) pt1(np.ndarray) : previous points. (Mx2) pt2(np.ndarray) : [Optional] current points estimate (Mx2) thresh(float) : Flow Back-projection Error threshold Returns: pt2(np.ndarray) : current points. (Mx2) idx(np.ndarray) : valid tracked indices from pt1 & pt2. """ if pt1.size <= 0: # soft fail pt2 = np.empty([0,2], dtype=np.float32) if return_msk: msk = np.empty([0], dtype=np.bool) return pt2, msk idx = np.empty([0], dtype=np.int32) return pt2, idx # stat img h, w = np.shape(img2)[:2] # convert to grayscale # TODO : check if already gray/mono if (np.ndim(img1) == 2) or img1.shape[2] == 1: # already monochromatic img1_gray = img1 img2_gray = img2 else: # handle image # 1 + pre-allocated data cache if self.tmp_['img1g'] is not None: cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY, self.tmp_['img1g']) img1_gray = self.tmp_['img1g'] else: img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) self.tmp_['img1g'] = np.empty_like(img1_gray) # handle image # 2 + pre-allocated data cache if self.tmp_['img2g'] is not None: cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY, self.tmp_['img2g']) img2_gray = self.tmp_['img2g'] else: img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) self.tmp_['img2g'] = np.empty_like(img2_gray) # forward flow if pt2 is not None: # set initial flow flags self.lk_.setFlags(self.lk_.getFlags() | cv2.OPTFLOW_USE_INITIAL_FLOW ) pt2, st, _ = self.lk_.calc( img1_gray, img2_gray, pt1, pt2 ) else: pt2, st, _ = self.lk_.calc( img1_gray, img2_gray, pt1, None ) st_fw = st[:,0].astype(np.bool) # backward flow # unset initial flow flags self.lk_.setFlags(self.lk_.getFlags() & ~cv2.OPTFLOW_USE_INITIAL_FLOW ) pt1_r, st, _ = self.lk_.calc( img2_gray, img1_gray, pt2, None ) st_bw = st[:,0].astype(np.bool) # override error with reprojection error # (default error doesn't make much sense anyways) err = np.linalg.norm(pt1 - pt1_r, axis=-1) # apply mask msk = np.logical_and.reduce([ # error check err < thresh, # bounds check 0 <= pt2[:,0], 0 <= pt2[:,1], pt2[:,0] < w, pt2[:,1] < h, # status check st_fw, st_bw, ]) if return_msk: return pt2, msk else: idx = np.where(msk)[0] return pt2, idx def main(): from matplotlib import pyplot as plt # params w = 2*640 h = 2*480 n = 2*1024 di = 8 dj = 32 track = Tracker() img1 = np.random.randint(0, 255, size=(h,w,3), dtype=np.uint8) #img2 = np.random.randint(0, 255, size=(480,640,3), dtype=np.uint8) img2 = np.roll(img1, di, axis=0) img2 = np.roll(img2, dj, axis=1) #img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) #img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) pt1 = np.random.uniform((0,0), (w,h), size=(n,2)).astype(np.float32) pt2, idx = track(img1, img2, pt1) #pt2, idx = track(img1, img2, pt1, pt2) fig, ax = plt.subplots(1,2) ax[0].imshow(img1, alpha=0.5) ax[0].plot(pt1[:,0], pt1[:,1], 'r+') ax[1].imshow(img2, alpha=0.5) ax[1].plot(pt1[:,0], pt1[:,1], 'bx') ax[1].plot(pt2[:,0], pt2[:,1], 'r+') plt.show() if __name__ == "__main__": main()

yycho0108/MoRoL

core/track.py

Python

import unittest from PyStacks.PyStacks.template import templateCF class TestTemplate(unittest.TestCase): def test_templateCF_Route53Zone(self): resources = { 'route53_zone': { 'testr53zone': { 'name': 'example.com', 'comment': 'testzonecomment', 'hostedzone': { 'Name': 'testname', 'Tag2': 'testtagstuff' }, 'vpcs': { 'vpc-12345678': 'ap-southeast-2', 'vpc-87654321': 'us-west-2' } } } } expected = { 'testr53zone': { 'Type': 'AWS::Route53::HostedZone', 'Properties': { 'HostedZoneConfig': { 'Comment': 'testzonecomment' }, 'HostedZoneTags': [ { 'Key': 'Name', 'Value': 'testname' }, { 'Key': 'Tag2', 'Value': 'testtagstuff' } ], 'VPCs': [ { 'VPCId': 'vpc-87654321', 'VPCRegion': 'us-west-2' }, { 'VPCId': 'vpc-12345678', 'VPCRegion': 'ap-southeast-2' } ], 'Name': 'example.com' } } } actual = templateCF(resources, 'resources') self.assertDictEqual(actual, expected) def test_templateCF_Route53Record(self): resources = { 'route53_record': { 'testr53record': { 'comment': 'testcomment', 'zoneid': 'testzoneid', 'recordsets': [ [ 'atest', 'A', '1.2.3.4', '900', '0', 'base' ], [ 'cnametest', 'CNAME', 'example.com', '900', '0', 'base' ] ] } } } expected = { 'testr53record': { 'Type': 'AWS::Route53::RecordSetGroup', 'Properties': { 'Comment': 'testcomment', 'HostedZoneId': { 'Fn::ImportValue': { 'Fn::Sub': [ '${DNSStack}-Route53-testzoneid-Zone', { 'DNSStack': { 'Ref': 'DNSStack' } } ] } }, 'RecordSets': [ { 'Name': 'atest', 'Type': 'A', 'ResourceRecords': ['1.2.3.4'], 'TTL': '900', 'Weight': '0', 'SetIdentifier': 'base' }, { 'Name': 'cnametest', 'Type': 'CNAME', 'ResourceRecords': ['example.com'], 'TTL': '900', 'Weight': '0', 'SetIdentifier': 'base' } ] } } } actual = templateCF(resources, 'resources') self.assertDictEqual(actual, expected) if __name__ == '__main__': unittest.main()

0xack13/PyStacks

PyStacks/test/templates/test_route53.py

Python

from escpos.printer import Usb from pathlib import Path image = Path("/tamamo-no-mae/me-cloudy.png") printer = Usb(0x0416, 0x5011, 0, profile="ZJ-5870") printer.image(image); printer.cut() # with printer() as that: # that.write('Hello, world!\n\n') # # 000000000111111111122222222223 # # 123456789012345678901234567890 # that.write('Soluta sed voluptatem ut\n') # that.write('facere aut. Modi placeat et\n') # that.write('eius voluptate sint ut.\n') # that.write('Facilis minima ex quia quia\n') # that.write('consectetur ex ipsa. Neque et\n') # that.write('voluptatem ipsa enim error\n') # that.write('rthatrehenderit ex dolore.\n') # that.write('Cupiditate ad voluptatem nisi.\n\n\n\n') # ZJ-5870

paulhoule/usb_receipt_printer

demo.py

Python

import operator import numpy import pytest import cupy from cupy import testing class TestArrayElementwiseOp: @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(rtol=1e-6, accept_error=TypeError) def check_array_scalar_op(self, op, xp, x_type, y_type, swap=False, no_bool=False, no_complex=False): x_dtype = numpy.dtype(x_type) y_dtype = numpy.dtype(y_type) if no_bool and x_dtype == '?' and y_dtype == '?': return xp.array(True) if no_complex and (x_dtype.kind == 'c' or y_dtype.kind == 'c'): return xp.array(True) a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) if swap: return op(y_type(3), a) else: return op(a, y_type(3)) def test_add_scalar(self): self.check_array_scalar_op(operator.add) def test_radd_scalar(self): self.check_array_scalar_op(operator.add, swap=True) def test_iadd_scalar(self): self.check_array_scalar_op(operator.iadd) def test_sub_scalar(self): self.check_array_scalar_op(operator.sub, no_bool=True) def test_rsub_scalar(self): self.check_array_scalar_op(operator.sub, swap=True, no_bool=True) def test_isub_scalar(self): self.check_array_scalar_op(operator.isub, no_bool=True) def test_mul_scalar(self): self.check_array_scalar_op(operator.mul) def test_rmul_scalar(self): self.check_array_scalar_op(operator.mul, swap=True) def test_imul_scalar(self): self.check_array_scalar_op(operator.imul) def test_truediv_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(operator.truediv) def test_rtruediv_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(operator.truediv, swap=True) def test_itruediv_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(operator.itruediv) def test_floordiv_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(operator.floordiv, no_complex=True) def test_rfloordiv_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(operator.floordiv, swap=True, no_complex=True) def test_ifloordiv_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(operator.ifloordiv, no_complex=True) def test_pow_scalar(self): self.check_array_scalar_op(operator.pow) def test_rpow_scalar(self): self.check_array_scalar_op(operator.pow, swap=True) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(atol=1.0, accept_error=TypeError) def check_ipow_scalar(self, xp, x_type, y_type): a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) return operator.ipow(a, y_type(3)) def test_ipow_scalar(self): self.check_ipow_scalar() def test_divmod0_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(lambda x, y: divmod(x, y)[0], no_complex=True) def test_divmod1_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(lambda x, y: divmod(x, y)[1], no_complex=True) def test_rdivmod0_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(lambda x, y: divmod(x, y)[0], swap=True, no_complex=True) def test_rdivmod1_scalar(self): with numpy.errstate(divide='ignore'): self.check_array_scalar_op(lambda x, y: divmod(x, y)[1], swap=True, no_complex=True) def test_lt_scalar(self): self.check_array_scalar_op(operator.lt, no_complex=False) def test_le_scalar(self): self.check_array_scalar_op(operator.le, no_complex=False) def test_gt_scalar(self): self.check_array_scalar_op(operator.gt, no_complex=False) def test_ge_scalar(self): self.check_array_scalar_op(operator.ge, no_complex=False) def test_eq_scalar(self): self.check_array_scalar_op(operator.eq) def test_ne_scalar(self): self.check_array_scalar_op(operator.ne) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_array_op(self, op, xp, x_type, y_type, no_bool=False, no_complex=False): x_dtype = numpy.dtype(x_type) y_dtype = numpy.dtype(y_type) if no_bool and x_dtype == '?' and y_dtype == '?': return xp.array(True) if no_complex and (x_dtype.kind == 'c' or y_dtype.kind == 'c'): return xp.array(True) a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) b = xp.array([[6, 5, 4], [3, 2, 1]], y_type) return op(a, b) def test_add_array(self): self.check_array_array_op(operator.add) def test_iadd_array(self): self.check_array_array_op(operator.iadd) def test_sub_array(self): self.check_array_array_op(operator.sub, no_bool=True) def test_isub_array(self): self.check_array_array_op(operator.isub, no_bool=True) def test_mul_array(self): self.check_array_array_op(operator.mul) def test_imul_array(self): self.check_array_array_op(operator.imul) def test_truediv_array(self): with numpy.errstate(divide='ignore'): self.check_array_array_op(operator.truediv) def test_itruediv_array(self): with numpy.errstate(divide='ignore'): self.check_array_array_op(operator.itruediv) def test_floordiv_array(self): with numpy.errstate(divide='ignore'): self.check_array_array_op(operator.floordiv, no_complex=True) def test_ifloordiv_array(self): if '1.16.1' <= numpy.lib.NumpyVersion(numpy.__version__) < '1.18.0': self.skipTest("NumPy Issue #12927") with numpy.errstate(divide='ignore'): self.check_array_array_op(operator.ifloordiv, no_complex=True) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(atol=1e-5, rtol=1e-6, accept_error=TypeError) def check_pow_array(self, xp, x_type, y_type): a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) b = xp.array([[6, 5, 4], [3, 2, 1]], y_type) return operator.pow(a, b) def test_pow_array(self): # There are some precission issues in HIP that prevent # checking with atol=0 if cupy.cuda.runtime.is_hip: self.check_pow_array() else: self.check_array_array_op(operator.pow) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(atol=1.0, accept_error=TypeError) def check_ipow_array(self, xp, x_type, y_type): a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) b = xp.array([[6, 5, 4], [3, 2, 1]], y_type) return operator.ipow(a, b) def test_ipow_array(self): self.check_ipow_array() def test_divmod0_array(self): with numpy.errstate(divide='ignore'): self.check_array_array_op(lambda x, y: divmod(x, y)[0]) def test_divmod1_array(self): with numpy.errstate(divide='ignore'): self.check_array_array_op(lambda x, y: divmod(x, y)[1]) def test_lt_array(self): self.check_array_array_op(operator.lt, no_complex=True) def test_le_array(self): self.check_array_array_op(operator.le, no_complex=True) def test_gt_array(self): self.check_array_array_op(operator.gt, no_complex=True) def test_ge_array(self): self.check_array_array_op(operator.ge, no_complex=True) def test_eq_array(self): self.check_array_array_op(operator.eq) def test_ne_array(self): self.check_array_array_op(operator.ne) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_broadcasted_op(self, op, xp, x_type, y_type, no_bool=False, no_complex=False): x_dtype = numpy.dtype(x_type) y_dtype = numpy.dtype(y_type) if no_bool and x_dtype == '?' and y_dtype == '?': return xp.array(True) if no_complex and (x_dtype.kind == 'c' or y_dtype.kind == 'c'): return xp.array(True) a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) b = xp.array([[1], [2]], y_type) return op(a, b) def test_broadcasted_add(self): self.check_array_broadcasted_op(operator.add) def test_broadcasted_iadd(self): self.check_array_broadcasted_op(operator.iadd) def test_broadcasted_sub(self): # TODO(unno): sub for boolean array is deprecated in numpy>=1.13 self.check_array_broadcasted_op(operator.sub, no_bool=True) def test_broadcasted_isub(self): # TODO(unno): sub for boolean array is deprecated in numpy>=1.13 self.check_array_broadcasted_op(operator.isub, no_bool=True) def test_broadcasted_mul(self): self.check_array_broadcasted_op(operator.mul) def test_broadcasted_imul(self): self.check_array_broadcasted_op(operator.imul) def test_broadcasted_truediv(self): with numpy.errstate(divide='ignore'): self.check_array_broadcasted_op(operator.truediv) def test_broadcasted_itruediv(self): with numpy.errstate(divide='ignore'): self.check_array_broadcasted_op(operator.itruediv) def test_broadcasted_floordiv(self): with numpy.errstate(divide='ignore'): self.check_array_broadcasted_op(operator.floordiv, no_complex=True) def test_broadcasted_ifloordiv(self): if '1.16.1' <= numpy.lib.NumpyVersion(numpy.__version__) < '1.18.0': self.skipTest("NumPy Issue #12927") with numpy.errstate(divide='ignore'): self.check_array_broadcasted_op(operator.ifloordiv, no_complex=True) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(atol=1e-5, rtol=1e-6, accept_error=TypeError) def check_broadcasted_pow(self, xp, x_type, y_type): a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) b = xp.array([[1], [2]], y_type) return operator.pow(a, b) def test_broadcasted_pow(self): # There are some precission issues in HIP that prevent # checking with atol=0 if cupy.cuda.runtime.is_hip: self.check_broadcasted_pow() else: self.check_array_broadcasted_op(operator.pow) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(atol=1.0, accept_error=TypeError) def check_broadcasted_ipow(self, xp, x_type, y_type): a = xp.array([[1, 2, 3], [4, 5, 6]], x_type) b = xp.array([[1], [2]], y_type) return operator.ipow(a, b) def test_broadcasted_ipow(self): self.check_broadcasted_ipow() def test_broadcasted_divmod0(self): with numpy.errstate(divide='ignore'): self.check_array_broadcasted_op(lambda x, y: divmod(x, y)[0], no_complex=True) def test_broadcasted_divmod1(self): with numpy.errstate(divide='ignore'): self.check_array_broadcasted_op(lambda x, y: divmod(x, y)[1], no_complex=True) def test_broadcasted_lt(self): self.check_array_broadcasted_op(operator.lt, no_complex=True) def test_broadcasted_le(self): self.check_array_broadcasted_op(operator.le, no_complex=True) def test_broadcasted_gt(self): self.check_array_broadcasted_op(operator.gt, no_complex=True) def test_broadcasted_ge(self): self.check_array_broadcasted_op(operator.ge, no_complex=True) def test_broadcasted_eq(self): self.check_array_broadcasted_op(operator.eq) def test_broadcasted_ne(self): self.check_array_broadcasted_op(operator.ne) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(rtol=1e-6) def check_array_doubly_broadcasted_op(self, op, xp, x_type, y_type, no_bool=False, no_complex=False): x_dtype = numpy.dtype(x_type) y_dtype = numpy.dtype(y_type) if no_bool and x_dtype == '?' and y_dtype == '?': return xp.array(True) if no_complex and (x_dtype.kind == 'c' or y_dtype.kind == 'c'): return xp.array(True) a = xp.array([[[1, 2, 3]], [[4, 5, 6]]], x_type) b = xp.array([[1], [2], [3]], y_type) return op(a, b) def test_doubly_broadcasted_add(self): self.check_array_doubly_broadcasted_op(operator.add) def test_doubly_broadcasted_sub(self): self.check_array_doubly_broadcasted_op(operator.sub, no_bool=True) def test_doubly_broadcasted_mul(self): self.check_array_doubly_broadcasted_op(operator.mul) def test_doubly_broadcasted_truediv(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_doubly_broadcasted_op(operator.truediv) def test_doubly_broadcasted_floordiv(self): with numpy.errstate(divide='ignore'): self.check_array_doubly_broadcasted_op(operator.floordiv, no_complex=True) def test_doubly_broadcasted_pow(self): self.check_array_doubly_broadcasted_op(operator.pow) def test_doubly_broadcasted_divmod0(self): with numpy.errstate(divide='ignore'): self.check_array_doubly_broadcasted_op( lambda x, y: divmod(x, y)[0], no_complex=True) def test_doubly_broadcasted_divmod1(self): with numpy.errstate(divide='ignore'): self.check_array_doubly_broadcasted_op( lambda x, y: divmod(x, y)[1], no_complex=True) def test_doubly_broadcasted_lt(self): self.check_array_doubly_broadcasted_op(operator.lt, no_complex=True) def test_doubly_broadcasted_le(self): self.check_array_doubly_broadcasted_op(operator.le, no_complex=True) def test_doubly_broadcasted_gt(self): self.check_array_doubly_broadcasted_op(operator.gt, no_complex=True) def test_doubly_broadcasted_ge(self): self.check_array_doubly_broadcasted_op(operator.ge, no_complex=True) def test_doubly_broadcasted_eq(self): self.check_array_doubly_broadcasted_op(operator.eq) def test_doubly_broadcasted_ne(self): self.check_array_doubly_broadcasted_op(operator.ne) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose() def check_array_reversed_op(self, op, xp, x_type, y_type, no_bool=False): if no_bool and x_type == numpy.bool_ and y_type == numpy.bool_: return xp.array(True) a = xp.array([1, 2, 3, 4, 5], x_type) b = xp.array([1, 2, 3, 4, 5], y_type) return op(a, b[::-1]) def test_array_reversed_add(self): self.check_array_reversed_op(operator.add) def test_array_reversed_sub(self): self.check_array_reversed_op(operator.sub, no_bool=True) def test_array_reversed_mul(self): self.check_array_reversed_op(operator.mul) @testing.for_all_dtypes(no_bool=True) def check_typecast(self, val, dtype): operators = [ operator.add, operator.sub, operator.mul, operator.truediv] for op in operators: with numpy.errstate(divide='ignore', invalid='ignore'): a = op(val, (testing.shaped_arange((5,), numpy, dtype) - 2)) b = op(val, (testing.shaped_arange((5,), cupy, dtype) - 2)) assert a.dtype == b.dtype def test_typecast_bool1(self): self.check_typecast(True) def test_typecast_bool2(self): self.check_typecast(False) def test_typecast_int1(self): self.check_typecast(0) def test_typecast_int2(self): self.check_typecast(-127) def test_typecast_int3(self): self.check_typecast(255) def test_typecast_int4(self): self.check_typecast(-32768) def test_typecast_int5(self): self.check_typecast(65535) def test_typecast_int6(self): self.check_typecast(-2147483648) def test_typecast_int7(self): self.check_typecast(4294967295) def test_typecast_float1(self): self.check_typecast(0.0) def test_typecast_float2(self): self.check_typecast(100000.0) # Skip float16 because of NumPy #19514 @testing.for_all_dtypes(name='x_type', no_float16=True) @testing.numpy_cupy_allclose() def check_array_boolarray_op(self, op, xp, x_type): a = xp.array([[2, 7, 1], [8, 2, 8]], x_type) # Cast from np.bool8 array should not read bytes b = xp.array([[3, 1, 4], [-1, -5, -9]], numpy.int8).view(bool) return op(a, b) def test_add_array_boolarray(self): self.check_array_boolarray_op(operator.add) def test_iadd_array_boolarray(self): self.check_array_boolarray_op(operator.iadd) class TestArrayIntElementwiseOp: @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_scalar_op(self, op, xp, x_type, y_type, swap=False): a = xp.array([[0, 1, 2], [1, 0, 2]], dtype=x_type) if swap: return op(y_type(2), a) else: return op(a, y_type(2)) def test_lshift_scalar(self): self.check_array_scalar_op(operator.lshift) def test_rlshift_scalar(self): self.check_array_scalar_op(operator.lshift, swap=True) def test_rshift_scalar(self): self.check_array_scalar_op(operator.rshift) def test_rrshift_scalar(self): self.check_array_scalar_op(operator.rshift, swap=True) def test_and_scalar(self): self.check_array_scalar_op(operator.and_) def test_rand_scalar(self): self.check_array_scalar_op(operator.and_, swap=True) def test_or_scalar(self): self.check_array_scalar_op(operator.or_) def test_ror_scalar(self): self.check_array_scalar_op(operator.or_, swap=True) def test_xor_scalar(self): self.check_array_scalar_op(operator.xor) def test_rxor_scalar(self): self.check_array_scalar_op(operator.xor, swap=True) def test_mod_scalar(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_scalar_op(operator.mod) def test_rmod_scalar(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_scalar_op(operator.mod, swap=True) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_scalarzero_op(self, op, xp, x_type, y_type, swap=False): a = xp.array([[0, 1, 2], [1, 0, 2]], dtype=x_type) if swap: return op(y_type(0), a) else: return op(a, y_type(0)) def test_lshift_scalarzero(self): self.check_array_scalarzero_op(operator.lshift) def test_rlshift_scalarzero(self): self.check_array_scalarzero_op(operator.lshift, swap=True) def test_rshift_scalarzero(self): self.check_array_scalarzero_op(operator.rshift) def test_rrshift_scalarzero(self): self.check_array_scalarzero_op(operator.rshift, swap=True) def test_and_scalarzero(self): self.check_array_scalarzero_op(operator.and_) def test_rand_scalarzero(self): self.check_array_scalarzero_op(operator.and_, swap=True) def test_or_scalarzero(self): self.check_array_scalarzero_op(operator.or_) def test_ror_scalarzero(self): self.check_array_scalarzero_op(operator.or_, swap=True) def test_xor_scalarzero(self): self.check_array_scalarzero_op(operator.xor) def test_rxor_scalarzero(self): self.check_array_scalarzero_op(operator.xor, swap=True) def test_mod_scalarzero(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_scalarzero_op(operator.mod) def test_rmod_scalarzero(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_scalarzero_op(operator.mod, swap=True) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_array_op(self, op, xp, x_type, y_type): a = xp.array([[0, 1, 2], [1, 0, 2]], dtype=x_type) b = xp.array([[0, 0, 1], [0, 1, 2]], dtype=y_type) return op(a, b) def test_lshift_array(self): self.check_array_array_op(operator.lshift) def test_ilshift_array(self): self.check_array_array_op(operator.ilshift) def test_rshift_array(self): self.check_array_array_op(operator.rshift) def test_irshift_array(self): self.check_array_array_op(operator.irshift) def test_and_array(self): self.check_array_array_op(operator.and_) def test_iand_array(self): self.check_array_array_op(operator.iand) def test_or_array(self): self.check_array_array_op(operator.or_) def test_ior_array(self): self.check_array_array_op(operator.ior) def test_xor_array(self): self.check_array_array_op(operator.xor) def test_ixor_array(self): self.check_array_array_op(operator.ixor) def test_mod_array(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_array_op(operator.mod) def test_imod_array(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_array_op(operator.imod) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_broadcasted_op(self, op, xp, x_type, y_type): a = xp.array([[0, 1, 2], [1, 0, 2], [2, 1, 0]], dtype=x_type) b = xp.array([[0, 0, 1]], dtype=y_type) return op(a, b) def test_broadcasted_lshift(self): self.check_array_broadcasted_op(operator.lshift) def test_broadcasted_ilshift(self): self.check_array_broadcasted_op(operator.ilshift) def test_broadcasted_rshift(self): self.check_array_broadcasted_op(operator.rshift) def test_broadcasted_irshift(self): self.check_array_broadcasted_op(operator.irshift) def test_broadcasted_and(self): self.check_array_broadcasted_op(operator.and_) def test_broadcasted_iand(self): self.check_array_broadcasted_op(operator.iand) def test_broadcasted_or(self): self.check_array_broadcasted_op(operator.or_) def test_broadcasted_ior(self): self.check_array_broadcasted_op(operator.ior) def test_broadcasted_xor(self): self.check_array_broadcasted_op(operator.xor) def test_broadcasted_ixor(self): self.check_array_broadcasted_op(operator.ixor) def test_broadcasted_mod(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_broadcasted_op(operator.mod) def test_broadcasted_imod(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_broadcasted_op(operator.imod) @testing.for_all_dtypes_combination(names=['x_type', 'y_type']) @testing.numpy_cupy_allclose(accept_error=TypeError) def check_array_doubly_broadcasted_op(self, op, xp, x_type, y_type): a = xp.array([[[0, 1, 2]], [[1, 0, 2]]], dtype=x_type) b = xp.array([[0], [0], [1]], dtype=y_type) return op(a, b) def test_doubly_broadcasted_lshift(self): self.check_array_doubly_broadcasted_op(operator.lshift) def test_doubly_broadcasted_rshift(self): self.check_array_doubly_broadcasted_op(operator.rshift) def test_doubly_broadcasted_and(self): self.check_array_doubly_broadcasted_op(operator.and_) def test_doubly_broadcasted_or(self): self.check_array_doubly_broadcasted_op(operator.or_) def test_doubly_broadcasted_xor(self): self.check_array_doubly_broadcasted_op(operator.xor) def test_doubly_broadcasted_mod(self): with numpy.errstate(divide='ignore', invalid='ignore'): self.check_array_doubly_broadcasted_op(operator.mod) @pytest.mark.parametrize('value', [ None, Ellipsis, object(), numpy._NoValue, ]) class TestArrayObjectComparison: @pytest.mark.parametrize('swap', [False, True]) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_eq_object(self, xp, dtype, value, swap): a = xp.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) if swap: return value == a else: return a == value @pytest.mark.parametrize('swap', [False, True]) @testing.for_all_dtypes() @testing.numpy_cupy_array_equal() def test_ne_object(self, xp, dtype, value, swap): a = xp.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) if swap: return value != a else: return a != value class HasEq: def __eq__(self, other): return (other == 2) | (other == 4) class HasNe: def __ne__(self, other): return (other == 2) | (other == 4) class HasEqSub(HasEq): pass class CustomInt(int): pass @pytest.mark.parametrize('dtype', ['int32', 'float64']) @pytest.mark.parametrize('value', [ HasEq(), HasNe(), # eq test passes because `==` does not fall back to `__ne__`. HasEqSub(), CustomInt(3), ]) class TestArrayObjectComparisonDifficult: # OK to raise TypeError. # If CuPy returns a result, it should match with NumPy's result. def test_eq_object(self, dtype, value): expected = numpy.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) == value a = cupy.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) try: res = a == value except TypeError: pytest.skip() cupy.testing.assert_array_equal(res, expected) def test_ne_object(self, dtype, value): expected = numpy.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) != value a = cupy.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) try: res = a != value except TypeError: pytest.skip() cupy.testing.assert_array_equal(res, expected)

Onkar627/cupy

tests/cupy_tests/core_tests/test_ndarray_elementwise_op.py

Python

def read_fasta(filename): """Returns a list of tuples of each header and sequence in a fasta (or multifasta) file. first element in tuple is header and second the sequence. Key Arguments: filename -- fasta file. """ tmp_seq = None seqs_list = [] with open(filename, 'r') as fasta_file: for line in fasta_file: line = line.replace('\n','') if '>' in line: if tmp_seq != None: seqs_list.append((hd, tmp_seq)) tmp_seq = '' hd = line.replace('>','') else: tmp_seq += line seqs_list.append((hd, tmp_seq)) try: assert len(seqs_list) > 0 except AssertionError: print('The selected file is not a Fasta file.') else: return seqs_list def write_fasta(outfile, seq_dict): """Writes fasta with dictionary where keys are headers and values sequences. Key Arguments: outfile. """ step = 70 with open(outfile, 'w') as file: for header, sequence in seq_dict.items(): sequence_list = [sequence[i - step: i] for i in range(step, len(sequence) + 1, step)] last = sequence[step * (len(sequence) // step):] if last != '': sequence_list.append(last) sequence = '\n'.join(sequence_list) file.write('>' + header + '\n' + sequence + '\n') def reads_generator(fasta_file, read_length, k): """This function simulates the reads generation from a fasta file with a coverage not less than 50. It will return a list of tuples. First element in tuple is read ID and second the sequence. Key Arguments: fasta_file -- fasta file. read_length -- size of reads. """ reads_list = [] overlap = k - 1 input_header, input_seq = read_fasta(fasta_file)[0] n = len(input_seq) for i in range(0, n - overlap, read_length - overlap): read_seq = input_seq[i: i + read_length] reads_list.append(read_seq) return [('{}_{}'.format(input_header, i), read) for i, read in enumerate(reads_list)] def write_fastq(reads_list, filename): """This function created a FASTQ file from a list of read generated by the reads_generator function. Key Arguments: reads_list -- list of reads generated with reads_generator. filename -- name of output file WITH EXTENSION. """ with open(filename, 'w') as fastq_file: for read_id, read in reads_list: fastq_file.write('@{}\n'.format(read_id)) fastq_file.write(read + '\n') fastq_file.write('+\n') fastq_file.write('I' * len(read) + '\n') # max possible score def read_fastq(filename): """This function reads a FASTQ file storing the read and its ID in a dictionary where keys are IDs and read value. This function does not consider + and score lines. Key Arguments: filename -- name of FASTQ input file. """ reads_dict = dict() with open(filename, 'r') as fastq_file: for line in fastq_file: if '@' in line: reads_dict[line[1:].replace('\n', '')] = next( fastq_file).replace('\n', '') next(fastq_file) next(fastq_file) return reads_dict

Mirindi95/PrIDcon

pridcon/utils.py

Python

from django import template from home.models import Recipe, MixingAgent, Base, Ingredient, FacePack, CustomFacePack import pdb register = template.Library() @register.inclusion_tag('facepack.html') def facepack_display(item_id): if not item_id: return mandatory = [] type = "primary" for cfp in CustomFacePack.objects.filter(facepack=item_id): ing = cfp.optional_ingredient if cfp.optional_ingredient else cfp.recipe.mandatory_ingredient mandatory.append({ 'name' : ing.name, 'id' : ing.id, 'r_id' : cfp.recipe.id, 'image' : ing.image, }) if cfp.optional_ingredient: type = "secondary" fp = FacePack.objects.get(pk=item_id) res = { 'item_id' : item_id, 'name' : fp.name, 'mandatory' : mandatory, 'base' : fp.base.name, 'mixing_agent' : fp.mixing_agent.name, 'image' : fp.image, 'type' : type, } return {'item': res } def facepack_display_abs(base_url, item_id): if not item_id: return mandatory = [] type = "primary" for cfp in CustomFacePack.objects.filter(facepack=item_id): ing = cfp.optional_ingredient if cfp.optional_ingredient else cfp.recipe.mandatory_ingredient mandatory.append({ 'name' : ing.name, 'id' : ing.id, 'r_id' : cfp.recipe.id, 'image' : ing.image, }) if cfp.optional_ingredient: type = "secondary" fp = FacePack.objects.get(pk=item_id) res = { 'item_id' : item_id, 'name' : fp.name, 'mandatory' : mandatory, 'base' : fp.base.name, 'mixing_agent' : fp.mixing_agent.name, 'image' : fp.image, 'type' : type, #'base_url' : request.get_raw_uri().replace(request.get_full_path(),''), 'base_url' : base_url, } return {'item': res }

dev1farms2face/f2f

f2f/farms2face/home/templatetags/common_tags.py

Python

# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. """ FlowClient is a Python client to FlowAPI. """ from ._version import get_versions __version__ = get_versions()["version"] del get_versions from flowclient.api_query import APIQuery from .connection import Connection from flowclient.client import connect from flowclient.async_api_query import ASyncAPIQuery from .async_connection import ASyncConnection from flowclient.async_client import connect_async from .client import ( get_geography, get_result, get_result_by_query_id, get_geojson_result, get_status, query_is_ready, run_query, get_available_dates, ) from .query_specs import ( daily_location_spec, modal_location_spec, modal_location_from_dates_spec, radius_of_gyration_spec, unique_location_counts_spec, topup_balance_spec, subscriber_degree_spec, topup_amount_spec, event_count_spec, displacement_spec, pareto_interactions_spec, nocturnal_events_spec, handset_spec, random_sample_spec, unique_locations_spec, most_frequent_location_spec, total_active_periods_spec, location_visits_spec, majority_location_spec, coalesced_location_spec, mobility_classification_spec, ) from . import aggregates from .aggregates import ( location_event_counts, meaningful_locations_aggregate, meaningful_locations_between_label_od_matrix, meaningful_locations_between_dates_od_matrix, flows, unique_subscriber_counts, location_introversion, total_network_objects, aggregate_network_objects, spatial_aggregate, joined_spatial_aggregate, histogram_aggregate, active_at_reference_location_counts, unmoving_at_reference_location_counts, unmoving_counts, consecutive_trips_od_matrix, trips_od_matrix, labelled_spatial_aggregate, labelled_flows, ) __all__ = [ "aggregates", "connect_async", "connect", "get_geography", "get_result", "get_result_by_query_id", "get_geojson_result", "get_status", "query_is_ready", "run_query", "get_available_dates", "APIQuery", "ASyncAPIQuery", "location_event_counts", "meaningful_locations_aggregate", "meaningful_locations_between_label_od_matrix", "meaningful_locations_between_dates_od_matrix", "flows", "unique_subscriber_counts", "location_introversion", "total_network_objects", "aggregate_network_objects", "spatial_aggregate", "joined_spatial_aggregate", "histogram_aggregate", "active_at_reference_location_counts", "unique_locations_spec", "unmoving_at_reference_location_counts", "unmoving_counts", "consecutive_trips_od_matrix", "trips_od_matrix", "labelled_spatial_aggregate", "labelled_flows", ]

Flowminder/FlowK

flowclient/flowclient/__init__.py

Python

import demistomock as demisto from CommonServerPython import * from CommonServerUserPython import * ''' IMPORTS ''' import requests # Disable insecure warnings requests.packages.urllib3.disable_warnings() API_KEY = demisto.getParam('APIKey') SERVER_URL = 'https://analyze.intezer.com/api' API_VERSION = '/v2-0' BASE_URL = SERVER_URL + API_VERSION IS_AVAILABLE_URL = 'is-available' ERROR_PREFIX = 'Error from Intezer:' ACCEPTABLE_HTTP_CODES = {200, 201, 202} USE_SSL = not demisto.params().get('insecure', False) http_status_to_error_massage = { 400: '400 Bad Request - Wrong or invalid parameters', 401: '401 Unauthorized - Wrong or invalid api key', 403: '403 Forbidden - The account is not allowed to preform this task', 404: '404 Not Found - Analysis was not found', 410: '410 Gone - Analysis no longer exists in the service', 500: '500 Internal Server Error - Internal error', 503: '503 Service Unavailable' } dbot_score_by_verdict = { 'malicious': 3, 'suspicious': 2, 'trusted': 1, 'neutral': 1, 'no_threats': 1 } ''' HELPER FUNCTIONS ''' def handle_response(response, acceptable_http_status_codes): if response.status_code not in acceptable_http_status_codes: error_msg = http_status_to_error_massage.get(response.status_code, "Failed to perform request") return_error(f'{ERROR_PREFIX} {error_msg}') try: return response.json() except json.decoder.JSONDecodeError: # This error is unlikely to happen, as the return code should indicate of error beforehand return_error(f'Response returned with no data. This might be an issue with Intezer.\nPlease try again later\n' f'Response content:\n{response.content}') def get_session(): response = requests.post(BASE_URL + '/get-access-token', json={'api_key': API_KEY}, verify=USE_SSL) response = handle_response(response, {200}) session = requests.session() session.headers['Authorization'] = f'Bearer {response["result"]}' return session ''' COMMANDS ''' def check_is_available(): url = f'{SERVER_URL}/{IS_AVAILABLE_URL}' result = SESSION.get(url, verify=USE_SSL) return 'ok' if result.json()['is_available'] else None def analyze_by_hash_command(): file_hash = demisto.getArg('file_hash') response = make_analyze_by_hash_request(file_hash) handle_analyze_by_hash_response(response, file_hash) def get_latest_result_command(): file_hash = demisto.getArg('file_hash') response = make_get_latest_report_request(file_hash) handle_get_latest_result_response(response, file_hash) def make_analyze_by_hash_request(file_hash): data = {'hash': file_hash} return SESSION.post(BASE_URL + '/analyze-by-hash', json=data, verify=USE_SSL) def make_get_latest_report_request(file_hash): return SESSION.get(f'{BASE_URL}/files/{file_hash}', verify=USE_SSL) def handle_analyze_by_hash_response(response, file_hash): if response.status_code == 404: dbot = { 'Vendor': 'Intezer', 'Type': 'hash', 'Indicator': file_hash, 'Score': 0 } hr = f'Hash {file_hash} does not exist on Intezer genome database' ec = {'DBotScore': dbot} return_outputs(hr, ec) return elif response.status_code == 400: return_error('File hash is not valid.\nIntezer file hash reputation supports only SHA-256, ' 'SHA-1 and MD5 hash formats.\n') handle_analyze_response(response) def handle_get_latest_result_response(response, file_hash): if response.status_code == 404: dbot = { 'Vendor': 'Intezer', 'Type': 'hash', 'Indicator': file_hash, 'Score': 0 } hr = f'Hash {file_hash} does not exist on Intezer genome database' ec = {'DBotScore': dbot} return_outputs(hr, ec) return elif response.status_code == 400: return_error('File hash is not valid.\nIntezer file hash reputation supports only SHA-256, ' 'SHA-1 and MD5 hash formats.\n') analysis_result = response.json() enrich_dbot_and_display_file_analysis_results(analysis_result['result']) def analyze_by_uploaded_file_command(): response = make_analyze_by_file_request(demisto.getArg('file_entry_id')) handle_analyze_response(response) def make_analyze_by_file_request(file_id): file_data = demisto.getFilePath(file_id) with open(file_data['path'], 'rb') as file_to_upload: files = {'file': (file_data['name'], file_to_upload)} return SESSION.post(BASE_URL + '/analyze', files=files, verify=USE_SSL) def handle_analyze_response(response): response = handle_response(response, ACCEPTABLE_HTTP_CODES) result_url = response['result_url'] analysis_id = result_url.rsplit('/', 1)[-1] context_json = {'Intezer.Analysis(obj.ID === val.ID)': {'ID': analysis_id, 'Status': 'Created', 'type': 'File'}} return_outputs('Analysis created successfully: {}'.format(analysis_id), context_json, response) def check_analysis_status_and_get_results_command(): analysis_type = demisto.args().get('analysis_type', 'File') analysis_ids = argToList(demisto.args().get('analysis_id')) indicator_name = demisto.args().get('indicator_name') for analysis_id in analysis_ids: response = make_analysis_status_request(analysis_id, analysis_type) analysis_result = handle_analysis_result(response) if analysis_result and analysis_type == 'Endpoint': enrich_dbot_and_display_endpoint_analysis_results(analysis_result, indicator_name) elif analysis_result and analysis_type == 'File': enrich_dbot_and_display_file_analysis_results(analysis_result) def make_analysis_status_request(analysis_id, analysis_type): analysis_endpoint = 'endpoint-analyses/' if analysis_type == 'Endpoint' else 'analyses/' result_url = f'{BASE_URL}/{analysis_endpoint}{analysis_id}' return SESSION.get(result_url, verify=USE_SSL) def handle_analysis_result(response): json_response = handle_response(response, ACCEPTABLE_HTTP_CODES) if response.status_code != 200: result_url = json_response['result_url'] analysis_id = result_url.rsplit('/', 1)[-1] context_json = {'Intezer.Analysis(val.ID === obj.ID)': {'ID': analysis_id, 'Status': 'InProgress'}} return_outputs('Analysis is still in progress', context_json) return return json_response['result'] def enrich_dbot_and_display_file_analysis_results(result): verdict = result.get('verdict') sha256 = result.get('sha256') analysis_id = result.get('analysis_id') dbot = { 'Vendor': 'Intezer', 'Type': 'hash', 'Indicator': sha256, 'Score': dbot_score_by_verdict.get(verdict, 0) } file = {'SHA256': sha256, 'Metadata': result, 'ExistsInIntezer': True} if verdict == 'malicious': file['Malicious'] = {'Vendor': 'Intezer'} presentable_result = '## Intezer File analysis result\n' presentable_result += f' SHA256: {sha256}\n' presentable_result += f' Verdict: **{verdict}** ({result["sub_verdict"]})\n' if 'family_name' in result: presentable_result += f'Family: **{result["family_name"]}**\n' presentable_result += f'[Analysis Link]({result["analysis_url"]})\n' demisto.results({ 'Type': entryTypes['note'], 'EntryContext': { outputPaths['dbotscore']: dbot, outputPaths['file']: file, 'Intezer.Analysis(val.ID === obj.ID)': {'ID': analysis_id, 'Status': 'Done'}}, 'HumanReadable': presentable_result, 'ContentsFormat': formats['json'], 'Contents': result }) def enrich_dbot_and_display_endpoint_analysis_results(result, indicator_name=None): verdict = result['verdict'] computer_name = result['computer_name'] analysis_id = result['analysis_id'] dbot = { 'Vendor': 'Intezer', 'Type': 'hostname', 'Indicator': indicator_name if indicator_name else computer_name, 'Score': dbot_score_by_verdict.get(verdict, 0) } endpoint = {'Metadata': result} presentable_result = '## Intezer Endpoint analysis result\n' presentable_result += f'Host Name: {computer_name}\n' presentable_result += f' Verdict: **{verdict}**\n' if result.get('families') is not None: presentable_result += f'Families: **{result["families"]}**\n' presentable_result += f' Scan Time: {result["scan_start_time"]}\n' presentable_result += f'[Analysis Link]({result["analysis_url"]})\n' ec = { 'DBotScore': dbot, 'Endpoint': endpoint, 'Intezer.Analysis(val.ID === obj.ID)': {'ID': analysis_id, 'Status': 'Done'} } return_outputs(presentable_result, ec, result) ''' EXECUTION CODE ''' try: SESSION = get_session() except Exception as e: return_error(str(e)) def main(): try: handle_proxy() if demisto.command() == 'test-module': demisto.results(check_is_available()) elif demisto.command() == 'intezer-analyze-by-hash': analyze_by_hash_command() elif demisto.command() == 'intezer-analyze-by-file': analyze_by_uploaded_file_command() elif demisto.command() == 'intezer-get-latest-report': get_latest_result_command() elif demisto.command() == 'intezer-get-analysis-result': check_analysis_status_and_get_results_command() except Exception as e: return_error(str(e)) # python2 uses __builtin__ python3 uses builtins if __name__ == "__builtin__" or __name__ == "builtins": main()

Axonius/conten

Packs/Intezer/Integrations/IntezerV2/IntezerV2.py

Python

from dataclasses import dataclass, field from typing import List, Optional @dataclass class Regex: att: Optional[str] = field( default=None, metadata={ "type": "Attribute", "pattern": r"[\C\?a-c\?]+", } ) @dataclass class Doc: class Meta: name = "doc" elem: List[Regex] = field( default_factory=list, metadata={ "type": "Element", "namespace": "", } )

tefra/xsdata-w3c-tests

output/models/ms_data/regex/re_g18_xsd/re_g18.py

Python

from django.db import models from django.contrib.auth.models import AbstractBaseUser, \ BaseUserManager, PermissionsMixin class UserManager(BaseUserManager): def create_user(self, email, password=None, **extra_fields): """Creates and saves a new user""" if not email: raise ValueError('User must have an email address') user = self.model(email=self.normalize_email(email), **extra_fields) user.set_password(password) user.save(using=self._db) return user def create_superuser(self, email, password): """Creates and saves a new super user""" user = self.create_user(email, password) user.is_superuser = True user.is_staff = True user.save(using=self._db) return user class User(AbstractBaseUser, PermissionsMixin): """Custom user model that supports using email instead of username""" email = models.EmailField(max_length=255, unique=True) name = models.CharField(max_length=255) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) objects = UserManager() USERNAME_FIELD = 'email'

StoikovOleh/recipe-app-api

app/core/models.py

Python

from django.apps import AppConfig class EnquiriesConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'src.enquiries'

kkamara/django-app

src/enquiries/apps.py

Python

# Copyright 2013 Hewlett-Packard Development Company, L.P. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Tests for manipulating Nodes via the DB API""" import datetime import mock from oslo_utils import timeutils from oslo_utils import uuidutils import six from ironic.common import exception from ironic.common import states from ironic.tests.unit.db import base from ironic.tests.unit.db import utils class DbNodeTestCase(base.DbTestCase): def test_create_node(self): node = utils.create_test_node() self.assertEqual([], node.tags) self.assertEqual([], node.traits) def test_create_node_with_tags(self): self.assertRaises(exception.InvalidParameterValue, utils.create_test_node, tags=['tag1', 'tag2']) def test_create_node_with_traits(self): self.assertRaises(exception.InvalidParameterValue, utils.create_test_node, traits=['trait1', 'trait2']) def test_create_node_already_exists(self): utils.create_test_node() self.assertRaises(exception.NodeAlreadyExists, utils.create_test_node) def test_create_node_instance_already_associated(self): instance = uuidutils.generate_uuid() utils.create_test_node(uuid=uuidutils.generate_uuid(), instance_uuid=instance) self.assertRaises(exception.InstanceAssociated, utils.create_test_node, uuid=uuidutils.generate_uuid(), instance_uuid=instance) def test_create_node_name_duplicate(self): node = utils.create_test_node(name='spam') self.assertRaises(exception.DuplicateName, utils.create_test_node, name=node.name) def test_get_node_by_id(self): node = utils.create_test_node() self.dbapi.set_node_tags(node.id, ['tag1', 'tag2']) utils.create_test_node_traits(node_id=node.id, traits=['trait1', 'trait2']) res = self.dbapi.get_node_by_id(node.id) self.assertEqual(node.id, res.id) self.assertEqual(node.uuid, res.uuid) self.assertItemsEqual(['tag1', 'tag2'], [tag.tag for tag in res.tags]) self.assertItemsEqual(['trait1', 'trait2'], [trait.trait for trait in res.traits]) def test_get_node_by_uuid(self): node = utils.create_test_node() self.dbapi.set_node_tags(node.id, ['tag1', 'tag2']) utils.create_test_node_traits(node_id=node.id, traits=['trait1', 'trait2']) res = self.dbapi.get_node_by_uuid(node.uuid) self.assertEqual(node.id, res.id) self.assertEqual(node.uuid, res.uuid) self.assertItemsEqual(['tag1', 'tag2'], [tag.tag for tag in res.tags]) self.assertItemsEqual(['trait1', 'trait2'], [trait.trait for trait in res.traits]) def test_get_node_by_name(self): node = utils.create_test_node() self.dbapi.set_node_tags(node.id, ['tag1', 'tag2']) utils.create_test_node_traits(node_id=node.id, traits=['trait1', 'trait2']) res = self.dbapi.get_node_by_name(node.name) self.assertEqual(node.id, res.id) self.assertEqual(node.uuid, res.uuid) self.assertEqual(node.name, res.name) self.assertItemsEqual(['tag1', 'tag2'], [tag.tag for tag in res.tags]) self.assertItemsEqual(['trait1', 'trait2'], [trait.trait for trait in res.traits]) def test_get_node_that_does_not_exist(self): self.assertRaises(exception.NodeNotFound, self.dbapi.get_node_by_id, 99) self.assertRaises(exception.NodeNotFound, self.dbapi.get_node_by_uuid, '12345678-9999-0000-aaaa-123456789012') self.assertRaises(exception.NodeNotFound, self.dbapi.get_node_by_name, 'spam-eggs-bacon-spam') def test_get_nodeinfo_list_defaults(self): node_id_list = [] for i in range(1, 6): node = utils.create_test_node(uuid=uuidutils.generate_uuid()) node_id_list.append(node.id) res = [i[0] for i in self.dbapi.get_nodeinfo_list()] self.assertEqual(sorted(res), sorted(node_id_list)) def test_get_nodeinfo_list_with_cols(self): uuids = {} extras = {} for i in range(1, 6): uuid = uuidutils.generate_uuid() extra = {'foo': i} node = utils.create_test_node(extra=extra, uuid=uuid) uuids[node.id] = uuid extras[node.id] = extra res = self.dbapi.get_nodeinfo_list(columns=['id', 'extra', 'uuid']) self.assertEqual(extras, dict((r[0], r[1]) for r in res)) self.assertEqual(uuids, dict((r[0], r[2]) for r in res)) def test_get_nodeinfo_list_with_filters(self): node1 = utils.create_test_node( driver='driver-one', instance_uuid=uuidutils.generate_uuid(), reservation='fake-host', uuid=uuidutils.generate_uuid()) node2 = utils.create_test_node( driver='driver-two', uuid=uuidutils.generate_uuid(), maintenance=True, fault='boom', resource_class='foo', conductor_group='group1') node3 = utils.create_test_node( driver='driver-one', uuid=uuidutils.generate_uuid(), reservation='another-fake-host') res = self.dbapi.get_nodeinfo_list(filters={'driver': 'driver-one'}) self.assertEqual(sorted([node1.id, node3.id]), sorted([r[0] for r in res])) res = self.dbapi.get_nodeinfo_list(filters={'driver': 'bad-driver'}) self.assertEqual([], [r[0] for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'associated': True}) self.assertEqual([node1.id], [r[0] for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'associated': False}) self.assertEqual(sorted([node2.id, node3.id]), sorted([r[0] for r in res])) res = self.dbapi.get_nodeinfo_list(filters={'reserved': True}) self.assertEqual(sorted([node1.id, node3.id]), sorted([r[0] for r in res])) res = self.dbapi.get_nodeinfo_list(filters={'reserved': False}) self.assertEqual([node2.id], [r[0] for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'maintenance': True}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'maintenance': False}) self.assertEqual(sorted([node1.id, node3.id]), sorted([r.id for r in res])) res = self.dbapi.get_nodeinfo_list(filters={'fault': 'boom'}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'fault': 'moob'}) self.assertEqual([], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'resource_class': 'foo'}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list( filters={'conductor_group': 'group1'}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list( filters={'conductor_group': 'group2'}) self.assertEqual([], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list( filters={'reserved_by_any_of': ['fake-host', 'another-fake-host']}) self.assertEqual(sorted([node1.id, node3.id]), sorted([r.id for r in res])) res = self.dbapi.get_nodeinfo_list(filters={'id': node1.id}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'uuid': node1.uuid}) self.assertEqual([node1.id], [r.id for r in res]) # ensure unknown filters explode filters = {'bad_filter': 'foo'} self.assertRaisesRegex(ValueError, 'bad_filter', self.dbapi.get_nodeinfo_list, filters=filters) # even with good filters present filters = {'bad_filter': 'foo', 'id': node1.id} self.assertRaisesRegex(ValueError, 'bad_filter', self.dbapi.get_nodeinfo_list, filters=filters) @mock.patch.object(timeutils, 'utcnow', autospec=True) def test_get_nodeinfo_list_provision(self, mock_utcnow): past = datetime.datetime(2000, 1, 1, 0, 0) next = past + datetime.timedelta(minutes=8) present = past + datetime.timedelta(minutes=10) mock_utcnow.return_value = past # node with provision_updated timeout node1 = utils.create_test_node(uuid=uuidutils.generate_uuid(), provision_updated_at=past) # node with None in provision_updated_at node2 = utils.create_test_node(uuid=uuidutils.generate_uuid(), provision_state=states.DEPLOYWAIT) # node without timeout utils.create_test_node(uuid=uuidutils.generate_uuid(), provision_updated_at=next) mock_utcnow.return_value = present res = self.dbapi.get_nodeinfo_list(filters={'provisioned_before': 300}) self.assertEqual([node1.id], [r[0] for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'provision_state': states.DEPLOYWAIT}) self.assertEqual([node2.id], [r[0] for r in res]) @mock.patch.object(timeutils, 'utcnow', autospec=True) def test_get_nodeinfo_list_inspection(self, mock_utcnow): past = datetime.datetime(2000, 1, 1, 0, 0) next = past + datetime.timedelta(minutes=8) present = past + datetime.timedelta(minutes=10) mock_utcnow.return_value = past # node with provision_updated timeout node1 = utils.create_test_node(uuid=uuidutils.generate_uuid(), inspection_started_at=past) # node with None in provision_updated_at node2 = utils.create_test_node(uuid=uuidutils.generate_uuid(), provision_state=states.INSPECTING) # node without timeout utils.create_test_node(uuid=uuidutils.generate_uuid(), inspection_started_at=next) mock_utcnow.return_value = present res = self.dbapi.get_nodeinfo_list( filters={'inspection_started_before': 300}) self.assertEqual([node1.id], [r[0] for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'provision_state': states.INSPECTING}) self.assertEqual([node2.id], [r[0] for r in res]) def test_get_nodeinfo_list_description(self): node1 = utils.create_test_node(uuid=uuidutils.generate_uuid(), description='Hello') node2 = utils.create_test_node(uuid=uuidutils.generate_uuid(), description='World!') res = self.dbapi.get_nodeinfo_list( filters={'description_contains': 'Hello'}) self.assertEqual([node1.id], [r[0] for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'description_contains': 'World!'}) self.assertEqual([node2.id], [r[0] for r in res]) def test_get_node_list(self): uuids = [] for i in range(1, 6): node = utils.create_test_node(uuid=uuidutils.generate_uuid()) uuids.append(six.text_type(node['uuid'])) res = self.dbapi.get_node_list() res_uuids = [r.uuid for r in res] six.assertCountEqual(self, uuids, res_uuids) for r in res: self.assertEqual([], r.tags) self.assertEqual([], r.traits) def test_get_node_list_with_filters(self): ch1 = utils.create_test_chassis(uuid=uuidutils.generate_uuid()) ch2 = utils.create_test_chassis(uuid=uuidutils.generate_uuid()) node1 = utils.create_test_node( driver='driver-one', instance_uuid=uuidutils.generate_uuid(), reservation='fake-host', uuid=uuidutils.generate_uuid(), chassis_id=ch1['id']) node2 = utils.create_test_node( driver='driver-two', uuid=uuidutils.generate_uuid(), chassis_id=ch2['id'], maintenance=True, fault='boom', resource_class='foo', conductor_group='group1', power_state='power on') res = self.dbapi.get_node_list(filters={'chassis_uuid': ch1['uuid']}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'chassis_uuid': ch2['uuid']}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'driver': 'driver-one'}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'driver': 'bad-driver'}) self.assertEqual([], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'associated': True}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'associated': False}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'reserved': True}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'reserved': False}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'maintenance': True}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'maintenance': False}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'fault': 'boom'}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_nodeinfo_list(filters={'fault': 'moob'}) self.assertEqual([], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'resource_class': 'foo'}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'conductor_group': 'group1'}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'conductor_group': 'group2'}) self.assertEqual([], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'id': node1.id}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'uuid': node1.uuid}) self.assertEqual([node1.id], [r.id for r in res]) uuids = [uuidutils.generate_uuid(), node1.uuid, uuidutils.generate_uuid()] res = self.dbapi.get_node_list(filters={'uuid_in': uuids}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'with_power_state': True}) self.assertEqual([node2.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={'with_power_state': False}) self.assertEqual([node1.id], [r.id for r in res]) # ensure unknown filters explode filters = {'bad_filter': 'foo'} self.assertRaisesRegex(ValueError, 'bad_filter', self.dbapi.get_node_list, filters=filters) # even with good filters present filters = {'bad_filter': 'foo', 'id': node1.id} self.assertRaisesRegex(ValueError, 'bad_filter', self.dbapi.get_node_list, filters=filters) def test_get_node_list_description(self): node1 = utils.create_test_node(uuid=uuidutils.generate_uuid(), description='Hello') node2 = utils.create_test_node(uuid=uuidutils.generate_uuid(), description='World!') res = self.dbapi.get_node_list(filters={ 'description_contains': 'Hello'}) self.assertEqual([node1.id], [r.id for r in res]) res = self.dbapi.get_node_list(filters={ 'description_contains': 'World!'}) self.assertEqual([node2.id], [r.id for r in res]) def test_get_node_list_chassis_not_found(self): self.assertRaises(exception.ChassisNotFound, self.dbapi.get_node_list, {'chassis_uuid': uuidutils.generate_uuid()}) def test_get_node_by_instance(self): node = utils.create_test_node( instance_uuid='12345678-9999-0000-aaaa-123456789012') self.dbapi.set_node_tags(node.id, ['tag1', 'tag2']) utils.create_test_node_traits(node_id=node.id, traits=['trait1', 'trait2']) res = self.dbapi.get_node_by_instance(node.instance_uuid) self.assertEqual(node.uuid, res.uuid) self.assertItemsEqual(['tag1', 'tag2'], [tag.tag for tag in res.tags]) self.assertItemsEqual(['trait1', 'trait2'], [trait.trait for trait in res.traits]) def test_get_node_by_instance_wrong_uuid(self): utils.create_test_node( instance_uuid='12345678-9999-0000-aaaa-123456789012') self.assertRaises(exception.InstanceNotFound, self.dbapi.get_node_by_instance, '12345678-9999-0000-bbbb-123456789012') def test_get_node_by_instance_invalid_uuid(self): self.assertRaises(exception.InvalidUUID, self.dbapi.get_node_by_instance, 'fake_uuid') def test_destroy_node(self): node = utils.create_test_node() self.dbapi.destroy_node(node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.get_node_by_id, node.id) def test_destroy_node_by_uuid(self): node = utils.create_test_node() self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.NodeNotFound, self.dbapi.get_node_by_uuid, node.uuid) def test_destroy_node_that_does_not_exist(self): self.assertRaises(exception.NodeNotFound, self.dbapi.destroy_node, '12345678-9999-0000-aaaa-123456789012') def test_ports_get_destroyed_after_destroying_a_node(self): node = utils.create_test_node() port = utils.create_test_port(node_id=node.id) self.dbapi.destroy_node(node.id) self.assertRaises(exception.PortNotFound, self.dbapi.get_port_by_id, port.id) def test_ports_get_destroyed_after_destroying_a_node_by_uuid(self): node = utils.create_test_node() port = utils.create_test_port(node_id=node.id) self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.PortNotFound, self.dbapi.get_port_by_id, port.id) def test_tags_get_destroyed_after_destroying_a_node(self): node = utils.create_test_node() tag = utils.create_test_node_tag(node_id=node.id) self.assertTrue(self.dbapi.node_tag_exists(node.id, tag.tag)) self.dbapi.destroy_node(node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.node_tag_exists, node.id, tag.tag) def test_tags_get_destroyed_after_destroying_a_node_by_uuid(self): node = utils.create_test_node() tag = utils.create_test_node_tag(node_id=node.id) self.assertTrue(self.dbapi.node_tag_exists(node.id, tag.tag)) self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.NodeNotFound, self.dbapi.node_tag_exists, node.id, tag.tag) def test_volume_connector_get_destroyed_after_destroying_a_node(self): node = utils.create_test_node() connector = utils.create_test_volume_connector(node_id=node.id) self.dbapi.destroy_node(node.id) self.assertRaises(exception.VolumeConnectorNotFound, self.dbapi.get_volume_connector_by_id, connector.id) def test_volume_connector_get_destroyed_after_destroying_a_node_uuid(self): node = utils.create_test_node() connector = utils.create_test_volume_connector(node_id=node.id) self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.VolumeConnectorNotFound, self.dbapi.get_volume_connector_by_id, connector.id) def test_volume_target_gets_destroyed_after_destroying_a_node(self): node = utils.create_test_node() target = utils.create_test_volume_target(node_id=node.id) self.dbapi.destroy_node(node.id) self.assertRaises(exception.VolumeTargetNotFound, self.dbapi.get_volume_target_by_id, target.id) def test_volume_target_gets_destroyed_after_destroying_a_node_uuid(self): node = utils.create_test_node() target = utils.create_test_volume_target(node_id=node.id) self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.VolumeTargetNotFound, self.dbapi.get_volume_target_by_id, target.id) def test_traits_get_destroyed_after_destroying_a_node(self): node = utils.create_test_node() trait = utils.create_test_node_trait(node_id=node.id) self.assertTrue(self.dbapi.node_trait_exists(node.id, trait.trait)) self.dbapi.destroy_node(node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.node_trait_exists, node.id, trait.trait) def test_traits_get_destroyed_after_destroying_a_node_by_uuid(self): node = utils.create_test_node() trait = utils.create_test_node_trait(node_id=node.id) self.assertTrue(self.dbapi.node_trait_exists(node.id, trait.trait)) self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.NodeNotFound, self.dbapi.node_trait_exists, node.id, trait.trait) def test_allocations_get_destroyed_after_destroying_a_node_by_uuid(self): node = utils.create_test_node() allocation = utils.create_test_allocation(node_id=node.id) self.dbapi.destroy_node(node.uuid) self.assertRaises(exception.AllocationNotFound, self.dbapi.get_allocation_by_id, allocation.id) def test_update_node(self): node = utils.create_test_node() old_extra = node.extra new_extra = {'foo': 'bar'} self.assertNotEqual(old_extra, new_extra) res = self.dbapi.update_node(node.id, {'extra': new_extra}) self.assertEqual(new_extra, res.extra) self.assertEqual([], res.tags) self.assertEqual([], res.traits) def test_update_node_with_tags(self): node = utils.create_test_node() tag = utils.create_test_node_tag(node_id=node.id) old_extra = node.extra new_extra = {'foo': 'bar'} self.assertNotEqual(old_extra, new_extra) res = self.dbapi.update_node(node.id, {'extra': new_extra}) self.assertEqual([tag.tag], [t.tag for t in res.tags]) def test_update_node_with_traits(self): node = utils.create_test_node() trait = utils.create_test_node_trait(node_id=node.id) old_extra = node.extra new_extra = {'foo': 'bar'} self.assertNotEqual(old_extra, new_extra) res = self.dbapi.update_node(node.id, {'extra': new_extra}) self.assertEqual([trait.trait], [t.trait for t in res.traits]) def test_update_node_not_found(self): node_uuid = uuidutils.generate_uuid() new_extra = {'foo': 'bar'} self.assertRaises(exception.NodeNotFound, self.dbapi.update_node, node_uuid, {'extra': new_extra}) def test_update_node_uuid(self): node = utils.create_test_node() self.assertRaises(exception.InvalidParameterValue, self.dbapi.update_node, node.id, {'uuid': ''}) def test_update_node_associate_and_disassociate(self): node = utils.create_test_node() new_i_uuid = uuidutils.generate_uuid() res = self.dbapi.update_node(node.id, {'instance_uuid': new_i_uuid}) self.assertEqual(new_i_uuid, res.instance_uuid) res = self.dbapi.update_node(node.id, {'instance_uuid': None}) self.assertIsNone(res.instance_uuid) def test_update_node_instance_already_associated(self): node1 = utils.create_test_node(uuid=uuidutils.generate_uuid()) new_i_uuid = uuidutils.generate_uuid() self.dbapi.update_node(node1.id, {'instance_uuid': new_i_uuid}) node2 = utils.create_test_node(uuid=uuidutils.generate_uuid()) self.assertRaises(exception.InstanceAssociated, self.dbapi.update_node, node2.id, {'instance_uuid': new_i_uuid}) @mock.patch.object(timeutils, 'utcnow', autospec=True) def test_update_node_provision(self, mock_utcnow): mocked_time = datetime.datetime(2000, 1, 1, 0, 0) mock_utcnow.return_value = mocked_time node = utils.create_test_node() res = self.dbapi.update_node(node.id, {'provision_state': 'fake'}) self.assertEqual(mocked_time, timeutils.normalize_time(res['provision_updated_at'])) def test_update_node_name_duplicate(self): node1 = utils.create_test_node(uuid=uuidutils.generate_uuid(), name='spam') node2 = utils.create_test_node(uuid=uuidutils.generate_uuid()) self.assertRaises(exception.DuplicateName, self.dbapi.update_node, node2.id, {'name': node1.name}) def test_update_node_no_provision(self): node = utils.create_test_node() res = self.dbapi.update_node(node.id, {'extra': {'foo': 'bar'}}) self.assertIsNone(res['provision_updated_at']) self.assertIsNone(res['inspection_started_at']) @mock.patch.object(timeutils, 'utcnow', autospec=True) def test_update_node_inspection_started_at(self, mock_utcnow): mocked_time = datetime.datetime(2000, 1, 1, 0, 0) mock_utcnow.return_value = mocked_time node = utils.create_test_node(uuid=uuidutils.generate_uuid(), inspection_started_at=mocked_time) res = self.dbapi.update_node(node.id, {'provision_state': 'fake'}) result = res['inspection_started_at'] self.assertEqual(mocked_time, timeutils.normalize_time(result)) self.assertIsNone(res['inspection_finished_at']) @mock.patch.object(timeutils, 'utcnow', autospec=True) def test_update_node_inspection_finished_at(self, mock_utcnow): mocked_time = datetime.datetime(2000, 1, 1, 0, 0) mock_utcnow.return_value = mocked_time node = utils.create_test_node(uuid=uuidutils.generate_uuid(), inspection_finished_at=mocked_time) res = self.dbapi.update_node(node.id, {'provision_state': 'fake'}) result = res['inspection_finished_at'] self.assertEqual(mocked_time, timeutils.normalize_time(result)) self.assertIsNone(res['inspection_started_at']) def test_reserve_node(self): node = utils.create_test_node() self.dbapi.set_node_tags(node.id, ['tag1', 'tag2']) utils.create_test_node_traits(node_id=node.id, traits=['trait1', 'trait2']) uuid = node.uuid r1 = 'fake-reservation' # reserve the node res = self.dbapi.reserve_node(r1, uuid) self.assertItemsEqual(['tag1', 'tag2'], [tag.tag for tag in res.tags]) self.assertItemsEqual(['trait1', 'trait2'], [trait.trait for trait in res.traits]) # check reservation res = self.dbapi.get_node_by_uuid(uuid) self.assertEqual(r1, res.reservation) def test_release_reservation(self): node = utils.create_test_node() uuid = node.uuid r1 = 'fake-reservation' self.dbapi.reserve_node(r1, uuid) # release reservation self.dbapi.release_node(r1, uuid) res = self.dbapi.get_node_by_uuid(uuid) self.assertIsNone(res.reservation) def test_reservation_of_reserved_node_fails(self): node = utils.create_test_node() uuid = node.uuid r1 = 'fake-reservation' r2 = 'another-reservation' # reserve the node self.dbapi.reserve_node(r1, uuid) # another host fails to reserve or release self.assertRaises(exception.NodeLocked, self.dbapi.reserve_node, r2, uuid) self.assertRaises(exception.NodeLocked, self.dbapi.release_node, r2, uuid) def test_reservation_after_release(self): node = utils.create_test_node() uuid = node.uuid r1 = 'fake-reservation' r2 = 'another-reservation' self.dbapi.reserve_node(r1, uuid) self.dbapi.release_node(r1, uuid) # another host succeeds self.dbapi.reserve_node(r2, uuid) res = self.dbapi.get_node_by_uuid(uuid) self.assertEqual(r2, res.reservation) def test_reservation_in_exception_message(self): node = utils.create_test_node() uuid = node.uuid r = 'fake-reservation' self.dbapi.reserve_node(r, uuid) exc = self.assertRaises(exception.NodeLocked, self.dbapi.reserve_node, 'another', uuid) self.assertIn(r, str(exc)) def test_reservation_non_existent_node(self): node = utils.create_test_node() self.dbapi.destroy_node(node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.reserve_node, 'fake', node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.reserve_node, 'fake', node.uuid) def test_release_non_existent_node(self): node = utils.create_test_node() self.dbapi.destroy_node(node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.release_node, 'fake', node.id) self.assertRaises(exception.NodeNotFound, self.dbapi.release_node, 'fake', node.uuid) def test_release_non_locked_node(self): node = utils.create_test_node() self.assertIsNone(node.reservation) self.assertRaises(exception.NodeNotLocked, self.dbapi.release_node, 'fake', node.id) self.assertRaises(exception.NodeNotLocked, self.dbapi.release_node, 'fake', node.uuid) @mock.patch.object(timeutils, 'utcnow', autospec=True) def test_touch_node_provisioning(self, mock_utcnow): test_time = datetime.datetime(2000, 1, 1, 0, 0) mock_utcnow.return_value = test_time node = utils.create_test_node() # assert provision_updated_at is None self.assertIsNone(node.provision_updated_at) self.dbapi.touch_node_provisioning(node.uuid) node = self.dbapi.get_node_by_uuid(node.uuid) # assert provision_updated_at has been updated self.assertEqual(test_time, timeutils.normalize_time(node.provision_updated_at)) def test_touch_node_provisioning_not_found(self): self.assertRaises( exception.NodeNotFound, self.dbapi.touch_node_provisioning, uuidutils.generate_uuid()) def test_get_node_by_port_addresses(self): wrong_node = utils.create_test_node( driver='driver-one', uuid=uuidutils.generate_uuid()) node = utils.create_test_node( driver='driver-two', uuid=uuidutils.generate_uuid()) addresses = [] for i in (1, 2, 3): address = '52:54:00:cf:2d:4%s' % i utils.create_test_port(uuid=uuidutils.generate_uuid(), node_id=node.id, address=address) if i > 1: addresses.append(address) utils.create_test_port(uuid=uuidutils.generate_uuid(), node_id=wrong_node.id, address='aa:bb:cc:dd:ee:ff') res = self.dbapi.get_node_by_port_addresses(addresses) self.assertEqual(node.uuid, res.uuid) self.assertEqual([], res.traits) def test_get_node_by_port_addresses_not_found(self): node = utils.create_test_node( driver='driver', uuid=uuidutils.generate_uuid()) utils.create_test_port(uuid=uuidutils.generate_uuid(), node_id=node.id, address='aa:bb:cc:dd:ee:ff') self.assertRaisesRegex(exception.NodeNotFound, 'was not found', self.dbapi.get_node_by_port_addresses, ['11:22:33:44:55:66']) def test_get_node_by_port_addresses_multiple_found(self): node1 = utils.create_test_node( driver='driver', uuid=uuidutils.generate_uuid()) node2 = utils.create_test_node( driver='driver', uuid=uuidutils.generate_uuid()) addresses = ['52:54:00:cf:2d:4%s' % i for i in (1, 2)] utils.create_test_port(uuid=uuidutils.generate_uuid(), node_id=node1.id, address=addresses[0]) utils.create_test_port(uuid=uuidutils.generate_uuid(), node_id=node2.id, address=addresses[1]) self.assertRaisesRegex(exception.NodeNotFound, 'Multiple nodes', self.dbapi.get_node_by_port_addresses, addresses)

Rachit7194/ironic

ironic/tests/unit/db/test_nodes.py

Python

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import print_function import sys import warnings from functools import reduce from threading import RLock if sys.version >= '3': basestring = unicode = str else: from itertools import imap as map from pyspark import since from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.sql.catalog import Catalog from pyspark.sql.conf import RuntimeConfig from pyspark.sql.dataframe import DataFrame from pyspark.sql.readwriter import DataFrameReader from pyspark.sql.streaming import DataStreamReader from pyspark.sql.types import Row, DataType, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string from pyspark.sql.utils import install_exception_handler __all__ = ["SparkSession"] def _monkey_patch_RDD(sparkSession): def toDF(self, schema=None, sampleRatio=None): """ Converts current :class:`RDD` into a :class:`DataFrame` This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)`` :param schema: a :class:`pyspark.sql.types.StructType` or list of names of columns :param samplingRatio: the sample ratio of rows used for inferring :return: a DataFrame >>> rdd.toDF().collect() [Row(name=u'Alice', age=1)] """ return sparkSession.createDataFrame(self, schema, sampleRatio) RDD.toDF = toDF class SparkSession(object): """The entry point to programming Spark with the Dataset and DataFrame API. A SparkSession can be used create :class:`DataFrame`, register :class:`DataFrame` as tables, execute SQL over tables, cache tables, and read parquet files. To create a SparkSession, use the following builder pattern: >>> spark = SparkSession.builder \\ ... .master("local") \\ ... .appName("Word Count") \\ ... .config("spark.some.config.option", "some-value") \\ ... .getOrCreate() """ class Builder(object): """Builder for :class:`SparkSession`. """ _lock = RLock() _options = {} @since(2.0) def config(self, key=None, value=None, conf=None): """Sets a config option. Options set using this method are automatically propagated to both :class:`SparkConf` and :class:`SparkSession`'s own configuration. For an existing SparkConf, use `conf` parameter. >>> from pyspark.conf import SparkConf >>> SparkSession.builder.config(conf=SparkConf()) <pyspark.sql.session... For a (key, value) pair, you can omit parameter names. >>> SparkSession.builder.config("spark.some.config.option", "some-value") <pyspark.sql.session... :param key: a key name string for configuration property :param value: a value for configuration property :param conf: an instance of :class:`SparkConf` """ with self._lock: if conf is None: self._options[key] = str(value) else: for (k, v) in conf.getAll(): self._options[k] = v return self @since(2.0) def master(self, master): """Sets the Spark master URL to connect to, such as "local" to run locally, "local[4]" to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster. :param master: a url for spark master """ return self.config("spark.master", master) @since(2.0) def appName(self, name): """Sets a name for the application, which will be shown in the Spark web UI. If no application name is set, a randomly generated name will be used. :param name: an application name """ return self.config("spark.app.name", name) @since(2.0) def enableHiveSupport(self): """Enables Hive support, including connectivity to a persistent Hive metastore, support for Hive serdes, and Hive user-defined functions. """ return self.config("spark.sql.catalogImplementation", "hive") @since(2.0) def getOrCreate(self): """Gets an existing :class:`SparkSession` or, if there is no existing one, creates a new one based on the options set in this builder. This method first checks whether there is a valid global default SparkSession, and if yes, return that one. If no valid global default SparkSession exists, the method creates a new SparkSession and assigns the newly created SparkSession as the global default. >>> s1 = SparkSession.builder.config("k1", "v1").getOrCreate() >>> s1.conf.get("k1") == s1.sparkContext.getConf().get("k1") == "v1" True In case an existing SparkSession is returned, the config options specified in this builder will be applied to the existing SparkSession. >>> s2 = SparkSession.builder.config("k2", "v2").getOrCreate() >>> s1.conf.get("k1") == s2.conf.get("k1") True >>> s1.conf.get("k2") == s2.conf.get("k2") True """ with self._lock: from pyspark.context import SparkContext from pyspark.conf import SparkConf session = SparkSession._instantiatedSession if session is None or session._sc._jsc is None: sparkConf = SparkConf() for key, value in self._options.items(): sparkConf.set(key, value) sc = SparkContext.getOrCreate(sparkConf) # This SparkContext may be an existing one. for key, value in self._options.items(): # we need to propagate the confs # before we create the SparkSession. Otherwise, confs like # warehouse path and metastore url will not be set correctly ( # these confs cannot be changed once the SparkSession is created). sc._conf.set(key, value) session = SparkSession(sc) for key, value in self._options.items(): session.conf.set(key, value) for key, value in self._options.items(): session.sparkContext._conf.set(key, value) return session builder = Builder() _instantiatedSession = None @ignore_unicode_prefix def __init__(self, sparkContext, jsparkSession=None): """Creates a new SparkSession. >>> from datetime import datetime >>> spark = SparkSession(sc) >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1, ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), ... time=datetime(2014, 8, 1, 14, 1, 5))]) >>> df = allTypes.toDF() >>> df.createOrReplaceTempView("allTypes") >>> spark.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' ... 'from allTypes where b and i > 0').collect() [Row((i + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \ dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)] >>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect() [(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ from pyspark.sql.context import SQLContext self._sc = sparkContext self._jsc = self._sc._jsc self._jvm = self._sc._jvm if jsparkSession is None: jsparkSession = self._jvm.SparkSession(self._jsc.sc()) self._jsparkSession = jsparkSession self._jwrapped = self._jsparkSession.sqlContext() self._wrapped = SQLContext(self._sc, self, self._jwrapped) _monkey_patch_RDD(self) install_exception_handler() # If we had an instantiated SparkSession attached with a SparkContext # which is stopped now, we need to renew the instantiated SparkSession. # Otherwise, we will use invalid SparkSession when we call Builder.getOrCreate. if SparkSession._instantiatedSession is None \ or SparkSession._instantiatedSession._sc._jsc is None: SparkSession._instantiatedSession = self @since(2.0) def newSession(self): """ Returns a new SparkSession as new session, that has separate SQLConf, registered temporary views and UDFs, but shared SparkContext and table cache. """ return self.__class__(self._sc, self._jsparkSession.newSession()) @property @since(2.0) def sparkContext(self): """Returns the underlying :class:`SparkContext`.""" return self._sc @property @since(2.0) def version(self): """The version of Spark on which this application is running.""" return self._jsparkSession.version() @property @since(2.0) def conf(self): """Runtime configuration interface for Spark. This is the interface through which the user can get and set all Spark and Hadoop configurations that are relevant to Spark SQL. When getting the value of a config, this defaults to the value set in the underlying :class:`SparkContext`, if any. """ if not hasattr(self, "_conf"): self._conf = RuntimeConfig(self._jsparkSession.conf()) return self._conf @property @since(2.0) def catalog(self): """Interface through which the user may create, drop, alter or query underlying databases, tables, functions etc. """ if not hasattr(self, "_catalog"): self._catalog = Catalog(self) return self._catalog @property @since(2.0) def udf(self): """Returns a :class:`UDFRegistration` for UDF registration. :return: :class:`UDFRegistration` """ from pyspark.sql.context import UDFRegistration return UDFRegistration(self._wrapped) @since(2.0) def range(self, start, end=None, step=1, numPartitions=None): """ Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named ``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with step value ``step``. :param start: the start value :param end: the end value (exclusive) :param step: the incremental step (default: 1) :param numPartitions: the number of partitions of the DataFrame :return: :class:`DataFrame` >>> spark.range(1, 7, 2).collect() [Row(id=1), Row(id=3), Row(id=5)] If only one argument is specified, it will be used as the end value. >>> spark.range(3).collect() [Row(id=0), Row(id=1), Row(id=2)] """ if numPartitions is None: numPartitions = self._sc.defaultParallelism if end is None: jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions)) else: jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions)) return DataFrame(jdf, self._wrapped) def _inferSchemaFromList(self, data): """ Infer schema from list of Row or tuple. :param data: list of Row or tuple :return: :class:`pyspark.sql.types.StructType` """ if not data: raise ValueError("can not infer schema from empty dataset") first = data[0] if type(first) is dict: warnings.warn("inferring schema from dict is deprecated," "please use pyspark.sql.Row instead") schema = reduce(_merge_type, map(_infer_schema, data)) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") return schema def _inferSchema(self, rdd, samplingRatio=None): """ Infer schema from an RDD of Row or tuple. :param rdd: an RDD of Row or tuple :param samplingRatio: sampling ratio, or no sampling (default) :return: :class:`pyspark.sql.types.StructType` """ first = rdd.first() if not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") if type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated. " "Use pyspark.sql.Row instead") if samplingRatio is None: schema = _infer_schema(first) if _has_nulltype(schema): for row in rdd.take(100)[1:]: schema = _merge_type(schema, _infer_schema(row)) if not _has_nulltype(schema): break else: raise ValueError("Some of types cannot be determined by the " "first 100 rows, please try again with sampling") else: if samplingRatio < 0.99: rdd = rdd.sample(False, float(samplingRatio)) schema = rdd.map(_infer_schema).reduce(_merge_type) return schema def _createFromRDD(self, rdd, schema, samplingRatio): """ Create an RDD for DataFrame from an existing RDD, returns the RDD and schema. """ if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchema(rdd, samplingRatio) converter = _create_converter(struct) rdd = rdd.map(converter) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) # convert python objects to sql data rdd = rdd.map(schema.toInternal) return rdd, schema def _createFromLocal(self, data, schema): """ Create an RDD for DataFrame from a list or pandas.DataFrame, returns the RDD and schema. """ # make sure data could consumed multiple times if not isinstance(data, list): data = list(data) if schema is None or isinstance(schema, (list, tuple)): struct = self._inferSchemaFromList(data) converter = _create_converter(struct) data = map(converter, data) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif not isinstance(schema, StructType): raise TypeError("schema should be StructType or list or None, but got: %s" % schema) # convert python objects to sql data data = [schema.toInternal(row) for row in data] return self._sc.parallelize(data), schema @since(2.0) @ignore_unicode_prefix def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True): """ Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`. When ``schema`` is a list of column names, the type of each column will be inferred from ``data``. When ``schema`` is ``None``, it will try to infer the schema (column names and types) from ``data``, which should be an RDD of :class:`Row`, or :class:`namedtuple`, or :class:`dict`. When ``schema`` is :class:`pyspark.sql.types.DataType` or :class:`pyspark.sql.types.StringType`, it must match the real data, or an exception will be thrown at runtime. If the given schema is not :class:`pyspark.sql.types.StructType`, it will be wrapped into a :class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value", each record will also be wrapped into a tuple, which can be converted to row later. If schema inference is needed, ``samplingRatio`` is used to determined the ratio of rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``. :param data: an RDD of any kind of SQL data representation(e.g. row, tuple, int, boolean, etc.), or :class:`list`, or :class:`pandas.DataFrame`. :param schema: a :class:`pyspark.sql.types.DataType` or a :class:`pyspark.sql.types.StringType` or a list of column names, default is ``None``. The data type string format equals to :class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use ``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`. We can also use ``int`` as a short name for ``IntegerType``. :param samplingRatio: the sample ratio of rows used for inferring :param verifySchema: verify data types of every row against schema. :return: :class:`DataFrame` .. versionchanged:: 2.0.1 Added verifySchema. >>> l = [('Alice', 1)] >>> spark.createDataFrame(l).collect() [Row(_1=u'Alice', _2=1)] >>> spark.createDataFrame(l, ['name', 'age']).collect() [Row(name=u'Alice', age=1)] >>> d = [{'name': 'Alice', 'age': 1}] >>> spark.createDataFrame(d).collect() [Row(age=1, name=u'Alice')] >>> rdd = sc.parallelize(l) >>> spark.createDataFrame(rdd).collect() [Row(_1=u'Alice', _2=1)] >>> df = spark.createDataFrame(rdd, ['name', 'age']) >>> df.collect() [Row(name=u'Alice', age=1)] >>> from pyspark.sql import Row >>> Person = Row('name', 'age') >>> person = rdd.map(lambda r: Person(*r)) >>> df2 = spark.createDataFrame(person) >>> df2.collect() [Row(name=u'Alice', age=1)] >>> from pyspark.sql.types import * >>> schema = StructType([ ... StructField("name", StringType(), True), ... StructField("age", IntegerType(), True)]) >>> df3 = spark.createDataFrame(rdd, schema) >>> df3.collect() [Row(name=u'Alice', age=1)] >>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP [Row(name=u'Alice', age=1)] >>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP [Row(0=1, 1=2)] >>> spark.createDataFrame(rdd, "a: string, b: int").collect() [Row(a=u'Alice', b=1)] >>> rdd = rdd.map(lambda row: row[1]) >>> spark.createDataFrame(rdd, "int").collect() [Row(value=1)] >>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... Py4JJavaError: ... """ if isinstance(data, DataFrame): raise TypeError("data is already a DataFrame") if isinstance(schema, basestring): schema = _parse_datatype_string(schema) try: import pandas has_pandas = True except Exception: has_pandas = False if has_pandas and isinstance(data, pandas.DataFrame): if schema is None: schema = [str(x) for x in data.columns] data = [r.tolist() for r in data.to_records(index=False)] verify_func = _verify_type if verifySchema else lambda _, t: True if isinstance(schema, StructType): def prepare(obj): verify_func(obj, schema) return obj elif isinstance(schema, DataType): dataType = schema schema = StructType().add("value", schema) def prepare(obj): verify_func(obj, dataType) return obj, else: if isinstance(schema, list): schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema] prepare = lambda obj: obj if isinstance(data, RDD): rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio) else: rdd, schema = self._createFromLocal(map(prepare, data), schema) jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) df = DataFrame(jdf, self._wrapped) df._schema = schema return df @ignore_unicode_prefix @since(2.0) def sql(self, sqlQuery): """Returns a :class:`DataFrame` representing the result of the given query. :return: :class:`DataFrame` >>> df.createOrReplaceTempView("table1") >>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> df2.collect() [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] """ return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped) @since(2.0) def table(self, tableName): """Returns the specified table as a :class:`DataFrame`. :return: :class:`DataFrame` >>> df.createOrReplaceTempView("table1") >>> df2 = spark.table("table1") >>> sorted(df.collect()) == sorted(df2.collect()) True """ return DataFrame(self._jsparkSession.table(tableName), self._wrapped) @property @since(2.0) def read(self): """ Returns a :class:`DataFrameReader` that can be used to read data in as a :class:`DataFrame`. :return: :class:`DataFrameReader` """ return DataFrameReader(self._wrapped) @property @since(2.0) def readStream(self): """ Returns a :class:`DataStreamReader` that can be used to read data streams as a streaming :class:`DataFrame`. .. note:: Experimental. :return: :class:`DataStreamReader` """ return DataStreamReader(self._wrapped) @property @since(2.0) def streams(self): """Returns a :class:`StreamingQueryManager` that allows managing all the :class:`StreamingQuery` StreamingQueries active on `this` context. .. note:: Experimental. :return: :class:`StreamingQueryManager` """ from pyspark.sql.streaming import StreamingQueryManager return StreamingQueryManager(self._jsparkSession.streams()) @since(2.0) def stop(self): """Stop the underlying :class:`SparkContext`. """ self._sc.stop() SparkSession._instantiatedSession = None @since(2.0) def __enter__(self): """ Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax. """ return self @since(2.0) def __exit__(self, exc_type, exc_val, exc_tb): """ Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax. Specifically stop the SparkSession on exit of the with block. """ self.stop() def _test(): import os import doctest from pyspark.context import SparkContext from pyspark.sql import Row import pyspark.sql.session os.chdir(os.environ["SPARK_HOME"]) globs = pyspark.sql.session.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc globs['spark'] = SparkSession(sc) globs['rdd'] = rdd = sc.parallelize( [Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")]) globs['df'] = rdd.toDF() (failure_count, test_count) = doctest.testmod( pyspark.sql.session, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE) globs['sc'].stop() if failure_count: exit(-1) if __name__ == "__main__": _test()

DislabNJU/Spark

python/pyspark/sql/session.py

Python

""" director subsystem's configuration - config-file schema - settings """ from typing import Dict import trafaret as T from aiohttp import ClientSession, web from yarl import URL from servicelib.application_keys import APP_CLIENT_SESSION_KEY, APP_CONFIG_KEY APP_DIRECTOR_API_KEY = __name__ + ".director_api" CONFIG_SECTION_NAME = "director" schema = T.Dict( { T.Key("enabled", default=True, optional=True): T.Bool(), T.Key("host", default="director",): T.String(), T.Key("port", default=8001): T.ToInt(), T.Key("version", default="v0"): T.Regexp( regexp=r"^v\d+" ), # storage API version basepath } ) def build_api_url(config: Dict) -> URL: api_baseurl = URL.build( scheme="http", host=config["host"], port=config["port"] ).with_path(config["version"]) return api_baseurl def get_config(app: web.Application) -> Dict: return app[APP_CONFIG_KEY][CONFIG_SECTION_NAME] def get_client_session(app: web.Application) -> ClientSession: return app[APP_CLIENT_SESSION_KEY]

KZzizzle/osparc-simcore

services/web/server/src/simcore_service_webserver/director/config.py

Python