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Python/Display/display.py | alexanderkrauck/Weatherstation | 0 | 61387 | # Display the ip's from all network interfaces on the display
import time
import Adafruit_Nokia_LCD as LCD
import Adafruit_GPIO.SPI as SPI
import netifaces
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
# Raspberry Pi hardware SPI config:
DC = 23
RST = 24
SPI_PORT = 0
SPI_DEVICE = 0
# Raspberry Pi software SPI config:
# SCLK = 4
# DIN = 17
# DC = 23
# RST = 24
# CS = 8
# Beaglebone Black hardware SPI config:
# DC = 'P9_15'
# RST = 'P9_12'
# SPI_PORT = 1
# SPI_DEVICE = 0
# Beaglebone Black software SPI config:
# DC = 'P9_15'
# RST = 'P9_12'
# SCLK = 'P8_7'
# DIN = 'P8_9'
# CS = 'P8_11'
# Hardware SPI usage:
disp = LCD.PCD8544(DC, RST, spi=SPI.SpiDev(SPI_PORT, SPI_DEVICE, max_speed_hz=4000000))
# Software SPI usage (defaults to bit-bang SPI interface):
#disp = LCD.PCD8544(DC, RST, SCLK, DIN, CS)
# Initialize library.
disp.begin(contrast=60)
# Clear display.
disp.clear()
disp.display()
# Create blank image for drawing.
# Make sure to create image with mode '1' for 1-bit color.
image = Image.new('1', (LCD.LCDWIDTH, LCD.LCDHEIGHT))
# Get drawing object to draw on image.
draw = ImageDraw.Draw(image)
# Draw a white filled box to clear the image.
draw.rectangle((0,0,LCD.LCDWIDTH,LCD.LCDHEIGHT), outline=255, fill=255)
# Load default font.
font = ImageFont.load_default()
# Refresh the ip's every second.
while True:
disp.clear();
# Draw a white filled box to clear the image.
draw.rectangle((0,0,LCD.LCDWIDTH,LCD.LCDHEIGHT), outline=255, fill=255)
cnt = 0
for i in netifaces.interfaces():
try:
ip = str(netifaces.ifaddresses(i)[2][0]['addr'])
# only display interfaces which aren't local
if ip != "127.0.0.1" :
draw.text((1,10 * cnt), i, font=font)
draw.text((6,10 * cnt + 10), ip, font=font)
cnt += 2
except:
continue
# Display image.
disp.image(image)
disp.display()
time.sleep(1.0)
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] |
agents/seqseq_agent.py | sidg54/textgeneration | 0 | 103251 | r'''
'''
# standard library imports
from __future__ import absolute_import
import math
import tqdm
import random
import shutil
from os import getcwd
from datetime import datetime
# third party imports
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.backends import cudnn
from torch.autograd import Variable
from torch.utils.tensorboard import SummaryWriter
from termcolor import cprint
# internal imports
from agents.base_agent import BaseAgent
cudnn.benchmark = True
class Seq2SeqAgent(BaseAgent):
'''
Class that implements BaseAgent to train
a sequence to sequence agent.
Arguments
---------
config : obj
Configuration object.
'''
def __init__(self, config):
''' Initializes an instance of the Seq2SeqAgent class. '''
super().__init__(config)
self.start = datetime.now()
self.end = None
self.save_filepath = getcwd() + self.config.checkpoint_dir
self.cuda_avail = torch.cuda.is_available()
if self.cuda_avail and not self.config.cuda:
self.logger.info('WARNING :: CUDA is available but is not being used.')
self.cuda = self.cuda_avail and self.config.cuda
self.manual_seed = self.config.seed
if self.manual_seed <= 0:
self.manual_seed = random.randint(1, 10000):
self.logger.info('INFO :: Random seed used.')
self.logger.info('SEED :: ', self.manual_seed)
random.seed(self.manual_seed)
if self.cuda:
self.device = torch.device('cuda')
torch.cuda.set_device(self.config.gpu_device)
torch.manual_seed(self.manual_seed)
self.logger.info('CUDA in use.')
show_gpu()
else:
self.device = torch.device('cpu')
torch.manual_seed(self.manual_seed)
self.logger.info('CPU in use (not on CUDA)')
# data loading
# set epoch and batch number tracking
self.current_epoch = 0
self.num_epochs = self.config.num_epochs
self.current_batch = 0
# define models and optimizers
self.enc_class = globals()[self.config.encoder]
self.dec_class = globals()[self.config.decoder]
self.encoder = self.
# loss
# send nn's to selected device
# load checkpoint if chosen
try:
self.load_checkpoint(self.config.checkpoint_file)
except AttributeError:
pass
# checkpoint file for saving models
self.checkpoint_file = self.config.checkpoint_file
self.is_best = False
# tensorboard
self.writer = SummaryWriter(
log_dir=getcwd() + self.config.log_dir,
comment='Seq2Seq'
)
def load_checkpoint(self, file_name='seq2seq.pth'):
''' Loads the checkpoint from the given file. '''
filepath = self.config.checkpoint_dir + file_name
try:
self.logger.info(f'Loading checkpoint from {filepath}')
checkpoint = torch.load(filepath)
self.current_epoch = checkpoint['epoch']
self.current_batch = checkpoint['batch']
self.manual_seed = checkpoint['manual_seed']
self.logger.info(f'Checkpoint loaded from {filepath}\t\t::\t\t\
Currently at batch {self.current_batch} of epoch {self.current_epoch}.')
except OSError:
self.logger.info(f'No checkpint at specified file path {filepath}.\
Skipping for now.')
self.logger.info('Checkpoint load failure most likely because filepath is incorrect \
or this is the first training run.')
def save_checkpoint(self, file_name='seq2seq.pth', is_best=False):
'''
Saves the model to the given file.
Arguments
---------
file_name : str (default='seq2seq.pth')
Name of the file to save the model to.
is_best : bool (default=False)
If true, this is the best model to date.
'''
state = {
'epoch': self.current_epoch,
'batch': self.current_batch,
'manual_seed': self.manual_seed
}
self.save_filepath = getcwd() + self.config.checkpoint_dir + file_name
while True:
try:
torch.save(state, self.save_filepath)
break
except FileNotFoundError:
cprint('ERROR :: File not found.', 'red')
cprint('INFO :: Creating file and trying to checkpoint again', 'yellow')
f = optn(self.save_filepath, 'w+')
if is_best:
shutil.copyfile(self.save_filepath, self.config.checkpoint_dir + 'best_model.pth')
def run(self):
''' Runs the agent. '''
try:
losses = self.train()
return losses
except Exception as e:
raise e
def train(self):
''' Function to train the model. '''
losses = 0
self.start = datetime.now()
for epoch in range(self.current_epoch, self.config.num_epochs):
self.current_epoch = epoch
self.train_one_epoch()
self.save_checkpoint(file_name=self.checkpoint_file, is_best=False)
self.end = datetime.now()
return losses
def train_one_epoch(self):
''' Runs a single training epoch. '''
pass
def validate(self):
''' Runs a cycle of validation on the model. '''
pass
def finalize(self):
''' Finalizes training. '''
pass | [
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testscripts/RDKB/component/WIFIAgent/TS_WIFIAGENT_CheckTelemetryMarkerCHUTIL_2_Logging.py | rdkcmf/rdkb-tools-tdkb | 0 | 37925 | <filename>testscripts/RDKB/component/WIFIAgent/TS_WIFIAGENT_CheckTelemetryMarkerCHUTIL_2_Logging.py
##########################################################################
# If not stated otherwise in this file or this component's Licenses.txt
# file the following copyright and licenses apply:
#
# Copyright 2021 RDK Management
#
# 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.
##########################################################################
'''
<?xml version="1.0" encoding="UTF-8"?><xml>
<id/>
<version>2</version>
<name>TS_WIFIAGENT_CheckTelemetryMarkerCHUTIL_2_Logging</name>
<primitive_test_id/>
<primitive_test_name>WIFIAgent_Get</primitive_test_name>
<primitive_test_version>1</primitive_test_version>
<status>FREE</status>
<synopsis>To check if the channel utilization marker CHUTIL_2 logging is happening according to the log interval set with Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval.</synopsis>
<groups_id/>
<execution_time>20</execution_time>
<long_duration>false</long_duration>
<advanced_script>false</advanced_script>
<remarks/>
<skip>false</skip>
<box_types>
<box_type>Broadband</box_type>
<box_type>RPI</box_type>
</box_types>
<rdk_versions>
<rdk_version>RDKB</rdk_version>
</rdk_versions>
<test_cases>
<test_case_id>TC_WIFIAGENT_148</test_case_id>
<test_objective>To check if the channel utilization marker CHUTIL_2 logging is happening according to the log interval set with Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval.</test_objective>
<test_type>Positive</test_type>
<test_setup>Broadband, RPI</test_setup>
<pre_requisite>1.Ccsp Components should be in a running state else invoke cosa_start.sh manually that includes all the ccsp components and TDK Component
2.TDK Agent should be in running state or invoke it through StartTdk.sh script</pre_requisite>
<api_or_interface_used>None</api_or_interface_used>
<input_parameters>ParamName : Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval
ParamValue : 30 or 60
Type : int
</input_parameters>
<automation_approch>1. Load the modules
2. Get the initial value of Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval.
3. If the initial value is 30, set it to 60 else set the value of Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval to 30.
4. Check if the log file wifihealth.txt is present under /rdklogs/logs.
5. Get the initial count of the telemetry marker CHUTIL_2 and store it.
6. Sleep for a wait time of sum of initial value of Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval and the new value set.
7. After the wait time, check the final count of the telemetry marker CHUTIL_2 and compute the difference with the initial value.
8. The difference should be greater than or equal to 2.
9. Revert Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval to initial value.
10. Unload the modules.</automation_approch>
<expected_output>The channel utilization marker CHUTIL_2 logging should happen according to the log interval set with Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval.</expected_output>
<priority>High</priority>
<test_stub_interface>wifiagent</test_stub_interface>
<test_script>TS_WIFIAGENT_CheckTelemetryMarkerCHUTIL_2_Logging</test_script>
<skipped>No</skipped>
<release_version>M93</release_version>
<remarks/>
</test_cases>
<script_tags/>
</xml>
'''
def getLogFileTotalLinesCount(tdkTestObj, string, step):
cmd = "grep -ire " + "\"" + string + "\" " + "/rdklogs/logs/wifihealth.txt | wc -l";
expectedresult="SUCCESS";
tdkTestObj.addParameter("command",cmd);
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details = tdkTestObj.getResultDetails();
print "\n*********************************************";
print "TEST STEP %d : Get the number of log lines currently present" %step;
print "EXPECTED RESULT %d : Should get the number of log lines currently present" %step;
print "Query : %s" %cmd;
count = 0;
if expectedresult in actualresult:
count = int(tdkTestObj.getResultDetails().strip().replace("\\n", ""));
tdkTestObj.setResultStatus("SUCCESS");
print "ACTUAL RESULT %d: Successfully captured the number of log lines present : %d" %(step, count);
print "[TEST EXECUTION RESULT] : SUCCESS";
else:
tdkTestObj.setResultStatus("FAILURE");
print "ACTUAL RESULT %d: Failed to capture the number of log lines present : %s" %(step, details);
print "[TEST EXECUTION RESULT] : FAILURE";
print "*********************************************\n";
return count,step;
# use tdklib library,which provides a wrapper for tdk testcase script
import tdklib;
from time import sleep;
#Test component to be tested
obj = tdklib.TDKScriptingLibrary("pam","RDKB");
sysObj = tdklib.TDKScriptingLibrary("sysutil","RDKB");
#IP and Port of box, No need to change,
#This will be replaced with corresponding DUT Ip and port while executing script
ip = <ipaddress>
port = <port>
obj.configureTestCase(ip,port,'TS_WIFIAGENT_CheckTelemetryMarkerCHUTIL_2_Logging');
sysObj.configureTestCase(ip,port,'TS_WIFIAGENT_CheckTelemetryMarkerCHUTIL_2_Logging');
#Get the result of connection with test component and DUT
loadmodulestatus=obj.getLoadModuleResult();
sysutilloadmodulestatus=sysObj.getLoadModuleResult();
if "SUCCESS" in loadmodulestatus.upper() and "SUCCESS" in sysutilloadmodulestatus.upper():
#Set the result status of execution
obj.setLoadModuleStatus("SUCCESS");
sysObj.setLoadModuleStatus("SUCCESS");
expectedresult="SUCCESS";
step = 1;
#Check whether the wifihealth.txt file is present or not
tdkTestObj = sysObj.createTestStep('ExecuteCmd');
cmd = "[ -f /rdklogs/logs/wifihealth.txt ] && echo \"File exist\" || echo \"File does not exist\"";
tdkTestObj.addParameter("command",cmd);
expectedresult="SUCCESS";
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details = tdkTestObj.getResultDetails().strip().replace("\\n", "");
print "\nTEST STEP %d: Check for wifihealth log file presence" %step;
print "EXPECTED RESULT %d:wifihealth log file should be present" %step;
if details == "File exist":
tdkTestObj.setResultStatus("SUCCESS");
print "ACTUAL RESULT %d:wifihealth log file is present" %step;
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
step = step + 1;
#Get the value of Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval
tdkTestObj = obj.createTestStep('pam_GetParameterValues');
tdkTestObj.addParameter("ParamName","Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval");
#Execute the test case in DUT
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
initial_value = tdkTestObj.getResultDetails();
print "\nTEST STEP %d: Get the TELEMETRY Channel Utility LogInterval from Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval" %step;
print "EXPECTED RESULT %d: Should get the TELEMETRY Channel Utility LogInterval from Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval" %step;
if expectedresult in actualresult and initial_value != "":
DeflogInt = int(initial_value);
tdkTestObj.setResultStatus("SUCCESS");
print "ACTUAL RESULT %d: TELEMETRY Channel Utility LogInterval: %d" %(step,DeflogInt);
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
if DeflogInt == 30:
newlogInt = "60";
else:
newlogInt = "30";
#Set the LogInterval to newlogInt, the set is cross checked with get
step = step + 1;
tdkTestObj = obj.createTestStep('pam_SetParameterValues');
tdkTestObj.addParameter("ParamName","Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval");
tdkTestObj.addParameter("ParamValue",newlogInt);
tdkTestObj.addParameter("Type","int");
expectedresult="SUCCESS";
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details = tdkTestObj.getResultDetails();
print "\nTEST STEP %d: Set the TELEMETRY Channel Utility LogInterval to %ss" %(step, newlogInt);
print "EXPECTED RESULT %d: Should set the TELEMETRY Channel Utility LogInterval to %ss" %(step, newlogInt);
if expectedresult in actualresult:
tdkTestObj.setResultStatus("SUCCESS");
print "ACTUAL RESULT %d: TELEMETRY Channel Utility LogInterval: %s" %(step,details);
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
print "\nGet the number of log lines \"CHUTIL_2\" in /rdklogs/logs/wifihealth.txt";
step = step + 1;
tdkTestObj1 = sysObj.createTestStep('ExecuteCmd');
log = "CHUTIL_2";
no_of_lines_initial,step = getLogFileTotalLinesCount(tdkTestObj1, log, step);
print "The initial number of log lines \"CHUTIL_2\" in wifihealth.txt is : %d" %no_of_lines_initial;
#Sleeping for initial telemetry interval DeflogInt + newlogInt
sleep_time = DeflogInt + int(newlogInt);
print "\nSleeping for duration : %d to check if the logging is happening according to the new log interval set" %sleep_time;
sleep(sleep_time);
print "\nGet the final number of log lines \"CHUTIL_2\" in /rdklogs/logs/wifihealth.txt";
step = step + 1;
tdkTestObj1 = sysObj.createTestStep('ExecuteCmd');
log = "CHUTIL_2";
no_of_lines_final,step = getLogFileTotalLinesCount(tdkTestObj1, log, step);
print "The initial number of log lines \"CHUTIL_2\" in wifihealth.txt is : %d" %no_of_lines_final;
step = step + 1;
difference = no_of_lines_final - no_of_lines_initial;
print "\nThe CHUTIL_2 log lines can be >= 2, after accounting for the initial log interval and the new log interval set";
print "TEST STEP %d: Should get CHUTIL_2 markers count greater than or equal to 2" %step;
print "EXPECTED RESULT %d: The CHUTIL_2 markers count should be greater than or equal to 2" %step;
if difference >= 2:
tdkTestObj.setResultStatus("SUCCESS");
print "ACTUAL RESULT %d: Number of new CHUTIL_2 markers are : %d" %(step, difference);
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
else:
tdkTestObj.setResultStatus("FAILURE");
print "ACTUAL RESULT %d: Number of new CHUTIL_2 markers are : %d" %(step, difference);
#Get the result of execution
print "[TEST EXECUTION RESULT] : FAILURE";
else:
tdkTestObj.setResultStatus("FAILURE");
print "ACTUAL RESULT %d: Set operation failed" %(step);
#Get the result of execution
print "[TEST EXECUTION RESULT] :FAILURE";
#Revert the Value
step = step + 1;
tdkTestObj = obj.createTestStep('pam_SetParameterValues');
tdkTestObj.addParameter("ParamName","Device.DeviceInfo.X_RDKCENTRAL-COM_WIFI_TELEMETRY.ChUtilityLogInterval");
tdkTestObj.addParameter("ParamValue",initial_value);
tdkTestObj.addParameter("Type","int");
expectedresult="SUCCESS";
tdkTestObj.executeTestCase(expectedresult);
actualresult = tdkTestObj.getResult();
details = tdkTestObj.getResultDetails();
print "\nTEST STEP %d: Revert the TELEMETRY Channel Utility LogInterval to initial value" %step;
print "EXPECTED RESULT %d: Should revert the TELEMETRY Channel Utility LogInterval to initial value" %step;
if expectedresult in actualresult:
tdkTestObj.setResultStatus("SUCCESS");
print "ACTUAL RESULT %d: Revert successful" %step;
#Get the result of execution
print "[TEST EXECUTION RESULT] : SUCCESS";
else:
tdkTestObj.setResultStatus("FAILURE");
print "ACTUAL RESULT %d: Revertion failed" %step;
#Get the result of execution
print "[TEST EXECUTION RESULT] : FAILURE";
else:
tdkTestObj.setResultStatus("FAILURE");
print "ACTUAL RESULT %d: TELEMETRY Channel Utility LogInterval: %s" %(step,details);
#Get the result of execution
print "[TEST EXECUTION RESULT] : FAILURE";
else:
tdkTestObj.setResultStatus("FAILURE");
print "ACTUAL RESULT %d:wifihealth log file is not present" %step;
#Get the result of execution
print "[TEST EXECUTION RESULT] :FAILURE";
obj.unloadModule("pam")
sysObj.unloadModule("sysutil");
else:
print "Failed to load module";
obj.setLoadModuleStatus("FAILURE");
sysObj.setLoadModuleStatus("FAILURE");
print "Module loading failed";
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] |
sectiondoc/sections/item_list.py | itziakos/sectiondoc | 0 | 124876 | # -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# License: LICENSE.TXT
# Author: <NAME>
#
# Copyright (c) 2011-14, Enthought, Inc.
# All rights reserved.
# -----------------------------------------------------------------------------
from sectiondoc.util import add_indent
from sectiondoc.items import OrDefinitionItem
from sectiondoc.renderers import ListItem
def item_list(doc, header, renderer=ListItem, item_class=OrDefinitionItem):
""" Render the section to sphinx friendly item list.
Arguments
---------
doc : DocRender
The docstring container.
header : str
The header name that is used for the fields (i.e. ``:<header>:``).
renderer : Renderer
A renderer instance to render the items.
item_class : type
The item parser class to use. Default is :class:`~.OrDefinitionItem`.
"""
items = doc.extract_items(item_class)
lines = [':{0}:'.format(header.lower())]
prefix = None if len(items) == 1 else '-'
renderer = renderer()
for item in items:
renderer.item = item
lines += add_indent(renderer.to_rst(prefix))
lines.append('')
return lines
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src/pysolo/c_wrapper/function_alias.py | NCAR/lrose-solo-python | 0 | 1600957 | # this module handles mapping of C-function names
# also handles which library to import based on platform
import platform
from pathlib import Path
import ctypes
aliases = {}
# list of functions PySolo uses
functions = [
"assert_bad_flags",
"assign_value",
"bad_flags_logic",
"BB_unfold_first_good_gate",
"BB_unfold_local_wind",
"clear_bad_flags",
"copy_bad_flags",
"do_clear_bad_flags_array",
"despeckle",
"flagged_add",
"flag_freckles",
"flag_glitches",
"funfold",
"merge_fields",
"radial_shear",
"rain_rate",
"ring_zap",
"set_bad_flags",
"threshold_field",
"remove_ac_motion",
"remove_storm_motion",
]
# from this script file, go up two directories (pysolo) then into libs/libSolo...
pysolo_dir = Path(__file__).parents[1].absolute()
# get appropriate library depending on platform
# the DLL from Windows has "extern C" so no name mangling occurs.
if (platform.system() == "Windows"):
path_to_file = pysolo_dir / Path('libs/solo.dll')
c_lib = ctypes.CDLL(str(path_to_file))
for function in functions:
aliases[function] = c_lib["se_" + function]
# Linux, on the other hand, has name manging.
# Algorithm was required to retrieve the functions from the mangled names.
else:
import os
import re
temp_dir = pysolo_dir / Path("temp")
shared_lib_path = Path(__file__).parents[1].absolute() / Path('libs/libSolo_18.04.so')
# run readelf to get a list of C-functions with their mangled names, save results to file
os.system("mkdir %s" % temp_dir)
os.system("readelf -Ws %s > %s" % (shared_lib_path, temp_dir / Path("readelf.txt")))
# read all content from the command
content = open(temp_dir / Path("readelf.txt")).read()
# use regex pattern to discover all mangled functions, retrieve the unmangled name from group
matches = re.findall(r'(_Z\w+se_\w+)', content, re.M)
# initialize c_types object for shared library
c_lib = ctypes.CDLL(str(shared_lib_path))
# iterate through matches, check if PySolo uses that function. If so, save to map.
for match in matches:
for func in functions:
if func in match:
aliases[func] = c_lib[match]
# remove command output.
os.system("rm -r %s" % temp_dir)
# make sure all the needed functions are mapped
for func in functions:
assert func in aliases, func
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pytorch_fm/torchfm/dataset/avazu.py | qianrenjian/hy_learn | 0 | 1613084 | import shutil
import struct
from collections import defaultdict
from pathlib import Path
import lmdb
import numpy as np
import torch.utils.data
from tqdm import tqdm
class AvazuDataset(torch.utils.data.Dataset):
"""
Avazu Click-Through Rate Prediction Dataset
Dataset preparation
Remove the infrequent features (appearing in less than threshold instances) and treat them as a single feature
:param dataset_path: avazu train path
:param cache_path: lmdb cache path
:param rebuild_cache: If True, lmdb cache is refreshed
:param min_threshold: infrequent feature threshold
Reference
https://www.kaggle.com/c/avazu-ctr-prediction
"""
def __init__(self, dataset_path=None, cache_path='.avazu', rebuild_cache=False, min_threshold=4):
self.NUM_FEATS = 22
self.min_threshold = min_threshold
if rebuild_cache or not Path(cache_path).exists():
shutil.rmtree(cache_path, ignore_errors=True)
if dataset_path is None:
raise ValueError('create cache: failed: dataset_path is None')
self.__build_cache(dataset_path, cache_path)
self.env = lmdb.open(cache_path, create=False, lock=False, readonly=True)
with self.env.begin(write=False) as txn:
self.length = txn.stat()['entries'] - 1
self.field_dims = np.frombuffer(txn.get(b'field_dims'), dtype=np.uint32)
def __getitem__(self, index):
with self.env.begin(write=False) as txn:
np_array = np.frombuffer(
txn.get(struct.pack('>I', index)), dtype=np.uint32).astype(dtype=np.long)
return np_array[1:], np_array[0]
def __len__(self):
return self.length
def __build_cache(self, path, cache_path):
feat_mapper, defaults = self.__get_feat_mapper(path)
with lmdb.open(cache_path, map_size=int(1e11)) as env:
field_dims = np.zeros(self.NUM_FEATS, dtype=np.uint32)
for i, fm in feat_mapper.items():
field_dims[i - 1] = len(fm) + 1
with env.begin(write=True) as txn:
txn.put(b'field_dims', field_dims.tobytes())
for buffer in self.__yield_buffer(path, feat_mapper, defaults):
with env.begin(write=True) as txn:
for key, value in buffer:
txn.put(key, value)
def __get_feat_mapper(self, path):
feat_cnts = defaultdict(lambda: defaultdict(int))
with open(path) as f:
f.readline()
pbar = tqdm(f, mininterval=1, smoothing=0.1)
pbar.set_description('Create avazu dataset cache: counting features')
for line in pbar:
values = line.rstrip('\n').split(',')
if len(values) != self.NUM_FEATS + 2:
continue
for i in range(1, self.NUM_FEATS + 1):
feat_cnts[i][values[i + 1]] += 1
feat_mapper = {i: {feat for feat, c in cnt.items() if c >= self.min_threshold} for i, cnt in feat_cnts.items()}
feat_mapper = {i: {feat: idx for idx, feat in enumerate(cnt)} for i, cnt in feat_mapper.items()}
defaults = {i: len(cnt) for i, cnt in feat_mapper.items()}
return feat_mapper, defaults
def __yield_buffer(self, path, feat_mapper, defaults, buffer_size=int(1e5)):
item_idx = 0
buffer = list()
with open(path) as f:
f.readline()
pbar = tqdm(f, mininterval=1, smoothing=0.1)
pbar.set_description('Create avazu dataset cache: setup lmdb')
for line in pbar:
values = line.rstrip('\n').split(',')
if len(values) != self.NUM_FEATS + 2:
continue
np_array = np.zeros(self.NUM_FEATS + 1, dtype=np.uint32)
np_array[0] = int(values[1])
for i in range(1, self.NUM_FEATS + 1):
np_array[i] = feat_mapper[i].get(values[i+1], defaults[i])
buffer.append((struct.pack('>I', item_idx), np_array.tobytes()))
item_idx += 1
if item_idx % buffer_size == 0:
yield buffer
buffer.clear()
yield buffer
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Movement/2DIK.py | IanSMoyes/SpiderPi | 7 | 197956 | #!/usr/bin/python3
# -*- coding: UTF-8 -*-
# Copyright <EMAIL>
# Programme to calculate inverse kinematics
import math
def switch_sides(coords):
'''
Take account of servo positions switching polarity on the other side of the hexapod
param: coords: Tuple containing shoulder, knee & ankle servo positions
return: Tuple containing shoulder, knee & ankle servo positions
'''
return (1000-coords[0], 1000-coords[1], 1000-coords[2])
def remap(angle):
'''
Function takes an angle and returns a servo position
param: angle: is the joint angle in radians
return: servo position 0-1000
'''
return int(((math.degrees(angle)/120)*500)+500)
def inverse_kin(leg, coords):
'''
Converts a XYZ coordinate into a set of servo positions using inverse kinematics
param:leg: 0~5
param: Coords:Tuple to store the coordinates of the foot (X, Y, Z)
'''
# Lengths of the 3 leg elements
# The thigh length is important because the mat assumes the shoulder joint is 1, 3D joint
thigh = 44.60 # This is the distance across the shoulder joint bracket
calf = 75.00 # This is the length of the upper leg member
foot = 126.50 # This is the length of the lower leg member
x = float(coords[0]) # distance forward the new toe position is
y = float(coords[1]) # distance out (from the shoulder joint) the new toe position is
if y == 0: y = 1 # Avoids division by 0
z = float(coords[2]) # distance down the new toe position is
if z == 0: z = 1 # Avoids division by 0
# The WZ axis is the verticle plane through the shoulder joint, toe position and knee joint
# w = the distance along the W axis the new toe position is
w = math.sqrt(x**2 + y**2)
if w > thigh + calf + foot: return "Position too far" # Goal exceeds reach
# The inverse kinematics needs to be calculated on the distance from the knee
# joint to the toe position. So the length of the shoulder joint needs to be deducted
w -= thigh
if w == 0: w = 1 # Avoids division by 0
servo_positions = (remap(math.atan(x/y)),) # shoulderjoint position in the XY plane
# toedist = hypoteneus of the triangle made by w, x & the knee joint.
# The distance between the nee toe position & the knee joint
toedist = math.sqrt(w**2 + z**2)
# kneeang = internal angle the knee joint makes between the thigh bone & the toe position
kneeang = math.acos((toedist**2 + calf**2 - foot**2)/(2*toedist*calf))
# kneeflex = angle between the W axis & direction to the ankle joint
kneeflex = kneeang - math.atan(w/z)
# kneepos = servo position to achieve kneeflex
servo_positions += (remap(kneeflex),)
# ankleang = internal angle between the shin bone and the thigh bone
ankleang = math.acos((foot**2+calf**2-toedist**2)/(2*foot*calf))
# ankleflex = external angle between straight and ankleang
ankleflex = math.pi - ankleang
# anklepos = servo position to achieve ankleflex
servo_positions += (remap(ankleflex),)
if leg > 2: return switch_sides(servo_positions)
return servo_positions
if __name__ == '__main__':
while True:
leg = int(input("Which leg are we working on? "))
x = float(input("Input x cordinate? "))
y = float(input("Input y cordinate? "))
z = float(input("Input z cordinate? "))
print(inverse_kin(leg, (x,y,z)))
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] |
examples/video.py | monchier/streamlit-1 | 1 | 71543 | <reponame>monchier/streamlit-1
# -*- coding: utf-8 -*-
# Copyright 2018-2020 Streamlit Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import streamlit as st
# These are the formats supported in Streamlit right now.
VIDEO_EXTENSIONS = ["mp4", "ogv", "m4v", "webm"]
# For sample video files, try the Internet Archive, or download a few samples here:
# http://techslides.com/sample-webm-ogg-and-mp4-video-files-for-html5
st.title("Video Widget Examples")
st.header("Local video files")
st.write(
"You can use st.video to play a locally-stored video by supplying it with a valid filesystem path."
)
def get_video_files_in_dir(directory):
out = []
for item in os.listdir(directory):
try:
name, ext = item.split(".")
except:
continue
if name and ext:
if ext in VIDEO_EXTENSIONS:
out.append(item)
return out
avdir = os.path.expanduser("~")
files = get_video_files_in_dir(avdir)
if len(files) == 0:
st.write(
"Put some video files in your home directory (%s) to activate this player."
% avdir
)
else:
filename = st.selectbox(
"Select a video file from your home directory (%s) to play" % avdir, files, 0,
)
st.video(os.path.join(avdir, filename))
st.header("Remote video playback")
st.write("st.video allows a variety of HTML5 supported video links, including YouTube.")
def shorten_vid_option(opt):
return opt.split("/")[-1]
# A random sampling of videos found around the web. We should replace
# these with those sourced from the streamlit community if possible!
vidurl = st.selectbox(
"Pick a video to play",
(
"https://youtu.be/_T8LGqJtuGc",
"https://www.youtube.com/watch?v=kmfC-i9WgH0",
"https://www.youtube.com/embed/sSn4e1lLVpA",
"http://www.rochikahn.com/video/videos/zapatillas.mp4",
"http://www.marmosetcare.com/video/in-the-wild/intro.webm",
"https://www.orthopedicone.com/u/home-vid-4.mp4",
),
0,
shorten_vid_option,
)
st.video(vidurl)
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2271,
29897,
13,
2
] |
manga_py/base_classes/callbacks.py | paulolimac/manga-py | 1 | 163171 | from typing import Callable
class Callbacks:
def _call_files_progress_callback(self):
if callable(self.progress):
_max, _current = len(self._storage['files']), self._storage['current_file']
self.progress(_max, _current, _current < 1)
def set_quest_callback(self, callback: Callable): # Required call from initiator (CLI, GUI)
setattr(self, 'quest', callback)
def set_progress_callback(self, callback: Callable): # Required call from initiator (CLI, GUI)
setattr(self, 'progress', callback)
def set_log_callback(self, callback: Callable): # Required call from initiator (CLI, GUI)
setattr(self, 'log', callback)
def set_quest_password_callback(self, callback: Callable): # Required call from iterator (CLI, GUI)
setattr(self, 'quest_password', callback)
def quest(self, *args, **kwargs):
pass
def quest_password(self, *args, **kwargs):
pass
def progress(self, *args, **kwargs):
pass
def log(self, *args, **kwargs):
pass
def book_meta(self) -> dict:
return {}
| [
1,
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tests/test_fluids_ecl.py | trhallam/digirock | 0 | 37370 | """Test functions for pem.fluid.ecl module
"""
import pytest
from pytest import approx
import numpy as np
import digirock.fluids.ecl as fluid_ecl
from inspect import getmembers, isfunction
@pytest.fixture
def tol():
return {
"rel": 0.05, # relative testing tolerance in percent
"abs": 0.00001, # absolute testing tolerance
}
@pytest.mark.parametrize(
"pres, extrap, ans",
[
(325, "const", 1.4615),
(325, "pchip", 1.4615),
(np.r_[325, 375], "const", np.r_[1.4615, 1.4505]),
(np.r_[325, 375], "pchip", np.r_[1.4615, 1.4505]),
],
)
def test_oil_fvf_table(test_data, pres, ans, extrap, tol):
tab = np.loadtxt(test_data / "PVT_BO.inc")
assert np.allclose(
fluid_ecl.oil_fvf_table(tab[:, 0], tab[:, 1], pres, extrap=extrap),
ans,
rtol=tol["rel"],
)
def test_oil_fvf_table_bad_pchi(test_data):
tab = np.loadtxt(test_data / "PVT_BO.inc")
# test bad extrap
with pytest.raises(ValueError):
assert fluid_ecl.oil_fvf_table(
tab[:, 0], tab[:, 1], 235, extrap="Unknown Extrap"
)
@pytest.mark.parametrize(
"pres, extrap, ans",
[
(325, "const", 1.4615),
(325, "pchip", 1.4615),
(np.r_[325, 375], "const", np.r_[1.4615, 1.4505]),
(np.r_[325, 375], "pchip", np.r_[1.4615, 1.4505]),
],
)
def test_oil_fvf_table(test_data, pres, ans, extrap, tol):
tab = np.loadtxt(test_data / "PVT_BO.inc")
assert np.allclose(
fluid_ecl.oil_fvf_table(tab[:, 0], tab[:, 1], pres, extrap=extrap),
ans,
rtol=tol["rel"],
)
@pytest.mark.parametrize("api,ans", ((20, 0.933993399339934), (45, 0.8016997167138812)))
def test_e100_oil_density(api, ans, tol):
assert fluid_ecl.e100_oil_density(api) == approx(ans)
assert np.allclose(
fluid_ecl.e100_oil_density(np.r_[api, api]), np.r_[ans, ans], atol=tol["abs"]
)
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30004,
13,
2
] |
lang/python/try-doctest.py | hmisty/coding | 3 | 92794 | #!/usr/bin/env python
#coding:utf-8
"""
doctest demonstration.
how to run:
$ python -m doctest -v try-doctest.py
"""
def multiply(a, b):
"""
Multiply a with b.
>>> multiply(1, 2)
2
>>> multiply('a', 3)
'aaa'
"""
return a * b
"""
main
"""
if __name__ == '__main__':
import doctest
doctest.testmod(verbose=True)
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] |
HW8.py | shng5175/Random-Python-Stuff | 0 | 115088 | <filename>HW8.py
import sys
import math
x = int(sys.argv[1]) #takes in the number as an arguement
y = math.sqrt(x) #gets the square root of x
y = math.ceil(y) #rounds up y
y = int(y) #did this because the for loop didn't want to take in a float as part of the range
i = 2
for i in range(i, y): #this whole section here finds prime numbers from 2 to the square root of x
j = 2
while (j <= (i/j)):
if (i%j == 0):
break #sends to end of for loop where we increment i
j = j + 1 #increments j and sends back top of loop to test i again
if (j > i/j): #only ever gets to this line if i is indeed prime because there were no js that evenly goes into it
if(x%i==0): #checks if the prime number we just found is a factor of the original number
p = i #sets the number to p if the if statement above is true
i = (y + 1) #since we've found p, sets i to y+1 to exit the loop
i = i + 1
q=x/p #since we've found p, we can calculate the value of q
print "p = ",p , "q = ",q #prints out the values of p and q | [
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tests/test_tbears_db.py | Transcranial-Solutions/t-bears | 35 | 9318 | # -*- coding: utf-8 -*-
# Copyright 2017-2018 ICON Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import unittest
from tbears.block_manager.tbears_db import TbearsDB
DIRECTORY_PATH = os.path.abspath((os.path.dirname(__file__)))
DB_PATH = os.path.join(DIRECTORY_PATH, './.tbears_db')
class TestTBearsDB(unittest.TestCase):
def setUp(self):
self.TBEARS_DB = TbearsDB(TbearsDB.make_db(DB_PATH))
self.test_key = b'test_key'
self.test_value = b'test_value'
def tearDown(self):
self.TBEARS_DB.close()
shutil.rmtree(DB_PATH)
def test_put_and_get(self):
# Put and get
self.TBEARS_DB.put(self.test_key, self.test_value)
ret = self.TBEARS_DB.get(self.test_key)
self.assertEqual(ret, self.test_value)
# overwrite
overwrite_value = b'test_value_overwrite'
self.TBEARS_DB.put(self.test_key, overwrite_value)
ret = self.TBEARS_DB.get(self.test_key)
self.assertEqual(ret, overwrite_value)
# get invalid key
ret = self.TBEARS_DB.get(b'invalid_key')
self.assertIsNone(ret)
# put invalid type
self.assertRaises(TypeError, self.TBEARS_DB.put, 'test_key', self.test_value)
self.assertRaises(TypeError, self.TBEARS_DB.put, self.test_key, 123)
def test_delete(self):
self.TBEARS_DB.put(self.test_key, self.test_value)
ret = self.TBEARS_DB.get(self.test_key)
self.assertEqual(ret, self.test_value)
self.TBEARS_DB.delete(self.test_key)
ret = self.TBEARS_DB.get(self.test_key)
self.assertIsNone(ret)
def test_iterator(self):
self.TBEARS_DB.put(b'key1', b'value1')
self.TBEARS_DB.put(b'key2', b'value2')
self.TBEARS_DB.put(b'key3', b'value3')
self.TBEARS_DB.put(b'key4', b'value4')
i = 1
for _, actual_value in self.TBEARS_DB.iterator():
expected_value = ('value' + str(i)).encode()
self.assertEqual(expected_value, actual_value)
i += 1
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] |
flowx/poisson/_poisson.py | akashdhruv/flowX | 0 | 64714 | <filename>flowx/poisson/_poisson.py
"""Interface for Poisson solver module"""
from . import _interface
class Poisson(object):
def __init__(self, grid=None, poisson_vars=[None]*2, poisson_info=None):
"""
Constructor for the Poisson unit
Arguments
---------
grid : object
Grid object where the poisson equation needs to be solved
poisson_vars : list
List of string for field variables required by poisson unit
poisson_vars[0] --> Phi (numerical solution)
poisson_vars[1] --> RHS
poisson_info : Dictionary of keyword arguments
'poisson_solver' keyword refers to the type of solver to be used
poisson_info['poisson_solver'] = 'serial_cg' --> default
= 'serial_jacobi'
poisson_info['maxiter'] = maximum number of iterations --> default 2000
poisson_info['tol'] = minimum tolerance of the residuals --> default 1e-9
poisson_info['verbose'] = bool to displacy poisson stats or not --> default False
"""
#---------------------Create images of other units and objects----------------
self._grid = grid
self._ivar, self._rvar = poisson_vars
#--------------------Set default parameters---------------------------------
self._options = {'poisson_solver' : 'superlu', \
'maxiter': 2000, \
'tol' : 1e-9, \
'verbose' : False}
self._serial_iterative_solvers = {'cg' : _interface.solve_cg, \
'jacobi': _interface.solve_jacobi}
self._serial_direct_solvers = {'direct' : _interface.solve_direct, \
'superlu' : _interface.solve_superlu}
#----------------------Read user parameters------------------------------------
if poisson_info:
for key in poisson_info: self._options[key] = poisson_info[key]
#----------------------Setup current unit with default/user parameters-----------
if not grid or None in poisson_vars:
self._solve = _interface.solve_stub
print('Warning: Poisson unit is a stub')
else:
self._solve = {**self._serial_iterative_solvers,
**self._serial_direct_solvers}[self._options['poisson_solver']]
self._options['lu'], self._options['matrix'] = _interface.build_sparse_matrix(self._grid, self._ivar)
return
def solve(self):
""" Subroutine to solve poisson equation
"""
ites, residual = self._solve(self._grid, self._ivar, self._rvar, self._options)
return ites, residual
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] |
hydras/utils.py | Gilnaa/Hydra | 5 | 149266 | """
Contains various utility methods.
:file: utils.py
:date: 27/08/2015
:authors:
- <NAME> <<EMAIL>>
"""
from typing import Any, Type, Union
import inspect
import enum
import sys
class Endianness(enum.Enum):
BIG = '>'
LITTLE = '<'
HOST = '='
TARGET = None
def is_equivalent_to_little_endian(self):
return self == Endianness.LITTLE or (self == Endianness.HOST and sys.byteorder == 'little')
def is_equivalent_to_big_endian(self):
return self == Endianness.LITTLE or (self == Endianness.HOST and sys.byteorder == 'little')
def create_array(size: Union[int, slice], underlying_type):
# Importing locally in order to avoid weird import-cycle issues
from .array import Array
return Array[size, underlying_type]
def fit_bytes_to_size(byte_string, length):
"""
Ensure the given byte_string is in the correct length
A long byte_string will be truncated, while a short one will be padded.
:param byte_string: The string to fit.
:param length: The required string size.
"""
if length is None:
return byte_string
if len(byte_string) < length:
return padto(byte_string, length)
return byte_string[:length]
def get_as_type(t):
return t if inspect.isclass(t) else type(t)
def get_as_value(v):
return v() if inspect.isclass(v) else v
def mask(length, offset=0):
"""
Generate a bitmask with the given parameter.
:param length: The bit length of the mask.
:param offset: The offset of the mask from the LSB bit. [default: 0]
:return: An integer representing the bit mask.
"""
return ((1 << length) - 1) << offset
def get_type_name(t: Union[Type, Any]) -> str:
return get_as_type(t).__name__
def padto(data, size, pad_val=b'\x00', leftpad=False):
assert type(pad_val) == bytes and len(pad_val) == 1, 'Padding value must be 1 byte'
if len(data) < size:
padding = pad_val * (size - len(data))
if not leftpad:
data += padding
else:
data = padding + data
return data
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] |
number_punch/number_punch.py | abcdHero/battlegame | 1 | 53295 | import sys
import random
import pygame
# Define Window Size
SCREEN_WIDTH, SCREEN_HEIGHT = 800, 600
# Define Frames Per Second
FRAMES_PER_SEC = 25
# Function: Draw a text to the screen
def blit_text(screen_object, font, msg_text, color_tuple, center_tuple, antialiased=True):
# Define a message in Main Menu with Color Tuple in RGB
msg_font = font.render(msg_text, antialiased, color_tuple)
# Find position of the message
font_pos = msg_font.get_rect(center=center_tuple)
# Draw (blit) the message at the calculated position
screen_object.blit(msg_font , font_pos)
# Function: Check which is winner
def check_winner(player1_value, player2_value, screen_object, font_object, clock):
frame_count = 0
while True:
for event in pygame.event.get():
# If Quit Signal is received, quit the game
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
screen_object.fill((0,0,0))
if frame_count < 100:
player1_message = 'Player 1: ' + str(random.randrange(1, 5+1))
blit_text(screen_object, font_object, player1_message, (255, 0, 0), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 40))
player2_message = 'Player 2: ' + str(random.randrange(1, 5+1))
blit_text(screen_object, font_object, player2_message, (0, 0, 255), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 80))
elif frame_count < 200:
player1_message = 'Player 1: ' + str(player1_value + 1)
blit_text(screen_object, font_object, player1_message, (255, 0, 0), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 40))
player2_message = 'Player 2: ' + str(player2_value + 1)
blit_text(screen_object, font_object, player2_message, (0, 0, 255), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 80))
elif frame_count < 300:
winner_message = 'Player 2 wins!'
if player1_value == player2_value:
winner_message = 'Draw Game!'
elif player1_value > player2_value or (player1_value == 0 and player2_value == 5):
winner_message = 'Player 1 wins!'
blit_text(screen_object, font_object, winner_message, (255, 255, 255), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2))
else:
break # leave this while-loop and this function
frame_count += 1
pygame.display.update()
clock.tick(FRAMES_PER_SEC)
# Function: Start the game
def start_game():
# Initialize pygame library
pygame.init()
# Set default Font: comic sans
font_object=pygame.font.SysFont('comicsans',40)
# Define Window Screen Display object
screen_object = pygame.display.set_mode((SCREEN_WIDTH,SCREEN_HEIGHT))
# Initial a CLOCK object to tick time
CLOCK = pygame.time.Clock()
# initial values
is_reset = True
# Game's main entry
print('DEBUG: about to enter the game')
while True:
if is_reset:
# value range: 0 - 4
player1_value = 0
player1_pressed_count = 0
player2_value = 0
player2_pressed_count = 0
is_reset = False
for event in pygame.event.get():
# If Quit Signal is received, quit the game
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
# If any key is pressed, go here
keys_pressed = pygame.key.get_pressed()
if keys_pressed[pygame.K_w]:
player1_value = (player1_value + 1) % 5
player1_pressed_count += 1
if keys_pressed[pygame.K_s]:
player1_value = (player1_value - 1 + 5) % 5
player1_pressed_count += 1
if keys_pressed[pygame.K_UP]:
player2_value = (player2_value + 1) % 5
player2_pressed_count += 1
if keys_pressed[pygame.K_DOWN]:
player2_value = (player2_value - 1 + 5) % 5
player2_pressed_count += 1
if keys_pressed[pygame.K_RETURN] and player1_pressed_count > 0 and player2_pressed_count > 0:
check_winner(player1_value, player2_value, screen_object, font_object, CLOCK)
is_reset = True
screen_object.fill((0,0,0))
blit_text(screen_object, font_object, 'Welcome to Number Punch!', (255, 255, 255), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2))
player1_message = 'Player 1, Press W to increase, S to decrease'
if player1_pressed_count > 0:
if player1_value % 2 == 0:
player1_message = 'Player 1: @@'
else:
player1_message = 'Player 1: ##'
blit_text(screen_object, font_object, player1_message, (255, 0, 0), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 40))
player2_message = 'Player 2, Press UP to increase, DOWN to decrease'
if player2_pressed_count > 0:
if player2_value % 2 == 0:
player2_message = 'Player 2: @@'
else:
player2_message = 'Player 2: ##'
blit_text(screen_object, font_object, player2_message, (0, 0, 255), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 80))
blit_text(screen_object, font_object, 'Press ENTER to confirm both', (255, 255, 255), (SCREEN_WIDTH//2, SCREEN_HEIGHT//2 + 120))
pygame.display.update()
CLOCK.tick(FRAMES_PER_SEC)
if __name__ == '__main__':
# Executed when invoked directly
start_game()
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rollservice/tests/test_dice_seq.py | stallmanifold/pnpdr | 0 | 38668 | from django.contrib.auth.models import User
from rollservice.models import DiceSequence
import rest_framework.test as rf_test
import rest_framework.status as status
import rest_framework.reverse as reverse
import hypothesis.extra.django
import hypothesis.strategies as strategies
import unittest
class DiceSeqStrategies:
dice_rolls = strategies.lists(
elements=strategies.sampled_from([4, 6, 8, 10, 12, 20, 100]),
min_size=1
)
user = strategies.just(dict(
username='dungeon_master',
email='<EMAIL>',
password='<PASSWORD>'
))
@strategies.composite
def seq_name(draw):
seq_number = draw(strategies.integers(min_value=1))
return f'Roll {seq_number}'
@strategies.composite
def dice_sequence(draw, seq_name=seq_name(), dice_rolls=dice_rolls):
seq_name = draw(seq_name)
dice_sequence = draw(dice_rolls)
return dict(
seq_name=seq_name,
dice_sequence=dice_sequence
)
dice_sequence_list = strategies.lists(elements=dice_sequence(), min_size=1)
@strategies.composite
def existing_uuid(draw, queryset):
max_value = len(queryset) - 1
index = draw(strategies.integers(min_value=0, max_value=max_value))
return queryset[index].uuid
non_existing_uuid = strategies.uuids()
invalid_uuid = strategies.text(max_size=100)
@strategies.composite
def existing_uuid_url(draw, queryset):
max_value = len(queryset) - 1
index = draw(strategies.integers(min_value=0, max_value=max_value))
uuid = queryset[index].uuid
url = reverse.reverse('dice-seq-by-uuid', args=[uuid])
return url
@strategies.composite
def non_existing_uuid_url(draw, queryset, non_existing_uuid=non_existing_uuid):
uuid = draw(non_existing_uuid)
url = reverse.reverse('dice-seq-by-uuid', args=[uuid])
return url
@strategies.composite
def invalid_uuid_url(draw, invalid_uuid=invalid_uuid):
uuid = draw(invalid_uuid)
url_root = reverse.reverse('dice-seq')
url = url_root + '/by_uuid/' + uuid + '/'
return url
class DiceSequenceByUUIDTests(hypothesis.extra.django.TestCase):
@classmethod
def setUpTestData(cls):
sequences = DiceSeqStrategies.dice_sequence_list.example()
new_user = DiceSeqStrategies.user.example()
owner = User.objects.create(**new_user)
for sequence in sequences:
dice_sequence = DiceSequence.objects.create(seq_name=sequence['seq_name'], owner=owner)
dice_sequence.sequence.set(sequence['dice_sequence'])
queryset = DiceSequence.objects.all()
client_class = rf_test.APIClient
@hypothesis.given(DiceSeqStrategies.existing_uuid_url(queryset=queryset))
def test_dice_seq_by_uuid_GET_with_existing_uuid_should_return_OK(self, url):
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
@hypothesis.given(DiceSeqStrategies.non_existing_uuid_url(queryset=queryset))
def test_dice_seq_by_uuid_GET_with_non_existing_uuid_should_return_NOT_FOUND(self, url):
response = self.client.get(url)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
@hypothesis.given(DiceSeqStrategies.invalid_uuid_url())
def test_dice_seq_by_uuid_GET_with_invalid_uuid_should_return_BAD_REQUEST(self, url):
response = self.client.get(url)
self.assertIn(response.status_code, [status.HTTP_404_NOT_FOUND, status.HTTP_400_BAD_REQUEST])
@hypothesis.given(strategies.one_of([
DiceSeqStrategies.existing_uuid_url(queryset=queryset),
DiceSeqStrategies.non_existing_uuid_url(queryset=queryset),
DiceSeqStrategies.invalid_uuid_url(),
]))
def test_dice_seq_by_uuid_GET_idempotent(self, url):
response1 = self.client.get(url)
response2 = self.client.get(url)
self.assertEqual(response1.status_code, response2.status_code)
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13,
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2
] |
reporter/uhl_reports/civicrm/practice_duplicate_name.py | LCBRU/reporter | 0 | 122348 | <filename>reporter/uhl_reports/civicrm/practice_duplicate_name.py<gh_stars>0
#!/usr/bin/env python3
from reporter.core import SqlReport, Schedule
from reporter.uhl_reports.civicrm import get_contact_link
from reporter.emailing import RECIPIENT_IT_DQ
class CivicrmPracticeDuplicateName(SqlReport):
def __init__(self):
super().__init__(
introduction=("The following GP Practices have "
"a duplicate name in CiviCRM"),
recipients=[RECIPIENT_IT_DQ],
sql='''
SELECT
con.id,
con.organization_name
FROM (
SELECT
organization_name
FROM STG_CiviCRM.dbo.civicrm_contact con
WHERE con.contact_type = 'Organization'
AND con.contact_sub_type LIKE '%GP_Surgery%'
AND con.is_deleted = 0
GROUP BY con.organization_name
HAVING COUNT(*) > 1
) x
JOIN STG_CiviCRM.dbo.civicrm_contact con
ON con.organization_name = x.organization_name
AND con.contact_sub_type LIKE '%GP_Surgery%'
AND con.is_deleted = 0
;
''',
schedule=Schedule.daily
)
def get_report_line(self, row):
return '- {}\r\n'.format(
get_contact_link(
row['organization_name'], row['id']))
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] |
Strings/count/Solution.py | Voley/AlgorithmicProblemsV2 | 0 | 135240 |
# Problem code
def countAndSay(n):
if n == 1:
return "1"
current = "1"
for i in range(2, n + 1):
current = helper(current)
return current
def helper(current):
group_count = 1
group_member = current[0]
result = ""
for i in range(1, len(current)):
if current[i] == group_member:
group_count += 1
continue
else:
result += str(group_count)
result += str(group_member)
group_count = 1
group_member = current[i]
result += str(group_count)
result += str(group_member)
return result
# Setup
print(countAndSay(4))
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] |
official/mnist/mnist.py | TuKJet/models | 3,326 | 16319 | <filename>official/mnist/mnist.py<gh_stars>1000+
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tensorflow as tf
import dataset
class Model(object):
"""Class that defines a graph to recognize digits in the MNIST dataset."""
def __init__(self, data_format):
"""Creates a model for classifying a hand-written digit.
Args:
data_format: Either 'channels_first' or 'channels_last'.
'channels_first' is typically faster on GPUs while 'channels_last' is
typically faster on CPUs. See
https://www.tensorflow.org/performance/performance_guide#data_formats
"""
if data_format == 'channels_first':
self._input_shape = [-1, 1, 28, 28]
else:
assert data_format == 'channels_last'
self._input_shape = [-1, 28, 28, 1]
self.conv1 = tf.layers.Conv2D(
32, 5, padding='same', data_format=data_format, activation=tf.nn.relu)
self.conv2 = tf.layers.Conv2D(
64, 5, padding='same', data_format=data_format, activation=tf.nn.relu)
self.fc1 = tf.layers.Dense(1024, activation=tf.nn.relu)
self.fc2 = tf.layers.Dense(10)
self.dropout = tf.layers.Dropout(0.4)
self.max_pool2d = tf.layers.MaxPooling2D(
(2, 2), (2, 2), padding='same', data_format=data_format)
def __call__(self, inputs, training):
"""Add operations to classify a batch of input images.
Args:
inputs: A Tensor representing a batch of input images.
training: A boolean. Set to True to add operations required only when
training the classifier.
Returns:
A logits Tensor with shape [<batch_size>, 10].
"""
y = tf.reshape(inputs, self._input_shape)
y = self.conv1(y)
y = self.max_pool2d(y)
y = self.conv2(y)
y = self.max_pool2d(y)
y = tf.layers.flatten(y)
y = self.fc1(y)
y = self.dropout(y, training=training)
return self.fc2(y)
def model_fn(features, labels, mode, params):
"""The model_fn argument for creating an Estimator."""
model = Model(params['data_format'])
image = features
if isinstance(image, dict):
image = features['image']
if mode == tf.estimator.ModeKeys.PREDICT:
logits = model(image, training=False)
predictions = {
'classes': tf.argmax(logits, axis=1),
'probabilities': tf.nn.softmax(logits),
}
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.PREDICT,
predictions=predictions,
export_outputs={
'classify': tf.estimator.export.PredictOutput(predictions)
})
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
logits = model(image, training=True)
loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
accuracy = tf.metrics.accuracy(
labels=tf.argmax(labels, axis=1), predictions=tf.argmax(logits, axis=1))
# Name the accuracy tensor 'train_accuracy' to demonstrate the
# LoggingTensorHook.
tf.identity(accuracy[1], name='train_accuracy')
tf.summary.scalar('train_accuracy', accuracy[1])
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.TRAIN,
loss=loss,
train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))
if mode == tf.estimator.ModeKeys.EVAL:
logits = model(image, training=False)
loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
return tf.estimator.EstimatorSpec(
mode=tf.estimator.ModeKeys.EVAL,
loss=loss,
eval_metric_ops={
'accuracy':
tf.metrics.accuracy(
labels=tf.argmax(labels, axis=1),
predictions=tf.argmax(logits, axis=1)),
})
def main(unused_argv):
data_format = FLAGS.data_format
if data_format is None:
data_format = ('channels_first'
if tf.test.is_built_with_cuda() else 'channels_last')
mnist_classifier = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=FLAGS.model_dir,
params={
'data_format': data_format
})
# Train the model
def train_input_fn():
# When choosing shuffle buffer sizes, larger sizes result in better
# randomness, while smaller sizes use less memory. MNIST is a small
# enough dataset that we can easily shuffle the full epoch.
ds = dataset.train(FLAGS.data_dir)
ds = ds.cache().shuffle(buffer_size=50000).batch(FLAGS.batch_size).repeat(
FLAGS.train_epochs)
(images, labels) = ds.make_one_shot_iterator().get_next()
return (images, labels)
# Set up training hook that logs the training accuracy every 100 steps.
tensors_to_log = {'train_accuracy': 'train_accuracy'}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=100)
mnist_classifier.train(input_fn=train_input_fn, hooks=[logging_hook])
# Evaluate the model and print results
def eval_input_fn():
return dataset.test(FLAGS.data_dir).batch(
FLAGS.batch_size).make_one_shot_iterator().get_next()
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print()
print('Evaluation results:\n\t%s' % eval_results)
# Export the model
if FLAGS.export_dir is not None:
image = tf.placeholder(tf.float32, [None, 28, 28])
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
'image': image,
})
mnist_classifier.export_savedmodel(FLAGS.export_dir, input_fn)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='Number of images to process in a batch')
parser.add_argument(
'--data_dir',
type=str,
default='/tmp/mnist_data',
help='Path to directory containing the MNIST dataset')
parser.add_argument(
'--model_dir',
type=str,
default='/tmp/mnist_model',
help='The directory where the model will be stored.')
parser.add_argument(
'--train_epochs', type=int, default=40, help='Number of epochs to train.')
parser.add_argument(
'--data_format',
type=str,
default=None,
choices=['channels_first', 'channels_last'],
help='A flag to override the data format used in the model. channels_first '
'provides a performance boost on GPU but is not always compatible '
'with CPU. If left unspecified, the data format will be chosen '
'automatically based on whether TensorFlow was built for CPU or GPU.')
parser.add_argument(
'--export_dir',
type=str,
help='The directory where the exported SavedModel will be stored.')
tf.logging.set_verbosity(tf.logging.INFO)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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] |
doc/paper/results/time-big-fft.py | wangxuesong29/bit-reversal-methods | 0 | 57576 | import numpy as np
from time import time
# Use the largest number of bits here:
b = 28
N = 2**b
x = np.array([ 2*i + (2*i+1)*1j for i in xrange(N)])
print 'Timing b =', b
t1=time()
y = np.fft.fft(x)
t2=time()
print t2-t1, (t2 - t2) / float(N)
| [
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] |
omw/blueprints/ili/__init__.py | tnaskret/OMW | 0 | 159530 | <filename>omw/blueprints/ili/__init__.py
from omw.blueprints.ili.views import ili
| [
1,
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2638,
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7406,
1053,
29040,
13,
2
] |
ca-hostpathogen.py | RachidStat/PyCX | 176 | 69972 | <reponame>RachidStat/PyCX
import pycxsimulator
from pylab import *
width = 50
height = 50
initProb = 0.01
infectionRate = 0.85
regrowthRate = 0.15
def initialize():
global time, config, nextConfig
time = 0
config = zeros([height, width])
for x in range(width):
for y in range(height):
if random() < initProb:
state = 2
else:
state = 1
config[y, x] = state
nextConfig = zeros([height, width])
def observe():
cla()
imshow(config, vmin = 0, vmax = 2, cmap = cm.jet)
axis('image')
title('t = ' + str(time))
def update():
global time, config, nextConfig
time += 1
for x in range(width):
for y in range(height):
state = config[y, x]
if state == 0:
for dx in range(-1, 2):
for dy in range(-1, 2):
if config[(y+dy)%height, (x+dx)%width] == 1:
if random() < regrowthRate:
state = 1
elif state == 1:
for dx in range(-1, 2):
for dy in range(-1, 2):
if config[(y+dy)%height, (x+dx)%width] == 2:
if random() < infectionRate:
state = 2
else:
state = 0
nextConfig[y, x] = state
config, nextConfig = nextConfig, config
pycxsimulator.GUI().start(func=[initialize, observe, update])
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2767,
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13,
2
] |
Codeforces/CF1433C.py | qinyihao/Luogu_Problem_Solver | 0 | 104801 | <reponame>qinyihao/Luogu_Problem_Solver<gh_stars>0
for _ in range(int(input())):
n = int(input())
r = [int(i) for i in input().split()]
o = max(r)
if r.count(o)==len(r):
print(-1)
continue
kq = -1
for i in range(1,len(r)):
if r[i]==o and (r[i]>r[i-1]):
kq = i+1
for i in range(len(r)-1):
if r[i]==o and(r[i]>r[i+1]):
kq = i+1
print(kq)
| [
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29895,
29939,
353,
474,
29974,
29896,
13,
12,
2158,
29898,
29895,
29939,
29897,
13,
2
] |
Cyber_Tyan.py | RuCybernetic/CyberTyanBot | 1 | 167386 | <gh_stars>1-10
import os
import discord
from discord.ext import commands
TOKEN = os.environ.get('BOT_TOKEN')
prefix = '!'
bot = commands.Bot(command_prefix=prefix)
bot.load_extension("jishaku")
bot.remove_command('help')
@bot.command()
async def ping(ctx):
latency = bot.latency
await ctx.send(latency)
@bot.command()
async def load(ctx, extensions):
bot.load_extension(f'cogs.{extensions}')
await ctx.send("loaded")
@bot.command()
async def unload(ctx, extensions):
bot.unload_extension(f'cogs.{extensions}')
await ctx.send("unloaded")
@bot.command()
async def reload(ctx, extensions):
bot.unload_extension(f'cogs.{extensions}')
bot.load_extension(f'cogs.{extensions}')
await ctx.send("reloaded")
for filename in os.listdir('./cogs'):
if filename.endswith('.py'):
bot.load_extension(f'cogs.{filename[:-3]}')
for filename in os.listdir('./cogs/commands'):
if filename.endswith('.py'):
bot.load_extension(f'cogs.commands.{filename[:-3]}')
for filename in os.listdir('./cogs/events'):
if filename.endswith('.py'):
bot.load_extension(f'cogs.events.{filename[:-3]}')
bot.run(TOKEN) | [
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4986,
29968,
1430,
29897,
2
] |
pyNastran/gui/gui_objects/alt_geometry_storage.py | luzpaz/pyNastran | 293 | 104616 | <gh_stars>100-1000
from copy import deepcopy
class AltGeometry:
representations = ['main', 'toggle', 'wire', 'point', 'surface',
'bar', 'wire+point', 'wire+surf']
displays = ['Wireframe', 'Surface', 'point', None]
def __repr__(self):
msg = ('AltGeometry(%r, color=%s, line_width=%s, opacity=%s,\n'
' point_size=%s, bar_scale=%s, representation=%r, display=%r, is_visible=%s,\n'
'is_pickable=%s, label_actors=%s)' % (
self.name, str(self.color), self.line_width, self.opacity, self.point_size,
self.bar_scale, self.representation, self.display, self.is_visible,
self.is_pickable, self.label_actors))
return msg
def __init__(self, parent, name, color=None, line_width=1, opacity=0.0,
point_size=1, bar_scale=1.0, representation='main', display=None, is_visible=True,
is_pickable=False, label_actors=None):
"""
Creates an AltGeometry object
Parameters
----------
line_width : int
the width of the line for 'surface' and 'main'
color : [int, int, int]
the RGB colors (0-255)
opacity : float
0.0 -> solid
1.0 -> transparent
point_size : int
the point size for 'point'
bar_scale : float
the scale for the CBAR / CBEAM elements
representation : str
main - change with main mesh
wire - always wireframe
point - always points
surface - always surface
bar - can use bar scale
toggle - follow the main mesh
wire+point - is this used???
wire+surf - two options
display : str
only relevant to wire+surf
the active state of the mesh
is_visible : bool; default=True
is this actor currently visible
is_pickable : bool; default=False
can you pick a node/cell on this actor
label_actors : List[annotation]; None -> []
stores annotations (e.g., for a control surface)
"""
representation_map = {
'main' : None,
'wire' : 'Wireframe',
'point' : 'point',
'surface' : 'Surface',
'wire+surf' : 'Surface',
'wire+point' : 'Wireframe',
'bar' : None,
'toggle' : None,
}
if display is None:
try:
display = representation_map[representation]
except KeyError:
valid_keys = list(representation_map.keys())
valid_keys.sort()
raise RuntimeError('%r is not a valid representation\nvalid=[%s]' % (
representation, ', '.join(valid_keys)))
if line_width is None:
line_width = 1
if opacity is None:
opacity = 1.0
if label_actors is None:
label_actors = []
self.parent = parent
self.name = name
self.display = display
assert display in self.displays, 'dislay=%r displays=%s' % (display, self.displays)
assert isinstance(name, str), 'name=%r' % name
assert isinstance(label_actors, list), 'name=%r label_actors=%s' % (name, str(label_actors))
self._color = None
if color is not None:
assert color is not None, color
self.color = color
self.line_width = line_width
self.point_size = point_size
self._opacity = opacity
self.bar_scale = bar_scale
self.label_actors = []
assert isinstance(is_visible, bool), is_visible
self.is_visible = is_visible
self.is_pickable = is_pickable
if representation not in self.representations:
msg = 'representation=%r is invalid\nrepresentations=%r' % (
representation, self.representations)
raise RuntimeError(msg)
self.representation = representation
def __deepcopy__(self, memo):
"""doesn't copy the label_actors to speed things up?"""
keys = ['name', '_color', 'display', 'line_width', 'point_size', '_opacity',
'_representation', 'is_visible', 'bar_scale', 'is_pickable']
cls = self.__class__
result = cls.__new__(cls)
idi = id(self)
memo[idi] = result
for key in keys:
value = self.__dict__[key]
setattr(result, key, deepcopy(value, memo))
#result.label_actors = [] #= memo['label_actors']
return result
@property
def opacity(self):
"""
0 -> transparent
1 -> solid
"""
assert 0.0 <= self._opacity <= 1.0, self._opacity
return self._opacity
@opacity.setter
def opacity(self, opacity):
assert 0.0 <= opacity <= 1.0, opacity
self._opacity = opacity
@property
def transparency(self):
"""
0 -> solid
1 -> transparent
"""
assert 0.0 <= self._opacity <= 1.0, self._opacity
return 1.0 - self._opacity
@transparency.setter
def transparency(self, transparency):
assert 0.0 <= transparency <= 1.0, transparency
self._opacity = 1.0 - transparency
@property
def color(self):
if self._color is None:
return (255, 0, 0) # the default color; red
return self._color
@color.setter
def color(self, color):
assert len(color) == 3, color
if isinstance(color[0], int):
assert isinstance(color[0], int), color[0]
assert isinstance(color[1], int), color[1]
assert isinstance(color[2], int), color[2]
self._color = tuple(color)
else:
assert isinstance(color[0], float), color[0]
assert isinstance(color[1], float), color[1]
assert isinstance(color[2], float), color[2]
self._color = (int(color[0] * 255), int(color[1] * 255), int(color[2] * 255))
@property
def color_float(self):
return tuple([i/255. for i in self._color])
def set_color(self, color, mode='rgb'):
assert mode == 'rgb', 'mode=%r' % mode
self.color = color
assert len(color) == 3, color
#self.mode = 'rgb'
@property
def representation(self):
"""
Gets the representation
* main - main mesh
* toggle - change with main mesh
* wire - always wireframe
* point - always points
* surface - always surface
* bar - this can use bar scale
* wire+point - point (vertex) and wireframe allowed
* wire+surf - the user can switch between surface and wireframe as a selection
"""
return self._representation
@representation.setter
def representation(self, representation):
"""Sets the representation"""
if representation not in self.representations:
msg = 'representation=%r is invalid\nrepresentations=%r' % (
representation, self.representations)
raise RuntimeError(msg)
self._representation = representation
| [
1,
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385,
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13,
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263,
2943,
29914,
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] |
tests/adv/test_pop_sfrd.py | jlashner/ares | 10 | 3442 | """
test_pop_models.py
Author: <NAME>
Affiliation: UCLA
Created on: Fri Jul 15 15:23:11 PDT 2016
Description:
"""
import ares
import matplotlib.pyplot as pl
PB = ares.util.ParameterBundle
def test():
# Create a simple population
pars_1 = PB('pop:fcoll') + PB('sed:bpass')
pop_fcoll = ares.populations.GalaxyPopulation(**pars_1)
#pop_fcoll_XR = ares.populations.GalaxyPopulation(**pars_1)
# Mimic the above population to check our different SFRD/SED techniques
sfrd_pars = {'pop_sfr_model': 'sfrd-func'}
sfrd_pars['pop_sfrd'] = pop_fcoll.SFRD
sfrd_pars['pop_sfrd_units'] = 'internal'
sed = PB('sed:toy')
sed['pop_Nion'] = pop_fcoll.src.Nion
sed['pop_Nlw'] = pop_fcoll.src.Nlw
# pop_Ex?
sed['pop_ion_src_igm'] = False
sed['pop_heat_src_igm'] = False
pars_2 = sed + sfrd_pars
pop_sfrd = ares.populations.GalaxyPopulation(**pars_2)
assert pop_fcoll.SFRD(20.) == pop_sfrd.SFRD(20.), "Error in SFRD."
# Check the emissivities too
#print(pop_fcoll.PhotonLuminosityDensity(20., Emin=10.2, Emax=13.6))
#print(pop_sfrd.PhotonLuminosityDensity(20., Emin=10.2, Emax=13.6))
#assert pop_fcoll.PhotonLuminosityDensity(20., Emin=10.2, Emax=13.6) \
# == pop_sfrd.PhotonLuminosityDensity(20., Emin=10.2, Emax=13.6), \
# "Error in photon luminosity density."
if __name__ == '__main__':
test()
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mlab_tools/camera.py | lukius/mlab-tools | 0 | 177224 | <reponame>lukius/mlab-tools<filename>mlab_tools/camera.py
from mayavi import mlab
class Camera(object):
"""Class that wraps some of the mlab functions that manipulate the camera.
A camera instance is automatically created by the animation.
"""
def __init__(self, focalpoint=None, distance=None,
azimuth=None, elevation=None, roll=None):
self.focalpoint = focalpoint or 'auto'
self.distance = distance or 'auto'
self.azimuth = azimuth or 0
self.elevation = elevation or 0
self.roll = roll or 0
self._set_view()
if self.distance == 'auto':
_, _, self.distance, self.focalpoint = mlab.view()
def _set_view(self):
# Sets current camera state into the animation.
mlab.view(azimuth=self.azimuth,
elevation=self.elevation,
distance=self.distance,
focalpoint=self.focalpoint,
roll=self.roll)
def parameters(self):
"""Get current camera parameters.
Returns a dictionary associating each parameter name (i.e., `azimuth`,
`focalpoint`, `distance`, `elevation` and `roll`) to its current value.
"""
self.azimuth, self.elevation, self.distance, self.focalpoint = mlab.view()
self.roll = mlab.roll()
return {'focalpoint' : self.focalpoint,
'distance' : self.distance,
'azimuth' : self.azimuth,
'elevation' : self.elevation,
'roll' : self.roll}
def update(self, focalpoint=None, distance=None,
azimuth=None, elevation=None, roll=None):
"""Updates the camera.
Parameters are additive (e.g., if distance=d is supplied, the camera
will have an increase of d in its distance after this call).
Keyword arguments:
:azimuth: the azimuthal angle (in degrees, 0-360), i.e. the angle
subtended by the position vector on a sphere projected on to the x-y
plane with the x-axis.
:distance: a positive floating point number representing the distance
from the focal point to place the camera.
:elevation: the zenith angle (in degrees, 0-180), i.e. the angle
subtended by the position vector and the z-axis.
:focalpoint: an array of 3 floating point numbers representing the
focal point of the camera.
:roll: the rotation of the camera around its axis.
See mlab documentation for further details.
"""
self.focalpoint = focalpoint or self.focalpoint
self.distance = self.distance + (distance or 0)
self.azimuth = self.azimuth + (azimuth or 0)
self.elevation = self.elevation + (elevation or 0)
self.roll = self.roll + (roll or 0)
self._set_view() | [
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src/MLP/utils.py | rudyn2/wine_market_temporal_prediction | 0 | 115918 | import pandas as pd
import torch
import torch.nn as nn
from src.MLP.mlp_models import WineDataset
def model_eval(trained_model: nn.Module, dataset: WineDataset):
"""
Takes a WineDataset and returns a pandas series (using datetime indexing) with the predictions.
"""
trained_model.eval()
y_pred = []
with torch.no_grad():
for index in range(len(dataset)):
temp_seq, true_pred, temp_seq_index, true_pred_index = dataset.get_with_index(index)
outputs = trained_model(temp_seq)
y_pred.append(pd.Series(outputs.numpy(), index=true_pred_index))
return pd.concat(y_pred)
| [
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src/backend/backend/users/migrations/0010_update_uuid_field.py | tejpratap545/E-Commerce-Application | 0 | 49803 | <reponame>tejpratap545/E-Commerce-Application
# Generated by Django 3.1.2 on 2020-10-21 15:40
from django.db import migrations, models
import uuid
class Migration(migrations.Migration):
dependencies = [
('users', '0009_auto_20201021_2108'),
]
operations = [
migrations.AlterField(
model_name='emailconfirmation',
name='token',
field=models.CharField(default='<KEY>', max_length=250),
),
migrations.AlterField(
model_name='emailconfirmation',
name='uuid',
field=models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False),
),
migrations.AlterField(
model_name='passwordreset',
name='url_token',
field=models.CharField(default='<KEY>', max_length=250),
),
migrations.AlterField(
model_name='passwordreset',
name='uuid',
field=models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False),
),
]
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OSeMOSYS_PuLP.py | OSeMOSYS/OSeMOSYS-PuLP | 4 | 53604 | <filename>OSeMOSYS_PuLP.py
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
# Author: <NAME>, Copyright 2019
# OSeMOSYS version: OSeMOSYS_2017_11_08
__doc__ = """
========================================================================================================================
OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling
========================================================================================================================
OSeMOSYS-PuLP
This is the educational version of OSeMOSYS-PuLP
========================================================================================================================
OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling
Please cite this software by using the following reference of the original scientific article:
<NAME>, <NAME>, OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling.
Energies 2019, 12, 1382, https://doi.org/10.3390/en12071382
Additional references to be cited for the OSeMOSYS modelling framework (see DOI links for complete references):
Howells et al. (2011), https://doi.org/10.1016/j.enpol.2011.06.033
Gardumi et al. (2018), https://doi.org/10.1016/j.esr.2018.03.005
Other sources:
OSeMOSYS GitHub: https://github.com/OSeMOSYS/
OSeMOSYS website: http://www.osemosys.org/
OpTIMUS community: http://www.optimus.community/
========================================================================================================================
"""
import os
import datetime as dt
import logging
import numpy as np
import pandas as pd
import pulp
logging.basicConfig(level=logging.DEBUG)
logging.info("{}\tScript started.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# SETUP - DATA SOURCES and MONTE CARLO SIMULATION
# ----------------------------------------------------------------------------------------------------------------------
# Input data
inputFile = "UTOPIA_BASE.xlsx" # Update with actual filename
# Settings
inputDir = ".\Input_Data\\"
modelName = inputFile.split('.')[0]
sheetSets = "SETS"
sheetParams = "PARAMETERS"
sheetParamsDefault = "PARAMETERS_DEFAULT"
sheetMcs = "MCS"
sheetMcsNum = "MCS_num"
outputDir = ".\Output_Data\\"
outputFile = f"{modelName}_result.xlsx"
# ----------------------------------------------------------------------------------------------------------------------
# FUNCTIONS
# ----------------------------------------------------------------------------------------------------------------------
def newVar(name, lb, ub, cat, *indices):
"""
This function create a new variable having a lower bound (lb),
upper bound (ub), category (cat), using indices from SETS
"""
_name = name
for index in indices:
_name = "{}_{}".format(_name, index)
return pulp.LpVariable(_name, lowBound=lb, upBound=ub, cat=cat)
def loadData(filePath, sheetSets, sheetParams, sheetParamsDefault, sheetMcs, sheetMcsNum):
"""
This function loads all data from the input data set to dataframes.
"""
# Data: SETS
sets_df = pd.read_excel(io=filePath, sheet_name=sheetSets)
sets_df['REGION'] = sets_df['REGION'].astype(str)
sets_df['REGION2'] = sets_df['REGION2'].astype(str)
sets_df['DAYTYPE'] = sets_df['DAYTYPE'].astype(str)
sets_df['EMISSION'] = sets_df['EMISSION'].astype(str)
sets_df['FUEL'] = sets_df['FUEL'].astype(str)
sets_df['DAILYTIMEBRACKET'] = sets_df['DAILYTIMEBRACKET'].astype(str)
sets_df['SEASON'] = sets_df['SEASON'].astype(str)
sets_df['TIMESLICE'] = sets_df['TIMESLICE'].astype(str)
sets_df['MODE_OF_OPERATION'] = sets_df['MODE_OF_OPERATION'].astype(str)
sets_df['STORAGE'] = sets_df['STORAGE'].astype(str)
sets_df['TECHNOLOGY'] = sets_df['TECHNOLOGY'].astype(str)
sets_df['YEAR'] = sets_df['YEAR'].astype(str)
sets_df['FLEXIBLEDEMANDTYPE'] = sets_df['FLEXIBLEDEMANDTYPE'].astype(str)
# Data: PARAMETERS
p_df = pd.read_excel(io=filePath, sheet_name=sheetParams)
p_df = p_df.fillna(0)
p_df['PARAM'] = p_df['PARAM'].astype(str)
p_df['VALUE'] = p_df['VALUE'].apply(pd.to_numeric, downcast='signed')
p_df['REGION'] = p_df['REGION'].astype(str)
p_df['REGION2'] = p_df['REGION2'].astype(str)
p_df['DAYTYPE'] = p_df['DAYTYPE'].astype(int)
p_df['DAYTYPE'] = p_df['DAYTYPE'].astype(str)
p_df['EMISSION'] = p_df['EMISSION'].astype(str)
p_df['FUEL'] = p_df['FUEL'].astype(str)
p_df['DAILYTIMEBRACKET'] = p_df['DAILYTIMEBRACKET'].astype(int)
p_df['DAILYTIMEBRACKET'] = p_df['DAILYTIMEBRACKET'].astype(str)
p_df['SEASON'] = p_df['SEASON'].astype(int)
p_df['SEASON'] = p_df['SEASON'].astype(str)
p_df['TIMESLICE'] = p_df['TIMESLICE'].astype(str)
p_df['MODE_OF_OPERATION'] = p_df['MODE_OF_OPERATION'].astype(int)
p_df['MODE_OF_OPERATION'] = p_df['MODE_OF_OPERATION'].astype(str)
p_df['STORAGE'] = p_df['STORAGE'].astype(str)
p_df['TECHNOLOGY'] = p_df['TECHNOLOGY'].astype(str)
p_df['YEAR'] = p_df['YEAR'].astype(int)
p_df['YEAR'] = p_df['YEAR'].astype(str)
# Data: Parameters default values
p_default_df = pd.read_excel(io=filePath, sheet_name=sheetParamsDefault)
p_default_df = p_default_df.fillna(0)
p_default_df['PARAM'] = p_default_df['PARAM'].astype(str)
p_default_df['VALUE'] = p_default_df['VALUE'].apply(pd.to_numeric, downcast='signed')
# Data: Monte Carlo Simulation (MCS)
mcs_df = pd.read_excel(io=filePath, sheet_name=sheetMcs)
mcs_df = mcs_df.fillna(0)
mcs_df['DEFAULT_SETTING'] = mcs_df['DEFAULT_SETTING'].apply(pd.to_numeric, downcast='signed')
mcs_df['DEFAULT_SETTING'] = mcs_df['DEFAULT_SETTING'].astype(int)
mcs_df['REL_SD'] = mcs_df['REL_SD'].apply(pd.to_numeric, downcast='signed')
mcs_df['REL_MIN'] = mcs_df['REL_MIN'].apply(pd.to_numeric, downcast='signed')
mcs_df['REL_MAX'] = mcs_df['REL_MAX'].apply(pd.to_numeric, downcast='signed')
mcs_df['DISTRIBUTION'] = mcs_df['DISTRIBUTION'].astype(str)
mcs_df['ARRAY'] = [[float(i) for i in str(x).split(",")] for x in mcs_df['ARRAY']]
mcs_df['PARAM'] = mcs_df['PARAM'].astype(str)
mcs_df['REGION'] = mcs_df['REGION'].astype(str)
mcs_df['DAYTYPE'] = mcs_df['DAYTYPE'].astype(int)
mcs_df['DAYTYPE'] = mcs_df['DAYTYPE'].astype(str)
mcs_df['EMISSION'] = mcs_df['EMISSION'].astype(str)
mcs_df['FUEL'] = mcs_df['FUEL'].astype(str)
mcs_df['DAILYTIMEBRACKET'] = mcs_df['DAILYTIMEBRACKET'].astype(int)
mcs_df['DAILYTIMEBRACKET'] = mcs_df['DAILYTIMEBRACKET'].astype(str)
mcs_df['SEASON'] = mcs_df['SEASON'].astype(int)
mcs_df['SEASON'] = mcs_df['SEASON'].astype(str)
mcs_df['TIMESLICE'] = mcs_df['TIMESLICE'].astype(str)
mcs_df['MODE_OF_OPERATION'] = mcs_df['MODE_OF_OPERATION'].astype(int)
mcs_df['MODE_OF_OPERATION'] = mcs_df['MODE_OF_OPERATION'].astype(str)
mcs_df['STORAGE'] = mcs_df['STORAGE'].astype(str)
mcs_df['TECHNOLOGY'] = mcs_df['TECHNOLOGY'].astype(str)
mcs_df['YEAR'] = mcs_df['YEAR'].astype(int)
mcs_df['YEAR'] = mcs_df['YEAR'].astype(str)
# Number of MCS simulations
mcs_num_df = pd.read_excel(io=filePath, sheet_name=sheetMcsNum)
mcs_num = mcs_num_df.at[0, 'MCS_num']
return sets_df, p_df, p_default_df, mcs_df, mcs_num
def generateRandomData(reference, list):
"""
This function generates random data for the parameters included in the Monte Carlo Simulations.
reference (format: float): mean for normal distribution, mode for both triangular and uniform distributions
dist: type of distribution. Choose from: "normal", "triangular", "uniform" (format: string)
rel_sd: relative standard deviation from mean or mode. Unit: percent as decimals (format: float)
rel_min: relative minimum deviation from mean or mode. Unit: percent as decimals (format: float), must be a negative value
rel_max: relative maximum deviation from mean or mode. Unit: percent as decimals (format: float), must be a positive value
array: array with potential values. One value out of the array will be randomly chosen.
==================================================================================================================
Note: To use the reference value without any distribution, then write as input in the excel file in the tab "MCS":
Columns: PARAM: "parameter name", DEFAULT_SETTING: "1", DIST: "normal", REL_SD: "0".
This will make the code to choose the reference value as defined for the model without MCS.
"""
dist, rel_sd, rel_min, rel_max, array = list[0], list[1], list[2], list[3], list[4]
if dist == "normal":
value = np.random.normal(reference, rel_sd * reference, 1)[
0] # mean, standard deviation, generate 1 value at the time
elif dist == "triangular":
value = np.random.triangular((1 + rel_min) * reference, reference, (1 + rel_max) * reference, 1)[
0] # minimum value, mode, maximum value, generate 1 value at the time
elif dist == "uniform":
value = np.random.uniform((1 + rel_min) * reference, (1 + rel_max) * reference, 1)[
0] # minimum value, maximum value, generate 1 value at the time
elif dist == "choice":
if len(array) > 1:
value = np.random.choice(array)
else:
logging.error("ERROR: Review MCS_df array column. Expected length of array: larger than 1, but is: 0 or 1")
else:
logging.error("ERROR: Select an available distribution, review input data and/or add default input data for this parameter.")
return
# This if condition prevents input errors caused by negative values for the parameters
if value >= 0:
return value
else:
return 0
def saveResultsTemporary(dataframe, model_name, scenario):
# Activate variable names in "var_dict" to be included in the results,
# or comment out all redundant variables.
df = dataframe
var_dict = {
######## Demands #############
#"RateOfDemand": ["r", "l", "f", "y"],
"Demand": ["r", "l", "f", "y"],
######## Storage #############
#"RateOfStorageCharge": ["r", "s", "ls", "ld", "lh", "y"],
#"RateOfStorageDischarge": ["r", "s", "ls", "ld", "lh", "y"],
#"NetChargeWithinYear": ["r", "s", "ls", "ld", "lh", "y"],
#"NetChargeWithinDay": ["r", "s", "ls", "ld", "lh", "y"],
#"StorageLevelYearStart": ["r", "s", "y"],
#"StorageLevelYearFinish": ["r", "s", "y"],
#"StorageLevelSeasonStart": ["r", "s", "ls", "y"],
#"StorageLevelDayTypeStart": ["r", "s", "ls", "ld", "y"],
#"StorageLevelDayTypeFinish": ["r", "s", "ls", "ld", "y"],
#"StorageLowerLimit": ["r", "s", "y"],
#"StorageUpperLimit": ["r", "s", "y"],
#"AccumulatedNewStorageCapacity": ["r", "s", "y"],
#"NewStorageCapacity": ["r", "s", "y"],
#"CapitalInvestmentStorage": ["r", "s", "y"],
#"DiscountedCapitalInvestmentStorage": ["r", "s", "y"],
#"SalvageValueStorage": ["r", "s", "y"],
#"DiscountedSalvageValueStorage": ["r", "s", "y"],
#"TotalDiscountedStorageCost": ["r", "s", "y"],
######### Capacity Variables #############
#"NumberOfNewTechnologyUnits": ["r", "t", "y"],
"NewCapacity": ["r", "t", "y"],
#"AccumulatedNewCapacity": ["r", "t", "y"],
"TotalCapacityAnnual": ["r", "t", "y"],
######### Activity Variables #############
#"RateOfActivity": ["r", "l", "t", "m", "y"],
#"RateOfTotalActivity": ["r", "t", "l", "y"],
#"TotalTechnologyAnnualActivity": ["r", "t", "y"],
#"TotalAnnualTechnologyActivityByMode": ["r", "t", "m", "y"],
#"TotalTechnologyModelPeriodActivity": ["r", "t"],
#"RateOfProductionByTechnologyByMode": ["r", "l", "t", "m", "f", "y"],
#"RateOfProductionByTechnology": ["r", "l", "t", "f", "y"],
#"ProductionByTechnology": ["r", "l", "t", "f", "y"],
#"ProductionByTechnologyAnnual": ["r", "t", "f", "y"],
#"RateOfProduction": ["r", "l", "f", "y"],
#"Production": ["r", "l", "f", "y"],
#"RateOfUseByTechnologyByMode": ["r", "l", "t", "m", "f", "y"],
#"RateOfUseByTechnology": ["r", "l", "t", "f", "y"],
#"UseByTechnologyAnnual": ["r", "t", "f", "y"],
#"RateOfUse": ["r", "l", "f", "y"],
#"UseByTechnology": ["r", "l", "t", "f", "y"],
#"Use": ["r", "l", "f", "y"],
#"Trade": ["r", "rr", "l", "f", "y"],
#"TradeAnnual": ["r", "rr", "f", "y"],
#"ProductionAnnual": ["r", "f", "y"],
"UseAnnual": ["r", "f", "y"],
######### Costing Variables #############
"CapitalInvestment": ["r", "t", "y"],
#"DiscountedCapitalInvestment": ["r", "t", "y"],
#"SalvageValue": ["r", "t", "y"],
#"DiscountedSalvageValue": ["r", "t", "y"],
#"OperatingCost": ["r", "t", "y"],
#"DiscountedOperatingCost": ["r", "t", "y"],
#"AnnualVariableOperatingCost": ["r", "t", "y"],
#"AnnualFixedOperatingCost": ["r", "t", "y"],
#"TotalDiscountedCostByTechnology": ["r", "t", "y"],
#"TotalDiscountedCost": ["r", "y"],
#"ModelPeriodCostByRegion": ["r"],
######### Reserve Margin #############
#"TotalCapacityInReserveMargin": ["r", "y"],
#"DemandNeedingReserveMargin": ["r", "l", "y"],
######### RE Gen Target #############
#"TotalREProductionAnnual": ["r", "y"],
#"RETotalProductionOfTargetFuelAnnual": ["r", "y"],
######### Emissions #############
#"AnnualTechnologyEmissionByMode": ["r", "t", "e", "m", "y"],
#"AnnualTechnologyEmission": ["r", "t", "e", "y"],
#"AnnualTechnologyEmissionPenaltyByEmission": ["r", "t", "e", "y"],
#"AnnualTechnologyEmissionsPenalty": ["r", "t", "y"],
#"DiscountedTechnologyEmissionsPenalty": ["r", "t", "y"],
"AnnualEmissions": ["r", "e", "y"],
"ModelPeriodEmissions": ["r", "e"]
}
# Objective value ("cost")
temp_df = pd.DataFrame(columns=[
'SCENARIO',
'VAR_NAME',
'VAR_VALUE',
'REGION',
'REGION2',
'DAYTYPE',
'EMISSION',
'FUEL',
'DAILYTIMEBRACKET',
'SEASON',
'TIMESLICE',
'MODE_OF_OPERATION',
'STORAGE',
'TECHNOLOGY',
'YEAR',
'FLEXIBLEDEMANDTYPE'])
temp_df.at[0, 'SCENARIO'] = scenario
temp_df.at[0, 'VAR_NAME'] = "cost"
temp_df.at[0, 'VAR_VALUE'] = model_name.objective.value()
temp_df.at[0, 'REGION'] = " "
temp_df.at[0, 'REGION2'] = " "
temp_df.at[0, 'DAYTYPE'] = " "
temp_df.at[0, 'EMISSION'] = " "
temp_df.at[0, 'FUEL'] = " "
temp_df.at[0, 'DAILYTIMEBRACKET'] = " "
temp_df.at[0, 'SEASON'] = " "
temp_df.at[0, 'TIMESLICE'] = " "
temp_df.at[0, 'MODE_OF_OPERATION'] = " "
temp_df.at[0, 'STORAGE'] = " "
temp_df.at[0, 'TECHNOLOGY'] = " "
temp_df.at[0, 'YEAR'] = " "
temp_df.at[0, 'FLEXIBLEDEMANDTYPE'] = " "
df = pd.concat([df, temp_df])
# Variables values (only variables that are included in var_dict)
selected_variables = [variable for key in var_dict.keys() for variable in model_name.variables() if key == variable.name.split("_")[0]]
for var in selected_variables:
# Temporal dataframe in loop
temp_df = pd.DataFrame(columns=[
'SCENARIO',
'VAR_NAME',
'VAR_VALUE',
'REGION',
'REGION2',
'DAYTYPE',
'EMISSION',
'FUEL',
'DAILYTIMEBRACKET',
'SEASON',
'TIMESLICE',
'MODE_OF_OPERATION',
'STORAGE',
'TECHNOLOGY',
'YEAR',
'FLEXIBLEDEMANDTYPE'])
# Variable name
var_name = var.name.split("_")[0]
# Variable indices
var_concrete_indices_list = var.name.split("_")[1:]
# Variable abstract indices
var_abstract_indices_list = var_dict[var_name]
# Dictionary
abstract_dict = {key: "" for key in ["r", "rr", "ld", "e", "f", "lh", "ls", "l", "m", "s", "t", "y", "fdt"]} # default value: " "
concrete_dict = {key: value for key, value in zip(var_abstract_indices_list, var_concrete_indices_list)}
data_dict = {**abstract_dict, **concrete_dict} # Merge dictionaries
# Write data to temporary dataframe
temp_df.at[0, 'SCENARIO'] = scenario
temp_df.at[0, 'VAR_NAME'] = var.name.split("_")[0]
temp_df.at[0, 'VAR_VALUE'] = var.varValue
temp_df.at[0, 'REGION'] = data_dict["r"]
temp_df.at[0, 'REGION2'] = data_dict["rr"]
temp_df.at[0, 'DAYTYPE'] = data_dict["ld"]
temp_df.at[0, 'EMISSION'] = data_dict["e"]
temp_df.at[0, 'FUEL'] = data_dict["f"]
temp_df.at[0, 'DAILYTIMEBRACKET'] = data_dict["lh"]
temp_df.at[0, 'SEASON'] = data_dict["ls"]
temp_df.at[0, 'TIMESLICE'] = data_dict["l"]
temp_df.at[0, 'MODE_OF_OPERATION'] = data_dict["m"]
temp_df.at[0, 'STORAGE'] = data_dict["s"]
temp_df.at[0, 'TECHNOLOGY'] = data_dict["t"]
temp_df.at[0, 'YEAR'] = data_dict["y"]
temp_df.at[0, 'FLEXIBLEDEMANDTYPE'] = data_dict["fdt"]
df = pd.concat([df, temp_df])
return df
def saveResults(dataframe, fileDir, fileName):
"""
This function saves all results to an Excel file.
"""
_df = dataframe
# Shorten abstract variable names to keep Excel worksheet name limit of 31 characters
_df['VAR_NAME'].replace(
regex={'Total': 'Tot', 'Annual': 'Ann', 'Technology': 'Tech', 'Discounted': 'Disc', 'Production': 'Prod'},
inplace=True)
name_list = _df['VAR_NAME'].unique()
dataframe_list = [_df[_df['VAR_NAME'] == str(name)] for name in name_list]
if not os.path.exists(fileDir):
os.makedirs(fileDir)
writer = pd.ExcelWriter(os.path.join(fileDir, fileName))
for d, name in zip(dataframe_list, name_list):
d.to_excel(writer, sheet_name=name, index=False)
writer.save()
return
# ----------------------------------------------------------------------------------------------------------------------
# LOAD DATA
# ----------------------------------------------------------------------------------------------------------------------
inputPath = os.path.join(inputDir, inputFile)
sets_df, p_df, p_default_df, mcs_df, mcs_num = loadData(inputPath, sheetSets, sheetParams, sheetParamsDefault, sheetMcs, sheetMcsNum)
mcs_parameters = mcs_df['PARAM'].unique() # list of parameters to be included in monte carlo simulation
logging.info("{}\tData is loaded.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# SETS
# ----------------------------------------------------------------------------------------------------------------------
REGION = [r for r in sets_df['REGION'] if r != 'nan']
REGION2 = [rr for rr in sets_df['REGION2'] if rr != 'nan']
DAYTYPE = [str(int(float(ld))) for ld in sets_df['DAYTYPE'] if ld != 'nan']
EMISSION = [e for e in sets_df['EMISSION'] if e != 'nan']
FUEL = [f for f in sets_df['FUEL'] if f != 'nan']
DAILYTIMEBRACKET = [str(int(float(lh))) for lh in sets_df['DAILYTIMEBRACKET'] if lh != 'nan']
SEASON = [str(int(float(ls))) for ls in sets_df['SEASON'] if ls != 'nan']
TIMESLICE = [l for l in sets_df['TIMESLICE'] if l != 'nan']
MODE_OF_OPERATION = [str(int(float(m))) for m in sets_df['MODE_OF_OPERATION'] if m != 'nan']
STORAGE = [s for s in sets_df['STORAGE'] if s != 'nan']
TECHNOLOGY = [t for t in sets_df['TECHNOLOGY'] if t != 'nan']
YEAR = [str(int(float(y))) for y in sets_df['YEAR'] if y != 'nan']
FLEXIBLEDEMANDTYPE = [fdt for fdt in sets_df['FLEXIBLEDEMANDTYPE'] if fdt != 'nan']
logging.info("{}\tSets are created.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# PARAMETERS AND DATA
# ----------------------------------------------------------------------------------------------------------------------
######## Global #########
# YearSplit
YearSplit = p_df[p_df['PARAM'] == "YearSplit"][['TIMESLICE', 'YEAR', 'VALUE']].groupby('TIMESLICE')\
.apply(lambda df: df.set_index('YEAR')['VALUE'].to_dict()).to_dict()
# DiscountRate
DiscountRate_default_value = p_default_df[p_default_df['PARAM'] == "DiscountRate"].VALUE.iat[0]
DiscountRate_specified = tuple([(str(r)) for r in p_df[p_df['PARAM'] == "DiscountRate"].REGION])
DiscountRate = {str(r): p_df[(p_df['PARAM'] == "DiscountRate") & (p_df['REGION'] == r)].VALUE.iat[0]\
if (str(r)) in DiscountRate_specified else DiscountRate_default_value for r in REGION}
# DaySplit
DaySplit_default_value = p_default_df[p_default_df['PARAM'] == "DaySplit"].VALUE.iat[0]
DaySplit_specified = tuple([(str(lh), str(y)) for lh, y in zip(
p_df[p_df['PARAM'] == "DaySplit"].DAILYTIMEBRACKET, p_df[p_df['PARAM'] == "DaySplit"].YEAR)])
DaySplit = {str(lh): {str(y): p_df[(p_df['PARAM'] == "DaySplit") & (p_df['DAILYTIMEBRACKET'] == lh) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(lh), str(y)) in DaySplit_specified else DaySplit_default_value for y in YEAR} for lh in DAILYTIMEBRACKET}
# Conversionls
Conversionls_default_value = p_default_df[p_default_df['PARAM'] == "Conversionls"].VALUE.iat[0]
Conversionls_specified = tuple([(str(l), str(ls)) for l, ls in zip(p_df[p_df['PARAM'] == "Conversionls"].TIMESLICE, p_df[p_df['PARAM'] == "Conversionls"].SEASON)])
Conversionls = {str(l): {str(ls): p_df[(p_df['PARAM'] == "Conversionls") & (p_df['TIMESLICE'] == l) & (p_df['SEASON'] == ls)].VALUE.iat[0] if (str(l), str(ls)) in Conversionls_specified else Conversionls_default_value for ls in SEASON} for l in TIMESLICE}
# Conversionld
Conversionld_default_value = p_default_df[p_default_df['PARAM'] == "Conversionld"].VALUE.iat[0]
Conversionld_specified = tuple([(str(l), str(ld)) for l, ld in zip(p_df[p_df['PARAM'] == "Conversionld"].TIMESLICE, p_df[p_df['PARAM'] == "Conversionld"].DAYTYPE)])
Conversionld = {str(l): {str(ld): p_df[(p_df['PARAM'] == "Conversionld") & (p_df['TIMESLICE'] == l) & (p_df['DAYTYPE'] == ld)].VALUE.iat[0] if (str(l), str(ld)) in Conversionld_specified else Conversionld_default_value for ld in DAYTYPE} for l in TIMESLICE}
# Conversionlh
Conversionlh_default_value = p_default_df[p_default_df['PARAM'] == "Conversionlh"].VALUE.iat[0]
Conversionlh_specified = tuple([(str(l), str(lh)) for l, lh in zip(p_df[p_df['PARAM'] == "Conversionlh"].TIMESLICE, p_df[p_df['PARAM'] == "Conversionlh"].DAILYTIMEBRACKET)])
Conversionlh = {str(l): {str(lh): p_df[(p_df['PARAM'] == "Conversionlh") & (p_df['TIMESLICE'] == l) & (p_df['DAILYTIMEBRACKET'] == lh)].VALUE.iat[0] if (str(l), str(lh)) in Conversionlh_specified else Conversionlh_default_value for lh in DAILYTIMEBRACKET} for l in TIMESLICE}
# DaysInDayType
DaysInDayType_default_value = p_default_df[p_default_df['PARAM'] == "DaysInDayType"].VALUE.iat[0]
DaysInDayType_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "DaysInDayType"].SEASON, p_df[p_df['PARAM'] == "DaysInDayType"].DAYTYPE, p_df[p_df['PARAM'] == "DaysInDayType"].YEAR)])
DaysInDayType = {str(ls): {str(ld): {str(y): p_df[(p_df['PARAM'] == "DaysInDayType") & (p_df['SEASON'] == ls) & (p_df['DAYTYPE'] == ld) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(ls),str(ld),str(y)) in DaysInDayType_specified else DaysInDayType_default_value for y in YEAR} for ld in DAYTYPE} for ls in SEASON}
# TradeRoute
TradeRoute_default_value = p_default_df[p_default_df['PARAM'] == "TradeRoute"].VALUE.iat[0]
TradeRoute_specified = tuple([(str(r),str(rr),str(f),str(y)) for r, rr, f, y in zip(p_df[p_df['PARAM'] == "TradeRoute"].REGION, p_df[p_df['PARAM'] == "TradeRoute"].REGION2, p_df[p_df['PARAM'] == "TradeRoute"].FUEL, p_df[p_df['PARAM'] == "TradeRoute"].YEAR)])
TradeRoute = {str(r): {str(rr): {str(f): {str(y): p_df[(p_df['PARAM'] == "TradeRoute") & (p_df['REGION'] == r) & (p_df['REGION2'] == rr) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(rr),str(f),str(y)) in TradeRoute_specified else TradeRoute_default_value for y in YEAR} for f in FUEL} for rr in REGION2} for r in REGION}
# DepreciationMethod
DepreciationMethod_default_value = p_default_df[p_default_df['PARAM'] == "DepreciationMethod"].VALUE.iat[0]
DepreciationMethod_specified = tuple([(str(r)) for r in p_df[p_df['PARAM'] == "DepreciationMethod"].REGION])
DepreciationMethod = {str(r): p_df[(p_df['PARAM'] == "DepreciationMethod") & (p_df['REGION'] == r)].VALUE.iat[0] if (str(r)) in DepreciationMethod_specified else DepreciationMethod_default_value for r in REGION}
######## Demands #########
# SpecifiedAnnualDemand
SpecifiedAnnualDemand_default_value = p_default_df[p_default_df['PARAM'] == "SpecifiedAnnualDemand"].VALUE.iat[0]
SpecifiedAnnualDemand_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "SpecifiedAnnualDemand"].REGION, p_df[p_df['PARAM'] == "SpecifiedAnnualDemand"].FUEL, p_df[p_df['PARAM'] == "SpecifiedAnnualDemand"].YEAR)])
SpecifiedAnnualDemand = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "SpecifiedAnnualDemand") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(f),str(y)) in SpecifiedAnnualDemand_specified else SpecifiedAnnualDemand_default_value for y in YEAR} for f in FUEL} for r in REGION}
# SpecifiedDemandProfile
SpecifiedDemandProfile_default_value = p_default_df[p_default_df['PARAM'] == "SpecifiedDemandProfile"].VALUE.iat[0]
SpecifiedDemandProfile_specified = tuple([(str(r),str(f),str(l),str(y)) for r, f, l, y in zip(p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].REGION, p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].FUEL, p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].TIMESLICE, p_df[p_df['PARAM'] == "SpecifiedDemandProfile"].YEAR)])
SpecifiedDemandProfile = {str(r): {str(f): {str(l): {str(y): p_df[(p_df['PARAM'] == "SpecifiedDemandProfile") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['TIMESLICE'] == l) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(f),str(l),str(y)) in SpecifiedDemandProfile_specified else SpecifiedDemandProfile_default_value for y in YEAR} for l in TIMESLICE} for f in FUEL} for r in REGION}
# AccumulatedAnnualDemand
AccumulatedAnnualDemand_default_value = p_default_df[p_default_df['PARAM'] == "AccumulatedAnnualDemand"].VALUE.iat[0]
AccumulatedAnnualDemand_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "AccumulatedAnnualDemand"].REGION, p_df[p_df['PARAM'] == "AccumulatedAnnualDemand"].FUEL, p_df[p_df['PARAM'] == "AccumulatedAnnualDemand"].YEAR)])
AccumulatedAnnualDemand = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "AccumulatedAnnualDemand") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(f),str(y)) in AccumulatedAnnualDemand_specified else AccumulatedAnnualDemand_default_value for y in YEAR} for f in FUEL} for r in REGION}
######### Performance #########
# CapacityToActivityUnit
CapacityToActivityUnit_default_value = p_default_df[p_default_df['PARAM'] == "CapacityToActivityUnit"].VALUE.iat[0]
CapacityToActivityUnit_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "CapacityToActivityUnit"].REGION, p_df[p_df['PARAM'] == "CapacityToActivityUnit"].TECHNOLOGY)])
CapacityToActivityUnit = {str(r): {str(t): p_df[(p_df['PARAM'] == "CapacityToActivityUnit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in CapacityToActivityUnit_specified else CapacityToActivityUnit_default_value for t in TECHNOLOGY} for r in REGION}
# TechWithCapacityNeededToMeetPeakTS
TechWithCapacityNeededToMeetPeakTS_default_value = p_default_df[p_default_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].VALUE.iat[0]
TechWithCapacityNeededToMeetPeakTS_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].REGION, p_df[p_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].TECHNOLOGY)])
TechWithCapacityNeededToMeetPeakTS = {str(r): {str(t): p_df[(p_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in TechWithCapacityNeededToMeetPeakTS_specified else TechWithCapacityNeededToMeetPeakTS_default_value for t in TECHNOLOGY} for r in REGION}
# CapacityFactor
CapacityFactor_default_value = p_default_df[p_default_df['PARAM'] == "CapacityFactor"].VALUE.iat[0]
CapacityFactor_specified = tuple([(str(r),str(t),str(l),str(y)) for r, t, l, y in zip(p_df[p_df['PARAM'] == "CapacityFactor"].REGION, p_df[p_df['PARAM'] == "CapacityFactor"].TECHNOLOGY, p_df[p_df['PARAM'] == "CapacityFactor"].TIMESLICE, p_df[p_df['PARAM'] == "CapacityFactor"].YEAR)])
CapacityFactor = {str(r): {str(t): {str(l): {str(y): p_df[(p_df['PARAM'] == "CapacityFactor") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y) & (p_df['TIMESLICE'] == l)].VALUE.iat[0] if (str(r),str(t),str(l),str(y)) in CapacityFactor_specified else CapacityFactor_default_value for y in YEAR} for l in TIMESLICE} for t in TECHNOLOGY} for r in REGION}
# AvailabilityFactor
AvailabilityFactor_default_value = p_default_df[p_default_df['PARAM'] == "AvailabilityFactor"].VALUE.iat[0]
AvailabilityFactor_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "AvailabilityFactor"].REGION, p_df[p_df['PARAM'] == "AvailabilityFactor"].TECHNOLOGY, p_df[p_df['PARAM'] == "AvailabilityFactor"].YEAR)])
AvailabilityFactor = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "AvailabilityFactor") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in AvailabilityFactor_specified else AvailabilityFactor_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# OperationalLife
OperationalLife_default_value = p_default_df[p_default_df['PARAM'] == "OperationalLife"].VALUE.iat[0]
OperationalLife_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "OperationalLife"].REGION, p_df[p_df['PARAM'] == "OperationalLife"].TECHNOLOGY)])
OperationalLife = {str(r): {str(t): p_df[(p_df['PARAM'] == "OperationalLife") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in OperationalLife_specified else OperationalLife_default_value for t in TECHNOLOGY} for r in REGION}
# ResidualCapacity
ResidualCapacity_default_value = p_default_df[p_default_df['PARAM'] == "ResidualCapacity"].VALUE.iat[0]
ResidualCapacity_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "ResidualCapacity"].REGION, p_df[p_df['PARAM'] == "ResidualCapacity"].TECHNOLOGY, p_df[p_df['PARAM'] == "ResidualCapacity"].YEAR)])
ResidualCapacity = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "ResidualCapacity") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in ResidualCapacity_specified else ResidualCapacity_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# InputActivityRatio
InputActivityRatio_default_value = p_default_df[p_default_df['PARAM'] == "InputActivityRatio"].VALUE.iat[0]
InputActivityRatio_specified = tuple([(str(r),str(t),str(f),str(m),str(y)) for r, t, f, m, y in zip(p_df[p_df['PARAM'] == "InputActivityRatio"].REGION, p_df[p_df['PARAM'] == "InputActivityRatio"].TECHNOLOGY, p_df[p_df['PARAM'] == "InputActivityRatio"].FUEL, p_df[p_df['PARAM'] == "InputActivityRatio"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "InputActivityRatio"].YEAR)])
InputActivityRatio = {str(r): {str(t): {str(f): {str(m): {str(y): p_df[(p_df['PARAM'] == "InputActivityRatio") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['FUEL'] == f) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(f),str(m),str(y)) in InputActivityRatio_specified else InputActivityRatio_default_value for y in YEAR} for m in MODE_OF_OPERATION} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
# OutputActivityRatio
OutputActivityRatio_default_value = p_default_df[p_default_df['PARAM'] == "OutputActivityRatio"].VALUE.iat[0]
OutputActivityRatio_specified = tuple([(str(r),str(t),str(f),str(m),str(y)) for r, t, f, m, y in zip(p_df[p_df['PARAM'] == "OutputActivityRatio"].REGION, p_df[p_df['PARAM'] == "OutputActivityRatio"].TECHNOLOGY, p_df[p_df['PARAM'] == "OutputActivityRatio"].FUEL, p_df[p_df['PARAM'] == "OutputActivityRatio"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "OutputActivityRatio"].YEAR)])
OutputActivityRatio = {str(r): {str(t): {str(f): {str(m): {str(y): p_df[(p_df['PARAM'] == "OutputActivityRatio") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['FUEL'] == f) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(f),str(m),str(y)) in OutputActivityRatio_specified else OutputActivityRatio_default_value for y in YEAR} for m in MODE_OF_OPERATION} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
######### Technology Costs #########
# CapitalCost
CapitalCost_default_value = p_default_df[p_default_df['PARAM'] == "CapitalCost"].VALUE.iat[0]
CapitalCost_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "CapitalCost"].REGION, p_df[p_df['PARAM'] == "CapitalCost"].TECHNOLOGY, p_df[p_df['PARAM'] == "CapitalCost"].YEAR)])
CapitalCost = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "CapitalCost") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in CapitalCost_specified else CapitalCost_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# VariableCost
VariableCost_default_value = p_default_df[p_default_df['PARAM'] == "VariableCost"].VALUE.iat[0]
VariableCost_specified = tuple([(str(r),str(t),str(m),str(y)) for r, t, m, y in zip(p_df[p_df['PARAM'] == "VariableCost"].REGION, p_df[p_df['PARAM'] == "VariableCost"].TECHNOLOGY, p_df[p_df['PARAM'] == "VariableCost"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "VariableCost"].YEAR)])
VariableCost = {str(r): {str(t): {str(m): {str(y): p_df[(p_df['PARAM'] == "VariableCost") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(m),str(y)) in VariableCost_specified else VariableCost_default_value for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for r in REGION}
# FixedCost
FixedCost_default_value = p_default_df[p_default_df['PARAM'] == "FixedCost"].VALUE.iat[0]
FixedCost_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "FixedCost"].REGION, p_df[p_df['PARAM'] == "FixedCost"].TECHNOLOGY, p_df[p_df['PARAM'] == "FixedCost"].YEAR)])
FixedCost = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "FixedCost") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(y)) in FixedCost_specified else FixedCost_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Storage #########
# TechnologyToStorage
TechnologyToStorage_default_value = p_default_df[p_default_df['PARAM'] == "TechnologyToStorage"].VALUE.iat[0]
TechnologyToStorage_specified = tuple([(str(r),str(t),str(s),str(m)) for r, t, s, m in zip(p_df[p_df['PARAM'] == "TechnologyToStorage"].REGION, p_df[p_df['PARAM'] == "TechnologyToStorage"].TECHNOLOGY, p_df[p_df['PARAM'] == "TechnologyToStorage"].STORAGE, p_df[p_df['PARAM'] == "TechnologyToStorage"].MODE_OF_OPERATION)])
TechnologyToStorage = {str(r): {str(t): {str(s): {str(m): p_df[(p_df['PARAM'] == "TechnologyToStorage") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['STORAGE'] == s) & (p_df['MODE_OF_OPERATION'] == m)].VALUE.iat[0] if (str(r),str(t),str(s),str(m)) in TechnologyToStorage_specified else TechnologyToStorage_default_value for m in MODE_OF_OPERATION} for s in STORAGE} for t in TECHNOLOGY} for r in REGION}
# TechnologyFromStorage
TechnologyFromStorage_default_value = p_default_df[p_default_df['PARAM'] == "TechnologyFromStorage"].VALUE.iat[0]
TechnologyFromStorage_specified = tuple([(str(r),str(t),str(s),str(m)) for r, t, s, m in zip(p_df[p_df['PARAM'] == "TechnologyFromStorage"].REGION, p_df[p_df['PARAM'] == "TechnologyFromStorage"].TECHNOLOGY, p_df[p_df['PARAM'] == "TechnologyFromStorage"].STORAGE, p_df[p_df['PARAM'] == "TechnologyFromStorage"].MODE_OF_OPERATION)])
TechnologyFromStorage = {str(r): {str(t): {str(s): {str(m): p_df[(p_df['PARAM'] == "TechnologyFromStorage") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['STORAGE'] == s) & (p_df['MODE_OF_OPERATION'] == m)].VALUE.iat[0] if (str(r),str(t),str(s),str(m)) in TechnologyFromStorage_specified else TechnologyFromStorage_default_value for m in MODE_OF_OPERATION} for s in STORAGE} for t in TECHNOLOGY} for r in REGION}
# StorageLevelStart
StorageLevelStart_default_value = p_default_df[p_default_df['PARAM'] == "StorageLevelStart"].VALUE.iat[0]
StorageLevelStart_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "StorageLevelStart"].REGION, p_df[p_df['PARAM'] == "StorageLevelStart"].STORAGE)])
StorageLevelStart = {str(r): {str(s): p_df[(p_df['PARAM'] == "StorageLevelStart") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in StorageLevelStart_specified else StorageLevelStart_default_value for s in STORAGE} for r in REGION}
# StorageMaxChargeRate
StorageMaxChargeRate_default_value = p_default_df[p_default_df['PARAM'] == "StorageMaxChargeRate"].VALUE.iat[0]
StorageMaxChargeRate_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "StorageMaxChargeRate"].REGION, p_df[p_df['PARAM'] == "StorageMaxChargeRate"].STORAGE)])
StorageMaxChargeRate = {str(r): {str(s): p_df[(p_df['PARAM'] == "StorageMaxChargeRate") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in StorageMaxChargeRate_specified else StorageMaxChargeRate_default_value for s in STORAGE} for r in REGION}
# StorageMaxDischargeRate
StorageMaxDischargeRate_default_value = p_default_df[p_default_df['PARAM'] == "StorageMaxDischargeRate"].VALUE.iat[0]
StorageMaxDischargeRate_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "StorageMaxDischargeRate"].REGION, p_df[p_df['PARAM'] == "StorageMaxDischargeRate"].STORAGE)])
StorageMaxDischargeRate = {str(r): {str(s): p_df[(p_df['PARAM'] == "StorageMaxDischargeRate") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in StorageMaxDischargeRate_specified else StorageMaxDischargeRate_default_value for s in STORAGE} for r in REGION}
# MinStorageCharge
MinStorageCharge_default_value = p_default_df[p_default_df['PARAM'] == "MinStorageCharge"].VALUE.iat[0]
MinStorageCharge_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(p_df[p_df['PARAM'] == "MinStorageCharge"].REGION, p_df[p_df['PARAM'] == "MinStorageCharge"].STORAGE, p_df[p_df['PARAM'] == "MinStorageCharge"].YEAR)])
MinStorageCharge = {str(r): {str(s): {str(y): p_df[(p_df['PARAM'] == "MinStorageCharge") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(s), str(y)) in MinStorageCharge_specified else MinStorageCharge_default_value for y in YEAR} for s in STORAGE} for r in REGION}
# OperationalLifeStorage
OperationalLifeStorage_default_value = p_default_df[p_default_df['PARAM'] == "OperationalLifeStorage"].VALUE.iat[0]
OperationalLifeStorage_specified = tuple([(str(r), str(s)) for r, s in zip(p_df[p_df['PARAM'] == "OperationalLifeStorage"].REGION, p_df[p_df['PARAM'] == "OperationalLifeStorage"].STORAGE)])
OperationalLifeStorage = {str(r): {str(s): p_df[(p_df['PARAM'] == "OperationalLifeStorage") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s)].VALUE.iat[0] if (str(r), str(s)) in OperationalLifeStorage_specified else OperationalLifeStorage_default_value for s in STORAGE} for r in REGION}
# CapitalCostStorage
CapitalCostStorage_default_value = p_default_df[p_default_df['PARAM'] == "CapitalCostStorage"].VALUE.iat[0]
CapitalCostStorage_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(p_df[p_df['PARAM'] == "CapitalCostStorage"].REGION, p_df[p_df['PARAM'] == "CapitalCostStorage"].STORAGE, p_df[p_df['PARAM'] == "CapitalCostStorage"].YEAR)])
CapitalCostStorage = {str(r): {str(s): {str(y): p_df[(p_df['PARAM'] == "CapitalCostStorage") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(s), str(y)) in CapitalCostStorage_specified else CapitalCostStorage_default_value for y in YEAR} for s in STORAGE} for r in REGION}
# ResidualStorageCapacity
ResidualStorageCapacity_default_value = p_default_df[p_default_df['PARAM'] == "ResidualStorageCapacity"].VALUE.iat[0]
ResidualStorageCapacity_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(p_df[p_df['PARAM'] == "ResidualStorageCapacity"].REGION, p_df[p_df['PARAM'] == "ResidualStorageCapacity"].STORAGE, p_df[p_df['PARAM'] == "ResidualStorageCapacity"].YEAR)])
ResidualStorageCapacity = {str(r): {str(s): {str(y): p_df[(p_df['PARAM'] == "ResidualStorageCapacity") & (p_df['REGION'] == r) & (p_df['STORAGE'] == s) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(s), str(y)) in ResidualStorageCapacity_specified else ResidualStorageCapacity_default_value for y in YEAR} for s in STORAGE} for r in REGION}
######### Capacity Constraints #########
# CapacityOfOneTechnologyUnit
CapacityOfOneTechnologyUnit_default_value = p_default_df[p_default_df['PARAM'] == "CapacityOfOneTechnologyUnit"].VALUE.iat[0]
CapacityOfOneTechnologyUnit_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "CapacityOfOneTechnologyUnit"].REGION, p_df[p_df['PARAM'] == "CapacityOfOneTechnologyUnit"].TECHNOLOGY, p_df[p_df['PARAM'] == "CapacityOfOneTechnologyUnit"].YEAR)])
CapacityOfOneTechnologyUnit = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "CapacityOfOneTechnologyUnit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in CapacityOfOneTechnologyUnit_specified else CapacityOfOneTechnologyUnit_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalAnnualMaxCapacity
TotalAnnualMaxCapacity_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMaxCapacity"].VALUE.iat[0]
TotalAnnualMaxCapacity_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMaxCapacity"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacity"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacity"].YEAR)])
TotalAnnualMaxCapacity = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMaxCapacity") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMaxCapacity_specified else TotalAnnualMaxCapacity_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalAnnualMinCapacity
TotalAnnualMinCapacity_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMinCapacity"].VALUE.iat[0]
TotalAnnualMinCapacity_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMinCapacity"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMinCapacity"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMinCapacity"].YEAR)])
TotalAnnualMinCapacity = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMinCapacity") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMinCapacity_specified else TotalAnnualMinCapacity_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Investment Constraints #########
# TotalAnnualMaxCapacityInvestment
TotalAnnualMaxCapacityInvestment_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].VALUE.iat[0]
TotalAnnualMaxCapacityInvestment_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].YEAR)])
TotalAnnualMaxCapacityInvestment = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMaxCapacityInvestment") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMaxCapacityInvestment_specified else TotalAnnualMaxCapacityInvestment_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalAnnualMinCapacityInvestment
TotalAnnualMinCapacityInvestment_default_value = p_default_df[p_default_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].VALUE.iat[0]
TotalAnnualMinCapacityInvestment_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].REGION, p_df[p_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].YEAR)])
TotalAnnualMinCapacityInvestment = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalAnnualMinCapacityInvestment") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalAnnualMinCapacityInvestment_specified else TotalAnnualMinCapacityInvestment_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Activity Constraints #########
# TotalTechnologyAnnualActivityUpperLimit
TotalTechnologyAnnualActivityUpperLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].VALUE.iat[0]
TotalTechnologyAnnualActivityUpperLimit_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].YEAR)])
TotalTechnologyAnnualActivityUpperLimit = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalTechnologyAnnualActivityUpperLimit_specified else TotalTechnologyAnnualActivityUpperLimit_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalTechnologyAnnualActivityLowerLimit
TotalTechnologyAnnualActivityLowerLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].VALUE.iat[0]
TotalTechnologyAnnualActivityLowerLimit_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].TECHNOLOGY, p_df[p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].YEAR)])
TotalTechnologyAnnualActivityLowerLimit = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in TotalTechnologyAnnualActivityLowerLimit_specified else TotalTechnologyAnnualActivityLowerLimit_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# TotalTechnologyModelPeriodActivityUpperLimit
TotalTechnologyModelPeriodActivityUpperLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].VALUE.iat[0]
TotalTechnologyModelPeriodActivityUpperLimit_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].TECHNOLOGY)])
TotalTechnologyModelPeriodActivityUpperLimit = {str(r): {str(t): p_df[(p_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in TotalTechnologyModelPeriodActivityUpperLimit_specified else TotalTechnologyModelPeriodActivityUpperLimit_default_value for t in TECHNOLOGY} for r in REGION}
# TotalTechnologyModelPeriodActivityLowerLimit
TotalTechnologyModelPeriodActivityLowerLimit_default_value = p_default_df[p_default_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].VALUE.iat[0]
TotalTechnologyModelPeriodActivityLowerLimit_specified = tuple([(str(r), str(t)) for r, t in zip(p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].REGION, p_df[p_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].TECHNOLOGY)])
TotalTechnologyModelPeriodActivityLowerLimit = {str(r): {str(t): p_df[(p_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t)].VALUE.iat[0] if (str(r), str(t)) in TotalTechnologyModelPeriodActivityLowerLimit_specified else TotalTechnologyModelPeriodActivityLowerLimit_default_value for t in TECHNOLOGY} for r in REGION}
######### Reserve Margin #########
# ReserveMarginTagTechnology
ReserveMarginTagTechnology_default_value = p_default_df[p_default_df['PARAM'] == "ReserveMarginTagTechnology"].VALUE.iat[0]
ReserveMarginTagTechnology_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "ReserveMarginTagTechnology"].REGION, p_df[p_df['PARAM'] == "ReserveMarginTagTechnology"].TECHNOLOGY, p_df[p_df['PARAM'] == "ReserveMarginTagTechnology"].YEAR)])
ReserveMarginTagTechnology = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "ReserveMarginTagTechnology") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in ReserveMarginTagTechnology_specified else ReserveMarginTagTechnology_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# ReserveMarginTagFuel
ReserveMarginTagFuel_default_value = p_default_df[p_default_df['PARAM'] == "ReserveMarginTagFuel"].VALUE.iat[0]
ReserveMarginTagFuel_specified = tuple([(str(r), str(f), str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "ReserveMarginTagFuel"].REGION, p_df[p_df['PARAM'] == "ReserveMarginTagFuel"].FUEL, p_df[p_df['PARAM'] == "ReserveMarginTagFuel"].YEAR)])
ReserveMarginTagFuel = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "ReserveMarginTagFuel") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(f), str(y)) in ReserveMarginTagFuel_specified else ReserveMarginTagFuel_default_value for y in YEAR} for f in FUEL} for r in REGION}
# ReserveMargin
ReserveMargin_default_value = p_default_df[p_default_df['PARAM'] == "ReserveMargin"].VALUE.iat[0]
ReserveMargin_specified = tuple([(str(r), str(y)) for r, y in zip(p_df[p_df['PARAM'] == "ReserveMargin"].REGION, p_df[p_df['PARAM'] == "ReserveMargin"].YEAR)])
ReserveMargin = {str(r): {str(y): p_df[(p_df['PARAM'] == "ReserveMargin") & (p_df['REGION'] == r) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(y)) in ReserveMargin_specified else ReserveMargin_default_value for y in YEAR} for r in REGION}
######### RE Generation Target #########
# RETagTechnology
RETagTechnology_default_value = p_default_df[p_default_df['PARAM'] == "RETagTechnology"].VALUE.iat[0]
RETagTechnology_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(p_df[p_df['PARAM'] == "RETagTechnology"].REGION, p_df[p_df['PARAM'] == "RETagTechnology"].TECHNOLOGY, p_df[p_df['PARAM'] == "RETagTechnology"].YEAR)])
RETagTechnology = {str(r): {str(t): {str(y): p_df[(p_df['PARAM'] == "RETagTechnology") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(t), str(y)) in RETagTechnology_specified else RETagTechnology_default_value for y in YEAR} for t in TECHNOLOGY} for r in REGION}
# RETagFuel
RETagFuel_default_value = p_default_df[p_default_df['PARAM'] == "RETagFuel"].VALUE.iat[0]
RETagFuel_specified = tuple([(str(r), str(f), str(y)) for r, f, y in zip(p_df[p_df['PARAM'] == "RETagFuel"].REGION, p_df[p_df['PARAM'] == "RETagFuel"].FUEL, p_df[p_df['PARAM'] == "RETagFuel"].YEAR)])
RETagFuel = {str(r): {str(f): {str(y): p_df[(p_df['PARAM'] == "RETagFuel") & (p_df['REGION'] == r) & (p_df['FUEL'] == f) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(f), str(y)) in RETagFuel_specified else RETagFuel_default_value for y in YEAR} for f in FUEL} for r in REGION}
# REMinProductionTarget
REMinProductionTarget_default_value = p_default_df[p_default_df['PARAM'] == "REMinProductionTarget"].VALUE.iat[0]
REMinProductionTarget_specified = tuple([(str(r), str(y)) for r, y in zip(p_df[p_df['PARAM'] == "REMinProductionTarget"].REGION, p_df[p_df['PARAM'] == "REMinProductionTarget"].YEAR)])
REMinProductionTarget = {str(r): {str(y): p_df[(p_df['PARAM'] == "REMinProductionTarget") & (p_df['REGION'] == r) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(y)) in REMinProductionTarget_specified else REMinProductionTarget_default_value for y in YEAR} for r in REGION}
######### Emissions & Penalties #########
# EmissionActivityRatio
EmissionActivityRatio_default_value = p_default_df[p_default_df['PARAM'] == "EmissionActivityRatio"].VALUE.iat[0]
EmissionActivityRatio_specified = tuple([(str(r),str(t),str(e),str(m),str(y)) for r, t, e, m, y in zip(p_df[p_df['PARAM'] == "EmissionActivityRatio"].REGION, p_df[p_df['PARAM'] == "EmissionActivityRatio"].TECHNOLOGY, p_df[p_df['PARAM'] == "EmissionActivityRatio"].EMISSION, p_df[p_df['PARAM'] == "EmissionActivityRatio"].MODE_OF_OPERATION, p_df[p_df['PARAM'] == "EmissionActivityRatio"].YEAR)])
EmissionActivityRatio = {str(r): {str(t): {str(e): {str(m): {str(y): p_df[(p_df['PARAM'] == "EmissionActivityRatio") & (p_df['REGION'] == r) & (p_df['TECHNOLOGY'] == t) & (p_df['EMISSION'] == e) & (p_df['MODE_OF_OPERATION'] == m) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r),str(t),str(e),str(m),str(y)) in EmissionActivityRatio_specified else EmissionActivityRatio_default_value for y in YEAR} for m in MODE_OF_OPERATION} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
# EmissionsPenalty
EmissionsPenalty_default_value = p_default_df[p_default_df['PARAM'] == "EmissionsPenalty"].VALUE.iat[0]
EmissionsPenalty_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(p_df[p_df['PARAM'] == "EmissionsPenalty"].REGION, p_df[p_df['PARAM'] == "EmissionsPenalty"].EMISSION, p_df[p_df['PARAM'] == "EmissionsPenalty"].YEAR)])
EmissionsPenalty = {str(r): {str(e): {str(y): p_df[(p_df['PARAM'] == "EmissionsPenalty") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(e), str(y)) in EmissionsPenalty_specified else EmissionsPenalty_default_value for y in YEAR} for e in EMISSION} for r in REGION}
# AnnualExogenousEmission
AnnualExogenousEmission_default_value = p_default_df[p_default_df['PARAM'] == "AnnualExogenousEmission"].VALUE.iat[0]
AnnualExogenousEmission_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(p_df[p_df['PARAM'] == "AnnualExogenousEmission"].REGION, p_df[p_df['PARAM'] == "AnnualExogenousEmission"].EMISSION, p_df[p_df['PARAM'] == "AnnualExogenousEmission"].YEAR)])
AnnualExogenousEmission = {str(r): {str(e): {str(y): p_df[(p_df['PARAM'] == "AnnualExogenousEmission") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(e), str(y)) in AnnualExogenousEmission_specified else AnnualExogenousEmission_default_value for y in YEAR} for e in EMISSION} for r in REGION}
# AnnualEmissionLimit
AnnualEmissionLimit_default_value = p_default_df[p_default_df['PARAM'] == "AnnualEmissionLimit"].VALUE.iat[0]
AnnualEmissionLimit_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(p_df[p_df['PARAM'] == "AnnualEmissionLimit"].REGION, p_df[p_df['PARAM'] == "AnnualEmissionLimit"].EMISSION, p_df[p_df['PARAM'] == "AnnualEmissionLimit"].YEAR)])
AnnualEmissionLimit = {str(r): {str(e): {str(y): p_df[(p_df['PARAM'] == "AnnualEmissionLimit") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e) & (p_df['YEAR'] == y)].VALUE.iat[0] if (str(r), str(e), str(y)) in AnnualEmissionLimit_specified else AnnualEmissionLimit_default_value for y in YEAR} for e in EMISSION} for r in REGION}
# ModelPeriodExogenousEmission
ModelPeriodExogenousEmission_default_value = p_default_df[p_default_df['PARAM'] == "ModelPeriodExogenousEmission"].VALUE.iat[0]
ModelPeriodExogenousEmission_specified = tuple([(str(r), str(e)) for r, e in zip(p_df[p_df['PARAM'] == "ModelPeriodExogenousEmission"].REGION, p_df[p_df['PARAM'] == "ModelPeriodExogenousEmission"].EMISSION)])
ModelPeriodExogenousEmission = {str(r): {str(e): p_df[(p_df['PARAM'] == "ModelPeriodExogenousEmission") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e)].VALUE.iat[0] if (str(r), str(e)) in ModelPeriodExogenousEmission_specified else ModelPeriodExogenousEmission_default_value for e in EMISSION} for r in REGION}
# ModelPeriodEmissionLimit
ModelPeriodEmissionLimit_default_value = p_default_df[p_default_df['PARAM'] == "ModelPeriodEmissionLimit"].VALUE.iat[0]
ModelPeriodEmissionLimit_specified = tuple([(str(r), str(e)) for r, e in zip(p_df[p_df['PARAM'] == "ModelPeriodEmissionLimit"].REGION, p_df[p_df['PARAM'] == "ModelPeriodEmissionLimit"].EMISSION)])
ModelPeriodEmissionLimit = {str(r): {str(e): p_df[(p_df['PARAM'] == "ModelPeriodEmissionLimit") & (p_df['REGION'] == r) & (p_df['EMISSION'] == e)].VALUE.iat[0] if (str(r), str(e)) in ModelPeriodEmissionLimit_specified else ModelPeriodEmissionLimit_default_value for e in EMISSION} for r in REGION}
logging.info("{}\tParameters are created.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
i = 0
while i <= mcs_num:
######### Simulation loops #########
logging.info("{}\tModel run #{}".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), i))
# ------------------------------------------------------------------------------------------------------------------
# MODEL INITIALIZATION
# ------------------------------------------------------------------------------------------------------------------
model = pulp.LpProblem(modelName, pulp.LpMinimize)
# ------------------------------------------------------------------------------------------------------------------
# MODEL VARIABLES
# ------------------------------------------------------------------------------------------------------------------
######## Demands #########
RateOfDemand = {str(r): {str(l): {str(f): {str(y): newVar("RateOfDemand", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
Demand = {str(r): {str(l): {str(f): {str(y): newVar("Demand", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
######## Storage #########
RateOfStorageCharge = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("RateOfStorageCharge", 0, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
RateOfStorageDischarge = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("RateOfStorageDischarge", None, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
NetChargeWithinYear = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("NetChargeWithinYear", None, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
NetChargeWithinDay = {str(r): {str(s): {str(ls): {str(ld): {str(lh): {str(y): newVar("NetChargeWithinDay", None, None, 'Continuous', r, s, ls, ld, lh, y) for y in YEAR} for lh in DAILYTIMEBRACKET} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLevelYearStart = {str(r): {str(s): {str(y): newVar("StorageLevelYearStart", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
StorageLevelYearFinish = {str(r): {str(s): {str(y): newVar("StorageLevelYearFinish", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
StorageLevelSeasonStart = {str(r): {str(s): {str(ls): {str(y): newVar("StorageLevelSeasonStart", 0, None, 'Continuous', r, s, ls, y) for y in YEAR} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLevelDayTypeStart = {str(r): {str(s): {str(ls): {str(ld): {str(y): newVar("StorageLevelDayTypeStart", 0, None, 'Continuous', r, s, ls, ld, y) for y in YEAR} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLevelDayTypeFinish = {str(r): {str(s): {str(ls): {str(ld): {str(y): newVar("StorageLevelDayTypeFinish", 0, None, 'Continuous', r, s, ls, ld, y) for y in YEAR} for ld in DAYTYPE} for ls in SEASON} for s in STORAGE} for r in REGION}
StorageLowerLimit = {str(r): {str(s): {str(y): newVar("StorageLowerLimit", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
StorageUpperLimit = {str(r): {str(s): {str(y): newVar("StorageUpperLimit", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
AccumulatedNewStorageCapacity = {str(r): {str(s): {str(y): newVar("AccumulatedNewStorageCapacity", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
NewStorageCapacity = {str(r): {str(s): {str(y): newVar("NewStorageCapacity", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
CapitalInvestmentStorage = {str(r): {str(s): {str(y): newVar("CapitalInvestmentStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
DiscountedCapitalInvestmentStorage = {str(r): {str(s): {str(y): newVar("DiscountedCapitalInvestmentStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
SalvageValueStorage = {str(r): {str(s): {str(y): newVar("SalvageValueStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
DiscountedSalvageValueStorage = {str(r): {str(s): {str(y): newVar("DiscountedSalvageValueStorage", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
TotalDiscountedStorageCost = {str(r): {str(s): {str(y): newVar("TotalDiscountedStorageCost", 0, None, 'Continuous', r, s, y) for y in YEAR} for s in STORAGE} for r in REGION}
######### Capacity Variables #########
NumberOfNewTechnologyUnits = {str(r): {str(t): {str(y): newVar("NumberOfNewTechnologyUnits", 0, None, 'Integer', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
NewCapacity = {str(r): {str(t): {str(y): newVar("NewCapacity", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AccumulatedNewCapacity = {str(r): {str(t): {str(y): newVar("AccumulatedNewCapacity", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalCapacityAnnual = {str(r): {str(t): {str(y): newVar("TotalCapacityAnnual", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Activity Variables #########
RateOfActivity = {str(r): {str(l): {str(t): {str(m): {str(y): newVar("RateOfActivity", 0, None, 'Continuous', r, l, t, m, y) for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
RateOfTotalActivity = {str(r): {str(t): {str(l): {str(y): newVar("RateOfTotalActivity", 0, None, 'Continuous', r, t, l, y) for y in YEAR} for l in TIMESLICE} for t in TECHNOLOGY} for r in REGION}
TotalTechnologyAnnualActivity = {str(r): {str(t): {str(y): newVar("TotalTechnologyAnnualActivity", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalAnnualTechnologyActivityByMode = {str(r): {str(t): {str(m): {str(y): newVar("TotalAnnualTechnologyActivityByMode", 0, None, 'Continuous', r, t, m, y) for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for r in REGION}
TotalTechnologyModelPeriodActivity = {str(r): {str(t): newVar("TotalTechnologyModelPeriodActivity", None, None, 'Continuous', r, t) for t in TECHNOLOGY} for r in REGION}
RateOfProductionByTechnologyByMode = {str(r): {str(l): {str(t): {str(m): {str(f): {str(y): newVar("RateOfProductionByTechnologyByMode", 0, None, 'Continuous', r, l, t, m, f, y) for y in YEAR} for f in FUEL} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
RateOfProductionByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("RateOfProductionByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
ProductionByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("ProductionByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
ProductionByTechnologyAnnual = {str(r): {str(t): {str(f): {str(y): newVar("ProductionByTechnologyAnnual", 0, None, 'Continuous', r, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
RateOfProduction = {str(r): {str(l): {str(f): {str(y): newVar("RateOfProduction", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
Production = {str(r): {str(l): {str(f): {str(y): newVar("Production", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
RateOfUseByTechnologyByMode = {str(r): {str(l): {str(t): {str(m): {str(f): {str(y): newVar("RateOfUseByTechnologyByMode", 0, None, 'Continuous', r, l, t, m, f, y) for y in YEAR} for f in FUEL} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
RateOfUseByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("RateOfUseByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
UseByTechnologyAnnual = {str(r): {str(t): {str(f): {str(y): newVar("UseByTechnologyAnnual", 0, None, 'Continuous', r, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
RateOfUse = {str(r): {str(l): {str(f): {str(y): newVar("RateOfUse", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
UseByTechnology = {str(r): {str(l): {str(t): {str(f): {str(y): newVar("UseByTechnology", 0, None, 'Continuous', r, l, t, f, y) for y in YEAR} for f in FUEL} for t in TECHNOLOGY} for l in TIMESLICE} for r in REGION}
Use = {str(r): {str(l): {str(f): {str(y): newVar("Use", 0, None, 'Continuous', r, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for r in REGION}
Trade = {str(r): {str(rr): {str(l): {str(f): {str(y): newVar("Trade", None, None, 'Continuous', r, rr, l, f, y) for y in YEAR} for f in FUEL} for l in TIMESLICE} for rr in REGION2} for r in REGION}
TradeAnnual = {str(r): {str(rr): {str(f): {str(y): newVar("TradeAnnual", None, None, 'Continuous', r, rr, f, y) for y in YEAR} for f in FUEL} for rr in REGION2} for r in REGION}
ProductionAnnual = {str(r): {str(f): {str(y): newVar("ProductionAnnual", 0, None, 'Continuous', r, f, y) for y in YEAR} for f in FUEL} for r in REGION}
UseAnnual = {str(r): {str(f): {str(y): newVar("UseAnnual", 0, None, 'Continuous', r, f, y) for y in YEAR} for f in FUEL} for r in REGION}
######### Costing Variables #########
CapitalInvestment = {str(r): {str(t): {str(y): newVar("CapitalInvestment", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedCapitalInvestment = {str(r): {str(t): {str(y): newVar("DiscountedCapitalInvestment", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
SalvageValue = {str(r): {str(t): {str(y): newVar("SalvageValue", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedSalvageValue = {str(r): {str(t): {str(y): newVar("DiscountedSalvageValue", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
OperatingCost = {str(r): {str(t): {str(y): newVar("OperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedOperatingCost = {str(r): {str(t): {str(y): newVar("DiscountedOperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AnnualVariableOperatingCost = {str(r): {str(t): {str(y): newVar("AnnualVariableOperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AnnualFixedOperatingCost = {str(r): {str(t): {str(y): newVar("AnnualFixedOperatingCost", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalDiscountedCostByTechnology = {str(r): {str(t): {str(y): newVar("TotalDiscountedCostByTechnology", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
TotalDiscountedCost = {str(r): {str(y): newVar("TotalDiscountedCost", 0, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
ModelPeriodCostByRegion = {str(r): newVar("ModelPeriodCostByRegion", 0, None, 'Continuous', r) for r in REGION}
######### Reserve Margin #########
TotalCapacityInReserveMargin = {str(r): {str(y): newVar("TotalCapacityInReserveMargin", 0, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
DemandNeedingReserveMargin = {str(r): {str(l): {str(y): newVar("DemandNeedingReserveMargin", 0, None, 'Continuous', r, l, y) for y in YEAR} for l in TIMESLICE} for r in REGION}
######### RE Gen Target #########
TotalREProductionAnnual = {str(r): {str(y): newVar("TotalREProductionAnnual", None, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
RETotalProductionOfTargetFuelAnnual = {str(r): {str(y): newVar("RETotalProductionOfTargetFuelAnnual", None, None, 'Continuous', r, y) for y in YEAR} for r in REGION}
######### Emissions #########
AnnualTechnologyEmissionByMode = {str(r): {str(t): {str(e): {str(m): {str(y): newVar("AnnualTechnologyEmissionByMode", 0, None, 'Continuous', r, t, e, m, y) for y in YEAR} for m in MODE_OF_OPERATION} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
AnnualTechnologyEmission = {str(r): {str(t): {str(e): {str(y): newVar("AnnualTechnologyEmission", 0, None, 'Continuous', r, t, e, y) for y in YEAR} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
AnnualTechnologyEmissionPenaltyByEmission = {str(r): {str(t): {str(e): {str(y): newVar("AnnualTechnologyEmissionPenaltyByEmission", 0, None, 'Continuous', r, t, e, y) for y in YEAR} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
AnnualTechnologyEmissionsPenalty = {str(r): {str(t): {str(y): newVar("AnnualTechnologyEmissionsPenalty", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
DiscountedTechnologyEmissionsPenalty = {str(r): {str(t): {str(y): newVar("DiscountedTechnologyEmissionsPenalty", 0, None, 'Continuous', r, t, y) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
AnnualEmissions = {str(r): {str(e): {str(y): newVar("AnnualEmissions", 0, None, 'Continuous', r, e, y) for y in YEAR} for e in EMISSION} for r in REGION}
ModelPeriodEmissions = {str(r): {str(e): newVar("ModelPeriodEmissions", 0, None, 'Continuous', r, e) for e in EMISSION} for r in REGION}
logging.info("{}\tVariables are created".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ------------------------------------------------------------------------------------------------------------------
# OBJECTIVE FUNCTION
# ------------------------------------------------------------------------------------------------------------------
cost = pulp.LpVariable("cost", cat='Continuous')
model += cost, "Objective"
model += cost == pulp.lpSum([TotalDiscountedCost[r][y] for r in REGION for y in YEAR]), "Cost_function"
# ------------------------------------------------------------------------------------------------------------------
# CONSTRAINTS
# ------------------------------------------------------------------------------------------------------------------
for r in REGION:
for l in TIMESLICE:
for f in FUEL:
for y in YEAR:
# EQ_SpecifiedDemand
model += RateOfDemand[r][l][f][y] == SpecifiedAnnualDemand[r][f][y] * SpecifiedDemandProfile[r][f][l][y] / YearSplit[l][y], ""
######### Capacity Adequacy A #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# CAa1_TotalNewCapacity
model += AccumulatedNewCapacity[r][t][y] == pulp.lpSum([NewCapacity[r][t][yy] for yy in YEAR if (float(int(y) - int(yy)) < OperationalLife[r][t]) and (int(y) - int(yy) >= 0)]), ""
# CAa2_TotalAnnualCapacity
model += TotalCapacityAnnual[r][t][y] == AccumulatedNewCapacity[r][t][y] + ResidualCapacity[r][t][y], ""
for l in TIMESLICE:
# CAa3_TotalActivityOfEachTechnology
model += RateOfTotalActivity[r][t][l][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] for m in MODE_OF_OPERATION]), ""
# CAa4_Constraint_Capacity
model += RateOfTotalActivity[r][t][l][y] <= TotalCapacityAnnual[r][t][y] * CapacityFactor[r][t][l][y] * CapacityToActivityUnit[r][t], ""
if CapacityOfOneTechnologyUnit[r][t][y] != 0:
# CAa5_TotalNewCapacity
model += NewCapacity[r][t][y] == CapacityOfOneTechnologyUnit[r][t][y] * NumberOfNewTechnologyUnits[r][t][y], ""
######### Capacity Adequacy B #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# CAb1_PlannedMaintenance
model += pulp.lpSum([RateOfTotalActivity[r][t][l][y] * YearSplit[l][y] for l in TIMESLICE]) <= pulp.lpSum([TotalCapacityAnnual[r][t][y] * CapacityFactor[r][t][l][y] * YearSplit[l][y] for l in TIMESLICE]) * AvailabilityFactor[r][t][y] * CapacityToActivityUnit[r][t], ""
######### Energy Balance A #########
for r in REGION:
for l in TIMESLICE:
for f in FUEL:
for y in YEAR:
for t in TECHNOLOGY:
for m in MODE_OF_OPERATION:
# EBa1_RateOfFuelProduction1
if OutputActivityRatio[r][t][f][m][y] != 0:
model += RateOfProductionByTechnologyByMode[r][l][t][m][f][y] == RateOfActivity[r][l][t][m][y] * OutputActivityRatio[r][t][f][m][y], ""
else:
model += RateOfProductionByTechnologyByMode[r][l][t][m][f][y] == 0, ""
# EBa2_RateOfFuelProduction2
model += RateOfProductionByTechnology[r][l][t][f][y] == pulp.lpSum([RateOfProductionByTechnologyByMode[r][l][t][m][f][y] for m in MODE_OF_OPERATION if OutputActivityRatio[r][t][f][m][y] != 0]), ""
# EBa3_RateOfFuelProduction3
model += RateOfProduction[r][l][f][y] == pulp.lpSum([RateOfProductionByTechnology[r][l][t][f][y] for t in TECHNOLOGY]), ""
for t in TECHNOLOGY:
for m in MODE_OF_OPERATION:
# EBa4_RateOfFuelUse1
if InputActivityRatio[r][t][f][m][y] != 0:
model += RateOfUseByTechnologyByMode[r][l][t][m][f][y] == RateOfActivity[r][l][t][m][y] * InputActivityRatio[r][t][f][m][y], ""
# EBa5_RateOfFuelUse2
model += RateOfUseByTechnology[r][l][t][f][y] == pulp.lpSum([RateOfUseByTechnologyByMode[r][l][t][m][f][y] for m in MODE_OF_OPERATION if InputActivityRatio[r][t][f][m][y] != 0]), ""
# EBa6_RateOfFuelUse3
model += RateOfUse[r][l][f][y] == pulp.lpSum([RateOfUseByTechnology[r][l][t][f][y] for t in TECHNOLOGY]), ""
# EBa7_EnergyBalanceEachTS1
model += Production[r][l][f][y] == RateOfProduction[r][l][f][y] * YearSplit[l][y], ""
# EBa8_EnergyBalanceEachTS2
model += Use[r][l][f][y] == RateOfUse[r][l][f][y] * YearSplit[l][y], ""
# EBa9_EnergyBalanceEachTS3
model += Demand[r][l][f][y] == RateOfDemand[r][l][f][y] * YearSplit[l][y], ""
for rr in REGION2:
# EBa10_EnergyBalanceEachTS4
model += Trade[r][rr][l][f][y] == -Trade[rr][r][l][f][y], ""
# EBa11_EnergyBalanceEachTS5
model += Production[r][l][f][y] >= Demand[r][l][f][y] + Use[r][l][f][y] + pulp.lpSum([Trade[r][rr][l][f][y] * TradeRoute[r][rr][f][y] for rr in REGION2]), ""
######### Energy Balance B #########
for r in REGION:
for f in FUEL:
for y in YEAR:
# EBb1_EnergyBalanceEachYear1
model += ProductionAnnual[r][f][y] == pulp.lpSum([Production[r][l][f][y] for l in TIMESLICE]), ""
# EBb2_EnergyBalanceEachYear2
model += UseAnnual[r][f][y] == pulp.lpSum([Use[r][l][f][y] for l in TIMESLICE]), ""
for rr in REGION2:
# EBb3_EnergyBalanceEachYear3
model += TradeAnnual[r][rr][f][y] == pulp.lpSum([Trade[r][rr][l][f][y] for l in TIMESLICE]), ""
# EBb4_EnergyBalanceEachYear4
model += ProductionAnnual[r][f][y] >= UseAnnual[r][f][y] + pulp.lpSum([TradeAnnual[r][rr][f][y] * TradeRoute[r][rr][f][y] for rr in REGION2]) + AccumulatedAnnualDemand[r][f][y], ""
######### Accounting Technology Production/Use #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
for l in TIMESLICE:
for f in FUEL:
# Acc1_FuelProductionByTechnology
model += ProductionByTechnology[r][l][t][f][y] == RateOfProductionByTechnology[r][l][t][f][y] * YearSplit[l][y], ""
# Acc2_FuelUseByTechnology
model += UseByTechnology[r][l][t][f][y] == RateOfUseByTechnology[r][l][t][f][y] * YearSplit[l][y], ""
for m in MODE_OF_OPERATION:
# Acc3_AverageAnnualRateOfActivity
model += TotalAnnualTechnologyActivityByMode[r][t][m][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] * YearSplit[l][y] for l in TIMESLICE]), ""
# Acc4_ModelPeriodCostByRegion
model += ModelPeriodCostByRegion[r] == pulp.lpSum([TotalDiscountedCost[r][y] for y in YEAR]), ""
######### Storage Equations #########
for r in REGION:
for s in STORAGE:
for y in YEAR:
for ls in SEASON:
for ld in DAYTYPE:
for lh in DAILYTIMEBRACKET:
# S1_RateOfStorageCharge
model += RateOfStorageCharge[r][s][ls][ld][lh][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] * TechnologyToStorage[r][t][s][m] * Conversionls[l][ls] * Conversionld[l][ld] * Conversionlh[l][lh] for t in TECHNOLOGY for m in MODE_OF_OPERATION for l in TIMESLICE if TechnologyToStorage[r][t][s][m] > 0]), ""
# S2_RateOfStorageDischarge
model += RateOfStorageDischarge[r][s][ls][ld][lh][y] == pulp.lpSum([RateOfActivity[r][l][t][m][y] * TechnologyFromStorage[r][t][s][m] * Conversionls[l][ls] * Conversionld[l][ld] * Conversionlh[l][lh] for t in TECHNOLOGY for m in MODE_OF_OPERATION for l in TIMESLICE if TechnologyFromStorage[r][t][s][m] > 0]), ""
# S3_NetChargeWithinYear
model += NetChargeWithinYear[r][s][ls][ld][lh][y] == pulp.lpSum([(RateOfStorageCharge[r][s][ls][ld][lh][y] - RateOfStorageDischarge[r][s][ls][ld][lh][y]) * YearSplit[l][y] * Conversionls[l][ls] * Conversionld[l][ld] * Conversionlh[l][lh] for l in TIMESLICE if (Conversionls[l][ls] > 0) and (Conversionld[l][ld] > 0) and (Conversionlh[l][lh] > 0)]), ""
# S4_NetChargeWithinDay
model += NetChargeWithinDay[r][s][ls][ld][lh][y] == (RateOfStorageCharge[r][s][ls][ld][lh][y] - RateOfStorageDischarge[r][s][ls][ld][lh][y]) * DaySplit[lh][y], ""
# S5_and_S6_StorageLevelYearStart
if int(y) == int(min(YEAR)):
model += StorageLevelYearStart[r][s][y] == StorageLevelStart[r][s], ""
else:
model += StorageLevelYearStart[r][s][y] == StorageLevelYearStart[r][s][str(int(y)-1)] + pulp.lpSum([NetChargeWithinYear[r][s][ls][ld][lh][str(int(y)-1)] for ls in SEASON for ld in DAYTYPE for lh in DAILYTIMEBRACKET]), ""
# S7_and_S8_StorageLevelYearFinish
if int(y) < int(max(YEAR)):
model += StorageLevelYearFinish[r][s][y] == StorageLevelYearStart[r][s][str(int(y) + 1)], ""
else:
model += StorageLevelYearFinish[r][s][y] == StorageLevelYearStart[r][s][y] + pulp.lpSum([NetChargeWithinYear[r][s][ls][ld][lh][y] for ls in SEASON for ld in DAYTYPE for lh in DAILYTIMEBRACKET]), ""
for ls in SEASON:
# S9_and_S10_StorageLevelSeasonStart
if int(ls) == int(min(SEASON)):
model += StorageLevelSeasonStart[r][s][ls][y] == StorageLevelYearStart[r][s][y], ""
else:
model += StorageLevelSeasonStart[r][s][ls][y] == StorageLevelSeasonStart[r][s][str(int(ls)-1)][y] + pulp.lpSum([NetChargeWithinYear[r][s][str(int(ls)-1)][ld][lh][y] for ld in DAYTYPE for lh in DAILYTIMEBRACKET]), ""
for ld in DAYTYPE:
# S11_and_S12_StorageLevelDayTypeStart
if int(ld) == int(min(DAYTYPE)):
model += StorageLevelDayTypeStart[r][s][ls][ld][y] == StorageLevelSeasonStart[r][s][ls][y], ""
else:
model += StorageLevelDayTypeStart[r][s][ls][ld][y] == StorageLevelDayTypeStart[r][s][ls][str(int(ld)-1)][y] + pulp.lpSum([NetChargeWithinDay[r][s][ls][str(int(ld)-1)][lh][y] * DaysInDayType[ls][str(int(ld)-1)][y] for lh in DAILYTIMEBRACKET]), ""
# S13_and_S14_and_S15_StorageLevelDayTypeFinish
if (int(ld) == int(max(DAYTYPE))) and (int(ls) == int(max(SEASON))):
model += StorageLevelDayTypeFinish[r][s][ls][ld][y] == StorageLevelYearFinish[r][s][y], ""
elif int(ld) == int(max(DAYTYPE)):
model += StorageLevelDayTypeFinish[r][s][ls][ld][y] == StorageLevelSeasonStart[r][s][str(int(ls)+1)][y], ""
else:
model += StorageLevelDayTypeFinish[r][s][ls][ld][y] == StorageLevelDayTypeFinish[r][s][ls][str(int(ld)+1)][y] - pulp.lpSum([NetChargeWithinDay[r][s][ls][str(int(ld)+1)][lh][y] * DaysInDayType[ls][str(int(ld)+1)][y] for lh in DAILYTIMEBRACKET]), ""
########## Storage Constraints #########
for r in REGION:
for s in STORAGE:
for y in YEAR:
for ls in SEASON:
for ld in DAYTYPE:
for lh in DAILYTIMEBRACKET:
# SC1_LowerLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInFirstWeekConstraint
model += (StorageLevelDayTypeStart[r][s][ls][ld][y] + pulp.lpSum([NetChargeWithinDay[r][s][ls][ld][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh)-int(lhlh) > 0])) - StorageLowerLimit[r][s][y] >= 0, ""
# SC1_UpperLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInFirstWeekConstraint
model += (StorageLevelDayTypeStart[r][s][ls][ld][y] + pulp.lpSum([NetChargeWithinDay[r][s][ls][ld][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh)-int(lhlh) > 0])) - StorageUpperLimit[r][s][y] <= 0, ""
# SC2_LowerLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInFirstWeekConstraint
if int(ld) > int(min(DAYTYPE)):
model += (StorageLevelDayTypeStart[r][s][ls][ld][y] - pulp.lpSum([NetChargeWithinDay[r][s][ls][str(int(ld)-1)][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh)-int(lhlh) < 0])) - StorageLowerLimit[r][s][y] >= 0, ""
# SC2_LowerLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInFirstWeekConstraint
if int(ld) > int(min(DAYTYPE)):
model += (StorageLevelDayTypeStart[r][s][ls][ld][y] - pulp.lpSum([NetChargeWithinDay[r][s][ls][str(int(ld)-1)][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh) - int(lhlh) < 0])) - StorageUpperLimit[r][s][y] <= 0, ""
# SC3_LowerLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInLastWeekConstraint
model += (StorageLevelDayTypeFinish[r][s][ls][ld][y] - pulp.lpSum([NetChargeWithinDay[r][s][ls][ld][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh) - int(lhlh) < 0])) - StorageLowerLimit[r][s][y] >= 0, ""
# SC3_UpperLimit_EndOfDailyTimeBracketOfLastInstanceOfDayTypeInLastWeekConstraint
model += (StorageLevelDayTypeFinish[r][s][ls][ld][y] - pulp.lpSum([NetChargeWithinDay[r][s][ls][ld][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh) - int(lhlh) < 0])) - StorageUpperLimit[r][s][y] <= 0, ""
# SC4_LowerLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInLastWeekConstraint
if int(ld) > int(min(DAYTYPE)):
model += (StorageLevelDayTypeFinish[r][s][ls][str(int(ld)-1)][y] + pulp.lpSum([NetChargeWithinDay[r][s][ls][ld][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh) - int(lhlh) > 0])) - StorageLowerLimit[r][s][y] >= 0, ""
# SC4_UpperLimit_BeginningOfDailyTimeBracketOfFirstInstanceOfDayTypeInLastWeekConstraint
if int(ld) > int(min(DAYTYPE)):
model += (StorageLevelDayTypeFinish[r][s][ls][str(int(ld)-1)][y] + pulp.lpSum([NetChargeWithinDay[r][s][ls][ld][lhlh][y] for lhlh in DAILYTIMEBRACKET if int(lh) - int(lhlh) > 0])) - StorageUpperLimit[r][s][y] <= 0, ""
# SC5_MaxChargeConstraint
model += RateOfStorageCharge[r][s][ls][ld][lh][y] <= StorageMaxChargeRate[r][s], ""
# SC6_MaxDischargeConstraint
model += RateOfStorageDischarge[r][s][ls][ld][lh][y] <= StorageMaxDischargeRate[r][s], ""
######### Storage Investments #########
for r in REGION:
for s in STORAGE:
for y in YEAR:
# SI1_StorageUpperLimit
model += StorageUpperLimit[r][s][y] == AccumulatedNewStorageCapacity[r][s][y] + ResidualStorageCapacity[r][s][y], ""
# SI2_StorageLowerLimit
model += StorageLowerLimit[r][s][y] == MinStorageCharge[r][s][y] * StorageUpperLimit[r][s][y], ""
# SI3_TotalNewStorage
model += AccumulatedNewStorageCapacity[r][s][y] == pulp.lpSum([NewStorageCapacity[r][s][yy] for yy in YEAR if (float(int(y) - int(yy)) < OperationalLifeStorage[r][s]) and (int(y)-int(yy) >= 0)]), ""
# SI4_UndiscountedCapitalInvestmentStorage
model += CapitalInvestmentStorage[r][s][y] == CapitalCostStorage[r][s][y] * NewStorageCapacity[r][s][y], ""
# SI5_DiscountingCapitalInvestmentStorage
model += DiscountedCapitalInvestmentStorage[r][s][y] == CapitalInvestmentStorage[r][s][y] * (1/ ((1+DiscountRate[r])**(int(y) - int(min(YEAR))))), ""
# SI6_SalvageValueStorageAtEndOfPeriod1
if float(int(y) + OperationalLifeStorage[r][s] - 1) <= float(max(YEAR)):
model += SalvageValueStorage[r][s][y] == 0, ""
# SI7_SalvageValueStorageAtEndOfPeriod2
if ((DepreciationMethod[r] == 1) and (float(int(y)+OperationalLifeStorage[r][s]-1) > float(max(YEAR))) and (DiscountRate[r] == 0)) or ((DepreciationMethod[r] == 2) and (float(int(y)+OperationalLifeStorage[r][s]-1) > float(max(YEAR)))):
model += SalvageValueStorage[r][s][y] == CapitalInvestmentStorage[r][s][y] * (1-(int(max(YEAR))-int(y)+1))/OperationalLifeStorage[r][s], ""
# SI8_SalvageValueStorageAtEndOfPeriod3
if (DepreciationMethod[r] == 1) and (float(int(y)+OperationalLifeStorage[r][s]-1) > float(max(YEAR))) and (DiscountRate[r] > 0):
model += SalvageValueStorage[r][s][y] == CapitalInvestmentStorage[r][s][y] * (1-(((1+DiscountRate[r])**(int(max(YEAR)) - int(y)+1)-1)/((1+DiscountRate[r])**OperationalLifeStorage[r][s]-1))), ""
# SI9_SalvageValueStorageDiscountedToStartYear
model += DiscountedSalvageValueStorage[r][s][y] == SalvageValueStorage[r][s][y] * (1 /((1+DiscountRate[r])**(int(max(YEAR))- int(min(YEAR))+1))), ""
# SI10_TotalDiscountedCostByStorage
model += TotalDiscountedStorageCost[r][s][y] == DiscountedCapitalInvestmentStorage[r][s][y]-DiscountedSalvageValueStorage[r][s][y], ""
######### Capital Costs #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# CC1_UndiscountedCapitalInvestment
model += CapitalInvestment[r][t][y] == CapitalCost[r][t][y] * NewCapacity[r][t][y], ""
# CC2_DiscountingCapitalInvestment
model += DiscountedCapitalInvestment[r][t][y] == CapitalInvestment[r][t][y] * (1/((1 + DiscountRate[r]) ** (int(y) - int(min(YEAR))))), ""
######### Salvage Value #########
for r in REGION:
for y in YEAR:
for t in TECHNOLOGY:
# SV1_SalvageValueAtEndOfPeriod1
if (DepreciationMethod[r] == 1) and (float(int(y) + OperationalLife[r][t] - 1) > float(max(YEAR))) and (DiscountRate[r] > 0):
model += SalvageValue[r][t][y] == CapitalCost[r][t][y] * NewCapacity[r][t][y] * (1 - (((1 + DiscountRate[r]) ** (int(max(YEAR)) - int(y) + 1) - 1) / ((1 + DiscountRate[r]) ** OperationalLife[r][t] - 1))), ""
# SV2_SalvageValueAtEndOfPeriod2
if ((DepreciationMethod[r] == 1) and (float(int(y) + OperationalLife[r][t] - 1) > float(max(YEAR))) and (DiscountRate[r] == 0)) or ((DepreciationMethod[r] == 2) and (float(int(y) + OperationalLife[r][t] - 1) > float(max(YEAR)))):
model += SalvageValue[r][t][y] == CapitalCost[r][t][y] * NewCapacity[r][t][y] * (1 - (int(max(YEAR)) - int(y) + 1) / OperationalLife[r][t]), ""
# SV3_SalvageValueAtEndOfPeriod3
if float(int(y) + OperationalLife[r][t] - 1) <= float(max(YEAR)):
model += SalvageValue[r][t][y] == 0, ""
# SV4_SalvageValueDiscountedToStartYear
model += DiscountedSalvageValue[r][t][y] == SalvageValue[r][t][y] * (1 / ((1 + DiscountRate[r]) ** (1 + int(max(YEAR)) - int(min(YEAR))))), ""
######### Operating Costs #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# OC1_OperatingCostsVariable
model += AnnualVariableOperatingCost[r][t][y] == pulp.lpSum([TotalAnnualTechnologyActivityByMode[r][t][m][y] * VariableCost[r][t][m][y] for m in MODE_OF_OPERATION]), ""
# OC2_OperatingCostsFixedAnnual
model += AnnualFixedOperatingCost[r][t][y] == TotalCapacityAnnual[r][t][y] * FixedCost[r][t][y], ""
# OC3_OperatingCostsTotalAnnual
model += OperatingCost[r][t][y] == AnnualFixedOperatingCost[r][t][y] + AnnualVariableOperatingCost[r][t][y], ""
# OC4_DiscountedOperatingCostsTotalAnnual
model += DiscountedOperatingCost[r][t][y] == OperatingCost[r][t][y] * (1 /((1 + DiscountRate[r]) ** (int(y) - int(min(YEAR)) + 0.5))), ""
######### Total Discounted Costs #########
for r in REGION:
for y in YEAR:
for t in TECHNOLOGY:
# TDC1_TotalDiscountedCostByTechnology
model += TotalDiscountedCostByTechnology[r][t][y] == DiscountedOperatingCost[r][t][y] + DiscountedCapitalInvestment[r][t][y] + DiscountedTechnologyEmissionsPenalty[r][t][y] - DiscountedSalvageValue[r][t][y], ""
# TDC2_TotalDiscountedCost
model += TotalDiscountedCost[r][y] == pulp.lpSum([TotalDiscountedCostByTechnology[r][t][y] for t in TECHNOLOGY]) + pulp.lpSum([TotalDiscountedStorageCost[r][s][y] for s in STORAGE]), ""
######### Total Capacity Constraints #########
for r in REGION:
for y in YEAR:
for t in TECHNOLOGY:
# TCC1_TotalAnnualMaxCapacityConstraint
model += TotalCapacityAnnual[r][t][y] <= TotalAnnualMaxCapacity[r][t][y], ""
# TCC2_TotalAnnualMinCapacityConstraint
if TotalAnnualMinCapacity[r][t][y] > 0:
model += TotalCapacityAnnual[r][t][y] >= TotalAnnualMinCapacity[r][t][y], ""
######### New Capacity Constraints #########
for r in REGION:
for y in YEAR:
for t in TECHNOLOGY:
# NCC1_TotalAnnualMaxNewCapacityConstraint
model += NewCapacity[r][t][y] <= TotalAnnualMaxCapacityInvestment[r][t][y], ""
# NCC2_TotalAnnualMinNewCapacityConstraint
if TotalAnnualMinCapacityInvestment[r][t][y] > 0:
model += NewCapacity[r][t][y] >= TotalAnnualMinCapacityInvestment[r][t][y], ""
######### Annual Activity Constraints #########
for r in REGION:
for t in TECHNOLOGY:
for y in YEAR:
# AAC1_TotalAnnualTechnologyActivity
model += TotalTechnologyAnnualActivity[r][t][y] == pulp.lpSum([RateOfTotalActivity[r][t][l][y] * YearSplit[l][y] for l in TIMESLICE]), ""
# AAC2_TotalAnnualTechnologyActivityUpperLimit
model += TotalTechnologyAnnualActivity[r][t][y] <= TotalTechnologyAnnualActivityUpperLimit[r][t][y], ""
# AAC3_TotalAnnualTechnologyActivityLowerLimit
if TotalTechnologyAnnualActivityLowerLimit[r][t][y] > 0:
model += TotalTechnologyAnnualActivity[r][t][y] >= TotalTechnologyAnnualActivityLowerLimit[r][t][y], ""
######### Total Activity Constraints #########
for r in REGION:
for t in TECHNOLOGY:
# TAC1_TotalModelHorizonTechnologyActivity
model += TotalTechnologyModelPeriodActivity[r][t] == pulp.lpSum([TotalTechnologyAnnualActivity[r][t][y] for y in YEAR]), ""
# TAC2_TotalModelHorizonTechnologyActivityUpperLimit
if TotalTechnologyModelPeriodActivityUpperLimit[r][t] > 0:
model += TotalTechnologyModelPeriodActivity[r][t] <= TotalTechnologyModelPeriodActivityUpperLimit[r][t], ""
# TAC3_TotalModelHorizenTechnologyActivityLowerLimit
if TotalTechnologyModelPeriodActivityLowerLimit[r][t] > 0:
model += TotalTechnologyModelPeriodActivity[r][t] >= TotalTechnologyModelPeriodActivityLowerLimit[r][t], ""
######### Reserve Margin Constraint #########
for r in REGION:
for y in YEAR:
# RM1_ReserveMargin_TechnologiesIncluded_In_Activity_Units
model += TotalCapacityInReserveMargin[r][y] == pulp.lpSum([TotalCapacityAnnual[r][t][y] * ReserveMarginTagTechnology[r][t][y] * CapacityToActivityUnit[r][t] for t in TECHNOLOGY]), ""
for l in TIMESLICE:
# RM2_ReserveMargin_FuelsIncluded
model += DemandNeedingReserveMargin[r][l][y] == pulp.lpSum([RateOfProduction[r][l][f][y] * ReserveMarginTagFuel[r][f][y] for f in FUEL]), ""
# RM3_ReserveMargin_Constraint
model += DemandNeedingReserveMargin[r][l][y] <= TotalCapacityInReserveMargin[r][y] * (1/ReserveMargin[r][y]), ""
######### RE Production Target #########
for r in REGION:
for y in YEAR:
for t in TECHNOLOGY:
for f in FUEL:
# RE1_FuelProductionByTechnologyAnnual
model += ProductionByTechnologyAnnual[r][t][f][y] == pulp.lpSum([ProductionByTechnology[r][l][t][f][y] for l in TIMESLICE]), ""
# RE2_TechIncluded
model += TotalREProductionAnnual[r][y] == pulp.lpSum([ProductionByTechnologyAnnual[r][t][f][y] * RETagTechnology[r][t][y] for t in TECHNOLOGY for f in FUEL]), ""
# RE3_FuelIncluded
model += RETotalProductionOfTargetFuelAnnual[r][y] == pulp.lpSum([RateOfProduction[r][l][f][y] * YearSplit[l][y] * RETagFuel[r][f][y] for l in TIMESLICE for f in FUEL]), ""
# RE4_EnergyConstraint
model += TotalREProductionAnnual[r][y] >= REMinProductionTarget[r][y] * RETotalProductionOfTargetFuelAnnual[r][y], ""
for t in TECHNOLOGY:
for f in FUEL:
# RE5_FuelUseByTechnologyAnnual
model += UseByTechnologyAnnual[r][t][f][y] == pulp.lpSum([RateOfUseByTechnology[r][l][t][f][y] * YearSplit[l][y] for l in TIMESLICE]), ""
######### Emissions Accounting #########
for r in REGION:
for y in YEAR:
for t in TECHNOLOGY:
for e in EMISSION:
for m in MODE_OF_OPERATION:
# E1_AnnualEmissionProductionByMode
model += AnnualTechnologyEmissionByMode[r][t][e][m][y] == EmissionActivityRatio[r][t][e][m][y] * TotalAnnualTechnologyActivityByMode[r][t][m][y], ""
# E2_AnnualEmissionProduction
model += AnnualTechnologyEmission[r][t][e][y] == pulp.lpSum([AnnualTechnologyEmissionByMode[r][t][e][m][y] for m in MODE_OF_OPERATION]), ""
# E3_EmissionsPenaltyByTechAndEmission
model += AnnualTechnologyEmissionPenaltyByEmission[r][t][e][y] == AnnualTechnologyEmission[r][t][e][y] * EmissionsPenalty[r][e][y], ""
# E4_EmissionsPenaltyByTechnology
model += AnnualTechnologyEmissionsPenalty[r][t][y] == pulp.lpSum([AnnualTechnologyEmissionPenaltyByEmission[r][t][e][y] for e in EMISSION]), ""
# E5_DiscountedEmissionsPenaltyByTechnology
model += DiscountedTechnologyEmissionsPenalty[r][t][y] == AnnualTechnologyEmissionsPenalty[r][t][y] * (1 / ((1 + DiscountRate[r]) ** (int(y) - int(min(YEAR)) + 0.5))), ""
for e in EMISSION:
# E6_EmissionsAccounting1
model += AnnualEmissions[r][e][y] == pulp.lpSum([AnnualTechnologyEmission[r][t][e][y] for t in TECHNOLOGY]), ""
for e in EMISSION:
# E7_EmissionsAccounting2
model += pulp.lpSum([AnnualEmissions[r][e][y] for y in YEAR]) == ModelPeriodEmissions[r][e] - ModelPeriodExogenousEmission[r][e], ""
for y in YEAR:
# E8_AnnualEmissionsLimit
model += AnnualEmissions[r][e][y] <= AnnualEmissionLimit[r][e][y] - AnnualExogenousEmission[r][e][y], ""
# E9_ModelPeriodEmissionsLimit
model += ModelPeriodEmissions[r][e] <= ModelPeriodEmissionLimit[r][e], ""
logging.info("{}\tModel is built.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ------------------------------------------------------------------------------------------------------------------
# SOLVE
# ------------------------------------------------------------------------------------------------------------------
model.solve()
logging.info("{}\tModel is solved.".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
if str(pulp.LpStatus[model.status]) == "Optimal":
logging.info("The optimal solution found a cost value of {}.".format(round(model.objective.value(), 2)))
# --------------------------------------------------------------------------------------------------------------
# SAVE RESULTS TO DATAFRAME
# --------------------------------------------------------------------------------------------------------------
# Create dataframe to save results after the model was run the first time
if i == 0:
res_df = pd.DataFrame(columns=[
'SCENARIO',
'VAR_NAME',
'VAR_VALUE',
'REGION',
'REGION2',
'DAYTYPE',
'EMISSION',
'FUEL',
'DAILYTIMEBRACKET',
'SEASON',
'TIMESLICE',
'MODE_OF_OPERATION',
'STORAGE',
'TECHNOLOGY',
'YEAR',
'FLEXIBLEDEMANDTYPE'])
res_df = saveResultsTemporary(res_df, model, "Scenario_" + str(i))
logging.info("{}\tResults are saved".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
else:
logging.error("{}\tError: Optimisation status for Scenario_{} is: {}".format(
dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), pulp.LpStatus[model.status]))
del model # Delete model
# ----------------------------------------------------------------------------------------------------------------------
# MONTE CARLO SIMULATION - START
# ----------------------------------------------------------------------------------------------------------------------
i += 1
# Note: Monte Carlo Simulation is applied to all selected parameters (mcs_parameters).
# For each parameter, the mcs_parameters is only applied to parameter values that are not equal to default values, i.e. values that were explicitly set.
######### Reference parameters and data #########
if (len(mcs_parameters) >= 1) and (mcs_num > 0) and (i == 1):
# Copy of previous paramteres. This is used to store already generated parameters with data to enhance the performance in the mcs_parameters loops
DiscountRate_ref = DiscountRate
DaySplit_ref = DaySplit
Conversionls_ref = Conversionls
Conversionld_ref = Conversionld
Conversionlh_ref = Conversionlh
DaysInDayType_ref = DaysInDayType
TradeRoute_ref = TradeRoute
DepreciationMethod_ref = DepreciationMethod
SpecifiedAnnualDemand_ref = SpecifiedAnnualDemand
SpecifiedDemandProfile_ref = SpecifiedDemandProfile
AccumulatedAnnualDemand_ref = AccumulatedAnnualDemand
CapacityToActivityUnit_ref = CapacityToActivityUnit
TechWithCapacityNeededToMeetPeakTS_ref = TechWithCapacityNeededToMeetPeakTS
CapacityFactor_ref = CapacityFactor
AvailabilityFactor_ref = AvailabilityFactor
OperationalLife_ref = OperationalLife
ResidualCapacity_ref = ResidualCapacity
InputActivityRatio_ref = InputActivityRatio
OutputActivityRatio_ref = OutputActivityRatio
CapitalCost_ref = CapitalCost
VariableCost_ref = VariableCost
FixedCost_ref = FixedCost
TechnologyToStorage_ref = TechnologyToStorage
TechnologyFromStorage_ref = TechnologyFromStorage
StorageLevelStart_ref = StorageLevelStart
StorageMaxChargeRate_ref = StorageMaxChargeRate
StorageMaxDischargeRate_ref = StorageMaxDischargeRate
MinStorageCharge_ref = MinStorageCharge
OperationalLifeStorage_ref = OperationalLifeStorage
CapitalCostStorage_ref = CapitalCostStorage
ResidualStorageCapacity_ref = ResidualStorageCapacity
CapacityOfOneTechnologyUnit_ref = CapacityOfOneTechnologyUnit
TotalAnnualMaxCapacity_ref = TotalAnnualMaxCapacity
TotalAnnualMinCapacity_ref = TotalAnnualMinCapacity
TotalAnnualMaxCapacityInvestment_ref = TotalAnnualMaxCapacityInvestment
TotalAnnualMinCapacityInvestment_ref = TotalAnnualMinCapacityInvestment
TotalTechnologyAnnualActivityUpperLimit_ref = TotalTechnologyAnnualActivityUpperLimit
TotalTechnologyAnnualActivityLowerLimit_ref = TotalTechnologyAnnualActivityLowerLimit
TotalTechnologyModelPeriodActivityUpperLimit_ref = TotalTechnologyModelPeriodActivityUpperLimit
TotalTechnologyModelPeriodActivityLowerLimit_ref = TotalTechnologyModelPeriodActivityLowerLimit
ReserveMarginTagTechnology_ref = ReserveMarginTagTechnology
ReserveMarginTagFuel_ref = ReserveMarginTagFuel
ReserveMargin_ref = ReserveMargin
RETagTechnology_ref = RETagTechnology
RETagFuel_ref = RETagFuel
REMinProductionTarget_ref = REMinProductionTarget
EmissionActivityRatio_ref = EmissionActivityRatio
EmissionsPenalty_ref = EmissionsPenalty
AnnualExogenousEmission_ref = AnnualExogenousEmission
AnnualEmissionLimit_ref = AnnualEmissionLimit
ModelPeriodExogenousEmission_ref = ModelPeriodExogenousEmission
ModelPeriodEmissionLimit_ref = ModelPeriodEmissionLimit
######### Generate random data and overwrite selected parameters for next MCS run #########
######## Global #########
if ("DiscountRate" in mcs_parameters) and (mcs_num > 0):
if i == 1:
DiscountRate_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "DiscountRate") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
DiscountRate_mcs_specified = tuple([(str(r)) for r in mcs_df[mcs_df['PARAM'] == "DiscountRate"].REGION])
DiscountRate = {str(r): generateRandomData(DiscountRate_ref[r], mcs_df[(mcs_df['PARAM'] == "DiscountRate") & (mcs_df['REGION'] == r)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r)) in DiscountRate_mcs_specified else generateRandomData(DiscountRate_ref[r], DiscountRate_mcs_default_list) for r in REGION}
######## Demands #########
if ("SpecifiedAnnualDemand" in mcs_parameters) and (mcs_num > 0):
if i == 1:
SpecifiedAnnualDemand_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "SpecifiedAnnualDemand") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
SpecifiedAnnualDemand_mcs_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(mcs_df[mcs_df['PARAM'] == "SpecifiedAnnualDemand"].REGION, mcs_df[mcs_df['PARAM'] == "SpecifiedAnnualDemand"].FUEL, mcs_df[mcs_df['PARAM'] == "SpecifiedAnnualDemand"].YEAR)])
SpecifiedAnnualDemand = {str(r): {str(f): {str(y): generateRandomData(SpecifiedAnnualDemand_ref[r][f][y], mcs_df[(mcs_df['PARAM'] == "SpecifiedAnnualDemand") & (mcs_df['REGION'] == r) & (mcs_df['FUEL'] == f) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(f),str(y)) in SpecifiedAnnualDemand_mcs_specified else generateRandomData(SpecifiedAnnualDemand_ref[r][f][y], SpecifiedAnnualDemand_mcs_default_list) for y in YEAR} for f in FUEL} for r in REGION}
if ("AccumulatedAnnualDemand" in mcs_parameters) and (mcs_num > 0):
if i == 1:
AccumulatedAnnualDemand_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "AccumulatedAnnualDemand") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
AccumulatedAnnualDemand_mcs_specified = tuple([(str(r),str(f),str(y)) for r, f, y in zip(mcs_df[mcs_df['PARAM'] == "AccumulatedAnnualDemand"].REGION, mcs_df[mcs_df['PARAM'] == "AccumulatedAnnualDemand"].FUEL, mcs_df[mcs_df['PARAM'] == "AccumulatedAnnualDemand"].YEAR)])
AccumulatedAnnualDemand = {str(r): {str(f): {str(y): generateRandomData(AccumulatedAnnualDemand_ref[r][f][y], mcs_df[(mcs_df['PARAM'] == "AccumulatedAnnualDemand") & (mcs_df['REGION'] == r) & (mcs_df['FUEL'] == f) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(f),str(y)) in AccumulatedAnnualDemand_mcs_specified else generateRandomData(AccumulatedAnnualDemand_ref[r][f][y], AccumulatedAnnualDemand_mcs_default_list) for y in YEAR} for f in FUEL} for r in REGION}
######### Performance #########
if ("TechWithCapacityNeededToMeetPeakTS" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TechWithCapacityNeededToMeetPeakTS_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TechWithCapacityNeededToMeetPeakTS_mcs_specified = tuple([(str(r), str(t)) for r, t in zip(mcs_df[mcs_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].REGION, mcs_df[mcs_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS"].TECHNOLOGY)])
TechWithCapacityNeededToMeetPeakTS = {str(r): {str(t): generateRandomData(TechWithCapacityNeededToMeetPeakTS_ref[r][t], mcs_df[(mcs_df['PARAM'] == "TechWithCapacityNeededToMeetPeakTS") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t)) in TechWithCapacityNeededToMeetPeakTS_mcs_specified else generateRandomData(TechWithCapacityNeededToMeetPeakTS_ref[r][t], TechWithCapacityNeededToMeetPeakTS_mcs_default_list) for t in TECHNOLOGY} for r in REGION}
if ("CapacityFactor" in mcs_parameters) and (mcs_num > 0):
if i == 1:
CapacityFactor_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "CapacityFactor") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
CapacityFactor_mcs_specified = tuple([(str(r),str(t),str(l),str(y)) for r, t, l, y in zip(mcs_df[mcs_df['PARAM'] == "CapacityFactor"].REGION, mcs_df[mcs_df['PARAM'] == "CapacityFactor"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "CapacityFactor"].TIMESLICE, mcs_df[mcs_df['PARAM'] == "CapacityFactor"].YEAR)])
CapacityFactor = {str(r): {str(t): {str(l): {str(y): generateRandomData(CapacityFactor_ref[r][t][l][y], mcs_df[(mcs_df['PARAM'] == "CapacityFactor") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(l),str(y)) in CapacityFactor_mcs_specified else generateRandomData(CapacityFactor_ref[r][t][l][y], CapacityFactor_mcs_default_list) for y in YEAR} for l in TIMESLICE} for t in TECHNOLOGY} for r in REGION}
if ("AvailabilityFactor" in mcs_parameters) and (mcs_num > 0):
if i == 1:
AvailabilityFactor_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "AvailabilityFactor") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
AvailabilityFactor_mcs_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "AvailabilityFactor"].REGION, mcs_df[mcs_df['PARAM'] == "AvailabilityFactor"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "AvailabilityFactor"].YEAR)])
AvailabilityFactor = {str(r): {str(t): {str(y): generateRandomData(AvailabilityFactor_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "AvailabilityFactor") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(y)) in AvailabilityFactor_mcs_specified else generateRandomData(AvailabilityFactor_ref[r][t][y], AvailabilityFactor_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
if ("OperationalLife" in mcs_parameters) and (mcs_num > 0):
if i == 1:
OperationalLife_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "OperationalLife") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
OperationalLife_mcs_specified = tuple([(str(r), str(t)) for r, t in zip(mcs_df[mcs_df['PARAM'] == "OperationalLife"].REGION, mcs_df[mcs_df['PARAM'] == "OperationalLife"].TECHNOLOGY)])
OperationalLife = {str(r): {str(t): int(generateRandomData(OperationalLife_ref[r][t], mcs_df[(mcs_df['PARAM'] == "OperationalLife") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0])) if (str(r), str(t)) in OperationalLife_mcs_specified else int(generateRandomData(OperationalLife_ref[r][t], OperationalLife_mcs_default_list)) for t in TECHNOLOGY} for r in REGION}
if ("InputActivityRatio" in mcs_parameters) and (mcs_num > 0):
if i == 1:
InputActivityRatio_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "InputActivityRatio") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
InputActivityRatio_mcs_specified = tuple([(str(r),str(t),str(f),str(m),str(y)) for r, t, f, m, y in zip(mcs_df[mcs_df['PARAM'] == "InputActivityRatio"].REGION, mcs_df[mcs_df['PARAM'] == "InputActivityRatio"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "InputActivityRatio"].FUEL, mcs_df[mcs_df['PARAM'] == "InputActivityRatio"].MODE_OF_OPERATION, mcs_df[mcs_df['PARAM'] == "InputActivityRatio"].YEAR)])
InputActivityRatio = {str(r): {str(t): {str(f): {str(m): {str(y): generateRandomData(InputActivityRatio_ref[r][t][f][m][y], mcs_df[(mcs_df['PARAM'] == "InputActivityRatio") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['FUEL'] == f) & (mcs_df['MODE_OF_OPERATION'] == m) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(f),str(m),str(y)) in InputActivityRatio_mcs_specified else generateRandomData(InputActivityRatio_ref[r][t][f][m][y], InputActivityRatio_mcs_default_list) for y in YEAR} for m in MODE_OF_OPERATION} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
if ("OutputActivityRatio" in mcs_parameters) and (mcs_num > 0):
if i == 1:
OutputActivityRatio_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "OutputActivityRatio") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
OutputActivityRatio_mcs_specified = tuple([(str(r),str(t),str(f),str(m),str(y)) for r, t, f, m, y in zip(mcs_df[mcs_df['PARAM'] == "OutputActivityRatio"].REGION, mcs_df[mcs_df['PARAM'] == "OutputActivityRatio"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "OutputActivityRatio"].FUEL, mcs_df[mcs_df['PARAM'] == "OutputActivityRatio"].MODE_OF_OPERATION, mcs_df[mcs_df['PARAM'] == "OutputActivityRatio"].YEAR)])
OutputActivityRatio = {str(r): {str(t): {str(f): {str(m): {str(y): generateRandomData(OutputActivityRatio_ref[r][t][f][m][y], mcs_df[(mcs_df['PARAM'] == "OutputActivityRatio") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['FUEL'] == f) & (mcs_df['MODE_OF_OPERATION'] == m) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(f),str(m),str(y)) in OutputActivityRatio_mcs_specified else generateRandomData(OutputActivityRatio_ref[r][t][f][m][y], OutputActivityRatio_mcs_default_list) for y in YEAR} for m in MODE_OF_OPERATION} for f in FUEL} for t in TECHNOLOGY} for r in REGION}
######### Technology Costs #########
if ("CapitalCost" in mcs_parameters) and (mcs_num > 0):
if i == 1:
CapitalCost_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "CapitalCost") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
CapitalCost_mcs_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "CapitalCost"].REGION, mcs_df[mcs_df['PARAM'] == "CapitalCost"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "CapitalCost"].YEAR)])
CapitalCost = {str(r): {str(t): {str(y): generateRandomData(CapitalCost_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "CapitalCost") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(y)) in CapitalCost_mcs_specified else generateRandomData(CapitalCost_ref[r][t][y], CapitalCost_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
if ("VariableCost" in mcs_parameters) and (mcs_num > 0):
if i == 1:
VariableCost_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "VariableCost") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
VariableCost_mcs_specified = tuple([(str(r),str(t),str(m),str(y)) for r, t, m, y in zip(mcs_df[mcs_df['PARAM'] == "VariableCost"].REGION, mcs_df[mcs_df['PARAM'] == "VariableCost"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "VariableCost"].MODE_OF_OPERATION, mcs_df[mcs_df['PARAM'] == "VariableCost"].YEAR)])
VariableCost = {str(r): {str(t): {str(m): {str(y): generateRandomData(VariableCost_ref[r][t][m][y], mcs_df[(mcs_df['PARAM'] == "VariableCost") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['MODE_OF_OPERATION'] == m) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(m),str(y)) in VariableCost_mcs_specified else generateRandomData(VariableCost_ref[r][t][m][y], VariableCost_mcs_default_list) for y in YEAR} for m in MODE_OF_OPERATION} for t in TECHNOLOGY} for r in REGION}
if ("FixedCost" in mcs_parameters) and (mcs_num > 0):
if i == 1:
FixedCost_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "FixedCost") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
FixedCost_mcs_specified = tuple([(str(r),str(t),str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "FixedCost"].REGION, mcs_df[mcs_df['PARAM'] == "FixedCost"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "FixedCost"].YEAR)])
FixedCost = {str(r): {str(t): {str(y): generateRandomData(FixedCost_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "FixedCost") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(y)) in FixedCost_mcs_specified else generateRandomData(FixedCost_ref[r][t][y], FixedCost_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Storage #########
if ("StorageLevelStart" in mcs_parameters) and (mcs_num > 0):
if i == 1:
StorageLevelStart_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "StorageLevelStart") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
StorageLevelStart_mcs_specified = tuple([(str(r), str(s)) for r, s in zip(mcs_df[mcs_df['PARAM'] == "StorageLevelStart"].REGION, mcs_df[mcs_df['PARAM'] == "StorageLevelStart"].STORAGE)])
StorageLevelStart = {str(r): {str(s): generateRandomData(StorageLevelStart_ref[r][s], mcs_df[(mcs_df['PARAM'] == "StorageLevelStart") & (mcs_df['REGION'] == r) & (mcs_df['STORAGE'] == s)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(s)) in StorageLevelStart_mcs_specified else generateRandomData(StorageLevelStart_ref[r][s], StorageLevelStart_mcs_default_list) for s in STORAGE} for r in REGION}
if ("StorageMaxChargeRate" in mcs_parameters) and (mcs_num > 0):
if i == 1:
StorageMaxChargeRate_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "StorageMaxChargeRate") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
StorageMaxChargeRate_mcs_specified = tuple([(str(r), str(s)) for r, s in zip(mcs_df[mcs_df['PARAM'] == "StorageMaxChargeRate"].REGION, mcs_df[mcs_df['PARAM'] == "StorageMaxChargeRate"].STORAGE)])
StorageMaxChargeRate = {str(r): {str(s): generateRandomData(StorageMaxChargeRate_ref[r][s], mcs_df[(mcs_df['PARAM'] == "StorageMaxChargeRate") & (mcs_df['REGION'] == r) & (mcs_df['STORAGE'] == s)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(s)) in StorageMaxChargeRate_mcs_specified else generateRandomData(StorageMaxChargeRate_ref[r][s], StorageMaxChargeRate_mcs_default_list) for s in STORAGE} for r in REGION}
if ("StorageMaxDischargeRate" in mcs_parameters) and (mcs_num > 0):
if i == 1:
StorageMaxDischargeRate_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "StorageMaxDischargeRate") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
StorageMaxDischargeRate_mcs_specified = tuple([(str(r), str(s)) for r, s in zip(mcs_df[mcs_df['PARAM'] == "StorageMaxDischargeRate"].REGION, mcs_df[mcs_df['PARAM'] == "StorageMaxDischargeRate"].STORAGE)])
StorageMaxDischargeRate = {str(r): {str(s): generateRandomData(StorageMaxDischargeRate_ref[r][s], mcs_df[(mcs_df['PARAM'] == "StorageMaxDischargeRate") & (mcs_df['REGION'] == r) & (mcs_df['STORAGE'] == s)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(s)) in StorageMaxDischargeRate_mcs_specified else generateRandomData(StorageMaxDischargeRate_ref[r][s], StorageMaxDischargeRate_mcs_default_list) for s in STORAGE} for r in REGION}
if ("OperationalLifeStorage" in mcs_parameters) and (mcs_num > 0):
if i == 1:
OperationalLifeStorage_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "OperationalLifeStorage") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
OperationalLifeStorage_mcs_specified = tuple([(str(r), str(s)) for r, s in zip(mcs_df[mcs_df['PARAM'] == "OperationalLifeStorage"].REGION, mcs_df[mcs_df['PARAM'] == "OperationalLifeStorage"].STORAGE)])
OperationalLifeStorage = {str(r): {str(s): generateRandomData(OperationalLifeStorage_ref[r][s], mcs_df[(mcs_df['PARAM'] == "OperationalLifeStorage") & (mcs_df['REGION'] == r) & (mcs_df['STORAGE'] == s)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(s)) in OperationalLifeStorage_mcs_specified else generateRandomData(OperationalLifeStorage_ref[r][s], OperationalLifeStorage_mcs_default_list) for s in STORAGE} for r in REGION}
if ("CapitalCostStorage" in mcs_parameters) and (mcs_num > 0):
if i == 1:
CapitalCostStorage_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "CapitalCostStorage") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
CapitalCostStorage_mcs_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(mcs_df[mcs_df['PARAM'] == "CapitalCostStorage"].REGION, mcs_df[mcs_df['PARAM'] == "CapitalCostStorage"].STORAGE, mcs_df[mcs_df['PARAM'] == "CapitalCostStorage"].YEAR)])
CapitalCostStorage = {str(r): {str(s): {str(y): generateRandomData(CapitalCostStorage_ref[r][s][y], mcs_df[(mcs_df['PARAM'] == "CapitalCostStorage") & (mcs_df['REGION'] == r) & (mcs_df['STORAGE'] == s) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(s), str(y)) in CapitalCostStorage_mcs_specified else generateRandomData(CapitalCostStorage_ref[r][s][y], CapitalCostStorage_mcs_default_list) for y in YEAR} for s in STORAGE} for r in REGION}
if ("ResidualStorageCapacity" in mcs_parameters) and (mcs_num > 0):
if i == 1:
ResidualStorageCapacity_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "ResidualStorageCapacity") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
ResidualStorageCapacity_mcs_specified = tuple([(str(r), str(s), str(y)) for r, s, y in zip(mcs_df[mcs_df['PARAM'] == "ResidualStorageCapacity"].REGION, mcs_df[mcs_df['PARAM'] == "ResidualStorageCapacity"].STORAGE, mcs_df[mcs_df['PARAM'] == "ResidualStorageCapacity"].YEAR)])
ResidualStorageCapacity = {str(r): {str(s): {str(y): generateRandomData(ResidualStorageCapacity_ref[r][s][y], mcs_df[(mcs_df['PARAM'] == "ResidualStorageCapacity") & (mcs_df['REGION'] == r) & (mcs_df['STORAGE'] == s) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(s), str(y)) in ResidualStorageCapacity_mcs_specified else generateRandomData(ResidualStorageCapacity_ref[r][s][y], ResidualStorageCapacity_mcs_default_list) for y in YEAR} for s in STORAGE} for r in REGION}
######### Capacity Constraints #########
if ("CapacityOfOneTechnologyUnit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
CapacityOfOneTechnologyUnit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "CapacityOfOneTechnologyUnit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
CapacityOfOneTechnologyUnit_mcs_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "CapacityOfOneTechnologyUnit"].REGION, mcs_df[mcs_df['PARAM'] == "CapacityOfOneTechnologyUnit"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "CapacityOfOneTechnologyUnit"].YEAR)])
CapacityOfOneTechnologyUnit = {str(r): {str(t): {str(y): generateRandomData(CapacityOfOneTechnologyUnit_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "CapacityOfOneTechnologyUnit") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t), str(y)) in CapacityOfOneTechnologyUnit_mcs_specified else generateRandomData(CapacityOfOneTechnologyUnit_ref[r][t][y], CapacityOfOneTechnologyUnit_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Investment Constraints #########
if ("TotalAnnualMaxCapacityInvestment" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TotalAnnualMaxCapacityInvestment_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TotalAnnualMaxCapacityInvestment") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TotalAnnualMaxCapacityInvestment_mcs_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].REGION, mcs_df[mcs_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "TotalAnnualMaxCapacityInvestment"].YEAR)])
TotalAnnualMaxCapacityInvestment = {str(r): {str(t): {str(y): generateRandomData(TotalAnnualMaxCapacityInvestment_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "TotalAnnualMaxCapacityInvestment") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t), str(y)) in TotalAnnualMaxCapacityInvestment_mcs_specified else generateRandomData(TotalAnnualMaxCapacityInvestment_ref[r][t][y], TotalAnnualMaxCapacityInvestment_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
if ("TotalAnnualMinCapacityInvestment" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TotalAnnualMinCapacityInvestment_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TotalAnnualMinCapacityInvestment") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TotalAnnualMinCapacityInvestment_mcs_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].REGION, mcs_df[mcs_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "TotalAnnualMinCapacityInvestment"].YEAR)])
TotalAnnualMinCapacityInvestment = {str(r): {str(t): {str(y): generateRandomData(TotalAnnualMinCapacityInvestment_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "TotalAnnualMinCapacityInvestment") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t), str(y)) in TotalAnnualMinCapacityInvestment_mcs_specified else generateRandomData(TotalAnnualMinCapacityInvestment_ref[r][t][y], TotalAnnualMinCapacityInvestment_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
######### Activity Constraints #########
if ("TotalTechnologyAnnualActivityUpperLimit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TotalTechnologyAnnualActivityUpperLimit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TotalTechnologyAnnualActivityUpperLimit_mcs_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].REGION, mcs_df[mcs_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit"].YEAR)])
TotalTechnologyAnnualActivityUpperLimit = {str(r): {str(t): {str(y): generateRandomData(TotalTechnologyAnnualActivityUpperLimit_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "TotalTechnologyAnnualActivityUpperLimit") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t), str(y)) in TotalTechnologyAnnualActivityUpperLimit_mcs_specified else generateRandomData(TotalTechnologyAnnualActivityUpperLimit_ref[r][t][y], TotalTechnologyAnnualActivityUpperLimit_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
if ("TotalTechnologyAnnualActivityLowerLimit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TotalTechnologyAnnualActivityLowerLimit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TotalTechnologyAnnualActivityLowerLimit_mcs_specified = tuple([(str(r), str(t), str(y)) for r, t, y in zip(mcs_df[mcs_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].REGION, mcs_df[mcs_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit"].YEAR)])
TotalTechnologyAnnualActivityLowerLimit = {str(r): {str(t): {str(y): generateRandomData(TotalTechnologyAnnualActivityLowerLimit_ref[r][t][y], mcs_df[(mcs_df['PARAM'] == "TotalTechnologyAnnualActivityLowerLimit") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t), str(y)) in TotalTechnologyAnnualActivityLowerLimit_mcs_specified else generateRandomData(TotalTechnologyAnnualActivityLowerLimit_ref[r][t][y], TotalTechnologyAnnualActivityLowerLimit_mcs_default_list) for y in YEAR} for t in TECHNOLOGY} for r in REGION}
if ("TotalTechnologyModelPeriodActivityUpperLimit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TotalTechnologyModelPeriodActivityUpperLimit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TotalTechnologyModelPeriodActivityUpperLimit_mcs_specified = tuple([(str(r), str(t)) for r, t in zip(mcs_df[mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].REGION, mcs_df[mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit"].TECHNOLOGY)])
TotalTechnologyModelPeriodActivityUpperLimit = {str(r): {str(t): generateRandomData(TotalTechnologyModelPeriodActivityUpperLimit_ref[r][t], mcs_df[(mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityUpperLimit") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t)) in TotalTechnologyModelPeriodActivityUpperLimit_mcs_specified else generateRandomData(TotalTechnologyModelPeriodActivityUpperLimit_ref[r][t], TotalTechnologyModelPeriodActivityUpperLimit_mcs_default_list) for t in TECHNOLOGY} for r in REGION}
if ("TotalTechnologyModelPeriodActivityLowerLimit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
TotalTechnologyModelPeriodActivityLowerLimit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
TotalTechnologyModelPeriodActivityLowerLimit_mcs_specified = tuple([(str(r), str(t)) for r, t in zip(mcs_df[mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].REGION, mcs_df[mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit"].TECHNOLOGY)])
TotalTechnologyModelPeriodActivityLowerLimit = {str(r): {str(t): generateRandomData(TotalTechnologyModelPeriodActivityLowerLimit_ref[r][t], mcs_df[(mcs_df['PARAM'] == "TotalTechnologyModelPeriodActivityLowerLimit") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(t)) in TotalTechnologyModelPeriodActivityLowerLimit_mcs_specified else generateRandomData(TotalTechnologyModelPeriodActivityLowerLimit_ref[r][t], TotalTechnologyModelPeriodActivityLowerLimit_mcs_default_list) for t in TECHNOLOGY} for r in REGION}
######### Emissions & Penalties #########
if ("EmissionActivityRatio" in mcs_parameters) and (mcs_num > 0):
if i == 1:
EmissionActivityRatio_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "EmissionActivityRatio") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
EmissionActivityRatio_mcs_specified = tuple([(str(r),str(t),str(e),str(m),str(y)) for r, t, e, m, y in zip(mcs_df[mcs_df['PARAM'] == "EmissionActivityRatio"].REGION, mcs_df[mcs_df['PARAM'] == "EmissionActivityRatio"].TECHNOLOGY, mcs_df[mcs_df['PARAM'] == "EmissionActivityRatio"].EMISSION, mcs_df[mcs_df['PARAM'] == "EmissionActivityRatio"].MODE_OF_OPERATION, mcs_df[mcs_df['PARAM'] == "EmissionActivityRatio"].YEAR)])
EmissionActivityRatio = {str(r): {str(t): {str(e): {str(m): {str(y): generateRandomData(EmissionActivityRatio_ref[r][t][e][m][y], mcs_df[(mcs_df['PARAM'] == "EmissionActivityRatio") & (mcs_df['REGION'] == r) & (mcs_df['TECHNOLOGY'] == t) & (mcs_df['EMISSION'] == e) & (mcs_df['MODE_OF_OPERATION'] == m) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r),str(t),str(e),str(m),str(y)) in EmissionActivityRatio_mcs_specified else generateRandomData(EmissionActivityRatio_ref[r][t][e][m][y], EmissionActivityRatio_mcs_default_list) for y in YEAR} for m in MODE_OF_OPERATION} for e in EMISSION} for t in TECHNOLOGY} for r in REGION}
if ("EmissionsPenalty" in mcs_parameters) and (mcs_num > 0):
if i == 1:
EmissionsPenalty_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "EmissionsPenalty") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
EmissionsPenalty_mcs_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(mcs_df[mcs_df['PARAM'] == "EmissionsPenalty"].REGION, mcs_df[mcs_df['PARAM'] == "EmissionsPenalty"].EMISSION, mcs_df[mcs_df['PARAM'] == "EmissionsPenalty"].YEAR)])
EmissionsPenalty = {str(r): {str(e): {str(y): generateRandomData(EmissionsPenalty_ref[r][e][y], mcs_df[(mcs_df['PARAM'] == "EmissionsPenalty") & (mcs_df['REGION'] == r) & (mcs_df['EMISSION'] == e) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(e), str(y)) in EmissionsPenalty_mcs_specified else generateRandomData(EmissionsPenalty_ref[r][e][y], EmissionsPenalty_mcs_default_list) for y in YEAR} for e in EMISSION} for r in REGION}
if ("AnnualExogenousEmission" in mcs_parameters) and (mcs_num > 0):
if i == 1:
AnnualExogenousEmission_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "AnnualExogenousEmission") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
AnnualExogenousEmission_mcs_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(mcs_df[mcs_df['PARAM'] == "AnnualExogenousEmission"].REGION, mcs_df[mcs_df['PARAM'] == "AnnualExogenousEmission"].EMISSION, mcs_df[mcs_df['PARAM'] == "AnnualExogenousEmission"].YEAR)])
AnnualExogenousEmission = {str(r): {str(e): {str(y): generateRandomData(AnnualExogenousEmission_ref[r][e][y], mcs_df[(mcs_df['PARAM'] == "AnnualExogenousEmission") & (mcs_df['REGION'] == r) & (mcs_df['EMISSION'] == e) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(e), str(y)) in AnnualExogenousEmission_mcs_specified else generateRandomData(AnnualExogenousEmission_ref[r][e][y], AnnualExogenousEmission_mcs_default_list) for y in YEAR} for e in EMISSION} for r in REGION}
if ("AnnualEmissionLimit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
AnnualEmissionLimit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "AnnualEmissionLimit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
AnnualEmissionLimit_mcs_specified = tuple([(str(r), str(e), str(y)) for r, e, y in zip(mcs_df[mcs_df['PARAM'] == "AnnualEmissionLimit"].REGION, mcs_df[mcs_df['PARAM'] == "AnnualEmissionLimit"].EMISSION, mcs_df[mcs_df['PARAM'] == "AnnualEmissionLimit"].YEAR)])
AnnualEmissionLimit = {str(r): {str(e): {str(y): generateRandomData(AnnualEmissionLimit_ref[r][e][y], mcs_df[(mcs_df['PARAM'] == "AnnualEmissionLimit") & (mcs_df['REGION'] == r) & (mcs_df['EMISSION'] == e) & (mcs_df['YEAR'] == y)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(e), str(y)) in AnnualEmissionLimit_mcs_specified else generateRandomData(AnnualEmissionLimit_ref[r][e][y], AnnualEmissionLimit_mcs_default_list) for y in YEAR} for e in EMISSION} for r in REGION}
if ("ModelPeriodExogenousEmission" in mcs_parameters) and (mcs_num > 0):
if i == 1:
ModelPeriodExogenousEmission_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "ModelPeriodExogenousEmission") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
ModelPeriodExogenousEmission_mcs_specified = tuple([(str(r), str(e)) for r, e in zip(mcs_df[mcs_df['PARAM'] == "ModelPeriodExogenousEmission"].REGION, mcs_df[mcs_df['PARAM'] == "ModelPeriodExogenousEmission"].EMISSION)])
ModelPeriodExogenousEmission = {str(r): {str(e): generateRandomData(ModelPeriodExogenousEmission_ref[r][e], mcs_df[(mcs_df['PARAM'] == "ModelPeriodExogenousEmission") & (mcs_df['REGION'] == r) & (mcs_df['EMISSION'] == e)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(e)) in ModelPeriodExogenousEmission_mcs_specified else generateRandomData(ModelPeriodExogenousEmission_ref[r][e], ModelPeriodExogenousEmission_mcs_default_list) for e in EMISSION} for r in REGION}
if ("ModelPeriodEmissionLimit" in mcs_parameters) and (mcs_num > 0):
if i == 1:
ModelPeriodEmissionLimit_mcs_default_list = mcs_df[(mcs_df['PARAM'] == "ModelPeriodEmissionLimit") & (mcs_df['DEFAULT_SETTING'] == 1)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]
ModelPeriodEmissionLimit_mcs_specified = tuple([(str(r), str(e)) for r, e in zip(mcs_df[mcs_df['PARAM'] == "ModelPeriodEmissionLimit"].REGION, mcs_df[mcs_df['PARAM'] == "ModelPeriodEmissionLimit"].EMISSION)])
ModelPeriodEmissionLimit = {str(r): {str(e): generateRandomData(ModelPeriodEmissionLimit_ref[r][e], mcs_df[(mcs_df['PARAM'] == "ModelPeriodEmissionLimit") & (mcs_df['REGION'] == r) & (mcs_df['EMISSION'] == e)][['DISTRIBUTION', 'REL_SD', 'REL_MIN', 'REL_MAX', 'ARRAY']].values.tolist()[0]) if (str(r), str(e)) in ModelPeriodEmissionLimit_mcs_specified else generateRandomData(ModelPeriodEmissionLimit_ref[r][e], ModelPeriodEmissionLimit_mcs_default_list) for e in EMISSION} for r in REGION}
logging.info("{}\tAnalysis is finished. Please wait until the results are saved!".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# ----------------------------------------------------------------------------------------------------------------------
# SAVE ALL RESULTS
# ----------------------------------------------------------------------------------------------------------------------
saveResults(res_df, outputDir, outputFile)
logging.info("{}\tAll results are saved".format(dt.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) | [
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12,
12,
7346,
29937,
30004,
13,
30004,
13,
1678,
363,
364,
297,
5195,
29954,
2725,
29901,
30004,
13,
4706,
363,
269,
297,
6850,
1955,
10461,
29901,
30004,
13,
9651,
363,
343,
297,
612,
26441,
29901,
30004,
13,
18884,
363,
19375,
297,
3725,
29909,
3094,
29901,
30004,
13,
462,
1678,
363,
301,
29881,
297,
21330,
29979,
11116,
29901,
30004,
13,
462,
4706,
363,
301,
29882,
297,
21330,
6227,
29979,
15307,
15176,
11375,
2544,
29901,
30004,
13,
462,
9651,
396,
317,
29896,
29918,
19907,
2776,
10486,
5914,
479,
30004,
13,
462,
9651,
1904,
4619,
390,
403,
2776,
10486,
5914,
479,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
1275,
9505,
29886,
29889,
22833,
11139,
4197,
19907,
2776,
3886,
29961,
29878,
3816,
29880,
3816,
29873,
3816,
29885,
3816,
29891,
29962,
334,
17968,
1762,
10486,
29961,
29878,
3816,
29873,
3816,
29879,
3816,
29885,
29962,
334,
1281,
3259,
3137,
29961,
29880,
3816,
3137,
29962,
334,
1281,
3259,
430,
29961,
29880,
3816,
430,
29962,
334,
1281,
3259,
29880,
29882,
29961,
29880,
3816,
29880,
29882,
29962,
363,
260,
297,
17067,
3210,
6632,
14480,
29979,
363,
286,
297,
16999,
2287,
29918,
9800,
29918,
4590,
1001,
8098,
363,
301,
297,
323,
8890,
12750,
12107,
565,
17968,
1762,
10486,
29961,
29878,
3816,
29873,
3816,
29879,
3816,
29885,
29962,
1405,
29871,
29900,
11724,
5124,
30004,
13,
462,
9651,
396,
317,
29906,
29918,
19907,
2776,
10486,
29928,
783,
279,
479,
30004,
13,
462,
9651,
1904,
4619,
390,
403,
2776,
10486,
29928,
783,
279,
479,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
1275,
9505,
29886,
29889,
22833,
11139,
4197,
19907,
2776,
3886,
29961,
29878,
3816,
29880,
3816,
29873,
3816,
29885,
3816,
29891,
29962,
334,
17968,
4591,
10486,
29961,
29878,
3816,
29873,
3816,
29879,
3816,
29885,
29962,
334,
1281,
3259,
3137,
29961,
29880,
3816,
3137,
29962,
334,
1281,
3259,
430,
29961,
29880,
3816,
430,
29962,
334,
1281,
3259,
29880,
29882,
29961,
29880,
3816,
29880,
29882,
29962,
363,
260,
297,
17067,
3210,
6632,
14480,
29979,
363,
286,
297,
16999,
2287,
29918,
9800,
29918,
4590,
1001,
8098,
363,
301,
297,
323,
8890,
12750,
12107,
565,
17968,
4591,
10486,
29961,
29878,
3816,
29873,
3816,
29879,
3816,
29885,
29962,
1405,
29871,
29900,
11724,
5124,
30004,
13,
462,
9651,
396,
317,
29941,
29918,
6779,
5914,
479,
3047,
262,
12883,
30004,
13,
462,
9651,
1904,
4619,
12670,
5914,
479,
3047,
262,
12883,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
1275,
9505,
29886,
29889,
22833,
11139,
4197,
29898,
19907,
2776,
10486,
5914,
479,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
448,
390,
403,
2776,
10486,
29928,
783,
279,
479,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
2314,
334,
8905,
18772,
29961,
29880,
3816,
29891,
29962,
334,
1281,
3259,
3137,
29961,
29880,
3816,
3137,
29962,
334,
1281,
3259,
430,
29961,
29880,
3816,
430,
29962,
334,
1281,
3259,
29880,
29882,
29961,
29880,
3816,
29880,
29882,
29962,
363,
301,
297,
323,
8890,
12750,
12107,
565,
313,
1168,
3259,
3137,
29961,
29880,
3816,
3137,
29962,
1405,
29871,
29900,
29897,
322,
313,
1168,
3259,
430,
29961,
29880,
3816,
430,
29962,
1405,
29871,
29900,
29897,
322,
313,
1168,
3259,
29880,
29882,
29961,
29880,
3816,
29880,
29882,
29962,
1405,
29871,
29900,
4638,
511,
5124,
30004,
13,
462,
9651,
396,
317,
29946,
29918,
6779,
5914,
479,
3047,
262,
12742,
30004,
13,
462,
9651,
1904,
4619,
12670,
5914,
479,
3047,
262,
12742,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
1275,
313,
19907,
2776,
10486,
5914,
479,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
448,
390,
403,
2776,
10486,
29928,
783,
279,
479,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
2314,
334,
8373,
18772,
29961,
29880,
29882,
3816,
29891,
1402,
5124,
30004,
13,
30004,
13,
18884,
396,
317,
29945,
29918,
392,
29918,
29903,
29953,
29918,
10486,
10108,
12883,
4763,
30004,
13,
18884,
565,
938,
29898,
29891,
29897,
1275,
938,
29898,
1195,
29898,
29979,
26441,
22164,
30004,
13,
462,
1678,
1904,
4619,
26162,
10108,
12883,
4763,
29961,
29878,
3816,
29879,
3816,
29891,
29962,
1275,
26162,
10108,
4763,
29961,
29878,
3816,
29879,
1402,
5124,
30004,
13,
18884,
1683,
29901,
30004,
13,
462,
1678,
1904,
4619,
26162,
10108,
12883,
4763,
29961,
29878,
3816,
29879,
3816,
29891,
29962,
1275,
26162,
10108,
12883,
4763,
29961,
29878,
3816,
29879,
3816,
710,
29898,
524,
29898,
29891,
6817,
29896,
4638,
718,
9505,
29886,
29889,
22833,
11139,
4197,
6779,
5914,
479,
3047,
262,
12883,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
710,
29898,
524,
29898,
29891,
6817,
29896,
4638,
363,
19375,
297,
3725,
29909,
3094,
363,
301,
29881,
297,
21330,
29979,
11116,
363,
301,
29882,
297,
21330,
6227,
29979,
15307,
15176,
11375,
2544,
11724,
5124,
30004,
13,
18884,
396,
317,
29955,
29918,
392,
29918,
29903,
29947,
29918,
10486,
10108,
12883,
12881,
728,
30004,
13,
18884,
565,
938,
29898,
29891,
29897,
529,
938,
29898,
3317,
29898,
29979,
26441,
22164,
30004,
13,
462,
1678,
1904,
4619,
26162,
10108,
12883,
12881,
728,
29961,
29878,
3816,
29879,
3816,
29891,
29962,
1275,
26162,
10108,
12883,
4763,
29961,
29878,
3816,
29879,
3816,
710,
29898,
524,
29898,
29891,
29897,
718,
29871,
29896,
29897,
1402,
5124,
30004,
13,
18884,
1683,
29901,
30004,
13,
462,
1678,
1904,
4619,
26162,
10108,
12883,
12881,
728,
29961,
29878,
3816,
29879,
3816,
29891,
29962,
1275,
26162,
10108,
12883,
4763,
29961,
29878,
3816,
29879,
3816,
29891,
29962,
718,
9505,
29886,
29889,
22833,
11139,
4197,
6779,
5914,
479,
3047,
262,
12883,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
363,
19375,
297,
3725,
29909,
3094,
363,
301,
29881,
297,
21330,
29979,
11116,
363,
301,
29882,
297,
21330,
6227,
29979,
15307,
15176,
11375,
2544,
11724,
5124,
30004,
13,
30004,
13,
18884,
363,
19375,
297,
3725,
29909,
3094,
29901,
30004,
13,
462,
1678,
396,
317,
29929,
29918,
392,
29918,
29903,
29896,
29900,
29918,
10486,
10108,
2008,
1658,
4763,
30004,
13,
462,
1678,
565,
938,
29898,
3137,
29897,
1275,
938,
29898,
1195,
29898,
1660,
29909,
3094,
22164,
30004,
13,
462,
4706,
1904,
4619,
26162,
10108,
2008,
1658,
4763,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
29891,
29962,
1275,
26162,
10108,
12883,
4763,
29961,
29878,
3816,
29879,
3816,
29891,
1402,
5124,
30004,
13,
462,
1678,
1683,
29901,
30004,
13,
462,
4706,
1904,
4619,
26162,
10108,
2008,
1658,
4763,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
29891,
29962,
1275,
26162,
10108,
2008,
1658,
4763,
29961,
29878,
3816,
29879,
3816,
710,
29898,
524,
29898,
3137,
6817,
29896,
29897,
3816,
29891,
29962,
718,
9505,
29886,
29889,
22833,
11139,
4197,
6779,
5914,
479,
3047,
262,
12883,
29961,
29878,
3816,
29879,
3816,
710,
29898,
524,
29898,
3137,
6817,
29896,
29897,
3816,
430,
3816,
29880,
29882,
3816,
29891,
29962,
363,
301,
29881,
297,
21330,
29979,
11116,
363,
301,
29882,
297,
21330,
6227,
29979,
15307,
15176,
11375,
2544,
11724,
5124,
30004,
13,
30004,
13,
462,
1678,
363,
301,
29881,
297,
21330,
29979,
11116,
29901,
30004,
13,
462,
4706,
396,
317,
29896,
29896,
29918,
392,
29918,
29903,
29896,
29906,
29918,
10486,
10108,
12742,
1542,
4763,
30004,
13,
462,
4706,
565,
938,
29898,
430,
29897,
1275,
938,
29898,
1195,
29898,
28658,
11116,
22164,
30004,
13,
462,
9651,
1904,
4619,
26162,
10108,
12742,
1542,
4763,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29891,
29962,
1275,
26162,
10108,
2008,
1658,
4763,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
29891,
1402,
5124,
30004,
13,
462,
4706,
1683,
29901,
30004,
13,
462,
9651,
1904,
4619,
26162,
10108,
12742,
1542,
4763,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29891,
29962,
1275,
26162,
10108,
12742,
1542,
4763,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
710,
29898,
524,
29898,
430,
6817,
29896,
29897,
3816,
29891,
29962,
718,
9505,
29886,
29889,
22833,
11139,
4197,
6779,
5914,
479,
3047,
262,
12742,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
710,
29898,
524,
29898,
430,
6817,
29896,
29897,
3816,
29880,
29882,
3816,
29891,
29962,
334,
24728,
797,
12742,
1542,
29961,
3137,
3816,
710,
29898,
524,
29898,
430,
6817,
29896,
29897,
3816,
29891,
29962,
363,
301,
29882,
297,
21330,
6227,
29979,
15307,
15176,
11375,
2544,
11724,
5124,
30004,
13,
462,
4706,
396,
317,
29896,
29941,
29918,
392,
29918,
29903,
29896,
29946,
29918,
392,
29918,
29903,
29896,
29945,
29918,
10486,
10108,
12742,
1542,
12881,
728,
30004,
13,
462,
4706,
565,
313,
524,
29898,
430,
29897,
1275,
938,
29898,
3317,
29898,
28658,
11116,
4961,
322,
313,
524,
29898,
3137,
29897,
1275,
938,
29898,
3317,
29898,
1660,
29909,
3094,
876,
1125,
30004,
13,
462,
9651,
1904,
4619,
26162,
10108,
12742,
1542,
12881,
728,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29891,
29962,
1275,
26162,
10108,
12883,
12881,
728,
29961,
29878,
3816,
29879,
3816,
29891,
1402,
5124,
30004,
13,
462,
4706,
25342,
938,
29898,
430,
29897,
1275,
938,
29898,
3317,
29898,
28658,
11116,
22164,
30004,
13,
462,
9651,
1904,
4619,
26162,
10108,
12742,
1542,
12881,
728,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29891,
29962,
1275,
26162,
10108,
2008,
1658,
4763,
29961,
29878,
3816,
29879,
3816,
710,
29898,
524,
29898,
3137,
7240,
29896,
29897,
3816,
29891,
1402,
5124,
30004,
13,
462,
4706,
1683,
29901,
30004,
13,
462,
9651,
1904,
4619,
26162,
10108,
12742,
1542,
12881,
728,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
430,
3816,
29891,
29962,
1275,
26162,
10108,
12742,
1542,
12881,
728,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
710,
29898,
524,
29898,
430,
7240,
29896,
29897,
3816,
29891,
29962,
448,
9505,
29886,
29889,
22833,
11139,
4197,
6779,
5914,
479,
3047,
262,
12742,
29961,
29878,
3816,
29879,
3816,
3137,
3816,
710,
29898,
524,
29898,
430,
7240,
29896,
29897,
3816,
29880,
29882,
3816,
29891,
29962,
334,
24728,
797,
12742,
1542,
29961,
3137,
3816,
710,
29898,
524,
29898,
430,
7240,
29896,
29897,
3816,
29891,
29962,
363,
301,
29882,
297,
21330,
6227,
29979,
15307,
15176,
11375,
2544,
11724,
5124,
30004,
13,
30004,
13,
1678,
835,
4136,
2277,
29937,
12,
12,
12,
10486,
1281,
4151,
9466,
12,
12,
12,
7346,
29937,
30004,
13,
30004,
13,
1678,
363,
364,
297,
5195,
29954,
2725,
29901,
30004,
13,
4706,
363,
269,
297,
6850,
1955,
10461,
29901,
30004,
13,
9651,
363,
343,
297,
612,
26441,
29901,
30004,
13,
18884,
363,
19375,
297,
3725,
29909,
3094,
29901,
30004,
13,
462,
1678,
363,
301,
29881,
297,
21330,
29979,
11116,
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algorithms/dataStructures/graph.py | mohrtw/algorithms | 0 | 173543 | from collections import defaultdict
class graph(object):
def __init__(self, arcList=[]):
self.arcs = defaultdict(list)
for arc in arcList:
self.arcs[arc[0]].append(arc[1])
if not self.check_vertex(arc[1]):
self.add_vertex(arc[1])
def check_vertex(self, v):
return v in self.arcs
def check_arc(self, v, a):
return a in self.arcs[v]
def add_vertex(self, v):
self.arcs[v] = []
def add_arc(self, v, a):
self.arcs[v].append(a)
def remove_vertex(self, v):
self.arcs.pop(v, None)
def remove_arc(self, v, a):
self.arcs[v].remove(a)
def get_arcs(self, v):
return self.arcs[v]
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] |
helpers/expr_to_dnf.py | hriener/party-elli | 9 | 128186 | <reponame>hriener/party-elli
from typing import List
from sympy import Symbol, to_dnf, Not, Or, And, simplify_logic
from sympy import false as sympy_false
from sympy import true as sympy_true
from helpers.expr_helper import get_sig_number
from helpers.spec_helper import prop
from interfaces.expr import Expr, BinOp, Number, Bool, UnaryOp
from parsing.visitor import Visitor
class SympyConverter(Visitor):
def visit_binary_op(self, binary_op:BinOp):
assert binary_op.name in '*+=', str(binary_op)
if binary_op.name == '=':
sig, num = get_sig_number(binary_op)
assert num == Number(1), str(num)
sympy_prop = Symbol(sig.name)
return sympy_prop
if binary_op.name == '*':
return self.dispatch(binary_op.arg1) & self.dispatch(binary_op.arg2)
if binary_op.name == '+':
return self.dispatch(binary_op.arg1) | self.dispatch(binary_op.arg2)
def visit_bool(self, bool_const:Bool):
return (sympy_false, sympy_true)[bool_const==Bool(True)]
def visit_unary_op(self, unary_op:UnaryOp):
assert unary_op.name == '!', str(unary_op)
return ~self.dispatch(unary_op.arg)
# end of SympyConverter
def to_dnf_set(dst_expr:Expr) -> List[List[Expr]]:
sympy_dst_expr = simplify_logic(SympyConverter().dispatch(dst_expr)) # the checks below need this `simplify`
if sympy_dst_expr == sympy_true:
return [[]]
if sympy_dst_expr == sympy_false:
return []
dnf_sympy_dst_expr = to_dnf(sympy_dst_expr, simplify=True)
cubes = _get_cubes(dnf_sympy_dst_expr)
cubes_list = []
for cube in cubes:
literals = _get_literals(cube)
props_list = []
for l in literals:
if isinstance(l, Not):
props_list.append(~prop(str(l.args[0]))) # it should print its name..
else:
props_list.append(prop(str(l))) # it should print its name..
cubes_list.append(props_list)
return cubes_list
def _get_cubes(dnf_expr) -> tuple:
assert dnf_expr not in (sympy_true, sympy_false)
if isinstance(dnf_expr, Or):
return dnf_expr.args
return dnf_expr,
def _get_literals(expr) -> tuple:
if isinstance(expr, Or) or isinstance(expr, And):
return expr.args
assert isinstance(expr, (Not, Symbol)), str(expr.__class__)
return expr, # tuple
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src/data/definitions.py | tamarakatic/patient_survival_prediction | 0 | 1610246 | <reponame>tamarakatic/patient_survival_prediction
import os
file_path = os.path.dirname(os.path.abspath(__file__))
ROOT_PATH = os.path.dirname(os.path.dirname(file_path))
TRAIN_RAW = os.path.join(ROOT_PATH, 'data/raw/training_v2.csv')
TEST_RAW = os.path.join(ROOT_PATH, 'data/raw/unlabeled.csv')
TRAIN_PROCESS = os.path.join(ROOT_PATH, 'data/processed/training_v2.csv')
TEST_PROCESS = os.path.join(ROOT_PATH, 'data/processed/unlabeled.csv')
SOLUTION_TEMPLATE = os.path.join(ROOT_PATH, 'data/raw/solution_template.csv')
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tests/unit_tests/test_system_program.py | 524119574/solana-py | 0 | 45877 | """Unit tests for solana.system_program."""
import solana.system_program as sp
from solana.account import Account
def test_transfer():
"""Test creating a transaction for transfer."""
params = sp.TransferParams(from_pubkey=Account().public_key(), to_pubkey=Account().public_key(), lamports=123)
txn = sp.transfer(params)
assert len(txn.instructions) == 1
assert sp.decode_transfer(txn.instructions[0]) == params
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src/split_data.py | sunsiche/DSCI522-2020-g22 | 0 | 29706 | # author: <NAME>
# date: 2020-11-25
"""
This script Splits the raw cleaned data to train and test splits
based on the user input and saves them into two separate csv files
Usage: clean_data.py --input_file_path=<input_file_path> --saving_path_train=<saving_path_train> --saving_path_test=<saving_path_test> --test_size=<test_size>
Options:
--input_file_path=<file_path> Path to the cleaned input data file
--saving_path_train=<saving_path_train> Path the training data file must be saved as csv file
--saving_path_test=<saving_path_test> Path the testing data file must be saved as csv file
--test_size=<saving_path_test> The proportion of test data to all the data. must be between 0 and 1.
"""
import pandas as pd
from docopt import docopt
from sklearn.model_selection import train_test_split
opt = docopt(__doc__)
def main(input_file_path, saving_path_train, saving_path_test, test_size):
"""
The main function of script
which splits the cleaned data to train and test
portions for the predictive model
Args:
input_file_path (string): the file path to cleaned data file
saving_path_train (string): the file path the script will
save the train data to the csv file.
saving_path_test (string): the file path the script will
save the test data to the csv file.
test_size (float) : the test portion of the data. must be
between 0 and 1.
Returns:
0 if main was successful
-1 if main failed.
"""
# read in data
try:
df = pd.read_csv(input_file_path)
except Exception as e:
print(f"The script failed to open the cleaned data file with the error {e}")
return -1
# Check test size is valid
try:
test_size = float(test_size)
if test_size < 0 or test_size > 1:
print("The test_size argument must be between 0 and 1")
return -1
except:
print("The test_size argument must be a numeric number")
return -1
# Split dataframe
try:
train_data, test_data = train_test_split(
df, test_size=test_size, random_state=123
)
except Exception as e:
print(f"The script failed to split data with error {e}")
return -1
# Save data
try:
# save train portion
train_data.to_csv(saving_path_train, index_label=False, index=False)
# save test portion
test_data.to_csv(saving_path_test, index_label=False, index=False)
except Exception as e:
print(f"The script failed to save the save train or test with the error {e}")
return -1
return 0
if __name__ == "__main__":
main(
opt["--input_file_path"],
opt["--saving_path_train"],
opt["--saving_path_test"],
opt["--test_size"],
)
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venv/Lib/site-packages/statsmodels/stats/tests/test_statstools.py | EkremBayar/bayar | 6,931 | 129645 | <gh_stars>1000+
# TODO: Test robust skewness
# TODO: Test robust kurtosis
import numpy as np
import pandas as pd
from numpy.testing import (assert_almost_equal, assert_raises, assert_equal,
assert_allclose)
from statsmodels.stats._adnorm import normal_ad
from statsmodels.stats.stattools import (omni_normtest, jarque_bera,
durbin_watson, _medcouple_1d, medcouple,
robust_kurtosis, robust_skewness)
# a random array, rounded to 4 decimals
x = np.array([-0.1184, -1.3403, 0.0063, -0.612, -0.3869, -0.2313, -2.8485,
-0.2167, 0.4153, 1.8492, -0.3706, 0.9726, -0.1501, -0.0337,
-1.4423, 1.2489, 0.9182, -0.2331, -0.6182, 0.183])
def test_durbin_watson():
#benchmark values from R car::durbinWatsonTest(x)
#library("car")
#> durbinWatsonTest(x)
#[1] 1.95298958377419
#> durbinWatsonTest(x**2)
#[1] 1.848802400319998
#> durbinWatsonTest(x[2:20]+0.5*x[1:19])
#[1] 1.09897993228779
#> durbinWatsonTest(x[2:20]+0.8*x[1:19])
#[1] 0.937241876707273
#> durbinWatsonTest(x[2:20]+0.9*x[1:19])
#[1] 0.921488912587806
st_R = 1.95298958377419
assert_almost_equal(durbin_watson(x), st_R, 14)
st_R = 1.848802400319998
assert_almost_equal(durbin_watson(x**2), st_R, 14)
st_R = 1.09897993228779
assert_almost_equal(durbin_watson(x[1:] + 0.5 * x[:-1]), st_R, 14)
st_R = 0.937241876707273
assert_almost_equal(durbin_watson(x[1:] + 0.8 * x[:-1]), st_R, 14)
st_R = 0.921488912587806
assert_almost_equal(durbin_watson(x[1:] + 0.9 * x[:-1]), st_R, 14)
X = np.array([x, x])
st_R = 1.95298958377419
assert_almost_equal(durbin_watson(X, axis=1), np.array([st_R, st_R]), 14)
assert_almost_equal(durbin_watson(X.T, axis=0), np.array([st_R, st_R]), 14)
def test_omni_normtest():
#tests against R fBasics
from scipy import stats
st_pv_R = np.array(
[[3.994138321207883, -1.129304302161460, 1.648881473704978],
[0.1357325110375005, 0.2587694866795507, 0.0991719192710234]])
nt = omni_normtest(x)
assert_almost_equal(nt, st_pv_R[:, 0], 14)
st = stats.skewtest(x)
assert_almost_equal(st, st_pv_R[:, 1], 14)
kt = stats.kurtosistest(x)
assert_almost_equal(kt, st_pv_R[:, 2], 11)
st_pv_R = np.array(
[[34.523210399523926, 4.429509162503833, 3.860396220444025],
[3.186985686465249e-08, 9.444780064482572e-06, 1.132033129378485e-04]])
x2 = x**2
#TODO: fix precision in these test with relative tolerance
nt = omni_normtest(x2)
assert_almost_equal(nt, st_pv_R[:, 0], 12)
st = stats.skewtest(x2)
assert_almost_equal(st, st_pv_R[:, 1], 12)
kt = stats.kurtosistest(x2)
assert_almost_equal(kt, st_pv_R[:, 2], 12)
def test_omni_normtest_axis(reset_randomstate):
#test axis of omni_normtest
x = np.random.randn(25, 3)
nt1 = omni_normtest(x)
nt2 = omni_normtest(x, axis=0)
nt3 = omni_normtest(x.T, axis=1)
assert_almost_equal(nt2, nt1, decimal=13)
assert_almost_equal(nt3, nt1, decimal=13)
def test_jarque_bera():
#tests against R fBasics
st_pv_R = np.array([1.9662677226861689, 0.3741367669648314])
jb = jarque_bera(x)[:2]
assert_almost_equal(jb, st_pv_R, 14)
st_pv_R = np.array([78.329987305556, 0.000000000000])
jb = jarque_bera(x**2)[:2]
assert_almost_equal(jb, st_pv_R, 13)
st_pv_R = np.array([5.7135750796706670, 0.0574530296971343])
jb = jarque_bera(np.log(x**2))[:2]
assert_almost_equal(jb, st_pv_R, 14)
st_pv_R = np.array([2.6489315748495761, 0.2659449923067881])
jb = jarque_bera(np.exp(-x**2))[:2]
assert_almost_equal(jb, st_pv_R, 14)
def test_shapiro():
#tests against R fBasics
#testing scipy.stats
from scipy.stats import shapiro
st_pv_R = np.array([0.939984787255526, 0.239621898000460])
sh = shapiro(x)
assert_almost_equal(sh, st_pv_R, 4)
#st is ok -7.15e-06, pval agrees at -3.05e-10
st_pv_R = np.array([5.799574255943298e-01, 1.838456834681376e-06 * 1e4])
sh = shapiro(x**2) * np.array([1, 1e4])
assert_almost_equal(sh, st_pv_R, 5)
st_pv_R = np.array([0.91730442643165588, 0.08793704167882448])
sh = shapiro(np.log(x**2))
assert_almost_equal(sh, st_pv_R, 5)
#diff is [ 9.38773155e-07, 5.48221246e-08]
st_pv_R = np.array([0.818361863493919373, 0.001644620895206969])
sh = shapiro(np.exp(-x**2))
assert_almost_equal(sh, st_pv_R, 5)
def test_adnorm():
#tests against R fBasics
st_pv = []
st_pv_R = np.array([0.5867235358882148, 0.1115380760041617])
ad = normal_ad(x)
assert_almost_equal(ad, st_pv_R, 12)
st_pv.append(st_pv_R)
st_pv_R = np.array([2.976266267594575e+00, 8.753003709960645e-08])
ad = normal_ad(x**2)
assert_almost_equal(ad, st_pv_R, 11)
st_pv.append(st_pv_R)
st_pv_R = np.array([0.4892557856308528, 0.1968040759316307])
ad = normal_ad(np.log(x**2))
assert_almost_equal(ad, st_pv_R, 12)
st_pv.append(st_pv_R)
st_pv_R = np.array([1.4599014654282669312, 0.0006380009232897535])
ad = normal_ad(np.exp(-x**2))
assert_almost_equal(ad, st_pv_R, 12)
st_pv.append(st_pv_R)
ad = normal_ad(np.column_stack((x, x**2, np.log(x**2), np.exp(-x**2))).T,
axis=1)
assert_almost_equal(ad, np.column_stack(st_pv), 11)
def test_durbin_watson_pandas(reset_randomstate):
x = np.random.randn(50)
x_series = pd.Series(x)
assert_almost_equal(durbin_watson(x), durbin_watson(x_series), decimal=13)
class TestStattools(object):
@classmethod
def setup_class(cls):
x = np.random.standard_normal(1000)
e1, e2, e3, e4, e5, e6, e7 = np.percentile(x, (12.5, 25.0, 37.5, 50.0, 62.5, 75.0, 87.5))
c05, c50, c95 = np.percentile(x, (5.0, 50.0, 95.0))
f025, f25, f75, f975 = np.percentile(x, (2.5, 25.0, 75.0, 97.5))
mean = np.mean
kr1 = mean(((x - mean(x)) / np.std(x))**4.0) - 3.0
kr2 = ((e7 - e5) + (e3 - e1)) / (e6 - e2) - 1.2330951154852172
kr3 = (mean(x[x > c95]) - mean(x[x < c05])) / (mean(x[x > c50]) - mean(x[x < c50])) - 2.5852271228708048
kr4 = (f975 - f025) / (f75 - f25) - 2.9058469516701639
cls.kurtosis_x = x
cls.expected_kurtosis = np.array([kr1, kr2, kr3, kr4])
cls.kurtosis_constants = np.array([3.0,1.2330951154852172,2.5852271228708048,2.9058469516701639])
def test_medcouple_no_axis(self):
x = np.reshape(np.arange(100.0), (50, 2))
mc = medcouple(x, axis=None)
assert_almost_equal(mc, medcouple(x.ravel()))
def test_medcouple_1d(self):
x = np.reshape(np.arange(100.0),(50,2))
assert_raises(ValueError, _medcouple_1d, x)
def test_medcouple_symmetric(self):
mc = medcouple(np.arange(5.0))
assert_almost_equal(mc, 0)
def test_medcouple_nonzero(self):
mc = medcouple(np.array([1, 2, 7, 9, 10.0]))
assert_almost_equal(mc, -0.3333333)
def test_medcouple_int(self):
# GH 4243
mc1 = medcouple(np.array([1, 2, 7, 9, 10]))
mc2 = medcouple(np.array([1, 2, 7, 9, 10.0]))
assert_equal(mc1, mc2)
def test_medcouple_symmetry(self, reset_randomstate):
x = np.random.standard_normal(100)
mcp = medcouple(x)
mcn = medcouple(-x)
assert_almost_equal(mcp + mcn, 0)
def test_medcouple_ties(self, reset_randomstate):
x = np.array([1, 2, 2, 3, 4])
mc = medcouple(x)
assert_almost_equal(mc, 1.0 / 6.0)
def test_durbin_watson(self, reset_randomstate):
x = np.random.standard_normal(100)
dw = sum(np.diff(x)**2.0) / np.dot(x, x)
assert_almost_equal(dw, durbin_watson(x))
def test_durbin_watson_2d(self, reset_randomstate):
shape = (1, 10)
x = np.random.standard_normal(100)
dw = sum(np.diff(x)**2.0) / np.dot(x, x)
x = np.tile(x[:, None], shape)
assert_almost_equal(np.squeeze(dw * np.ones(shape)), durbin_watson(x))
def test_durbin_watson_3d(self, reset_randomstate):
shape = (10, 1, 10)
x = np.random.standard_normal(100)
dw = sum(np.diff(x)**2.0) / np.dot(x, x)
x = np.tile(x[None, :, None], shape)
assert_almost_equal(np.squeeze(dw * np.ones(shape)), durbin_watson(x, axis=1))
def test_robust_skewness_1d(self):
x = np.arange(21.0)
sk = robust_skewness(x)
assert_almost_equal(np.array(sk), np.zeros(4))
def test_robust_skewness_1d_2d(self, reset_randomstate):
x = np.random.randn(21)
y = x[:, None]
sk_x = robust_skewness(x)
sk_y = robust_skewness(y, axis=None)
assert_almost_equal(np.array(sk_x), np.array(sk_y))
def test_robust_skewness_symmetric(self, reset_randomstate):
x = np.random.standard_normal(100)
x = np.hstack([x, np.zeros(1), -x])
sk = robust_skewness(x)
assert_almost_equal(np.array(sk), np.zeros(4))
def test_robust_skewness_3d(self, reset_randomstate):
x = np.random.standard_normal(100)
x = np.hstack([x, np.zeros(1), -x])
x = np.tile(x, (10, 10, 1))
sk_3d = robust_skewness(x, axis=2)
result = np.zeros((10, 10))
for sk in sk_3d:
assert_almost_equal(sk, result)
def test_robust_skewness_4(self, reset_randomstate):
x = np.random.standard_normal(1000)
x[x > 0] *= 3
m = np.median(x)
s = x.std(ddof=0)
expected = (x.mean() - m) / s
_, _, _, sk4 = robust_skewness(x)
assert_allclose(expected, sk4)
def test_robust_kurtosis_1d_2d(self, reset_randomstate):
x = np.random.randn(100)
y = x[:, None]
kr_x = np.array(robust_kurtosis(x))
kr_y = np.array(robust_kurtosis(y, axis=None))
assert_almost_equal(kr_x, kr_y)
def test_robust_kurtosis(self):
x = self.kurtosis_x
assert_almost_equal(np.array(robust_kurtosis(x)), self.expected_kurtosis)
def test_robust_kurtosis_3d(self):
x = np.tile(self.kurtosis_x, (10, 10, 1))
kurtosis = np.array(robust_kurtosis(x, axis=2))
for i, r in enumerate(self.expected_kurtosis):
assert_almost_equal(r * np.ones((10, 10)), kurtosis[i])
def test_robust_kurtosis_excess_false(self):
x = self.kurtosis_x
expected = self.expected_kurtosis + self.kurtosis_constants
kurtosis = np.array(robust_kurtosis(x, excess=False))
assert_almost_equal(expected, kurtosis)
def test_robust_kurtosis_ab(self):
# Test custom alpha, beta in kr3
x = self.kurtosis_x
alpha, beta = (10.0, 45.0)
kurtosis = robust_kurtosis(self.kurtosis_x, ab=(alpha,beta), excess=False)
num = np.mean(x[x>np.percentile(x,100.0 - alpha)]) - np.mean(x[x<np.percentile(x,alpha)])
denom = np.mean(x[x>np.percentile(x,100.0 - beta)]) - np.mean(x[x<np.percentile(x,beta)])
assert_almost_equal(kurtosis[2], num/denom)
def test_robust_kurtosis_dg(self):
# Test custom delta, gamma in kr4
x = self.kurtosis_x
delta, gamma = (10.0, 45.0)
kurtosis = robust_kurtosis(self.kurtosis_x, dg=(delta,gamma), excess=False)
q = np.percentile(x,[delta, 100.0-delta, gamma, 100.0-gamma])
assert_almost_equal(kurtosis[3], (q[1] - q[0]) / (q[3] - q[2]))
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] |
facexlib/headpose/hopenet_arch.py | spy14414/facexlib | 164 | 101521 | import torch
import torch.nn as nn
import torchvision
class HopeNet(nn.Module):
# Hopenet with 3 output layers for yaw, pitch and roll
# Predicts Euler angles by binning and regression with the expected value
def __init__(self, block, layers, num_bins):
super(HopeNet, self).__init__()
if block == 'resnet':
block = torchvision.models.resnet.Bottleneck
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7)
self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
self.idx_tensor = torch.arange(66).float()
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
@staticmethod
def softmax_temperature(tensor, temperature):
result = torch.exp(tensor / temperature)
result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result))
return result
def bin2degree(self, predict):
predict = self.softmax_temperature(predict, 1)
return torch.sum(predict * self.idx_tensor.type_as(predict), 1) * 3 - 99
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
pre_yaw = self.fc_yaw(x)
pre_pitch = self.fc_pitch(x)
pre_roll = self.fc_roll(x)
yaw = self.bin2degree(pre_yaw)
pitch = self.bin2degree(pre_pitch)
roll = self.bin2degree(pre_roll)
return yaw, pitch, roll
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] |
chroma-agent/chroma_agent/action_plugins/manage_fail_node.py | GarimaVishvakarma/intel-chroma | 0 | 147626 | # Copyright (c) 2017 Intel Corporation. All rights reserved.
# Use of this source code is governed by a MIT-style
# license that can be found in the LICENSE file.
from chroma_agent.lib.shell import AgentShell
def fail_node(args):
# force a manual failover by failing a node
AgentShell.try_run(["sync"])
AgentShell.try_run(["sync"])
AgentShell.try_run(["init", "0"])
ACTIONS = [fail_node]
CAPABILITIES = []
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] |
opencoverage/api/upload.py | kotlas92/opencoverage | 0 | 196679 | import logging
import os
import zlib
from typing import Optional
from urllib.parse import parse_qs
from starlette.requests import Request
from starlette.responses import PlainTextResponse
from opencoverage import tasks
from opencoverage.clients.scm import get_client
from .app import router
logger = logging.getLogger(__name__)
@router.post("/api/upload/v4")
async def upload_coverage_v4(request: Request):
settings = request.app.settings
path = os.path.join("/api", "upload-report")
if path[0] != "/":
path = "/" + path
logger.info(request)
upload_url = str(request.url.replace(path=path))
scheme = request.headers.get(
"x-scheme", request.headers.get("x-forwarded-proto", "http")
)
if not upload_url.startswith(f"{scheme}://"):
_, _, url_part = upload_url.partition("://")
upload_url = f"{scheme}://{url_part}"
logger.info(f"Redirecting to {upload_url}")
return PlainTextResponse(f"success\n{upload_url}")
@router.put("/api/upload-report")
async def upload_report(
request: Request,
branch: str,
commit: str,
slug: str,
token: Optional[str] = None,
):
data = await request.body()
logger.info(f"Upload body {len(data)}: {request.headers}")
if request.headers.get("content-type") == "application/x-gzip":
gzip_worker = zlib.decompressobj(zlib.MAX_WBITS | 16)
data = gzip_worker.decompress(data) + gzip_worker.flush()
logger.info(f"decompressed gzip data {len(data)}")
organization, _, repo = slug.partition("/")
if token in ("dummy", "-", ".", "y", None):
installation_id = None
else:
installation_id = token
project = None
query = parse_qs(request.url.query)
if query.get("flags"):
for flag in query["flags"]:
name, _, value = flag.partition(":")
if name == "project":
project = value
async with get_client(request.app.settings, installation_id) as scm:
await request.app.db.update_organization(organization, scm.installation_id)
await request.app.taskrunner.add(
name="coveragereport",
config=tasks.CoverageTaskConfig(
organization=organization,
repo=repo,
branch=branch,
commit=commit,
project=project,
installation_id=installation_id,
data=data,
),
)
logger.info("Task scheduled")
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ProcessImagePipeline.py | SamaraLove/Project-1-Lane-Following | 0 | 83594 | # -*- coding: utf-8 -*-
"""
Created on Fri Dec 6 13:45:30 2019
@author: LOVESA
"""
#importing relevant packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import os
import cv2
import math
from os.path import isfile, join
import Helper as Functions
#Parameters definition
low_threshold = 50
high_threshold = 150
kernel_size = 7
# Define the Hough transform parameters
# Make a blank the same size as our image to draw on
rho = 2 # distance resolution in pixels of the Hough grid
theta = np.pi/180 # angular resolution in radians of the Hough grid
threshold = 160 # minimum number of votes (intersections in Hough grid cell)
min_line_len = 10 #minimum number of pixels making up a line
max_line_gap = 20 # maximum gap in pixels between connectable line segments
# image processing pipeline function
def pipeline(image):
plt.imshow(image) #plot original image
initial_img = np.copy(image)*0 # creating a blank to draw lines on
#from PipelineFunctions import grayscale, gaussian blur and canny functions on the whole image
gray = Functions.grayscale(image)
plt.imshow(gray, cmap='gray') #grayscale image
gaussian_blur = Functions.gaussian_blur(gray, kernel_size)
plt.imshow(gaussian_blur)
edges = Functions.canny(gaussian_blur, low_threshold, high_threshold)
plt.imshow(edges) #plots binary image
# dilate()ing edge image.
#This will make the lines thicker which will help fit the Hough lines better
dilated = cv2.dilate(edges, np.ones((3,3), dtype=np.uint8))
plt.imshow(dilated)
#Cropping the image to within the region of interest
#imshape = image.shape
#vertices = np.array([[(140,imshape[0]),(440, 325), (520, 325), (960,imshape[0])]], dtype=np.int32)
vertices = np.array([[(140,550),(440, 325), (550, 325), (920,550)]], dtype=np.int32)
#the co-ordinate origin in the image is top-left corner of image
# create a masked edges image
masked_image = Functions.region_of_interest(dilated, vertices)
plt.imshow(masked_image) #plots binary image with lines within boundary
#Shows the region
#x, y = vertices.T
#plt.plot(x, y, 'b--', lw=4)
#Detecting shape edges in the remaining (cropped) image data
#Applying the hough transform
hough_img = Functions.hough_lines(masked_image, rho, theta, threshold, min_line_len, max_line_gap)
#Testing output of hough_img
#print(hough_img.shape)
#print(hough_img)
#for i in range(hough_img.shape[0]):
# for j in range(hough_img.shape[1]):
# for k in range(3):
# if hough_img[i,j,k] > 1e-4:
# print(i,j,k,hough_img[i,j,k])
plt.imshow(hough_img) #Prints full binary image with red lines only
weighted_img = Functions.weighted_img(hough_img,image, α=0.8, β=1.,γ=0.)
plt.imshow(weighted_img) #Prints original image with lines highlighted red
return weighted_img | [
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tests/timers/PeriodicTimer.py | SLAcM/SLAcM | 1 | 72700 | <filename>tests/timers/PeriodicTimer.py<gh_stars>1-10
'''
Created on Sep 19, 2020
@author: esdev
'''
from slacm.component import Component
class PeriodicTimer(Component):
def __init__(self):
super(PeriodicTimer, self).__init__()
def on_periodic(self):
now = self.periodic.recv_pyobj() # Receive time (as float)
self.logger.info('on_periodic():%s' % str(now))
period = self.periodic.getPeriod() # Query the period
if period > 1.0:
period = period - 1.0
else:
period = 5.0
self.periodic.setPeriod(period) # Set the period
self.logger.info('on_periodic(): setting period to %f' % period)
msg = ('periodic',now)
self.ticker.send_pyobj(msg)
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src/genomehubs/lib/parse.py | genomehubs/genomehubs | 7 | 139746 | <filename>src/genomehubs/lib/parse.py
#!/usr/bin/env python3
"""
Parse a local or remote data source.
Usage:
genomehubs parse [--btk] [--btk-root STRING...]
[--wikidata PATH] [--wikidata-root STRING...] [--wikidata-xref STRING...]
[--gbif] [--gbif-root STRING...] [--gbif-xref STRING...]
[--ncbi-datasets-genome PATH] [--outfile PATH]
[--refseq-mitochondria] [--refseq-organelles]
[--refseq-plastids] [--refseq-root NAME]
[-h|--help] [-v|--version]
Options:
--btk Parse assemblies in BlobToolKit
--btk-root STRING Scientific name of root taxon
--gbif Parse taxa in GBIF
--gbif-root STRING GBIF taxon ID of root taxon
--gbif-xref STRING Include link to external reference from GBIF (e.g. NBN, BOLD)
--wikidata PATH Parse taxa in WikiData dump
--wikidata-root STRING WikiData taxon ID of root taxon
--wikidata-xref STRING Include link to external reference from WikiData (e.g. NBN, BOLD)
--ncbi-datasets-genome PATH Parse NCBI Datasets genome directory
--outfile PATH Save parsed output to file
--refseq-mitochondria Parse mitochondrial genomes from the NCBI RefSeq
organelle collection
--refseq-organelles Parse all genomes from the NCBI RefSeq organelle
collection
--refseq-plastids Parse plastid genomes from the NCBI RefSeq organelle
collection
--refseq-root NAME Name (not taxId) of root taxon
-h, --help Show this
-v, --version Show version number
"""
import re
import sys
from pathlib import Path
from docopt import docopt
from tolkein import tofile
from tolkein import tolog
from .btk import btk_parser
from .config import config
from .hub import load_types
from .hub import order_parsed_fields
# from .ncbi import ncbi_datasets_summary_parser
from .ncbi import ncbi_genome_parser
from .ncbi import refseq_organelle_parser
from .version import __version__
from .wikidata import wikidata_parser
LOGGER = tolog.logger(__name__)
PARSERS = {
"btk": {"func": btk_parser, "params": None, "types": "btk"},
"ncbi-datasets-genome": {
"func": ncbi_genome_parser,
"params": None,
"types": "assembly",
},
# "ncbi-datasets-summary": {
# "func": ncbi_datasets_summary_parser,
# "params": None,
# "types": "assembly",
# },
"refseq-mitochondria": {
"func": refseq_organelle_parser,
"params": ("mitochondrion"),
"types": "organelle",
},
"refseq-organelles": {
"func": refseq_organelle_parser,
"params": ("mitochondrion", "plastid"),
"types": "organelle",
},
"refseq-plastids": {
"func": refseq_organelle_parser,
"params": ("plastid"),
"types": "organelle",
},
"wikidata": {"func": wikidata_parser, "params": None, "types": "wikidata"},
}
def main(args):
"""Parse data sources."""
options = config("parse", **args)
for option in options["parse"]:
if option in PARSERS:
params = PARSERS[option]["params"]
if params is None:
params = options["parse"][option]
LOGGER.info("Parsing %s" % option)
types = load_types(PARSERS[option]["types"])
names = load_types(PARSERS[option]["types"], part="names")
parsed = PARSERS[option]["func"](
params, options["parse"], types=types, names=names
)
files = []
if isinstance(parsed, tuple):
parsed, files = parsed
data = order_parsed_fields(parsed, types, names)
tofile.write_file(options["parse"]["outfile"], data)
filepath = Path(options["parse"]["outfile"])
outdir = filepath.parent
suff = re.compile(r"\.[^\.]+$")
if filepath.name.endswith(".gz"):
stem = re.sub(suff, "", filepath.stem)
else:
stem = filepath.stem
if types:
types["file"]["name"] = filepath.name
tofile.write_file("%s/%s.types.yaml" % (outdir, stem), types)
if names:
names["file"]["name"] = filepath.name
tofile.write_file("%s/%s.names.yaml" % (outdir, stem), names)
if files:
tofile.write_file("%s/%s.files.yaml" % (outdir, stem), files)
def cli():
"""Entry point."""
if len(sys.argv) == sys.argv.index("parse") + 1:
args = docopt(__doc__, argv=[])
else:
args = docopt(__doc__, version=__version__)
main(args)
if __name__ == "__main__":
cli()
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] |
piptools/repositories/local.py | simon-graham/pip-tools | 0 | 195491 | <reponame>simon-graham/pip-tools
# coding: utf-8
from __future__ import absolute_import, division, print_function, unicode_literals
from contextlib import contextmanager
from pip._internal.utils.hashes import FAVORITE_HASH
from .._compat import PIP_VERSION
from .base import BaseRepository
from piptools.utils import as_tuple, key_from_ireq, make_install_requirement
def ireq_satisfied_by_existing_pin(ireq, existing_pin):
"""
Return True if the given InstallationRequirement is satisfied by the
previously encountered version pin.
"""
version = next(iter(existing_pin.req.specifier)).version
return ireq.req.specifier.contains(
version, prereleases=existing_pin.req.specifier.prereleases
)
class LocalRequirementsRepository(BaseRepository):
"""
The LocalRequirementsRepository proxied the _real_ repository by first
checking if a requirement can be satisfied by existing pins (i.e. the
result of a previous compile step).
In effect, if a requirement can be satisfied with a version pinned in the
requirements file, we prefer that version over the best match found in
PyPI. This keeps updates to the requirements.txt down to a minimum.
"""
def __init__(self, existing_pins, proxied_repository, reuse_hashes=True):
self._reuse_hashes = reuse_hashes
self.repository = proxied_repository
self.existing_pins = existing_pins
@property
def options(self):
return self.repository.options
@property
def finder(self):
return self.repository.finder
@property
def session(self):
return self.repository.session
@property
def DEFAULT_INDEX_URL(self):
return self.repository.DEFAULT_INDEX_URL
def clear_caches(self):
self.repository.clear_caches()
def freshen_build_caches(self):
self.repository.freshen_build_caches()
def find_best_match(self, ireq, prereleases=None, prefer_local=""):
key = key_from_ireq(ireq)
existing_pin = self.existing_pins.get(key)
if existing_pin and ireq_satisfied_by_existing_pin(ireq, existing_pin):
project, version, _ = as_tuple(existing_pin)
return make_install_requirement(
project, version, ireq.extras, constraint=ireq.constraint
)
else:
return self.repository.find_best_match(ireq, prereleases, prefer_local)
def get_dependencies(self, ireq):
return self.repository.get_dependencies(ireq)
def get_hashes(self, ireq):
existing_pin = self._reuse_hashes and self.existing_pins.get(
key_from_ireq(ireq)
)
if existing_pin and ireq_satisfied_by_existing_pin(ireq, existing_pin):
if PIP_VERSION[:2] <= (20, 0):
hashes = existing_pin.options.get("hashes", {})
else:
hashes = existing_pin.hash_options
hexdigests = hashes.get(FAVORITE_HASH)
if hexdigests:
return {
":".join([FAVORITE_HASH, hexdigest]) for hexdigest in hexdigests
}
return self.repository.get_hashes(ireq)
@contextmanager
def allow_all_wheels(self):
with self.repository.allow_all_wheels():
yield
def copy_ireq_dependencies(self, source, dest):
self.repository.copy_ireq_dependencies(source, dest)
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] |
carrito_app/baseclasses/compras.py | fortegaseguel/Mi-Carrito | 0 | 1613756 | <gh_stars>0
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.tabbedpanel import TabbedPanel
from kivy.uix.popup import Popup
import sqlite3
def connect_to_database(path):
try:
con = sqlite3.connect(path)
cursor = con.cursor()
create_table_insumos(cursor)
con.commit()
con.close()
except Exception as e:
print(e)
def create_table_insumos(cursor):
cursor.execute(
'''
CREATE TABLE if not exists insumos(
id INTEGER,
producto VARCHAR(100),
cantidad INTEGER,
unidad VARCHAR(5),
categoria VARCHAR(100),
costo INTEGER,
fecha DATE DEFAULT(DATE('now', 'localtime')),
PRIMARY KEY(id AUTOINCREMENT)
)'''
)
class MessagePopup(Popup):
pass
class DataBaseWidCompras(BoxLayout):
def __init__(self, mainwid, **kwargs):
super(DataBaseWidCompras, self).__init__()
self.mainwid = mainwid
def check_memory_insumos(self):
self.ids.container.clear_widgets()
con = sqlite3.connect(self.mainwid.DB_PATH)
cursor = con.cursor()
cursor.execute(
'select ID, producto, cantidad, unidad, categoria, costo, fecha from insumos')
lista = cursor.fetchall()
lista.reverse()
for i in lista:
wid = DataWidCompras(self.mainwid)
r0 = '\n'
r1 = f'id: {i[0]}\n'
r2 = f'Producto: {i[1]}\n'
r3 = f'En almacen: {i[2]} {i[3]}\n'
r4 = f'Categoria: {i[4]}\n'
r5 = f'Costo por unidad: ${i[5]}\n'
r6 = f'Fecha de Ingreso: {i[6]}\n'
wid.data_id = str(i[0])
wid.data = r0+r1+r2+r3+r4+r5+r6
self.ids.container.add_widget(wid)
con.close()
def create_new_product(self):
self.mainwid.goto_insertdata_compras()
class UpdateDataWidCompras(BoxLayout):
def __init__(self, mainwid, data_id, **kwargs):
super(UpdateDataWidCompras, self).__init__()
self.mainwid = mainwid
self.data_id = data_id
self.check_memory_insumos()
def check_memory_insumos(self):
con = sqlite3.connect(self.mainwid.DB_PATH)
cursor = con.cursor()
s = 'select ID, producto, cantidad, unidad, categoria, costo from insumos where ID='
cursor.execute(s+self.data_id)
for i in cursor:
self.ids.ti_producto.text = i[1]
self.ids.ti_cantidad.text = str(i[2])
self.ids.ti_unidad.text = i[3]
self.ids.ti_categoria.text = i[4]
self.ids.ti_costo.text = str(i[5])
con.close()
def select_unidad(self, value):
self.ids.ti_unidad.text = f'{value}'
def select_categoria(self, value):
self.ids.ti_categoria.text = f'{value}'
def update_data_compras(self):
con = sqlite3.connect(self.mainwid.DB_PATH)
cursor = con.cursor()
d1 = str(self.ids.ti_producto.text)
d2 = str(self.ids.ti_cantidad.text)
d3 = str(self.ids.ti_unidad.text)
d4 = str(self.ids.ti_categoria.text)
d5 = str(self.ids.ti_costo.text)
a1 = (d1, d2, d3, d4, d5)
s1 = 'UPDATE insumos SET'
s2 = 'producto= "%s",cantidad= "%s", unidad= "%s", categoria= "%s", costo= "%s"' % a1
s3 = 'WHERE ID= %s' % self.data_id
try:
cursor.execute(s1+' '+s2+' '+s3)
con.commit()
con.close()
self.mainwid.goto_database_compras()
except Exception as e:
message = self.mainwid.Popup.ids.message
self.mainwid.Popup.open()
self.mainwid.Popup.title = "Data base error"
if '' in a1:
message.text = 'Uno o más campos están vacíos'
else:
message.text = str(e)
con.close()
def delete_data(self):
con = sqlite3.connect(self.mainwid.DB_PATH)
cursor = con.cursor()
s = 'delete from insumos where ID='+self.data_id
cursor.execute(s)
con.commit()
con.close()
self.mainwid.goto_database_compras()
def back_to_dbw(self):
self.mainwid.goto_database_compras()
class InsertDataWidCompras(TabbedPanel):
def __init__(self, mainwid, **kwargs):
super(InsertDataWidCompras, self).__init__()
self.mainwid = mainwid
def ingreso_rapido(self, value):
self.ids.ti_categoria.text = f'{value}'
def insert_datafast(self):
if self.ids.ti_categoria.text == 'Pack Bebidas':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Un'
d4 = 'Bebidas'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Papas Fritas':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Kg'
d4 = 'Congelados'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Proteina Congelada':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Kg'
d4 = 'Congelados'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Pan':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Un'
d4 = 'Pan'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Fajitas':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Un'
d4 = 'Pan'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Verduras':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Kg'
d4 = 'No Congelados'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Bencina':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Lts'
d4 = 'Combustible'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
elif self.ids.ti_categoria.text == 'Pago Jornada':
d1 = self.ids.ti_producto_rapido.text
d2 = self.ids.ti_cantidadd.text
d3 = 'Un'
d4 = 'Personal'
d5 = self.ids.ti_costoo.text
a1 = (d1,d2,d3,d4,d5)
print(a1)
con = sqlite3.connect(self.mainwid.DB_PATH)
cursor = con.cursor()
s1 = 'INSERT INTO insumos(producto, cantidad, unidad, categoria, costo)'
s2 = 'VALUES("%s", "%s", "%s", "%s", "%s")' % a1
try:
cursor.execute(s1+' '+s2)
con.commit()
con.close()
self.mainwid.goto_insertdata_compras()
except Exception as e:
message = self.mainwid.Popup.ids.message
self.mainwid.Popup.open()
self.mainwid.Popup.title = "Data base error"
if '' in a1:
message.text = 'Uno o más campos están vacíos'
else:
message.text = str(e)
con.close()
def select_unidad(self, value):
self.ids.ti_unidad.text = f'{value}'
def select_categoria(self, value):
self.ids.ti_categoria.text = f'{value}'
def insert_data(self):
con = sqlite3.connect(self.mainwid.DB_PATH)
cursor = con.cursor()
d1 = self.ids.ti_producto.text
d2 = self.ids.ti_cantidad.text
d3 = self.ids.ti_unidad.text
d4 = self.ids.ti_categoria.text
d5 = self.ids.ti_costo.text
a1 = (d1, d2, d3, d4, d5)
s1 = 'INSERT INTO insumos(producto, cantidad, unidad, categoria, costo)'
s2 = 'VALUES("%s", "%s", "%s", "%s", "%s")' % a1
try:
cursor.execute(s1+' '+s2)
con.commit()
con.close()
self.mainwid.goto_insertdata_compras()
except Exception as e:
message = self.mainwid.Popup.ids.message
self.mainwid.Popup.open()
self.mainwid.Popup.title = "Data base error"
if '' in a1:
message.text = 'Uno o más campos están vacíos'
else:
message.text = str(e)
con.close()
def back_to_dbw(self):
self.mainwid.goto_database_compras()
class DataWidCompras(BoxLayout):
def __init__(self, mainwid, **kwargs):
super(DataWidCompras, self).__init__()
self.mainwid = mainwid
def update_data_compras(self, data_id):
self.mainwid.goto_updatedata_compras(data_id)
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pcc/ast/ast.py | jiamo/pcc | 4 | 1603178 | <filename>pcc/ast/ast.py
# AST hierarchy
class ASTNode(object):
def dump(self, indent=0):
raise NotImplementedError
class ExprAST(ASTNode):
pass
class NumberExprAST(ExprAST):
def __init__(self, val):
self.val = val
def dump(self, indent=0):
return '{0}{1}[{2}]'.format(
' ' * indent, self.__class__.__name__, self.val)
class VariableExprAST(ExprAST):
def __init__(self, name):
self.name = name
def dump(self, indent=0):
return '{0}{1}[{2}]'.format(
' ' * indent, self.__class__.__name__, self.name)
class VarExprAST(ExprAST):
def __init__(self, vars, body):
# vars is a sequence of (name, init) pairs
self.vars = vars
self.body = body
def dump(self, indent=0):
prefix = ' ' * indent
s = '{0}{1}\n'.format(prefix, self.__class__.__name__)
for name, init in self.vars:
s += '{0} {1}'.format(prefix, name)
if init is None:
s += '\n'
else:
s += '=\n' + init.dump(indent+2) + '\n'
s += '{0} Body:\n'.format(prefix)
s += self.body.dump(indent + 2)
return s
class UnaryExprAST(ExprAST):
def __init__(self, op, operand):
self.op = op
self.operand = operand
def dump(self, indent=0):
s = '{0}{1}[{2}]\n'.format(
' ' * indent, self.__class__.__name__, self.op)
s += self.operand.dump(indent + 2)
return s
class BinaryExprAST(ExprAST):
def __init__(self, op, lhs, rhs):
self.op = op
self.lhs = lhs
self.rhs = rhs
def dump(self, indent=0):
s = '{0}{1}[{2}]\n'.format(
' ' * indent, self.__class__.__name__, self.op)
s += self.lhs.dump(indent + 2) + '\n'
s += self.rhs.dump(indent + 2)
return s
class IfExprAST(ExprAST):
def __init__(self, cond_expr, then_expr, else_expr):
self.cond_expr = cond_expr
self.then_expr = then_expr
self.else_expr = else_expr
def dump(self, indent=0):
prefix = ' ' * indent
s = '{0}{1}\n'.format(prefix, self.__class__.__name__)
s += '{0} Condition:\n{1}\n'.format(
prefix, self.cond_expr.dump(indent + 2))
s += '{0} Then:\n{1}\n'.format(
prefix, self.then_expr.dump(indent + 2))
s += '{0} Else:\n{1}'.format(
prefix, self.else_expr.dump(indent + 2))
return s
class ForExprAST(ExprAST):
def __init__(self, id_name, start_expr, end_expr, step_expr, body):
self.id_name = id_name
self.start_expr = start_expr
self.end_expr = end_expr
self.step_expr = step_expr
self.body = body
def dump(self, indent=0):
prefix = ' ' * indent
s = '{0}{1}\n'.format(prefix, self.__class__.__name__)
s += '{0} Start [{1}]:\n{2}\n'.format(
prefix, self.id_name, self.start_expr.dump(indent + 2))
s += '{0} End:\n{1}\n'.format(
prefix, self.end_expr.dump(indent + 2))
s += '{0} Step:\n{1}\n'.format(
prefix, self.step_expr.dump(indent + 2))
s += '{0} Body:\n{1}\n'.format(
prefix, self.body.dump(indent + 2))
return s
class CallExprAST(ExprAST):
def __init__(self, callee, args):
self.callee = callee
self.args = args
def dump(self, indent=0):
s = '{0}{1}[{2}]\n'.format(
' ' * indent, self.__class__.__name__, self.callee)
for arg in self.args:
s += arg.dump(indent + 2) + '\n'
return s[:-1] # snip out trailing '\n'
class PrototypeAST(ASTNode):
def __init__(self, name, argnames, isoperator=False, prec=0):
self.name = name
self.argnames = argnames
self.isoperator = isoperator
self.prec = prec
def is_unary_op(self):
return self.isoperator and len(self.argnames) == 1
def is_binary_op(self):
return self.isoperator and len(self.argnames) == 2
def get_op_name(self):
assert self.isoperator
return self.name[-1]
def dump(self, indent=0):
s = '{0}{1} {2}({3})'.format(
' ' * indent, self.__class__.__name__, self.name,
', '.join(self.argnames))
if self.isoperator:
s += '[operator with prec={0}]'.format(self.prec)
return s
class FunctionAST(ASTNode):
def __init__(self, proto, body):
self.proto = proto
self.body = body
_anonymous_function_counter = 0
@classmethod
def create_anonymous(klass, expr):
"""Create an anonymous function to hold an expression."""
klass._anonymous_function_counter += 1
return klass(
PrototypeAST('_anon{0}'.format(klass._anonymous_function_counter),
[]),
expr)
def is_anonymous(self):
return self.proto.name.startswith('_anon')
def dump(self, indent=0):
s = '{0}{1}[{2}]\n'.format(
' ' * indent, self.__class__.__name__, self.proto.dump())
s += self.body.dump(indent + 2) + '\n'
return s
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] |
scripts/window_stats.py | aakashsur/docker-hirise | 0 | 110471 | <gh_stars>0
#!/usr/bin/env python3
#
# Copyright 2015 Dovetail Genomics LLC
#
#
from __future__ import division
from __future__ import print_function
from builtins import range
from past.utils import old_div
from hirise_assembly import HiriseAssembly
import random
import re
if __name__=="__main__":
import sys
import argparse
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-d','--debug',default=False ,action="store_true",help="Turn on debugging ouput")
# parser.add_argument('-L','--layout',default=False ,help="A file containing a layout of contigs.")
parser.add_argument('-T','--test', default=False ,help="Compute stats for an explicitly specified region.")
parser.add_argument('-B','--bin', default=False ,action="store_true",help="Bin reference sequences.")
parser.add_argument('-N','--nsamples',default=10000,type=int ,help="Number of winows to sample.")
parser.add_argument('-w','--window',default=1000, type=int ,help="Size of windows to examine.")
parser.add_argument('-W','--binwindow',default=100000, type=int ,help="Size of windows to break reference contigs into.")
parser.add_argument('-i','--infile',default=False ,help="Filename for serialised assembly input file.")
# parser.add_argument('-o','--outfile',default=False,help="Filename for writing a list of segments on the raw contigs to mask for being promiscuous in linking.")
args = parser.parse_args()
if args.infile:
asf = HiriseAssembly()
asf.load_assembly(args.infile)
n=0
asf.binsize = args.binwindow
while n<args.nsamples:
try:
c,a,b=asf.random_window(wlen=args.window)
if args.test:
m=re.match("^(.*):(\d+)-(\d+)$",args.test)
c=m.group(1)
a=int(m.group(2))
b=int(m.group(3))
asf.window_stats(c,a,b,debug=args.debug,bins=args.bin)
n+=1
except Exception as e:
print("#",e,c,a,b)
raise e
if args.test: break
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qcmetadataprinter/struct.py | x2dev/device_leeco_x2 | 0 | 1820 | <reponame>x2dev/device_leeco_x2
#!/bin/python3
with open('../camera/QCamera2/stack/common/cam_intf.h', 'r') as f:
data = f.read()
f.closed
start = data.find(' INCLUDE(CAM_INTF_META_HISTOGRAM')
end = data.find('} metadata_data_t;')
data = data[start:end]
metadata = data.split("\n")
metalist = list()
for line in metadata:
if (line.startswith(' INCLUDE')):
foo = line.split(',')
foo[0] = foo[0].replace('INCLUDE', 'PRINT')
metalist.append(foo[0] + ", pMetadata);")
with open('list.txt', 'w') as f:
for item in metalist:
f.write("%s\n" % item)
f.closed
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pixette/application.py | EXLER/pixette | 0 | 25582 | <reponame>EXLER/pixette
import logging
import os
import pygame
from pixette.scenes.admin import AdminScene
from pixette.scenes.clock import ClockScene
from pixette.scenes.currencies import CurrenciesScene
from pixette.scenes.weather import WeatherScene
class Application:
def __init__(self, device, title, resolution, update_rate, debug=False):
self.device = device
if not debug:
os.environ["SDL_FBDEV"] = "/dev/fb1"
os.environ["SDL_VIDEODRIVER"] = "fbcon"
os.environ["SDL_VIDEO_CENTERED"] = "1"
pygame.init()
pygame.mouse.set_visible(False)
self._scene = None
self._screen = None
self.title = title
self.resolution = resolution
self.update_rate = update_rate
logging.info("Display initialized")
self.scenes = [ClockScene(), CurrenciesScene(), WeatherScene(lat=51.11, lon=17.04), AdminScene()]
logging.info("Scenes loaded")
self.device.left_btn.when_pressed = self.previous_scene
self.device.right_btn.when_pressed = self.next_scene
@property
def title(self):
return pygame.display.get_caption()
@title.setter
def title(self, value):
pygame.display.set_caption(value)
@property
def resolution(self):
return self._screen.get_size()
@resolution.setter
def resolution(self, value):
self._screen = pygame.display.set_mode(value)
@property
def width(self):
return self.resolution[0]
@property
def height(self):
return self.resolution[1]
@property
def active_scene(self):
return self._scene
def next_scene(self):
index = self.scenes.index(self.active_scene)
if index == len(self.scenes) - 1:
self.change_scene(self.scenes[0])
else:
self.change_scene(self.scenes[index + 1])
def previous_scene(self):
index = self.scenes.index(self.active_scene)
if index == 0:
self.change_scene(self.scenes[-1])
else:
self.change_scene(self.scenes[index - 1])
def change_scene(self, scene):
"""
Change the currently active scene.
This will invoke `scene.on_exit` and `scene.on_enter` methods on the switching scenes.
If `None` is provided, the application's execution will end.
"""
if self.active_scene is not None:
self.active_scene.on_exit(next_scene=scene)
self.active_scene._application = None
self._scene, old_scene = scene, self.active_scene
if self.active_scene is not None:
self.active_scene._application = self
self.active_scene.on_enter(previous_scene=old_scene)
def run(self, scene=None):
"""
Start the application at given scene.
"""
if scene is not None:
for s in self.scenes:
if type(s) == scene:
self.change_scene(s)
break
elif self.active_scene is not None:
self.change_scene(scene)
else:
raise ValueError("No scene provided")
clock = pygame.time.Clock()
while self.active_scene is not None:
try:
for event in pygame.event.get():
self.active_scene.handle_event(event)
if event.type == pygame.QUIT:
self.change_scene(None) # Trigger scene.on_exit()
return
self.device.keys(event)
dt = clock.tick(self.update_rate)
self.active_scene.update(dt)
self.active_scene.draw(self._screen)
pygame.display.update()
except KeyboardInterrupt:
logging.info("Shutting down")
break
except Exception as e:
logging.exception("Caught exception: %s" % e, exc_info=True)
break
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tests/verbs/test_drop_verb.py | RathmoreChaos/intficpy | 25 | 35124 | from ..helpers import IFPTestCase
from intficpy.things import Thing, Container, Liquid
class TestDropVerb(IFPTestCase):
def test_verb_func_drops_item(self):
item = Thing(self.game, self._get_unique_noun())
item.invItem = True
self.me.addThing(item)
self.assertIn(item.ix, self.me.contains)
self.assertEqual(len(self.me.contains[item.ix]), 1)
self.assertIn(item, self.me.contains[item.ix])
self.game.turnMain(f"drop {item.verbose_name}")
self.assertItemNotIn(
item, self.me.contains, "Dropped item, but item still in inventory"
)
def test_drop_item_not_in_inv(self):
item = Thing(self.game, "shoe")
item.invItem = True
self.start_room.addThing(item)
self.assertFalse(self.me.containsItem(item))
self.game.turnMain(f"drop {item.verbose_name}")
self.assertIn("You are not holding", self.app.print_stack.pop())
def test_drop_liquid_in_container(self):
cup = Container(self.game, "cup")
water = Liquid(self.game, "water", "water")
water.moveTo(cup)
cup.moveTo(self.me)
self.game.turnMain("drop water")
self.assertIn("You drop the cup", self.app.print_stack.pop())
self.assertFalse(self.game.me.containsItem(cup))
self.assertTrue(cup.containsItem(water))
def test_drop_composite_child(self):
machine = Thing(self.game, "machine")
wheel = Thing(self.game, "wheel")
machine.addComposite(wheel)
machine.moveTo(self.me)
self.game.turnMain("drop wheel")
self.assertIn("wheel is attached to the machine", self.app.print_stack.pop())
self.assertTrue(self.me.containsItem(wheel))
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bot.py | finneh4249/PTBot | 0 | 69680 | import discord
import asyncio
import re
import os
import datetime
# import git
import sys
import json
import dbl
import psycopg2
import logging
import pytz
from discord.ext import commands
from classes.PTVFormatter import PTVFormatter
import sys, traceback
debug_mode = False
logging.basicConfig(level=int(os.environ['LOGGING_LEVEL']))
if debug_mode:
TOKEN = os.environ['VPTBOT_DEVELOPMENT']
# logging.basicConfig(level=logging.DEBUG)
else:
TOKEN = os.environ['VPTBOT']
initial_extensions = ['cogs.error_handler',
'cogs.train_commands',
'cogs.config_commands',
'cogs.help_command']
def get_prefix(bot, message):
try:
DATABASE_URL = os.environ['DATABASE_URL']
except KeyError:
return commands.when_mentioned_or('pt!', 'Pt!', 'pT!', 'PT!')(bot, message)
conn = psycopg2.connect(DATABASE_URL, sslmode='require')
cur = conn.cursor()
prefix = None
if message.guild:
cur.execute(f"SELECT * FROM prefixes WHERE guild_id = {message.guild.id}")
prefix = cur.fetchone()
if prefix == None:
return commands.when_mentioned_or('pt!', 'Pt!', 'pT!', 'PT!')(bot, message)
else:
return commands.when_mentioned_or(prefix[1])(bot, message)
bot = commands.Bot(command_prefix=get_prefix, description='Shows Victorian Train Information', case_insensitive=True)
PTV = PTVFormatter(os.environ['PTV_DEV_ID'],os.environ['PTV_DEV_KEY'])
dblpy = dbl.DBLClient(bot, os.environ['DBL_TOKEN'])
try:
with open ("./InfoFiles/disruptions.json", 'r') as f:
disruptions = json.load(f)
except FileNotFoundError:
with open ("./InfoFiles/disruptions.json", 'w+') as f:
f.write('{"1":["l"], "2":["l"],"3":["l"],"4":["l"],"5":["l"],"6":["l"],"7":["l"],"8":["l"],"9":["l"],"10":[],"11":["l"],"12":["l"],"13":["l"],"14":["l"],"15":["l"],"16":["l"],"17":["l"]}')
with open ("./InfoFiles/routes.json", 'r') as f:
bot.routes = json.load(f)
bot.remove_command('help')
if __name__ == '__main__':
for extension in initial_extensions:
try:
bot.load_extension(extension)
except Exception as e:
print(f'Failed to load extension {extension}.', file=sys.stderr)
traceback.print_exc()
def _splicegen(maxchars, stringlist):
"""
Return a list of slices to print based on maxchars string-length boundary.
"""
runningcount = 0 # start at 0
tmpslice = [] # tmp list where we append slice numbers.
for item in stringlist:
runningcount += len(item)
if runningcount <= int(maxchars):
tmpslice.append(item)
else:
yield tmpslice
tmpslice = [item]
runningcount = len(item)
yield(tmpslice)
async def my_background_task():
await bot.wait_until_ready()
# disruptionschannel = bot.get_channel(545113422283669514)
while not bot.is_closed():
await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name=f'Trains with {len(bot.guilds)} servers! || pt!help'))
try:
await dblpy.post_guild_count()
except:
print('An error has occured posting server count')
await asyncio.sleep(60)
async def disruptions_task():
await bot.wait_until_ready()
print('BOT READY')
while not bot.is_closed():
# {"1":["l"], "2":["l"],"3":["l"],"4":["l"],"5":["l"],"6":["l"],"7":["l"],"8":["l"],"9":["l"],"10":[],"11":["l"],"12":["l"],"13":["l"],"14":["l"],"15":["l"],"16":["l"],"17":["l"]}
try:
bot.disruptions, notuptodatedisruption = PTV.UpdateDisruptions(bot)
# channelstodelete = []
disruptionchannels = {}
DATABASE_URL = os.environ['DATABASE_URL']
conn = psycopg2.connect(DATABASE_URL, sslmode='require')
cur = conn.cursor()
cur.execute("SELECT * FROM disruption_channels")
disruptionchannelsstorage = cur.fetchall()
for row in disruptionchannelsstorage:
disruptionchannels[str(row[0])] = {"1":row[1],"2":row[2],"3":row[3],"4":row[4],"5":row[5],"6":row[6],"7":row[7],"8":row[8],"9":row[9],"11":row[10],"12":row[11],"13":row[12],"14":row[13],"15":row[14],"16":row[15],"17":row[16],"18":row[17]}
cur.close()
conn.close()
# with open('./InfoFiles/disruptionchannels.json', 'r') as f:
# disruptionchannels = json.load(f)
# # disruptionchannelsold = dict(disruptionchannels)
for j in notuptodatedisruption:
for channel, messages in disruptionchannels.items():
if messages[j]:
messagerouteid = j
messageid = messages[j]
disruptionsmsg = discord.Embed(name="Disruptions", description=bot.routes[str(messagerouteid)]["route_name"], timestamp=datetime.datetime.now(), color=3447003)
disruptionsmsg = PTV.disruptions_to_embed(disruptionsmsg, bot.disruptions[str(messagerouteid)], messagerouteid, bot)
disruptionsmsg.set_footer(icon_url=bot.user.avatar_url, text=f'Last Disruption Update ')
try:
disruptionmsgobj = await bot.get_channel(int(channel)).fetch_message(messageid)
except AttributeError:
# if not channel in channelstodelete:
# channelstodelete.append(channel)
continue
except discord.errors.NotFound:
continue
try:
await disruptionmsgobj.edit(content = "", embed = disruptionsmsg)
except discord.errors.HTTPException:
await disruptionmsgobj.edit(content = "ERROR - Disruptions have too many characters for an embed field.", embed = None)
for channel, messages in disruptionchannels.items():
if messages["18"]:
try:
updatedtime = await bot.get_channel(int(channel)).fetch_message(messages["18"])
except AttributeError:
# if not channel in channelstodelete:
# channelstodelete.append(channel)
continue
except discord.errors.NotFound:
continue
await updatedtime.edit(content = f"Last Checked for Disruptions at {(datetime.datetime.now().astimezone(pytz.timezone('Australia/Melbourne'))).strftime('%I:%M%p %d %b %Y')}\nThe side bar will be yellow if a Planned Work is currently active.\nSource: Licensed from Public Transport Victoria under a Creative Commons Attribution 4.0 International Licence.\nBe sure to join my discord server for official VPTBot support/feedback! https://discord.gg/KEhCS8U")
# for channel in channelstodelete:
# del disruptionchannels[channel]
# with open('./InfoFiles/disruptionchannels.json', 'r') as f:
# disruptionchannelsnew = json.load(f)
# if disruptionchannelsnew == disruptionchannelsold:
# with open('./InfoFiles/disruptionchannels.json', 'w') as f:
# f.write(json.dumps(disruptionchannels))
await asyncio.sleep(60)
except Exception as e:
print(f"ERROR OCCURED {e}")
traceback.print_exc()
await asyncio.sleep(5)
pass
@bot.event
async def on_ready():
global json
"""http://discordpy.readthedocs.io/en/rewrite/api.html#discord.on_ready"""
print(f'\n\nLogged in as: {bot.user.name} - {bot.user.id}\nVersion: {discord.__version__}\n')
# Changes our bots Playing Status.
await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name='Trains! || pt!help'))
print(f'Successfully logged in and booted...!')
updatesmessage = await bot.get_channel(553466650347962372).fetch_message(557735165896425492)
Updates = discord.Embed(timestamp=datetime.datetime.now())
Updates.set_author(name='VPT Bot', icon_url=bot.user.avatar_url)
Updates.add_field(name='Feature Updates', value=':white_check_mark: Show disruptions in nexttrain command.\n:white_check_mark: NextTrain Command\n:white_check_mark: Better looking help command! \n:white_check_mark: Customisable prefixes.\n:white_check_mark: A configurable disruptions channel!\n:white_check_mark: More detailed nexttrain command.\n:ok_hand: Feedback message after commands.', inline=False)
Updates.add_field(name='General Improvements', value=':white_check_mark: Speed Improvements for nexttrain command.\n:white_check_mark: Made ETA: NOW if train was at station.')
Updates.add_field(name='Bugs', value=':white_check_mark: NextTrain command for City Loop Stations don\'t work. \n:white_check_mark: Case sensitivity for stations.\n:white_check_mark: Fixes message if station not found.\n:thumbsup: Saved Disruptions Channel info gets deleted upon error code from PTV resulting in not up-to-date disruptions info.', inline=False)
Updates.add_field(name='Key', value=':white_check_mark: Updated and deployed into bot\n:thumbsup: Beta testing, don\'t expect much. \n:ok_hand: Developing/Fixing\n:raised_hand: Under Investigation', inline=False)
Updates.set_footer(icon_url=bot.user.avatar_url, text=f'Last Updated ')
if debug_mode == False:
await updatesmessage.edit(embed=Updates)
bot.loop.create_task(my_background_task())
bot.loop.create_task(disruptions_task())
@bot.event
async def on_message(message):
# print(f"Message by {message.author} in {message.channel} in {message.guild} at {datetime.datetime.now()}")
# print(message.content)
if not message.guild:
if message.author.id != <PASSWORD> and message.author.id != <PASSWORD> and not message.content.lower().startswith("pt!"):
await message.channel.send('Thank you for your message, it will be passed on to lucky962. If you instead wanted to run a command, please make sure to include the prefix ("pt!") at the start of your message.')
await bot.get_user(244596682531143680).send(f'You received a message from {message.author.name} ({message.author.id}) saying:\n{message.content}')
await bot.process_commands(message)
@bot.command(name='await', description='**lucky962 only** does an await command', brief='**lucky962 only** does an await command')
@commands.is_owner()
async def do_await(ctx, *, args):
await ctx.message.delete()
await eval(args)
bot.run(TOKEN, bot=True, reconnect=True)
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] |
setup.py | sdpython/td3a_cpp | 1 | 199007 | <gh_stars>1-10
# -*- coding: utf-8 -*-
import os
import sys
from distutils.command.build_ext import build_ext
from setuptools import setup, Extension
from setuptools import find_packages
import numpy
##########
# helpers
##########
# Placeholder to change the default
# compilation for command ``build_ext --inplace``.
def get_cmd_classes():
class build_ext_subclass(build_ext):
def build_extensions(self):
build_ext.build_extensions(self)
return {'build_ext': build_ext_subclass}
def get_defined_args():
if sys.platform.startswith("win"):
# windows
define_macros = [('USE_OPENMP', None)]
libraries = ['kernel32']
extra_compile_args = ['/EHsc', '/O2', '/Gy', '/openmp']
extra_link_args = None
elif sys.platform.startswith("darwin"):
# mac osx
define_macros = [('USE_OPENMP', None)]
libraries = None
extra_compile_args = ['-lpthread', '-stdlib=libc++',
'-mmacosx-version-min=10.7', '-Xpreprocessor',
'-fopenmp']
extra_link_args = ["-lomp"]
else:
# linux
define_macros = [('USE_OPENMP', None)]
libraries = None
extra_compile_args = ['-lpthread', '-fopenmp']
# option '-mavx2' forces the compiler to use
# AVX instructions the processor might not have
extra_link_args = ['-lgomp']
return {
'define_macros': define_macros,
'libraries': libraries,
'extra_compile_args': extra_compile_args,
'extra_link_args': extra_link_args,
}
def get_extension_tutorial(name):
pattern1 = "td3a_cpp.tutorial.%s"
srcs = ['td3a_cpp/tutorial/%s.pyx' % name]
args = get_defined_args()
if name in ['dot_cython', 'experiment_cython', 'dot_cython_omp',
'mul_cython_omp', 'td_mul_cython']:
srcs.extend(['td3a_cpp/tutorial/%s_.cpp' % name])
args['language'] = "c++"
ext = Extension(pattern1 % name, srcs,
include_dirs=[numpy.get_include()],
**args)
opts = dict(boundscheck=False, cdivision=True,
wraparound=False, language_level=3,
cdivision_warnings=True)
ext_modules = []
from Cython.Build import cythonize
ext_modules.extend(cythonize([ext], compiler_directives=opts))
return ext_modules
######################
# beginning of setup
######################
here = os.path.dirname(__file__)
if here == "":
here = '.'
packages = find_packages(where=here)
package_dir = {k: os.path.join(here, k.replace(".", "/")) for k in packages}
package_data = {
"td3a_cpp.tutorial": ["*.pyx", '*.cpp', '*.h'],
}
try:
with open(os.path.join(here, "requirements.txt"), "r") as f:
requirements = f.read().strip(' \n\r\t').split('\n')
except FileNotFoundError:
requirements = []
if len(requirements) == 0 or requirements == ['']:
requirements = []
try:
with open(os.path.join(here, "readme.rst"), "r", encoding='utf-8') as f:
long_description = "td3a_cpp:" + f.read().split('td3a_cpp:')[1]
except FileNotFoundError:
long_description = ""
version_str = '0.0.1'
with open(os.path.join(here, 'td3a_cpp/__init__.py'), "r") as f:
line = [_ for _ in [_.strip("\r\n ")
for _ in f.readlines()]
if _.startswith("__version__")]
if len(line) > 0:
version_str = line[0].split('=')[1].strip('" ')
ext_modules = []
for ext in ['dot_blas_lapack', 'dot_cython',
'experiment_cython', 'dot_cython_omp',
'mul_cython_omp', 'td_mul_cython']:
ext_modules.extend(get_extension_tutorial(ext))
setup(name='td3a_cpp',
version=version_str,
description="Example of a python module including cython and openmp",
long_description=long_description,
author='<NAME>',
author_email='<EMAIL>',
url='https://github.com/sdpython/td3a_cpp',
ext_modules=ext_modules,
packages=packages,
package_dir=package_dir,
package_data=package_data,
setup_requires=["cython", "numpy", "scipy"],
install_requires=["cython", "numpy", "scipy"],
cmdclass=get_cmd_classes())
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Source_Code/Python/labinstrument/SS/CMW500/__init__.py | fenglwh/instruments | 0 | 164651 | from .CMW500_WIFI.CMW500_WIFI import CMW_WIFI | [
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firecam/lib/rect_to_squares.py | open-climate-tech/firecam | 9 | 171719 | # Copyright 2020 Open Climate Tech Contributors
#
# 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.
# ==============================================================================
"""
Simple utility to break up rectangle into squares
"""
import os
import pathlib
import math
import logging
import numpy as np
def getSegmentRanges(fullSize, segmentSize):
"""Break the given fullSize into ranges of segmentSize
Divide the range (0,fullSize) into multiple ranges of size
segmentSize that are equally spaced apart and have approximately
15% overlap (overlapRatio)
Args:
fullSize (int): size of the full range (0, fullSize)
segmentSize (int): size of each segment
Returns:
(list): list of tuples (start, end) marking each segment's range
"""
overlapRatio = 1.15
if fullSize < segmentSize:
return [] # all segments must be exactly segmentSize
elif fullSize == segmentSize:
return [(0,segmentSize)]
firstCenter = int(segmentSize/2)
lastCenter = fullSize - int(segmentSize/2)
assert lastCenter > firstCenter
flexSize = lastCenter - firstCenter
numSegments = math.ceil(flexSize / (segmentSize/overlapRatio))
offset = flexSize / numSegments
ranges = []
for i in range(numSegments):
center = firstCenter + round(i * offset)
start = center - int(segmentSize/2)
if (start + segmentSize) > fullSize:
break
ranges.append((start,start + segmentSize))
ranges.append((fullSize - segmentSize, fullSize))
# print('ranges', fullSize, segmentSize, ranges)
# lastC = 0
# for i, r in enumerate(ranges):
# c = (r[0] + r[1])/2
# print(i, r[0], r[1], c, c - lastC)
# lastC = c
return ranges
def getRangeFromCenter(center, size, minLimit, maxLimit):
"""Get linear range from center given constraints
Return (min,max) pair with given range within (minLimit, maxLimit)
ideally centered at given center
Args:
cneter (int): desired center
size (int): size of the output range
minLimit (int): absolute minimum value of the output range
maxLimit (int): absolute maximum value of the output range
Returns:
(int, int): start, end of the range
"""
if (center - int(size/2)) <= minLimit: # left edge limited
val0 = minLimit
val1 = min(val0 + size, maxLimit)
# print('left', val0, val1, center, size)
elif (center + int(size/2)) >= maxLimit: # right edge limited
val1 = maxLimit
val0 = max(val1 - size, minLimit)
# print('right', val0, val1, center, size)
else: # unlimited
val0 = center - int(size/2)
val1 = min(val0 + size, maxLimit)
# print('center', val0, val1, center, size)
return (val0, val1)
def cutBoxesFiles(imgOrig, outputDirectory, imageFileName, callBackFn=None):
"""Cut the given image into fixed size boxes and store to files
Divide the given image into square segments of 299x299 (segmentSize below)
to match the size of images used by InceptionV3 image classification
machine learning model. This function uses the getSegmentRanges() function
above to calculate the exact start and end of each square
Args:
imgOrig (Image): Image object of the original image
outputDirectory (str): name of directory to store the segments
imageFileName (str): nane of image file (used as segment file prefix)
callBackFn (function): callback function that's called for each square
Returns:
(list): list of segments with filename and coordinates
"""
segmentSize = 299
segments = []
imgName = pathlib.PurePath(imageFileName).name
imgNameNoExt = str(os.path.splitext(imgName)[0])
xRanges = getSegmentRanges(imgOrig.size[0], segmentSize)
yRanges = getSegmentRanges(imgOrig.size[1], segmentSize)
for yRange in yRanges:
for xRange in xRanges:
coords = (xRange[0], yRange[0], xRange[1], yRange[1])
if callBackFn != None:
skip = callBackFn(coords)
if skip:
continue
# output cropped image
cropImgName = imgNameNoExt + '_Crop_' + 'x'.join(list(map(lambda x: str(x), coords))) + '.jpg'
cropImgPath = os.path.join(outputDirectory, cropImgName)
cropped_img = imgOrig.crop(coords)
cropped_img.save(cropImgPath, format='JPEG', quality=95)
cropped_img.close()
segments.append({
'imgPath': cropImgPath,
'MinX': coords[0],
'MinY': coords[1],
'MaxX': coords[2],
'MaxY': coords[3]
})
return segments
def cutBoxesArray(imgOrig, startX=0, endX=None, startY=0, endY=None):
"""Cut the given image into fixed size boxes, normalize data, and return as np arrays
Divide the given image into square segments of 299x299 (segmentSize below)
to match the size of images used by InceptionV3 image classification
machine learning model. This function uses the getSegmentRanges() function
above to calculate the exact start and end of each square
Args:
imgOrig (Image): Image object of the original image
Returns:
(list, list): pair of lists (cropped numpy arrays) and (metadata on boundaries)
"""
segmentSize = 299
if endX == None:
endX = imgOrig.size[0]
elif endX < 0:
endX = imgOrig.size[0] + endX
startX = max(0, startX)
endX = min(endX, imgOrig.size[0])
xRanges = getSegmentRanges(endX - startX, segmentSize)
xRanges = list(map(lambda x: (x[0] + startX, x[1] + startX), xRanges))
if endY == None:
endY = imgOrig.size[1]
elif endY < 0:
endY = imgOrig.size[1] + endY
startY = max(0, startY)
endY = min(endY, imgOrig.size[1])
yRanges = getSegmentRanges(endY - startY, segmentSize)
yRanges = list(map(lambda x: (x[0] + startY, x[1] + startY), yRanges))
crops = []
segments = []
imgNpArray = np.asarray(imgOrig, dtype=np.float32)
imgNormalized = np.divide(np.subtract(imgNpArray,128),128)
for yRange in yRanges:
for xRange in xRanges:
crops.append(imgNormalized[yRange[0]:yRange[1], xRange[0]:xRange[1]])
coords = (xRange[0], yRange[0], xRange[1], yRange[1])
coordStr = 'x'.join(list(map(lambda x: str(x), coords)))
segments.append({
'coords': coords,
'coordStr': coordStr,
'MinX': coords[0],
'MinY': coords[1],
'MaxX': coords[2],
'MaxY': coords[3]
})
crops = np.array(crops)
return crops, segments
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decode.py | k-kha/Steganography-Program | 1 | 94505 |
#converts the pixel bytes to binary
def decToBin(dec):
secret_bin = []
for i in dec:
secret_bin.append(f'{i:08b}')
return secret_bin
#gets the last 2 LSB of each byte
def get2LSB(secret_bin):
last2 = []
for i in secret_bin:
for j in i[6:8]:
last2.append(j)
return last2
def filter2LSB(listdict, last2):
piclsb = []
replaceNum = 0
index = 0
#the lower even or odd occurence gets replaced
if listdict['0']<2 or listdict['1']<2:
replaceNum = 0
listdict['2'] = '01'
elif listdict['0'] <= listdict['1']:
replaceNum = 0
else:
replaceNum = 1
#filters the right matching bits out of the image
for i in last2:
if int(listdict['2'][index])%2 == replaceNum:
piclsb.append(i)
index += 1
if index >= len(listdict['2']):
index = 0
else:
index += 1
if index >= len(listdict['2']):
index = 0
return piclsb
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pyrtshm/metrics.py | pappacena/pyrtshm | 0 | 152818 | class Metrics:
received_packets: int = 0
sent_packets: int = 0
forward_key_set: int = 0
forward_key_del: int = 0
lost_packet_count: int = 0
out_of_order_count: int = 0
delete_unknown_key_count: int = 0
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Code/Python/limits.py | james-alvey-42/BoostedDM | 0 | 138868 | <filename>Code/Python/limits.py
# File: limits.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sys
import scipy.stats
from atmos_dm_flux import kdemultifit
from mean_free_path import TzMin
def get_kernel(particle):
r"""
Obtains the kernel and the normalisation factor for the dark matter flux from pions and eta particle decays. This can then be called directly on an array of energies as `kernel.evaluate(energies)`.
Parameters
----------
particle : str ('pion' or 'eta')
the particle from which the dark matter particles are produced
Returns
-------
kernel : scipy.stats.gaussian_kde
the kernel, which can be called directly on an array of energies
normalisation factor : float
the normalisation factor for the flux, this is because the scipy.stats.gaussian_kde is normalised by default
Examples
--------
>>> kernel, norm = get_kernel('eta')
>>> kernel.evaluate(np.power(10.0, -1))
1.24491518
>>> norm
8.905796945913948e-27
"""
if particle == 'pion':
save_directory = '/mnt/james/atmos/'
elif particle == 'eta':
save_directory = '../data/'
chi_energies = np.load('{}chienergies.npy'.format(save_directory))
chi_weights = np.load('{}chiweights.npy'.format(save_directory))
bw = np.power(10.0, -1.5)
kernel = scipy.stats.gaussian_kde(dataset=chi_energies, weights=chi_weights, bw_method=bw)
counts, _ = np.histogram(chi_energies, bins=1, weights=chi_weights)
norm = counts[0]
return kernel, norm
def get_Gdet_fit():
r"""
Returns the 1d interpolation for the :py:func:`Gdet` function, trained on 100 cross sections between :math:`10^{-34}` and :math:`10^{-28}\,\textrm{cm}^2`.
Returns
-------
GdetFit : scipy.interpolate.interp1d
Gdet interpolation [:math:`\textrm{cm}^{-2}`]
Examples
--------
>>> GdetFit = get_Gdet_fit()
>>> GdetFit(np.power(10.0, -34))
array(2.52933574e+25)
"""
GdetFit = np.load('GdetFit.npy')[0]
return GdetFit
def limit_prefactor(mchi=0.001):
r"""
Returns the prefactor for the limit plot.
Parameters
----------
mchi : float
mass of the dark matter particle [:math:`\textrm{GeV}`]
Returns
-------
prefactor : float
the prefactor [:math:`\textrm{s}^{-1}`]
Notes
-----
We are implementing the following definition for the prefactor,
.. math:: \kappa (\bar{v} \rho_{\textrm{DM}})^{\textrm{ local}}\left(\frac{m_\chi + m_N}{m_\chi + m_p}\right)^2\left(\frac{\sigma^{\textrm{ SI, lim}}_{\textrm{ DM}}}{m_{\textrm{ DM}}}\right)_{m_{\textrm{DM}}\rightarrow \infty}
In terms of parameter values we are taking :math:`\kappa = 0.23`, :math:`\bar{v} = 250\,\textrm{km}\,\textrm{s}^{-1}`, :math:`\rho_{\textrm{ DM}} = 0.3\,\textrm{GeV}\,\textrm{cm}^{-3}`, :math:`m_N = m_{\textrm{Xe}} = 115.3909 \,\textrm{GeV}`, :math:`\left(\frac{\sigma^{\textrm{ SI, lim}}_{\textrm{ DM}}}{m_{\textrm{ DM}}}\right)_{m_{\textrm{DM}}\rightarrow \infty} = 8.3 \times 10^{-49}\,\textrm{cm}^2 \,\textrm{GeV}^{-1}`, :math:`m_p = 0.938272\,\textrm{GeV}`.
Examples
--------
>>> limit_prefactor(mchi=0.001)
2.1609020839512918e-32
"""
k = 0.23
vbar = 250*np.power(10.0, 5) # cm s^-1
rhodm = 0.3 # GeV cm^-3
mXe = 115.3909 # GeV
mp = 0.938272 # GeV
sigmamchi = 8.3*np.power(10.0, -49) # cm^2 GeV^-1
return k*vbar*rhodm*np.power(mchi + mXe, 2.0)*np.power(mchi + mp, -2.0)*sigmamchi
def get_integral(particle, mchi=0.001, dTN=np.power(10.0, -7), dTchi=np.power(10.0, -3)):
r"""
Computes the integral in the denominator of equation (16) in `1810.10543 <https://arxiv.org/pdf/1810.10543.pdf>`_. Computes it in the case of both the pions and the eta fluxes.
Parameters
----------
particle : str ('pion' or 'dm')
the dm flux being considered
mchi : float
mass of the dark matter particle [:math:`\textrm{GeV}`]
dTN : float
nuclear recoil interval [:math:`\textrm{GeV}`]
dTchi : float
dark matter energy interval [:math:`\textrm{GeV}`]
Returns
-------
integral : float
the result of the double integral [:math:`\textrm{s}^{-1}`]
Notes
-----
We are computing,
.. math:: \int_{T_1}^{T_2}{\textrm{d}T_N\,\int^{\infty}_{T_\chi(T_\chi^{z, \textrm{min}})}{\textrm{d}T_\chi}\,\frac{1}{T^{\textrm{max}}_{r, N}}\frac{\textrm{d}\phi_\chi}{\textrm{d}T_\chi} }
Here :math:`T_1 = 4.9 \,\textrm{keV}` and :math:`40.9\,\textrm{keV}` are the recoil energy bounds in the Xenon 1T detector.
"""
#TzMin(mchi=mchi, TN=TN)
TchiMax = np.power(10.0, 1)
T1 = 4.9*np.power(10.0, -6) # GeV
T2 = 40.9*np.power(10.0, -6) # GeV
kernel, norm = get_kernel(particle)
TNarr = np.arange(T1, T2, dTN)
integral = 0.0
count = 1
print('Starting Integration')
for TN in TNarr:
TchiMin = TzMin(mchi=mchi, TN=TN)
Tchiarr = np.arange(TchiMin, TchiMax, dTchi)
TrNarr = TrNMax(Tchiarr, mchi)
fluxarr = norm*kernel.evaluate(Tchiarr)
farr = fluxarr*np.power(TrNarr, -1.0)
integral += farr.sum()*dTN*dTchi
print('Completed {} out of {} recoil energies'.format(count, len(TNarr)), end='\r')
count += 1
print('\n--------\nResults:\n--------\n\nIntegral: {}, particle = {}\n'.format(integral, particle))
return integral
def get_integral_v2(particle, mchi=0.001, dTchi=np.power(10.0, -3.0), TchiMax=np.power(10.0, 1.0)):
r"""
Computes the integral in the denominator of equation (16) in `1810.10543 <https://arxiv.org/pdf/1810.10543.pdf>`_. Computes it in the case of both the pions and the eta fluxes. [version 2]
Parameters
----------
particle : str ('pion' or 'dm')
the dm flux being considered
mchi : float
mass of the dark matter particle [:math:`\textrm{GeV}`]
dTN : float
nuclear recoil
Returns
-------
integral : float
the result of the double integral
Notes
-----
We are computing,
.. math:: \int_{T_1}^{T_2}{\textrm{d}T_N\,\int^{\infty}_{T_\chi(T_\chi^{z, \textrm{min}})}{\textrm{d}T_\chi}\,\frac{1}{T^{\textrm{max}}_{r, N}}\frac{\textrm{d}\phi_\chi}{\textrm{d}T_\chi} }
Here :math:`T_1 = 4.9 \,\textrm{keV}` and :math:`40.9\,\textrm{keV}` are the recoil energy bounds in the Xenon 1T detector.
"""
TchiMin = TzMin(mchi=mchi)
TchiArr = np.arange(TchiMin, TchiMax, dTchi)
TNBoundArr = TNBound(TchiArr, mchi=mchi)
dTNArr = TNBoundArr - 4.9*np.power(10.0, -6.0)
kernel, norm = get_kernel(particle)
fluxArr = norm*kernel.evaluate(TchiArr)
TrNArr = TrNMax(TchiArr, mchi)
integrands = fluxArr*np.power(TrNArr, -1.0)*dTNArr*dTchi
integral = integrands.sum()
print('\n--------\nResults:\n--------\n\nIntegral: {}, particle = {}\n'.format(integral, particle))
return integral
def TrNMax(Tchi, mchi=0.001):
r"""
Returns the maximum recoil energy using equation (1) in `1810.10543 <https://arxiv.org/pdf/1810.10543.pdf>`_.
Parameters
----------
Tchi : float
kinetic energy of the dark matter particle [:math:`\textrm{GeV}`]
mchi : float
mass of the dark matter particle [:math:`\textrm{GeV}`]
Returns
-------
TrNMax : float or np.array
maximum recoil energy [:math:`\textrm{GeV}`]
Notes
-----
We are implementing the expression,
.. math:: \frac{T_\chi^2 + 2m_\chi T_\chi}{T_\chi + \frac{(m_\chi + m_{\textrm{Xe}})^2}{2m_\textrm{Xe}}}
Examples
--------
>>> TrNMax(Tchi=np.power(10.0, -2), mchi=0.001)
2.0794902474678193e-06
>>> TrNMax(Tchi=np.array([np.power(10.0, -2.0), np.power(10.0, -1.0)]), mchi=0.001)
array([2.07949025e-06, 1.76481427e-04])
"""
mXe = 115.3909 # GeV
num = np.power(Tchi, 2.0) + 2*mchi*Tchi
denom = Tchi + np.power(mchi + mXe, 2.0)*np.power(2*mXe, -1.0)
return num*np.power(denom, -1.0)
def get_xenon_rate(gu=5.0*np.power(10.0, -5.0), ms=0.001, dTN=np.power(10.0, -8.0), dTchi=np.power(10.0, -3.0), TchiMax=np.power(10.0, 0)):
r"""
Computes the rate for the specific hadrophilic model given in `Batell et al. <http://inspirehep.net/record/1708854>`_ in the Xenon 1T detector. Given an exposure time (278.8 days), the number of expected events can be calculated as :math:`\Gamma_N T_{\textrm{exp}}`. The 90% confidence level is then found by comparing this event number to 3.56 events, from Table I in the most recent `Xenon 1T paper <https://arxiv.org/pdf/1805.12562.pdf>`_.
Parameters
----------
gu : float
coupling to up-quark
ms : float
mass of the scalar mediator [:math:`\textrm{GeV}`]
dTN : float
recoil energy integration resolution [:math:`\textrm{GeV}`]
dTchi : float
dark matter energy resolution [:math:`\textrm{GeV}`]
TchiMax : float
maximum dark matter energy [:math:`\textrm{GeV}`]
Returns
-------
gN : float
rate [:math:`\textrm{s}^{-1}`]
Notes
-----
We are implementing the following definition,
.. math:: \Gamma_N = N_T \int_{T_1}^{T_2}{\textrm{d}T_N \, \int_{T_\chi^{\textrm{min}}(T_N)}^{\infty}{\textrm{d}T_\chi \, \epsilon(T_N)\frac{\textrm{d}\phi_\chi}{\textrm{d}T_\chi}\frac{\textrm{d}\sigma_{\chi N}}{\textrm{d}T_N} } }
Examples
--------
>>> get_xenon_rate(gu=5.0*np.power(10.0, -5.0), ms=0.001)
"""
print('-----------------------------------------------------\nXenon 1T Rate Calculator\n-----------------------------------------------------\nComputing rate for gu = {}, ms = {} GeV'.format(gu, ms))
print('dTchi = {}, dTN = {}'.format(dTchi, dTN))
Gdet = 2.529335739713634*np.power(10.0, 25.0) # Assuming Earth is transparent on the lower boundary
mchi = ms/3
gchi = 1.0
A = 131.293
Z = 54.0
NT = 1300/(A*1.66054*np.power(10.0, -27))
epsilon = 0.89
mN = 0.9314941*A
mp = 0.938272 # GeV
mn = 0.939565 # GeV
mu = 0.0022 # GeV
yspp = 0.014*gu*mp/mu
ysnn = 0.012*gu*mn/mu
br = get_branching_ratio(gu=gu, ms=ms)
print('\nBranching Ratio = {}'.format(br))
TchiArr = np.arange(0.015, TchiMax, dTchi)
TNArr = np.arange(4.9*np.power(10.0, -6.0), 40.9*np.power(10.0, -6.0), dTN)
TN, TCHI = np.meshgrid(TNArr, TchiArr)
kernel, norm = get_kernel('eta')
FluxArr = norm*Gdet*br*kernel.evaluate(TchiArr)
_, FLUX = np.meshgrid(TNArr, FluxArr)
prefactor = (np.power(Z*yspp + (A - Z)*ysnn, 2.0)*np.power(gchi, 2.0)/(8*np.pi))
DSIGMATN = prefactor*(2*mN + TN)*(2*np.power(mchi, 2.0) + mN*TN)*np.power(2*mN*TN + np.power(ms, 2.0), -2.0)*form_factor(TN)
DSIGMATCHI = np.power(np.power(TCHI, 2.0) + 2*mchi*TCHI, -1.0)
DSIGMA = DSIGMATN*DSIGMATCHI*np.power(1/(5.06*np.power(10.0, 13)), 2.0)
INTEGRAND = NT*epsilon*DSIGMA*FLUX*np.heaviside(TNBound(TCHI) - TN, 1.0)
gN = INTEGRAND.sum()*dTchi*dTN
events = gN*278.8*86400
print('\n--------\nResults:\n--------\n\nRate: {} per second, Events = {}\n'.format(gN, events))
return gN, events
def form_factor(TN):
r"""
Returns the Helm Form Factor from `0608.035 <https://arxiv.org/pdf/hep-ph/0608035.pdf>`_.
Parameters
----------
TN : np.array
nuclear recoil energy [:math:`\textrm{GeV}`]
Returns
-------
FHsq : np.array
square of the Helm Form Factor
Examples
--------
>>> form_factor(TN=5.0*np.power(10.0, -6.0))
0.7854737713313549
"""
A = 131.293
mN = 0.9314941*A
q = np.sqrt(2*mN*TN)
fmGeV = 1/(1.98*np.power(10.0, -1.0))
s = 0.9*fmGeV
a = 0.52*fmGeV
c = (1.23*np.power(A, 1/3) - 0.60)*fmGeV
R1 = np.sqrt(np.power(c, 2.0) + (7/3)*np.power(np.pi, 2.0)*np.power(a, 2.0) - 5*np.power(s, 2.0))
FHsq = 9*np.exp(-np.power(q*s, 2.0))*np.power(q*R1, -2.0)*np.power(np.sin(q*R1)*np.power(q*R1, -2.0) - np.cos(q*R1)*np.power(q*R1, -1.0), 2.0)
return FHsq
def get_branching_ratio(gu, ms):
r"""
Returns the branching ratio for the process :math:`\eta \rightarrow \pi^0 \chi\chi` from the model in `Batell et al. <https://arxiv.org/pdf/1812.05103.pdf>`_.
Parameters
----------
gu : float
coupling to up-quark
ms : float
scalar mediator mass [:math:`\textrm{GeV}`]
Returns
-------
br : float
branching ratio :math:`\textrm{BR}(\eta \rightarrow \pi^0 \chi\chi)`
Notes
-----
We are implementing the following expression,
.. math:: \textrm(\eta \rightarrow \pi^0 S) = \frac{C^2 g_u^2 B^2}{16\pi m_\eta \Gamma_\eta}\lambda^{1/2}\left(1, \frac{m_S^2}{m_\eta^2}, \frac{m_\pi^2}{m_\eta^2}\right)
where we are assuming a branching ratio :math:`\textrm{BR}(S \rightarrow \chi\chi) = 1`, and :math:`\lambda(a, b, c) = a^2 + b^2 + c^2 - 2(ab + bc + ac)`.
Examples
--------
>>> get_branching_ratio(gu=7*np.power(10.0, -4.0), ms=0.1)
0.05765234404584737
"""
thetapr = -np.pi/9
mpi = 0.1349770 # GeV
meta = 0.547862 # GeV
mu = 0.0022 # GeV
md = 0.0047 # GeV
geta = 1.31*np.power(10.0, -6.0) # GeV
C = np.sqrt(1/3)*np.cos(thetapr) - np.sqrt(2/3)*np.sin(thetapr)
B = np.power(mpi, 2.0)*np.power(mu + md, -1.0)
a = 1.0
b = np.power(ms/meta, 2.0)
c = np.power(mpi/meta, 2.0)
br = np.power(C*gu*B, 2.0)*np.power(16*np.pi*meta*geta, -1.0)*np.sqrt(np.power(a, 2) + np.power(b, 2) + np.power(c, 2) - 2*a*b - 2*b*c - 2*a*c)
return br
def TNBound(Tchi, mchi=0.001, mN=115.3909, Trecoil_max=40.9*np.power(10.0, -6.0)):
r"""
Inverts the relation for the expression in :py:func:`TzMin` to find the maximum recoil energy that can be generated from a dark matter kinetic energy :math:`T_\chi`.
Parameters
----------
Tchi : float
kinetic energies of dark matter particle [:math:`\textrm{GeV}`]
mchi : float
mass of the dark matter particle [:math:`\textrm{GeV}`]
mN : float
mass of the recoil nucleus [:math:`\textrm{GeV}`]
Trecoil_max : float
maximum detector sensitivity for recoil energies [:math:`\textrm{GeV}`]
Returns
-------
TNBound : np.array
maximum recoil energy, either kinematically or by detector sensitivity
Notes
-----
We are implementing the following,
.. math:: T_N^{\textrm{bound}} = \textrm{min}\left[\frac{2(2 m_\chi + T_\chi)m_N T_\chi}{m_\chi^2 + 2m_\chi m_N + m_N^2 + 2m_N T_\chi}, T_N^{\textrm{recoil}}\right]
For Xenon 1T, :math:`T_N^{\textrm{recoil}} = 40.9\,\textrm{keV}` and :math:`m_N = 115.3909\,\textrm{GeV}`.
Examples
--------
>>> TNBound(np.array([0.015, 0.020]), mchi=0.001, mN=115.3909, Trecoil_max=40.9*np.power(10.0, -6.0))
array([4.41853393e-06, 7.62347650e-06])
>>> TNBound(np.array([0.040, 0.050, 0.055]), mchi=0.001, mN=115.3909, Trecoil_max=40.9*np.power(10.0, -6.0))
array([2.90977363e-05, 4.09000000e-05, 4.09000000e-05])
"""
TNBound = 2*mN*Tchi*(2*mchi + Tchi)*np.power(np.power(mchi, 2.0) + 2*mchi*mN + np.power(mN, 2.0) + 2*mN*Tchi, -1.0)
mask = (TNBound > Trecoil_max)
TNBound[mask] = Trecoil_max
return TNBound
def get_miniboone_data():
r"""
Loads miniboone data from miniboone.csv. Data taken from `Batell et al. <http://inspirehep.net/record/1708854>`_.
Returns
-------
mini_df : pd.DataFrame
miniboone dataframe with columns [:math:`m_S` (in GeV), :math:`g_u`]
"""
mini_df = pd.read_csv('miniboone.csv', header=None)
mini_df.columns = ['ms [GeV]', 'gu']
return mini_df
if __name__ == '__main__':
# dTchiArr = np.logspace(-3.0, -2.0)
# eventsarr = np.empty(len(dTchiArr))
# idx = 0
# for dTchi in dTchiArr:
# rate, events = get_xenon_rate(dTchi=dTchi)
# eventsarr[idx] = events
# idx += 1
# plt.figure()
# plt.semilogx(dTchiArr, eventsarr)
# plt.title(r'Rate Integral Convergence')
# plt.xlabel(r'$\Delta T_\chi\,\textrm{[GeV]}$')
# plt.ylabel(r'Events')
# plt.savefig('plots/events.pdf')
# HADROPHILIC PLOT
import matplotlib
plt.rcParams['axes.linewidth'] = 1.75
plt.rcParams['xtick.minor.size'] = 5
plt.rcParams['xtick.major.size'] = 7
plt.rcParams['ytick.minor.size'] = 5
plt.rcParams['ytick.major.size'] = 7
plt.rcParams['xtick.major.width'] = 1.0
plt.rcParams['ytick.major.width'] = 1.0
plt.rcParams['xtick.minor.visible'] = True
plt.rcParams['ytick.minor.visible'] = True
matplotlib.rcParams['text.latex.preamble'] = [r"\usepackage{amsmath}"]
guArr = np.logspace(-5.0, -3.0, 25)
msArr = np.logspace(-3.0, np.log10(0.5), 25)
MS, GU = np.meshgrid(msArr, guArr)
# EVENTS = np.empty([len(guArr), len(msArr)])
# idx = 1
# for i in range(0, len(guArr)):
# for j in range(0, len(msArr)):
# print('Completed {} out of {}'.format(idx, len(msArr)*len(guArr)))
# rate, events = get_xenon_rate(ms=msArr[j], gu=guArr[i])
# EVENTS[i][j] = events
# idx += 1
# np.save('events.npy', EVENTS)
EVENTS = np.load('events.npy')
LZ_EVENTS = (1.0/0.89)*((5.6*1000)/(1.3*278.8))*EVENTS
#print(EVENTS.shape)
event_lim = 3.56
cmap = 'Greens'
from matplotlib import ticker
miniboone_df = get_miniboone_data()
plt.figure()
plt.plot(miniboone_df['ms [GeV]'], miniboone_df['gu'], color='#62656E', lw=1.0)
#plt.contourf(MS, GU, EVENTS, locator=ticker.LogLocator(), cmap=cmap, alpha=1.0)
#plt.colorbar(label='Event Count')
CS = plt.contour(MS, GU, EVENTS, levels=[event_lim], colors=['#D81159'], linewidths=1.0, alpha=1.0, linestyles='-')
plt.contour(MS, GU, LZ_EVENTS, levels=[event_lim], colors=['#D81159'], linewidths=1.0, alpha=1.0, linestyles='--')
mss, guu = CS.allsegs[0][0].T
plt.fill(np.append(miniboone_df['ms [GeV]'], mss[::-1]), np.append(miniboone_df['gu'], guu[::-1]), edgecolor='k', facecolor='#62656E', alpha=0.3, linewidth=0.0)
mss = np.append(mss, np.array([mss[-1], mss[0]]))
guu = np.append(guu, np.array([np.power(10.0, -3), np.power(10.0, -3)]))
plt.fill(mss, guu, edgecolor='k', facecolor='#D81159', alpha=0.3, linewidth=0.0)
plt.xlabel(r'$m_S\,\mathrm{[GeV]}$')
plt.ylabel(r'$g_u$')
#plt.title('Hadrophilic Exclusion Limits')
plt.gca().set_xscale('log')
plt.gca().set_yscale('log')
axes = plt.axis()
plt.axis([np.power(10.0, -3.0), mss.max(), 5*np.power(10.0, -6.0), np.power(10.0, -3.0)])
#plt.scatter(MS, GU, c='k', marker='x', s=4.0)
axes = plt.axis()
plt.text(2*np.power(10.0, -2.0), 1.05*miniboone_df['gu'][0], 'MINIBOONE', fontsize=12, color='#62656E')
plt.text(1.1*axes[0], 5.5*np.power(10.0, -5.0), r'ICRDM ($\eta$) (XENON1T)', fontsize=12, color='#D81159')
plt.text(1.1*axes[0], 2.55*np.power(10.0, -5.0), r'ICRDM ($\eta$) (LZ)', fontsize=12, color='#D81159')
plt.text(np.power(10.0, -2.9), np.power(10.0, -5.1), r'$g_\chi = 1$')
plt.text(np.power(10.0, -2.9), np.power(10.0, -5.25), r'$m_\chi = m_S/3$')
xmin, xmax = axes[0], axes[1]
ymin, ymax = axes[2], axes[3]
xy = (xmin, ymin)
width = xmax - xmin
height = ymax - ymin
import matplotlib.patches as patches
# create the patch and place it in the back of countourf (zorder!)
p = patches.Rectangle(xy, width, height, hatch=4*'/', fill=None, alpha=0.2, zorder=-10)
ax = plt.gca()
ax.tick_params(which='minor', length=4)
#ax.add_patch(p)
plt.savefig('plots/hadrophilic.pdf')
# dTchi = np.power(10.0, -4)
# dtc = 'minus4'
# TchiMaxArr = np.logspace(-1.0, 0.0, 25)
# integrals = np.empty(len(TchiMaxArr))
# idx = 0
# for TchiMax in TchiMaxArr:
# integrals[idx] = get_integral_v2('eta', mchi=0.001, TchiMax=TchiMax, dTchi=dTchi)
# idx += 1
# np.save('TchiMax{}.npy'.format(dtc), TchiMaxArr)
# np.save('integrals{}.npy'.format(dtc), integrals)
# print(integrals)
# plt.semilogx(TchiMaxArr, integrals)
# plt.show()
# plt.figure()
# # dtc = 'minus1'
# # integrals = np.load('integrals{}.npy'.format(dtc))
# # TchiMaxArr = np.load('TchiMax{}.npy'.format(dtc))
# # plt.semilogx(TchiMaxArr, integrals/np.power(10.0, -28.0), label = r'$10^{-1}$')
# dtc = 'minus2'
# integrals = np.load('integrals{}.npy'.format(dtc))
# TchiMaxArr = np.load('TchiMax{}.npy'.format(dtc))
# plt.semilogx(TchiMaxArr, integrals/np.power(10.0, -28.0), label = r'$10^{-2}$')
# dtc = 'minus3'
# integrals = np.load('integrals{}.npy'.format(dtc))
# TchiMaxArr = np.load('TchiMax{}.npy'.format(dtc))
# plt.semilogx(TchiMaxArr, integrals/np.power(10.0, -28.0), label = r'$10^{-3}$')
# dtc = 'minus4'
# integrals = np.load('integrals{}.npy'.format(dtc))
# TchiMaxArr = np.load('TchiMax{}.npy'.format(dtc))
# plt.semilogx(TchiMaxArr, integrals/np.power(10.0, -28.0), label = r'$10^{-4}$')
# plt.title('Flux Integral Convergence')
# plt.xlabel(r'$T_\chi^{\textrm{\small max}}\,\textrm{[GeV]}$')
# plt.ylabel(r'Integral $\textrm{[}10^{-28}\,s^{-1}\textrm{]}$')
# plt.legend(loc='lower right', fontsize=10, title=r'$\Delta T_\chi$', title_fontsize=10)
# plt.savefig('plots/int_convergence.pdf')
from matplotlib import ticker
from scipy.interpolate import interp1d
prefactor = limit_prefactor()
integral = 1.871660096591386*np.power(10.0, -26)
integral = 2.061418762860559*np.power(10.0, -27)
integral = 5.863077015127929*np.power(10.0, -28)
eta_integral = 5.863064797490202*np.power(10.0, -28.0) # get_integral_v2('eta')
pion_integral = 7.190987691388251*np.power(10.0, -27.0) # get_integral_v2('pion')
eta_level = prefactor*np.power(eta_integral, -1.0)
pion_level = prefactor*np.power(pion_integral, -1.0)
LZ_FACTOR = (3.3217544185377643e-47/1757.9236139586912)/(8.3*np.power(10.0, -49))
pion_color = '#01295F'
pion_color = '#29AB87'
eta_color = '#D81159'
pospelov_color = '#FF8514'
# size = 100
# sigmaarray = np.logspace(-34.0, -28.0, size)
# Gdetarr = np.load('Gdetarr.npy')
# sigmaarray = np.append(np.logspace(-40.0, -34.0, size)[:-1], sigmaarray)
# Gdetarr = np.append(np.full(size - 1, Gdetarr[0]), Gdetarr)
#
# GdetFun = interp1d(sigmaarray, Gdetarr, kind='slinear')
# GdetFun = get_Gdet_fit()
# B = np.array([np.power(10.0, -2.0), np.power(10.0, -4.0), np.power(10.0, -6.0), np.power(10.0, -7.0), np.power(10.0, -8.0)])
# labels = np.array([r'$10^{-2}$', r'$10^{-4}$', r'$10^{-6}$', r'$10^{-7}$', r'$10^{-8}$'])
# S = np.logspace(-35.0, -28.0)
# idx = 0
# for br in B:
# plt.loglog(S, S*GdetFun(S)*br, label=labels[idx])
# idx += 1
# plt.plot([S[0], S[-1]], [eta_level, eta_level], 'k--')
# plt.plot([S[0], S[-1]], [pion_level, pion_level], 'k--')
# plt.text(np.power(10.0, -34.8), np.power(10.0, -10.3), 'Eta Flux', fontsize=8)
# plt.text(np.power(10.0, -34.2), np.power(10.0, -11.4), 'Pion Flux', fontsize=8)
# plt.title('Limits for different branching ratios')
# plt.legend(fontsize=9, title=r'$\textrm{BR}(\eta \rightarrow \chi\chi)$', title_fontsize=9, frameon=True, fancybox=True)
# plt.xlabel(r'$\sigma_\chi^{\textrm{\tiny SI}}\,\textrm{[cm}^2\textrm{]}$')
# plt.ylabel(r'$\sigma_\chi^{\textrm{\tiny SI}} G(\sigma_\chi^{\textrm{\tiny SI}})\textrm{BR}(\eta \rightarrow \chi\chi)$')
# plt.savefig('plots/gdetbrs.pdf')
GdetFun = get_Gdet_fit()
lw = 1.0
alpha = 0.3
pospelov_top = 2.411345e-28
pospelov_low = 1.543418e-31
plt.figure()
plt.plot([np.power(10.0, -10.0), np.power(10.0, -1.0)], [pospelov_top, pospelov_top], c=pospelov_color, label=r'Elastic CRDM', lw=lw)
plt.plot([np.power(10.0, -10.0), np.power(10.0, -1.0)], [pospelov_low, pospelov_low], c=pospelov_color, lw=lw)
plt.fill([np.power(10.0, -10.0), np.power(10.0, -1.0), np.power(10.0, -1.0), np.power(10.0, -10.0)], [pospelov_low, pospelov_low, pospelov_top, pospelov_top], edgecolor=pospelov_color, facecolor=pospelov_color, alpha=alpha, linewidth=0.0), #fill=False, hatch=2*'//')
B, S = np.meshgrid(np.logspace(-9.0, np.log10(5*10**(-2)), 10000), np.logspace(-38.0, -28.0, 10000))
C = S*GdetFun(S)*B
CS = plt.contour(B, S, C, levels=[eta_level], colors=[eta_color], linewidths=0.0)
plt.contour(B, S, C, levels=[eta_level*LZ_FACTOR], colors=[eta_color], linewidths=1.0, linestyles='--')
br, sig = CS.allsegs[0][0].T
plt.fill(br, sig, edgecolor='k', facecolor=eta_color, alpha=alpha, linewidth=0.0)
plt.plot(br, sig, c=eta_color, lw=lw, label=r'Inelastic CRDM ($\eta$)')
#plt.plot([br[1], br[-1]], [sig[1], sig[-1]], c=eta_color, lw=1.0)
B, S = np.meshgrid(np.logspace(-10.0, np.log10(6*10**(-4)), 10000), np.logspace(-38.0, -28.0, 10000))
C = S*GdetFun(S)*B
CS = plt.contour(B, S, C, levels=[pion_level], colors=[pion_color], linewidths=0.0)
br, sig = CS.allsegs[0][0].T
plt.fill(br, sig, edgecolor='k', facecolor=pion_color, alpha=alpha, linewidth=0.0)
plt.plot(br, sig, c=pion_color, lw=lw, label=r'Inelastic CRDM ($\pi$)')
plt.plot([br[1], br[-1]], [sig[1], sig[-1]], c=pion_color, lw=lw)
CS = plt.contour(B, S, C, levels=[pion_level*LZ_FACTOR], colors=[pion_color], linewidths=0.0)
br, sig = CS.allsegs[0][0].T
#plt.fill(br, sig, edgecolor='k', facecolor=pion_color, alpha=alpha, linewidth=0.0)
plt.plot(br, sig, c=pion_color, lw=lw, ls='--')
plt.plot([br[1], br[-1]], [sig[1], sig[-1]], c=pion_color, lw=lw, ls='--')
plt.xlabel(r'$\mathrm{BR}(M \rightarrow X\chi\chi)$')
plt.ylabel(r'$\sigma_\chi^{\mathrm{\tiny SI}}\,\mathrm{[cm}^2\mathrm{]}$')
#plt.title(r'Exclusion Limits')
plt.gca().set_xscale('log')
plt.gca().set_yscale('log')
ax = plt.gca()
ax.xaxis.set_major_locator(ticker.LogLocator(numticks=6))
ax.tick_params(which='minor', length=4)
plt.legend(fontsize=14, loc='lower left')
axes = plt.axis()
plt.axis([np.power(10.0, -10.0), 5*np.power(10.0, -3.0), np.power(10.0, -37), np.power(10.0, -27)])
fontsize = 12
plt.text(np.power(10.0, -6.35), np.power(10.0, -28.85), 'XENON1T', color=eta_color, fontsize=fontsize, rotation=-43.0)
plt.text(np.power(10.0, -3.1), np.power(10.0, -34.2), 'LZ', color=eta_color, fontsize=fontsize, rotation=-41.0)
plt.text(np.power(10.0, -7.45), np.power(10.0, -28.85), 'XENON1T', color=pion_color, fontsize=fontsize, rotation=-43.0)
plt.text(np.power(10.0, -8.0), np.power(10.0, -30.15), 'LZ', color=pion_color, fontsize=fontsize, rotation=-47.0)
plt.text(1.1*np.power(10.0, -9.85), np.power(10.0, -30.73), 'XENON1T', color=pospelov_color, fontsize=fontsize, rotation=0.0)
plt.text(np.power(10.0, -9.5), np.power(10.0, -33.9), r'$m_\chi = 1\,\mathrm{MeV}$')
plt.text(np.power(10.0, -9.5), np.power(10.0, -34.4), r'$m_{\mathrm{med}} = 10\,\mathrm{MeV}$')
plt.savefig('plots/limit_no_title.pdf')
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] |
Firmware/Py Scripts/smartmeshsdk-master/libs/SmartMeshSDK/protocols/xivelyConnector/xivelyConnector.py | ECE-412-Capstone-Sensor-Suite/Team-20-Sensor-Suite | 2 | 135401 | #!/usr/bin/python
import logging
class NullHandler(logging.Handler):
def emit(self, record):
pass
log = logging.getLogger('xivelyConnector')
log.setLevel(logging.ERROR)
log.addHandler(NullHandler())
import json
import threading
import socket
import traceback
import re
import time
import copy
import httplib
from SmartMeshSDK.utils import FormatUtils
#============================ exceptions ======================================
class xivelyError(Exception):
pass
class xivelyRateError(xivelyError):
pass
class xivelyTimeoutError(xivelyError):
pass
#============================ helpers =========================================
class xivelyStats(object):
RATE_WINDOW_MIN = 3
SOCKET_TX = 'SOCKET_TX'
SOCKET_TX_OK = 'SOCKET_TX_OK'
SOCKET_TX_TIMEOUT = 'SOCKET_TX_TIMEOUT'
SOCKET_RX = 'SOCKET_RX'
HTTP_TX = 'HTTP_TX'
HTTP_TX_OK = 'HTTP_TX_OK'
HTTP_TX_RATELIMIT = 'HTTP_TX_RATELIMIT'
HTTP_TX_ERROR = 'HTTP_TX_ERROR'
#======================== singleton pattern ===============================
_instance = None
_init = False
def __new__(cls, *args, **kwargs):
if not cls._instance:
log.info("Creating xivelyStats instance")
cls._instance = super(xivelyStats, cls).__new__(cls, *args, **kwargs)
return cls._instance
#======================== init ============================================
def __init__(self):
# don't re-initialize an instance (needed because singleton)
if self._init:
return
self._init = True
# store params
# local variables
self.dataLock = threading.RLock()
self.reset()
#======================== public ==========================================
def increment(self,statName):
with self.dataLock:
if statName not in self.stats:
self.stats[statName] = 0
self.stats[statName] += 1
if statName in [self.SOCKET_TX,self.SOCKET_RX,self.HTTP_TX]:
self.numTransactions += 1
self.rateBuf += [time.time()]
self._cleanupRateBuf()
if statName in [self.HTTP_TX_RATELIMIT]:
try:
self.rateBuf.pop()
except IndexError:
pass
def getNumTransactions(self):
with self.dataLock:
return self.numTransactions
def getRate(self):
with self.dataLock:
self._cleanupRateBuf()
return len(self.rateBuf)/float(self.RATE_WINDOW_MIN)
def getStats(self):
with self.dataLock:
return copy.deepcopy(self.stats)
def reset(self):
with self.dataLock:
self.numTransactions = 0
self.rateBuf = []
self.stats = {}
#======================== private =========================================
def _cleanupRateBuf(self):
now = time.time()
with self.dataLock:
while self.rateBuf and (self.rateBuf[0]<(now-self.RATE_WINDOW_MIN*60)):
self.rateBuf.pop(0)
class xivelySubscriber(threading.Thread):
XIVELY_SOCKETSERVER_HOST = 'api.xively.com'
XIVELY_SOCKETSERVER_PORT = 8081
XIVELY_SUBSCRIPTION_TIMEOUT = 10
XIVELY_KEEPALIVE_TIMEOUT = (30*60) # in s
#======================== thread ==========================================
def __init__(self):
# store params
# local variables
self.dataLock = threading.Lock()
self.stats = xivelyStats()
self.keepaliveTimer = None
self.subscriptions = {}
self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_KEEPALIVE, 1)
self.socket.connect(
(
self.XIVELY_SOCKETSERVER_HOST,
self.XIVELY_SOCKETSERVER_PORT,
)
)
# reset keepalive timer
self._resetKeepaliveTimer()
# initialize parent
threading.Thread.__init__(self)
self.name = 'xivelySubscriber'
# log
log.info("creating {0} instance".format(self.name))
# start myself
self.start()
def run(self):
# log
log.info("{0} thread starts".format(self.name))
try:
while True:
# read from socket
try:
dataRaw = self.socket.recv(1024)
except socket.error:
# log
log.info('Stopping thread {0}, error when reading socket'.format(self.name))
# stop thread
return
if not dataRaw:
# log
log.info('Stopping thread {0}, error no data received'.format(self.name))
# stop thread
return
# update stats
self.stats.increment(xivelyStats.SOCKET_RX)
# reset keepalive timer
self._resetKeepaliveTimer()
# convert to JSON
data = json.loads(dataRaw)
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['xivelySubscriber received data: {0}'.format(data)]
output = '\n'.join(output)
log.debug(output)
# no 'resource' field in data when sending keepalive
if not 'resource' in data:
continue
# retrieve feedId and datastream
m = re.search('/feeds/(\S+)/datastreams/(\S+)', data['resource'])
feedId = int(m.group(1))
datastream = str(m.group(2))
if sorted(data.keys())==sorted(['status','resource']):
# this is a reply to a subscribe/unsubscribe command
# release subscriptionEvent
if data['status']==200:
with self.dataLock:
self.subscriptions[(feedId,datastream)]['subscriptionEvent'].set()
else:
log.warning("received response code {0} for subscription/unsubscription".format(
data['status']
)
)
elif sorted(data.keys())==sorted(['body','resource']):
# this is a new data notification
# get the mac and callback
with self.dataLock:
mac = self.subscriptions[(feedId,datastream)]['mac']
callback = self.subscriptions[(feedId,datastream)]['callback']
# retrieve value
if 'current_value' in data['body']:
value = str(data['body']['current_value'])
try:
value = int(value)
except Exception as err:
try:
value = float(value)
except Exception:
pass
# call the callback
try:
callback(
mac = mac,
datastream = datastream,
value = value,
)
except:
output = []
output += ['exception when calling callback for mac={0} datastream={1}'.format(mac,datastream)]
output += ['\nerror:\n']
output += [type(err)]
output += [str(err)]
output += ['\ncall stack:\n']
output += [traceback.format_exc()]
output = '\n'.join(output)
log.error(output)
except Exception as err:
output = []
output += ['===== crash in thread {0} ====='.format(self.name)]
output += ['\nerror:\n']
output += [str(type(err))]
output += [str(err)]
output += ['\ncall stack:\n']
output += [traceback.format_exc()]
output = '\n'.join(output)
print output # critical error
log.critical(output)
raise
#======================== public ==========================================
def subscribe(self,apiKey,mac,feedId,datastream,callback):
# log
log.info(
" subscribing to {0}/{1}".format(
FormatUtils.formatMacString(mac),
datastream,
)
)
# validate params
with self.dataLock:
if (feedId,datastream) in self.subscriptions:
raise ValueError("Subscription to {0}:{1} already exists".format(feedId,datastream))
# add to subscriptions
event = threading.Event()
with self.dataLock:
self.subscriptions[(feedId,datastream)] = {
'mac': mac,
'subscriptionEvent': threading.Event(),
'callback': callback,
}
# prepare
method = 'subscribe'
resource = '/feeds/{0}/datastreams/{1}'.format(feedId,datastream)
headers = {
'X-ApiKey' : apiKey,
}
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['{0} send:'.format(self.name)]
output += ['- method : {0}'.format(method)]
output += ['- resource : {0}'.format(resource)]
output += ['- headers : {0}'.format(headers)]
output = '\n'.join(output)
log.debug(output)
# send
self.socket.send(
json.dumps(
{
'method' : method,
'resource' : resource,
'headers' : headers,
}
)
)
# update stats
self.stats.increment(xivelyStats.SOCKET_TX)
# reset keepalive timer
self._resetKeepaliveTimer()
# wait for reply
if not self.subscriptions[(feedId,datastream)]['subscriptionEvent'].wait(
self.XIVELY_SUBSCRIPTION_TIMEOUT
):
# timeout
# update stats
self.stats.increment(xivelyStats.SOCKET_TX_TIMEOUT)
# subscription failed, delete from subscriptions
del self.subscriptions[(feedId,datastream)]
# raise error
raise xivelyTimeoutError("timeout to subscribe to {0}:{1}".format(feedId,datastream))
else:
# update stats
self.stats.increment(xivelyStats.SOCKET_TX_OK)
# log
log.info(
"successfully subscribed to {0}/{1}".format(
FormatUtils.formatMacString(mac),
datastream,
)
)
# remove subscriptionEvent
with self.dataLock:
self.subscriptions[(feedId,datastream)]['subscriptionEvent'] = None
def unsubscribe(self,apiKey,mac,feedId,datastream):
# log
log.info("unsubscribing from {0}/{1}".format(
FormatUtils.formatMacString(mac),
datastream,
)
)
# validate params
with self.dataLock:
if (feedId,datastream) not in self.subscriptions:
raise ValueError("No active subscription to {0}:{1}".format(feedId,datastream))
if self.subscriptions[(feedId,datastream)]['subscriptionEvent']:
raise SystemError("Ongoing subscription/unsubscription process to {0}:{1}".format(feedId,datastream))
# add to event subscriptions
with self.dataLock:
self.subscriptions[(feedId,datastream)]['subscriptionEvent'] = threading.Event()
# send
self.socket.send(
json.dumps(
{
'method' : 'unsubscribe',
'resource' : '/feeds/{0}/datastreams/{1}'.format(feedId,datastream),
'headers' :
{
'X-ApiKey' : apiKey,
},
}
)
)
# update stats
self.stats.increment(xivelyStats.SOCKET_TX)
# reset keepalive timer
self._resetKeepaliveTimer()
# wait for reply
if not self.subscriptions[(feedId,datastream)]['subscriptionEvent'].wait(
self.XIVELY_SUBSCRIPTION_TIMEOUT
):
# timeout
# remove subscriptionEvent
with self.dataLock:
self.subscriptions[(feedId,datastream)]['subscriptionEvent'] = None
# update stats
self.stats.increment(xivelyStats.SOCKET_TX_TIMEOUT)
# raise error
raise xivelyTimeoutError("timeout to unsubscribe from {0}:{1}".format(feedId,datastream))
else:
# successful reply received
# update stats
self.stats.increment(xivelyStats.SOCKET_TX_OK)
# unsubscription succeeded, delele from subscriptions
del self.subscriptions[(feedId,datastream)]
def close(self):
# log
log.info("closing {0}".format(self.name))
# cancel the keepalive timer
if self.keepaliveTimer:
self.keepaliveTimer.cancel()
# shut down the socket. Will cause self.socket.recv() to return ''.
self.socket.shutdown(socket.SHUT_RDWR)
#======================== private =========================================
def _resetKeepaliveTimer(self):
if self.keepaliveTimer:
self.keepaliveTimer.cancel()
self.keepaliveTimer = threading.Timer(self.XIVELY_KEEPALIVE_TIMEOUT, self._keepalive)
self.keepaliveTimer.start()
def _keepalive(self):
# log
if log.isEnabledFor(logging.DEBUG):
log.debug("sending TCP keepalive to Xively")
# send dummy data over TCP socket to force TCP to stay open
try:
self.socket.send('keepalive')
except Exception as err:
# log
log.error(
"Error while sending keepalive to Xively: [{0}] {1}".format(
type(err),
err,
)
)
# close
self.close()
else:
# reset keepalive timer
self._resetKeepaliveTimer()
class xivelyClient(object):
XIVELY_HOST = 'api.xively.com'
XIVELY_BASE_URL = '/v2/'
XIVELY_HTTP_TIMEOUT_S = 10.0
def __init__(self,apiKey):
# store params
self.apiKey = apiKey
self.stats = xivelyStats()
#======================== public ==========================================
def get(self,url,body=''):
return self._request(
method = 'GET',
url = url,
body = body,
)
def post(self,url,body=''):
return self._request(
method = 'POST',
url = url,
body = body,
)
def put(self,url,body=''):
return self._request(
method = 'PUT',
url = url,
body = body,
)
def delete(self,url,body=''):
return self._request(
method = 'DELETE',
url = url,
body = body,
)
#======================== private =========================================
def _request(self,method,url,body):
# create connection to Xively server
connection = httplib.HTTPSConnection(
host = self.XIVELY_HOST,
timeout = self.XIVELY_HTTP_TIMEOUT_S,
)
# send the request
try:
args = {
'method': method,
'url': self.XIVELY_BASE_URL+url,
'headers': {
'X-ApiKey': self.apiKey,
'Content-Type': 'application/json',
},
}
if body:
args['body'] = json.dumps(body)
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['_request() called with:']
for (k,v) in args.items():
output += ['- {0:<20}: {1}'.format(k,v)]
output = '\n'.join(output)
log.debug(output)
# send the request
connection.request(**args)
# update stats
self.stats.increment(xivelyStats.HTTP_TX)
# parse response
response = connection.getresponse()
responseBody = response.readUI().strip()
if responseBody:
responseBody = json.loads(responseBody)
else:
responseBody = None
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['response:']
output += ['- status : {0}'.format(response.status)]
output += ['- reason : {0}'.format(response.reason)]
output += ['- body : {0}'.format(responseBody)]
output = '\n'.join(output)
log.debug(output)
# raise returned error code
if response.status not in [httplib.OK,httplib.CREATED]:
output = '{0} {1} response.status={2}'.format(
method,
url,
response.status,
)
log.error(output)
if response.status ==httplib.FORBIDDEN and responseBody['title']=='Rate too fast':
# update stats
self.stats.increment(xivelyStats.HTTP_TX_RATELIMIT)
# raise
raise xivelyRateError(output)
else:
# update stats
self.stats.increment(xivelyStats.HTTP_TX_ERROR)
# raise
raise SystemError(output)
# update stats
self.stats.increment(xivelyStats.HTTP_TX_OK)
return responseBody
finally:
# close the connection
connection.close()
class xivelyConnector(object):
def __init__(self,apiKey,productName,productDesc):
assert type(apiKey)==str
assert type(productName)==str
assert type(productDesc)==str
# log
log.info("Creating xivelyConnector instance")
# store params
self.apiKey = apiKey
self.productName = productName
self.productDesc = productDesc
self.datastreams = {} # a cache on the datastreams currently on each device
self.dataLock = threading.RLock()
self.subscriber = None
# local variables
self.client = None
self.product_id = None
self.stats = xivelyStats()
self.xivelyDevices = []
# connect
self._connect()
self.product_id = self.getProductId()
self._retrieveXivelyDevices()
#======================== public ==========================================
def createDatastream(self,mac,datastream):
'''
\brief Create a particular datastream on a device, or the one that
already exists on Xively.
\return The feed_id.
'''
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['createDatastream() called with:']
output += ['- mac {0}'.format(mac)]
output += ['- datastream {0}'.format(datastream)]
output = '\n'.join(output)
log.debug(output)
if type(mac) in [tuple,list]:
mac = FormatUtils.formatMacString(mac)
assert self.product_id
# retrieve device's information (create if needed)
dev = None
while dev is None:
# get device, if exists
with self.dataLock:
for d in self.xivelyDevices:
if d['serial']==mac:
dev = d
break
# create mote
if dev is None:
# log
log.info("Creating mote {0}".format(mac))
# create
response = self.client.post(
url = 'products/{0}/devices'.format(
self.product_id,
),
body = {
'devices': [
{"serial": mac}
]
},
)
# update list of devices
self._retrieveXivelyDevices()
isActive = (dev['activated_at'] is not None)
activation_code = dev['activation_code']
# activate mote if needed
if not isActive:
# log
log.info("Activating mote {0}".format(mac))
# activate
self.client.get(
url = 'devices/{0}/activate'.format(
activation_code,
),
)
# update list of devices
self._retrieveXivelyDevices()
# get datastreams
if mac not in self.datastreams:
# log
log.info(" retrieving datastreams of {0}".format(mac))
# retrieve
response = self.client.get(
url = 'feeds/{0}.json'.format(
dev['feed_id'],
),
)
if 'datastreams' in response:
self.datastreams[mac] = [ds['id'] for ds in response['datastreams']]
else:
self.datastreams[mac] = []
# create datastream
if datastream not in self.datastreams[mac]:
# log
log.info("Create datastream {0} on mote {1}".format(
datastream,
mac,
)
)
# create
self.client.put(
url = 'feeds/{0}.json'.format(
dev['feed_id'],
),
body = {
'version': '1.0.0',
'datastreams': [
{
'id' : datastream,
},
]
},
)
# cache
self.datastreams[mac] += [datastream]
return dev['feed_id']
def publish(self,mac,datastream,value):
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['publish() called with:']
output += ['- mac {0}'.format(FormatUtils.formatMacString(mac))]
output += ['- datastream {0}'.format(datastream)]
output += ['- value {0}'.format(value)]
output = '\n'.join(output)
log.debug(output)
# verify subscriber alive, if exists
if self.subscriber:
if not self.subscriber.isAlive():
self.subscriber = None
raise SystemError("subscriber is not alive during publish()")
# create the datastream (or retrieve feed_id if exists)
feed_id = self.createDatastream(mac,datastream)
# publish to datastream
self.client.put(
url = 'feeds/{0}.json'.format(
feed_id,
),
body = {
'version': '1.0.0',
'datastreams': [
{
'id' : datastream,
'current_value': value,
},
]
},
)
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['published to Xively:']
output += ['- mac {0}'.format(FormatUtils.formatMacString(mac))]
output += ['- datastream {0}'.format(datastream)]
output += ['- value {0}'.format(value)]
output = '\n'.join(output)
log.debug(output)
def subscribe(self,mac,datastream,callback):
feedId = self.mac2feedId(mac)
if not feedId:
raise ValueError("unknown MAC {0}".format(mac))
# create subscriber if needed
if not self.subscriber:
self.subscriber = xivelySubscriber()
# verify subscriber alive
if not self.subscriber.isAlive():
self.subscriber = None
raise SystemError("subscriber is not alive during subscribe()")
# subscribe
self.subscriber.subscribe(self.apiKey,mac,feedId,datastream,callback)
def unsubscribe(self,mac,datastream):
feedId = self.mac2feedId(mac)
if not feedId:
raise ValueError("unknown MAC {0}".format(mac))
if not self.subscriber:
raise SystemError("no subscriber")
# verify subscriber alive
if not self.subscriber.isAlive():
self.subscriber = None
raise SystemError("subscriber is not alive during subscribe()")
# unsubscribe
self.subscriber.unsubscribe(self.apiKey,mac,feedId,datastream)
def mac2feedId(self,mac):
if type(mac) in [tuple,list]:
mac = FormatUtils.formatMacString(mac)
returnVal = None
with self.dataLock:
for d in self.xivelyDevices:
if d['serial']==mac:
returnVal = d['feed_id']
break
return returnVal
def feedId2mac(self,feedId):
returnVal = None
with self.dataLock:
for d in self.xivelyDevices:
if d['feed_id']==feedId:
returnVal = d['serial']
break
return returnVal
def getProductId(self):
'''
\brief Retrieve the product ID. Creates product if necessary.
'''
with self.dataLock:
if self.product_id:
return self.product_id
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['Get productID for:']
output += ['- name: \"{0}\"'.format(self.productName)]
output += ['- description: \"{0}\"'.format(self.productDesc)]
output = '\n'.join(output)
log.debug(output)
product_id = None
while product_id is None:
# retrieve list of current products
response = self.client.get(
url = 'products.json',
)
# retrieve product ID if exists
for product in response['products']:
if (product['name']==self.productName and product['description']==self.productDesc):
if product_id:
raise SystemError(
"More than one product with name \"{0}\" and description \"{1}\"".format(
self.productName,
self.productDesc,
)
)
product_id = product['product_id']
# create product if needed
if product_id is None:
# log
log.info("Creating product")
# create
self.client.post(
url = 'products',
body = {
'product': {
'name': self.productName,
'description': self.productDesc,
}
}
)
# log
if log.isEnabledFor(logging.DEBUG):
log.debug("product_id: \"{0}\"".format(product_id))
return product_id
def deleteProduct(self):
'''
\brief Deletes the product from Xively.
\warning Calling this function will delete all the devices and
associated data.
'''
assert self.product_id
# log
log.info("Deleting product \"{0}\"".format(self.product_id))
# delete
self.client.delete(
url = 'products/{0}'.format(
self.product_id,
),
)
# record
with self.dataLock:
self.product_id = None
self.xivelyDevices = []
def close(self):
try:
self.subscriber.close()
except AttributeError:
pass # happens when no subscriber
self.subscriber = None
#======================== private =========================================
def _connect(self):
'''
\brief Connect to Xively.
'''
# log
log.info("Connecting to Xively service")
# record
with self.dataLock:
self.client = xivelyClient(self.apiKey)
def _retrieveXivelyDevices(self):
'''
\brief Retrieve the list of devices currently on Xively.
'''
assert self.product_id
PER_PAGE = 10
# log
if log.isEnabledFor(logging.DEBUG):
log.debug("Retrieving devices")
duplicates = True
while duplicates:
# gather all devices
xivelyDevices = []
doneListing = False
page = 1
while not doneListing:
# get next devices
response = self.client.get(
url = 'products/{0}/devices?per_page={1}&page={2}'.format(
self.product_id,
PER_PAGE,
page,
)
)
# add to local devices
xivelyDevices += response['devices']
# decide whether next
if len(response['devices'])<PER_PAGE:
doneListing=True
else:
page += 1
# make sure no duplicates
serials = [d['serial'] for d in xivelyDevices]
duplicateSerials = [s for s in serials if serials.count(s)>1]
if duplicateSerials:
duplicates = True
else:
duplicates = False
# log
if log.isEnabledFor(logging.DEBUG):
output = []
output += ['Found {0} devices:'.format(len(xivelyDevices))]
for d in xivelyDevices:
output += ['- {0}'.format(d['serial'])]
output = '\n'.join(output)
log.debug(output)
# record
with self.dataLock:
self.xivelyDevices = xivelyDevices
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1962,
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1962,
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6024,
2751,
29922,
8095,
297,
3244,
426,
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29913,
1275,
1360,
2433,
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1962,
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1962,
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1962,
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1962,
396,
12187,
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13,
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1480,
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4905,
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13,
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12020,
13,
268,
13,
1678,
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4936,
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304,
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1980,
13,
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1741,
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292,
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2624,
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13,
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411,
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1272,
16542,
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13,
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1491,
7588,
1980,
15625,
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1204,
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4130,
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1633,
4638,
353,
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13,
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525,
8628,
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13,
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1158,
268,
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29915,
13,
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6503,
259,
353,
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1725,
5779,
19248,
29900,
6822,
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1633,
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29913,
4286,
4830,
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13,
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9066,
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13,
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29990,
29899,
11713,
2558,
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584,
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2558,
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13,
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500,
13,
308,
13,
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396,
1480,
13,
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565,
1480,
29889,
275,
10861,
2831,
29898,
21027,
29889,
18525,
1125,
13,
9651,
1962,
418,
353,
5159,
13,
9651,
1962,
268,
4619,
6024,
29912,
29900,
29913,
3638,
29901,
4286,
4830,
29898,
1311,
29889,
978,
4638,
13,
9651,
1962,
268,
4619,
6024,
29899,
1158,
795,
584,
426,
29900,
29913,
4286,
4830,
29898,
5696,
4638,
13,
9651,
1962,
268,
4619,
6024,
29899,
6503,
9651,
584,
426,
29900,
29913,
4286,
4830,
29898,
10314,
4638,
13,
9651,
1962,
268,
4619,
6024,
29899,
9066,
632,
584,
426,
29900,
29913,
4286,
4830,
29898,
13662,
4638,
13,
9651,
1962,
418,
353,
11297,
29876,
4286,
7122,
29898,
4905,
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13,
9651,
1480,
29889,
8382,
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4905,
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13,
308,
13,
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396,
3638,
29871,
13,
4706,
1583,
29889,
11514,
29889,
6717,
29898,
13,
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4390,
29889,
29881,
17204,
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13,
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13,
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29915,
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1158,
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13,
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10314,
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13,
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584,
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9066,
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13,
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500,
13,
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13,
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1723,
13,
308,
13,
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396,
2767,
22663,
13,
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1583,
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16202,
29889,
25629,
29898,
29916,
3598,
25060,
29889,
6156,
7077,
2544,
29918,
28627,
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13,
308,
13,
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396,
10092,
3013,
284,
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12237,
13,
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3032,
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9598,
1022,
284,
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396,
4480,
363,
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13,
4706,
565,
451,
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1583,
29889,
1491,
7588,
1980,
15625,
18798,
1204,
29892,
4130,
579,
1633,
4638,
1839,
1491,
22371,
2624,
13359,
10685,
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13,
462,
1678,
1583,
29889,
29990,
5667,
6670,
29979,
29918,
20633,
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24290,
2725,
29918,
15307,
12015,
13,
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13,
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13,
632,
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13,
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3598,
25060,
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12015,
29897,
13,
632,
13,
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396,
25691,
5229,
29892,
5217,
515,
21696,
1980,
13,
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628,
1583,
29889,
1491,
7588,
1980,
15625,
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4130,
579,
1633,
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12020,
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12020,
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29889,
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2544,
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28627,
29918,
8949,
29897,
13,
632,
13,
9651,
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] |
aiolifx/products.py | bdraco/aiolifx | 0 | 128378 | product_map = { 1: 'Original 1000',
3: 'Color 650',
10: 'White 800 (Low Voltage)',
11: 'White 800 (High Voltage)',
18: 'White 900 BR30 (Low Voltage)',
20: 'Color 1000 BR30',
22: 'Color 1000',
27: 'LIFX A19',
28: 'LIFX BR30',
29: 'LIFX+ A19',
30: 'LIFX+ BR30',
31: 'LIFX Z',
32: 'LIFX Z 2',
36: 'LIFX Downlight',
37: 'LIFX Downlight',
38: 'LIFX Beam',
43: 'LIFX A19',
44: 'LIFX BR30',
45: 'LIFX+ A19',
46: 'LIFX+ BR30',
49: 'LIFX Mini',
50: 'LIFX Mini Day and Dusk',
51: 'LIFX Mini White',
52: 'LIFX GU10',
55: 'LIFX Tile',
59: 'LIFX Mini Color',
60: 'LIFX Mini Day and Dusk',
61: 'LIFX Mini White'}
features_map = { 1: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
3: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
10: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 6500,
'min_kelvin': 2700,
'multizone': False},
11: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 6500,
'min_kelvin': 2700,
'multizone': False},
18: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
20: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
22: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
27: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
28: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
29: { 'chain': False,
'color': True,
'infrared': True,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
30: { 'chain': False,
'color': True,
'infrared': True,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
31: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': True},
32: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': True},
36: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
37: { 'chain': False,
'color': True,
'infrared': False,
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'min_kelvin': 2500,
'multizone': False},
38: { 'chain': False,
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'multizone': True},
43: { 'chain': False,
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'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
44: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
45: { 'chain': False,
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'infrared': True,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
46: { 'chain': False,
'color': True,
'infrared': True,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
49: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
50: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 4000,
'min_kelvin': 1500,
'multizone': False},
51: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 2700,
'min_kelvin': 2700,
'multizone': False},
52: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
55: { 'chain': True,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
59: { 'chain': False,
'color': True,
'infrared': False,
'max_kelvin': 9000,
'min_kelvin': 2500,
'multizone': False},
60: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 4000,
'min_kelvin': 1500,
'multizone': False},
61: { 'chain': False,
'color': False,
'infrared': False,
'max_kelvin': 2700,
'min_kelvin': 2700,
'multizone': False}}
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src/GaussianDistributionGenerator.py | jachallbound/kmeans_python | 0 | 129917 | <reponame>jachallbound/kmeans_python<filename>src/GaussianDistributionGenerator.py
import numpy as np
import numpy.random as npr
def generate_gaussian_data(D,
ndim,
samples,
priors = None,
means = None,
covs = None
):
"""Generate D N-dimension Gaussian Distributions of size samples.
Parameters:
---
D: *int*
Amount of IID Gaussian clusters to create.
When used in K-Means, should probably be equal to K.
ndim: *int*
Dimensionality of data.
Returned data with be ndim arrays of sample data (in a matrix)
samples: *int*
Amount of data samples per Gaussian cluster.
priors: *array_like*
D element array of priors of corresponding Gaussian distributions.
Default: random values from a Dirichlet distribution.
means: *array_like*
D by ndim matrix of means.
Default: rand(ndim) values times randint(1,high=10) (D times)
covs = *array_like*
D by ndim by ndim matrix of covariances.
Default: rand(ndim,ndim) values times randint(1,high=3) (D times)
This can lead to 'not positive-semidefinite' covs for high ndim.
Returns:
---
gaussian_dict: *dict*
Dictionary of data, labels, and Gaussian distribution parameters.
"""
"Generate distribution parameters if they weren't passed"
if priors == None:
priors = npr.dirichlet(np.ones(D), size=1)
if means == None:
means = np.array([npr.rand(ndim)*npr.randint(10,high=200) for i in range(D)])
if covs == None:
covs = np.array([npr.rand(ndim,ndim)*npr.randint(10, high=30) for i in range(D)])
"Fix not positive-semidefinite covariance matrices"
"https://stackoverflow.com/questions/41515522/numpy-positive-semi-definite-warning"
for d in range(D):
min_eigen_value = np.min(np.real(np.linalg.eigvals(covs[d, ])))
if min_eigen_value < 0:
covs[d, ] -= 10*min_eigen_value * np.eye(*covs[d, ].shape)
"Distribution Parameters"
# Number of means to search for, also number of distributions to create
priors_csum = np.r_[0, np.cumsum(priors)]
pd = npr.rand(samples) # Decision vector for choosing observed data based on priors
"Data matrix"
data_D = np.zeros((D,ndim,samples)) # All distribution data
data = np.zeros((ndim,samples)) # Observation data
labels = np.zeros((samples), dtype=int)
"Generate K Gaussian Distributions"
for d in range(D):
data_D[d, ] = npr.multivariate_normal(means[d, ], covs[d, ], size=samples).T
observation = np.logical_and(pd>priors_csum[d], pd<=priors_csum[d+1])
data = np.where(observation, data_D[d, ], data)
labels = np.where(observation, d, labels)
gaussian_dict = {'data': data,
'labels': labels,
'data_D': data_D,
'priors': priors,
'means': means,
'covs': covs}
return gaussian_dict
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research/active_learning/_selection_methods.py | joaopfonseca/research | 1 | 110558 | <gh_stars>1-10
"""
Uncertainty estimators to be used along with the ALWrapper object. All functions must be
set up so that a higher value means higher uncertainty (higher likelihood of selection)
and vice-versa.
"""
import numpy as np
def breaking_ties(probabilities):
"""Breaking Ties uncertainty measurement. The output is scaled and reversed."""
probs_sorted = np.sort(probabilities, axis=1)[:, ::-1]
# The extra minus is redundant but I kept as a highlight of the change in the
# original formula.
bt = -(probs_sorted[:, 0] - probs_sorted[:, 1])
return bt
UNCERTAINTY_FUNCTIONS = {
"entropy": (
lambda probabilities: (-probabilities * np.log2(probabilities)).sum(axis=1)
),
"breaking_ties": breaking_ties,
"random": (
lambda probabilities: np.ones(probabilities.shape[0]) / probabilities.shape[0]
),
}
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modules/client/gui/optsmenu.py | bullseyestudio/guns-game | 0 | 66909 | <gh_stars>0
from modules.client.gui import common
from modules.pgu import gui as pgui
from modules.client import config
hgt = common.screen.get_height()
t = pgui.Table(width=common.screen.get_width(), height=hgt)
t.tr()
t.td(pgui.Spacer(width=150, height=80), colspan=2)
t.tr()
t.td(pgui.Label('Guns-game.com username', height=30))
input_user = pgui.Input(value=config.cp.get('auth', 'canonical_name'))
t.td(input_user)
t.tr()
t.td(pgui.Label('More options later...', height=30), colspan=2)
t.tr()
t.td(pgui.Spacer(width=150, height=hgt - (2 * 80) - (3 * 40)), colspan=2)
t.tr()
backbtn = pgui.Button('Back to Main Menu', width=150, height=30)
t.td(backbtn)
savebtn = pgui.Button('Save Settings', width=150, height=30)
t.td(savebtn)
t.tr()
t.td(pgui.Label('(without saving)'))
t.td(pgui.Label('(and back to main menu)'))
t.tr()
t.td(pgui.Spacer(width=150, height=40), colspan=2)
initialized = False
def init():
global initialized
if initialized:
return
global backbtn, savebtn
backbtn.connect(pgui.CLICK, hide)
savebtn.connect(pgui.CLICK, save_and_hide)
initialized = True
def show():
init()
global input_user
input_user = pgui.Input(value=config.cp.get('auth', 'canonical_name'))
common.draw_background()
common.pguapp.init(t, common.screen)
def hide():
import mainmenu
mainmenu.show()
def save_and_hide():
config.cp.set('auth', 'canonical_name', input_user.value)
# TODO: Save the rest of the config settings
hide()
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] |
app.py | AmirValeev/auto-ml-classifier | 0 | 2022 | <reponame>AmirValeev/auto-ml-classifier<gh_stars>0
import os, ast
import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
import pickle
def main():
# Get the dataset from the users GitHub repository
dataset_path = "https://raw.githubusercontent.com/" + os.environ["GITHUB_REPOSITORY"] + "/master/dataset.csv"
data = pd.read_csv(dataset_path)
print()
print(data.describe())
x=data.iloc[:,:-1]
y=data.iloc[:,-1]
column_trans = make_column_transformer((OneHotEncoder(),[-1]),remainder='passthrough') # apply encoding on output variable
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state=0)
#define a pipeline
pipe = make_pipeline(column_trans,SVC())
pipe.fit(x_train,y_train) #training the model
print("\nModel Training Finished")
accuracy = pipe.score(x_test,y_test)
print("\nAccuracy of the Model: "+str(accuracy*100))
if pipe:
pickle.dump(pipe,open('model.pkl','wb')) # store the artifact in docker container
if not os.environ["INPUT_MYINPUT"] == 'zeroinputs':
inputs = ast.literal_eval(os.environ["INPUT_MYINPUT"])
print("\nThe Predicted Ouput is :")
output = pipe.predict([inputs])
print(output)
else:
output = ["None"]
print("\nUser didn't provided inputs to predict")
print("\n=======================Action Completed========================")
print(f"::set-output name=myOutput::{output[0]}")
if __name__ == "__main__":
main()
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formfactory/templatetags/formfactory_tags.py | AltusBarry/django-formfactory | 0 | 102455 | from django import template
from django.db.models import Model
from django.http import Http404
from django.urls import reverse, resolve
from formfactory import models
register = template.Library()
@register.tag()
def render_form(parser, token):
"""{% render_form <form_slug> %}"""
tokens = token.split_contents()
if len(tokens) != 2:
raise template.TemplateSyntaxError(
"{% render_form <form_slug>/<object> %}"
)
return RenderFormNode(variable=tokens[1])
class RenderFormNode(template.Node):
def __init__(self, variable):
self.variable = template.Variable(variable)
def render(self, context):
try:
variable = self.variable.resolve(context)
except template.VariableDoesNotExist:
variable = self.variable.var
default_msg = "No FormFactory Form matches the given query. %s" % self.variable
# If the variable is a string type, attempt to find object based on
# slug field, otherwise pass the object along directly.
if not isinstance(variable, Model):
try:
form = models.Form.objects.get(slug=variable)
except models.Form.DoesNotExist:
raise Http404(default_msg)
elif isinstance(variable, models.Form):
form = variable
else:
raise Http404(default_msg)
url = form.absolute_url
view, args, kwargs = resolve(url)
request = context["request"]
# Store original request values.
original_method = request.method
original_path = request.path
original_info = request.path_info
# Assign new request values.
request.method = "GET"
request.path = url
request.path_info = url
# Call view to get result.
kwargs["inclusion_tag"] = True
result = view(request, *args, **kwargs)
# Replace request values with originals.
request.method = original_method
request.path = original_path
request.path_info = original_path
# This does not expect anything other than a TemplateResponse here.
result.render()
html = result.rendered_content
return html
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] |
Python_MD_toHTML/Marked.py | StevenDias33/Marked | 1 | 50649 | import markdown2
import re
import os
from os.path import splitext
link_patterns = [(re.compile(
r'((([A-Za-z]{3,9}:(?:\/\/)?)(?:[\-;:&=\+\$,\w]+@)?[A-Za-z0-9\.\-]+(:[0-9]+)?|(?:www\.|[\-;:&=\+\$,\w]+@)[A-Za-z0-9\.\-]+)((?:\/[\+~%\/\.\w\-_]*)?\??(?:[\-\+=&;%@\.\w_]*)#?(?:[\.\!\/\\\w]*))?)'), r'\1')]
head = "<!DOCTYPE html>\n<html>\n<head>\n\t<meta charset='utf-8'/>\n\t<title>" + "Marked" + \
"</title>\n\t\n</head>\n<body>\n"
finHead = "</body>\n</html>"
def convert(md_input, html_output):
malist = os.listdir(f'./'+md_input)
for i in malist:
f = open(f'./{md_input}/{i}', "r")
html = markdown2.markdown(
f.read(), extras=["link-patterns"], link_patterns=link_patterns)
nomFichier = os.path.splitext(i)[0]
print(f'Le fichier "{nomFichier}" est convertie !')
html_file = open(f'./{html_output}/{nomFichier}.html', 'w')
html_file.write(head)
html_file.write(html)
html_file.write(finHead)
"""
<?php
$command = escapeshellcmd('/usr/custom/test.py');
$output = shell_exec($command);
echo $output;
?>
""" | [
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] |
3D_CNNs/S3DG_small.py | Seunghoon-Yi/Paper_review-PyTorch | 2 | 40090 | import torch.nn as nn
import torch
import os
class BasicConv3d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(BasicConv3d, self).__init__()
self.conv = nn.Conv3d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) # verify bias false
# verify defalt value in sonnet
#self.bn = nn.BatchNorm3d(out_planes, eps=1.e-5, momentum=0.1, affine=True)
self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=True)
def forward(self, x):
x = self.conv(x)
#x = self.bn(x)
x = self.relu(x)
return x
class STConv3d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
super(STConv3d, self).__init__()
self.conv = nn.Conv3d(in_planes, out_planes, kernel_size=(1, kernel_size, kernel_size),
stride=(1, stride, stride), padding=(0, padding, padding))
self.conv2 = nn.Conv3d(out_planes, out_planes, kernel_size=(kernel_size, 1, 1), stride=(stride, 1, 1),
padding=(padding, 0, 0))
#self.bn = nn.BatchNorm3d(out_planes, eps=1.e-5, momentum=0.1, affine=True)
self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=True)
#self.bn2 = nn.BatchNorm3d(out_planes, eps=1.e-5, momentum=0.1, affine=True)
self.relu2 = nn.LeakyReLU(negative_slope=0.01, inplace=True)
nn.init.normal_(self.conv2.weight, mean=0, std=0.01)
nn.init.constant_(self.conv2.bias, 0)
def forward(self, x):
x=self.conv(x)
#x=self.bn(x)
x=self.relu(x)
x=self.conv2(x)
#x=self.bn2(x)
x=self.relu2(x)
return x
# Note the operations here for S3D-G:
# If we set two convs: 1xkxk + kx1x1, it's as follows: (p=(k-1)/2)
# BasicConv3d(input,output,kernel_size=(1,k,k),stride=1,padding=(0,p,p))
# Then BasicConv3d(output,output,kernel_size=(k,1,1),stride=1,padding=(p,0,0))
class Mixed_3b(nn.Module):
def __init__(self):
super(Mixed_3b, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(64, 16, kernel_size=1, stride=1),
)
self.branch1 = nn.Sequential(
BasicConv3d(64, 32, kernel_size=1, stride=1),
STConv3d(32, 24, kernel_size=3, stride=1, padding=1),
)
self.branch2 = nn.Sequential(
BasicConv3d(64, 16, kernel_size=1, stride=1),
STConv3d(16, 12, kernel_size=3, stride=1, padding=1),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(64, 12, kernel_size=1, stride=1),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_3c(nn.Module):
def __init__(self):
super(Mixed_3c, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(64, 24, kernel_size=1, stride=1),
)
self.branch1 = nn.Sequential(
BasicConv3d(64, 24, kernel_size=1, stride=1),
STConv3d(24, 40, kernel_size=3, stride=1, padding=1),
)
self.branch2 = nn.Sequential(
BasicConv3d(64, 12, kernel_size=1, stride=1),
STConv3d(12, 16, kernel_size=3, stride=1, padding=1),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(64, 16, kernel_size=1, stride=1),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_4b(nn.Module):
def __init__(self):
super(Mixed_4b, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(96, 32, kernel_size=1, stride=1),
)
self.branch1 = nn.Sequential(
BasicConv3d(96, 32, kernel_size=1, stride=1),
STConv3d(32, 64, kernel_size=3, stride=1, padding=1),
)
self.branch2 = nn.Sequential(
BasicConv3d(96, 32, kernel_size=1, stride=1),
STConv3d(32, 16, kernel_size=3, stride=1, padding=1),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(96, 16, kernel_size=1, stride=1),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_4c(nn.Module):
def __init__(self):
super(Mixed_4c, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(128, 60, kernel_size=1, stride=1),
)
self.branch1 = nn.Sequential(
BasicConv3d(128, 64, kernel_size=1, stride=1),
STConv3d(64, 84, kernel_size=3, stride=1, padding=1),
)
self.branch2 = nn.Sequential(
BasicConv3d(128, 16, kernel_size=1, stride=1),
STConv3d(16, 24, kernel_size=3, stride=1, padding=1),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(128, 24, kernel_size=1, stride=1),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_4d(nn.Module):
def __init__(self):
super(Mixed_4d, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(512, 128, kernel_size=1, stride=1),
)
self.branch1 = nn.Sequential(
BasicConv3d(512, 128, kernel_size=1, stride=1),
STConv3d(128, 256, kernel_size=3, stride=1, padding=1),
)
self.branch2 = nn.Sequential(
BasicConv3d(512, 24, kernel_size=1, stride=1),
STConv3d(24, 64, kernel_size=3, stride=1, padding=1),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(512, 64, kernel_size=1, stride=1),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_4e(nn.Module):
def __init__(self):
super(Mixed_4e, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(512, 112, kernel_size=1, stride=1),
)
self.branch1 = nn.Sequential(
BasicConv3d(512, 144, kernel_size=1, stride=1),
STConv3d(144, 288, kernel_size=3, stride=1, padding=1),
)
self.branch2 = nn.Sequential(
BasicConv3d(512, 32, kernel_size=1, stride=1),
STConv3d(32, 64, kernel_size=3, stride=1, padding=1),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(512, 64, kernel_size=1, stride=1),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_4f(nn.Module):
def __init__(self):
super(Mixed_4f, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(128, 32, kernel_size=1, stride=1), # 4
)
self.branch1 = nn.Sequential(
BasicConv3d(128, 36, kernel_size=1, stride=1),
STConv3d(36, 64, kernel_size=3, stride=1, padding=1), # 6
)
self.branch2 = nn.Sequential(
BasicConv3d(128, 12, kernel_size=1, stride=1),
STConv3d(12, 16, kernel_size=3, stride=1, padding=1), # 3
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(128, 16, kernel_size=1, stride=1), # 3
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_5b(nn.Module):
def __init__(self):
super(Mixed_5b, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(96, 32, kernel_size=1, stride=1), # 4
)
self.branch1 = nn.Sequential(
BasicConv3d(96, 24, kernel_size=1, stride=1),
STConv3d(24, 48, kernel_size=3, stride=1, padding=1), # 6
)
self.branch2 = nn.Sequential(
BasicConv3d(96, 12, kernel_size=1, stride=1),
STConv3d(12, 24, kernel_size=3, stride=1, padding=1), # 3
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(96, 24, kernel_size=1, stride=1), # 3
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Mixed_5c(nn.Module):
def __init__(self):
super(Mixed_5c, self).__init__()
self.branch0 = nn.Sequential(
BasicConv3d(192, 96, kernel_size=1, stride=1), # 3
)
self.branch1 = nn.Sequential(
BasicConv3d(192, 64, kernel_size=1, stride=1),
STConv3d(64, 96, kernel_size=3, stride=1, padding=1), # 3
)
self.branch2 = nn.Sequential(
BasicConv3d(192, 16, kernel_size=1, stride=1),
STConv3d(16, 32, kernel_size=3, stride=1, padding=1), # 1
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
BasicConv3d(192, 32, kernel_size=1, stride=1), # 1
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class Permute(nn.Module):
def __init__(self):
super(Permute, self).__init__()
def forward(self, x):
return x.permute(0,2,1,3,4)
class S3DG(nn.Module):
def __init__(self, embed_size = 512, device = None, dropout = 0.5, input_channel=3, spatial_squeeze=True):
super(S3DG, self).__init__()
self.features = nn.Sequential(
############################ The Stem Network ############################
BasicConv3d(input_channel, 16, kernel_size=6, stride=2, padding=2), # (16, 112, 114, 98)
BasicConv3d(16, 32, kernel_size=3, stride=1, padding=1), # (32, 112, 114, 98)
nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0)), # (32, 112, 57, 49)
STConv3d(32, 64, kernel_size=3, stride=1, padding=1), # (64, 112, 57, 49)
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 0, 0)), # (64, 112, 28, 24)
######################## Incption + Residual conv3d ########################
Mixed_3c(), # (96, 112, 28, 24)
nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=(0, 0, 0)), # (96, 112, 14, 12)
Mixed_4b(), # (128, 112, 14, 12)
Mixed_4c(), # (192, 112, 14, 12)
nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0)), # (192, 56, 7, 6)
Mixed_5c(), # (256, 56, 7, 6)
nn.AdaptiveAvgPool3d(output_size=(56, 2, 2)),
)
self.fc_out_1 = nn.Linear(1024, embed_size)
#self.fc_out_2 = nn.Linear(1660, 64*4*2)
self.dropout = nn.Dropout(p=0.5)
self.spatial_squeeze = spatial_squeeze
self.softmax = nn.Softmax()
self.device = device
self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=True)
def forward(self, x):
#print("initial shape : ", x.shape)
x = x.permute(0,2,1,3,4).to(self.device)
logits = self.features(x)
if self.spatial_squeeze:
logits = logits.squeeze(3)
logits = logits.squeeze(3)
BS = logits.shape[0]
print("feature shape : ", logits.shape)
out = self.relu(self.fc_out_1(self.dropout(logits.permute(0,2,1,3,4).reshape(BS, 56, -1))))
return out
def load_state_dict(self, path):
target_weights = torch.load(path)
own_state = self.state_dict()
for name, param in target_weights.items():
if name in own_state:
if isinstance(param, nn.Parameter):
param = param.data
try:
if len(param.size()) == 5 and param.size()[3] in [3, 7]:
own_state[name][:, :, 0, :, :] = torch.mean(param, 2)
else:
own_state[name].copy_(param)
except Exception:
raise RuntimeError('While copying the parameter named {}.\
whose dimensions in the model are {} and \
whose dimensions in the checkpoint are {}.\
'.format(name, own_state[name].size(), param.size()))
else:
print('{} meets error in locating parameters'.format(name))
missing = set(own_state.keys()) - set(target_weights.keys())
print('{} keys are not holded in target checkpoints'.format(len(missing)))
if __name__ == '__main__':
model = S3DG(embed_size=512)
# Initialize the weights with pretrained I3D net. In detail,
# please refer to specific reproduced load_state_dict() function
#if not os.path.exists('modelweights/RGB_imagenet.pkl'):
#print 'No weights Found! please download first, or comment 382~384th line'
#model.load_state_dict('modelweights/RGB_imagenet.pkl')
model = model.cuda()
data = torch.autograd.Variable(torch.rand(3, 224, 3, 228, 196)).cuda() # [BS, C, T, H, W]
print(model)
out = model(data)
print(out.shape) | [
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] |
certbot/certbot/plugins/dns_test_common_lexicon.py | vivithemage/certbot | 3 | 77534 | <gh_stars>1-10
"""Base test class for DNS authenticators built on Lexicon."""
from typing import Any
from typing import TYPE_CHECKING
from unittest.mock import MagicMock
import josepy as jose
from requests.exceptions import HTTPError
from requests.exceptions import RequestException
from certbot import errors
from certbot.achallenges import AnnotatedChallenge
from certbot.plugins import dns_test_common
from certbot.plugins.dns_common_lexicon import LexiconClient
from certbot.plugins.dns_test_common import _AuthenticatorCallableTestCase
from certbot.tests import util as test_util
try:
import mock
except ImportError: # pragma: no cover
from unittest import mock # type: ignore
if TYPE_CHECKING:
from typing_extensions import Protocol
else:
Protocol = object
DOMAIN = 'example.com'
KEY = jose.JWKRSA.load(test_util.load_vector("rsa512_key.pem"))
class _AuthenticatorCallableLexiconTestCase(_AuthenticatorCallableTestCase, Protocol):
"""
Protocol describing a TestCase suitable to test challenges against
a mocked LexiconClient instance.
"""
mock_client: MagicMock
achall: AnnotatedChallenge
class _LexiconAwareTestCase(Protocol):
"""
Protocol describing a TestCase suitable to test a real LexiconClient instance.
"""
client: LexiconClient
provider_mock: MagicMock
record_prefix: str
record_name: str
record_content: str
DOMAIN_NOT_FOUND: Exception
GENERIC_ERROR: Exception
LOGIN_ERROR: Exception
UNKNOWN_LOGIN_ERROR: Exception
def assertRaises(self, *unused_args: Any) -> None:
"""
See
https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertRaises
"""
...
# These classes are intended to be subclassed/mixed in, so not all members are defined.
# pylint: disable=no-member
class BaseLexiconAuthenticatorTest(dns_test_common.BaseAuthenticatorTest):
@test_util.patch_display_util()
def test_perform(self: _AuthenticatorCallableLexiconTestCase,
unused_mock_get_utility: Any) -> None:
self.auth.perform([self.achall])
expected = [mock.call.add_txt_record(DOMAIN, '_acme-challenge.'+DOMAIN, mock.ANY)]
self.assertEqual(expected, self.mock_client.mock_calls)
def test_cleanup(self: _AuthenticatorCallableLexiconTestCase) -> None:
self.auth._attempt_cleanup = True # _attempt_cleanup | pylint: disable=protected-access
self.auth.cleanup([self.achall])
expected = [mock.call.del_txt_record(DOMAIN, '_acme-challenge.'+DOMAIN, mock.ANY)]
self.assertEqual(expected, self.mock_client.mock_calls)
class BaseLexiconClientTest:
DOMAIN_NOT_FOUND = Exception('No domain found')
GENERIC_ERROR = RequestException
LOGIN_ERROR = HTTPError('400 Client Error: ...')
UNKNOWN_LOGIN_ERROR = HTTPError('500 Surprise! Error: ...')
record_prefix = "_acme-challenge"
record_name = record_prefix + "." + DOMAIN
record_content = "bar"
def test_add_txt_record(self: _LexiconAwareTestCase) -> None:
self.client.add_txt_record(DOMAIN, self.record_name, self.record_content)
self.provider_mock.create_record.assert_called_with(rtype='TXT',
name=self.record_name,
content=self.record_content)
def test_add_txt_record_try_twice_to_find_domain(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = [self.DOMAIN_NOT_FOUND, '']
self.client.add_txt_record(DOMAIN, self.record_name, self.record_content)
self.provider_mock.create_record.assert_called_with(rtype='TXT',
name=self.record_name,
content=self.record_content)
def test_add_txt_record_fail_to_find_domain(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = [self.DOMAIN_NOT_FOUND,
self.DOMAIN_NOT_FOUND,
self.DOMAIN_NOT_FOUND,]
self.assertRaises(errors.PluginError,
self.client.add_txt_record,
DOMAIN, self.record_name, self.record_content)
def test_add_txt_record_fail_to_authenticate(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = self.LOGIN_ERROR
self.assertRaises(errors.PluginError,
self.client.add_txt_record,
DOMAIN, self.record_name, self.record_content)
def test_add_txt_record_fail_to_authenticate_with_unknown_error(
self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = self.UNKNOWN_LOGIN_ERROR
self.assertRaises(errors.PluginError,
self.client.add_txt_record,
DOMAIN, self.record_name, self.record_content)
def test_add_txt_record_error_finding_domain(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = self.GENERIC_ERROR
self.assertRaises(errors.PluginError,
self.client.add_txt_record,
DOMAIN, self.record_name, self.record_content)
def test_add_txt_record_error_adding_record(self: _LexiconAwareTestCase) -> None:
self.provider_mock.create_record.side_effect = self.GENERIC_ERROR
self.assertRaises(errors.PluginError,
self.client.add_txt_record,
DOMAIN, self.record_name, self.record_content)
def test_del_txt_record(self: _LexiconAwareTestCase) -> None:
self.client.del_txt_record(DOMAIN, self.record_name, self.record_content)
self.provider_mock.delete_record.assert_called_with(rtype='TXT',
name=self.record_name,
content=self.record_content)
def test_del_txt_record_fail_to_find_domain(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = [self.DOMAIN_NOT_FOUND,
self.DOMAIN_NOT_FOUND,
self.DOMAIN_NOT_FOUND, ]
self.client.del_txt_record(DOMAIN, self.record_name, self.record_content)
def test_del_txt_record_fail_to_authenticate(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = self.LOGIN_ERROR
self.client.del_txt_record(DOMAIN, self.record_name, self.record_content)
def test_del_txt_record_fail_to_authenticate_with_unknown_error(
self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = self.UNKNOWN_LOGIN_ERROR
self.client.del_txt_record(DOMAIN, self.record_name, self.record_content)
def test_del_txt_record_error_finding_domain(self: _LexiconAwareTestCase) -> None:
self.provider_mock.authenticate.side_effect = self.GENERIC_ERROR
self.client.del_txt_record(DOMAIN, self.record_name, self.record_content)
def test_del_txt_record_error_deleting_record(self: _LexiconAwareTestCase) -> None:
self.provider_mock.delete_record.side_effect = self.GENERIC_ERROR
self.client.del_txt_record(DOMAIN, self.record_name, self.record_content)
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bert_multitask_learning/model_fn.py | akashnd/bert-multitask-learning | 0 | 5330 | # AUTOGENERATED! DO NOT EDIT! File to edit: source_nbs/13_model_fn.ipynb (unless otherwise specified).
__all__ = ['variable_summaries', 'filter_loss', 'BertMultiTaskBody', 'BertMultiTaskTop', 'BertMultiTask']
# Cell
from typing import Dict, Tuple
from inspect import signature
import tensorflow as tf
import transformers
from .modeling import MultiModalBertModel
from .params import BaseParams
from .top import (Classification, MultiLabelClassification, PreTrain,
Seq2Seq, SequenceLabel, MaskLM)
from .utils import get_embedding_table_from_model, get_transformer_main_model
def variable_summaries(var, name):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
with tf.compat.v1.name_scope(name):
mean = tf.reduce_mean(input_tensor=var)
tf.compat.v1.summary.scalar('mean', mean)
with tf.compat.v1.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(
input_tensor=tf.square(var - mean)))
tf.compat.v1.summary.scalar('stddev', stddev)
tf.compat.v1.summary.scalar('max', tf.reduce_max(input_tensor=var))
tf.compat.v1.summary.scalar('min', tf.reduce_min(input_tensor=var))
tf.compat.v1.summary.histogram('histogram', var)
@tf.function
def filter_loss(loss, features, problem):
if tf.reduce_mean(input_tensor=features['%s_loss_multiplier' % problem]) == 0:
return_loss = 0.0
else:
return_loss = loss
return return_loss
class BertMultiTaskBody(tf.keras.Model):
"""Model to extract bert features and dispatch corresponding rows to each problem_chunk.
for each problem chunk, we extract corresponding features
and hidden features for that problem. The reason behind this
is to save computation for downstream processing.
For example, we have a batch of two instances and they're from
problem a and b respectively:
Input:
[{'input_ids': [1,2,3], 'a_loss_multiplier': 1, 'b_loss_multiplier': 0},
{'input_ids': [4,5,6], 'a_loss_multiplier': 0, 'b_loss_multiplier': 1}]
Output:
{
'a': {'input_ids': [1,2,3], 'a_loss_multiplier': 1, 'b_loss_multiplier': 0}
'b': {'input_ids': [4,5,6], 'a_loss_multiplier': 0, 'b_loss_multiplier': 1}
}
"""
def __init__(self, params: BaseParams, name='BertMultiTaskBody'):
super(BertMultiTaskBody, self).__init__(name=name)
self.params = params
self.bert = MultiModalBertModel(params=self.params)
if self.params.custom_pooled_hidden_size:
self.custom_pooled_layer = tf.keras.layers.Dense(
self.params.custom_pooled_hidden_size, activation=tf.keras.activations.selu)
else:
self.custom_pooled_layer = None
@tf.function
def get_features_for_problem(self, features, hidden_feature, problem, mode):
# get features with ind == 1
if mode == tf.estimator.ModeKeys.PREDICT:
feature_this_round = features
hidden_feature_this_round = hidden_feature
else:
multiplier_name = '%s_loss_multiplier' % problem
record_ind = tf.where(tf.cast(
tf.squeeze(features[multiplier_name]), tf.bool))
hidden_feature_this_round = {}
for hidden_feature_name in hidden_feature:
if hidden_feature_name != 'embed_table':
hidden_feature_this_round[hidden_feature_name] = tf.squeeze(tf.gather(
hidden_feature[hidden_feature_name], record_ind, axis=0
), axis=1)
hidden_feature_this_round[hidden_feature_name].set_shape(
hidden_feature[hidden_feature_name].shape.as_list())
else:
hidden_feature_this_round[hidden_feature_name] = hidden_feature[hidden_feature_name]
feature_this_round = {}
for features_name in features:
feature_this_round[features_name] = tf.gather_nd(
features[features_name],
record_ind)
return feature_this_round, hidden_feature_this_round
def call(self, inputs: Dict[str, tf.Tensor],
mode: str) -> Tuple[Dict[str, Dict[str, tf.Tensor]], Dict[str, Dict[str, tf.Tensor]]]:
_ = self.bert(inputs, mode == tf.estimator.ModeKeys.TRAIN)
# extract bert hidden features
inputs['model_input_mask'] = self.bert.get_input_mask()
inputs['model_token_type_ids'] = self.bert.get_token_type_ids()
hidden_feature = {}
for logit_type in ['seq', 'pooled', 'all', 'embed', 'embed_table']:
if logit_type == 'seq':
# tensor, [batch_size, seq_length, hidden_size]
hidden_feature[logit_type] = self.bert.get_sequence_output()
elif logit_type == 'pooled':
# tensor, [batch_size, hidden_size]
hidden_feature[logit_type] = self.bert.get_pooled_output()
if self.custom_pooled_layer:
hidden_feature[logit_type] = self.custom_pooled_layer(
hidden_feature[logit_type])
elif logit_type == 'all':
# list, num_hidden_layers * [batch_size, seq_length, hidden_size]
hidden_feature[logit_type] = self.bert.get_all_encoder_layers()
elif logit_type == 'embed':
# for res connection
hidden_feature[logit_type] = self.bert.get_embedding_output()
elif logit_type == 'embed_table':
hidden_feature[logit_type] = self.bert.get_embedding_table()
# for each problem chunk, we extract corresponding features
# and hidden features for that problem. The reason behind this
# is to save computation for downstream processing.
# For example, we have a batch of two instances and they're from
# problem a and b respectively:
# Input:
# [{'input_ids': [1,2,3], 'a_loss_multiplier': 1, 'b_loss_multiplier': 0},
# {'input_ids': [4,5,6], 'a_loss_multiplier': 0, 'b_loss_multiplier': 1}]
# Output:
# {
# 'a': {'input_ids': [1,2,3], 'a_loss_multiplier': 1, 'b_loss_multiplier': 0}
# 'b': {'input_ids': [4,5,6], 'a_loss_multiplier': 0, 'b_loss_multiplier': 1}
# }
features = inputs
return_feature = {}
return_hidden_feature = {}
for problem_dict in self.params.run_problem_list:
for problem in problem_dict:
if self.params.task_transformer:
# hidden_feature = task_tranformer_hidden_feature[problem]
raise NotImplementedError
if len(self.params.run_problem_list) > 1:
feature_this_round, hidden_feature_this_round = self.get_features_for_problem(
features, hidden_feature, problem, mode)
else:
feature_this_round, hidden_feature_this_round = features, hidden_feature
if self.params.label_transfer and self.params.grid_transformer:
raise ValueError(
'Label Transfer and grid transformer cannot be enabled in the same time.'
)
if self.params.grid_transformer:
raise NotImplementedError
return_hidden_feature[problem] = hidden_feature_this_round
return_feature[problem] = feature_this_round
return return_feature, return_hidden_feature
# Cell
class BertMultiTaskTop(tf.keras.Model):
"""Model to create top layer, aka classification layer, for each problem.
"""
def __init__(self, params: BaseParams, name='BertMultiTaskTop', input_embeddings: tf.Tensor = None):
super(BertMultiTaskTop, self).__init__(name=name)
self.params = params
problem_type_layer = {
'seq_tag': SequenceLabel,
'cls': Classification,
'seq2seq_tag': Seq2Seq,
'seq2seq_text': Seq2Seq,
'multi_cls': MultiLabelClassification,
'pretrain': PreTrain,
'masklm': MaskLM
}
problem_type_layer.update(self.params.top_layer)
self.top_layer_dict = {}
for problem_dict in self.params.run_problem_list:
for problem in problem_dict:
problem_type = self.params.problem_type[problem]
# some layers has different signatures, assign inputs accordingly
layer_signature_name = signature(
problem_type_layer[problem_type].__init__).parameters.keys()
inputs_kwargs = {
'params': self.params,
'problem_name': problem
}
for signature_name in layer_signature_name:
if signature_name == 'input_embeddings':
inputs_kwargs.update(
{signature_name: input_embeddings})
self.top_layer_dict[problem] = problem_type_layer[problem_type](
**inputs_kwargs)
def call(self,
inputs: Tuple[Dict[str, Dict[str, tf.Tensor]], Dict[str, Dict[str, tf.Tensor]]],
mode: str) -> Dict[str, tf.Tensor]:
features, hidden_feature = inputs
return_dict = {}
for problem_dict in self.params.run_problem_list:
for problem in problem_dict:
feature_this_round = features[problem]
hidden_feature_this_round = hidden_feature[problem]
problem_type = self.params.problem_type[problem]
# if pretrain, return pretrain logit
if problem_type == 'pretrain':
pretrain = self.top_layer_dict[problem]
return_dict[problem] = pretrain(
(feature_this_round, hidden_feature_this_round), mode)
return return_dict
if self.params.label_transfer and self.params.grid_transformer:
raise ValueError(
'Label Transfer and grid transformer cannot be enabled in the same time.'
)
with tf.name_scope(problem):
layer = self.top_layer_dict[problem]
return_dict[problem] = layer(
(feature_this_round, hidden_feature_this_round), mode)
if self.params.augument_mask_lm and mode == tf.estimator.ModeKeys.TRAIN:
raise NotImplementedError
# try:
# mask_lm_top = MaskLM(self.params)
# return_dict['augument_mask_lm'] = \
# mask_lm_top(features,
# hidden_feature, mode, 'dummy')
# except ValueError:
# pass
return return_dict
# Cell
class BertMultiTask(tf.keras.Model):
def __init__(self, params: BaseParams, name='BertMultiTask') -> None:
super(BertMultiTask, self).__init__(name=name)
self.params = params
# initialize body model, aka transformers
self.body = BertMultiTaskBody(params=self.params)
# mlm might need word embedding from bert
# build sub-model
_ = get_embedding_table_from_model(self.body.bert.bert_model)
main_model = get_transformer_main_model(self.body.bert.bert_model)
# input_embeddings = self.body.bert.bert_model.bert.embeddings
input_embeddings = main_model.embeddings
self.top = BertMultiTaskTop(
params=self.params, input_embeddings=input_embeddings)
def call(self, inputs, mode=tf.estimator.ModeKeys.TRAIN):
feature_per_problem, hidden_feature_per_problem = self.body(
inputs, mode)
pred_per_problem = self.top(
(feature_per_problem, hidden_feature_per_problem), mode)
return pred_per_problem
def compile(self):
super(BertMultiTask, self).compile()
logger = tf.get_logger()
logger.info('Initial lr: {}'.format(self.params.lr))
logger.info('Train steps: {}'.format(self.params.train_steps))
logger.info('Warmup steps: {}'.format(self.params.num_warmup_steps))
self.optimizer, self.lr_scheduler = transformers.optimization_tf.create_optimizer(
init_lr=self.params.lr,
num_train_steps=self.params.train_steps,
num_warmup_steps=self.params.num_warmup_steps,
weight_decay_rate=0.01
)
self.mean_acc = tf.keras.metrics.Mean(name='mean_acc')
def train_step(self, data):
with tf.GradientTape() as tape:
# Forward pass
_ = self(data, mode=tf.estimator.ModeKeys.TRAIN)
# gather losses from all problems
loss_dict = {'{}_loss'.format(problem_name): tf.reduce_sum(top_layer.losses) for problem_name,
top_layer in self.top.top_layer_dict.items()}
# metric_dict = {'{}_metric'.format(problem_name): tf.reduce_mean(top_layer.metrics) for problem_name,
# top_layer in self.top.top_layer_dict.items()}
metric_dict = {m.name: m.result() for m in self.metrics}
# Compute gradients
trainable_vars = self.trainable_variables
gradients = tape.gradient(self.losses, trainable_vars)
# Update weights
self.optimizer.apply_gradients(zip(gradients, trainable_vars))
self.mean_acc.update_state(
[v for n, v in metric_dict.items() if n != 'mean_acc'])
return_dict = metric_dict
return_dict.update(loss_dict)
return_dict[self.mean_acc.name] = self.mean_acc.result()
# Return a dict mapping metric names to current value.
# Note that it will include the loss (tracked in self.metrics).
return return_dict
def test_step(self, data):
"""The logic for one evaluation step.
This method can be overridden to support custom evaluation logic.
This method is called by `Model.make_test_function`.
This function should contain the mathemetical logic for one step of
evaluation.
This typically includes the forward pass, loss calculation, and metrics
updates.
Configuration details for *how* this logic is run (e.g. `tf.function` and
`tf.distribute.Strategy` settings), should be left to
`Model.make_test_function`, which can also be overridden.
Arguments:
data: A nested structure of `Tensor`s.
Returns:
A `dict` containing values that will be passed to
`tf.keras.callbacks.CallbackList.on_train_batch_end`. Typically, the
values of the `Model`'s metrics are returned.
"""
y_pred = self(data, mode=tf.estimator.ModeKeys.EVAL)
# Updates stateful loss metrics.
self.compiled_loss(
None, y_pred, None, regularization_losses=self.losses)
self.compiled_metrics.update_state(None, y_pred, None)
# get metrics to calculate mean
m_list = []
for metric in self.metrics:
if 'mean_acc' in metric.name:
continue
if 'acc' in metric.name:
m_list.append(metric.result())
if 'f1' in metric.name:
m_list.append(metric.result())
self.mean_acc.update_state(
m_list)
return {m.name: m.result() for m in self.metrics}
def predict_step(self, data):
return self(data, mode=tf.estimator.ModeKeys.PREDICT)
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] |
bmt/util.py | patrickkwang/bmt-lite | 0 | 7722 | """Utilities."""
from functools import wraps
import re
from typing import Callable, List, Optional, TypeVar, Union
from .data import (
all_classes, all_slots,
)
def pascal_to_snake(s: str, sep: str = "_") -> str:
"""Convert Pascal case to snake case.
Assumes that
a) all words are either all-lowercase or all-uppercase
b) all 1-letter words are lowercase
c) there are no adjacent 1-letter words
d) there are no adjacent uppercase words
Examples:
PhenotypicFeature -> phenotypic_feature
RNAProduct -> RNA_product
FeedACamel -> feed_a_camel
Optionally specify `sep` (default "_").
"""
# add an underscore before each capital letter
underscored = re.sub(
r"(?<!^)(?=[A-Z])",
sep,
s,
)
# collapse any adjacent one-letter words
collapsed = re.sub(
r"(?<![a-zA-Z])[A-Z](?:_[A-Z](?=$|_))+",
lambda match: match.group(0).replace("_", ""),
underscored,
)
# lower-case any words containing only one uppercase letter
lowercased = re.sub(
r"(?<![A-Z])[A-Z](?![A-Z])",
lambda match: match.group(0).lower(),
collapsed,
)
return lowercased
def snake_to_pascal(s: str, sep: str = "_") -> str:
"""Convert snake case to Pascal case.
This is the inverse of pascal_to_snake() when its assumptions
are true.
Optionally specify `sep` (default "_").
"""
return re.sub(
fr"(?:^|{sep})([a-zA-Z])",
lambda match: match.group(1).upper(),
s
)
def guess_casing(s: str) -> str:
"""Guess snake case or Pascal case."""
if "_" in s:
return "snake"
if any(c.isupper() for c in s):
return "pascal"
return "snake"
def normalize(s: str) -> str:
"""Normalize string input."""
if s.startswith("biolink:"):
s = s[8:]
if "_" in s:
# it's snake case
return s.replace("_", " ")
if " " in s:
return s
return pascal_to_snake(s, " ")
T = TypeVar("T")
def listify(func: Callable) -> Callable:
"""Expand function to take list of arguments."""
@wraps(func)
def wrapper(arg: Union[T, List[T]], **kwargs) -> Union[T, List[T]]:
"""Apply function to each element in list."""
if isinstance(arg, list):
return [
func(el, **kwargs)
for el in arg
]
else:
return func(arg, **kwargs)
return wrapper
@listify
def format(s: str, case: Optional[str] = None, **kwargs) -> str:
"""Format space-case string as biolink CURIE."""
if isinstance(case, str) and case.lower() == "pascal":
return "biolink:" + snake_to_pascal(s, " ")
elif isinstance(case, str) and case.lower() == "snake":
return "biolink:" + s.replace(" ", "_")
else:
return "biolink:" + s
def with_formatting():
"""Add format conversions to method."""
def decorator(func: Callable) -> Callable:
"""Generate decorator."""
@wraps(func)
def wrapper(self, s: str, *args, formatted=False, **kwargs):
"""Wrap in format conversions."""
case = guess_casing(s)
normalized = normalize(s)
output: Union[str, List[str]] = func(self, normalized, *args, **kwargs)
if formatted:
if normalized in all_classes:
output = format(output, case="pascal")
elif normalized in all_slots:
output = format(output, case="snake")
else:
output = format(output, case=case)
return output
return wrapper
return decorator
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notebook/book/python/head-first-python/Chapter-6/kelly.py | JMwill/note | 0 | 182772 | def sanitize(time_string):
if '-' in time_string:
spliter = '-'
elif ':' in time_string:
spliter = ':'
else:
return time_string
(mins, secs) = time_string.split(spliter)
return mins + '.' + secs
def get_coach_data(filepath):
try:
with open(filepath) as f:
data = f.readline()
data = data.strip().split(',')
return {
'name': data.pop(0),
'dob': data.pop(0),
'times': sorted(set([sanitize(time) for time in data]))[:3]
}
except IOError as err:
print('File error: ' + str(err))
return None
sarah = get_coach_data('hfpy_ch6_data/sarah2.txt')
james = get_coach_data('hfpy_ch6_data/james2.txt')
julie = get_coach_data('hfpy_ch6_data/julie2.txt')
mikey = get_coach_data('hfpy_ch6_data/mikey2.txt')
print(sarah['name'] + "'s fastest times are: " + str(sarah['times']))
print(james['name'] + "'s fastest times are: " + str(james['times']))
print(julie['name'] + "'s fastest times are: " + str(julie['times']))
print(mikey['name'] + "'s fastest times are: " + str(mikey['times']))
print('\n==========================> line break <============================\n')
# 改进2
class Athlete():
def __init__(self, name, dob=None, times=[]):
self.name = name
self.dob = dob
self.times = times
def top3(self):
return sorted(set([sanitize(time) for time in self.times]))[:3]
def add_time(self, data):
self.times.append(data)
def add_times(self, data_arr=[]):
self.times.extend(data_arr)
def get_coach_data(filepath):
try:
with open(filepath) as f:
data = f.readline().strip().split(',')
return Athlete(data.pop(0), data.pop(0), data)
except IOError as err:
print('File error: ' + str(err))
return None
james = get_coach_data('hfpy_ch6_data/james2.txt')
julie = get_coach_data('hfpy_ch6_data/julie2.txt')
mikey = get_coach_data('hfpy_ch6_data/mikey2.txt')
sarah = get_coach_data('hfpy_ch6_data/sarah2.txt')
print(james.name + "'s fastest times are: " + str(james.top3()))
print(julie.name + "'s fastest times are: " + str(julie.top3()))
print(mikey.name + "'s fastest times are: " + str(mikey.top3()))
print(sarah.name + "'s fastest times are: " + str(sarah.top3()))
# 改进3
print('\n==========================> line break <============================\n')
class Athlete(list):
def __init__(self, name, dob=None, times=[]):
list.__init__([])
self.name = name
self.dob = dob
self.extend(times)
def top3(self):
return sorted(set([sanitize(time) for time in self]))[:3]
james = get_coach_data('hfpy_ch6_data/james2.txt')
julie = get_coach_data('hfpy_ch6_data/julie2.txt')
mikey = get_coach_data('hfpy_ch6_data/mikey2.txt')
sarah = get_coach_data('hfpy_ch6_data/sarah2.txt')
print(james.name + "'s fastest times are: " + str(james.top3()))
print(julie.name + "'s fastest times are: " + str(julie.top3()))
print(mikey.name + "'s fastest times are: " + str(mikey.top3()))
print(sarah.name + "'s fastest times are: " + str(sarah.top3()))
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] |
tests/integration/test_logs.py | matthall88/localstack | 1 | 131710 | # -*- coding: utf-8 -*-
import unittest
from localstack.constants import APPLICATION_AMZ_JSON_1_1
from localstack.utils.aws import aws_stack
from localstack.utils import testutil
from localstack.utils.common import short_uid, retry
from localstack.services.awslambda.lambda_api import LAMBDA_RUNTIME_PYTHON36, func_arn
from tests.integration.test_lambda import TEST_LAMBDA_PYTHON3, TEST_LAMBDA_NAME_PY3, TEST_LAMBDA_LIBS
class CloudWatchLogsTest(unittest.TestCase):
def setUp(self):
self.logs_client = aws_stack.connect_to_service('logs')
def test_put_events_multi_bytes_msg(self):
group = 'g-%s' % short_uid()
stream = 's-%s' % short_uid()
response = self.logs_client.create_log_group(logGroupName=group)
self.assertEqual(response['ResponseMetadata']['HTTPStatusCode'], 200)
response = self.logs_client.create_log_stream(logGroupName=group, logStreamName=stream)
self.assertEqual(response['ResponseMetadata']['HTTPStatusCode'], 200)
# send message with non-ASCII (multi-byte) chars
body_msg = '🙀 - 参よ - 日本語'
events = [{
'timestamp': 1546300800,
'message': body_msg
}]
response = self.logs_client.put_log_events(logGroupName=group, logStreamName=stream, logEvents=events)
self.assertEqual(response['ResponseMetadata']['HTTPStatusCode'], 200)
events = self.logs_client.get_log_events(logGroupName=group, logStreamName=stream)['events']
self.assertEqual(events[0]['message'], body_msg)
# clean up
self.logs_client.delete_log_group(
logGroupName=group
)
def test_filter_log_events_response_header(self):
group = 'lg-%s' % short_uid()
stream = 'ls-%s' % short_uid()
self.logs_client.create_log_group(logGroupName=group)
self.logs_client.create_log_stream(logGroupName=group, logStreamName=stream)
events = [
{'timestamp': 1585902800, 'message': 'log message 1'},
{'timestamp': 1585902961, 'message': 'log message 2'}
]
self.logs_client.put_log_events(logGroupName=group, logStreamName=stream, logEvents=events)
rs = self.logs_client.filter_log_events(
logGroupName=group
)
self.assertEqual(rs['ResponseMetadata']['HTTPStatusCode'], 200)
self.assertEqual(rs['ResponseMetadata']['HTTPHeaders']['content-type'], APPLICATION_AMZ_JSON_1_1)
# clean up
self.logs_client.delete_log_group(
logGroupName=group
)
def test_list_tags_log_group(self):
group = 'lg-%s' % short_uid()
self.logs_client.create_log_group(
logGroupName=group,
tags={
'env': 'testing1'
}
)
rs = self.logs_client.list_tags_log_group(
logGroupName=group
)
self.assertEqual(rs['ResponseMetadata']['HTTPStatusCode'], 200)
self.assertIn('tags', rs)
self.assertEqual(rs['tags']['env'], 'testing1')
# clean up
self.logs_client.delete_log_group(
logGroupName=group
)
def test_put_subscribe_policy(self):
lambda_client = aws_stack.connect_to_service('lambda')
log_client = aws_stack.connect_to_service('logs')
testutil.create_lambda_function(
handler_file=TEST_LAMBDA_PYTHON3, libs=TEST_LAMBDA_LIBS,
func_name=TEST_LAMBDA_NAME_PY3, runtime=LAMBDA_RUNTIME_PYTHON36)
lambda_client.invoke(
FunctionName=TEST_LAMBDA_NAME_PY3, Payload=b'{}')
log_group_name = '/aws/lambda/{}'.format(TEST_LAMBDA_NAME_PY3)
log_client.put_subscription_filter(
logGroupName=log_group_name,
filterName='test',
filterPattern='',
destinationArn=func_arn(TEST_LAMBDA_NAME_PY3),
)
stream = 'ls-%s' % short_uid()
log_client.create_log_stream(logGroupName=log_group_name, logStreamName=stream)
log_client.put_log_events(
logGroupName=log_group_name,
logStreamName=stream,
logEvents=[
{'timestamp': 0, 'message': 'test'},
{'timestamp': 0, 'message': 'test 2'},
],
)
resp2 = log_client.describe_subscription_filters(logGroupName=log_group_name)
self.assertEqual(len(resp2['subscriptionFilters']), 1)
def check_invocation():
events = testutil.get_lambda_log_events(TEST_LAMBDA_NAME_PY3)
self.assertEqual(len(events), 2)
retry(check_invocation, retries=6, sleep=3.0)
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] |
example.py | mpainenz/tnas-client | 0 | 126639 | from connection import api_client
username = 'something'
password = '<PASSWORD>'
carrier_id = '12356'
tnas_connection = api_client.TNASConnection(api_client.TNAS_TEST)
tnas_connection.login(username=username, password=password, carrier_id=carrier_id)
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keystone/common/policies/policy.py | rajivmucheli/keystone | 0 | 144676 | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from oslo_policy import policy
from keystone.common.policies import base
policy_policies = [
policy.DocumentedRuleDefault(
name=base.IDENTITY % 'get_policy',
check_str=base.RULE_ADMIN_REQUIRED,
# This API isn't really exposed to usable, it's actually deprecated.
# More-or-less adding scope_types to be consistent with other policies.
scope_types=['system'],
description='Show policy details.',
operations=[{'path': '/v3/policy/{policy_id}',
'method': 'GET'}]),
policy.DocumentedRuleDefault(
name=base.IDENTITY % 'list_policies',
check_str=base.RULE_ADMIN_REQUIRED,
scope_types=['system'],
description='List policies.',
operations=[{'path': '/v3/policies',
'method': 'GET'}]),
policy.DocumentedRuleDefault(
name=base.IDENTITY % 'create_policy',
check_str=base.RULE_ADMIN_REQUIRED,
scope_types=['system'],
description='Create policy.',
operations=[{'path': '/v3/policies',
'method': 'POST'}]),
policy.DocumentedRuleDefault(
name=base.IDENTITY % 'update_policy',
check_str=base.RULE_ADMIN_REQUIRED,
scope_types=['system'],
description='Update policy.',
operations=[{'path': '/v3/policies/{policy_id}',
'method': 'PATCH'}]),
policy.DocumentedRuleDefault(
name=base.IDENTITY % 'delete_policy',
check_str=base.RULE_ADMIN_REQUIRED,
scope_types=['system'],
description='Delete policy.',
operations=[{'path': '/v3/policies/{policy_id}',
'method': 'DELETE'}])
]
def list_rules():
return policy_policies
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] |
virt/ansible-latest/lib/python2.7/site-packages/ansible/modules/cloud/ovirt/ovirt_api_facts.py | lakhlaifi/RedHat-Ansible | 1 | 49697 | <gh_stars>1-10
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (c) 2017 Ansible Project
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {
'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'
}
DOCUMENTATION = '''
---
module: ovirt_api_facts
short_description: Retrieve facts about the oVirt/RHV API
author: "<NAME> (@machacekondra)"
version_added: "2.5"
description:
- "Retrieve facts about the oVirt/RHV API."
notes:
- "This module creates a new top-level C(ovirt_api) fact,
which contains a information about oVirt/RHV API."
extends_documentation_fragment: ovirt_facts
'''
EXAMPLES = '''
# Examples don't contain auth parameter for simplicity,
# look at ovirt_auth module to see how to reuse authentication:
# Gather facts oVirt API:
- ovirt_api_facts:
- debug:
var: ovirt_api
'''
RETURN = '''
ovirt_api:
description: "Dictionary describing the oVirt API information.
Api attributes are mapped to dictionary keys,
all API attributes can be found at following
url: https://ovirt.example.com/ovirt-engine/api/model#types/api."
returned: On success.
type: dict
'''
import traceback
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.ovirt import (
check_sdk,
create_connection,
get_dict_of_struct,
ovirt_facts_full_argument_spec,
)
def main():
argument_spec = ovirt_facts_full_argument_spec()
module = AnsibleModule(argument_spec)
check_sdk(module)
try:
auth = module.params.pop('auth')
connection = create_connection(auth)
api = connection.system_service().get()
module.exit_json(
changed=False,
ansible_facts=dict(
ovirt_api=get_dict_of_struct(
struct=api,
connection=connection,
fetch_nested=module.params.get('fetch_nested'),
attributes=module.params.get('nested_attributes'),
)
),
)
except Exception as e:
module.fail_json(msg=str(e), exception=traceback.format_exc())
finally:
connection.close(logout=auth.get('token') is None)
if __name__ == '__main__':
main()
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stamper/migrations/0004_auto_20161208_1658.py | uploadcare/stump | 0 | 19254 | # -*- coding: utf-8 -*-
# Generated by Django 1.9.9 on 2016-12-08 16:58
from __future__ import unicode_literals
import datetime
from django.db import migrations, models
from django.utils.timezone import utc
class Migration(migrations.Migration):
dependencies = [
('stamper', '0003_auto_20161122_1253'),
]
operations = [
migrations.AddField(
model_name='fileuploadmessage',
name='original_file_url',
field=models.CharField(default=datetime.datetime(2016, 12, 8, 16, 57, 50, 623808, tzinfo=utc), max_length=255),
preserve_default=False,
),
migrations.AddField(
model_name='imageuploadmessage',
name='original_file_url',
field=models.CharField(default=datetime.datetime(2016, 12, 8, 16, 58, 2, 437475, tzinfo=utc), max_length=255),
preserve_default=False,
),
]
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trimmed_match/design/tests/geo_assignment_test.py | Praneethp09/Geo_Test | 31 | 111224 | <reponame>Praneethp09/Geo_Test
# Copyright 2020 Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Tests for trimmed_match.design.tests.util."""
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
import pandas as pd
from trimmed_match.design import geo_assignment
sign_test = geo_assignment.binomial_sign_test
iroas_test = geo_assignment.trimmed_match_aa_test
generate_assignment = geo_assignment.generate_balanced_random_assignment
generate_paired_assignment = geo_assignment._generate_random_paired_assignment
calculate_paired_difference = geo_assignment.calculate_paired_difference
class TestSupportingFunctions(parameterized.TestCase):
def testBinomialSignAaTest(self):
self.assertTrue(sign_test([-1, -1, 1], 0.6))
self.assertFalse(sign_test([-1, -1, -1], 0.6))
self.assertTrue(sign_test([0, 0, 0], 0.6))
def testTrimmedMatchAaTest(self):
self.assertFalse(iroas_test([1, 2, 3], [1, 2, 3], 0.8))
self.assertTrue(iroas_test([0, -1, 1], [1, 2, 3], 0.8))
def testGenerateRandomPairedAssignment(self):
with self.assertRaises(ValueError):
_ = generate_paired_assignment(-1)
assignment = generate_paired_assignment(10)
for i in range(10):
self.assertEqual(assignment[2 * i] + assignment[2 * i + 1], 1)
@parameterized.parameters(
(np.array([1, 2]), np.array([True, True])),
(np.array([1, 2]), np.array([1.0, 0.0])),
(np.array([1, 2]), np.array([[True], [False]])),
(np.array([1, 2]), np.array([True, True, False])),
(np.array([1, 2, 3]), np.array([0, 1], dtype=bool)))
def testCalculatePairedDifferenceValueError(self, geo_values, assignment):
with self.assertRaises(ValueError):
calculate_paired_difference(geo_values, assignment)
def testCalculatePairedDifferenceSuccess(self):
geo_values = np.array([1, 2, 3, 4])
assignment = np.array([True, False, True, False])
result = calculate_paired_difference(geo_values, assignment)
np.testing.assert_array_equal(result, np.array([-1, -1]))
class GeoAssignmentTest(absltest.TestCase):
def setUp(self):
super().setUp()
self._sign_test_data = pd.DataFrame({
'geo': [1, 2, 3, 4, 5, 6],
'pair': [1, 1, 2, 2, 3, 3],
'response': [1, 2, 3, 4, 5, 6],
'spend': [1, 2, 3, 4, 5, 6]
})
self._aa_test_data = pd.DataFrame({
'geo': [1, 2, 3, 4, 5, 6],
'pair': [1, 1, 2, 2, 3, 3],
'response': [1, 2, 3, 4, 5, 6],
'spend': [1, 1, 2, 4, 5, 6]
})
def testGenerateBalancedRandomAssignmentValueError(self):
# Incorrect columns in aa_test_data
with self.assertRaises(ValueError):
_ = generate_assignment(
self._sign_test_data,
self._aa_test_data.drop(columns=['spend'], axis=1))
# Incorrect columns in sign_test_data
with self.assertRaises(ValueError):
_ = generate_assignment(
self._sign_test_data.drop(columns=['spend'], axis=1),
self._aa_test_data)
# Mismatch on the number of geos
with self.assertRaises(ValueError):
_ = generate_assignment(
self._sign_test_data,
self._aa_test_data.append(
pd.DataFrame({
'geo': [10],
'pair': [10],
'response': [10],
'spend': 10
})))
# Two test data differ on (geo, pair)
with self.assertRaises(ValueError):
aa_test_data = self._aa_test_data.copy()
aa_test_data['geo'] = [3, 1, 2, 4, 5, 6]
_ = generate_assignment(self._sign_test_data, aa_test_data)
def testGenerateBalancedRandomAssignmentSuccess(self):
balanced_assignment = generate_assignment(self._sign_test_data,
self._aa_test_data)
assignment = np.array(balanced_assignment['assignment'])
# passes binomial sign test
self.assertTrue(
sign_test(
calculate_paired_difference(
np.array(self._sign_test_data['response']), assignment), 0.2))
# passes trimmed match aa test
delta_responses = calculate_paired_difference(
np.array(self._aa_test_data['response']), assignment)
delta_spends = calculate_paired_difference(
np.array(self._aa_test_data['spend']), assignment)
self.assertTrue(iroas_test(delta_responses, delta_spends, 0.8))
def testGenerateBalancedRandomAssignmentWithTies(self):
aa_data = pd.DataFrame({
'geo': [1, 2, 3, 4, 5, 6],
'pair': [1, 1, 2, 2, 3, 3],
'response': [1, 1, 1, 1, 1, 1],
'spend': [1, 1, 1, 1, 1, 1]
})
assignment = generate_assignment(aa_data, aa_data)['assignment']
delta_responses = calculate_paired_difference(
np.array(aa_data['response']), assignment)
delta_spends = (
calculate_paired_difference(np.array(aa_data['spend']), assignment) +
geo_assignment._INCREMENTAL_SPEND_RATIO_FOR_AA_TEST)
self.assertTrue(iroas_test(delta_responses, delta_spends, 0.8))
if __name__ == '__main__':
absltest.main()
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] |
code.py | aashray18521/parallelModifiedGrepPython | 0 | 20068 | import multiprocessing
import os
import time
rootdir = input()
keyword = input()
batch_size = 1
def try_multiple_operations(file_path):
try:
with open(file_path, "rb") as f: # open the file for reading
for line in f: # use: for i, line in enumerate(f) if you need line numbers
try:
line = line.decode("utf-8") # try to decode the contents to utf-8
except ValueError: # decoding failed, skip the line
continue
if keyword in line: # if the keyword exists on the current line...
print(file_path) # print the file path
except (IOError, OSError): # ignore read and permission errors
pass
def walk_dirs(directory, batch_size):
walk_dirs_generator = os.walk(directory)
for dirname, subdirectories, filenames in walk_dirs_generator:
for i in range(0, len(filenames), batch_size):
the_queue.put(os.path.join(dirname, filenames[i]))
the_queue = multiprocessing.Queue()
walk_dirs(rootdir, batch_size)
def worker_main(queue):
while True:
item = queue.get(True)
try_multiple_operations(item)
the_pool = multiprocessing.Pool(3, worker_main,(the_queue,))
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] |
demo/infer_demo.py | mrlooi/maskrcnn-benchmark | 344 | 168231 | <filename>demo/infer_demo.py
from maskrcnn_benchmark.config import cfg
from predictor import COCODemo, LOVDemo
import numpy as np
import torch
import torch.nn.functional as F
import glob
import cv2
def expand_boxes(boxes, scale):
w_half = (boxes[:, 2] - boxes[:, 0]) * .5
h_half = (boxes[:, 3] - boxes[:, 1]) * .5
x_c = (boxes[:, 2] + boxes[:, 0]) * .5
y_c = (boxes[:, 3] + boxes[:, 1]) * .5
w_half *= scale
h_half *= scale
boxes_exp = torch.zeros_like(boxes)
boxes_exp[:, 0] = x_c - w_half
boxes_exp[:, 2] = x_c + w_half
boxes_exp[:, 1] = y_c - h_half
boxes_exp[:, 3] = y_c + h_half
return boxes_exp
def expand_masks(mask, padding):
N = mask.shape[0]
M = mask.shape[-1]
pad2 = 2 * padding
scale = float(M + pad2) / M
padded_mask = mask.new_zeros((N, 1, M + pad2, M + pad2))
padded_mask[:, :, padding:-padding, padding:-padding] = mask
return padded_mask, scale
def paste_mask_in_image(mask, box, im_h, im_w, padding=1):
padded_mask, scale = expand_masks(mask[None], padding=padding)
mask = padded_mask[0, 0]
box = expand_boxes(box[None], scale)[0]
box = box.to(dtype=torch.int32)
TO_REMOVE = 1
w = box[2] - box[0] + TO_REMOVE
h = box[3] - box[1] + TO_REMOVE
w = max(w, 1)
h = max(h, 1)
# Set shape to [batchxCxHxW]
mask = mask.expand((1, 1, -1, -1))
# Resize mask
mask = mask.to(torch.float32)
mask = F.interpolate(mask, size=(h, w), mode='bilinear', align_corners=False)
mask = mask[0][0]
# if thresh >= 0:
# mask = mask > thresh
# else:
# # for visualization and debugging, we also
# # allow it to return an unmodified mask
# mask = (mask * 255).to(torch.uint8)
im_mask = torch.zeros((im_h, im_w), dtype=torch.float32)
x_0 = max(box[0], 0)
x_1 = min(box[2] + 1, im_w)
y_0 = max(box[1], 0)
y_1 = min(box[3] + 1, im_h)
im_mask[y_0:y_1, x_0:x_1] = mask[
(y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
]
return im_mask
def normalize(x, xmin=None, xmax=None):
xmin = np.min(x) if xmin is None else xmin
xmax = np.max(x) if xmax is None else xmax
nx = x - xmin
nx /= (xmax - xmin)
return nx
def visualize_vertmap(vertmap,bbox,h,w):
cx = normalize(paste_mask_in_image(vertmap[0, :, :],bbox,h,w).numpy(), -1, 1)
cy = normalize(paste_mask_in_image(vertmap[1, :, :],bbox,h,w).numpy(), -1, 1)
cz = normalize(paste_mask_in_image(vertmap[2, :, :],bbox,h,w).numpy(), -1, 1)
cv2.imshow("vertmap x", cx)
cv2.imshow("vertmap y", cy)
cv2.imshow("vertmap z", cz)
return np.stack((cx,cy,cz), axis=0)
if __name__ == '__main__':
# config_file = "./configs/mrcnn.yaml"
config_file = "./configs/lov_debug.yaml"
# update the config options with the config file
cfg.merge_from_file(config_file)
# manual override some options
# cfg.merge_from_list(["MODEL.DEVICE", "cpu"])
# coco_demo = COCODemo(
# cfg,
# min_image_size=480,
# confidence_threshold=0.95,
# )
lov_demo = LOVDemo(
cfg,
min_image_size=480,
confidence_threshold=0.95,
)
# image_dir = "/home/vincent/Documents/py/ml/Detectron.pytorch/demo/coco_debug_images"
# image_ext = "jpg"
image_dir = "./datasets/LOV/data/0001"
image_ext = "1-color.png"
for image_file in glob.glob("%s/*%s"%(image_dir, image_ext)):
# load image and then run prediction
# image_file = "/COCO_val2014_000000010012.jpg"
img = cv2.imread(image_file)
if img is None:
print("Could not find %s"%(image_file))
continue
# img = np.expand_dims(img,0)
res, predictions = lov_demo.run_on_opencv_image(img)
print("Showing pred results for %s"%(image_file))
h,w,_ = img.shape
cv2.imshow("pred", res)
cv2.waitKey(0)
verts = predictions.get_field("vertex")#.numpy()
bboxes = predictions.bbox
for v, bbox in zip(verts, bboxes):
vm = visualize_vertmap(v,bbox,h,w)
# vm = paste_mask_in_image(v[0],bbox,h,w)
# visualize_vertmap(vm)
# cv2.imshow("cx", normalize(vm.numpy(), -1, 1))
cv2.waitKey(0)
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2
] |
colosseumrl/envs/poker/__init__.py | carletonz/colosseumrl | 8 | 186657 | <filename>colosseumrl/envs/poker/__init__.py
from .KuhnPokerEnvironment import KuhnPokerEnvironment | [
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] |
scripts/crTestCaseGen.py | VRFamilyMan/CRTest | 0 | 101780 | <filename>scripts/crTestCaseGen.py
#!/usr/bin/env python
import re
import math
import crTemplate
def csvBooleanExpand(csvLineData):
"""Each line which contains 'X' is duplicated and the X replace with 0,1. Only one expansion per line is processed"""
ExpandOccurred = 0
newCsvLineData = []
for line in csvLineData:
#print(line)
newCsvLine = []
booleanExpand = 0
csvLine = line.split(',')
tempLine = ''
for variable in csvLine:
#search for an x,X included in alphanumeric string before it, if true don't expand
if re.search('[\s]*[a-zA-Z0-9]+x[\s]*', variable) or re.search('[\s]*[a-zA-Z0-9]+X[\s]*', variable):
tempLine = tempLine + variable + ','
#search for an x,X included in alphanumeric string after it, if true don't expand
elif re.search('[\s]*x[a-zA-Z0-9]+[\s]*', variable) or re.search('[\s]*X[a-zA-Z0-9]+[\s]*', variable):
tempLine = tempLine + variable + ','
#search for an x,X with only spaces before or after, if true EXPAND,
#replace x,X with special string to be used later.
#This is done to ensure other x,X in the line remain untouched
elif booleanExpand is 0 and (re.search('[\s]*x[\s]*', variable) or re.search('[\s]*X[\s]*', variable)):
booleanExpand = 1
tempLine = tempLine + "abcdefghijklmnopqrstuvwxyz" + ','
#anything else also don't expand
else:
tempLine = tempLine + variable + ','
#print(tempLine)
if booleanExpand is 0:
newCsvLineData.append(line)
else:
#remove extra end ','
tempLine = tempLine[0:-1]
#put back the original line and create the extra one
newCsvLine.append(tempLine)
newCsvLine.append(tempLine)
#remove the special string with 0 and 1
newCsvLine[0] = newCsvLine[0].replace('abcdefghijklmnopqrstuvwxyz','0',1)
newCsvLine[1] = newCsvLine[1].replace('abcdefghijklmnopqrstuvwxyz','1',1)
#add the new line to the final processed csv data
newCsvLineData.append(newCsvLine[0])
newCsvLineData.append(newCsvLine[1])
ExpandOccurred = 1
return ExpandOccurred, newCsvLineData
def csvRangeExpand(csvLineData):
"""Each line which contains 'MIN..STEP..MAX' is duplicated with the (MAX-MIN)/STEP replacements. Only one expansion per line is processed"""
ExpandOccurred = 0
newCsvLineData = []
minRange = 0
stepSize = 0
maxRange = 0
direction = 'UP'
newRangeValue = 0
requiredExpandLine = 0
tempString = ''
for line in csvLineData:
#print(line)
newCsvLine = []
booleanExpand = 0
csvLine = line.split(',')
tempLine = ''
for variable in csvLine:
#search for an x,X with only spaces before or after, if true EXPAND,
#replace x,X with special string to be used later.
#This is done to ensure other x,X in the line remain untouched
if booleanExpand is 0 and re.search('[\s]*[0-9][0-9]*[\s]*\.\.[\s]*[0-9][0-9]*[\s]*\.\.[\s]*[0-9][0-9]*[\s]*', variable):
booleanExpand = 1
tempString = variable.split('..')
minRange = int(tempString[0])
stepSize = int(tempString[1])
maxRange = int(tempString[2])
if minRange < maxRange:
direction = 'UP'
else:
direction = 'DOWN'
minRange = int(tempString[2])
maxRange = int(tempString[0])
tempLine = tempLine + "abcdefghijklmnopqrstuvwxyz" + ','
#anything else also don't expand
else:
tempLine = tempLine + variable + ','
#print(tempLine)
if booleanExpand is 0:
newCsvLineData.append(line)
else:
#remove extra end ','
tempLine = tempLine[0:-1]
#pull back to orignial line
newCsvLine.append(tempLine)
if direction is 'UP':
newCsvLine[0] = newCsvLine[0].replace('abcdefghijklmnopqrstuvwxyz',str(minRange),1)
newRangeValue = minRange
else:
newCsvLine[0] = newCsvLine[0].replace('abcdefghijklmnopqrstuvwxyz',str(maxRange),1)
newRangeValue = maxRange
#add the new line to the final processed csv data
newCsvLineData.append(newCsvLine[0])
#add a new line for each value in the range (MAX-MIN)/STEP
#print(minRange)
#print(stepSize)
#print(maxRange)
requiredExpandLine = int(math.ceil((maxRange - minRange)/stepSize))
#print(requiredExpandLine)
#remove the special string and replace with ranges values (minRange + stepSize + stepSize + stepSize + stepSize + MaxRange)
for i in range(0,requiredExpandLine,1):
newCsvLine.append(tempLine)
if direction is 'UP':
newRangeValue = newRangeValue + stepSize
if newRangeValue <= maxRange:
newCsvLine[i+1] = newCsvLine[i+1].replace('abcdefghijklmnopqrstuvwxyz',str(newRangeValue),1)
#add the new line to the final processed csv data
newCsvLineData.append(newCsvLine[i+1])
else:
newCsvLine[i+1] = newCsvLine[i+1].replace('abcdefghijklmnopqrstuvwxyz',str(maxRange),1)
#add the new line to the final processed csv data
newCsvLineData.append(newCsvLine[i+1])
else:
newRangeValue = newRangeValue - stepSize
if newRangeValue >= minRange:
newCsvLine[i+1] = newCsvLine[i+1].replace('abcdefghijklmnopqrstuvwxyz',str(newRangeValue),1)
#add the new line to the final processed csv data
newCsvLineData.append(newCsvLine[i+1])
else:
newCsvLine[i+1] = newCsvLine[i+1].replace('abcdefghijklmnopqrstuvwxyz',str(minRange),1)
#add the new line to the final processed csv data
newCsvLineData.append(newCsvLine[i+1])
ExpandOccurred = 1
return ExpandOccurred, newCsvLineData
def testCaseCodeGen(sourceTemplate, headerTemplate, testCaseDirAndFile, testCaseFileOnlyNoExt):
"""Takes the Test Case Data and generates C code to run with the Module under test"""
#file handling
fh = open(testCaseDirAndFile, 'r')
raw_testCase_data = fh.read()
fh.close()
testCaseLineData = raw_testCase_data.split('\n')
#open the c file template
fh = open(sourceTemplate, 'r')
raw_sourceFile = fh.read()
fh.close()
fh = open(headerTemplate, 'r')
raw_headerFile = fh.read()
fh.close()
#Required sections
variableTypes = []
variableNames = []
actualTestData = []
csvLine = []
rawRowStringArray = []
rawRowString = ''
valueArray = []
controlArray = []
valueMaxArray = []
accummulatedTime = 0
numberTestCases = 0
numberVariables = 0
numberRangeRows = 0
finalVarStructType = ''
finalCtrlStructType = ''
finalValueArray = ''
finalControlArray = ''
finalValueMaxArray = ''
reqRefString = ''
commentString = ''
tempNumberVariables = 0
#************* TEST CASE *******************
#***************************** Variable Types and Names ******************************
#search for the line with the variable types
sectionfound = 0
for line in testCaseLineData:
#print(line)
if re.search('CR_Test,Types', line):
sectionfound = 1
line = line.replace('CR_Test,','',1) #only replace the first as the string "Types" may occur else where in row
line = line.replace('Types,','',1) #only replace the first as the string "Types" may occur else where in row
variableTypes = line.split(',')
break
if sectionfound is 0:
print("Warning: No Variable Types found. \n")
#search for the Variable names
sectionfound = 0
for line in testCaseLineData:
#print(line)
if re.search('Update_time,Check_time', line):
sectionfound = 1
line = line.replace('Update_time,','',1) #only replace the first as the string "Types" may occur else where in row
line = line.replace('Check_time,','',1) #only replace the first as the string "Types" may occur else where in row
variableNames = line.split(',')
#print(variableNames)
count = 0
#determine the number of variable
for string in variableNames:
if re.search('Req_Ref', string):
numberVariables = count
count = count + 1
break
if sectionfound is 0:
print("Warning: No Variable Types found. \n")
#remove the Reg_Ref and Comments from variable name list
variableNames = variableNames[0:-2]
#check variable types list it could have extra cell on the end
#print(variableTypes)
if len(variableTypes) > numberVariables:
extraCells = len(variableTypes) - numberVariables
for x in range(0,extraCells,1):
variableTypes = variableTypes[0:-1]
#print(variableTypes)
#print(numberVariables)
#combine the variable type and names with standard C code formatting
count = 0
for count in range(0,numberVariables,1):
finalVarStructType = finalVarStructType + ' ' + variableTypes[count] + ' ' + variableNames[count] + ';\n'
#print(finalVarStructType)
#***************************** Actual Test Data ******************************
#search for the line with the actual test data
#remove the variable type and names row to leave the actual test case
sectionfound = 0
#Also create a raw row string of the test case
#add c code array sytax
rawRowString = "\n{ \n"
for line in testCaseLineData:
#print(line)
if re.search('CR_Test,Types', line):
pass
elif re.search('Update_time,Check_time', line):
pass
else:
sectionfound = 1
#Check if the row has any text
if re.search('[a-zA-Z0-9][a-zA-Z0-9_,]*', line):
actualTestData.append(line)
#Add the row to the raw row string with c code sytax, after removing the Reg_Ref
rawRowString = rawRowString + " \"" + line + "\",\n"
else:
break
if sectionfound is 0:
print("Warning: No Test Data found. \n")
#print(*actualTestData)
#remove extra end ',' and return
rawRowString = rawRowString[0:-2]
rawRowString = rawRowString + line + '\n}'
#print(rawRowString)
#**************************************************************************
#TestData = ['10, 20,FxED, X,x,56 =,34<','40,50,BXB,70, x,78=,12>','40,50..1..60,BXB,70, x,78=,12>']
#print(*TestData)
#for string in TestData:
#print(string)
#print('\n')
#finished, newTestData = csvBooleanExpand(TestData)
#for string in newTestData:
#print(string)
#print('\n')
#finished, newTestData = csvBooleanExpand(newTestData)
#finished, newTestData = csvRangeExpand(newTestData)
#for string in newTestData:
# print(string)
#print('\n')
#*****************************************************************************
#find for boolean expansion (Xx means don't care and with be replaced with 0,1)
temp = actualTestData[0].split(',')
count = 0
for variable in temp:
count = count + 1
#print(count)
for i in range(0,count,1):
itRan, actualTestData = csvBooleanExpand(actualTestData)
#print(*actualTestData)
if itRan is 0:
break
for i in range(0,count,1):
itRan, actualTestData = csvRangeExpand(actualTestData)
#print(*actualTestData)
if itRan is 0:
break
#print(*actualTestData)
#generate the control and Value arrays
lineCount = 0
sectionfound = 0
tempArray = []
for line in actualTestData:
csvLine = line.split(',')
#check for the end of the test case
if csvLine[0] is '':
break
#added formatting for the c structure
valueArray.append(" ")
controlArray.append(" ")
valueMaxString = ''
numberTestCases = numberTestCases + 1
count = 0
rangeFound = 0
for value in csvLine:
if (( numberVariables + 2 ) > count ):
#first value in the row is the updateTime
#(time between the end of the previous row and the stimulus of this one)
if count == 0:
accummulatedTime = accummulatedTime + int(value)
controlArray[lineCount] = controlArray[lineCount] + "{ " + str(accummulatedTime) + ", "
count = 1
#second value in the row is the checkTime - NOT IMPLIMENTED
#(time between the stimulus and the expected of this one)
elif count == 1:
controlArray[lineCount] = controlArray[lineCount] + value
valueArray[lineCount] = valueArray[lineCount] + "{ "
count = 2
else:
#replace the evaluation symbols with the control enumerations
#must be in this order
if re.search('[\s]*[0-9][0-9]*[\s]*\.\.[\s]*[0-9][0-9]*[\s]*!=', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_RANGE_NOT_EQU"
tempValue = value.replace('!=','')
tempArray = tempValue.split('..')
value = tempArray[0]
valueMax = tempArray[1]
rangeFound = 1
elif re.search('[\s]*[0-9][0-9]*[\s]*\.\.[\s]*[0-9][0-9]*[\s]*=', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_RANGE_EQUAL"
tempValue = value.replace('=','')
tempArray = tempValue.split('..')
#check which way the range goes.
if int(tempArray[0]) < int(tempArray[1]):
value = tempArray[0]
valueMax = tempArray[1]
else:
valueMax = tempArray[0]
value = tempArray[1]
rangeFound = 1
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*\<=', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_GREATER_EQU"
value = value.replace('<=','')
valueMax = '0'
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*\>=', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_LESS_EQU"
value = value.replace('>=','')
valueMax = '0'
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*!=', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_NOT_EQU"
value = value.replace('!=','')
valueMax = '0'
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*\>', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_LESS"
value = value.replace('>','')
valueMax = '0'
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*\<', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_GREATER"
value = value.replace('<','')
valueMax = '0'
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*=', value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_EQUAL"
value = value.replace('=','')
valueMax = '0'
#check if value only and if yes it is stimulus only
elif re.search('[\s]*[a-zA-Z0-9][a-zA-Z0-9_]*[\s]*',value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_STIMULUS"
valueMax = '0'
#check for a blank field, if blank make value zero and mark control to do nothing
elif re.search('',value) or re.search('[\s]*',value):
controlArray[lineCount] = controlArray[lineCount] + ", TC_NOTHING"
value = '0'
valueMax = '0'
else:
pass
#remove the evaluation sybols from value array
#must be in this order
valueArray[lineCount] = valueArray[lineCount] + value + " ,"
#build up the max values row string even if it is not required
valueMaxString = valueMaxString + valueMax + " ,"
count = count + 1
elif (( numberVariables + 2 ) == count ):
if value is not '':
reqRefString = reqRefString + value + ','
count = count + 1
elif (( numberVariables + 3 ) == count ):
if value is not '':
commentString = commentString + value + ','
count = count + 1
else:
pass
#remove extra end ','
valueArray[lineCount] = valueArray[lineCount][0:-1]
#check if max value row string actually contained a range
if rangeFound:
#add row to Max array and remove extra end ','
valueMaxArray.append(" { " + valueMaxString[0:-1] + " },")
numberRangeRows = numberRangeRows + 1
#first time through record the number of variables (not counting the 2 time columns)
if sectionfound == 0:
#numberVariables = count - 2
sectionfound = 1
#finish formatting the line
controlArray[lineCount] = controlArray[lineCount] + " },"
valueArray[lineCount] = valueArray[lineCount] + " },"
lineCount = lineCount + 1
#Remove the extra ',' on the last row of the c array
controlArray[lineCount-1] = controlArray[lineCount-1][0:-1]
valueArray[lineCount-1] = valueArray[lineCount-1][0:-1]
#check if there were any range evaluation found
if numberRangeRows is not 0:
#remove extra end ','
valueMaxArray[numberRangeRows-1] = valueMaxArray[numberRangeRows-1][0:-1]
#combine the right number of Control type and names with standard C code formatting
count = 0
for count in range(0,numberVariables,1):
finalCtrlStructType = finalCtrlStructType + ' TestCase_Control_en ' + variableNames[count] + '_Control;\n'
#print(finalCtrlStructType)
#combine the lines of the actual test data into one long string
count = 0
for string in valueArray:
finalValueArray = finalValueArray + string + '\n'
count = count + 1
#print(finalValueArray)
#combine the lines of the control data into one long string
count = 0
for string in controlArray:
finalControlArray = finalControlArray + string + '\n'
count = count + 1
#print(finalControlArray)
#combine the lines of the actual test data into one long string
count = 0
for string in valueMaxArray:
finalValueMaxArray = finalValueMaxArray + string + '\n'
count = count + 1
#print(finalValueMaxArray)
reqRefString = reqRefString[0:-1]
commentString = commentString[0:-1]
#print(reqRefString)
#print(commentString)
#***************************** Write Test C file *******************
# SOURCE FILE GENERATOR
#search for the key words to replace with c code
raw_sourceFile = raw_sourceFile.replace('##TestCase_HeaderFile##', testCaseFileOnlyNoExt + "_testcase.h")
raw_sourceFile = raw_sourceFile.replace('##TestCase_NoTests##', str(numberTestCases))
raw_sourceFile = raw_sourceFile.replace('##TestCase_NoVariables##', str(numberVariables))
raw_sourceFile = raw_sourceFile.replace('##TestCase_NoRangeRows##', str(numberRangeRows))
raw_sourceFile = raw_sourceFile.replace('##TestCase_Name##', testCaseFileOnlyNoExt)
raw_sourceFile = raw_sourceFile.replace('##TestCase_RawRowString##', rawRowString)
raw_sourceFile = raw_sourceFile.replace('##TestCase_ReqString##', reqRefString)
raw_sourceFile = raw_sourceFile.replace('##TestCase_ValueType##', finalVarStructType)
raw_sourceFile = raw_sourceFile.replace('##TestCase_ControlType##', finalCtrlStructType)
raw_sourceFile = raw_sourceFile.replace('##TestCase_Values##', finalValueArray)
raw_sourceFile = raw_sourceFile.replace('##TestCase_Control##', finalControlArray)
raw_sourceFile = raw_sourceFile.replace('##TestCase_ValuesMax##', finalValueMaxArray)
#Generating test functions which contain the actual test variables
# Stimulate function
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_StimulateFn##', '##VarName##', '##NL##', variableNames)
# Expected function
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_ExpectedFn##', '##VarName##', '##NL##', variableNames)
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_ExpectedV1##', '##VarName##', '##NL##', variableNames)
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_ExpectedV2##', '##VarName##', '##NL##', variableNames)
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_ExpectedV3##', '##VarName##', '##NL##', variableNames)
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_ExpectedV4##', '##VarName##', '##NL##', variableNames)
raw_sourceFile = crTemplate.varMLTemplateGen(raw_sourceFile, '##TestCase_ExpectedV5##', '##VarName##', '##NL##', variableNames)
# Variable Check function
raw_sourceFile = crTemplate.varAndTypeMLTemplateGen(raw_sourceFile, '##TestCase_VarCheckFn1##', '##VarName##', '##VarType##', '##NL##', variableNames, variableTypes)
raw_sourceFile = crTemplate.varAndTypeMLTemplateGen(raw_sourceFile, '##TestCase_VarCheckFn2##', '##VarName##', '##VarType##', '##NL##', variableNames, variableTypes)
# HEADER FILE GENERATOR
#search for the key words to replace with c code
raw_headerFile = raw_headerFile.replace('##TestCase_Name##', str.upper(testCaseFileOnlyNoExt))
return raw_sourceFile, raw_headerFile, reqRefString
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] |
mctl/favicons.py | jgeboski/mctl | 4 | 152808 | <filename>mctl/favicons.py
# Copyright 2012-2020 <NAME> <<EMAIL>>
#
# 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.
# Image origin: http://en.wikipedia.org/wiki/File:DIN_4844-2_Warnung_vor_einer_Gefahrenstelle_D-W000.svg
CAUTION_BASE64 = (
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAABhWlDQ1BJQ0MgcHJvZmlsZQAA"
"<KEY>"
"zro6uAqC4A+Ik6OToouUeF9SaBHjhcf7OO+ew3v3AUKzylQzMAGommWkE3Exl18Vg68IQIAP"
"g4hIzNSTmcUsPOvrnjqp7mI8y7vvz+pXCiYDfCLxHNMNi3iDeGbT0jnvE4dZWVKIz4nHDbog"
"8SPXZZffOJccFnhm2Mim54nDxGKpi+UuZmVDJZ4mjiqqRvlCzmWF8xZntVpn7XvyF4YK2kqG"
"67QiSGAJSaQgQkYdFVRhIUa7RoqJNJ3HPfwjjj9FLplcFTByLKAGFZLjB/+D37M1i1OTblIo"
"DvS82PbHKBDcBVoN2/4+tu3WCeB/Bq60jr/WBGY/SW90tOgRMLANXFx3NHkPuNwBhp90yZAc"
"yU9LKBaB9zP6pjwwdAv0rblza5/j9AHI0qyWb4CDQ2CsRNnrHu/u7Z7bvz3t+f0ADmVyf6Xa"
"xBUAAAAGYktHRAD/AP8A/6C9p5MAAAAJcEhZcwAADdcAAA3XAUIom3gAAAkhSURBVHja7Zp7"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>fHygL5ge6ecKhQzSLMGHQEdDUxDeJiSpQs1mRX8+Hy28XwmWY"
"RXHp4+OR69c5NNis/9S777qv+Q05FqlzWKXOYZXXN1jc2t55BxEhdbCAD6iq4reICBXg6NFa"
"sVVFuAiotkXImDE6V3twMFJeTokIhsFAwOr0dHfr79oV5ALfVj7/PNitz5IliAgrb3bwI86c"
"waHXq8BmztR7gG8rf3pY7+qn0yGFhdS<KEY>lBTcQB07FtopAT+eCBU/P7V/cjIiwt9v"
"VvBJBw7Q0t6tFy0ydgq+rSxebHRbCl9+SYsIM2428JqGBo6OHasCCQrSyH/LwrsloPz3CAkP"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"oIHX1dRQNHKkOtHISK1UXInwADR5sl+nBEye7OfRv/JqhNxxh9bVZ8QIxG6nWAT9QNoEX8rP"
"J76kRFXkrD<KEY>dNc06b5d7ip"
"1TmsMmVK5x6QmOjX4TOOWqvcd5+/q19AAHL+PDUiWAeCB6x79VWCamvVIyz/DfUI+3/p6jLU"
"WZuiOI9FTWtzXR1kZGAE1v2hBIgQV1DAizt3qrp58wJJTPTr9JmQLgjoqu3uu/145plAV/3T"
"T+Gbb1ggwtQ/jIDmZrYsWYJWWpeu2aywZrW5y2eCQzRetQGsW2tmmEV1rSVL0DQ18ZaI9zda"
"TR+sn/bhh8w6elTVvbrCRERE10MGe+kBAKGhGlasUAn+8Uf44APuBZ66oQSI4F9dzRvZ2apu"
"<KEY>"
"<KEY>"
"Uu9BW7bA6dMsF2HkjfCA3PR0TA0NzopO54z2eiohXhyDHUlengW/1sOmsRGWLsUA5PUrASIk"
"HDzIs3v2qLoXnjcyPr7nsa7Vqu30jhAe3vPpxMbqeOF5ddl/9RXs28fc3n5mV3oBXmls5N8J"
"CUwvKnLqgoI0FP0c2ivLiUCEtRy7vcU9E2RWuHQpwnXZ6YnYbC2Mn3CFq1edY8XEQFERhXo9"
"CYpCs6894Olt21TwAKtWmXsFvu1W19FaDw7R9go8wPDhGl57Td0Qz52D7duZCPzFpx4gQkBF"
"BWdiYxlx9ap3aa72Mn1GBT/80OimS0jw4z+<KEY>WFPze25hTh1ClskZFEKwqVvvKA"
"jOxsFbw3aa72EhXl+SNIZKR3P4dotfDmm2rsYbfD2rUM72n6TNMD699eVMSy999XdampBmbN"
"<KEY>4WJ3X4yUl+ZOaqh7D770Hx47xsgjxvvCA/PR0DE1Nzoper7B+nblPEdi99/p3"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"<KEY>"
"YnOngdL54jCfvOfy5RYmTrxMtd25yUyaBEePUqDVMr31Vz1PDxAh+eOPVfAAK5abfAbemdDw"
"rq23EhamYdkyk6t+4gR89BFJwNwOl4AIOpuNtzMz1cZRo7QsWmTElzJ7tsGrNm9k8WKjW/os"
"IwOqqtjcPn3W3gMW5eYS55bmyrVgMPj2zM/KNBM3zvPaGzdOR3aW2afv8vdXyMlxT5/l5hIJ"
"<KEY>
"<KEY>"
"<KEY>"
"lPJy6sPDffOt/WaTS5eoRYS9aWm3ngekpiIifKaIEAr89eJFZjsc1N0Klg8IgKgojgDPMiRD"
"MiS3tPwPyDEn525xOZcAAAAASUVORK5CYII="
)
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coder_directory_api/auth/__init__.py | seekheart/coder_directory_api | 3 | 101423 | <reponame>seekheart/coder_directory_api<filename>coder_directory_api/auth/__init__.py<gh_stars>1-10
"""
Authorization Package
Copyright (c) 2017 by <NAME>.
MIT License, see LICENSE for details
"""
__all__ = ['check_token', 'token_required', 'make_token', 'refresh_token']
from .jwt_authorization import *
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gpdist/tanh_saturate.py | saalfeldlab/gunpowder-distances | 0 | 29932 | import logging
import numpy as np
from gunpowder.nodes.batch_filter import BatchFilter
logger = logging.getLogger(__name__)
class TanhSaturate(BatchFilter):
'''Saturate the values of an array to be floats between -1 and 1 by applying the tanh function.
Args:
array (:class:`ArrayKey`):
The key of the array to modify.
factor (scalar, optional):
The factor to divide by before applying the tanh, controls how quickly the values saturate to -1, 1.
'''
def __init__(self, array, scale=None):
self.array = array
if scale is not None:
self.scale = scale
else:
self.scale = 1.
def process(self, batch, request):
if self.array not in batch.arrays:
return
array = batch.arrays[self.array]
array.data = np.tanh(array.data/self.scale)
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create_alert.py | miwiwa/sample-miwalker-no-git-issues | 0 | 126771 | <gh_stars>0
#!/usr/bin/env python
# Program submits PagerDuty incidents or Git issues upon request
import pip
package = 'requests'
pip.main(['install', package])
import requests
import json
import argparse
from os import environ
import subprocess
import re
import collections
import sys
# Read in argument(s)
description = 'Specify creation of incident/issue in Pagerduty and Git Issues'
parser = argparse.ArgumentParser( description=__doc__)
parser.add_argument('-a','--ALERTS', nargs='+', type=str.lower, dest='ALERTS', help="Enter 'PagerDuty' and/or 'Git' to open incident/issue", required=True)
args = parser.parse_args()
alerts = args.ALERTS
# Import Pipeline environment variables
print(str(environ.get('IDS_JOB_NAME')))
ids_job_name = environ.get('IDS_JOB_NAME')
ids_job_id = environ.get('IDS_JOB_ID')
ids_stage_name = str(environ.get('IDS_STAGE_NAME'))
print("ids_stage_name:", ids_stage_name)
ids_project_name = environ.get('IDS_PROJECT_NAME')
ids_url = environ.get('IDS_URL')
git_url = environ.get('GIT_URL')
task_id = environ.get('TASK_ID')
archive_dir = environ.get('ARCHIVE_DIR')
pipeline_id = environ.get('PIPELINE_ID')
pipeline_stage_id = environ.get('PIPELINE_STAGE_ID')
pipeline_stage_name = environ.get('PIPELINE_STAGE_NAME')
pipeline_stage_input_job_id = environ.get('PIPELINE_STAGE_INPUT_JOB_ID')
pipeline_initial_stage_execution_id = environ.get('PIPELINE_INITIAL_STAGE_EXECUTION_ID')
workspace = environ.get('WORKSPACE')
github_token = environ.get('github_accessToken')
# Load toolchain json to dict for parsing
toolchain_json = "%s/_toolchain.json" % workspace
with open(toolchain_json) as f:
data = json.load(f)
ids_region_id = data['region_id']
instance_id = [i['instance_id'] for i in data["services"] if 'pipeline' in i['broker_id']]
ids_instance_id = instance_id[0]
pipeline_base_url = "https://console.bluemix.net/devops/pipelines/"
pipeline_full_url = pipeline_base_url + pipeline_id + "/" + pipeline_stage_id + "/" + ids_job_id + "?env_id=" + ids_region_id
print("pipeline_full_url:", pipeline_full_url)
def trigger_incident():
# Function creates request to create new PagerDuty incident and submits
# Parse dict for PagerDuty parameters
try:
pd_service_id = [i['parameters']['service_id'] for i in data["services"] if 'pagerduty' in i['broker_id']]
pd_api_key = [i['parameters']['api_key'] for i in data["services"] if 'pagerduty' in i['broker_id']]
pd_user_email = [i['parameters']['user_email'] for i in data["services"] if 'pagerduty' in i['broker_id']]
api_key = pd_api_key[0]
service_id = pd_service_id[0]
user_email = pd_user_email[0]
except (KeyError, IndexError):
print("ERROR: Pager Duty is not configured correctly with the toolchain")
return 1
# Develop request to create incident through API
url = 'https://api.pagerduty.com/incidents'
headers = {
'Content-Type': 'application/json',
'Accept': 'application/vnd.pagerduty+json;version=2',
'Authorization': 'Token token={token}'.format(token=api_key),
'From': user_email
}
payload = {
"incident": {
"type": "incident",
"title": "Job: " + ids_job_name + " in Stage: " + ids_stage_name + " and Project: " + ids_project_name + " failed" ,
"service": {
"id": service_id,
"type": "service_reference"
},
"body": {
"type": "incident_body",
"details": pipeline_full_url
}
}
}
# Send request to PagerDuty
r = requests.post(url, headers=headers, data=json.dumps(payload))
code=r.status_code
# Check if request was successful. If not successful then fail the job
if code != 201:
print("ERROR: PagerDuty incident request did not complete successfully")
exit()
else:
print("PagerDuty incident request created successfully")
def trigger_issue(title, body=None):
# Function creates request to create Git Issue and submits
git_repo_owner = environ.get('GIT_REPO_OWNER')
git_repo_name = environ.get('GIT_REPO_NAME')
git_issue_label = environ.get('GIT_ISSUE_LABEL')
print("git_repo_owner:", git_repo_owner)
print("git_repo_name:", git_repo_name)
#api_base_url = "https://api.github.ibm.com/"
api_base_url = "https://api.github.com/"
if git_repo_owner is not None and git_repo_name is not None:
print("Values already given")
print("git_repo_owner:", git_repo_owner)
print("git_repo_name:", git_repo_name)
elif git_url is not None:
print("Parsing git_url")
git_repo_name = re.search(r'(.*)/(.*)', git_url).group(2).split('.')[0]
git_repo_owner = re.search(r'(.*)/(.*)', git_url).group(1).split('/')[3]
print("git_repo_owner:", git_repo_owner)
print("git_repo_name:", git_repo_name)
else:
try:
repo_owner = [i['parameters']['owner_id'] for i in data["services"] if 'git' in i['broker_id']]
repo_name = [i['parameters']['repo_name'] for i in data["services"] if 'git' in i['broker_id']]
except (KeyError, IndexError):
print("ERROR: Git Issues is not configured correctly with the toolchain")
return 1
if git_issue_label is None:
git_issue_label = 'feature'
headers = {
"Content-Type": "application/json",
"Authorization": "token " + github_token
}
issue = {'title': title,
'body': body,
'labels': git_issue_label}
# Specifies URL for github api
print("Specifying URL for base call")
url = api_base_url + "repos/" + git_repo_owner + "/" + git_repo_name + "/issues"
print("url", url)
r = requests.post(url, headers=headers, data=json.dumps(issue))
if r.status_code != 201:
print("status code:",r.status_code)
print("status text:",r.text)
print("status_content:",r.content)
print ('ERROR: Could not create Git Issue {0:s}'.format(title))
exit()
else:
print ('Successfully created Git Issue {0:s}'.format(title))
if __name__ == '__main__':
print("=============================")
print("Creating alerts")
if all(alert_type in alerts for alert_type in ('incident', 'issue')):
trigger_incident()
trigger_issue("Job: " + ids_job_name + " in Stage: " + ids_stage_name + " and Project: " + ids_project_name + " failed", pipeline_full_url)
elif 'incident' in alerts:
trigger_incident()
elif 'issue' in alerts:
trigger_issue("Job: " + ids_job_name + " in Stage: " + ids_stage_name + " failed", pipeline_full_url)
else:
print("Alert type was not specified in call")
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sources/t08/t08ej02.py | workready/pythonbasic | 0 | 34892 | class Empleado(object):
"Clase para definir a un empleado"
def __init__(self, nombre, email):
self.nombre = nombre
self.email = email
def getNombre(self):
return self.nombre
jorge = Empleado("Jorge", "<EMAIL>")
jorge.guapo = "Por supuesto"
# Probando hasattr, getattr, setattr
print(hasattr(jorge, 'guapo')) # True
print(getattr(jorge, 'guapo')) # Por supuesto
print(hasattr(jorge, 'listo')) # False
setattr(jorge, 'listo', 'Más que las monas')
print(getattr(jorge, 'listo')) # Mas que las monas | [
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molmap/data-processing/pubchem/03_statistics_NumOnBits.py | riversdark/bidd-molmap | 75 | 126429 | import numpy as np
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
import os
bitsize = 1024
total_sample = 110913349
data_save_folder = './data'
file = './data/%s_%s.npy' % (total_sample, bitsize)
f = np.memmap(file, dtype = np.bool, shape = (total_sample, bitsize))
def _sum(memmap, x):
return memmap[x, ].sum()
P = Parallel(n_jobs=16) #
res = P(delayed(_sum)(f, i) for i in tqdm(range(total_sample)))
pd.Series(res).to_pickle('./data/%s_%s_NumOnBits.pkl' % (total_sample, bitsize))
print('Done!') | [
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hover/utils/misc.py | phurwicz/hover | 251 | 1608812 | <gh_stars>100-1000
"""Mini-functions that do not belong elsewhere."""
from datetime import datetime
from abc import ABC, abstractmethod
def current_time(template="%Y%m%d %H:%M:%S"):
return datetime.now().strftime(template)
class BaseUnionFind(ABC):
"""
???+ note "Data attached to union-find."
"""
def __init__(self, data):
self._data = data
self._parent = None
self._count = 1
def __repr__(self):
return self.data.__repr__()
@property
def count(self):
if self.parent is None:
return self._count
return self.find().count
@count.setter
def count(self, count):
self._count = count
@property
def parent(self):
return self._parent
@parent.setter
def parent(self, other):
assert isinstance(other, BaseUnionFind)
self._parent = other
def find(self):
if self.parent:
self.parent = self.parent.find()
return self.parent
return self
@abstractmethod
def union(self, other):
pass
class NodeUnionFind(BaseUnionFind):
"""
???+ note "Each node keeps its own data."
"""
@property
def data(self):
return self._data
@data.setter
def data(self, data):
self._data = data
def union(self, other):
root = self.find()
other_root = other.find()
if root is other_root:
return
# merge the smaller trees into the larger
if root.count < other_root.count:
other_root.count += root.count
root.parent = other_root
else:
root.count += other_root.count
other_root.parent = root
class RootUnionFind(BaseUnionFind):
"""
???+ note "Union always uses left as root. Each node looks up its root for data."
"""
@property
def data(self):
root = self.find()
if self is root:
return self._data
return root.data
@data.setter
def data(self, data):
root = self.find()
if self is root:
self._data = data
root._data = data
def union(self, other):
root = self.find()
other_root = other.find()
# clear the data on the other root
other_root.data = None
root.count += other_root.count
other_root.parent = root
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] |
scripts/embed_seqs.py | susiegriggo/ProtvecHierachy | 0 | 68873 | """Code to embed a set of sequences in an embedding space using a trained protvec model and an embedding set of sequences. Creates a .csv file of the embedded sequences. Also returns file of sequences which could not be successfully embedded"""
import pickle
import numpy as np
from Bio import SeqIO
import pandas as pd
from sklearn.preprocessing import minmax_scale
import warnings
from random import shuffle
warnings.filterwarnings(action='once')
def seq_3mers(sequence):
"""Takes a sequence to overlapping 3-mers"""
seq_size = len(sequence)
seq_3mers = list() #intialise list
#iterate through sequence to obtain 3mers
for i in range (1,seq_size-1):
seq_3mers.append(sequence[i-1]+sequence[i]+sequence[i+1])
return seq_3mers
def seq_vector(seq_3mers, vec_dict, d, seq):
"""Converts a list of 3-mers to a vector, vec_dict is the model vectors and d is the number of dimensions in the embedding"""
#intialise empty array
seq_arr = np.zeros((len(seq_3mers), 100))
#populate the array
for i in range (0,len(seq_arr)):
if seq_3mers[i] in vec_dict:
seq_arr[i] = vec_dict.get(seq_3mers[i])
else:
print('\n3-mer not in model!!!!')
print('BAD 3MER: '+seq_3mers[i])
print(seq)
#take the array to a vector
sequence_vec = pd.DataFrame(seq_arr, index = seq_3mers).sum(axis = 0, skipna = True)
return sequence_vec
def normalise_vec(embedded_seqs, d):
"""Normalises embedded sequences in d dimensions. Got this from Phage-Prot https://github.com/mikejhuang/PhageProtVec/blob/master/protvec.py"""
features = []
for i in range(d):
tempvec = [vec[i] for vec in embedded_seqs]
print(tempvec)
mean = np.mean(tempvec)
var = np.var(tempvec)
features.append([(vec[i]-mean)/var for vec in embedded_seqs])
features = np.array(features).reshape(len(embedded_seqs),len(features))
return features
def standardise_vec(embedded_seqs,d):
"""Standardisation for embedded sequences in d dimensions"""
#intialise an empty matrix
stand_embed = np.zeros((len(embedded_seqs),d))
#normalise each vector
for i in range(0,len(embedded_seqs)):
x = embedded_seqs[i]
z = (x-np.mean(x))/np.std(x)
stand_embed[i] = z
return stand_embed
def missing_3mers(three_mers, vec_dict):
"""Checks that the 3mers are in the model"""
missing = [x for x in three_mers if x not in list(vec_dict.keys())]
return missing
def embed_sequences(sequences,vec_dict, d):
"""Embeds sequences in d dimensions using some protvec model vec_dict"""
#intialise an empty matrix for sequence vectors
embedded_seqs = np.zeros((len(sequences), d))
#intialise an empty matrix for the id of sequences embedded
embedded_seqs_keys = list()
#intialise a list of dictionaries for sequences with 3mers which are not in the model
bad_seqs = list()
#get the keys of the sequences
seq_keys = list(sequences.keys()) #list of the sequence ids
#reshuffle the sequence keys
shuffle(seq_keys)
#iterate through the sequence to obtain vectors
for i in range(0, len(sequences)):
#get 3mers in the sequence
three_mers = seq_3mers(str(sequences.get(seq_keys[i]).seq))
#check the 3-mers are in the model (if not exclude these sequences)
missing = missing_3mers(three_mers, vec_dict)
if len(missing) == 0 and len(sequences.get(seq_keys[i]).seq) <= 1024:
#get the vector of the sequence
vec = seq_vector(three_mers, vec_dict, d, sequences.get(seq_keys[i]).seq)
#add vector to array
embedded_seqs[i] = vec
#add corresponding sequence key to the list
embedded_seqs_keys.append(seq_keys[i])
else:
bad_seqs.append({'seq_ID': seq_keys[i], 'sequence':str(sequences.get(seq_keys[i]).seq), 'bad_3mers': missing})
#drop rows with zeroes (where there was a sequence could not be embedded)
embedded_seqs = embedded_seqs[~np.all(embedded_seqs == 0, axis=1)]
#standardise the vectors
standard_embedding = standardise_vec(embedded_seqs,d)
return standard_embedding, embedded_seqs_keys, bad_seqs
#import the pickled vector dictionary
with open('../protvec_models/bacillus_3mervectors.pkl', 'rb') as f:
vec_dict = pickle.load(f)
#load in real sequences to embed (see what the speed is like when I run it locally)
seqs = SeqIO.index("../sequences/bacillus_embeddingset.fa", 'fasta')
#embed the sequences
print('EMBEDDING SEQUENCES')
embedding,seqs_keys, missing = embed_sequences(seqs, vec_dict, 100)
print('sequences embedded')
#Assemble the embedding in a dataframe and drop the empty rows
embedding_df = pd.DataFrame(embedding)
embedding_df.index = seqs_keys
#save the embedding and missing 3mers so we can evaluate them locally
print('Saving the embedding')
embedding_df.to_csv('../embedded_sequences/bacillus_filtered_embedded.csv', sep = '\t')
(pd.DataFrame(missing)).to_csv('../embedded_sequences/bacillus_filtered_notembedded.csv', sep = '\t')
print('Embedding saved!')
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] |
dtc/message_types/open_orders_request.py | jseparovic/python-ws-dtc-client | 15 | 49627 | <filename>dtc/message_types/open_orders_request.py
from dtc.enums.message_types import MessageTypes
from lib.base_message_type import BaseMessageType
class OpenOrdersRequest(BaseMessageType):
def __init__(self,
request_id=None,
request_all_orders=None,
server_order_id=None,
trade_account=None):
self.Type = MessageTypes.OPEN_ORDERS_REQUEST
self.RequestID = request_id
self.RequestAllOrders = request_all_orders
self.ServerOrderID = server_order_id
self.TradeAccount = trade_account
@staticmethod
def from_message_short(message_obj):
packet = message_obj.get('F')
return OpenOrdersRequest(
request_id=packet[0],
request_all_orders=packet[1],
server_order_id=packet[2],
trade_account=packet[3]
)
@staticmethod
def from_message_long(message_obj):
return OpenOrdersRequest(
request_id=message_obj.get('RequestID'),
request_all_orders=message_obj.get('RequestAllOrders'),
server_order_id=message_obj.get('ServerOrderID'),
trade_account=message_obj.get('TradeAccount')
)
@staticmethod
def from_message(message_obj):
if 'F' in message_obj:
return OpenOrdersRequest.from_message_short(message_obj)
else:
return OpenOrdersRequest.from_message_long(message_obj)
@staticmethod
def get_message_type_name():
return "OpenOrdersRequest"
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] |
rollbar/examples/starlette/app_logger.py | jackton1/pyrollbar | 177 | 132928 | <gh_stars>100-1000
#!/usr/bin/env python
# This example uses Uvicorn package that must be installed. However, it can be
# replaced with any other ASGI-compliant server.
#
# NOTE: Python 3.6 requires aiocontextvars package to be installed.
# Optional asynchronous reporting requires HTTPX package to be installed.
#
# Run: python app_logger.py
import logging
import rollbar
import uvicorn
from rollbar.contrib.starlette import LoggerMiddleware
from rollbar.logger import RollbarHandler
from starlette.applications import Starlette
from starlette.responses import PlainTextResponse
# Initialize Rollbar SDK with your server-side ACCESS_TOKEN
rollbar.init(
'ACCESS_TOKEN',
environment='staging',
handler='async', # For asynchronous reporting use: default, async or httpx
)
# Set root logger to log DEBUG and above
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
# Report ERROR and above to Rollbar
rollbar_handler = RollbarHandler()
rollbar_handler.setLevel(logging.ERROR)
# Attach Rollbar handler to the root logger
logger.addHandler(rollbar_handler)
# Integrate Rollbar with Starlette application
app = Starlette()
app.add_middleware(LoggerMiddleware) # should be added as the last middleware
# GET query params will be sent to Rollbar and available in the UI
# $ curl http://localhost:8888?param1=hello¶m2=world
@app.route('/')
async def root(request):
# Report log entries
logger.critical('Critical message sent to Rollbar')
logger.error('Error message sent to Rollbar')
# Ignore log entries
logger.warning('Warning message is not sent to Rollbar')
logger.info('Info message is not sent to Rollbar')
logger.debug('Debug message is not sent to Rollbar')
return PlainTextResponse('hello world')
if __name__ == '__main__':
uvicorn.run(app, host='localhost', port=8888)
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data/quaro/csv_to_tsv.py | Nstats/my_bert | 1 | 1606707 | import re
import pandas as pd
dev_size = 20000
train_dir_csv = './train.csv'
train_balanced_dir_csv = './train_balanced.csv'
dev_dir_csv = './dev.csv'
train_dir_tsv = './train.tsv'
dev_dir_tsv = './dev.tsv'
def remove_link_and_slash_split(sen):
regex_link = r'(http|ftp|https):\/\/[\w\-_]+(\.[\w\-_]+)+([\w\-\.,@?^=%&:/~\+#]*[\w\-\@?^=%&/~\+#])?'
regex_slash_3 = r'[a-zA-Z]{3,}/[a-zA-Z]{3,}/[a-zA-Z]{3,}'
regex_slash_2 = r'[a-zA-Z]{3,}/[a-zA-Z]{3,}'
sen, number = re.subn(regex_link, ' link ', sen)
result = re.findall(regex_slash_3, sen, re.S)
for word in result:
new_word = word.replace('/', ' / ')
sen = sen.replace(word, new_word)
result = re.findall(regex_slash_2, sen, re.S)
for word in result:
new_word = word.replace('/', ' / ')
sen = sen.replace(word, new_word)
return sen
def remove_formula(sen):
for i in range(100):
judge1 = '[math]' in sen and '[/math]' in sen
judge2 = '[math]' in sen and '[\math]'in sen
judge3 = '[math]' in sen and '[math]'in sen.replace('[math]', '', 1)
if judge1 or judge2:
index1 = sen.find('[math]')
index2 = max(sen.find('[\math]'), sen.find('[/math]'))+7
(index1, index2) = (min(index1, index2), max(index1, index2))
sen = sen.replace(sen[index1: index2], ' formula ')
elif judge3:
index1 = sen.find('[math]')
index2 = sen.replace('[math]', ' ', 1).find('[math]') + 6
sen = sen.replace(sen[index1: index2], ' formula ')
else:
break
return sen
def preprocessing(file_dir):
df = pd.DataFrame(pd.read_csv(file_dir, encoding='utf-8', engine='python'))
question_text = df['question_text'].fillna('').apply(
lambda x: remove_link_and_slash_split(remove_formula(x)))
df['question_text'] = question_text
df = df.sample(frac=1.0)
dev_df = df[:dev_size]
train_df = df[dev_size:]
df_target_1 = train_df[train_df['target'] == 1]
for i in range(1):
train_df = train_df.append(df_target_1)
train_df = train_df.sample(frac=1.0)
return train_df, dev_df
train_df, dev_df = preprocessing(train_dir_csv)
train_df.to_csv(train_balanced_dir_csv)
dev_df.to_csv(dev_dir_csv)
train_df = pd.DataFrame(pd.read_csv(train_balanced_dir_csv, encoding='utf-8', engine='python'))
id = train_df['qid']
text = train_df['question_text'].fillna('').values
target = train_df['target']
size = train_df.shape[0]
ftrain = open(train_dir_tsv, 'w', encoding='utf-8')
for i in range(size):
line = text[i].replace('\n', ' ', 100).replace('\t', ' ', 100)
ftrain.write(str(target[i])+'\t'+str(id[i])+'\t'+line+'\n')
ftrain.close()
dev_df = pd.DataFrame(pd.read_csv(dev_dir_csv, encoding='utf-8', engine='python'))
dev_id = dev_df['qid']
dev_text = dev_df['question_text'].fillna('').values
dev_target = dev_df['target']
fdev = open(dev_dir_tsv, 'w', encoding='utf-8')
for i in range(dev_size):
line = dev_text[i].replace('\n', ' ', 100).replace('\t', ' ', 100)
fdev.write(str(dev_target[i])+'\t'+str(dev_id[i])+'\t'+line+'\n')
fdev.close()
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myapp/library/cache.py | lalanza808/lza-quart-app | 0 | 152805 | from json import loads as json_loads
from json import dumps as json_dumps
from datetime import timedelta
from redis import Redis
from app.library.market import get_market_data
from app import config
class Cache(object):
def __init__(self):
self.redis = Redis(host=config.REDIS_HOST, port=config.REDIS_PORT)
def store_data(self, item_name, expiration_minutes, data):
self.redis.setex(
item_name,
timedelta(minutes=expiration_minutes),
value=data
)
def get_coin_price(self, coin_name):
key_name = f'{coin_name}_price'
data = self.redis.get(key_name)
if data:
return json_loads(data)
else:
d = get_market_data(coin_name)
data = {
key_name: d['market_data']['current_price'],
}
self.store_data(key_name, 4, json_dumps(data))
return data
cache = Cache()
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feather-rp2040/pio-hx711-driver/hx711/__init__.py | fivesixzero/circuitpython-experiments | 3 | 1611871 | """
`HX711`
====================================================
Driver superclass for the HX711 load cell amplifier/DAC.
* Author(s): <NAME>
Implementation Notes
--------------------
**Driver Subclasses**
* `HX711_GPIO`, works with all boards but may be subject to timing issues
* `HX711_PIO`, works with RP2040's PIO to provide more consistent pulse timing
**Hardware:**
* SparkFun `HX711 Load Cell Amplifier Breakout <https://www.sparkfun.com/products/13879>`_
"""
__version__ = "0.0.0-auto.0"
__repo__ = "https://github.com/fivesixzero/CircuitPython_HX711"
class HX711():
def __init__(
self,
gain: int = 1,
offset: int = 0,
scale: float = 1,
tare: bool = False
):
self.gain = gain
self.offset = offset
self.scale = scale
if tare:
self.read_raw() # Pull a reading to avoid first-read issues
self.tare()
@property
def gain(self) -> int:
return self._gain
@gain.setter
def gain(self, gain: int) -> None:
if gain < 1 or gain > 3:
raise ValueError()
else:
self._gain = gain
def tare(self) -> None:
self.offset = self.read_raw()
def determine_scale(self, weight: float) -> float:
if not self.offset:
raise ValueError()
reading = self.read_average()
diff = abs(reading - self.offset)
self.scale = diff / weight
return self.scale
def read(self, average_count: int = 1) -> int:
if average_count > 1:
return (self.read_average() - self.offset) / self.scale
else:
return (self.read_raw() - self.offset) / self.scale
def read_average(self, count: int = 10):
readings = []
for i in range(count):
readings.append(self.read_raw(clear_fifo=False))
return sum(readings) // len(readings)
def read_raw(self) -> int:
raise NotImplementedError()
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privatise.py | seykuyinu/privatise-spotify-playlists | 1 | 71684 | <reponame>seykuyinu/privatise-spotify-playlists
import sys
import spotipy
import spotipy.util as util
def filter_public(playlist_items):
'''
Returns all public playlsits present in the given playlist array
'''
public_playlists = list(filter(lambda playlist: playlist['public'] == True, playlist_items))
return public_playlists
def pretty_print_playlists(playlists):
list(map(lambda playlist: print(playlist['name']), playlists))
def make_playlists_private(sp: spotipy.Spotify, username, playlists):
list(map(lambda playlist: sp.user_playlist_change_details(username, playlist['id'], public=False), playlists))
def main():
scope = 'user-library-read playlist-modify-public'
if len(sys.argv) > 1:
username = sys.argv[1]
else:
print(f"Usage: {sys.argv[0]} username")
sys.exit()
token = util.prompt_for_user_token(username, scope)
if token:
sp = spotipy.Spotify(auth=token)
offset = 0
limit = 50
count = 0
while True:
playlists = sp.current_user_playlists(offset=offset, limit=limit)
public_playlists = filter_public(playlists['items'])
if len(public_playlists) > 0:
print("Privatising the following public playlists.. \n")
pretty_print_playlists(public_playlists)
make_playlists_private(sp, username, public_playlists)
count += len(public_playlists)
next_page = playlists['next']
if next_page:
offset += limit
else:
if count == 0:
print("No public playlists were found.")
else:
print(f"{count} public playlists have been made private.")
break
else:
print ("Can't get token for", username)
if __name__ == "__main__":
main()
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] |
simpleredial/model/LatentInteractionModels/dual_bert_compare.bak2.py | gmftbyGMFTBY/SimpleReDial-v1 | 36 | 114139 | <reponame>gmftbyGMFTBY/SimpleReDial-v1
from model.utils import *
class BERTDualCompEncoder(nn.Module):
'''This model needs the gray(hard negative) samples, which cannot be used for recall'''
def __init__(self, **args):
super(BERTDualCompEncoder, self).__init__()
model = args['pretrained_model']
s = args['smoothing']
self.gray_num = args['gray_cand_num']
nhead = args['nhead']
dim_feedforward = args['dim_feedforward']
dropout = args['dropout']
num_encoder_layers = args['num_encoder_layers']
self.bce_loss_scale = args['bce_loss_scale']
# ====== Model ====== #
self.ctx_encoder = BertEmbedding(model=model)
self.can_encoder = BertEmbedding(model=model)
hidden_size = self.ctx_encoder.model.config.hidden_size
encoder_layer = nn.TransformerEncoderLayer(
hidden_size*2,
nhead=nhead,
dim_feedforward=dim_feedforward,
dropout=dropout,
)
encoder_norm = nn.LayerNorm(2*hidden_size)
self.trs_encoder = nn.TransformerEncoder(
encoder_layer,
num_encoder_layers,
encoder_norm,
)
self.trs_head = nn.Sequential(
self.trs_encoder,
nn.Tanh(),
nn.Dropout(p=dropout),
nn.Linear(hidden_size*2, hidden_size)
)
self.gate = nn.Sequential(
nn.Linear(hidden_size*3, hidden_size*2),
nn.Tanh(),
nn.Dropout(p=dropout),
nn.Linear(hidden_size*2, hidden_size),
nn.Sigmoid()
)
self.cls_head = nn.Sequential(
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Dropout(p=dropout),
nn.Linear(hidden_size, 1)
)
self.criterion = nn.BCEWithLogitsLoss()
def _encode(self, cid, rid, cid_mask, rid_mask):
cid_rep = self.ctx_encoder(cid, cid_mask)
rid_rep = self.can_encoder(rid, rid_mask)
b_c = len(cid_rep)
rid_reps = rid_rep.unsqueeze(0).repeat(b_c, 1, 1) # [B_c, B_r*gray, E]
# fuse context into the response
cid_reps = cid_rep.unsqueeze(1).repeat(1, len(rid), 1) # [B_c, B_r*gray, E]
for_comp = torch.cat([rid_reps, cid_reps], dim=-1) # [B_c, B_r*gray, 2*E]
comp_reps = self.trs_head(for_comp.permute(1, 0, 2)).permute(1, 0, 2) # [B_c, B_r*gray, E]
for_gate = torch.cat([comp_reps, rid_reps, cid_reps], dim=-1)
gate_score = self.gate(for_gate) # [B_c, B_r*gray, E]
rid_fusion_reps = gate_score * rid_reps + (1-gate_score) * comp_reps
# rid_reps: [B_c, G, E]; cid_rep: [B_c, E]
rid_fusion_reps = rid_fusion_reps.permute(1, 0, 2) # [G, B_c, E]
# [B_c, E]; [G, B_c, E], [B_c, G, E]
return cid_rep, rid_fusion_reps, comp_reps
@torch.no_grad()
def get_cand(self, ids, attn_mask):
rid_rep = self.can_encoder(ids, attn_mask)
return rid_rep
@torch.no_grad()
def get_ctx(self, ids, attn_mask):
cid_rep = self.ctx_encoder(ids, attn_mask)
return cid_rep
@torch.no_grad()
def predict(self, batch):
cid = batch['ids']
rid = batch['rids']
rid_mask = batch['rids_mask']
cid = cid.unsqueeze(0)
cid_mask = torch.ones_like(cid)
batch_size = rid.shape[0]
cid_rep, rid_rep, _ = self._encode(cid, rid, cid_mask, rid_mask)
dot_product = torch.einsum('ijk,jk->ij', rid_rep, cid_rep).t() # [B_c, G]
dot_product /= np.sqrt(768) # scale dot product
return dot_product.squeeze(0) # [G] = [B_r*gray]
def forward(self, batch):
cid = batch['ids']
rid = batch['rids']
cid_mask = batch['ids_mask']
rid_mask = batch['rids_mask']
b_c, b_r = len(cid), int(len(rid)//(self.gray_num+1))
assert b_c == b_r
# cid_rep: [B, E]; rid_rep: [G, B, E]; [B_c, G, E]
cid_rep, rid_fusion_rep, comp_rid_rep = self._encode(cid, rid, cid_mask, rid_mask)
# loss1: advantage loss
comp_rid_rep = self.cls_head(comp_rid_rep).squeeze(-1) # [B, G]
label = torch.zeros_like(comp_rid_rep)
label[torch.arange(b_c), torch.arange(0, len(rid), self.gray_num+1)] = 1.
loss = self.bce_loss_scale * self.criterion(comp_rid_rep, label)
# loss2: constrastive loss
dot_product = torch.einsum('ijk,jk->ij', rid_fusion_rep, cid_rep).t() # [B_c, G]
dot_product /= np.sqrt(768) # scale dot product
mask = torch.zeros_like(dot_product).cuda()
mask[torch.arange(b_c), torch.arange(0, len(rid), self.gray_num+1)] = 1.
loss_ = F.log_softmax(dot_product, dim=-1) * mask
loss += (-loss_.sum(dim=1)).mean()
# acc
acc_num = (F.softmax(dot_product, dim=-1).max(dim=-1)[1] == torch.LongTensor(torch.arange(0, len(rid), self.gray_num+1)).cuda()).sum().item()
acc = acc_num / b_c
return loss, acc
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] |
SHM prototype/arima/ma.py | acharaakshit/Structural-Health-Monitoring | 1 | 1617946 | <gh_stars>1-10
import matplotlib.pyplot as plt
from pandas import read_csv
import pandas as pd
import numpy as np
def main():
series = read_csv('/home/akshit/Documents/Downloads/2.csv', header=0, index_col=0, squeeze=True)
series.columns = ['a', 'b', 'c', 'd']
series['e'] = pow((series['a']*series['a'] + series['b']*series['b'] + series['c']*series['c']), 0.5)
df = pd.DataFrame({'$a': series['e']})
fd = np.array(df)
#mn_for_trailing_fd_for_centered
mn = fd
#original_plot
plt.plot(fd)
#Centered_Averaging
for x in range(0, len(fd)-1):
if(x>=2 and x<=137990):
fd[x]=(fd[x-2]+fd[x-1]+fd[x]+fd[x+1]+fd[x+2])/5
#after_centered
print("asdasd")
plt.plot(fd)
plt.show()
#Trailing_Moving_Average
for x in range(0, len(fd)-1):
if(x>=2 and x<=137995):
mn[x] = (mn[x-4] + mn[x-3] + mn[x-2] + mn[x-1] + mn[x])/5
#after_trailing
print("Asdqwwe")
plt.plot(mn)
plt.show()
if __name__ == '__main__':
main() | [
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sahara/service/edp/oozie/engine.py | ksshanam/sahara | 161 | 33497 | <gh_stars>100-1000
# Copyright (c) 2014 OpenStack Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
import os
import xml.dom.minidom as xml
from oslo_config import cfg
from oslo_utils import uuidutils
import six
from sahara import conductor as c
from sahara import context
from sahara.service.edp import base_engine
from sahara.service.edp import hdfs_helper as h
from sahara.service.edp.job_binaries import manager as jb_manager
from sahara.service.edp import job_utils
from sahara.service.edp.oozie import oozie as o
from sahara.service.edp.oozie.workflow_creator import workflow_factory
from sahara.service.validations.edp import job_execution as j
from sahara.utils import edp
from sahara.utils import remote
from sahara.utils import xmlutils as x
CONF = cfg.CONF
conductor = c.API
@six.add_metaclass(abc.ABCMeta)
class OozieJobEngine(base_engine.JobEngine):
def __init__(self, cluster):
self.cluster = cluster
self.plugin = job_utils.get_plugin(self.cluster)
def get_remote_client(self):
return o.RemoteOozieClient(self.get_oozie_server_uri(self.cluster),
self.get_oozie_server(self.cluster),
self.get_hdfs_user())
def get_client(self):
# by default engine will return standard oozie client implementation
return o.OozieClient(self.get_oozie_server_uri(self.cluster),
self.get_oozie_server(self.cluster))
def _get_oozie_job_params(self, hdfs_user, path_to_workflow,
oozie_params, use_hbase_lib,
scheduled_params=None, job_dir=None,
job_execution_type=None):
oozie_libpath_key = "oozie.libpath"
oozie_libpath = ""
rm_path = self.get_resource_manager_uri(self.cluster)
nn_path = self.get_name_node_uri(self.cluster)
hbase_common_lib_path = "%s%s" % (nn_path, h.HBASE_COMMON_LIB_PATH)
if use_hbase_lib:
if oozie_libpath_key in oozie_params:
oozie_libpath = "%s,%s" % (oozie_params.get(oozie_libpath_key,
""), hbase_common_lib_path)
else:
oozie_libpath = hbase_common_lib_path
if job_execution_type == "scheduled":
app_path = "oozie.coord.application.path"
job_parameters = {
"start": scheduled_params.get('start'),
"end": scheduled_params.get('end'),
"frequency": scheduled_params.get('frequency'),
"workflowAppUri": "%s%s" % (nn_path, job_dir),
app_path: "%s%s" % (nn_path, job_dir)}
else:
app_path = "oozie.wf.application.path"
job_parameters = {
app_path: "%s%s" % (nn_path, path_to_workflow)}
job_parameters["nameNode"] = nn_path
job_parameters["user.name"] = hdfs_user
job_parameters["jobTracker"] = rm_path
job_parameters[oozie_libpath_key] = oozie_libpath
job_parameters["oozie.use.system.libpath"] = "true"
# Don't let the application path be overwritten, that can't
# possibly make any sense
if app_path in oozie_params:
del oozie_params[app_path]
if oozie_libpath_key in oozie_params:
del oozie_params[oozie_libpath_key]
job_parameters.update(oozie_params)
return job_parameters
def _upload_workflow_file(self, where, job_dir, wf_xml, hdfs_user):
with remote.get_remote(where) as r:
h.put_file_to_hdfs(r, wf_xml, "workflow.xml", job_dir, hdfs_user)
return "%s/workflow.xml" % job_dir
def _upload_coordinator_file(self, where, job_dir, wf_xml, hdfs_user):
with remote.get_remote(where) as r:
h.put_file_to_hdfs(r, wf_xml, "coordinator.xml", job_dir,
hdfs_user)
return "%s/coordinator.xml" % job_dir
def cancel_job(self, job_execution):
if job_execution.engine_job_id is not None:
client = self.get_client()
client.kill_job(job_execution)
return client.get_job_info(job_execution)
def get_job_status(self, job_execution):
if job_execution.engine_job_id is not None:
return self.get_client().get_job_info(job_execution)
def _prepare_run_job(self, job_execution):
ctx = context.ctx()
# This will be a dictionary of tuples, (native_url, runtime_url)
# keyed by data_source id
data_source_urls = {}
prepared_job_params = {}
job = conductor.job_get(ctx, job_execution.job_id)
input_source, output_source = job_utils.get_input_output_data_sources(
job_execution, job, data_source_urls, self.cluster)
# Updated_job_configs will be a copy of job_execution.job_configs with
# any name or uuid references to data_sources resolved to paths
# assuming substitution is enabled.
# If substitution is not enabled then updated_job_configs will
# just be a reference to job_execution.job_configs to avoid a copy.
# Additional_sources will be a list of any data_sources found.
additional_sources, updated_job_configs = (
job_utils.resolve_data_source_references(job_execution.job_configs,
job_execution.id,
data_source_urls,
self.cluster)
)
job_execution = conductor.job_execution_update(
ctx, job_execution,
{"data_source_urls": job_utils.to_url_dict(data_source_urls)})
# Now that we've recorded the native urls, we can switch to the
# runtime urls
data_source_urls = job_utils.to_url_dict(data_source_urls,
runtime=True)
data_sources = additional_sources + [input_source, output_source]
job_utils.prepare_cluster_for_ds(data_sources,
self.cluster, updated_job_configs,
data_source_urls)
proxy_configs = updated_job_configs.get('proxy_configs')
configs = updated_job_configs.get('configs', {})
use_hbase_lib = configs.get('edp.hbase_common_lib', {})
# Extract all the 'oozie.' configs so that they can be set in the
# job properties file. These are config values for Oozie itself,
# not the job code
oozie_params = {}
for k in list(configs):
if k.startswith('oozie.'):
oozie_params[k] = configs[k]
external_hdfs_urls = self._resolve_external_hdfs_urls(
job_execution.job_configs)
for url in external_hdfs_urls:
h.configure_cluster_for_hdfs(self.cluster, url)
hdfs_user = self.get_hdfs_user()
# TODO(tmckay): this should probably be "get_namenode"
# but that call does not exist in the oozie engine api now.
oozie_server = self.get_oozie_server(self.cluster)
wf_dir = self._create_hdfs_workflow_dir(oozie_server, job)
self._upload_job_files_to_hdfs(oozie_server, wf_dir, job, configs,
proxy_configs)
wf_xml = workflow_factory.get_workflow_xml(
job, self.cluster, updated_job_configs,
input_source, output_source,
hdfs_user, data_source_urls)
path_to_workflow = self._upload_workflow_file(oozie_server, wf_dir,
wf_xml, hdfs_user)
prepared_job_params['context'] = ctx
prepared_job_params['hdfs_user'] = hdfs_user
prepared_job_params['path_to_workflow'] = path_to_workflow
prepared_job_params['use_hbase_lib'] = use_hbase_lib
prepared_job_params['job_execution'] = job_execution
prepared_job_params['oozie_params'] = oozie_params
prepared_job_params['wf_dir'] = wf_dir
prepared_job_params['oozie_server'] = oozie_server
return prepared_job_params
def run_job(self, job_execution):
prepared_job_params = self._prepare_run_job(job_execution)
path_to_workflow = prepared_job_params['path_to_workflow']
hdfs_user = prepared_job_params['hdfs_user']
oozie_params = prepared_job_params['oozie_params']
use_hbase_lib = prepared_job_params['use_hbase_lib']
ctx = prepared_job_params['context']
job_execution = prepared_job_params['job_execution']
job_params = self._get_oozie_job_params(hdfs_user,
path_to_workflow,
oozie_params,
use_hbase_lib)
client = self.get_client()
oozie_job_id = client.add_job(x.create_hadoop_xml(job_params),
job_execution)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
conductor.job_execution_update(
context.ctx(), job_execution.id,
{'info': {'status': edp.JOB_STATUS_READYTORUN},
'engine_job_id': oozie_job_id})
client.run_job(job_execution, oozie_job_id)
try:
status = client.get_job_info(job_execution, oozie_job_id)['status']
except Exception:
status = None
return (oozie_job_id, status, None)
def run_scheduled_job(self, job_execution):
prepared_job_params = self._prepare_run_job(job_execution)
oozie_server = prepared_job_params['oozie_server']
wf_dir = prepared_job_params['wf_dir']
hdfs_user = prepared_job_params['hdfs_user']
oozie_params = prepared_job_params['oozie_params']
use_hbase_lib = prepared_job_params['use_hbase_lib']
ctx = prepared_job_params['context']
job_execution = prepared_job_params['job_execution']
coord_configs = {"jobTracker": "${jobTracker}",
"nameNode": "${nameNode}"}
coord_xml = self._create_coordinator_xml(coord_configs)
self._upload_coordinator_file(oozie_server, wf_dir, coord_xml,
hdfs_user)
job_params = self._get_oozie_job_params(
hdfs_user, None, oozie_params, use_hbase_lib,
job_execution.job_configs.job_execution_info, wf_dir,
"scheduled")
client = self.get_client()
oozie_job_id = client.add_job(x.create_hadoop_xml(job_params),
job_execution)
job_execution = conductor.job_execution_get(ctx, job_execution.id)
if job_execution.info['status'] == edp.JOB_STATUS_TOBEKILLED:
return (None, edp.JOB_STATUS_KILLED, None)
try:
status = client.get_job_status(job_execution,
oozie_job_id)['status']
except Exception:
status = None
return (oozie_job_id, status, None)
@abc.abstractmethod
def get_hdfs_user(self):
pass
@abc.abstractmethod
def create_hdfs_dir(self, remote, dir_name):
pass
@abc.abstractmethod
def get_oozie_server_uri(self, cluster):
pass
@abc.abstractmethod
def get_oozie_server(self, cluster):
pass
@abc.abstractmethod
def get_name_node_uri(self, cluster):
pass
@abc.abstractmethod
def get_resource_manager_uri(self, cluster):
pass
def validate_job_execution(self, cluster, job, data):
# Shell job type requires no specific fields
if job.type == edp.JOB_TYPE_SHELL:
return
# All other types except Java require input and output
# objects and Java require main class
if job.type == edp.JOB_TYPE_JAVA:
j.check_main_class_present(data, job)
else:
j.check_data_sources(data, job)
job_type, subtype = edp.split_job_type(job.type)
if job_type == edp.JOB_TYPE_MAPREDUCE and (
subtype == edp.JOB_SUBTYPE_STREAMING):
j.check_streaming_present(data, job)
@staticmethod
def get_possible_job_config(job_type):
return workflow_factory.get_possible_job_config(job_type)
@staticmethod
def get_supported_job_types():
return [edp.JOB_TYPE_HIVE,
edp.JOB_TYPE_JAVA,
edp.JOB_TYPE_MAPREDUCE,
edp.JOB_TYPE_MAPREDUCE_STREAMING,
edp.JOB_TYPE_PIG,
edp.JOB_TYPE_SHELL]
def _prepare_job_binaries(self, job_binaries, r):
for jb in job_binaries:
jb_manager.JOB_BINARIES.get_job_binary_by_url(jb.url). \
prepare_cluster(jb, remote=r)
def _upload_job_files_to_hdfs(self, where, job_dir, job, configs,
proxy_configs=None):
mains = list(job.mains) if job.mains else []
libs = list(job.libs) if job.libs else []
builtin_libs = edp.get_builtin_binaries(job, configs)
uploaded_paths = []
hdfs_user = self.get_hdfs_user()
job_dir_suffix = 'lib' if job.type != edp.JOB_TYPE_SHELL else ''
lib_dir = os.path.join(job_dir, job_dir_suffix)
with remote.get_remote(where) as r:
job_binaries = mains + libs
self._prepare_job_binaries(job_binaries, r)
# upload mains
uploaded_paths.extend(self._upload_job_binaries(r, mains,
proxy_configs,
hdfs_user,
job_dir))
# upload libs
if len(libs) and job_dir_suffix:
# HDFS 2.2.0 fails to put file if the lib dir does not exist
self.create_hdfs_dir(r, lib_dir)
uploaded_paths.extend(self._upload_job_binaries(r, libs,
proxy_configs,
hdfs_user,
lib_dir))
# upload buitin_libs
for lib in builtin_libs:
h.put_file_to_hdfs(r, lib['raw'], lib['name'], lib_dir,
hdfs_user)
uploaded_paths.append(lib_dir + lib['name'])
return uploaded_paths
def _upload_job_binaries(self, r, job_binaries, proxy_configs,
hdfs_user, job_dir):
uploaded_paths = []
for jb in job_binaries:
path = jb_manager.JOB_BINARIES. \
get_job_binary_by_url(jb.url). \
copy_binary_to_cluster(jb, proxy_configs=proxy_configs,
remote=r, context=context.ctx())
h.copy_from_local(r, path, job_dir, hdfs_user)
uploaded_paths.append(path)
return uploaded_paths
def _create_hdfs_workflow_dir(self, where, job):
constructed_dir = '/user/%s/' % self.get_hdfs_user()
constructed_dir = self._add_postfix(constructed_dir)
constructed_dir += '%s/%s' % (job.name, uuidutils.generate_uuid())
with remote.get_remote(where) as r:
self.create_hdfs_dir(r, constructed_dir)
return constructed_dir
def _create_coordinator_xml(self, coord_configs, config_filter=None,
appname='coord'):
doc = xml.Document()
# Create the <coordinator-app> base element
coord = doc.createElement('coordinator-app')
coord.attributes['name'] = appname
coord.attributes['start'] = "${start}"
coord.attributes['end'] = "${end}"
coord.attributes['frequency'] = "${frequency}"
coord.attributes['timezone'] = 'UTC'
coord.attributes['xmlns'] = 'uri:oozie:coordinator:0.2'
doc.appendChild(coord)
action = doc.createElement('action')
workflow = doc.createElement('workflow')
coord.appendChild(action)
action.appendChild(workflow)
x.add_text_element_to_tag(doc, "workflow", 'app-path',
"${workflowAppUri}")
configuration = doc.createElement('configuration')
workflow.appendChild(configuration)
default_configs = []
if config_filter is not None:
default_configs = [cfg['name'] for cfg in config_filter]
for name in sorted(coord_configs):
if name in default_configs or config_filter is None:
x.add_property_to_configuration(doc, name, coord_configs[name])
# Return newly created XML
return doc.toprettyxml(indent=" ")
def _add_postfix(self, constructed_dir):
def _append_slash_if_needed(path):
if path[-1] != '/':
path += '/'
return path
constructed_dir = _append_slash_if_needed(constructed_dir)
if CONF.job_workflow_postfix:
constructed_dir = ''.join([str(constructed_dir),
str(CONF.job_workflow_postfix)])
return _append_slash_if_needed(constructed_dir)
def _resolve_external_hdfs_urls(self, job_configs):
external_hdfs_urls = []
for k, v in six.iteritems(job_configs.get('configs', {})):
if isinstance(v, six.string_types) and v.startswith("hdfs://"):
external_hdfs_urls.append(v)
for k, v in six.iteritems(job_configs.get('params', {})):
if isinstance(v, six.string_types) and v.startswith("hdfs://"):
external_hdfs_urls.append(v)
for v in job_configs.get('args', []):
if isinstance(v, six.string_types) and v.startswith("hdfs://"):
external_hdfs_urls.append(v)
return external_hdfs_urls
def suspend_job(self, job_execution):
return self._manage_job(job_execution, edp.JOB_ACTION_SUSPEND)
def _manage_job(self, job_execution, action):
if job_execution.oozie_job_id is not None:
client = self.get_client()
if action == edp.JOB_ACTION_SUSPEND:
client.suspend_job(job_execution)
return client.get_job_status(job_execution)
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1202,
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13,
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565,
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4882,
2033,
1275,
1226,
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29889,
29967,
14824,
29918,
27047,
29918,
4986,
15349,
29968,
6227,
20566,
29901,
13,
9651,
736,
313,
8516,
29892,
1226,
29886,
29889,
29967,
14824,
29918,
27047,
29918,
29968,
6227,
20566,
29892,
6213,
29897,
13,
13,
4706,
7512,
272,
29889,
9057,
29918,
22256,
29918,
5504,
29898,
13,
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3030,
29889,
13073,
3285,
4982,
29918,
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333,
29892,
13,
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11117,
3888,
2396,
11117,
4882,
2396,
1226,
29886,
29889,
29967,
14824,
29918,
27047,
29918,
16310,
29979,
29911,
1955,
3904,
1118,
13,
632,
525,
10599,
29918,
9057,
29918,
333,
2396,
288,
2112,
347,
29918,
9057,
29918,
333,
1800,
13,
13,
4706,
3132,
29889,
3389,
29918,
9057,
29898,
9057,
29918,
22256,
29892,
288,
2112,
347,
29918,
9057,
29918,
333,
29897,
13,
4706,
1018,
29901,
13,
9651,
4660,
353,
3132,
29889,
657,
29918,
9057,
29918,
3888,
29898,
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29918,
22256,
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288,
2112,
347,
29918,
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29918,
333,
29897,
1839,
4882,
2033,
13,
4706,
5174,
8960,
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13,
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4660,
353,
6213,
13,
4706,
736,
313,
29877,
2112,
347,
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333,
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4660,
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6213,
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13,
13,
1678,
822,
1065,
29918,
816,
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7529,
353,
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3032,
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3389,
29918,
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2112,
347,
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2974,
2033,
13,
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281,
29888,
29918,
3972,
353,
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29918,
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7529,
1839,
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29888,
29918,
3972,
2033,
13,
4706,
298,
29069,
29918,
1792,
353,
13240,
29918,
9057,
29918,
7529,
1839,
29882,
29069,
29918,
1792,
2033,
13,
4706,
288,
2112,
347,
29918,
7529,
353,
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29918,
9057,
29918,
7529,
1839,
29877,
2112,
347,
29918,
7529,
2033,
13,
4706,
671,
29918,
29882,
3188,
29918,
1982,
353,
13240,
29918,
9057,
29918,
7529,
1839,
1509,
29918,
29882,
3188,
29918,
1982,
2033,
13,
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13,
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7529,
1839,
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13,
13,
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29311,
29918,
2917,
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353,
8853,
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5323,
4937,
1115,
11568,
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5323,
4937,
17671,
13,
462,
308,
376,
978,
4247,
1115,
11568,
978,
4247,
5038,
29913,
13,
13,
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3134,
353,
1583,
3032,
3258,
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1061,
29918,
3134,
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1111,
536,
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13,
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1583,
3032,
657,
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2112,
347,
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29918,
7529,
29898,
13,
9651,
298,
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29918,
1792,
29892,
6213,
29892,
288,
2112,
347,
29918,
7529,
29892,
671,
29918,
29882,
3188,
29918,
1982,
29892,
13,
9651,
4982,
29918,
22256,
29889,
9057,
29918,
2917,
29879,
29889,
9057,
29918,
22256,
29918,
3888,
29892,
281,
29888,
29918,
3972,
29892,
13,
9651,
376,
816,
14989,
1159,
13,
13,
4706,
3132,
353,
1583,
29889,
657,
29918,
4645,
580,
13,
4706,
288,
2112,
347,
29918,
9057,
29918,
333,
353,
3132,
29889,
1202,
29918,
9057,
29898,
29916,
29889,
3258,
29918,
22075,
29918,
3134,
29898,
9057,
29918,
7529,
511,
13,
462,
462,
418,
4982,
29918,
22256,
29897,
13,
13,
4706,
4982,
29918,
22256,
353,
7512,
272,
29889,
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29918,
22256,
29918,
657,
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13073,
29892,
4982,
29918,
22256,
29889,
333,
29897,
13,
13,
4706,
565,
4982,
29918,
22256,
29889,
3888,
1839,
4882,
2033,
1275,
1226,
29886,
29889,
29967,
14824,
29918,
27047,
29918,
4986,
15349,
29968,
6227,
20566,
29901,
13,
9651,
736,
313,
8516,
29892,
1226,
29886,
29889,
29967,
14824,
29918,
27047,
29918,
29968,
6227,
20566,
29892,
6213,
29897,
13,
4706,
1018,
29901,
13,
9651,
4660,
353,
3132,
29889,
657,
29918,
9057,
29918,
4882,
29898,
9057,
29918,
22256,
29892,
13,
462,
462,
965,
288,
2112,
347,
29918,
9057,
29918,
333,
29897,
1839,
4882,
2033,
13,
4706,
5174,
8960,
29901,
13,
9651,
4660,
353,
6213,
13,
4706,
736,
313,
29877,
2112,
347,
29918,
9057,
29918,
333,
29892,
4660,
29892,
6213,
29897,
13,
13,
1678,
732,
10736,
29889,
16595,
5696,
13,
1678,
822,
679,
29918,
29882,
29069,
29918,
1792,
29898,
1311,
1125,
13,
4706,
1209,
13,
13,
1678,
732,
10736,
29889,
16595,
5696,
13,
1678,
822,
1653,
29918,
29882,
29069,
29918,
3972,
29898,
1311,
29892,
7592,
29892,
4516,
29918,
978,
1125,
13,
4706,
1209,
13,
13,
1678,
732,
10736,
29889,
16595,
5696,
13,
1678,
822,
679,
29918,
29877,
2112,
347,
29918,
2974,
29918,
5338,
29898,
1311,
29892,
9867,
1125,
13,
4706,
1209,
13,
13,
1678,
732,
10736,
29889,
16595,
5696,
13,
1678,
822,
679,
29918,
29877,
2112,
347,
29918,
2974,
29898,
1311,
29892,
9867,
1125,
13,
4706,
1209,
13,
13,
1678,
732,
10736,
29889,
16595,
5696,
13,
1678,
822,
679,
29918,
978,
29918,
3177,
29918,
5338,
29898,
1311,
29892,
9867,
1125,
13,
4706,
1209,
13,
13,
1678,
732,
10736,
29889,
16595,
5696,
13,
1678,
822,
679,
29918,
10314,
29918,
12847,
29918,
5338,
29898,
1311,
29892,
9867,
1125,
13,
4706,
1209,
13,
13,
1678,
822,
12725,
29918,
9057,
29918,
22256,
29898,
1311,
29892,
9867,
29892,
4982,
29892,
848,
1125,
13,
4706,
396,
1383,
514,
4982,
1134,
6858,
694,
2702,
4235,
13,
4706,
565,
4982,
29889,
1853,
1275,
1226,
29886,
29889,
29967,
14824,
29918,
11116,
29918,
7068,
29923,
2208,
29901,
13,
9651,
736,
13,
4706,
396,
2178,
916,
4072,
5174,
3355,
1996,
1881,
322,
1962,
13,
4706,
396,
3618,
322,
3355,
1996,
1667,
770,
13,
4706,
565,
4982,
29889,
1853,
1275,
1226,
29886,
29889,
29967,
14824,
29918,
11116,
29918,
29967,
26612,
29901,
13,
9651,
432,
29889,
3198,
29918,
3396,
29918,
1990,
29918,
6338,
29898,
1272,
29892,
4982,
29897,
13,
4706,
1683,
29901,
13,
9651,
432,
29889,
3198,
29918,
1272,
29918,
29879,
2863,
29898,
1272,
29892,
4982,
29897,
13,
13,
9651,
4982,
29918,
1853,
29892,
1014,
1853,
353,
1226,
29886,
29889,
5451,
29918,
9057,
29918,
1853,
29898,
9057,
29889,
1853,
29897,
13,
9651,
565,
4982,
29918,
1853,
1275,
1226,
29886,
29889,
29967,
14824,
29918,
11116,
29918,
1529,
15094,
14849,
4741,
322,
313,
13,
462,
1678,
1014,
1853,
1275,
1226,
29886,
29889,
29967,
14824,
29918,
20633,
11116,
29918,
1254,
1525,
5194,
4214,
1125,
13,
18884,
432,
29889,
3198,
29918,
5461,
292,
29918,
6338,
29898,
1272,
29892,
4982,
29897,
13,
13,
1678,
732,
7959,
5696,
13,
1678,
822,
679,
29918,
27338,
29918,
9057,
29918,
2917,
29898,
9057,
29918,
1853,
1125,
13,
4706,
736,
27321,
29918,
14399,
29889,
657,
29918,
27338,
29918,
9057,
29918,
2917,
29898,
9057,
29918,
1853,
29897,
13,
13,
1678,
732,
7959,
5696,
13,
1678,
822,
679,
29918,
23765,
29918,
9057,
29918,
8768,
7295,
13,
4706,
736,
518,
287,
29886,
29889,
29967,
14824,
29918,
11116,
29918,
29950,
18474,
29892,
13,
18884,
1226,
29886,
29889,
29967,
14824,
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29889,
657,
29918,
9057,
29918,
4882,
29898,
9057,
29918,
22256,
29897,
13,
2
] |
www/tests/test_browser_html.py | ranjian0/brython | 5,926 | 1617850 | <filename>www/tests/test_browser_html.py
import browser.html as html
assert html.H2 is html.tags['H2']
class MyH1:
pass
html.tags['H1'] = MyH1
assert html.tags['H1'] == html.H1
assert html.tags == html.tags
nb = len(html.tags)
html.tags['GGHHJJ'] = 77
assert len(html.tags) == nb + 1
assert(len(html.tags.items())) == nb + 1
assert(len(html.tags.keys())) == nb + 1
assert(len(html.tags.values())) == nb + 1
assert 'GGHHJJ' in html.tags
del html.tags['GGHHJJ']
assert len(html.tags) == nb
assert(len(html.tags.items())) == nb
assert(len(html.tags.keys())) == nb
assert(len(html.tags.values())) == nb
assert 'GGHHJJ' not in html.tags
del html.tags['H6']
assert len(html.tags) == nb - 1
assert(len(html.tags.items())) == nb - 1
assert(len(html.tags.keys())) == nb - 1
assert(len(html.tags.values())) == nb - 1
assert 'H6' not in html.tags
print('all tests passed...') | [
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pablo/envs/canvas.py | trstruth/pablo | 0 | 170761 | <reponame>trstruth/pablo<filename>pablo/envs/canvas.py
# coding: utf-8
"""A module for the Canvas where the image will be created
analogous to the environment in a reinforcement learning problem - accepts actions,
performs transformations to the images stored as members, thus updating the environment
and returning a reward scalar
"""
import numpy as np
import os
import gym
from PIL import Image
from gym import spaces, logger
from numpy import linalg as LA
from scipy.spatial import KDTree
from skimage.measure import compare_ssim
class Canvas(gym.Env):
metadata = {
'render.modes': ['human'],
'video.frames_per_second' : 50
}
def __init__(self,
target_image_filename,
images_directory=os.path.abspath(os.path.join(__file__,'../../../','images/'))):
self.target_image_filename = target_image_filename
self.generated_image = None
self.images_directory = images_directory
self.emoji_directory = os.path.join(self.images_directory, 'emojis')
self.num_available_emojis = len(os.listdir(self.emoji_directory)) - 1
self.emoji_count = 0
self.max_emojis = 300
self.similarity = None
self.similarity_threshold = 0.1
# These values control the quality of the output image
self.min_dim = 4000
self.image_res_ratio = 1
self.emoji_KDT = self._construct_emoji_KDTree()
self.emoji_cache = self._construct_emoji_cache()
self.viewer = None
self._load_target_image_from_file(self.target_image_filename)
self.reset()
self.target_image_w, self.target_image_h = self.target_image.size
self.action_space = spaces.Dict({
'y': spaces.Discrete(self.target_image_h),
'x': spaces.Discrete(self.target_image_w),
'r': spaces.Discrete(256),
'g': spaces.Discrete(256),
'b': spaces.Discrete(256),
'scale': spaces.Discrete(1000),
'rotation': spaces.Discrete(360)
})
self.observation_space = spaces.Dict({
'target': spaces.Box(0, 255, shape=(self.target_image_h, self.target_image_w, 3), dtype=np.uint8),
'generated': spaces.Box(0, 255, shape=(self.target_image_h, self.target_image_w, 3), dtype=np.uint8)
})
self.action_space.n = len(self.action_space.spaces.items())
def reset(self):
"""Resets the state of the canvas by:
- reinitializing the generated image
- returning the inital observation
Returns: observation (object): The observation object that is described by
self.observation_space
"""
assert self.target_image is not None
self.emoji_count = 0
self._init_generated_image()
self.similarity = 0
return {
'target': self.target_image,
'generated': self.generated_image
}
def step(self, action):
"""
step returns four values. These are:
* observation (object): an environment-specific object representing your observation of the environment. For
example, pixel data from a camera, joint angles and joint velocities of a robot, or the board state in a board
game.
* reward (float): amount of reward achieved by the previous action. The scale varies between environments, but
the goal is always to increase your total reward.
* done (boolean): whether it’s time to reset the environment again. Most (but not all) tasks are divided up into
well-defined episodes, and done being True indicates the episode has terminated. (For example, perhaps the pole
tipped too far, or you lost your last life.)
* info (dict): diagnostic information useful for debugging. It can sometimes be useful for learning (for example,
it might contain the raw probabilities behind the environment’s last state change). However, official evaluations
of your agent are not allowed to use this for learning.
"""
assert self.action_space.contains(action), "%r (%s) invalid"%(action, type(action))
index = self._find_nearest_emoji_index(action['r'], action['g'], action['b'])
selected_emoji = self._get_emoji(index)
# Place emoji as described by the action
coordinate = (
int(action['x']*self.image_res_ratio),
int(action['y']*self.image_res_ratio)
)
scale = action['scale']
cur_size = selected_emoji.size
scaled_size = (int(cur_size[0] / scale), int(cur_size[1] / scale))
selected_emoji = selected_emoji.resize(scaled_size, Image.ANTIALIAS)
selected_emoji = selected_emoji.rotate(action['rotation'], expand=1)
self.generated_image.paste(selected_emoji, coordinate, selected_emoji)
# construct the observation object
observation = {
'target': self.target_image,
'generated': self.generated_image
}
# TODO: calculate reward
# new_similarity = self._calculate_mssim()
new_similarity = 0
reward = new_similarity - self.similarity
self.similarity = new_similarity
# increment emoji count and set done flag
self.emoji_count += 1
done = (self.emoji_count >= self.max_emojis) # or (self.similarity < self.similarity_threshold)
# construct diagnositic info dict
info = {
'position': (action['x'], action['y']),
'emoji_count': self.emoji_count,
'mssim': self.similarity,
}
return observation, reward, done, info
def render(self, mode='human', close=False):
if close:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(np.array(self.generated_image.convert('RGB')))
return self.viewer.isopen
def _load_target_image_from_file(self, filename):
"""Load the image file stored at <filename>
filename will be appended to self.images_directory to create the path where the image is stored
Args:
filename (str): The name of the file
Returns:
None
"""
target_image_filepath = os.path.join(self.images_directory, filename)
try:
self.target_image = Image.open(target_image_filepath).convert('RGB')
except IOError:
print('There was an error opening the file {}'.format(target_image_filepath))
self.target_image = None
def _init_generated_image(self):
"""Initialize the generated image
in order to avoid unnecessary downsampling of the emojis, we create a high resolution canvas
to paste them on. The aspect ratio of of the generated image should be the same as that of
the target image. For the generated image, the min(width, height) should equal some parameter
set for the canvas.
Returns:
None
"""
assert self.target_image is not None
# divide 5000 by each dim, store ratios in scale_ratios
scale_ratios = [self.min_dim/dim for dim in self.target_image.size]
# the min(width, height) will create the larger res ratio
self.image_res_ratio = max(scale_ratios)
scaled_size = tuple([int(self.image_res_ratio*dim) for dim in self.target_image.size])
self.generated_image = Image.new('RGBA', scaled_size, (255, 255, 255))
def _write_generated_image_to_file(self, filename):
"""Write self.generated_image to <filename>
Args:
filename (str): The name of the file
Returns:
None
"""
generated_image_filepath = os.path.join(self.images_directory, filename)
self.generated_image.save(generated_image_filepath, quality=95)
def _calculate_error(self):
""" Calculate the error between self.target_image and self.generated_image
Returns:
Int: the error scalar
"""
assert self.target_image.size == self.generated_image.size
return LA.norm(self.target_image - self.generated_image)
def _calculate_mssim(self):
""" Calculate the mean structural similarity between self.target_image and self.generated_image
Returns:
Float: The mean structural similarity over the image
"""
assert self.target_image.size == self.generated_image.size
thumb_size = (128, 128)
target_thumb = self.target_image.copy().convert('RGB')
generated_thumb = self.generated_image.copy().convert('RGB')
target_thumb.thumbnail(thumb_size, resample=Image.ANTIALIAS)
generated_thumb.thumbnail(thumb_size, resample=Image.ANTIALIAS)
return compare_ssim(np.array(target_thumb), np.array(generated_thumb), multichannel=True)
def _construct_emoji_KDTree(self):
""" The rgb color space can be visualized in 3 dimensions, with
the intensity of each color along each dim. We can achieve
logarithmic lookup performance by indexing each emoji in a KDTree.
This method constructs such a tree. We iterate through each of
the emojis, construct a list of those values, then use them to
construct a KDTree.
Returns:
KDTree: The KDTree indexing each of the emojis
"""
avg_rgb_list = np.zeros((self.num_available_emojis, 3))
for i in range(self.num_available_emojis):
emoji = Image.open('{}/{}.png'.format(self.emoji_directory, i))
average_rgb = self._get_average_rgb(emoji)
avg_rgb_list[i, :] = average_rgb
return KDTree(avg_rgb_list)
def _construct_emoji_cache(self):
""" Instead of loading each emoji from disk each time, on first load
we cache the emoji in a dictionary. On each lookup, we first check
to see if the emoji is loaded in the cache, and if so we can retrieve
it from memory as opposed to disk
"""
return {i: None for i in range(self.num_available_emojis)}
def _get_average_rgb(self, image):
""" Calculate the average rgb values given an image. Ignore pixels
that have a 0 alpha value
Args:
image (Image): the input image
Returns:
np.array: Array with [r, g, b] values
"""
im = np.array(image)
h, w, d = im.shape
im_vector = im.reshape((h*w, d))
im_mask = im_vector[:,3] != 0
average_rgb = np.average(im_vector, axis=0, weights=im_mask)[:3]
return average_rgb
def _find_nearest_emoji_index(self, r, g, b):
"""Find and return the emoji with average rgb values closest to supplied rgb
Args:
r (int): The red component
g (int): The green component
b (int): The blue component
Returns:
int: the index of the nearest emoji
"""
dist, emoji_index = self.emoji_KDT.query([r, g, b])
return emoji_index
def _get_emoji(self, index):
""" Return the emoji that corresponds with the given index.
Check cache for the emoji first. If it doesnt exist, make a record of it
otherwise, return the emoji in the cache.
Args:
index (int): The index of the desired emoji
Returns:
Image: the Image object that represents the emoji
"""
if self.emoji_cache[index] is None:
image = Image.open('{}/{}.png'.format(self.emoji_directory, index))
self.emoji_cache[index] = image
return image
else:
return self.emoji_cache[index]
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29918,
8173,
29961,
2248,
29962,
13,
13,
2
] |
mattspy/stats/__init__.py | beckermr/mattspy | 0 | 134829 | <reponame>beckermr/mattspy
from mad import mad
| [
1,
529,
276,
1112,
420,
29958,
19645,
837,
29878,
29914,
29885,
1131,
1028,
29891,
13,
3166,
10395,
1053,
10395,
13,
2
] |
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