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functions/is_the_cat.py | mirumirumi/your-cat-sam | 3 | 87325 | <reponame>mirumirumi/your-cat-sam
import os
import img
import json
import boto3
import proxy_response
from boto3 import Session
from aws_lambda_powertools import Logger
# PowerTools
logger = Logger()
# Rekognition
session = Session(region_name="ap-northeast-1")
rekognition_client = session.client("rekognition")
CONFIDENCE_THRESHOLD = 60.0
@logger.inject_lambda_context()
def lambda_handler(event, context):
print("🍊")
body = json.loads(event["body"])
logger.info(body)
key = img.to_jpg(body["image_data"]["base64"])
# rekognition
with open("/tmp/" + key, mode="rb") as f:
file = f.read()
try:
res = rekognition_client.detect_labels(Image={
"Bytes": file
})
except Exception as e:
logger.exception(f"failed detect labels: {e}")
return proxy_response._500()
else:
logger.info(res)
# delete file in this runtime
os.remove("/tmp/" + key)
result = {
"cat": get_label_data(res, "Cat"),
"dog": get_label_data(res, "Dog")
}
logger.info(result)
print("🍊")
return proxy_response._200(result)
def get_label_data(label_datas: dict, name: str) -> dict:
result = {}
for label in label_datas["Labels"]:
if label["Name"] == name:
if CONFIDENCE_THRESHOLD < label["Confidence"]:
result["judge"] = True
result["bounding_box"] = []
for bbs in label["Instances"]:
result["bounding_box"].append({
"width": bbs["BoundingBox"]["Width"],
"height": bbs["BoundingBox"]["Height"],
"left": bbs["BoundingBox"]["Left"],
"top": bbs["BoundingBox"]["Top"]
})
break
else:
result["judge"] = False
return result
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c2_python-operating-system/6_bash-scripting/_scripts/random_exit.py | DrShams/google-it-automation | 0 | 56761 | #!/usr/bin/python3
import sys
import random
value=random.randint(0, 3)
print("Returning: " + str(value))
sys.exit(value)
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Apps/phciscowebex/ciscowebex_connector.py | emanuel2rw/phantom-apps | 74 | 134456 | <filename>Apps/phciscowebex/ciscowebex_connector.py
# File: ciscowebex_connector.py
#
# Copyright (c) 2021 Splunk Inc.
#
# Licensed under Apache 2.0 (https://www.apache.org/licenses/LICENSE-2.0.txt)
# --
# Phantom App imports
import os
import time
import phantom.app as phantom
from phantom.base_connector import BaseConnector
from phantom.action_result import ActionResult
# Usage of the consts file is recommended
import requests
import json
from bs4 import BeautifulSoup
from django.http import HttpResponse
from ciscowebex_consts import *
class RetVal(tuple):
def __new__(cls, val1, val2):
return tuple.__new__(RetVal, (val1, val2))
def _get_error_message_from_exception(e):
""" This function is used to get appropriate error message from the exception.
:param e: Exception object
:return: error code and message
"""
error_msg = UNKNOWN_ERR_MSG
error_code = UNKNOWN_ERR_CODE_MSG
try:
if e.args:
if len(e.args) > 1:
error_code = e.args[0]
error_msg = e.args[1]
elif len(e.args) == 1:
error_code = UNKNOWN_ERR_CODE_MSG
error_msg = e.args[0]
else:
error_code = UNKNOWN_ERR_CODE_MSG
error_msg = UNKNOWN_ERR_MSG
except:
error_code = UNKNOWN_ERR_CODE_MSG
error_msg = UNKNOWN_ERR_MSG
return error_code, error_msg
def _handle_rest_request(request, path_parts):
""" Handle requests for authorization.
:param request: Data given to REST endpoint
:param path_parts: Parts of the URL passed
:return: Dictionary containing response parameters
"""
if len(path_parts) < 2:
return HttpResponse('error: True, message: Invalid REST endpoint request', content_type=WEBEX_STR_TEXT, status=404)
call_type = path_parts[1]
# To handle authorize request in test connectivity action
if call_type == 'start_oauth':
return _handle_login_redirect(request, 'authorization_url')
# To handle response from Webex login page
if call_type == 'result':
return_val = _handle_login_response(request)
asset_id = request.GET.get('state')
if asset_id and asset_id.isalnum():
app_dir = os.path.dirname(os.path.abspath(__file__))
auth_status_file_path = '{0}/{1}_{2}'.format(app_dir, asset_id, 'oauth_task.out')
real_auth_status_file_path = os.path.abspath(auth_status_file_path)
if not os.path.dirname(real_auth_status_file_path) == app_dir:
return HttpResponse("Error: Invalid asset_id", content_type=WEBEX_STR_TEXT, status=400)
open(auth_status_file_path, 'w').close()
return return_val
return HttpResponse('error: Invalid endpoint', content_type=WEBEX_STR_TEXT, status=404)
def _handle_login_response(request):
""" This function is used to get the login response of authorization request from Webex login page.
:param request: Data given to REST endpoint
:return: HttpResponse. The response displayed on authorization URL page
"""
asset_id = request.GET.get('state')
if not asset_id:
return HttpResponse('ERROR: Asset ID not found in URL\n{}'.format(json.dumps(request.GET)), content_type=WEBEX_STR_TEXT, status=400)
# Check for error in URL
error = request.GET.get('error')
error_description = request.GET.get('error_description')
# If there is an error in response
if error:
message = 'Error: {0}'.format(error)
if error_description:
message = '{0} Details: {1}'.format(message, error_description)
return HttpResponse('Server returned {0}'.format(message), content_type=WEBEX_STR_TEXT, status=400)
code = request.GET.get(WEBEX_STR_CODE)
# If code is not available
if not code:
return HttpResponse('Error while authenticating\n{0}'.format(json.dumps(request.GET)), content_type=WEBEX_STR_TEXT, status=400)
state = _load_app_state(asset_id)
state[WEBEX_STR_CODE] = code
_save_app_state(state, asset_id, None)
return HttpResponse(WEBEX_SUCCESS_CODE_RECEIVED_MESSAGE, content_type=WEBEX_STR_TEXT)
def _handle_login_redirect(request, key):
""" This function is used to redirect login request to Cisco webex login page.
:param request: Data given to REST endpoint
:param key: Key to search in state file
:return: response authorization_url/admin_consent_url
"""
asset_id = request.GET.get('asset_id')
if not asset_id:
return HttpResponse('ERROR: Asset ID not found in URL', content_type=WEBEX_STR_TEXT, status=400)
state = _load_app_state(asset_id)
if not state:
return HttpResponse('ERROR: Invalid asset_id', content_type=WEBEX_STR_TEXT, status=400)
url = state.get(key)
if not url:
return HttpResponse('App state is invalid, {key} not found.'.format(key=key), content_type=WEBEX_STR_TEXT, status=400)
response = HttpResponse(status=302)
response['Location'] = url
return response
def _load_app_state(asset_id, app_connector=None):
""" This function is used to load the current state file.
:param asset_id: asset_id
:param app_connector: Object of app_connector class
:return: state: Current state file as a dictionary
"""
asset_id = str(asset_id)
if not asset_id or not asset_id.isalnum():
if app_connector:
app_connector.debug_print('In _load_app_state: Invalid asset_id')
return {}
app_dir = os.path.dirname(os.path.abspath(__file__))
state_file = '{0}/{1}_state.json'.format(app_dir, asset_id)
real_state_file_path = os.path.abspath(state_file)
if not os.path.dirname(real_state_file_path) == app_dir:
if app_connector:
app_connector.debug_print('In _load_app_state: Invalid asset_id')
return {}
state = {}
try:
with open(real_state_file_path, 'r') as state_file_obj:
state = json.load(state_file_obj)
except Exception as e:
if app_connector:
error_code, error_msg = _get_error_message_from_exception(e)
app_connector.debug_print('In _load_app_state: Error Code: {0}. Error Message: {1}'.format(error_code, error_msg))
if app_connector:
app_connector.debug_print('Loaded state: ', state)
return state
def _save_app_state(state, asset_id, app_connector=None):
""" This function is used to save current state in file.
:param state: Dictionary which contains data to write in state file
:param asset_id: asset_id
:param app_connector: Object of app_connector class
:return: status: phantom.APP_SUCCESS|phantom.APP_ERROR
"""
asset_id = str(asset_id)
if not asset_id or not asset_id.isalnum():
if app_connector:
app_connector.debug_print('In _save_app_state: Invalid asset_id')
return {}
app_dir = os.path.split(__file__)[0]
state_file = '{0}/{1}_state.json'.format(app_dir, asset_id)
real_state_file_path = os.path.abspath(state_file)
if not os.path.dirname(real_state_file_path) == app_dir:
if app_connector:
app_connector.debug_print('In _save_app_state: Invalid asset_id')
return {}
if app_connector:
app_connector.debug_print('Saving state: ', state)
try:
with open(real_state_file_path, 'w+') as state_file_obj:
state_file_obj.write(json.dumps(state))
except Exception as e:
error_code, error_msg = _get_error_message_from_exception(e)
if app_connector:
app_connector.debug_print('Unable to save state file: Error Code: {0}. Error Message: {1}'.format(error_code, error_msg))
return phantom.APP_ERROR
return phantom.APP_SUCCESS
def _get_dir_name_from_app_name(app_name):
""" Get name of the directory for the app.
:param app_name: Name of the application for which directory name is required
:return: app_name: Name of the directory for the application
"""
app_name = ''.join([x for x in app_name if x.isalnum()])
app_name = app_name.lower()
if not app_name:
app_name = 'app_for_phantom'
return app_name
class CiscoWebexConnector(BaseConnector):
def __init__(self):
# Call the BaseConnectors init first
super(CiscoWebexConnector, self).__init__()
self._api_key = None
self._state = None
self._client_id = None
self._client_secret = None
self._access_token = None
self._refresh_token = None
# Variable to hold a base_url in case the app makes REST calls
# Do note that the app json defines the asset config, so please
# modify this as you deem fit.
self._base_url = None
def _process_empty_reponse(self, response, action_result):
if response.status_code == 200:
return RetVal(phantom.APP_SUCCESS, {})
return RetVal(action_result.set_status(phantom.APP_ERROR, WEBEX_ERR_EMPTY_RESPONSE), None)
def _process_html_response(self, response, action_result):
# An html response, treat it like an error
status_code = response.status_code
try:
soup = BeautifulSoup(response.text, "html.parser")
error_text = soup.text
split_lines = error_text.split('\n')
split_lines = [x.strip() for x in split_lines if x.strip()]
error_text = '\n'.join(split_lines)
except:
error_text = "Cannot parse error details"
message = "Status Code: {0}. Data from server:\n{1}\n".format(status_code, error_text)
message = message.replace('{', '{{').replace('}', '}}')
return RetVal(action_result.set_status(phantom.APP_ERROR, message), None)
def _process_json_response(self, r, action_result):
# Try a json parse
try:
resp_json = r.json()
except Exception as e:
return RetVal(action_result.set_status(phantom.APP_ERROR, "Unable to parse JSON response. Error: {0}".format(str(e))), None)
# Please specify the status codes here
if 200 <= r.status_code < 399:
return RetVal(phantom.APP_SUCCESS, resp_json)
# You should process the error returned in the json
message = "Error from server. Status Code: {0} Data from server: {1}".format(
r.status_code, r.text.replace('{', '{{').replace('}', '}}'))
return RetVal(action_result.set_status(phantom.APP_ERROR, message), None)
def _process_response(self, r, action_result):
# store the r_text in debug data, it will get dumped in the logs if the action fails
if r.status_code == 401:
return RetVal(action_result.set_status(phantom.APP_ERROR, "Access token is expired or invalid"), None)
if hasattr(action_result, 'add_debug_data'):
action_result.add_debug_data({'r_status_code': r.status_code})
action_result.add_debug_data({'r_text': r.text})
action_result.add_debug_data({'r_headers': r.headers})
# Process each 'Content-Type' of response separately
# Process a json response
if 'json' in r.headers.get('Content-Type', ''):
return self._process_json_response(r, action_result)
# Process an HTML resonse, Do this no matter what the api talks.
# There is a high chance of a PROXY in between phantom and the rest of
# world, in case of errors, PROXY's return HTML, this function parses
# the error and adds it to the action_result.
if 'html' in r.headers.get('Content-Type', ''):
return self._process_html_response(r, action_result)
# it's not content-type that is to be parsed, handle an empty response
if not r.text:
return self._process_empty_reponse(r, action_result)
# everything else is actually an error at this point
message = "Can't process response from server. Status Code: {0} Data from server: {1}".format(
r.status_code, r.text.replace('{', '{{').replace('}', '}}'))
return RetVal(action_result.set_status(phantom.APP_ERROR, message), None)
def _make_rest_call(self, endpoint, action_result, headers=None, params=None, data=None, method="get", verify=False):
resp_json = None
try:
request_func = getattr(requests, method)
except AttributeError:
return RetVal(action_result.set_status(phantom.APP_ERROR, "Invalid method: {0}".format(method)), resp_json)
try:
r = request_func(endpoint, json=data, headers=headers, verify=verify, params=params)
except Exception as e:
error_msg = _get_error_message_from_exception(e)
return RetVal(action_result.set_status(phantom.APP_ERROR, "Error Connecting to server. Details: {0}"
.format(error_msg)), resp_json)
return self._process_response(r, action_result)
def _get_asset_name(self, action_result):
""" Get name of the asset using Phantom URL.
:param action_result: object of ActionResult class
:return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message), asset name
"""
asset_id = self.get_asset_id()
rest_endpoint = PHANTOM_ASSET_ENDPOINT.format(asset_id=asset_id)
url = '{}{}'.format(self.get_phantom_base_url() + 'rest', rest_endpoint)
ret_val, resp_json = self._make_rest_call(action_result=action_result, endpoint=url)
if phantom.is_fail(ret_val):
return ret_val, None
asset_name = resp_json.get('name')
if not asset_name:
return action_result.set_status(phantom.APP_ERROR, WEBEX_ERR_ASSET_NAME_NOT_FOUND.format(asset_id), None)
return phantom.APP_SUCCESS, asset_name
def _get_phantom_base_url(self, action_result):
""" Get base url of phantom.
:param action_result: object of ActionResult class
:return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message),
base url of phantom
"""
url = '{0}{1}{2}'.format(BaseConnector._get_phantom_base_url(), 'rest', PHANTOM_SYSTEM_INFO_ENDPOINT)
ret_val, resp_json = self._make_rest_call(action_result=action_result, endpoint=url, verify=False)
if phantom.is_fail(ret_val):
return ret_val, None
phantom_base_url = resp_json.get('base_url')
if not phantom_base_url:
return action_result.set_status(phantom.APP_ERROR, WEBEX_ERR_PHANTOM_BASE_URL_NOT_FOUND), None
return phantom.APP_SUCCESS, phantom_base_url.rstrip('/')
def _get_app_rest_url(self, action_result):
""" Get URL for making rest calls.
:param action_result: object of ActionResult class
:return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message),
URL to make rest calls
"""
ret_val, phantom_base_url = self._get_phantom_base_url(action_result)
if phantom.is_fail(ret_val):
return action_result.get_status(), None
ret_val, asset_name = self._get_asset_name(action_result)
if phantom.is_fail(ret_val):
return action_result.get_status(), None
self.save_progress('Using Phantom base URL as: {0}'.format(phantom_base_url))
app_json = self.get_app_json()
app_name = app_json['name']
app_dir_name = _get_dir_name_from_app_name(app_name)
url_to_app_rest = '{0}/rest/handler/{1}_{2}/{3}'.format(phantom_base_url, app_dir_name, app_json['appid'],
asset_name)
return phantom.APP_SUCCESS, url_to_app_rest
def _generate_new_access_token(self, action_result, data):
""" This function is used to generate new access token using the code obtained on authorization.
s
:param action_result: object of ActionResult class
:param data: Data to send in REST call
:return: status phantom.APP_ERROR/phantom.APP_SUCCESS
"""
req_url = '{}{}'.format(self._base_url, WEBEX_ACCESS_TOKEN_ENDPOINT)
ret_val, resp_json = self._make_rest_call(action_result=action_result, endpoint=req_url,
data=data, method='post')
if phantom.is_fail(ret_val):
return action_result.get_status()
# If there is any error while generating access_token, API returns 200 with error and error_description fields
if not resp_json.get(WEBEX_STR_ACCESS_TOKEN):
if resp_json.get('message'):
return action_result.set_status(phantom.APP_ERROR, status_message=resp_json['message'])
return action_result.set_status(phantom.APP_ERROR, status_message='Error while generating access_token')
self._state[WEBEX_STR_TOKEN] = resp_json
self.save_state(self._state)
_save_app_state(self._state, self.get_asset_id(), self)
self._state = self.load_state()
self._access_token = resp_json[WEBEX_STR_ACCESS_TOKEN]
self._refresh_token = resp_json[WEBEX_STR_REFRESH_TOKEN]
# Scenario -
#
# If the corresponding state file doesn't have the correct owner, owner group or permissions,
# the newly generated token is not being saved to state file and automatic workflow for the token has been stopped.
# So we have to check that token from response and token which is saved to state file after successful generation of the new token are the same or not.
if self._access_token != self._state.get(WEBEX_STR_TOKEN, {}).get(WEBEX_STR_ACCESS_TOKEN):
message = "Error occurred while saving the newly generated access token (in place of the expired token) in the state file."
message += " Please check the owner, owner group, and the permissions of the state file. The Phantom "
message += "user should have the correct access rights and ownership for the corresponding state file (refer to the readme file for more information)."
return action_result.set_status(phantom.APP_ERROR, message)
return phantom.APP_SUCCESS
def _wait(self, action_result):
""" This function is used to hold the action till user login.
:param action_result: Object of ActionResult class
:return: status (success/failed)
"""
app_dir = os.path.dirname(os.path.abspath(__file__))
# file to check whether the request has been granted or not
auth_status_file_path = '{0}/{1}_{2}'.format(app_dir, self.get_asset_id(), 'oauth_task.out')
time_out = False
# wait-time while request is being granted
for _ in range(OAUTH_WAIT_INTERVALS):
self.send_progress('Waiting...')
if os.path.isfile(auth_status_file_path):
time_out = True
os.unlink(auth_status_file_path)
break
time.sleep(OAUTH_WAIT_TIME)
if not time_out:
return action_result.set_status(phantom.APP_ERROR, status_message=WEBEX_ERR_TIMEOUT)
self.send_progress('Authenticated')
return phantom.APP_SUCCESS
def _update_request(self, action_result, endpoint, headers=None, params=None, data=None, method='get'):
""" This function is used to update the headers with access_token before making REST call.
:param endpoint: REST endpoint that needs to appended to the service address
:param action_result: object of ActionResult class
:param headers: request headers
:param params: request parameters
:param data: request body
:param method: GET/POST/PUT/DELETE/PATCH (Default will be GET)
:return: status phantom.APP_ERROR/phantom.APP_SUCCESS(along with appropriate message),
response obtained by making an API call
"""
if not endpoint.startswith(self._base_url):
endpoint = '{0}{1}'.format(self._base_url, endpoint)
if headers is None:
headers = {}
token_data = {
WEBEX_STR_CLIENT_ID: self._client_id,
WEBEX_STR_SECRET: self._client_secret,
WEBEX_STR_GRANT_TYPE: WEBEX_STR_REFRESH_TOKEN,
WEBEX_STR_REFRESH_TOKEN: self._refresh_token
}
if not self._access_token:
if not self._refresh_token:
# If none of the access_token and refresh_token is available
return action_result.set_status(phantom.APP_ERROR, status_message=WEBEX_ERR_TOKEN_NOT_AVAILABLE), None
# If refresh_token is available and access_token is not available, generate new access_token
status = self._generate_new_access_token(action_result=action_result, data=token_data)
if phantom.is_fail(status):
return action_result.get_status(), None
headers.update({'Authorization': 'Bearer {0}'.format(self._access_token)})
if not headers.get('Content-Type'):
headers.update({'Content-Type': 'application/json'})
ret_val, resp_json = self._make_rest_call(action_result=action_result, endpoint=endpoint, headers=headers,
params=params, data=data, method=method)
# If token is expired, generate new token
if 'Access token is expired or invalid' in action_result.get_message():
status = self._generate_new_access_token(action_result=action_result, data=token_data)
if phantom.is_fail(status):
return action_result.get_status(), None
headers.update({'Authorization': 'Bearer {0}'.format(self._access_token)})
ret_val, resp_json = self._make_rest_call(action_result=action_result, endpoint=endpoint, headers=headers,
params=params, data=data, method=method)
if phantom.is_fail(ret_val):
return action_result.get_status(), None
return phantom.APP_SUCCESS, resp_json
def _handle_test_connectivity(self, param):
action_result = self.add_action_result(ActionResult(dict(param)))
app_state = {}
self.save_progress("Validating API Key")
# If API key exists, skipping oAuth authentication
if self._api_key:
ret_val, response = self._make_rest_call_using_api_key(WEBEX_GET_ROOMS_ENDPOINT, action_result, params=None)
if phantom.is_fail(ret_val):
self.save_progress(WEBEX_ERR_TEST_CONNECTIVITY)
return action_result.get_status()
self.save_progress(WEBEX_SUCCESS_TEST_CONNECTIVITY)
return action_result.set_status(phantom.APP_SUCCESS)
# Get initial REST URL
ret_val, app_rest_url = self._get_app_rest_url(action_result)
if phantom.is_fail(ret_val):
self.save_progress("Rest URL not available. Error: {error}".format(error=action_result.get_message()))
return action_result.set_status(phantom.APP_ERROR, status_message="Test Connectivity Failed")
# Append /result to create redirect_uri
redirect_uri = '{0}/result'.format(app_rest_url)
app_state[WEBEX_STR_REDIRECT_URI] = redirect_uri
self.save_progress("Using OAuth URL:")
self.save_progress(redirect_uri)
# Authorization URL used to make request for getting code which is used to generate access token
authorization_url = AUTHORIZATION_URL.format(client_id=self._client_id,
redirect_uri=redirect_uri,
response_type=WEBEX_STR_CODE,
state=self.get_asset_id(),
scope=SCOPE)
authorization_url = '{}{}'.format(self._base_url, authorization_url)
app_state['authorization_url'] = authorization_url
# URL which would be shown to the user
url_for_authorize_request = '{0}/start_oauth?asset_id={1}&'.format(app_rest_url, self.get_asset_id())
_save_app_state(app_state, self.get_asset_id(), self)
self.save_progress('Please authorize user in a separate tab using URL')
self.save_progress(url_for_authorize_request)
# Wait time for authorization
time.sleep(15)
# Wait for some while user login to Cisco webex
status = self._wait(action_result=action_result)
# Empty message to override last message of waiting
self.send_progress('')
if phantom.is_fail(status):
return action_result.get_status()
self.save_progress('Code Received')
self._state = _load_app_state(self.get_asset_id(), self)
# if code is not available in the state file
if not self._state or not self._state.get(WEBEX_STR_CODE):
return action_result.set_status(phantom.APP_ERROR, status_message=WEBEX_ERR_TEST_CONNECTIVITY)
current_code = self._state[WEBEX_STR_CODE]
self.save_state(self._state)
_save_app_state(self._state, self.get_asset_id(), self)
self.save_progress('Generating access token')
data = {
WEBEX_STR_CLIENT_ID: self._client_id,
WEBEX_STR_SECRET: self._client_secret,
WEBEX_STR_GRANT_TYPE: 'authorization_code',
WEBEX_STR_REDIRECT_URI: redirect_uri,
WEBEX_STR_CODE: current_code
}
# For first time access, new access token is generated
ret_val = self._generate_new_access_token(action_result=action_result, data=data)
if phantom.is_fail(ret_val):
self.save_progress(WEBEX_ERR_TEST_CONNECTIVITY)
return action_result.get_status()
self.save_progress('Getting info about the rooms to verify token')
url = '{}{}'.format(self._base_url, WEBEX_GET_ROOMS_ENDPOINT)
ret_val, response = self._update_request(action_result=action_result, endpoint=url)
if phantom.is_fail(ret_val):
self.save_progress(WEBEX_ERR_TEST_CONNECTIVITY)
return action_result.get_status()
self.save_progress('Got room details successfully')
self.save_progress(WEBEX_SUCCESS_TEST_CONNECTIVITY)
return action_result.set_status(phantom.APP_SUCCESS)
def _make_rest_call_using_api_key(self, endpoint, action_result, params=None, data=None, method="get", verify=False):
# Create a URL to connect to
url = self._base_url + endpoint
authToken = "Bearer " + self._api_key
headers = {'Content-Type': 'application/json', 'Authorization': authToken}
return self._make_rest_call(url, action_result, params=params, headers=headers, data=data, method=method, verify=verify)
def _handle_list_rooms(self, param):
self.save_progress("In action handler for: {0}".format(self.get_action_identifier()))
action_result = self.add_action_result(ActionResult(dict(param)))
if self._api_key:
ret_val, response = self._make_rest_call_using_api_key(WEBEX_GET_ROOMS_ENDPOINT, action_result)
else:
ret_val, response = self._update_request(action_result, WEBEX_GET_ROOMS_ENDPOINT)
if phantom.is_fail(ret_val):
return action_result.get_status()
summary = action_result.update_summary({'total_rooms': 0})
resp_value = response.get('items', [])
if type(resp_value) != list:
resp_value = [resp_value]
for curr_item in resp_value:
action_result.add_data(curr_item)
summary['total_rooms'] = action_result.get_data_size()
if phantom.is_fail(ret_val):
return action_result.get_status()
return action_result.set_status(phantom.APP_SUCCESS)
def _handle_get_user(self, param):
self.save_progress("In action handler for: {0}".format(self.get_action_identifier()))
action_result = self.add_action_result(ActionResult(dict(param)))
uri_endpoint = WEBEX_GET_USER_ENDPOINT.format(param['email_address'])
if self._api_key:
ret_val, response = self._make_rest_call_using_api_key(uri_endpoint, action_result, params=None)
else:
ret_val, response = self._update_request(action_result, uri_endpoint)
if phantom.is_fail(ret_val):
return action_result.get_status()
summary = action_result.update_summary({'found_user': False})
resp_value = response.get('items', [])
for resp in resp_value:
action_result.add_data(resp)
is_user_found = True if action_result.get_data_size() > 0 else False
summary['found_user'] = is_user_found
if phantom.is_fail(ret_val):
return action_result.get_status()
if not is_user_found:
return action_result.set_status(phantom.APP_ERROR, WEBEX_ERR_USER_NOT_FOUND)
return action_result.set_status(phantom.APP_SUCCESS)
def _handle_send_message(self, param):
self.save_progress("In action handler for: {0}".format(self.get_action_identifier()))
action_result = self.add_action_result(ActionResult(dict(param)))
type = param['destination_type']
if type == "user":
uri_endpoint = WEBEX_SEND_MESSAGE_ENDPOINT
user_id = param['endpoint_id']
message = param['message']
data = {'toPersonId': user_id, 'text': message}
else:
uri_endpoint = WEBEX_SEND_MESSAGE_ENDPOINT
user_id = param['endpoint_id']
message = param['message']
data = {"roomId": user_id, "text": message}
if self._api_key:
ret_val, response = self._make_rest_call_using_api_key(uri_endpoint, action_result, data=data, method="post")
else:
ret_val, response = self._update_request(action_result, uri_endpoint, data=data, method="post")
if phantom.is_fail(ret_val):
return action_result.get_status()
action_result.add_data(response)
message = WEBEX_SUCCESS_SEND_MESSAGE
summary = action_result.update_summary({})
summary['message'] = message
if phantom.is_fail(ret_val):
return action_result.get_status()
return action_result.set_status(phantom.APP_SUCCESS, message)
def handle_action(self, param):
ret_val = phantom.APP_SUCCESS
action_id = self.get_action_identifier()
if action_id == 'test_connectivity':
ret_val = self._handle_test_connectivity(param)
elif action_id == 'list_rooms':
ret_val = self._handle_list_rooms(param)
elif action_id == 'get_user':
ret_val = self._handle_get_user(param)
elif action_id == 'send_message':
ret_val = self._handle_send_message(param)
return ret_val
def initialize(self):
# Load the state in initialize, use it to store data
# that needs to be accessed across actions
self._state = self.load_state()
config = self.get_config()
self._base_url = BASE_URL
self._api_key = config.get('authorization_key', None)
self._client_id = config.get(WEBEX_STR_CLIENT_ID, None)
self._client_secret = config.get(WEBEX_STR_SECRET, None)
self._access_token = self._state.get(WEBEX_STR_TOKEN, {}).get(WEBEX_STR_ACCESS_TOKEN)
self._refresh_token = self._state.get(WEBEX_STR_TOKEN, {}).get(WEBEX_STR_REFRESH_TOKEN)
if not self._api_key and (not self._client_id and not self._client_secret):
return self.set_status(phantom.APP_ERROR, status_message=WEBEX_ERR_REQUIRED_CONFIG_PARAMS)
if not self._api_key and ((self._client_id and not self._client_secret) or (self._client_secret and not self._client_id)):
return self.set_status(phantom.APP_ERROR, status_message=WEBEX_ERR_REQUIRED_CONFIG_PARAMS)
return phantom.APP_SUCCESS
def finalize(self):
# Save the state, this data is saved accross actions and app upgrades
self.save_state(self._state)
return phantom.APP_SUCCESS
if __name__ == '__main__':
import sys
import pudb
import argparse
pudb.set_trace()
argparser = argparse.ArgumentParser()
argparser.add_argument('input_test_json', help='Input Test JSON file')
argparser.add_argument('-u', '--username', help='username', required=False)
argparser.add_argument('-p', '--password', help='password', required=False)
args = argparser.parse_args()
session_id = None
username = args.username
password = <PASSWORD>
if username is not None and password is None:
# User specified a username but not a password, so ask
import getpass
password = getpass.getpass("Password: ")
if username and password:
try:
print("Accessing the Login page")
r = requests.get(BaseConnector._get_phantom_base_url() + "login", verify=False)
csrftoken = r.cookies['csrftoken']
data = dict()
data['username'] = username
data['password'] = password
data['csrfmiddlewaretoken'] = csrftoken
headers = dict()
headers['Cookie'] = 'csrftoken=' + csrftoken
headers['Referer'] = BaseConnector._get_phantom_base_url()
print("Logging into Platform to get the session id")
r2 = requests.post(BaseConnector._get_phantom_base_url(), verify=False, data=data, headers=headers)
session_id = r2.cookies['sessionid']
except Exception as e:
print("Unable to get session id from the platfrom. Error: " + str(e))
exit(1)
if len(sys.argv) < 2:
print("No test json specified as input")
exit(0)
with open(args.input_test_json) as f:
in_json = f.read()
in_json = json.loads(in_json)
print(json.dumps(in_json, indent=4))
connector = CiscoWebexConnector()
connector.print_progress_message = True
if session_id is not None:
in_json['user_session_token'] = session_id
ret_val = connector._handle_action(json.dumps(in_json), None)
print(json.dumps(json.loads(ret_val), indent=4))
exit(0)
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3.拓展模块/3.摄像头模块/1.拍摄照片/photo.py | 01studio-lab/RaspberryPi_Examples | 7 | 74799 | '''
实验名称：摄像头模块
版本：v1.0
日期：2020.3
作者：01Studio
说明：使用摄像头模块拍照并保存
社区：www.01studio.org
'''
#导入相关库
from picamera import PiCamera
from time import sleep
#构建摄像头
camera = PiCamera()
#打开摄像头
camera.start_preview()
sleep(3) #等待摄像头稳定
#拍摄照片5张并保存到桌面
for i in range(5):
camera.capture('/home/pi/Desktop/'+str(i)+'.jpg')
#关闭摄像头
camera.stop_preview() | [
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match.py | pammirato/image_vatic | 571 | 92050 | <reponame>pammirato/image_vatic<filename>match.py
import munkres
def match(first, second, method):
"""
Attempts to match every path in 'first' with a path in 'second'. Returns
the association along with its score.
Note: if two paths have nothing to do with each other, but there is no
other suitable candidate, the two seeminly unrelated paths will be
associated. It is up to the caller to handle this situation. The 'validate'
method may provide some help. Further, if len(first) != len(second), then
some elements will be associated with None.
"""
if len(first) == len(second) == 0:
return []
costs = buildmatrix(first, second, method)
response = []
for f, s in munkres.Munkres().compute(costs):
response.append((first[f] if f < len(first) else None,
second[s] if s < len(second) else None,
costs[f][s]))
return response
def buildmatrix(first, second, method):
"""
Builds the matrix for the Hungarian algorithm. Pads with the worst to make
the matrix square.
"""
costs = [[method(f,s) for s in second] for f in first]
if len(first) and len(second):
horrible = [max(max(costs)) + 1]
else:
horrible = [1e10]
if len(first) > len(second):
for row in costs:
row.extend(horrible * (len(first) - len(second)))
elif len(first) < len(second):
costs.extend([horrible * len(second)] * (len(second) - len(first)))
return costs
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SC101/SC101_week2/zonegraphics.py | ariel98po/SC101-projects | 0 | 25749 | from campy.graphics.gwindow import GWindow
from campy.graphics.gobjects import GOval, GRect
from campy.gui.events.mouse import onmouseclicked
import random
WINDOW_WIDTH = 600
WINDOW_HEIGHT = 400
ZONE_WIDTH = 100
ZONE_HEIGHT = 100
BALL_RADIUS = 15
MAX_SPEED = 6
MIN_Y_SPEED = 2
class ZoneGraphics:
def __init__(self, window_width=WINDOW_WIDTH, window_height=WINDOW_HEIGHT,
zone_width=ZONE_WIDTH, zone_height=ZONE_HEIGHT, ball_radius=BALL_RADIUS):
# Create window
self.window = GWindow(window_width, window_height, title='Zone Game')
# Create zone
self.zone = GRect(zone_width, zone_height, x=(window_width - zone_width) / 2,
y=(window_height - zone_height) / 2)
self.zone.color = 'blue'
self.window.add(self.zone)
# Create ball and initialize velocity/position
self.ball = GOval(2 * ball_radius, 2 * ball_radius)
self.ball.filled = True
self.ball.fill_color = 'salmon'
self.dx = 0
self.dy = 0
self.reset_ball()
# Initialize mouse listeners
onmouseclicked(self.handle_click)
# Set ball position at random inside the window
def set_ball_position(self):
self.ball.x = random.randint(0, self.window.width - self.ball.width)
self.ball.y = random.randint(0, self.window.height - self.ball.height)
def set_ball_velocity(self):
self.dx = random.randint(0, MAX_SPEED)
if random.random() > 0.5:
self.dx = -self.dx
self.dy = random.randint(MIN_Y_SPEED, MAX_SPEED)
if random.random() > 0.5:
self.dy = -self.dy
def reset_ball(self):
self.set_ball_position()
while self.ball_in_zone():
self.set_ball_position()
self.set_ball_velocity()
self.window.add(self.ball)
def move_ball(self):
self.ball.move(self.dx, self.dy)
def handle_wall_collisions(self):
if self.ball.x + self.ball.width >= self.window.width or self.ball.x <= 0:
self.dx = -self.dx
if self.ball.y + self.ball.height >= self.window.height or self.ball.y <= 0:
self.dy = -self.dy
def ball_in_zone(self):
zone_left_side = self.zone.x
zone_right_side = self.zone.x + self.zone.width
ball_x_in_zone = zone_left_side <= self.ball.x <= zone_right_side - self.ball.width
zone_top_side = self.zone.y
zone_bottom_side = self.zone.y + self.zone.height
ball_y_in_zone = zone_top_side <= self.ball.y <= zone_bottom_side - self.ball.height
return ball_x_in_zone and ball_y_in_zone
def handle_click(self, event):
obj = self.window.get_object_at(event.x, event.y)
if self.ball == obj:
self.reset_ball()
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] |
scripts/generate_spec_tfrecords.py | dave6892/single_word_speech | 0 | 102425 | import sys
sys.path.append('..')
import glob
from typing import NoReturn
from multiprocessing import cpu_count
from sklearn.model_selection import train_test_split
from src.utilities.tfrecords_save import multi_create_tfrecords_in_shards
def main(
num_files_to_create: int = 96,
num_workeers: int = cpu_count() - 1
) -> NoReturn:
num_files_to_create = 96
num_workers = cpu_count()-1
filenames = {
'train': None,
'test' : None,
'validation' : None
}
image_files = glob.glob(f'/data/spec/*/*')
train_filenames, validation_test_filenames = train_test_split(
image_files,
train_size = 0.8,
test_size = 0.2,
random_state = 24601
)
validation_filenames, test_filenames = train_test_split(
validation_test_filenames,
train_size = 0.5,
test_size = 0.5,
random_state = 24601
)
num_test_samples, test_tfrecords = multi_create_tfrecords_in_shards(
data_filenames = test_filenames,
dataset_name = 'test',
num_files_to_create = num_files_to_create,
num_workers = num_workers
)
num_validation_samples, validation_tfrecords = multi_create_tfrecords_in_shards(
data_filenames = validation_filenames,
dataset_name = 'validation',
num_files_to_create = num_files_to_create,
num_workers = num_workers
)
num_train_samples, train_tfrecords = multi_create_tfrecords_in_shards(
data_filenames = train_filenames,
dataset_name = 'train',
num_files_to_create = num_files_to_create,
num_workers = num_workers
)
filenames['train'] = train_tfrecords
filenames['test'] = test_tfrecords
filenames['validation'] = validation_tfrecords
return filenames
if __name__ == "__main__":
main()
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] |
tests/kernel/test_task_api_state.py | tmontes/ppytty | 1 | 144712 | # ----------------------------------------------------------------------------
# ppytty
# ----------------------------------------------------------------------------
# Copyright (c) <NAME>.
# See LICENSE for details.
# ----------------------------------------------------------------------------
from ppytty.kernel import run, api
from ppytty.kernel.exceptions import TrapException
from . import helper_io
from . import helper_log
from . import helper_state
class TestNeedingOutput(helper_io.NoOutputTestCase):
def setUp(self):
self.log_handler = helper_log.create_and_add_handler()
def tearDown(self):
helper_log.remove_handler(self.log_handler)
def test_state_dump_logs_all_state_fields(self):
async def task():
await api.state_dump()
success, result = run(task)
self.assertTrue(success)
self.assertIsNone(result)
for state_attr in helper_state.STATE_ATTRS:
with self.subTest(state_attr=state_attr):
expected_pattern = f'state.{state_attr}='
for level, message in self.log_handler.messages:
if expected_pattern in message and level=='CRITICAL':
break
else:
raise AssertionError(f'{expected_pattern!r} not found')
class Test(helper_io.NoOutputAutoTimeTestCase):
def setUp(self):
super().setUp()
self.log_handler = helper_log.create_and_add_handler()
def tearDown(self):
helper_log.remove_handler(self.log_handler)
super().tearDown()
def _assert_state_dump_logs_top_task(self, runnable=False):
async def task():
await api.state_dump()
user_level_task = task() if runnable else task
success, result = run(user_level_task)
self.assertTrue(success)
self.assertIsNone(result)
expected = str(user_level_task)
for level, message in self.log_handler.messages:
if expected in message and level=='CRITICAL':
break
if 'DATA STRUCTURES' in message:
# if we get to this point, the task hierarchy is gone
raise AssertionError(f'{expected!r} not found')
else:
raise AssertionError(f'{expected!r} not found')
def test_state_dump_logs_top_task_gen_function(self):
self._assert_state_dump_logs_top_task(runnable=False)
def test_state_dump_logs_top_task_gen_object(self):
self._assert_state_dump_logs_top_task(runnable=True)
def test_state_dump_logs_correct_task_states(self):
async def sleeping():
await api.sleep(42)
async def wait_child():
await api.task_spawn(sleeping)
await api.task_wait()
async def wait_inbox():
await api.message_wait()
async def wait_key():
await api.key_read()
async def completed():
await api.sleep(0)
async def runnable():
await api.sleep(0)
async def parent():
# Nuance: `runnable` spawned last means it will still be in the
# runnable state when the `state-dump` trap is called.
all_tasks = (wait_child, wait_inbox, wait_key, completed, runnable)
for task in all_tasks:
await api.task_spawn(task)
await api.state_dump()
for task in all_tasks:
try:
await api.task_destroy(task)
except TrapException:
# the `completed` task triggers this: it was waited by one
# of the previous `task-waits` and it no longer exists.
pass
finally:
await api.task_wait()
success, result = run(parent)
self.assertTrue(success)
self.assertIsNone(result)
expected_states = {
parent: 'RR', # actually running, having called `state-dump`
sleeping: 'WT', # waiting on time via `sleep`
wait_child: 'WC', # waiting child via `task-wait`
wait_inbox: 'WM', # waiting message via `message-wait`
wait_key: 'WK', # waiting keyboard via `read-key`
completed: 'CC', # completed, not waited for yet
runnable: 'RN', # runnable, no chance to run yet
}
for task, expected_state in expected_states.items():
with self.subTest(task=task, expected_state=expected_state):
expected_parts = (str(task), expected_state)
for level, message in self.log_handler.messages:
if all(p in message for p in expected_parts) and level=='CRITICAL':
break
if 'DATA STRUCTURES' in message:
# if we get to this point, the task hierarchy is gone
raise AssertionError(f'matching task/state not found')
else:
# Should not be reached because `state-dump` output should
# include a 'DATA STRUCTURES' line, after the task state
# lines; keeping it here, just in case, with an *explicitly*
# different assertion error message.
raise AssertionError('no DATA STRUCTURES line in log')
# ----------------------------------------------------------------------------
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Practic/task_8/swapcase1.py | zevgenia/Python_shultais | 0 | 125921 | <filename>Practic/task_8/swapcase1.py<gh_stars>0
import sys
str1 = sys.argv[1]
str2 = sys.argv[2]
print(str1, str2)
print(str1.swapcase(), str2.swapcase())
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sccsClientLocalNetworkPythonGear-ReceivingOnlyWIP/sccsClient/sccsClient.py | ninekorn/Python-2-way-local-network-communication-WIP | 0 | 175771 | <reponame>ninekorn/Python-2-way-local-network-communication-WIP
#https://realpython.com/python-sockets/
#!/usr/bin/env python3
import socket
import sys
#import time
UDP_IP = "127.0.0.1" # set it to destination IP.. RPi in this case #127.0.0.1 #192.168.127.12
UDP_PORT = 5000
#print("UDP target IP:", UDP_IP)
#print("UDP target port:", UDP_PORT)
sock = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)
#sock.bind((UDP_IP, UDP_PORT))
#print >>sys.stderr, 'starting up on %s port %s' % sock.getsockname()
#sock.listen(1)
#HOST = '127.0.0.1' # The server's hostname or IP address
#PORT = 5000 # The port used by the server #65432
someswtc = True;
somecounter = 0;
somecountermax =10;
somecounterinit = 0;
somecounterinitmax = 100;
somecounterinitswtc = 0;
while someswtc == True:
if somecounterinitswtc == 0:
if somecounterinit >= somecounterinitmax:
somecounterinitswtc = 1
somecounterinit = 0
somecounterinit+=1
else:
if somecounter >= somecountermax:
with sock as s:
s.connect((UDP_IP, UDP_PORT))
s.sendall(b'Hello, world')
data = s.recv(1024)
#message = bytearray([0,1,2,3,4,5,6,7,8,9])
#s.sendto(message, ('127.0.0.1',5000))
#print('Received', repr(data))
somecounter = 0;
somecounter+=1
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] |
backend/config.py | mrkrause/penn-events-calendar | 0 | 56007 | <reponame>mrkrause/penn-events-calendar<gh_stars>0
import os
# Module containing scrapers for extracting data from webpages
# Every public function (i.e., those not beginning with _) should return a list
# of event dictionaries when called with no arguments.
import local.upenn.scrapers
scrapers = local.upenn.scrapers
# Module containing a list of locations (for feature generation)
import local.upenn.locations
locations = local.upenn.locations.locations
# Path to save event data (as json)
PATH_DATA = os.path.join('data', 'events.json') # path to save events
PATH_FETCH_DATA = os.path.join('data', 'fetch_record.json')
PATH_VECTOR = os.path.join('data', 'events_vector.json')
# GROBID stuff for parsing PDFs
GROBID_URL = 'http://localhost:8070'
GROBID_PDF_URL = '{}/api/processFulltextDocument'.format(GROBID_URL)
# ElasticSearch configuration
ELASTIC_HOST = 'localhost'
ELASTIC_PORT = 9200
ELASTIC_INDEX = 'penn-events'
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homeassistant/components/plugwise/sensor.py | prwood80/core | 5 | 130602 | """Plugwise Sensor component for Home Assistant."""
import logging
from homeassistant.components.sensor import (
DEVICE_CLASS_BATTERY,
DEVICE_CLASS_ENERGY,
DEVICE_CLASS_GAS,
DEVICE_CLASS_ILLUMINANCE,
DEVICE_CLASS_POWER,
DEVICE_CLASS_PRESSURE,
DEVICE_CLASS_TEMPERATURE,
STATE_CLASS_MEASUREMENT,
STATE_CLASS_TOTAL,
STATE_CLASS_TOTAL_INCREASING,
SensorEntity,
)
from homeassistant.const import (
ENERGY_KILO_WATT_HOUR,
ENERGY_WATT_HOUR,
PERCENTAGE,
POWER_WATT,
PRESSURE_BAR,
TEMP_CELSIUS,
VOLUME_CUBIC_METERS,
)
from homeassistant.core import callback
from .const import (
COOL_ICON,
COORDINATOR,
DEVICE_STATE,
DOMAIN,
FLAME_ICON,
IDLE_ICON,
SENSOR_MAP_DEVICE_CLASS,
SENSOR_MAP_MODEL,
SENSOR_MAP_STATE_CLASS,
SENSOR_MAP_UOM,
UNIT_LUMEN,
)
from .gateway import SmileGateway
_LOGGER = logging.getLogger(__name__)
ATTR_TEMPERATURE = [
"Temperature",
TEMP_CELSIUS,
DEVICE_CLASS_TEMPERATURE,
STATE_CLASS_MEASUREMENT,
]
ATTR_BATTERY_LEVEL = [
"Charge",
PERCENTAGE,
DEVICE_CLASS_BATTERY,
STATE_CLASS_MEASUREMENT,
]
ATTR_ILLUMINANCE = [
"Illuminance",
UNIT_LUMEN,
DEVICE_CLASS_ILLUMINANCE,
STATE_CLASS_MEASUREMENT,
]
ATTR_PRESSURE = [
"Pressure",
PRESSURE_BAR,
DEVICE_CLASS_PRESSURE,
STATE_CLASS_MEASUREMENT,
]
TEMP_SENSOR_MAP = {
"setpoint": ATTR_TEMPERATURE,
"temperature": ATTR_TEMPERATURE,
"intended_boiler_temperature": ATTR_TEMPERATURE,
"temperature_difference": ATTR_TEMPERATURE,
"outdoor_temperature": ATTR_TEMPERATURE,
"water_temperature": ATTR_TEMPERATURE,
"return_temperature": ATTR_TEMPERATURE,
}
ENERGY_SENSOR_MAP = {
"electricity_consumed": [
"Current Consumed Power",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"electricity_produced": [
"Current Produced Power",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"electricity_consumed_interval": [
"Consumed Power Interval",
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
"electricity_consumed_peak_interval": [
"Consumed Power Interval",
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
"electricity_consumed_off_peak_interval": [
"Consumed Power Interval (off peak)",
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
"electricity_produced_interval": [
"Produced Power Interval",
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
"electricity_produced_peak_interval": [
"Produced Power Interval",
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
"electricity_produced_off_peak_interval": [
"Produced Power Interval (off peak)",
ENERGY_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
"electricity_consumed_off_peak_point": [
"Current Consumed Power (off peak)",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"electricity_consumed_peak_point": [
"Current Consumed Power",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"electricity_consumed_off_peak_cumulative": [
"Cumulative Consumed Power (off peak)",
ENERGY_KILO_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL_INCREASING,
],
"electricity_consumed_peak_cumulative": [
"Cumulative Consumed Power",
ENERGY_KILO_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL_INCREASING,
],
"electricity_produced_off_peak_point": [
"Current Produced Power (off peak)",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"electricity_produced_peak_point": [
"Current Produced Power",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"electricity_produced_off_peak_cumulative": [
"Cumulative Produced Power (off peak)",
ENERGY_KILO_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL_INCREASING,
],
"electricity_produced_peak_cumulative": [
"Cumulative Produced Power",
ENERGY_KILO_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL_INCREASING,
],
"gas_consumed_interval": [
"Current Consumed Gas Interval",
VOLUME_CUBIC_METERS,
DEVICE_CLASS_GAS,
STATE_CLASS_TOTAL,
],
"gas_consumed_cumulative": [
"Consumed Gas",
VOLUME_CUBIC_METERS,
DEVICE_CLASS_GAS,
STATE_CLASS_TOTAL_INCREASING,
],
"net_electricity_point": [
"Current net Power",
POWER_WATT,
DEVICE_CLASS_POWER,
STATE_CLASS_MEASUREMENT,
],
"net_electricity_cumulative": [
"Cumulative net Power",
ENERGY_KILO_WATT_HOUR,
DEVICE_CLASS_ENERGY,
STATE_CLASS_TOTAL,
],
}
MISC_SENSOR_MAP = {
"battery": ATTR_BATTERY_LEVEL,
"illuminance": ATTR_ILLUMINANCE,
"modulation_level": [
"Heater Modulation Level",
PERCENTAGE,
None,
STATE_CLASS_MEASUREMENT,
],
"valve_position": ["Valve Position", PERCENTAGE, None, STATE_CLASS_MEASUREMENT],
"water_pressure": ATTR_PRESSURE,
}
INDICATE_ACTIVE_LOCAL_DEVICE = [
"cooling_state",
"flame_state",
]
CUSTOM_ICONS = {
"gas_consumed_interval": "mdi:fire",
"gas_consumed_cumulative": "mdi:fire",
"modulation_level": "mdi:percent",
"valve_position": "mdi:valve",
}
async def async_setup_entry(hass, config_entry, async_add_entities):
"""Set up the Smile sensors from a config entry."""
api = hass.data[DOMAIN][config_entry.entry_id]["api"]
coordinator = hass.data[DOMAIN][config_entry.entry_id][COORDINATOR]
entities = []
all_devices = api.get_all_devices()
single_thermostat = api.single_master_thermostat()
for dev_id, device_properties in all_devices.items():
data = api.get_device_data(dev_id)
for sensor, sensor_type in {
**TEMP_SENSOR_MAP,
**ENERGY_SENSOR_MAP,
**MISC_SENSOR_MAP,
}.items():
if data.get(sensor) is None:
continue
if "power" in device_properties["types"]:
model = None
if "plug" in device_properties["types"]:
model = "Metered Switch"
entities.append(
PwPowerSensor(
api,
coordinator,
device_properties["name"],
dev_id,
sensor,
sensor_type,
model,
)
)
else:
entities.append(
PwThermostatSensor(
api,
coordinator,
device_properties["name"],
dev_id,
sensor,
sensor_type,
)
)
if single_thermostat is False:
for state in INDICATE_ACTIVE_LOCAL_DEVICE:
if state not in data:
continue
entities.append(
PwAuxDeviceSensor(
api,
coordinator,
device_properties["name"],
dev_id,
DEVICE_STATE,
)
)
break
async_add_entities(entities, True)
class SmileSensor(SmileGateway, SensorEntity):
"""Represent Smile Sensors."""
def __init__(self, api, coordinator, name, dev_id, sensor):
"""Initialise the sensor."""
super().__init__(api, coordinator, name, dev_id)
self._sensor = sensor
self._dev_class = None
self._icon = None
self._state = None
self._state_class = None
self._unit_of_measurement = None
if dev_id == self._api.heater_id:
self._entity_name = "Auxiliary"
sensorname = sensor.replace("_", " ").title()
self._name = f"{self._entity_name} {sensorname}"
if dev_id == self._api.gateway_id:
self._entity_name = f"Smile {self._entity_name}"
self._unique_id = f"{dev_id}-{sensor}"
@property
def device_class(self):
"""Device class of this entity."""
return self._dev_class
@property
def icon(self):
"""Return the icon of this entity."""
return self._icon
@property
def native_value(self):
"""Return the state of this entity."""
return self._state
@property
def native_unit_of_measurement(self):
"""Return the unit of measurement of this entity, if any."""
return self._unit_of_measurement
@property
def state_class(self):
"""Return the state_class of this entity."""
return self._state_class
class PwThermostatSensor(SmileSensor):
"""Thermostat (or generic) sensor devices."""
def __init__(self, api, coordinator, name, dev_id, sensor, sensor_type):
"""Set up the Plugwise API."""
super().__init__(api, coordinator, name, dev_id, sensor)
self._icon = None
self._model = sensor_type[SENSOR_MAP_MODEL]
self._unit_of_measurement = sensor_type[SENSOR_MAP_UOM]
self._dev_class = sensor_type[SENSOR_MAP_DEVICE_CLASS]
self._state_class = sensor_type[SENSOR_MAP_STATE_CLASS]
@callback
def _async_process_data(self):
"""Update the entity."""
data = self._api.get_device_data(self._dev_id)
if not data:
_LOGGER.error("Received no data for device %s", self._entity_name)
self.async_write_ha_state()
return
if data.get(self._sensor) is not None:
self._state = data[self._sensor]
self._icon = CUSTOM_ICONS.get(self._sensor, self._icon)
self.async_write_ha_state()
class PwAuxDeviceSensor(SmileSensor):
"""Auxiliary Device Sensors."""
def __init__(self, api, coordinator, name, dev_id, sensor):
"""Set up the Plugwise API."""
super().__init__(api, coordinator, name, dev_id, sensor)
self._cooling_state = False
self._heating_state = False
@callback
def _async_process_data(self):
"""Update the entity."""
data = self._api.get_device_data(self._dev_id)
if not data:
_LOGGER.error("Received no data for device %s", self._entity_name)
self.async_write_ha_state()
return
if data.get("heating_state") is not None:
self._heating_state = data["heating_state"]
if data.get("cooling_state") is not None:
self._cooling_state = data["cooling_state"]
self._state = "idle"
self._icon = IDLE_ICON
if self._heating_state:
self._state = "heating"
self._icon = FLAME_ICON
if self._cooling_state:
self._state = "cooling"
self._icon = COOL_ICON
self.async_write_ha_state()
class PwPowerSensor(SmileSensor):
"""Power sensor entities."""
def __init__(self, api, coordinator, name, dev_id, sensor, sensor_type, model):
"""Set up the Plugwise API."""
super().__init__(api, coordinator, name, dev_id, sensor)
self._icon = None
self._model = model
if model is None:
self._model = sensor_type[SENSOR_MAP_MODEL]
self._unit_of_measurement = sensor_type[SENSOR_MAP_UOM]
self._dev_class = sensor_type[SENSOR_MAP_DEVICE_CLASS]
self._state_class = sensor_type[SENSOR_MAP_STATE_CLASS]
if dev_id == self._api.gateway_id:
self._model = "P1 DSMR"
@callback
def _async_process_data(self):
"""Update the entity."""
data = self._api.get_device_data(self._dev_id)
if not data:
_LOGGER.error("Received no data for device %s", self._entity_name)
self.async_write_ha_state()
return
if data.get(self._sensor) is not None:
self._state = data[self._sensor]
self._icon = CUSTOM_ICONS.get(self._sensor, self._icon)
self.async_write_ha_state()
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19713,
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13,
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11501,
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12636,
488,
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29918,
2350,
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3859,
580,
13,
13,
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DigiroadPreDataAnalysis/digiroad/util/__init__.py | DigitalGeographyLab/Car-Routing | 2 | 119206 | <filename>DigiroadPreDataAnalysis/digiroad/util/__init__.py
import configparser
import csv
import datetime
import json
import logging
import logging.config
import numpy
import os
import shutil
import time
import zipfile
from digiroad import carRoutingExceptions as exc
from digiroad.entities import Point
from pandas.io.json import json_normalize
def enum(**enums):
return type('Enum', (), enums)
carRountingDictionary = {
"pituus": "distance",
"digiroa_aa": "speed_limit_time",
"kokopva_aa": "day_average_delay_time",
"keskpva_aa": "midday_delay_time",
"ruuhka_aa": "rush_hour_delay_time"
}
CostAttributes = enum(DISTANCE='pituus',
SPEED_LIMIT_TIME='digiroa_aa',
DAY_AVG_DELAY_TIME='kokopva_aa',
MIDDAY_DELAY_TIME='keskpva_aa',
RUSH_HOUR_DELAY='ruuhka_aa',
BICYCLE_FAST_TIME='fast_time',
BICYCLE_SLOW_TIME='slow_time')
TransportModes = enum(PRIVATE_CAR='PRIVATE_CAR', BICYCLE='BICYCLE')
GeometryType = enum(POINT="Point", MULTI_POINT='MultiPoint', LINE_STRING='LineString')
PostfixAttribute = enum(EUCLIDEAN_DISTANCE="EuclideanDistance", AVG_WALKING_DISTANCE="AVGWalkingDistance",
WALKING_TIME="WalkingTime", PARKING_TIME="ParkingTime")
GPD_CRS = enum(WGS_84={'init': 'EPSG:4326'}, PSEUDO_MERCATOR={'init': 'EPSG:3857'})
def getEnglishMeaning(cost_attribute=None):
if cost_attribute in carRountingDictionary:
return carRountingDictionary[cost_attribute]
else:
return cost_attribute
def getFormattedDatetime(timemilis=time.time(), format='%Y-%m-%d %H:%M:%S'):
formattedDatetime = datetime.datetime.fromtimestamp(timemilis).strftime(format)
return formattedDatetime
def timeDifference(startTime, endTime):
totalTime = (endTime - startTime) / 60 # min
return totalTime
def getConfigurationProperties(section="WFS_CONFIG"):
config = configparser.ConfigParser()
configurationPath = os.getcwd() + "%resources%configuration.properties".replace("%", os.sep)
config.read(configurationPath)
return config[section]
def extractCRS(geojson):
epsgCode = geojson["crs"]["properties"]["name"].split(":")[-3] + ":" + \
geojson["crs"]["properties"]["name"].split(":")[-1]
return epsgCode
def createPointFromPointFeature(newFeaturePoint, epsgCode):
if newFeaturePoint["geometry"]["type"] == GeometryType.MULTI_POINT:
startNearestVertexCoordinates = newFeaturePoint["geometry"]["coordinates"][0]
elif newFeaturePoint["geometry"]["type"] == GeometryType.POINT:
startNearestVertexCoordinates = newFeaturePoint["geometry"]["coordinates"]
nearestStartPoint = Point(latitute=startNearestVertexCoordinates[1],
longitude=startNearestVertexCoordinates[0],
epsgCode=epsgCode)
return nearestStartPoint
def dgl_timer(func):
def func_wrapper(*args, **kwargs):
timerEnabled = "True".__eq__(getConfigurationProperties(section="WFS_CONFIG")["timerEnabled"])
if timerEnabled:
functionName = func.__name__
startTime = time.time()
Logger.getInstance().info("%s Start Time: %s" % (functionName, getFormattedDatetime(timemilis=startTime)))
###############################
returns = func(*args, **kwargs)
###############################
endTime = time.time()
Logger.getInstance().info("%s End Time: %s" % (functionName, getFormattedDatetime(timemilis=endTime)))
totalTime = timeDifference(startTime, endTime)
Logger.getInstance().info("%s Total Time: %s m" % (functionName, totalTime))
return returns
else:
return func(*args, **kwargs)
return func_wrapper
def dgl_timer_enabled(func):
def func_wrapper(*args, **kwargs):
functionName = func.__name__
startTime = time.time()
Logger.getInstance().info("%s Start Time: %s" % (functionName, getFormattedDatetime(timemilis=startTime)))
###############################
returns = func(*args, **kwargs)
###############################
endTime = time.time()
Logger.getInstance().info("%s End Time: %s" % (functionName, getFormattedDatetime(timemilis=endTime)))
totalTime = timeDifference(startTime, endTime)
Logger.getInstance().info("%s Total Time: %s m" % (functionName, totalTime))
return returns
return func_wrapper
class AbstractLinkedList(object):
def __init__(self):
self._next = None
def hasNext(self):
return self._next is not None
def next(self):
self._next
def setNext(self, next):
self._next = next
class Node:
def __init__(self, item):
"""
A node contains an item and a possible next node.
:param item: The referenced item.
"""
self._item = item
self._next = None
def getItem(self):
return self._item
def setItem(self, item):
self._item = item
def getNext(self):
return self._next
def setNext(self, next):
self._next = next
class LinkedList(AbstractLinkedList):
def __init__(self):
"""
Linked List implementation.
The _head is the first node in the linked list.
_next refers to the possible next node into the linked list.
And the _tail is the last node added into the linked list.
"""
self._head = None
self._next = None
self._tail = None
def hasNext(self):
"""
Veryfy if there is a possible next node in the queue of the linked list.
:return: True if there is a next node.
"""
if self._next:
return True
return False
def next(self):
"""
:return: The next available item in the queue of the linked list.
"""
item = self._next.getItem()
self._next = self._next.getNext()
return item
def add(self, newItem):
"""
Add new items into the linked list. The _tail is moving forward and create a new node ecah time that a new item
is added.
:param newItem: Item to be added.
"""
if self._head is None:
self._head = Node(newItem)
self._next = self._head
self._tail = self._head
else:
node = Node(newItem)
self._tail.setNext(node)
self._tail = node
def restart(self):
"""
Move the linked list to its initial node.
"""
self._next = self._head
self._tail = self._head
class FileActions:
def readJson(self, url):
"""
Read a json file
:param url: URL for the Json file
:return: json dictionary data
"""
with open(url) as f:
data = json.load(f)
return data
def readMultiPointJson(self, url):
"""
Read a MultiPoint geometry geojson file, in case the file do not be a MultiPoint
geometry, then an NotMultiPointGeometryException is thrown.
:param url: URL for the Json file
:return: json dictionary data
"""
data = None
with open(url) as f:
data = json.load(f)
self.checkGeometry(data, GeometryType.MULTI_POINT)
return data
def readPointJson(self, url):
"""
Read a MultiPoint geometry geojson file, in case the file do not be a MultiPoint
geometry, then an NotMultiPointGeometryException is thrown.
:param url: URL for the Json file
:return: json dictionary data
"""
data = None
with open(url) as f:
data = json.load(f)
self.checkGeometry(data, GeometryType.POINT)
return data
def checkGeometry(self, data, geometryType=GeometryType.MULTI_POINT):
"""
Check the content of the Json to verify if it is a specific geoemtry type. By default is MultiPoint.
In case the geojson do not be the given geometry type then an
:param data: json dictionary
:param geometryType: Geometry type (i.e. MultiPoint, LineString)
:return: None
"""
for feature in data["features"]:
if feature["geometry"]["type"] != geometryType:
raise exc.IncorrectGeometryTypeException("Expected %s" % geometryType)
def convertToGeojson(self, dataframe):
jsonResult = dataframe.to_json()
newJson = json.loads(jsonResult)
newJson["crs"] = {
"properties": {
"name": "urn:ogc:def:crs:%s" % (GPD_CRS.PSEUDO_MERCATOR["init"].replace(":", "::"))
},
"type": "name"
}
return newJson
def writeFile(self, folderPath, filename, data):
if not os.path.exists(folderPath):
os.makedirs(folderPath)
fileURL = folderPath + "%s%s" % (os.sep, filename)
with open(fileURL, 'w+') as outfile:
json.dump(data, outfile, sort_keys=True)
return fileURL
def createFile(self, folderPath, filename):
if not os.path.exists(folderPath):
os.makedirs(folderPath)
with open(folderPath + os.sep + filename, 'w+') as outfile:
outfile.close()
def deleteFile(self, folderPath, filename):
Logger.getInstance().info("Deleting FILE %s" % os.path.join(folderPath, filename))
if os.path.exists(folderPath + os.sep + filename):
os.remove(folderPath + os.sep + filename)
Logger.getInstance().info("The FILE %s was deleted" % os.path.join(folderPath, filename))
def deleteFolder(self, path):
Logger.getInstance().info("Deleting FOLDER %s" % path)
if os.path.exists(path):
shutil.rmtree(path)
Logger.getInstance().info("The FOLDER %s was deleted" % path)
@dgl_timer
def compressOutputFile(self, folderPath, zip_filename, filepath):
zipf = zipfile.ZipFile(folderPath + os.sep + zip_filename, "a", zipfile.ZIP_DEFLATED, allowZip64=True)
zipf.write(filepath, os.path.basename(filepath))
@dgl_timer
def transformGeojsonInDataFrame(self, geojson):
if "features" in geojson:
df = json_normalize(geojson["features"])
columns = numpy.asarray([column.replace("properties.", "") for column in df.columns.values])
df.columns = columns
return df
return None
def writeInCSV(self, folderPath, filename, valueList):
if not os.path.exists(folderPath):
os.makedirs(folderPath)
file = folderPath + os.sep + filename
if not os.path.isfile(file):
fieldList = []
attributes = getConfigurationProperties(section="ATTRIBUTES_MAPPING")
for attribute_key in attributes:
attribute_splitted = attributes[attribute_key].split(",")
key = attribute_splitted[0]
value = attribute_splitted[1]
fieldList.append(value)
with open(file, 'w', newline='') as outputFile:
writer = csv.writer(outputFile, delimiter=';')
writer.writerow(fieldList)
writer.writerow(valueList)
else:
with open(file, 'a', newline='') as outputFile:
writer = csv.writer(outputFile, delimiter=';')
writer.writerow(valueList)
def parallel_job_print(msg, msg_args):
""" Display the message on stout or stderr depending on verbosity
"""
# XXX: Not using the logger framework: need to
# learn to use logger better.
# if not self.verbose:
# return
# if self.verbose < 50:
# writer = sys.stderr.write
# else:
# writer = sys.stdout.write
msg = msg % msg_args
self = "Parallel(n_jobs=%s)" % getConfigurationProperties(section="PARALLELIZATION")["jobs"]
# writer('[%s]: %s\n' % (self, msg))
Logger.getInstance().info('[%s]: %s' % (self, msg))
class Logger:
__instance = None
__handler = None
def __init__(self):
raise Exception("Instances must be constructed with Logger.getInstance()")
@staticmethod
def configureLogger(outputFolder, prefix):
# Logger.__instance = None
log_filename = prefix + "_log - %s.log" % getFormattedDatetime(timemilis=time.time(),
format='%Y-%m-%d %H_%M_%S')
logs_folder = outputFolder + os.sep + "logs"
FileActions().createFile(logs_folder, log_filename)
if Logger.__handler is not None:
Logger.getInstance().removeHandler(Logger.__handler)
fileHandler = logging.FileHandler(logs_folder + os.sep + log_filename, 'w')
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fileHandler.setFormatter(formatter)
Logger.__handler = fileHandler
Logger.getInstance().addHandler(fileHandler)
@staticmethod
def getInstance():
if not Logger.__instance:
# configurationPath = os.getcwd() + "%resources%logging.properties".replace("%", os.sep)
# logging.config.fileConfig(configurationPath)
# create logger
Logger.__instance = logging.getLogger("CARDAT")
# "application" code
# Logger.instance.debug("debug message")
# Logger.instance.info("info message")
# Logger.instance.warn("warn message")
# Logger.instance.error("error message")
# Logger.instance.critical("critical message")
return Logger.__instance
class GeneralLogger:
def __init__(self, loggerName, outputFolder, prefix=""):
self.logger = self._createLogger(loggerName=loggerName)
self.handler = self._createLogFileHandler(outputFolder=outputFolder, prefix=prefix)
self.logger.addHandler(self.handler)
def _createLogger(self, loggerName):
configurationPath = os.getcwd() + "%resources%logging.properties".replace("%", os.sep)
logging.config.fileConfig(configurationPath)
# create logger
logger = logging.getLogger(loggerName)
return logger
def _createLogFileHandler(self, outputFolder, prefix):
log_filename = prefix + "_log - %s.log" % getFormattedDatetime(
timemilis=time.time(),
format='%Y-%m-%d %H_%M_%S'
)
logs_folder = outputFolder + os.sep + "logs"
FileActions().createFile(logs_folder, log_filename)
fileHandler = logging.FileHandler(logs_folder + os.sep + log_filename, 'w')
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
fileHandler.setFormatter(formatter)
return fileHandler
def getLogger(self):
return self.logger
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613,
13,
462,
4706,
399,
1964,
29968,
4214,
29918,
15307,
543,
29956,
2235,
292,
2481,
613,
349,
1718,
29968,
4214,
29918,
15307,
543,
29925,
935,
292,
2481,
1159,
13,
13,
19903,
29928,
29918,
11341,
29903,
353,
14115,
29898,
29956,
10749,
29918,
29947,
29946,
3790,
29915,
2344,
2396,
525,
29923,
7024,
29954,
29901,
29946,
29941,
29906,
29953,
16675,
349,
1660,
29965,
3970,
29918,
29924,
1001,
23972,
1955,
3790,
29915,
2344,
2396,
525,
29923,
7024,
29954,
29901,
29941,
29947,
29945,
29955,
29915,
1800,
13,
13,
13,
1753,
679,
24636,
6816,
273,
292,
29898,
18253,
29918,
12715,
29922,
8516,
1125,
13,
1678,
565,
3438,
29918,
12715,
297,
1559,
29934,
792,
292,
11513,
29901,
13,
4706,
736,
1559,
29934,
792,
292,
11513,
29961,
18253,
29918,
12715,
29962,
13,
1678,
1683,
29901,
13,
4706,
736,
3438,
29918,
12715,
13,
13,
13,
1753,
679,
2500,
19667,
16390,
5410,
29898,
9346,
331,
23285,
29922,
2230,
29889,
2230,
3285,
3402,
2433,
29995,
29979,
19222,
29885,
19222,
29881,
1273,
29950,
16664,
29924,
16664,
29903,
29374,
13,
1678,
20917,
16390,
5410,
353,
12865,
29889,
12673,
29889,
3166,
16394,
29898,
9346,
331,
23285,
467,
710,
615,
603,
29898,
4830,
29897,
13,
1678,
736,
20917,
16390,
5410,
13,
13,
13,
1753,
931,
29928,
17678,
29898,
2962,
2481,
29892,
1095,
2481,
1125,
13,
1678,
3001,
2481,
353,
313,
355,
2481,
448,
1369,
2481,
29897,
847,
29871,
29953,
29900,
29871,
396,
1375,
13,
1678,
736,
3001,
2481,
13,
13,
13,
1753,
679,
8614,
11857,
29898,
2042,
543,
29956,
9998,
29918,
25903,
29908,
1125,
13,
1678,
2295,
353,
2295,
16680,
29889,
3991,
11726,
580,
13,
1678,
5285,
2605,
353,
2897,
29889,
657,
29883,
9970,
580,
718,
11860,
13237,
29995,
13305,
29889,
11330,
1642,
6506,
11702,
613,
2897,
29889,
19570,
29897,
13,
1678,
2295,
29889,
949,
29898,
13305,
2605,
29897,
13,
1678,
736,
2295,
29961,
2042,
29962,
13,
13,
13,
1753,
6597,
11341,
29903,
29898,
24756,
3126,
1125,
13,
1678,
321,
567,
29887,
3399,
353,
1737,
29877,
3126,
3366,
29883,
2288,
3108,
3366,
11330,
3108,
3366,
978,
16862,
5451,
703,
29901,
1159,
14352,
29941,
29962,
718,
376,
6160,
718,
320,
13,
1669,
1737,
29877,
3126,
3366,
29883,
2288,
3108,
3366,
11330,
3108,
3366,
978,
16862,
5451,
703,
29901,
1159,
14352,
29896,
29962,
13,
1678,
736,
321,
567,
29887,
3399,
13,
13,
13,
1753,
1653,
5228,
4591,
5228,
19132,
29898,
1482,
19132,
5228,
29892,
321,
567,
29887,
3399,
1125,
13,
1678,
565,
716,
19132,
5228,
3366,
19156,
3108,
3366,
1853,
3108,
1275,
1879,
7843,
1542,
29889,
29924,
8647,
29902,
29918,
29925,
6992,
29911,
29901,
13,
4706,
1369,
29940,
799,
342,
22479,
7967,
24266,
353,
716,
19132,
5228,
3366,
19156,
3108,
3366,
1111,
24266,
3108,
29961,
29900,
29962,
13,
1678,
25342,
716,
19132,
5228,
3366,
19156,
3108,
3366,
1853,
3108,
1275,
1879,
7843,
1542,
29889,
29925,
6992,
29911,
29901,
13,
4706,
1369,
29940,
799,
342,
22479,
7967,
24266,
353,
716,
19132,
5228,
3366,
19156,
3108,
3366,
1111,
24266,
3108,
13,
13,
1678,
20471,
4763,
5228,
353,
8984,
29898,
5066,
12356,
29922,
2962,
29940,
799,
342,
22479,
7967,
24266,
29961,
29896,
1402,
13,
462,
795,
28745,
29922,
2962,
29940,
799,
342,
22479,
7967,
24266,
29961,
29900,
1402,
13,
462,
795,
321,
567,
29887,
3399,
29922,
8961,
29887,
3399,
29897,
13,
1678,
736,
20471,
4763,
5228,
13,
13,
13,
1753,
270,
3820,
29918,
20404,
29898,
9891,
1125,
13,
1678,
822,
3653,
29918,
17699,
10456,
5085,
29892,
3579,
19290,
1125,
13,
4706,
12237,
10861,
353,
376,
5574,
1642,
1649,
1837,
12035,
657,
8614,
11857,
29898,
2042,
543,
29956,
9998,
29918,
25903,
1159,
3366,
20404,
10861,
20068,
13,
4706,
565,
12237,
10861,
29901,
13,
9651,
740,
1170,
353,
3653,
17255,
978,
1649,
13,
9651,
1369,
2481,
353,
931,
29889,
2230,
580,
13,
9651,
28468,
29889,
20958,
2141,
3888,
11702,
29879,
7370,
5974,
29901,
1273,
29879,
29908,
1273,
313,
2220,
1170,
29892,
679,
2500,
19667,
16390,
5410,
29898,
9346,
331,
23285,
29922,
2962,
2481,
4961,
13,
13,
9651,
835,
13383,
7346,
4136,
13,
9651,
3639,
353,
3653,
10456,
5085,
29892,
3579,
19290,
29897,
13,
9651,
835,
13383,
7346,
4136,
13,
13,
9651,
1095,
2481,
353,
931,
29889,
2230,
580,
13,
9651,
28468,
29889,
20958,
2141,
3888,
11702,
29879,
2796,
5974,
29901,
1273,
29879,
29908,
1273,
313,
2220,
1170,
29892,
679,
2500,
19667,
16390,
5410,
29898,
9346,
331,
23285,
29922,
355,
2481,
4961,
13,
13,
9651,
3001,
2481,
353,
931,
29928,
17678,
29898,
2962,
2481,
29892,
1095,
2481,
29897,
13,
9651,
28468,
29889,
20958,
2141,
3888,
11702,
29879,
14990,
5974,
29901,
1273,
29879,
286,
29908,
1273,
313,
2220,
1170,
29892,
3001,
2481,
876,
13,
13,
9651,
736,
3639,
13,
4706,
1683,
29901,
13,
9651,
736,
3653,
10456,
5085,
29892,
3579,
19290,
29897,
13,
13,
1678,
736,
3653,
29918,
17699,
13,
13,
13,
1753,
270,
3820,
29918,
20404,
29918,
17590,
29898,
9891,
1125,
13,
1678,
822,
3653,
29918,
17699,
10456,
5085,
29892,
3579,
19290,
1125,
13,
4706,
740,
1170,
353,
3653,
17255,
978,
1649,
13,
4706,
1369,
2481,
353,
931,
29889,
2230,
580,
13,
4706,
28468,
29889,
20958,
2141,
3888,
11702,
29879,
7370,
5974,
29901,
1273,
29879,
29908,
1273,
313,
2220,
1170,
29892,
679,
2500,
19667,
16390,
5410,
29898,
9346,
331,
23285,
29922,
2962,
2481,
4961,
13,
13,
4706,
835,
13383,
7346,
4136,
13,
4706,
3639,
353,
3653,
10456,
5085,
29892,
3579,
19290,
29897,
13,
4706,
835,
13383,
7346,
4136,
13,
13,
4706,
1095,
2481,
353,
931,
29889,
2230,
580,
13,
4706,
28468,
29889,
20958,
2141,
3888,
11702,
29879,
2796,
5974,
29901,
1273,
29879,
29908,
1273,
313,
2220,
1170,
29892,
679,
2500,
19667,
16390,
5410,
29898,
9346,
331,
23285,
29922,
355,
2481,
4961,
13,
13,
4706,
3001,
2481,
353,
931,
29928,
17678,
29898,
2962,
2481,
29892,
1095,
2481,
29897,
13,
4706,
28468,
29889,
20958,
2141,
3888,
11702,
29879,
14990,
5974,
29901,
1273,
29879,
286,
29908,
1273,
313,
2220,
1170,
29892,
3001,
2481,
876,
13,
13,
4706,
736,
3639,
13,
13,
1678,
736,
3653,
29918,
17699,
13,
13,
13,
1990,
25513,
6595,
287,
1293,
29898,
3318,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
13,
4706,
1583,
3032,
4622,
353,
6213,
13,
13,
1678,
822,
756,
9190,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
4622,
338,
451,
6213,
13,
13,
1678,
822,
2446,
29898,
1311,
1125,
13,
4706,
1583,
3032,
4622,
13,
13,
1678,
822,
731,
9190,
29898,
1311,
29892,
2446,
1125,
13,
4706,
1583,
3032,
4622,
353,
2446,
13,
13,
13,
1990,
9071,
29901,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2944,
1125,
13,
4706,
9995,
13,
4706,
319,
2943,
3743,
385,
2944,
322,
263,
1950,
2446,
2943,
29889,
13,
4706,
584,
3207,
2944,
29901,
450,
16180,
2944,
29889,
13,
4706,
9995,
13,
4706,
1583,
3032,
667,
353,
2944,
13,
4706,
1583,
3032,
4622,
353,
6213,
13,
13,
1678,
822,
679,
2001,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
667,
13,
13,
1678,
822,
731,
2001,
29898,
1311,
29892,
2944,
1125,
13,
4706,
1583,
3032,
667,
353,
2944,
13,
13,
1678,
822,
679,
9190,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
4622,
13,
13,
1678,
822,
731,
9190,
29898,
1311,
29892,
2446,
1125,
13,
4706,
1583,
3032,
4622,
353,
2446,
13,
13,
13,
1990,
28547,
1293,
29898,
9118,
6595,
287,
1293,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
13,
4706,
9995,
13,
4706,
28547,
2391,
5314,
29889,
13,
13,
4706,
450,
903,
2813,
338,
278,
937,
2943,
297,
278,
9024,
1051,
29889,
13,
4706,
903,
4622,
14637,
304,
278,
1950,
2446,
2943,
964,
278,
9024,
1051,
29889,
13,
4706,
1126,
278,
903,
18237,
338,
278,
1833,
2943,
2715,
964,
278,
9024,
1051,
29889,
13,
4706,
9995,
13,
4706,
1583,
3032,
2813,
353,
6213,
13,
4706,
1583,
3032,
4622,
353,
6213,
13,
4706,
1583,
3032,
18237,
353,
6213,
13,
13,
1678,
822,
756,
9190,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
18064,
29888,
29891,
565,
727,
338,
263,
1950,
2446,
2943,
297,
278,
9521,
310,
278,
9024,
1051,
29889,
13,
13,
4706,
584,
2457,
29901,
5852,
565,
727,
338,
263,
2446,
2943,
29889,
13,
4706,
9995,
13,
4706,
565,
1583,
3032,
4622,
29901,
13,
9651,
736,
5852,
13,
13,
4706,
736,
7700,
13,
13,
1678,
822,
2446,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
584,
2457,
29901,
450,
2446,
3625,
2944,
297,
278,
9521,
310,
278,
9024,
1051,
29889,
13,
4706,
9995,
13,
4706,
2944,
353,
1583,
3032,
4622,
29889,
657,
2001,
580,
13,
4706,
1583,
3032,
4622,
353,
1583,
3032,
4622,
29889,
657,
9190,
580,
13,
4706,
736,
2944,
13,
13,
1678,
822,
788,
29898,
1311,
29892,
716,
2001,
1125,
13,
4706,
9995,
13,
4706,
3462,
716,
4452,
964,
278,
9024,
1051,
29889,
450,
903,
18237,
338,
8401,
6375,
322,
1653,
263,
716,
2943,
21226,
801,
931,
393,
263,
716,
2944,
13,
4706,
338,
2715,
29889,
13,
13,
4706,
584,
3207,
716,
2001,
29901,
10976,
304,
367,
2715,
29889,
13,
4706,
9995,
13,
4706,
565,
1583,
3032,
2813,
338,
6213,
29901,
13,
9651,
1583,
3032,
2813,
353,
9071,
29898,
1482,
2001,
29897,
13,
9651,
1583,
3032,
4622,
353,
1583,
3032,
2813,
13,
9651,
1583,
3032,
18237,
353,
1583,
3032,
2813,
13,
4706,
1683,
29901,
13,
9651,
2943,
353,
9071,
29898,
1482,
2001,
29897,
13,
9651,
1583,
3032,
18237,
29889,
842,
9190,
29898,
3177,
29897,
13,
9651,
1583,
3032,
18237,
353,
2943,
13,
13,
1678,
822,
10715,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
25249,
278,
9024,
1051,
304,
967,
2847,
2943,
29889,
13,
4706,
9995,
13,
4706,
1583,
3032,
4622,
353,
1583,
3032,
2813,
13,
4706,
1583,
3032,
18237,
353,
1583,
3032,
2813,
13,
13,
13,
1990,
3497,
26525,
29901,
13,
1678,
822,
1303,
8148,
29898,
1311,
29892,
3142,
1125,
13,
4706,
9995,
13,
4706,
7523,
263,
4390,
934,
13,
4706,
584,
3207,
3142,
29901,
3988,
363,
278,
14355,
934,
13,
4706,
584,
2457,
29901,
4390,
8600,
848,
13,
4706,
9995,
13,
4706,
411,
1722,
29898,
2271,
29897,
408,
285,
29901,
13,
9651,
848,
353,
4390,
29889,
1359,
29898,
29888,
29897,
13,
4706,
736,
848,
13,
13,
1678,
822,
1303,
15329,
5228,
8148,
29898,
1311,
29892,
3142,
1125,
13,
4706,
9995,
13,
4706,
7523,
263,
14974,
5228,
16303,
1737,
29877,
3126,
934,
29892,
297,
1206,
278,
934,
437,
451,
367,
263,
14974,
5228,
13,
4706,
16303,
29892,
769,
385,
2216,
15329,
5228,
7999,
7843,
2451,
338,
12005,
29889,
13,
13,
4706,
584,
3207,
3142,
29901,
3988,
363,
278,
14355,
934,
13,
4706,
584,
2457,
29901,
4390,
8600,
848,
13,
4706,
9995,
13,
4706,
848,
353,
6213,
13,
4706,
411,
1722,
29898,
2271,
29897,
408,
285,
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] |
diana/toolbox/parse_mesh.py | quimaguirre/diana | 3 | 70315 | import networkx
def main():
base_dir = "/home/emre/data/ontology/mesh/"
rel_file = base_dir + "mtrees2013.bin"
m = MESH(rel_file)
g = m.get_ontology()
#print len(g.nodes()), len(g.edges())
#concept = "Mycoses"
concept = "Diabetes Mellitus, Type 2"
concept_ids = m.get_concept_ids(concept)
for concept_id in concept_ids:
print concept_id, g.edges([concept_id])
for u, v in g.edges([concept_id]):
print v, m.get_concept(v)
return
def get_mesh_term_to_tree_ids(rel_file):
m = MESH(rel_file)
g = m.get_ontology()
return m.concept_to_concept_ids
class MESH(object):
def __init__(self, file_name):
self.file_name = file_name
self.delim = ";"
self.ontology = None
self.concept_to_concept_ids = None
self.concept_id_to_concept = None
self.root_concept_ids = None
return
def get_concept_ids(self, concept):
return self.concept_to_concept_ids[concept]
def get_concept(self, concept_id):
return self.concept_id_to_concept[concept_id]
def get_ontology(self):
if self.ontology is not None:
return self.ontology
self.ontology = networkx.DiGraph()
self.concept_to_concept_ids = {}
self.concept_id_to_concept = {}
self.root_concept_ids = []
f = open(self.file_name)
for line in f:
words = line.strip("\n").split(self.delim)
concept = words[0]
concept_id = words[1]
self.concept_id_to_concept[concept_id] = concept
self.concept_to_concept_ids.setdefault(concept, []).append(concept_id)
inner_words = concept_id.split(".")
if len(inner_words) == 1:
self.root_concept_ids.append(inner_words[0])
else:
parent_id = ".".join(inner_words[:-1])
self.ontology.add_edge(parent_id, concept_id)
return self.ontology
if __name__ == "__main__":
main()
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] |
day_24/day_24.py | GuillaumeGandon/advent-of-code-2015 | 0 | 136872 | from math import prod
from itertools import combinations
weights = tuple(map(int, open('input').read().splitlines()))
def solve(nb_group):
targeted_weight = sum(weights) // nb_group
i = 0
valid_combinations = None
while not valid_combinations:
i += 1
valid_combinations = tuple(
comb
for comb in combinations(weights, i)
if sum(comb) == targeted_weight
)
return min(map(prod, valid_combinations))
print(f"Answer part one: {solve(nb_group=3)}")
print(f"Answer part two: {solve(nb_group=4)}")
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2
] |
tests/test_overlap.py | opendatacube/odc-geo | 5 | 127666 | # This file is part of the Open Data Cube, see https://opendatacube.org for more information
#
# Copyright (c) 2015-2020 ODC Contributors
# SPDX-License-Identifier: Apache-2.0
import math
from random import uniform
import numpy as np
import pytest
from affine import Affine
from odc.geo import CRS, geom, resyx_, wh_, xy_
from odc.geo.geobox import GeoBox, scaled_down_geobox
from odc.geo.gridspec import GridSpec
from odc.geo.math import affine_from_pts, decompose_rws, is_affine_st, stack_xy
from odc.geo.overlap import (
LinearPointTransform,
ReprojectInfo,
_can_paste,
compute_axis_overlap,
compute_output_geobox,
compute_reproject_roi,
get_scale_at_point,
native_pix_transform,
)
from odc.geo.roi import (
roi_is_empty,
roi_normalise,
roi_shape,
scaled_down_roi,
scaled_up_roi,
)
from odc.geo.testutils import AlbersGS, epsg3577, epsg3857, epsg4326, mkA
def diff_affine(A: Affine, B: Affine) -> float:
return math.sqrt(sum((a - b) ** 2 for a, b in zip(A, B)))
def test_affine_checks():
assert is_affine_st(mkA(scale=(1, 2), translation=(3, -10))) is True
assert is_affine_st(mkA(scale=(1, -2), translation=(-3, -10))) is True
assert is_affine_st(mkA(rot=0.1)) is False
assert is_affine_st(mkA(shear=0.4)) is False
def test_affine_rsw():
def run_test(a, scale, shear=0, translation=(0, 0), tol=1e-8):
A = mkA(a, scale=scale, shear=shear, translation=translation)
R, W, S = decompose_rws(A)
assert diff_affine(A, R * W * S) < tol
assert diff_affine(S, mkA(0, scale)) < tol
assert diff_affine(R, mkA(a, translation=translation)) < tol
for a in (0, 12, 45, 33, 67, 89, 90, 120, 170):
run_test(a, (1, 1))
run_test(a, (0.5, 2))
run_test(-a, (0.5, 2))
run_test(a, (1, 2))
run_test(-a, (1, 2))
run_test(a, (2, -1))
run_test(-a, (2, -1))
run_test(0, (3, 4), 10)
run_test(-33, (3, -1), 10, translation=(100, -333))
def test_fit():
def run_test(A, n, tol=1e-5):
X = [xy_(uniform(0, 1), uniform(0, 1)) for _ in range(n)]
Y = [xy_(A * pt.xy) for pt in X]
A_ = affine_from_pts(X, Y)
assert diff_affine(A, A_) < tol
A = mkA(13, scale=(3, 4), shear=3, translation=(100, -3000))
run_test(A, 3)
run_test(A, 10)
run_test(mkA(), 3)
run_test(mkA(), 10)
def test_scale_at_point():
def mk_transform(sx, sy):
A = mkA(37, scale=(sx, sy), translation=(2127, 93891))
return LinearPointTransform(A)
tol = 1e-4
pt = xy_(0, 0)
for sx, sy in [(3, 4), (0.4, 0.333)]:
tr = mk_transform(sx, sy)
sx_, sy_ = get_scale_at_point(pt, tr).xy
assert abs(sx - sx_) < tol
assert abs(sy - sy_) < tol
sx_, sy_ = get_scale_at_point(pt, tr, 0.1).xy
assert abs(sx - sx_) < tol
assert abs(sy - sy_) < tol
def test_pix_transform():
pt = tuple(
int(x / 10) * 10
for x in geom.point(145, -35, epsg4326).to_crs(epsg3577).coords[0]
)
A = mkA(scale=(20, -20), translation=pt)
src = GeoBox((512, 1024), A, epsg3577)
dst = GeoBox.from_geopolygon(src.geographic_extent, resyx_(0.0001, -0.0001))
tr = native_pix_transform(src, dst)
pts_src = [xy_(0, 0), xy_(10, 20), xy_(300, 200)]
pts_dst = tr(pts_src)
pts_src_ = tr.back(pts_dst)
np.testing.assert_almost_equal(stack_xy(pts_src), stack_xy(pts_src_))
assert tr.linear is None
assert repr(tr).startswith("GbxPointTransform(")
# check identity transform
tr = native_pix_transform(src, src)
pts_src = [xy_(0, 0), xy_(10, 20), xy_(300, 200)]
pts_dst = tr(pts_src)
pts_src_ = tr.back(pts_dst)
np.testing.assert_almost_equal(stack_xy(pts_src), stack_xy(pts_src_))
np.testing.assert_almost_equal(stack_xy(pts_src), stack_xy(pts_dst))
assert tr.linear is not None
assert tr.back.linear is not None
assert tr.back.back is tr
assert repr(tr).startswith("LinearPointTransform(")
# check scale only change
tr = native_pix_transform(src, scaled_down_geobox(src, 2))
pts_dst = tr(pts_src)
pts_src_ = tr.back(pts_dst)
assert tr.linear is not None
assert tr.back.linear is not None
assert tr.back.back is tr
np.testing.assert_almost_equal(
stack_xy(pts_dst), [(pt.x / 2, pt.y / 2) for pt in pts_src]
)
np.testing.assert_almost_equal(stack_xy(pts_src), stack_xy(pts_src_))
def test_compute_reproject_roi():
src = AlbersGS.tile_geobox((15, -40))
dst = GeoBox.from_geopolygon(
src.extent.to_crs(epsg3857).buffer(10), resolution=src.resolution
)
rr = compute_reproject_roi(src, dst)
assert rr.roi_src == np.s_[0 : src.height, 0 : src.width]
assert 0 < rr.scale < 1
assert rr.transform.linear is None
assert rr.transform.back is not None
assert rr.transform.back.linear is None
# check pure translation case
roi_ = np.s_[113:-100, 33:-10]
rr = compute_reproject_roi(src, src[roi_])
assert rr.roi_src == roi_normalise(roi_, src.shape)
assert rr.scale == 1
rr = compute_reproject_roi(src, src[roi_], padding=0, align=0)
assert rr.roi_src == roi_normalise(roi_, src.shape)
assert rr.scale == 1
# check pure translation case
roi_ = np.s_[113:-100, 33:-10]
rr = compute_reproject_roi(src, src[roi_], align=256)
assert rr.roi_src == np.s_[0 : src.height, 0 : src.width]
assert rr.scale == 1
roi_ = np.s_[113:-100, 33:-10]
rr = compute_reproject_roi(src, src[roi_])
assert rr.scale == 1
assert roi_shape(rr.roi_src) == roi_shape(rr.roi_dst)
assert roi_shape(rr.roi_dst) == src[roi_].shape
# check pasteable zoom_out
dst = src.zoom_out(2)
rr = compute_reproject_roi(src, dst)
assert rr.paste_ok is True
assert rr.read_shrink == 2
assert roi_shape(rr.roi_src) == src.shape
assert roi_shape(rr.roi_dst) == dst.shape
# check non-pasteable zoom_out
dst = src[1:, :].zoom_out(2)
rr = compute_reproject_roi(src, dst)
assert rr.paste_ok is False
assert rr.read_shrink == 2
def test_compute_reproject_roi_paste():
src = GeoBox(
wh_(1000, 2000),
mkA(scale=(10, -10), translation=(10 * 123, -10 * 230)),
epsg3857,
)
def _check(src: GeoBox, dst: GeoBox, rr: ReprojectInfo):
assert rr.read_shrink >= 1
if roi_is_empty(rr.roi_src):
assert roi_is_empty(rr.roi_dst)
return
if rr.paste_ok:
if rr.read_shrink == 1:
assert roi_shape(rr.roi_src) == roi_shape(rr.roi_dst)
assert src[rr.roi_src].shape == dst[rr.roi_dst].shape
else:
# roi source must align to read scale
# => round-triping roi to overview and back should not change roi
assert (
scaled_up_roi(
scaled_down_roi(rr.roi_src, rr.read_shrink), rr.read_shrink
)
== rr.roi_src
)
src_ = src[rr.roi_src].zoom_out(rr.read_shrink)
assert src_.shape == dst[rr.roi_dst].shape
if rr.read_shrink == 1:
assert rr.scale <= 1.1
else:
assert rr.scale >= rr.read_shrink
if src.crs == dst.crs:
_src = src[rr.roi_src].extent
_dst = dst[rr.roi_dst].extent
else:
_src = src[rr.roi_src].geographic_extent
_dst = dst[rr.roi_dst].geographic_extent
assert _src.intersection(_dst).area > 0
def _yes(src: GeoBox, dst: GeoBox, **kw):
rr = compute_reproject_roi(src, dst, **kw)
assert rr.paste_ok is True
_check(src, dst, rr)
def _no_(src: GeoBox, dst: GeoBox, **kw):
rr = compute_reproject_roi(src, dst, **kw)
assert rr.paste_ok is False
_check(src, dst, rr)
t_ = Affine.translation
s_ = Affine.scale
# plain pixel aligned translation
_yes(src, src)
_yes(src, src[10:, 29:])
_yes(src[10:, 29:], src)
# subpixel translation below threshhold
_no_(src, src * t_(0.3, 0.3))
_yes(src, src * t_(0.3, 0.3), ttol=0.5)
_no_(src, src * t_(0.0, 0.1))
_yes(src, src * t_(0.0, 0.1), ttol=0.15)
_no_(src, src * t_(-0.1, 0.0))
_yes(src, src * t_(-0.1, 0.0), ttol=0.15)
# tiny scale deviations
_no_(src, src[20:, :30] * s_(1.003, 1.003))
_yes(src, src[20:, :30] * s_(1.003, 1.003), stol=0.01)
# integer shrink
_no_(src, src.zoom_out(2.3))
_yes(src, src.zoom_out(2))
_yes(src, src.zoom_out(3))
_yes(src, src.zoom_out(2 + 1e-5)) # rounding issues should not matter
_no_(src.zoom_out(3), src)
_no_(src.zoom_out(2), src)
# integer shrink but with sub-pixel translation after shrinking
_yes(src, src[4:, 8:].zoom_out(4))
_no_(src, src[2:, 8:].zoom_out(4))
_no_(src, src[8:, 3:].zoom_out(4))
_yes(src, src[8:, 3:].zoom_out(4), ttol=0.5)
def test_compute_reproject_roi_issue647():
"""In some scenarios non-overlapping geoboxes will result in non-empty
`roi_dst` even though `roi_src` is empty.
Test this case separately.
"""
src = GeoBox(
(10980, 10980), Affine(10, 0, 300000, 0, -10, 5900020), CRS("epsg:32756")
)
dst = GeoBox((976, 976), Affine(10, 0, 1730240, 0, -10, -4170240), CRS("EPSG:3577"))
assert src.extent.overlaps(dst.extent.to_crs(src.crs)) is False
rr = compute_reproject_roi(src, dst)
assert roi_is_empty(rr.roi_src)
assert roi_is_empty(rr.roi_dst)
def test_compute_reproject_roi_issue1047():
"""`compute_reproject_roi(geobox, geobox[roi])` sometimes returns
`src_roi != roi`, when `geobox` has (1) tiny pixels and (2) oddly
sized `alignment`.
Test this issue is resolved.
"""
geobox = GeoBox(
(3000, 3000),
Affine(
0.00027778, 0.0, 148.72673054908861, 0.0, -0.00027778, -34.98825802556622
),
"EPSG:4326",
)
src_roi = np.s_[2800:2810, 10:30]
rr = compute_reproject_roi(geobox, geobox[src_roi])
assert rr.paste_ok is True
assert rr.roi_src == src_roi
assert rr.roi_dst == np.s_[0:10, 0:20]
def test_compute_reproject_roi_overhang():
"""
Images with global coverage in epsg:4326 often have slightly
wrong georegistration that causes image boundaries to reach outside
of the [-180, -90, 180, 90] bounding box.
Reproject roi introduces clipping to deal with that issue.
"""
tol = 1e-3
src_geobox = GeoBox.from_bbox(
(-180 - tol, -90 - tol, 180 + tol, 90 + tol),
epsg4326,
shape=wh_(2000, 1000),
tight=True,
)
assert src_geobox.shape.wh == (2000, 1000)
assert src_geobox.extent.boundingbox[0] < -180
assert src_geobox.extent.boundingbox[1] < -90
assert src_geobox.extent.boundingbox[2] > +180
assert src_geobox.extent.boundingbox[3] > +90
dst_geobox = GridSpec.web_tiles(0)[0, 0]
rr = compute_reproject_roi(src_geobox, dst_geobox)
assert rr.paste_ok is False
assert dst_geobox[rr.roi_dst] == dst_geobox
def test_axis_overlap():
s_ = np.s_
# Source overlaps destination fully
#
# S: |<--------------->|
# D: |<----->|
assert compute_axis_overlap(100, 20, 1, 10) == s_[10:30, 0:20]
assert compute_axis_overlap(100, 20, 2, 10) == s_[10:50, 0:20]
assert compute_axis_overlap(100, 20, 0.25, 10) == s_[10:15, 0:20]
assert compute_axis_overlap(100, 20, -1, 80) == s_[60:80, 0:20]
assert compute_axis_overlap(100, 20, -0.5, 50) == s_[40:50, 0:20]
assert compute_axis_overlap(100, 20, -2, 90) == s_[50:90, 0:20]
# Destination overlaps source fully
#
# S: |<-------->|
# D: |<----------------->|
assert compute_axis_overlap(10, 100, 1, -10) == s_[0:10, 10:20]
assert compute_axis_overlap(10, 100, 2, -10) == s_[0:10, 5:10]
assert compute_axis_overlap(10, 100, 0.5, -10) == s_[0:10, 20:40]
assert compute_axis_overlap(10, 100, -1, 11) == s_[0:10, 1:11]
# Partial overlaps
#
# S: |<----------->|
# D: |<----------->|
assert compute_axis_overlap(10, 10, 1, 3) == s_[3:10, 0:7]
assert compute_axis_overlap(10, 15, 1, 3) == s_[3:10, 0:7]
# S: |<----------->|
# D: |<----------->|
assert compute_axis_overlap(10, 10, 1, -5) == s_[0:5, 5:10]
assert compute_axis_overlap(50, 10, 1, -5) == s_[0:5, 5:10]
# No overlaps
# S: |<--->|
# D: |<--->|
assert compute_axis_overlap(10, 10, 1, 11) == s_[10:10, 0:0]
assert compute_axis_overlap(10, 40, 1, 11) == s_[10:10, 0:0]
# S: |<--->|
# D: |<--->|
assert compute_axis_overlap(10, 10, 1, -11) == s_[0:0, 10:10]
assert compute_axis_overlap(40, 10, 1, -11) == s_[0:0, 10:10]
def test_can_paste():
assert _can_paste(mkA(translation=(10, -20))) == (True, None)
assert _can_paste(mkA(scale=(10, 10))) == (True, None)
assert _can_paste(mkA(scale=(-10, 10), translation=(0, -4 * 10))) == (True, None)
assert _can_paste(mkA(shear=0.3)) == (False, "has rotation or shear")
assert _can_paste(mkA(rot=30)) == (False, "has rotation or shear")
assert _can_paste(mkA(scale=(-11.1, 11.1))) == (False, "non-integer scale")
assert _can_paste(mkA(scale=(0.5, 0.5))) == (False, "non-integer scale")
assert _can_paste(mkA(scale=(2, 3))) == (False, "sx!=sy, probably")
assert _can_paste(mkA(scale=(-10, 10), translation=(0, -4))) == (
False,
"sub-pixel translation",
)
assert _can_paste(mkA(scale=(-10, 10), translation=(-4, 10))) == (
False,
"sub-pixel translation",
)
assert _can_paste(mkA(translation=(0, 0.4))) == (False, "sub-pixel translation")
assert _can_paste(mkA(translation=(0.4, 0))) == (False, "sub-pixel translation")
def test_compute_output_geobox():
# sentinel2 over Gibraltar strait
src = GeoBox.from_bbox(
[199980, 3890220, 309780, 4000020], "EPSG:32630", resolution=10
)
# just copy resolution since both in meters
dst = compute_output_geobox(src, "epsg:6933")
assert dst.crs.units == src.crs.units
assert dst.crs == "epsg:6933"
assert dst.resolution == src.resolution
assert dst.geographic_extent.contains(src.geographic_extent)
assert compute_output_geobox(src, "epsg:6933") == src.to_crs("epsg:6933")
assert compute_output_geobox(
src, "epsg:6933", resolution="auto"
) == compute_output_geobox(src, "epsg:6933", resolution="same")
# force estimation of new resolution
dst = compute_output_geobox(src, "epsg:6933", resolution="fit")
assert dst.crs == "epsg:6933"
assert dst.resolution != src.resolution
assert dst.resolution.x == -dst.resolution.y
assert dst.geographic_extent.contains(src.geographic_extent)
# check conversion to lon/lat
dst = compute_output_geobox(src, "epsg:4326")
assert dst.crs == "epsg:4326"
assert dst.resolution != src.resolution
assert dst.resolution.x == -dst.resolution.y
assert dst.geographic_extent.contains(src.geographic_extent)
npix_change = (src.shape[0] * src.shape[1]) / (dst.shape[0] * dst.shape[1])
assert 0.8 < npix_change < 1.1
# go back from 4326
_src = dst
dst = compute_output_geobox(_src, src.crs)
npix_change = (_src.shape[0] * _src.shape[1]) / (dst.shape[0] * dst.shape[1])
assert 0.8 < npix_change < 1.1
assert dst.geographic_extent.contains(_src.geographic_extent)
# test bad input
with pytest.raises(ValueError):
_ = compute_output_geobox(src, "epsg:6933", resolution="bad-one")
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weekly_challenge/week3/solution.py | WooJin1993/coding_test | 0 | 179563 | <reponame>WooJin1993/coding_test
def get_rotated(matrix):
return [list(reversed(m)) for m in zip(*matrix)]
def get_expanded(game_board):
N = len(game_board)
expanded = [[1] * (N+2) for _ in range(N + 2)]
for x in range(N):
for y in range(N):
expanded[1 + x][1 + y] = game_board[x][y]
return expanded
def can_fill(puzzle, expanded):
M, N = len(puzzle), len(expanded)
N -= 2
def check(x, y):
for i in range(M):
for j in range(M):
if puzzle[i][j] == 1:
if expanded[x + i][y + j] == 0 and expanded[x + i][y + j] == 0
return
for x in range(1, N + 1):
for y in range(1, N + 1):
def extract_puzzle(table):
def solution(game_board, table):
answer = -1
return answer
# --- 모범 답안 ---
from collections import Counter
from dataclasses import dataclass
from itertools import product
@dataclass(frozen=True)
class Pos:
x: int
y: int
def neighbors(self):
return [
Pos(self.x, self.y - 1),
Pos(self.x + 1, self.y),
Pos(self.x, self.y + 1),
Pos(self.x - 1, self.y),
]
def get_rotated(matrix):
return tuple(tuple(reversed(m)) for m in zip(*matrix))
def make_tile_from_positions(positions):
# Smallest possible representation with rotation
positions = set(positions)
xs = [pos.x for pos in positions]
min_x = min(xs)
max_x = max(xs)
ys = [pos.y for pos in positions]
min_y = min(ys)
max_y = max(ys)
tile_representations = [
tuple(
tuple(Pos(i, j) in positions for j in range(min_y, max_y + 1)) for i in range(min_x, max_x + 1)
)
]
for __ in range(3):
tile_representations.append(get_rotated(tile_representations[-1]))
return min(tile_representations)
def get_tile_size(tile):
return sum(sum(row) for row in tile)
def parse_tiles(board, tile_value=1):
n = len(board)
# Add sentinel boundaries
sentinel = 1 - tile_value
board = [
[sentinel] * (n + 2),
*([sentinel] + row + [sentinel] for row in board),
[sentinel] * (n + 2),
]
# Detect tiles
tile_positions = []
for i, j in product(range(1, n + 1), range(1, n + 1)):
if board[i][j] == tile_value:
stack = [Pos(i, j)]
squares = []
while stack:
curr = stack.pop()
board[curr.x][curr.y] = sentinel
squares.append(curr)
for neighbor in curr.neighbors():
if board[neighbor.x][neighbor.y] == tile_value:
stack.append(neighbor)
tile_positions.append(squares)
# Make tiles
tiles = [make_tile_from_positions(p) for p in tile_positions]
return tiles
def solution(game_board, table):
tiles = parse_tiles(table, 1)
empty_spaces = parse_tiles(game_board, 0)
tile_counter = Counter(tiles)
empty_space_counter = Counter(empty_spaces)
used_tiles = tile_counter & empty_space_counter
return sum(get_tile_size(tile) * occ for tile, occ in used_tiles.items())
# --- 모범 답안 2 ---
from collections import defaultdict, deque
# 아래, 오른쪽, 위, 왼쪽
x_move = [1,0,-1,0]
y_move = [0,1,0,-1]
dirs = [(0, 1), (1, 0) , (0, -1), (-1, 0)]
table_num_dict = defaultdict(list)
board_num_dict = defaultdict(list)
def bfs(table, table_num, init, x, y):
n = len(table)
queue = deque()
queue.append((x, y))
table[x][y] = table_num
tmp_path = [x, y]
visited = [[0 for _ in range(n)] for _ in range(n)]
if init == False:
visited[x][y] = 1
while queue:
x, y = queue.popleft()
for dx, dy in dirs:
nx, ny = x + dx, y + dy
if not (0 <= nx < n and 0 <= ny < n):
continue
if init == True and table[nx][ny] == 1:
queue.append((nx, ny))
table[nx][ny] = table_num
tmp_path.extend((nx, ny))
if init == False and visited[nx][ny] != 1 and table[nx][ny] == table_num:
queue.append((nx, ny))
visited[nx][ny] = 1
tmp_path.extend((nx, ny))
table_num_dict[table_num].append(tmp_path)
def board_bfs(board, board_num, x, y):
q = deque()
q.append([x,y])
board[x][y] = board_num
tmp_path = [x,y]
while q:
now_x, now_y = q.popleft()
for i in range(len(x_move)):
next_x, next_y = now_x+x_move[i], now_y+y_move[i]
if 0 <= next_x < len(board) and 0 <= next_y < len(board):
if board[next_x][next_y] == 0:
q.append([next_x, next_y])
board[next_x][next_y] = board_num
tmp_path.extend([next_x, next_y])
board_num_dict[board_num].append(tmp_path)
def table_rotate(table):
tmp_table = [[0 for y in range(len(table))] for x in range(len(table))]
for x in range(len(tmp_table)):
for y in range(len(tmp_table)):
tmp_table[y][len(table)-1-x] = table[x][y]
return tmp_table
def solution(game_board, table):
answer = 0
#table num 2부터
table_num = 2
for x in range(len(table)):
for y in range(len(table)):
if table[x][y] == 1:
bfs(table, table_num, True, x, y)
table_num += 1
# 3번 돌림
for i in range(3):
table = table_rotate(table)
table_num_visited = []
for x in range(len(table)):
for y in range(len(table)):
if table[x][y] >= 2 and table[x][y] not in table_num_visited:
table_num = table[x][y]
table_num_visited.append(table_num)
bfs(table, table_num, False, x, y)
#board
board_num = 2
for x in range(len(game_board)):
for y in range(len(game_board)):
if game_board[x][y] == 0:
board_bfs(game_board, board_num, x, y)
board_num += 1
table_num_visited = []
for i in range(len(board_num_dict)):
board_cmp = board_num_dict[i+2][0]
find_answer = False
for j in range(len(table_num_dict)):
if (j+2) in table_num_visited:
continue
if find_answer == True:
break
for k in range(4):
table_cmp = table_num_dict[j+2][k]
if len(board_cmp) != len(table_cmp):
break
if len(board_cmp) == 1 and len(table_cmp) == 1:
answer += 1
find_answer = True
table_num_visited.append(j+2)
break
cmp_right = True
cmp_first, cmp_second = board_cmp[0]-table_cmp[0], board_cmp[1]-table_cmp[1]
for l in range(2, len(board_cmp)):
if l%2 == 0 and board_cmp[l]-table_cmp[l] != cmp_first:
cmp_right = False
break
if l%2 == 1 and board_cmp[l]-table_cmp[l] != cmp_second:
cmp_right = False
break
if cmp_right == True:
answer += len(table_cmp)//2
find_answer = True
table_num_visited.append(j+2)
break
return answer | [
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] |
scripts/utils.py | carbonplan/cdr-database | 13 | 1611579 | <filename>scripts/utils.py
import pathlib
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from pandas import DataFrame, MultiIndex
SECRET_FILE = pathlib.Path(__file__).parents[1] / 'secrets/google-sheets-key.json'
def ffill(data):
"""
helper function to forward fill column labels
"""
last = data[0]
new = []
for line in data:
if line:
new.append(line)
last = line
else:
new.append(last)
return new
def get_sheet(sheet, doc):
"""
helper function to open a specific google sheet
"""
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
credentials = ServiceAccountCredentials.from_json_keyfile_name(SECRET_FILE, scope)
gc = gspread.authorize(credentials)
wks = gc.open(doc)
sheet = wks.worksheet(sheet)
data = sheet.get_all_values()
h1 = ffill(data[0])
# remove extra whitespace
h1 = [k.strip() for k in h1]
h2 = [k.strip() for k in data[1]]
# create a multiindex
columns = MultiIndex.from_tuples(zip(h1, h2))
# populate the dataframe
df = DataFrame(data[2:], columns=columns)
return df
def make_project(id):
"""
return a template project
"""
return {
"type": "Project",
"metrics": [],
"tags": [],
"id": id,
"description": "",
"applicant": "",
}
def make_metric(name):
"""
return a template metric
"""
return {
"type": "Metric",
"name": name,
"value": "",
"units": "",
"rating": "",
"notes": "",
"comment": "",
}
def maybe_float(value):
new = value.replace("$", "").replace(",", "")
try:
return float(new)
except Exception:
return value.strip()
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src/chia_log/log_handler.py | Connor-Knabe/chiadog | 503 | 69749 | <gh_stars>100-1000
# std
from typing import Optional
# project
from src.chia_log.handlers.daily_stats.stats_manager import StatsManager
from src.chia_log.handlers.harvester_activity_handler import HarvesterActivityHandler
from src.chia_log.handlers.partial_handler import PartialHandler
from src.chia_log.handlers.block_handler import BlockHandler
from src.chia_log.handlers.finished_signage_point_handler import FinishedSignagePointHandler
from src.chia_log.handlers.wallet_added_coin_handler import WalletAddedCoinHandler
from src.chia_log.log_consumer import LogConsumerSubscriber, LogConsumer
from src.notifier.notify_manager import NotifyManager
class LogHandler(LogConsumerSubscriber):
"""This class holds a list of handlers that analyze
specific parts of the logs and generate events that
are consumed by the notifier (for user notifications).
Data flow:
LogConsumer -> LogHandler -> Notifier
Three easy steps to extend monitoring functionality
1. Create a parser for a new part of the log stream
2. Create a handler for analysing the parsed information
3. Add the new handler to the list of handlers below
"""
def __init__(
self, log_consumer: LogConsumer, notify_manager: NotifyManager, stats_manager: Optional[StatsManager] = None
):
self._notify_manager = notify_manager
self._stats_manager = stats_manager
self._handlers = [
HarvesterActivityHandler(),
PartialHandler(),
BlockHandler(),
FinishedSignagePointHandler(),
WalletAddedCoinHandler(),
]
log_consumer.subscribe(self)
def consume_logs(self, logs: str):
for handler in self._handlers:
events = handler.handle(logs, self._stats_manager)
self._notify_manager.process_events(events)
| [
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utils/perm_utils.py | IBM/NeuronAlignment | 3 | 36449 | <filename>utils/perm_utils.py
import torch
import numpy as np
def train_perm_orth(train_loader, model, optimizer, scheduler, criterion, regularizer=None, rho=1E-4, delta=0.5,
nu=1E-2, eps=1E-3, tau=1E-2, lagrange_pen=1E-2, perm_flag=True, t_step=40):
if perm_flag:
tau_min = 1E-24
tau_max = 1E-1
c = None
lam_lm = []
for p in optimizer.param_groups[0]['params']:
lam_lm.append(torch.zeros_like(p))
k_iter = 0
ts = torch.empty(len(train_loader), device=model.device).uniform_(0.0, 1.0)
with torch.no_grad():
for p in optimizer.param_groups[0]['params']:
p.data = torch.rand_like(p.data)
p.data, _, _ = torch.svd(p.data)
input_cml = []
target_cml = []
t_cml = []
inner_iter = 0
loss = 0.0
loss_obj = 0.0
for iter, (input, target) in enumerate(train_loader):
t = ts[iter]
input = input.to(model.device, non_blocking=False)
target = target.to(model.device, non_blocking=False)
output = model(input, perm_train=True, t=t)
input_all = input
target_all = target
new_loss = criterion(output, target_all)
loss_obj += new_loss
# This part is for the augmented Lagrangian method
int_pen = integer_penalty(optimizer.param_groups[0]['params'], lam_lm, lagrange_pen)
loss += new_loss + int_pen
inner_iter += 1
input_cml.append(input.clone())
target_cml.append(target.clone())
t_cml.append(t.clone())
if inner_iter % t_step == 0:
optimizer.zero_grad()
loss.backward()
grad_norm = 0.0
violator = 0.0
for p in optimizer.param_groups[0]['params']:
param_norm = p.grad.data.norm(2)
grad_norm += param_norm.item() ** 2
violator += torch.sum((torch.matmul(p.data.t(), p.data) - torch.eye(p.data.shape[0],
device=p.device)) ** 2)
grad_norm = grad_norm ** (1. / 2)
if c is None:
c = loss.clone().item()
q_opt = 1
loss_inner = loss.clone()
print('Iteration: %03d, Loss %.2E, Objective %.2E, Negative Penalty: %.2E,'
'Grad Norm: %.2E, Ortho Violation: %.2E, tau: %.2E' %
(k_iter, loss_inner.item(), loss_obj.item(), int_pen.item(), grad_norm, violator.item(), tau))
# Compute F for defining Y function
F_list = []
with torch.no_grad():
for p in optimizer.param_groups[0]['params']:
f = torch.matmul(p.grad.data, p.t().data) - torch.matmul(p.data, p.grad.t().data)
F_list.append(f)
# Store old parameters
params_old = [None] * len(optimizer.param_groups[0]['params'])
for idx, param in enumerate(optimizer.param_groups[0]['params']):
params_old[idx] = param.clone()
grads_old = [p.grad.data.clone() for p in optimizer.param_groups[0]['params']]
# Compute the values of Y(tau) and Y'(tau), store them into the model
Y_t, Y_ft_prime = compute_ytau(tau, F_list, optimizer.param_groups[0]['params'])
for p, y_t in zip(optimizer.param_groups[0]['params'], Y_t):
p.data = y_t.clone()
loss_inner = 0.0
for t_2, input_2, target_2 in zip(t_cml, input_cml, target_cml):
output = model(input_2, perm_train=True, t=t_2)
loss_inner += criterion(output, target_2)
int_pen = integer_penalty(optimizer.param_groups[0]['params'], lam_lm, lagrange_pen)
loss_inner += int_pen
optimizer.zero_grad()
loss_inner.backward()
grads_new = [p.grad.data.clone() for p in optimizer.param_groups[0]['params']]
with torch.no_grad():
dF_dt = 0.0
for g_new, y_ft_p in zip(grads_new, Y_ft_prime):
df = g_new * (y_ft_p / torch.norm(y_ft_p.data))
df = torch.sum(df)
dF_dt += df.item()
threshold_flag = True
k_inner = 0
while threshold_flag:
with torch.no_grad():
threshold = c + rho * tau * dF_dt
if loss_inner.item() >= threshold:
# Compute Y for smaller value of tau
with torch.no_grad():
tau *= delta
Y_t, Y_ft_prime = compute_ytau(tau, F_list, optimizer.param_groups[0]['params'])
for p, y_t in zip(optimizer.param_groups[0]['params'], Y_t):
p.data = y_t.clone()
loss_old = loss_inner.clone()
loss_inner = 0.0
for t_2, input_2, target_2 in zip(t_cml, input_cml, target_cml):
output = model(input_2, perm_train=True, t=t_2)
loss_inner += criterion(output, target_2)
int_pen = integer_penalty(optimizer.param_groups[0]['params'], lam_lm, lagrange_pen)
loss_inner += int_pen
optimizer.zero_grad()
loss_inner.backward()
grads_new = [p.grad.data.clone() for p in optimizer.param_groups[0]['params']]
k_inner += 1
if (loss_inner.item() - loss_old.item()) / (1 + loss_old.item()) < 1E-5:
threshold_flag = False
else:
threshold_flag = False
with torch.no_grad():
c = (nu * q_opt * c + loss_inner.item())
q_opt = nu * q_opt + 1
c = c / q_opt
bb_num = 0.0
bb_denom = 0.0
yy_sum = 0.0
for p_old, g_old, p_new, g_new in zip(params_old, grads_old, optimizer.param_groups[0]['params'],
grads_new):
s_bb = p_new - p_old
y_bb = g_new - g_old
bb_num += torch.sum(s_bb ** 2)
bb_denom += torch.sum(s_bb * y_bb)
yy_sum += torch.sum(y_bb ** 2)
tau_bb = bb_num / torch.abs(bb_denom)
tau_bb = tau_bb.item()
tau_bb2 = torch.abs(bb_denom) / yy_sum
tau_bb2 = tau_bb2.item()
tau_bb = np.minimum(tau_bb, tau_bb2)
tau = np.minimum(tau_bb, tau_max)
tau = np.maximum(tau, tau_min)
lam_lm, lagrange_pen = integer_penalty_update(optimizer.param_groups[0]['params'], lam_lm,
lagrange_pen)
loss_inner = 0.0
for t_2, input_2, target_2 in zip(t_cml, input_cml, target_cml):
output = model(input_2, perm_train=True, t=t_2)
loss_obj = criterion(output, target_2)
int_pen = integer_penalty(optimizer.param_groups[0]['params'], lam_lm, lagrange_pen)
loss_inner += loss_obj + int_pen
optimizer.zero_grad()
loss_inner.backward()
grads_new = [p.grad.data.clone() for p in optimizer.param_groups[0]['params']]
grad_norm = 0.0
for g_new in grads_new:
gn = g_new.norm(2)
grad_norm += gn.item() ** 2
grad_norm = grad_norm ** (1. / 2)
k_iter += 1
input_cml = []
target_cml = []
t_cml = []
loss = 0.0
loss_obj = 0.0
model.train()
loss_sum = 0.0
correct = 0.0
change_P = np.nan
params_before = [None] * len(optimizer.param_groups[0]['params'])
if nu is not None:
for idx, param in enumerate(optimizer.param_groups[0]['params']):
params_before[idx] = param.clone().detach()
optimizer.step()
lr = scheduler.get_lr()[0]
with torch.no_grad():
for param, param_o in zip(optimizer.param_groups[0]['params'], params_old):
param.data = 1 / (1 + lr / nu) * (param + lr / nu * param_o)
output = model(input_all, perm_train=True)
loss = criterion(output, target_all)
if regularizer is not None:
loss += regularizer(model)
loss_sum += loss.item() * input.size(0)
pred = output.data.argmax(1, keepdim=True)
correct += pred.eq(target_all.data.view_as(pred)).sum().item()
return {
'loss': loss_sum / len(train_loader.dataset),
'accuracy': correct * 100.0 / len(train_loader.dataset),
'change_perm': change_P
}
def hard_int_penalty(p_list, pen=1E1):
pen_loss = 0.0
for p in p_list:
p_mask = p.data * (p.data <= 0)
pen_loss += pen * torch.sum(p_mask ** 2)
return pen_loss
def integer_penalty(p_list, lam_list, mu):
pen_loss = 0.0
for p, lam in zip(p_list, lam_list):
mask = (p - lam / mu) <= 0
mask_alt = (p - lam / mu) > 0
p_l = torch.sum((- lam * p + 0.5 * mu * (p ** 2)) * mask)
p_l += torch.sum((-1/(2 * mu) * lam ** 2) * mask_alt)
pen_loss += p_l
return pen_loss
def integer_penalty_update(p_list, lam_list, mu):
new_lam_list = []
with torch.no_grad():
for p, lam in zip(p_list, lam_list):
upd = lam - mu * p
new_lam_list.append(upd * (upd > 0))
new_mu = mu * 1.01
return new_lam_list, new_mu
def compute_ytau(tau, f_list, p_list):
y_tau = []
y_tau_prime = []
for p, f in zip(p_list, f_list):
eye = torch.eye(f.shape[0], device=f.device)
qmat_inv = torch.inverse(eye + tau / 2 * f)
y_ft = torch.matmul(qmat_inv, eye - tau / 2 * f)
y_ft = torch.matmul(y_ft, p)
y_ft_prime = - torch.matmul(qmat_inv, f)
y_ft_prime = torch.matmul(y_ft_prime, (p + y_ft) / 2)
y_tau.append(y_ft.clone())
y_tau_prime.append(y_ft_prime.clone())
return y_tau, y_tau_prime | [
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] |
jiant/utils/python/checks.py | yzpang/jiant | 1,108 | 1614389 | from typing import Dict
def dict_equal(dict1: Dict, dict2: Dict) -> bool:
if not len(dict1) == len(dict2):
return False
for (k1, v1), (k2, v2) in zip(dict1.items(), dict2.items()):
if k1 != k2:
return False
if v1 != v2:
return False
return True
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server/favorite_things/apps/favorite/migrations/0001_initial.py | el-Joft/favorite-things | 1 | 1616323 | <reponame>el-Joft/favorite-things
# Generated by Django 2.1.4 on 2019-08-05 22:38
import django.contrib.postgres.fields.jsonb
from django.db import migrations, models
import django.db.models.deletion
import uuid
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='AuditLog',
fields=[
('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('table_name', models.CharField(blank=True, max_length=132)),
('table_fields', django.contrib.postgres.fields.jsonb.JSONField(default=dict)),
('action', models.CharField(blank=True, max_length=200)),
('timestamp', models.DateTimeField(auto_now_add=True)),
],
options={
'db_table': 'audit_log',
},
),
migrations.CreateModel(
name='Category',
fields=[
('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('name', models.CharField(max_length=100, unique=True)),
],
),
migrations.CreateModel(
name='Favorite',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100)),
('description', models.TextField(max_length=7000)),
('ranking', models.IntegerField(default=0)),
('created_at', models.DateTimeField(auto_now_add=True)),
('modified_at', models.DateTimeField(auto_now=True, null=True)),
('category', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='favorite', to='favorite.Category')),
],
),
migrations.CreateModel(
name='MetaData',
fields=[
('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('key', models.CharField(blank=True, max_length=200, null=True)),
('value', models.CharField(blank=True, max_length=200, null=True)),
('favorite', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='favorite.Favorite')),
],
),
]
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] |
Python/easy/0136_single_number.py | CalmScout/LeetCode | 0 | 1609720 | <filename>Python/easy/0136_single_number.py
"""
Given a non-empty array of integers, every element appears twice except for one. Find that single one.
Note:
Your algorithm should have a linear runtime complexity. Could you implement it without using extra memory?
Example 1:
Input: [2,2,1]
Output: 1
Example 2:
Input: [4,1,2,1,2]
Output: 4
"""
from typing import List
class Solution:
def singleNumber(self, nums: List[int]) -> int:
result = 0
for num in nums:
result ^= num
return result
if __name__ == "__main__":
assert Solution().singleNumber([2, 2, 1]) == 1
assert Solution().singleNumber([4,1,2,1,2]) == 4
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chapter15/exercises/exercise06/average.py | YordanIH/Intro_to_CS_w_Python | 0 | 1617119 | <filename>chapter15/exercises/exercise06/average.py
from typing import List
def average(values: List[float]) -> float:
"""Return the average of the numbers in values. Some items in values are None,
and they are not counted toward the average.
>>> average([20, 30])
25.0
>>> average([None, 20, 30]) 25.0
"""
count = 0 # The number of values seen so far.
total = 0 # The sum of the values seen so far.
for value in values:
if value is not None:
total += value
count += 1
if count == 0:
return None
return total / count | [
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src/sensor/electricity/__init__.py | Jochen-TPO/internet-of-washingmachines | 0 | 114701 | import time
import json
from math import sqrt
from typing import List
from multiprocessing import Process, Event, Queue, Lock
from src.base import EdgiseBase
from grove.adc import ADC
from config import cfg
import json
class ACSensor(Process, EdgiseBase):
def __init__(self, stop_event: Event, logging_q: Queue, input_q: Queue, output_q: Queue,
config_dict, resource_lock: Lock, **kwargs):
self._stop_event = stop_event
self._logging_q: Queue = logging_q
self._input_q: Queue = input_q
self._output_q: Queue = output_q
self.RMS_voltage = 230
self.VCC = 5
self._config_dict = config_dict
self._name = self._config_dict['name']
self.adc = ADC()
self.i2c_lock = resource_lock
Process.__init__(self)
EdgiseBase.__init__(self, name=self._name, logging_q=logging_q)
# config = {
# "name":str
# "PINNR":int,
# "SensorI bD":int,
# "Unit":"cm"
# "SensorType":""
# }
def read_sensor(self):
sensor_value = self.adc.read(self._config_dict['pin'])
return sensor_value
def amplitude_current(self, sensor_value):
return float(sensor_value / 1024 * self.VCC / 800 * 2000000)
def RMS_current(self, amplitude_current):
return amplitude_current / sqrt(2)
def avg_power_consumption(self, RMS_current):
return self.RMS_voltage * RMS_current
def run(self) -> None:
self.info("Starting AC sensor")
print(self._config_dict['name'])
while not self._stop_event.is_set():
self.i2c_lock.acquire()
try:
raw_val = self.read_sensor()
finally:
self.i2c_lock.release()
self.info("Raw Value: {}".format(raw_val))
amplitude_current = self.amplitude_current(raw_val)
self.info("A I Value: {}".format(amplitude_current))
rms_current = self.RMS_current(amplitude_current)
self.info("RMS I Value: {}".format(rms_current))
avg_power = self.avg_power_consumption(rms_current)
self.info("AVG W Value: {}".format(avg_power))
data = {'electricitySensorData': {
'rawVal': raw_val,
'currentAmp': amplitude_current,
'rmsCurrent': rms_current,
'avgPower': avg_power
}}
measurement = {'data': data}
self._output_q.put_nowait({'event': json.dumps(measurement)})
time.sleep(10)
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] |
PySide2Wrapper/widget.py | toodef/PySide2Wrapper | 0 | 190896 | import math
import os
from PySide2.QtOpenGL import QGLWidget
from PySide2.QtWidgets import QVBoxLayout, QHBoxLayout, QLabel, QLineEdit, QPushButton, QCheckBox, QRadioButton, \
QComboBox, QProgressBar, QTableWidget, QHeaderView, QTableWidgetItem, QFileDialog, QToolButton, QTabWidget, \
QWidget, QListWidget, QListWidgetItem, QGroupBox, QStackedLayout, QSplitter, QGraphicsView, QGraphicsScene
from PySide2.QtGui import QPixmap, QImage, QDoubleValidator, QIntValidator, QRegExpValidator, QPainterPath
from PySide2.QtCore import QObject, Signal, QDir, Qt, QRectF
from PySide2 import QtCore
from abc import ABCMeta, abstractmethod
from .utils import StateSaver
class Checkable(metaclass=ABCMeta):
@abstractmethod
def add_clicked_callback(self, callback: callable):
"""
Add callback, that will call for every click
:param callback: callback
:return: Widget object
:rtype: Widget
"""
@abstractmethod
def set_value(self, state: bool):
"""
Set state of Widget: checked or not
:return: State
:rtype: self instance
"""
@abstractmethod
def get_value(self):
"""
Get state of Widget: checked or not
:return: State
:rtype: bool
"""
class Widget:
def __init__(self, instance: QObject = None):
self._layout = QVBoxLayout()
if instance is not None:
self._instance = instance
self._enabled_dependencies = []
self._state_saver = None
self._layouts = [QVBoxLayout()]
self._cur_tab_widget = None
self._cur_splitter = None
def set_state_saver(self, saver: StateSaver):
self._state_saver = saver
def get_layout(self):
"""
Return layout of widget
:return: layout, contains Widget instance
@:rtype: QLayout
"""
self._layout.addLayout(self._layouts[-1])
return self._layout
def get_instance(self):
"""
Get Qt instance of widget
:return: instance of widget (PySide object)
:rtype QWidget
"""
return self._instance
def _may_be_enabled(self, is_enabled):
if is_enabled:
for depends in self._enabled_dependencies:
if not depends.get_value():
return False
return is_enabled
def set_enabled(self, is_enabled: bool = True):
"""
Set widget enabled
:param is_enabled: state of widget: enabled or not
:return: Widget object (self)
:rtype: Widget
"""
self._instance.setEnabled(self._may_be_enabled(is_enabled))
return self
def add_enabled_dependency(self, dependency: Checkable):
self.set_enabled(dependency.get_value())
self._enabled_dependencies.append(dependency)
dependency.add_clicked_callback(self.set_enabled)
def add_widget(self, widget: "Widget instance", need_store=False, need_stretch=True):
"""
Add widget to window layout
:param widget: Widget unit
:param need_store: is need to store state of specified widget
:param need_stretch: is need to insert stretch around widget
:return: widget instance
"""
if need_stretch:
self.get_current_layout().addStretch()
self.get_current_layout().addLayout(widget.get_layout())
if need_stretch:
self.get_current_layout().addStretch()
if need_store and self._state_saver is not None:
self._state_saver.add_widget(widget)
return widget
def add_widgets(self, widgets: "list of Widget instances"):
"""
Add list of widgets to window layout
:param widgets: Widget units
:return: None
"""
for widget in widgets:
self.get_current_layout().addLayout(widget.get_layout())
def start_horizontal(self):
"""
Start horizontal components insertion
:return: None
"""
if isinstance(self.get_current_layout(), QHBoxLayout):
return
layout = QHBoxLayout()
self.get_current_layout().addLayout(layout)
self._layouts.append(layout)
def start_vertical(self):
"""
Start vertical components insertion
:return: None
"""
if isinstance(self.get_current_layout(), QVBoxLayout):
return
layout = QVBoxLayout()
self.get_current_layout().addLayout(layout)
self._layouts.append(layout)
def group_horizontal(self, widgets: list):
"""
Place list of widgets horizontal
:param widgets: list of widgets
:return: None
"""
self.start_horizontal()
self.add_widgets(widgets)
self.cancel()
def group_vertical(self, widgets: "list of Widget instances"):
"""
Place list of widgets vertical
:param widgets: list of widgets
:return: None
"""
self.start_vertical()
self.add_widgets(widgets)
self.cancel()
def start_splitter(self, orientation='vertical'):
self._cur_splitter = QSplitter()
self._cur_splitter.setOrientation(Qt.Orientation.Horizontal if orientation == 'horizontal' else Qt.Orientation.Vertical)
self.get_current_layout().addWidget(self._cur_splitter)
def add_splitter_space(self):
self._layouts.append(QVBoxLayout())
widget = QWidget()
widget.setLayout(self.get_current_layout())
self._cur_splitter.addWidget(widget)
def get_current_layout(self):
"""
Return current layout
:return: layout of type QLayout
"""
return self._layouts[-1]
def add_to_group_box(self, group_name: str, widgets: "list of Widget instances"):
"""
Place layout to group box
:param group_name: name of group
:param widgets: list of widgets, that been placed to group
:return: None
"""
self.start_group_box(group_name)
for widget in widgets:
self.add_widget(widget)
self.cancel()
def start_group_box(self, name: str):
"""
Start group box
:return:
"""
group_box = QGroupBox(name)
group_box_layout = QVBoxLayout()
group_box.setLayout(group_box_layout)
self.get_current_layout().addWidget(group_box)
self._layouts.append(group_box_layout)
def cancel(self):
"""
Cnacel last format append
:return: None
"""
del self._layouts[-1]
def insert_text_label(self, text, is_link=False):
widget = QLabel(text)
widget.setOpenExternalLinks(is_link)
self.get_current_layout().addWidget(widget)
def insert_tab_space(self):
self._cur_tab_widget = QTabWidget()
self.get_current_layout().addWidget(self._cur_tab_widget)
def add_tab(self, name):
self._layouts.append(QVBoxLayout())
widget = QWidget()
widget.setLayout(self.get_current_layout())
self._cur_tab_widget.addTab(widget, name)
class LabeledWidget(Widget, metaclass=ABCMeta):
def __init__(self, instance: QObject = None):
super().__init__(instance)
self.__is_assembled = False
def add_label(self, text: str, position: str):
"""
Add label to widget
:param text: label text
:param position: position of label ['top', 'left', 'right', 'bottom']
:return: self value
"""
if not (position in ['top', 'bottom', 'left', 'right']):
raise Exception("Wrong position for label: " + text)
if position in ['top', 'bottom']:
self._layout = QVBoxLayout()
elif position in ['left', 'right']:
self._layout = QHBoxLayout()
if position in ['top', 'left']:
if position == 'top':
self._layout.addStretch()
self._layout.addWidget(QLabel(text))
self._layout.addWidget(self._instance)
if position == 'top':
self._layout.addStretch()
elif position in ['bottom', 'right']:
if position == 'bottom':
self._layout.addStretch()
self._layout.addWidget(self._instance)
self._layout.addWidget(QLabel(text))
if position == 'bottom':
self._layout.addStretch()
self.__is_assembled = True
return self
def get_layout(self):
"""
Return layout of widget
:return: layout
@:rtype: QLayout
"""
if not self.__is_assembled:
self._assembly()
return self._layout
@abstractmethod
def _assembly(self):
"""
Assembly object
:return: object instance
"""
class ValueContains(metaclass=ABCMeta):
@abstractmethod
def set_value(self, value):
"""
Set value to
:param value: value to set
:return: None
"""
@abstractmethod
def get_value(self):
"""
Get value
:return: current value
"""
@abstractmethod
def set_value_changed_callback(self, callback: callable):
"""
Set callback? that called when value in widget changed
:return: self object
"""
class LineEdit(LabeledWidget, ValueContains):
def __init__(self):
super().__init__(QLineEdit())
def set_data_type(self, data_type: str, regexp: str = None):
"""
Set data type? contains in LineEdit
:param data_type: type of data. String with possible values: {'int', 'double', 'regexp'}. If set 'regexp' type, need to define regexp parameter
:param regexp: regexp, that filter contain in LineEdit
:return: self instance
"""
if data_type == 'int':
self._instance.setValidator(QIntValidator())
elif data_type == 'double':
self._instance.setValidator(QDoubleValidator())
elif data_type == 'regexp':
if regexp is None:
raise Exception("Regexp doesn't specified for LineEdit")
self._instance.setValidator(QRegExpValidator(regexp))
else:
raise Exception("Incorrect data_type parameter value: '{}' in LineEdit.set_data_type".format(data_type))
return self
def set_value(self, value: str):
"""
Set value to
:param value:
:return:
"""
self._instance.setText(value)
def get_value(self):
"""
Get current value
:return: current value
"""
return self._instance.text()
def set_value_changed_callback(self, callback: callable):
self._instance.textChanged.connect(callback)
return self
def _assembly(self):
self._layout.addWidget(self._instance)
class Button(Widget):
def __init__(self, title: str, is_tool_button: bool = False):
super().__init__(QToolButton() if is_tool_button else QPushButton())
self._layout.addWidget(self._instance)
self._instance.setText(title)
def set_on_click_callback(self, callback: callable):
"""
Set callback on click event
:param callback:
:return:
"""
self._instance.clicked.connect(callback)
return self
class ImageLayout(Widget):
class QtImageViewer(QGraphicsView):
""" PyQt image viewer widget for a QPixmap in a QGraphicsView scene with mouse zooming and panning.
Displays a QImage or QPixmap (QImage is internally converted to a QPixmap).
To display any other image format, you must first convert it to a QImage or QPixmap.
Some useful image format conversion utilities:
qimage2ndarray: NumPy ndarray <==> QImage (https://github.com/hmeine/qimage2ndarray)
ImageQt: PIL Image <==> QImage (https://github.com/python-pillow/Pillow/blob/master/PIL/ImageQt.py)
Mouse interaction:
Left mouse button drag: Pan image.
Right mouse button drag: Zoom box.
Right mouse button doubleclick: Zoom to show entire image.
"""
# Mouse button signals emit image scene (x, y) coordinates.
# !!! For image (row, column) matrix indexing, row = y and column = x.
leftMouseButtonPressed = Signal(float, float)
rightMouseButtonPressed = Signal(float, float)
leftMouseButtonReleased = Signal(float, float)
rightMouseButtonReleased = Signal(float, float)
leftMouseButtonDoubleClicked = Signal(float, float)
rightMouseButtonDoubleClicked = Signal(float, float)
def __init__(self):
QGraphicsView.__init__(self)
# Image is displayed as a QPixmap in a QGraphicsScene attached to this QGraphicsView.
self.scene = QGraphicsScene()
self.setScene(self.scene)
# Store a local handle to the scene's current image pixmap.
self._pixmapHandle = None
# Image aspect ratio mode.
# !!! ONLY applies to full image. Aspect ratio is always ignored when zooming.
# Qt.IgnoreAspectRatio: Scale image to fit viewport.
# Qt.KeepAspectRatio: Scale image to fit inside viewport, preserving aspect ratio.
# Qt.KeepAspectRatioByExpanding: Scale image to fill the viewport, preserving aspect ratio.
self.aspectRatioMode = Qt.KeepAspectRatio
# Scroll bar behaviour.
# Qt.ScrollBarAlwaysOff: Never shows a scroll bar.
# Qt.ScrollBarAlwaysOn: Always shows a scroll bar.
# Qt.ScrollBarAsNeeded: Shows a scroll bar only when zoomed.
self.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOn)
self.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn)
self.setTransformationAnchor(QGraphicsView.AnchorUnderMouse)
# Stack of QRectF zoom boxes in scene coordinates.
self.zoomStack = []
# Flags for enabling/disabling mouse interaction.
self.canZoom = True
self.canPan = True
def hasImage(self):
""" Returns whether or not the scene contains an image pixmap.
"""
return self._pixmapHandle is not None
def clearImage(self):
""" Removes the current image pixmap from the scene if it exists.
"""
if self.hasImage():
self.scene.removeItem(self._pixmapHandle)
self._pixmapHandle = None
def pixmap(self):
""" Returns the scene's current image pixmap as a QPixmap, or else None if no image exists.
:rtype: QPixmap | None
"""
if self.hasImage():
return self._pixmapHandle.pixmap()
return None
def image(self):
""" Returns the scene's current image pixmap as a QImage, or else None if no image exists.
:rtype: QImage | None
"""
if self.hasImage():
return self._pixmapHandle.pixmap().toImage()
return None
def setImage(self, image):
""" Set the scene's current image pixmap to the input QImage or QPixmap.
Raises a RuntimeError if the input image has type other than QImage or QPixmap.
:type image: QImage | QPixmap
"""
if type(image) is QPixmap:
pixmap = image
elif type(image) is QImage:
pixmap = QPixmap.fromImage(image)
else:
raise RuntimeError("ImageViewer.setImage: Argument must be a QImage or QPixmap.")
if self.hasImage():
self._pixmapHandle.setPixmap(pixmap)
else:
self._pixmapHandle = self.scene.addPixmap(pixmap)
self.setSceneRect(QRectF(pixmap.rect())) # Set scene size to image size.
self.updateViewer()
def loadImageFromFile(self, fileName):
""" Load an image from file.
Without any arguments, loadImageFromFile() will popup a file dialog to choose the image file.
With a fileName argument, loadImageFromFile(fileName) will attempt to load the specified image file directly.
"""
if len(fileName) and os.path.isfile(fileName):
image = QImage(fileName)
self.setImage(image)
def updateViewer(self):
""" Show current zoom (if showing entire image, apply current aspect ratio mode).
"""
if not self.hasImage():
return
if len(self.zoomStack) and self.sceneRect().contains(self.zoomStack[-1]):
self.fitInView(self.zoomStack[-1], Qt.IgnoreAspectRatio) # Show zoomed rect (ignore aspect ratio).
else:
self.zoomStack = [] # Clear the zoom stack (in case we got here because of an invalid zoom).
self.fitInView(self.sceneRect(), self.aspectRatioMode) # Show entire image (use current aspect ratio mode).
def resizeEvent(self, event):
""" Maintain current zoom on resize.
"""
self.updateViewer()
def wheelEvent(self, event):
scale_factor = 1.1
if event.delta() > 0:
self.scale(scale_factor, scale_factor)
else:
self.scale(1 / scale_factor, 1 / scale_factor)
def mousePressEvent(self, event):
""" Start mouse pan or zoom mode.
"""
scenePos = self.mapToScene(event.pos())
if event.button() == Qt.LeftButton:
if self.canPan:
self.setDragMode(QGraphicsView.ScrollHandDrag)
self.leftMouseButtonPressed.emit(scenePos.x(), scenePos.y())
QGraphicsView.mousePressEvent(self, event)
def mouseReleaseEvent(self, event):
""" Stop mouse pan or zoom mode (apply zoom if valid).
"""
QGraphicsView.mouseReleaseEvent(self, event)
scenePos = self.mapToScene(event.pos())
if event.button() == Qt.LeftButton:
self.setDragMode(QGraphicsView.NoDrag)
self.leftMouseButtonReleased.emit(scenePos.x(), scenePos.y())
def mouseDoubleClickEvent(self, event):
""" Show entire image.
"""
scenePos = self.mapToScene(event.pos())
if event.button() == Qt.LeftButton:
self.leftMouseButtonDoubleClicked.emit(scenePos.x(), scenePos.y())
elif event.button() == Qt.RightButton:
if self.canZoom:
self.zoomStack = [] # Clear zoom stack.
self.updateViewer()
self.rightMouseButtonDoubleClicked.emit(scenePos.x(), scenePos.y())
QGraphicsView.mouseDoubleClickEvent(self, event)
def __init__(self):
super().__init__(self.QtImageViewer())
self._layout.addWidget(self._instance)
def set_image_from_data(self, image, width, height, bytes_per_line):
img = QImage(image, width, height, bytes_per_line, QImage.Format_RGB888)
pixmap = QPixmap.fromImage(img)
self._instance.setImage(pixmap)
return self
def set_image_from_file(self, file_path: str):
self._instance.setPixmap(file_path)
return self
def set_size(self, width, height):
# self.__pixmap = self.__pixmap.scaledToWidth(width).scaledToHeight(height)
# self._instance.setPixmap(self.__pixmap)
return self
def get_size(self):
return 0, 0 # self.__pixmap.width(), self.__pixmap.height()
class CheckBox(Widget, Checkable):
def __init__(self, title: str):
super().__init__(QCheckBox(title))
self._layout.addWidget(self._instance)
def add_clicked_callback(self, callback: callable):
self._instance.toggled.connect(callback)
return self
def set_value(self, state: bool):
self._instance.setChecked(state)
return self
def get_value(self):
return self._instance.isChecked()
class RadioButton(Widget, Checkable):
def __init__(self, title: str):
super().__init__(QRadioButton(title))
self._layout.addWidget(self._instance)
def set_value(self, state: bool):
self._instance.setChecked(state)
return self
def get_value(self):
return self._instance.isChecked()
def add_clicked_callback(self, callback: callable):
self._instance.toggled.connect(callback)
return self
class ComboBox(LabeledWidget, ValueContains):
def __init__(self):
super().__init__(QComboBox())
def _assembly(self):
self._layout.addWidget(self._instance)
def add_items(self, values: []):
"""
Add values to combo box
@param values: list of values
@return: self instance
"""
for v in values:
self._instance.addItem(v)
return self
def set_value(self, value: int):
self._instance.setCurrentIndex(value)
def get_value(self):
return self._instance.currentIndex()
def set_value_changed_callback(self, callback: callable):
self._instance.currentIndexChanged.connect(callback)
return self
class ProgressBar(Widget, ValueContains):
class Instance(QObject):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
value_changed = Signal(int, str)
def set_value(self, value: int, status: str = ""):
self.value_changed.emit(value, status)
def __init__(self):
super().__init__(QProgressBar())
self._layout.addWidget(self._instance)
self.__status = QLabel()
self._layout.addWidget(self.__status)
self.__InstanceCls = self.Instance()
self.__InstanceCls.value_changed.connect(self.__set_value)
def set_value(self, value: int, status: str = ""):
self.__InstanceCls.value_changed.emit(value, status)
def get_value(self):
return self._instance.getValue()
def set_value_changed_callback(self, callback: callable):
self._instance.valueChanged.connect(callback)
return self
def __set_value(self, value: int, status: str = ""):
self._instance.setValue(value)
self.__status.setText(status)
class Table(Widget):
def __init__(self):
super().__init__(QTableWidget())
self._instance.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)
self._layout.addWidget(self._instance)
def add_row(self, items: []):
row_idx = self._instance.rowCount()
self._instance.setRowCount(row_idx + 1)
for i, item in enumerate(items):
self._instance.setItem(row_idx, i, QTableWidgetItem(item))
return self
def del_row(self):
current_row = self._instance.currentRow()
self._instance.removeRow(current_row)
return self
def set_columns_headers(self, headers: []):
self._instance.setColumnCount(len(headers))
self._instance.setHorizontalHeaderLabels(headers)
return self
class PathDialog(Widget, ValueContains, metaclass=ABCMeta):
def __init__(self, label: str, button_label: str):
super().__init__(QFileDialog())
self.__label = label
self.__button_label = button_label
self._default_path = QDir.homePath()
self.__value_changed_callbacks = []
self.__line_edit = None
self.__button = None
def set_default_path(self, default_path: str):
self._default_path = default_path
return self
def get_default_path(self):
return self._default_path
def get_value(self):
res = self.__line_edit.get_value()
return None if len(res) < 1 else res
def set_value(self, value):
if self.__line_edit is not None:
self.__line_edit.set_value(value)
self.set_default_path(os.path.dirname(value))
for c in self.__value_changed_callbacks:
c(value)
def set_value_changed_callback(self, callback: callable):
self.__value_changed_callbacks.append(callback)
return self
def _update_value(self) -> None:
"""
Update path value by dialog
"""
res = self.call()
if res is None or len(res) < 1:
return
self.set_value(res)
@abstractmethod
def _call(self) -> str:
"""
Call dialog window
:return: result path
"""
def call(self) -> str:
path = self._call()
if path is None or len(path) < 1:
return None
self.set_value(path)
return path
def get_layout(self):
super().get_layout()
self._layout.addWidget(QLabel(self.__label))
self.__line_edit = LineEdit()
h_layout = QHBoxLayout()
h_layout.addLayout(self.__line_edit.get_layout())
self.__button = Button(self.__button_label, is_tool_button=True).set_on_click_callback(self._update_value)
h_layout.addLayout(self.__button.get_layout())
self._layout.addLayout(h_layout)
return self._layout
def set_enabled(self, is_enabled: bool = True):
res = self._may_be_enabled(is_enabled)
if self.__line_edit is not None:
self.__line_edit.set_enabled(res)
if self.__button is not None:
self.__button.set_enabled(res)
class OpenFile(PathDialog):
def __init__(self, label: str):
super().__init__(label, "...")
self.__files_types = ""
def set_files_types(self, types: str):
self.__files_types = types
return self
def _call(self):
return self._instance.getOpenFileName(caption="Open File", dir=self._default_path, filter=self.__files_types)[0]
class SaveFile(PathDialog):
def __init__(self, label: str):
super().__init__(label, "...")
self.__files_types = ""
def set_files_types(self, types: str):
self.__files_types = types
return self
def _call(self):
return self._instance.getSaveFileName(caption="Save File", dir=self._default_path, filter=self.__files_types)[0]
class OpenDirectory(PathDialog):
def __init__(self, label: str):
super().__init__(label, "...")
def _call(self):
return self._instance.getExistingDirectory(caption="Open Directory", dir=self._default_path,
options=QFileDialog.ShowDirsOnly | QFileDialog.DontResolveSymlinks)
class ListWidget(Widget, ValueContains):
def __init__(self):
super().__init__(QListWidget())
self._layout.addWidget(self._instance)
self.__items = []
def add_item(self, item: str, is_editable=True):
self.__items.append(QListWidgetItem(item, self._instance))
if is_editable:
self.__items[-1].setFlags(self.__items[-1].flags() | Qt.ItemIsEditable)
self._instance.addItem(self.__items[-1])
def add_items(self, items: [str], is_editable=True) -> Widget:
for item in items:
self.add_item(item, is_editable)
return self
def remove_item(self, idx: int):
self._instance.takeItem(idx)
del self.__items[idx]
def remove_current(self):
self.remove_item(self.get_current_idx())
def get_current_idx(self):
return self.__get_item_idx(self._instance.currentItem())
def clear(self):
for i in range(len(self.__items)):
self.remove_item(i)
def set_value(self, value: int):
self._instance.setItem(self.__items[value])
def get_value(self):
return self.__items.index(self._instance.currentItem())
def set_value_changed_callback(self, callback: callable):
self._instance.currentItemChanged.connect(lambda cur, prev: callback(self.__get_item_idx(cur)))
return self
def set_item_renamed_callback(self, callback: callable):
def internal(item: QListWidgetItem):
idx = self.__get_item_idx(self._instance.currentItem())
callback(idx, item.text())
self._instance.itemChanged.connect(internal)
def __get_item_idx(self, item: QListWidgetItem):
for idx, it in enumerate(self.__items):
if it is item:
return idx
return None
class DynamicView(Widget):
def __init__(self):
super().__init__()
self.__stacked_layout = QStackedLayout()
self._layout.addLayout(self.__stacked_layout)
self.__widgets = []
def add_item(self, item: Widget):
w = QWidget()
w.setLayout(item.get_layout())
self.__stacked_layout.addWidget(w)
self.__widgets.append(w)
def remove_item(self, idx: int):
self.__stacked_layout.takeAt(idx)
del self.__widgets[idx]
def clear(self):
for i in range(len(self.__widgets) - 1):
self.remove_item(i)
def set_index(self, idx: int):
self.__stacked_layout.setCurrentIndex(idx)
class OpenGLWidget(Widget):
def __init__(self, init_callback: callable, resize_callback: callable, draw_callback: callable):
super().__init__(QGLWidget())
self._layout.addWidget(self._instance)
self._instance.initializeGL = init_callback
self._instance.resizeGL = resize_callback
self._instance.paintGL = draw_callback
self._instance.setFocusPolicy(Qt.StrongFocus)
self._key_buf = [False for _ in range(256)]
def set_mouse_move_callback(self, callback: callable):
def with_update(event):
callback(event.pos().x(), event.pos().y())
self._instance.updateGL()
self._instance.mouseMoveEvent = with_update
def set_mouse_press_callback(self, callback: callable):
self._instance.mousePressEvent = lambda event: callback(event.pos().x(), event.pos().y(), event.buttons() == Qt.LeftButton, True)
self._instance.mouseReleaseEvent = lambda event: callback(event.pos().x(), event.pos().y(), event.buttons() == Qt.LeftButton, False)
def set_wheel_scroll_event(self, callback: callable):
def with_update(event):
callback(1 if event.delta() > 0 else -1)
self._instance.updateGL()
self._instance.wheelEvent = lambda event: with_update(event)
def set_keyboard_event(self, callback: callable):
def get_code(event):
code = event.key() + 32
if code < 0 or code > 255:
return None
return code
def on_press(event):
code = get_code(event)
if code is None:
return
self._key_buf[code] = True
callback(self._key_buf)
self._instance.updateGL()
def on_release(event):
code = get_code(event)
if code is None:
return
self._key_buf[code] = False
self._instance.keyPressEvent = on_press
self._instance.keyReleaseEvent = on_release
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2457,
29901,
13,
4706,
9995,
13,
4706,
1583,
3032,
8758,
29889,
12038,
29898,
1767,
29897,
13,
13,
1678,
822,
679,
29918,
1767,
29898,
1311,
1125,
13,
4706,
9995,
13,
4706,
3617,
1857,
995,
13,
4706,
584,
2457,
29901,
1857,
995,
13,
4706,
9995,
13,
4706,
736,
1583,
3032,
8758,
29889,
726,
580,
13,
13,
1678,
822,
731,
29918,
1767,
29918,
15033,
29918,
14035,
29898,
1311,
29892,
6939,
29901,
1246,
519,
1125,
13,
4706,
1583,
3032,
8758,
29889,
726,
7590,
29889,
6915,
29898,
14035,
29897,
13,
4706,
736,
1583,
13,
13,
1678,
822,
903,
26936,
29898,
1311,
1125,
13,
4706,
1583,
3032,
2680,
29889,
1202,
8801,
29898,
1311,
3032,
8758,
29897,
13,
13,
13,
1990,
11025,
29898,
8801,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
3611,
29901,
851,
29892,
338,
29918,
10154,
29918,
3092,
29901,
6120,
353,
7700,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
29984,
12229,
3125,
580,
565,
338,
29918,
10154,
29918,
3092,
1683,
660,
27031,
3125,
3101,
13,
4706,
1583,
3032,
2680,
29889,
1202,
8801,
29898,
1311,
3032,
8758,
29897,
13,
4706,
1583,
3032,
8758,
29889,
12038,
29898,
3257,
29897,
13,
13,
1678,
822,
731,
29918,
265,
29918,
3808,
29918,
14035,
29898,
1311,
29892,
6939,
29901,
1246,
519,
1125,
13,
4706,
9995,
13,
4706,
3789,
6939,
373,
2828,
1741,
13,
4706,
584,
3207,
6939,
29901,
13,
4706,
584,
2457,
29901,
13,
4706,
9995,
13,
4706,
1583,
3032,
8758,
29889,
3808,
287,
29889,
6915,
29898,
14035,
29897,
13,
4706,
736,
1583,
13,
13,
13,
1990,
7084,
3453,
29898,
8801,
1125,
13,
1678,
770,
14705,
2940,
29963,
15580,
29898,
29984,
17290,
1043,
1125,
13,
4706,
9995,
10772,
17303,
1967,
6316,
556,
11109,
363,
263,
660,
29925,
861,
1958,
297,
263,
660,
17290,
1043,
9088,
411,
9495,
19342,
292,
322,
282,
9450,
29889,
13,
4706,
3295,
12922,
263,
660,
2940,
470,
660,
29925,
861,
1958,
313,
29984,
2940,
338,
25106,
11543,
304,
263,
660,
29925,
861,
1958,
467,
13,
4706,
1763,
2479,
738,
916,
1967,
3402,
29892,
366,
1818,
937,
3588,
372,
304,
263,
660,
2940,
470,
660,
29925,
861,
1958,
29889,
13,
4706,
3834,
5407,
1967,
3402,
11301,
3667,
1907,
29901,
13,
9651,
3855,
3027,
29906,
299,
2378,
29901,
11848,
19737,
29871,
299,
2378,
529,
1360,
29958,
660,
2940,
1678,
313,
991,
597,
3292,
29889,
510,
29914,
29882,
1004,
457,
29914,
29939,
3027,
29906,
299,
2378,
29897,
13,
9651,
7084,
17303,
29901,
349,
6227,
7084,
529,
1360,
29958,
660,
2940,
29871,
313,
991,
597,
3292,
29889,
510,
29914,
4691,
29899,
29886,
453,
340,
29914,
29925,
453,
340,
29914,
10054,
29914,
6207,
29914,
2227,
29931,
29914,
2940,
17303,
29889,
2272,
29897,
13,
4706,
25992,
14881,
29901,
13,
9651,
19941,
9495,
2826,
8338,
29901,
6518,
1967,
29889,
13,
9651,
10428,
9495,
2826,
8338,
29901,
17421,
290,
3800,
29889,
13,
9651,
10428,
9495,
2826,
3765,
3808,
29901,
17421,
290,
304,
1510,
4152,
1967,
29889,
13,
4706,
9995,
13,
13,
4706,
396,
25992,
2826,
18470,
20076,
1967,
9088,
313,
29916,
29892,
343,
29897,
10350,
29889,
13,
4706,
396,
1738,
6824,
1152,
1967,
313,
798,
29892,
1897,
29897,
4636,
26190,
29892,
1948,
353,
343,
322,
1897,
353,
921,
29889,
13,
4706,
2175,
14346,
3125,
24104,
353,
9954,
284,
29898,
7411,
29892,
5785,
29897,
13,
4706,
1492,
14346,
3125,
24104,
353,
9954,
284,
29898,
7411,
29892,
5785,
29897,
13,
4706,
2175,
14346,
3125,
1123,
4611,
353,
9954,
284,
29898,
7411,
29892,
5785,
29897,
13,
4706,
1492,
14346,
3125,
1123,
4611,
353,
9954,
284,
29898,
7411,
29892,
5785,
29897,
13,
4706,
2175,
14346,
3125,
11843,
4164,
287,
353,
9954,
284,
29898,
7411,
29892,
5785,
29897,
13,
4706,
1492,
14346,
3125,
11843,
4164,
287,
353,
9954,
284,
29898,
7411,
29892,
5785,
29897,
13,
13,
4706,
822,
4770,
2344,
12035,
1311,
1125,
13,
9651,
660,
17290,
1043,
17255,
2344,
12035,
1311,
29897,
13,
13,
9651,
396,
7084,
338,
8833,
408,
263,
660,
29925,
861,
1958,
297,
263,
660,
17290,
23472,
10959,
304,
445,
660,
17290,
1043,
29889,
13,
9651,
1583,
29889,
24645,
353,
660,
17290,
23472,
580,
13,
9651,
1583,
29889,
842,
23472,
29898,
1311,
29889,
24645,
29897,
13,
13,
9651,
396,
14491,
263,
1887,
4386,
304,
278,
9088,
29915,
29879,
1857,
1967,
9277,
1958,
29889,
13,
9651,
1583,
3032,
29886,
861,
1958,
13554,
353,
6213,
13,
13,
9651,
396,
7084,
9565,
11959,
4464,
29889,
13,
9651,
396,
1738,
6824,
6732,
16786,
16058,
304,
2989,
1967,
29889,
1094,
1103,
11959,
338,
2337,
17262,
746,
19342,
292,
29889,
13,
9651,
396,
259,
14705,
29889,
23805,
2887,
1103,
29934,
20819,
29901,
2522,
744,
1967,
304,
6216,
1776,
637,
29889,
13,
9651,
396,
259,
14705,
29889,
9598,
1022,
2887,
1103,
29934,
20819,
29901,
2522,
744,
1967,
304,
6216,
2768,
1776,
637,
29892,
2225,
29530,
9565,
11959,
29889,
13,
9651,
396,
259,
14705,
29889,
9598,
1022,
2887,
1103,
29934,
20819,
2059,
29777,
292,
29901,
2522,
744,
1967,
304,
5445,
278,
1776,
637,
29892,
2225,
29530,
9565,
11959,
29889,
13,
9651,
1583,
29889,
294,
1103,
29934,
20819,
6818,
353,
14705,
29889,
9598,
1022,
2887,
1103,
29934,
20819,
13,
13,
9651,
396,
28797,
2594,
10468,
29889,
13,
9651,
396,
259,
14705,
29889,
10463,
4297,
2499,
1994,
6880,
29901,
12391,
3697,
263,
6355,
2594,
29889,
13,
9651,
396,
259,
14705,
29889,
10463,
4297,
2499,
1994,
2951,
29901,
29849,
3697,
263,
6355,
2594,
29889,
13,
9651,
396,
259,
14705,
29889,
10463,
4297,
2887,
8139,
19226,
29901,
1383,
1242,
263,
6355,
2594,
871,
746,
19342,
287,
29889,
13,
9651,
1583,
29889,
842,
24932,
10463,
4297,
15644,
29898,
17303,
29889,
10463,
4297,
2499,
1994,
2951,
29897,
13,
9651,
1583,
29889,
842,
29270,
10463,
4297,
15644,
29898,
17303,
29889,
10463,
4297,
2499,
1994,
2951,
29897,
13,
13,
9651,
1583,
29889,
842,
4300,
5404,
24458,
29898,
29984,
17290,
1043,
29889,
24458,
29177,
14346,
29897,
13,
13,
9651,
396,
10292,
310,
660,
7364,
29943,
19342,
16273,
297,
9088,
10350,
29889,
13,
9651,
1583,
29889,
2502,
290,
7264,
353,
5159,
13,
13,
9651,
396,
2379,
810,
363,
427,
17961,
29914,
2218,
17961,
9495,
14881,
29889,
13,
9651,
1583,
29889,
3068,
29999,
29667,
353,
5852,
13,
9651,
1583,
29889,
3068,
23684,
353,
5852,
13,
13,
4706,
822,
756,
2940,
29898,
1311,
1125,
13,
9651,
9995,
16969,
3692,
470,
451,
278,
9088,
3743,
385,
1967,
9277,
1958,
29889,
13,
9651,
9995,
13,
9651,
736,
1583,
3032,
29886,
861,
1958,
13554,
338,
451,
6213,
13,
13,
4706,
822,
2821,
2940,
29898,
1311,
1125,
13,
9651,
9995,
5240,
586,
267,
278,
1857,
1967,
9277,
1958,
515,
278,
9088,
565,
372,
4864,
29889,
13,
9651,
9995,
13,
9651,
565,
1583,
29889,
5349,
2940,
7295,
13,
18884,
1583,
29889,
24645,
29889,
5992,
2001,
29898,
1311,
3032,
29886,
861,
1958,
13554,
29897,
13,
18884,
1583,
3032,
29886,
861,
1958,
13554,
353,
6213,
13,
13,
4706,
822,
9277,
1958,
29898,
1311,
1125,
13,
9651,
9995,
16969,
278,
9088,
29915,
29879,
1857,
1967,
9277,
1958,
408,
263,
660,
29925,
861,
1958,
29892,
470,
1683,
6213,
565,
694,
1967,
4864,
29889,
13,
9651,
584,
29878,
1853,
29901,
660,
29925,
861,
1958,
891,
6213,
13,
9651,
9995,
13,
9651,
565,
1583,
29889,
5349,
2940,
7295,
13,
18884,
736,
1583,
3032,
29886,
861,
1958,
13554,
29889,
29886,
861,
1958,
580,
13,
9651,
736,
6213,
13,
13,
4706,
822,
1967,
29898,
1311,
1125,
13,
9651,
9995,
16969,
278,
9088,
29915,
29879,
1857,
1967,
9277,
1958,
408,
263,
660,
2940,
29892,
470,
1683,
6213,
565,
694,
1967,
4864,
29889,
13,
9651,
584,
29878,
1853,
29901,
660,
2940,
891,
6213,
13,
9651,
9995,
13,
9651,
565,
1583,
29889,
5349,
2940,
7295,
13,
18884,
736,
1583,
3032,
29886,
861,
1958,
13554,
29889,
29886,
861,
1958,
2141,
517,
2940,
580,
13,
9651,
736,
6213,
13,
13,
4706,
822,
731,
2940,
29898,
1311,
29892,
1967,
1125,
13,
9651,
9995,
3789,
278,
9088,
29915,
29879,
1857,
1967,
9277,
1958,
304,
278,
1881,
660,
2940,
470,
660,
29925,
861,
1958,
29889,
13,
9651,
390,
1759,
267,
263,
24875,
2392,
565,
278,
1881,
1967,
756,
1134,
916,
1135,
660,
2940,
470,
660,
29925,
861,
1958,
29889,
13,
9651,
584,
1853,
1967,
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660,
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891,
660,
29925,
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1958,
13,
9651,
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13,
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565,
1134,
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3027,
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660,
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1958,
29901,
13,
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1958,
353,
1967,
13,
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25342,
1134,
29898,
3027,
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660,
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29901,
13,
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1958,
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1958,
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3166,
2940,
29898,
3027,
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13,
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1683,
29901,
13,
18884,
12020,
24875,
2392,
703,
2940,
29963,
15580,
29889,
842,
2940,
29901,
23125,
1818,
367,
263,
660,
2940,
470,
660,
29925,
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1958,
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13,
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565,
1583,
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3032,
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1202,
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1958,
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1958,
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13,
9651,
1583,
29889,
842,
23472,
7364,
29898,
29984,
7364,
29943,
29898,
29886,
861,
1958,
29889,
1621,
22130,
29871,
396,
3789,
9088,
2159,
304,
1967,
2159,
29889,
13,
9651,
1583,
29889,
5504,
29963,
15580,
580,
13,
13,
4706,
822,
2254,
2940,
4591,
2283,
29898,
1311,
29892,
29729,
1125,
13,
9651,
9995,
16012,
385,
1967,
515,
934,
29889,
13,
9651,
13932,
738,
6273,
29892,
2254,
2940,
4591,
2283,
580,
674,
18218,
263,
934,
7928,
304,
6755,
278,
1967,
934,
29889,
13,
9651,
2973,
263,
29729,
2980,
29892,
2254,
2940,
4591,
2283,
29898,
28926,
29897,
674,
4218,
304,
2254,
278,
6790,
1967,
934,
4153,
29889,
13,
9651,
9995,
13,
9651,
565,
7431,
29898,
28926,
29897,
322,
2897,
29889,
2084,
29889,
275,
1445,
29898,
28926,
1125,
13,
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1967,
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660,
2940,
29898,
28926,
29897,
13,
18884,
1583,
29889,
842,
2940,
29898,
3027,
29897,
13,
13,
4706,
822,
2767,
29963,
15580,
29898,
1311,
1125,
13,
9651,
9995,
7704,
1857,
19342,
313,
361,
6445,
4152,
1967,
29892,
3394,
1857,
9565,
11959,
4464,
467,
13,
9651,
9995,
13,
9651,
565,
451,
1583,
29889,
5349,
2940,
7295,
13,
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736,
13,
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7431,
29898,
1311,
29889,
2502,
290,
7264,
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11516,
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29889,
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29962,
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1402,
14705,
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23805,
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1103,
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20819,
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19342,
287,
7705,
313,
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9565,
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29901,
13,
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1583,
29889,
2502,
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29871,
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17732,
278,
19342,
5096,
313,
262,
1206,
591,
2355,
1244,
1363,
310,
385,
8340,
19342,
467,
13,
18884,
1583,
29889,
9202,
797,
1043,
29898,
1311,
29889,
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7364,
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13,
1990,
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8801,
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1202,
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1990,
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13,
1990,
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770,
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353,
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284,
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1202,
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1990,
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1948,
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2001,
29898,
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2001,
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1583,
13,
13,
1678,
822,
628,
29918,
798,
29898,
1311,
1125,
13,
4706,
1857,
29918,
798,
353,
1583,
3032,
8758,
29889,
3784,
4301,
580,
13,
4706,
1583,
3032,
8758,
29889,
5992,
4301,
29898,
3784,
29918,
798,
29897,
13,
4706,
736,
1583,
13,
13,
1678,
822,
731,
29918,
13099,
29918,
13662,
29898,
1311,
29892,
9066,
29901,
5159,
1125,
13,
4706,
1583,
3032,
8758,
29889,
842,
4409,
3981,
29898,
2435,
29898,
13662,
876,
13,
4706,
1583,
3032,
8758,
29889,
842,
24932,
7850,
4775,
29879,
29898,
13662,
29897,
13,
4706,
736,
1583,
13,
13,
13,
1990,
10802,
7647,
29898,
8801,
29892,
7865,
21409,
29892,
1539,
562,
605,
29922,
19658,
19346,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
3858,
29901,
851,
29892,
2826,
29918,
1643,
29901,
851,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
29984,
2283,
7647,
3101,
13,
4706,
1583,
17255,
1643,
353,
3858,
13,
4706,
1583,
17255,
3092,
29918,
1643,
353,
2826,
29918,
1643,
13,
4706,
1583,
3032,
4381,
29918,
2084,
353,
660,
9170,
29889,
5184,
2605,
580,
13,
13,
4706,
1583,
17255,
1767,
29918,
15033,
29918,
14035,
29879,
353,
5159,
13,
13,
4706,
1583,
17255,
1220,
29918,
5628,
353,
6213,
13,
4706,
1583,
17255,
3092,
353,
6213,
13,
13,
1678,
822,
731,
29918,
4381,
29918,
2084,
29898,
1311,
29892,
2322,
29918,
2084,
29901,
851,
1125,
13,
4706,
1583,
3032,
4381,
29918,
2084,
353,
2322,
29918,
2084,
13,
4706,
736,
1583,
13,
13,
1678,
822,
679,
29918,
4381,
29918,
2084,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
4381,
29918,
2084,
13,
13,
1678,
822,
679,
29918,
1767,
29898,
1311,
1125,
13,
4706,
620,
353,
1583,
17255,
1220,
29918,
5628,
29889,
657,
29918,
1767,
580,
13,
4706,
736,
6213,
565,
7431,
29898,
690,
29897,
529,
29871,
29896,
1683,
620,
13,
13,
1678,
822,
731,
29918,
1767,
29898,
1311,
29892,
995,
1125,
13,
4706,
565,
1583,
17255,
1220,
29918,
5628,
338,
451,
6213,
29901,
13,
9651,
1583,
17255,
1220,
29918,
5628,
29889,
842,
29918,
1767,
29898,
1767,
29897,
13,
4706,
1583,
29889,
842,
29918,
4381,
29918,
2084,
29898,
359,
29889,
2084,
29889,
25721,
29898,
1767,
876,
13,
13,
4706,
363,
274,
297,
1583,
17255,
1767,
29918,
15033,
29918,
14035,
29879,
29901,
13,
9651,
274,
29898,
1767,
29897,
13,
13,
1678,
822,
731,
29918,
1767,
29918,
15033,
29918,
14035,
29898,
1311,
29892,
6939,
29901,
1246,
519,
1125,
13,
4706,
1583,
17255,
1767,
29918,
15033,
29918,
14035,
29879,
29889,
4397,
29898,
14035,
29897,
13,
4706,
736,
1583,
13,
13,
1678,
822,
903,
5504,
29918,
1767,
29898,
1311,
29897,
1599,
6213,
29901,
13,
4706,
9995,
13,
4706,
10318,
2224,
995,
491,
7928,
13,
4706,
9995,
13,
4706,
620,
353,
1583,
29889,
4804,
580,
13,
4706,
565,
620,
338,
6213,
470,
7431,
29898,
690,
29897,
529,
29871,
29896,
29901,
13,
9651,
736,
13,
4706,
1583,
29889,
842,
29918,
1767,
29898,
690,
29897,
13,
13,
1678,
732,
16595,
5696,
13,
1678,
822,
903,
4804,
29898,
1311,
29897,
1599,
851,
29901,
13,
4706,
9995,
13,
4706,
8251,
7928,
3474,
13,
4706,
584,
2457,
29901,
1121,
2224,
13,
4706,
9995,
13,
13,
1678,
822,
1246,
29898,
1311,
29897,
1599,
851,
29901,
13,
4706,
2224,
353,
1583,
3032,
4804,
580,
13,
4706,
565,
2224,
338,
6213,
470,
7431,
29898,
2084,
29897,
529,
29871,
29896,
29901,
13,
9651,
736,
6213,
13,
4706,
1583,
29889,
842,
29918,
1767,
29898,
2084,
29897,
13,
4706,
736,
2224,
13,
13,
1678,
822,
679,
29918,
2680,
29898,
1311,
1125,
13,
4706,
2428,
2141,
657,
29918,
2680,
580,
13,
4706,
1583,
3032,
2680,
29889,
1202,
8801,
29898,
2239,
1107,
29898,
1311,
17255,
1643,
876,
13,
13,
4706,
1583,
17255,
1220,
29918,
5628,
353,
7407,
6103,
580,
13,
4706,
298,
29918,
2680,
353,
660,
29950,
3313,
3453,
580,
13,
4706,
298,
29918,
2680,
29889,
1202,
3453,
29898,
1311,
17255,
1220,
29918,
5628,
29889,
657,
29918,
2680,
3101,
13,
4706,
1583,
17255,
3092,
353,
11025,
29898,
1311,
17255,
3092,
29918,
1643,
29892,
338,
29918,
10154,
29918,
3092,
29922,
5574,
467,
842,
29918,
265,
29918,
3808,
29918,
14035,
29898,
1311,
3032,
5504,
29918,
1767,
29897,
13,
4706,
298,
29918,
2680,
29889,
1202,
3453,
29898,
1311,
17255,
3092,
29889,
657,
29918,
2680,
3101,
13,
4706,
1583,
3032,
2680,
29889,
1202,
3453,
29898,
29882,
29918,
2680,
29897,
13,
4706,
736,
1583,
3032,
2680,
13,
13,
1678,
822,
731,
29918,
17590,
29898,
1311,
29892,
338,
29918,
17590,
29901,
6120,
353,
5852,
1125,
13,
4706,
620,
353,
1583,
3032,
13029,
29918,
915,
29918,
17590,
29898,
275,
29918,
17590,
29897,
13,
4706,
565,
1583,
17255,
1220,
29918,
5628,
338,
451,
6213,
29901,
13,
9651,
1583,
17255,
1220,
29918,
5628,
29889,
842,
29918,
17590,
29898,
690,
29897,
13,
4706,
565,
1583,
17255,
3092,
338,
451,
6213,
29901,
13,
9651,
1583,
17255,
3092,
29889,
842,
29918,
17590,
29898,
690,
29897,
13,
13,
13,
1990,
4673,
2283,
29898,
2605,
7647,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
3858,
29901,
851,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
1643,
29892,
29804,
1159,
13,
4706,
1583,
17255,
5325,
29918,
8768,
353,
5124,
13,
13,
1678,
822,
731,
29918,
5325,
29918,
8768,
29898,
1311,
29892,
4072,
29901,
851,
1125,
13,
4706,
1583,
17255,
5325,
29918,
8768,
353,
4072,
13,
4706,
736,
1583,
13,
13,
1678,
822,
903,
4804,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
8758,
29889,
657,
6585,
17020,
29898,
6671,
543,
6585,
3497,
613,
4516,
29922,
1311,
3032,
4381,
29918,
2084,
29892,
4175,
29922,
1311,
17255,
5325,
29918,
8768,
9601,
29900,
29962,
13,
13,
13,
1990,
16913,
2283,
29898,
2605,
7647,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
3858,
29901,
851,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
1643,
29892,
29804,
1159,
13,
4706,
1583,
17255,
5325,
29918,
8768,
353,
5124,
13,
13,
1678,
822,
731,
29918,
5325,
29918,
8768,
29898,
1311,
29892,
4072,
29901,
851,
1125,
13,
4706,
1583,
17255,
5325,
29918,
8768,
353,
4072,
13,
4706,
736,
1583,
13,
13,
1678,
822,
903,
4804,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
8758,
29889,
657,
11371,
17020,
29898,
6671,
543,
11371,
3497,
613,
4516,
29922,
1311,
3032,
4381,
29918,
2084,
29892,
4175,
29922,
1311,
17255,
5325,
29918,
8768,
9601,
29900,
29962,
13,
13,
13,
1990,
4673,
9882,
29898,
2605,
7647,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
3858,
29901,
851,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
1643,
29892,
29804,
1159,
13,
13,
1678,
822,
903,
4804,
29898,
1311,
1125,
13,
4706,
736,
1583,
3032,
8758,
29889,
657,
1252,
15423,
9882,
29898,
6671,
543,
6585,
18862,
613,
4516,
29922,
1311,
3032,
4381,
29918,
2084,
29892,
13,
462,
462,
462,
259,
3987,
29922,
29984,
2283,
7647,
29889,
8964,
9170,
29879,
11730,
891,
660,
2283,
7647,
29889,
29928,
609,
12375,
345,
29903,
21053,
19363,
29897,
13,
13,
13,
1990,
2391,
8801,
29898,
8801,
29892,
7865,
21409,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
13,
4706,
2428,
2141,
1649,
2344,
12035,
29984,
1293,
8801,
3101,
13,
4706,
1583,
3032,
2680,
29889,
1202,
8801,
29898,
1311,
3032,
8758,
29897,
13,
4706,
1583,
17255,
7076,
353,
5159,
13,
13,
1678,
822,
788,
29918,
667,
29898,
1311,
29892,
2944,
29901,
851,
29892,
338,
29918,
5628,
519,
29922,
5574,
1125,
13,
4706,
1583,
17255,
7076,
29889,
4397,
29898,
29984,
1293,
8801,
2001,
29898,
667,
29892,
1583,
3032,
8758,
876,
13,
4706,
565,
338,
29918,
5628,
519,
29901,
13,
9651,
1583,
17255,
7076,
14352,
29896,
1822,
842,
15675,
29898,
1311,
17255,
7076,
14352,
29896,
1822,
15764,
580,
891,
14705,
29889,
2001,
3624,
6103,
519,
29897,
13,
4706,
1583,
3032,
8758,
29889,
1202,
2001,
29898,
1311,
17255,
7076,
14352,
29896,
2314,
13,
13,
1678,
822,
788,
29918,
7076,
29898,
1311,
29892,
4452,
29901,
518,
710,
1402,
338,
29918,
5628,
519,
29922,
5574,
29897,
1599,
27080,
29901,
13,
4706,
363,
2944,
297,
4452,
29901,
13,
9651,
1583,
29889,
1202,
29918,
667,
29898,
667,
29892,
338,
29918,
5628,
519,
29897,
13,
4706,
736,
1583,
13,
13,
1678,
822,
3349,
29918,
667,
29898,
1311,
29892,
22645,
29901,
938,
1125,
13,
4706,
1583,
3032,
8758,
29889,
19730,
2001,
29898,
13140,
29897,
13,
4706,
628,
1583,
17255,
7076,
29961,
13140,
29962,
13,
13,
1678,
822,
3349,
29918,
3784,
29898,
1311,
1125,
13,
4706,
1583,
29889,
5992,
29918,
667,
29898,
1311,
29889,
657,
29918,
3784,
29918,
13140,
3101,
13,
13,
1678,
822,
679,
29918,
3784,
29918,
13140,
29898,
1311,
1125,
13,
4706,
736,
1583,
17255,
657,
29918,
667,
29918,
13140,
29898,
1311,
3032,
8758,
29889,
3784,
2001,
3101,
13,
13,
1678,
822,
2821,
29898,
1311,
1125,
13,
4706,
363,
474,
297,
3464,
29898,
2435,
29898,
1311,
17255,
7076,
22164,
13,
9651,
1583,
29889,
5992,
29918,
667,
29898,
29875,
29897,
13,
13,
1678,
822,
731,
29918,
1767,
29898,
1311,
29892,
995,
29901,
938,
1125,
13,
4706,
1583,
3032,
8758,
29889,
842,
2001,
29898,
1311,
17255,
7076,
29961,
1767,
2314,
13,
13,
1678,
822,
679,
29918,
1767,
29898,
1311,
1125,
13,
4706,
736,
1583,
17255,
7076,
29889,
2248,
29898,
1311,
3032,
8758,
29889,
3784,
2001,
3101,
13,
13,
1678,
822,
731,
29918,
1767,
29918,
15033,
29918,
14035,
29898,
1311,
29892,
6939,
29901,
1246,
519,
1125,
13,
4706,
1583,
3032,
8758,
29889,
3784,
2001,
7590,
29889,
6915,
29898,
2892,
3151,
29892,
12379,
29901,
6939,
29898,
1311,
17255,
657,
29918,
667,
29918,
13140,
29898,
2764,
4961,
13,
4706,
736,
1583,
13,
13,
1678,
822,
731,
29918,
667,
29918,
1267,
2795,
29918,
14035,
29898,
1311,
29892,
6939,
29901,
1246,
519,
1125,
13,
4706,
822,
7463,
29898,
667,
29901,
660,
1293,
8801,
2001,
1125,
13,
9651,
22645,
353,
1583,
17255,
657,
29918,
667,
29918,
13140,
29898,
1311,
3032,
8758,
29889,
3784,
2001,
3101,
13,
9651,
6939,
29898,
13140,
29892,
2944,
29889,
726,
3101,
13,
13,
4706,
1583,
3032,
8758,
29889,
667,
7590,
29889,
6915,
29898,
7564,
29897,
13,
13,
1678,
822,
4770,
657,
29918,
667,
29918,
13140,
29898,
1311,
29892,
2944,
29901,
660,
1293,
8801,
2001,
1125,
13,
4706,
363,
22645,
29892,
372,
297,
26985,
29898,
1311,
17255,
7076,
1125,
13,
9651,
565,
372,
338,
2944,
29901,
13,
18884,
736,
22645,
13,
4706,
736,
6213,
13,
13,
13,
1990,
27747,
1043,
29898,
8801,
1125,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
13,
4706,
2428,
2141,
1649,
2344,
1649,
580,
13,
4706,
1583,
17255,
1429,
287,
29918,
2680,
353,
660,
7264,
287,
3453,
580,
13,
4706,
1583,
3032,
2680,
29889,
1202,
3453,
29898,
1311,
17255,
1429,
287,
29918,
2680,
29897,
13,
13,
4706,
1583,
17255,
8030,
29879,
353,
5159,
13,
13,
1678,
822,
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2
] |
bot/logic/spending/build_order/standard_zvp_hl.py | Scottdecat/HiveMind | 0 | 137943 | from typing import List, Dict
from sc2 import UnitTypeId, Race
from sc2.ids.upgrade_id import UpgradeId
from bot.logic.spending.build_order.build_order import BOStep, BOAction
from bot.services.state_service import StateService
def get_build(state : StateService):
return [
BOStep(
13,
lambda: state.get_unit_count(UnitTypeId.OVERLORD) >= 2,
lambda: state.get_unit_count(UnitTypeId.OVERLORD) < 2,
UnitTypeId.OVERLORD
),
BOStep(
17,
lambda: state.pending_townhalls() >= 2,
lambda: state.pending_townhalls() < 2,
UnitTypeId.HATCHERY
),
BOStep(
18,
lambda: state.get_unit_count(UnitTypeId.EXTRACTOR) > 1,
lambda: state.get_unit_count(UnitTypeId.EXTRACTOR) < 1,
UnitTypeId.EXTRACTOR
),
BOStep(
17,
lambda: state.get_unit_count(UnitTypeId.SPAWNINGPOOL) > 1,
lambda: (state.get_unit_count(UnitTypeId.SPAWNINGPOOL) < 1
and state.get_unit_count(UnitTypeId.EXTRACTOR) >= 1),
UnitTypeId.SPAWNINGPOOL
),
BOStep(
17,
lambda: state.get_unit_count(UnitTypeId.ZERGLING) > 1,
lambda: state.own_structures(UnitTypeId.SPAWNINGPOOL).ready.exists,
UnitTypeId.ZERGLING
),
BOStep(
17,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGLINGMOVEMENTSPEED) > 0
or UpgradeId.ZERGLINGMOVEMENTSPEED in state.upgrades),
lambda: (state.own_units(UnitTypeId.SPAWNINGPOOL).ready.exists
and state.get_unit_count(UnitTypeId.EXTRACTOR) > 0),
UpgradeId.ZERGLINGMOVEMENTSPEED
),
BOStep(
19,
lambda: state.get_unit_count(UnitTypeId.OVERLORD) >= 3,
lambda: state.get_unit_count(UnitTypeId.OVERLORD) < 3,
UnitTypeId.OVERLORD
),
BOStep(
0,
lambda: False,
lambda: state.get_unit_count(UnitTypeId.OVERLORD) >= 3,
BOAction.BUILD_OVERLORDS, True
),
BOStep(
0,
lambda: False,
lambda: state.own_structures(UnitTypeId.SPAWNINGPOOL).ready.exists,
BOAction.BUILD_QUEENS, True
),
BOStep(
30,
lambda: state.pending_townhalls() >= 3,
lambda: (state.pending_townhalls() < 3
and state.get_unit_count(UnitTypeId.DRONE) >= 24),
UnitTypeId.HATCHERY
),
BOStep(
30,
lambda: False,
lambda: state.pending_townhalls() > 2,
BOAction.BUILD_HATCHERIES, True
),
BOStep(
38,
lambda: state.get_unit_count(UnitTypeId.EXTRACTOR) >= 3,
lambda: (state.get_unit_count(UnitTypeId.EXTRACTOR) < 3
and state.get_unit_count(UnitTypeId.DRONE) >= 38),
UnitTypeId.EXTRACTOR
),
BOStep(
40,
lambda: (state.get_unit_count(UnitTypeId.LAIR) >= 1
or state.get_unit_count(UnitTypeId.HIVE) >= 1),
lambda: state.get_unit_count(UnitTypeId.DRONE) >= 32,
UnitTypeId.LAIR
),
BOStep(
40,
lambda: state.get_unit_count(UnitTypeId.OVERSEER) >= 1,
lambda: state.own_structures(UnitTypeId.LAIR).exists,
UnitTypeId.OVERSEER
),
BOStep(
44,
lambda: state.get_unit_count(UnitTypeId.HYDRALISKDEN) >= 1,
lambda: (state.get_unit_count(UnitTypeId.HYDRALISKDEN) < 1
and state.get_unit_count(UnitTypeId.LAIR) > 0
and state.get_unit_count(UnitTypeId.DRONE) >= 44),
UnitTypeId.HYDRALISKDEN
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.EVOLVEGROOVEDSPINES) > 0
or UpgradeId.EVOLVEGROOVEDSPINES in state.upgrades),
lambda: state.own_structures(UnitTypeId.HYDRALISKDEN).ready.exists,
UpgradeId.EVOLVEGROOVEDSPINES
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.EVOLVEMUSCULARAUGMENTS) > 0
or UpgradeId.EVOLVEMUSCULARAUGMENTS in state.upgrades),
lambda: (state.own_structures(UnitTypeId.HYDRALISKDEN).ready.exists
and UpgradeId.EVOLVEGROOVEDSPINES in state.upgrades),
UpgradeId.EVOLVEMUSCULARAUGMENTS
),
#BOStep(
# 52,
# lambda: state.get_unit_count(UnitTypeId.EXTRACTOR) >= 5,
# lambda: (state.get_unit_count(UnitTypeId.EXTRACTOR) < 5
# and state.get_unit_count(UnitTypeId.DRONE) >= 52),
# UnitTypeId.EXTRACTOR
#),
BOStep(
56,
lambda: state.get_unit_count(UnitTypeId.EVOLUTIONCHAMBER) >= 2,
lambda: (state.get_unit_count(UnitTypeId.EVOLUTIONCHAMBER) < 2
and state.get_unit_count(UnitTypeId.DRONE) >= 56),
UnitTypeId.EVOLUTIONCHAMBER
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGMISSILEWEAPONSLEVEL1) > 0
or UpgradeId.ZERGMISSILEWEAPONSLEVEL1 in state.upgrades),
lambda: state.own_structures(UnitTypeId.EVOLUTIONCHAMBER).ready.exists,
UpgradeId.ZERGMISSILEWEAPONSLEVEL1
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGMISSILEWEAPONSLEVEL2) > 0
or UpgradeId.ZERGMISSILEWEAPONSLEVEL2 in state.upgrades),
lambda: (state.own_structures(UnitTypeId.EVOLUTIONCHAMBER).ready.exists
and UpgradeId.ZERGMISSILEWEAPONSLEVEL1 in state.upgrades),
UpgradeId.ZERGMISSILEWEAPONSLEVEL2
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGMISSILEWEAPONSLEVEL3) > 0
or UpgradeId.ZERGMISSILEWEAPONSLEVEL3 in state.upgrades),
lambda: (state.own_structures(UnitTypeId.EVOLUTIONCHAMBER).ready.exists
and state.own_structures(UnitTypeId.HIVE).ready.exists
and UpgradeId.ZERGMISSILEWEAPONSLEVEL2 in state.upgrades),
UpgradeId.ZERGMISSILEWEAPONSLEVEL3
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGMELEEWEAPONSLEVEL1) > 0
or UpgradeId.ZERGMELEEWEAPONSLEVEL1 in state.upgrades),
lambda: state.own_structures(UnitTypeId.EVOLUTIONCHAMBER).ready.exists,
UpgradeId.ZERGMELEEWEAPONSLEVEL1
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGMELEEWEAPONSLEVEL2) > 0
or UpgradeId.ZERGGROUNDARMORSLEVEL2 in state.upgrades),
lambda: (state.own_structures(UnitTypeId.EVOLUTIONCHAMBER).ready.exists
and UpgradeId.ZERGMELEEWEAPONSLEVEL1 in state.upgrades),
UpgradeId.ZERGMELEEWEAPONSLEVEL2
),
BOStep(
0,
lambda: (state.already_pending_upgrade(UpgradeId.ZERGMELEEWEAPONSLEVEL3) > 0
or UpgradeId.ZERGGROUNDARMORSLEVEL3 in state.upgrades),
lambda: (state.own_structures(UnitTypeId.EVOLUTIONCHAMBER).ready.exists
and state.own_structures(UnitTypeId.HIVE).ready.exists
and UpgradeId.ZERGMELEEWEAPONSLEVEL2 in state.upgrades),
UpgradeId.ZERGMELEEWEAPONSLEVEL3
),
BOStep(
58,
lambda: (state.already_pending_upgrade(UpgradeId.OVERLORDSPEED) > 0
or UpgradeId.OVERLORDSPEED in state.upgrades),
lambda: (state.own_structures(UnitTypeId.LAIR).exists
and state.get_unit_count(UnitTypeId.DRONE) > 58),
UpgradeId.OVERLORDSPEED
),
BOStep(
180,
lambda: state.get_unit_count(UnitTypeId.INFESTATIONPIT) >= 1,
lambda: (state.get_unit_count(UnitTypeId.INFESTATIONPIT) < 1
and state.get_unit_count(UnitTypeId.DRONE) >= 70),
UnitTypeId.INFESTATIONPIT
),
BOStep(
180,
lambda: (state.get_unit_count(UnitTypeId.SPIRE) >= 1
or state.get_unit_count(UnitTypeId.GREATERSPIRE) >= 1),
lambda: (state.get_unit_count(UnitTypeId.SPIRE) < 1
and state.get_unit_count(UnitTypeId.DRONE) >= 75),
UnitTypeId.SPIRE
),
BOStep(
180,
lambda: state.get_unit_count(UnitTypeId.HIVE) >= 1,
lambda: (state.get_unit_count(UnitTypeId.HIVE) < 1
and state.get_unit_count(UnitTypeId.DRONE) >= 75),
UnitTypeId.HIVE
),
#BOStep(
# 75,
# lambda: state.get_unit_count(UnitTypeId.EXTRACTOR) >= 7,
# lambda: (state.get_unit_count(UnitTypeId.EXTRACTOR) < 7
# and state.get_unit_count(UnitTypeId.DRONE) >= 75),
# UnitTypeId.EXTRACTOR
#),
BOStep(
160,
lambda: state.get_unit_count(UnitTypeId.GREATERSPIRE) >= 1,
lambda: (state.get_unit_count(UnitTypeId.GREATERSPIRE) < 1
and state.get_unit_count(UnitTypeId.SPIRE) >= 1
and state.get_unit_count(UnitTypeId.HIVE) >= 1
and state.get_unit_count(UnitTypeId.DRONE) >= 75),
UnitTypeId.GREATERSPIRE
),
#BOStep(
# 75,
# lambda: state.get_unit_count(UnitTypeId.EXTRACTOR) >= 8,
# lambda: (state.get_unit_count(UnitTypeId.EXTRACTOR) < 8
# and state.get_unit_count(UnitTypeId.DRONE) >= 75),
# UnitTypeId.EXTRACTOR
#),
]
def get_army_comp(state : StateService):
goal_army_composition: Dict[UnitTypeId, int] = {}
fallback_units: List[UnitTypeId] = []
# ORDER IS PRIORITY
if state.own_structures(UnitTypeId.GREATERSPIRE).ready:
goal_army_composition[UnitTypeId.BROODLORD] = 0
if state.own_structures({UnitTypeId.SPIRE, UnitTypeId.GREATERSPIRE}).ready:
goal_army_composition[UnitTypeId.CORRUPTOR] = 0
if state.own_structures(UnitTypeId.HYDRALISKDEN).ready:
goal_army_composition[UnitTypeId.HYDRALISK] = 0
if state.own_structures(UnitTypeId.BANELINGNEST).ready:
goal_army_composition[UnitTypeId.BANELING] = 0
if state.own_structures(UnitTypeId.SPAWNINGPOOL).ready:
goal_army_composition[UnitTypeId.ZERGLING] = 0
fallback_units.append(UnitTypeId.ZERGLING)
goal_army_composition[UnitTypeId.QUEEN] = 0
if UnitTypeId.BROODLORD in goal_army_composition:
goal_army_composition[UnitTypeId.CORRUPTOR] = 12 - (state.get_unit_count(UnitTypeId.CORRUPTOR) + state.get_unit_count(UnitTypeId.BROODLORD))
own_corruptors = state.own_army_units(UnitTypeId.CORRUPTOR)
if own_corruptors.exists and UnitTypeId.BROODLORD in goal_army_composition:
goal_army_composition[UnitTypeId.BROODLORD] += own_corruptors.amount
# ----#
# ZvP # (only matchup implemented)
# ----#
zealots = state.enemy_army_units(UnitTypeId.ZEALOT)
if zealots.exists:
if UnitTypeId.HYDRALISK in goal_army_composition:
goal_army_composition[UnitTypeId.HYDRALISK] += zealots.amount
elif UnitTypeId.ZERGLING in goal_army_composition:
goal_army_composition[UnitTypeId.ZERGLING] += 4 * zealots.amount
adepts = state.enemy_army_units(UnitTypeId.ADEPT)
if adepts.exists:
if UnitTypeId.HYDRALISK in goal_army_composition:
goal_army_composition[UnitTypeId.HYDRALISK] += adepts.amount
elif UnitTypeId.ZERGLING in goal_army_composition:
goal_army_composition[UnitTypeId.ZERGLING] += 4 * adepts.amount
sentries = state.enemy_army_units(UnitTypeId.SENTRY)
if sentries.exists:
if UnitTypeId.HYDRALISK in goal_army_composition:
goal_army_composition[UnitTypeId.HYDRALISK] += sentries.amount
elif UnitTypeId.ZERGLING in goal_army_composition:
goal_army_composition[UnitTypeId.ZERGLING] += 4 * sentries.amount
stalkers = state.enemy_army_units(UnitTypeId.STALKER)
if stalkers.exists:
if UnitTypeId.HYDRALISK in goal_army_composition:
if 2 * state.get_unit_count(UnitTypeId.ZERGLING) < stalkers.amount:
goal_army_composition[UnitTypeId.ZERGLING] += 4 * stalkers.amount
else:
goal_army_composition[UnitTypeId.HYDRALISK] += stalkers.amount
elif UnitTypeId.ZERGLING in goal_army_composition:
goal_army_composition[UnitTypeId.ZERGLING] += 4 * stalkers.amount
immortals = state.enemy_army_units(UnitTypeId.IMMORTAL)
if immortals.exists:
if UnitTypeId.ZERGLING in goal_army_composition:
if state.enemy_army_units(UnitTypeId.COLOSSUS).exists:
goal_army_composition[UnitTypeId.ZERGLING] += 8 * immortals.amount
else:
goal_army_composition[UnitTypeId.ZERGLING] += 16 * immortals.amount
voidrays = state.enemy_army_units(UnitTypeId.VOIDRAY)
if voidrays.exists and UnitTypeId.HYDRALISK in goal_army_composition:
goal_army_composition[UnitTypeId.HYDRALISK] += 3 * voidrays.amount
if voidrays.exists and not UnitTypeId.HYDRALISK in goal_army_composition:
goal_army_composition[UnitTypeId.QUEEN] += 2 + 2 * voidrays.amount
prisms = state.enemy_army_units(UnitTypeId.WARPPRISM)
if prisms.exists and UnitTypeId.HYDRALISK in goal_army_composition:
goal_army_composition[UnitTypeId.HYDRALISK] += 2 * prisms.amount
colossi = state.enemy_army_units(UnitTypeId.COLOSSUS)
if colossi.exists and UnitTypeId.CORRUPTOR in goal_army_composition:
goal_army_composition[UnitTypeId.CORRUPTOR] += 3 * colossi.amount
goal_army_composition[UnitTypeId.ZERGLING] += 4 * colossi.amount
return goal_army_composition, fallback_units, [] | [
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348,
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2
] |
autogluon/utils/tabular/utils/decorators.py | zhanghang1989/autogluon | 6 | 160871 | import time, logging
logger = logging.getLogger(__name__)
# decorator to calculate duration taken by any function. Logs times at debug level (logging level 10).
def calculate_time(func):
# added arguments inside the inner1,
# if function takes any arguments,
# can be added like this.
def inner1(*args, **kwargs):
# storing time before function execution
begin = time.time()
output = func(*args, **kwargs)
# storing time after function execution
end = time.time()
logger.debug("Total time taken in " + str(func.__name__)+": "+str(end - begin))
return output
return inner1
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IMPACTA/Unid02_Sem4.py/exe02A.py | FR7/Meus-Projetos | 0 | 136048 | #Faça um programa que leia três números inteiros e colocá-los em ordem crescente.
#Sugestão: Sejam 3 números A, B e C. A ideia principal é:
# – armazenar em A o menor valor
# – armazenar em B o valor intermediário
# – armazenar em C o maior valor
a = int(input("Digite um número para a: "))
b = int(input("Digite um número para b: "))
c = int(input("Digite um número para c: "))
#a é o menor:
if a < b and a < c:
if b < c:
print("{}, {}, {}".format(a, b, c))
else:
print("{},{},{}".format(a, c, b))
#b é menor:
elif b < a and b < c:
if a < c:
print("{}, {}, {}".format(b, a, c))
else:
print("{}, {}, {}".format(b, c, a))
else:
if a > b:
print("{}, {}, {}".format(c, b, a))
else:
print("{}, {}, {}".format(c, a, b))
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] |
model.py | linsamtw/finmind-analysis | 0 | 111124 | <filename>model.py
import datetime
from collections import Counter
from random import seed
from tqdm import tqdm
import numpy as np
import pandas as pd
import xgboost as xgb
from loguru import logger
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
def load_taiwan_stock_price_data():
df = pd.read_csv("taiwan_stock_price.csv")
df["stock_id"] = df["stock_id"].astype(str)
return df
def clean_df(df):
df = df[df["Trading_Volume"] > 0]
df = df.sort_values("date")
df["stock_id"] = df["stock_id"].astype(str)
logger.info(df["Trading_Volume"].describe()["mean"])
df = df[df["Trading_Volume"] > df["Trading_Volume"].describe()["mean"]]
df = df.reset_index(drop=True)
return df
def create_target(spread_per):
if spread_per > 0.03:
return 1
elif spread_per < -0.03:
return -1
else:
return 0
def feature_engineer(df: pd.DataFrame, last_days: int = 7):
# create target
logger.info("feature_engineer")
df["spread_per"] = df["spread"] / df["open"]
df["target"] = df["spread_per"].apply(create_target)
logger.info(f'target: {Counter(df["target"])}')
# create feature
for day in range(1, last_days + 1):
df[f"open_minus_{day}"] = df.groupby("stock_id")["open"].shift(day)
df[f"max_minus_{day}"] = df.groupby("stock_id")["max"].shift(day)
df[f"min_minus_{day}"] = df.groupby("stock_id")["min"].shift(day)
df[f"close_minus_{day}"] = df.groupby("stock_id")["close"].shift(day)
df[f"Trading_Volume_minus_{day}"] = df.groupby("stock_id")[
"Trading_Volume"
].shift(day)
df[f"Trading_turnover_minus_{day}"] = df.groupby("stock_id")[
"Trading_turnover"
].shift(day)
df[f"spread_per_{day}"] = df.groupby("stock_id")["spread_per"].shift(
day
)
df = df.dropna()
return df
def split_train_test(df):
logger.info("split_train_test")
train_test_split_index = int(len(df) * 0.7)
train_df = df[:train_test_split_index]
test_df = df[train_test_split_index:]
return train_df, test_df
def create_feature_variable(df, last_days: int = 7):
logger.info("create_feature_variable")
colname_list = [
[
f"open_minus_{day}",
f"max_minus_{day}",
f"min_minus_{day}",
f"close_minus_{day}",
f"Trading_Volume_minus_{day}",
f"Trading_turnover_minus_{day}",
# f"spread_per_{day}",
]
for day in range(1, last_days + 1)
]
feature_list = []
[feature_list.extend(col) for col in colname_list]
feature_list = [
feature for feature in feature_list if feature in list(df.columns)
]
return feature_list
def create_model(train_df, test_df, feature_list):
logger.info("create_model")
logger.info(f"feature: {feature_list}")
x_train = train_df[feature_list]
x_test = test_df[feature_list]
y_train = train_df["target"].values
y_test = test_df["target"].values
watchlist = [(x_train, y_train), (x_test, y_test)]
seed(100)
clf = xgb.XGBClassifier(
eta=0.1,
max_depth=5,
eval_metric="merror",
n_estimators=100,
verbose=True,
num_classes=3,
# min_child_weight=0.5,
# subsample =0.5,
# alpha=1,
)
clf.fit(x_train, y_train, eval_set=watchlist, early_stopping_rounds=5)
train_pred = clf.predict(x_train)
train_df["pred"] = train_pred
train_array = confusion_matrix(train_df["target"], train_df["pred"])
train_confusion_matrix = pd.DataFrame(
train_array, range(-1, 2), range(-1, 2)
)
logger.info(f"train_confusion_matrix \n {train_confusion_matrix}")
test_pred = clf.predict(x_test)
test_df["pred"] = test_pred
test_array = confusion_matrix(test_df["target"], test_df["pred"])
test_confusion_matrix = pd.DataFrame(test_array, range(-1, 2), range(-1, 2))
logger.info(f"test_confusion_matrix \n {test_confusion_matrix}")
return train_df, test_df
def calculate_return_profit(pred_df):
pred_1 = pred_df[pred_df["pred"] == 1]
pred_1["profit"] = pred_1["close"] - pred_1["open"]
pred_1 = pred_1[["open", "date", "profit"]]
pred_minut_1 = pred_df[pred_df["pred"] == -1]
pred_minut_1["profit"] = pred_minut_1["open"] - pred_minut_1["close"]
pred_minut_1 = pred_minut_1[["open", "date", "profit"]]
pred_df = pd.concat([pred_1, pred_minut_1], axis=0)
mean_df = pred_df.groupby("date")["profit"].mean()
mean_df = mean_df.reset_index()
open_df = pred_df.groupby("date")["open"].sum()
open_df = open_df.reset_index()
count_df = pred_df.groupby("date")["open"].count()
count_df = count_df.reset_index()
count_df.columns = ["date", "deal_times"]
pred_df = mean_df.merge(open_df, on=["date"]).merge(count_df, on=["date"])
pred_df["date"] = pd.to_datetime(pred_df["date"])
pred_df["year"] = pred_df["date"].dt.year
pred_df["fee"] = pred_df["open"] * 0.001425 * 2 * 0.5
pred_df["tax"] = pred_df["open"] * 0.003 * 0.5
pred_df["profit_fee_tax"] = (
pred_df["profit"] - pred_df["fee"] - pred_df["tax"]
)
pred_df["total_profit_fee_tax"] = (
pred_df["profit_fee_tax"].cumsum()
).round(2)
pred_df["total_profit"] = (pred_df["profit_fee_tax"].cumsum()).round(2)
for col in [
"fee",
"tax",
"profit_fee_tax",
"total_profit_fee_tax",
"total_profit",
]:
pred_df[col] = pred_df[col].round(2)
return pred_df
def create_win_rate(profit_df, profit_col):
win_rate = (
round(
len(profit_df[profit_df[profit_col] > 0]) / len(profit_df),
2,
)
* 100
)
return win_rate
def show_profit(profit_df, text: str):
train_win_rate = round(create_win_rate(profit_df, "profit"), 2)
train_win_rate_with_fee_tax = round(
create_win_rate(profit_df, "profit_fee_tax"), 2
)
profit_df["return"] = [
0 if x > 0 else x for x in list(profit_df["profit_fee_tax"])
]
max_return = 0
max_ret = 0
for ret in list(profit_df["return"]):
if ret == 0:
max_ret = 0
max_ret += ret
if max_ret < max_return:
max_return = max_ret
logger.info(
f"""
{text}
---------------------------------------------------------------------------
平均資金: {round(profit_df["open"].mean(),2)*1000}
平均每天交易資金: {round(profit_df['open'].mean() ,2)*1000}
最大交易資金: {round(profit_df['open'].max() ,2)*1000}
平均每天交易次數: {round(profit_df['deal_times'].mean() ,2)}
最大每天交易次數: {round(profit_df['deal_times'].max() ,2)}
最小每天交易次數: {round(profit_df['deal_times'].min() ,2)}
---------------------------------------------------------------------------
平均報酬: {round(profit_df["profit"].mean(),2)*1000}
平均年報酬: {profit_df.groupby('year')['profit'].sum().mean().round(2)*1000}
勝率: {train_win_rate}%
---------------------------------------------------------------------------
含手續費 0.001425、交易稅 0.003*0.5
平均報酬: {round(profit_df["profit_fee_tax"].mean(),2)*1000}
最大單次損失: {round(profit_df["profit_fee_tax"].min(),2)*1000}
最大累積損失: {round(max_return,2)*1000}
平均年報酬: {profit_df.groupby('year')['profit_fee_tax'].sum().mean().round(2)*1000}
勝率: {train_win_rate_with_fee_tax}%
"""
)
def main():
stock_price_df = load_taiwan_stock_price_data()
stock_price_df = clean_df(stock_price_df)
last_days = 5
df = feature_engineer(stock_price_df, last_days=last_days)
train_df, test_df = split_train_test(df)
feature_list = create_feature_variable(df, last_days=last_days)
train_df, test_df = create_model(train_df, test_df, feature_list)
train_profit_df = calculate_return_profit(train_df)
test_profit_df = calculate_return_profit(test_df)
show_profit(train_profit_df, "train")
show_profit(test_profit_df, "test")
test_profit_df.to_csv('test_profit_df.csv', index=False)
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] |
pipeline/fast_gbdt.py | LasTAD/FATE_FL_IoT_Intrusions | 0 | 95252 | <gh_stars>0
import argparse
from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform
from pipeline.component.hetero_fast_secureboost import HeteroFastSecureBoost
from pipeline.component.intersection import Intersection
from pipeline.component.reader import Reader
from pipeline.interface.data import Data
from pipeline.component.evaluation import Evaluation
from pipeline.interface.model import Model
from pipeline.utils.tools import load_job_config
from pipeline.runtime.entity import JobParameters
import datasets
def main(config="config.yaml", namespace="", num_host=4):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
parties = config.parties
guest = parties.guest[0]
hosts = config.parties.host[:num_host]
# data sets
data = datasets.dataset.swat_ait_hetero
label = data['label']
guest_train_data = data['train']["guest"]
host_train_data = data['train']["host"][:num_host]
for d in [guest_train_data, *host_train_data]:
d["namespace"] = f"{d['namespace']}{namespace}"
guest_test_data = data['test']["guest"]
host_test_data = data['test']["host"][:num_host]
for d in [guest_test_data, *host_test_data]:
d["namespace"] = f"{d['namespace']}{namespace}"
# init pipeline
pipeline = PipeLine().set_initiator(role="guest", party_id=guest).set_roles(guest=guest, host=hosts, )
# set data reader and data-io
reader_0, reader_1 = Reader(name="reader_0"), Reader(name="reader_1")
reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
for i in range(num_host):
reader_0.get_party_instance(role='host', party_id=hosts[i]) \
.component_param(table=host_train_data[i])
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_test_data)
for i in range(num_host):
reader_1.get_party_instance(role='host', party_id=hosts[i]) \
.component_param(table=host_test_data[i])
datatr_0, datatr_1 = DataTransform(name="datatr_0"), DataTransform(name="datatr_1")
datatr_0.get_party_instance(role="guest", party_id=guest).component_param(with_label=True, output_format="dense",
label_name=label, label_type='int')
datatr_0.get_party_instance(role="host", party_id=hosts).component_param(with_label=False)
datatr_1.get_party_instance(role="guest", party_id=guest).component_param(with_label=True, output_format="dense",
label_name=label)
datatr_1.get_party_instance(role="host", party_id=hosts).component_param(with_label=False)
# data intersect component
intersect_0 = Intersection(name="intersection_0")
intersect_1 = Intersection(name="intersection_1")
# secure boost component
hetero_fast_secure_boost_0 = HeteroFastSecureBoost(name="hetero_fast_secure_boost_0",
num_trees=15,
tree_num_per_party=1,
task_type="classification",
objective_param={"objective": "cross_entropy"},
encrypt_param={"method": "iterativeAffine"},
tree_param={"max_depth": 3},
validation_freqs=1)
# evaluation component
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(datatr_0, data=Data(data=reader_0.output.data))
pipeline.add_component(datatr_1, data=Data(data=reader_1.output.data), model=Model(datatr_0.output.model))
pipeline.add_component(intersect_0, data=Data(data=datatr_0.output.data))
pipeline.add_component(intersect_1, data=Data(data=datatr_1.output.data))
pipeline.add_component(hetero_fast_secure_boost_0,
data=Data(train_data=intersect_0.output.data, validate_data=intersect_1.output.data))
pipeline.add_component(evaluation_0, data=Data(data=hetero_fast_secure_boost_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode,
adaptation_parameters={'task_cores_per_node': 2, 'request_task_cores': 2})
pipeline.fit(job_parameters)
pipeline.dump("fast_gbdt_model.pkl")
print("fitting hetero secureboost done, result:")
print(pipeline.get_component("hetero_secure_boost_0").get_summary())
# pipeline = PipeLine.load_model_from_file('pipeline_saved.pkl')
print('start to predict')
# predict
# deploy required components
pipeline.deploy_component([datatr_0, intersect_0, hetero_fast_secure_boost_0, evaluation_0])
predict_pipeline = PipeLine()
# add data reader onto predict pipeline
predict_pipeline.add_component(reader_0)
# add selected components from train pipeline onto predict pipeline
# specify data source
predict_pipeline.add_component(pipeline,
data=Data(predict_input={pipeline.datatr_0.input.data: reader_0.output.data}))
# run predict model
predict_pipeline.predict(job_parameters)
if __name__ == "__main__":
parser = argparse.ArgumentParser("PIPELINE DEMO")
parser.add_argument("-config", type=str,
help="config file")
args = parser.parse_args()
if args.config is not None:
main(args.config)
else:
main()
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runserver.py | chintal/tendril-monitor-vendor | 0 | 23006 | <filename>runserver.py
#!/usr/bin/env python
# encoding: utf-8
# Copyright (C) 2015 <NAME>
# Released under the MIT license.
"""
Simple Deployment Example
-------------------------
"""
from vendor_monitor import worker
from twisted.internet import reactor
import logging
logging.basicConfig(level=logging.INFO)
if __name__ == '__main__':
worker.start()
reactor.run()
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test/api/test_histories.py | mmiladi/galaxy | 1 | 2740 | <filename>test/api/test_histories.py
# -*- coding: utf-8 -*-
from requests import (
get,
post,
put
)
from base import api # noqa: I100
from base.populators import ( # noqa: I100
DatasetCollectionPopulator,
DatasetPopulator,
wait_on
)
class HistoriesApiTestCase(api.ApiTestCase):
def setUp(self):
super(HistoriesApiTestCase, self).setUp()
self.dataset_populator = DatasetPopulator(self.galaxy_interactor)
self.dataset_collection_populator = DatasetCollectionPopulator(self.galaxy_interactor)
def test_create_history(self):
# Create a history.
create_response = self._create_history("TestHistory1")
created_id = create_response["id"]
# Make sure new history appears in index of user's histories.
index_response = self._get("histories").json()
indexed_history = [h for h in index_response if h["id"] == created_id][0]
self.assertEquals(indexed_history["name"], "TestHistory1")
def test_show_history(self):
history_id = self._create_history("TestHistoryForShow")["id"]
show_response = self._show(history_id)
self._assert_has_key(
show_response,
'id', 'name', 'annotation', 'size', 'contents_url',
'state', 'state_details', 'state_ids'
)
state_details = show_response["state_details"]
state_ids = show_response["state_ids"]
states = [
'discarded', 'empty', 'error', 'failed_metadata', 'new',
'ok', 'paused', 'queued', 'running', 'setting_metadata', 'upload'
]
assert isinstance(state_details, dict)
assert isinstance(state_ids, dict)
self._assert_has_keys(state_details, *states)
self._assert_has_keys(state_ids, *states)
def test_show_most_recently_used(self):
history_id = self._create_history("TestHistoryRecent")["id"]
show_response = self._get("histories/most_recently_used").json()
assert show_response["id"] == history_id
def test_index_order(self):
slightly_older_history_id = self._create_history("TestHistorySlightlyOlder")["id"]
newer_history_id = self._create_history("TestHistoryNewer")["id"]
index_response = self._get("histories").json()
assert index_response[0]["id"] == newer_history_id
assert index_response[1]["id"] == slightly_older_history_id
def test_delete(self):
# Setup a history and ensure it is in the index
history_id = self._create_history("TestHistoryForDelete")["id"]
index_response = self._get("histories").json()
assert index_response[0]["id"] == history_id
show_response = self._show(history_id)
assert not show_response["deleted"]
# Delete the history
self._delete("histories/%s" % history_id)
# Check can view it - but it is deleted
show_response = self._show(history_id)
assert show_response["deleted"]
# Verify it is dropped from history index
index_response = self._get("histories").json()
assert len(index_response) == 0 or index_response[0]["id"] != history_id
# Add deleted filter to index to view it
index_response = self._get("histories", {"deleted": "true"}).json()
assert index_response[0]["id"] == history_id
def test_purge(self):
history_id = self._create_history("TestHistoryForPurge")["id"]
data = {'purge': True}
self._delete("histories/%s" % history_id, data=data)
show_response = self._show(history_id)
assert show_response["deleted"]
assert show_response["purged"]
def test_undelete(self):
history_id = self._create_history("TestHistoryForDeleteAndUndelete")["id"]
self._delete("histories/%s" % history_id)
self._post("histories/deleted/%s/undelete" % history_id)
show_response = self._show(history_id)
assert not show_response["deleted"]
def test_update(self):
history_id = self._create_history("TestHistoryForUpdating")["id"]
self._update(history_id, {"name": "New Name"})
show_response = self._show(history_id)
assert show_response["name"] == "New Name"
unicode_name = u'桜ゲノム'
self._update(history_id, {"name": unicode_name})
show_response = self._show(history_id)
assert show_response["name"] == unicode_name, show_response
quoted_name = "'MooCow'"
self._update(history_id, {"name": quoted_name})
show_response = self._show(history_id)
assert show_response["name"] == quoted_name
self._update(history_id, {"deleted": True})
show_response = self._show(history_id)
assert show_response["deleted"], show_response
self._update(history_id, {"deleted": False})
show_response = self._show(history_id)
assert not show_response["deleted"]
self._update(history_id, {"published": True})
show_response = self._show(history_id)
assert show_response["published"]
self._update(history_id, {"genome_build": "hg18"})
show_response = self._show(history_id)
assert show_response["genome_build"] == "hg18"
self._update(history_id, {"annotation": "The annotation is cool"})
show_response = self._show(history_id)
assert show_response["annotation"] == "The annotation is cool"
self._update(history_id, {"annotation": unicode_name})
show_response = self._show(history_id)
assert show_response["annotation"] == unicode_name, show_response
self._update(history_id, {"annotation": quoted_name})
show_response = self._show(history_id)
assert show_response["annotation"] == quoted_name
def test_update_invalid_attribute(self):
history_id = self._create_history("TestHistoryForInvalidUpdating")["id"]
put_response = self._update(history_id, {"invalidkey": "moo"})
assert "invalidkey" not in put_response.json()
def test_update_invalid_types(self):
history_id = self._create_history("TestHistoryForUpdatingInvalidTypes")["id"]
for str_key in ["name", "annotation"]:
assert self._update(history_id, {str_key: False}).status_code == 400
for bool_key in ['deleted', 'importable', 'published']:
assert self._update(history_id, {bool_key: "a string"}).status_code == 400
assert self._update(history_id, {"tags": "a simple string"}).status_code == 400
assert self._update(history_id, {"tags": [True]}).status_code == 400
def test_invalid_keys(self):
invalid_history_id = "1234123412341234"
assert self._get("histories/%s" % invalid_history_id).status_code == 400
assert self._update(invalid_history_id, {"name": "new name"}).status_code == 400
assert self._delete("histories/%s" % invalid_history_id).status_code == 400
assert self._post("histories/deleted/%s/undelete" % invalid_history_id).status_code == 400
def test_create_anonymous_fails(self):
post_data = dict(name="CannotCreate")
# Using lower-level _api_url will cause key to not be injected.
histories_url = self._api_url("histories")
create_response = post(url=histories_url, data=post_data)
self._assert_status_code_is(create_response, 403)
def test_import_export(self):
history_name = "for_export"
history_id = self.dataset_populator.new_history(name=history_name)
self.dataset_populator.new_dataset(history_id, content="1 2 3")
imported_history_id = self._reimport_history(history_id, history_name)
contents_response = self._get("histories/%s/contents" % imported_history_id)
self._assert_status_code_is(contents_response, 200)
contents = contents_response.json()
assert len(contents) == 1
imported_content = self.dataset_populator.get_history_dataset_content(
history_id=imported_history_id,
dataset_id=contents[0]["id"]
)
assert imported_content == "1 2 3\n"
def test_import_export_collection(self):
from nose.plugins.skip import SkipTest
raise SkipTest("Collection import/export not yet implemented")
history_name = "for_export_with_collections"
history_id = self.dataset_populator.new_history(name=history_name)
self.dataset_collection_populator.create_list_in_history(history_id, contents=["Hello", "World"])
imported_history_id = self._reimport_history(history_id, history_name)
contents_response = self._get("histories/%s/contents" % imported_history_id)
self._assert_status_code_is(contents_response, 200)
contents = contents_response.json()
assert len(contents) == 3
def _reimport_history(self, history_id, history_name):
# Ensure the history is ready to go...
self.dataset_populator.wait_for_history(history_id, assert_ok=True)
# Export the history.
download_path = self._export(history_id)
# Create download for history
full_download_url = "%s%s?key=%s" % (self.url, download_path, self.galaxy_interactor.api_key)
download_response = get(full_download_url)
self._assert_status_code_is(download_response, 200)
def history_names():
history_index = self._get("histories")
return dict((h["name"], h) for h in history_index.json())
import_name = "imported from archive: %s" % history_name
assert import_name not in history_names()
import_data = dict(archive_source=full_download_url, archive_type="url")
import_response = self._post("histories", data=import_data)
self._assert_status_code_is(import_response, 200)
def has_history_with_name():
histories = history_names()
return histories.get(import_name, None)
imported_history = wait_on(has_history_with_name, desc="import history")
imported_history_id = imported_history["id"]
self.dataset_populator.wait_for_history(imported_history_id)
return imported_history_id
def test_create_tag(self):
post_data = dict(name="TestHistoryForTag")
history_id = self._post("histories", data=post_data).json()["id"]
tag_data = dict(value="awesometagvalue")
tag_url = "histories/%s/tags/awesometagname" % history_id
tag_create_response = self._post(tag_url, data=tag_data)
self._assert_status_code_is(tag_create_response, 200)
def _export(self, history_id):
export_url = self._api_url("histories/%s/exports" % history_id, use_key=True)
put_response = put(export_url)
self._assert_status_code_is(put_response, 202)
def export_ready_response():
put_response = put(export_url)
if put_response.status_code == 202:
return None
return put_response
put_response = wait_on(export_ready_response, desc="export ready")
self._assert_status_code_is(put_response, 200)
response = put_response.json()
self._assert_has_keys(response, "download_url")
download_path = response["download_url"]
return download_path
def _show(self, history_id):
return self._get("histories/%s" % history_id).json()
def _update(self, history_id, data):
update_url = self._api_url("histories/%s" % history_id, use_key=True)
put_response = put(update_url, json=data)
return put_response
def _create_history(self, name):
post_data = dict(name=name)
create_response = self._post("histories", data=post_data).json()
self._assert_has_keys(create_response, "name", "id")
self.assertEquals(create_response["name"], name)
return create_response
# TODO: (CE) test_create_from_copy
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] |
tests/integration/mci/test_happy_path.py | qateam123/eq | 0 | 2393 | from tests.integration.create_token import create_token
from tests.integration.integration_test_case import IntegrationTestCase
class TestHappyPath(IntegrationTestCase):
def test_happy_path_203(self):
self.happy_path('0203', '1')
def test_happy_path_205(self):
self.happy_path('0205', '1')
def happy_path(self, form_type_id, eq_id):
# Get a token
token = create_token(form_type_id, eq_id)
resp = self.client.get('/session?token=' + token.decode(), follow_redirects=True)
self.assertEqual(resp.status_code, 200)
# We are on the landing page
content = resp.get_data(True)
self.assertRegex(content, '<title>Introduction</title>')
self.assertRegex(content, '>Start survey<')
self.assertRegex(content, 'Monthly Business Survey - Retail Sales Index')
# We proceed to the questionnaire
post_data = {
'action[start_questionnaire]': 'Start Questionnaire'
}
resp = self.client.post('/questionnaire/' + eq_id + '/' + form_type_id + '/789/introduction', data=post_data, follow_redirects=False)
self.assertEqual(resp.status_code, 302)
block_one_url = resp.location
resp = self.client.get(block_one_url, follow_redirects=False)
self.assertEqual(resp.status_code, 200)
# We are in the Questionnaire
content = resp.get_data(True)
self.assertRegex(content, '<title>Survey</title>')
self.assertRegex(content, '>Monthly Business Survey - Retail Sales Index</')
self.assertRegex(content, "What are the dates of the sales period you are reporting for?")
self.assertRegex(content, ">Save and continue<")
# check with have some guidance
self.assertRegex(content, "alcoholic drink")
# We fill in our answers
form_data = {
# Start Date
"period-from-day": "01",
"period-from-month": "4",
"period-from-year": "2016",
# End Date
"period-to-day": "30",
"period-to-month": "04",
"period-to-year": "2016",
# Total Turnover
"total-retail-turnover": "100000",
# User Action
"action[save_continue]": "Save & Continue"
}
# We submit the form
resp = self.client.post(block_one_url, data=form_data, follow_redirects=False)
self.assertEqual(resp.status_code, 302)
# There are no validation errors
self.assertRegex(resp.location, r'\/questionnaire\/1\/' + form_type_id + r'\/789\/summary$')
summary_url = resp.location
resp = self.client.get(summary_url, follow_redirects=False)
self.assertEqual(resp.status_code, 200)
# We are on the review answers page
content = resp.get_data(True)
self.assertRegex(content, '<title>Summary</title>')
self.assertRegex(content, '>Monthly Business Survey - Retail Sales Index</')
self.assertRegex(content, '>Your responses<')
self.assertRegex(content, 'Please check carefully before submission.')
self.assertRegex(content, '>Submit answers<')
# We submit our answers
post_data = {
"action[submit_answers]": "Submit answers"
}
resp = self.client.post(summary_url, data=post_data, follow_redirects=False)
self.assertEqual(resp.status_code, 302)
self.assertRegex(resp.location, r'\/questionnaire\/1\/' + form_type_id + r'\/789\/thank-you$')
resp = self.client.get(resp.location, follow_redirects=True)
self.assertEqual(resp.status_code, 200)
# We are on the thank you page
content = resp.get_data(True)
self.assertRegex(content, '<title>Submission Successful</title>')
self.assertRegex(content, '(?s)Monthly Business Survey - Retail Sales Index.*?Monthly Business Survey - Retail Sales Index')
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genkey/migrations/0007_auto_20190328_1149.py | MoreNiceJay/CAmanager_web | 0 | 41217 | <filename>genkey/migrations/0007_auto_20190328_1149.py
# Generated by Django 2.0.4 on 2019-03-28 02:49
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('genkey', '0006_auto_20190327_1004'),
]
operations = [
migrations.RenameModel(
old_name='CA',
new_name='Company',
),
]
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jmetex/main.py | innovocloud/jmetex | 2 | 21811 | <reponame>innovocloud/jmetex
import sys
import time
import argparse
from prometheus_client import start_http_server, Metric, REGISTRY, Summary
from .interfacecollector import InterfaceCollector
from .opticalcollector import OpticalCollector
def main():
parser = argparse.ArgumentParser(description='JunOS API to Prometheus exporter')
parser.add_argument('--port', type=int, required=True,
help='listen port')
parser.add_argument('--instance', type=str, required=True,
help='instance name')
parser.add_argument('--rpc_url', type=str, required=True,
help='URL of the junos RPC endpoint')
parser.add_argument('--user', type=str, required=True,
help='junos user name')
parser.add_argument('--password', type=str, required=True,
help='junos password')
args = parser.parse_args()
start_http_server(args.port)
REGISTRY.register(InterfaceCollector(args.instance, args.rpc_url, args.user, args.password))
REGISTRY.register(OpticalCollector(args.instance, args.rpc_url, args.user, args.password))
while True:
time.sleep(1)
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test_thermal/test_CompanyAdmin/test_RoleManagement/RoleManageBase.py | pyamour/Selenium_Python | 0 | 115878 | from seleniumbase import BaseCase
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from parameterized import parameterized
import pytest
from utilities import utilities
from utilities import gmail
from utilities.Authority import Authority
from utilities.Role import Role
from config.constants import *
from test_thermal.ThermalBase import ThermalBase
from test_thermal.test_CompanyAdmin.test_RoleManagement.RoleManageBaseComponent import RoleManageBaseComponent
import math
import datetime
import time
import json
class RoleManageBase(RoleManageBaseComponent):
def check_authority_info(self, company, role, email_list, expectation="pass"):
self.start_test(locals())
self.login_company_admin()
self.choose_company(companyname=company)
self.role_manage_enter()
self.verify_role_authority(rolename=role, email_list=email_list)
self.logout(COMPANYADMIN_ACCOUNT)
self.end_test(self._testMethodName)
def role_create(self, companyname, rolename, service_list, user_list, expectation):
self.start_test(locals())
companyname, rolename, service_list, user_list, expectation = self.process_create_input(companyname=companyname, rolename=rolename, service_list=service_list, user_list=user_list, expectation=expectation)
self.login_company_admin()
self.choose_company(companyname=companyname)
self.role_manage_enter()
self.role_create_start()
self.role_create_update_set_rolename(rolename, expectation)
self.role_create_update_set_service_list(service_list, expectation)
self.role_create_update_set_user_list(user_list, expectation)
self.role_create_update_submit(expectation)
if expectation == "pass":
time.sleep(10) # Too slowly to refresh record in page
self.verify_role_info(rolename, service_list, user_list)
self.add_role_record(company=companyname, custome_role=[rolename], service={rolename: service_list})
self.add_authority_record(email_list=user_list, company=[companyname], role={companyname: [rolename]})
self.logout(COMPANYADMIN_ACCOUNT)
self.end_test(self._testMethodName)
def role_update(self, companyname, rolename, valuedict, expectation="pass"):
self.start_test(locals())
companyname, rolename, key, value, expectation = self.process_update_input(companyname=companyname,
rolename=rolename,
valuedict=valuedict,
expectation=expectation)
self.login_company_admin()
self.choose_company(companyname=companyname)
self.role_manage_enter()
found_role = self.role_search(rolename=rolename)
if found_role == {}:
self.assertTrue(False, "Cannot find role: " + rolename)
found_correct = found_role["rolename"] == rolename
print("Found info: \n" + str(found_role))
self.assert_true(found_correct,
"Found info doesn't match \n" + "Try to find: " + rolename + "\n But find " + str(
found_role))
edit_selector = str(found_role["edit_selector"])
self.role_update_start(edit_selector)
role_entity = Role()
role = rolename
service_list = None
user_list = None
if "rolename" in key:
role = str(value["rolename"])
self.role_create_update_set_rolename(role)
if "service" in key:
service_list = str(value["service"])
service_list = utilities.str_to_list(service_list)
fail_msg = None
self.role_create_update_set_service_list(service_list=service_list, fail_msg=fail_msg, expectation=expectation)
if "user" in key:
user_list = str(value["user"])
user_list = utilities.str_to_list(user_list)
fail_msg = None
self.role_create_update_set_user_list(user_list=user_list, fail_msg=fail_msg, expectation=expectation)
self.role_create_update_submit(expectation)
if expectation == "pass":
time.sleep(10) # Too slowly to refresh record in page
self.verify_role_info(role, service_list, user_list)
self.logout(COMPANYADMIN_ACCOUNT)
self.end_test(self._testMethodName)
def role_read(self, companyname, rolename, expectation):
self.start_test(locals())
self.login_company_admin()
self.choose_company(companyname=companyname)
self.role_manage_enter()
found_role = self.role_search(rolename)
if rolename == "":
self.assert_true(found_role == {}, "Should not locate individual role")
if rolename != "" and expectation == 'fail':
self.assert_true(found_role == {}, "Should not found role: " + rolename)
if rolename != "" and expectation == "pass":
print("Try to find role: " + rolename)
print("Find: " + str(found_role))
self.assert_true(found_role != {} and found_role["rolename"] == rolename,
"Try to find: " + rolename + "\n But find " + str(found_role))
print("Try to find: " + rolename + "\n and find " + str(found_role))
self.logout(COMPANYADMIN_ACCOUNT)
self.end_test(self._testMethodName)
def role_delete(self, companyname, rolename, expectation):
self.start_test(locals())
self.login_company_admin()
self.choose_company(companyname=companyname)
self.role_manage_enter()
found_role = self.role_search(rolename)
if expectation != "pass":
self.assert_true(found_role == {}, "Should not find role: " + rolename)
else:
self.assertTrue(found_role != {}, "Can not find role: " + rolename)
all_deleted = False
authority = Authority()
while not all_deleted:
delete_selector = found_role["delete_selector"]
self.role_delete_action(delete_selector)
self.remove_authority_record(
email_list=authority.get_email_list(bycolumn="role", value=rolename, company=companyname),
role={companyname: [rolename]})
# self.remove_role_record(company=companyname, custome_role=[rolename])
time.sleep(6) # wait for refresh
found_role = self.role_search(rolename)
if found_role == {}:
all_deleted = True
self.logout(COMPANYADMIN_ACCOUNT)
self.end_test(self._testMethodName)
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handedness/c2_ukb_analysisPheno.py | Conxz/brainskew | 0 | 68534 | #
import pandas as pd
import numpy as np
import os
import statsmodels.api as sm
from scipy.stats import pearsonr, bartlett, levene
data_file = './ukb_pheno_T1pos.csv'
dat = pd.read_csv(data_file)
print 'Handedness: 1=right, 2=left'
print dat[dat['Handedness']==1]['Handedness'].count()
print dat[dat['Handedness']==2]['Handedness'].count()
print dat[dat['Handedness']==3]['Handedness'].count()
#conf_str = 'plusPCs' # SexAge or plusPCs or plusPos
conf_str = 'plusPos'#'plusPCs' # SexAge or plusPCs or plusPos
#conf_str = 'plusPosScales'#'plusPCs' # SexAge or plusPCs or plusPos
if conf_str == 'SexAge':
col_covars = ['Sex', 'Age', 'ZAge2'#, 'BrainV'
]
elif conf_str == 'plusPCs':
col_covars = ['Sex', 'Age','ZAge2', #'BrainV',
'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7',
'PC8', 'PC9', 'PC10']
elif conf_str == 'plusPos':
col_covars = ['Sex', 'Age','ZAge2', #'BrainV',
'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7',
'PC8', 'PC9', 'PC10',
'PosX','PosY','PosZ', 'T1SNR', 'T1CNR', 'Center']#+ ['SkewsXY']
else: # plusPosScales
col_covars = ['Sex', 'Age','ZAge2', 'BrainV',
'PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7',
'PC8', 'PC9', 'PC10',
'PosX','PosY','PosZ', 'T1SNR', 'T1CNR', 'Center',
'ScalesAvg','ScalesX','ScalesY', 'ScalesZ']
skew_data_file = '../out_2/skewsAll.out'
skew_dat = pd.read_csv(skew_data_file)
#col_skews = ['SkewsXY', 'SkewsXZ'] # 'SkewsYZ'
col_skews = ['ScalesAvg', 'ScalesX', 'ScalesY', 'ScalesZ'] # Do not use plusPosScales for using this line
# Deal with outliers, 4SD
#TagOutliers = False#True
TagOutliers = True
if TagOutliers:
thr_xy = skew_dat['SkewsXY'].mean()+4.0*skew_dat['SkewsXY'].std()
skew_dat.mask(skew_dat['SkewsXY']>thr_xy, inplace=True) # Edit
skew_dat.mask(skew_dat['SkewsXY']<-thr_xy, inplace=True) # Edit
thr_xz = skew_dat['SkewsXZ'].mean()+4.0*skew_dat['SkewsXZ'].std()
skew_dat.mask(skew_dat['SkewsXZ']>thr_xz, inplace=True) # Edit
skew_dat.mask(skew_dat['SkewsXZ']<-thr_xz, inplace=True) # Edit
## For Handedness ----------------------------------------------------------
hand_dat = pd.merge(left=skew_dat, right=dat, left_on='SID', right_on='SID')
hand_dat.mask(hand_dat['Handedness']==-3, inplace=True)
hand_dat = hand_dat.dropna()
#hand_dat.to_csv('./hand_skew_pheno_T1pos.csv', index=False)
hand_dat['Hand'] = np.nan
hand_dat['Hand'][hand_dat['Handedness']==1] = 1
hand_dat['Hand'][hand_dat['Handedness']==2] = -1
hand_dat['Hand'][hand_dat['Handedness']==3] = 0
#hand_dat.mask(hand_dat['Hand']==0, inplace=True) # Edit
#hand_dat.mask(hand_dat['Hand']==-1, inplace=True) #Edit
#hand_dat.mask(hand_dat['Hand']==1, inplace=True) #Edit
hand_dat = hand_dat.dropna()
hand_dat.to_csv('./hand_skew_pheno_T1pos_clean.csv', index=False)
col_vars = ['Hand']
for col_skew in col_skews:
print col_skew
y = hand_dat[col_skew]
for col_var in col_vars:
print '-', col_var
#print pearsonr(hand_dat[col_skew], hand_dat[col_var])
X = hand_dat[[col_var]+col_covars]
X = sm.add_constant(X)
model = sm.OLS(y,X).fit()
predictions = model.predict(X)
#print model.summary()
print 't=', model.tvalues[1], ',p =',model.pvalues[1]
print 'Handedness: 1=right, 2=left, 3=both'
print hand_dat[hand_dat['Handedness']==1]['Handedness'].count()
print hand_dat[hand_dat['Handedness']==2]['Handedness'].count()
print hand_dat[hand_dat['Handedness']==3]['Handedness'].count()
print bartlett(hand_dat[hand_dat['Hand']==-1]['SkewsXY'], hand_dat[hand_dat['Hand']==1]['SkewsXY'])
print bartlett(hand_dat[hand_dat['Hand']==-1]['SkewsXZ'], hand_dat[hand_dat['Hand']==1]['SkewsXZ'])
print levene(hand_dat[hand_dat['Hand']==-1]['SkewsXY'], hand_dat[hand_dat['Hand']==1]['SkewsXY'])
print levene(hand_dat[hand_dat['Hand']==-1]['SkewsXZ'], hand_dat[hand_dat['Hand']==1]['SkewsXZ'])
print hand_dat.groupby('Hand').apply(np.std)
## For Grip Asymmetry --------------------------------------------
dat['Grip'] = dat[['GripL','GripR']].max(axis=1)
z1 = []
z2 = []
z3 = []
z4 = []
for index, row in dat.iterrows():
if row.Handedness==1:
z1.append(row.GripR)
z2.append(row.GripL)
z3.append(2.0*(row.GripR-row.GripL)/(row.GripR+row.GripL))
z4.append(2.0*(row.GripR-row.GripL)/(row.GripR+row.GripL))
elif row.Handedness==2:
z1.append(row.GripL)
z2.append(row.GripR)
z3.append(2.0*(row.GripL-row.GripR)/(row.GripR+row.GripL))
z4.append(2.0*(row.GripR-row.GripL)/(row.GripR+row.GripL))
else:
z1.append(np.nan)
z2.append(np.nan)
z3.append(np.nan)
z4.append(np.nan)
dat['yGrip'] = z1 # dominance hand
dat['nGrip'] = z2
dat['GripAsyAbs'] = z3
dat['GripAsy'] = z4 # for the main analysis, grip strength asymmetry !!!
print dat.shape
col_vars = ['GripAsy']
print 'GripAsy #: ', dat['GripAsy'].count()
grip_dat = pd.merge(left=skew_dat, right=dat, left_on='SID', right_on='SID')
grip_dat = grip_dat.dropna()
grip_dat.to_csv('./grip_skew_pheno_T1pos.csv', index=False)
col_vars = ['GripAsy']
for col_skew in col_skews:
print col_skew
y = grip_dat[col_skew]
for col_var in col_vars:
print '-', col_var
#print pearsonr(grip_dat[col_skew], grip_dat[col_var])
X = grip_dat[[col_var]+col_covars]
X = sm.add_constant(X)
model = sm.OLS(y,X).fit()
predictions = model.predict(X)
#print model.summary()
print 't=', model.tvalues[1], ',p =',model.pvalues[1]
"""
# for subject info of interest
info_fields = {'SID': 'eid',
'Sex': '31-0.0',
'Age': '21003-2.0',
'Handedness': '1707-2.0',
'GripL':'46-2.0',
'GripR':'47-2.0',
'Height':'12144-2.0',
'Weight':'12143-2.0'}
# N = 9932 for subcortical volume, overall white/gray matter
# N = 9931 for cortical volume
# N = 8839 for DTI measures !!!!!!!!!!!!
# N = 9306 for rfMRI matrix
#
# N = 11671 for handedness
# N = 12551 for age for imaging
# N = 502629 for sex for all
# here we focus on subject info with DTI meaures, take collumn 25062-2.0, ...
# ... Mean FA in corticospinal tract on FA skeleton (right), as the selection rule
# here we focus on subject info with T1 data, take collumn 25006-2.0, ...
my_index = dat['25006-2.0'].notnull()
col_names = ['SID', 'Sex', 'Age', 'Handedness', 'GripL', 'GripR', 'Height', 'Weight']
col_list = [info_fields['SID'], info_fields['Sex'], info_fields['Age'],
info_fields['Handedness'],
info_fields['GripL'], info_fields['GripR'],
info_fields['Height'], info_fields['Weight']]
info_dat = dat[col_list][my_index]
info_dat.columns = col_names
info_dat.to_csv('../UKB_Dat/ukb_pheno.csv', index=False)
"""
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src/craft_ml/data/split.py | OptimizationGuys/CraftML | 15 | 100535 | import numpy as np
from sklearn import model_selection
import typing as t
from copy import copy
from ..mltypes import RandomState
from ..data.dataset import Dataset
class DataSplit:
def get_splits(self, dataset: Dataset) -> t.Generator[t.Tuple[Dataset, Dataset], None, None]:
raise NotImplementedError
class TrainTestSplit(DataSplit):
def __init__(self,
train_size: t.Union[float, int] = 0.7,
random_state: RandomState = None,
shuffle: bool = True
):
self.train_size = train_size
self.random_state = random_state
self.shuffle = shuffle
def get_splits(self, dataset: Dataset) -> t.Generator[t.Tuple[Dataset, Dataset], None, None]:
all_rows = dataset.ids
train_rows, test_rows = model_selection.train_test_split(all_rows,
train_size=self.train_size,
random_state=self.random_state,
shuffle=self.shuffle)
train_dataset = copy(dataset)
train_dataset.ids = train_rows
test_dataset = copy(dataset)
test_dataset.ids = test_rows
yield train_dataset, test_dataset
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nlt_gol2_3-autorun.py | kooi/nlt-robotica-snippets | 0 | 1615901 | <reponame>kooi/nlt-robotica-snippets
import pygame
import random
# grid = [
# [0, 1, 0, 1, 0, 1],
# [1, 1, 1, 1, 1, 1],
# ]
# cell = grid[y vanaf boven][x vanaf links]
# cell = grid[0][1]
# print(grid[0][1])
PALETTE = [
(255, 255, 255), # dood
( 0, 0, 0), # levend
(255, 255, 0), # 2
( 0, 255, 255), # 3
(255, 0, 255), # 4
(128, 128, 0), # 5
( 0, 128, 128), # 6
(128, 0, 128), # 7
(128, 128, 128), # 8
(255, 255, 255), # achtergrond
]
PIXEL_SIZE = 10
PIXEL_MARGIN = 2
GRID_SIZE_X = 64
GRID_SIZE_Y = 48
petridish = [[random.randint(0,1) for x in range(GRID_SIZE_X)]
for y in range(GRID_SIZE_Y)]
#petridish = [[0 for x in range(GRID_SIZE_X)] for y in range(GRID_SIZE_Y)]
def count_neighbours(x, y, p):
sum = 0
sum = sum + p[(y-1)%GRID_SIZE_Y][(x-1)%GRID_SIZE_X]
sum = sum + p[(y-1)%GRID_SIZE_Y][(x )%GRID_SIZE_X]
sum = sum + p[(y-1)%GRID_SIZE_Y][(x+1)%GRID_SIZE_X]
sum = sum + p[(y )%GRID_SIZE_Y][(x+1)%GRID_SIZE_X]
sum = sum + p[(y+1)%GRID_SIZE_Y][(x+1)%GRID_SIZE_X]
sum = sum + p[(y+1)%GRID_SIZE_Y][(x )%GRID_SIZE_X]
sum = sum + p[(y+1)%GRID_SIZE_Y][(x-1)%GRID_SIZE_X]
sum = sum + p[(y )%GRID_SIZE_Y][(x-1)%GRID_SIZE_X]
return sum
def life_or_death(state, neighbours):
"""
1. Any live cell with two or three live neighbors survives.
2. Any dead cell with three live neighbors becomes a live cell.
3. All other live cells die in the next generation.
Similarly, all other dead cells stay dead.
"""
if state == 1:
if neighbours == 2 or neighbours == 3:
return 1
else:
return 0
else: # state = 0
if neighbours == 3:
return 1
else:
return 0
def update_petridish(old):
new = [[0 for x in range(GRID_SIZE_X)] for y in range(GRID_SIZE_Y)]
for j in range(GRID_SIZE_Y):
for i in range(GRID_SIZE_X):
new[j][i] = life_or_death( old[j][i], count_neighbours(i, j, old) )
return new
pygame.init()
screen = pygame.display.set_mode([
GRID_SIZE_X*(PIXEL_SIZE+PIXEL_MARGIN)+PIXEL_MARGIN,
GRID_SIZE_Y*(PIXEL_SIZE+PIXEL_MARGIN)+PIXEL_MARGIN,
])
done = False
clock = pygame.time.Clock()
autorun = False
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
elif event.type == pygame.MOUSEBUTTONDOWN:
pos = pygame.mouse.get_pos()
col = pos[0] // (PIXEL_SIZE+PIXEL_MARGIN)
row = pos[1] // (PIXEL_SIZE+PIXEL_MARGIN)
if event.button == 1:
petridish[row][col] = 1
elif event.button == 3:
petridish[row][col] = 0
print("mouseclick(", pos, ") -> (", row,",", col, ")")
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_SPACE:
petridish = update_petridish(petridish)
elif event.key == pygame.K_a:
autorun = True
elif event.key == pygame.K_s:
autorun = False
if autorun == True:
petridish = update_petridish(petridish)
# teken petrischaal naar scherm
screen.fill( PALETTE[9] )
for j in range(GRID_SIZE_Y):
for i in range(GRID_SIZE_X):
pygame.draw.rect( screen, PALETTE[petridish[j][i]],
[(PIXEL_SIZE + PIXEL_MARGIN)*i + PIXEL_MARGIN,
(PIXEL_SIZE + PIXEL_MARGIN)*j + PIXEL_MARGIN,
PIXEL_SIZE, PIXEL_SIZE] )
clock.tick(60)
pygame.display.flip()
pygame.quit()
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] |
BioKlustering-Website/mlmodel/KMtests/websiteScripts.py | solislemuslab/mycovirus-website | 1 | 170425 | import pandas as pd
from Bio import SeqIO
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.cluster import MeanShift
from sklearn import preprocessing
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
def parseFasta(data):
d = {fasta.id : str(fasta.seq) for fasta in SeqIO.parse(data, "fasta")}
pd.DataFrame([d])
s = pd.Series(d, name='Sequence')
s.index.name = 'ID'
s.reset_index()
return pd.DataFrame(s)
def kmerXTable(s, a, b):
tfid_vector = TfidfVectorizer(analyzer='char', ngram_range=(a,b))
s_hat = tfid_vector.fit_transform(s.Sequence)
kmerNames = tfid_vector.get_feature_names()
kmers = s_hat.toarray()
return pd.DataFrame(kmers,columns=kmerNames, index = s.index)
def kmeans(fasta, cNum, klength_min = 6, klength_max = 6, rNum = 50):
inputData = parseFasta(fasta)
# temp = virus01.append(inputData)
# temp = temp.drop_duplicates(keep="last")
inputData["Sequence"] = inputData["Sequence"].apply(lambda x: x.replace("-", ""))
kmerXTableInput = kmerXTable(inputData, klength_min, klength_max)
km = KMeans(random_state = rNum, n_clusters = cNum)
km.fit(kmerXTableInput)
y_hat = km.predict(kmerXTableInput)
return y_hat, kmerXTableInput
def kmeans_semiSupervised(fasta, y_hat, klength_min = 6, klength_max = 6, rNum = 50):
inputData = parseFasta(fasta)
inputData["Sequence"] = inputData["Sequence"].apply(lambda x: x.replace("-", ""))
kmerXTableInput = kmerXTable(inputData, klength_min, klength_max)
PCAembedding = PCA(n_components=10)
NkmerXTableInput = preprocessing.normalize(kmerXTableInput)
PCAembedding_low = PCAembedding.fit_transform(NkmerXTableInput)
ms = MeanShift()
ms.fit(PCAembedding_low)
cluster_centers = ms.cluster_centers_
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
kmms = KMeans(init = cluster_centers, n_clusters = len(cluster_centers))
kmms_labels = kmms.fit_predict(PCAembedding_low)
# convert all clusters into two clusters
kmerXTableInput["pLabels"] = kmms_labels
kmerXTableInput["aLabels"] = y_hat
newLabels_clusters_1 = kmerXTableInput[kmerXTableInput["aLabels"] == 1]["pLabels"].tolist()
newLabels_clusters_0 = kmerXTableInput[kmerXTableInput["aLabels"] == 0]["pLabels"].tolist()
newLabels = []
for label in kmms_labels:
if (newLabels_clusters_1.count(label) > newLabels_clusters_0.count(label)):
newLabels.append(1)
else:
newLabels.append(0)
return newLabels, kmerXTableInput.drop(columns=["pLabels", "aLabels"])
def PCA2d(kTable, y_hat, filename):
embedding = PCA(n_components= 2)
nkTable = preprocessing.normalize(kTable)
embedding.fit(nkTable)
show = pd.DataFrame(embedding.transform(nkTable))
# show kmeans clustering
show["labels"] = y_hat
ax = show[show["labels"]==1].plot.scatter(x=0, y=1, style="o", color="red", s=2)
show[show["labels"]==0].plot.scatter(x=0, y=1, style="o", color="blue", s=2, ax=ax)
show[(show["labels"]!= 0) & (show["labels"]!= 1) ].plot.scatter(x=0, y=1, style="o", color="gray", s=2, ax=ax)
red = mpatches.Patch(color='red', label='Label 1')
blue = mpatches.Patch(color='blue', label='Label 0')
grey = mpatches.Patch(color='grey', label='Label -1')
plt.legend(handles=[red, blue, grey])
plt.title('PCA Visualization')
plt.xlabel('First Principal Component')
plt.ylabel('Second Principal Component')
plt.savefig('nonNotebookFiles/' + filename + '.png', bbox_inches='tight')
plt.close()
def tSNE2d(kTable, y_hat, filename):
tSNEembedding = TSNE(n_components= 2, random_state = 0)
tSNEembedding_low = tSNEembedding.fit_transform(kTable)
show = pd.DataFrame(tSNEembedding_low)
# show kmeans clustering
show["labels"] = y_hat
ax = show[show["labels"]==1].plot.scatter(x=0, y=1, style="o", color="red", s=2)
show[show["labels"]==0].plot.scatter(x=0, y=1, style="o", color="blue", s=2, ax=ax)
show[(show["labels"]!= 0) & (show["labels"]!= 1) ].plot.scatter(x=0, y=1, style="o", color="gray", s=2, ax=ax)
red = mpatches.Patch(color='red', label='Label 1')
blue = mpatches.Patch(color='blue', label='Label 0')
grey = mpatches.Patch(color='grey', label='Label -1')
plt.legend(handles=[red, blue, grey])
plt.title('tSNE Visualization\n')
plt.xlabel('First Component')
plt.ylabel('Second Component')
plt.savefig('nonNotebookFiles/' + filename + '.png', bbox_inches='tight')
plt.close() | [
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] |
data/geometry.py | hash451722/FlowPredictionWithDeepNeuralNetwork | 0 | 70839 | <gh_stars>0
import pathlib
import numpy as np
import matplotlib.pyplot as plt
class AirfoilStl():
def __init__(self, case_path) -> None:
self.case_path = case_path
self.database_path = None
self.airfoil_list = None
self.init_path_list()
def init_path_list(self) -> None:
dir_path = pathlib.Path(__file__).parent
self.database_path = dir_path.joinpath("airfoil_database")
self.airfoil_list = list(self.database_path.glob("*.dat"))
def create_airfoil_stl(self, airfoil_name="random"):
p = self.select_airfoil(airfoil_name)
dat = self.format_dat(p)
s = create_stl_solid(dat, "airfoil")
stl_file_path = self.case_path.joinpath("constant", "geometry", "airfoil.stl")
stl_file_path.write_text(s)
return p.stem
def select_airfoil(self, airfoil_name="random"):
''' ファイルの絶対パスを返す '''
if airfoil_name == "random":
i = np.random.randint(0, len(self.airfoil_list))
return self.airfoil_list[i]
else:
al = list( map(lambda x: x.stem, self.airfoil_list) )
if airfoil_name in al:
k = al.index(airfoil_name)
return self.airfoil_list[k]
else:
raise RuntimeError('[{}] does not exist.'.format(airfoil_name))
def format_dat(self, airfoil_path):
airfoil_dat = np.loadtxt(airfoil_path, skiprows=1) # <class 'numpy.ndarray'>
# 閉じた形状にする
clearance = np.sqrt( np.sum( np.square(airfoil_dat[0]-airfoil_dat[-1]) ) )
if clearance > 1e-6:
airfoil_dat = np.vstack([ airfoil_dat, airfoil_dat[0]] )
return airfoil_dat
def draw_airfoil(self, airfoil_name="random"):
path = self.select_airfoil(airfoil_name)
dat = self.format_dat(path)
fig, ax = plt.subplots()
ax.plot(dat[:, 0], dat[:,1])
ax.grid()
ax.axis('equal')
ax.set_title(path.stem, fontsize=10)
plt.show()
def create_stl_solid(dat, solid_name="walls") -> str:
''' 2次元座標からSTLの文字列を作成 '''
if dat.shape[1] != 2:
return False
z = 1
s = "solid {}\n".format(solid_name)
for i in range(dat.shape[0]-1):
v0 = np.hstack( (dat[i], 0) )
v1 = np.hstack( (dat[i+1], 0) )
v2 = np.hstack( (dat[i], z) )
s += facet( v0, v1, v2)
v0 = np.hstack( (dat[i], z) )
v1 = np.hstack( (dat[i+1], z) )
v2 = np.hstack( (dat[i+1], 0) )
s += facet( v0, v1, v2)
s += "endsolid {}".format(solid_name)
return s
def facet(v0, v1, v2) -> str:
nx, ny, nz = normal_vector(v0, v1, v2)
s0 = "facet normal {} {} {}\n".format(nx, ny, nz)
s1 = " outer loop\n"
s2 = " vertex {} {} {}\n".format(v0[0], v0[1], v0[2])
s3 = " vertex {} {} {}\n".format(v1[0], v1[1], v1[2])
s4 = " vertex {} {} {}\n".format(v2[0], v2[1], v2[2])
s5 = " endloop\n"
s6 = "endfacet\n"
return s0 + s1 +s2 + s3 +s4 + s5 + s6
def normal_vector(v0, v1, v2):
''' 法線ベクトル '''
vec01 = v1 - v0
vec02 = v2 - v0
x = vec01[1]*vec02[2] - vec01[2]*vec02[1]
y = vec01[2]*vec02[0] - vec01[0]*vec02[2]
z = vec01[0]*vec02[1] - vec01[1]*vec02[0]
return x, y, z
if __name__ == '__main__':
stl = AirfoilStl(None)
print( len(stl.airfoil_list) )
fig, ax = plt.subplots()
for airfoil_path in stl.airfoil_list:
dat = stl.format_dat(airfoil_path)
ax.plot(dat[:, 0], dat[:,1])
ax.grid()
ax.axis('equal')
# ax.set_title(path.stem, fontsize=10)
plt.show()
# stl.draw_airfoil()
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] |
management/commands/imageload.py | NikDark/ManagementCommands | 0 | 50047 | import os
import re
import shutil
from django.core.management.base import BaseCommand, CommandError
from wagtail.images.models import Image
class Command(BaseCommand):
help = 'Add Image from folder that you indicate'
IMAGE_FORMAT = (
'jpg',
'jpeg',
'webp',
'png',
'gif',
)
def add_arguments(self,parser):
parser.add_argument(
'-f',
'--folder',
nargs=1,
required=True,
help='Path to the folder with different images',
)
def handle(self, *args, **options):
path_folder = options['folder'][0]
if not os.path.exists(r'media'):
os.mkdir(r'media')
if os.path.exists(path_folder) and os.path.isdir(path_folder):
files_dir = os.listdir(path_folder)
images = []
image_names = []
for file in files_dir:
try:
format = re.search(r'\.(.*)$', file).group(1)
except:
continue
if format in self.IMAGE_FORMAT:
images.append(path_folder+'/'+file)
image_names.append(file)
for image in images:
shutil.copy2(image,r'media')
for name in image_names:
self.stdout.write(name + ' --- ' + self.style.SUCCESS('OK'))
Image.objects.create(file=name,title=name.split('.')[0])
else:
self.stdout.write(self.style.ERROR('Not dir or not exists'))
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] |
webapp/app/__init__.py | siddharthsudheer/Alysida | 0 | 68115 | <filename>webapp/app/__init__.py
#!/usr/bin/env python3
from flask import Flask
from flask_socketio import SocketIO
app = Flask(__name__, static_url_path='')
app.url_map.strict_slashes = False
socketio = SocketIO(app)
from app.routes import index
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] |
src/safe_diet_project/safe_ingredient.py | hiwett18/Safe-Diet | 0 | 92513 | <reponame>hiwett18/Safe-Diet
from patients import PatientType
from menu import EntireMenu
from ingredient import Food
from restrictor import Restriction
p1 = PatientType()
patient = p1.select_patient()
print(patient)
f1 = Food()
food = f1.food_ingredeients()
food1 = food[:]
r1 = Restriction(patient, food1)
safe_ingredients = r1.food_restriction()
ommited_ingredient = ''
for unwanted_ingrid in food:
if unwanted_ingrid not in safe_ingredients:
ommited_ingredient = unwanted_ingrid
if len(ommited_ingredient) == 0:
ommited_ingredient = "nothing is ommited from your food. Your ingriedients are safe to eat"
print("--------------------------------------------------------------------------------------------")
print(f'Since you are {patient}, please avoid adding: {ommited_ingredient}')
print("--------------------------------------------------------------------------------------------")
print(f'Your safe ingriedient list is: {safe_ingredients}')
print("--------------------------------------------------------------------------------------------")
print(f'Omitted ingredient: {ommited_ingredient}')
print("--------------------------------------------------------------------------------------------")
print(f'(Original ingredient list: {food})')
print("--------------------------------------------------------------------------------------------")
if len(ommited_ingredient) > 1:
if ommited_ingredient == 'white pasta':
safe_ingredients.append('whole-grain pasta')
print('Suggestion:\n White pasta is high in carb.\n Substitute white pasta with whole-grain pasta. \n Your updated ingredient list is: ', safe_ingredients)
if len(ommited_ingredient) > 1:
if ommited_ingredient == 'maple syrup':
safe_ingredients.append('Raw honey')
print('Suggestion:\n Maple syrup is high in carb.\n Substitute maple syrup with Raw honey. \n Your updated ingredient list is: ', safe_ingredients)
if len(ommited_ingredient) > 1:
if ommited_ingredient == 'dressing':
safe_ingredients.append('olive oil and vinegar dressing')
print('Suggestion:\n Dressing is high in carb.\n Substitute dressing with olive oil and vinegar dressing. \n Your updated ingredient list is: ', safe_ingredients)
if len(ommited_ingredient) > 1:
if ommited_ingredient == 'salt':
safe_ingredients.append('Mrs Dash')
print('Suggestion:\n Salt will increase your blood presssure.\n Substitute salt with Mrs Dash.\n Your updated ingredient list is: ', safe_ingredients)
if len(ommited_ingredient) > 1:
if ommited_ingredient == 'spinach':
safe_ingredients.append('Arugula')
print('Suggestion:\n Spinach is high in potassium.\n Substitute spinach with arugula.\n Your updated ingredient list is: ', safe_ingredients)
if len(ommited_ingredient) > 1:
if ommited_ingredient == 'tomato':
safe_ingredients.append('broccoli')
print('Suggestion:\n Tomato is high in potassium.\n Substitute tomato with broccoli.\n Your updated ingredient list is: ', safe_ingredients)
print("---------- ---------- ---------- // ---------- ---------- ---------- ")
print("--------------------------------------------------------------------------------------------")
print("--------------------------------------------------------------------------------------------")
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ArcGIS Code/CutFillStatistics.py | uob-hydrology/OptimizedPitRemoval | 4 | 1600502 | <gh_stars>1-10
# Script Name: CutFillStatistics 1.0
#
# Created By: <NAME>
# Date: 01/16/2013
# Import ArcPy site-package and os modules
#
import arcpy
import os
import sys
import time
import string
import subprocess
#set executable program location
executablepath = os.path.dirname(os.path.abspath(__file__))
arcpy.AddMessage(executablepath)
executablename = '\CutFillStatistics.exe'
executablestr = '"' + executablepath + executablename + '"'
arcpy.AddMessage(executablestr)
# Get Original DEM (ASCII)
#
inLyr = arcpy.GetParameterAsText(0)
desc = arcpy.Describe(inLyr)
OrigDEM=str(desc.catalogPath)
arcpy.AddMessage("\nOriginal Elevation file: "+OrigDEM)
OriginalDEMstr = ' -orig "' + OrigDEM + '"'
arcpy.AddMessage(OriginalDEMstr)
# Get Modified DEM (ASCII)
ModDEM = arcpy.GetParameterAsText(1)
arcpy.AddMessage("\Modified Elevation file: "+ModDEM)
ModDEMstr = ' -mod "' + ModDEM + '"'
# Get Output Statistics File (ASCII)
StatFile = arcpy.GetParameterAsText(2)
arcpy.AddMessage("\nOutput Statistics file: "+StatFile)
StatFilestr = ' -stat "' + StatFile + '"'
# Construct the command line. Put quotes around file names in case there are spaces
cmd = executablestr + OriginalDEMstr + ModDEMstr + StatFilestr
arcpy.AddMessage(cmd)
os.system(cmd)
pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
# print the output lines as they happen
while True:
line = pipe.stdout.readline()
if not line:
break
arcpy.AddMessage(line)
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drams/vagrant-settings.py | FAIRDataPipeline/data-registry | 0 | 89090 | <reponame>FAIRDataPipeline/data-registry
from .base_settings import *
ALLOWED_HOSTS = ['192.168.20.10', '127.0.0.1', 'localhost']
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src/narmax/utils.py | DavidContrerasFranco/Data-driven-Dynamics-Understanding | 0 | 142244 |
import time
import copy
import numpy as np
from functools import reduce
from operator import indexOf, iconcat
from sysidentpy.utils.display_results import results
from sysidentpy.model_structure_selection import FROLS
def narmax_state_space(nx_model:FROLS, X_train, X_test, states_names):
xlag = nx_model.xlag if type(nx_model.xlag) == int else max(reduce(iconcat, nx_model.xlag, []))
max_lag = max(xlag, nx_model.ylag)
narmax_model = {}
narmax_time = 0
coeffs = []
regs = []
sim = []
for s_id, state in enumerate(states_names):
model = copy.deepcopy(nx_model)
x_train = np.delete(X_train, s_id, axis=1)[:-1]
y_train = X_train[1:, s_id].reshape(-1, 1)
x_test = np.delete(X_test, s_id, axis=1)
y_test = X_test[0:nx_model.ylag, s_id].reshape(-1, 1)
# Train model for one state and get time
tic = time.time()
model.fit(X=x_train, y=y_train)
toc = time.time()
narmax_time += toc - tic
# Print resulting model
param = results(model.final_model, model.theta, model.err, model.n_terms, dtype='sci')
param_names = np.delete(states_names, s_id, axis=0).tolist()
display_nx_model(param, model.theta, state, param_names, max_lag)
# Simulate model for the state Z
coeffs += [np.pad(model.theta.flatten(), (0, model.basis_function.__sizeof__() - len(model.theta)))]
regs += [[list(eq) for eq in model.final_model]]
sim += [model.predict(X=x_test, y=y_test)]
# Stack results for models and predictions
narmax_model['features'] = states_names
narmax_model['coeffs'] = np.vstack(coeffs)
narmax_model['regs'] = regs
narmax_sim = np.hstack(sim)
return narmax_model, narmax_sim, narmax_time
def display_nx_model(results, theta, output:str, input_vars, max_lag=1):
regressors = ''
for idx, regressor in enumerate(results):
if idx > 0:
regressors += ' +'
for lag in range(0, max_lag):
regressor[0] = regressor[0].replace('(k-' + str(lag + 1) + ')', '[k-' + str(lag) + ']')
regressors += ' ' + f'{theta[idx][0]:.3E}' + ' ' + regressor[0].replace('-0', '')
regressors = regressors.replace('y', output).replace('+ -', '- ')
for idx, var in enumerate(input_vars):
regressors = regressors.replace('x' + str(idx+1), var)
print(output + '[k+1] =' + regressors)
def solution_to_regressors(sol_terms, feature_names, order):
'''Convert an standard list of terms describing the solution into the NARMAX regressors format'''
# TODO: Division with powers in conversion
regressors = []
for idx_eq, eq in enumerate(sol_terms):
eq_regs = []
eq_features = [feature_names[idx_eq]] + np.delete(feature_names, idx_eq).tolist()
for term in eq:
n = 0
reg = np.zeros(order, dtype='int')
split_terms = term.split()
# Replace powers by repetitions
new_split_terms = []
for factor in split_terms:
new_factor = [factor]
if '^' in factor:
pos_power = indexOf(factor, '^')
new_factor = [factor[pos_power - 1]] * int(factor[pos_power + 1])
elif '/' in factor:
pos_div = indexOf(factor, '/')
new_factor = [factor[pos_div - 1], factor[pos_div + 1]]
new_split_terms += new_factor
# Convert terms into regressors
for factor in new_split_terms:
if factor.isalpha():
reg[n] = 1000*indexOf(eq_features, factor) + 1001
n += 1
elif '_k-' in factor:
pos_k = indexOf(factor, 'k')
base = factor[:pos_k - 1]
delay =int(factor[pos_k + 2])
reg[n] = 1000*indexOf(eq_features, base) + 1000 + delay
n += 1
eq_regs += [list(np.sort(reg))[::-1]]
regressors += [eq_regs]
return regressors | [
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JumpscaleCore/servers/graphql/GraphqlBottle.py | gneumann333/jumpscaleX_core | 1 | 151261 | import json
from collections import namedtuple
from bottle import request, response
from graphql import GraphQLError, format_error as format_graphql_error
def format_error(error):
if isinstance(error, GraphQLError):
return format_graphql_error(error)
return {"message": str(error)}
def handle_graphql_response(func, graphene_schema):
def wrapper():
response.content_type = "application/json"
try:
result = func(graphene_schema)
output = {"data": result.data}
if result.errors is not None:
output["errors"] = [format_error(e) for e in result.errors]
if result.invalid:
response.status = 400
return json.dumps(output)
except Exception as e:
return json.dumps({"data": None, "errors": [e]})
return wrapper
def execute_get(graphene_schema):
graphql_query = request.query["query"]
graphql_variables = json.loads(request.query.get("variables", "{}"))
graphql_operation_name = request.query.get("operationName", None)
return graphene_schema.execute(
graphql_query, variable_values=graphql_variables, operation_name=graphql_operation_name
)
def execute_post(graphene_schema):
graphql_data = request.json
graphql_query = graphql_data["query"]
return graphene_schema.execute(graphql_query)
def execute_introspection(graphene_schema):
DataItem = namedtuple("DataItem", ["data", "errors"])
return DataItem(data=graphene_schema.introspect(), errors=None)
def graphql_middleware(bottle_app, endpoint_name, graphene_schema):
bottle_app.route(endpoint_name, "OPTIONS", handle_graphql_response(execute_introspection, graphene_schema))
bottle_app.route(endpoint_name, "POST", handle_graphql_response(execute_post, graphene_schema))
bottle_app.route(endpoint_name, "GET", handle_graphql_response(execute_get, graphene_schema))
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iconary/models/t5_decoding.py | allenai/iconary | 6 | 103633 | <reponame>allenai/iconary<gh_stars>1-10
"""Logic to use T5 in allennlp's BeamSearch
This allows us to use T5 to create the initial_state, starting predictions, and `StepFunctionType`
functions needed to run `BeamSearch` from allennlp. It also includes the constraint logic we use to
ensure Drawers produce valid drawings and Guesser produce valid guesses.
We use allennlp because it BeamSearch is easy to use and add constraints to, and hugging faces
version is much more difficult to modify.
"""
import torch
from transformers import T5Tokenizer
from transformers.modeling_outputs import Seq2SeqLMOutput
from iconary.data.datasets import GamePhraseConstraints
from iconary.utils import utils
from torch.nn import functional as F
_t5_tokens_with_spaces = None
def get_tokens_with_space(tokenizer):
assert isinstance(tokenizer, T5Tokenizer)
global _t5_tokens_with_spaces
if _t5_tokens_with_spaces is None:
start_of_word_tokens_ix = []
for k, ix in tokenizer.get_vocab().items():
if k.startswith("▁"):
start_of_word_tokens_ix.append(ix)
start_of_word_tokens_ix = torch.tensor(start_of_word_tokens_ix, dtype=torch.long)
_t5_tokens_with_spaces = start_of_word_tokens_ix
return _t5_tokens_with_spaces
def get_starts_word(tokenizer, voc_size=None):
if voc_size is None:
voc_size = len(tokenizer)
start_of_word_tokens_ix = get_tokens_with_space(tokenizer)
starts_new_word = torch.zeros(voc_size, dtype=torch.bool)
starts_new_word[start_of_word_tokens_ix] = 1
return starts_new_word
def build_allowed_word_matrix(
constraints: GamePhraseConstraints, starts_new_word, continues_word, tokenizer):
"""
:param constraints: from `ImsituGame.get_constraints()`
:param starts_new_word: [vocab_size] tensor with 1 indicating word pieces that add a space
:param tokenizer: T5 Tokenizer to use
:return: A matrix of (number of spaces so far -> allowed token pieces) this matrix constrains
generation in a way that model produces the right number of words, and so that the
model (more or less) has to produce the words that are known to be correct.
"""
# allowed_words[i] is words that are allowed after the ith space has been added
allowed_words = []
for i, const in enumerate(constraints):
if isinstance(const, list):
if i == 0:
# Must start a new word at step 1
allowed = starts_new_word.clone()
elif isinstance(constraints[i - 1], str):
# Either start any new word
allowed = starts_new_word.clone()
# Or continue the previous correct word
allowed[tokenizer.encode(constraints[i - 1], add_special_tokens=False)[1:]] = 1
else:
# Anything goes, except special tokens
allowed = torch.ones_like(starts_new_word, dtype=torch.float)
allowed[tokenizer.all_special_ids] = 0
allowed_words.append(allowed)
else:
input_id = tokenizer.encode(const, add_special_tokens=False)
correct_word = input_id[0]
if i == 0:
# After the PAD token, can only start the new, correct word
allowed = torch.zeros_like(starts_new_word)
allowed[correct_word] = 1
elif isinstance(constraints[i-1], str):
allowed = torch.zeros_like(starts_new_word)
# Anything that could continue the previous correct word
allowed[tokenizer.encode_with_cache(constraints[i-1])[1:]] = 1
allowed[correct_word] = 1 # And the start of the correct word
else:
allowed = continues_word.clone() # Anything that does NOT start a new word
allowed[correct_word] = 1 # And the start of the correct word
allowed_words.append(allowed)
allowed = continues_word.clone() # Anything that does NOT start a new word
allowed[tokenizer.eos_token_id] = 1 # And the EOS token
allowed_words.append(allowed)
return torch.stack(allowed_words, dim=0)
def t5_initialize_decoding(
tokenizer, decoder, encoder_out, encoder_mask, constraints,
constraints_before_softmax=False, post_process=None,
prevent_known_incorrect_words=False
):
batch_size = encoder_out.size(0)
device = encoder_out.device
starts_new_word = get_starts_word(tokenizer, decoder.config.vocab_size).to(device)
continues_word = torch.logical_not(starts_new_word)
continues_word[tokenizer.all_special_ids] = 0
mat_size = max(len(x) for x in constraints) + 1
allowed_word_mat = []
for const in constraints:
mat = build_allowed_word_matrix(const, starts_new_word, continues_word, tokenizer)
mat = F.pad(mat, [0, 0, 0, mat_size - mat.size(0)])
allowed_word_mat.append(mat)
allowed_word_mat = torch.stack(allowed_word_mat, 0)
allowed_word_mat = allowed_word_mat.to(torch.bool)
initial_state = dict(
encoder_mask=encoder_mask,
batch_id=torch.arange(0, batch_size, device=device, dtype=torch.long),
num_spaces=torch.zeros(batch_size, device=device, dtype=torch.long),
encoder_outputs=encoder_out
)
banned_tensor = None
if prevent_known_incorrect_words:
banned_lists = [x for x in utils.flatten_list(constraints) if isinstance(x, list)]
n_words = 0 if len(banned_lists) == 0 else max(len(x) for x in banned_lists)
if n_words > 0:
n_word_peices = max(len(tokenizer.encode(x, add_special_tokens=False)) for x in utils.flatten_list(banned_lists))
banned_tensor = torch.full((len(constraints), mat_size, n_words, n_word_peices+1), -1, dtype=torch.long)
banned_lens = torch.full((len(constraints), mat_size, n_words), -1, dtype=torch.long)
for game_ix, const in enumerate(constraints):
for word_ix, c in enumerate(const, start=1):
if isinstance(c, list):
for banned_ix, word in enumerate(c):
word_pieces = tokenizer.encode(word, add_special_tokens=False)
banned_lens[game_ix, word_ix, banned_ix] = len(word_pieces)
for word_piece_ix, wp in enumerate(word_pieces):
banned_tensor[game_ix, word_ix, banned_ix, word_piece_ix] = wp
banned_tensor = banned_tensor.to(device=device).transpose(2, 3)
banned_lens = banned_lens.to(device=device)
banned_tensor = (banned_tensor, banned_lens)
initial_state["num_word_pieces"] = torch.zeros(batch_size, device=device, dtype=torch.long)
initial_state["on_banned"] = torch.zeros(batch_size, n_words, device=device, dtype=torch.bool)
# extra tokens count as generating a space
starts_new_word[32000:] = 1.0
def _decode_step(predictions, prev_state):
return decoding_step(decoder, predictions, prev_state, starts_new_word, continues_word,
allowed_word_mat, constraints_before_softmax, post_process, banned_tensor)
initial_out = torch.full(
(batch_size,), tokenizer.pad_token_id, dtype=torch.long, device=device)
return initial_out, initial_state, _decode_step
def decoding_step(decoder, predictions, prev_state, starts_new_word, continues_word,
allowed_words_mat, constraints_before_softmax,
post_process=None, banned_tensor=None):
num_spaces = prev_state["num_spaces"]
batch_ids = prev_state["batch_id"]
generated_space = starts_new_word[predictions]
num_spaces = num_spaces + generated_space.to(torch.long)
past = utils.flat_to_nested_struct({k: v.contiguous() for k, v in prev_state.items()
if isinstance(k, tuple)})
model_inputs = decoder.prepare_inputs_for_generation(
predictions.unsqueeze(1), past=past, attention_mask=prev_state["encoder_mask"],
encoder_outputs=(prev_state["encoder_outputs"],),
use_cache=True)
out: Seq2SeqLMOutput = decoder(**model_inputs, return_dict=True)
next_state = dict(
encoder_mask=prev_state["encoder_mask"],
num_spaces=num_spaces,
batch_id=batch_ids,
encoder_outputs=prev_state["encoder_outputs"],
)
if post_process is not None:
logits = post_process(out, model_inputs)
else:
logits = out.logits
logits = logits.squeeze(1)
cur_const = allowed_words_mat[batch_ids, num_spaces]
if banned_tensor is not None:
# Remove word pieces that would result in generating a banned (aka already-guessed) word
# The high-level idea if we track what word number we are generating and what
# word peice in that word we are on, then use that to figure out if we generating a
# banned word for that word slot. If we are have completely generated a banned word,
# we prevent the model from generating any word piece that would result in a space
# so that generation complete that banned word.
banned_tensor, banned_lens = banned_tensor
on_piece = prev_state["num_word_pieces"]
on_piece = torch.logical_not(generated_space).to(torch.long)*(1 + on_piece)
next_state["num_word_pieces"] = on_piece
on_banned = prev_state["on_banned"] # [batch, n_words]
on_banned[generated_space] = True
batch, _, n_pieces, n_word = banned_tensor.size()
on_piece = torch.min(on_piece, torch.as_tensor(n_pieces-1).to(on_piece))
# [batch, n_words]
continued_banned = banned_tensor[prev_state["batch_id"], num_spaces, on_piece]
# [batch, n_words]
continue_banned_lens = banned_lens[prev_state["batch_id"], num_spaces]
on_banned = torch.logical_and(continued_banned == predictions.unsqueeze(1), on_banned)
next_state["on_banned"] = on_banned
is_banned_word = torch.any(torch.logical_and(on_banned, continue_banned_lens == (on_piece+1).unsqueeze(1)), 1)
cur_const = cur_const.to(torch.bool)
cur_const = torch.logical_and(cur_const, torch.logical_not(torch.logical_and(is_banned_word.unsqueeze(1), torch.logical_not(continues_word).unsqueeze(0))))
cur_const = torch.logical_not(cur_const).float()
if constraints_before_softmax:
logits -= cur_const * 100000
logits = F.log_softmax(logits, -1)
if not constraints_before_softmax:
logits -= cur_const * 100000
utils.nested_struct_to_flat(out.past_key_values, (), cur_dict=next_state)
return logits, next_state
def t5_decoding_token_constraints(tokenizer, decoder, encoder_out, encoder_mask,
constraints, constraints_before_softmax=False):
"""
Decode with per-token constraints, used for the drawer
"""
batch_size = encoder_out.size(0)
device = encoder_out.device
initial_state = dict(
encoder_mask=encoder_mask,
encoder_outputs=encoder_out,
time=torch.zeros(batch_size, device=encoder_out.device, dtype=torch.long)
)
def _decode_step(predictions, prev_state):
return decoding_step_token_constraints(
decoder, predictions, prev_state, constraints, constraints_before_softmax)
initial_out = torch.full(
(batch_size,), tokenizer.pad_token_id, dtype=torch.long, device=device)
return initial_out, initial_state, _decode_step
def decoding_step_token_constraints(
decoder, predictions, prev_state, constraints, constraints_before_softmax):
time = prev_state["time"]
past = utils.flat_to_nested_struct({k: v.contiguous() for k, v in prev_state.items()
if isinstance(k, tuple)})
model_inputs = decoder.prepare_inputs_for_generation(
predictions.unsqueeze(1), past=past, attention_mask=prev_state["encoder_mask"],
encoder_outputs=(prev_state["encoder_outputs"],),
use_cache=True)
out = decoder(**model_inputs, return_dict=True)
next_state = dict(
encoder_mask=prev_state["encoder_mask"],
time=(time+1) % constraints.size(0),
encoder_outputs=prev_state["encoder_outputs"]
)
logits = out.logits.squeeze(1)
constraint = constraints[time[0]]
if len(constraint.size()) == 2:
constraint = constraint.unsqueeze(1)
if constraints_before_softmax:
logits -= (1 - constraint.float()) * 100000
logits = F.log_softmax(logits, -1)
if not constraints_before_softmax:
logits -= (1 - constraint.float()) * 100000
utils.nested_struct_to_flat(out.past_key_values, (), cur_dict=next_state)
return logits, next_state
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29892,
2446,
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tests/test_doctest.py | nlaurance/sourceparse | 0 | 75649 | import doctest
def test_doctests():
doctest.testfile("../README.rst")
if __name__ == "__main__":
test_doctests()
| [
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src/a05obiekty/Knife.py | tborzyszkowski/PSPI_Wstep_do_programowania | 2 | 129977 | from .Tool import *
class Knife(Tool):
def __init__(self) -> None:
super().__init__()
def can_drill(self):
return False
def can_cut(self):
return True
def can_sweep(self):
return False
| [
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docs/assets/greeting.py | pablodevs/StarwarsBlog | 0 | 149424 | <reponame>pablodevs/StarwarsBlog
def blue(_str):
return f"\033[0;33m{_str}\033[0m"
print(f"""
Hello 😁 ! Use the terminal to code!
1. Start the dev server by running {blue("$ npm run start")}
2. You can find a video tutorial and explanation on the README.md file.
3. Always read the terminal output, it's your best tool for debugging!
""") | [
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network.py | QiaoZhongzheng/EWC-sample-PMNIST | 0 | 2718 | # -*- coding: UTF-8 -*-
'''=================================================
@Project -> File ：EWC -> network
@IDE ：PyCharm
@Author ：<NAME>
@Date ：2021/6/23 20:28
@Desc ：
=================================================='''
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Conv2D,LeakyReLU,MaxPool2D,Flatten,Input
def fcnn():
input = Input(shape=784,dtype='float32',name='input')
# x = Dense(128,activation='relu')(input)
# x = Dense(64,activation='relu')(x)
# x = Dense(32,activation='relu')(x)
x = Dense(256,activation='relu')(input)
x = Dense(256,activation='relu')(x)
output = Dense(10,activation='softmax')(x)
return Model(input, output) | [
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] |
setup.py | aychedee/sniffer | 0 | 199927 | <reponame>aychedee/sniffer<gh_stars>0
from setuptools import setup, find_packages
# will fail to install via pip/easy_install
#from sniffer.metadata import __version__, __author__, __author_email__
__version__, __author__, __author_email__ = "0.3.4", "<NAME>", "<EMAIL>"
setup(
name='sniffer',
version=__version__,
description='An automatic test runner. Supports nose out of the box.',
long_description=open('README.rst').read(),
author=__author__,
author_email=__author_email__,
url='http://github.com/jeffh/sniffer/',
install_requires=[
'colorama',
'python-termstyle',
'nose',
],
extras_require = {
'Growl': ['gntp==0.7'],
'LibNotify': ['py-notify==0.3.1'],
'OSX': ['MacFSEvents==0.2.8'],
#'Windows': ['pywin'], # not part of PYPI
'Linux': ['pyinotify==0.9.0'],
},
entry_points = {
'console_scripts': ['sniffer = sniffer:main'],
},
packages=find_packages(),
license='MIT',
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Console',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Topic :: Software Development :: Testing',
'Topic :: Software Development :: Libraries',
],
)
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py/543. Diameter of Binary Tree.py | longwangjhu/LeetCode | 3 | 105417 | <gh_stars>1-10
# https://leetcode.com/problems/diameter-of-binary-tree/
# Given the root of a binary tree, return the length of the diameter of the tree.
# The diameter of a binary tree is the length of the longest path between any two
# nodes in a tree. This path may or may not pass through the root.
# The length of a path between two nodes is represented by the number of edges
# between them.
################################################################################
# longest path must be leaf-to-leaf
# -> sum of longest left and right branches
# -> divide and conquer
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def diameterOfBinaryTree(self, root: TreeNode) -> int:
self.ans = 0
self.longest_path(root)
return self.ans
def longest_path(self, node):
if not node: return 0
left_longest = self.longest_path(node.left)
right_longest = self.longest_path(node.right)
self.ans = max(self.ans, left_longest + right_longest)
return max(left_longest, right_longest) + 1
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src/cstar-cli/cstar/cli/bpd/awardperiod.py | pagopa/cstar-cli | 1 | 182488 | <reponame>pagopa/cstar-cli
import psycopg2
import psycopg2.extras
import pandas as pd
import pandas.io.sql as psql
import random
from datetime import timedelta, datetime, date
import string
from .constants import CITIZEN_RANKING_EXT_SCHEMA
class Awardperiod() :
def __init__(self, args):
self.args = args
self.db_connection = psycopg2.connect(args.connection_string)
def set_grace_period(self):
table = "bpd_award_period.bpd_award_period"
set_values = "aw_grace_period_n = (%s)"
conditions = "award_period_id_n = (%s)"
update_q = "UPDATE {} SET {} WHERE {};".format(
table, set_values, conditions)
cursor = self.db_connection.cursor()
cursor.execute(update_q, [self.args.grace_period, self.args.id])
self.db_connection.commit()
cursor.close()
def set_properties(self):
award_period_df = self._read_award_period()
award_period_start = award_period_df.at[0,'aw_period_start_d']
delta = date.today() - award_period_start
insert_datetime = ( datetime.combine( award_period_start, datetime.min.time())
+ timedelta(seconds=random.random() * delta.total_seconds()) )
award_period_ext_df = pd.DataFrame(columns=CITIZEN_RANKING_EXT_SCHEMA)
award_period_ext_df = award_period_ext_df.append({
'award_period_id_n' : self.args.id,
'max_transaction_n' : 2000,
'min_transaction_n' : 0,
'total_participants' : 10000000,
'ranking_min_n': 0,
'period_cashback_max_n' : f'{150:.2f}',
'insert_date_t': insert_datetime.isoformat(),
'insert_user_s': 'PAGOPATEST',
'update_date_t' : None,
'update_user_s' : None
}, True)
if len(award_period_ext_df) > 0:
columns = list(award_period_ext_df)
# create VALUES('%s', '%s",...) one '%s' per column
set_values = ",".join(["{} = (%s)".format(_) for _ in columns])
#create INSERT INTO table (columns) VALUES('%s',...)
update = "UPDATE {} SET {} WHERE award_period_id_n = {};".format(
"bpd_citizen.bpd_ranking_ext", set_values, self.args.id)
cursor = self.db_connection.cursor()
psycopg2.extras.execute_batch(cursor, update, award_period_ext_df.values)
self.db_connection.commit()
cursor.close()
def _read_award_period(self) :
self.db_cursor = self.db_connection.cursor()
award_period_q = self.db_cursor.mogrify(
"SELECT * "
"FROM bpd_award_period.bpd_award_period "
"WHERE award_period_id_n = %(id)s;", {"id": self.args.id})
return pd.read_sql(award_period_q, self.db_connection)
def read_all(self) :
self.db_cursor = self.db_connection.cursor()
award_period_q = self.db_cursor.mogrify(
"SELECT award_period_id_n, aw_period_start_d, aw_period_end_d "
"FROM bpd_award_period.bpd_award_period ")
print(pd.read_sql(award_period_q, self.db_connection))
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quests/forms.py | czambuk/dnd | 0 | 97570 | <filename>quests/forms.py
from django import forms
from .models import (
Quest,
QuestStage
)
class QuestForm(forms.ModelForm):
class Meta:
model = Quest
fields = (
'name',
'description',
'which_campaign',
'characters',
)
widgets = {
'name': forms.TextInput,
'description': forms.Textarea,
'which_campaign': forms.Select,
'characters': forms.SelectMultiple,
}
labels = {
'name': 'Nazwa zadania',
'description': 'Opis zadania',
'which_campaign': 'Kampania',
'characters': 'Bohaterowie',
}
# class CampaignEntryForm(forms.ModelForm):
# class Meta:
# model = CampaignEntry
# fields = (
# 'entry_name',
# 'entry_content',
# )
# widgets = {
# 'entry_name': forms.TextInput,
# 'entry_content': forms.Textarea,
# }
# labels = {
# 'entry_name': 'Tytuł wpisu',
# 'entry_content': 'Treść',
# }
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] |
src/pycropml/render_fortran.py | sielenk-yara/PyCrop2ML | 0 | 139776 | <filename>src/pycropml/render_fortran.py
"""Add License, Header.
Use pkglts
Problems:
- name of a model unit?
"""
from __future__ import print_function
from __future__ import absolute_import
from path import Path
from pycropml.formater_f90 import formater
import six
import os
from pycropml.composition import model_parser
class Model2Package(object):
""" TODO
"""
DATATYPE = {}
DATATYPE['FLOAT'] = 'REAL'
DATATYPE['INT'] = 'INTEGER'
DATATYPE['STRING'] = 'CHARACTER(65)'
DATATYPE['DATE'] = 'CHARACTER(65)'
DATATYPE['DOUBLE'] = 'REAL'
DATATYPE['DOUBLELIST'] = 'REAL, ALLOCATABLE, DIMENSION(:)'
DATATYPE['INTLIST'] = 'INTEGER, ALLOCATABLE, DIMENSION(:)'
DATATYPE['STRINGLIST'] = 'CHARACTER(65), ALLOCATABLE, DIMENSION(:)'
DATATYPE['INTARRAY'] = 'INTEGER, ALLOCATABLE, DIMENSION'
DATATYPE['DOUBLEARRAY'] = 'REAL, ALLOCATABLE, DIMENSION'
DATATYPE['STRINGARRAY'] = 'CHARACTER(65), ALLOCATABLE, DIMENSION'
DATATYPE['BOOLEAN'] = 'LOGICAL'
DATATYPE['DATELIST'] = 'CHARACTER(65), ALLOCATABLE, DIMENSION(:)'
num = 0
def __init__(self, models, directory=None, pkg_name=None):
"""TODO."""
self.models = models
self.directory = directory
self.with_import = True
if pkg_name is None:
self.pkg_name = "CropModel"
else: self.pkg_name = pkg_name
def my_input(self,_input):
name = _input.name
_type = _input.datatype
if _type=="DOUBLEARRAY" or _type=="INTARRAY" or _type =="STRINGARRAY":
size = _input.len
return ("%s(%s) :: %s \n"%(self.DATATYPE[_type],size, name))
else:
return ("%s:: %s\n"%(self.DATATYPE[_type], name))
def generate_test(self, model_unit):
tab = ' '*4
m = model_unit
model_name = signature(m)
psets = m.parametersets
list_var=[]
list_inouts=[]
for inp in m.inputs:
list_var.append(inp.name)
list_inouts.append(inp)
for out in m.outputs:
if out.name not in list_var:
list_var.append(out.name)
list_inouts.append(out)
self.codetest = "PROGRAM test\n"
self.codetest += tab+ "USE" + " %smod\n"%model_unit.name.capitalize()
test_codes = []
decl_ins= [ tab + self.my_input(var) for var in list_inouts]
test_codes.append(''.join(decl_ins))
for v_tests in m.testsets:
test_runs = v_tests.test # different run in the thest
test_paramsets = v_tests.parameterset # name of paramsets
# map the paramsets
params = {}
if test_paramsets.strip()!="" and test_paramsets not in list(psets.keys()):
print(' Unknow parameter %s'%test_paramsets)
else:
if test_paramsets.strip()!="" : params.update(psets[test_paramsets].params)
for each_run in test_runs :
# make a function that transforms a title into a function name
tname = list(each_run.keys())[0].replace(' ', '_')
tname = tname.replace('-', '_')
(run, inouts) = list(each_run.items())[0]
ins = inouts['inputs']
outs = inouts['outputs']
#code = "PROGRAM test_%s_%s \n"%(tname,m.name)
#code += tab+ "USE" + " %smod"%model_unit.name.capitalize()
#test_codes.append(code)
#test_codes.append('\n'.join(decl_ins))
code = tab +'print *, "--------test_%s_%s-------" \n'%(tname,m.name)
run_param = params.copy()
run_param.update(ins)
for testinp in m.inputs:
if testinp.name not in list(run_param.keys()):
run_param[testinp.name]=testinp.default
for k, v in six.iteritems(run_param):
typ = [inp.datatype for inp in list_inouts if inp.name == k][0]
if typ == "STRING" or typ == "DATE": code += ' %s = "%s"\n'%(k,v.replace('"', ''))
elif typ != "BOOLEAN" and typ != "STRINGLIST" and typ!= "DATELIST": code += " %s = %s\n"%(k,v)
elif typ == "BOOLEAN": code +=" %s = .%s.\n"%(k, v.capitalize())
else:
value = ""
for val in eval(v):
value += ' CALL Add(%s, "%s")\n'%(k,val)
code += value
test_codes.append(code)
code=" call model_{0}({1})\n".format(model_name, ', '.join(list_var))
for k, v in six.iteritems(outs):
type_o = [out.datatype for out in m.outputs if out.name==k][0]
code += tab + "!%s: %s\n"%(k, v[0])
code += tab + 'print *, "%s estimated :" \n'%(k)
if type_o.find("LIST")!=-1:
code += tab +"Do i_cyml = %s, %s\n"%(1, len(eval(v[0])))
code += 2*tab + "print *, %s(i_cyml);\n"%(k)
code += tab + "END DO\n"
else:
code += tab + "print *, %s\n"%(k)
test_codes.append(code)
code = ''.join(test_codes)
self.codetest += code+'\nEND PROGRAM\n\n'
return formater(self.codetest)
def write_tests(self):
""" TODO: Manage several models rather than just one.
"""
files = []
count = 0
for model in self.models:
codetest = self.generate_test(model)
filename = Path(self.directory/"test_%s.f90"%signature(model))
codetest = "!Test generation\n\n"+codetest
with open(filename, "wb") as f90_file:
f90_file.write(codetest.encode('utf-8'))
files.append(filename)
count +=1
return files
def signature(model):
name = model.name
name = name.strip()
name = name.replace(' ', '_').lower()
return name
def generate_doc(model):
desc = model.description
_doc = """
%s
Author: %s
Reference: %s
Institution: %s
Abstract: %s
""" %(desc.Title, desc.Authors, desc.Reference, desc.Institution, desc.Abstract)
code = '\n'
code += _doc
return code
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connect/reports/renderers/j2.py | effrenus/connect-reports-core | 0 | 86607 | # Copyright © 2021 CloudBlue. All rights reserved.
import os
from jinja2 import (
Environment,
FileSystemLoader,
select_autoescape,
)
from connect.reports.renderers.base import BaseRenderer
from connect.reports.renderers.registry import register
@register('jinja2')
class Jinja2Renderer(BaseRenderer):
"""
Jinja2 Renderer class.
Inherits from BaseRenderer class and implements
the generation report function, exporting the data
to a j2 file.
"""
def generate_report(self, data, output_file):
path, name = self.template.rsplit('/', 1)
loader = FileSystemLoader(os.path.join(self.root_dir, path))
env = Environment(
loader=loader,
autoescape=select_autoescape(['html', 'xml']),
)
template = env.get_template(name)
_, ext, _ = name.rsplit('.', 2)
report_file = f'{output_file}.{ext}'
template.stream(self.get_context(data)).dump(open(report_file, 'w'))
return report_file
@classmethod
def validate(cls, definition):
errors = []
if definition.template is None:
errors.append('`template` is required for jinja2 renderer.')
else:
if not os.path.isfile(
os.path.join(definition.root_path, definition.template),
):
errors.append(f'template `{definition.template}` not found.')
tokens = definition.template.rsplit('.', 2)
if len(tokens) < 3 or tokens[-1] != 'j2':
errors.append(
f'invalid template name: `{definition.template}` '
'(must be in the form <name>.<ext>.j2).',
)
return errors
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Inference_Model.py | CODEJIN/WaveRNN | 1 | 29485 | <reponame>CODEJIN/WaveRNN
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import functools
import itertools
import threading
import numpy as np
from six.moves import zip # pylint: disable=redefined-builtin
from google.protobuf import json_format
from tensorflow.core.framework import node_def_pb2
from tensorflow.python.autograph.core import ag_ctx
from tensorflow.python.autograph.impl import api as autograph
from tensorflow.python.distribute import distribution_strategy_context as ds_context
from tensorflow.python.eager import context
from tensorflow.python.eager import execute
from tensorflow.python.eager import function
from tensorflow.python.eager import monitoring
from tensorflow.python.framework import auto_control_deps
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import func_graph
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import tensor_util
from tensorflow.python.keras import backend
from tensorflow.python.keras import constraints
from tensorflow.python.keras import initializers
from tensorflow.python.keras import regularizers
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.keras.engine import input_spec
from tensorflow.python.keras.engine import node as node_module
from tensorflow.python.keras.mixed_precision.experimental import autocast_variable
from tensorflow.python.keras.mixed_precision.experimental import policy
from tensorflow.python.keras.saving.saved_model import layer_serialization
from tensorflow.python.keras.utils import generic_utils
from tensorflow.python.keras.utils import tf_utils
# A module that only depends on `keras.layers` import these from here.
from tensorflow.python.keras.utils.generic_utils import to_snake_case # pylint: disable=unused-import
from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list # pylint: disable=unused-import
from tensorflow.python.module import module
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.platform import tf_logging
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.training.tracking import data_structures
from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
from tensorflow.python.training.tracking import tracking
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import nest
from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import keras_export
from tensorflow.tools.docs import doc_controls
import tensorflow as tf
class Keras_Model_for_Inference(tf.keras.Model):
'''
This class is created to solve an issue that 'tf.Tensor'
cannot be used in 'tf.cond' or 'tf.while_loop' while inference
due to an autograph problem. The inference function is a
clone of __call__ of 'tf.kreas.layers.Layer'. Inference
is implemented at '_inference'.
'''
def __init__(self, *args, **kwargs): # pylint: disable=super-init-not-called
super()
def inference(self, inputs, *args, **kwargs):
call_context = base_layer_utils.call_context()
input_list = nest.flatten(inputs)
# We will attempt to build a TF graph if & only if all inputs are symbolic.
# This is always the case in graph mode. It can also be the case in eager
# mode when all inputs can be traced back to `keras.Input()` (when building
# models using the functional API).
build_graph = tf_utils.are_all_symbolic_tensors(input_list)
# Accept NumPy and scalar inputs by converting to Tensors.
if any(isinstance(x, (np.ndarray, float, int)) for x in input_list):
def _convert_non_tensor(x):
# Don't call `ops.convert_to_tensor` on all `inputs` because
# `SparseTensors` can't be converted to `Tensor`.
if isinstance(x, (np.ndarray, float, int)):
return ops.convert_to_tensor(x)
return x
inputs = nest.map_structure(_convert_non_tensor, inputs)
input_list = nest.flatten(inputs)
# Handle `mask` propagation from previous layer to current layer. Masks can
# be propagated explicitly via the `mask` argument, or implicitly via
# setting the `_keras_mask` attribute on the inputs to a Layer. Masks passed
# explicitly take priority.
mask_arg_passed_by_framework = False
input_masks = self._collect_input_masks(inputs, args, kwargs)
if (self._expects_mask_arg and input_masks is not None and
not self._call_arg_was_passed('mask', args, kwargs)):
mask_arg_passed_by_framework = True
kwargs['mask'] = input_masks
# If `training` argument was not explicitly passed, propagate `training`
# value from this layer's calling layer.
training_arg_passed_by_framework = False
# Priority 1: `training` was explicitly passed.
if self._call_arg_was_passed('training', args, kwargs):
training_value = self._get_call_arg_value('training', args, kwargs)
if not self._expects_training_arg:
kwargs.pop('training')
else:
training_value = None
# Priority 2: `training` was passed to a parent layer.
if call_context.training is not None:
training_value = call_context.training
# Priority 3a: `learning_phase()` has been set.
elif backend.global_learning_phase_is_set():
training_value = backend.learning_phase()
# Priority 3b: Pass the `learning_phase()` if in the Keras FuncGraph.
elif build_graph:
with backend.get_graph().as_default():
if base_layer_utils.is_in_keras_graph():
training_value = backend.learning_phase()
if self._expects_training_arg and training_value is not None:
# Force the training_value to be bool type which matches to the contract
# for layer/model call args.
if tensor_util.is_tensor(training_value):
training_value = math_ops.cast(training_value, dtypes.bool)
else:
training_value = bool(training_value)
kwargs['training'] = training_value
training_arg_passed_by_framework = True
# Only create Keras history if at least one tensor originates from a
# `keras.Input`. Otherwise this Layer may be being used outside the Keras
# framework.
if build_graph and base_layer_utils.needs_keras_history(inputs):
base_layer_utils.create_keras_history(inputs)
# Clear eager losses on top level model call.
# We are clearing the losses only on the top level model call and not on
# every layer/model call because layer/model may be reused.
if (base_layer_utils.is_in_eager_or_tf_function() and
not call_context.in_call):
self._clear_losses()
with call_context.enter(self, inputs, build_graph, training_value):
# Check input assumptions set after layer building, e.g. input shape.
if build_graph:
# Symbolic execution on symbolic tensors. We will attempt to build
# the corresponding TF subgraph inside `backend.get_graph()`
# TODO(reedwm): We should assert input compatibility after the inputs
# are casted, not before.
input_spec.assert_input_compatibility(self.input_spec, inputs,
self.name)
if (any(isinstance(x, ragged_tensor.RaggedTensor) for x in input_list)
and self._supports_ragged_inputs is False): # pylint: disable=g-bool-id-comparison
raise ValueError('Layer %s does not support RaggedTensors as input. '
'Inputs received: %s. You can try converting your '
'input to an uniform tensor.' % (self.name, inputs))
graph = backend.get_graph()
with graph.as_default(), backend.name_scope(self._name_scope()):
# Build layer if applicable (if the `build` method has been
# overridden).
self._maybe_build(inputs)
cast_inputs = self._maybe_cast_inputs(inputs)
# Wrapping `call` function in autograph to allow for dynamic control
# flow and control dependencies in call. We are limiting this to
# subclassed layers as autograph is strictly needed only for
# subclassed layers and models.
# tf_convert will respect the value of autograph setting in the
# enclosing tf.function, if any.
if (base_layer_utils.is_subclassed(self) and
not base_layer_utils.from_saved_model(self)):
call_fn = autograph.tf_convert(
self._inference, ag_ctx.control_status_ctx())
else:
call_fn = self._inference
if not self.dynamic:
try:
with base_layer_utils.autocast_context_manager(
self._compute_dtype):
# Add auto_control_deps in V2 when they are not already added by
# a `tf.function`.
if (ops.executing_eagerly_outside_functions() and
not base_layer_utils.is_in_eager_or_tf_function()):
with auto_control_deps.AutomaticControlDependencies() as acd:
outputs = call_fn(cast_inputs, *args, **kwargs)
# Wrap Tensors in `outputs` in `tf.identity` to avoid
# circular dependencies.
outputs = base_layer_utils.mark_as_return(outputs, acd)
else:
outputs = call_fn(cast_inputs, *args, **kwargs)
except errors.OperatorNotAllowedInGraphError as e:
raise TypeError('You are attempting to use Python control '
'flow in a layer that was not declared to be '
'dynamic. Pass `dynamic=True` to the class '
'constructor.\nEncountered error:\n"""\n' +
str(e) + '\n"""')
else:
# We will use static shape inference to return symbolic tensors
# matching the specifications of the layer outputs.
# Since `self.dynamic` is True, we will never attempt to
# run the underlying TF graph (which is disconnected).
# TODO(fchollet): consider py_func as an alternative, which
# would enable us to run the underlying graph if needed.
outputs = self._symbolic_call(inputs)
if outputs is None:
raise ValueError('A layer\'s `call` method should return a '
'Tensor or a list of Tensors, not None '
'(layer: ' + self.name + ').')
if base_layer_utils.have_all_keras_metadata(inputs):
if training_arg_passed_by_framework:
kwargs.pop('training')
if mask_arg_passed_by_framework:
kwargs.pop('mask')
inputs, outputs = self._set_connectivity_metadata_(
inputs, outputs, args, kwargs)
self._handle_activity_regularization(inputs, outputs)
self._set_mask_metadata(inputs, outputs, input_masks)
if hasattr(self, '_set_inputs') and not self.inputs:
# Subclassed network: explicitly set metadata normally set by
# a call to self._set_inputs().
# TODO(b/120997007): This should be done in Eager as well, but
# causes garbage collection issues because of the placeholders
# created on the default Keras graph.
self._set_inputs(inputs, outputs)
else:
# Eager execution on data tensors.
with backend.name_scope(self._name_scope()):
self._maybe_build(inputs)
cast_inputs = self._maybe_cast_inputs(inputs)
with base_layer_utils.autocast_context_manager(
self._compute_dtype):
outputs = self._inference(cast_inputs, *args, **kwargs)
self._handle_activity_regularization(inputs, outputs)
self._set_mask_metadata(inputs, outputs, input_masks)
return outputs
def _inference(self, inputs, *args, **kwargs):
raise NotImplementedError
@trackable.no_automatic_dependency_tracking
def _init_graph_network(self, inputs, outputs, name=None, **kwargs):
generic_utils.validate_kwargs(
kwargs, {'trainable'},
'Functional models may only specify `name` and `trainable` keyword '
'arguments during initialization. Got an unexpected argument:')
# Normalize and set self.inputs, self.outputs.
if isinstance(inputs, list) and len(nest.flatten(inputs)) == 1:
inputs = inputs[0]
if isinstance(outputs, list) and len(nest.flatten(outputs)) == 1:
outputs = outputs[0]
self._nested_outputs = outputs
self._nested_inputs = inputs
self.inputs = nest.flatten(inputs)
self.outputs = nest.flatten(outputs)
if any(not hasattr(tensor, '_keras_history') for tensor in self.outputs):
base_layer_utils.create_keras_history(self._nested_outputs)
self._base_init(name=name, **kwargs)
self._validate_graph_inputs_and_outputs()
# A Network does not create weights of its own, thus it is already
# built.
self.built = True
self._compute_output_and_mask_jointly = True
self._is_graph_network = True
# `_expects_training_arg` is True since the `training` argument is always
# present in the signature of the `call` method of a graph network.
self._expects_training_arg = True
self._expects_mask_arg = True
# A graph network does not autocast inputs, as its layers will cast them
# instead.
self._autocast = False
self._input_layers = []
self._output_layers = []
self._input_coordinates = []
self._output_coordinates = []
self._supports_ragged_inputs = None
# This is for performance optimization when calling the Network on new
# inputs. Every time the Network is called on a set on input tensors,
# we compute the output tensors, output masks and output shapes in one pass,
# then cache them here. When any of these outputs is queried later, we
# retrieve it from there instead of recomputing it.
self._output_mask_cache = {}
self._output_tensor_cache = {}
self._output_shape_cache = {}
# Build self._output_layers:
for x in self.outputs:
layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access
self._output_layers.append(layer)
self._output_coordinates.append((layer, node_index, tensor_index))
# Build self._input_layers:
for x in self.inputs:
layer, node_index, tensor_index = x._keras_history # pylint: disable=protected-access
# It's supposed to be an input layer, so only one node
# and one tensor output.
assert node_index == 0
assert tensor_index == 0
self._input_layers.append(layer)
self._input_coordinates.append((layer, node_index, tensor_index))
# Keep track of the network's nodes and layers.
nodes, nodes_by_depth, layers, _ = _map_graph_network(
self.inputs, self.outputs)
self._network_nodes = nodes
self._nodes_by_depth = nodes_by_depth
self._layers = layers
self._layer_call_argspecs = {}
for layer in self._layers:
self._layer_call_argspecs[layer] = tf_inspect.getfullargspec(layer.call)
layer._attribute_sentinel.add_parent(self._attribute_sentinel)
self._track_layers(layers)
# Create the node linking internal inputs to internal outputs.
node_module.Node(
outbound_layer=self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=self._nested_inputs,
output_tensors=self._nested_outputs)
# Build self.input_names and self.output_names.
self._set_output_names()
self.input_names = []
self._feed_input_names = []
self._feed_inputs = []
self._feed_input_shapes = []
for layer in self._input_layers:
self.input_names.append(layer.name)
if layer.is_placeholder:
self._feed_input_names.append(layer.name)
# Use batch_input_shape here because non-eager composite tensors may not
# have a shape attribute that's meaningful (sparse, for instance, has
# a tensor that's non-constant and needs to be fed). This means that
# input layers that create placeholders will need to have the
# batch_input_shape attr to allow for input shape validation.
self._feed_input_shapes.append(layer._batch_input_shape)
self._feed_inputs.append(layer.input) | [
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] |
experiments/_pytorch/_grpc_server/protofiles/imagedata_pb2.py | RedisAI/benchmarks | 6 | 4468 | <gh_stars>1-10
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: imagedata.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='imagedata.proto',
package='',
syntax='proto3',
serialized_options=None,
serialized_pb=_b('\n\x0fimagedata.proto\"H\n\tImageData\x12\r\n\x05image\x18\x01 \x01(\x0c\x12\x0e\n\x06height\x18\x02 \x01(\x05\x12\r\n\x05width\x18\x03 \x01(\x05\x12\r\n\x05\x64type\x18\x04 \x01(\t\"!\n\x0fPredictionClass\x12\x0e\n\x06output\x18\x01 \x03(\x02\x32<\n\tPredictor\x12/\n\rGetPrediction\x12\n.ImageData\x1a\x10.PredictionClass\"\x00\x62\x06proto3')
)
_IMAGEDATA = _descriptor.Descriptor(
name='ImageData',
full_name='ImageData',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='image', full_name='ImageData.image', index=0,
number=1, type=12, cpp_type=9, label=1,
has_default_value=False, default_value=_b(""),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='height', full_name='ImageData.height', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='width', full_name='ImageData.width', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
_descriptor.FieldDescriptor(
name='dtype', full_name='ImageData.dtype', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=19,
serialized_end=91,
)
_PREDICTIONCLASS = _descriptor.Descriptor(
name='PredictionClass',
full_name='PredictionClass',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='output', full_name='PredictionClass.output', index=0,
number=1, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=93,
serialized_end=126,
)
DESCRIPTOR.message_types_by_name['ImageData'] = _IMAGEDATA
DESCRIPTOR.message_types_by_name['PredictionClass'] = _PREDICTIONCLASS
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
ImageData = _reflection.GeneratedProtocolMessageType('ImageData', (_message.Message,), dict(
DESCRIPTOR = _IMAGEDATA,
__module__ = 'imagedata_pb2'
# @@protoc_insertion_point(class_scope:ImageData)
))
_sym_db.RegisterMessage(ImageData)
PredictionClass = _reflection.GeneratedProtocolMessageType('PredictionClass', (_message.Message,), dict(
DESCRIPTOR = _PREDICTIONCLASS,
__module__ = 'imagedata_pb2'
# @@protoc_insertion_point(class_scope:PredictionClass)
))
_sym_db.RegisterMessage(PredictionClass)
_PREDICTOR = _descriptor.ServiceDescriptor(
name='Predictor',
full_name='Predictor',
file=DESCRIPTOR,
index=0,
serialized_options=None,
serialized_start=128,
serialized_end=188,
methods=[
_descriptor.MethodDescriptor(
name='GetPrediction',
full_name='Predictor.GetPrediction',
index=0,
containing_service=None,
input_type=_IMAGEDATA,
output_type=_PREDICTIONCLASS,
serialized_options=None,
),
])
_sym_db.RegisterServiceDescriptor(_PREDICTOR)
DESCRIPTOR.services_by_name['Predictor'] = _PREDICTOR
# @@protoc_insertion_point(module_scope)
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] |
orangery/cli/cutfill.py | mrahnis/orangery | 2 | 6682 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import logging
import time
import json
import click
import matplotlib.pyplot as plt
import orangery as o
from orangery.cli import defaults, util
from orangery.tools.plotting import get_scale_factor
@click.command(options_metavar='<options>')
@click.argument('file1', nargs=1, type=click.Path(exists=True), metavar='<file_t0>') # help="survey representing the initial condition"
@click.argument('file2', nargs=1, type=click.Path(exists=True), metavar='<file_t1>') # help="survey representing the final condition"
@click.argument('fields', nargs=1, metavar='<fields>') # help="character string identifying the columns"
@click.argument('xs_name', nargs=1, metavar='<name>') # help="name of the cross-section to plot"
@click.option('--codes', 'codes_f', nargs=1, type=click.Path(exists=True), metavar='<codes_file>', help="JSON file representing the usage intent of a set of survey codes")
@click.option('--show/--save', is_flag=True, default=True, help="Show the plot or save to files; --show is the default")
@click.option('--summary/--no-summary', default=True, help="Print summary information; --summary is the default")
@click.option('--units', type=click.Choice(['m','sft','ft']), default='m', help="Unit to show in axis labels")
@click.option('--labels', nargs=2, metavar='<text text>', help="Labels to display in the legend")
@click.option('--exaggeration', metavar='<int>', default=3, help="Vertical exaggeration of plot")
@click.option('--scale', nargs=2, metavar='<float int>', type=click.Tuple([float, int]), default=(10, 300), help="Scale where first argument is units per-inch on the horizontal axis and second argument is output DPI")
@click.option('--close/--no-close', default=True, help="Close the line ends; --close is the default")
@click.option('--reverse', type=click.Choice(['t0','t1','tx']), help="Reverse a line or lines of section (t0=initial, t1=final, tx=both)")
@click.option('--exclude', nargs=2, type=click.Tuple([str, click.Choice(['t0','t1','tx'])]), multiple=True, metavar='<str choice>', help="Exclude a survey code from a line or lines of section (t0=initial, t1=final, tx=both)")
@click.option('--overlay', nargs=1, type=click.Path(exists=True))
@click.option('-v', '--verbose', is_flag=True, help="Enables verbose mode")
def cutfill(file1, file2, fields, xs_name, codes_f, show, summary, units, labels, exaggeration, scale, close, reverse, exclude, overlay, verbose):
"""Displays a plot of a repeat survey with cut and fill.
\b
The cutfill subcommand takes four arguments:
<file_t0> : survey data representing the initial condition in csv format
<file_t1> : survey data representing the final condition in csv format
<fields> : series of characters describing the data columns
<name> : name of cross-section to plot
Options allow to set various properties of the plot. The default is to --show the plot.
With the --save option the plot will be saved as an image along with a csv file containing
data about cross-sectional cut-and-fill areas along the line of secion.
\b
Example:
orangery cutfill file_2004.csv file_2010.csv pxyzctr XS-7 --reverse t0
"""
if verbose is True:
loglevel = 2
else:
loglevel = 0
logging.basicConfig(stream=sys.stderr, level=loglevel or logging.INFO)
# load the configuration
codes = defaults.codes.copy()
if codes_f:
user_codes = util.load_config(codes_f)
codes.update(user_codes)
# load the survey data
s1 = o.Survey(file1, fields, codes, 0)
s2 = o.Survey(file2, fields, codes, 0)
if overlay:
s3 = o.Survey(overlay, fields, codes, 0)
exclude_t0 = []
exclude_t1 = []
for code in exclude:
if code[1] in ('t0', 'tx'):
exclude_t0.append(code[0])
if code[1] in ('t1', 'tx'):
exclude_t1.append(code[0])
# select a group of points, in this case a cross section
xs_pts1 = o.group(s1.data, s1.code_table, group=xs_name, exclude=exclude_t0)
xs_pts2 = o.group(s2.data, s2.code_table, group=xs_name, exclude=exclude_t1)
# xs_pts_overlay = o.group(s3.data, s3.code_table, group=xs_name)
# get the endpoints of the group
p1, p2 = o.endpoints(xs_pts1, reverse=reverse in ('t0','tx'))
# make the sections
xs1 = o.Section(xs_pts1, p1, p2, reverse=reverse in ('t0','tx'))
xs2 = o.Section(xs_pts2, p1, p2, reverse=reverse in ('t1','tx'))
# xs_overlay = o.Section(xs_pts_overlay, p1, p2)
if labels:
label_t0 = labels[0]
label_t1 = labels[1]
label_overlay = labels[3]
elif 't' in fields:
label_t0 = (xs1.data.iloc[0]['t']).split('T')[0]
label_t1 = (xs2.data.iloc[0]['t']).split('T')[0]
# label_overlay = (xs_overlay.data.iloc[0]['t']).split('T')[0]
else:
label_t0 = 't0'
label_t1 = 't1'
# label_overlay = 'pre-restoration'
# calculate the change
chg = o.Change(xs1, xs2, close_ends=close)
if summary:
chg.summarize()
import matplotlib
font = {'family':'normal','weight':'normal','size':16}
matplotlib.rc('font', **font)
# plot the change between two cross-sections
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_aspect(exaggeration)
# xs_overlay.plot(ax=ax, marker='None', linestyle='-', linewidth=3, color='tab:red', label=label_overlay)
xs1.plot(ax=ax, marker='o', markersize=4, markerfacecolor='white', markeredgecolor='black', linestyle='-', color='gray', label=label_t0)
xs2.plot(ax=ax, marker='o', markersize=4, markerfacecolor='black', markeredgecolor='black', linestyle='-', color='black', label=label_t1)
chg.polygon_plot(ax=ax, fill_label='Fill', cut_label='Cut')
chg.annotate_plot(ax=ax)
ax.set_xlabel('Distance ({0})'.format(units))
ax.set_ylabel('Elevation ({0}), {1}x exaggeration'.format(units, exaggeration))
plt.legend(loc='best')
plt.title('Cross-section {0}'.format(xs_name))
if show:
plt.show()
else:
fname = xs_name + '-' + label_t0.replace('-', '') + '-' + label_t1.replace('-', '')
scale_factor = get_scale_factor(fig, ax, scale[0])
dims = fig.get_size_inches()
fig.set_size_inches(dims[0]*scale_factor, dims[1]*scale_factor)
fig.savefig(fname+'.png', dpi=scale[1])
click.echo('Figure saved to: {}'.format(fname+'.png'))
chg.save(fname+'.csv')
click.echo('Data saved to: {}'.format(fname+'.csv'))
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] |
src/evolvepy/integrations/__init__.py | EltonCN/evolvepy | 1 | 21132 | <filename>src/evolvepy/integrations/__init__.py
'''
EvolvePy's integrations with other modules.
''' | [
1,
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411,
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] |
blog/articles/views.py | dotHashemi/django-practice | 1 | 118399 | <filename>blog/articles/views.py
from django.shortcuts import render
from .models import Article
def index(request):
articles = Article.objects.order_by("-published")[:10]
return render(request, 'index.html', {
'title': 'Articles Page',
'articles': articles
})
def show(request, id):
article = Article.objects.get(id=id)
return render(request, 'show.html', {
'title': article.title,
'article': article
})
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] |
recommender/main.py | TxConvergentAdmin/f20-bt-impact-local-business | 0 | 189390 | <gh_stars>0
from script import get_rec
def main(request):
"""Responds to any HTTP request.
Args:
request (flask.Request): HTTP request object.
Returns:
The response text or any set of values that can be turned into a
Response object using
`make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`.
"""
request_json = request.get_json()
if request.args and 'user_id' and 'user_friends' in request.args:
return get_rec(request.args.get('user_id'), request.args.get('user_friends'))
elif 'user_id' in request_json:
return get_rec(request_json['user_id'])
else:
return f'Incorrect Data'
| [
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2
] |
tests/utils_tests/test_glob.py | MikeAmy/django | 5,079 | 92046 | <reponame>MikeAmy/django
from __future__ import unicode_literals
from django.test import SimpleTestCase
from django.utils.glob import glob_escape
class TestUtilsGlob(SimpleTestCase):
def test_glob_escape(self):
filename = '/my/file?/name[with special chars*'
expected = '/my/file[?]/name[[]with special chars[*]'
filename_b = b'/my/file?/name[with special chars*'
expected_b = b'/my/file[?]/name[[]with special chars[*]'
self.assertEqual(glob_escape(filename), expected)
self.assertEqual(glob_escape(filename_b), expected_b)
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] |
gcloud/datastores/tests/STUB_test_bigquery.py | pantheon-ci-bot/etl-framework | 2 | 10542 | <filename>gcloud/datastores/tests/STUB_test_bigquery.py
"""tests bigquery client"""
import unittest
from gcloud.datastores.bigquery import BigqueryClient
class BigqueryClientTestCases(unittest.TestCase):
"""stuff"""
@classmethod
def setUpClass(cls):
cls.project_id = 'test'
cls.dataset_id = 'etl_test'
cls.table_id = 'etl_test'
cls.table_schema = {
"fields": [
{
"type": "STRING",
"name": "a_key",
"mode": "REQUIRED",
}
]
}
cls.rows = [
{
"insertId": "some_uuid",
"json": {
"a_key": "a_value"
},
},
]
cls.query = "SELECT a_key FROM [{}:{}.{}]".format(
cls.project_id,
cls.dataset_id,
cls.table_id,
)
cls.client = BigqueryClient(
project_name=cls.project_id,
dataset_id=cls.dataset_id
)
# Create a dataset and table (this indirectly tests create and delete)
cls.client.insert_dataset(cls.dataset_id)
cls.client.insert_table(
table_id=cls.table_id,
schema=cls.table_schema
)
@classmethod
def tearDownClass(cls):
# Remove table and dataset (this indirectly tests create and delete)
cls.client.delete_table(cls.table_id)
cls.client.delete_dataset(cls.dataset_id)
def test_get_dataset(self):
self.client.get_dataset(self.dataset_id)
def test_get_table(self):
self.client.get_table(self.table_id)
def test_insert_data(self):
self.client.insert_data(
table_id=self.table_id,
rows=self.rows
)
def test_list_data(self):
self.client.list_data(
table_id=self.table_id
)
def test_list_datasets(self):
self.client.list_datasets()
def test_list_tables(self):
self.client.list_tables(
dataset_id=self.dataset_id
)
def test_patch_table(self):
self.client.patch_table(
table_id=self.table_id,
schema=self.table_schema,
)
def test_query(self):
self.client.query(
query=self.query,
)
def test_update_table(self):
self.client.update_table(
table_id=self.table_id,
schema=self.table_schema,
)
| [
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2
] |
ppo/train.py | esthergu/pytorch-a2c-ppo-acktr | 0 | 183829 | import os, sys, time, copy, glob
from collections import deque
import gym
from gym import spaces
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from ppo.a2c_ppo_acktr import algo
from ppo.a2c_ppo_acktr.arguments import get_args
from ppo.a2c_ppo_acktr.envs import make_vec_envs
from ppo.a2c_ppo_acktr.model import Policy
from ppo.a2c_ppo_acktr.storage import RolloutStorage
from ppo.a2c_ppo_acktr.utils import get_vec_normalize, update_linear_schedule
from ppo.a2c_ppo_acktr.visualize import visdom_plot
args = get_args()
assert args.algo in ['a2c', 'ppo', 'acktr']
if args.recurrent_policy:
assert args.algo in ['a2c', 'ppo'], \
'Recurrent policy is not implemented for ACKTR'
if args.num_rollouts > 0:
assert args.num_rollouts % args.num_processes == 0, 'num_rollouts must be divisable by num_processes'
num_updates = int(args.num_env_steps) // args.num_steps // args.num_processes
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
try:
os.makedirs(args.log_dir)
except OSError:
files = glob.glob(os.path.join(args.log_dir, '*.monitor.csv'))
try:
for f in files:
os.remove(f)
except PermissionError as e:
pass
eval_log_dir = args.log_dir + "_eval"
try:
os.makedirs(eval_log_dir)
except OSError:
files = glob.glob(os.path.join(eval_log_dir, '*.monitor.csv'))
try:
for f in files:
os.remove(f)
except PermissionError as e:
pass
def main():
torch.set_num_threads(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
if args.vis:
from visdom import Visdom
viz = Visdom(port=args.port)
win = None
envs = make_vec_envs(args.env_name, args.seed, 1,
args.gamma, args.log_dir, args.add_timestep, device, False)
# Determine if this is a dual robot (multi agent) environment.
obs = envs.reset()
action = torch.tensor([envs.action_space.sample()])
_, _, _, info = envs.step(action)
dual_robots = 'dual_robots' in info[0]
if dual_robots:
obs_robot_len = info[0]['obs_robot_len'] // 2
action_robot_len = info[0]['action_robot_len'] // 2
obs_robot1 = obs[:, :obs_robot_len]
obs_robot2 = obs[:, obs_robot_len:]
if len(obs_robot1[0]) != obs_robot_len or len(obs_robot2[0]) != obs_robot_len:
print('robot 1 obs shape:', len(obs_robot1[0]), 'obs space robot shape:', (obs_robot_len,))
print('robot 2 obs shape:', len(obs_robot2[0]), 'obs space robot shape:', (obs_robot_len,))
exit()
envs = make_vec_envs(args.env_name, args.seed, args.num_processes,
args.gamma, args.log_dir, args.add_timestep, device, False)
if dual_robots:
# Reset environment
obs = envs.reset()
obs_robot1 = obs[:, :obs_robot_len]
obs_robot2 = obs[:, obs_robot_len:]
action_space_robot1 = spaces.Box(low=np.array([-1.0]*action_robot_len), high=np.array([1.0]*action_robot_len), dtype=np.float32)
action_space_robot2 = spaces.Box(low=np.array([-1.0]*action_robot_len), high=np.array([1.0]*action_robot_len), dtype=np.float32)
if args.load_policy is not None:
if dual_robots:
actor_critic_robot1, actor_critic_robot2, ob_rms = torch.load(args.load_policy)
else:
actor_critic, ob_rms = torch.load(args.load_policy)
vec_norm = get_vec_normalize(envs)
if vec_norm is not None:
vec_norm.ob_rms = ob_rms
else:
if dual_robots:
actor_critic_robot1 = Policy([obs_robot_len], action_space_robot1,
base_kwargs={'recurrent': args.recurrent_policy})
actor_critic_robot2 = Policy([obs_robot_len], action_space_robot2,
base_kwargs={'recurrent': args.recurrent_policy})
else:
actor_critic = Policy(envs.observation_space.shape, envs.action_space,
base_kwargs={'recurrent': args.recurrent_policy, 'hidden_size': args.hidden_size})
if dual_robots:
actor_critic_robot1.to(device)
actor_critic_robot2.to(device)
else:
actor_critic.to(device)
if args.algo == 'a2c':
agent = algo.A2C_ACKTR(actor_critic, args.value_loss_coef,
args.entropy_coef, lr=args.lr,
eps=args.eps, alpha=args.alpha,
max_grad_norm=args.max_grad_norm)
elif args.algo == 'ppo':
if dual_robots:
agent_robot1 = algo.PPO(actor_critic_robot1, args.clip_param, args.ppo_epoch, args.num_mini_batch,
args.value_loss_coef, args.entropy_coef, lr=args.lr,
eps=args.eps,
max_grad_norm=args.max_grad_norm)
agent_robot2 = algo.PPO(actor_critic_robot2, args.clip_param, args.ppo_epoch, args.num_mini_batch,
args.value_loss_coef, args.entropy_coef, lr=args.lr,
eps=args.eps,
max_grad_norm=args.max_grad_norm)
else:
agent = algo.PPO(actor_critic, args.clip_param, args.ppo_epoch, args.num_mini_batch,
args.value_loss_coef, args.entropy_coef, lr=args.lr,
eps=args.eps,
max_grad_norm=args.max_grad_norm)
elif args.algo == 'acktr':
agent = algo.A2C_ACKTR(actor_critic, args.value_loss_coef,
args.entropy_coef, acktr=True)
if dual_robots:
rollouts_robot1 = RolloutStorage(args.num_steps, args.num_rollouts if args.num_rollouts > 0 else args.num_processes,
[obs_robot_len], action_space_robot1,
actor_critic_robot1.recurrent_hidden_state_size)
rollouts_robot2 = RolloutStorage(args.num_steps, args.num_rollouts if args.num_rollouts > 0 else args.num_processes,
[obs_robot_len], action_space_robot2,
actor_critic_robot2.recurrent_hidden_state_size)
if args.num_rollouts > 0:
rollouts_robot1.obs[0].copy_(torch.cat([obs_robot1 for _ in range(args.num_rollouts // args.num_processes)] + [obs_robot1[:(args.num_rollouts % args.num_processes)]], dim=0))
rollouts_robot2.obs[0].copy_(torch.cat([obs_robot2 for _ in range(args.num_rollouts // args.num_processes)] + [obs_robot2[:(args.num_rollouts % args.num_processes)]], dim=0))
else:
rollouts_robot1.obs[0].copy_(obs_robot1)
rollouts_robot2.obs[0].copy_(obs_robot2)
rollouts_robot1.to(device)
rollouts_robot2.to(device)
else:
rollouts = RolloutStorage(args.num_steps, args.num_rollouts if args.num_rollouts > 0 else args.num_processes,
envs.observation_space.shape, envs.action_space,
actor_critic.recurrent_hidden_state_size)
obs = envs.reset()
if args.num_rollouts > 0:
rollouts.obs[0].copy_(torch.cat([obs for _ in range(args.num_rollouts // args.num_processes)] + [obs[:(args.num_rollouts % args.num_processes)]], dim=0))
else:
rollouts.obs[0].copy_(obs)
rollouts.to(device)
deque_len = args.num_rollouts if args.num_rollouts > 0 else (args.num_processes if args.num_processes > 10 else 10)
if dual_robots:
episode_rewards_robot1 = deque(maxlen=deque_len)
episode_rewards_robot2 = deque(maxlen=deque_len)
else:
episode_rewards = deque(maxlen=deque_len)
start = time.time()
for j in range(num_updates):
if args.use_linear_lr_decay:
# decrease learning rate linearly
if args.algo == "acktr":
# use optimizer's learning rate since it's hard-coded in kfac.py
update_linear_schedule(agent.optimizer, j, num_updates, agent.optimizer.lr)
else:
if dual_robots:
update_linear_schedule(agent_robot1.optimizer, j, num_updates, args.lr)
update_linear_schedule(agent_robot2.optimizer, j, num_updates, args.lr)
else:
update_linear_schedule(agent.optimizer, j, num_updates, args.lr)
if args.algo == 'ppo' and args.use_linear_clip_decay:
if dual_robots:
agent_robot1.clip_param = args.clip_param * (1 - j / float(num_updates))
agent_robot2.clip_param = args.clip_param * (1 - j / float(num_updates))
else:
agent.clip_param = args.clip_param * (1 - j / float(num_updates))
reward_list_robot1 = [[] for _ in range(args.num_processes)]
reward_list_robot2 = [[] for _ in range(args.num_processes)]
for step in range(args.num_steps):
# Sample actions
# obs = self.apply_attack(obs, args.phi, args.epsilon)
with torch.no_grad():
if dual_robots:
value_robot1, action_robot1, action_log_prob_robot1, recurrent_hidden_states_robot1 = actor_critic_robot1.act(
rollouts_robot1.obs[step, :args.num_processes],
rollouts_robot1.recurrent_hidden_states[step, :args.num_processes],
rollouts_robot1.masks[step, :args.num_processes])
value_robot2, action_robot2, action_log_prob_robot2, recurrent_hidden_states_robot2 = actor_critic_robot2.act(
rollouts_robot2.obs[step, :args.num_processes],
rollouts_robot2.recurrent_hidden_states[step, :args.num_processes],
rollouts_robot2.masks[step, :args.num_processes])
else:
value, action, action_log_prob, recurrent_hidden_states = actor_critic.act(
rollouts.obs[step, :args.num_processes],
rollouts.recurrent_hidden_states[step, :args.num_processes],
rollouts.masks[step, :args.num_processes])
# Obser reward and next obs
if dual_robots:
action = torch.cat((action_robot1, action_robot2), dim=-1)
obs, reward, done, infos = envs.step(action)
obs_robot1 = obs[:, :obs_robot_len]
obs_robot2 = obs[:, obs_robot_len:]
for i, info in enumerate(infos):
reward_list_robot1[i].append(info['reward_robot1'])
reward_list_robot2[i].append(info['reward_robot2'])
reward_robot1 = torch.tensor([[info['reward_robot1']] for info in infos])
reward_robot2 = torch.tensor([[info['reward_robot2']] for info in infos])
else:
obs, reward, done, infos = envs.step(action)
for i, info in enumerate(infos):
if 'episode' in info.keys():
if dual_robots:
episode_rewards_robot1.append(np.sum(reward_list_robot1[i]))
episode_rewards_robot2.append(np.sum(reward_list_robot2[i]))
else:
episode_rewards.append(info['episode']['r'])
# If done then clean the history of observations.
masks = torch.FloatTensor([[0.0] if done_ else [1.0]
for done_ in done])
if dual_robots:
rollouts_robot1.insert(obs_robot1, recurrent_hidden_states_robot1, action_robot1, action_log_prob_robot1, value_robot1, reward_robot1, masks)
rollouts_robot2.insert(obs_robot2, recurrent_hidden_states_robot2, action_robot2, action_log_prob_robot2, value_robot2, reward_robot2, masks)
else:
rollouts.insert(obs, recurrent_hidden_states, action, action_log_prob, value, reward, masks)
if args.num_rollouts > 0 and (j % (args.num_rollouts // args.num_processes) != 0):
# Only update the policies when we have performed num_rollouts simulations
continue
with torch.no_grad():
if dual_robots:
next_value_robot1 = actor_critic_robot1.get_value(rollouts_robot1.obs[-1],
rollouts_robot1.recurrent_hidden_states[-1],
rollouts_robot1.masks[-1]).detach()
next_value_robot2 = actor_critic_robot2.get_value(rollouts_robot2.obs[-1],
rollouts_robot2.recurrent_hidden_states[-1],
rollouts_robot2.masks[-1]).detach()
else:
next_value = actor_critic.get_value(rollouts.obs[-1],
rollouts.recurrent_hidden_states[-1],
rollouts.masks[-1]).detach()
if dual_robots:
rollouts_robot1.compute_returns(next_value_robot1, args.use_gae, args.gamma, args.tau)
rollouts_robot2.compute_returns(next_value_robot2, args.use_gae, args.gamma, args.tau)
value_loss_robot1, action_loss_robot1, dist_entropy_robot1 = agent_robot1.update(rollouts_robot1)
value_loss_robot2, action_loss_robot2, dist_entropy_robot2 = agent_robot2.update(rollouts_robot2)
rollouts_robot1.after_update()
rollouts_robot2.after_update()
else:
rollouts.compute_returns(next_value, args.use_gae, args.gamma, args.tau)
value_loss, action_loss, dist_entropy = agent.update(rollouts)
rollouts.after_update()
# save for every interval-th episode or for the last epoch
if (j % args.save_interval == 0 or j == num_updates - 1) and args.save_dir != "":
save_path = os.path.join(args.save_dir, args.algo)
try:
os.makedirs(save_path)
except OSError:
pass
# A really ugly way to save a model to CPU
if dual_robots:
save_model_robot1 = actor_critic_robot1
save_model_robot2 = actor_critic_robot2
if args.cuda:
save_model_robot1 = copy.deepcopy(actor_critic_robot1).cpu()
save_model_robot2 = copy.deepcopy(actor_critic_robot2).cpu()
save_model = [save_model_robot1, save_model_robot2,
getattr(get_vec_normalize(envs), 'ob_rms', None)]
else:
save_model = actor_critic
if args.cuda:
save_model = copy.deepcopy(actor_critic).cpu()
save_model = [save_model,
getattr(get_vec_normalize(envs), 'ob_rms', None)]
torch.save(save_model, os.path.join(save_path, args.env_name + ".pt"))
total_num_steps = (j + 1) * args.num_processes * args.num_steps
if j % args.log_interval == 0 and (len(episode_rewards_robot1) > 1 if dual_robots else len(episode_rewards) > 1):
end = time.time()
if dual_robots:
print("Robot1 updates {}, num timesteps {}, FPS {} \n Last {} training episodes: mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}".
format(j, total_num_steps,
int(total_num_steps / (end - start)),
len(episode_rewards_robot1),
np.mean(episode_rewards_robot1),
np.median(episode_rewards_robot1),
np.min(episode_rewards_robot1),
np.max(episode_rewards_robot1), dist_entropy_robot1,
value_loss_robot1, action_loss_robot1))
print("Robot2 updates {}, Last {} training episodes: mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}\n".
format(j, len(episode_rewards_robot2),
np.mean(episode_rewards_robot2),
np.median(episode_rewards_robot2),
np.min(episode_rewards_robot2),
np.max(episode_rewards_robot2), dist_entropy_robot2,
value_loss_robot2, action_loss_robot2))
else:
print("Updates {}, num timesteps {}, FPS {} \n Last {} training episodes: mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}\n".
format(j, total_num_steps,
int(total_num_steps / (end - start)),
len(episode_rewards),
np.mean(episode_rewards),
np.median(episode_rewards),
np.min(episode_rewards),
np.max(episode_rewards), dist_entropy,
value_loss, action_loss))
sys.stdout.flush()
if (args.eval_interval is not None
and len(episode_rewards) > 1
and j % args.eval_interval == 0):
eval_envs = make_vec_envs(
args.env_name, args.seed + args.num_processes, args.num_processes,
args.gamma, eval_log_dir, args.add_timestep, device, True)
vec_norm = get_vec_normalize(eval_envs)
if vec_norm is not None:
vec_norm.eval()
vec_norm.ob_rms = get_vec_normalize(envs).ob_rms
if dual_robots:
eval_episode_rewards_robot1 = []
eval_episode_rewards_robot2 = []
else:
eval_episode_rewards = []
obs = eval_envs.reset()
if dual_robots:
obs_robot1 = obs[:, :obs_robot_len]
obs_robot2 = obs[:, obs_robot_len:]
eval_recurrent_hidden_states_robot1 = torch.zeros(args.num_processes,
actor_critic_robot1.recurrent_hidden_state_size, device=device)
eval_recurrent_hidden_states_robot2 = torch.zeros(args.num_processes,
actor_critic_robot2.recurrent_hidden_state_size, device=device)
else:
eval_recurrent_hidden_states = torch.zeros(args.num_processes,
actor_critic.recurrent_hidden_state_size, device=device)
eval_masks = torch.zeros(args.num_processes, 1, device=device)
eval_reward_list_robot1 = [[] for _ in range(args.num_processes)]
eval_reward_list_robot2 = [[] for _ in range(args.num_processes)]
while (len(eval_episode_rewards_robot1) < 10 if dual_robots else len(eval_episode_rewards) < 10):
with torch.no_grad():
if dual_robots:
_, action_robot1, _, eval_recurrent_hidden_states_robot1 = actor_critic_robot1.act(
obs_robot1, eval_recurrent_hidden_states_robot1, eval_masks, deterministic=True)
_, action_robot2, _, eval_recurrent_hidden_states_robot2 = actor_critic_robot2.act(
obs_robot2, eval_recurrent_hidden_states_robot2, eval_masks, deterministic=True)
else:
_, action, _, eval_recurrent_hidden_states = actor_critic.act(
obs, eval_recurrent_hidden_states, eval_masks, deterministic=True)
# Obser reward and next obs
if dual_robots:
action = torch.cat((action_robot1, action_robot2), dim=-1)
obs, reward, done, infos = eval_envs.step(action)
obs_robot1 = obs[:, :obs_robot_len]
obs_robot2 = obs[:, obs_robot_len:]
for i, info in enumerate(infos):
eval_reward_list_robot1[i].append(info['reward_robot1'])
eval_reward_list_robot2[i].append(info['reward_robot2'])
else:
obs, reward, done, infos = eval_envs.step(action)
eval_masks = torch.FloatTensor([[0.0] if done_ else [1.0]
for done_ in done])
reset_rewards = False
for info in infos:
if 'episode' in info.keys():
if dual_robots:
reset_rewards = True
eval_episode_rewards_robot1.append(np.sum(eval_reward_list_robot1[i]))
eval_episode_rewards_robot2.append(np.sum(eval_reward_list_robot2[i]))
else:
eval_episode_rewards.append(info['episode']['r'])
if reset_rewards:
eval_reward_list_robot1 = [[] for _ in range(args.num_processes)]
eval_reward_list_robot2 = [[] for _ in range(args.num_processes)]
eval_envs.close()
if dual_robots:
print(" Evaluation using {} episodes: robot1 mean reward {:.5f}, robot2 mean reward {:.5f}\n".
format(len(eval_episode_rewards_robot1),
np.mean(eval_episode_rewards_robot1), np.mean(eval_episode_rewards_robot2)))
else:
print(" Evaluation using {} episodes: mean reward {:.5f}\n".
format(len(eval_episode_rewards),
np.mean(eval_episode_rewards)))
sys.stdout.flush()
if args.vis and j % args.vis_interval == 0:
try:
# Sometimes monitor doesn't properly flush the outputs
win = visdom_plot(viz, win, args.log_dir, args.env_name,
args.algo, args.num_env_steps)
except IOError:
pass
if __name__ == "__main__":
main()
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] |
Project--Make-Sense-of-Census/code.py | sahil-mehta/ga-learner-dsmp-repo | 0 | 184583 | <filename>Project--Make-Sense-of-Census/code.py
# --------------
# Importing header files
import numpy as np
# Path of the file has been stored in variable called 'path'
dfile='path'
data=np.genfromtxt(path,delimiter=",", skip_header=1)
data
#New record
new_record=[[50, 9, 4, 1, 0, 0, 40, 0]]
census=np.concatenate([new_record,data])
# --------------
#Code starts here
age = census[:,0]
max_age=age.max()
max_age
min_age=age.min()
min_age
age_mean=age.mean()
age_mean
age_std=age.std()
age_std
# --------------
#Code starts here
race_0=census[census[:,2]==0]
race_1=census[census[:,2]==1]
race_2=census[census[:,2]==2]
race_3=census[census[:,2]==3]
race_4=census[census[:,2]==4]
len_0=len(race_0)
len_1=len(race_1)
len_2=len(race_2)
len_3=len(race_3)
len_4=len(race_4)
listmin=(len_0,len_1,len_2,len_3,len_4)
minority_race=listmin.index(min(listmin))
# --------------
#Code starts here
senior_citizens=census[census[:,0]>60]
#Calculating the sum of all the values of array
working_hours_sum=senior_citizens.sum(axis=0)[6]
#Finding the length of the array
senior_citizens_len=len(senior_citizens)
#Finding the average working hours
avg_working_hours=working_hours_sum/senior_citizens_len
#Printing the average working hours
print((avg_working_hours))
# --------------
#Code starts here
high=census[census[:,1]>10]
low=census[census[:,1]<=10]
avg_pay_high=high[:,7].mean()
avg_pay_low=low[:,7].mean()
avg_pay_high,avg_pay_low
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] |
scripts/data/topple_dataset.py | davrempe/predicting-physical-dynamics | 16 | 2956 | import numpy as np
import pickle
from os.path import exists, realpath
import sys
import math
from topple_data_loader import ToppleData, ToppleDataLoader
import transforms3d
class ToppleNormalizationInfo():
'''
Structure to hold all the normalization information for a dataset.
'''
def __init__(self):
# max element of any linear vel vector
self.max_lin_vel = None
# max element of any angular vel vector
self.max_ang_vel = None
# max distance between positions in two contiguous timesteps
self.max_pos = None
# max change in rotation around any axis between two contiguous timesteps (for euler rot)
self.max_rot = None
# max angle of rotation between two steps for axis-angle representation
self.max_delta_rot = None
# max 2-norm of applied impulse vector
self.force_vec_max = None
# max 2-norm of a point in an object point cloud (used for point cloud and force pos)
self.pc_max = None
# normalization values for shape-related stuff
self.density_offset = None
self.density_max = None
self.mass_offset = None
self.mass_max = None
self.inertia_offset = None
self.inertia_max = None
self.friction_offset = None
self.friction_max = None
def print_out(self):
print({'max_lin_vel' : self.max_lin_vel, 'max_ang_vel' : self.max_ang_vel, 'max_pos' : self.max_pos, \
'max_rot' : self.max_rot, 'max_delta_rot' : self.max_delta_rot, 'force_vec_max' : self.force_vec_max, 'pc_max' : self.pc_max, \
'density_off' : self.density_offset, 'density_max' : self.density_max, 'mass_off' : self.mass_offset, \
'mass_max' : self.mass_max, 'inertia_off' : self.inertia_offset, 'inertia_max' : self.inertia_max, \
'friction_off' : self.friction_offset, 'friction_max' : self.friction_max
})
def save(self, pkl_file):
''' Saves normalization info object to a specified .pkl file. '''
with open(pkl_file, 'wb') as f:
pickle.dump(self, f)
def load_from(self, pkl_file):
''' Load normalization info into this object from a specified .pkl file. '''
with open(pkl_file, 'rb') as f:
norm_info = pickle.load(f)
self.copy_from(norm_info)
def copy_from(self, norm_info):
'''
Takes values from the given normalization info object and copies them to this one
'''
self.max_lin_vel = norm_info.max_lin_vel
self.max_ang_vel = norm_info.max_ang_vel
self.max_pos = norm_info.max_pos
self.max_rot = norm_info.max_rot
try:
self.max_delta_rot = norm_info.max_delta_rot
except:
# old versions of data doesn't have max delta rot
pass
self.force_vec_max = norm_info.force_vec_max
self.pc_max = norm_info.pc_max
self.density_offset = norm_info.density_offset
self.density_max = norm_info.density_max
self.mass_offset = norm_info.mass_offset
self.mass_max = norm_info.mass_max
self.inertia_offset = norm_info.inertia_offset
self.inertia_max = norm_info.inertia_max
try:
self.friction_offset = norm_info.friction_offset
self.friction_max = norm_info.friction_max
except:
# old version doesn't have this
pass
class ToppleBatch(object):
'''
Structure to hold a single batch of data.
'''
def __init__(self, size, seq_len, num_pts):
self.size = size
self.num_steps = seq_len
self.num_pts = num_pts
self.point_cloud = np.zeros((self.size, self.num_pts, 3))
self.lin_vel = np.zeros((self.size, self.num_steps, 3))
self.ang_vel = np.zeros((self.size, self.num_steps, 3))
self.pos = np.zeros((self.size, self.num_steps, 3))
# cummulative euler angles
self.rot = np.zeros((self.size, self.num_steps, 3))
# change in rotation in quaternion rep (w, x, y, z)
self.delta_quat = np.zeros((self.size, self.num_steps, 4))
# change in rotation between steps in axis-angle rep (scaled 3 vec)
self.delta_rot = np.zeros((self.size, self.num_steps, 3))
# change in rotation between steps in split axis-angle rep (4-vec)
self.delta_rot_split = np.zeros((self.size, self.num_steps, 4))
# 0 if before topple idx, 1 if after
self.topple_label = np.zeros((self.size, self.num_steps), dtype=int)
# other meta-data not directly used in network
self.toppled = []
self.shape_name = []
self.body_friction = np.zeros((self.size))
self.mass = np.zeros((self.size))
self.scale = np.zeros((self.size, 3))
self.rot_euler = np.zeros((self.size, self.num_steps, 3))
class ToppleDataset(object):
'''
Loads toppling data and provides batches for training and model evaluation.
'''
def __init__(self, roots, norm_info_file, batch_size=32, num_steps=15, shuffle=False, num_pts=None, perturb_pts=0.0):
'''
- roots : list of directories containing data to load for this dataset
- norm_info_file : .pkl file containing normalization information
- batch_size : number of sequences to return in each batch
- num_steps : number of timesteps to return in each sequence
- shuffle : randomly shuffles the returned sequence ordering
- num_pts : the number of points to use in the returned point cloud. If None uses all points in the data.
- perturb_pts : the stdev to randomly perturb point clouds with. If None no perturbation is performed.
-
'''
# settings
self.batch_size = batch_size
self.steps_per_seq = num_steps
self.shuffle = shuffle
self.perturb_std = perturb_pts
self.num_pts = num_pts
# load in data
for root in roots:
if not exists(root):
print('Could not find dataset at ' + root)
return
data_loader = ToppleDataLoader()
self.data = data_loader.load_data(roots)
if num_pts is None:
# use all the points in the point cloud
self.num_pts = self.data.point_cloud.shape[1]
# load in normalization info
if not exists(norm_info_file):
print('Could not find normalization info at ' + norm_info_file)
return
self.norm_info = ToppleNormalizationInfo()
self.norm_info.load_from(norm_info_file)
print('Loaded normalization info!')
# see if we have axis-angle info (for backwards compat)
self.use_aa = False
self.use_aa_split = False
self.use_topple_idx = False
self.use_delta_quat = False
if len(self.data.delta_rot) > 0:
self.use_aa = True
if len(self.data.delta_rot_split) > 0:
self.use_aa_split = True
if len(self.data.topple_idx) > 0:
self.use_topple_idx = True
if len(self.data.body_friction) > 0:
self.use_body_friction = True
if len(self.data.delta_quat) > 0:
self.use_delta_quat = True
# normalize the data
print('Normalizing data...')
self.normalize_data(self.data, self.norm_info)
print('Finished normalizing!')
# order to iterate through data when returning batches (in order by default)
self.iter_inds = range(0, self.data.size)
# prepare to iterate through
self.reset()
def normalize_data(self, data, norm_info):
'''
Normalizes (in place) the given ToppleData using the ToppleNormalizationInfo.
'''
# point clouds -> [-1, 1]
data.point_cloud /= norm_info.pc_max
# force pos -> [-1, 1]
data.force_pos /= norm_info.pc_max
# force vec -> [-1, 1]
data.force_vec /= norm_info.force_vec_max
# density -> [0, 1]
data.density = (data.density - norm_info.density_offset) / norm_info.density_max
# mass -> [0, 1]
data.mass = (data.mass - norm_info.mass_offset) / norm_info.mass_max
# inertia -> [0, 1]
data.inertia = (data.inertia - norm_info.inertia_offset) / norm_info.inertia_max
# friction -> [0, 1]
if norm_info.friction_offset is not None:
data.body_friction = (data.body_friction - norm_info.friction_offset) / norm_info.friction_max
# now time sequence data
# velocities -> [-1, 1]
for i, lin_vel_steps in enumerate(data.lin_vel):
data.lin_vel[i] = [(x / norm_info.max_lin_vel) for x in lin_vel_steps]
for i, ang_vel_steps in enumerate(data.ang_vel):
data.ang_vel[i] = [(x / norm_info.max_ang_vel) for x in ang_vel_steps]
# delta position -> [-1, 1]
for i, pos_steps in enumerate(data.pos):
data.pos[i] = [(x / norm_info.max_pos) for x in pos_steps]
# delta rotation -> [-1, 1]
for i, rot_steps in enumerate(data.total_rot):
data.total_rot[i] = [(x / norm_info.max_rot) for x in rot_steps]
# delta rot axis-angle -> [-1, 1] norm
if self.use_aa:
for i, delta_rot_steps in enumerate(data.delta_rot):
data.delta_rot[i] = [(x / norm_info.max_delta_rot) for x in delta_rot_steps]
# make axes unit and and normalize angle -> [-1, 1]
if self.use_aa_split:
for i, delta_rot_split_steps in enumerate(data.delta_rot_split):
data.delta_rot_split[i] = [np.append(x[:3] / np.linalg.norm(x[:3]), x[3] / norm_info.max_delta_rot) for x in delta_rot_split_steps]
def reset(self):
'''
Prepares to iterate through dataset.
'''
if self.shuffle:
np.random.shuffle(self.iter_inds)
# we consider an epoch as returning one sequence from every single simulation
# ( though if the sequence length is shorter than sim length the unique sequences contained
# in the dataset will be much more than an epoch length )
self.num_batches = (self.data.size + self.batch_size - 1) // self.batch_size
self.batch_idx = 0
def has_next_batch(self):
'''
Returns false if done with the current "epoch" (seen each sim once).
'''
return self.batch_idx < self.num_batches
def next_batch(self, random_window=True, focus_toppling=False):
'''
Returns the next batch of data. if random_window=True will get a random sequence of correct length (otherwise
starts at 0). If focus_toppling=True, will make sure this sequence includes the part of the sequence where toppling occurs.
'''
# size is either batch_size, or shorter if we're at the end of the data
start_idx = self.batch_idx * self.batch_size
end_idx = min((self.batch_idx + 1) * self.batch_size, self.data.size)
batch_size = end_idx - start_idx
# get batch data
batch = ToppleBatch(self.batch_size, self.steps_per_seq, self.num_pts)
for i in range(batch_size):
pc, lin_vel, ang_vel, pos, rot, delta_quat, delta_rot, delta_rot_split, topple_label, meta_info = \
self.get_seq(self.iter_inds[start_idx + i], self.steps_per_seq, random_window, focus_toppling)
batch.point_cloud[i] = pc
batch.lin_vel[i] = lin_vel
batch.ang_vel[i] = ang_vel
batch.pos[i] = pos
batch.rot[i] = rot
if self.use_delta_quat:
batch.delta_quat[i] = delta_quat
if self.use_aa:
batch.delta_rot[i] = delta_rot
if self.use_aa_split:
batch.delta_rot_split[i] = delta_rot_split
if self.use_topple_idx:
batch.topple_label[i] = topple_label
batch.toppled.append(meta_info[0])
batch.shape_name.append(meta_info[1])
batch.scale[i] = meta_info[2]
batch.rot_euler[i] = meta_info[3]
if self.use_body_friction:
batch.body_friction[i] = meta_info[4]
batch.mass[i] = meta_info[5]
if batch_size != self.batch_size:
# need to pad the end with repeat of data
for i in range(self.batch_size - batch_size):
batch.point_cloud[batch_size + i] = batch.point_cloud[i]
batch.lin_vel[batch_size + i] = batch.lin_vel[i]
batch.ang_vel[batch_size + i] = batch.ang_vel[i]
batch.pos[batch_size + i] = batch.pos[i]
batch.rot[batch_size + i] = batch.rot[i]
if self.use_delta_quat:
batch.delta_quat[batch_size + i] = batch.delta_quat[i]
batch.toppled.append(batch.toppled[i])
batch.shape_name.append(batch.shape_name[i])
batch.scale[batch_size + i] = batch.scale[i]
batch.rot_euler[batch_size + i] = batch.rot_euler[i]
batch.mass[batch_size + i] = batch.mass[i]
if self.use_aa:
batch.delta_rot[batch_size + i] = batch.delta_rot[i]
if self.use_aa_split:
batch.delta_rot_split[batch_size + i] = batch.delta_rot_split[i]
if self.use_topple_idx:
batch.topple_label[batch_size + i] = batch.topple_label[i]
if self.use_body_friction:
batch.body_friction[batch_size + i] = batch.body_friction[i]
self.batch_idx += 1
return batch
def get_seq(self, idx, num_steps, random_window=True, focus_toppling=False):
'''
Returns a random contiguous sequence from the simulation at the given idx and length num_steps.
If num_steps > sim_length the final (sim_length-num_steps) steps are padded with the value at
sim[sim_length].
'''
# get the normalized canonical point cloud for this simulation
pc = np.copy(self.data.point_cloud[self.data.shape_idx[idx]])
scale = self.data.scale[idx]
# scale accordingly
pc *= np.reshape(scale, (1, -1))
# randomly perturb point cloud
pc += np.random.normal(0.0, self.perturb_std, pc.shape)
# randomly draw a subset of points if desired
if self.num_pts < pc.shape[0]:
pc_inds = np.random.choice(pc.shape[0], self.num_pts, replace=False)
pc = pc[pc_inds, :]
# randomly choose a size num_steps sequence from the simulation to return time-series data
total_steps = len(self.data.lin_vel[idx])
max_start_step = total_steps - num_steps
start_step = 0
if max_start_step < 0:
# simulation is shorter than desired sequence length
pad_len = abs(max_start_step)
lin_vel_list = self.data.lin_vel[idx]
lin_vel_out = np.array(lin_vel_list + [lin_vel_list[-1]]*pad_len)
ang_vel_list = self.data.ang_vel[idx]
ang_vel_out = np.array(ang_vel_list + [ang_vel_list[-1]]*pad_len)
pos_list = self.data.pos[idx]
pos_out = np.array(pos_list + [pos_list[-1]]*pad_len)
rot_list = self.data.total_rot[idx]
rot_out = np.array(rot_list + [rot_list[-1]]*pad_len)
if self.use_delta_quat:
delta_quat_list = self.data.delta_quat[idx]
delta_quat_out = np.array(delta_quat_list + [delta_quat_list[-1]]*pad_len)
euler_rot_list = self.data.rot_euler[idx]
euler_rot_out = np.array(euler_rot_list + [euler_rot_list[-1]]*pad_len)
if self.use_aa:
delta_rot_list = self.data.delta_rot[idx]
delta_rot_out = np.array(delta_rot_list + [delta_rot_list[-1]]*pad_len)
if self.use_aa_split:
delta_rot_split_list = self.data.delta_rot_split[idx]
delta_rot_split_out = np.array(delta_rot_split_list + [delta_rot_split_list[-1]]*pad_len)
if self.use_topple_idx:
topple_label_out = np.zeros((total_steps + pad_len), dtype=int)
seq_topple_idx = self.data.topple_idx[idx]
if seq_topple_idx > 0:
topple_label_out[seq_topple_idx:] = 1
else:
start_step = 0
if random_window:
if focus_toppling and self.data.toppled[idx]:
# choose window around the index where it topples
topple_idx = self.data.topple_idx[idx]
min_idx = max([topple_idx - num_steps + 1, 0])
if min_idx >= max_start_step:
# just pick the max index
start_step = max_start_step
else:
# our window is guaranteed to see some part of toppling
start_step = np.random.randint(min_idx, max_start_step+1)
else:
start_step = np.random.randint(0, max_start_step+1)
end_step = start_step + num_steps
# print('Range: %d, %d' % (start_step, end_step))
lin_vel_out = np.array(self.data.lin_vel[idx][start_step:end_step])
ang_vel_out = np.array(self.data.ang_vel[idx][start_step:end_step])
pos_out = np.array(self.data.pos[idx][start_step:end_step])
rot_out = np.array(self.data.total_rot[idx][start_step:end_step])
if self.use_delta_quat:
delta_quat_out = np.array(self.data.delta_quat[idx][start_step:end_step])
euler_rot_out = np.array(self.data.rot_euler[idx][start_step:end_step])
if self.use_aa:
delta_rot_out = np.array(self.data.delta_rot[idx][start_step:end_step])
if self.use_aa_split:
delta_rot_split_out = np.array(self.data.delta_rot_split[idx][start_step:end_step])
if self.use_topple_idx:
topple_label_out = np.zeros((num_steps), dtype=int)
seq_topple_idx = self.data.topple_idx[idx]
if seq_topple_idx > 0:
if seq_topple_idx <= start_step:
topple_label_out[:] = 1
elif seq_topple_idx < end_step:
topple_label_out[seq_topple_idx-start_step:] = 1
# rotate point cloud to align with first frame of sequence
init_rot = self.data.rot_euler[idx][start_step]
xrot, yrot, zrot = np.radians(init_rot)
R = transforms3d.euler.euler2mat(zrot, xrot, yrot, axes='szxy') # unity applies euler angles in z, x, y ordering
pc = np.dot(pc, R.T)
toppled = self.data.toppled[idx]
shape_name = self.data.shape_name[idx]
mass = self.data.mass[idx]
body_fric = -1.0
if self.use_body_friction:
body_fric = self.data.body_friction[idx]
meta_info = (toppled, shape_name, scale, euler_rot_out, body_fric, mass)
if not self.use_aa:
delta_rot_out = None
if not self.use_aa_split:
delta_rot_split_out = None
if not self.use_topple_idx:
topple_label_out = None
if not self.use_delta_quat:
delta_quat_out = None
return pc, lin_vel_out, ang_vel_out, pos_out, rot_out, delta_quat_out, delta_rot_out, delta_rot_split_out, topple_label_out, meta_info
def get_norm_info(self):
return self.norm_info
if __name__=='__main__':
# norm_info = ToppleNormalizationInfo()
# norm_info.load_from('../../data/sim/normalization_info/cube_train.pkl')
# norm_info.print_out()
topple_data = ToppleDataset(roots=['./data/sim/Cube/Cube30k_ObjSplit/Cube30kVal'], norm_info_file='./data/sim/normalization_info/cube_30k.pkl', \
batch_size=5, num_steps=10, shuffle=True, num_pts=None, perturb_pts=0.01)
count = 0
while topple_data.has_next_batch():
batch = topple_data.next_batch(random_window=True, focus_toppling=False)
count += 1
# print(batch.lin_vel[0])
# print(batch.toppled[0])
# print(batch.delta_rot_split[0])
# print(batch.delta_rot[0])
# print(batch.topple_label[0])
# print(batch.pos)
# print(batch.body_friction)
# print(batch.delta_quat[0])
# print(np.degrees(2*np.arccos(batch.delta_quat[0, :, 0])))
print('Total num batches: ' + str(count))
topple_data.reset()
count = 0
while topple_data.has_next_batch():
batch = topple_data.next_batch()
count += 1
print(batch.size)
print('Total num batches: ' + str(count))
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Project_Euler/01_mults_3_or_5/find_sum.py | perlygatekeeper/glowing-robot | 2 | 47757 | #!/opt/local/bin/python
sum_3_5 = 0
for i in range(1,1000):
if i % 3 == 0 or i % 5 == 0:
print(i)
sum_3_5 += i
print(sum_3_5)
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load_dataset/custom_datasets.py | propelwise/ct-net-models | 6 | 138900 | <filename>load_dataset/custom_datasets.py
#custom_datasets.py
#Copyright (c) 2020 <NAME> <NAME>
#MIT License
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation the rights
#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#copies of the Software, and to permit persons to whom the Software is
#furnished to do so, subject to the following conditions:
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE
import os
import pickle
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from . import utils
#Set seeds
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
###################################################
# PACE Dataset for Data Stored in 2019-10-BigData #-----------------------------
###################################################
class CTDataset_2019_10(Dataset):
def __init__(self, setname, label_type_ld,
label_meanings, num_channels, pixel_bounds,
data_augment, crop_type,
selected_note_acc_files):
"""CT Dataset class that works for preprocessed data in 2019-10-BigData.
A single example (for crop_type == 'single') is a 4D CT volume:
if num_channels == 3, shape [134,3,420,420]
if num_channels == 1, shape [402,420,420]
Variables:
<setname> is either 'train' or 'valid' or 'test'
<label_type_ld> is 'disease_new'
<label_meanings>: list of strings indicating which labels should
be kept. Alternatively, can be the string 'all' in which case
all labels are kept.
<num_channels>: number of channels to reshape the image to.
== 3 if the model uses a pretrained feature extractor.
== 1 if the model uses only 3D convolutions.
<pixel_bounds>: list of ints e.g. [-1000,200]
Determines the lower bound, upper bound of pixel value clipping
and normalization.
<data_augment>: if True, perform data augmentation.
<crop_type>: is 'single' for an example consisting of one 4D numpy array
<selected_note_acc_files>: This should be a dictionary
with key equal to setname and value that is a string. If the value
is a path to a file, the file must be a CSV. Only note accessions
in this file will be used. If the value is not a valid file path,
all available note accs will be used, i.e. the model will be
trained on the whole dataset."""
self.setname = setname
self.define_subsets_list()
self.label_type_ld = label_type_ld
self.label_meanings = label_meanings
self.num_channels = num_channels
self.pixel_bounds = pixel_bounds
if self.setname == 'train':
self.data_augment = data_augment
else:
self.data_augment = False
print('For dataset',self.setname,'data_augment is',self.data_augment)
self.crop_type = crop_type
assert self.crop_type == 'single'
self.selected_note_acc_files = selected_note_acc_files
#Define location of the CT volumes
self.main_clean_path = './load_dataset/fakedata'
self.volume_log_df = pd.read_csv('./load_dataset/fakedata/CT_Scan_Preprocessing_Log_File_FINAL_SMALL.csv',header=0,index_col=0)
#Get the example ids
self.volume_accessions = self.get_volume_accessions()
#Get the ground truth labels
self.labels_df = self.get_labels_df()
# Pytorch Required Methods #------------------------------------------------
def __len__(self):
return len(self.volume_accessions)
def __getitem__(self, idx):
"""Return a single sample at index <idx>. The sample is a Python
dictionary with keys 'data' and 'gr_truth' for the image and label,
respectively"""
return self._get_pace(self.volume_accessions[idx])
# Volume Accession Methods #------------------------------------------------
def get_note_accessions(self):
setname_file = self.selected_note_acc_files[self.setname]
if os.path.isfile(setname_file):
print('\tObtaining note accessions from',setname_file)
sel_accs = pd.read_csv(setname_file,header=0)
assert sorted(list(set(sel_accs['Subset_Assigned'].values.tolist())))==sorted(self.subsets_list)
note_accs = sel_accs.loc[:,'Accession'].values.tolist()
print('\tTotal theoretical note accessions in subsets:',len(note_accs))
return note_accs
else:
print('\tObtaining note accessions from complete identifiers file')
#Read in identifiers file, which contains note_accessions
#Columns are MRN, Accession, Set_Assigned, Set_Should_Be, Subset_Assigned
all_ids = pd.read_csv('./load_dataset/fakedata/all_identifiers.csv',header=0)
#Extract the note_accessions
note_accs = []
for subset in self.subsets_list: #e.g. ['imgvalid_a','imgvalid_b']
subset_note_accs = all_ids[all_ids['Subset_Assigned']==subset].loc[:,'Accession'].values.tolist()
note_accs += subset_note_accs
print('\tTotal theoretical note accessions in subsets:',len(note_accs))
return note_accs
def get_volume_accessions(self):
note_accs = self.get_note_accessions()
#Translate note_accessions to volume_accessions based on what data has been
#preprocessed successfully. volume_log_df has note accessions as the
#index, and the column 'full_filename_npz' for the volume accession.
#The column 'status' should equal 'success' if the volume has been
#preprocessed correctly.
print('\tTotal theoretical volumes in whole dataset:',self.volume_log_df.shape[0])
self.volume_log_df = self.volume_log_df[self.volume_log_df['status']=='success']
print('\tTotal successfully preprocessed volumes in whole dataset:',self.volume_log_df.shape[0])
volume_accs = []
for note_acc in note_accs:
if note_acc in self.volume_log_df.index.values.tolist():
volume_accs.append(self.volume_log_df.at[note_acc,'full_filename_npz'])
print('\tFinal total successfully preprocessed volumes in requested subsets:',len(volume_accs))
#According to this thread: https://github.com/pytorch/pytorch/issues/13246
#it is better to use a numpy array than a list to reduce memory leaks.
return np.array(volume_accs)
# Ground Truth Label Methods #----------------------------------------------
def get_labels_df(self):
#Get the ground truth labels based on requested label type.
labels_df = read_in_labels(self.label_type_ld, self.setname)
#Now filter the ground truth labels based on the desired label meanings:
if self.label_meanings != 'all': #i.e. if you want to filter
labels_df = labels_df[self.label_meanings]
return labels_df
# Fetch a CT Volume (__getitem__ implementation) #--------------------------
def _get_pace(self, volume_acc):
"""<volume_acc> is for example RHAA12345_6.npz"""
#Load compressed npz file: [slices, square, square]
ctvol = np.load(os.path.join(self.main_clean_path, volume_acc))['ct']
#Prepare the CT volume data (already torch Tensors)
data = utils.prepare_ctvol_2019_10_dataset(ctvol, self.pixel_bounds, self.data_augment, self.num_channels, self.crop_type)
#Get the ground truth:
note_acc = self.volume_log_df[self.volume_log_df['full_filename_npz']==volume_acc].index.values.tolist()[0]
gr_truth = self.labels_df.loc[note_acc, :].values
gr_truth = torch.from_numpy(gr_truth).squeeze().type(torch.float)
#When training on only one abnormality you must unsqueeze to prevent
#a dimensions error when training the model:
if len(self.label_meanings)==1:
gr_truth = gr_truth.unsqueeze(0)
#Create the sample
sample = {'data': data, 'gr_truth': gr_truth, 'volume_acc': volume_acc}
return sample
# Sanity Check #------------------------------------------------------------
def define_subsets_list(self):
assert self.setname in ['train','valid','test']
if self.setname == 'train':
self.subsets_list = ['imgtrain']
elif self.setname == 'valid':
self.subsets_list = ['imgvalid_a']
elif self.setname == 'test':
self.subsets_list = ['imgtest_a','imgtest_b','imgtest_c','imgtest_d']
print('Creating',self.setname,'dataset with subsets',self.subsets_list)
#######################
# Ground Truth Labels #---------------------------------------------------------
#######################
def read_in_labels(label_type_ld, setname):
"""Return a pandas dataframe with the dataset labels.
Accession numbers are the index and labels (e.g. "pneumonia") are the columns.
<setname> can be 'train', 'valid', or 'test'."""
assert label_type_ld == 'disease_new'
labels_file = './load_dataset/fakedata/2019-12-18_duke_disease/img'+setname+'_BinaryLabels.csv'
return pd.read_csv(labels_file, header=0, index_col = 0)
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] |
applications/structural_application/test_examples/balken.gid/balken_include.py | jiaqiwang969/Kratos-test | 0 | 144207 | <reponame>jiaqiwang969/Kratos-test
##################################################################
######################## include.py ############################
##################################################################
##### ekate - Enhanced KRATOS for Advanced Tunnel Enineering #####
##### copyright by CIMNE, Barcelona, Spain #####
##### and <NAME> for TUNCONSTRUCT #####
##### all rights reserved #####
##################################################################
##### note: KRATOS is released under LGPL #####
##################################################################
##################################################################
##################################################################
## ATTENTION: here the order is important #####
##################################################################
## including kratos path #####
## ATTENTION: the following lines have to be adapted to #####
## match your acrtual configuration #####
##################################################################
kratos_libs_path = '/home/hurga/kratos_local/kratos/libs/' ##kratos_root/libs
#kratos_libs_path = '/home/hurga/kratosR1/libs/' ##kratos_root/libs
kratos_applications_path = '/home/hurga/kratos_local/kratos/applications/' ##kratos_root/applications
#kratos_applications_path = '/home/hurga/kratosR1/applications/' ##kratos_root/applications
ekate_auxiliary_path = '/home/hurga/kratos_local/kratos/applications/ekate_auxiliary_application/python_scripts'
#ekate_auxiliary_path = '/home/hurga/kratosR1/applications/ekate_auxiliary_application/python_scripts'
##################################################################
##################################################################
import sys
sys.path.append(kratos_libs_path)
sys.path.append(kratos_applications_path)
sys.path.append(ekate_auxiliary_path)
#importing Kratos main library
from Kratos import *
kernel = Kernel() #defining kernel
#importing applications
import applications_interface
applications_interface.Import_StructuralApplication = True
applications_interface.Import_EkateAuxiliaryApplication = True
applications_interface.Import_ExternalConstitutiveLawsApplication = True
applications_interface.ImportApplications(kernel, kratos_applications_path)
from KratosStructuralApplication import *
from KratosExternalSolversApplication import *
from KratosEkateAuxiliaryApplication import *
from KratosExternalConstitutiveLawsApplication import *
##################################################################
##################################################################
class Model:
def __init__( self, problem_name, path ):
#setting the domain size for the problem to be solved
self.domain_size = 3
##################################################################
## DEFINE MODELPART ##############################################
##################################################################
self.model_part = ModelPart("ekate_simulation")
self.path = path
self.problem_name = problem_name
##################################################################
## DEFINE SOLVER #################################################
##################################################################
# reading simulation parameters
number_of_time_steps = 1
self.analysis_parameters = []
# content of analysis_parameters:
# perform_contact_analysis_flag
# penalty value for normal contact
# maximum number of uzawa iterations
# friction coefficient
# penalty value for frictional contact
# contact_double_check_flag
# contact_ramp_penalties_flag
# maximum penalty value for normal contact
# ramp criterion for normal contact
# ramp factor for normal contact
# maximum penalty value for frictional contact
# ramp criterion for frictional contact
# ramp factor for frictional contact
perform_contact_analysis_flag = True
# performing contact analysis: reading contact parameters
penalty = 1e+10
maxuzawa = 25
friction = 0
frictionpenalty = 1e+05
contact_double_check_flag = False
contact_ramp_penalties_flag = False
maxpenalty = penalty
rampcriterion = 0.0
rampfactor = 0.0
fricmaxpenalty = penalty
fricrampcriterion = 0.0
fricrampfactor = 0.0
self.analysis_parameters.append(perform_contact_analysis_flag)
self.analysis_parameters.append(penalty)
self.analysis_parameters.append(maxuzawa)
self.analysis_parameters.append(friction)
self.analysis_parameters.append(frictionpenalty)
self.analysis_parameters.append(contact_double_check_flag)
self.analysis_parameters.append(contact_ramp_penalties_flag)
self.analysis_parameters.append(maxpenalty)
self.analysis_parameters.append(rampcriterion)
self.analysis_parameters.append(rampfactor)
self.analysis_parameters.append(fricmaxpenalty)
self.analysis_parameters.append(fricrampcriterion)
self.analysis_parameters.append(fricrampfactor)
abs_tol = 1e-06
rel_tol = 0.0001
## generating solver
import ekate_solver
self.solver = ekate_solver.EkateSolver( self.model_part, self.domain_size, number_of_time_steps, self.analysis_parameters, abs_tol, rel_tol )
ekate_solver.AddVariables( self.model_part )
##################################################################
## READ MODELPART ################################################
##################################################################
#reading a model
write_deformed_flag = WriteDeformedMeshFlag.WriteUndeformed
write_elements = WriteConditionsFlag.WriteElementsOnly
post_mode = GiDPostMode.GiD_PostBinary
multi_file_flag = MultiFileFlag.SingleFile
self.gid_io = StructuralGidIO( self.path+self.problem_name, post_mode, multi_file_flag, write_deformed_flag, write_elements )
self.gid_io.ReadModelPart(self.model_part)
self.meshWritten = False
## READ DEACTIVATION FILE ########################################
self.deac_file = open(self.path+self.problem_name+".deac",'r' )
self.activation_flags = [0]
for line in self.deac_file:
val_set = line.split(' ')
elem_num = int(val_set[0])
act_level = int(val_set[1])
self.activation_flags.append(act_level)
print "input data read OK"
#print "+++++++++++++++++++++++++++++++++++++++"
#for node in self.model_part.Nodes:
# print node
#print "+++++++++++++++++++++++++++++++++++++++"
#the buffer size should be set up here after the mesh is read for the first time
self.model_part.SetBufferSize(2)
##################################################################
## ADD DOFS ######################################################
##################################################################
ekate_solver.AddDofs( self.model_part )
##################################################################
## INITIALISE SOLVER FOR PARTICULAR SOLUTION #####################
##################################################################
#defining linear solver
plinear_solver = SkylineLUFactorizationSolver()
self.solver.structure_linear_solver = plinear_solver
self.solver.Initialize()
(self.solver.solver).SetEchoLevel(2);
##################################################################
## INITIALISE RESTART UTILITY ####################################
##################################################################
#restart_utility= RestartUtility( self.problem_name )
def SetUpActivationLevels( self, model_part, activation_list ):
for element in self.model_part.Elements:
element.SetValue(ACTIVATION_LEVEL, activation_list[element.Id])
# def write_restart_file( self, time ):
# print("------------> restart file written for time step: "+str(time))
# self.restart_utility.ChangeFileName(problem_name+str(time))
# self.restart_utility.StoreNodalVariables(model_part)
# self.restart_utility.StoreInSituStress(model_part)
# self.restart_utility.StoreConstitutiveLawVariables(model_part)
#
# def restart_time_step( self, time, Dt ):
# print("############ time step solution has to be restarted ############")
# time = time-Dt
# model_part.CloneTimeStep(time)
# for step in range(1,11):
# time = time+ Dt/10.0
# model_part.CloneTimeStep(time)
# #####################################################################################################
# model_part.ProcessInfo.SetValue( QUASI_STATIC_ANALYSIS, True )
# model_part.ProcessInfo.SetValue( FIRST_TIME_STEP, False )
# #####################################################################################################
# solver.Solve()
# print("~~~~~~~~~~~~~~ RESTARTED STEP ( DT= "+str(Dt/10.0)+" / Step= "+str(step)+" ) ~~~~~~~~~~~~~~")
# print("############ restart finished ############")
#
# def write_to_file( self, time ):
# for i in range(0, len(self.layer_nodes_sets['top'])):
# settlements.write(str(time)+"/"+str(model_part.Nodes[layer_nodes_sets['top'][i]].GetZ())+"/"+str(model_part.Nodes[layer_nodes_sets['top'][i]].GetSolutionStepValue(DISPLACEMENT_Z))+"\n")
# for i in range(0, len(layer_nodes_sets['side'])):
# pressure_air.write(str(time)+"/"+str(model_part.Nodes[layer_nodes_sets['side'][i]].GetZ())+"/"+str(model_part.Nodes[layer_nodes_sets['side'][i]].GetSolutionStepValue(AIR_PRESSURE))+"\n")
# pressure_water.write(str(time)+"/"+str(model_part.Nodes[layer_nodes_sets['side'][i]].GetZ())+"/"+str(model_part.Nodes[layer_nodes_sets['side'][i]].GetSolutionStepValue(WATER_PRESSURE))+"\n")
#
def ApplyInsituWaterPressure( self, free_node_list_water, free_node_list_air, z_zero):
gravity_z = -50;
water_density= 1000.0;
for i in range(1, len(self.model_part.Nodes)+1):
if self.model_part.Nodes[i].HasDofFor(WATER_PRESSURE):
if (self.model_part.Nodes[i].IsFixed(WATER_PRESSURE)==0):
water_pressure= 0.0
self.model_part.Nodes[i].SetSolutionStepValue(WATER_PRESSURE, water_pressure)
self.model_part.Nodes[i].SetSolutionStepValue(WATER_PRESSURE_EINS, water_pressure)
self.model_part.Nodes[i].SetSolutionStepValue(WATER_PRESSURE_NULL, water_pressure)
self.model_part.Nodes[i].Fix(WATER_PRESSURE)
free_node_list_water.append(i)
if self.model_part.Nodes[i].HasDofFor(AIR_PRESSURE):
if (self.model_part.Nodes[i].IsFixed(AIR_PRESSURE)==0):
self.model_part.Nodes[i].SetSolutionStepValue(AIR_PRESSURE, 0.0)
self.model_part.Nodes[i].SetSolutionStepValue(AIR_PRESSURE_EINS, 0.0)
self.model_part.Nodes[i].SetSolutionStepValue(AIR_PRESSURE_NULL, 0.0)
self.model_part.Nodes[i].Fix(AIR_PRESSURE)
free_node_list_air.append(i)
def FreePressureNodes(self,free_node_list_water, free_node_list_air):
for item in free_node_list_water:
self.model_part.Nodes[item].Free(WATER_PRESSURE)
for item in free_node_list_air:
self.model_part.Nodes[item].Free(AIR_PRESSURE)
def WriteMaterialParameters( self, time, indices ):
self.gid_io.OpenResultFile( self.path+self.problem_name, GiDPostMode.GiD_PostBinary)
for index in indices:
self.gid_io.SuperPrintOnGaussPoints(MATERIAL_PARAMETERS, self.model_part, time, index)
self.gid_io.CloseResultFile()
def WriteOutput( self, time ):
if( self.meshWritten == False ):
self.gid_io.InitializeMesh( 0.0 )
mesh = self.model_part.GetMesh()
self.gid_io.WriteMesh( mesh )
self.meshWritten = True
self.gid_io.FinalizeMesh()
self.gid_io.InitializeResults( 0.0, self.model_part.GetMesh() )
print("write nodal displacements")
self.gid_io.WriteNodalResults(DISPLACEMENT, self.model_part.Nodes, time, 0)
self.gid_io.FinalizeResults()
def InitializeModel( self ):
##################################################################
## INITIALISE CONSTITUTIVE LAWS ##################################
##################################################################
#set material parameters
params1 = Vector(7)
params1[0] = 2.1e+11 #Young's Modulus
params1[1] = 0.3 #Poisson Ratio
params1[2] = 0.0 #Internal Friction Angle
params1[3] = 0.0 #Cohesion
params1[4] = 0.0 #Compressive Strength
params1[5] = 0.0 #Tensile Strength
params1[6] = 0.0 #RMR Variance
self.model_part.Properties[1].SetValue(MATERIAL_PARAMETERS, params1 )
params2 = Vector(7)
params2[0] = 3e+10 #Young's Modulus
params2[1] = 0.3 #Poisson Ratio
params2[2] = 0.0 #Internal Friction Angle
params2[3] = 0.0 #Cohesion
params2[4] = 0.0 #Compressive Strength
params2[5] = 0.0 #Tensile Strength
params2[6] = 0.0 #RMR Variance
self.model_part.Properties[2].SetValue(MATERIAL_PARAMETERS, params2 )
self.model_part.Properties[1].SetValue(CONSTITUTIVE_LAW, Isotropic3D() )
print "Linear elastic model selected"
self.model_part.Properties[2].SetValue(CONSTITUTIVE_LAW, Isotropic3D() )
print "Linear elastic model selected"
##################################################################
## STORE LAYER SETS ##############################################
##################################################################
## ELEMENTS on layers ############################################
self.layer_sets = {}
layer_elements_list = [
1 ,
2 ,
3 ,
4 ,
5 ,
6 ,
7 ,
8 ,
9 ,
10 ,
11 ,
12 ,
13 ,
14 ,
15 ,
16 ,
17 ,
18 ,
19 ,
20 ,
21 ,
22 ,
23 ,
24 ,
25 ,
]
self.layer_sets['Layer0'] = layer_elements_list
## ELEMENTS on inner boundaries ##################################
self.inner_boundary_elements = [
]
## NODES on layers ###############################################
self.layer_nodes_sets = {}
layer_nodes_list = [
1 ,
2 ,
3 ,
4 ,
5 ,
6 ,
7 ,
8 ,
9 ,
10 ,
11 ,
12 ,
13 ,
14 ,
15 ,
16 ,
17 ,
18 ,
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33 ,
34 ,
35 ,
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38 ,
39 ,
40 ,
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43 ,
44 ,
45 ,
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47 ,
48 ,
49 ,
50 ,
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53 ,
54 ,
55 ,
56 ,
57 ,
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67 ,
68 ,
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70 ,
71 ,
72 ,
73 ,
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75 ,
76 ,
77 ,
78 ,
79 ,
80 ,
81 ,
82 ,
83 ,
84 ,
85 ,
86 ,
87 ,
88 ,
89 ,
90 ,
91 ,
92 ,
93 ,
94 ,
95 ,
96 ,
97 ,
98 ,
99 ,
100 ,
101 ,
102 ,
103 ,
104 ,
105 ,
106 ,
107 ,
108 ,
109 ,
110 ,
111 ,
112 ,
113 ,
114 ,
115 ,
116 ,
117 ,
118 ,
119 ,
120 ,
121 ,
122 ,
123 ,
124 ,
125 ,
126 ,
127 ,
128 ,
129 ,
130 ,
131 ,
132 ,
133 ,
134 ,
135 ,
136 ,
137 ,
138 ,
139 ,
140 ,
141 ,
142 ,
143 ,
144 ,
145 ,
146 ,
147 ,
148 ,
149 ,
150 ,
151 ,
152 ,
153 ,
154 ,
155 ,
156 ,
157 ,
158 ,
159 ,
160 ,
161 ,
162 ,
163 ,
164 ,
165 ,
166 ,
167 ,
168 ,
169 ,
170 ,
171 ,
172 ,
173 ,
174 ,
175 ,
176 ,
177 ,
178 ,
179 ,
180 ,
181 ,
182 ,
183 ,
184 ,
185 ,
186 ,
187 ,
188 ,
189 ,
190 ,
191 ,
192 ,
193 ,
194 ,
195 ,
196 ,
197 ,
198 ,
199 ,
200 ,
201 ,
202 ,
203 ,
204 ,
205 ,
206 ,
207 ,
208 ,
209 ,
210 ,
211 ,
212 ,
213 ,
214 ,
215 ,
216 ,
217 ,
218 ,
219 ,
220 ,
221 ,
222 ,
223 ,
224 ,
225 ,
226 ,
227 ,
228 ,
229 ,
230 ,
231 ,
232 ,
233 ,
234 ,
235 ,
236 ,
237 ,
238 ,
239 ,
240 ,
241 ,
242 ,
243 ,
244 ,
245 ,
246 ,
247 ,
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250 ,
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252 ,
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254 ,
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256 ,
257 ,
258 ,
259 ,
260 ,
261 ,
262 ,
263 ,
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265 ,
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291 ,
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293 ,
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295 ,
296 ,
297 ,
298 ,
299 ,
300 ,
301 ,
302 ,
303 ,
304 ,
305 ,
306 ,
307 ,
308 ,
309 ,
310 ,
311 ,
312 ,
313 ,
314 ,
315 ,
316 ,
317 ,
318 ,
319 ,
320 ,
321 ,
322 ,
323 ,
324 ,
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326 ,
327 ,
328 ,
329 ,
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331 ,
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455 ,
456 ,
457 ,
458 ,
459 ,
460 ,
461 ,
462 ,
463 ,
464 ,
465 ,
466 ,
467 ,
468 ,
]
self.layer_nodes_sets['Layer0'] = layer_nodes_list
## CONTACT MASTER NODES ##########################################
self.contact_master_nodes = [
6 ,
8 ,
15 ,
22 ,
25 ,
33 ,
37 ,
41 ,
49 ,
55 ,
56 ,
67 ,
73 ,
74 ,
79 ,
91 ,
92 ,
97 ,
109 ,
110 ,
115 ,
127 ,
128 ,
133 ,
145 ,
146 ,
151 ,
163 ,
164 ,
169 ,
181 ,
182 ,
187 ,
199 ,
200 ,
205 ,
217 ,
218 ,
223 ,
231 ,
233 ,
241 ,
244 ,
245 ,
250 ,
262 ,
263 ,
268 ,
280 ,
281 ,
286 ,
298 ,
299 ,
304 ,
316 ,
317 ,
322 ,
334 ,
335 ,
340 ,
352 ,
353 ,
358 ,
370 ,
371 ,
376 ,
388 ,
389 ,
394 ,
406 ,
407 ,
412 ,
424 ,
425 ,
430 ,
442 ,
443 ,
448 ,
456 ,
458 ,
466 ,
]
## CONTACT SLAVE NODES ###########################################
self.contact_slave_nodes = [
3 ,
7 ,
12 ,
20 ,
21 ,
32 ,
40 ,
45 ,
52 ,
62 ,
66 ,
72 ,
83 ,
87 ,
90 ,
102 ,
105 ,
108 ,
120 ,
123 ,
126 ,
138 ,
141 ,
144 ,
156 ,
159 ,
162 ,
174 ,
177 ,
180 ,
192 ,
195 ,
198 ,
210 ,
213 ,
216 ,
228 ,
232 ,
239 ,
253 ,
257 ,
261 ,
273 ,
276 ,
279 ,
291 ,
294 ,
297 ,
309 ,
312 ,
315 ,
327 ,
330 ,
333 ,
345 ,
348 ,
351 ,
363 ,
366 ,
369 ,
381 ,
384 ,
387 ,
399 ,
402 ,
405 ,
417 ,
420 ,
423 ,
435 ,
438 ,
441 ,
453 ,
457 ,
464 ,
]
## INNER BOUNDARY NODES ##########################################
self.inner_boundary_nodes = [
]
##################################################################
print "layer sets stored"
##################################################################
## STORE NODES ON GROUND SURFACE #################################
##################################################################
self.top_surface_nodes = []
print "nodes on ground surface stored"
##################################################################
## ACTIVATION ####################################################
##################################################################
self.deac = DeactivationUtility()
self.SetUpActivationLevels( self.model_part, self.activation_flags )
self.deac.Initialize( self.model_part )
print "activation utility initialized"
##################################################################
## MESH TYING ####################################################
##################################################################
self.mesh_tying_utility= MeshTyingUtility()
self.mesh_tying_utility.InitializeMeshTyingUtility(self.model_part)
print "mesh-tying utility successfully initialized"
print "model successfully initialized"
self.SetCalculateInSituStress( False )
def FinalizeModel( self ):
self.gid_io.CloseResultFile()
def SetCalculateInSituStress( self, calculation_flag ):
self.insitu_stress_flag = calculation_flag
def Solve( self, time, from_deac, to_deac, from_reac, to_reac ):
self.deac.Reactivate( self.model_part, from_reac, to_reac )
self.deac.Deactivate( self.model_part, from_deac, to_deac )
self.model_part.CloneTimeStep(time)
self.model_part.ProcessInfo.SetValue( CALCULATE_INSITU_STRESS, self.insitu_stress_flag )
self.solver.Solve()
##################################################################
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2
] |
setup.py | davidrpugh/solowPy | 31 | 24481 | <reponame>davidrpugh/solowPy
import os
from distutils.core import setup
def read(*paths):
"""Build a file path from *paths* and return the contents."""
with open(os.path.join(*paths), 'r') as f:
return f.read()
DESCRIPTION = ("Library for solving, simulating, and estimating the " +
"Solow (1956) model of economic growth.")
CLASSIFIERS = ['Development Status :: 3 - Alpha',
'Intended Audience :: Education',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
]
setup(
name="solowpy",
packages=['solowpy',
'solowpy.tests'],
version='0.2.0-alpha',
license="MIT License",
author="davidrpugh",
author_email="<EMAIL>",
url='https://github.com/solowPy/solowPy',
description=DESCRIPTION,
long_description=read('README.rst'),
classifiers=CLASSIFIERS,
)
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2
] |
wow.py | aswinmurali-io/magiclist | 1 | 175799 | <gh_stars>1-10
import sqlite3
import time
import os
print('\nsqlite3')
file = 'example.db'
if os.path.exists(file):
os.remove(file)
conn = sqlite3.connect(file)
c = conn.cursor()
# Create table
start = time.time()
c.execute('''CREATE TABLE stocks
(date text, trans text, symbol text, qty real, price real)''')
e = time.time() - start
print(f'Creating table {e}s')
# Insert a row of data
start = time.time()
c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")
e2 = time.time() - start
print(f'Inserting table {e2}s')
# Save (commit) the changes
start = time.time()
conn.commit()
e3 = time.time() - start
print(f'Saving table {e3}s')
print(f'Creating & Inserting table combined {e + e2 + e3}s')
from magiclist import Magic
magic = Magic('example')
print('\nMagiclist')
start = time.time()
magic['stock'] = {
'date': ['2006-01-05'],
'trans': ['BUY'],
'symbol': ['RHAT'],
'qty': [100],
'price': [35.14],
}
print(f'Creating & Inserting table combined {time.time() - start}s')
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Code/binarytree.py | siko408/CS-1.3-Core-Data-Structures | 0 | 187192 | #!python
from collections import deque
class User(object):
def __init__(self, age, testscore):
self.age = age
self.testscore = testscore
def getAge(self):
return self.age
def getTestScore(self):
return self.testscore
class BinaryTreeNode(object):
def __init__(self, data):
"""Initialize this binary tree node with the given data."""
self.data = data
self.left = None
self.right = None
def __repr__(self):
"""Return a string representation of this binary tree node."""
return 'BinaryTreeNode({!r})'.format(self.data)
def is_leaf(self):
"""Return True if this node is a leaf (has no children)."""
# TODO: Check if both left child and right child have no value
if self.right is None and self.left is None:
return True
return False
def is_branch(self):
"""Return True if this node is a branch (has at least one child)."""
# TODO: Check if either left child or right child has a value
if self.right is not None or self.left is not None:
return True
return False
def height(self):
"""Return the height of this node (the number of edges on the longest
downward path from this node to a descendant leaf node).
TODO: Best and worst case running time: ??? under what conditions?"""
# Best Case running Time O(1) if parent is None/Root
# Worst Case Running Time O(n) If have to search through each node
# TODO: Check if left child has a value and if so calculate its height
# TODO: Check if right child has a value and if so calculate its height
# Return one more than the greater of the left height and right height
left_height = 0
right_height = 0
node = self
node_List = []
while True:
if node.left is not None and node.right is not None:
node_List.append((node, "right"))
if node.left is not None:
if node.right is None:
left_height += 1
node = node.left
else:
left_height += 1
node = node.left
if node.right is not None:
if node.left is None:
right_height += 1
node = node.right
else:
right_height += 1
node = node.right
if node.right is None and node.left is None:
if len(node_List) == 0:
""" Only break if there is no more top nodes to redirect"""
print("left", left_height, "right", right_height)
if left_height >= right_height:
return left_height
else:
return right_height
else:
""" Give the node of the first tuple of nodes"""
if node_List[0][1] == "right":
node = node_List[0][0].right
node_List.pop()
class BinarySearchTree(object):
def __init__(self, items=None):
"""Initialize this binary search tree and insert the given items."""
self.root = None
self.size = 0
if items is not None:
for item in items:
self.insert(item)
def __repr__(self):
"""Return a string representation of this binary search tree."""
return 'BinarySearchTree({} nodes)'.format(self.size)
def is_empty(self):
"""Return True if this binary search tree is empty (has no nodes)."""
return self.root is None
def height(self):
"""Return the height of this tree (the number of edges on the longest
downward path from this tree's root node to a descendant leaf node).
TODO: Best and worst case running time: ??? under what conditions?"""
# Best Case running Time O(1) if there is only the root
# Worst Case Running Time O(n) Have to search through each node
return self.root.height()
def contains(self, item):
"""Return True if this binary search tree contains the given item.
TODO: Best case running time: ??? under what conditions?
TODO: Worst case running time: ??? under what conditions?"""
# Best Case running Time O(1) if parent is None/Root
# Worst Case Running Time O(n) If have to search through each node
# Find a node with the given item, if any
node = self._find_node_iterative(item)
# Return True if a node as found, or False
if node is not None:
return True
return False
def search(self, item):
"""Return an item in this binary search tree matching the given item,
or None if the given item is not found.
TODO: Best case running time: ??? under what conditions?
TODO: Worst case running time: ??? under what conditions?"""
# Best Case running Time O(1) if parent is None/Root
# Worst Case Running Time O(n) If have to search through each node
# Find a node with the given item, if any
node = self._find_node_recursive(item, self.root)
# TODO: Return the node's data if found, or None
if node is not None:
return node.data
return None
def insert(self, item):
# Best Case running Time O(1) if parent is None/Root
# Worst Case Running Time O(n) If have to search through each node in parent_node method
# Handle the case where the tree is empty
if self.is_empty():
# TODO: Create a new root node
self.root = BinaryTreeNode(item)
# TODO: Increase the tree size
self.size += 1
return
# Find the parent node of where the given item should be inserted
parent = self._find_parent_node_iterative(item)
if parent.is_branch():
if parent.left is not None:
parent.right = BinaryTreeNode(item)
else:
parent.left = BinaryTreeNode(item)
if parent.is_leaf():
if item > parent.data:
parent.right = BinaryTreeNode(item)
else:
parent.left = BinaryTreeNode(item)
self.size += 1
return
def _find_node_iterative(self, item):
"""Return the node containing the given item in this binary search tree,
or None if the given item is not found. Search is performed iteratively
starting from the root node."""
node = self.root
while node is not None:
if node.data == item:
return node
elif item < node.data:
node = node.left
elif item > node.data:
node = node.right
# Not found
return None
def _find_node_recursive(self, item, node):
"""Return the node containing the given item in this binary search tree,
or None if the given item is not found. Search is performed recursively
starting from the given node (give the root node to start recursion).
TODO: Best case running time: ??? under what conditions?
TODO: Worst case running time: ??? under what conditions?"""
# Best Case running Time O(1) if parent is None/Root
# Worst Case Running Time O(n) If have to search through each node
# node = self._find_parent_node_recursive(item, node)
if node is None:
return None
if node.data == item:
return node
elif node.data < item:
return self._find_node_recursive(item, node.right)
else:
return self._find_node_recursive(item, node.left)
def _find_parent_node_iterative(self, item):
"""Return the parent node of the node containing the given item
(or the parent node of where the given item would be if inserted)
in this tree, or None if this tree is empty or has only a root node.
Search is performed iteratively starting from the root node.
TODO: Best case running time: ??? under what conditions?
TODO: Worst case running time: ??? under what conditions?"""
# Best Case running Time O(1) if parent is None/Root
# Worst Case Running Time O(n) If have to search through each node
# Start with the root node and keep track of its parent
node = self.root
parent = node
if self.is_empty() is True or self.root is None:
return parent
while node is not None:
parent_Full = True
if node.data == item:
return parent
if node.left is None and node.right is not None or node.right is None and node.left is not None:
parent_Full = False
if node.is_leaf() is True or parent_Full is False:
""" If there are no branches or only has a single branch"""
return parent
if item < node.data:
""" Parent will be left node"""
parent = node.left
node = node.left
else:
parent = node.right
node = node.right
def _find_parent_node_recursive(self, item, node, parent=None):
"""Return the parent node of the node containing the given item
(or the parent node of where the given item would be if inserted)
in this tree, or None if this tree is empty or has only a root node.
Search is performed recursively starting from the given node
(give the root node to start recursion)."""
if node is None:
return parent
if item == node.data:
return parent
elif item < node.data:
return self._find_parent_node_recursive(item, node.left, node)
elif item > node.data:
return self._find_parent_node_recursive(item, node.right, node)
def delete(self, item):
"""Remove given item from this tree, if present, or raise ValueError.
TODO: Best case running time: ??? under what conditions?
TODO: Worst case running time: ??? under what conditions?"""
# TODO: Use helper methods and break this algorithm down into 3 cases
# based on how many children the node containing the given item has and
# implement new helper methods for subtasks of the more complex cases
parent_node = self._find_parent_node_iterative(item)
node = self._find_node_iterative(item)
if parent_node is None:
""" Tree is empty"""
return None
if parent_node.data == item:
print("Parent node deletion")
""" Must be root node """
if node.left is None:
self.root = node.right
node.right = None
elif node.right is None:
self.root = node.left
node.left = None
elif node.right is None and node.left is None:
self.root = None
return None
else:
# First step is to detach root to left side
self.root = node.left
if self.root.right is None:
self.root.right = node.right
else:
lost_data = self.root.right
self.root.right = node.right
self.insert(lost_data.data)
return
# Second step is to reorganize
if node.is_leaf() is True:
""" If node has no children But has parent"""
print("Leaf deletion")
if parent_node.data > node.data:
parent_node.left = None
else:
parent_node.right = None
return
""" Best case scenerio, node has a parent and children"""
if node.left is not None:
if parent_node.data > node.left.data:
parent_node.left = node.left
else:
parent_node.right = node.left
if node.right is not None:
if node.right.data > node.left.data:
node.left.right = node.right
else:
node.left.left = node.right
return
def items_in_order(self):
"""Return an in-order list of all items in this binary search tree."""
items = []
if not self.is_empty():
# Traverse tree in-order from root, appending each node's item
self._traverse_in_order_recursive(self.root, items.append)
# Return in-order list of all items in tree
return items
def _traverse_in_order_recursive(self, node, visit):
"""Traverse this binary tree with recursive in-order traversal (DFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# Running Time 0(n)
# Space Complexity O(h)
if node is not None:
# TODO: Traverse left subtree, if it exists
self._traverse_in_order_recursive(node.left, visit)
# TODO: Visit this node's data with given function
visit(node.data)
# TODO: Traverse right subtree, if it exists
self._traverse_in_order_recursive(node.right, visit)
def _traverse_in_order_iterative(self, node, visit):
"""Traverse this binary tree with iterative in-order traversal (DFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# TODO: Traverse in-order without using recursion (stretch challenge)
def items_pre_order(self):
"""Return a pre-order list of all items in this binary search tree."""
items = []
if not self.is_empty():
# Traverse tree pre-order from root, appending each node's item
self._traverse_pre_order_recursive(self.root, items.append)
# Return pre-order list of all items in tree
return items
def _traverse_pre_order_recursive(self, node, visit):
"""Traverse this binary tree with recursive pre-order traversal (DFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# Running Time 0(n)
# Space Complexity O(h)
# TODO: Visit this node's data with given function
if node is not None:
visit(node.data)
# TODO: Traverse left subtree, if it exists
self._traverse_pre_order_recursive(node.left, visit)
# TODO: Traverse right subtree, if it exists
self._traverse_pre_order_recursive(node.right, visit)
def _traverse_pre_order_iterative(self, node, visit):
"""Traverse this binary tree with iterative pre-order traversal (DFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# TODO: Traverse pre-order without using recursion (stretch challenge)
def items_post_order(self):
"""Return a post-order list of all items in this binary search tree."""
items = []
if not self.is_empty():
# Traverse tree post-order from root, appending each node's item
self._traverse_post_order_recursive(self.root, items.append)
# Return post-order list of all items in tree
return items
def _traverse_post_order_recursive(self, node, visit):
"""Traverse this binary tree with recursive post-order traversal (DFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# Running Time O(n)
# Time complexity O(h)
if node is not None:
# TODO: Traverse left subtree, if it exists
self._traverse_post_order_recursive(node.left, visit)
# TODO: Traverse right subtree, if it exists
self._traverse_post_order_recursive(node.right, visit)
# TODO: Visit this node's data with given function
visit(node.data)
def _traverse_post_order_iterative(self, node, visit):
"""Traverse this binary tree with iterative post-order traversal (DFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# TODO: Traverse post-order without using recursion (stretch challenge)
def items_level_order(self):
"""Return a level-order list of all items in this binary search tree."""
items = []
if not self.is_empty():
# Traverse tree level-order from root, appending each node's item
self._traverse_level_order_iterative(self.root, items.append)
# Return level-order list of all items in tree
return items
def _traverse_level_order_iterative(self, start_node, visit):
"""Traverse this binary tree with iterative level-order traversal (BFS).
Start at the given node and visit each node with the given function.
TODO: Running time: ??? Why and under what conditions?
TODO: Memory usage: ??? Why and under what conditions?"""
# Running Time O(n)
# Space Complexity O(n)
# TODO: Create queue to store nodes not yet traversed in level-order
queue = deque()
# TODO: Enqueue given starting node
queue.append(start_node)
# TODO: Loop until queue is empty
while len(queue) > 0:
# TODO: Dequeue node at front of queue
node = queue.popleft()
# TODO: Visit this node's data with given function
visit(node.data)
# TODO: Enqueue this node's left child, if it exists
if node.left is not None:
queue.append(node.left)
# TODO: Enqueue this node's right child, if it exists
if node.right is not None:
queue.append(node.right)
def test_binary_search_tree():
# Create a complete binary search tree of 3, 7, or 15 items in level-order
# items = [2, 1, 3]
items = [4, 2, 6, 1, 3, 5, 7]
# items = [8, 4, 12, 2, 6, 10, 14, 1, 3, 5, 7, 9, 11, 13, 15]
print('items: {}'.format(items))
tree = BinarySearchTree()
print('tree: {}'.format(tree))
print('root: {}'.format(tree.root))
print('\nInserting items:')
for item in items:
tree.insert(item)
print('insert({}), size: {}'.format(item, tree.size))
print('root: {}'.format(tree.root))
print('\nSearching for items:')
for item in items:
result = tree.search(item)
print('search({}): {}'.format(item, result))
item = 123
result = tree.search(item)
print('search({}): {}'.format(item, result))
print('\nTraversing items:')
print('items in-order: {}'.format(tree.items_in_order()))
print('items pre-order: {}'.format(tree.items_pre_order()))
print('items post-order: {}'.format(tree.items_post_order()))
print('items level-order: {}'.format(tree.items_level_order()))
def articleExample():
userTree = BinarySearchTree() # Empty tree
users = [User(18, 0), User(12, 34), User(13, 24), User(50, 100), User(34, 4), User(20, 90)] # List of users
for user in users:
userTree.insert(user.age)
return userTree.items_in_order()
if __name__ == '__main__':
# test_binary_search_tree()
print(articleExample())
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] |
Python/add_networks_from_ddi.py | rexyim/netmri-toolkit | 17 | 117932 | <gh_stars>10-100
import requests
import json
requests.packages.urllib3.disable_warnings()
gm_user = "admin"
gm_pwd = "<PASSWORD>"
url = "https://192.168.0.200/wapi/v2.7/"
s = requests.Session()
s.auth = (gm_user, gm_pwd)
mri_url = "192.168.0.201"
mri_user = "admin"
mri_pwd = "<PASSWORD>"
def get_networks_to_add():
# We are going to search an EA called NetMRI and look for any value of ADD
querystring = {"_return_fields+": "extattrs", "*NetMRI:": "Add"}
# We are using the API Network method
url2 = url + "network"
response = s.get(url2, params=querystring, verify=False)
if response.status_code == 200:
return json.loads(response.content.decode('utf-8'))
else:
return None
def add_network_mri(net_new):
url3 = "https://" + mri_url + "/api/3.3/discovery_settings/create"
payload = "{\n\t\"range_value\" : \"" + net_new + \
"\",\n\t\"range_type\" : \"CIDR\",\n\t\"discovery_status\" : \"INCLUDE\"\n}"
headers = {'Content-Type': "application/json"}
response = requests.post(url3, data=payload, verify=False,
auth=(mri_user, mri_pwd), headers=headers)
if response.status_code == 201:
return json.loads(response.content.decode('utf-8'))
else:
return None
def update_network_ea(ea):
url1 = url + ea
payload = "{\"extattrs\": { \"NetMRI\": { \"value\": \"Done\"}} }"
headers = {'Content-Type': "application/json"}
response = s.put( url1, data=payload, verify=False, auth=(gm_user, gm_pwd), headers=headers)
if response.status_code == 200:
return json.loads(response.content.decode('utf-8'))
else:
return None
account_info = get_networks_to_add()
#print(account_info)
if account_info is not None:
print("Found the following network(s) will be added to NetMRI: ")
for k in account_info:
new_net_mri = k['network']
add_netmri = add_network_mri(new_net_mri)
if add_netmri is not None:
print("[+] Added network " + new_net_mri + " for discovery")
ea = k['_ref']
done = update_network_ea(ea)
if done is not None:
print("[+] Updated EA for " + k['network'])
else:
print('[-] EA Update Request Failed' )
else:
print('[-] Update to NetMRI Failed' + k['network'])
else:
print('[-] Request Failed')
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transfer_model/losses/losses.py | ribeiro-hugo/smplx | 776 | 94920 | <filename>transfer_model/losses/losses.py
# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2020 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: <NAME>, <EMAIL>
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import sys
import time
from typing import Callable, Iterator, Union, Optional, List
import os.path as osp
import yaml
from loguru import logger
import pickle
import numpy as np
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
from .utils import get_reduction_method
__all__ = [
'VertexEdgeLoss',
'build_loss',
]
def build_loss(type='l2', reduction='mean', **kwargs) -> nn.Module:
logger.debug(f'Building loss: {type}')
if type == 'l2':
return WeightedMSELoss(reduction=reduction, **kwargs)
elif type == 'vertex-edge':
return VertexEdgeLoss(reduction=reduction, **kwargs)
elif type == 'l1':
return nn.L1Loss()
else:
raise ValueError(f'Unknown loss type: {type}')
class WeightedMSELoss(nn.Module):
def __init__(self, reduction='mean', **kwargs):
super(WeightedMSELoss, self).__init__()
self.reduce_str = reduction
self.reduce = get_reduction_method(reduction)
def forward(self, input, target, weights=None):
diff = input - target
if weights is None:
return diff.pow(2).sum() / diff.shape[0]
else:
return (
weights.unsqueeze(dim=-1) * diff.pow(2)).sum() / diff.shape[0]
class VertexEdgeLoss(nn.Module):
def __init__(self, norm_type='l2',
gt_edges=None,
gt_edge_path='',
est_edges=None,
est_edge_path='',
robustifier=None,
edge_thresh=0.0, epsilon=1e-8,
reduction='sum',
**kwargs):
super(VertexEdgeLoss, self).__init__()
assert norm_type in ['l1', 'l2'], 'Norm type must be [l1, l2]'
self.norm_type = norm_type
self.epsilon = epsilon
self.reduction = reduction
assert self.reduction in ['sum', 'mean']
logger.info(f'Building edge loss with'
f' norm_type={norm_type},'
f' reduction={reduction},'
)
gt_edge_path = osp.expandvars(gt_edge_path)
est_edge_path = osp.expandvars(est_edge_path)
assert osp.exists(gt_edge_path) or gt_edges is not None, (
'gt_edges must not be None or gt_edge_path must exist'
)
assert osp.exists(est_edge_path) or est_edges is not None, (
'est_edges must not be None or est_edge_path must exist'
)
if osp.exists(gt_edge_path) and gt_edges is None:
gt_edges = np.load(gt_edge_path)
if osp.exists(est_edge_path) and est_edges is None:
est_edges = np.load(est_edge_path)
self.register_buffer(
'gt_connections', torch.tensor(gt_edges, dtype=torch.long))
self.register_buffer(
'est_connections', torch.tensor(est_edges, dtype=torch.long))
def extra_repr(self):
msg = [
f'Norm type: {self.norm_type}',
]
if self.has_connections:
msg.append(
f'GT Connections shape: {self.gt_connections.shape}'
)
msg.append(
f'Est Connections shape: {self.est_connections.shape}'
)
return '\n'.join(msg)
def compute_edges(self, points, connections):
edge_points = torch.index_select(
points, 1, connections.view(-1)).reshape(points.shape[0], -1, 2, 3)
return edge_points[:, :, 1] - edge_points[:, :, 0]
def forward(self, gt_vertices, est_vertices, weights=None):
gt_edges = self.compute_edges(
gt_vertices, connections=self.gt_connections)
est_edges = self.compute_edges(
est_vertices, connections=self.est_connections)
raw_edge_diff = (gt_edges - est_edges)
batch_size = gt_vertices.shape[0]
if self.norm_type == 'l2':
edge_diff = raw_edge_diff.pow(2)
elif self.norm_type == 'l1':
edge_diff = raw_edge_diff.abs()
else:
raise NotImplementedError(
f'Loss type not implemented: {self.loss_type}')
if self.reduction == 'sum':
return edge_diff.sum()
elif self.reduction == 'mean':
return edge_diff.sum() / batch_size
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FT_to_prediction/finetune_launch.py | bm2-lab/X-MOL | 19 | 164314 | import sys
import subprocess
import commands
import os
import six
import copy
import argparse
import time
from utils.stream import stream_by_running as get_stream_m
from utils.args import ArgumentGroup, print_arguments, inv_arguments
from finetune_args import parser as finetuning_parser
from extend_pos import extend_word_emb, extend_sent_emb
import subprocess
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
multip_g = ArgumentGroup(parser, "multiprocessing",
"start paddle training using multi-processing mode.")
multip_g.add_arg("node_ips", str, None,
"paddle trainer ips")
multip_g.add_arg("node_id", int, None,
"the trainer id of the node for multi-node distributed training.")
multip_g.add_arg("print_config", bool, True,
"print the config of multi-processing mode.")
multip_g.add_arg("current_node_ip", str, None,
"the ip of current node.")
multip_g.add_arg("split_log_path", str, "log",
"log path for each trainer.")
multip_g.add_arg("log_prefix", str, "",
"the prefix name of job log.")
multip_g.add_arg("nproc_per_node", int, 4,
"the number of process to use on each node.")
multip_g.add_arg("selected_gpus", str, "0,1,2,3",
"the gpus selected to use.")
multip_g.add_arg("training_script", str, None, "the program/script to be lauched "
"in parallel followed by all the arguments", positional_arg=True)
multip_g.add_arg("training_script_args", str, None,
"training script args", positional_arg=True, nargs=argparse.REMAINDER)
# yapf: enable
def start_procs(args):
procs = []
log_fns = []
default_env = os.environ.copy()
node_id = args.node_id
node_ips = [x.strip() for x in args.node_ips.split(',')]
current_ip = args.current_node_ip
num_nodes = len(node_ips)
selected_gpus = [x.strip() for x in args.selected_gpus.split(',')]
selected_gpu_num = len(selected_gpus)
all_trainer_endpoints = ""
if selected_gpu_num < args.nproc_per_node:
for ip in node_ips:
for i in range(selected_gpu_num):
if all_trainer_endpoints != "":
all_trainer_endpoints += ","
all_trainer_endpoints += "%s:617%d" % (ip, int(selected_gpus[i]))
nranks = num_nodes * selected_gpu_num
else:
for ip in node_ips:
for i in range(args.nproc_per_node):
if all_trainer_endpoints != "":
all_trainer_endpoints += ","
all_trainer_endpoints += "%s:617%d" % (ip, i)
nranks = num_nodes * args.nproc_per_node
gpus_per_proc = args.nproc_per_node % selected_gpu_num
if gpus_per_proc == 0:
if selected_gpu_num < args.nproc_per_node:
gpus_per_proc = 1
else:
gpus_per_proc = selected_gpu_num / args.nproc_per_node
else:
gpus_per_proc = selected_gpu_num / args.nproc_per_node + 1
selected_gpus_per_proc = [selected_gpus[i:i + gpus_per_proc] for i in range(0, len(selected_gpus), gpus_per_proc)]
if args.print_config:
print("all_trainer_endpoints: ", all_trainer_endpoints,
", node_id: ", node_id,
", current_ip: ", current_ip,
", num_nodes: ", num_nodes,
", node_ips: ", node_ips,
", gpus_per_proc: ", gpus_per_proc,
", selected_gpus_per_proc: ", selected_gpus_per_proc,
", nranks: ", nranks)
current_env = copy.copy(default_env)
procs = []
cmds = []
log_fns = []
for i in range(0, args.nproc_per_node):
trainer_id = node_id * args.nproc_per_node + i
current_env.update({
"FLAGS_selected_gpus": "%s" % ",".join([str(s) for s in selected_gpus_per_proc[i]]),
"PADDLE_TRAINER_ID" : "%d" % trainer_id,
"PADDLE_CURRENT_ENDPOINT": "%s:617%d" % (current_ip, i),
"PADDLE_TRAINERS_NUM": "%d" % nranks,
"PADDLE_TRAINER_ENDPOINTS": all_trainer_endpoints,
"PADDLE_NODES_NUM": "%d" % num_nodes
})
cmd = [sys.executable, "-u",
args.training_script] + args.training_script_args
cmds.append(cmd)
if args.split_log_path:
fn = open("%s/%sjob.log.%d" % (args.split_log_path, args.log_prefix, trainer_id), "a")
log_fns.append(fn)
process = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn)
else:
process = subprocess.Popen(cmd, env=current_env)
procs.append(process)
for i in range(len(procs)):
proc = procs[i]
proc.wait()
if len(log_fns) > 0:
log_fns[i].close()
if proc.returncode != 0:
raise subprocess.CalledProcessError(returncode=procs[i].returncode,
cmd=cmds[i])
else:
print("proc %d finsh" % i)
def stream(args, lanch_args):
#stream model list
#stream_m = get_stream_m(args.stream_job, args.stream_cluster)
stream_m = get_stream_m(args.stream_job)
while len(stream_m) == 0:
time.sleep(600)
stream_m = get_stream_m(args.stream_job)
download, tar, init_path = stream_m[-1]
retcode, ret = commands.getstatusoutput(download)
if not os.path.exists(init_path):
retcode, ret = commands.getstatusoutput(tar)
if not args.use_fp16:
retcode, ret = commands.getstatusoutput(
'rename .master "" ' + init_path + '/*.master'
)
arg_name = '--init_pretraining_params'
val_index = -1
if arg_name in lanch_args.training_script_args:
val_index = lanch_args.training_script_args.index(arg_name) + 1
lanch_args.training_script_args[val_index] = init_path
else:
lanch_args.training_script_args += [arg_name, init_path]
main(lanch_args)
while True:
#updated_m = get_stream_m(args.stream_job, args.stream_cluster)
updated_m = get_stream_m(args.stream_job)
download, tar, init_path = updated_m[-1]
if len(updated_m) > len(stream_m):
retcode, ret = commands.getstatusoutput(download)
if not os.path.exists(init_path):
retcode, ret = commands.getstatusoutput(tar)
if not args.use_fp16:
retcode, ret = commands.getstatusoutput(
'rename .master "" ' + init_path + '/*.master'
)
lanch_args.training_script_args[val_index] = init_path
main(lanch_args)
#update
stream_m = updated_m
def main(args):
extend_vocab = False
extend_sent = False
if args.print_config:
print_arguments(args)
"""
zs = '*'*20
print(zs+'\n')
print(args.training_script_args)
print(type(args.training_script_args))
print(zs+'\n')
"""
def get_param(name):
key = "--" + name
if key not in args.training_script_args:
return None
index = args.training_script_args.index(key) + 1
return args.training_script_args[index]
if extend_vocab:
extend_word_emb(get_param("ernie_config_path"), get_param("init_pretraining_params"))
if extend_sent:
extend_sent_emb(get_param("ernie_config_path"), get_param("init_pretraining_params"))
start_procs(args)
if __name__ == "__main__":
fine_tune_rep = 0
lanch_args = parser.parse_args()
finetuning_args = finetuning_parser.parse_args(
lanch_args.training_script_args)
if finetuning_args.stream_job and finetuning_args.stream_job != "":
stream(finetuning_args, lanch_args)
else:
init_path = finetuning_args.init_pretraining_params
print("init model: %s" % init_path)
finetuning_data = os.path.dirname(finetuning_args.train_set)
task_name = finetuning_data.split('/')[-1]
if not finetuning_args.use_fp16:
retcode, ret = commands.getstatusoutput(
'rename .master "" ' + init_path + '/*.master'
)
#while True:
#"""
while fine_tune_rep < 20:
with open('finetune_reprec.txt','a') as f:
f.write('finetune repeat {0} start @ {1}\n'.format(fine_tune_rep, time.strftime("%Y-%m-%d %X",time.localtime())))
subprocess.call("python3 pt_scaffold_split1.py {0}".format(task_name), shell=True)
#file_dir = './package/task_data/clintox_0'
#subprocess.call("cp {0}/train_{1}.tsv {2}/train.tsv".format(file_dir, fine_tune_rep, file_dir), shell=True)
#subprocess.call("cp {0}/test_{1}.tsv {2}/test.tsv".format(file_dir, fine_tune_rep, file_dir), shell=True)
#subprocess.call("cp {0}/dev_{1}.tsv {2}/dev.tsv".format(file_dir, fine_tune_rep, file_dir), shell=True)
#subprocess.call("python3 package/task_data/reactAf/reactA_split.py", shell=True)
main(lanch_args)
with open('finetune_reprec.txt','a') as f:
f.write('finetune repeat {0} finished @ {1}\n\n'.format(fine_tune_rep, time.strftime("%Y-%m-%d %X",time.localtime())))
fine_tune_rep += 1
#"""
#main(lanch_args)
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pyregion/conftest.py | stefanor/pyregion | 21 | 104193 | # this contains imports plugins that configure py.test for astropy tests.
# by importing them here in conftest.py they are discoverable by py.test
# no matter how it is invoked within the source tree.
from astropy.version import version as astropy_version
if astropy_version < '3.0':
# With older versions of Astropy, we actually need to import the pytest
# plugins themselves in order to make them discoverable by pytest.
from astropy.tests.pytest_plugins import *
else:
# As of Astropy 3.0, the pytest plugins provided by Astropy are
# automatically made available when Astropy is installed. This means it's
# not necessary to import them here, but we still need to import global
# variables that are used for configuration.
from astropy.tests.plugins.display import PYTEST_HEADER_MODULES, TESTED_VERSIONS
from astropy.tests.helper import enable_deprecations_as_exceptions
# Uncomment the following line to treat all DeprecationWarnings as exceptions
enable_deprecations_as_exceptions()
PYTEST_HEADER_MODULES.clear()
PYTEST_HEADER_MODULES.update([
('numpy', 'numpy'),
('cython', 'cython'),
('Astropy', 'astropy'),
('pyparsing', 'pyparsing'),
('matplotlib', 'matplotlib'),
])
# This is to figure out the affiliated package version, rather than
# using Astropy's
try:
from .version import version
except ImportError:
version = 'dev'
import os
packagename = os.path.basename(os.path.dirname(__file__))
TESTED_VERSIONS[packagename] = version
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] |
esppy/helper/helpers.py | Mentos05/SAS_AskTheExpert | 0 | 87285 | <gh_stars>0
#Helpfer Function:
def create_scoring_schema(number_objects):
_field = "id*:int64,image:blob,_image_:blob,_nObjects_:double,"
for obj in range(0,number_objects):
_field += "_Object" + str(obj) + "_:string,"
_field += "_P_Object" + str(obj) + "_:double,"
_field += "_Object" + str(obj) + "_x:double,"
_field += "_Object" + str(obj) + "_y:double,"
_field += "_Object" + str(obj) + "_width:double,"
_field += "_Object" + str(obj) + "_height:double,"
return _field[:-1]
def create_scoring_schema(number_objects):
_field = "id*:int64,image:blob,_image_:blob,_nObjects_:double,"
for obj in range(0,number_objects):
_field += "_Object" + str(obj) + "_:string,"
_field += "_P_Object" + str(obj) + "_:double,"
_field += "_Object" + str(obj) + "_x:double,"
_field += "_Object" + str(obj) + "_y:double,"
_field += "_Object" + str(obj) + "_width:double,"
_field += "_Object" + str(obj) + "_height:double,"
return _field[:-1]
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python/rikai/pytorch/models/__init__.py | askintution/rikai | 1 | 106302 | <filename>python/rikai/pytorch/models/__init__.py
# Copyright 2022 Rikai 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.
"""Rikai-implemented PyTorch models and executors."""
from abc import ABC
from typing import Optional
import torch
from rikai.spark.sql.model import ModelSpec, ModelType
__all__ = ["TorchModelType"]
class TorchModelType(ModelType, ABC):
"""Base ModelType for PyTorch models."""
def __init__(self):
self.model: Optional[torch.nn.Module] = None
self.spec: Optional[ModelSpec] = None
def load_model(self, spec: ModelSpec, **kwargs):
self.model = spec.load_model()
self.model.eval()
if "device" in kwargs:
self.model.to(kwargs.get("device"))
self.spec = spec
# Release GPU memory
# https://blog.paperspace.com/pytorch-memory-multi-gpu-debugging/?ref=tfrecipes
def release(self):
model = self.model.cpu()
del model
torch.cuda.empty_cache()
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summer-of-code/week-01/Nessa/soc-wk1-cert-nessa-carson.py | supersciencegrl/toolkitten | 0 | 120180 | # ### CALC ###
print("")
print("### CALC ###")
print("")
print("Hours in a year:")
print(24*365)
print("Minutes in a decade:")
print(60*24*365*10)
print("My age in seconds:")
print((365*27+6+2+31+30+31+30+16)*24*1440)
# Correct to the nearest day at the time of writing!
print("Andreea's age:")
print(48618000/(365*24*1440))
print("?!")
# ### FULL NAME ###
print("")
print("### WHAT'S YOUR NAME ###")
print("")
import random
first_name = input("What is your first name? ")
middle_name = input("What is your middle name? ")
last_name = input("What is your surname? ")
greeting = ["Hello, ", "Hi, ", "Bonjour, ", "salve, "]
print(random.choice(greeting) + first_name + " " + middle_name + " " + last_name + "!")
# ### BETTER FAVOURITE NUMBER ###
print("")
print("### BETTER FAVOURITE NUMBER ###")
print("")
fave = str(int(float(input("What is your favourite number? ")) + 1))
print("Oh come on, how about " + fave + "... that's bigger and better!")
# ### TEN GREEN BOTTLES ###
print("")
print("### TEN GREEN BOTTLES ###")
print("")
x = 10
while x > 1:
y = str(x)
print((y + " green bottles, standing on a wall, ") * 2)
print("And if one green bottle should accidentally fall")
x = x - 1
y = str(x)
if x > 1:
print("There'll be " + y + " green bottles, standing on the wall.")
print("")
else:
print("There'll be " + y + " green bottle, standing on the wall.")
print("")
while x == 1:
y = str(x)
print((y + " green bottle, standing on a wall, ") * 2)
print("And if one green bottle should accidentally fall")
print("There'll be no green bottles, standing on the wall.")
x = x - 1
print("")
print("Fin.")
# ### DEAF GRANDMA ###
import random
year = random.randint(1938, 1950)
text = ""
utext = " "
while utext != text:
text = input("WHAT'S UP WITH YOU?! ")
utext = text.upper()
if utext != text:
print("HUH?! SPEAK UP, GIRL! ")
print("NO, NOT SINCE " + str(year) + "!")
# ### LEAP YEARS ###
print("")
print("### LEAP YEARS ###")
print("")
i_year = int(input("First year: "))
f_year = int(input("Last year: "))
c_year = i_year
while c_year != f_year + 1:
if c_year % 4 == 0:
if c_year % 100 != 0:
print(c_year)
elif c_year % 400 == 0:
print(c_year)
c_year = c_year + 1
if c_year == f_year + 1:
break
# ### ALPHABETIZE ###
text = " "
words = []
while text != "":
text = input("Add your word here: ")
if text != "":
words.append(text)
print(sorted(words))
# ### MOO ###
print("")
print("### MOO ###")
print("")
n = int(input("How many cows do you have? "))
def moo(n):
print("moo " * n)
moo(n)
# ### EARLY ROMAN NUMERALS ###
print("")
print("### EARLY ROMAN NUMERALS ###")
print("")
n = int(input("Input a number. "))
m = n // 1000
n = n - m * 1000
d = n // 500
n = n - d * 500
c = n // 100
n = n - c * 100
l = n // 50
n = n - l * 50
x = n // 10
n = n - x * 10
v = n // 5
n = n - v * 5
print("M" * m + "D" * d + "C" * c + "L" * l + "X" * x + "V" * v + "I" * n)
# ### LATE ROMAN NUMERALS ###
print("")
print("### LATE ROMAN NUMERALS ###")
print("")
n = int(input("Input a number. "))
m = n // 1000
n = n - m * 1000
n1 = n
d = n // 500
n = n - d * 500
c = n // 100
n = n - c * 100
if n1 // 100 == 9:
c1 = "CM"
else:
c1 = "D" * d + "C" * c
n1 = n1
l = n // 50
n = n - l * 50
x = n // 10
n = n - x * 10
if n1 // 10 == 9:
x1 = "XC"
else:
x1 = "L" * l + "X" * x
n1 = n
v = n // 5
n = n - v * 5
if n1 == 9:
i1 = "IX"
else:
i1 = "V" * v + "I" * n
print("M" * m + c1 + x1 + i1)
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] |
pyyeti/cla/_rptpct1.py | twmacro/pyye | 0 | 38847 | # -*- coding: utf-8 -*-
"""
Low level tool for writing percent difference reports. Typically, this
is called via: :func:`cla.DR_Results.rptpct`.
"""
from io import StringIO
from types import SimpleNamespace
import warnings
import numpy as np
import matplotlib.pyplot as plt
from pyyeti import ytools, locate, writer
from ._utilities import _get_rpt_headers, _get_numform, _proc_filterval
from ._magpct import magpct
__all__ = ["rptpct1"]
# FIXME: We need the str/repr formatting used in Numpy < 1.14.
try:
np.set_printoptions(legacy="1.13")
except TypeError:
pass
def _apply_pv(value, pv, oldlen):
# if value has a len that's > 1, try to partition it down;
# otherwise, return it as is:
try:
n = len(value)
except TypeError:
return value
else:
if n == 1:
return value
# `value` is a vector with len > 1 ... ensure it is a true numpy
# array:
value = np.atleast_1d(value)
# oldlen is either 0 (for `value` vectors that are expected to be
# full size ... currently, only the `filterval` and
# `magpct_filterval` vectors), or it is the length of the
# dimension that the `value` index type of partition vector
# (currently, only the `ignorepv` vector) was originally defined
# to partition.
if oldlen == 0:
# `value` is `filterval` or `magpct_filterval` ... these just
# need to be partitioned down:
newvalue = value[pv]
else:
# `value` is `ignorepv` ... it needs to be redefined to
# correspond to reduced size:
truefalse = locate.index2bool(value, oldlen)
newvalue = truefalse[pv].nonzero()[0]
return newvalue
def _align_mxmn(mxmn1, mxmn2, labels2, row_number, infodct):
if infodct["labels"] and infodct["labels"] != labels2:
n = len(infodct["labels"])
pv1, pv2 = locate.list_intersect(infodct["labels"], labels2)
mxmn1 = mxmn1[pv1]
mxmn2 = mxmn2[pv2]
infodct["labels"] = [infodct["labels"][i] for i in pv1]
row_number = row_number[pv1]
infodct["filterval"] = _apply_pv(infodct["filterval"], pv1, 0)
infodct["magpct_filterval"] = _apply_pv(infodct["magpct_filterval"], pv1, 0)
infodct["ignorepv"] = _apply_pv(infodct["ignorepv"], pv1, n)
return mxmn1, mxmn2, row_number
def _get_filtline(filterval):
if len(filterval) > 1:
filtline = "Filter: <defined row-by-row>\n"
else:
filtline = f"Filter: {filterval[0]}\n"
return filtline
def _get_noteline(use_range, names, prtbads, flagbads):
noteline = "Notes: "
tab = " "
if not use_range:
noteline += "% Diff = +/- abs(({0}-{1})/{1})*100\n".format(*names)
else:
noteline += "% Diff = +/- abs({0}-{1})/max(abs({1}(max,min)))*100\n".format(
*names
)
noteline += tab + "Sign set such that positive % differences indicate exceedances\n"
prtbad, prtbadh, prtbadl = prtbads
flagbad, flagbadh, flagbadl = flagbads
if prtbad is not None or prtbadh is not None or prtbadl is not None:
if prtbad is not None:
prtbad = abs(prtbad)
noteline += tab + f"Printing rows where abs(% Diff) > {prtbad}%\n"
elif prtbadh is not None:
noteline += tab + f"Printing rows where % Diff > {prtbadh}%\n"
else:
noteline += tab + f"Printing rows where % Diff < {prtbadl}%\n"
if flagbad is not None or flagbadh is not None or flagbadl is not None:
if flagbad is not None:
flagbad = abs(flagbad)
noteline += tab + f"Flagging (*) rows where abs(% Diff) > {flagbad}%\n"
elif flagbadh is not None:
noteline += tab + f"Flagging (*) rows where % Diff > {flagbadh}%\n"
else:
noteline += tab + f"Flagging (*) rows where % Diff < {flagbadl}%\n"
return noteline
def _get_badpv(pct, pv, bad, badh, badl, defaultpv=False):
if bad is not None or badh is not None or badl is not None:
badpv = pv.copy()
if bad is not None:
badpv &= abs(pct) > bad
elif badh is not None:
badpv &= pct > badh
else:
badpv &= pct < badl
else:
badpv = np.empty(len(pct), bool)
badpv[:] = defaultpv
return badpv
def _get_pct_diff(a, b, filt, pv, nastring, mxmn_b=None, ismax=True, flagbads=None):
# either can pass filter to be kept:
pv &= (abs(a) > filt) | (abs(b) > filt)
if mxmn_b is not None:
denom = np.nanmax(abs(mxmn_b), axis=1)
else:
denom = abs(b)
# put 1's in for filtered values ... this is temporary
a = a.copy()
b = b.copy()
a[~pv] = 1.0
b[~pv] = 1.0
z = denom == 0.0
denom[z] = 1.0
pct = 100 * abs(a - b) / denom
pct[z] = 100.0 # np.inf
# make less extreme values negative
neg = a < b if ismax else a > b
pct[neg] *= -1.0
# put nan's in for the filtered or n/a rows:
pct[~pv] = np.nan
# make 7 char version:
spct = [f"{p:7.2f}" for p in pct]
badpv = _get_badpv(pct, pv, *flagbads, False)
for j in badpv.nonzero()[0]:
spct[j] += "*"
for j in (~pv).nonzero()[0]:
spct[j] = nastring
return pct, spct
def _get_histogram_str(desc, hdr, pctinfo):
pctcount = pctinfo["hsto"]
s = [
(f"\n\n {desc} - {hdr} Comparison Histogram\n\n"),
(" % Diff Count Percent\n -------- -------- -------\n"),
]
with StringIO() as f:
writer.vecwrite(f, " {:8.2f} {:8.0f} {:7.2f}\n", pctcount)
s.append(f.getvalue())
s.append("\n")
# total_percent_10 will either be 0 or 1000:
# - 0 if all % diffs are "n/a"
# - 1000 otherwise
total_percent_10 = np.round(pctcount[:, 2].sum() * 10)
last = -1.0
for pdiff in [1, 2, 5, 10, 15, 20, 25, 50, 100, 500]:
pvdiff = abs(pctcount[:, 0]) <= pdiff
num = pctcount[pvdiff, 2].sum()
if num > last:
s.append(f" {num:.1f}% of values are within {pdiff}%\n")
if np.round(num * 10) == total_percent_10:
break
last = num
pct = pctinfo["pct"]
n = len(pct)
if n == 0:
s.append(
"\n % Diff Statistics: [Min, Max, Mean, StdDev]"
" = [n/a, n/a, n/a, n/a]\n"
)
else:
stddev = 0.0 if n <= 1 else pct.std(ddof=1)
s.append(
"\n % Diff Statistics: [Min, Max, Mean, StdDev]"
f" = [{pct.min():.2f}, {pct.max():.2f}, {pct.mean():.4f}, {stddev:.4f}]\n"
)
return "".join(s)
def _proc_pct(
ext1,
ext2,
filterval,
magpct_filterval,
*,
names,
mxmn1,
comppv,
mxmn_b,
ismax,
histogram_inc,
prtbads,
flagbads,
numform,
valhdr,
maxhdr,
minhdr,
absmhdr,
pdhdr,
nastring,
doabsmax,
shortabsmax,
print_info,
):
# handle magpct stuff here:
mag = ext1[comppv], ext2[comppv]
if magpct_filterval is not None and len(magpct_filterval) > 1:
magfilt = magpct_filterval[comppv]
else:
magfilt = magpct_filterval
pv = comppv.copy()
pct, spct = _get_pct_diff(
ext1,
ext2,
filterval,
pv,
nastring,
mxmn_b=mxmn_b,
ismax=ismax,
flagbads=flagbads,
)
pct_ret = pct[pv]
hsto = ytools.histogram(pct_ret, histogram_inc)
# for trimming down if prtbad set:
prtpv = _get_badpv(pct, pv, *prtbads, True)
pctlen = max(len(pdhdr), len(max(spct, key=len)))
sformatpd = f"{{:{pctlen}}}"
# for writer.formheader:
numlen = max(13, len(max(names, key=len)), len(numform.format(np.pi)))
if not doabsmax:
print_info.headers1.extend([*names, ""])
print_info.headers2.extend([valhdr, valhdr, pdhdr])
print_info.formats.extend([numform, numform, sformatpd])
print_info.printargs.extend([ext1, ext2, spct])
print_info.widths.extend([numlen, numlen, pctlen])
print_info.seps.extend([4, 2, 2])
print_info.justs.extend(["c", "c", "c"])
elif shortabsmax:
print_info.headers1.extend([*names, ""])
print_info.headers2.extend([absmhdr, absmhdr, pdhdr])
print_info.formats.extend([numform, numform, sformatpd])
print_info.printargs.extend([ext1, ext2, spct])
print_info.widths.extend([numlen, numlen, pctlen])
print_info.seps.extend([4, 2, 2])
print_info.justs.extend(["c", "c", "c"])
else:
print_info.headers1.extend([names[0], names[0], names[0], names[1], ""])
print_info.headers2.extend([maxhdr, minhdr, absmhdr, absmhdr, pdhdr])
print_info.formats.extend([numform, numform, numform, numform, sformatpd])
print_info.printargs.extend([mxmn1[:, 0], mxmn1[:, 1], ext1, ext2, spct])
print_info.widths.extend([numlen, numlen, numlen, numlen, pctlen])
print_info.seps.extend([4, 2, 2, 2, 2])
print_info.justs.extend(["c", "c", "c", "c", "c"])
return dict(
pct=pct_ret, spct=spct, hsto=hsto, prtpv=prtpv, mag=mag, magfilt=magfilt
)
def _figure_on(name, doabsmax, show_figures):
figsize = [8.5, 11.0]
if doabsmax:
figsize[1] /= 3.0
if show_figures:
plt.figure(name, figsize=figsize)
plt.clf()
else:
plt.figure(figsize=figsize)
def _figure_off(show_figures):
if not show_figures:
plt.close()
def _prep_subplot(pctinfo, sp):
if "mx" in pctinfo:
# if not just doing absmax
if sp > 311:
plt.subplot(sp, sharex=plt.gca())
else:
plt.subplot(sp)
def _plot_magpct(
pctinfo,
names,
desc,
doabsmax,
filename,
magpct_options,
use_range,
maxhdr,
minhdr,
absmhdr,
show_figures,
tight_layout_args,
):
ptitle = f"{desc} - {{}} Comparison vs Magnitude"
xl = f"{names[1]} Magnitude"
yl = f"% Diff of {names[0]} vs {names[1]}"
_figure_on("Magpct - " + desc, doabsmax, show_figures)
try:
for lbl, hdr, sp, ismax in (
("mx", maxhdr, 311, True),
("mn", minhdr, 312, False),
("amx", absmhdr, 313, True),
):
_prep_subplot(pctinfo, sp)
if lbl in pctinfo:
if use_range:
ref = pctinfo["amx"]["mag"][1]
else:
ref = None
magpct(
pctinfo[lbl]["mag"][0],
pctinfo[lbl]["mag"][1],
Ref=ref,
ismax=ismax,
filterval=pctinfo[lbl]["magfilt"],
**magpct_options,
)
plt.title(ptitle.format(hdr))
plt.xlabel(xl)
plt.ylabel(yl)
plt.grid(True)
plt.tight_layout(**tight_layout_args)
if isinstance(filename, str):
plt.savefig(filename + ".magpct.png")
finally:
_figure_off(show_figures)
def _plot_histogram(
pctinfo,
names,
desc,
doabsmax,
filename,
histogram_inc,
maxhdr,
minhdr,
absmhdr,
show_figures,
tight_layout_args,
):
ptitle = f"{desc} - {{}} Comparison Histogram"
xl = f"% Diff of {names[0]} vs {names[1]}"
yl = "Percent Occurrence (%)"
_figure_on("Histogram - " + desc, doabsmax, show_figures)
try:
for lbl, hdr, sp in (
("mx", maxhdr, 311),
("mn", minhdr, 312),
("amx", absmhdr, 313),
):
_prep_subplot(pctinfo, sp)
if lbl in pctinfo:
width = histogram_inc
x = pctinfo[lbl]["hsto"][:, 0]
y = pctinfo[lbl]["hsto"][:, 2]
colors = ["b"] * len(x)
ax = abs(x)
pv1 = ((ax > 5) & (ax <= 10)).nonzero()[0]
pv2 = (ax > 10).nonzero()[0]
for pv, c in ((pv1, "m"), (pv2, "r")):
for i in pv:
colors[i] = c
plt.bar(x, y, width=width, color=colors, align="center")
plt.title(ptitle.format(hdr))
plt.xlabel(xl)
plt.ylabel(yl)
x = abs(max(plt.xlim(), key=abs))
if x < 5:
plt.xlim(-5, 5)
plt.grid(True)
plt.tight_layout(**tight_layout_args)
if isinstance(filename, str):
plt.savefig(filename + ".histogram.png")
finally:
_figure_off(show_figures)
def rptpct1(
mxmn1,
mxmn2,
filename,
*,
title="PERCENT DIFFERENCE REPORT",
names=("Self", "Reference"),
desc=None,
filterval=None,
labels=None,
units=None,
ignorepv=None,
uf_reds=None,
use_range=True,
numform=None,
prtbad=None,
prtbadh=None,
prtbadl=None,
flagbad=None,
flagbadh=None,
flagbadl=None,
dohistogram=True,
histogram_inc=1.0,
domagpct=True,
magpct_options=None,
doabsmax=False,
shortabsmax=False,
roundvals=-1,
rowhdr="Row",
deschdr="Description",
maxhdr="Maximum",
minhdr="Minimum",
absmhdr="Abs-Max",
perpage=-1,
tight_layout_args=None,
show_figures=False,
align_by_label=True,
):
"""
Write a percent difference report between 2 sets of max/min data
Parameters
----------
mxmn1 : 2d array_like or SimpleNamespace
The max/min data to compare to the `mxmn2` set. If 2-column
array_like, its columns are: [max, min]. If SimpleNamespace,
it must be as defined in :class:`DR_Results` and have these
members:
.. code-block:: none
.ext = [max, min]
.drminfo = SimpleNamespace which has (at least):
.desc = one line description of category
.filterval = the filter value; (see `filterval`
description below)
.labels = a list of descriptions; one per row
.ignorepv = these rows will get 'n/a' for % diff
.units = string with units
.uf_reds = uncertainty factors
Note that the inputs `desc`, `labels`, etc, override the
values above.
mxmn2 : 2d array_like or SimpleNamespace
The reference set of max/min data. Format is the same as
`mxmn1`.
.. note::
If both `mxmn1` and `mxmn2` are SimpleNamespaces and have
the ``.drminfo.labels`` attribute, this routine will, by
default, use the labels to align the data sets for
comparison. To prevent this, set the `align_by_label`
parameter to False.
filename : string or file_like or 1 or None
Either a name of a file, or is a file_like object as returned
by :func:`open` or :class:`io.StringIO`. Input as integer 1 to
write to stdout. Can also be the name of a directory or None;
in these cases, a GUI is opened for file selection.
title : string; must be named; optional
Title for the report
names : list/tuple; must be named; optional
Two (short) strings identifying the two sets of data
desc : string or None; must be named; optional
A one line description of the table. Overrides
`mxmn1.drminfo.desc`. If neither are input,
'No description provided' is used.
filterval : scalar, 1d array_like or None; must be named; optional
Numbers with absolute value <= than `filterval` will get a
'n/a' % diff. If vector, length must match number of rows in
`mxmn1` and `mxmn2` data. Overrides `mxmn1.drminfo.filterval`.
If neither are input, `filterval` is set to 1.e-6.
labels : list or None; must be named; optional
A list of strings briefly describing each row. Overrides
`mxmn1.drminfo.labels`. If neither are input,
``['Row 1','Row 2',...]`` is used.
units : string or None; must be named; optional
Specifies the units. Overrides `mxmn1.drminfo.units`. If
neither are input, 'Not specified' is used.
ignorepv : 1d array or None; must be named; optional
0-offset index vector specifying which rows of `mxmn1` to
ignore (they get the 'n/a' % diff). Overrides
`mxmn1.drminfo.ignorepv`. If neither are input, no rows are
ignored (though `filterval` is still used).
.. note::
`ignorepv` applies *before* any alignment by labels is
done (when `align_by_label` is True, which is the
default).
uf_reds : 1d array or None; must be named; optional
Uncertainty factors: [rigid, elastic, dynamic, static].
Overrides `mxmn1.drminfo.uf_reds`. If neither is input,
'Not specified' is used.
use_range : bool; must be named, optional
If True, the denominator of the % diff calc for both the max
& min for each row is the absolute maximum of the reference
max & min for that row. If False, the denominator is the
applicable reference max or min. A quick example shows why
``use_range=True`` might be useful:
.. code-block:: none
If [max1, min1] = [12345, -10] and
[max2, min2] = [12300, 50]
Then:
% diff = [0.37%, 0.49%] if use_range is True
% diff = [0.37%, 120.00%] if use_range is False
Note that the sign of the % diff is defined such that a
positive % diff means an exceedance: where ``max1 > max2`` or
``min1 < min2``.
`use_range` is ignored if `doabsmax` is True.
numform : string or None; must be named; optional
Format of the max & min numbers. If None, it is set internally
to be 13 chars wide and depends on the range of numbers to
print:
- if range is "small", numform='{:13.xf}' where "x" ranges
from 0 to 7
- if range is "large", numform='{:13.6e}'
prtbad : scalar or None; must be named; optional
Only print rows where ``abs(%diff) > prtbad``. For example, to
print rows off by more than 5%, use ``prtbad=5``. `prtbad`
takes precedence over `prtbadh` and `prtbadl`.
prtbadh : scalar or None; must be named; optional
Only print rows where ``%diff > prtbadh``. Handy for showing
just the exceedances. `prtbadh` takes precedence over
`prtbadl`.
prtbadl : scalar or None; must be named; optional
Only print rows where ``%diff < prtbadl``. Handy for showing
where reference rows are higher.
flagbad : scalar or None; must be named; optional
Flag % diffs where ``abs(%diff) > flagbad``. Works similar to
`prtbad`. The flag is an asterisk (*).
flagbadh : scalar or None; must be named; optional
Flag % diffs where ``%diff > flagbadh``. Works similar to
`prtbadh`. Handy for flagging exceedances. `flagbadh` takes
precedence over `flagbadl`.
flagbadl : scalar or None; must be named; optional
Flag % diffs where ``%diff < flagbadl``. Works similar to
`prtbadl`.
dohistogram : bool; must be named; optional
If True, plot the histograms. Plots will be written to
"`filename`.histogram.png".
histogram_inc : scalar; must be named; optional
The histogram increment; defaults to 1.0 (for 1%).
domagpct : bool; must be named; optional
If True, plot the percent differences versus magnitude via
:func:`magpct`. Plots will be written to
"`filename`.magpct.png". Filtering for the "magpct" plot is
controlled by the ``magpct_options['filterval']`` and
``magpct_options['symlogy']`` options. By default, all percent
differences are shown, but the larger values (according to the
`filterval` filter) are emphasized by using a mixed linear/log
y-axis. The percent differences for the `ignorepv` rows are
not plotted.
magpct_options : None or dict; must be named; optional
If None, it is internally reset to::
magpct_options = {'filterval': 'filterval'}
Use this parameter to provide any options to :func:`magpct`
but note that the `filterval` option for :func:`magpct` is
treated specially. Here, in addition to any of the values that
:func:`magpct` accepts, it can also be set to the string
"filterval" as in the default case shown above. In that case,
``magpct_options['filterval']`` gets internally reset to the
initial value of `filterval` (which is None by default).
.. note::
The call to :func:`magpct` is *after* applying `ignorepv`
and doing any data aligning by labels.
.. note::
The two filter value options (`filterval` and
``magpct_options['filterval']``) have different defaults:
None and 'filterval`, respectively. They also differ on how
the ``None`` setting is used: for `filterval`, None is
replaced by 1.e-6 while for `magpct_filterval`, None means
that the "magpct" plot will not have any filters applied at
all.
.. note::
The above means that, if you accept the default values for
`filterval` and for ``magpct_options['filterval']``, then
tables and the histogram plots will use a `filterval` of
1.e-6 while the "magpct" plots will use no filter (it
compares everything except perfect zeros).
doabsmax : bool; must be named; optional
If True, compare only absolute maximums.
shortabsmax : bool; must be named; optional
If True, set ``doabsmax=True`` and do not print the max1 and
min1 columns.
roundvals : integer; must be named; optional
Round max & min numbers at specified decimal. If negative, no
rounding.
rowhdr : string; must be named; optional
Header for row number column
deschdr : string; must be named; optional
Header for description column
maxhdr : string; must be named; optional
Header for the column 1 data
minhdr : string; must be named; optional
Header for the column 2 data
absmhdr : string; must be named; optional
Header for abs-max column
perpage : integer; must be named; optional
The number of lines to write perpage. If < 1, there is no
limit (one page).
tight_layout_args : dict or None; must be named; optional
Arguments for :func:`matplotlib.pyplot.tight_layout`. If None,
defaults to ``{'pad': 3.0}``.
show_figures : bool; must be named; optional
If True, plot figures will be displayed on the screen for
interactive viewing. Warning: there may be many figures.
align_by_label : bool; must be named; optional
If True, use labels to align the two sets of data for
comparison. See note above under the `mxmn2` option.
Returns
-------
pdiff_info : dict
Dictionary with 'amx' (abs-max), 'mx' (max), and 'mn' keys:
.. code-block:: none
<class 'dict'>[n=3]
'amx': <class 'dict'>[n=5]
'hsto' : float64 ndarray 33 elems: (11, 3)
'mag' : [n=2]: (float64 ndarray: (100,), ...
'pct' : float64 ndarray 100 elems: (100,)
'prtpv': bool ndarray 100 elems: (100,)
'spct' : [n=100]: [' -2.46', ' -1.50', ...
'mn' : <class 'dict'>[n=5]
'hsto' : float64 ndarray 33 elems: (11, 3)
'mag' : [n=2]: (float64 ndarray: (100,), ...
'pct' : float64 ndarray 100 elems: (100,)
'prtpv': bool ndarray 100 elems: (100,)
'spct' : [n=100]: [' 1.55', ' 1.53', ...
'mx' : <class 'dict'>[n=5]
'hsto' : float64 ndarray 27 elems: (9, 3)
'mag' : [n=2]: (float64 ndarray: (100,), ...
'pct' : float64 ndarray 100 elems: (100,)
'prtpv': bool ndarray 100 elems: (100,)
'spct' : [n=100]: [' -2.46', ' -1.50', ...
Where:
.. code-block:: none
'hsto' : output of :func:`histogram`: [center, count, %]
'mag' : inputs to :func:`magpct`
'pct' : percent differences
'prtpv' : rows to print partition vector
'spct' : string version of 'pct'
Examples
--------
>>> import numpy as np
>>> from pyyeti import cla
>>> ext1 = [[120.0, -8.0],
... [8.0, -120.0]]
>>> ext2 = [[115.0, -5.0],
... [10.0, -125.0]]
Run :func:`rptpct1` multiple times to get a more complete picture
of all the output (the table is very wide). Also, the plots will
be turned off for this example.
First, the header:
>>> opts = {'domagpct': False, 'dohistogram': False}
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
<BLANKLINE>
Description: No description provided
Uncertainty: Not specified
Units: Not specified
Filter: 1e-06
Notes: % Diff = +/- abs(Self-Reference)/max(abs(Reference...
Sign set such that positive % differences indicate...
Date: ...
...
Then, the max/min/absmax percent difference table in 3 calls:
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
Self Reference ...
Row Description Maximum Maximum % Diff ...
------- ----------- ------------- ------------- ------- ...
1 Row 1 120.00000 115.00000 4.35 ...
2 Row 2 8.00000 10.00000 -1.60 ...
...
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
... Self Reference ...
Row Description ... Minimum Minimum % Diff ...
------- ----------- ...------------- ------------- ------- ...
1 Row 1 ... -8.00000 -5.00000 2.61 ...
2 Row 2 ... -120.00000 -125.00000 -4.00 ...
...
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
... Self Reference
Row Description ... Abs-Max Abs-Max % Diff
------- ----------- ...------------- ------------- -------
1 Row 1 ... 120.00000 115.00000 4.35
2 Row 2 ... 120.00000 125.00000 -4.00
...
Finally, the histogram summaries:
>>> dct = cla.rptpct1(ext1, ext2, 1, **opts) # doctest: +ELLIPSIS
PERCENT DIFFERENCE REPORT
...
No description provided - Maximum Comparison Histogram
<BLANKLINE>
% Diff Count Percent
-------- -------- -------
-2.00 1 50.00
4.00 1 50.00
<BLANKLINE>
0.0% of values are within 1%
50.0% of values are within 2%
100.0% of values are within 5%
<BLANKLINE>
% Diff Statistics: [Min, Max, Mean, StdDev] = [-1.60, 4.35,...
<BLANKLINE>
<BLANKLINE>
No description provided - Minimum Comparison Histogram
<BLANKLINE>
% Diff Count Percent
-------- -------- -------
-4.00 1 50.00
3.00 1 50.00
<BLANKLINE>
0.0% of values are within 1%
100.0% of values are within 5%
<BLANKLINE>
% Diff Statistics: [Min, Max, Mean, StdDev] = [-4.00, 2.61,...
<BLANKLINE>
<BLANKLINE>
No description provided - Abs-Max Comparison Histogram
<BLANKLINE>
% Diff Count Percent
-------- -------- -------
-4.00 1 50.00
4.00 1 50.00
<BLANKLINE>
0.0% of values are within 1%
100.0% of values are within 5%
<BLANKLINE>
% Diff Statistics: [Min, Max, Mean, StdDev] = [-4.00, 4.35,...
"""
if tight_layout_args is None:
tight_layout_args = {"pad": 3.0}
if magpct_options is None:
magpct_options = {"filterval": "filterval"}
else:
magpct_options = magpct_options.copy()
# magpct_options['filterval'] get special treatment:
magpct_filterval = magpct_options["filterval"]
del magpct_options["filterval"]
if isinstance(magpct_filterval, str):
if magpct_filterval != "filterval":
raise ValueError(
"``magpct_options['filterval']`` is an invalid "
f"string: {magpct_filterval!r} (can only "
"be 'filterval' if a string)"
)
# copy the initial `filterval` setting:
magpct_filterval = filterval
infovars = (
"desc",
"filterval",
"magpct_filterval",
"labels",
"units",
"ignorepv",
"uf_reds",
)
dct = locals()
infodct = {n: dct[n] for n in infovars}
del dct
# check mxmn1:
if isinstance(mxmn1, SimpleNamespace):
sns = mxmn1.drminfo
for key, value in infodct.items():
if value is None:
infodct[key] = getattr(sns, key, None)
del sns
mxmn1 = mxmn1.ext
else:
mxmn1 = np.atleast_2d(mxmn1)
row_number = np.arange(1, mxmn1.shape[0] + 1)
# check mxmn2:
if isinstance(mxmn2, SimpleNamespace) and getattr(mxmn2, "drminfo", None):
labels2 = mxmn2.drminfo.labels
mxmn2 = mxmn2.ext
if align_by_label:
# use labels and labels2 to align data; this is in case
# the two sets of results recover some of the same items,
# but not all
mxmn1, mxmn2, row_number = _align_mxmn(
mxmn1, mxmn2, labels2, row_number, infodct
)
else:
mxmn2 = np.atleast_2d(mxmn2)
desc = infodct["desc"]
if desc is None:
desc = "No description provided"
R = mxmn1.shape[0]
if R != mxmn2.shape[0]:
raise ValueError(
f"`mxmn1` and `mxmn2` have a different number of rows: "
f"{R} vs {mxmn2.shape[0]} for category with `desc` = {desc}"
)
filterval = infodct["filterval"]
magpct_filterval = infodct["magpct_filterval"]
labels = infodct["labels"]
units = infodct["units"]
ignorepv = infodct["ignorepv"]
uf_reds = infodct["uf_reds"]
del infodct
if filterval is None:
filterval = 1.0e-6
filterval = _proc_filterval(filterval, R, "filterval")
magpct_filterval = _proc_filterval(
magpct_filterval, R, "magpct_options['filterval']"
)
if labels is None:
labels = [f"Row {i + 1:6d}" for i in range(R)]
elif len(labels) != R:
raise ValueError(
"length of `labels` does not match number"
f" of rows in `mxmn1`: {len(labels)} vs {R} for "
f"category with `desc` = {desc}"
)
if units is None:
units = "Not specified"
if numform is None:
numform = _get_numform(mxmn1)
pdhdr = "% Diff"
nastring = "n/a "
comppv = np.ones(R, bool)
if ignorepv is not None:
comppv[ignorepv] = False
# for row labels:
w = max(11, len(max(labels, key=len)))
frm = f"{{:{w}}}"
# start preparing for writer.formheader:
print_info = SimpleNamespace(
headers1=["", ""],
headers2=[rowhdr, deschdr],
formats=["{:7d}", frm],
printargs=[row_number, labels],
widths=[7, w],
seps=[0, 2],
justs=["c", "l"],
)
if shortabsmax:
doabsmax = True
if doabsmax:
use_range = False
if roundvals > -1:
mxmn1 = np.round(mxmn1, roundvals)
mxmn2 = np.round(mxmn2, roundvals)
prtbads = (prtbad, prtbadh, prtbadl)
flagbads = (flagbad, flagbadh, flagbadl)
# compute percent differences
pctinfo = {}
kwargs = dict(
names=names,
mxmn1=mxmn1,
comppv=comppv,
histogram_inc=histogram_inc,
numform=numform,
prtbads=prtbads,
flagbads=flagbads,
maxhdr=maxhdr,
minhdr=minhdr,
absmhdr=absmhdr,
pdhdr=pdhdr,
nastring=nastring,
doabsmax=doabsmax,
shortabsmax=shortabsmax,
print_info=print_info,
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", r"All-NaN (slice|axis) encountered")
mx1 = np.nanmax(abs(mxmn1), axis=1)
mx2 = np.nanmax(abs(mxmn2), axis=1)
if not doabsmax:
max1, min1 = mxmn1[:, 0], mxmn1[:, 1]
max2, min2 = mxmn2[:, 0], mxmn2[:, 1]
mxmn_b = mxmn2 if use_range else None
prtpv = np.zeros(R, bool)
for i in zip(
("mx", "mn", "amx"),
(max1, min1, mx1),
(max2, min2, mx2),
(True, False, True),
(maxhdr, minhdr, absmhdr),
):
lbl, ext1, ext2, ismax, valhdr = i
pctinfo[lbl] = _proc_pct(
ext1,
ext2,
filterval,
magpct_filterval,
mxmn_b=mxmn_b,
ismax=ismax,
valhdr=valhdr,
**kwargs,
)
prtpv |= pctinfo[lbl]["prtpv"]
prtpv &= comppv
else:
pctinfo["amx"] = _proc_pct(
mx1,
mx2,
filterval,
magpct_filterval,
mxmn_b=None,
ismax=True,
valhdr=absmhdr,
**kwargs,
)
prtpv = pctinfo["amx"]["prtpv"]
hu, frm = writer.formheader(
[print_info.headers1, print_info.headers2],
print_info.widths,
print_info.formats,
sep=print_info.seps,
just=print_info.justs,
)
# format page header:
misc = _get_filtline(filterval) + _get_noteline(use_range, names, prtbads, flagbads)
hdrs = _get_rpt_headers(desc=desc, uf_reds=uf_reds, units=units, misc=misc)
header = title + "\n\n" + hdrs + "\n"
imode = plt.isinteractive()
plt.interactive(show_figures)
try:
if domagpct:
_plot_magpct(
pctinfo,
names,
desc,
doabsmax,
filename,
magpct_options,
use_range,
maxhdr,
minhdr,
absmhdr,
show_figures,
tight_layout_args,
)
if dohistogram:
_plot_histogram(
pctinfo,
names,
desc,
doabsmax,
filename,
histogram_inc,
maxhdr,
minhdr,
absmhdr,
show_figures,
tight_layout_args,
)
finally:
plt.interactive(imode)
# write results
@ytools.write_text_file
def _wtcmp(f, header, hu, frm, printargs, perpage, prtpv, pctinfo, desc):
prtpv = prtpv.nonzero()[0]
if perpage < 1:
# one additional in case size is zero
perpage = prtpv.size + 1
pages = (prtpv.size + perpage - 1) // perpage
if prtpv.size < len(printargs[0]):
for i, item in enumerate(printargs):
printargs[i] = [item[j] for j in prtpv]
tabhead = header + hu
pager = "\n" # + chr(12)
for p in range(pages):
if p > 0:
f.write(pager)
f.write(tabhead)
b = p * perpage
e = b + perpage
writer.vecwrite(f, frm, *printargs, so=slice(b, e))
f.write(pager)
for lbl, hdr in zip(("mx", "mn", "amx"), (maxhdr, minhdr, absmhdr)):
if lbl in pctinfo:
f.write(_get_histogram_str(desc, hdr, pctinfo[lbl]))
_wtcmp(
filename, header, hu, frm, print_info.printargs, perpage, prtpv, pctinfo, desc
)
return pctinfo
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plugins/matplotlib/komand_matplotlib/actions/create_line_plot/action.py | lukaszlaszuk/insightconnect-plugins | 46 | 1608510 | <filename>plugins/matplotlib/komand_matplotlib/actions/create_line_plot/action.py
import komand
from .schema import CreateLinePlotInput, CreateLinePlotOutput
# Custom imports below
import base64
import pandas as pd
import seaborn as sns
from io import BytesIO
class CreateLinePlot(komand.Action):
def __init__(self):
super(self.__class__, self).__init__(
name="create_line_plot",
description="Create a line plot with an X/Y axis",
input=CreateLinePlotInput(),
output=CreateLinePlotOutput(),
)
def run(self, params={}):
# Set styles
sns.set_palette(params.get("color_palette"))
sns.set(style=params.get("margin_style"))
# Process the data and create the plot
try:
decoded_data = base64.b64decode(params.get("csv_data"))
except Exception as e:
error = f"Failed to decode base64 encoded CSV data with error: {e}"
self.logger.error(error)
raise e
df = pd.read_csv(BytesIO(decoded_data))
x = params.get("x_value")
y = params.get("y_value")
hue = params.get("hue")
args = {"data": df, "x": x, "y": y}
if not x or (x not in df):
error = f"Column ({x}) not in data set, cannot create plot..."
self.logger.error(error)
return Exception(error)
elif not y or (y not in df):
error = f"Column ({y}) not in data set, cannot create plot..."
self.logger.error(error)
return Exception(error)
if hue and (len(hue) > 0):
args["hue"] = hue
if hue not in df:
error = f"Column for hue ({hue}) not in data set, cannot create plot..."
self.logger.error(error)
return Exception(error)
# AxesSubplots (the plot object returned) don't have the savefig method, get the figure, then save it
self.logger.info("Creating plot...")
plot = sns.lineplot(**args)
fig = plot.get_figure()
# bbox_inches is required to ensure that labels are cut off
fig.savefig("plot.png", bbox_inches="tight")
with open(
"plot.png",
"rb",
) as f:
plot = base64.b64encode(f.read())
return {"csv": params.get("csv_data"), "plot": plot.decode("utf-8")}
def test(self):
self.logger.info("No connection required, success!")
return {"csv": "", "plot": ""}
| [
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] |
wagtailrelated/utils.py | torchbox/wagtail-related | 2 | 33710 | <reponame>torchbox/wagtail-related<gh_stars>1-10
from bs4 import BeautifulSoup
from wagtail.core.fields import StreamField
def extract_text(obj):
"""Extracts data, concatenates and removes html tags
from fields listed in a obj.related_source_fields list.
"""
related_source_fields = getattr(obj._meta.model, 'related_source_fields', None)
if not related_source_fields:
return
html_pieces = []
for source_field in related_source_fields:
field = source_field.get_field(obj.__class__)
field_value = source_field.get_value(obj)
if isinstance(field, StreamField):
field_value = ' '.join(field_value)
html_pieces.append(field_value)
text = ' '.join(html_pieces)
text = BeautifulSoup(text, 'html5lib').text
return text
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] |
python/testData/inspections/PyTypeCheckerInspection/FieldWithNoneInStub/m1.py | truthiswill/intellij-community | 2 | 128524 | class C(object):
foo = None
| [
1,
770,
315,
29898,
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1125,
13,
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7953,
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] |
crosstab/mega_analysis/lateralised_intensities.py | karandahele/Epilepsy-Repository | 2 | 24549 | import pandas as pd
from crosstab.mega_analysis.pivot_result_to_pixel_intensities import *
def lateralisation_to_pixel_intensities(all_combined_gifs, df,
semiology_term,
quantiles, method='non-linear', scale_factor=10,
intensity_label='lateralised intensity',
use_semiology_dictionary=False):
"""
runs pivot_result_to_pixel_intensities when the input has already been mapped to gifs as a result of
running QUERY_LATERALISATION.
This is the final step in the query_lateralisation pathway.
<NAME> 2019
"""
# isn't really a pivot_result but let's use consistent notations:
pivot_result = all_combined_gifs[['pt #s']].T
all_combined_gifs_intensities = pivot_result_to_pixel_intensities(pivot_result, df,
method=method, scale_factor=scale_factor, quantiles=quantiles,
use_main_df_calibration=False)
# now we just need to transpose it and add the other columns back
a2 = all_combined_gifs[['Gif Parcellations']].T
a3 = all_combined_gifs[['Semiology Term']].T
all_combined_gifs_intensities.index = [intensity_label]
all_lateralised_gifs = pd.concat([a3, a2, pivot_result, all_combined_gifs_intensities], sort=False).T
all_lateralised_gifs.loc[0, 'Semiology Term'] = str(semiology_term)
all_lateralised_gifs.loc[1, 'Semiology Term'] = 'use_semiology_dictionary='+str(use_semiology_dictionary)
return all_lateralised_gifs | [
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models/discriminator_net.py | hbin0701/c3vqg-official | 13 | 166396 | from torch import nn
class Discriminator(nn.Module):
def __init__(self, hidden_size=512):
super(Discriminator,self).__init__()
self.model = nn.Sequential(
nn.Linear(hidden_size, hidden_size//2),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(hidden_size//2, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256, 1),
# nn.Softmax()
nn.Sigmoid()
)
def forward(self, vqg, questions, qlengths): # pass object of vqg
encoder_hidden = vqg.encode_questions_discriminator(questions, qlengths)
x = encoder_hidden.view(-1, vqg.hidden_size)
verdict = self.model(x)
return verdict | [
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examples/mmLogin.py | michaelrosejr/pyaos8 | 2 | 92102 | <gh_stars>1-10
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import requests
import pprint as pp
import re
from requests.packages.urllib3.exceptions import InsecureRequestWarning
# requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
# MM Login IP and credentials
userName = 'admin'
passWord = '<PASSWORD>'
deviceIP = '10.0.1.33'
values = {'username': userName,'password': passWord}
baseURL = "https://" + deviceIP + ":4343/v1"
loginURL = "https://" + deviceIP + ":4343/v1/api/login"
logoutURL = "https://" + deviceIP + ":4343/v1/api/logout"
#node_hierarchy = "https://10.3.3.233/v1/configuration/object/node_hierarchy"
node_hierarchy = baseURL + "/configuration/object/node_hierarchy"
def getCookie(authString):
print(authString)
cookieString = str(authString)
tmpList = cookieString.split(',')
#print(tmpList[2])
tmpStr = tmpList[2]
tmpStr = tmpStr.replace('{','')
tmpStr = tmpStr.replace('}','')
tmpStr = tmpStr.replace('\"','')
tmpStr = tmpStr.strip()
print(tmpStr)
tmpList = tmpStr.split(':')
print(tmpList[1])
return(tmpList[1])
with requests.Session() as query:
#authentication login call
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
auth = query.post(loginURL, verify=False, data=values)
#print (auth)
#print("Request Content")
#print(auth.content)
#print("Cookie Jar")
#print (auth.cookies)
#print("Status code")
#print(auth.status_code)
## Clean up cookie string and extract the Session ID
UIDARUBA = getCookie(auth.content)
node_hierarchy = node_hierarchy + '?UIDARUBA=' + UIDARUBA
print(node_hierarchy)
resp = query.get(node_hierarchy, cookies=auth.cookies,verify=False)
# print(resp.content)
print(resp.text)
print("Logging out of MM")
auth = query.post(logoutURL, cookies=auth.cookies,verify=False)
#print(auth)
#print(auth.content)
print(auth.status_code)
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docs/examples/models_abc.py | jasujm/pydantic | 25 | 86099 | <filename>docs/examples/models_abc.py
import abc
from pydantic import BaseModel
class FooBarModel(BaseModel, abc.ABC):
a: str
b: int
@abc.abstractmethod
def my_abstract_method(self):
pass
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core/translator.py | bfu4/mdis | 13 | 23605 | from typing import List
from parser import parse_bytes, split_bytes_from_lines, get_bytes, parse_instruction_set, wrap_parsed_set
from reader import dump_file_hex_with_locs
class Translator:
"""
Class handling file translations from *.mpy to hex dumps and opcodes
"""
def __init__(self, file: str):
"""
Create new translator
:param file: location of the file
"""
self.file = file
def get_file_hex(self):
"""
Get a full hex dump of the file
:return:
"""
return dump_file_hex_with_locs(self.file)
def get_file_hex_at(self, _from: str, _to: str):
"""
Get a byte dump at a specified location
:param _from: from address
:param _to: to address
:return: bytes from address {_from} to address {_to}
"""
return parse_bytes(self.get_file_hex(), _from, _to)
def get_file(self):
"""
Get the file name
:return:
"""
return self.file
def get_magic(self) -> str:
"""
Get the magic number
:return:
"""
return "".join(self.get_all_bytes()[0][:8])
def get_all_bytes(self):
"""
Get all of the bytes
:return: all of the bytes
"""
return get_bytes(self.get_file_hex().split("\n"))
def get_split_bytes(self) -> List[List[str]]:
"""
Get all of the bytes per line
:return: bytes in list form
"""
split = split_bytes_from_lines(self.get_all_bytes())
split[0] = split[0][4:]
return split
def get_bytes_at(self, _from: str, _to: str) -> List[List[str]]:
"""
Get the bytes between the specified locations
:param _from: start address
:param _to: end address
:return: bytes
"""
return split_bytes_from_lines(self.get_file_hex_at(_from, _to))
def get_instruction_set(self) -> List[str]:
"""
Get the file's instruction set
:return: set
"""
bl = self.get_split_bytes()
# offset of 8, start at first BC_BASE_RESERVED
list_with_offset = bl[0][4:]
_bytes = self.__flatten([list_with_offset, bl[1]])
_set = parse_instruction_set(_bytes)
return wrap_parsed_set(_set)
def get_instructions_at(self, _from: str, _to: str) -> List[str]:
"""
Get the instructions between addresses
:param _from: start address
:param _to: end address
:return: instructions
"""
_bytes = self.__flatten(self.get_bytes_at(_from, _to))
_set = parse_instruction_set(_bytes)
return wrap_parsed_set(_set)
def __flatten(self, _list):
# Lambda replaced by def flatten due to E731
return [item for sublist in _list for item in sublist]
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Algo_Ds_Notes-master/Algo_Ds_Notes-master/A_Star_Search_Algorithm/A_Star_Search_Algorithm.py | rajatenzyme/Coding-Journey- | 0 | 21224 | class Node:
"""
A node class used in A* Pathfinding.
parent: it is parent of current node
position: it is current position of node in the maze.
g: cost from start to current Node
h: heuristic based estimated cost for current Node to end Node
f: total cost of present node i.e. : f = g + h
"""
def __init__(self , parent=None , poistion=None):
self.parent = parent
self.position = poistion
self.g = 0
self.f = 0
self.h = 0
def __eq__(self , other):
return self.position == other.position
class FindPath():
def __init__(self , maze , cost , start , end):
self.maze = maze
self.cost = cost
self.start = start
self.end = end
self.move = [ [-1, 0] , # go up
[ 0,-1] , # go left
[ 1, 0] , # go down
[ 0, 1] , # go right
[-1,-1] , # go left-up
[-1, 1] , # go left down
[ 1,-1] , # go right down
[ 1, 1] ] # go right up
def return_path(self,curr_node,cost_matrix):
path = []
no_rows , no_columns = np.shape(cost_matrix)
# here we create the initialized result maze with -1 in every position
res = [[-1 for i in range(no_columns)] for j in range(no_rows)]
#we will iterate over all parents of node and store in path
curr = curr_node
while curr is not None:
path.append(curr.position)
curr = curr.parent
path = path[::-1]
initial_value = 0
# we will insert the path in matrix
for i in range(len(path)):
res[path[i][0]][path[i][1]] = initial_value
initial_value += 1
return res
def search(self):
"""
Returns a list of tuples as a path from the given start to the given end in the given maze
"""
# we will create start node and end node
# we will initialize g, h and f value zero
start_node = Node(None, tuple(self.start))
start_node.g = 0
start_node.h = 0
start_node.f = 0
end_node = Node(None, tuple(self.end))
end_node.g = 0
end_node.h = 0
end_node.f = 0
# we need to initialize both queue and visited list
# we will find the lowest cost node to expand next
queue = []
# we will store all visited node
visited_list = []
# Add the start node
queue.append(start_node)
# calculate the maximiuim number of steps we can move in the matrix
counter = 0
max_steps = (len(self.maze) // 2) ** 10
# Get number of rows and columns
no_rows, no_columns = np.shape(self.maze)
# Loop until you find the end
while len(queue) > 0:
# Every time any node is visited increase the counter
counter += 1
# Get the current node
current_node = queue[0]
current_index = 0
for index, item in enumerate(queue):
if item.f < current_node.f:
current_node = item
current_index = index
# if we hit this point return the path such as it may be no solution or
# computation cost is too high
if counter > max_steps:
print ("Destination cannot be reached")
return self.return_path(current_node , self.maze)
# Pop current node out off
queue.pop(current_index)
# mark it visited
visited_list.append(current_node)
# check if goal is reached or not
if current_node == end_node:
return self.return_path(current_node , self.maze)
# Generate coordinate from all adjacent coordinates
coordinates = []
for move in self.move:
# Get node position
current_node_position = (current_node.position[0] + move[0] , current_node.position[1] + move[1])
# check if all the moves are in maze limit
if (current_node_position[0] > (no_rows - 1) or current_node_position[0] < 0 or current_node_position[1] > (no_columns -1) or current_node_position[1] < 0):
continue
# Make sure walkable terrain
if self.maze[current_node_position[0]][current_node_position[1]] != 0:
continue
# Create new node
new_node = Node(current_node , current_node_position)
# Append
coordinates.append(new_node)
# Loop through children
for child in coordinates:
# Child is on the visited list (search entire visited list)
if len([visited_child for visited_child in visited_list if visited_child == child]) > 0:
continue
# calculate f, g, and h values
child.g = current_node.g + self.cost
# calculated Heuristic costs, this is using eucledian distance
child.h = (((child.position[0] - end_node.position[0]) ** 2) + ((child.position[1] - end_node.position[1]) ** 2))
child.f = child.g + child.h
# Child if already in queue and g cost is already lower
if len([i for i in queue if child == i and child.g > i.g]) > 0:
continue
queue.append(child)
class Preprocess:
def __init__(self , maze , n , m):
self.maze = maze
self.n = n
self.m = m
def check(self , value):
data=''
for i in range(len(value)):
if(value[i] == '[' or value[i] == ']'):
continue
else:
data+=value[i]
return data
def process_text(self):
c=0
matrix = self.maze
matrix = matrix.split(',')
data = []
for i in range(self.n):
l = []
for j in range(self.m):
l.append(int(self.check(matrix[c])))
c += 1
data.append(l)
return data
if __name__ == '__main__':
no_rows = int(input("Enter number of rows: "))
no_cols = int(input("Enter number of columns: "))
matrix = Preprocess(str(input("Enter Matrix: ")) , no_rows , no_cols).process_text()
start_x = int(input("Enter x coordinate of starting node: "))
start_y = int(input("Enter y coordinate of starting node: "))
end_x = int(input("Enter x coordinate of ending node: "))
end_y = int(input("Enter y coordinate of ending node: "))
cost = int(input("Enter cost: "))
start = [start_x , start_y]
end = [end_x , end_y]
path = FindPath(matrix , cost , start , end).search()
if(path != None):
print("Path found: ")
for i in range(len(path)):
for j in range(len(path[i])):
if(path[i][j] == -1):
print(0 , end=" ")
else:
print(path[i][j] , end=" ")
print()
else:
print("No Path found")
#input:
# Enter number of rows: 5
# Enter number of columns: 6
# Enter Matrix: [[0, 1, 0, 0, 0, 0],
# [0, 1, 0, 0, 0, 0],
# [0, 1, 0, 1, 0, 0],
# [0, 1, 0, 0, 1, 0],
# [0, 0, 0, 0, 1, 0]]
# Enter x coordinate of starting node: 0
# Enter y coordinate of starting node: 0
# Enter x coordinate of ending node: 4
# Enter y coordinate of ending node: 5
# Enter cost: 1
#Path found:
# 0 0 0 0 0 0
# 1 0 0 0 0 0
# 2 0 0 0 7 0
# 3 0 0 6 0 8
# 0 4 5 0 0 9
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] |
aws-refresh-ssh-config.py | Eximchain/deployment-scripts | 0 | 84510 | import argparse
import boto3
import json
import time
IN_FILE = "initial-servers.json"
OUT_FILE = "temp-servers.json"
# Network ID for main network, gamma, and beta
NETWORK_ID="1"
NETWORK_ID_FILTER = {'Name': 'tag:NetworkId', 'Values': [NETWORK_ID]}
RUNNING_FILTER={'Name': 'instance-state-name', 'Values': ['running']}
ELASTIC_IP_GROUPS = ['bootnode-a', 'bootnode-b', 'observer-a', 'observer-b']
def parse_args():
parser = argparse.ArgumentParser(description='Create config for ssh-to via AWS')
parser.add_argument('--ssh-user', dest='ssh_user', default='ubuntu', action='store', help='Username that will be used to ssh instances')
parser.add_argument('--refresh-group', dest='refresh_group', required=True, action='store', help='Group from the input that is being replaced')
parser.add_argument('--no-elastic-ips', dest='no_elastic_ips', default=False, action='store_true', help='Disable the expectation for elastic IPs on bootnodes and observers')
return parser.parse_args()
def check_for_replacement(old_exim_node, expect_elastic_ips, replacements_so_far):
name_filter = {'Name': 'tag:Name', 'Values': [old_exim_node.name]}
conn = boto3.resource('ec2', region_name=old_exim_node.region)
# We distinguish 3 cases based on the contents of 'instances'
# 1. The old instance is still up and the new one is not up yet
# 2. The old instance is down but the new one is not up yet
# 3. The new instance is up
instances = conn.instances.filter(Filters=[NETWORK_ID_FILTER, RUNNING_FILTER, name_filter])
# In case 2 this loop has no iterations
for instance in instances:
temp_exim_node = EximNode.from_boto_instance(instance)
# Skip this node because it's already in our replacements list
if temp_exim_node in replacements_so_far:
print(f'Skipping matching node {temp_exim_node.instance_id} because it is already in replacements list')
continue
# This catches case 1, and case 3 where the old node still exists
if old_exim_node.instance_id == temp_exim_node.instance_id:
print(f'Old instance {old_exim_node.instance_id} for {old_exim_node.name} not yet replaced')
continue
# If we expect elastic IPs for this group and we haven't assigned it yet, keep waiting
if expect_elastic_ips and (len(instance.network_interfaces) != 1 or instance.network_interfaces[0].association_attribute["IpOwnerId"] == 'amazon'):
print(f'New instance {temp_exim_node.instance_id} expected to use elastic IP, but isn\'t associated yet')
continue
# If the hostname didn't change with the instance id this is probably a bug
assert(old_exim_node.hostname != temp_exim_node.hostname)
return temp_exim_node
return None
def wait_for_replacements(nodes_to_replace, expect_elastic_ips):
original_to_replacement = {node: None for node in nodes_to_replace}
num_unreplaced_nodes = len(original_to_replacement)
while num_unreplaced_nodes > 0:
for original_node,replacement_node in original_to_replacement.items():
if replacement_node == None:
replacements_so_far = [replacement for replacement in original_to_replacement.values() if replacement != None]
original_to_replacement[original_node] = check_for_replacement(original_node, expect_elastic_ips, replacements_so_far)
num_unreplaced_nodes = len([node for node in original_to_replacement.values() if node == None])
print(f'Still waiting for {num_unreplaced_nodes} unreplaced nodes')
if num_unreplaced_nodes > 0:
sleep_seconds = 20
print(f'Sleeping for {sleep_seconds} seconds before polling again')
time.sleep(sleep_seconds)
return original_to_replacement.values()
class EximNode:
def __init__(self, hostname, name, region, instance_id):
self.hostname = hostname
self.name = name
self.region = region
self.instance_id = instance_id
def __eq__(self, other):
if not isinstance(other, type(self)):
return False
return (self.hostname, self.name, self.region, self.instance_id) == (other.hostname, other.name, other.region, other.instance_id)
def __ne__(self, other):
return not(self == other)
def __hash__(self):
return hash((self.hostname, self.name, self.region, self.instance_id))
@classmethod
def from_json_list(cls, json_list):
assert(len(json_list) == 3)
hostname = json_list[0]
name = json_list[1]
comment = json_list[2]
split_comment = comment.split(':')
assert(len(split_comment) == 2)
region = split_comment[0]
instance_id = split_comment[1]
return cls(hostname, name, region, instance_id)
@classmethod
def from_boto_instance(cls, instance):
hostname = instance.public_dns_name
name_tag = [tag for tag in instance.tags if tag['Key'] == 'Name']
name = name_tag[0]['Value']
region_tag = [tag for tag in instance.tags if tag['Key'] == 'Region']
region = region_tag[0]['Value']
instance_id = instance.instance_id
return cls(hostname, name, region, instance_id)
# Serializes to ssh-to input style list
def to_json_list(self, args):
ssh_hostname = f'{args.ssh_user}@{self.hostname}'
comment = f'{self.region}:{self.instance_id}'
return [ssh_hostname, self.name, comment]
# Main script body
args = parse_args()
expect_elastic_ips = args.refresh_group in ELASTIC_IP_GROUPS and not args.no_elastic_ips
with open(IN_FILE, 'r') as f:
input = json.load(f)
nodes_to_replace = [EximNode.from_json_list(node) for node in input[args.refresh_group]]
replacement_nodes = wait_for_replacements(nodes_to_replace, expect_elastic_ips)
replacement_node_json_list = [node.to_json_list(args) for node in replacement_nodes]
output = {args.refresh_group: replacement_node_json_list}
print(f'Dumping server config with replacement instances for group {args.refresh_group} to {OUT_FILE}')
with open(OUT_FILE, 'w') as f:
json.dump(output, f, indent=2) | [
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tests/autobahn_test.py | andreasbossard/deutschland | 445 | 141231 | from pprint import pprint
from deutschland import autobahn
from deutschland.autobahn.api import default_api
autobahn_api_instance = default_api.DefaultApi()
try:
# Auflistung aller Autobahnen
api_response = autobahn_api_instance.list_autobahnen()
pprint(api_response)
# Details zu einer Ladestation
station_id = "RUxFQ1RSSUNfQ0hBUkdJTkdfU1RBVElPTl9fMTczMzM=" # str |
api_response = autobahn_api_instance.get_charging_station(station_id)
pprint(api_response)
except autobahn.ApiException as e:
print("Exception when calling DefaultApi->get_charging_station: %s\n" % e)
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server/processes/migrations/0132_auto_20201108_0540.py | CloudReactor/task_manager | 0 | 5448 | # Generated by Django 2.2.14 on 2020-11-08 05:40
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('processes', '0131_auto_20201107_2316'),
]
operations = [
migrations.RunSQL(
"UPDATE processes_workflow SET run_environment_id = scheduling_run_environment_id WHERE run_environment_id IS NULL;",
reverse_sql='',
),
]
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test/tool_shed/base/test_db_util.py | thata/galaxy-dist | 2 | 61139 | import galaxy.model
import galaxy.webapps.community.model as model
from galaxy.model.orm import *
from galaxy.webapps.community.model.mapping import context as sa_session
from galaxy.model.mapping import context as ga_session
def delete_obj( obj ):
sa_session.delete( obj )
sa_session.flush()
def delete_user_roles( user ):
for ura in user.roles:
sa_session.delete( ura )
sa_session.flush()
def flush( obj ):
sa_session.add( obj )
sa_session.flush()
def get_all_repositories():
return sa_session.query( model.Repository ).all()
def get_all_installed_repositories( actually_installed=False ):
if actually_installed:
return ga_session.query( galaxy.model.ToolShedRepository ) \
.filter( and_( galaxy.model.ToolShedRepository.table.c.deleted == False,
galaxy.model.ToolShedRepository.table.c.uninstalled == False,
galaxy.model.ToolShedRepository.table.c.status == galaxy.model.ToolShedRepository.installation_status.INSTALLED ) ) \
.all()
else:
return ga_session.query( galaxy.model.ToolShedRepository ).all()
def get_category_by_name( name ):
return sa_session.query( model.Category ) \
.filter( model.Category.table.c.name == name ) \
.first()
def get_default_user_permissions_by_role( role ):
return sa_session.query( model.DefaultUserPermissions ) \
.filter( model.DefaultUserPermissions.table.c.role_id == role.id ) \
.all()
def get_default_user_permissions_by_user( user ):
return sa_session.query( model.DefaultUserPermissions ) \
.filter( model.DefaultUserPermissions.table.c.user_id==user.id ) \
.all()
def get_galaxy_repository_by_name_owner_changeset_revision( repository_name, owner, changeset_revision ):
return ga_session.query( galaxy.model.ToolShedRepository ) \
.filter( and_( galaxy.model.ToolShedRepository.table.c.name == repository_name,
galaxy.model.ToolShedRepository.table.c.owner == owner,
galaxy.model.ToolShedRepository.table.c.changeset_revision == changeset_revision ) ) \
.first()
def get_installed_repository_by_id( repository_id ):
return ga_session.query( galaxy.model.ToolShedRepository ) \
.filter( galaxy.model.ToolShedRepository.table.c.id == repository_id ) \
.first()
def get_installed_repository_by_name_owner( repository_name, owner ):
return ga_session.query( galaxy.model.ToolShedRepository ) \
.filter( and_( galaxy.model.ToolShedRepository.table.c.name == repository_name,
galaxy.model.ToolShedRepository.table.c.owner == owner ) ) \
.first()
def get_private_role( user ):
for role in user.all_roles():
if role.name == user.email and role.description == 'Private Role for %s' % user.email:
return role
raise AssertionError( "Private role not found for user '%s'" % user.email )
def get_repository_by_id( repository_id ):
return sa_session.query( model.Repository ) \
.filter( model.Repository.table.c.id == repository_id ) \
.first()
def get_repository_review_by_user_id_changeset_revision( user_id, repository_id, changeset_revision ):
review = sa_session.query( model.RepositoryReview ) \
.filter( and_( model.RepositoryReview.table.c.user_id == user_id,
model.RepositoryReview.table.c.repository_id == repository_id,
model.RepositoryReview.table.c.changeset_revision == changeset_revision ) ) \
.first()
return review
def get_role_by_name( role_name ):
return sa_session.query( model.Role ) \
.filter( model.Role.table.c.name == role_name ) \
.first()
def get_user( email ):
return sa_session.query( model.User ) \
.filter( model.User.table.c.email==email ) \
.first()
def get_user_by_name( username ):
return sa_session.query( model.User ) \
.filter( model.User.table.c.username==username ) \
.first()
def mark_obj_deleted( obj ):
obj.deleted = True
sa_session.add( obj )
sa_session.flush()
def refresh( obj ):
sa_session.refresh( obj )
def ga_refresh( obj ):
ga_session.refresh( obj )
def get_galaxy_private_role( user ):
for role in user.all_roles():
if role.name == user.email and role.description == 'Private Role for %s' % user.email:
return role
raise AssertionError( "Private role not found for user '%s'" % user.email )
def get_galaxy_user( email ):
return ga_session.query( galaxy.model.User ) \
.filter( galaxy.model.User.table.c.email==email ) \
.first()
def get_repository_by_name_and_owner( name, owner_username ):
owner = get_user_by_name( owner_username )
repository = sa_session.query( model.Repository ) \
.filter( and_( model.Repository.table.c.name == name,
model.Repository.table.c.user_id == owner.id ) ) \
.first()
return repository
def get_repository_metadata_by_repository_id_changeset_revision( repository_id, changeset_revision ):
repository_metadata = sa_session.query( model.RepositoryMetadata ) \
.filter( and_( model.RepositoryMetadata.table.c.repository_id == repository_id,
model.RepositoryMetadata.table.c.changeset_revision == changeset_revision ) ) \
.first()
return repository_metadata
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] |
backstack/__init__.py | pixlie/platform | 2 | 21837 | <reponame>pixlie/platform
from .models import SystemModel, BaseModel
from .errors import ServerError, Errors
from .config import settings
from .db import db, Base
from .commands import Commands
name = "platform"
__all__ = [
"name",
"SystemModel",
"BaseModel",
"ServerError",
"Errors",
"settings",
"db",
"Base",
"Commands",
]
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backend/security/migrations/8036f244db77_create_user_and_role.py | anu-act-health-covid19-support/hospital-demand-webapp-backup | 558 | 143754 | """
create user and role
Revision ID: 8036f244db77
Revises:
Create Date: 2017-10-11 17:22:00.467780
"""
from alembic import op
import sqlalchemy as sa
import backend
# revision identifiers, used by Alembic.
revision = '8036f244db77'
down_revision = None
branch_labels = ('security',)
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('role',
sa.Column('id', sa.BigInteger(), nullable=False),
sa.Column('created_at', backend.database.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('updated_at', backend.database.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('name', sa.String(length=50), nullable=False),
sa.Column('description', sa.String(length=255), nullable=True),
sa.PrimaryKeyConstraint('id', name=op.f('pk_role'))
)
op.create_index(op.f('ix_role_name'), 'role', ['name'], unique=True)
op.create_table('user',
sa.Column('id', sa.BigInteger(), nullable=False),
sa.Column('created_at', backend.database.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('updated_at', backend.database.types.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
sa.Column('username', sa.String(length=50), nullable=False),
sa.Column('email', sa.String(length=50), nullable=False),
sa.Column('first_name', sa.String(length=32), nullable=False),
sa.Column('last_name', sa.String(length=64), nullable=False),
sa.Column('password', sa.String(), nullable=True),
sa.Column('active', sa.Boolean(name='active'), nullable=False),
sa.Column('confirmed_at', backend.database.types.DateTime(timezone=True), nullable=True),
sa.PrimaryKeyConstraint('id', name=op.f('pk_user'))
)
op.create_index(op.f('ix_user_email'), 'user', ['email'], unique=True)
op.create_index(op.f('ix_user_username'), 'user', ['username'], unique=True)
op.create_table('user_role',
sa.Column('user_id', sa.BigInteger(), nullable=False),
sa.Column('role_id', sa.BigInteger(), nullable=False),
sa.ForeignKeyConstraint(['role_id'], ['role.id'], name=op.f('fk_user_role_role_id_role')),
sa.ForeignKeyConstraint(['user_id'], ['user.id'], name=op.f('fk_user_role_user_id_user')),
sa.PrimaryKeyConstraint('user_id', 'role_id', name=op.f('pk_user_role'))
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('user_role')
op.drop_index(op.f('ix_user_username'), table_name='user')
op.drop_index(op.f('ix_user_email'), table_name='user')
op.drop_table('user')
op.drop_index(op.f('ix_role_name'), table_name='role')
op.drop_table('role')
# ### end Alembic commands ###
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] |
Subsets and Splits