code
stringlengths 22
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| apis
listlengths 1
3.31k
| extract_api
stringlengths 75
3.25M
|
|---|---|---|
#<NAME>
#Weekly Task 7
#Read in a file from a Command line argument
#Return total amount of times a letter (case sensitive) appears in a file
#Program has an input option to select which letter the user wants a return for
# SYS module used for command line arguments
#Ref https://www.tutorialspoint.com/python/python_command_line_arguments.htm
import sys
#error handling - more detail in comments above exceptions
#Ref http://hplgit.github.io/primer.html/doc/pub/input/._input-solarized007.html
try:
#takes an argument from the command line at position 2 i.e Filename
filename = sys.argv[1]
#opens file in readmode
#using with means no nead to have f.close() at the end
with open(filename,'r') as f:
#splits file into individual words
#Ref https://docs.python.org/3.3/tutorial/inputoutput.html
readfile = f.read().split()
#for loop to create variable that splits the individual words into a list with letters/characters split
#Ref https://www.w3schools.com/python/ref_string_strip.asp
letter = [list(line.strip()) for line in readfile]
#Ref http://hplgit.github.io/primer.html/doc/pub/input/._input-solarized007.html
#error handling if command line argument left blank
except IndexError:
print('No File entered on command line')
#system exit when an exception or error occurs
sys.exit(1)
#both the following needed for readfile and letter variables above
#error handling if incorrect filename with extension entered on command line
except NameError:
print('File not Found')
sys.exit(1)
#error handling if incorrect name entered on command line
except FileNotFoundError:
print('File not Found')
sys.exit(1)
#input letters to main program
letter_input = input("enter letter: ")
#dictionary for a count of each letter and charcter in file
# Ref https://stackoverflow.com/questions/41970992/appending-values-to-dictionary-in-for-loop
freq = {}
#for loop for each list in letter
for line in letter:
#loops through characters in each list from letter variable
for char in line:
if char in freq:
#adds 1 to each time a character is present
freq[char] +=1
else:
#if character is only present once give it a value of 1
freq[char] = 1
#print count of charcter frequency by passing input value to dictionary
print(freq[letter_input])
|
[
"sys.exit"
] |
[((1393, 1404), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (1401, 1404), False, 'import sys\n'), ((1600, 1611), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (1608, 1611), False, 'import sys\n'), ((1733, 1744), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (1741, 1744), False, 'import sys\n')]
|
#!/usr/bin/env python
PROGRAM_NAME = "cg-add-route.py"
PROGRAM_DESCRIPTION = """
CloudGenix script
---------------------------------------
This script quickly adds a global route to a site for all elements at that site
"""
from cloudgenix import API, jd
import os
import sys
import argparse
from fuzzywuzzy import fuzz
import ipaddress
CLIARGS = {}
cgx_session = API() #Instantiate a new CG API Session for AUTH
global_vars = {}
def parse_arguments():
global CLIARGS
parser = argparse.ArgumentParser(
prog=PROGRAM_NAME,
formatter_class=argparse.RawDescriptionHelpFormatter,
description=PROGRAM_DESCRIPTION
)
parser.add_argument('--token', '-t', metavar='"MYTOKEN"', type=str,
help='specify an authtoken to use for CloudGenix authentication')
parser.add_argument('--authtokenfile', '-f', metavar='"MYTOKENFILE.TXT"', type=str,
help='a file containing the authtoken')
parser.add_argument('--site-name', '-s', metavar='SiteName', type=str,
help='The site to run the site health check for', required=True)
parser.add_argument('--prefix', '-p', metavar='prefix', type=str,
help='The IP Prefix for the destination route', default="")
parser.add_argument('--next-hop', '-n', metavar='nexthop', type=str,
help='The IP Next hop for the route', default="")
parser.add_argument('--admin-distance', '-a', metavar='admindistance', type=str,
help='The admin distance for the route (default: 1)', default="1")
args = parser.parse_args()
CLIARGS.update(vars(args)) ##ASSIGN ARGUMENTS to our DICT
def authenticate():
print("AUTHENTICATING...")
user_email = None
user_password = None
##First attempt to use an AuthTOKEN if defined
if CLIARGS['token']: #Check if AuthToken is in the CLI ARG
CLOUDGENIX_AUTH_TOKEN = CLIARGS['token']
print(" ","Authenticating using Auth-Token in from CLI ARGS")
elif CLIARGS['authtokenfile']: #Next: Check if an AuthToken file is used
tokenfile = open(CLIARGS['authtokenfile'])
CLOUDGENIX_AUTH_TOKEN = tokenfile.read().strip()
print(" ","Authenticating using Auth-token from file",CLIARGS['authtokenfile'])
elif "X_AUTH_TOKEN" in os.environ: #Next: Check if an AuthToken is defined in the OS as X_AUTH_TOKEN
CLOUDGENIX_AUTH_TOKEN = os.environ.get('X_AUTH_TOKEN')
print(" ","Authenticating using environment variable X_AUTH_TOKEN")
elif "AUTH_TOKEN" in os.environ: #Next: Check if an AuthToken is defined in the OS as AUTH_TOKEN
CLOUDGENIX_AUTH_TOKEN = os.environ.get('AUTH_TOKEN')
print(" ","Authenticating using environment variable AUTH_TOKEN")
else: #Next: If we are not using an AUTH TOKEN, set it to NULL
CLOUDGENIX_AUTH_TOKEN = None
print(" ","Authenticating using interactive login")
##ATTEMPT AUTHENTICATION
if CLOUDGENIX_AUTH_TOKEN:
cgx_session.interactive.use_token(CLOUDGENIX_AUTH_TOKEN)
if cgx_session.tenant_id is None:
print(" ","ERROR: AUTH_TOKEN login failure, please check token.")
sys.exit()
else:
while cgx_session.tenant_id is None:
cgx_session.interactive.login(user_email, user_password)
# clear after one failed login, force relogin.
if not cgx_session.tenant_id:
user_email = None
user_password = None
print(" ","SUCCESS: Authentication Complete")
def match_site():
print_array = []
global CLIARGS, global_vars
search_site = CLIARGS['site_name']
search_ratio = 0
resp = cgx_session.get.sites()
if resp.cgx_status:
tenant_name = resp.cgx_content.get("name", None)
print("TENANT NAME:",tenant_name)
site_list = resp.cgx_content.get("items", None) #EVENT_LIST contains an list of all returned events
for site in site_list: #Loop through each EVENT in the EVENT_LIST
check_ratio = fuzz.ratio(search_site.lower(),site['name'].lower())
if (check_ratio > search_ratio ):
site_id = site['id']
site_name = site['name']
search_ratio = check_ratio
site_dict = site
else:
logout()
print("ERROR: API Call failure when enumerating SITES in tenant! Exiting!")
sys.exit((jd(resp)))
print("Found SITE ")
print(" Site Name: " , site_dict['name'])
print(" Site ID: " , site_dict['id'])
print(" Description: " , site_dict["description"])
global_vars['site_id'] = site_id
global_vars['site_name'] = site_name
global_vars['site_dict'] = site_dict
def go():
global CLIARGS, global_vars
site_id = global_vars['site_id']
####CODE GOES BELOW HERE#########
resp = cgx_session.get.tenants()
if resp.cgx_status:
tenant_name = resp.cgx_content.get("name", None)
else:
logout()
print("ERROR: API Call failure when enumerating TENANT Name! Exiting!")
print(resp.cgx_status)
sys.exit((vars(resp)))
change_elem_array = []
element_count = 0
resp = cgx_session.get.elements()
if resp.cgx_status:
element_change_list = {}
element_list = resp.cgx_content.get("items", None) #EVENT_LIST contains an list of all returned events
for element in element_list: #Loop through each EVENT in the EVENT_LIST
if (element['site_id'] == site_id):
element_count += 1
print("Found ION to add static route to: ", element['name'])
change_elem_array.append(element)
else:
logout()
print("ERROR: API Call failure when enumerating SITES in tenant! Exiting!")
sys.exit((jd(resp)))
#get ip prefix
ip_valid = False
ip_prefix_str = CLIARGS['prefix']
while (ip_valid == False):
try:
ip_prefix = ipaddress.ip_network(ip_prefix_str,strict=False)
ip_valid = True
except:
if (ip_prefix_str != ""):
print("")
print("Invalid IP Prefix Detected...")
ip_valid = False
ip_prefix_str = str(input("Please enter the DEST PREFIX (x.x.x.x/z): "))
#get ip address/next-hop
ip_valid = False
ip_next_hop_str = CLIARGS['next_hop']
while (ip_valid == False):
try:
ip_next_hop = ipaddress.ip_address(ip_next_hop_str)
ip_valid = True
except:
if (ip_next_hop_str != ""):
print("")
print("Invalid IP Next-HOP Detected...")
ip_valid = False
ip_next_hop_str = str(input("Please enter the NEXTHOP IP (x.x.x.x): "))
#get METRIC
ip_valid = False
ip_metric_str = CLIARGS['admin_distance']
while (ip_valid == False):
try:
ip_metric = str(int(ip_metric_str))
ip_valid = True
except:
print("Invalid IP Admin Distance Detected...")
ip_valid = False
ip_metric_str = str(input("Please enter the ADMIN Distance (Default 1): "))
#post to site_id and elements in site
json_request = '{"description":null,"tags":null,"destination_prefix":"' + str(ip_prefix) + '","nexthops":[{"nexthop_ip":"' + str(ip_next_hop) + '","nexthop_interface_id":null,"admin_distance":"'+ str(ip_metric) + '","self":false}],"scope":"global","network_context_id":null}'
for element in change_elem_array:
user_input = ""
while (user_input != "y" and user_input != "n"):
user_input = str(input("Would you like to add the static route to " + str(element['name'] + " ?(y/n) ")))
if (user_input == "y"):
result = cgx_session.post.staticroutes(site_id, element['id'], json_request)
if result.cgx_status:
print("Route added Successfully")
else:
print("ERROR: API Call failure when enumerating TENANT Name! Exiting!")
print(result.cgx_status)
####CODE GOES ABOVE HERE#########
def logout():
print("Logging out")
cgx_session.get.logout()
if __name__ == "__main__":
parse_arguments()
authenticate()
match_site()
go()
logout()
|
[
"ipaddress.ip_network",
"argparse.ArgumentParser",
"cloudgenix.jd",
"ipaddress.ip_address",
"os.environ.get",
"cloudgenix.API",
"sys.exit"
] |
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|
from flask_restful import Resource
from models.organization import OrganizationModel
class OrganizationName(Resource):
# GET method
def get(self, name):
org = OrganizationModel.find_by_name(name)
if org:
return org.json()
return {'message': 'Organization not found'}, 404
class Organizations(Resource):
# GET method
def get(self):
return [organization.json() for organization in OrganizationModel.objects()]
|
[
"models.organization.OrganizationModel.find_by_name",
"models.organization.OrganizationModel.objects"
] |
[((180, 216), 'models.organization.OrganizationModel.find_by_name', 'OrganizationModel.find_by_name', (['name'], {}), '(name)\n', (210, 216), False, 'from models.organization import OrganizationModel\n'), ((450, 477), 'models.organization.OrganizationModel.objects', 'OrganizationModel.objects', ([], {}), '()\n', (475, 477), False, 'from models.organization import OrganizationModel\n')]
|
import numpy as np
import sklearn
def sequences_from_log_and_indices(log, indices):
"""
Creates sequences from log and indices.
:param log: event log.
:param indices: indices of instances in the event log.
:return: list of sequences.
"""
sequences = []
for index in indices:
sequences.append(log[index])
return sequences
def data_tensor_from_sequences(sequences, mapping_activities, max_seq_length, name_of_activity):
"""
Creates data tensor of third order from sequences.
:param name_of_activity: name of the activity.
:param max_seq_length: length of the longest sequence in the event log.
:param mapping_activities: mapping of activities to indices.
:param sequences: list of sequences.
:return: data_tensor.
"""
data_tensor = np.zeros((len(sequences), max_seq_length, len(mapping_activities.keys())), dtype=np.float32)
for index, sequence in enumerate(sequences):
for index_, event in enumerate(sequence):
index_activity = mapping_activities[event[name_of_activity]]
data_tensor[index, index_, index_activity] = 1
return data_tensor
def label_tensor_from_sequences(sequences, num_classes=2):
"""
Creates label tensor of second order (matrix) from sequences.
:param num_classes: number of classes.
:param sequences: list of sequences.
:return: label_tensor.
"""
label_tensor = np.zeros((len(sequences), num_classes), dtype=np.int_)
for index, sequence in enumerate(sequences):
for index_, event in enumerate(sequence):
if event['label'] == 0:
label_tensor[index, 0] = 1
else:
label_tensor[index, 1] = 1
return label_tensor
def max_sequence_length_from_log(log):
"""
Returns length of the longest sequence in the event log.
:param log: event log.
:return: max_seq_length.
"""
max_seq_length = 0
for sequence in log:
max_seq_length_temp = 0
for activity_ in sequence:
max_seq_length_temp += 1
if max_seq_length_temp > max_seq_length:
max_seq_length = max_seq_length_temp
return max_seq_length
def unique_activities_from_log(log, name_of_activity):
"""
Returns unique activities from event log.
:param name_of_activity: name of activity.
:param log: event log.
:return: unique activities.
"""
unique_activities = []
for sequence in log:
for activity in sequence:
unique_activities.append(activity[name_of_activity])
return sorted(list(set(unique_activities)))
def mapping_activities_from_log(log, name_of_activity):
"""
Returns mapping activities of activities.
:param name_of_activity:
:param log:
:return: mapping
"""
mapping_activities = dict()
unique_activities = unique_activities_from_log(log, name_of_activity)
for index, activity in enumerate(unique_activities):
mapping_activities[activity] = index
return mapping_activities
def label_sequence_from_tensor(tensor):
"""
Returns vector with label values from tensor of second order.
:param tensor: label tensor of second order.
:return: sequence of labels.
"""
labels = []
for index in range(0, tensor.shape[0]):
if tensor[index, 0] == 1:
labels.append(0)
else:
labels.append(1)
return labels
def create_index_from_sequences(sequences):
"""
Creates indices from sequences.
:param sequences: list of sequences.
:return: indices.
"""
indices = []
for index in range(0, len(sequences)):
indices.append(index)
return indices
def split_train_test(sequences):
"""
Splits data set into train- and test set.
:param sequences: list of sequences.
:return: sequences for training and testing.
"""
return sklearn.model_selection.train_test_split(sequences, sequences, test_size=0.1, random_state=0)
|
[
"sklearn.model_selection.train_test_split"
] |
[((3931, 4029), 'sklearn.model_selection.train_test_split', 'sklearn.model_selection.train_test_split', (['sequences', 'sequences'], {'test_size': '(0.1)', 'random_state': '(0)'}), '(sequences, sequences, test_size=\n 0.1, random_state=0)\n', (3971, 4029), False, 'import sklearn\n')]
|
import datetime
from django.core.management import BaseCommand
from django.conf import settings
from django.utils import timezone
from jobs.models import Job
class Command(BaseCommand):
""" Expire jobs older than settings.JOB_THRESHOLD_DAYS """
def handle(self, **options):
days = getattr(settings, 'JOB_THRESHOLD_DAYS', 90)
expiration = timezone.now() - datetime.timedelta(days=days)
Job.objects.filter(
status=Job.STATUS_APPROVED,
expires__lte=expiration,
).update(
status=Job.STATUS_EXPIRED
)
|
[
"django.utils.timezone.now",
"jobs.models.Job.objects.filter",
"datetime.timedelta"
] |
[((367, 381), 'django.utils.timezone.now', 'timezone.now', ([], {}), '()\n', (379, 381), False, 'from django.utils import timezone\n'), ((384, 413), 'datetime.timedelta', 'datetime.timedelta', ([], {'days': 'days'}), '(days=days)\n', (402, 413), False, 'import datetime\n'), ((423, 494), 'jobs.models.Job.objects.filter', 'Job.objects.filter', ([], {'status': 'Job.STATUS_APPROVED', 'expires__lte': 'expiration'}), '(status=Job.STATUS_APPROVED, expires__lte=expiration)\n', (441, 494), False, 'from jobs.models import Job\n')]
|
# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified).
__all__ = ['say_hello', 'HelloSayer', 'bubble_scatter']
# Cell
import numpy as np
import matplotlib.pyplot as plt
# Cell
def say_hello(to):
"Say hello to anybody"
return f'Hello {to}!'
# Cell
class HelloSayer:
"Say hello to `to` using `say_hello`"
def __init__(self, to): self.to = to
def say(self): return say_hello(self.to)
# Cell
def bubble_scatter(n):
"Create a scatter plot of n different sized bubbles"
np.random.seed(19680801)
x = np.random.rand(n)
y = np.random.rand(n)
colors = np.random.rand(n)
area = (30 * np.random.rand(n))**2 # 0 to 15 point radii
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()
|
[
"numpy.random.rand",
"matplotlib.pyplot.scatter",
"numpy.random.seed",
"matplotlib.pyplot.show"
] |
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|
import unittest
import os
from collections import Counter
import pandas as pd
from installed_clients.WorkspaceClient import Workspace
from installed_clients.DataFileUtilClient import DataFileUtil
class DownloadMetagenomeProteins(unittest.TestCase):
@classmethod
def setUpClass(self):
self.token = "<KEY>"
ws_url = "https://kbase.us/services/ws"
self.wsClient = Workspace (ws_url, token=self.token)
pass
@classmethod
def get_protein_handle_from_metagenome(self, ama_ref):
protein_handle = self.wsClient.get_object_subset([{
'included': ['/protein_handle_ref'],
'ref': ama_ref
}])[0]['data']['protein_handle_ref']
return (protein_handle)
@classmethod
def get_protein_file(self, ama_ref):
protein_handle = self.get_protein_handle_from_metagenome(ama_ref)
print ("################")
print (protein_handle)
print ("################")
protein_file = "/kb/module/work/tmp/test.fa.gz"
dfu = DataFileUtil(os.environ['SDK_CALLBACK_URL'], token=self.token)
shock_to_file = dfu.shock_to_file({'handle_id': protein_handle, 'file_path': protein_file})
return (shock_to_file)
def test_download(self):
m =DownloadMetagenomeProteins()
ama_ref = "100072/31/1"
x = m.get_protein_file(ama_ref)
print (x)
|
[
"installed_clients.WorkspaceClient.Workspace",
"installed_clients.DataFileUtilClient.DataFileUtil"
] |
[((396, 431), 'installed_clients.WorkspaceClient.Workspace', 'Workspace', (['ws_url'], {'token': 'self.token'}), '(ws_url, token=self.token)\n', (405, 431), False, 'from installed_clients.WorkspaceClient import Workspace\n'), ((1064, 1126), 'installed_clients.DataFileUtilClient.DataFileUtil', 'DataFileUtil', (["os.environ['SDK_CALLBACK_URL']"], {'token': 'self.token'}), "(os.environ['SDK_CALLBACK_URL'], token=self.token)\n", (1076, 1126), False, 'from installed_clients.DataFileUtilClient import DataFileUtil\n')]
|
import os
import datetime
import weasyprint
import pandas
from jinja2 import Environment, FileSystemLoader
from ..config import SETTINGS
from .PlotsMixin.give_quotes_html_locations import give_quotes_html_locations
from ...version import __version__ as version
class PdfReportMixin:
def make_html_report(self):
"""Return a HTML version of the assembly report (later converted to PDF)."""
def _anchor_span(op):
return "<span id='anchor_%s'>%s</span>" % (op.id, op.id)
operations = self.to_steps_list()
def sorting_key(op):
location = op.get("final_location", None)
if location is None:
location = (-1, 1)
return (location[0], -location[1])
operations = sorted(operations, key=sorting_key)
give_quotes_html_locations(operations, len_sequence=len(self.plan.sequence))
env = Environment(loader=FileSystemLoader(SETTINGS["template_path"]))
template = env.get_template("report_template.html")
orders_dataframe = pandas.DataFrame.from_records(
[
{
"ID": _anchor_span(op),
"Company": op.source,
"Sequence": op.sequence,
"Length": len(op.sequence),
"Price": op.price,
"Lead time": op.lead_time,
"Deadline": op.get("deadline", None),
"Location": op.html_location,
}
for op in operations
if self.sources[op.source].operation_type == "order"
],
columns=[
"ID",
"Company",
"Sequence",
"Length",
"Sequence",
"Price",
"Lead time",
"Location",
],
)
pcrs_dataframe = pandas.DataFrame.from_records(
[
{
"ID": _anchor_span(op),
"Primers": ", ".join(
[_anchor_span(child) for child in op.get("assembly_plan", [])]
),
"Source": op.source,
"Sequence": op.sequence,
"Length": len(op.sequence),
"Price": op.price,
"Lead time": op.lead_time,
"Deadline": op.get("deadline", None),
"Location": op.html_location,
}
for op in operations
if self.sources[op.source].operation_type == "PCR"
],
columns=[
"ID",
"Source",
"infos",
"Primers",
"Sequence",
"Length",
"Price",
"Lead time",
"Location",
],
)
parts_dataframe = pandas.DataFrame.from_records(
[
{
"ID": _anchor_span(op),
"Source": op.source,
"Sequence": op.sequence,
"Length": len(op.sequence),
"Price": op.price,
"Lead time": op.lead_time,
"Deadline": op.get("deadline", None),
"Location": op.html_location,
}
for op in operations
if self.sources[op.source].operation_type == "library"
],
columns=[
"ID",
"Source",
"Sequence",
"Length",
"Price",
"Lead time",
"Location",
],
)
assembly_operations = [
op
for op in operations
if self.sources[op.source].operation_type == "assembly"
]
asm_dataframe = pandas.DataFrame.from_records(
[
{
"ID": _anchor_span(op),
"Station": op.source,
"Sequence": op.sequence,
"Length": len(op.sequence),
"Price": op.price,
"Lead time": op.lead_time,
"Deadline": op.get("deadline", None),
"Location": op.html_location,
"Primers": None,
}
for op in assembly_operations
],
columns=[
"ID",
"Station",
"infos",
"Sequence",
"Length",
"Price",
"Lead time",
"Location",
],
)
cost_orders = orders_dataframe["Price"].sum()
render_parameters = {
"sequence": self.plan.sequence,
"sequence_name": self.plan.get("sequence_name", "unknown"),
"sequence_hash": "%02x" % hash(self.plan.sequence),
"quote": self.plan,
"date_submitted": datetime.datetime.now(),
"n_orders": len(orders_dataframe),
"cost_orders": cost_orders,
"total_cost": self.plan.price,
"orders": orders_dataframe,
"asm_dataframe": asm_dataframe,
"assembly_operations": assembly_operations,
"user_name": self.plan.get("user_name", "unknown"),
"version": version,
}
if len(pcrs_dataframe) > 0:
render_parameters["pcr_reuses"] = pcrs_dataframe
if len(parts_dataframe) > 0:
render_parameters["parts_reuses"] = parts_dataframe
html_out = template.render(render_parameters)
return html_out
def write_pdf_report(self, target):
"""Return a PDF version of the report with general infos and details of
each intermediary constructs."""
base_path = SETTINGS["template_path"]
css_path = os.path.join(base_path, "static", "css")
stylesheets = [
os.path.join(css_path, "dnaweaver_report.css"),
# os.path.join(css_path, "font-awesome.min.css")
]
html_out = self.make_html_report()
weasy_html = weasyprint.HTML(string=html_out, base_url=base_path)
weasy_html.write_pdf(target, stylesheets=stylesheets)
|
[
"jinja2.FileSystemLoader",
"os.path.join",
"weasyprint.HTML",
"datetime.datetime.now"
] |
[((5983, 6023), 'os.path.join', 'os.path.join', (['base_path', '"""static"""', '"""css"""'], {}), "(base_path, 'static', 'css')\n", (5995, 6023), False, 'import os\n'), ((6243, 6295), 'weasyprint.HTML', 'weasyprint.HTML', ([], {'string': 'html_out', 'base_url': 'base_path'}), '(string=html_out, base_url=base_path)\n', (6258, 6295), False, 'import weasyprint\n'), ((5076, 5099), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (5097, 5099), False, 'import datetime\n'), ((6060, 6106), 'os.path.join', 'os.path.join', (['css_path', '"""dnaweaver_report.css"""'], {}), "(css_path, 'dnaweaver_report.css')\n", (6072, 6106), False, 'import os\n'), ((921, 964), 'jinja2.FileSystemLoader', 'FileSystemLoader', (["SETTINGS['template_path']"], {}), "(SETTINGS['template_path'])\n", (937, 964), False, 'from jinja2 import Environment, FileSystemLoader\n')]
|
from unittest.mock import patch
from tests.test_rrpproxy_base import TestRRPProxyBase
class TestRRPProxyTransferDomain(TestRRPProxyBase):
@patch('rrpproxy.RRPProxy.call')
def test_calls_call_correctly(self, call_mock):
response = self.proxy.transfer_domain(domain='example.com', action='REQUEST', ownercontact0='CONTACT-A')
call_mock.assert_called_once_with('TransferDomain', domain='example.com', action='REQUEST', ownercontact0='CONTACT-A')
self.assertEqual(response, call_mock.return_value)
|
[
"unittest.mock.patch"
] |
[((146, 177), 'unittest.mock.patch', 'patch', (['"""rrpproxy.RRPProxy.call"""'], {}), "('rrpproxy.RRPProxy.call')\n", (151, 177), False, 'from unittest.mock import patch\n')]
|
import os
import html
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.linear_model import LogisticRegression
def train_with_reg_cv(trX, trY, vaX, vaY, teX=None, teY=None, penalty='l1',
C=2 ** np.arange(-8, 1).astype(np.float), seed=42):
scores = []
for i, c in enumerate(C):
model = LogisticRegression(C=c, penalty=penalty, random_state=seed + i)
model.fit(trX, trY)
score = model.score(vaX, vaY)
scores.append(score)
c = C[np.argmax(scores)]
model = LogisticRegression(C=c, penalty=penalty, random_state=seed + len(C))
model.fit(trX, trY)
nnotzero = np.sum(model.coef_ != 0)
if teX is not None and teY is not None:
score = model.score(teX, teY) * 100.
else:
score = model.score(vaX, vaY) * 100.
return score, c, nnotzero, model
def load_sst(path):
data = pd.read_csv(path)
X = data['sentence'].values.tolist()
Y = data['label'].values
return X, Y
def sst_binary(data_dir='character_rnn/data/'):
"""
Most standard models make use of a preprocessed/tokenized/lowercased version
of Stanford Sentiment Treebank. Our model extracts features from a version
of the dataset using the raw text instead which we've included in the data
folder.
"""
trX, trY = load_sst(os.path.join(data_dir, 'train_binary_sent.csv'))
vaX, vaY = load_sst(os.path.join(data_dir, 'dev_binary_sent.csv'))
teX, teY = load_sst(os.path.join(data_dir, 'test_binary_sent.csv'))
return trX, vaX, teX, trY, vaY, teY
def find_trainable_variables(key):
return tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, ".*{}.*".format(key))
def preprocess(text, front_pad='\n ', end_pad=' '):
text = html.unescape(text)
text = text.replace('\n', ' ').strip()
text = front_pad + text + end_pad
text = text.encode()
return text
def iter_data(*data, **kwargs):
size = kwargs.get('size', 128)
try:
n = len(data[0])
except:
n = data[0].shape[0]
batches = n // size
if n % size != 0:
batches += 1
for b in range(batches):
start = b * size
end = (b + 1) * size
if end > n:
end = n
if len(data) == 1:
yield data[0][start:end]
else:
yield tuple([d[start:end] for d in data])
class HParams(object):
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
|
[
"html.unescape",
"numpy.sum",
"numpy.argmax",
"pandas.read_csv",
"sklearn.linear_model.LogisticRegression",
"numpy.arange",
"os.path.join"
] |
[((659, 683), 'numpy.sum', 'np.sum', (['(model.coef_ != 0)'], {}), '(model.coef_ != 0)\n', (665, 683), True, 'import numpy as np\n'), ((898, 915), 'pandas.read_csv', 'pd.read_csv', (['path'], {}), '(path)\n', (909, 915), True, 'import pandas as pd\n'), ((1771, 1790), 'html.unescape', 'html.unescape', (['text'], {}), '(text)\n', (1784, 1790), False, 'import html\n'), ((351, 414), 'sklearn.linear_model.LogisticRegression', 'LogisticRegression', ([], {'C': 'c', 'penalty': 'penalty', 'random_state': '(seed + i)'}), '(C=c, penalty=penalty, random_state=seed + i)\n', (369, 414), False, 'from sklearn.linear_model import LogisticRegression\n'), ((520, 537), 'numpy.argmax', 'np.argmax', (['scores'], {}), '(scores)\n', (529, 537), True, 'import numpy as np\n'), ((1343, 1390), 'os.path.join', 'os.path.join', (['data_dir', '"""train_binary_sent.csv"""'], {}), "(data_dir, 'train_binary_sent.csv')\n", (1355, 1390), False, 'import os\n'), ((1416, 1461), 'os.path.join', 'os.path.join', (['data_dir', '"""dev_binary_sent.csv"""'], {}), "(data_dir, 'dev_binary_sent.csv')\n", (1428, 1461), False, 'import os\n'), ((1487, 1533), 'os.path.join', 'os.path.join', (['data_dir', '"""test_binary_sent.csv"""'], {}), "(data_dir, 'test_binary_sent.csv')\n", (1499, 1533), False, 'import os\n'), ((244, 260), 'numpy.arange', 'np.arange', (['(-8)', '(1)'], {}), '(-8, 1)\n', (253, 260), True, 'import numpy as np\n')]
|
# -*- coding: utf-8 -*-
import re
import pandas as pd
import scrapy
from scrapy import Request
from scrapy import Selector
from scrapy import signals
from fooltrader.api.quote import get_security_list
from fooltrader.consts import DEFAULT_KDATA_HEADER
from fooltrader.contract.files_contract import get_finance_report_event_path
from fooltrader.utils.utils import index_df_with_time
class StockFinanceReportEventSpider(scrapy.Spider):
name = "stock_finance_report_event"
custom_settings = {
'DOWNLOAD_DELAY': 2,
'CONCURRENT_REQUESTS_PER_DOMAIN': 8,
'SPIDER_MIDDLEWARES': {
'fooltrader.middlewares.FoolErrorMiddleware': 1000,
}
}
def start_requests(self):
security_item = self.settings.get("security_item")
if security_item is not None:
for request in self.yield_request(security_item):
yield request
else:
for _, item in get_security_list().iterrows():
for request in self.yield_request(item):
yield request
def yield_request(self, item):
# 一季度报告,中期报告,三季度报告,年度报告
for period_type in ['yjdbg', 'zqbg', 'sjdbg', 'ndbg']:
url = self.get_finance_report_event_url(item['code'], period_type)
yield Request(url=url, headers=DEFAULT_KDATA_HEADER,
meta={'item': item,
'period_type': period_type},
callback=self.download_fi_report_event_data)
@staticmethod
def report_period_from_title(title, period_type, report_event_date):
try:
year = re.match('.*(\d{4}).*', title).group(1)
report_event_year = pd.Timestamp(report_event_date).date().year
if int(year) < int(report_event_year) - 2 or int(year) > int(report_event_year):
raise Exception('wrong report year')
except Exception as e:
year = pd.Timestamp(report_event_date).date().year
if period_type == 'ndbg':
year -= 1
if period_type == 'yjdbg':
return "{}-03-31".format(year)
elif period_type == 'zqbg':
return "{}-06-30".format(year)
elif period_type == 'sjdbg':
return "{}-09-30".format(year)
elif period_type == 'ndbg':
return "{}-12-31".format(year)
def download_fi_report_event_data(self, response):
security_item = response.meta['item']
period_type = response.meta['period_type']
path = get_finance_report_event_path(security_item)
df = pd.DataFrame()
try:
report_timestamps = response.xpath('//*[@id="con02-7"]/table[2]/tr/td[2]//ul/text()').extract()
report_timestamps = [date.strip() for date in report_timestamps if date.strip()]
report_contents = response.xpath('//*[@id="con02-7"]/table[2]/tr/td[2]//ul//a').extract()
for i, tr in enumerate(report_contents):
href = Selector(text=tr).xpath('//@href').extract()[0]
title = Selector(text=tr).xpath('//text()').extract()[0]
report_period = self.report_period_from_title(title, period_type, report_timestamps[i])
df = df.append({
"securityId": security_item['id'],
"timestamp": report_timestamps[i],
"url": "http://vip.stock.finance.sina.com.cn" + href,
"title": title,
"reportPeriod": report_period}, ignore_index=True)
if not df.empty:
df = df.drop_duplicates()
df = index_df_with_time(df)
df.to_csv(path, index=False)
except Exception as e:
self.logger.exception('error when getting k data url={}'.format(response.url))
@classmethod
def from_crawler(cls, crawler, *args, **kwargs):
spider = super(StockFinanceReportEventSpider, cls).from_crawler(crawler, *args, **kwargs)
crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed)
return spider
def spider_closed(self, spider, reason):
spider.logger.info('Spider closed: %s,%s\n', spider.name, reason)
def get_finance_report_event_url(self, code, report_period):
return 'http://vip.stock.finance.sina.com.cn/corp/go.php/vCB_BulletinYi/stockid/{}/page_type/{}.phtml'.format(
code, report_period)
|
[
"pandas.DataFrame",
"scrapy.Selector",
"fooltrader.utils.utils.index_df_with_time",
"fooltrader.api.quote.get_security_list",
"pandas.Timestamp",
"scrapy.Request",
"fooltrader.contract.files_contract.get_finance_report_event_path",
"re.match"
] |
[((2558, 2602), 'fooltrader.contract.files_contract.get_finance_report_event_path', 'get_finance_report_event_path', (['security_item'], {}), '(security_item)\n', (2587, 2602), False, 'from fooltrader.contract.files_contract import get_finance_report_event_path\n'), ((2617, 2631), 'pandas.DataFrame', 'pd.DataFrame', ([], {}), '()\n', (2629, 2631), True, 'import pandas as pd\n'), ((1304, 1448), 'scrapy.Request', 'Request', ([], {'url': 'url', 'headers': 'DEFAULT_KDATA_HEADER', 'meta': "{'item': item, 'period_type': period_type}", 'callback': 'self.download_fi_report_event_data'}), "(url=url, headers=DEFAULT_KDATA_HEADER, meta={'item': item,\n 'period_type': period_type}, callback=self.download_fi_report_event_data)\n", (1311, 1448), False, 'from scrapy import Request\n'), ((3669, 3691), 'fooltrader.utils.utils.index_df_with_time', 'index_df_with_time', (['df'], {}), '(df)\n', (3687, 3691), False, 'from fooltrader.utils.utils import index_df_with_time\n'), ((953, 972), 'fooltrader.api.quote.get_security_list', 'get_security_list', ([], {}), '()\n', (970, 972), False, 'from fooltrader.api.quote import get_security_list\n'), ((1653, 1684), 're.match', 're.match', (['""".*(\\\\d{4}).*"""', 'title'], {}), "('.*(\\\\d{4}).*', title)\n", (1661, 1684), False, 'import re\n'), ((1725, 1756), 'pandas.Timestamp', 'pd.Timestamp', (['report_event_date'], {}), '(report_event_date)\n', (1737, 1756), True, 'import pandas as pd\n'), ((1965, 1996), 'pandas.Timestamp', 'pd.Timestamp', (['report_event_date'], {}), '(report_event_date)\n', (1977, 1996), True, 'import pandas as pd\n'), ((3027, 3044), 'scrapy.Selector', 'Selector', ([], {'text': 'tr'}), '(text=tr)\n', (3035, 3044), False, 'from scrapy import Selector\n'), ((3099, 3116), 'scrapy.Selector', 'Selector', ([], {'text': 'tr'}), '(text=tr)\n', (3107, 3116), False, 'from scrapy import Selector\n')]
|
from django.db import models
class Foo(models.Model):
bar = models.CharField(max_length=10)
|
[
"django.db.models.CharField"
] |
[((65, 96), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(10)'}), '(max_length=10)\n', (81, 96), False, 'from django.db import models\n')]
|
from __future__ import annotations
import logging
from math import floor, sqrt
import numpy as np
from numpy.linalg import inv, norm
from cctbx.array_family import flex
from dxtbx import flumpy
from scitbx import matrix
from dials.algorithms.profile_model.ellipsoid import chisq_quantile
from dials.algorithms.statistics.fast_mcd import FastMCD, maha_dist_sq
logger = logging.getLogger("dials")
def _index(reflection_table, experiment, fail_on_bad_index=False):
"""Index the strong spots"""
# Get some stuff from experiment
A = np.array(experiment.crystal.get_A(), dtype=np.float64).reshape(3, 3)
s0 = np.array([experiment.beam.get_s0()], dtype=np.float64).reshape(3, 1)
s0_length = norm(s0)
detector = experiment.detector
# Create array if necessary
if "miller_index" not in reflection_table:
reflection_table["miller_index"] = flex.miller_index(len(reflection_table))
# Index all the reflections
miller_index = reflection_table["miller_index"]
selection = flex.size_t()
num_reindexed = 0
for i, xyz in enumerate(reflection_table["xyzobs.px.value"]):
# Get the observed pixel coordinate
x, y, _ = xyz
# Get the lab coord
s1 = np.array(
detector[0].get_pixel_lab_coord((x, y)), dtype=np.float64
).reshape(3, 1)
s1_norm = norm(s1)
s1 *= s0_length / s1_norm
# Get the reciprocal lattice vector
r = s1 - s0
# Compute the fractional miller index
hf = np.matmul(inv(A), r)
# Compute the integer miller index
h = np.array([int(floor(j + 0.5)) for j in hf[:, 0]], dtype=int).reshape(3, 1)
# Print warning if reindexing
if tuple(h) != miller_index[i]:
logger.warn(
"Reindexing (% 3d, % 3d, % 3d) -> (% 3d, % 3d, % 3d)"
% (miller_index[i] + tuple(h))
)
num_reindexed += 1
miller_index[i] = matrix.col(flumpy.from_numpy(h))
if fail_on_bad_index:
raise RuntimeError("Bad index")
# If its not indexed as 0, 0, 0 then append
if h.any() and norm(h - hf) < 0.3:
selection.append(i)
# Print some info
logger.info(
"Reindexed %d/%d input reflections" % (num_reindexed, len(reflection_table))
)
logger.info(
"Selected %d/%d input reflections" % (len(selection), len(reflection_table))
)
# Select all the indexed reflections
reflection_table.set_flags(selection, reflection_table.flags.indexed)
reflection_table = reflection_table.select(selection)
return reflection_table
def _predict(reflection_table, experiment):
"""
Predict the position of the spots
"""
# Get some stuff from experiment
A = np.array(experiment.crystal.get_A(), dtype=np.float64).reshape((3, 3))
s0 = np.array([experiment.beam.get_s0()], dtype=np.float64).reshape(3, 1)
s0_length = norm(s0)
# Compute the vector to the reciprocal lattice point
# since this is not on the ewald sphere, lets call it s2
s1 = flex.vec3_double(reflection_table.size())
s2 = flex.vec3_double(reflection_table.size())
for i, h in enumerate(reflection_table["miller_index"]):
r = np.matmul(A, np.array([h], dtype=np.float64).reshape(3, 1))
s2_i = r + s0
s2[i] = matrix.col(flumpy.from_numpy(s2_i))
s1[i] = matrix.col(flumpy.from_numpy(s2_i * s0_length / norm(s2_i)))
reflection_table["s1"] = s1
reflection_table["s2"] = s2
reflection_table["entering"] = flex.bool(reflection_table.size(), False)
# Compute the ray intersections
xyzpx = flex.vec3_double()
xyzmm = flex.vec3_double()
for ss in s1:
mm = experiment.detector[0].get_ray_intersection(ss)
px = experiment.detector[0].millimeter_to_pixel(mm)
xyzpx.append(px + (0,))
xyzmm.append(mm + (0,))
reflection_table["xyzcal.mm"] = xyzmm
reflection_table["xyzcal.px"] = xyzpx
logger.info("Do prediction for %d reflections" % len(reflection_table))
return reflection_table
def _filter_reflections_based_on_centroid_distance(
reflection_table,
experiment,
outlier_probability=0.975,
max_separation=2,
):
"""
Filter reflections too far from predicted position
"""
# Compute the x and y residuals
Xobs, Yobs, _ = reflection_table["xyzobs.px.value"].parts()
Xcal, Ycal, _ = reflection_table["xyzcal.px"].parts()
Xres = Xobs - Xcal
Yres = Yobs - Ycal
# Compute the epsilon residual
s0_length = 1.0 / experiment.beam.get_wavelength()
s1x, s1y, s1z = reflection_table["s2"].parts()
s1_length = flex.sqrt(s1x**2 + s1y**2 + s1z**2)
Eres = s1_length - s0_length
# Initialise the fast_mcd outlier algorithm
# fast_mcd = FastMCD((Xres, Yres, Eres))
fast_mcd = FastMCD((Xres, Yres))
# get location and MCD scatter estimate
T, S = fast_mcd.get_corrected_T_and_S()
# get squared Mahalanobis distances
# d2s = maha_dist_sq((Xres, Yres, Eres), T, S)
d2s = maha_dist_sq((Xres, Yres), T, S)
# Compute the cutoff
mahasq_cutoff = chisq_quantile(2, outlier_probability)
# compare to the threshold and select reflections
selection1 = d2s < mahasq_cutoff
selection2 = flex.sqrt(Xres**2 + Yres**2) < max_separation
selection = selection1 & selection2
reflection_table = reflection_table.select(selection)
n_refl = reflection_table.size()
# Print some stuff
logger.info("-" * 80)
logger.info("Centroid outlier rejection")
logger.info(f" Using MCD algorithm with probability = {outlier_probability}")
logger.info(" Max X residual: %f" % flex.max(flex.abs(Xres)))
logger.info(" Max Y residual: %f" % flex.max(flex.abs(Yres)))
logger.info(" Max E residual: %f" % flex.max(flex.abs(Eres)))
logger.info(" Mean X RMSD: %f" % (sqrt(flex.sum(Xres**2) / len(Xres))))
logger.info(" Mean Y RMSD: %f" % (sqrt(flex.sum(Yres**2) / len(Yres))))
logger.info(" Mean E RMSD: %f" % (sqrt(flex.sum(Eres**2) / len(Eres))))
logger.info(" MCD location estimate: %.4f, %.4f" % tuple(T))
logger.info(
""" MCD scatter estimate:
%.7f, %.7f,
%.7f, %.7f"""
% tuple(S)
)
logger.info(" Number of outliers: %d" % selection1.count(False))
logger.info(
" Number of reflections with residual > %0.2f pixels: %d"
% (max_separation, selection2.count(False))
)
logger.info(f"Number of reflections selection for refinement: {n_refl}")
logger.info("-" * 80)
return reflection_table
def reindex(
reflection_table,
experiment,
outlier_probability=0.975,
max_separation=2,
fail_on_bad_index=False,
):
"""Reindex strong spots and perform filtering"""
reflection_table = _index(reflection_table, experiment, fail_on_bad_index)
reflection_table = _predict(reflection_table, experiment)
reflection_table = _filter_reflections_based_on_centroid_distance(
reflection_table,
experiment,
outlier_probability=outlier_probability,
max_separation=max_separation,
)
return reflection_table
|
[
"cctbx.array_family.flex.sqrt",
"cctbx.array_family.flex.sum",
"cctbx.array_family.flex.abs",
"dials.algorithms.statistics.fast_mcd.FastMCD",
"math.floor",
"dials.algorithms.profile_model.ellipsoid.chisq_quantile",
"cctbx.array_family.flex.vec3_double",
"numpy.linalg.norm",
"numpy.linalg.inv",
"dxtbx.flumpy.from_numpy",
"dials.algorithms.statistics.fast_mcd.maha_dist_sq",
"numpy.array",
"cctbx.array_family.flex.size_t",
"logging.getLogger"
] |
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|
import os
import pandas as pd
import os
import logging
import json
from util import StockAnalysis, AllStocks, TightMinMax, EnvFile
class volumeSpreadAnalysis:
def __init__(self):
self.barCount:int = 5
self.averagingBarCount:int = 20
self.factor3 = 1.2
self.factor4 = 1.2
self.factor5 = 1.8
self.factor8 = 1.8
def isCanCalculate(self, df: pd.DataFrame, period: int):
return False if len(df) < period else True
def avgSwing(self, df: pd.DataFrame, swingFunc) -> float:
swingTotal: float = 0
swingPeriod: int = self.averagingBarCount if len(
df) > self.averagingBarCount else len(df)
swingMax: float = 0
for _, row in df[:swingPeriod].iterrows():
swing = swingFunc(row)
swingMax = max(swingMax, swing)
swingTotal += swing
oneAvgSwing: float = (swingTotal - swingMax) / (swingPeriod - 1)
return oneAvgSwing
def avgHighLowSwing(self, df: pd.DataFrame) -> float:
return self.avgSwing(df, lambda row: abs(row.High - row.Low))
def avgVolumeSwing(self, df: pd.DataFrame) -> float:
return self.avgSwing(df, lambda row: abs(row.Volume))
def avgOpenCloseSwing(self, df: pd.DataFrame) -> float:
return self.avgSwing(df, lambda row: abs(row.Close - row.Open))
def getVariables(self, df:pd.DataFrame, avgSpread: float, avgVolume:float):
spreads = []
volumes = []
for _, row in df[0:5].iterrows():
spread = (row.Close - row.Open) / avgSpread
volume = row.Volume / avgVolume
spreads.append(spread)
volumes.append(volume)
return spreads, volumes
def isPositive(self, number: float):
if number < 0:
return False
return True
def isSameSign(self, number1: float, number2: float):
if number1 > 0 and number2 > 0:
return True
if number1 < 0 and number2 < 0:
return True
return False
def isAboutSameSize(self, number1: float, number2: float):
newPercent = abs(number2) / number1 if number1 != 0 else 0
if newPercent > 0.8 and newPercent <= 1.2:
return True
return False
def localMinMax(self, df: pd.DataFrame, index: int, barCount=None):
barCount = 15 if barCount is None else barCount
localMin = min(df.iloc[index]['Close'], df.iloc[index]['Open'])
localMax = max(df.iloc[index]['Close'], df.iloc[index]['Open'])
lowCount = 0
highCount = 0
for ix in range(index, barCount + index):
row = df.iloc[ix]
if row['Close'] <= localMin or row['Open'] <= localMin:
lowCount += 1
if row['Close'] >= localMax or row['Open'] >= localMax:
highCount +=1
return False if lowCount >= barCount-1 or highCount >= barCount + 1 else True
def wyckoffDoji(self, df:pd.DataFrame, spreads:list, volumes:list, period:int) -> int:
rangeLength = period - 2
for ix in range(rangeLength):
s1 = spreads[ix]
s2 = spreads[ix+1]
s3 = spreads[ix+2]
if abs(s1) > 0.1:
return 0
if abs(s1) > abs(s2) or abs(s2) > abs(s2):
return 0
v1 = volumes[ix]
v2 = volumes[ix+1]
v3 = volumes[ix+2]
if abs(v1) > abs(v2) or abs(v1) > abs(v3):
return 0
c1 = df.iloc[ix].Close
c2 = df.iloc[ix+1].Close
c3 = df.iloc[ix+2].Close
o1 = df.iloc[ix].Open
o2 = df.iloc[ix+1].Open
o3 = df.iloc[ix+2].Open
if c1 >= c2 and c2 > c3 and c2 > o2 and c3 > o3:
h1 = df.iloc[ix].High
h2 = df.iloc[ix+1].High
h3 = df.iloc[ix+2].High
if h1 >= h2 and self.localMinMax(df, ix):
return 10
if c1 <= c2 and c2 < c3 and c2 < o2 and o3 < c3:
l1 = df.iloc[ix].Low
l2 = df.iloc[ix+1].Low
l3 = df.iloc[ix+2].Low
if l1 <= l2 and self.localMinMax(df, ix):
return 10
return 0
def isVsaOk(self, df:pd.DataFrame, spreads:list, volumes:list, period:int) -> int:
rangeLength = period - 3
for ix in range(rangeLength):
s1 = spreads[ix]
s2 = spreads[ix+1]
s3 = spreads[ix+2]
s4 = spreads[ix+3]
v1 = volumes[ix]
v2 = volumes[ix+1]
v3 = volumes[ix+2]
v4 = volumes[ix+3]
# down thrust
if abs(s2) < 0.2 and v2 > 3 and not self.isSameSign(s1, s2) :
if self.localMinMax(df, ix):
return 1
# selling climax
if abs(s2) > 3 and v2 > 3 and not self.isSameSign(s1, s2) and abs(s2) > abs(s1) :
if self.localMinMax(df, ix):
return 2
if self.isSameSign(s4, s3) and self.isSameSign(s2, s3) and not self.isSameSign(s2, s1):
# effort is less than result
if (abs(s4) < abs(s3) < abs(s2)) and (v4 > v3 > v2):
return 3
# effort is more than result
if (abs(s4) > abs(s3) > abs(s2)) and (v4 < v3 < v2):
return 4
if self.isSameSign(s2, s1) and self.isSameSign(s3, s2) and self.isSameSign(s4, s3):
# no supply bar, pseudo down thrust, inverse downard thrust
if (abs(s2) * self.factor5) < abs(s3) and (v2 * self.factor5) < v3 and (v2 * self.factor5) < v4:
if self.localMinMax(df, ix, 10):
return 5
# if self.isSameSign(s1, s2):
# # effort is less than result
# if (abs(s2)*self.factor3) < abs(s1) and v2 > (v1 * self.factor3):
# return 3
# # effort is more than result
# if abs(s2) > (abs(s1) * self.factor4) and (v2 * self.factor4) < v1:
# return 4
# if self.isSameSign(s2, s3):
# # no supply bar, pseudo down thrust, inverse downard thrust
# if (abs(s1) * self.factor5) < abs(s2) and (v1 * self.factor5) < v2 and (v1 * self.factor5) < v3:
# return 5
# # inverse downward thrust
# if abs(s1) < 0.2 and v1 > 4:
# return 8
# # failed effort selling climax
# if abs(s2) > (abs(s3) * self.factor8) and v2 > (v3 * self.factor8) and (not self.isSameSign(s1, s2) and self.isAboutSameSize(s1, s2)):
# return 9
return 0
def Run(self, symbol: str, df: pd.DataFrame) -> int:
period = self.averagingBarCount
if self.isCanCalculate(df, period):
avgV = self.avgVolumeSwing(df)
avgS = self.avgOpenCloseSwing(df)
spreads, volumes = self.getVariables(df, avgS, avgV)
vsa = self.isVsaOk(df, spreads, volumes, self.barCount)
vsa = vsa if vsa > 0 else self.wyckoffDoji(
df, spreads, volumes, self.barCount)
return vsa
else:
return 0
class FilterVolumeSpreadAnalysis:
def __init__(self):
self.sa = StockAnalysis()
self.jsonData = self.sa.GetJson
self.voa = volumeSpreadAnalysis()
def Run(self, symbol: str):
try:
isLoaded, df = AllStocks.GetDailyStockData(symbol)
if isLoaded:
data = self.voa.Run(symbol, df)
self.sa.UpdateFilter(self.jsonData, symbol, 'vs', data)
return False
except Exception as e:
self.sa.UpdateFilter(self.jsonData, symbol, 'vs', 0)
logging.error(f'FilterVolumeSpreadAnalysis.Run: {symbol} - {e}')
print(f'FilterVolumeSpreadAnalysis.Run: {symbol} - {e}')
return False
@staticmethod
def All():
filter = FilterVolumeSpreadAnalysis()
AllStocks.Run(filter.Run, False)
filter.sa.WriteJson(filter.jsonData)
if __name__ == '__main__':
FilterVolumeSpreadAnalysis.All()
print('------------------------done ------------------------')
# data = {'Date':
# ['2021-01-07T00:00:00.000000000', '2021-02-01T00:00:00.000000000',
# '2021-03-12T00:00:00.000000000', '2021-04-23T00:00:00.000000000',
# '2021-05-28T00:00:00.000000000', '2021-09-01T00:00:00.000000000'],
# 'Close':
# [120.00, 100.01, 99.51623309,
# 93.63782723, 91.50373968, 91.8385245]
# }
# df = pd.DataFrame(data)
# price = 110.01
# isFirstMin = False
# fib = fibonachiRetracement(price, isFirstMin, df)
# result = fib.Run()
# print(result)
|
[
"util.AllStocks.Run",
"logging.error",
"util.AllStocks.GetDailyStockData",
"util.StockAnalysis"
] |
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|
#!/usr/bin/env python
import wfdb
import hmm
import numpy as np
import matplotlib.pyplot as plt
import pickle
def preprocess(count = 30):
print("Preprocessing")
sig, fields = wfdb.srdsamp('data/mitdb/100')
ecg = sig[:500000,0]
diff = np.diff(ecg)
emax = np.max(ecg)
emin = np.min(ecg)
count = count - 2
bins = [ emin + (emax - emin)/count * i for i in range(count+1) ]
quantized = np.digitize(ecg, bins)
dequant = np.empty(len(quantized))
# dmax = np.max(diff)-0.01
# dmin = np.min(diff)+0.01
# count = count - 2
# print(dmin,dmax)
# bins = [ dmin + (dmax - dmin)/count * i for i in range(count+1) ]
# print("bins",len(bins))
# quantized = np.digitize(diff, bins)
#
# dequant = np.empty(len(quantized))
running = 0
for i in range(len(dequant)):
v = quantized[i]
running += bins[v-1]
dequant[i] = running
return ecg,quantized,dequant,bins
def dequant(quantized, bins):
dequant = np.empty(len(quantized))
running = 0
for i in range(len(dequant)):
v = quantized[i]
running += bins[v-1]
dequant[i] = running
return dequant
# def train():
# print("Model training")
# states = 10
# model = hmm.random_model(states, count+2)
#
# model = hmm.baum_welch(quantized, model)
def query():
print("Questions answering")
hmm.probabilities(model, 5)
def train():
symbols = 100
states = 30
e, q, d, bins = preprocess(symbols)
model = hmm.random_model(states,symbols)
gen = hmm.synthetic(model)
sampl = [next(gen) for _ in range(1000)]
plt.ion()
plt.clf()
plt.subplot(311)
plt.imshow(model.transitions,interpolation='nearest', shape=model.transitions.shape)
plt.subplot(312)
plt.imshow(model.emissions,interpolation='nearest', shape=model.emissions.shape)
plt.subplot(313)
plt.plot(sampl)
plt.show()
plt.pause(0.001)
i = 0
plt.savefig("out{}.png".format(i))
# try:
step = 10000
sig = q
length = len(sig)
fro = 0
to = step
print("\nIteration {}".format(i))
while True:
with open("db_ecg.pickle","wb") as pfile:
pickle.dump(model,pfile)
print("batch from {} to {}".format(fro,to),end="\r")
i+=1
if to >= length - 9*step:
print("\nIteration {}".format(i))
fro = 0
to = step
obs = [ ]
# tmp_fro = fro
# tmp_to = to
# for x in range(8):
# obs.append(sig[tmp_fro:tmp_to])
# tmp_fro += step
# tmp_to += step
o = sig[fro:to]
fro += step
to += step
# for o in obs:
# model = hmm.baum_welch(o,model)
for i in range(100):
model = hmm.baum_welch(o,model)
gen = hmm.synthetic(model)
sampl = [next(gen) for _ in range(1000)]
# model = hmm.batch_baum_welch(obs,model)
plt.clf()
plt.subplot(311)
plt.imshow(model.transitions,interpolation='nearest', shape=model.transitions.shape)
plt.subplot(312)
plt.imshow(model.emissions,interpolation='nearest', shape=model.emissions.shape)
plt.subplot(313)
plt.plot(sampl)
plt.show()
plt.pause(0.001)
plt.savefig("out{}.png".format(i))
# except:
# pass
plt.ioff()
plt.subplot(311)
plt.imshow(model.transitions,interpolation='nearest', shape=model.transitions.shape)
plt.subplot(312)
plt.imshow(model.emissions,interpolation='nearest', shape=model.emissions.shape)
plt.subplot(313)
plt.plot(sampl)
plt.show()
return model, bins
|
[
"matplotlib.pyplot.subplot",
"pickle.dump",
"hmm.probabilities",
"matplotlib.pyplot.show",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.clf",
"matplotlib.pyplot.ioff",
"matplotlib.pyplot.imshow",
"matplotlib.pyplot.ion",
"numpy.max",
"numpy.diff",
"numpy.min",
"hmm.random_model",
"hmm.synthetic",
"hmm.baum_welch",
"wfdb.srdsamp",
"numpy.digitize",
"matplotlib.pyplot.pause"
] |
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'plt.imshow', (['model.transitions'], {'interpolation': '"""nearest"""', 'shape': 'model.transitions.shape'}), "(model.transitions, interpolation='nearest', shape=model.\n transitions.shape)\n", (3445, 3525), True, 'import matplotlib.pyplot as plt\n'), ((3524, 3540), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(312)'], {}), '(312)\n', (3535, 3540), True, 'import matplotlib.pyplot as plt\n'), ((3545, 3631), 'matplotlib.pyplot.imshow', 'plt.imshow', (['model.emissions'], {'interpolation': '"""nearest"""', 'shape': 'model.emissions.shape'}), "(model.emissions, interpolation='nearest', shape=model.emissions.\n shape)\n", (3555, 3631), True, 'import matplotlib.pyplot as plt\n'), ((3630, 3646), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(313)'], {}), '(313)\n', (3641, 3646), True, 'import matplotlib.pyplot as plt\n'), ((3651, 3666), 'matplotlib.pyplot.plot', 'plt.plot', (['sampl'], {}), '(sampl)\n', (3659, 3666), True, 'import matplotlib.pyplot as plt\n'), ((3671, 3681), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (3679, 3681), True, 'import matplotlib.pyplot as plt\n'), ((2858, 2878), 'hmm.synthetic', 'hmm.synthetic', (['model'], {}), '(model)\n', (2871, 2878), False, 'import hmm\n'), ((2987, 2996), 'matplotlib.pyplot.clf', 'plt.clf', ([], {}), '()\n', (2994, 2996), True, 'import matplotlib.pyplot as plt\n'), ((3005, 3021), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(311)'], {}), '(311)\n', (3016, 3021), True, 'import matplotlib.pyplot as plt\n'), ((3030, 3120), 'matplotlib.pyplot.imshow', 'plt.imshow', (['model.transitions'], {'interpolation': '"""nearest"""', 'shape': 'model.transitions.shape'}), "(model.transitions, interpolation='nearest', shape=model.\n transitions.shape)\n", (3040, 3120), True, 'import matplotlib.pyplot as plt\n'), ((3123, 3139), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(312)'], {}), '(312)\n', (3134, 3139), True, 'import matplotlib.pyplot as plt\n'), ((3148, 3234), 'matplotlib.pyplot.imshow', 'plt.imshow', (['model.emissions'], {'interpolation': '"""nearest"""', 'shape': 'model.emissions.shape'}), "(model.emissions, interpolation='nearest', shape=model.emissions.\n shape)\n", (3158, 3234), True, 'import matplotlib.pyplot as plt\n'), ((3237, 3253), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(313)'], {}), '(313)\n', (3248, 3253), True, 'import matplotlib.pyplot as plt\n'), ((3262, 3277), 'matplotlib.pyplot.plot', 'plt.plot', (['sampl'], {}), '(sampl)\n', (3270, 3277), True, 'import matplotlib.pyplot as plt\n'), ((3286, 3296), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (3294, 3296), True, 'import matplotlib.pyplot as plt\n'), ((3305, 3321), 'matplotlib.pyplot.pause', 'plt.pause', (['(0.001)'], {}), '(0.001)\n', (3314, 3321), True, 'import matplotlib.pyplot as plt\n'), ((2211, 2236), 'pickle.dump', 'pickle.dump', (['model', 'pfile'], {}), '(model, pfile)\n', (2222, 2236), False, 'import pickle\n'), ((2819, 2843), 'hmm.baum_welch', 'hmm.baum_welch', (['o', 'model'], {}), '(o, model)\n', (2833, 2843), False, 'import hmm\n')]
|
def linha(simb='=',tam=42):
"""[Cria uma linha]
Args:
simb (str, optional): [O símbolo que Vai Formar a linha]. Defaults to '='.
tam (int, optional): [Tamanho da Linha]. Defaults to 42.
Criado por:
Murilo_Henrique060
"""
print(f'{simb}'*tam)
def cabeçalho(simb='=',txt='ESCREVA O CABEÇALHO',tam=0):
"""[Cria um Cabeçalho, com uma linha em cima, o texto e uma linha em baixo.]
Args:
simb (str, optional): [O símbolo que vai formar as linhas]. Defaults to '='.
txt (str, optional): [O texto Do Cabeçalho]. Defaults to 'ESCREVA O CABEÇALHO'.
tam (int, optional): [O tamanho do cabeçalho, 0 = acompanha o tamanho do texto]. Defaults to 0
Criado por:
Murilo_Henrique060
"""
if tam <= 0:
linha(f'{simb}',(len(txt)+4))
print(f' {txt}')
linha(f'{simb}',(len(txt)+4))
else:
linha(f'{simb}',tam)
print(f'{" "*((tam-len(txt))//2)}{txt}')
linha(f'{simb}',tam)
def leiaInt(texto):
while True:
try:
numero = int(input(texto))
except:
print('ERRO! Digite um número inteiro válido')
else:
return numero
def leiaInRange(numero=0,n1=0,n2=1,outRange='ERRO! Número Fora Do Alcance.',cor=False):
"""[Verifica se Um Número Está Dentro de um Range.]
Args:
numero (int, optional): [Númaero à Ser Verificado.]. Defaults to 0.
n1 (int, optional): [começo do range]. Defaults to 0.
n2 (int, optional): [final do range]. Defaults to 1.
outRange (str, optional): [Mensagem se o número Estiver Fora do Range]. Defaults to 'ERRO! Número Fora Do Alcance.'.
cor (bool, optional): [Adicionar ou Não Cores]. Defaults to False.
Returns:
[bool]: [retorna True ou False]
Criado por:
Murilo_Henrique060
"""
if cor:
from colorama import init
init()
v = 0
for n in range(n1,n2+1):
if n == numero:
v += 1
if v != 1:
print(outRange)
return False
else:
return True
def menu(lista,cabecalho,input='Sua Opcão: '):
cabeçalho('=',cabecalho,42)
c = 1
for itens in lista:
print(f'{c} - {itens}')
c += 1
linha('=',42)
while True:
opc = leiaInt(input)
r = leiaInRange(opc,1,(len(lista)))
if r:
return opc
break
def regras():
print(f'{"REGRAS":^42}')
linha()
print('''Palavras:
As palavras do jogo da forca são guardadas em um arquivo de texto chamado \'palavras.txt\', podem ser mudadas no menu principal
ps: as palavras devem ser escritas sem acento.
O Jogo:
o jogo começa sorteando uma palavra do banco de palavras, e mostra um hub:
|======|
|
|
|
| _ _ _ _ _ _ _
=====
e a cada erro ele mostra uma parte do corpo (cabeça, tronco, braço direito, braço esquerdo, perna esquera, perna direita. Nessa ordem.)
até completar o corpo inteiro como o abaixo:
|======|
| o
| /|\
| / \
| _ _ _ _ _ _ _
=====
e a cada acerto o \'_\' é trocado pela letra escolhida:
|======|
|
|
|
| _ A _ A _ _ A
=====
e quando você perde:
|======|
| o VOCÊ PERDEU!!!
| /|\
| / \
| P A L A V R A
=====''')
|
[
"colorama.init"
] |
[((1916, 1922), 'colorama.init', 'init', ([], {}), '()\n', (1920, 1922), False, 'from colorama import init\n')]
|
import os
os.chdir("Render2018/lib/")
os.system("python3 create_config_bodyflow.py")
|
[
"os.system",
"os.chdir"
] |
[((10, 37), 'os.chdir', 'os.chdir', (['"""Render2018/lib/"""'], {}), "('Render2018/lib/')\n", (18, 37), False, 'import os\n'), ((38, 84), 'os.system', 'os.system', (['"""python3 create_config_bodyflow.py"""'], {}), "('python3 create_config_bodyflow.py')\n", (47, 84), False, 'import os\n')]
|
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
from typing import TYPE_CHECKING
from cdm.enums import CdmObjectType
from cdm.enums.cdm_operation_type import OperationTypeConvertor, CdmOperationType
from cdm.persistence.cdmfolder.types import OperationAddAttributeGroup
from cdm.utilities.logging import logger
from cdm.utilities.string_utils import StringUtils
if TYPE_CHECKING:
from cdm.objectmodel import CdmCorpusContext, CdmOperationAddAttributeGroup
from cdm.utilities import ResolveOptions, CopyOptions
_TAG = 'OperationAddAttributeGroupPersistence'
class OperationAddAttributeGroupPersistence:
"""Operation AddAttributeGroup persistence"""
@staticmethod
def from_data(ctx: 'CdmCorpusContext', data: 'OperationAddAttributeGroup') -> 'CdmOperationAddAttributeGroup':
if not data:
return None
add_attribute_group_op = ctx.corpus.make_object(CdmObjectType.OPERATION_ADD_ATTRIBUTE_GROUP_DEF)
if data.type and not StringUtils.equals_with_ignore_case(data.type, OperationTypeConvertor._operation_type_to_string(CdmOperationType.ADD_ATTRIBUTE_GROUP)):
logger.error(_TAG, ctx, '$type {} is invalid for this operation.'.format(data.type))
else:
add_attribute_group_op.type = CdmOperationType.ADD_ATTRIBUTE_GROUP
# TODO (sukanyas): Property to be defined
return add_attribute_group_op
@staticmethod
def to_data(instance: 'CdmOperationAddAttributeGroup', res_opt: 'ResolveOptions', options: 'CopyOptions') -> 'OperationAddAttributeGroup':
if not instance:
return None
obj = OperationAddAttributeGroup()
obj.type = OperationTypeConvertor._operation_type_to_string(CdmOperationType.ADD_ATTRIBUTE_GROUP)
obj.explanation = instance.explanation
# TODO (sukanyas): Property to be defined
return obj
|
[
"cdm.persistence.cdmfolder.types.OperationAddAttributeGroup",
"cdm.enums.cdm_operation_type.OperationTypeConvertor._operation_type_to_string"
] |
[((1731, 1759), 'cdm.persistence.cdmfolder.types.OperationAddAttributeGroup', 'OperationAddAttributeGroup', ([], {}), '()\n', (1757, 1759), False, 'from cdm.persistence.cdmfolder.types import OperationAddAttributeGroup\n'), ((1779, 1870), 'cdm.enums.cdm_operation_type.OperationTypeConvertor._operation_type_to_string', 'OperationTypeConvertor._operation_type_to_string', (['CdmOperationType.ADD_ATTRIBUTE_GROUP'], {}), '(CdmOperationType.\n ADD_ATTRIBUTE_GROUP)\n', (1827, 1870), False, 'from cdm.enums.cdm_operation_type import OperationTypeConvertor, CdmOperationType\n'), ((1136, 1227), 'cdm.enums.cdm_operation_type.OperationTypeConvertor._operation_type_to_string', 'OperationTypeConvertor._operation_type_to_string', (['CdmOperationType.ADD_ATTRIBUTE_GROUP'], {}), '(CdmOperationType.\n ADD_ATTRIBUTE_GROUP)\n', (1184, 1227), False, 'from cdm.enums.cdm_operation_type import OperationTypeConvertor, CdmOperationType\n')]
|
# Copyright (c) 2018, 2021 Oracle and/or its affiliates. All rights reserved.
# Licensed under the Universal Permissive License v 1.0 as shown
# at http://oss.oracle.com/licenses/upl.
import os
import unittest
from tools.decorators import skipUnlessOCI
from tools.oci_test_case import OciTestCase
os.environ['LC_ALL'] = 'en_US.UTF8'
class TestPlatformHelpers(OciTestCase):
""" Test around lib/oci_utils/impl/platform_helpers.py.
"""
@skipUnlessOCI()
def test_get_phys_device(self):
"""
Test get_phys_device.
Returns
-------
No return value.
"""
import oci_utils.impl.platform_helpers
self.assertIsNotNone(oci_utils.impl.platform_helpers.get_phys_device(),
'None returned by get_phys_device()')
@skipUnlessOCI()
def test_get_block_device(self):
"""
Test get_block_devices.
Returns
-------
No return value.
"""
import oci_utils.impl.platform_helpers
self.assertIsNotNone(
oci_utils.impl.platform_helpers.get_block_devices(),
'None returned by get_block_devices()')
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromTestCase(TestPlatformHelpers)
unittest.TextTestRunner().run(suite)
|
[
"tools.decorators.skipUnlessOCI",
"unittest.TestLoader",
"unittest.TextTestRunner"
] |
[((452, 467), 'tools.decorators.skipUnlessOCI', 'skipUnlessOCI', ([], {}), '()\n', (465, 467), False, 'from tools.decorators import skipUnlessOCI\n'), ((820, 835), 'tools.decorators.skipUnlessOCI', 'skipUnlessOCI', ([], {}), '()\n', (833, 835), False, 'from tools.decorators import skipUnlessOCI\n'), ((1226, 1247), 'unittest.TestLoader', 'unittest.TestLoader', ([], {}), '()\n', (1245, 1247), False, 'import unittest\n'), ((1295, 1320), 'unittest.TextTestRunner', 'unittest.TextTestRunner', ([], {}), '()\n', (1318, 1320), False, 'import unittest\n')]
|
# -*- coding: utf-8 -*-
""" Test functions in tools.py.
"""
import os
"""License:
Copyright 2020-2022 The Cytoscape Consortium
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions
of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS
OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
import unittest
import time
from requests import HTTPError
from test_utils import *
class ToolsTests(unittest.TestCase):
def setUp(self):
# Close all browser windows if possible
try:
for browser in cybrowser_list():
cybrowser_close(browser['id'])
except:
pass
def tearDown(self):
pass
BROWSER_HELLO = {'id': 'Browser Hello ID',
'show': {'func': lambda x, y: cybrowser_show(id=x, title=y,
text='<HTML><HEAD><TITLE>Hello</TITLE></HEAD><BODY>Hello, world!</BODY></HTML>'),
'title': 'Browser Hello Page'},
'dialog': {'func': lambda x, y: cybrowser_dialog(id=x, title=y,
text='<HTML><HEAD><TITLE>Hello</TITLE></HEAD><BODY>Hello, world!</BODY></HTML>'),
'title': 'Hello'}}
CYTOSCAPE_HOME_PAGE = {'id': 'Cytoscape Home Page ID',
'show': {
'func': lambda x, y: cybrowser_show(id=x, title=y, url='http://www.cytoscape.org'),
'title': 'Cytoscape Home Page'},
'dialog': {
'func': lambda x, y: cybrowser_dialog(id=x, title=y,
url='http://www.cytoscape.org'),
'title': 'Cytoscape: An Open Source Platform for Complex Network Analysis and Visualization'}}
CYTOSCAPE_MANUAL = {'id': 'Cytoscape Manual ID',
'show': {'func': lambda x, y: cybrowser_show(id=x, title=y,
url='http://manual.cytoscape.org/en/3.7.2/'),
'title': 'Cytoscape Manual Page'},
'dialog': {
'func': lambda x, y: cybrowser_dialog(id=x, title=y,
url='http://manual.cytoscape.org/en/3.7.2/'),
'title': 'Cytoscape 3.7.2 User Manual — Cytoscape User Manual 3.7.2 documentation'}}
@print_entry_exit
def test_cybrowser_version(self):
# Verify that a version is reported
version = cybrowser_version()
self.assertIsInstance(version, dict)
self.assertIsInstance(version['version'], str)
@unittest.skipIf(skip_for_ui(), 'Avoiding test that requires user response')
@print_entry_exit
def test_cybrowser_show_list_hide(self):
self._cybrowser_windows('show')
@unittest.skipIf(skip_for_ui(), 'Avoiding test that requires user response')
@print_entry_exit
def test_cybrowser_dialog_list_hide(self):
self._cybrowser_windows('dialog')
@unittest.skipIf(skip_for_ui(), 'Avoiding test that requires user response')
@print_entry_exit
def test_cybrowser_send(self):
self._check_show('dialog', ToolsTests.CYTOSCAPE_HOME_PAGE)
window_id = ToolsTests.CYTOSCAPE_HOME_PAGE['id']
# Verify that the user agent variable can be fetched
res = cybrowser_send(window_id, 'navigator.userAgent')
self.assertIsInstance(res, dict)
self.assertEqual(res['browserId'], window_id)
self.assertIsInstance(res['result'], str)
# Verify that the window can be moved to a different URL
res = cybrowser_send(window_id, "window.location='http://google.com'")
self.assertEqual(res['browserId'], window_id)
self.assertEqual(res['result'], 'http://google.com')
self.assertRaises(CyError, cybrowser_send, 'bogus window', 'navigator.userAgent')
self.assertDictEqual(cybrowser_send(window_id, 'bogus_statement'), {})
@print_entry_exit
def test_diffusion_basic(self):
# Initialization
load_test_session()
# Verify that selecting a node and calling diffusion returns a bunch of nodes
select_nodes(['RAP1'], by_col='COMMON')
res = diffusion_basic()
self.assertIsInstance(res, dict)
self.assertEqual(res['heatColumn'], 'diffusion_output_heat')
self.assertEqual(res['rankColumn'], 'diffusion_output_rank')
self.assertTrue(len(get_selected_nodes()) > 0)
# Verify that diffusion returns nodes even when nothing is selected
clear_selection()
res = diffusion_basic()
self.assertIsInstance(res, dict)
self.assertEqual(res['heatColumn'], 'diffusion_output_1_heat')
self.assertEqual(res['rankColumn'], 'diffusion_output_1_rank')
self.assertTrue(len(get_selected_nodes()) > 0)
@print_entry_exit
def test_diffusion_advanced(self):
# Initialization
load_test_session()
# Verify that selecting a node and calling diffusion returns a bunch of nodes
select_nodes(['RAP1'], by_col='COMMON')
res = diffusion_advanced(heat_column_name='', time=0.1)
self.assertIsInstance(res, dict)
self.assertEqual(res['heatColumn'], 'diffusion_output_heat')
self.assertEqual(res['rankColumn'], 'diffusion_output_rank')
self.assertTrue(len(get_selected_nodes()) > 0)
# Verify that diffusion returns nodes even when nothing is selected
clear_selection()
res = diffusion_advanced(heat_column_name='diffusion_output_heat', time=0.2)
self.assertIsInstance(res, dict)
self.assertEqual(res['heatColumn'], 'diffusion_output_1_heat')
self.assertEqual(res['rankColumn'], 'diffusion_output_1_rank')
self.assertTrue(len(get_selected_nodes()) > 0)
# Verify that a bad parameter causes an exception
self.assertRaises(CyError, diffusion_advanced, heat_column_name='diffusion_output_heat', time='x')
@print_entry_exit
def test_analyze_network(self):
def check_analysis(actual, expected, compare_props):
self.assertIsInstance(actual, dict)
self.assertTrue(set(expected).issubset(set(actual)))
member_equal = [expected[prop] == actual[prop] for prop in compare_props]
self.assertFalse(False in member_equal)
# Initialization
load_test_session()
check_analysis(analyze_network(),
{'networkTitle': 'galFiltered.sif (undirected)', 'nodeCount': '330', 'edgeCount': '359',
'avNeighbors': '2.379032258064516', 'diameter': '27', 'radius': '14',
'avSpl': '9.127660963823953', 'cc': '0.06959203036053131', 'density': '0.009631709546819902',
'heterogeneity': '0.8534500004035027', 'centralization': '0.06375695335900727', 'ncc': '26'},
{'networkTitle', 'nodeCount', 'edgeCount', 'diameter', 'radius', 'ncc'})
check_analysis(analyze_network(True),
{'networkTitle': 'galFiltered.sif (directed)', 'nodeCount': '330', 'edgeCount': '359',
'avNeighbors': '2.16969696969697', 'diameter': '10', 'radius': '1',
'avSpl': '3.4919830756382395', 'cc': '0.03544266191325015', 'density': '0.003297411808050106',
'ncc': '26', 'mnp': '1', 'nsl': '0'},
{'networkTitle', 'nodeCount', 'edgeCount', 'diameter', 'radius', 'ncc', 'mnp', 'nsl'})
@print_entry_exit
def test_network_merge(self):
BASIC_MERGED_NODE_PROPS = {'SUID': 'Long', 'shared name': 'String', 'name': 'String', 'selected': 'Boolean',
'age': 'Integer', 'first name': 'String', 'given name': 'String', 'group': 'String',
'id': 'String', 'score': 'Integer'}
BASIC_MERGED_EDGE_PROPS = {'SUID': 'Long', 'shared name': 'String', 'shared interaction': 'String',
'name': 'String',
'selected': 'Boolean', 'interaction': 'String', 'data.key.column': 'Integer',
'owes': 'Integer', 'relationship': 'String', 'source': 'String', 'target': 'String',
'weight': 'Double'}
BASIC_MERGED_NETWORK_PROPS = {'SUID': 'Long', 'shared name': 'String', 'name': 'String', 'selected': 'Boolean',
'__Annotations': 'List'}
BASIC_MERGED_NODES = {'node X', 'node 12', 'node 13', 'node 10', 'node 11', 'node 2', 'node 3', 'node 0',
'node 1'}
BASIC_MERGED_EDGES = {'node 2 (interacts) node 3', 'node X (destroys) node 0',
'node 10 (interacts with) node 13', 'node 12 (interacts with) node 13',
'node 10 (interacts with) node 11', 'node 10 (interacts with) node 12',
'node 0 (interacts) node 2', 'node 0 (activates) node 3',
'node X (interacts with) node 10', 'node 0 (inhibits) node 1'}
def check_merge(new_suid, new_title, node_count=9, edge_count=10,
extra_node_props={}, extra_edge_props={}, extra_network_props={},
merged_nodes=BASIC_MERGED_NODES, merged_edges=BASIC_MERGED_EDGES):
self.assertEqual(get_network_name(suid=new_suid), new_title, 'Check title')
self.assertEqual(get_node_count(network=new_suid), node_count, 'Check node count')
self.assertEqual(get_edge_count(network=new_suid), edge_count, 'Check edge count')
if node_count:
self.assertEqual(set(get_all_nodes(network=new_suid)), merged_nodes, 'Check all nodes')
if edge_count:
self.assertSetEqual(set(get_all_edges(network=new_suid)), merged_edges, 'Check all edges')
actual_nodes = BASIC_MERGED_NODE_PROPS.copy()
actual_nodes.update(extra_node_props)
actual_edges = BASIC_MERGED_EDGE_PROPS.copy()
actual_edges.update(extra_edge_props)
actual_networks = BASIC_MERGED_NETWORK_PROPS.copy()
actual_networks.update(extra_network_props)
self.assertDictEqual(get_table_column_types('node', network=new_suid), actual_nodes, 'Check column types')
self.assertDictEqual(get_table_column_types('edge', network=new_suid), actual_edges, 'Check edge types')
self.assertDictEqual(get_table_column_types('network', network=new_suid), actual_networks, 'Check network types')
# Setup: clean out previous test
close_session(False)
# Setup: Create the first network (Network_0)
node_data_0 = {'id': ["node 0", "node 1", "node 2", "node 3", 'node X'],
'given name': ["Barry", "Karen", "Scott", "Robyn", "X"],
'group': ["A", "A", "B", "B", "C"],
'score': [20, 10, 15, 5, -10]}
nodes_0 = df.DataFrame(data=node_data_0, columns=['id', 'given name', 'group', 'score'])
edge_data_0 = {'source': ["node 0", "node 0", "node 0", "node 2", "node X"],
'target': ["node 1", "node 2", "node 3", "node 3", "node 0"],
'interaction': ["inhibits", "interacts", "activates", "interacts", "destroys"],
'weight': [5.1, 3.0, 5.2, 9.9, -100]}
edges_0 = df.DataFrame(data=edge_data_0, columns=['source', 'target', 'interaction', 'weight'])
create_network_from_data_frames(nodes_0, edges_0, title='Network_0')
# Setup: Create the second network (Network_1)
node_data_1 = {'id': ["node 10", "node 11", "node 12", "node 13", "node X"],
'first name': ["Barry", "Karen", "Scott", "Robyn", "X"],
'age': [7, 5, 4, 0, -1]}
nodes_1 = df.DataFrame(data=node_data_1, columns=['id', 'first name', 'age'])
edge_data_1 = {'source': ["node 10", "node 10", "node 10", "node 12", 'node X'],
'target': ["node 11", "node 12", "node 13", "node 13", "node 10"],
'relationship': ["sister", "brother", "brother", "sister", "cousin"],
'owes': [10, 20, 30, 40, -1000]}
edges_1 = df.DataFrame(data=edge_data_1, columns=['source', 'target', 'relationship', 'owes'])
# Verify that a network can be created containing dataframe encoding both nodes and edges
create_network_from_data_frames(nodes_1, edges_1, title='Network_1')
# Verify that the simplest union merge works
check_merge(merge_networks(['Network_0', 'Network_1']), 'union: Network_0,Network_2')
check_merge(merge_networks(['Network_0', 'Network_1'], title='My Cool Network'), 'My Cool Network')
# Verify that a node merge map works -- 'called' and 'score_m' were created and have String/Integer types
res = merge_networks(['Network_0', 'Network_1'],
node_merge_map=[['given name', 'first name', 'called', 'String'],
['score', 'age', 'score_m', 'Integer']],
title='nodes mapped')
check_merge(res, 'nodes mapped', 9, 10, extra_node_props={'called': 'String', 'score_m': 'Integer'})
# Verify that an edge merge map works -- 'profile' was created and has Double type
res = merge_networks(['Network_0', 'Network_1'],
edge_merge_map=[['weight', 'owes', 'profile', 'Double']],
title='edges mapped')
check_merge(res, 'edges mapped', 9, 10, extra_edge_props={'profile': 'Double'})
# Verify that a network merge map works -- 'jumble forward' and 'jumble backward' were created and have String type
res = merge_networks(['Network_0', 'Network_1'],
network_merge_map=[['shared name', 'name', 'jumble forward', 'String'],
['shared name', 'name', 'jumble backward', 'String']],
title='networks mapped')
check_merge(res, 'networks mapped', 9, 10,
extra_network_props={'jumble forward': 'String', 'jumble backward': 'String'})
# It would be very reasonable to add more tests for the nodes_only, edge_keys, node_keys, and in_network_merge
# parameters, but I can't get a good definition of what these parameters do, so I don't know what to test for. So,
# for now, we pass them on and hope for the best.
# For the operation='difference' parameter, the best test would be to execute a merge that corresponds to the
# GUI's "Remove all nodes that are in the 2nd network". I don't think the parameter for this is exposed, so
# I have to punt on checking the 'difference' operation.
# Verify that an intersection leaves only the single node 'node X'
res = merge_networks(['Network_0', 'Network_1'], operation='intersection', title='Cool Intersection')
check_merge(res, 'Cool Intersection', node_count=1, edge_count=0, merged_nodes={'node X'}, merged_edges={})
# Verify that exception is thrown for bad cases
self.assertRaises(CyError, merge_networks, [])
self.assertRaises(CyError, merge_networks, None)
@print_entry_exit
def test_import_file_from_url(self):
_TEST_SANDBOX_NAME = 'test_sandbox'
_TEST_FILE = 'test file'
_ALTTEST_SANDBOX_NAME = '.test.sandbox'
_FROM_URL = 'https://www.dropbox.com/s/r15azh0xb53smu1/GDS112_full.soft?dl=0'
_FROM_URL_BYTES = 5536880
_ALT_FROM_URL = 'https://www.dropbox.com/s/8wc8o897tsxewt1/BIOGRID-ORGANISM-Saccharomyces_cerevisiae-3.2.105.mitab?dl=0'
_ALT_FROM_URL_BYTES = 166981992
_NESTED_DIR = '1/2/3/'
_ESCAPE_DIR = '1/../1/2/3/'
def check_url_to_result(res, sandbox_path, file_name, expected_length):
self.assertIsInstance(res, dict)
self.assertSetEqual(set(res.keys()), {'filePath', 'fileByteCount'})
expected_file = os.path.join(sandbox_path, file_name)
self.assertTrue(os.path.samefile(res['filePath'], expected_file))
self.assertTrue(os.path.exists(expected_file))
self.assertEqual(os.path.getsize(res['filePath']), expected_length)
def check_url_to_local_dir(sandbox_path):
# Get rid of the local test file if it already exists
if os.path.exists(_TEST_FILE):
os.remove(_TEST_FILE)
# Verify that a file can be transferred to the sandbox
res = import_file_from_url(_FROM_URL, _TEST_FILE)
check_url_to_result(res, sandbox_path, _TEST_FILE, _FROM_URL_BYTES)
# Verify that the file can't be overwritten if we don't want it to be
self.assertRaises(CyError, import_file_from_url, source_url=_ALT_FROM_URL, dest_file=_TEST_FILE,
overwrite=False)
check_url_to_result(res, sandbox_path, _TEST_FILE, _FROM_URL_BYTES)
# Verify that a different file can overwrite it if we allow it
res = import_file_from_url(_ALT_FROM_URL, _TEST_FILE)
check_url_to_result(res, sandbox_path, _TEST_FILE, _ALT_FROM_URL_BYTES)
# Get rid of the local test file if it already exists
if os.path.exists(_TEST_FILE):
os.remove(_TEST_FILE)
# Verify that a file can be written to a directory nested in the sandbox, with path to be created during write
nested_test_file = _NESTED_DIR + _TEST_FILE
res = import_file_from_url(_FROM_URL, nested_test_file)
check_url_to_result(res, sandbox_path, nested_test_file, _FROM_URL_BYTES)
if os.path.exists(nested_test_file):
os.remove(nested_test_file)
# Verify that a file can be written to a directory nested in the sandbox, with path to be created during write
escaped_test_file = _ESCAPE_DIR + _TEST_FILE
res = import_file_from_url(_FROM_URL, escaped_test_file)
check_url_to_result(res, sandbox_path, escaped_test_file, _FROM_URL_BYTES)
if os.path.exists(escaped_test_file):
os.remove(escaped_test_file)
# Verify that trying to send a non-existent file fails
self.assertRaises(Exception, import_file_from_url, source_url=_FROM_URL)
self.assertRaises(CyError, import_file_from_url, source_url='totally bogus', dest_file=_TEST_FILE)
self.assertRaises(CyError, import_file_from_url, source_url=None, dest_file=_TEST_FILE)
self.assertRaises(CyError, import_file_from_url, source_url=' ', dest_file=_TEST_FILE)
# Check sending to empty sandbox (Python kernel directory)
default_sandbox_path = sandbox_set(None) # Should be py4cytoscape test directory
self.assertTrue(os.path.samefile(default_sandbox_path, os.getcwd()))
check_url_to_local_dir(default_sandbox_path)
def _cybrowser_windows(self, operation='show'):
def check_browser_list(browser_list, expected_list): # doesn't support duplicate ID keys
browser = {b['id']: b for b in browser_list}
expected = {b['id']: b for b in expected_list}
self.assertEqual(len(browser), len(expected))
for id, val in browser.items():
self.assertIn(id, expected)
self.assertEqual(val['title'], expected[id][operation]['title'])
# Verify that the browser list starts out empty ... no browser windows displayed
check_browser_list(cybrowser_list(), [])
# Verify that a browser can be launched with all of its options
self._check_show(operation, ToolsTests.BROWSER_HELLO)
self._check_show(operation, ToolsTests.CYTOSCAPE_HOME_PAGE)
self._check_show(operation, ToolsTests.CYTOSCAPE_MANUAL)
# Verify that the browser list contains all of the new pages
time.sleep(2) # wait for windowing system to catch up
check_browser_list(cybrowser_list(),
[ToolsTests.BROWSER_HELLO, ToolsTests.CYTOSCAPE_HOME_PAGE, ToolsTests.CYTOSCAPE_MANUAL])
# Verify that adding the same pages doesn't change the browser list
self._check_show(operation, ToolsTests.BROWSER_HELLO, skip_verify=True)
self._check_show(operation, ToolsTests.BROWSER_HELLO, skip_verify=True)
self._check_show(operation, ToolsTests.BROWSER_HELLO, skip_verify=True)
time.sleep(2) # wait for windowing system to catch up
check_browser_list(cybrowser_list(),
[ToolsTests.BROWSER_HELLO, ToolsTests.CYTOSCAPE_HOME_PAGE, ToolsTests.CYTOSCAPE_MANUAL])
# Verify that hiding a browser removes it from the browser list, and bogus browser windows don't cause error
self.assertDictEqual(cybrowser_hide(ToolsTests.BROWSER_HELLO['id']), {})
self.assertDictEqual(cybrowser_close(ToolsTests.CYTOSCAPE_HOME_PAGE['id']), {})
time.sleep(2) # wait for windowing system to catch up
check_browser_list(cybrowser_list(), [ToolsTests.CYTOSCAPE_MANUAL, ToolsTests.BROWSER_HELLO])
# Verify that closing a browser twice does no harm
self.assertDictEqual(cybrowser_close(ToolsTests.CYTOSCAPE_HOME_PAGE['id']), {})
time.sleep(2) # wait for windowing system to catch up
check_browser_list(cybrowser_list(), [ToolsTests.CYTOSCAPE_MANUAL, ToolsTests.BROWSER_HELLO])
# Verify that closing the last browser window results in a clean browser list
self.assertDictEqual(cybrowser_close(ToolsTests.BROWSER_HELLO['id']), {})
self.assertDictEqual(cybrowser_close(ToolsTests.CYTOSCAPE_MANUAL['id']), {})
time.sleep(2) # wait for windowing system to catch up
check_browser_list(cybrowser_list(), [])
def _check_show(self, operation, window_def, skip_verify=False):
show_result = window_def[operation]['func'](window_def['id'], window_def[operation]['title'])
self.assertIsInstance(show_result, dict)
self.assertEqual(show_result['id'], window_def['id'])
if not skip_verify: input('Verify that the "' + window_def[operation]['title'] + '" is visible; hit Enter')
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"os.remove",
"os.getcwd",
"os.path.samefile",
"os.path.getsize",
"os.path.exists",
"time.sleep",
"os.path.join"
] |
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|
from django.urls import path
from My_music_app.music_app.views import home_page, add_album, album_details, album_edit, album_delete, profile_details, \
profile_delete, create_profile
urlpatterns =(
path('',home_page,name='home page'),
path('album/add',add_album,name='add album'),
path('album/details/<int:pk>/',album_details,name='album details'),
path('album/edit/<int:pk>/',album_edit,name='album edit'),
path('album/delete/<int:pk>/',album_delete,name='album delete'),
path('profile/create/', create_profile, name='create profile'),
path('profile/details/',profile_details,name='profile details'),
path('profile/delete/',profile_delete,name='profile delete'),
)
|
[
"django.urls.path"
] |
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|
from gym.envs.registration import register
from simglucose.sensor.cgm import CGMSensor,CGMSensorWithoutNoise
from simglucose.actuator.pump import InsulinPump
from simglucose.patient.t1dpatient import T1DPatient
from simglucose.simulation.env import T1DSimEnv
from simglucose.simulation.scenario import CustomScenario,ZeroScenario,DDPGScenario
from examples.apply_customized_controller import MyController
from simglucose.simulation.sim_engine import batch_sim,SimObjForKeras, SimObjForKeras2
from simglucose.controller.basal_bolus_ctrller import BBController
from datetime import timedelta
from datetime import datetime
register(
id='simglucose-adolescent2-v0',
entry_point='simglucose.envs:T1DSimEnv',
kwargs={'patient_name': 'adolescent#002'}
)
path = './results/phase3test1'
sim_time = timedelta(days=3)
now = datetime.now()
start_time = datetime.combine(now.date(), datetime.min.time())
patient = T1DPatient.withName('adolescent#002')
sensor = CGMSensorWithoutNoise.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
scenario = DDPGScenario(start_time=start_time,sim_time=sim_time, skip_meal=False)
print(scenario.my_scenario)
env = T1DSimEnv(patient, sensor, pump, scenario)
controller1 = MyController('simglucose-adolescent2-v0')
base_controller = BBController()
s1 = SimObjForKeras2(env, controller1, sim_time, base_controller, animate=False, path=path)
sim_instances = [s1]
results = batch_sim(sim_instances)
|
[
"simglucose.simulation.sim_engine.SimObjForKeras2",
"simglucose.simulation.env.T1DSimEnv",
"simglucose.simulation.sim_engine.batch_sim",
"examples.apply_customized_controller.MyController",
"simglucose.sensor.cgm.CGMSensorWithoutNoise.withName",
"datetime.timedelta",
"simglucose.actuator.pump.InsulinPump.withName",
"simglucose.simulation.scenario.DDPGScenario",
"simglucose.controller.basal_bolus_ctrller.BBController",
"datetime.datetime.min.time",
"datetime.datetime.now",
"simglucose.patient.t1dpatient.T1DPatient.withName",
"gym.envs.registration.register"
] |
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|
##################################################################
# Algorithm module using look-up tables
##################################################################
import math
import numpy as np
import modelicares
from scipy.optimize import minimize
import scipy.interpolate as interpolate
from scipy.optimize import differential_evolution
from scipy.optimize import brute
""" Execute only once """
def CalcBCvalues(amount_vals_BCs, exp_BCs, center_vals_BCs, factors_BCs, amount_lower_vals_BCs=None, amount_upper_vals_BCs=None):
"""
Calculate the boundary conditions around a center value. Typically, this
is a equidistant grid but it can also be an exponentially increasing
increment
inputs:
amount_vals_BCs: amount of discrete values of boundary conditions
exp_BCs: exponent for increasing increments
center_vals_BCs: center value that the other values are arranged around
factors_BCs: for an exponent of 1, this is the uniform increment
amount_lower_vals_BCs: amount of values below the center value
amount_upper_vals_BCs: amount of values above the center value
returns:
values: list of boundary conditions
"""
if amount_lower_vals_BCs is None or amount_upper_vals_BCs is None:
amount_lower_vals_BCs =[x/2 for x in amount_vals_BCs]
amount_lower_vals_BCs = [math.floor(x) for x in amount_lower_vals_BCs]
amount_upper_vals_BCs = [amount_vals_BCs[x]-amount_lower_vals_BCs[x]-1 for x in range(len(amount_vals_BCs))]
values_BCs = []
for i in range(len(amount_vals_BCs)):
temp_list = []
for j in range(amount_lower_vals_BCs[i]):
temp_list.append(center_vals_BCs[i] - (amount_lower_vals_BCs[i]-j)**exp_BCs[i]*factors_BCs[i])
for j in range(amount_upper_vals_BCs[i]+1):
temp_list.append(center_vals_BCs[i] + j**exp_BCs[i]*factors_BCs[i])
values_BCs.append(temp_list)
values_BCs[0].insert(0,0)
return values_BCs
def CalcLookUpTables(s, time_storage, init_conds):
"""
Calculate decision variables and costs and put them into look-up tables
inputs:
s: subsystem object
time_storage: the current time step
init_conds: initialization of optimization algorithm
returns:
storage_cost: list of costs depending on boundary conditions
storage_DV: decision variables depending on boundary conditions
storage_out: outputs depending on boundary conditions
exDestArr: two-dimensional list of costs
storage_grid: result grid as output of optimization algorithm
"""
import Objective_Function
# Use the fullfactorial design of experiment: Use predefined BCs
if len(s.values_BCs) == 1:
BCsettings = modelicares.exps.doe.fullfact(s.values_BCs[0])
counter = len(s.values_BCs)
elif len(s.values_BCs) == 2:
BCsettings = modelicares.exps.doe.fullfact(s.values_BCs[0],s.values_BCs[1])
counter = len(s.values_BCs[0])*len(s.values_BCs[1])
elif len(s.values_BCs) == 3:
BCsettings = modelicares.exps.doe.fullfact(s.values_BCs[0],s.values_BCs[1],s.values_BCs[2])
counter = len(s.values_BCs[0])*len(s.values_BCs[1])*len(s.values_BCs[2])
elif len(s.values_BCs) == 4:
BCsettings = modelicares.exps.doe.fullfact(s.values_BCs[0],s.values_BCs[1],s.values_BCs[2],s.values_BCs[3])
counter = len(s.values_BCs[0])*len(s.values_BCs[1])*len(s.values_BCs[2])*len(s.values_BCs[3])
# Look-up tables:
storage_cost = np.zeros([counter,(len(s.values_BCs)+1)])
storage_DV = np.zeros([counter,len(s.values_BCs)+s._num_DVs])
storage_out = np.zeros([counter,len(s.values_BCs)+s.num_VarsOut])
storage_grid = np.zeros([counter+1,2])
storage_grid[0,0] = storage_grid[0,1] = None
storage_grid_alt2 = np.zeros([2*counter,2])
# 2D exDestArr to communicate:
exDestArr = np.empty([len(s.values_BCs[0])+1,len(s.values_BCs[1])+1])
for i,val in enumerate(s.values_BCs[0]):
exDestArr[i+1,0]=val
for i,val in enumerate(s.values_BCs[1]):
exDestArr[0,i+1]=val
exDestArr[0,0] = None
counter = 0
for BC in BCsettings:
j = 0
while j < len(s.values_BCs):
storage_cost[counter,j] = storage_DV[counter,j] = storage_out[counter,j] = BC[j]
j += 1
"""" Perform a minimization using the defined objective function """
Objective_Function.ChangeDir(s._name)
boundaries = s._bounds_DVs #[low, high]
if boundaries[0] != boundaries[1]:
ranges = [slice(boundaries[0],boundaries[1]+5, 10)]
""" First conduct a brute force search """
obj_fun_val = brute(Objective_Function.Obj,ranges,args = (BC, s), disp=True, full_output=True, finish = None)
init_conds = obj_fun_val[0]
cons = ({'type':'ineq','fun': lambda x: x-boundaries[0]},
{'type':'ineq','fun': lambda x: boundaries[1]-x})
""" Perform an addtional optimization to refine the previous findings """
obj_fun_val = minimize(Objective_Function.Obj,init_conds,args = (BC, s),method='SLSQP',constraints=cons, options={'maxiter':100, 'ftol':0.01})
else:
ranges = [slice(boundaries[0],boundaries[1]+1, 1)]
obj_fun_val = brute(Objective_Function.Obj,ranges,args = (BC, s), disp=True, full_output=True, finish = None)
"""
Post processing of the different returned results of the
optimization algorithms
"""
if isinstance(obj_fun_val, tuple):
""" fill storage_grid """
if counter ==0:
res_grid = np.concatenate((obj_fun_val[2][np.newaxis], obj_fun_val[3][np.newaxis]),axis = 0)
else:
res_grid = np.append(res_grid,obj_fun_val[3][np.newaxis],axis = 0)
else:
res_grid_new = Objective_Function.GetOptTrack()
Objective_Function.DelLastTrack()
if counter ==0:
res_grid = res_grid_new
else:
stor_shape = res_grid.shape
new_shape = res_grid_new.shape
if stor_shape[1] != new_shape[1]:
to_add = abs(stor_shape[1]-new_shape[1])
if stor_shape[1] > new_shape[1]:
add_array = np.zeros([new_shape[0],to_add])
res_grid_new = np.append(res_grid_new,add_array, axis = 1)
else:
add_array = np.zeros([stor_shape[0],to_add])
res_grid = np.append(res_grid,add_array, axis = 1)
res_grid = np.append(res_grid,res_grid_new,axis = 0)
""" fill look-up table DV and Cost """
j = len(s.values_BCs)
while j < (s._num_DVs+len(s.values_BCs)):
if isinstance(obj_fun_val, tuple):
DV_values = obj_fun_val[0]
else:
DV_values = obj_fun_val.get('x')
storage_DV[counter,j] = DV_values.item(j-len(s.values_BCs))
j += 1
ix1 = np.isin(exDestArr, BC[0])
ix2 = np.isin(exDestArr, BC[1])
index1 = np.where(ix1)
index2 = np.where(ix2)
if isinstance(obj_fun_val, tuple):
storage_cost[counter,len(s.values_BCs)] = obj_fun_val[1]
exDestArr[index1[0].tolist(), index2[1].tolist()] = obj_fun_val[1]
else:
storage_cost[counter,len(s.values_BCs)] = obj_fun_val.get('fun')
exDestArr[index1[0].tolist(), index2[1].tolist()] = obj_fun_val.get('fun')
""" Get Output Variables """
output_vals = Objective_Function.GetOutputVars()
""" Fill look-up table Out """
k = len(s.values_BCs)
while k < (s.num_VarsOut+len(s.values_BCs)):
storage_out[counter,k] = output_vals[k-len(s.values_BCs)]
k += 1
counter = counter + 1
if isinstance(obj_fun_val, tuple):
storage_grid = np.append(storage_grid,res_grid,axis=1)
else:
storage_grid = np.append(storage_grid_alt2,res_grid,axis=1)
storage_grid = res_grid
return [storage_cost, storage_DV, storage_out, exDestArr, storage_grid]
def Interpolation(measurements_SubSys, storage_DV, bounds_DVs, storage_cost, storage_out):
"""
Interpolate the values of the decision variables, costs and outputs
inputs:
measurements_SubSys: the inflow measurements of a subsystem
storage_DV: the decision variables depending on boundary conditions
bounds_DVs: upper and lower bounds of the decision variables
storage_cost: the costs depending on boundary conditions
storage_out: the outputs depending on boundary conditions
returns:
commands: interpolated command for current measurement
costs: interpolated costs for current measurement
out: interpolated outputs for current measurement
"""
"""
Reformat the boundary conditions, decision variables, outputs and
costs
"""
cond_BC = [True if L<len(measurements_SubSys) else False for L in range(len(storage_DV[0]))]
cond_DV = [False if L<len(measurements_SubSys) else True for L in range(len(storage_DV[0]))]
cond_Out = [False if L<len(measurements_SubSys) else True for L in range(len(storage_out[0]))]
cond_Costs = cond_DV
grid_points = np.compress(cond_BC,storage_DV, axis = 1)
grid_point_values = np.compress(cond_DV,storage_DV, axis = 1)
grid_measurements = measurements_SubSys[::-1]
grid_point_values_costs = np.compress(cond_Costs,storage_cost, axis = 1)
grid_point_values_out = np.compress(cond_Out,storage_out, axis = 1)
print("Grid points:")
print(grid_points)
print("values:")
print(grid_point_values)
print("measurements:")
print(grid_measurements)
""" Interpolation of reformatted data """
try:
commands = interpolate.griddata(grid_points, grid_point_values,grid_measurements ,method='linear')
costs = interpolate.griddata(grid_points, grid_point_values_costs,grid_measurements ,method='linear')
out = interpolate.griddata(grid_points, grid_point_values_out, grid_measurements ,method='linear')
print("commands: " + str(commands))
print("costs: " + str(costs))
print("outputs: " + str(out))
# Check if commands are in range, else set to boundary values
for i, val in enumerate(commands):
if val < bounds_DVs[i]:
commands[i] = bounds_DVs[i]
print('Val < lower Bound')
elif val > bounds_DVs[i+1]:
commands[i] = bounds_DVs[i+1]
print('Val > higher Bound')
elif val >= bounds_DVs[i] and val <= bounds_DVs[i+1]:
commands[i] = val
# last case: invalid interpolation
else:
commands[i] = bounds_DVs[i]
print('interpolation failed!')
return [commands, costs, out]
except:
commands = []
costs = []
out = []
for i in range(0,len(storage_DV)):
commands.append(storage_DV[0,0])
costs.append(0)
out.append(0)
print('interpolation failed!')
|
[
"numpy.isin",
"modelicares.exps.doe.fullfact",
"scipy.optimize.minimize",
"scipy.interpolate.griddata",
"Objective_Function.DelLastTrack",
"numpy.zeros",
"Objective_Function.GetOutputVars",
"math.floor",
"Objective_Function.ChangeDir",
"Objective_Function.GetOptTrack",
"numpy.append",
"numpy.where",
"scipy.optimize.brute",
"numpy.compress",
"numpy.concatenate"
] |
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|
import factory
from sushi.models import CounterReportType
from .report_types import ReportTypeFactory
class CounterReportTypeFactory(factory.django.DjangoModelFactory):
class Meta:
model = CounterReportType
django_get_or_create = ('code',)
code = 'TR'
counter_version = 5
report_type = factory.SubFactory(ReportTypeFactory)
active = True
|
[
"factory.SubFactory"
] |
[((323, 360), 'factory.SubFactory', 'factory.SubFactory', (['ReportTypeFactory'], {}), '(ReportTypeFactory)\n', (341, 360), False, 'import factory\n')]
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Import necessary packages.
import argparse
import logging
import os
import os.path as osp
import warnings
from keras import backend as K
from keras.models import load_model
import tensorflow as tf
from tensorflow.python.framework import graph_io
from tensorflow.python.framework.graph_util import convert_variables_to_constants
from config import output_log
# Set necessary environment.
warnings.filterwarnings("ignore")
# Create logger for debugging.
output_log.init_log('./log/update_url')
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
""" Freezes the state of a session into a pruned computation graph.
Creates a new computation graph where variable nodes are replaced by
constants taking their current value in the session. The new graph will be
pruned so sub-graphs that are not necessary to compute the requested
outputs are removed.
:param session: The TensorFlow session to be frozen.
:param keep_var_names: A list of variable names that should not be frozen,
or None to freeze all the variables in the graph.
:param output_names: Names of the relevant graph outputs.
:param clear_devices: Remove the device directives from the graph for better portability.
:return: The frozen graph definition.
"""
# Fetch graph of session.
graph = session.graph
# Set graph as default graph and freeze graph.
with graph.as_default():
# Filter variable names which should be frozen.
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
# Set output names.
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
# Set input graph.
input_graph_def = graph.as_graph_def()
# Clear devices if not None.
if clear_devices:
for node in input_graph_def.node:
node.device = ""
# Convert variables to constants and freeze the graph.
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
def convert_model(input_file_path, target_fold, target_file_name):
"""Convert Keras .h5 model file into Tensorflow .pb model file.
Freeze session and convert Keras model into Tensorflow model.
:param input_file_path: The input file path of Keras model file, in .h5.
:param target_fold: The target fold where the Tensorflow model file is saved in.
:param target_file_name: The output file name of Tensorflow model file, in .pb.
:return: None. The output file has been written in function.
"""
# Load Keras model.
K.set_learning_phase(0)
net_model = load_model(input_file_path)
# Record input and output nodes' names.
logging.info('The input node name is : %s, and the output node name is : %s' %
(net_model.input.name, net_model.output.name))
# Create session and freeze graph, then save frozen graph into model file.
output_fold = target_fold + '/tensorflow_model/'
frozen_graph = freeze_session(K.get_session(), output_names=[net_model.output.op.name])
graph_io.write_graph(frozen_graph, output_fold, target_file_name, as_text=False)
logging.info('Saved the constant graph (ready for inference) at %s' %
osp.join(output_fold, target_file_name))
def main():
"""The main function to convert Keras model into TensorFlow model.
:return: Write the TensorFlow model into .pb file.
"""
# Parse the argument and read into the date.
# ------------------------------------------
parser = argparse.ArgumentParser(description='This is script to convert Keras model file.')
parser.add_argument('-p', required=True, help='The target product.')
args = parser.parse_args()
# path = args.p
# Define related file path, as well as file name.
input_fold = 'THE/PATH/TO/INPUT/FOLD'
weight_file = 'XXX.h5'
output_graph_name = 'XXX.pb'
output_fold = input_fold + 'tensorflow_model/'
# Combine path with file name.
if not os.path.isdir(output_fold):
os.mkdir(output_fold)
weight_file_path = osp.join(input_fold, weight_file)
# Load Keras model.
K.set_learning_phase(0)
net_model = load_model(weight_file_path)
# Record input and output nodes' names.
logging.info('The input node name is : %s, and the output node name is : %s' %
(net_model.input.name, net_model.output.name))
# Create session and freeze graph, then save frozen graph into model file.
frozen_graph = freeze_session(K.get_session(), output_names=[net_model.output.op.name])
graph_io.write_graph(frozen_graph, output_fold, output_graph_name, as_text=False)
logging.info('Saved the constant graph (ready for inference) at %s' %
osp.join(output_fold, output_graph_name))
if __name__ == '__main__':
main()
|
[
"keras.models.load_model",
"os.mkdir",
"config.output_log.init_log",
"argparse.ArgumentParser",
"warnings.filterwarnings",
"keras.backend.get_session",
"os.path.isdir",
"logging.info",
"keras.backend.set_learning_phase",
"tensorflow.python.framework.graph_util.convert_variables_to_constants",
"tensorflow.global_variables",
"tensorflow.python.framework.graph_io.write_graph",
"os.path.join"
] |
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|
import unittest
import torch
from cortex.modules.backbones import *
from cortex.modules.necks import *
class TestNecks(unittest.TestCase):
def setUp(self):
self.use_cpu = False
self.cuda = False if self.use_cpu else torch.cuda.is_available()
self.device = torch.device('cuda:0' if self.cuda else 'cpu')
self.input = torch.rand((2, 3, 512, 512)).to(self.device)
def test_fpn(self):
nets = {
'resnet18': resnet18(pretrained=True, out_layers=[
'layer1', 'layer2', 'layer3', 'layer4']),
'resnet50': resnet50(pretrained=True, out_layers=[
'layer1', 'layer2', 'layer3', 'layer4']),
'resnet101': resnet101(pretrained=True, out_layers=[
'layer1', 'layer2', 'layer3'])}
fpns = [
FPN(in_channels=[64, 128, 256, 512], out_channels=256),
FPN(in_channels=[256, 512, 1024, 2048], out_channels=256),
FPN(in_channels=[256, 512, 1024], out_channels=256)]
for i, name in enumerate(nets):
net = nets[name].to(self.device)
fpn = fpns[i].to(self.device)
# random input tensor
if hasattr(net, 'input_size'):
size = net.input_size
input = torch.rand(2, 3, size, size).to(self.device)
else:
input = self.input
# forward pass
outs = net(self.input)
if isinstance(outs, dict):
outs = list(outs.values())
fpn_outs = fpn(outs)
# print inference information
print('{}_FPN:'.format(name))
for layer, out, fpn_out in zip(
net.out_layers, outs, fpn_outs):
print(' [{}] input: {} output: {}'.format(
layer,
tuple(out.shape),
tuple(fpn_out.shape)))
if __name__ == '__main__':
unittest.main()
|
[
"unittest.main",
"torch.cuda.is_available",
"torch.rand",
"torch.device"
] |
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|
import json
try:
from urllib.parse import urlencode
except ImportError:
from urllib import urlencode
from django.core.urlresolvers import reverse
from django.db import models
from goal.models import GlobalUser, Goal
from suggestion.models import Suggestion
from review.models import Comment, Review
from django.template import loader
from django.shortcuts import get_object_or_404
class Notification(models.Model):
TEMPLATE_NEW_COMMENT = 0
TEMPLATE_UPDATED_REVIEW = 1
TEMPLATE_NEW_REVIEW = 2
TEMPLATE_NEW_SUGGESTION = 3
TEMPLATE_CHOICES = (
(TEMPLATE_NEW_COMMENT, "notification/notify_new_comment.html"),
(TEMPLATE_UPDATED_REVIEW, "notification/notify_updated_review.html"),
(TEMPLATE_NEW_REVIEW, "notification/notify_new_review.html"),
(TEMPLATE_NEW_SUGGESTION, "notification/notify_new_suggestion.html"),
)
template = models.PositiveSmallIntegerField(choices=TEMPLATE_CHOICES)
context = models.TextField()
is_read = models.BooleanField(default=False)
owner = models.ForeignKey(GlobalUser, related_name="notifications")
goal = models.ForeignKey(Goal, related_name="notifications")
pub_date = models.DateTimeField('date published', auto_now_add=True)
def __str__(self):
return self.get_template_display()
@staticmethod
def create_for_comment(comment):
n = Notification()
n.owner = (
comment.reply_to.owner
if comment.reply_to else
comment.review.owner
)
n.goal = comment.review.revision.suggestion.goal
n.template = Notification.TEMPLATE_NEW_COMMENT
n.context = json.dumps(dict(
comment_pk=comment.pk,
))
return n
@staticmethod
def create_for_review(review):
n = Notification()
n.owner = review.revision.suggestion.owner
n.goal = review.revision.suggestion.goal
n.template = Notification.TEMPLATE_NEW_REVIEW
n.context = json.dumps(dict(
review_pk=review.pk,
))
return n
@staticmethod
def create_for_suggestion(suggestion):
n = Notification()
n.goal = suggestion.goal
n.owner = n.goal.owner
n.template = Notification.TEMPLATE_NEW_SUGGESTION
n.context = json.dumps(dict(
suggestion_pk=suggestion.pk,
))
return n
def html(self):
t = loader.get_template(self.get_template_display())
c = json.loads(self.context)
if self.template == self.TEMPLATE_NEW_COMMENT:
self.__context_for_new_comment(c)
if self.template == self.TEMPLATE_UPDATED_REVIEW:
self.__context_for_new_or_updated_review(c)
if self.template == self.TEMPLATE_NEW_REVIEW:
self.__context_for_new_or_updated_review(c)
if self.template == self.TEMPLATE_NEW_SUGGESTION:
self.__context_for_new_suggestion(c)
c['is_read'] = 1 if self.is_read else 0
parameters = urlencode({'next': c['next_url']})
c['target_url'] = reverse(
'follow-notification',
kwargs=dict(
notification_id=self.pk
)) + "?" + parameters
return t.render(c)
def __context_for_new_comment(self, c):
comment = get_object_or_404(Comment, pk=c['comment_pk'])
c['comment'] = comment
review = comment.review
suggestion = review.revision.suggestion
c['next_url'] = suggestion.get_url() + "#sg-comment-%d" % comment.pk
if comment.reply_to:
c['did_what'] = 'replied to your comment'
else:
c['did_what'] = 'commented on your review'
def __context_for_new_or_updated_review(self, c):
review = get_object_or_404(Review, pk=c['review_pk'])
suggestion = review.revision.suggestion
c['review'] = review
c['suggestion'] = suggestion
c['next_url'] = suggestion.get_url() + "#sg-review-%d" % review.pk
def __context_for_new_suggestion(self, c):
suggestion = get_object_or_404(Suggestion, pk=c['suggestion_pk'])
c['suggestion'] = suggestion
c['next_url'] = suggestion.get_url()
|
[
"django.db.models.TextField",
"json.loads",
"django.db.models.ForeignKey",
"django.db.models.BooleanField",
"django.db.models.PositiveSmallIntegerField",
"django.shortcuts.get_object_or_404",
"urllib.urlencode",
"django.db.models.DateTimeField"
] |
[((897, 955), 'django.db.models.PositiveSmallIntegerField', 'models.PositiveSmallIntegerField', ([], {'choices': 'TEMPLATE_CHOICES'}), '(choices=TEMPLATE_CHOICES)\n', (929, 955), False, 'from django.db import models\n'), ((970, 988), 'django.db.models.TextField', 'models.TextField', ([], {}), '()\n', (986, 988), False, 'from django.db import models\n'), ((1003, 1037), 'django.db.models.BooleanField', 'models.BooleanField', ([], {'default': '(False)'}), '(default=False)\n', (1022, 1037), False, 'from django.db import models\n'), ((1050, 1109), 'django.db.models.ForeignKey', 'models.ForeignKey', (['GlobalUser'], {'related_name': '"""notifications"""'}), "(GlobalUser, related_name='notifications')\n", (1067, 1109), False, 'from django.db import models\n'), ((1121, 1174), 'django.db.models.ForeignKey', 'models.ForeignKey', (['Goal'], {'related_name': '"""notifications"""'}), "(Goal, related_name='notifications')\n", (1138, 1174), False, 'from django.db import models\n'), ((1190, 1247), 'django.db.models.DateTimeField', 'models.DateTimeField', (['"""date published"""'], {'auto_now_add': '(True)'}), "('date published', auto_now_add=True)\n", (1210, 1247), False, 'from django.db import models\n'), ((2489, 2513), 'json.loads', 'json.loads', (['self.context'], {}), '(self.context)\n', (2499, 2513), False, 'import json\n'), ((3018, 3052), 'urllib.urlencode', 'urlencode', (["{'next': c['next_url']}"], {}), "({'next': c['next_url']})\n", (3027, 3052), False, 'from urllib import urlencode\n'), ((3312, 3358), 'django.shortcuts.get_object_or_404', 'get_object_or_404', (['Comment'], {'pk': "c['comment_pk']"}), "(Comment, pk=c['comment_pk'])\n", (3329, 3358), False, 'from django.shortcuts import get_object_or_404\n'), ((3773, 3817), 'django.shortcuts.get_object_or_404', 'get_object_or_404', (['Review'], {'pk': "c['review_pk']"}), "(Review, pk=c['review_pk'])\n", (3790, 3817), False, 'from django.shortcuts import get_object_or_404\n'), ((4077, 4129), 'django.shortcuts.get_object_or_404', 'get_object_or_404', (['Suggestion'], {'pk': "c['suggestion_pk']"}), "(Suggestion, pk=c['suggestion_pk'])\n", (4094, 4129), False, 'from django.shortcuts import get_object_or_404\n')]
|
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2016 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Module exports :class:`FrankelEtAl1996MblgAB1987NSHMP2008`,
:class:`FrankelEtAl1996MblgJ1996NSHMP2008`,
:class:`FrankelEtAl1996MwNSHMP2008`.
"""
from __future__ import division
import numpy as np
from scipy.interpolate import RectBivariateSpline
from openquake.hazardlib.gsim.base import CoeffsTable, GMPE
from openquake.hazardlib.gsim.utils import (
mblg_to_mw_atkinson_boore_87,
mblg_to_mw_johnston_96,
clip_mean
)
from openquake.hazardlib import const
from openquake.hazardlib.imt import PGA, SA
class FrankelEtAl1996MblgAB1987NSHMP2008(GMPE):
"""
Implements GMPE developed by <NAME> et al. and documented in
"National Seismic-Hazard Maps: Documentation June 1996" (USGS - Open File
Report 96-532) available at:
http://earthquake.usgs.gov/hazards/products/conterminous/1996/documentation/ofr96-532.pdf
The GMPE is used by the National Seismic Hazard Mapping Project (NSHMP) for
the 2008 central and eastern US hazard model.
This class replicates the algorithm as implemented in
``subroutine getFEA`` in the ``hazgridXnga2.f`` Fortran code available
at: http://earthquake.usgs.gov/hazards/products/conterminous/2008/software/
The GMPE is defined by a set of lookup tables (see Appendix A) defined
from minimum magnitude Mw=4.4 to maximum magnitude Mw=8.2, and from
(hypocentral) distance 10 km to 1000 km. Values outside these range
are clipped.
Lookup tables are defined for PGA, and SA at the following periods: 0.1,
0.2, 0.3, 0.5, 1.0, 2.0. The GMPE does not allow for interpolation on
unsupported periods.
The class assumes rupture magnitude to be in Mblg scale (given that
MFDs for central and eastern US are given in this scale). However lookup
tables are defined for Mw. Therefore Mblg is converted to Mw by using
Atkinson and Boore 1987 conversion equation.
Coefficients are given for the B/C site conditions.
"""
#: Supported tectonic region type is stable continental crust,
#: given that the equations have been derived for central and eastern
#: north America
DEFINED_FOR_TECTONIC_REGION_TYPE = const.TRT.STABLE_CONTINENTAL
#: Supported intensity measure types are spectral acceleration,
#: and peak ground acceleration
DEFINED_FOR_INTENSITY_MEASURE_TYPES = set([
PGA,
SA
])
#: Supported intensity measure component is the geometric mean of
#two : horizontal components
#:attr:`~openquake.hazardlib.const.IMC.AVERAGE_HORIZONTAL`,
DEFINED_FOR_INTENSITY_MEASURE_COMPONENT = const.IMC.AVERAGE_HORIZONTAL
#: Supported standard deviation type is only total.
DEFINED_FOR_STANDARD_DEVIATION_TYPES = set([
const.StdDev.TOTAL
])
#: No site parameters required
REQUIRES_SITES_PARAMETERS = set()
#: Required rupture parameter is only magnitude (Mblg).
REQUIRES_RUPTURE_PARAMETERS = set(('mag', ))
#: Required distance measure is rhypo
REQUIRES_DISTANCES = set(('rhypo', ))
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types):
"""
See :meth:`superclass method
<.base.GroundShakingIntensityModel.get_mean_and_stddevs>`
for spec of input and result values.
:raises ValueError:
if imt is instance of :class:`openquake.hazardlib.imt.SA` with
unsupported period.
"""
assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES
for stddev_type in stddev_types)
if imt not in self.IMTS_TABLES:
raise ValueError(
'IMT %s not supported in FrankelEtAl1996NSHMP. ' % repr(imt) +
'FrankelEtAl1996NSHMP does not allow interpolation for ' +
'unsupported periods.'
)
mean = self._compute_mean(imt, rup.mag, dists.rhypo.copy())
mean = clip_mean(imt, mean)
stddevs = self._compute_stddevs(imt, dists.rhypo.shape, stddev_types)
return mean, stddevs
def _compute_mean(self, imt, mag, rhypo):
"""
Compute mean value from lookup table.
Lookup table defines log10(IMT) (in g) for combinations of Mw and
log10(rhypo) values. ``mag`` is therefore converted from Mblg to Mw
using Atkinson and Boore 1987 conversion equation. Mean value is
finally converted from base 10 to base e.
"""
mag = np.zeros_like(rhypo) + self._convert_magnitude(mag)
# to avoid run time warning in case rhypo is zero set minimum distance
# to 10, which is anyhow the minimum distance allowed by the tables
rhypo[rhypo < 10] = 10
rhypo = np.log10(rhypo)
# create lookup table and interpolate it at magnitude/distance values
table = RectBivariateSpline(
self.MAGS, self.DISTS, self.IMTS_TABLES[imt].T
)
mean = table.ev(mag, rhypo)
# convert mean from base 10 to base e
return mean * np.log(10)
def _convert_magnitude(self, mag):
"""
Convert magnitude from Mblg to Mw using Atkinson and Boore 1987
equation
"""
return mblg_to_mw_atkinson_boore_87(mag)
def _compute_stddevs(self, imt, num_sites, stddev_types):
"""
Return total standard deviation (converted to base e)
"""
C = self.COEFFS[imt]
stddevs = []
for _ in stddev_types:
stddevs.append(np.zeros(num_sites) + C['sigma'] * np.log(10))
return stddevs
# magnitude values for lookup table from minimum magnitude 4.4 to maximum
# magnitude 8.2
MAGS = np.linspace(4.4, 8.2, 20)
# hypocentral distance values (log10) for lookup table
# from minimum distance 10 km to maximum distance 1000 km
DISTS = np.linspace(1., 3., 21)
# lookup table for PGA
PGA_TBL = np.array([
[-0.71, -0.62, -0.53, -0.44, -0.36, -0.29, -0.21, -0.14, -0.07, 0.00, 0.06, 0.12, 0.19, 0.25, 0.31, 0.37, 0.43, 0.49, 0.54, 0.60],
[-0.84, -0.75, -0.66, -0.57, -0.49, -0.41, -0.33, -0.26, -0.19, -0.12, -0.05, 0.01, 0.07, 0.14, 0.20, 0.26, 0.32, 0.38, 0.43, 0.49],
[-0.98, -0.88, -0.79, -0.70, -0.62, -0.53, -0.46, -0.38, -0.31, -0.24, -0.17, -0.10, -0.04, 0.02, 0.08, 0.14, 0.20, 0.26, 0.32, 0.38],
[-1.12, -1.02, -0.93, -0.84, -0.75, -0.66, -0.58, -0.51, -0.43, -0.36, -0.29, -0.22, -0.16, -0.10, -0.03, 0.03, 0.09, 0.15, 0.21, 0.27],
[-1.27, -1.17, -1.07, -0.98, -0.89, -0.80, -0.72, -0.64, -0.56, -0.49, -0.42, -0.35, -0.28, -0.22, -0.15, -0.09, -0.03, 0.03, 0.09, 0.15],
[-1.43, -1.32, -1.22, -1.13, -1.03, -0.94, -0.86, -0.78, -0.70, -0.62, -0.55, -0.48, -0.41, -0.34, -0.28, -0.22, -0.15, -0.09, -0.03, 0.03],
[-1.59, -1.48, -1.38, -1.28, -1.19, -1.09, -1.01, -0.92, -0.84, -0.76, -0.69, -0.61, -0.54, -0.48, -0.41, -0.35, -0.28, -0.22, -0.16, -0.10],
[-1.76, -1.65, -1.54, -1.44, -1.35, -1.25, -1.16, -1.07, -0.99, -0.91, -0.83, -0.76, -0.69, -0.62, -0.55, -0.48, -0.42, -0.35, -0.29, -0.23],
[-1.93, -1.82, -1.72, -1.61, -1.51, -1.42, -1.33, -1.24, -1.15, -1.07, -0.99, -0.91, -0.83, -0.76, -0.69, -0.63, -0.56, -0.50, -0.43, -0.37],
[-2.07, -1.95, -1.84, -1.74, -1.64, -1.54, -1.44, -1.35, -1.26, -1.18, -1.10, -1.02, -0.94, -0.86, -0.79, -0.72, -0.65, -0.59, -0.53, -0.46],
[-2.17, -2.05, -1.94, -1.83, -1.73, -1.63, -1.53, -1.43, -1.34, -1.25, -1.17, -1.09, -1.01, -0.93, -0.86, -0.78, -0.71, -0.65, -0.58, -0.52],
[-2.28, -2.16, -2.04, -1.93, -1.83, -1.72, -1.62, -1.53, -1.43, -1.34, -1.25, -1.17, -1.09, -1.01, -0.93, -0.86, -0.78, -0.71, -0.65, -0.58],
[-2.44, -2.32, -2.20, -2.09, -1.98, -1.87, -1.77, -1.67, -1.58, -1.48, -1.39, -1.30, -1.22, -1.14, -1.06, -0.98, -0.91, -0.83, -0.77, -0.70],
[-2.63, -2.50, -2.38, -2.26, -2.15, -2.04, -1.94, -1.84, -1.74, -1.64, -1.55, -1.46, -1.37, -1.29, -1.20, -1.12, -1.05, -0.97, -0.90, -0.83],
[-2.83, -2.70, -2.57, -2.45, -2.34, -2.23, -2.12, -2.01, -1.91, -1.81, -1.72, -1.62, -1.53, -1.45, -1.36, -1.28, -1.20, -1.12, -1.05, -0.98],
[-3.05, -2.92, -2.78, -2.66, -2.54, -2.42, -2.31, -2.20, -2.10, -2.00, -1.90, -1.80, -1.71, -1.62, -1.53, -1.45, -1.36, -1.28, -1.21, -1.13],
[-3.29, -3.15, -3.01, -2.88, -2.75, -2.63, -2.52, -2.41, -2.30, -2.19, -2.09, -1.99, -1.90, -1.80, -1.71, -1.62, -1.54, -1.46, -1.38, -1.30],
[-3.55, -3.40, -3.25, -3.11, -2.98, -2.86, -2.74, -2.62, -2.51, -2.40, -2.30, -2.19, -2.09, -2.00, -1.90, -1.81, -1.72, -1.64, -1.56, -1.48],
[-3.84, -3.67, -3.52, -3.37, -3.23, -3.10, -2.97, -2.85, -2.73, -2.62, -2.51, -2.41, -2.30, -2.20, -2.10, -2.01, -1.92, -1.83, -1.74, -1.66],
[-4.15, -3.97, -3.80, -3.65, -3.50, -3.36, -3.22, -3.09, -2.97, -2.85, -2.74, -2.63, -2.52, -2.42, -2.31, -2.22, -2.12, -2.03, -1.94, -1.85],
[-4.48, -4.29, -4.11, -3.94, -3.78, -3.63, -3.49, -3.35, -3.22, -3.10, -2.98, -2.86, -2.75, -2.64, -2.53, -2.43, -2.33, -2.23, -2.14, -2.05]
])
# lookup table for SA 0.1
SA01_TBL = np.array([
[-0.43, -0.32, -0.23, -0.14, -0.05, 0.03, 0.11, 0.19, 0.26, 0.33, 0.40, 0.47, 0.54, 0.60, 0.66, 0.72, 0.78, 0.84, 0.90, 0.96],
[-0.55, -0.44, -0.35, -0.26, -0.17, -0.08, 0.00, 0.07, 0.15, 0.22, 0.29, 0.36, 0.43, 0.49, 0.55, 0.62, 0.68, 0.74, 0.80, 0.86],
[-0.67, -0.57, -0.47, -0.38, -0.29, -0.20, -0.12, -0.04, 0.03, 0.11, 0.18, 0.25, 0.31, 0.38, 0.44, 0.51, 0.57, 0.63, 0.69, 0.75],
[-0.80, -0.69, -0.60, -0.50, -0.41, -0.33, -0.24, -0.16, -0.09, -0.01, 0.06, 0.13, 0.20, 0.27, 0.33, 0.39, 0.46, 0.52, 0.58, 0.64],
[-0.93, -0.83, -0.73, -0.63, -0.54, -0.45, -0.37, -0.29, -0.21, -0.13, -0.06, 0.01, 0.08, 0.15, 0.21, 0.28, 0.34, 0.40, 0.46, 0.52],
[-1.07, -0.97, -0.86, -0.77, -0.68, -0.59, -0.50, -0.42, -0.34, -0.26, -0.18, -0.11, -0.04, 0.03, 0.09, 0.16, 0.22, 0.28, 0.35, 0.41],
[-1.22, -1.11, -1.01, -0.91, -0.82, -0.73, -0.64, -0.55, -0.47, -0.39, -0.32, -0.24, -0.17, -0.10, -0.03, 0.03, 0.10, 0.16, 0.22, 0.28],
[-1.37, -1.26, -1.16, -1.06, -0.97, -0.87, -0.78, -0.70, -0.61, -0.53, -0.45, -0.38, -0.30, -0.23, -0.16, -0.10, -0.03, 0.03, 0.10, 0.16],
[-1.53, -1.42, -1.32, -1.22, -1.12, -1.03, -0.94, -0.85, -0.76, -0.68, -0.60, -0.52, -0.45, -0.37, -0.30, -0.23, -0.17, -0.10, -0.04, 0.02],
[-1.65, -1.54, -1.43, -1.33, -1.24, -1.14, -1.05, -0.96, -0.87, -0.79, -0.70, -0.62, -0.54, -0.47, -0.40, -0.33, -0.26, -0.19, -0.13, -0.06],
[-1.73, -1.62, -1.51, -1.41, -1.31, -1.22, -1.12, -1.03, -0.94, -0.86, -0.77, -0.69, -0.61, -0.53, -0.46, -0.39, -0.32, -0.25, -0.18, -0.12],
[-1.83, -1.72, -1.61, -1.51, -1.41, -1.31, -1.21, -1.12, -1.03, -0.94, -0.85, -0.77, -0.69, -0.61, -0.53, -0.46, -0.39, -0.32, -0.25, -0.18],
[-1.98, -1.87, -1.76, -1.66, -1.56, -1.46, -1.36, -1.27, -1.18, -1.09, -1.00, -0.91, -0.83, -0.75, -0.67, -0.59, -0.52, -0.44, -0.37, -0.31],
[-2.17, -2.05, -1.95, -1.84, -1.74, -1.64, -1.54, -1.45, -1.35, -1.26, -1.17, -1.08, -0.99, -0.91, -0.83, -0.75, -0.67, -0.60, -0.53, -0.46],
[-2.38, -2.26, -2.15, -2.05, -1.94, -1.84, -1.74, -1.64, -1.55, -1.45, -1.36, -1.27, -1.18, -1.10, -1.01, -0.93, -0.85, -0.78, -0.70, -0.63],
[-2.61, -2.50, -2.39, -2.28, -2.17, -2.07, -1.97, -1.87, -1.77, -1.68, -1.58, -1.49, -1.40, -1.31, -1.22, -1.14, -1.06, -0.98, -0.91, -0.83],
[-2.89, -2.77, -2.66, -2.55, -2.44, -2.33, -2.23, -2.13, -2.03, -1.93, -1.83, -1.74, -1.64, -1.55, -1.46, -1.38, -1.29, -1.21, -1.13, -1.06],
[-3.20, -3.08, -2.96, -2.85, -2.73, -2.62, -2.51, -2.41, -2.31, -2.20, -2.10, -2.01, -1.91, -1.82, -1.72, -1.63, -1.55, -1.46, -1.38, -1.30],
[-3.56, -3.43, -3.30, -3.18, -3.06, -2.94, -2.82, -2.71, -2.60, -2.49, -2.39, -2.29, -2.19, -2.09, -1.99, -1.90, -1.81, -1.72, -1.64, -1.56],
[-3.96, -3.81, -3.67, -3.53, -3.39, -3.26, -3.14, -3.02, -2.90, -2.78, -2.67, -2.57, -2.46, -2.36, -2.26, -2.16, -2.07, -1.97, -1.88, -1.80],
[-4.37, -4.20, -4.04, -3.88, -3.73, -3.59, -3.45, -3.32, -3.19, -3.07, -2.95, -2.83, -2.72, -2.61, -2.51, -2.40, -2.31, -2.21, -2.12, -2.03]
])
# lookup table for SA 0.2
SA02_TBL = np.array([
[-0.71, -0.57, -0.46, -0.35, -0.25, -0.15, -0.06, 0.02, 0.10, 0.18, 0.25, 0.32, 0.39, 0.46, 0.53, 0.59, 0.65, 0.72, 0.78, 0.84],
[-0.82, -0.69, -0.57, -0.46, -0.36, -0.26, -0.17, -0.09, -0.01, 0.07, 0.14, 0.22, 0.29, 0.35, 0.42, 0.48, 0.55, 0.61, 0.67, 0.73],
[-0.93, -0.80, -0.68, -0.57, -0.47, -0.38, -0.29, -0.20, -0.12, -0.04, 0.03, 0.11, 0.18, 0.25, 0.31, 0.38, 0.44, 0.50, 0.56, 0.63],
[-1.05, -0.92, -0.80, -0.69, -0.59, -0.49, -0.40, -0.32, -0.23, -0.15, -0.08, 0.00, 0.07, 0.14, 0.20, 0.27, 0.33, 0.40, 0.46, 0.52],
[-1.17, -1.04, -0.92, -0.81, -0.71, -0.61, -0.52, -0.44, -0.35, -0.27, -0.19, -0.12, -0.05, 0.02, 0.09, 0.16, 0.22, 0.29, 0.35, 0.41],
[-1.30, -1.17, -1.05, -0.94, -0.84, -0.74, -0.65, -0.56, -0.47, -0.39, -0.31, -0.24, -0.16, -0.09, -0.02, 0.04, 0.11, 0.17, 0.24, 0.30],
[-1.43, -1.30, -1.18, -1.07, -0.97, -0.87, -0.77, -0.69, -0.60, -0.52, -0.44, -0.36, -0.28, -0.21, -0.14, -0.07, -0.01, 0.06, 0.12, 0.18],
[-1.57, -1.44, -1.32, -1.21, -1.10, -1.00, -0.91, -0.82, -0.73, -0.65, -0.56, -0.49, -0.41, -0.34, -0.27, -0.20, -0.13, -0.06, 0.00, 0.06],
[-1.72, -1.58, -1.46, -1.35, -1.25, -1.15, -1.05, -0.96, -0.87, -0.78, -0.70, -0.62, -0.54, -0.47, -0.39, -0.32, -0.25, -0.19, -0.12, -0.06],
[-1.82, -1.68, -1.56, -1.45, -1.34, -1.24, -1.14, -1.05, -0.96, -0.87, -0.79, -0.70, -0.63, -0.55, -0.47, -0.40, -0.33, -0.26, -0.20, -0.13],
[-1.88, -1.74, -1.62, -1.51, -1.40, -1.30, -1.20, -1.10, -1.01, -0.92, -0.84, -0.75, -0.67, -0.59, -0.52, -0.44, -0.37, -0.30, -0.24, -0.17],
[-1.95, -1.82, -1.69, -1.58, -1.47, -1.37, -1.27, -1.17, -1.08, -0.99, -0.90, -0.81, -0.73, -0.65, -0.57, -0.50, -0.42, -0.35, -0.28, -0.22],
[-2.08, -1.94, -1.82, -1.70, -1.59, -1.49, -1.39, -1.29, -1.20, -1.11, -1.02, -0.93, -0.84, -0.76, -0.68, -0.60, -0.53, -0.45, -0.38, -0.32],
[-2.23, -2.09, -1.97, -1.85, -1.74, -1.64, -1.53, -1.44, -1.34, -1.25, -1.15, -1.06, -0.98, -0.89, -0.81, -0.73, -0.65, -0.58, -0.51, -0.44],
[-2.40, -2.26, -2.13, -2.01, -1.90, -1.80, -1.70, -1.60, -1.50, -1.40, -1.31, -1.22, -1.13, -1.04, -0.96, -0.88, -0.80, -0.72, -0.65, -0.58],
[-2.58, -2.44, -2.32, -2.20, -2.09, -1.98, -1.88, -1.78, -1.68, -1.58, -1.49, -1.39, -1.30, -1.22, -1.13, -1.04, -0.96, -0.88, -0.81, -0.73],
[-2.80, -2.66, -2.53, -2.41, -2.30, -2.19, -2.09, -1.98, -1.88, -1.79, -1.69, -1.60, -1.50, -1.41, -1.32, -1.24, -1.15, -1.07, -0.99, -0.92],
[-3.04, -2.90, -2.77, -2.65, -2.54, -2.43, -2.32, -2.22, -2.12, -2.02, -1.92, -1.83, -1.73, -1.64, -1.55, -1.46, -1.37, -1.29, -1.21, -1.13],
[-3.33, -3.19, -3.06, -2.93, -2.82, -2.71, -2.60, -2.49, -2.39, -2.29, -2.19, -2.09, -1.99, -1.90, -1.80, -1.71, -1.62, -1.54, -1.45, -1.37],
[-3.66, -3.52, -3.38, -3.26, -3.14, -3.02, -2.91, -2.80, -2.69, -2.59, -2.48, -2.38, -2.28, -2.18, -2.09, -1.99, -1.90, -1.81, -1.73, -1.64],
[-4.05, -3.90, -3.76, -3.63, -3.50, -3.38, -3.26, -3.14, -3.03, -2.92, -2.81, -2.70, -2.59, -2.49, -2.39, -2.29, -2.19, -2.10, -2.01, -1.92]
])
# lookup table for SA 0.3
SA03_TBL = np.array([
[-0.97, -0.81, -0.66, -0.53, -0.42, -0.31, -0.21, -0.12, -0.03, 0.05, 0.13, 0.20, 0.28, 0.35, 0.41, 0.48, 0.54, 0.61, 0.67, 0.73],
[-1.08, -0.91, -0.77, -0.64, -0.53, -0.42, -0.32, -0.23, -0.14, -0.06, 0.02, 0.10, 0.17, 0.24, 0.31, 0.38, 0.44, 0.50, 0.57, 0.63],
[-1.19, -1.02, -0.88, -0.75, -0.64, -0.53, -0.43, -0.34, -0.25, -0.17, -0.09, -0.01, 0.06, 0.13, 0.20, 0.27, 0.33, 0.40, 0.46, 0.52],
[-1.30, -1.14, -0.99, -0.86, -0.75, -0.64, -0.54, -0.45, -0.36, -0.28, -0.20, -0.12, -0.05, 0.03, 0.09, 0.16, 0.23, 0.29, 0.36, 0.42],
[-1.41, -1.25, -1.11, -0.98, -0.86, -0.76, -0.66, -0.57, -0.48, -0.39, -0.31, -0.23, -0.16, -0.09, -0.02, 0.05, 0.12, 0.18, 0.25, 0.31],
[-1.53, -1.37, -1.23, -1.10, -0.98, -0.88, -0.78, -0.68, -0.59, -0.51, -0.43, -0.35, -0.27, -0.20, -0.13, -0.06, 0.01, 0.07, 0.14, 0.20],
[-1.66, -1.50, -1.35, -1.23, -1.11, -1.00, -0.90, -0.81, -0.72, -0.63, -0.55, -0.47, -0.39, -0.31, -0.24, -0.17, -0.11, -0.04, 0.02, 0.09],
[-1.79, -1.63, -1.48, -1.36, -1.24, -1.13, -1.03, -0.93, -0.84, -0.75, -0.67, -0.59, -0.51, -0.44, -0.36, -0.29, -0.22, -0.16, -0.09, -0.03],
[-1.93, -1.77, -1.62, -1.49, -1.37, -1.26, -1.16, -1.07, -0.97, -0.88, -0.80, -0.72, -0.64, -0.56, -0.49, -0.41, -0.34, -0.28, -0.21, -0.15],
[-2.02, -1.85, -1.71, -1.58, -1.46, -1.35, -1.25, -1.15, -1.06, -0.97, -0.88, -0.80, -0.71, -0.64, -0.56, -0.49, -0.42, -0.35, -0.28, -0.21],
[-2.07, -1.91, -1.76, -1.63, -1.51, -1.40, -1.30, -1.20, -1.10, -1.01, -0.92, -0.84, -0.75, -0.67, -0.60, -0.52, -0.45, -0.38, -0.31, -0.24],
[-2.13, -1.97, -1.82, -1.69, -1.57, -1.46, -1.35, -1.25, -1.16, -1.06, -0.97, -0.89, -0.80, -0.72, -0.64, -0.56, -0.49, -0.42, -0.35, -0.28],
[-2.25, -2.08, -1.93, -1.80, -1.68, -1.57, -1.46, -1.36, -1.26, -1.17, -1.08, -0.99, -0.90, -0.82, -0.74, -0.66, -0.58, -0.51, -0.44, -0.37],
[-2.38, -2.21, -2.06, -1.93, -1.81, -1.70, -1.59, -1.49, -1.39, -1.29, -1.20, -1.11, -1.02, -0.94, -0.85, -0.77, -0.70, -0.62, -0.55, -0.48],
[-2.53, -2.36, -2.21, -2.08, -1.96, -1.84, -1.73, -1.63, -1.53, -1.43, -1.34, -1.25, -1.16, -1.07, -0.99, -0.90, -0.82, -0.75, -0.67, -0.60],
[-2.69, -2.52, -2.37, -2.24, -2.12, -2.00, -1.89, -1.79, -1.69, -1.59, -1.50, -1.40, -1.31, -1.22, -1.13, -1.05, -0.97, -0.89, -0.81, -0.74],
[-2.88, -2.71, -2.56, -2.42, -2.30, -2.18, -2.08, -1.97, -1.87, -1.77, -1.67, -1.58, -1.48, -1.39, -1.30, -1.22, -1.13, -1.05, -0.97, -0.90],
[-3.09, -2.92, -2.77, -2.63, -2.51, -2.39, -2.28, -2.18, -2.07, -1.97, -1.87, -1.78, -1.68, -1.59, -1.50, -1.41, -1.32, -1.24, -1.16, -1.08],
[-3.33, -3.16, -3.01, -2.87, -2.75, -2.63, -2.52, -2.41, -2.31, -2.20, -2.11, -2.01, -1.91, -1.82, -1.72, -1.63, -1.54, -1.46, -1.37, -1.29],
[-3.61, -3.44, -3.29, -3.15, -3.02, -2.91, -2.79, -2.68, -2.58, -2.47, -2.37, -2.27, -2.17, -2.08, -1.98, -1.89, -1.80, -1.71, -1.62, -1.54],
[-3.95, -3.77, -3.62, -3.48, -3.35, -3.22, -3.11, -3.00, -2.89, -2.78, -2.67, -2.57, -2.47, -2.37, -2.27, -2.17, -2.08, -1.99, -1.90, -1.81]
])
# lookup table for SA 0.5
SA05_TBL = np.array([
[-1.44, -1.23, -1.03, -0.87, -0.72, -0.58, -0.46, -0.35, -0.25, -0.16, -0.07, 0.01, 0.09, 0.16, 0.23, 0.30, 0.37, 0.44, 0.50, 0.57],
[-1.54, -1.33, -1.14, -0.97, -0.82, -0.69, -0.57, -0.46, -0.36, -0.27, -0.18, -0.10, -0.02, 0.06, 0.13, 0.20, 0.27, 0.34, 0.40, 0.46],
[-1.64, -1.43, -1.24, -1.07, -0.92, -0.79, -0.67, -0.57, -0.47, -0.37, -0.29, -0.20, -0.12, -0.05, 0.03, 0.10, 0.16, 0.23, 0.30, 0.36],
[-1.74, -1.53, -1.35, -1.18, -1.03, -0.90, -0.78, -0.67, -0.57, -0.48, -0.39, -0.31, -0.23, -0.15, -0.08, -0.01, 0.06, 0.13, 0.19, 0.26],
[-1.85, -1.64, -1.46, -1.29, -1.14, -1.01, -0.89, -0.79, -0.69, -0.59, -0.50, -0.42, -0.34, -0.26, -0.19, -0.12, -0.05, 0.02, 0.09, 0.15],
[-1.96, -1.75, -1.57, -1.40, -1.25, -1.12, -1.01, -0.90, -0.80, -0.70, -0.62, -0.53, -0.45, -0.37, -0.30, -0.23, -0.16, -0.09, -0.02, 0.04],
[-2.08, -1.87, -1.68, -1.52, -1.37, -1.24, -1.12, -1.02, -0.91, -0.82, -0.73, -0.65, -0.56, -0.49, -0.41, -0.34, -0.27, -0.20, -0.13, -0.07],
[-2.20, -1.99, -1.81, -1.64, -1.49, -1.36, -1.24, -1.14, -1.03, -0.94, -0.85, -0.76, -0.68, -0.60, -0.53, -0.45, -0.38, -0.31, -0.24, -0.18],
[-2.33, -2.12, -1.93, -1.77, -1.62, -1.49, -1.37, -1.26, -1.16, -1.06, -0.97, -0.89, -0.80, -0.72, -0.64, -0.57, -0.50, -0.43, -0.36, -0.29],
[-2.41, -2.20, -2.01, -1.84, -1.70, -1.56, -1.45, -1.34, -1.23, -1.14, -1.04, -0.96, -0.87, -0.79, -0.71, -0.64, -0.56, -0.49, -0.42, -0.36],
[-2.45, -2.24, -2.05, -1.88, -1.73, -1.60, -1.48, -1.37, -1.27, -1.17, -1.08, -0.99, -0.90, -0.82, -0.74, -0.66, -0.59, -0.52, -0.45, -0.38],
[-2.49, -2.28, -2.10, -1.93, -1.78, -1.65, -1.53, -1.42, -1.31, -1.21, -1.12, -1.03, -0.94, -0.86, -0.78, -0.70, -0.62, -0.55, -0.48, -0.41],
[-2.59, -2.38, -2.19, -2.03, -1.88, -1.74, -1.62, -1.51, -1.41, -1.31, -1.21, -1.12, -1.03, -0.94, -0.86, -0.78, -0.70, -0.63, -0.56, -0.49],
[-2.71, -2.50, -2.31, -2.14, -1.99, -1.86, -1.74, -1.62, -1.52, -1.42, -1.32, -1.23, -1.14, -1.05, -0.96, -0.88, -0.80, -0.73, -0.65, -0.58],
[-2.84, -2.63, -2.44, -2.27, -2.12, -1.98, -1.86, -1.75, -1.64, -1.54, -1.44, -1.34, -1.25, -1.16, -1.08, -0.99, -0.91, -0.83, -0.76, -0.68],
[-2.98, -2.77, -2.58, -2.41, -2.26, -2.12, -2.00, -1.88, -1.77, -1.67, -1.57, -1.48, -1.38, -1.29, -1.20, -1.12, -1.04, -0.96, -0.88, -0.80],
[-3.14, -2.92, -2.73, -2.56, -2.41, -2.27, -2.15, -2.04, -1.93, -1.82, -1.72, -1.63, -1.53, -1.44, -1.35, -1.26, -1.18, -1.09, -1.01, -0.94],
[-3.32, -3.10, -2.91, -2.74, -2.59, -2.45, -2.32, -2.21, -2.10, -1.99, -1.89, -1.79, -1.70, -1.60, -1.51, -1.42, -1.34, -1.25, -1.17, -1.09],
[-3.52, -3.30, -3.11, -2.94, -2.78, -2.65, -2.52, -2.40, -2.29, -2.19, -2.08, -1.98, -1.89, -1.79, -1.70, -1.61, -1.52, -1.43, -1.35, -1.26],
[-3.75, -3.53, -3.34, -3.17, -3.01, -2.87, -2.75, -2.63, -2.52, -2.41, -2.31, -2.20, -2.11, -2.01, -1.91, -1.82, -1.73, -1.64, -1.55, -1.47],
[-4.02, -3.80, -3.60, -3.43, -3.27, -3.13, -3.01, -2.89, -2.77, -2.67, -2.56, -2.46, -2.36, -2.26, -2.16, -2.07, -1.97, -1.88, -1.79, -1.70],
])
# lookup table for SA 1.0
SA1_TBL = np.array([
[-2.22, -1.98, -1.75, -1.52, -1.31, -1.12, -0.95, -0.80, -0.66, -0.54, -0.43, -0.33, -0.24, -0.15, -0.07, 0.01, 0.08, 0.16, 0.23, 0.29],
[-2.32, -2.08, -1.85, -1.62, -1.41, -1.22, -1.05, -0.90, -0.77, -0.64, -0.54, -0.43, -0.34, -0.25, -0.17, -0.09, -0.02, 0.05, 0.12, 0.19],
[-2.43, -2.18, -1.95, -1.72, -1.51, -1.32, -1.15, -1.00, -0.87, -0.75, -0.64, -0.54, -0.45, -0.36, -0.28, -0.20, -0.12, -0.05, 0.02, 0.09],
[-2.53, -2.29, -2.05, -1.82, -1.61, -1.42, -1.25, -1.11, -0.97, -0.85, -0.74, -0.64, -0.55, -0.46, -0.38, -0.30, -0.23, -0.15, -0.08, -0.01],
[-2.63, -2.39, -2.15, -1.92, -1.71, -1.53, -1.36, -1.21, -1.08, -0.96, -0.85, -0.75, -0.66, -0.57, -0.49, -0.41, -0.33, -0.26, -0.19, -0.12],
[-2.74, -2.49, -2.25, -2.03, -1.82, -1.63, -1.46, -1.32, -1.18, -1.06, -0.96, -0.86, -0.76, -0.68, -0.59, -0.51, -0.44, -0.36, -0.29, -0.22],
[-2.84, -2.60, -2.36, -2.13, -1.92, -1.74, -1.57, -1.42, -1.29, -1.17, -1.07, -0.97, -0.87, -0.78, -0.70, -0.62, -0.54, -0.47, -0.40, -0.33],
[-2.95, -2.70, -2.47, -2.24, -2.03, -1.85, -1.68, -1.53, -1.40, -1.29, -1.18, -1.08, -0.98, -0.90, -0.81, -0.73, -0.65, -0.58, -0.51, -0.44],
[-3.07, -2.82, -2.58, -2.35, -2.15, -1.96, -1.80, -1.65, -1.52, -1.40, -1.29, -1.19, -1.10, -1.01, -0.92, -0.84, -0.77, -0.69, -0.62, -0.55],
[-3.14, -2.88, -2.64, -2.42, -2.21, -2.02, -1.86, -1.71, -1.58, -1.46, -1.36, -1.26, -1.16, -1.07, -0.99, -0.90, -0.83, -0.75, -0.68, -0.61],
[-3.16, -2.91, -2.67, -2.44, -2.24, -2.05, -1.88, -1.74, -1.61, -1.49, -1.38, -1.28, -1.18, -1.09, -1.01, -0.92, -0.84, -0.77, -0.69, -0.62],
[-3.20, -2.94, -2.70, -2.47, -2.27, -2.08, -1.91, -1.77, -1.63, -1.52, -1.41, -1.31, -1.21, -1.12, -1.03, -0.95, -0.87, -0.79, -0.71, -0.64],
[-3.28, -3.03, -2.78, -2.55, -2.35, -2.16, -1.99, -1.84, -1.71, -1.59, -1.48, -1.38, -1.29, -1.19, -1.10, -1.02, -0.94, -0.86, -0.78, -0.71],
[-3.38, -3.12, -2.88, -2.65, -2.44, -2.25, -2.09, -1.94, -1.81, -1.69, -1.58, -1.47, -1.37, -1.28, -1.19, -1.11, -1.02, -0.94, -0.87, -0.79],
[-3.49, -3.23, -2.98, -2.75, -2.55, -2.36, -2.19, -2.04, -1.91, -1.79, -1.68, -1.57, -1.47, -1.38, -1.29, -1.20, -1.12, -1.03, -0.96, -0.88],
[-3.61, -3.35, -3.10, -2.87, -2.66, -2.47, -2.30, -2.15, -2.02, -1.90, -1.79, -1.68, -1.58, -1.49, -1.39, -1.30, -1.22, -1.14, -1.05, -0.98],
[-3.74, -3.48, -3.23, -3.00, -2.79, -2.60, -2.43, -2.28, -2.14, -2.02, -1.91, -1.80, -1.70, -1.60, -1.51, -1.42, -1.33, -1.25, -1.16, -1.09],
[-3.88, -3.62, -3.37, -3.14, -2.93, -2.74, -2.57, -2.41, -2.28, -2.16, -2.04, -1.94, -1.83, -1.74, -1.64, -1.55, -1.46, -1.37, -1.29, -1.21],
[-4.04, -3.78, -3.53, -3.30, -3.08, -2.89, -2.72, -2.57, -2.43, -2.31, -2.20, -2.09, -1.98, -1.88, -1.79, -1.69, -1.60, -1.51, -1.43, -1.35],
[-4.22, -3.96, -3.71, -3.47, -3.26, -3.07, -2.90, -2.74, -2.61, -2.48, -2.37, -2.26, -2.15, -2.05, -1.96, -1.86, -1.77, -1.68, -1.59, -1.50],
[-4.43, -4.16, -3.91, -3.68, -3.46, -3.27, -3.10, -2.94, -2.81, -2.68, -2.56, -2.45, -2.35, -2.25, -2.15, -2.05, -1.95, -1.86, -1.77, -1.69]
])
# lookup table for SA 2.0
SA2_TBL = np.array([
[-2.87, -2.66, -2.45, -2.24, -2.03, -1.81, -1.60, -1.39, -1.20, -1.02, -0.87, -0.73, -0.61, -0.50, -0.40, -0.30, -0.21, -0.13, -0.05, 0.02],
[-3.00, -2.78, -2.57, -2.36, -2.14, -1.92, -1.70, -1.49, -1.30, -1.12, -0.97, -0.83, -0.71, -0.60, -0.50, -0.40, -0.32, -0.23, -0.16, -0.08],
[-3.14, -2.91, -2.69, -2.47, -2.24, -2.02, -1.80, -1.59, -1.40, -1.22, -1.07, -0.93, -0.81, -0.70, -0.60, -0.51, -0.42, -0.34, -0.26, -0.18],
[-3.27, -3.04, -2.81, -2.58, -2.35, -2.12, -1.90, -1.69, -1.50, -1.32, -1.17, -1.04, -0.91, -0.80, -0.70, -0.61, -0.52, -0.44, -0.36, -0.28],
[-3.40, -3.16, -2.92, -2.69, -2.45, -2.22, -2.00, -1.79, -1.60, -1.43, -1.27, -1.14, -1.02, -0.91, -0.81, -0.71, -0.62, -0.54, -0.46, -0.39],
[-3.52, -3.28, -3.03, -2.79, -2.55, -2.32, -2.10, -1.89, -1.70, -1.53, -1.38, -1.24, -1.12, -1.01, -0.91, -0.82, -0.73, -0.65, -0.57, -0.49],
[-3.64, -3.39, -3.14, -2.90, -2.66, -2.42, -2.20, -1.99, -1.80, -1.63, -1.48, -1.35, -1.22, -1.12, -1.02, -0.92, -0.83, -0.75, -0.67, -0.60],
[-3.76, -3.50, -3.25, -3.00, -2.76, -2.52, -2.30, -2.09, -1.90, -1.73, -1.58, -1.45, -1.33, -1.22, -1.12, -1.03, -0.94, -0.86, -0.78, -0.70],
[-3.87, -3.61, -3.36, -3.11, -2.86, -2.63, -2.40, -2.19, -2.01, -1.84, -1.69, -1.56, -1.44, -1.33, -1.23, -1.14, -1.05, -0.96, -0.89, -0.81],
[-3.93, -3.67, -3.42, -3.16, -2.92, -2.68, -2.45, -2.25, -2.06, -1.89, -1.75, -1.61, -1.49, -1.39, -1.29, -1.19, -1.10, -1.02, -0.94, -0.86],
[-3.95, -3.69, -3.43, -3.18, -2.93, -2.69, -2.47, -2.26, -2.07, -1.90, -1.76, -1.63, -1.51, -1.40, -1.30, -1.20, -1.12, -1.03, -0.95, -0.87],
[-3.97, -3.71, -3.45, -3.20, -2.95, -2.71, -2.48, -2.27, -2.09, -1.92, -1.77, -1.64, -1.52, -1.42, -1.32, -1.22, -1.13, -1.05, -0.97, -0.89],
[-4.05, -3.78, -3.52, -3.26, -3.01, -2.77, -2.55, -2.34, -2.15, -1.99, -1.84, -1.71, -1.59, -1.48, -1.38, -1.28, -1.19, -1.11, -1.03, -0.95],
[-4.14, -3.87, -3.60, -3.34, -3.09, -2.85, -2.62, -2.42, -2.23, -2.06, -1.91, -1.78, -1.66, -1.56, -1.45, -1.36, -1.27, -1.18, -1.10, -1.02],
[-4.23, -3.96, -3.69, -3.43, -3.18, -2.94, -2.71, -2.50, -2.31, -2.15, -2.00, -1.87, -1.75, -1.64, -1.54, -1.44, -1.35, -1.26, -1.18, -1.10],
[-4.34, -4.06, -3.79, -3.53, -3.27, -3.03, -2.80, -2.59, -2.40, -2.24, -2.09, -1.96, -1.84, -1.73, -1.62, -1.53, -1.44, -1.35, -1.26, -1.18],
[-4.45, -4.18, -3.90, -3.64, -3.38, -3.13, -2.90, -2.69, -2.51, -2.34, -2.19, -2.06, -1.94, -1.83, -1.72, -1.62, -1.53, -1.44, -1.35, -1.27],
[-4.58, -4.30, -4.02, -3.75, -3.49, -3.25, -3.02, -2.81, -2.62, -2.45, -2.30, -2.17, -2.04, -1.93, -1.83, -1.73, -1.63, -1.54, -1.46, -1.37],
[-4.71, -4.43, -4.15, -3.88, -3.62, -3.37, -3.14, -2.93, -2.74, -2.57, -2.42, -2.29, -2.17, -2.05, -1.95, -1.85, -1.75, -1.66, -1.57, -1.48],
[-4.86, -4.58, -4.30, -4.03, -3.76, -3.51, -3.28, -3.07, -2.88, -2.71, -2.56, -2.42, -2.30, -2.19, -2.08, -1.98, -1.88, -1.79, -1.70, -1.61],
[-5.03, -4.74, -4.46, -4.19, -3.92, -3.67, -3.44, -3.23, -3.03, -2.86, -2.71, -2.58, -2.45, -2.34, -2.23, -2.13, -2.03, -1.93, -1.84, -1.75]
])
# dictionary relating supported imts and associated tables
IMTS_TABLES = {
PGA(): PGA_TBL,
SA(period=0.1, damping=5): SA01_TBL,
SA(period=0.2, damping=5): SA02_TBL,
SA(period=0.3, damping=5): SA03_TBL,
SA(period=0.5, damping=5): SA05_TBL,
SA(period=1.0, damping=5): SA1_TBL,
SA(period=2.0, damping=5): SA2_TBL
}
# period dependent standard deviations (in base 10)
COEFFS = CoeffsTable(sa_damping=5, table="""\
IMT sigma
pga 0.326
0.1 0.326
0.2 0.326
0.3 0.326
0.5 0.326
1.0 0.347
2.0 0.347
""")
class FrankelEtAl1996MblgJ1996NSHMP2008(FrankelEtAl1996MblgAB1987NSHMP2008):
"""
Extend :class:`FrankelEtAl1996MblgAB1987NSHMP2008` but uses Johnston
1996 equation for converting from Mblg to Mw.
"""
def _convert_magnitude(self, mag):
"""
Convert magnitude from Mblg to Mw using Johnston 1996 equation
"""
return mblg_to_mw_johnston_96(mag)
class FrankelEtAl1996MwNSHMP2008(FrankelEtAl1996MblgAB1987NSHMP2008):
"""
Extend :class:`FrankelEtAl1996MblgAB1987NSHMP2008` but assumes magnitude
to be in Mw scale and therefore no conversion is applied.
"""
def _convert_magnitude(self, mag):
"""
Return magnitude value unchanged
"""
return mag
|
[
"numpy.zeros_like",
"numpy.log",
"numpy.zeros",
"openquake.hazardlib.gsim.utils.mblg_to_mw_johnston_96",
"openquake.hazardlib.imt.SA",
"openquake.hazardlib.gsim.base.CoeffsTable",
"openquake.hazardlib.imt.PGA",
"openquake.hazardlib.gsim.utils.clip_mean",
"numpy.array",
"scipy.interpolate.RectBivariateSpline",
"numpy.linspace",
"openquake.hazardlib.gsim.utils.mblg_to_mw_atkinson_boore_87",
"numpy.log10"
] |
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-2.79, -2.55, -2.32, -2.1, -1.89, -1.7, -1.53, -1.38, -\n 1.24, -1.12, -1.01, -0.91, -0.82, -0.73, -0.65, -0.57, -0.49], [-3.64, \n -3.39, -3.14, -2.9, -2.66, -2.42, -2.2, -1.99, -1.8, -1.63, -1.48, -\n 1.35, -1.22, -1.12, -1.02, -0.92, -0.83, -0.75, -0.67, -0.6], [-3.76, -\n 3.5, -3.25, -3.0, -2.76, -2.52, -2.3, -2.09, -1.9, -1.73, -1.58, -1.45,\n -1.33, -1.22, -1.12, -1.03, -0.94, -0.86, -0.78, -0.7], [-3.87, -3.61, \n -3.36, -3.11, -2.86, -2.63, -2.4, -2.19, -2.01, -1.84, -1.69, -1.56, -\n 1.44, -1.33, -1.23, -1.14, -1.05, -0.96, -0.89, -0.81], [-3.93, -3.67, \n -3.42, -3.16, -2.92, -2.68, -2.45, -2.25, -2.06, -1.89, -1.75, -1.61, -\n 1.49, -1.39, -1.29, -1.19, -1.1, -1.02, -0.94, -0.86], [-3.95, -3.69, -\n 3.43, -3.18, -2.93, -2.69, -2.47, -2.26, -2.07, -1.9, -1.76, -1.63, -\n 1.51, -1.4, -1.3, -1.2, -1.12, -1.03, -0.95, -0.87], [-3.97, -3.71, -\n 3.45, -3.2, -2.95, -2.71, -2.48, -2.27, -2.09, -1.92, -1.77, -1.64, -\n 1.52, -1.42, -1.32, -1.22, -1.13, -1.05, -0.97, -0.89], [-4.05, -3.78, \n -3.52, -3.26, -3.01, -2.77, -2.55, -2.34, -2.15, -1.99, -1.84, -1.71, -\n 1.59, -1.48, -1.38, -1.28, -1.19, -1.11, -1.03, -0.95], [-4.14, -3.87, \n -3.6, -3.34, -3.09, -2.85, -2.62, -2.42, -2.23, -2.06, -1.91, -1.78, -\n 1.66, -1.56, -1.45, -1.36, -1.27, -1.18, -1.1, -1.02], [-4.23, -3.96, -\n 3.69, -3.43, -3.18, -2.94, -2.71, -2.5, -2.31, -2.15, -2.0, -1.87, -\n 1.75, -1.64, -1.54, -1.44, -1.35, -1.26, -1.18, -1.1], [-4.34, -4.06, -\n 3.79, -3.53, -3.27, -3.03, -2.8, -2.59, -2.4, -2.24, -2.09, -1.96, -\n 1.84, -1.73, -1.62, -1.53, -1.44, -1.35, -1.26, -1.18], [-4.45, -4.18, \n -3.9, -3.64, -3.38, -3.13, -2.9, -2.69, -2.51, -2.34, -2.19, -2.06, -\n 1.94, -1.83, -1.72, -1.62, -1.53, -1.44, -1.35, -1.27], [-4.58, -4.3, -\n 4.02, -3.75, -3.49, -3.25, -3.02, -2.81, -2.62, -2.45, -2.3, -2.17, -\n 2.04, -1.93, -1.83, -1.73, -1.63, -1.54, -1.46, -1.37], [-4.71, -4.43, \n -4.15, -3.88, -3.62, -3.37, -3.14, -2.93, -2.74, -2.57, -2.42, -2.29, -\n 2.17, -2.05, -1.95, -1.85, -1.75, -1.66, -1.57, -1.48], [-4.86, -4.58, \n -4.3, -4.03, -3.76, -3.51, -3.28, -3.07, -2.88, -2.71, -2.56, -2.42, -\n 2.3, -2.19, -2.08, -1.98, -1.88, -1.79, -1.7, -1.61], [-5.03, -4.74, -\n 4.46, -4.19, -3.92, -3.67, -3.44, -3.23, -3.03, -2.86, -2.71, -2.58, -\n 2.45, -2.34, -2.23, -2.13, -2.03, -1.93, -1.84, -1.75]]'], {}), '([[-2.87, -2.66, -2.45, -2.24, -2.03, -1.81, -1.6, -1.39, -1.2, -\n 1.02, -0.87, -0.73, -0.61, -0.5, -0.4, -0.3, -0.21, -0.13, -0.05, 0.02],\n [-3.0, -2.78, -2.57, -2.36, -2.14, -1.92, -1.7, -1.49, -1.3, -1.12, -\n 0.97, -0.83, -0.71, -0.6, -0.5, -0.4, -0.32, -0.23, -0.16, -0.08], [-\n 3.14, -2.91, -2.69, -2.47, -2.24, -2.02, -1.8, -1.59, -1.4, -1.22, -\n 1.07, -0.93, -0.81, -0.7, -0.6, -0.51, -0.42, -0.34, -0.26, -0.18], [-\n 3.27, -3.04, -2.81, -2.58, -2.35, -2.12, -1.9, -1.69, -1.5, -1.32, -\n 1.17, -1.04, -0.91, -0.8, -0.7, -0.61, -0.52, -0.44, -0.36, -0.28], [-\n 3.4, -3.16, -2.92, -2.69, -2.45, -2.22, -2.0, -1.79, -1.6, -1.43, -1.27,\n -1.14, -1.02, -0.91, -0.81, -0.71, -0.62, -0.54, -0.46, -0.39], [-3.52,\n -3.28, -3.03, -2.79, -2.55, -2.32, -2.1, -1.89, -1.7, -1.53, -1.38, -\n 1.24, -1.12, -1.01, -0.91, -0.82, -0.73, -0.65, -0.57, -0.49], [-3.64, \n -3.39, -3.14, -2.9, -2.66, -2.42, -2.2, -1.99, -1.8, -1.63, -1.48, -\n 1.35, -1.22, -1.12, -1.02, -0.92, -0.83, -0.75, -0.67, -0.6], [-3.76, -\n 3.5, -3.25, -3.0, -2.76, -2.52, -2.3, -2.09, -1.9, -1.73, -1.58, -1.45,\n -1.33, -1.22, -1.12, -1.03, -0.94, -0.86, -0.78, -0.7], [-3.87, -3.61, \n -3.36, -3.11, -2.86, -2.63, -2.4, -2.19, -2.01, -1.84, -1.69, -1.56, -\n 1.44, -1.33, -1.23, -1.14, -1.05, -0.96, -0.89, -0.81], [-3.93, -3.67, \n -3.42, -3.16, -2.92, -2.68, -2.45, -2.25, -2.06, -1.89, -1.75, -1.61, -\n 1.49, -1.39, -1.29, -1.19, -1.1, -1.02, -0.94, -0.86], [-3.95, -3.69, -\n 3.43, -3.18, -2.93, -2.69, -2.47, -2.26, -2.07, -1.9, -1.76, -1.63, -\n 1.51, -1.4, -1.3, -1.2, -1.12, -1.03, -0.95, -0.87], [-3.97, -3.71, -\n 3.45, -3.2, -2.95, -2.71, -2.48, -2.27, -2.09, -1.92, -1.77, -1.64, -\n 1.52, -1.42, -1.32, -1.22, -1.13, -1.05, -0.97, -0.89], [-4.05, -3.78, \n -3.52, -3.26, -3.01, -2.77, -2.55, -2.34, -2.15, -1.99, -1.84, -1.71, -\n 1.59, -1.48, -1.38, -1.28, -1.19, -1.11, -1.03, -0.95], [-4.14, -3.87, \n -3.6, -3.34, -3.09, -2.85, -2.62, -2.42, -2.23, -2.06, -1.91, -1.78, -\n 1.66, -1.56, -1.45, -1.36, -1.27, -1.18, -1.1, -1.02], [-4.23, -3.96, -\n 3.69, -3.43, -3.18, -2.94, -2.71, -2.5, -2.31, -2.15, -2.0, -1.87, -\n 1.75, -1.64, -1.54, -1.44, -1.35, -1.26, -1.18, -1.1], [-4.34, -4.06, -\n 3.79, -3.53, -3.27, -3.03, -2.8, -2.59, -2.4, -2.24, -2.09, -1.96, -\n 1.84, -1.73, -1.62, -1.53, -1.44, -1.35, -1.26, -1.18], [-4.45, -4.18, \n -3.9, -3.64, -3.38, -3.13, -2.9, -2.69, -2.51, -2.34, -2.19, -2.06, -\n 1.94, -1.83, -1.72, -1.62, -1.53, -1.44, -1.35, -1.27], [-4.58, -4.3, -\n 4.02, -3.75, -3.49, -3.25, -3.02, -2.81, -2.62, -2.45, -2.3, -2.17, -\n 2.04, -1.93, -1.83, -1.73, -1.63, -1.54, -1.46, -1.37], [-4.71, -4.43, \n -4.15, -3.88, -3.62, -3.37, -3.14, -2.93, -2.74, -2.57, -2.42, -2.29, -\n 2.17, -2.05, -1.95, -1.85, -1.75, -1.66, -1.57, -1.48], [-4.86, -4.58, \n -4.3, -4.03, -3.76, -3.51, -3.28, -3.07, -2.88, -2.71, -2.56, -2.42, -\n 2.3, -2.19, -2.08, -1.98, -1.88, -1.79, -1.7, -1.61], [-5.03, -4.74, -\n 4.46, -4.19, -3.92, -3.67, -3.44, -3.23, -3.03, -2.86, -2.71, -2.58, -\n 2.45, -2.34, -2.23, -2.13, -2.03, -1.93, -1.84, -1.75]])\n', (25900, 29030), True, 'import numpy as np\n'), ((29510, 29699), 'openquake.hazardlib.gsim.base.CoeffsTable', 'CoeffsTable', ([], {'sa_damping': '(5)', 'table': '""" IMT sigma\n pga 0.326\n 0.1 0.326\n 0.2 0.326\n 0.3 0.326\n 0.5 0.326\n 1.0 0.347\n 2.0 0.347\n """'}), '(sa_damping=5, table=\n """ IMT sigma\n pga 0.326\n 0.1 0.326\n 0.2 0.326\n 0.3 0.326\n 0.5 0.326\n 1.0 0.347\n 2.0 0.347\n """\n )\n', (29521, 29699), False, 'from openquake.hazardlib.gsim.base import CoeffsTable, GMPE\n'), ((4641, 4661), 'openquake.hazardlib.gsim.utils.clip_mean', 'clip_mean', (['imt', 'mean'], {}), '(imt, mean)\n', (4650, 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|
# Copyright (c) RedFantom 2017
# For license see LICENSE
from setuptools import setup
long_description = """
yandex
==========
A python wrapper around the Yandex API
License
-------
MIT License
Copyright (c) 2019 Dogeek
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
setup(
name="yandex",
packages=["yandex"],
py_modules=["yandex"],
version="0.0.2",
description=" A python wrapper around the Yandex API ",
long_description=long_description,
author="Dogeek",
url="https://www.github.com/Dogeek/yandex",
download_url="https://www.github.com/Dogeek/yandex/releases",
license="MIT",
classifiers=["Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License"],
install_requires=["requests"]
)
|
[
"setuptools.setup"
] |
[((1256, 1719), 'setuptools.setup', 'setup', ([], {'name': '"""yandex"""', 'packages': "['yandex']", 'py_modules': "['yandex']", 'version': '"""0.0.2"""', 'description': '""" A python wrapper around the Yandex API """', 'long_description': 'long_description', 'author': '"""Dogeek"""', 'url': '"""https://www.github.com/Dogeek/yandex"""', 'download_url': '"""https://www.github.com/Dogeek/yandex/releases"""', 'license': '"""MIT"""', 'classifiers': "['Programming Language :: Python :: 3',\n 'License :: OSI Approved :: MIT License']", 'install_requires': "['requests']"}), "(name='yandex', packages=['yandex'], py_modules=['yandex'], version=\n '0.0.2', description=' A python wrapper around the Yandex API ',\n long_description=long_description, author='Dogeek', url=\n 'https://www.github.com/Dogeek/yandex', download_url=\n 'https://www.github.com/Dogeek/yandex/releases', license='MIT',\n classifiers=['Programming Language :: Python :: 3',\n 'License :: OSI Approved :: MIT License'], install_requires=['requests'])\n", (1261, 1719), False, 'from setuptools import setup\n')]
|
import pytest
from asyncclick.testing import CliRunner
from iperon.commands.generate_openapi import generate_openapi
@pytest.mark.asyncio
async def test_generate_openapi(app):
runner = CliRunner()
await runner.invoke(generate_openapi)
|
[
"asyncclick.testing.CliRunner"
] |
[((192, 203), 'asyncclick.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (201, 203), False, 'from asyncclick.testing import CliRunner\n')]
|
# Copyright (c) 2019 <NAME>
# License: MIT License
# Created 2019-02-15
from typing import TYPE_CHECKING, Tuple
from .dxfentity import DXFEntity, SubclassProcessor, DXFNamespace
from ezdxf.lldxf.attributes import DXFAttr, DXFAttributes, DefSubclass
from ezdxf.lldxf.const import DXF2004, DXF2000
from .factory import register_entity
if TYPE_CHECKING:
from ezdxf.eztypes import Drawing, ExtendedTags, TagWriter
__all__ = ['DXFClass']
class_def = DefSubclass(None, {
# Class DXF record name; always unique
'name': DXFAttr(1),
# C++ class name. Used to bind with software that defines object class behavior; always unique
'cpp_class_name': DXFAttr(2),
# Application name. Posted in Alert box when a class definition listed in this section is not currently loaded
'app_name': DXFAttr(3),
# Proxy capabilities flag. Bit-coded value that indicates the capabilities of this object as a proxy:
# 0 = No operations allowed (0)
# 1 = Erase allowed (0x1)
# 2 = Transform allowed (0x2)
# 4 = Color change allowed (0x4)
# 8 = Layer change allowed (0x8)
# 16 = Linetype change allowed (0x10)
# 32 = Linetype scale change allowed (0x20)
# 64 = Visibility change allowed (0x40)
# 128 = Cloning allowed (0x80)
# 256 = Lineweight change allowed (0x100)
# 512 = Plot Style Name change allowed (0x200)
# 895 = All operations except cloning allowed (0x37F)
# 1023 = All operations allowed (0x3FF)
# 1024 = Disables proxy warning dialog (0x400)
# 32768 = R13 format proxy (0x8000)
'flags': DXFAttr(90, default=0),
# Instance count for a custom class
'instance_count': DXFAttr(91, dxfversion=DXF2004, default=0),
# Was-a-proxy flag. Set to 1 if class was not loaded when this DXF file was created, and 0 otherwise
'was_a_proxy': DXFAttr(280, default=0),
# Is-an-entity flag. Set to 1 if class was derived from the AcDbEntity class and can reside in the BLOCKS or
# ENTITIES section. If 0, instances may appear only in the OBJECTS section
'is_an_entity': DXFAttr(281, default=0),
})
@register_entity
class DXFClass(DXFEntity):
DXFTYPE = 'CLASS'
DXFATTRIBS = DXFAttributes(class_def)
MIN_DXF_VERSION_FOR_EXPORT = DXF2000
@classmethod
def new(cls, handle: str = None, owner: str = None, dxfattribs: dict = None, doc: 'Drawing' = None) -> 'DXFClass':
""" New CLASS constructor - has no handle, no owner and do not need document reference """
dxf_class = cls()
dxf_class.update_dxf_attribs(dxfattribs)
return dxf_class
def load_tags(self, tags: 'ExtendedTags') -> None:
""" Called by load constructor. CLASS is special. """
if tags:
# do not process base class!!!
self.dxf = DXFNamespace(entity=self)
processor = SubclassProcessor(tags)
processor.load_dxfattribs_into_namespace(self.dxf, class_def)
def export_dxf(self, tagwriter: 'TagWriter'):
""" Do complete export here, because CLASS is special. """
dxfversion = tagwriter.dxfversion
if dxfversion < DXF2000:
return
attribs = self.dxf
tagwriter.write_tag2(0, self.DXFTYPE)
attribs.export_dxf_attribs(tagwriter, [
'name', 'cpp_class_name', 'app_name', 'flags', 'instance_count', 'was_a_proxy', 'is_an_entity',
])
@property
def key(self) -> Tuple[str, str]:
return self.dxf.name, self.dxf.cpp_class_name
|
[
"ezdxf.lldxf.attributes.DXFAttr",
"ezdxf.lldxf.attributes.DXFAttributes"
] |
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|
import argparse
import math
import random
from pathlib import Path
import h5py
import yaml
def make_random_splits(keys, num_test, num_splits):
splits = []
for _ in range(num_splits):
random.shuffle(keys)
test_keys = keys[:num_test]
train_keys = list(set(keys) - set(test_keys))
splits.append({
'train_keys': train_keys,
'test_keys': test_keys
})
return splits
def make_cross_val_splits(keys, num_videos, num_test):
random.shuffle(keys)
splits = []
for i in range(0, num_videos, num_test):
test_keys = keys[i: i + num_test]
train_keys = list(set(keys) - set(test_keys))
splits.append({
'train_keys': train_keys,
'test_keys': test_keys
})
return splits
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, required=True,
help='Path to h5 dataset')
parser.add_argument('--save-path', type=str, default='splits/resnet_goals_train.yml',
help='Path to save generated splits')
parser.add_argument('--num-splits', type=int, default=4,
help='How many splits to generate')
parser.add_argument('--train-ratio', type=float, default=0.6,
help='Percentage of training data')
parser.add_argument('--method', type=str, default='cross',
choices=['random', 'cross'],
help='Random selection or cross validation')
args = parser.parse_args()
dataset = h5py.File(args.dataset, 'r')
keys = list(dataset.keys())
keys = [str(Path(args.dataset) / key) for key in keys]
if args.num_splits == 1:
num_test = len(keys)
num_videos = len(keys)
else:
num_videos = len(keys)
num_train = int(math.ceil(num_videos * args.train_ratio))
num_test = num_videos - num_train
if args.method == 'random':
splits = make_random_splits(keys, num_test, args.num_splits)
elif args.method == 'cross':
splits = make_cross_val_splits(keys, num_videos, num_test)
else:
raise ValueError(f'Invalid method {args.method}')
# save splits
save_path = Path(args.save_path)
save_path.parent.mkdir(parents=True, exist_ok=True)
with open(save_path, 'w') as f:
yaml.dump(splits, f)
if __name__ == '__main__':
main()
|
[
"h5py.File",
"argparse.ArgumentParser",
"math.ceil",
"random.shuffle",
"yaml.dump",
"pathlib.Path"
] |
[((500, 520), 'random.shuffle', 'random.shuffle', (['keys'], {}), '(keys)\n', (514, 520), False, 'import random\n'), ((831, 856), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (854, 856), False, 'import argparse\n'), ((1600, 1628), 'h5py.File', 'h5py.File', (['args.dataset', '"""r"""'], {}), "(args.dataset, 'r')\n", (1609, 1628), False, 'import h5py\n'), ((2263, 2283), 'pathlib.Path', 'Path', (['args.save_path'], {}), '(args.save_path)\n', (2267, 2283), False, 'from pathlib import Path\n'), ((202, 222), 'random.shuffle', 'random.shuffle', (['keys'], {}), '(keys)\n', (216, 222), False, 'import random\n'), ((2384, 2404), 'yaml.dump', 'yaml.dump', (['splits', 'f'], {}), '(splits, f)\n', (2393, 2404), False, 'import yaml\n'), ((1874, 1914), 'math.ceil', 'math.ceil', (['(num_videos * args.train_ratio)'], {}), '(num_videos * args.train_ratio)\n', (1883, 1914), False, 'import math\n'), ((1677, 1695), 'pathlib.Path', 'Path', (['args.dataset'], {}), '(args.dataset)\n', (1681, 1695), False, 'from pathlib import Path\n')]
|
import logging
from datetime import datetime
from unittest import TestCase, skipIf
import sys
from unittest.mock import patch, mock_open, call
from tests import MockArgParse, fixture
from tests.test_providers import MockProvider
from virga.providers.abstract import AbstractProvider, SUCCESS
class TestAbstractProvider(TestCase):
def setUp(self):
self.arg_parse = MockArgParse(
debug=False,
silent=True,
logfile=None,
output='/tmp',
definitions='any'
)
self.provider = MockProvider(self.arg_parse)
def test_abstract_method(self):
class Provider(AbstractProvider):
pass
provider = Provider(MockArgParse(debug=False, silent=False, logfile=None))
with self.assertRaises(NotImplementedError):
provider.action()
with self.assertRaises(NotImplementedError):
provider.lookup('', '', '')
@patch('logging.addLevelName')
def test_set_logger_add_success(self, mock_add_level):
arg_parse = MockArgParse(debug=False, silent=False, logfile=None)
provider = MockProvider(arg_parse)
provider.set_logger(arg_parse)
mock_add_level.assert_called_once_with(SUCCESS, 'SUCCESS')
@patch('logging.Formatter')
def test_set_logger_set_formatter(self, mock_formatter):
arg_parse = MockArgParse(debug=False, silent=False, logfile=None)
provider = MockProvider(arg_parse)
provider.set_logger(arg_parse)
mock_formatter.assert_called_once_with('%(asctime)s %(levelname)s - %(message)s')
def test_set_logger_logging_level_info(self):
arg_parse = MockArgParse(debug=False, silent=False, logfile=None)
provider = MockProvider(arg_parse)
provider.set_logger(arg_parse)
self.assertEqual(logging.INFO, provider.logger.level)
def test_set_logger_logging_level_debug(self):
arg_parse = MockArgParse(debug=True, silent=False, logfile=None)
provider = MockProvider(arg_parse)
provider.set_logger(arg_parse)
self.assertEqual(logging.DEBUG, provider.logger.level)
def test_set_logger_logging_level_silent(self):
self.provider.set_logger(self.arg_parse)
self.assertEqual(logging.CRITICAL, self.provider.logger.level)
@patch('logging.FileHandler')
@skipIf(sys.platform.startswith('win'), 'Seriously?')
def test_set_logger_log_on_file(self, mock_file_handler):
arg_parse = MockArgParse(debug=False, silent=True, logfile='/dev/null')
provider = MockProvider(arg_parse)
provider.set_logger(arg_parse)
mock_file_handler.assert_called_once_with('/dev/null')
@patch('logging.StreamHandler')
def test_set_logger_log_on_stream(self, mock_stream_handler):
self.provider.set_logger(self.arg_parse)
mock_stream_handler.assert_called_once_with()
@patch('logging.getLogger')
def test_set_logger_instantiate_the_logger(self, mock_get_logger):
self.provider.set_logger(self.arg_parse)
mock_get_logger.assert_called_once_with('virga.providers.abstract')
self.assertIsNotNone(self.provider.logger)
@patch('logging.Logger.setLevel')
@patch('logging.Logger.addHandler')
def test_set_logger_set_the_logger_properties(self, mock_add_handler, mock_set_level):
self.provider.set_logger(self.arg_parse)
mock_add_handler.assert_called_once()
mock_set_level.assert_called_once_with(logging.CRITICAL)
@patch('virga.providers.abstract.AbstractProvider.set_logger')
def test_logs_instantiate_the_logger_if_it_is_none(self, mock_set_logger):
self.provider.logs([{}])
mock_set_logger.assert_called_once_with(self.arg_parse)
@patch('logging.Logger.log')
def test_logs_empty_outcomes(self, mock_log):
self.provider.logs([])
mock_log.assert_not_called()
@patch('logging.Logger.log')
def test_logs_outcomes_success(self, mock_log):
self.provider.logs([{
'level': SUCCESS,
'message': 'Success'
}])
mock_log.assert_called_once_with(SUCCESS, 'Success')
@patch('logging.Logger.log')
def test_logs_outcomes_failure(self, mock_log):
self.provider.logs([{
'level': logging.ERROR,
'message': 'Failure'
}])
mock_log.assert_called_once_with(logging.ERROR, 'Failure')
@patch('logging.Logger.log')
def test_logs_default_outcomes_failure(self, mock_log):
self.provider.logs([{}])
mock_log.assert_called_once_with(logging.CRITICAL, 'No message')
def test_flatten_empty_list(self):
self.assertListEqual([], self.provider.flatten([]))
def test_flatten_one_string(self):
self.assertListEqual(['t1'], self.provider.flatten('t1'))
def test_flatten_one_none(self):
self.assertListEqual([None], self.provider.flatten(None))
def test_flatten_list_one_level(self):
self.assertListEqual(['t1', 't2', 't3'], self.provider.flatten(['t1', 't2', 't3']))
def test_flatten_list_one_level_multiple_types(self):
self.assertListEqual(['t1', None, False], self.provider.flatten(['t1', None, False]))
def test_flatten_list_two_levels(self):
data = [
['t1', 't2', 't3'],
['t4', 't5', 't6'],
]
expected = ['t1', 't2', 't3', 't4', 't5', 't6']
self.assertListEqual(expected, self.provider.flatten(data))
def test_flatten_list_three_levels(self):
data = [
['t1', 't2', ['t3', 't4']],
['t5', ['t6', 't7', 't8'], ['t9', None]],
]
expected = ['t1', 't2', 't3', 't4', 't5', 't6', 't7', 't8', 't9', None]
self.assertListEqual(expected, self.provider.flatten(data))
def test_outcome_result_none(self):
self.assertFalse(self.provider.outcome(None))
@patch('virga.providers.abstract.AbstractProvider.flatten')
def test_outcome_flatten_result(self, mock_flatten):
self.provider.outcome('any')
mock_flatten.called_once_with('any')
def test_outcome_result_empty(self):
self.assertFalse(self.provider.outcome([]))
def test_outcome_all_result_are_none(self):
self.assertFalse(self.provider.outcome([None, None]))
def test_outcome_all_result_are_false(self):
self.assertFalse(self.provider.outcome([False, False]))
def test_outcome_all_result_are_empty(self):
self.assertFalse(self.provider.outcome(['', '']))
def test_outcome_any_result_exists(self):
self.assertTrue(self.provider.outcome(['test', '']))
def test_lookup_not_there(self):
data = 'Any=`any`'
self.assertEqual(data, self.provider._lookup(data))
# mocking the mock!!!
@patch('tests.test_providers.MockProvider.lookup')
def test_lookup_with_one_lookup(self, mock_lookup):
mock_lookup.return_value = 'lookup-return'
data = 'Any=`_lookup(section, identifier, id)`'
expected = 'Any=`lookup-return`'
self.assertEqual(expected, self.provider._lookup(data))
@patch('tests.test_providers.MockProvider.lookup')
def test_lookup_with_multiple_lookups(self, mock_lookup):
mock_lookup.return_value = 'lookup-return'
data = 'Any=`_lookup(section_1, identifier1, id-1)` && Any=`_lookup(section_2, identifier2, id-2)`'
expected = 'Any=`lookup-return` && Any=`lookup-return`'
self.assertEqual(expected, self.provider._lookup(data))
@patch('builtins.open', new_callable=mock_open)
def test_output_save_resource_on_file(self, mock_opened):
data = {
'k1': 'v1',
'k2': 'v2'
}
# this one is copied from the debug
# I expected the calls where split only by the CR
expected = [
call('{'), call('\n '),
call('"k1"'), call(': '), call('"v1"'), call(',\n '),
call('"k2"'), call(': '), call('"v2"'), call('\n'),
call('}')
]
self.provider.output(data, 'any')
mock_opened.assert_called_once_with('/tmp/any.json', 'w')
handle = mock_opened()
handle.write.assert_has_calls(expected)
@patch('builtins.open', new_callable=mock_open)
def test_output_save_resource_with_datetime(self, mock_opened):
data = {
'k1': 'v1',
'k2': datetime(2015, 1, 1, 12, 30, 59)
}
expected = [
call('{'), call('\n '),
call('"k1"'), call(': '), call('"v1"'), call(',\n '),
call('"k2"'), call(': '), call('"2015-01-01T12:30:59"'), call('\n'),
call('}')
]
self.provider.output(data, 'any')
mock_opened.assert_called_once_with('/tmp/any.json', 'w')
handle = mock_opened()
handle.write.assert_has_calls(expected)
@patch('virga.providers.abstract.AbstractProvider.output')
def test_assertion_call_output(self, mock_output):
self.provider.assertion("AnyKey=='any-value'", 'Context', {}, 'resource-id')
mock_output.assert_called_once_with({}, 'resource-id')
@patch('virga.providers.abstract.AbstractProvider._lookup')
def test_assertion_call_lookup(self, mock_lookup):
mock_lookup.return_value = "AnyKey=='any-value'"
self.provider.assertion("AnyKey=='any-value'", 'Context', {}, 'resource-id')
mock_lookup.assert_called_once_with("AnyKey=='any-value'")
@patch('jmespath.search')
def test_assertion_call_search(self, mock_search):
self.provider.assertion("AnyKey=='any-value'", 'Context', {}, 'resource-id')
mock_search.assert_called_once_with("AnyKey=='any-value'", {})
@patch('logging.Logger.debug')
def test_assertion_call_debug(self, mock_debug):
self.provider.assertion("AnyKey=='any-value'", 'Context', {}, 'resource-id')
mock_debug.assert_called_once_with("resource-id: AnyKey=='any-value' eval False == False")
@patch('virga.providers.abstract.AbstractProvider.outcome')
def test_assertion_call_outcome(self, mock_outcome):
self.provider.assertion("AnyKey=='any-value'", 'Context', {}, 'resource-id')
mock_outcome.assert_called_once_with(False)
@patch('os.listdir', return_value=['bare-valid.yaml'])
@patch('builtins.open', new_callable=mock_open, read_data=fixture('bare-valid.yaml'))
def test_definition_file(self, *args):
self.provider.definitions_path = 'any'
self.assertDictEqual({'test': 'ok'}, self.provider.definitions)
@patch('builtins.open', new_callable=mock_open, read_data=fixture('bare-valid.yaml'))
def test_definition_file_missing(self, *args):
with self.assertRaisesRegex(NotImplementedError, 'Implement definition_file property'):
_ = self.provider.definitions
@patch('sys.stderr.write')
@patch('sys.exit')
def test_result_no_messages(self, mock_exit, mock_write):
self.provider.result([])
mock_exit.assert_not_called()
mock_write.assert_not_called()
@patch('sys.stderr.write')
@patch('sys.exit')
def test_result_only_successful_messages(self, mock_exit, mock_write):
messages = [
{'success': True},
{'success': True},
{'success': True},
]
self.provider.result(messages)
mock_exit.assert_not_called()
mock_write.assert_not_called()
@patch('sys.stderr.write')
@patch('sys.exit')
def test_result_only_one_failing_message(self, mock_exit, mock_write):
messages = [
{'success': True},
{'success': True},
{'success': False},
]
self.provider.result(messages)
mock_exit.assert_called_once_with(1)
mock_write.assert_called_once_with('There is an error on 3 tests.\n')
@patch('sys.stderr.write')
@patch('sys.exit')
def test_result_only_failing_messages(self, mock_exit, mock_write):
messages = [
{'success': False},
{'success': False},
{'success': False},
]
self.provider.result(messages)
mock_exit.assert_called_once_with(1)
mock_write.assert_called_once_with('There are 3 errors on 3 tests.\n')
|
[
"sys.platform.startswith",
"datetime.datetime",
"unittest.mock.patch",
"tests.test_providers.MockProvider",
"tests.MockArgParse",
"tests.fixture",
"unittest.mock.call"
] |
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|
#!/usr/bin/env python3
from utils.logger import Logger
from utils import args
import logging
import sys
#--------------------------------------------------------------------#
# GLOBAL VARIABLES #
#--------------------------------------------------------------------#
#--------------------------------------------------------------------#
# MAIN #
#--------------------------------------------------------------------#
if __name__ == "__main__":
# Parse arguments
args = args.parseArguments()
# Prepare the logger and write start log
log = Logger()
log.writer.info("Starting script")
print("Change me !")
|
[
"utils.args.parseArguments",
"utils.logger.Logger"
] |
[((593, 614), 'utils.args.parseArguments', 'args.parseArguments', ([], {}), '()\n', (612, 614), False, 'from utils import args\n'), ((671, 679), 'utils.logger.Logger', 'Logger', ([], {}), '()\n', (677, 679), False, 'from utils.logger import Logger\n')]
|
#MAIN_DATA_CURATION--------run it with spark-submit
import time
from elasticsearch import Elasticsearch
from pyspark.sql import SQLContext, SparkSession
from pyspark.sql.functions import col
from spark_functions import elastic2df, df2elastic
start_time = time.time()
#spark connexion
sc = SparkSession.builder.appName("sensors processing").getOrCreate()
sqlContext = SQLContext(sc)
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
#---------------------ELASTIC 2 SPARK-----------------------------#
#elasticsearch variables
index = "clean_sensors"
index_output = "curated_sensors"
doc_type = "IoT"
#spark processing: delete strange float values on results (86 records)
df = elastic2df(es,index,doc_type)
df_float = df.select(col('bike'),
col('location'),
df.result.cast('float').alias('result'),
col('sampling_time'),
col('variable'))
df_curation = df_float.where('result is not null')
df2elastic(es,index_output,doc_type,df_curation)
print("--- %s seconds ---" % (time.time() - start_time))
#---------------------------PIVOTING-----------------------------------#
#elasticsearch variables
index = "bike2_sensors"
index_output = "pivoted_sensors"
doc_type = "IoT"
#input
df = sf.elastic2df(es,index,doc_type,sqlContext)
#transformations
df = sf.transform(df)
df_full = sf.pivoting(df)
sf.df2elastic(es,index_output,doc_type,df_full)
path = '/home/marcroig/Desktop/data/reports/pivoted_report.html'
sf.createReport(df_full,path)
|
[
"elasticsearch.Elasticsearch",
"pyspark.sql.SQLContext",
"spark_functions.elastic2df",
"time.time",
"pyspark.sql.functions.col",
"spark_functions.df2elastic",
"pyspark.sql.SparkSession.builder.appName"
] |
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|
import cv2 as cv
import numpy as np
from PIL import Image
from util.richlog import get_logger
from . import imgops
from . import resources
from . import util
from . import ocr
LOGFILE = 'recruit.html'
logger = get_logger(__name__)
from resources.recruit_database import recruit_database
known_tagchars = set(z for x in recruit_database for y in x[2] for z in y)
known_tagchars.update('高级资深')
def remove_unknown_chars(s, known_chars):
result = ''.join(c for c in s if c in known_chars)
return result
def get_recruit_tags(img):
vw, vh = util.get_vwvh(img)
tagimgs = [
img.crop((50*vw-36.481*vh, 50.185*vh, 50*vw-17.315*vh, 56.111*vh)).convert('L'),
img.crop((50*vw-13.241*vh, 50.185*vh, 50*vw+6.111*vh, 56.111*vh)).convert('L'),
img.crop((50*vw+10.000*vh, 50.185*vh, 50*vw+29.259*vh, 56.111*vh)).convert('L'),
img.crop((50*vw-36.481*vh, 60.278*vh, 50*vw-17.315*vh, 66.019*vh)).convert('L'),
img.crop((50*vw-13.241*vh, 60.278*vh, 50*vw+6.111*vh, 66.019*vh)).convert('L')
]
eng = ocr.acquire_engine_global_cached('zh-cn')
recognize = lambda img: eng.recognize(imgops.invert_color(img), int(vh * 20), hints=[ocr.OcrHint.SINGLE_LINE]).text
tags = [remove_unknown_chars(recognize(img), known_tagchars) for img in tagimgs]
for tagimg, tagtext in zip(tagimgs, tags):
logger.logimage(tagimg)
logger.logtext(tagtext)
return tags
|
[
"util.richlog.get_logger"
] |
[((212, 232), 'util.richlog.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (222, 232), False, 'from util.richlog import get_logger\n')]
|
from bs4 import BeautifulSoup, element # type: ignore
from typing import List
from urllib.parse import urljoin
from .base import NewsScraper
from .errors import FormatError
from .feed import NewsItem
from .utils import get_base_url, is_covid_related, parse_datetime
class SonomaNews(NewsScraper):
"""
Scrape official county COVID-related news from Sonoma County. This scrapes
the county's main news page (there's no RSS feed) for news and filters out
non-COVID news based on key terms.
NOTE: this page includes spanish-language versions of some articles, but
we don't have an effective way to link them up with the english ones and
they tend to get filtered out because our key terms are in english.
There are some other sources that might be useful to look into here:
- The county emergency site (socoemergency.org) has a news page and a
public health orders page:
- https://socoemergency.org/emergency/novel-coronavirus/latest-news/
- https://socoemergency.org/emergency/novel-coronavirus/health-orders/
These pages are specific to COVID, but they tend to be a little more
technical in approach (compare these two about the same topic:
https://sonomacounty.ca.gov/CAO/Press-Releases/Sonoma-County-Public-Health-Officer-Amends-Shelter-in-Place-Order-to-Allow-Additional-Businesses-to-Reopen/
vs. https://socoemergency.org/amendment-no-3-to-health-order-no-c19-09/).
These pages are *largely* a subset of items on main county feed, but not
exclusively. If we could find a good way to reconcile matching articles,
it might be good. to join these feeds together.
- The county emergency site also has an RSS feed, but it appears to be
about any/every page on the site (i.e. it tracks page updates, rather
than focusing on what we might think of as "news" here).
https://socoemergency.org/feed
Examples
--------
>>> scraper = SonomaNews()
>>> scraper.scrape()
"""
FEED_INFO = dict(
title='Sonoma County COVID-19 News',
home_page_url='https://sonomacounty.ca.gov/News/'
)
URL = 'https://sonomacounty.ca.gov/News/'
def parse_page(self, html: str, url: str) -> List[NewsItem]:
soup = BeautifulSoup(html, 'html5lib')
base_url = get_base_url(soup, url)
articles = soup.select('.teaserContainer.srchResults .teaserContainer')
if len(articles) == 0:
raise FormatError('Could not find any news items on page')
parsed = (self.parse_news_item(article, base_url)
for article in articles)
return list(filter(is_covid_related, parsed))
def parse_news_item(self, item_element: element.Tag, base_url: str) -> NewsItem:
title_element = item_element.select_one('.titlePrimary')
title_link = title_element.find('a')
url = title_link['href']
if not url:
raise FormatError('No URL found for article')
else:
url = urljoin(base_url, url)
title = title_link.get_text(strip=True)
if not title:
raise FormatError('No title content found')
date_string = item_element.select_one('.published .date').get_text(strip=True)
time_element = item_element.select_one('.published .time')
if time_element:
time_string = time_element.get_text(strip=True)
date_string = f'{date_string} at {time_string}'
date = parse_datetime(date_string)
summary = item_element.select_one('.summary').get_text().strip()
tags = []
source_element = item_element.select_one('.source')
if source_element:
tags.append(source_element.get_text(strip=True))
return NewsItem(id=url, url=url, title=title, date_published=date,
summary=summary, tags=tags)
|
[
"bs4.BeautifulSoup",
"urllib.parse.urljoin"
] |
[((2270, 2301), 'bs4.BeautifulSoup', 'BeautifulSoup', (['html', '"""html5lib"""'], {}), "(html, 'html5lib')\n", (2283, 2301), False, 'from bs4 import BeautifulSoup, element\n'), ((3023, 3045), 'urllib.parse.urljoin', 'urljoin', (['base_url', 'url'], {}), '(base_url, url)\n', (3030, 3045), False, 'from urllib.parse import urljoin\n')]
|
from __future__ import absolute_import
import unittest
from pymaze.maze import Maze
from pymaze.cell import Cell
def generate_maze():
# Used to generate a 5x5 maze for testing, Feel free to modify as needed
cols = 5
rows = 5
return Maze(rows, cols)
class TestMaze(unittest.TestCase):
def test_ctor(self):
"""Make sure that the constructor values are getting properly set."""
cols = 5
rows = 5
maze = Maze(rows, cols)
self.assertEqual(maze.num_cols, cols)
self.assertEqual(maze.num_rows, rows)
self.assertEqual(maze.id, 0)
self.assertEqual(maze.grid_size, rows * cols)
id = 33
maze2 = Maze(rows, cols, id)
self.assertEqual(maze2.num_cols, cols)
self.assertEqual(maze2.num_rows, rows)
self.assertEqual(maze2.id, id)
self.assertEqual(maze2.grid_size, rows * cols)
def test_generate_grid(self):
maze = generate_maze()
grid = maze.generate_grid()
self.assertEqual(len(grid), maze.num_cols)
self.assertGreater(len(grid), 2)
self.assertEqual(len(grid[0]), maze.num_rows)
def test_find_neighbors(self):
maze = Maze(2, 2)
neighbors = maze.find_neighbours(0, 1)
self.assertIsNotNone(neighbors)
if __name__ == "__main__":
unittest.main()
|
[
"unittest.main",
"pymaze.maze.Maze"
] |
[((264, 280), 'pymaze.maze.Maze', 'Maze', (['rows', 'cols'], {}), '(rows, cols)\n', (268, 280), False, 'from pymaze.maze import Maze\n'), ((1381, 1396), 'unittest.main', 'unittest.main', ([], {}), '()\n', (1394, 1396), False, 'import unittest\n'), ((478, 494), 'pymaze.maze.Maze', 'Maze', (['rows', 'cols'], {}), '(rows, cols)\n', (482, 494), False, 'from pymaze.maze import Maze\n'), ((720, 740), 'pymaze.maze.Maze', 'Maze', (['rows', 'cols', 'id'], {}), '(rows, cols, id)\n', (724, 740), False, 'from pymaze.maze import Maze\n'), ((1244, 1254), 'pymaze.maze.Maze', 'Maze', (['(2)', '(2)'], {}), '(2, 2)\n', (1248, 1254), False, 'from pymaze.maze import Maze\n')]
|
import argparse
import io
import os
import shutil
import sys
import mammoth
from . import writers
def main():
args = _parse_args()
if args.style_map is None:
style_map = None
else:
with open(args.style_map) as style_map_fileobj:
style_map = style_map_fileobj.read()
with open(args.path, "rb") as docx_fileobj:
if args.output_dir is None:
convert_image = None
output_path = args.output
else:
convert_image = mammoth.images.img_element(ImageWriter(args.output_dir))
output_filename = "{0}.html".format(os.path.basename(args.path).rpartition(".")[0])
output_path = os.path.join(args.output_dir, output_filename)
result = mammoth.convert(
docx_fileobj,
style_map=style_map,
convert_image=convert_image,
output_format=args.output_format,
)
for message in result.messages:
sys.stderr.write(message.message)
sys.stderr.write("\n")
_write_output(output_path, result.value)
class ImageWriter(object):
def __init__(self, output_dir):
self._output_dir = output_dir
self._image_number = 1
def __call__(self, element):
extension = element.content_type.partition("/")[2]
image_filename = "{0}.{1}".format(self._image_number, extension)
with open(os.path.join(self._output_dir, image_filename), "wb") as image_dest:
with element.open() as image_source:
shutil.copyfileobj(image_source, image_dest)
self._image_number += 1
return {"src": image_filename}
def _write_output(path, contents):
if path is None:
if sys.version_info[0] <= 2:
stdout = sys.stdout
else:
stdout = sys.stdout.buffer
stdout.write(contents.encode("utf-8"))
stdout.flush()
else:
with io.open(path, "w", encoding="utf-8") as fileobj:
fileobj.write(contents)
def _parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"path",
metavar="docx-path",
help="Path to the .docx file to convert.")
output_group = parser.add_mutually_exclusive_group()
output_group.add_argument(
"output",
nargs="?",
metavar="output-path",
help="Output path for the generated document. Images will be stored inline in the output document. Output is written to stdout if not set.")
output_group.add_argument(
"--output-dir",
help="Output directory for generated HTML and images. Images will be stored in separate files. Mutually exclusive with output-path.")
parser.add_argument(
"--output-format",
required=False,
choices=writers.formats(),
help="Output format.")
parser.add_argument(
"--style-map",
required=False,
help="File containg a style map.")
return parser.parse_args()
if __name__ == "__main__":
main()
|
[
"argparse.ArgumentParser",
"os.path.basename",
"io.open",
"sys.stderr.write",
"shutil.copyfileobj",
"os.path.join",
"mammoth.convert"
] |
[((2107, 2132), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2130, 2132), False, 'import argparse\n'), ((768, 886), 'mammoth.convert', 'mammoth.convert', (['docx_fileobj'], {'style_map': 'style_map', 'convert_image': 'convert_image', 'output_format': 'args.output_format'}), '(docx_fileobj, style_map=style_map, convert_image=\n convert_image, output_format=args.output_format)\n', (783, 886), False, 'import mammoth\n'), ((695, 741), 'os.path.join', 'os.path.join', (['args.output_dir', 'output_filename'], {}), '(args.output_dir, output_filename)\n', (707, 741), False, 'import os\n'), ((993, 1026), 'sys.stderr.write', 'sys.stderr.write', (['message.message'], {}), '(message.message)\n', (1009, 1026), False, 'import sys\n'), ((1039, 1061), 'sys.stderr.write', 'sys.stderr.write', (['"""\n"""'], {}), "('\\n')\n", (1055, 1061), False, 'import sys\n'), ((1988, 2024), 'io.open', 'io.open', (['path', '"""w"""'], {'encoding': '"""utf-8"""'}), "(path, 'w', encoding='utf-8')\n", (1995, 2024), False, 'import io\n'), ((1446, 1492), 'os.path.join', 'os.path.join', (['self._output_dir', 'image_filename'], {}), '(self._output_dir, image_filename)\n', (1458, 1492), False, 'import os\n'), ((1580, 1624), 'shutil.copyfileobj', 'shutil.copyfileobj', (['image_source', 'image_dest'], {}), '(image_source, image_dest)\n', (1598, 1624), False, 'import shutil\n'), ((621, 648), 'os.path.basename', 'os.path.basename', (['args.path'], {}), '(args.path)\n', (637, 648), False, 'import os\n')]
|
from setuptools import setup
setup(
name='system monitor',
version='0.1',
description='Network enabled system monitor',
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3.6',
],
keywords='Network system monitor',
author='<NAME>',
author_email='<EMAIL>',
license='MIT',
packages=['system_monitor'],
install_requires=[
'pysdl2',
'pysdl2-dll',
'psutil',
],
scripts=['monitor_server.py'],
zip_safe=False)
|
[
"setuptools.setup"
] |
[((30, 501), 'setuptools.setup', 'setup', ([], {'name': '"""system monitor"""', 'version': '"""0.1"""', 'description': '"""Network enabled system monitor"""', 'classifiers': "['Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3.6']", 'keywords': '"""Network system monitor"""', 'author': '"""<NAME>"""', 'author_email': '"""<EMAIL>"""', 'license': '"""MIT"""', 'packages': "['system_monitor']", 'install_requires': "['pysdl2', 'pysdl2-dll', 'psutil']", 'scripts': "['monitor_server.py']", 'zip_safe': '(False)'}), "(name='system monitor', version='0.1', description=\n 'Network enabled system monitor', classifiers=[\n 'Development Status :: 4 - Beta',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3.6'], keywords=\n 'Network system monitor', author='<NAME>', author_email='<EMAIL>',\n license='MIT', packages=['system_monitor'], install_requires=['pysdl2',\n 'pysdl2-dll', 'psutil'], scripts=['monitor_server.py'], zip_safe=False)\n", (35, 501), False, 'from setuptools import setup\n')]
|
import discord
import requests
client = discord.Client()
cmd1 = '$olc'
cmd2 = '$phrase'
@client.event
async def on_ready():
print('Logged in successfully.')
@client.event
async def on_message(message):
if message.content.startswith(cmd1):
olc = message.content[len(cmd1):] # rest of code
req = requests.get(
'http://localhost:8000/lookup_olc?olc=' + olc)
json = req.json()
await message.channel.send(olc + "'s phrase is: " + '**'
+ json['phrase'] + '**')
if message.content.startswith(cmd2):
phrase = message.content[len(cmd2):] # rest of phrase
req = requests.get(
'http://localhost:8000/lookup_phrase?'
'phrase=white.horse.body.girl.stead')
json = req.json()
await message.channel.send(phrase + "'s OLC is: " + '**'
+ json['olc'] + '**')
client.run('INSERT-TOKEN-HERE')
|
[
"requests.get",
"discord.Client"
] |
[((44, 60), 'discord.Client', 'discord.Client', ([], {}), '()\n', (58, 60), False, 'import discord\n'), ((343, 402), 'requests.get', 'requests.get', (["('http://localhost:8000/lookup_olc?olc=' + olc)"], {}), "('http://localhost:8000/lookup_olc?olc=' + olc)\n", (355, 402), False, 'import requests\n'), ((694, 785), 'requests.get', 'requests.get', (['"""http://localhost:8000/lookup_phrase?phrase=white.horse.body.girl.stead"""'], {}), "(\n 'http://localhost:8000/lookup_phrase?phrase=white.horse.body.girl.stead')\n", (706, 785), False, 'import requests\n')]
|
import configparser
import logging
from pprint import pprint
import telegram
from emoji import emojize
from jira import JIRA
from jira.exceptions import JIRAError
from telegram.ext import CommandHandler, Filters, MessageHandler, Updater
def escape(x):
special = [".", "-", "(", ")", "[", "]", ">", "<", "=", "+", "{", "}", "#", "_"]
for c in special:
x = x.replace(c, fr"\{c}")
return x
jira = JIRA("https://bugreports.qt.io")
def control_test(update, context):
chat_id = str(update.effective_chat.id).replace("-", r"\-")
pprint(update)
context.bot.send_message(
chat_id=update.effective_chat.id,
text=f"It's working [Qt for Python Site](https://pyside.org)\nID:{chat_id}",
parse_mode=telegram.ParseMode.MARKDOWN_V2,
)
def doc(update, context):
doc_url = r"https://doc\.qt\.io/qtforpython/"
wiki_url = r"https://pyside\.org"
icon1 = emojize(":green_book:", use_aliases=True)
icon2 = emojize(":notebook:", use_aliases=True)
msg = f"{icon1} Documentation: {doc_url}\n" f"{icon2} Wiki: {wiki_url}"
context.bot.send_message(
chat_id=update.effective_chat.id,
text=msg,
parse_mode=telegram.ParseMode.MARKDOWN_V2,
)
def gerrit(update, context):
gerrit_url = r"https://codereview.qt-project.org/q/project:pyside%252Fpyside-setup+status:open"
msg = f"Gerrit Code Review\n`pyside-setup` repository: [latest changes]({gerrit_url})"
context.bot.send_message(
chat_id=update.effective_chat.id,
text=msg,
parse_mode=telegram.ParseMode.MARKDOWN_V2,
)
def meetings(update, context):
meetings_url = r"https://wiki.qt.io/Qt_for_Python_Development_Notes"
msg = f"Qt for Python [meeting notes]({meetings_url})"
context.bot.send_message(
chat_id=update.effective_chat.id,
text=msg,
parse_mode=telegram.ParseMode.MARKDOWN_V2,
)
def qthelp_handler(update, context):
msg = (
"```\n"
"/help - This message\n"
"/issue N - Information about the PYSIDE-N issue\n"
" - Without arguments it shows a summary\n"
"/doc - Links to documentation\n"
"/module - Differences between PyQt/PySide\n"
"/gerrit - Link to the latest open changes\n"
"/meetings - Link to the public meeting notes\n"
"```"
)
context.bot.send_message(
chat_id=update.effective_chat.id,
text=msg,
parse_mode=telegram.ParseMode.MARKDOWN_V2,
)
def module(update, context):
pyqt_url = r"https://www\.riverbankcomputing\.com/software/pyqt/intro"
pyside_url = r"https://qt\.io/qt-for-python"
snake = emojize(":snake:", use_aliases=True)
msg = (
f"{snake} Python modules\n\n"
f"• [PySide]({pyside_url}): Developed by The Qt Company \(LGPL/Commercial\)\n"
f"• [PyQt]({pyqt_url}): Developed by Riverbank Computing \(GPL/Commercial\)\n\n"
f"This channel is about the first module, *PySide*, which "
f"belongs to the *Qt for Python* project, including the"
f"binding generator tool called *Shiboken*"
)
context.bot.send_message(
chat_id=update.effective_chat.id,
text=msg,
parse_mode=telegram.ParseMode.MARKDOWN_V2,
disable_web_page_preview=True,
)
def issue(update, context):
base_url = "https://bugreports.qt.io/browse/"
content = ""
link = ""
error_icon = emojize(":x:", use_aliases=True)
try:
arg = context.args[0]
arg = arg.replace("PYSIDE-", "")
try:
status = None
issue_number = int(arg)
issue_id = f"PYSIDE-{issue_number}"
try:
issue = jira.issue(issue_id)
summary = issue.raw["fields"]["summary"]
status = issue.raw["fields"]["status"]["name"]
link = f"[{issue_id}]({base_url}{issue_id})"
content = f"{summary}]"
except JIRAError as e:
link = error_icon
if e.status_code == 404:
content = "Error: Issue not found"
else:
content = "Undefined Error"
except ValueError:
link = error_icon
content = f"Invalid argument '{arg}', use an issue number"
content = escape(content)
link = link.replace("-", r"\-")
msg = f"{link}: {content}"
if status:
icon = ""
if status == "Open":
icon = emojize(":large_blue_circle:", use_aliases=True)
elif status == "Closed":
icon = emojize(":white_check_mark:", use_aliases=True)
else:
icon = emojize(":red_circle:", use_aliases=True)
msg += f"\n*Status*: {icon} {status}"
except IndexError:
issues = jira.search_issues(
"project=PYSIDE and resolution=Unresolved and type=Bug order by created DESC",
maxResults=200,
)
icon = emojize(":information_source:", use_aliases=True)
msg = f"*Total open issues*: {len(issues)}\n\nLast 5 open issues:"
for issue in issues[:5]:
key = issue.key
key_clean = key.replace("-", r"\-")
summary = issue.fields.summary
msg += f"\n• [{key_clean}]({base_url}{key}): {escape(summary)}"
msg += (
"\n\nCheck all the issues [on this link](https://bugreports.qt.io/issues/?filter=20338)"
)
context.bot.send_message(
chat_id=update.effective_chat.id,
text=msg,
parse_mode=telegram.ParseMode.MARKDOWN_V2,
)
def welcome(update, context):
for new_user_obj in update.message.new_chat_members:
chat_id = update.message.chat.id
new_user = ""
try:
new_user = "@" + new_user_obj["username"]
except Exception:
new_user = new_user_obj["first_name"]
party = emojize(":tada:", use_aliases=True)
confetti = emojize(":confetti_ball:", use_aliases=True)
doc_url = "https://doc.qt.io/qtforpython/"
wiki_url = "https://pyside.org"
msg = (
f"Welcome {new_user} {party} {confetti}\n"
f"Verify you are not a bot solving the captcha!\n\n"
f"Remember to check the documentation page {doc_url} "
f"and the wiki {wiki_url} "
f"if you have some questions.\n"
)
context.bot.send_message(chat_id=chat_id, text=msg, parse_mode="HTML")
def unknown(update, context):
context.bot.send_message(chat_id=update.message.chat.id,
text="Command not found, check `/help`",
parse_mode=telegram.ParseMode.MARKDOWN_V2)
if __name__ == "__main__":
logging.basicConfig(
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO
)
config = configparser.ConfigParser()
config.read("config.ini")
TOKEN = config["DEFAULT"]["Token"]
updater = Updater(token=TOKEN, use_context=True)
dispatcher = updater.dispatcher
dispatcher.add_handler(CommandHandler("test", control_test))
dispatcher.add_handler(CommandHandler("doc", doc))
dispatcher.add_handler(CommandHandler("module", module))
dispatcher.add_handler(CommandHandler("qthelp", qthelp_handler))
dispatcher.add_handler(CommandHandler("issue", issue))
dispatcher.add_handler(CommandHandler("gerrit", gerrit))
dispatcher.add_handler(CommandHandler("meetings", meetings))
dispatcher.add_handler(MessageHandler(Filters.command, unknown))
dispatcher.add_handler(MessageHandler(Filters.status_update.new_chat_members, welcome))
updater.start_polling()
|
[
"logging.basicConfig",
"emoji.emojize",
"telegram.ext.Updater",
"telegram.ext.MessageHandler",
"pprint.pprint",
"telegram.ext.CommandHandler",
"configparser.ConfigParser",
"jira.JIRA"
] |
[((419, 451), 'jira.JIRA', 'JIRA', (['"""https://bugreports.qt.io"""'], {}), "('https://bugreports.qt.io')\n", (423, 451), False, 'from jira import JIRA\n'), ((557, 571), 'pprint.pprint', 'pprint', (['update'], {}), '(update)\n', (563, 571), False, 'from pprint import pprint\n'), ((914, 955), 'emoji.emojize', 'emojize', (['""":green_book:"""'], {'use_aliases': '(True)'}), "(':green_book:', use_aliases=True)\n", (921, 955), False, 'from emoji import emojize\n'), ((968, 1007), 'emoji.emojize', 'emojize', (['""":notebook:"""'], {'use_aliases': '(True)'}), "(':notebook:', use_aliases=True)\n", (975, 1007), False, 'from emoji import emojize\n'), ((2686, 2722), 'emoji.emojize', 'emojize', (['""":snake:"""'], {'use_aliases': '(True)'}), "(':snake:', use_aliases=True)\n", (2693, 2722), False, 'from emoji import emojize\n'), ((3454, 3486), 'emoji.emojize', 'emojize', (['""":x:"""'], {'use_aliases': '(True)'}), "(':x:', use_aliases=True)\n", (3461, 3486), False, 'from emoji import emojize\n'), ((6816, 6923), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '"""%(asctime)s - %(name)s - %(levelname)s - %(message)s"""', 'level': 'logging.INFO'}), "(format=\n '%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)\n", (6835, 6923), False, 'import logging\n'), ((6947, 6974), 'configparser.ConfigParser', 'configparser.ConfigParser', ([], {}), '()\n', (6972, 6974), False, 'import configparser\n'), ((7059, 7097), 'telegram.ext.Updater', 'Updater', ([], {'token': 'TOKEN', 'use_context': '(True)'}), '(token=TOKEN, use_context=True)\n', (7066, 7097), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((5980, 6015), 'emoji.emojize', 'emojize', (['""":tada:"""'], {'use_aliases': '(True)'}), "(':tada:', use_aliases=True)\n", (5987, 6015), False, 'from emoji import emojize\n'), ((6035, 6079), 'emoji.emojize', 'emojize', (['""":confetti_ball:"""'], {'use_aliases': '(True)'}), "(':confetti_ball:', use_aliases=True)\n", (6042, 6079), False, 'from emoji import emojize\n'), ((7162, 7198), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""test"""', 'control_test'], {}), "('test', control_test)\n", (7176, 7198), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7227, 7253), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""doc"""', 'doc'], {}), "('doc', doc)\n", (7241, 7253), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7282, 7314), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""module"""', 'module'], {}), "('module', module)\n", (7296, 7314), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7343, 7383), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""qthelp"""', 'qthelp_handler'], {}), "('qthelp', qthelp_handler)\n", (7357, 7383), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7412, 7442), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""issue"""', 'issue'], {}), "('issue', issue)\n", (7426, 7442), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7471, 7503), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""gerrit"""', 'gerrit'], {}), "('gerrit', gerrit)\n", (7485, 7503), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7532, 7568), 'telegram.ext.CommandHandler', 'CommandHandler', (['"""meetings"""', 'meetings'], {}), "('meetings', meetings)\n", (7546, 7568), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7597, 7637), 'telegram.ext.MessageHandler', 'MessageHandler', (['Filters.command', 'unknown'], {}), '(Filters.command, unknown)\n', (7611, 7637), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((7667, 7730), 'telegram.ext.MessageHandler', 'MessageHandler', (['Filters.status_update.new_chat_members', 'welcome'], {}), '(Filters.status_update.new_chat_members, welcome)\n', (7681, 7730), False, 'from telegram.ext import CommandHandler, Filters, MessageHandler, Updater\n'), ((5038, 5087), 'emoji.emojize', 'emojize', (['""":information_source:"""'], {'use_aliases': '(True)'}), "(':information_source:', use_aliases=True)\n", (5045, 5087), False, 'from emoji import emojize\n'), ((4543, 4591), 'emoji.emojize', 'emojize', (['""":large_blue_circle:"""'], {'use_aliases': '(True)'}), "(':large_blue_circle:', use_aliases=True)\n", (4550, 4591), False, 'from emoji import emojize\n'), ((4652, 4699), 'emoji.emojize', 'emojize', (['""":white_check_mark:"""'], {'use_aliases': '(True)'}), "(':white_check_mark:', use_aliases=True)\n", (4659, 4699), False, 'from emoji import emojize\n'), ((4741, 4782), 'emoji.emojize', 'emojize', (['""":red_circle:"""'], {'use_aliases': '(True)'}), "(':red_circle:', use_aliases=True)\n", (4748, 4782), False, 'from emoji import emojize\n')]
|
__author__ = 'oddBit'
from kivy.uix.scatterlayout import ScatterLayout
from kivy.animation import Animation
from kivy.clock import Clock
from data.gameData import *
from HexTile import HexTile
from UpDownBubble import UpDownBubble
from random import choice, shuffle, random
from kivy.properties import NumericProperty, BooleanProperty
from kivy.core.audio import SoundLoader
from kivy.uix.bubble import Bubble
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.popup import Popup
from Actor import Actor
def level_diff(from_tile, to_tile):
if from_tile.row > to_tile.row:
from_level = from_tile.level + 3
to_level = to_tile.level
elif from_tile.row < to_tile.row:
from_level = from_tile.level
to_level = to_tile.level + 3
else:
from_level = from_tile.level
to_level = to_tile.level
return from_level, to_level
class HexScatter(ScatterLayout):
def __init__(self, hex_tiles, difficulty, soundsOption, musicOption, **kwargs):
super(HexScatter, self).__init__(**kwargs)
self.soundsOption = soundsOption
self.musicOption = musicOption
self.difficulty = difficulty
self.x_offset = 1080
self.y_offset = 1920
self.hex_tiles = hex_tiles
self.has_bubble = False
self.bubble = None
self.bubble_id = None
self.selected_hex = None
self.tiles = []
self.is_move_mode = False
self.is_moving = False
self.from_hex = None
self.to_hex = None
self.enemy_turn = False
self.tile_with_player = None
self.tile_with_portal = None
self.is_just_selected = False
self.move_anim = None
self.death_anim = None
self.player_death = False
self.enemy_death = False
self.actor_move_data = []
self.player = None
self.player_is_jumping = False
self.player_has_keys = []
self.player_has_exits = 0
self.enemy_list = []
self.skip_turn_bubble = None
self.score = 0
self.popup = None
self.player_won = False
self.create_map_from_data()
# for some reason the first time a bubble is loaded, it leads to a one or two second lag, preloading solves this
self.shadow_bubble = UpDownBubble(pos=(1000, 1000))
self.add_widget(self.shadow_bubble)
self.remove_widget(self.shadow_bubble)
self.hex_tiles = None
for enemy in self.enemy_list:
self.add_widget(enemy)
def create_map_from_data(self):
tileradius = 120
for tile in self.hex_tiles:
if tile.has_land:
tile_pos = ((tile.col * tileradius + (tileradius / 2) * (tile.row % 2)) + self.x_offset,
(tile.row * 95) + tile.row * 102 + tile.level * 34 + self.y_offset)
hex_tile = HexTile(tile.row, tile.col, tile.type, tile.level, tile.has_actor, tile.has_star,
tile.has_player, tile.key_type, tile.exit_type,
id='hexTile{0}'.format(str(tile.col) + str(tile.row)),
source='assets/graphics/tiles/' + tile.type + '.png',
pos=tile_pos)
self.tiles.append(hex_tile)
self.add_widget(hex_tile)
if tile.has_actor:
self.add_actor_to_map(tile, tile_pos)
hex_tile.post_init()
if tile.has_portal:
self.tile_with_portal = hex_tile
self.tile_with_portal.add_portal()
if tile.has_player:
self.tile_with_player = hex_tile
self.tile_with_player.add_player()
def add_actor_to_map(self, tile, tile_pos, add_to_enemy_list=False):
delay = choice([x / 1000. for x in range(80, 200, 5)])
actor = Actor(tile.col, tile.row, pos=(tile_pos[0] + 10, tile_pos[1] + 253), anim_delay=delay)
self.enemy_list.append(actor)
if add_to_enemy_list:
self.add_widget(actor)
def add_tile(self, tile):
self.tiles.append(tile)
self.add_widget(tile)
def move_mode(self, from_hex):
self.from_hex = from_hex
self.is_just_selected = True
Clock.schedule_once(self.set_move_mode, 0.08)
def set_move_mode(self, dt):
if self.from_hex.player and not self.enemy_turn:
self.enemy_turn = True
self.is_move_mode = True
self.is_just_selected = False
self.from_hex.player.select_player()
self.skip_turn_bubble = SkipTurnBubble(self, pos=(self.from_hex.x + 36, self.from_hex.y + 360))
self.add_widget(self.skip_turn_bubble)
def check_proximity(self, to_hex):
from_col = self.from_hex.col
from_row = self.from_hex.row
to_col = to_hex.col
to_row = to_hex.row
vector = [from_col - to_col, from_row - to_row]
if from_row & 1:
if vector in odd_directions:
return False
else:
return True
else:
if vector in even_directions:
return False
else:
return True
def schedule_move_to_hex(self, to_hex):
self.to_hex = to_hex
Clock.schedule_once(self.move_to_hex_prep)
def move_to_hex_prep(self, dt):
self.remove_skip_turn()
self.player_death = False
self.enemy_death = False
self.death_anim = Animation(y=self.to_hex.pos[1] + 500, duration=0.3)
from_level, to_level = level_diff(self.from_hex, self.to_hex)
if abs(to_level - from_level) > 1 or self.check_proximity(self.to_hex):
self.end_without_movement(no_move=True, show_too_far=True)
return
elif to_level - from_level == 1:
if self.to_hex.actor:
self.player_death = True
self.move_anim = Animation(x=self.to_hex.pos[0] + 10, duration=0.1)
self.move_anim &= Animation(y=self.to_hex.pos[1] + 279, t='out_back', duration=0.3)
jump = True
elif to_level - from_level == -1:
if self.to_hex.actor:
self.enemy_death = True
self.move_anim = Animation(x=self.to_hex.pos[0] + 10, duration=0.1)
self.move_anim &= Animation(y=self.to_hex.pos[1] + 279, t='out_back', duration=0.3)
jump = True
else:
if self.to_hex.actor:
self.player_death = True
jump = False
self.move_anim = Animation(x=self.to_hex.pos[0] + 10, y=self.to_hex.pos[1] + 279, duration=0.5)
self.is_moving = True
if jump:
self.player_is_jumping = True
else:
self.player_is_jumping = False
if not self.is_move_mode:
return
self.player = self.from_hex.player
self.from_hex.remove_player()
self.add_widget(self.player)
self.complete_player_movement(jump)
def complete_player_movement(self, jump):
if self.from_hex.col > self.to_hex.col or (self.from_hex.col >= self.to_hex.col and self.from_hex.row % 2):
if jump:
self.player.jump_left()
else:
self.player.move_left()
else:
if jump:
self.player.jump_right()
else:
self.player.move_right()
self.move_anim.start(self.player)
if jump:
jump_sound = SoundLoader.load('assets/audio/player_jump.ogg')
jump_sound.play()
else:
if self.soundsOption:
walk_sound = SoundLoader.load('assets/audio/player_walk.ogg')
walk_sound.play()
self.move_anim.bind(on_complete=self.end_with_player_transfer)
if self.player_death:
pass
elif self.enemy_death:
self.score += 5 * self.difficulty
self.parent.counter.update_counter(0, 5 * self.difficulty)
actor = self.get_enemy_by_tile(self.to_hex.row, self.to_hex.col)
self.death_anim.start(actor)
if self.soundsOption:
enemy_death_sound = SoundLoader.load('assets/audio/enemy_death.ogg')
enemy_death_sound.play()
self.to_hex.remove_actor()
for enemy in self.enemy_list:
if enemy == actor:
self.enemy_list.pop(self.enemy_list.index(enemy))
break
self.death_anim.bind(on_complete=self.remove_enemy)
def remove_enemy(self, a, b):
self.remove_widget(b)
def remove_skip_turn(self):
if self.skip_turn_bubble:
self.remove_widget(self.skip_turn_bubble)
self.skip_turn_bubble = None
def player_remove_on_death(self, a, b):
self.tile_with_player.remove_player()
def move_portal(self):
self.selected_hex = self.tile_with_portal
if self.tile_with_portal.level == MAX_LEVEL:
self.move_hex_down(False)
elif self.tile_with_portal.level == 0:
self.move_hex_up(False)
else:
self.move_hex_up(False) if random() > 0.5 else self.move_hex_down(False)
def spawn_enemy_on_portal(self):
if not self.tile_with_portal.actor:
self.tile_with_portal.add_actor()
Clock.schedule_once(self.add_actor_to_portal, 0.7)
def add_actor_to_portal(self, dt):
self.add_actor_to_map(self.tile_with_portal, self.tile_with_portal.pos, True)
if self.tile_with_portal.player:
self.player_death = True
self.complete_level()
def handle_portal(self):
if self.tile_with_portal:
self.move_portal()
if not self.tile_with_portal.actor and random() > 0.2:
self.spawn_enemy_on_portal()
def is_player_dead(self):
if self.player_death:
self.tile_with_player.player.player_dead()
death_anim = Animation(x=self.tile_with_player.player.y + 500, duration=0.1)
death_anim.start(self.tile_with_player.player)
death_anim.bind(on_complete=self.player_remove_on_death)
self.player_death = True
self.complete_level()
return True
def handle_star(self):
if self.tile_with_player.star:
self.score += 1 * self.difficulty
self.parent.counter.update_counter(1, 1 * self.difficulty)
if self.soundsOption:
pick_star_sound = SoundLoader.load('assets/audio/pick_star.ogg')
pick_star_sound.play()
star_anim = Animation(x=self.tile_with_player.star.x,
y=self.tile_with_player.star.y + 80,
duration=0.15)
star_anim.start(self.tile_with_player.star)
star_anim.bind(on_complete=self.tile_with_player.remove_star)
self.tile_with_player.player.add_star()
def handle_key(self):
if self.tile_with_player.key:
self.score += 5 * self.difficulty
self.parent.counter.update_counter(0, 5 * self.difficulty)
self.player_has_keys.append(self.tile_with_player.key_type)
key_anim = Animation(x=self.tile_with_player.key.x,
y=self.tile_with_player.key.y + 80,
duration=0.15)
key_anim.start(self.tile_with_player.key)
key_id = self.tile_with_player.key_type
if key_id == 'keyYellow':
self.parent.star_counter.ids.keyYellow.source = key_sources[self.tile_with_player.key_type]
self.parent.star_counter.ids.keyYellow.reload()
elif key_id == 'keyGreen':
self.parent.star_counter.ids.keyGreen.source = key_sources[self.tile_with_player.key_type]
self.parent.star_counter.ids.keyGreen.reload()
elif key_id == 'keyOrange':
self.parent.star_counter.ids.keyOrange.source = key_sources[self.tile_with_player.key_type]
self.parent.star_counter.ids.keyOrange.reload()
elif key_id == 'keyBlue':
self.parent.star_counter.ids.keyBlue.source = key_sources[self.tile_with_player.key_type]
self.parent.star_counter.ids.keyBlue.reload()
key_anim.bind(on_complete=self.tile_with_player.remove_key)
if self.soundsOption:
pick_key_sound = SoundLoader.load('assets/audio/pick_key.ogg')
pick_key_sound.play()
def player_has_all_exits(self):
if self.tile_with_player.exit:
self.score += 10 * self.difficulty
self.parent.counter.update_counter(0, 10 * self.difficulty)
for key in self.player_has_keys:
if self.tile_with_player.exit_type == key_exit[key]:
self.player_has_exits += 1
exit_anim = Animation(x=self.tile_with_player.exit.x,
y=self.tile_with_player.exit.y + 80,
duration=0.15)
exit_anim.start(self.tile_with_player.exit)
exit_anim.bind(on_complete=self.tile_with_player.remove_exit)
if self.tile_with_player.exit_type == 'lockYellow':
self.parent.star_counter.ids.keyYellow.source = lock_sources[self.tile_with_player.exit_type]
self.parent.star_counter.ids.keyYellow.reload()
if self.tile_with_player.exit_type == 'lockGreen':
self.parent.star_counter.ids.keyGreen.source = lock_sources[self.tile_with_player.exit_type]
self.parent.star_counter.ids.keyGreen.reload()
if self.tile_with_player.exit_type == 'lockBlue':
self.parent.star_counter.ids.keyBlue.source = lock_sources[self.tile_with_player.exit_type]
self.parent.star_counter.ids.keyBlue.reload()
elif self.tile_with_player.exit_type == 'lockOrange':
self.parent.star_counter.ids.keyOrange.source = lock_sources[self.tile_with_player.exit_type]
self.parent.star_counter.ids.keyOrange.reload()
if len(self.player_has_keys) == self.difficulty and self.player_has_exits == self.difficulty:
self.player_won = True
self.complete_level()
return True
def end_player_turn(self):
self.end_without_movement()
self.handle_portal()
if self.is_player_dead():
return
self.handle_star()
self.handle_key()
if self.player_has_all_exits():
return
self.player_is_jumping = False
self.parent.show_enemy_turn_splash()
Clock.schedule_once(self.process_enemy_turn, 0.7)
def complete_level(self):
self.show_reload_popup(True)
def get_tile_by_row_col(self, row, col):
for tile in self.tiles:
if tile.row == row and tile.col == col:
return tile
def get_enemy_by_tile(self, row, col):
for enemy in self.enemy_list:
if enemy.col == col and enemy.row == row:
return enemy
return
def get_neighbouring_tiles(self, tile):
n_tiles = []
if tile.row & 1:
lookup = even_directions
else:
lookup = odd_directions
for i in lookup:
n_tile = self.get_tile_by_row_col(tile.row + i[1], tile.col + i[0])
if n_tile:
from_level, to_level = level_diff(tile, n_tile)
if not n_tile.actor and not n_tile.occupied and not abs(from_level - to_level) > 1 and self.get_enemy_by_tile(tile.row, tile.col):
if abs(from_level - to_level) == 1 and not self.get_enemy_by_tile(tile.row, tile.col).can_fly:
continue
else:
n_tiles.append(n_tile)
return n_tiles
def prepare_actor_move_data(self):
for tile in self.tiles:
if tile.actor:
for e in self.enemy_list:
if e.col == tile.col and e.row == tile.row:
enemy = e
break
if not enemy.ended_turn:
n_tiles = self.get_neighbouring_tiles(tile)
if n_tiles:
self.from_hex = tile
for t in n_tiles:
if t.player:
tile_choice = t
self.player_death = True
break
else:
tile_choice = choice(n_tiles)
tile_choice.occupied = True
self.actor_move_data.append((tile, enemy, tile_choice))
tile.remove_actor()
def process_actor_movement(self):
for data in self.actor_move_data:
tile = data[0]
actor = data[1]
to_hex = data[2]
if tile.col > to_hex.col or (tile.col >= to_hex.col and tile.row % 2):
actor.move_left()
else:
actor.move_right()
from_level, to_level = level_diff(tile, to_hex)
if abs(from_level - to_level) == 1:
move_anim = Animation(x=to_hex.pos[0] + 10, duration=0.1)
move_anim &= Animation(y=to_hex.pos[1] + 249, t='out_back', duration=0.2)
else:
move_anim = Animation(x=to_hex.pos[0] + 10, y=to_hex.pos[1] + 249, duration=0.2)
move_anim.start(actor)
def process_enemy_turn(self, dt):
shuffle(self.enemy_list)
self.is_moving = True
self.is_move_mode = True
self.prepare_actor_move_data()
self.process_actor_movement()
if self.actor_move_data:
Clock.schedule_once(self.end_enemy_turn, 0.4)
else:
self.end_enemy_turn(0)
def end_enemy_turn(self, dt):
for data in self.actor_move_data:
data[2].add_actor()
data[1].col = data[2].col
data[1].row = data[2].row
for enemy in self.enemy_list:
enemy.ended_turn = False
for tile in self.tiles:
if tile.actor:
tile.occupied = True
else:
tile.occupied = False
self.is_move_mode = False
self.is_moving = False
self.actor_move_data = []
if self.player_death:
self.complete_level()
self.enemy_turn = False
self.parent.remove_enemy_turn_splash()
def end_without_movement(self, no_move=False, show_too_far=False):
self.is_move_mode = False
self.is_moving = False
if self.tile_with_player.player:
self.tile_with_player.player.stop_player()
self.remove_skip_turn()
if no_move:
self.enemy_turn = False
if show_too_far:
self.parent.show_too_far_splash()
Clock.schedule_once(self.parent.remove_tile_too_far_splash, 0.6)
def end_with_player_transfer(self, a, b):
self.to_hex.add_player(self.player.stars, self.player.pos)
self.is_move_mode = False
self.is_moving = False
self.to_hex.player.stop_player()
self.tile_with_player = self.to_hex
self.remove_widget(self.player)
self.player = None
self.end_player_turn()
def add_bubble(self, pos, id, hex):
if not self.bubble:
bubble_pos = [pos[0], pos[1] + 344]
self.selected_hex = hex
if self.selected_hex.level == MAX_LEVEL:
up_disabled = True
else:
up_disabled = False
if self.selected_hex.level == 0:
down_disabled = True
else:
down_disabled = False
self.bubble = UpDownBubble(up_disabled, down_disabled, pos=bubble_pos)
self.add_widget(self.bubble)
self.has_bubble = True
self.bubble_id = id
def remove_bubble(self, id):
if self.bubble and id == self.bubble_id:
self.remove_widget(self.bubble)
self.bubble = None
self.selected_hex = None
self.enemy_turn = False
Clock.schedule_once(self.clear_bubble, 0.1)
def move_hex_up(self, use_star=True):
if self.tile_with_player.player.stars > 0:
self.selected_hex.move_up(use_star)
for enemy in self.enemy_list:
if self.selected_hex.actor and enemy.col == self.selected_hex.col and enemy.row == self.selected_hex.row:
actor_anim = Animation(y=enemy.pos[1] + 34, t='in_out_elastic', duration=.7)
actor_anim.start(enemy)
break
if use_star:
self.parent.counter.update_counter(-1)
def move_hex_down(self, use_star=True):
if self.tile_with_player.player.stars > 0:
self.selected_hex.move_down(use_star)
for enemy in self.enemy_list:
if self.selected_hex.actor and enemy.col == self.selected_hex.col and enemy.row == self.selected_hex.row:
actor_anim = Animation(y=enemy.pos[1] - 34, t='in_out_elastic', duration=.7)
actor_anim.start(enemy)
break
if use_star:
self.parent.counter.update_counter(-1)
def clear_bubble(self, dt):
self.has_bubble = False
def show_reload_popup(self, death_or_victory=False):
new_difficulty = choice([1,2,3,4])
self.popup = ReloadPopup(self, death_or_victory, new_difficulty, self.player_won)
self.popup.open()
class ReloadPopup(Popup):
player_death = BooleanProperty(False)
player_won = BooleanProperty(False)
def __init__(self, context, player_death, new_difficulty, player_won, **kwargs):
super(ReloadPopup, self).__init__(**kwargs)
self.image = 'assets/graphics/ui/popupImg.png'
self.context = context
self.player_death = player_death
self.player_won = player_won
self.new_difficulty = new_difficulty
class StarCounter(BoxLayout):
stars = NumericProperty(0)
score = NumericProperty(0)
def __init__(self, stars, score=0, **kwargs):
super(StarCounter, self).__init__(**kwargs)
self.stars = stars
self.score = score
def update_counter(self, stars, score=0):
self.stars += stars
self.score += score
class SkipTurnBubble(Bubble):
def __init__(self, context, **kwargs):
super(SkipTurnBubble, self).__init__(**kwargs)
self.context = context
|
[
"random.shuffle",
"kivy.properties.BooleanProperty",
"kivy.clock.Clock.schedule_once",
"Actor.Actor",
"random.choice",
"random.random",
"kivy.animation.Animation",
"UpDownBubble.UpDownBubble",
"kivy.core.audio.SoundLoader.load",
"kivy.properties.NumericProperty"
] |
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0.7)\n', (15053, 15083), False, 'from kivy.clock import Clock\n'), ((18005, 18029), 'random.shuffle', 'shuffle', (['self.enemy_list'], {}), '(self.enemy_list)\n', (18012, 18029), False, 'from random import choice, shuffle, random\n'), ((21965, 21985), 'random.choice', 'choice', (['[1, 2, 3, 4]'], {}), '([1, 2, 3, 4])\n', (21971, 21985), False, 'from random import choice, shuffle, random\n'), ((7585, 7633), 'kivy.core.audio.SoundLoader.load', 'SoundLoader.load', (['"""assets/audio/player_jump.ogg"""'], {}), "('assets/audio/player_jump.ogg')\n", (7601, 7633), False, 'from kivy.core.audio import SoundLoader\n'), ((9453, 9503), 'kivy.clock.Clock.schedule_once', 'Clock.schedule_once', (['self.add_actor_to_portal', '(0.7)'], {}), '(self.add_actor_to_portal, 0.7)\n', (9472, 9503), False, 'from kivy.clock import Clock\n'), ((10090, 10153), 'kivy.animation.Animation', 'Animation', ([], {'x': '(self.tile_with_player.player.y + 500)', 'duration': '(0.1)'}), '(x=self.tile_with_player.player.y + 500, duration=0.1)\n', (10099, 10153), False, 'from kivy.animation import Animation\n'), ((10739, 10837), 'kivy.animation.Animation', 'Animation', ([], {'x': 'self.tile_with_player.star.x', 'y': '(self.tile_with_player.star.y + 80)', 'duration': '(0.15)'}), '(x=self.tile_with_player.star.x, y=self.tile_with_player.star.y + \n 80, duration=0.15)\n', (10748, 10837), False, 'from kivy.animation import Animation\n'), ((11360, 11455), 'kivy.animation.Animation', 'Animation', ([], {'x': 'self.tile_with_player.key.x', 'y': '(self.tile_with_player.key.y + 80)', 'duration': '(0.15)'}), '(x=self.tile_with_player.key.x, y=self.tile_with_player.key.y + 80,\n duration=0.15)\n', (11369, 11455), False, 'from kivy.animation import Animation\n'), ((18216, 18261), 'kivy.clock.Clock.schedule_once', 'Clock.schedule_once', (['self.end_enemy_turn', '(0.4)'], {}), '(self.end_enemy_turn, 0.4)\n', (18235, 18261), False, 'from kivy.clock import Clock\n'), ((19369, 19433), 'kivy.clock.Clock.schedule_once', 'Clock.schedule_once', (['self.parent.remove_tile_too_far_splash', '(0.6)'], {}), '(self.parent.remove_tile_too_far_splash, 0.6)\n', (19388, 19433), False, 'from kivy.clock import Clock\n'), ((20255, 20311), 'UpDownBubble.UpDownBubble', 'UpDownBubble', (['up_disabled', 'down_disabled'], {'pos': 'bubble_pos'}), '(up_disabled, down_disabled, pos=bubble_pos)\n', (20267, 20311), False, 'from UpDownBubble import UpDownBubble\n'), ((20663, 20706), 'kivy.clock.Clock.schedule_once', 'Clock.schedule_once', (['self.clear_bubble', '(0.1)'], {}), '(self.clear_bubble, 0.1)\n', (20682, 20706), False, 'from kivy.clock import Clock\n'), ((6011, 6061), 'kivy.animation.Animation', 'Animation', ([], {'x': '(self.to_hex.pos[0] + 10)', 'duration': '(0.1)'}), '(x=self.to_hex.pos[0] + 10, duration=0.1)\n', (6020, 6061), False, 'from kivy.animation import Animation\n'), ((6092, 6157), 'kivy.animation.Animation', 'Animation', ([], {'y': '(self.to_hex.pos[1] + 279)', 't': '"""out_back"""', 'duration': '(0.3)'}), "(y=self.to_hex.pos[1] + 279, t='out_back', duration=0.3)\n", (6101, 6157), False, 'from kivy.animation import Animation\n'), ((7741, 7789), 'kivy.core.audio.SoundLoader.load', 'SoundLoader.load', (['"""assets/audio/player_walk.ogg"""'], {}), "('assets/audio/player_walk.ogg')\n", (7757, 7789), False, 'from kivy.core.audio import SoundLoader\n'), ((10629, 10675), 'kivy.core.audio.SoundLoader.load', 'SoundLoader.load', (['"""assets/audio/pick_star.ogg"""'], {}), "('assets/audio/pick_star.ogg')\n", (10645, 10675), False, 'from kivy.core.audio import SoundLoader\n'), ((12600, 12645), 'kivy.core.audio.SoundLoader.load', 'SoundLoader.load', (['"""assets/audio/pick_key.ogg"""'], {}), "('assets/audio/pick_key.ogg')\n", (12616, 12645), False, 'from kivy.core.audio import SoundLoader\n'), ((17672, 17717), 'kivy.animation.Animation', 'Animation', ([], {'x': '(to_hex.pos[0] + 10)', 'duration': '(0.1)'}), '(x=to_hex.pos[0] + 10, duration=0.1)\n', (17681, 17717), False, 'from kivy.animation import Animation\n'), ((17747, 17807), 'kivy.animation.Animation', 'Animation', ([], {'y': '(to_hex.pos[1] + 249)', 't': '"""out_back"""', 'duration': '(0.2)'}), "(y=to_hex.pos[1] + 249, t='out_back', duration=0.2)\n", (17756, 17807), False, 'from kivy.animation import Animation\n'), ((17854, 17922), 'kivy.animation.Animation', 'Animation', ([], {'x': '(to_hex.pos[0] + 10)', 'y': '(to_hex.pos[1] + 249)', 'duration': '(0.2)'}), '(x=to_hex.pos[0] + 10, y=to_hex.pos[1] + 249, duration=0.2)\n', (17863, 17922), False, 'from kivy.animation import Animation\n'), ((6327, 6377), 'kivy.animation.Animation', 'Animation', ([], {'x': '(self.to_hex.pos[0] + 10)', 'duration': '(0.1)'}), '(x=self.to_hex.pos[0] + 10, duration=0.1)\n', (6336, 6377), False, 'from kivy.animation import Animation\n'), ((6408, 6473), 'kivy.animation.Animation', 'Animation', ([], {'y': '(self.to_hex.pos[1] + 279)', 't': '"""out_back"""', 'duration': '(0.3)'}), "(y=self.to_hex.pos[1] + 279, t='out_back', duration=0.3)\n", (6417, 6473), False, 'from kivy.animation import Animation\n'), ((6641, 6719), 'kivy.animation.Animation', 'Animation', ([], {'x': '(self.to_hex.pos[0] + 10)', 'y': '(self.to_hex.pos[1] + 279)', 'duration': '(0.5)'}), '(x=self.to_hex.pos[0] + 10, y=self.to_hex.pos[1] + 279, duration=0.5)\n', (6650, 6719), False, 'from kivy.animation import Animation\n'), ((8278, 8326), 'kivy.core.audio.SoundLoader.load', 'SoundLoader.load', (['"""assets/audio/enemy_death.ogg"""'], {}), "('assets/audio/enemy_death.ogg')\n", (8294, 8326), False, 'from kivy.core.audio import SoundLoader\n'), ((9888, 9896), 'random.random', 'random', ([], {}), '()\n', (9894, 9896), False, 'from random import choice, shuffle, random\n'), ((13072, 13170), 'kivy.animation.Animation', 'Animation', ([], {'x': 'self.tile_with_player.exit.x', 'y': '(self.tile_with_player.exit.y + 80)', 'duration': '(0.15)'}), '(x=self.tile_with_player.exit.x, y=self.tile_with_player.exit.y + \n 80, duration=0.15)\n', (13081, 13170), False, 'from kivy.animation import Animation\n'), ((21046, 21110), 'kivy.animation.Animation', 'Animation', ([], {'y': '(enemy.pos[1] + 34)', 't': '"""in_out_elastic"""', 'duration': '(0.7)'}), "(y=enemy.pos[1] + 34, t='in_out_elastic', duration=0.7)\n", (21055, 21110), False, 'from kivy.animation import Animation\n'), ((21603, 21667), 'kivy.animation.Animation', 'Animation', ([], {'y': '(enemy.pos[1] - 34)', 't': '"""in_out_elastic"""', 'duration': '(0.7)'}), "(y=enemy.pos[1] - 34, t='in_out_elastic', duration=0.7)\n", (21612, 21667), False, 'from kivy.animation import Animation\n'), ((9267, 9275), 'random.random', 'random', ([], {}), '()\n', (9273, 9275), False, 'from random import choice, shuffle, random\n'), ((17009, 17024), 'random.choice', 'choice', (['n_tiles'], {}), '(n_tiles)\n', (17015, 17024), False, 'from random import choice, shuffle, random\n')]
|
import os
import warnings
import itertools
from operator import itemgetter
from nose.tools import assert_equal, with_setup, assert_almost_equal, assert_raises
from random import uniform, seed
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal
try:
from pyne.mesh import Mesh
# see if the source sampling module exists but do not import it
import imp
pyne_info = imp.find_module("pyne")
pyne_mod = imp.load_module("pyne", *pyne_info)
imp.find_module("source_sampling", pyne_mod.__path__)
except ImportError:
from nose.plugins.skip import SkipTest
raise SkipTest
from pyne.source_sampling import Sampler, AliasTable
from pyne.mesh import Mesh, NativeMeshTag
from pyne.r2s import tag_e_bounds
from pymoab import core as mb_core, types
from pyne.utils import QAWarning
warnings.simplefilter("ignore", QAWarning)
# Define modes
DEFAULT_ANALOG = 0
DEFAULT_UNIFORM = 1
DEFAULT_USER = 2
SUBVOXEL_ANALOG = 3
SUBVOXEL_UNIFORM = 4
SUBVOXEL_USER = 5
def try_rm_file(filename):
return lambda: os.remove(filename) if os.path.exists(filename) else None
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_single_hex_tag_names_map():
"""This test tests uniform sampling within a single hex volume element.
This is done by dividing the volume element in 4 smaller hex and ensuring
that each sub-hex is sampled equally.
"""
seed(1953)
m = Mesh(
structured=True, structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]], mats=None
)
m.src = NativeMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
e_bounds = np.array([0, 0.5, 1.0])
m = tag_e_bounds(m, e_bounds)
m.bias = NativeMeshTag(2, float)
cell_fracs = np.zeros(
2,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0), (1, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
m.bias[:] = [[1.0, 2.0], [3.0, 3.0]]
filename = "sampling_mesh.h5m"
m.write_hdf5(filename)
# right condition with e_bounds in 'source.h5m'
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, DEFAULT_ANALOG)
# right condition with e_bounds provided by both 'e_bounds' and 'source.h5m'
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, DEFAULT_ANALOG)
# src_tag_name not given
tag_names = {
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_ANALOG)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_UNIFORM)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_USER)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_ANALOG)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_UNIFORM)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_USER)
# bias_tag_name not given
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_USER)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_USER)
# cell_number_tag_name not given
tag_names = {
"src_tag_name": "src",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_ANALOG)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_UNIFORM)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_USER)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_ANALOG)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_UNIFORM)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_USER)
# cell_fracs_tag_name not given
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"e_bounds_tag_name": "e_bounds",
}
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_ANALOG)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_UNIFORM)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, DEFAULT_USER)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_ANALOG)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_UNIFORM)
assert_raises(ValueError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_USER)
# wrong bias_tag data (non-zero source_density biased to zero -> NAN weight)
m.src = NativeMeshTag(2, float)
m.src[:] = [[1.0, 1.0]]
m.bias = NativeMeshTag(2, float)
m.bias[:] = [[0.0, 0.0]]
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"bias_tag_name": "bias",
"e_bounds_tag_name": "e_bounds",
}
assert_raises(RuntimeError, Sampler, filename, tag_names, e_bounds, SUBVOXEL_USER)
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_analog_single_hex():
"""This test tests that particles of sampled evenly within the phase-space
of a single mesh volume element with one energy group in an analog sampling
scheme. This done by dividing each dimension (x, y, z, E) in half, then
sampling particles and tallying on the basis of which of the 2^4 = 8 regions
of phase space the particle is born into.
"""
seed(1953)
m = Mesh(structured=True, structured_coords=[[0, 1], [0, 1], [0, 1]], mats=None)
m.src = NativeMeshTag(1, float)
m.src[0] = 1.0
cell_fracs = np.zeros(
1,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
e_bounds = np.array([0, 1.0])
m = tag_e_bounds(m, e_bounds)
filename = "sampling_mesh.h5m"
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, DEFAULT_ANALOG)
num_samples = 5000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(num_divs, num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
assert_equal(s.w, 1.0) # analog: all weights must be one
tally[
int(s.x * num_divs),
int(s.y * num_divs),
int(s.z * num_divs),
int(s.e * num_divs),
] += score
# Test that each half-space of phase space (e.g. x > 0.5) is sampled about
# half the time.
for i in range(0, 4):
for j in range(0, 2):
assert abs(np.sum(np.rollaxis(tally, i)[j, :, :, :]) - 0.5) < 0.05
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_analog_multiple_hex():
"""This test tests that particle are sampled uniformly from a uniform source
defined on eight mesh volume elements in two energy groups. This is done
using the exact same method ass test_analog_multiple_hex.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0, 0.5, 1], [0, 0.5, 1], [0, 0.5, 1]],
mats=None,
)
m.src = NativeMeshTag(2, float)
m.src[:] = np.ones(shape=(8, 2))
cell_fracs = np.zeros(
8,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [
(0, 11, 1.0, 0.0),
(1, 11, 1.0, 0.0),
(2, 11, 1.0, 0.0),
(3, 11, 1.0, 0.0),
(4, 11, 1.0, 0.0),
(5, 11, 1.0, 0.0),
(6, 11, 1.0, 0.0),
(7, 11, 1.0, 0.0),
]
m.tag_cell_fracs(cell_fracs)
filename = "sampling_mesh.h5m"
e_bounds = np.array([0, 0.5, 1])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, DEFAULT_ANALOG)
num_samples = 5000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(num_divs, num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth([uniform(0, 1) for x in range(6)])
assert_equal(s.w, 1.0)
tally[
int(s.x * num_divs),
int(s.y * num_divs),
int(s.z * num_divs),
int(s.e * num_divs),
] += score
for i in range(0, 4):
for j in range(0, 2):
halfspace_sum = np.sum(np.rollaxis(tally, i)[j, :, :, :])
assert abs(halfspace_sum - 0.5) / 0.5 < 0.1
@with_setup(None, try_rm_file("tet.h5m"))
def test_analog_single_tet():
"""This test tests uniform sampling within a single tetrahedron. This is
done by dividing the tetrahedron in 4 smaller tetrahedrons and ensuring
that each sub-tet is sampled equally.
"""
seed(1953)
mesh = mb_core.Core()
v1 = [0.0, 0.0, 0.0]
v2 = [1.0, 0.0, 0.0]
v3 = [0.0, 1.0, 0.0]
v4 = [0.0, 0.0, 1.0]
verts = mesh.create_vertices([v1, v2, v3, v4])
mesh.create_element(types.MBTET, verts)
m = Mesh(structured=False, mesh=mesh)
m.src = NativeMeshTag(1, float)
m.src[:] = np.array([1])
filename = "tet.h5m"
e_bounds = np.array([0.0, 1.0])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
center = m.ve_center(list(m.iter_ve())[0])
subtets = [
[center, v1, v2, v3],
[center, v1, v2, v4],
[center, v1, v3, v4],
[center, v2, v3, v4],
]
tag_names = {"src_tag_name": "src", "e_bounds_tag_name": "e_bounds"}
sampler = Sampler(filename, tag_names, np.array([0.0, 1.0]), DEFAULT_ANALOG)
num_samples = 5000
score = 1.0 / num_samples
tally = np.zeros(shape=(4))
for i in range(num_samples):
s = sampler.particle_birth([uniform(0.0, 1.0) for x in range(6)])
assert_equal(s.w, 1.0)
for i, tet in enumerate(subtets):
if point_in_tet(tet, [s.x, s.y, s.z]):
tally[i] += score
break
for t in tally:
assert abs(t - 0.25) / 0.25 < 0.2
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_uniform():
"""This test tests that the uniform biasing scheme:
1. Samples space uniformly. This is checked using the same method
described in test_analog_single_hex().
2. Adjusts weights accordingly. Sample calculations are provided in Case 1
in the Theory Manual.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0, 3, 3.5], [0.0, 1.0], [0.0, 1.0]],
mats=None,
)
m.src = NativeMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
e_bounds = np.array([0.0, 0.5, 1.0])
filename = "sampling_mesh.h5m"
cell_fracs = np.zeros(
2,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0), (1, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, DEFAULT_UNIFORM)
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
num_e = 2
spatial_tally = np.zeros(shape=(num_divs, num_divs, num_divs))
e_tally = np.zeros(shape=(4)) # number of phase space groups
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s.x < 3.0:
assert_almost_equal(s.w, 0.7) # hand calcs
else:
assert_almost_equal(s.w, 2.8) # hand calcs
spatial_tally[
int(s.x * num_divs / 3.5),
int(s.y * num_divs / 1.0),
int(s.z * num_divs / 1.0),
] += score
if s.x < 3 and s.e < 0.5:
e_tally[0] += score
elif s.x < 3 and s.e > 0.5:
e_tally[1] += score
if s.x > 3 and s.e < 0.5:
e_tally[2] += score
if s.x > 3 and s.e > 0.5:
e_tally[3] += score
for i in range(0, 3):
for j in range(0, 2):
halfspace_sum = np.sum(np.rollaxis(spatial_tally, i)[j, :, :])
assert abs(halfspace_sum - 0.5) / 0.5 < 0.1
expected_e_tally = [4.0 / 7, 2.0 / 7, 3.0 / 28, 1.0 / 28] # hand calcs
for i in range(4):
assert abs(e_tally[i] - expected_e_tally[i]) / expected_e_tally[i] < 0.1
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_single_hex_single_subvoxel_analog():
"""This test tests that particles of sampled evenly within the phase-space
of a single mesh volume element (also a sub-voxel) with one energy group
in an analog sampling scheme. This done by dividing each dimension
(x, y, z, E) in half, then sampling particles and tallying on the basis of
which of the 2^4 = 16 regions of phase space the particle is born into.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]],
mats=None,
)
m.src = NativeMeshTag(1, float)
m.src[0] = 1.0
cell_fracs = np.zeros(
1,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
filename = "sampling_mesh.h5m"
e_bounds = np.array([0.0, 1.0])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_ANALOG)
num_samples = 5000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(num_divs, num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0.0, 1.0) for x in range(6)]))
assert_equal(s.w, 1.0) # analog: all weights must be one
assert_equal(s.cell_list[0], 11) # analog: the cell number
tally[
int(s.x * num_divs),
int(s.y * num_divs),
int(s.z * num_divs),
int(s.e * num_divs),
] += score
# Test that each half-space of phase space (e.g. x > 0.5) is sampled about
# half the time.
for i in range(0, 4):
for j in range(0, 2):
assert abs(np.sum(np.rollaxis(tally, i)[j, :, :, :]) - 0.5) < 0.05
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_single_hex_multiple_subvoxel_analog():
"""This test tests that particles of sampled analog within the phase-space
of a single mesh volume element but multiple sub-voxels with one energy
group in an analog sampling scheme. Then sampling particles and tallying
the particles and check the probability of particles born in each
sub-voxel and the cell_number.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]],
mats=None,
)
m.src = NativeMeshTag(3, float)
m.src[:] = np.empty(shape=(1, 3))
m.src[0] = [0, 0.2, 0.8]
cell_fracs = np.zeros(
3,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 0.4, 0.0), (0, 12, 0.3, 0.0), (0, 13, 0.3, 0.0)]
m.tag_cell_fracs(cell_fracs) # cell_fracs will be sorted
filename = "sampling_mesh.h5m"
e_bounds = np.array([0, 1])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_ANALOG)
num_samples = 50000
score = 1.0 / num_samples
num_divs = 2
tally = [0.0] * 3
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
assert_equal(s.w, 1.0) # analog: all weights must be one
if s.cell_list[0] == 11:
tally[0] += score
elif s.cell_list[0] == 12:
tally[1] += score
elif s.cell_list[0] == 13:
tally[2] += score
# Test that each source particle in each cell has right frequency
assert_equal(tally[0], 0.0)
assert abs(tally[1] - 0.2) / 0.2 < 0.05
assert abs(tally[2] - 0.8) / 0.8 < 0.05
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_multiple_hex_multiple_subvoxel_analog():
"""This test tests that particle are sampled analog from a uniform source
defined on eight mesh volume elements in two energy groups.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0, 0.5, 1], [0, 0.5, 1], [0, 0.5, 1]],
mats=None,
)
m.src = NativeMeshTag(2, float)
m.src[:] = np.ones(shape=(8, 2))
cell_fracs = np.zeros(
8,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [
(0, 1, 1.0, 0.0),
(1, 2, 1.0, 0.0),
(2, 3, 1.0, 0.0),
(3, 4, 1.0, 0.0),
(4, 5, 1.0, 0.0),
(5, 6, 1.0, 0.0),
(6, 7, 1.0, 0.0),
(7, 8, 1.0, 0.0),
]
m.tag_cell_fracs(cell_fracs)
filename = "sampling_mesh.h5m"
e_bounds = np.array([0, 0.5, 1])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_ANALOG)
num_samples = 5000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(num_divs, num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth([uniform(0, 1) for x in range(6)])
assert_equal(s.w, 1.0)
assert_equal(
s.cell_list[0],
4 * int(s.x * num_divs) + 2 * int(s.y * num_divs) + int(s.z * num_divs) + 1,
)
tally[
int(s.x * num_divs),
int(s.y * num_divs),
int(s.z * num_divs),
int(s.e * num_divs),
] += score
for i in range(0, 4):
for j in range(0, 2):
halfspace_sum = np.sum(np.rollaxis(tally, i)[j, :, :, :])
assert abs(halfspace_sum - 0.5) / 0.5 < 0.1
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_single_hex_subvoxel_uniform():
"""This test tests that particles of sampled evenly within the phase-space
of a single mesh volume element with one energy group in an uniform sampling
scheme. This done by dividing each dimension (x, y, z, E) in half, then
sampling particles and tallying on the basis of which of the 2^4 = 8 regions
of phase space the particle is born into.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]],
mats=None,
)
m.src = NativeMeshTag(1, float)
m.src[0] = 1.0
cell_fracs = np.zeros(
1,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
filename = "sampling_mesh.h5m"
e_bounds = np.array([0.0, 1.0])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_UNIFORM)
num_samples = 5000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(num_divs, num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0.0, 1.0) for x in range(6)]))
assert_equal(s.w, 1.0) # analog: all weights must be one
assert_equal(s.cell_list[0], 11) # analog: the cell number
tally[
int(s.x * num_divs),
int(s.y * num_divs),
int(s.z * num_divs),
int(s.e * num_divs),
] += score
# Test that each half-space of phase space (e.g. x > 0.5) is sampled about
# half the time.
for i in range(0, 4):
for j in range(0, 2):
assert abs(np.sum(np.rollaxis(tally, i)[j, :, :, :]) - 0.5) < 0.05
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_single_hex_multiple_subvoxel_uniform():
"""This test tests that particles of sampled evenly within the phase-space
of a single mesh volume element with one energy group in an uniform sampling
scheme. This done by dividing each dimension (x, y, z, E) in half, then
sampling particles and tallying on the basis of which of the 2^4 = 8 regions
of phase space the particle is born into.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]],
mats=None,
)
m.src = NativeMeshTag(3, float)
m.src[:] = np.empty(shape=(1, 3))
m.src[0] = [0, 0.2, 0.8]
cell_fracs = np.zeros(
3,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 0.4, 0.0), (0, 12, 0.3, 0.0), (0, 13, 0.3, 0.0)]
m.tag_cell_fracs(cell_fracs)
filename = "sampling_mesh.h5m"
e_bounds = np.array([0.0, 1.0])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_UNIFORM)
num_samples = 5000
score = 1.0 / num_samples
num_divs = 2
tally = [0.0] * 3
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0.0, 1.0) for x in range(6)]))
if s.cell_list[0] == 11:
tally[0] += score
if s.cell_list[0] == 12:
tally[1] += score
# analog: all weights must be one
assert abs(s.w - 0.4) / 0.4 < 0.05
if s.cell_list[0] == 13:
tally[2] += score
assert abs(s.w - 1.6) / 1.6 < 0.05
# Test that each source particle in each cell has right frequency
assert_equal(tally[0], 0.0)
assert abs(tally[1] - 0.5) < 0.05
assert abs(tally[2] - 0.5) < 0.05
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_multiple_hex_multiple_subvoxel_uniform():
"""This test tests that particle are sampled uniformly from a uniform source
defined on eight mesh volume elements in two energy groups.
"""
seed(1953)
m = Mesh(
structured=True,
structured_coords=[[0, 0.5, 1], [0, 0.5, 1], [0, 0.5, 1]],
mats=None,
)
m.src = NativeMeshTag(2, float)
m.src[:] = np.empty(shape=(8, 2), dtype=float)
m.src[:] = [[0, 0], [1, 0], [0, 0], [2, 0], [0, 0], [3, 0], [0, 0], [4, 0]]
cell_fracs = np.zeros(
8,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [
(0, 0, 1.0, 0.0),
(1, 1, 1.0, 0.0),
(2, 2, 1.0, 0.0),
(3, 3, 1.0, 0.0),
(4, 4, 1.0, 0.0),
(5, 5, 1.0, 0.0),
(6, 6, 1.0, 0.0),
(7, 7, 1.0, 0.0),
]
empty_cells = [0, 2, 4, 6]
m.tag_cell_fracs(cell_fracs)
filename = "sampling_mesh.h5m"
e_bounds = np.array([0, 0.5, 1])
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_UNIFORM)
num_samples = 50000
score = 1.0 / num_samples
num_divs = 2
tally = [0.0] * 8
for i in range(num_samples):
s = sampler.particle_birth([uniform(0, 1) for x in range(6)])
# check the cell_number
assert_equal(
s.cell_list[0],
4 * int(s.x * num_divs) + 2 * int(s.y * num_divs) + int(s.z * num_divs),
)
# check the weight of each subvoxel
if s.cell_list[0] not in empty_cells:
# weight for cell 1, 3, 5, 7 should be: 0.4, 0.8, 1.2, 1.6
exp_w = (s.cell_list[0] + 1) / 2 * 0.4
out_w = s.w
assert abs(out_w - exp_w) / exp_w < 0.05 # hand calculate
# count the tally
tally[s.cell_list[0]] += score
# check the real sample rate
for i, item in enumerate(tally):
if i not in empty_cells:
assert abs(item - 0.25) / 0.25 < 0.05
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_bias():
"""This test tests that a user-specified biasing scheme:
1. Samples space uniformly according to the scheme.
2. Adjusts weights accordingly. Sample calculations are provided in Case 2
in the Theory Manual.
"""
seed(1953)
m = Mesh(
structured=True, structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]], mats=None
)
m.src = NativeMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
e_bounds = np.array([0, 0.5, 1.0])
m.bias = NativeMeshTag(2, float)
m.bias[:] = [[1.0, 2.0], [3.0, 3.0]]
cell_fracs = np.zeros(
2,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0), (1, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"bias_tag_name": "bias",
"e_bounds_tag_name": "e_bounds",
}
filename = "sampling_mesh.h5m"
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
sampler = Sampler(filename, tag_names, e_bounds, DEFAULT_USER)
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
tally = np.zeros(shape=(4))
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s.x < 3:
if s.e < 0.5:
assert_almost_equal(s.w, 1.6) # hand calcs
tally[0] += score
else:
assert_almost_equal(s.w, 0.4) # hand calcs
tally[1] += score
else:
if s.e < 0.5:
assert_almost_equal(s.w, 2.4) # hand calcs
tally[2] += score
else:
assert_almost_equal(s.w, 0.8) # hand calcs
tally[3] += score
expected_tally = [0.25, 0.5, 0.125, 0.125] # hand calcs
for a, b in zip(tally, expected_tally):
assert abs(a - b) / b < 0.25
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_bias_spatial():
"""This test tests a user-specified biasing scheme for which the only 1
bias group is supplied for a source distribution containing two energy
groups. This bias group is applied to both energy groups. In this test,
the user-supplied bias distribution that was choosen, correspondes to
uniform sampling, so that results can be checked against Case 1 in the
theory manual.
"""
seed(1953)
m = Mesh(
structured=True, structured_coords=[[0, 3, 3.5], [0, 1], [0, 1]], mats=None
)
m.src = NativeMeshTag(2, float)
m.src[:] = [[2.0, 1.0], [9.0, 3.0]]
m.bias = NativeMeshTag(1, float)
m.bias[:] = [1, 1]
e_bounds = np.array([0, 0.5, 1.0])
filename = "sampling_mesh.h5m"
cell_fracs = np.zeros(
2,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [(0, 11, 1.0, 0.0), (1, 11, 1.0, 0.0)]
m.tag_cell_fracs(cell_fracs)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"bias_tag_name": "bias",
"e_bounds_tag_name": "e_bounds",
}
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
sampler = Sampler(filename, tag_names, e_bounds, DEFAULT_USER)
num_samples = 10000
score = 1.0 / num_samples
num_divs = 2
num_e = 2
spatial_tally = np.zeros(shape=(num_divs, num_divs, num_divs))
e_tally = np.zeros(shape=(4)) # number of phase space groups
for i in range(num_samples):
s = sampler.particle_birth(np.array([uniform(0, 1) for x in range(6)]))
if s.x < 3.0:
assert_almost_equal(s.w, 0.7) # hand calcs
else:
assert_almost_equal(s.w, 2.8) # hand calcs
spatial_tally[
int(s.x * num_divs / 3.5),
int(s.y * num_divs / 1.0),
int(s.z * num_divs / 1.0),
] += score
if s.x < 3 and s.e < 0.5:
e_tally[0] += score
elif s.x < 3 and s.e > 0.5:
e_tally[1] += score
if s.x > 3 and s.e < 0.5:
e_tally[2] += score
if s.x > 3 and s.e > 0.5:
e_tally[3] += score
for i in range(0, 3):
for j in range(0, 2):
halfspace_sum = np.sum(np.rollaxis(spatial_tally, i)[j, :, :])
assert abs(halfspace_sum - 0.5) / 0.5 < 0.1
expected_e_tally = [4.0 / 7, 2.0 / 7, 3.0 / 28, 1.0 / 28] # hand calcs
for i in range(4):
assert abs(e_tally[i] - expected_e_tally[i]) / expected_e_tally[i] < 0.1
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_subvoxel_multiple_hex_bias_1():
"""This test tests that particle are sampled from a biased source
defined on two voxels (2*2 = 4 sub-voxels) with the biased tag length of 1.
"""
seed(1953)
# mesh contains two voxels. 2 * 1 * 1 = 2
m = Mesh(
structured=True, structured_coords=[[0, 0.5, 1], [0, 1], [0, 1]], mats=None
)
# max_num_cells = 2. 4 sub-voxels
cell_fracs = np.zeros(
4,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [
(0, 11, 0.5, 0.0),
(0, 12, 0.5, 0.0),
(1, 21, 0.5, 0.0),
(1, 22, 0.5, 0.0),
]
m.tag_cell_fracs(cell_fracs)
# the photon emitting rate of 4 sub-voxels is 0.1, 0.2, 0.3, 0.4
m.src = NativeMeshTag(4, float)
m.src[:] = np.empty(shape=(2, 4), dtype=float)
m.src[:] = [[0.05, 0.05, 0.10, 0.10], [0.15, 0.15, 0.20, 0.20]]
e_bounds = np.array([0, 0.5, 1.0])
# bias, tag size = 1
m.bias = NativeMeshTag(1, float)
m.bias[:] = [[0.4], [0.6]]
filename = "sampling_mesh.h5m"
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"bias_tag_name": "bias",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_USER)
num_samples = 50000
score = 1.0 / num_samples
num_divs = 2
# tally shape (v, c, e)
tally = np.zeros(shape=(num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth([uniform(0, 1) for x in range(6)])
vid = s.cell_list[0] // 10 - 1
cid = s.cell_list[0] % 10 - 1
eid = 0 if s.e < 0.5 else 1
# check the cell_number
if s.x < 0.5:
assert s.cell_list[0] in [11, 12]
if s.x > 0.5:
assert s.cell_list[0] in [21, 22]
# check the weight of each subvoxel
if vid == 0:
assert abs(s.w - 0.746) / 0.746 < 0.05
if vid == 1:
assert abs(s.w - 1.163) / 1.163 < 0.05
# count the tally
tally[vid, cid, eid] += score
# check the real sample rate
# exp_tally calculated by hand
exp_tally = np.zeros(shape=(2, 2, 2))
exp_tally[:] = [[[0.067, 0.067], [0.133, 0.133]], [[0.129, 0.129], [0.171, 0.171]]]
for v in range(2):
for c in range(2):
for e in range(2):
assert (
abs(tally[v, c, e] - exp_tally[v, c, e]) / exp_tally[v, c, e] < 0.05
)
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_subvoxel_multiple_hex_bias_max_num_cells_num_e_groups():
"""This test tests that particle are sampled from a biased source
defined on two voxels (2*2 = 4 sub-voxels) with the biased tag length
of max_num_cells*num_e_group.
"""
seed(1953)
# mesh contains two voxels. 2 * 1 * 1 = 2
m = Mesh(
structured=True, structured_coords=[[0, 0.5, 1], [0, 1], [0, 1]], mats=None
)
# max_num_cells = 2. 4 sub-voxels
cell_fracs = np.zeros(
4,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [
(0, 11, 0.5, 0.0),
(0, 12, 0.5, 0.0),
(1, 21, 0.5, 0.0),
(1, 22, 0.5, 0.0),
]
m.tag_cell_fracs(cell_fracs)
# the photon emitting rate of 4 sub-voxels is 0.1, 0.2, 0.3, 0.4
m.src = NativeMeshTag(4, float)
m.src[:] = np.empty(shape=(2, 4), dtype=float)
m.src[:] = [[0.125, 0.125, 0.125, 0.125], [0.125, 0.125, 0.125, 0.125]]
e_bounds = np.array([0, 0.5, 1.0])
# bias, tag size = 1
m.bias = NativeMeshTag(4, float)
m.bias[:] = [[0.125, 0.125, 0.1, 0.15], [0.1, 0.1, 0.15, 0.15]]
filename = "sampling_mesh.h5m"
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"bias_tag_name": "bias",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_USER)
num_samples = 50000
score = 1.0 / num_samples
num_divs = 2
# tally shape (v, c, e)
tally = np.zeros(shape=(num_divs, num_divs, num_divs))
exp_wgt = np.zeros(shape=(num_divs, num_divs, num_divs))
exp_wgt[:] = [[[1.0, 1.0], [1.25, 0.83]], [[1.25, 1.25], [0.83, 0.83]]]
for i in range(num_samples):
s = sampler.particle_birth([uniform(0, 1) for x in range(6)])
vid = s.cell_list[0] // 10 - 1
cid = s.cell_list[0] % 10 - 1
eid = 0 if s.e < 0.5 else 1
# check the cell_number
if s.x < 0.5:
assert s.cell_list[0] in [11, 12]
if s.x > 0.5:
assert s.cell_list[0] in [21, 22]
# check the weight of each subvoxel
assert abs(s.w - exp_wgt[vid, cid, eid]) / exp_wgt[vid, cid, eid] < 0.05
# count the tally
tally[vid, cid, eid] += score
# check the real sample rate
exp_tally = np.zeros(shape=(2, 2, 2))
exp_tally[:] = [[[0.125, 0.125], [0.100, 0.150]], [[0.100, 0.100], [0.150, 0.150]]]
for v in range(2):
for c in range(2):
for e in range(2):
assert (
abs(tally[v, c, e] - exp_tally[v, c, e]) / exp_tally[v, c, e] < 0.05
)
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def test_subvoxel_multiple_hex_bias_e_groups():
"""This test tests that particle are sampled from a biased source
defined on two voxels (2*2 = 4 sub-voxels) with the biased tag length
of energy groups.
"""
seed(1953)
# mesh contains two voxels. 2 * 1 * 1 = 2
m = Mesh(
structured=True, structured_coords=[[0, 0.5, 1], [0, 1], [0, 1]], mats=None
)
# max_num_cells = 2. 4 sub-voxels
cell_fracs = np.zeros(
4,
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = [
(0, 11, 0.5, 0.0),
(0, 12, 0.5, 0.0),
(1, 21, 0.5, 0.0),
(1, 22, 0.5, 0.0),
]
m.tag_cell_fracs(cell_fracs)
# the photon emitting rate of 4 sub-voxels is 0.1, 0.2, 0.3, 0.4
m.src = NativeMeshTag(4, float)
m.src[:] = np.empty(shape=(2, 4), dtype=float)
m.src[:] = [[0.05, 0.05, 0.10, 0.10], [0.15, 0.15, 0.20, 0.20]]
e_bounds = np.array([0, 0.5, 1.0])
# bias, tag size = 1
m.bias = NativeMeshTag(2, float)
m.bias[:] = [[0.1, 0.3], [0.2, 0.4]]
filename = "sampling_mesh.h5m"
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"bias_tag_name": "bias",
"e_bounds_tag_name": "e_bounds",
}
sampler = Sampler(filename, tag_names, e_bounds, SUBVOXEL_USER)
num_samples = 50000
score = 1.0 / num_samples
num_divs = 2
# tally shape (v, c, e)
tally = np.zeros(shape=(num_divs, num_divs, num_divs))
for i in range(num_samples):
s = sampler.particle_birth([uniform(0, 1) for x in range(6)])
vid = s.cell_list[0] // 10 - 1
cid = s.cell_list[0] % 10 - 1
eid = 0 if s.e < 0.5 else 1
# check the cell_number
if s.x < 0.5:
assert s.cell_list[0] in [11, 12]
if s.x > 0.5:
assert s.cell_list[0] in [21, 22]
# check the weight of each subvoxel
if vid == 0 and eid == 0:
assert abs(s.w - 1.5) / 1.5 < 0.05
if vid == 0 and eid == 1:
assert abs(s.w - 0.5) / 0.5 < 0.05
if vid == 1 and eid == 0:
assert abs(s.w - 1.75) / 1.75 < 0.05
if vid == 1 and eid == 1:
assert abs(s.w - 0.875) / 0.875 < 0.05
# count the tally
tally[vid, cid, eid] += score
# check the real sample rate
exp_tally = np.zeros(shape=(2, 2, 2))
exp_tally[:] = [
[[0.0333, 0.1000], [0.0667, 0.2000]],
[[0.0857, 0.1714], [0.1143, 0.2286]],
]
for v in range(2):
for c in range(2):
for e in range(2):
assert (
abs(tally[v, c, e] - exp_tally[v, c, e]) / exp_tally[v, c, e] < 0.05
)
def test_alias_table():
"""This tests that the AliasTable class produces samples in the ratios
consistant with the supplied PDF.
"""
seed(1953)
pdf = np.array([0.1, 0.2, 0.7])
at = AliasTable(pdf)
num_samples = 50000
score = 1.0 / num_samples
tally = np.zeros(shape=(3))
for i in range(num_samples):
s = at.sample_pdf(uniform(0, 1), uniform(0, 1))
tally[s] += score
for i in range(0, 3):
assert abs(tally[i] - pdf[i]) / pdf[i] < 0.05
def point_in_tet(t, p):
"""This function determines if some point <p> lies within some tetrahedron
<t> using the method described here:
http://steve.hollasch.net/cgindex/geometry/ptintet.html
"""
matricies = [
np.array(
[
[t[0][0], t[0][1], t[0][2], 1],
[t[1][0], t[1][1], t[1][2], 1],
[t[2][0], t[2][1], t[2][2], 1],
[t[3][0], t[3][1], t[3][2], 1],
]
),
np.array(
[
[p[0], p[1], p[2], 1],
[t[1][0], t[1][1], t[1][2], 1],
[t[2][0], t[2][1], t[2][2], 1],
[t[3][0], t[3][1], t[3][2], 1],
]
),
np.array(
[
[t[0][0], t[0][1], t[0][2], 1],
[p[0], p[1], p[2], 1],
[t[2][0], t[2][1], t[2][2], 1],
[t[3][0], t[3][1], t[3][2], 1],
]
),
np.array(
[
[t[0][0], t[0][1], t[0][2], 1],
[t[1][0], t[1][1], t[1][2], 1],
[p[0], p[1], p[2], 1],
[t[3][0], t[3][1], t[3][2], 1],
]
),
np.array(
[
[t[0][0], t[0][1], t[0][2], 1],
[t[1][0], t[1][1], t[1][2], 1],
[t[2][0], t[2][1], t[2][2], 1],
[p[0], p[1], p[2], 1],
]
),
]
determinates = [np.linalg.det(x) for x in matricies]
return all(x >= 0 for x in determinates) or all(x < 0 for x in determinates)
def test_template_examples():
"""
An example of using source_sampling test template to do the test
"""
# DEFAULT and SUBVOXEL
for mode in (
DEFAULT_ANALOG,
DEFAULT_UNIFORM,
DEFAULT_USER,
SUBVOXEL_ANALOG,
SUBVOXEL_UNIFORM,
SUBVOXEL_USER,
):
for num_e_groups in (1, 2):
# num_bias_groups could be:
# 1, num_e_groups, and max_num_cells*num_e_groups
# test case: 1 voxel, 1 subvoxel
cell_fracs_list = [(0, 1, 1.0, 0.0)]
src_tag = [[1.0] * num_e_groups]
if mode == DEFAULT_USER or mode == SUBVOXEL_USER:
for num_bias_groups in (1, num_e_groups):
bias_tag = [[1.0] * num_bias_groups]
_source_sampling_test_template(
mode, cell_fracs_list, src_tag, bias_tag
)
else:
_source_sampling_test_template(mode, cell_fracs_list, src_tag)
# test case: 1 voxel, 2 subvoxels
# create src and cell_fracs tag data
if mode in (0, 1, 2):
src_tag = [[1.0] * num_e_groups]
cell_fracs_list = [(0, 1, 1.0, 0.0)]
elif mode in (3, 4, 5):
src_tag = [[1.0, 1.0] * num_e_groups]
cell_fracs_list = [(0, 1, 0.5, 0.0), (0, 2, 0.5, 0.0)]
if mode == DEFAULT_USER:
for num_bias_groups in (1, num_e_groups):
bias_tag = [[1.0] * num_bias_groups]
_source_sampling_test_template(
mode, cell_fracs_list, src_tag, bias_tag
)
elif mode == SUBVOXEL_USER:
for num_bias_groups in (1, num_e_groups, 2 * num_e_groups):
bias_tag = [[1.0] * num_bias_groups]
_source_sampling_test_template(
mode, cell_fracs_list, src_tag, bias_tag
)
else:
_source_sampling_test_template(mode, cell_fracs_list, src_tag)
# test case: 2 voxel, 2 subvoxels
cell_fracs_list = [(0, 1, 1.0, 0.0), (1, 2, 1.0, 0.0)]
src_tag = [[1.0] * num_e_groups, [1.0] * num_e_groups]
if mode == DEFAULT_USER or mode == SUBVOXEL_USER:
for num_bias_groups in (1, num_e_groups):
bias_tag = [[1.0] * num_bias_groups, [1.0] * num_bias_groups]
_source_sampling_test_template(
mode, cell_fracs_list, src_tag, bias_tag
)
else:
_source_sampling_test_template(mode, cell_fracs_list, src_tag)
# test case: 2 voxel, 4 subvoxels
# create src and cell_fracs tag data
if mode in (0, 1, 2):
src_tag = [[1.0] * num_e_groups, [1.0] * num_e_groups]
cell_fracs_list = [(0, 1, 1.0, 0.0), (1, 2, 1.0, 0.0)]
elif mode in (3, 4, 5):
src_tag = [[1.0, 1.0] * num_e_groups, [1.0, 1.0] * num_e_groups]
cell_fracs_list = [
(0, 1, 0.5, 0.0),
(0, 2, 0.5, 0.0),
(1, 3, 0.5, 0.0),
(1, 4, 0.5, 0.0),
]
if mode == DEFAULT_USER:
for num_bias_groups in (1, num_e_groups):
bias_tag = [[1.0] * num_bias_groups, [1.0] * num_bias_groups]
_source_sampling_test_template(
mode, cell_fracs_list, src_tag, bias_tag
)
elif mode == SUBVOXEL_USER:
for num_bias_groups in (1, num_e_groups, 2 * num_e_groups):
bias_tag = [[1.0] * num_bias_groups, [1.0] * num_bias_groups]
_source_sampling_test_template(
mode, cell_fracs_list, src_tag, bias_tag
)
else:
_source_sampling_test_template(mode, cell_fracs_list, src_tag)
def _get_num_ve_sve_and_max_num_cells(cell_fracs):
"""
Calculate the num_ve, num_sve and max_num_cells
Parameters
----------
cell_fracs : structured array, optional
A sorted, one dimensional array,
each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
Returns
-------
num_ve : int
Number of the total voxels
num_sve : int
Number of the total subvoxels, eqaul to or greater than num_ve
max_num_cells : int
Max number of cells (subvoxels) in a voxel
"""
num_sve = len(cell_fracs)
num_ve = len(set(cell_fracs["idx"]))
max_num_cells = -1
for i in range(num_sve):
max_num_cells = max(max_num_cells, len(cell_fracs[cell_fracs["idx"] == i]))
return num_ve, num_sve, max_num_cells
def _create_mesh_via_num_ve(num_ve):
"""
This function creates mesh from number of voxels
Parameters
----------
num_ve : int
Number of voxels
Returns
-------
mesh. MOAB mesh.
"""
x_bounds = [v * 1.0 / (num_ve) for v in range(num_ve + 1)]
mesh = Mesh(
structured=True, structured_coords=[x_bounds, [0, 1], [0, 1]], mats=None
)
return mesh
def _cal_pdf_and_biased_pdf(cell_fracs, src_tag, bias_tag=None):
"""
This function calcualtes the normalized pdf of source.
Parameters
----------
cell_fracs : structured array
A sorted, one dimensional array,
each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
src_tag : numpy array
An one or two dimentional array contains data of the source tag.
bias_tag : numpy array, optional
An one or two dimentional array contains data of bias tag
Returns
-------
pdf : numpy array
A three dimentional numpy array, shape=(num_ve, num_sve, num_e_groups)
biased_pdf : numpy array
A three dimentional numpy array, shape=(num_ve, num_sve, num_e_groups)
"""
num_ve, num_sve, max_num_cells = _get_num_ve_sve_and_max_num_cells(cell_fracs)
num_e_groups = len(src_tag[0]) // max_num_cells
pdf = np.empty(shape=(num_ve, max_num_cells, num_e_groups), dtype=np.float64)
pdf.fill(0.0)
for vid in range(num_ve):
for svid in range(max_num_cells):
for eid in range(num_e_groups):
pdf[vid, svid, eid] = (
src_tag[vid][svid * num_e_groups + eid]
* cell_fracs[vid * max_num_cells + svid]["vol_frac"]
)
# normalize
pdf = pdf / pdf.sum()
# calculate biased_pdf
biased_pdf = np.empty(shape=(num_ve, max_num_cells, num_e_groups), dtype=np.float64)
biased_pdf.fill(0.0)
# set up bias_array to proper value
if bias_tag == None:
# UNIFORM mode, set default bias_group and bias_array
num_bias_groups = 1
bias_array = np.empty(
shape=(num_ve, max_num_cells, num_e_groups), dtype=np.float64
)
for vid in range(num_ve):
for svid in range(max_num_cells):
for eid in range(num_e_groups):
bias_array[vid, svid, eid] = (
src_tag[vid][svid * num_e_groups + eid]
/ np.array(src_tag[vid]).sum()
)
else:
# USER mode, set bias_array according to bias_tag
num_bias_groups = len(bias_tag[0])
bias_array = np.empty(
shape=(num_ve, max_num_cells, num_e_groups), dtype=np.float64
)
bias_array.fill(0.0)
for vid in range(num_ve):
for svid in range(max_num_cells):
for eid in range(num_e_groups):
if num_bias_groups == 1:
bias_array[vid, svid, eid] = bias_tag[vid][0]
elif num_bias_groups == num_e_groups:
bias_array[vid, svid, eid] = bias_tag[vid][eid]
elif num_bias_groups == max_num_cells * num_e_groups:
bias_array[vid, svid, eid] = bias_tag[vid][
svid * num_e_groups + eid
]
else:
raise ValueError("Wrong bias_tag length")
# calculate biased_pdf
if num_bias_groups == 1:
for vid in range(num_ve):
for svid in range(max_num_cells):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
biased_pdf[vid, svid, :] = (
bias_array[vid, svid, :] * current_ve[svid]["vol_frac"]
)
elif num_bias_groups == num_e_groups:
for vid in range(num_ve):
for eid in range(num_e_groups):
for svid in range(max_num_cells):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
biased_pdf[vid, svid, eid] = (
bias_array[vid, svid, eid] * current_ve[svid]["vol_frac"]
)
elif num_bias_groups == max_num_cells * num_e_groups:
for vid in range(num_ve):
for svid in range(max_num_cells):
for eid in range(num_e_groups):
biased_pdf[vid, svid, eid] = (
bias_array[vid, svid, eid] * cell_fracs[vid]["vol_frac"]
)
# normalize biased_pdf
biased_pdf = np.divide(biased_pdf, biased_pdf.sum())
return pdf, biased_pdf
def _cal_exp_w_c(s, mode, cell_fracs, src_tag, bias_tag):
"""
This function calcualtes the exptected weight and cell_number
for a given particle (according to it's x coordinate)
Parameters
----------
s : SourceParticle
The given particle
mode : int
Mode of the source_sampling
cell_fracs : structured array
A sorted, one dimensional array,
each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
src_tag : numpy array
An one or two dimentional array contains data of the source tag.
bias_tag : numpy array, optional
An one or two dimentional array contains data of bias tag
Returns
-------
exp_w : float
Expected weight of the source particle
exp_c : set of available cell numbers
Expected cell number of the source particle
"""
num_ve, num_sve, max_num_cells = _get_num_ve_sve_and_max_num_cells(cell_fracs)
# calculate vid
x_bounds = [v * 1.0 / (num_ve) for v in range(num_ve + 1)]
vid = -1
for i in range(num_ve):
if x_bounds[i] <= s.x <= x_bounds[i + 1]:
vid = i
break
if vid == -1:
raise ValueError(
"x coordinate of particle not in (0, 1), s.x = {0}".format(str(s.x))
)
# calculate svid
# get number of cells/subvoxels of current voxel
current_cell_fracs = cell_fracs[cell_fracs["idx"] == vid]
num_cells = len(current_cell_fracs)
x_bounds = np.array([0.0] * (num_cells + 1))
# the x_bounds of the vid start from 1.0/num_ve*vid
x_bounds[0] = 1.0 / num_ve * vid
for svid in range(num_cells):
x_bounds[svid + 1] = (
x_bounds[svid] + 1.0 / num_ve * current_cell_fracs[svid]["vol_frac"]
)
svid = -1
for i in range(num_cells):
if x_bounds[i] <= s.x <= x_bounds[i + 1]:
svid = i
break
if svid == -1:
raise ValueError("x coordinate not in the voxel, s.x = {0}".format(str(s.x)))
# get the cell_number
exp_c = set(list(current_cell_fracs["cell"]))
# calculate eid
if mode in (0, 1, 2):
num_e_groups = len(src_tag[0])
elif mode in (3, 4, 5):
num_e_groups = len(src_tag[0]) // max_num_cells
e_bounds = np.array([i * 1.0 / num_e_groups for i in range(num_e_groups + 1)])
eid = -1
for i in range(num_e_groups):
if e_bounds[i] <= s.e <= e_bounds[i + 1]:
eid = i
break
if eid == -1:
raise ValueError("energy not in (0, 1), s.e = ".format(str(s.e)))
# calculate exp_w, weight is determined by mode, vid, svid and energy
if mode in (0, 3):
# ANALOG
exp_w = 1.0
elif mode in (1, 4):
# UNIFORM
pdf, biased_pdf = _cal_pdf_and_biased_pdf(cell_fracs, src_tag, bias_tag)
exp_w = pdf[vid, svid, eid] / biased_pdf[vid, svid, eid]
else:
# USER
pdf, biased_pdf = _cal_pdf_and_biased_pdf(cell_fracs, src_tag, bias_tag)
exp_w = pdf[vid, svid, eid] / biased_pdf[vid, svid, eid]
return exp_w, exp_c
def _get_p_y_z_halfspace(particles):
"""
This function calcualtes the probabilities of y and z half space
for a given set of particles
Parameters
----------
particles : list
List of SourceParticle
Returns
-------
p_y_halfspace : float
The probability of y half space
p_z_halfspace : float
The probability of z half space
"""
y_count, z_count = 0, 0
for s in particles:
if s.y < 0.5:
y_count = y_count + 1
if s.z < 0.5:
z_count = z_count + 1
p_y_halfspace = float(y_count) / len(particles)
p_z_halfspace = float(z_count) / len(particles)
return p_y_halfspace, p_z_halfspace
def _get_x_dis(particles, num_ve):
"""
This function calcualtes the particle distribution along x direction
for a given set of particles
Parameters
----------
particles : list
List of SourceParticle
num_ve : int
Number of voxels
Returns
-------
x_dis : one dimentional numpy array
The particle direction along x direction
"""
x_bounds = [v * 1.0 / (num_ve) for v in range(num_ve + 1)]
x_dis = np.array([0.0] * num_ve)
for i in range(num_ve):
for s in particles:
if x_bounds[i] <= s.x <= x_bounds[i + 1]:
x_dis[i] = x_dis[i] + 1
x_dis = np.divide(x_dis, len(particles))
return x_dis
def _get_x_dis_exp(mode, cell_fracs, src_tag, bias_tag=None):
"""
This function calcualtes the exptected particle distribution along x
direction.
Parameters
----------
mode : int
Mode of the source_sampling
cell_fracs : structured array
A sorted, one dimensional array,
each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
src_tag : numpy array
An one or two dimentional array contains data of the source tag.
bias_tag : numpy array, optional
An one or two dimentional array contains data of bias tag
Returns
-------
x_dis_exp : one dimentional numpy array
The expected particle direction along x direction
"""
num_ve, num_sve, max_num_cells = _get_num_ve_sve_and_max_num_cells(cell_fracs)
if mode in (0, 1, 2):
num_e_groups = len(src_tag[0])
elif mode in (3, 4, 5):
num_e_groups = len(src_tag[0]) // max_num_cells
x_bounds = [v * 1.0 / (num_ve) for v in range(num_ve + 1)]
x_dis_exp = np.array([0.0] * num_ve)
if mode in (0, 3):
# ANALOG, particles distribution according to the src_tag
for vid in range(num_ve):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
for svid in range(len(current_ve)):
x_dis_exp[vid] += (
current_ve[svid]["vol_frac"]
* np.array(
src_tag[vid][svid * num_e_groups : (svid + 1) * num_e_groups]
).sum()
)
elif mode in (1, 4):
# UNIFORM, particles distribution uniformly in x direction
x_dis_exp = np.array([1.0 / num_ve] * num_ve)
elif mode in (2, 5):
if bias_tag == None:
raise ValueError(
"bias_tag must be provided when mode is {0}".format(str(mode))
)
# USER, particles distribute accroding to the bias_tag
for vid in range(num_ve):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
for svid in range(len(current_ve)):
x_dis_exp[vid] += (
current_ve[svid]["vol_frac"]
* np.array(
bias_tag[vid][svid * num_e_groups : (svid + 1) * num_e_groups]
).sum()
)
# normalize x_dis_exp
x_dis_exp = np.divide(x_dis_exp, x_dis_exp.sum())
return x_dis_exp
def _get_e_dis(particles, num_e_groups):
"""
This function calcualtes the particle distribution along energy
for a given set of particles
Parameters
----------
particles : list
List of SourceParticle
num_e_groups : int
Number of energy groups
Returns
-------
e_dis : one dimentional numpy array
The particle direction along energy
"""
e_bounds = [e * 1.0 / (num_e_groups) for e in range(num_e_groups + 1)]
e_dis = np.array([0.0] * num_e_groups)
for i in range(num_e_groups):
for s in particles:
if e_bounds[i] <= s.e <= e_bounds[i + 1]:
e_dis[i] = e_dis[i] + 1
e_dis = np.divide(e_dis, len(particles))
return e_dis
def _get_e_dis_exp(mode, cell_fracs, src_tag, bias_tag=None):
"""
This function calcualtes the exptected particle distribution along energy
Parameters
----------
mode : int
Mode of the source_sampling
cell_fracs : structured array
A sorted, one dimensional array,
each entry containing the following fields:
:idx: int
The volume element index.
:cell: int
The geometry cell number.
:vol_frac: float
The volume fraction of the cell withing the mesh ve.
:rel_error: float
The relative error associated with the volume fraction.
src_tag : numpy array
An one or two dimentional array contains data of the source tag.
bias_tag : numpy array, optional
An one or two dimentional array contains data of bias tag
Returns
-------
e_dis_exp : one dimentional numpy array
The expected particle direction along energy
"""
# input check
if mode in (2, 5) and bias_tag == None:
raise ValueError("bias_tag must be provided when mode is {0}".format(str(mode)))
num_ve, num_sve, max_num_cells = _get_num_ve_sve_and_max_num_cells(cell_fracs)
if mode in (0, 1, 2):
num_e_groups = len(src_tag[0])
elif mode in (3, 4, 5):
num_e_groups = len(src_tag[0]) // max_num_cells
e_bounds = [e * 1.0 / (num_e_groups) for e in range(num_e_groups + 1)]
e_dis_exp = np.array([0.0] * num_e_groups)
if mode in (0, 1, 3, 4) or (mode in (2, 5) and len(bias_tag[0]) == 1):
# when mode is ANALOG and UNIFORM, or mode is USER but num_bias_groups is 1
# particles distribution according to the src_tag
for vid in range(num_ve):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
for svid in range(len(current_ve)):
for eid in range(num_e_groups):
e_dis_exp[eid] += (
current_ve[svid]["vol_frac"]
* src_tag[vid][svid * num_e_groups + eid]
)
elif mode == 2 or (mode == 5 and len(bias_tag[0]) == num_e_groups):
# Energy is biased according to the bias_tag
for vid in range(num_ve):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
for svid in range(len(current_ve)):
for eid in range(num_e_groups):
e_dis_exp[eid] += current_ve[svid]["vol_frac"] * bias_tag[vid][eid]
else:
for vid in range(num_ve):
current_ve = cell_fracs[cell_fracs["idx"] == vid]
for svid in range(len(current_ve)):
for eid in range(num_e_groups):
e_dis_exp[eid] += (
current_ve[svid]["vol_frac"]
* bias_tag[vid][svid * num_e_groups + eid]
)
# normalize x_dis_exp
e_dis_exp = np.divide(e_dis_exp, e_dis_exp.sum())
return e_dis_exp
@with_setup(None, try_rm_file("sampling_mesh.h5m"))
def _source_sampling_test_template(mode, cell_fracs_list, src_tag, bias_tag=None):
"""
This function serve as a template for all source_sampling test cases.
It constrcut Sampler from input parameters.
And then perform a standardized sampling and tally,
Finally, it compares tallied results with exp_answers.
Assumptions:
* Use unit cube for all the meshes
* Use structured meshes for all the tests
* filename will always be: "sampling_mesh.h5m"
* distribution changes only on X direction
* uniform distribution in Y and Z directions
* cell_number always equal to the index of sve + 1, no void cell
* voxels have the same volume
For example:
cell_fracs = [(0, 1, 0.4, 0.0),
(0, 2, 0.6, 0.0),
(1, 3, 1.0, 0.0),
(2, 4, 1.0, 0.0), ...]
voxel idx v0 v1 v2
|------------|------------|------------|--- y
| | | | | ^ z
subvoxel | sve0| sve1 | sve2 | sve3 | . . . | /
| | | | | |/
|------------|------------|------------|--- ----> x
cell_number c1 c2 c3 c4
* Energy have only two options:
- [0.0, 1.0]
- [0.0, 0.5, 1.0]
* Voxel number of meshes could be:
- 1 voxel 1 subvoxel -> Single voxel single subvoxel
- 1 voxel 2 subvoxel -> Single voxel multiple subvoxel
- 2 voxel 2 subvoxel -> Multiple voxel multiple subvoxel
- 2 voxel 4 subvoxel -> Multiple voxel multiple subvoxel
Under these assumptions:
* Mesh could be derived from cell_fracs
* e_bounds could be derived from src_tag
* construct_paras contain:
- mode
- cell_fracs
- src_tag
- bias_tag (optional, required for bias_mode == USER)
Check items:
* weight for each particle
* cell_number for each particle
* position distribution
* energy distribution
Parameters
----------
mode : int
Mode of the source sampling, could be 0, 1, 2, 3, 4 or 5
cell_fracs_list : numpy array
A one dimentional numpy array used to construct cell_fracs,
Element: (idx, cell, vol_frac, rel_error)
src_tag : numpy array
An one or two dimentional array contains data of the source tag.
bias_tag : numpy array, optional
An one or two dimentional array contains data of bias tag
Returns
-------
None
"""
sub_mode_r2s = (0, 1, 2)
sub_mode_subvoxel = (3, 4, 5)
avail_mode = (0, 1, 2, 3, 4, 5)
# input check
# check mode
if mode not in avail_mode:
raise ValueError("mode must be in (0, 1, 2, 3, 4, 5)")
# set cell_fracs
cell_fracs = np.zeros(
len(cell_fracs_list),
dtype=[
("idx", np.int64),
("cell", np.int64),
("vol_frac", np.float64),
("rel_error", np.float64),
],
)
cell_fracs[:] = cell_fracs_list
# check bias_tag
if mode in (2, 5) and bias_tag == None: # bias_mode == USER
raise ValueError("bias_tag must be given when mode is {0}".format(str(mode)))
# get number of voxel, max_num_cells
num_ve, num_sve, max_num_cells = _get_num_ve_sve_and_max_num_cells(cell_fracs)
# set up e_bounds
if mode in (0, 1, 2):
num_e_groups = len(src_tag[0])
elif mode in (3, 4, 5):
num_e_groups = len(src_tag[0]) // max_num_cells
e_bounds = [i * 1.0 / num_e_groups for i in range(num_e_groups + 1)]
e_bounds = np.array(e_bounds)
# set up mesh
m = _create_mesh_via_num_ve(num_ve)
# set up src tag
if mode in (0, 1, 2):
m.src = NativeMeshTag(num_e_groups, float)
elif mode in (3, 4, 5):
m.src = NativeMeshTag(max_num_cells * num_e_groups, float)
m.src[:] = src_tag
# set up cell_number and cell_fracs tag
m.tag_cell_fracs(cell_fracs)
# set up bias tag
if mode in (2, 5):
bias_tag_lenght = len(bias_tag[0])
m.bias = NativeMeshTag(bias_tag_lenght, float)
m.bias[:] = bias_tag
# set up tag_names
tag_names = {
"src_tag_name": "src",
"cell_number_tag_name": "cell_number",
"cell_fracs_tag_name": "cell_fracs",
"e_bounds_tag_name": "e_bounds",
}
if mode in (2, 5):
tag_names["bias_tag_name"] = "bias"
# save the mesh into h5m file
filename = "sampling_mesh.h5m"
m = tag_e_bounds(m, e_bounds)
m.write_hdf5(filename)
# construct Sampler
sampler = Sampler(filename, tag_names, mode)
# remove the temporary file
os.remove(filename)
# sampling and tally, tally should be defined by the mesh cell_fracs
num_samples = 5000
particles = []
seed(1953)
for i in range(num_samples):
rands = np.array([uniform(0, 1) for x in range(6)])
s = sampler.particle_birth(rands)
# check w, and c for each particle
# calculate the expected weight and cell_number
exp_w, exp_c = _cal_exp_w_c(s, mode, cell_fracs, src_tag, bias_tag)
assert_equal(s.w, exp_w)
# when mode in (0, 1, 2), the set exp_c is (-1), otherwise it contains
# several available cell number
if mode in (0, 1, 2):
assert set(s.cell_list) == exp_c
elif mode in (3, 4, 5):
assert set(s.cell_list).issubset(exp_c)
# store all the particles for the convinent of distribution check
particles.append(s)
# check position distribution
# X direction follow specified distribution
x_dis = _get_x_dis(particles, num_ve)
x_dis_exp = _get_x_dis_exp(mode, cell_fracs, src_tag, bias_tag)
for i in range(len(x_dis)):
assert abs(x_dis[i] - x_dis_exp[i]) / x_dis_exp[i] < 0.05
# uniform in Y and Z directions
p_y_halfspace, p_z_halfspace = _get_p_y_z_halfspace(particles)
assert abs(p_y_halfspace - 0.5) / 0.5 < 0.05
assert abs(p_z_halfspace - 0.5) / 0.5 < 0.05
# check energy distribution
e_dis = _get_e_dis(particles, num_e_groups)
e_dis_exp = _get_e_dis_exp(mode, cell_fracs, src_tag, bias_tag)
for i in range(len(e_dis)):
if e_dis_exp[i] > 0:
assert abs(e_dis[i] - e_dis_exp[i]) / e_dis_exp[i] < 0.05
else:
assert_equal(e_dis[i], 0.0)
|
[
"os.remove",
"pyne.r2s.tag_e_bounds",
"numpy.empty",
"pymoab.core.Core",
"numpy.ones",
"pyne.source_sampling.AliasTable",
"warnings.simplefilter",
"os.path.exists",
"pyne.mesh.NativeMeshTag",
"random.seed",
"nose.tools.assert_raises",
"numpy.rollaxis",
"numpy.linalg.det",
"imp.load_module",
"imp.find_module",
"pyne.source_sampling.Sampler",
"random.uniform",
"numpy.zeros",
"nose.tools.assert_almost_equal",
"nose.tools.assert_equal",
"pyne.mesh.Mesh",
"numpy.array"
] |
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'assert_almost_equal', (['s.w', '(0.7)'], {}), '(s.w, 0.7)\n', (30938, 30948), False, 'from nose.tools import assert_equal, with_setup, assert_almost_equal, assert_raises\n'), ((30989, 31018), 'nose.tools.assert_almost_equal', 'assert_almost_equal', (['s.w', '(2.8)'], {}), '(s.w, 2.8)\n', (31008, 31018), False, 'from nose.tools import assert_equal, with_setup, assert_almost_equal, assert_raises\n'), ((40911, 40924), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (40918, 40924), False, 'from random import uniform, seed\n'), ((40926, 40939), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (40933, 40939), False, 'from random import uniform, seed\n'), ((59501, 59534), 'numpy.array', 'np.array', (['([1.0 / num_ve] * num_ve)'], {}), '([1.0 / num_ve] * num_ve)\n', (59509, 59534), True, 'import numpy as np\n'), ((68121, 68171), 'pyne.mesh.NativeMeshTag', 'NativeMeshTag', (['(max_num_cells * num_e_groups)', 'float'], {}), '(max_num_cells * num_e_groups, float)\n', 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(36666, 36672), False, 'from random import uniform, seed\n'), ((39381, 39394), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (39388, 39394), False, 'from random import uniform, seed\n'), ((69173, 69186), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (69180, 69186), False, 'from random import uniform, seed\n'), ((7302, 7315), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (7309, 7315), False, 'from random import uniform, seed\n'), ((9748, 9769), 'numpy.rollaxis', 'np.rollaxis', (['tally', 'i'], {}), '(tally, i)\n', (9759, 9769), True, 'import numpy as np\n'), ((12937, 12950), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (12944, 12950), False, 'from random import uniform, seed\n'), ((13639, 13668), 'numpy.rollaxis', 'np.rollaxis', (['spatial_tally', 'i'], {}), '(spatial_tally, i)\n', (13650, 13668), True, 'import numpy as np\n'), ((15497, 15514), 'random.uniform', 'uniform', (['(0.0)', '(1.0)'], {}), '(0.0, 1.0)\n', (15504, 15514), False, 'from random import uniform, seed\n'), ((17673, 17686), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (17680, 17686), False, 'from random import uniform, seed\n'), ((20182, 20203), 'numpy.rollaxis', 'np.rollaxis', (['tally', 'i'], {}), '(tally, i)\n', (20193, 20203), True, 'import numpy as np\n'), ((21830, 21847), 'random.uniform', 'uniform', (['(0.0)', '(1.0)'], {}), '(0.0, 1.0)\n', (21837, 21847), False, 'from random import uniform, seed\n'), ((24008, 24025), 'random.uniform', 'uniform', (['(0.0)', '(1.0)'], {}), '(0.0, 1.0)\n', (24015, 24025), False, 'from random import uniform, seed\n'), ((28413, 28426), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (28420, 28426), False, 'from random import uniform, seed\n'), ((30850, 30863), 'random.uniform', 'uniform', (['(0)', '(1)'], {}), '(0, 1)\n', (30857, 30863), False, 'from random import uniform, seed\n'), ((31552, 31581), 'numpy.rollaxis', 'np.rollaxis', (['spatial_tally', 'i'], {}), '(spatial_tally, i)\n', (31563, 31581), True, 'import numpy as np\n'), ((59247, 59316), 'numpy.array', 'np.array', (['src_tag[vid][svid * num_e_groups:(svid + 1) * num_e_groups]'], {}), '(src_tag[vid][svid * num_e_groups:(svid + 1) * num_e_groups])\n', (59255, 59316), True, 'import numpy as np\n'), ((7756, 7777), 'numpy.rollaxis', 'np.rollaxis', (['tally', 'i'], {}), '(tally, i)\n', (7767, 7777), True, 'import numpy as np\n'), ((16023, 16044), 'numpy.rollaxis', 'np.rollaxis', (['tally', 'i'], {}), '(tally, i)\n', (16034, 16044), True, 'import numpy as np\n'), ((22356, 22377), 'numpy.rollaxis', 'np.rollaxis', (['tally', 'i'], {}), '(tally, i)\n', (22367, 22377), True, 'import numpy as np\n'), ((50548, 50570), 'numpy.array', 'np.array', (['src_tag[vid]'], {}), '(src_tag[vid])\n', (50556, 50570), True, 'import numpy as np\n'), ((60026, 60096), 'numpy.array', 'np.array', (['bias_tag[vid][svid * num_e_groups:(svid + 1) * num_e_groups]'], {}), '(bias_tag[vid][svid * num_e_groups:(svid + 1) * num_e_groups])\n', (60034, 60096), True, 'import numpy as np\n')]
|
import itertools
from elasticsearch_dsl import Index, Q, Search
import pytest
from django.conf import settings
from api.applications import helpers, models
from api.flags.enums import SystemFlags
from api.parties.enums import PartyType
from test_helpers.clients import DataTestClient
from api.external_data.documents import SanctionDocumentType
def prepare_index():
SanctionDocumentType._index.delete(ignore=[404])
SanctionDocumentType.init()
class AbstractAutoMatchTests:
def create_application(self):
raise NotImplementedError
def get_party(self, application):
raise NotImplementedError
@pytest.mark.elasticsearch
def test_auto_match_sanctions_no_matches(self):
prepare_index()
application = self.create_application()
party = self.get_party(application)
helpers.auto_match_sanctions(application)
party_on_application = application.parties.get(party=party)
self.assertEqual(party_on_application.sanction_matches.all().count(), 0)
self.assertEqual(party_on_application.flags.count(), 0)
@pytest.mark.elasticsearch
def test_auto_match_sanctions_match_name(self):
prepare_index()
application = self.create_application()
party = self.get_party(application)
party.signatory_name_euu = "<NAME>"
party.save()
document = SanctionDocumentType(
name=party.signatory_name_euu,
address="123 fake street",
flag_uuid=SystemFlags.SANCTION_UK_MATCH,
reference="123",
)
document.save()
SanctionDocumentType._index.refresh()
helpers.auto_match_sanctions(application)
party_on_application = application.parties.get(party=party)
self.assertEqual(party_on_application.sanction_matches.count(), 1)
self.assertEqual(party_on_application.sanction_matches.first().elasticsearch_reference, "123")
self.assertEqual(str(party_on_application.flags.first().pk), SystemFlags.SANCTION_UK_MATCH)
@pytest.mark.elasticsearch
def test_auto_match_sanctions_avoid_false_positives(self):
names = [
"<NAME>",
"<NAME>",
"<NAME>",
"<NAME>",
"JeremyJackson",
"<NAME>",
"<NAME>",
"Mr. <NAME>",
"Mr. Jackson",
]
for name_variant in names:
prepare_index()
application = self.create_application()
party = self.get_party(application)
party.signatory_name_euu = "<NAME>"
party.address = "123 Plank Street, London, E19 8NX"
party.save()
document = SanctionDocumentType(
name=name_variant,
address="123 Plank Street, London, E19 8NX",
flag_uuid=SystemFlags.SANCTION_UK_MATCH,
reference="123",
)
document.save()
SanctionDocumentType._index.refresh()
helpers.auto_match_sanctions(application)
party_on_application = application.parties.get(party=party)
self.assertEqual(party_on_application.sanction_matches.count(), 0)
self.assertEqual(party_on_application.flags.count(), 0)
@pytest.mark.elasticsearch
def test_auto_match_sanctions_match_name_similar(self):
""" This test exposes the fact that ES tokenization etc. would mean
that the following names will be matched to "<NAME>". There is
no simple way around this.
"""
names = [
"<NAME>",
"<NAME>",
"Mr. <NAME>",
]
for name_variant in names:
prepare_index()
application = self.create_application()
party = self.get_party(application)
party.signatory_name_euu = "<NAME>"
party.address = "123 Fake Street, London, E14 9IX"
party.save()
document = SanctionDocumentType(
name=name_variant,
address="123 Fake Street, London, E14 9IX",
postcode="E14 9IX",
flag_uuid=SystemFlags.SANCTION_UK_MATCH,
reference="123",
)
document.save()
SanctionDocumentType._index.refresh()
helpers.auto_match_sanctions(application)
party_on_application = application.parties.get(party=party)
self.assertEqual(party_on_application.sanction_matches.count(), 1, msg=f'tried "{name_variant}"')
self.assertEqual(str(party_on_application.flags.first().pk), SystemFlags.SANCTION_UK_MATCH)
class AutoMatchStandardApplicationTests(AbstractAutoMatchTests, DataTestClient):
def create_application(self):
return self.create_standard_application_case(self.organisation)
def get_party(self, application):
return application.end_user.party
class AutoMatchOpenApplicationTests(AbstractAutoMatchTests, DataTestClient):
def create_application(self):
application = self.create_open_application_case(self.organisation)
self.create_party("Ultimate end User", self.organisation, PartyType.ULTIMATE_END_USER, application)
return application
def get_party(self, application):
return application.ultimate_end_users[0].party
|
[
"api.external_data.documents.SanctionDocumentType.init",
"api.external_data.documents.SanctionDocumentType._index.refresh",
"api.applications.helpers.auto_match_sanctions",
"api.external_data.documents.SanctionDocumentType",
"api.external_data.documents.SanctionDocumentType._index.delete"
] |
[((375, 423), 'api.external_data.documents.SanctionDocumentType._index.delete', 'SanctionDocumentType._index.delete', ([], {'ignore': '[404]'}), '(ignore=[404])\n', (409, 423), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((428, 455), 'api.external_data.documents.SanctionDocumentType.init', 'SanctionDocumentType.init', ([], {}), '()\n', (453, 455), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((839, 880), 'api.applications.helpers.auto_match_sanctions', 'helpers.auto_match_sanctions', (['application'], {}), '(application)\n', (867, 880), False, 'from api.applications import helpers, models\n'), ((1383, 1529), 'api.external_data.documents.SanctionDocumentType', 'SanctionDocumentType', ([], {'name': 'party.signatory_name_euu', 'address': '"""123 fake street"""', 'flag_uuid': 'SystemFlags.SANCTION_UK_MATCH', 'reference': '"""123"""'}), "(name=party.signatory_name_euu, address=\n '123 fake street', flag_uuid=SystemFlags.SANCTION_UK_MATCH, reference='123'\n )\n", (1403, 1529), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((1611, 1648), 'api.external_data.documents.SanctionDocumentType._index.refresh', 'SanctionDocumentType._index.refresh', ([], {}), '()\n', (1646, 1648), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((1658, 1699), 'api.applications.helpers.auto_match_sanctions', 'helpers.auto_match_sanctions', (['application'], {}), '(application)\n', (1686, 1699), False, 'from api.applications import helpers, models\n'), ((2710, 2862), 'api.external_data.documents.SanctionDocumentType', 'SanctionDocumentType', ([], {'name': 'name_variant', 'address': '"""123 Plank Street, London, E19 8NX"""', 'flag_uuid': 'SystemFlags.SANCTION_UK_MATCH', 'reference': '"""123"""'}), "(name=name_variant, address=\n '123 Plank Street, London, E19 8NX', flag_uuid=SystemFlags.\n SANCTION_UK_MATCH, reference='123')\n", (2730, 2862), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((2972, 3009), 'api.external_data.documents.SanctionDocumentType._index.refresh', 'SanctionDocumentType._index.refresh', ([], {}), '()\n', (3007, 3009), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((3023, 3064), 'api.applications.helpers.auto_match_sanctions', 'helpers.auto_match_sanctions', (['application'], {}), '(application)\n', (3051, 3064), False, 'from api.applications import helpers, models\n'), ((3995, 4166), 'api.external_data.documents.SanctionDocumentType', 'SanctionDocumentType', ([], {'name': 'name_variant', 'address': '"""123 Fake Street, London, E14 9IX"""', 'postcode': '"""E14 9IX"""', 'flag_uuid': 'SystemFlags.SANCTION_UK_MATCH', 'reference': '"""123"""'}), "(name=name_variant, address=\n '123 Fake Street, London, E14 9IX', postcode='E14 9IX', flag_uuid=\n SystemFlags.SANCTION_UK_MATCH, reference='123')\n", (4015, 4166), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((4292, 4329), 'api.external_data.documents.SanctionDocumentType._index.refresh', 'SanctionDocumentType._index.refresh', ([], {}), '()\n', (4327, 4329), False, 'from api.external_data.documents import SanctionDocumentType\n'), ((4343, 4384), 'api.applications.helpers.auto_match_sanctions', 'helpers.auto_match_sanctions', (['application'], {}), '(application)\n', (4371, 4384), False, 'from api.applications import helpers, models\n')]
|
import json
import sys
import boto3
client = boto3.client('glue')
def lambda_handler(event, context):
print(event)
file_name = event['Records'][0]['s3']['object']['key'].split("/")[1]
if file_name == "csv":
client.start_crawler( Name='csv_crawler' )
else:
client.start_crawler( Name='json_crawler' )
|
[
"boto3.client"
] |
[((49, 69), 'boto3.client', 'boto3.client', (['"""glue"""'], {}), "('glue')\n", (61, 69), False, 'import boto3\n')]
|
"""Fields for Zinnia admin"""
from __future__ import unicode_literals
from django import forms
from django.utils.encoding import smart_unicode
class MPTTModelChoiceIterator(forms.models.ModelChoiceIterator):
"""
MPTT version of ModelChoiceIterator
"""
def choice(self, obj):
"""
Overloads the choice method to add the position
of the object in the tree for future sorting.
"""
tree_id = getattr(obj, self.queryset.model._mptt_meta.tree_id_attr, 0)
left = getattr(obj, self.queryset.model._mptt_meta.left_attr, 0)
return super(MPTTModelChoiceIterator,
self).choice(obj) + ((tree_id, left),)
class MPTTModelMultipleChoiceField(forms.ModelMultipleChoiceField):
"""
MPTT version of ModelMultipleChoiceField
"""
def __init__(self, level_indicator='|--', *args, **kwargs):
self.level_indicator = level_indicator
super(MPTTModelMultipleChoiceField, self).__init__(*args, **kwargs)
def label_from_instance(self, obj):
"""
Creates labels which represent the tree level of each node
when generating option labels.
"""
label = smart_unicode(obj)
prefix = self.level_indicator * getattr(obj, obj._mptt_meta.level_attr)
if prefix:
return '%s %s' % (prefix, label)
return label
def _get_choices(self):
"""
Overrides the _get_choices method to use MPTTModelChoiceIterator.
"""
return MPTTModelChoiceIterator(self)
choices = property(_get_choices, forms.ChoiceField._set_choices)
|
[
"django.utils.encoding.smart_unicode"
] |
[((1193, 1211), 'django.utils.encoding.smart_unicode', 'smart_unicode', (['obj'], {}), '(obj)\n', (1206, 1211), False, 'from django.utils.encoding import smart_unicode\n')]
|
"""
"""
import textwrap
import collections
from clldutils.clilib import Table, add_format
from termcolor import colored
def register(parser):
add_format(parser, 'simple')
parser.add_argument('--id', help='Issue ID', type=int, default=None)
def run(args):
issues = collections.OrderedDict([(i.id, i) for i in args.repos.issues])
if args.id:
issue = issues[args.id]
print(colored('{0.created} {0.user}'.format(issue), 'green'))
print(colored(issue.title, attrs={'bold', 'underline'}))
print('')
print(issue.body)
for comment in issue.comments:
print(colored('\n {0.created} {0.user}'.format(comment), 'green'))
for line in comment.stripped_body.split('\n'):
print(' ' + line)
return
with Table(args, 'ID', 'Title', 'User', 'Comments') as t:
for issue in issues.values():
if issue.coding_problem:
t.append([
issue.id,
textwrap.shorten(issue.title, 50, placeholder=' […]'),
issue.user,
len(issue.comments)])
|
[
"textwrap.shorten",
"clldutils.clilib.add_format",
"clldutils.clilib.Table",
"termcolor.colored",
"collections.OrderedDict"
] |
[((150, 178), 'clldutils.clilib.add_format', 'add_format', (['parser', '"""simple"""'], {}), "(parser, 'simple')\n", (160, 178), False, 'from clldutils.clilib import Table, add_format\n'), ((282, 345), 'collections.OrderedDict', 'collections.OrderedDict', (['[(i.id, i) for i in args.repos.issues]'], {}), '([(i.id, i) for i in args.repos.issues])\n', (305, 345), False, 'import collections\n'), ((816, 862), 'clldutils.clilib.Table', 'Table', (['args', '"""ID"""', '"""Title"""', '"""User"""', '"""Comments"""'], {}), "(args, 'ID', 'Title', 'User', 'Comments')\n", (821, 862), False, 'from clldutils.clilib import Table, add_format\n'), ((479, 528), 'termcolor.colored', 'colored', (['issue.title'], {'attrs': "{'bold', 'underline'}"}), "(issue.title, attrs={'bold', 'underline'})\n", (486, 528), False, 'from termcolor import colored\n'), ((1021, 1074), 'textwrap.shorten', 'textwrap.shorten', (['issue.title', '(50)'], {'placeholder': '""" […]"""'}), "(issue.title, 50, placeholder=' […]')\n", (1037, 1074), False, 'import textwrap\n')]
|
from django.contrib import admin
from listenclosely.models import Message, Chat, Agent, Asker
admin.site.register(Message)
admin.site.register(Chat)
admin.site.register(Agent)
admin.site.register(Asker)
|
[
"django.contrib.admin.site.register"
] |
[((95, 123), 'django.contrib.admin.site.register', 'admin.site.register', (['Message'], {}), '(Message)\n', (114, 123), False, 'from django.contrib import admin\n'), ((124, 149), 'django.contrib.admin.site.register', 'admin.site.register', (['Chat'], {}), '(Chat)\n', (143, 149), False, 'from django.contrib import admin\n'), ((150, 176), 'django.contrib.admin.site.register', 'admin.site.register', (['Agent'], {}), '(Agent)\n', (169, 176), False, 'from django.contrib import admin\n'), ((177, 203), 'django.contrib.admin.site.register', 'admin.site.register', (['Asker'], {}), '(Asker)\n', (196, 203), False, 'from django.contrib import admin\n')]
|
##########################################################################
# MediPy - Copyright (C) Universite de Strasbourg
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
import numpy
from vtk import vtkActor, vtkContourFilter, vtkPolyDataMapper
from layer import Layer
class ContourLayer(Layer):
"Layer showing its data as a set of isocontours."
@staticmethod
def can_create(image):
return (image.data_type == "scalar" and
image.image_type == "spectroscopy" and
image.ndim <= 3)
def __init__(self, world_to_slice, image, display_coordinates="physical",
colormap=None, opacity = 1.0, levels=None) :
############################
# Property-related members #
############################
# List of levels, or None to use default-generated values
self._levels = None
self._actor = vtkActor()
###################
# Private members #
###################
self._contour_filter = vtkContourFilter()
self._mapper = vtkPolyDataMapper()
##################
# Initialization #
##################
super(ContourLayer, self).__init__(
world_to_slice, image, display_coordinates, colormap, opacity)
self._contour_filter.SetInput(self._change_information.GetOutput())
self._contour_filter.UseScalarTreeOn()
self._mapper.SetInputConnection(self._contour_filter.GetOutputPort())
self._mapper.ScalarVisibilityOn()
self._mapper.SetLookupTable(self._colormap.vtk_colormap)
self._mapper.UseLookupTableScalarRangeOn()
self._actor.SetMapper(self._mapper)
if levels is None :
# Generate 20 evenly spaced contour values
levels = numpy.linspace(self._image.data.min(),
self._image.data.max(), 20)
self._set_levels(levels)
##############
# Properties #
##############
def _set_image(self, image):
super(ContourLayer, self)._set_image(image)
self._mapper.SetScalarRange(image.data.min(), image.data.max())
def _set_colormap(self, colormap):
super(ContourLayer, self)._set_colormap(colormap)
self._mapper.SetLookupTable(self._colormap.vtk_colormap)
self.colormap.add_observer("vtk_colormap", self._on_vtk_colormap)
def _set_opacity(self, opacity):
super(ContourLayer, self)._set_opacity(opacity)
self._actor.GetProperty().SetOpacity(opacity)
def _get_levels(self) :
"Levels at which contours are computed."
return self._levels
def _set_levels(self, levels):
self._levels = levels
for i, level in enumerate(self._levels) :
self._contour_filter.SetValue(i, level)
def _get_actor(self):
return self._actor
levels = property(_get_levels, _set_levels)
#####################
# Private interface #
#####################
def _on_vtk_colormap(self, dummy) :
""" Event handler called when the ID of colormap.vtk_colormap changes.
"""
self._set_colormap(self._colormap)
Layer.derived_classes.append(ContourLayer)
|
[
"layer.Layer.derived_classes.append",
"vtk.vtkContourFilter",
"vtk.vtkActor",
"vtk.vtkPolyDataMapper"
] |
[((3530, 3572), 'layer.Layer.derived_classes.append', 'Layer.derived_classes.append', (['ContourLayer'], {}), '(ContourLayer)\n', (3558, 3572), False, 'from layer import Layer\n'), ((1171, 1181), 'vtk.vtkActor', 'vtkActor', ([], {}), '()\n', (1179, 1181), False, 'from vtk import vtkActor, vtkContourFilter, vtkPolyDataMapper\n'), ((1306, 1324), 'vtk.vtkContourFilter', 'vtkContourFilter', ([], {}), '()\n', (1322, 1324), False, 'from vtk import vtkActor, vtkContourFilter, vtkPolyDataMapper\n'), ((1348, 1367), 'vtk.vtkPolyDataMapper', 'vtkPolyDataMapper', ([], {}), '()\n', (1365, 1367), False, 'from vtk import vtkActor, vtkContourFilter, vtkPolyDataMapper\n')]
|
import datetime
import json
import typing
import pandas as pd
import requests
URL = "https://www.twse.com.tw/fund/T86?response=json&date={}&selectType=ALL&_={}"
# 網頁瀏覽時, 所帶的 request header 參數, 模仿瀏覽器發送 request
HEADER = {
"Accept": "application/json, text/javascript, */*; q=0.01",
"Accept-Encoding": "gzip, deflate, br",
"Accept-Language": "zh-TW,zh;q=0.9,en-US;q=0.8,en;q=0.7",
"Connection": "keep-alive",
"Host": "www.twse.com.tw",
"Referer": 'https://www.twse.com.tw/zh/page/trading/fund/T86.htmlsec-ch-ua: " Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
"sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": "Windows",
"Sec-Fetch-Dest": "empty",
"Sec-Fetch-Mode": "cors",
"Sec-Fetch-Site": "same-origin",
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.99 Safari/537.36",
"X-Requested-With": "XMLHttpRequest",
}
def crawler(parameters:typing.Dict[str, str]) -> pd.DataFrame:
crawler_date = parameters.get("crawler_date", "")
crawler_date = crawler_date.replace("-", "")
crawler_timestamp = int(datetime.datetime.now().timestamp())
resp = requests.get(
url=URL.format(crawler_date, crawler_timestamp), headers=HEADER
)
columns = [
"stock_id",
"stock_name",
"Foreign_Investors_include_Mainland_Area_Buy",
"Foreign_Investors_include_Mainland_Area_Sell",
"Foreign_Investors_include_Mainland_Area_Net",
"Foreign_Dealer_Self_Buy",
"Foreign_Dealer_Self_Sell",
"Foreign_Dealer_Self_Net",
"Investment_Trust_Buy",
"Investment_Trust_Sell",
"Investment_Trust_Net",
"Dealer_Net",
"Dealer_self_Buy",
"Dealer_self_Sell",
"Dealer_self_Net",
"Dealer_Hedging_Buy",
"Dealer_Hedging_Sell",
"Dealer_Hedging_Net",
"Total_Net",
]
if resp.ok:
resp_data = json.loads(resp.text)
data = resp_data.get("data", "")
data = pd.DataFrame(data, columns=columns)
data["date"] = parameters.get("crawler_date", "")
else:
data = pd.DataFrame(columns=columns)
return data
if __name__ == "__main__":
parameters = {
"crawler_date": "2022-01-26",
}
data = crawler(parameters)
print(data)
|
[
"pandas.DataFrame",
"datetime.datetime.now",
"json.loads"
] |
[((2055, 2076), 'json.loads', 'json.loads', (['resp.text'], {}), '(resp.text)\n', (2065, 2076), False, 'import json\n'), ((2133, 2168), 'pandas.DataFrame', 'pd.DataFrame', (['data'], {'columns': 'columns'}), '(data, columns=columns)\n', (2145, 2168), True, 'import pandas as pd\n'), ((2252, 2281), 'pandas.DataFrame', 'pd.DataFrame', ([], {'columns': 'columns'}), '(columns=columns)\n', (2264, 2281), True, 'import pandas as pd\n'), ((1229, 1252), 'datetime.datetime.now', 'datetime.datetime.now', ([], {}), '()\n', (1250, 1252), False, 'import datetime\n')]
|
import unittest
import library
import os
import glob
class TestLibrary(unittest.TestCase):
def setUp(self):
self.lib_fn = r'v:\workspace\PersistentStorage\src\lib.shelve'
self.lib = library.Library(self.lib_fn)
self.fixture_author1 = library.Author('Octavia', 'Estelle', 'Butler')
self.fixture_book1 = library.Book('0807083100', 'Kindred', [self.fixture_author1])
self.fixture_author2 = library.Author('Robert', 'Anson', 'Heinlein')
self.fixture_book2 = library.Book('0441790384', 'Stranger in a Strange Land', [self.fixture_author2])
self.lib.add(self.fixture_book1)
self.lib.add(self.fixture_book2)
def testGetByIsbn(self):
observed = self.lib.get_by_isbn(self.fixture_book1.isbn)
self.assertEqual(observed, self.fixture_book1)
def testGetByTitle(self):
observed = self.lib.get_by_title(self.fixture_book2.title)
self.assertEqual(observed, self.fixture_book2)
def testGetByAuthor(self):
observed = self.lib.get_by_author(self.fixture_book1.authors[0])
self.assertEqual(observed, self.fixture_book1)
def tearDown(self):
self.lib.close()
shelve_files = glob.glob(self.lib_fn + '*')
for fn in shelve_files:
os.remove(fn)
if __name__ == "__main__":
unittest.main()
|
[
"unittest.main",
"os.remove",
"library.Book",
"library.Library",
"library.Author",
"glob.glob"
] |
[((1365, 1380), 'unittest.main', 'unittest.main', ([], {}), '()\n', (1378, 1380), False, 'import unittest\n'), ((203, 231), 'library.Library', 'library.Library', (['self.lib_fn'], {}), '(self.lib_fn)\n', (218, 231), False, 'import library\n'), ((263, 309), 'library.Author', 'library.Author', (['"""Octavia"""', '"""Estelle"""', '"""Butler"""'], {}), "('Octavia', 'Estelle', 'Butler')\n", (277, 309), False, 'import library\n'), ((339, 400), 'library.Book', 'library.Book', (['"""0807083100"""', '"""Kindred"""', '[self.fixture_author1]'], {}), "('0807083100', 'Kindred', [self.fixture_author1])\n", (351, 400), False, 'import library\n'), ((432, 477), 'library.Author', 'library.Author', (['"""Robert"""', '"""Anson"""', '"""Heinlein"""'], {}), "('Robert', 'Anson', 'Heinlein')\n", (446, 477), False, 'import library\n'), ((507, 592), 'library.Book', 'library.Book', (['"""0441790384"""', '"""Stranger in a Strange Land"""', '[self.fixture_author2]'], {}), "('0441790384', 'Stranger in a Strange Land', [self.fixture_author2]\n )\n", (519, 592), False, 'import library\n'), ((1234, 1262), 'glob.glob', 'glob.glob', (["(self.lib_fn + '*')"], {}), "(self.lib_fn + '*')\n", (1243, 1262), False, 'import glob\n'), ((1307, 1320), 'os.remove', 'os.remove', (['fn'], {}), '(fn)\n', (1316, 1320), False, 'import os\n')]
|
import logging
from r2vstk.config_manager import ConfigManager
from r2vstk.house import House
import argparse
import os.path
import json
def run(conf: ConfigManager, source, output_path, scale_factor, save_previews,
save_room_json, skip_objects=False, adjust_short_walls=False,
classify_doors_and_windows=False, skip_rdr=False, r2v_annot=False):
"""
Converts Raster-to-Vector output/annotation to scene.json format.
"""
house = House()
# Pass in the scale factor
house.multiplication_factor = scale_factor
if r2v_annot:
# User has provided a r2v annotation file
house.load_r2v_annot_file(source)
else:
# User has provided a r2v output file
house.load_r2v_output_file(conf, source)
# Split walls that intersect with other walls into separate wall segments.
if conf.parser_config.split_walls.enabled:
house.split_source_walls(conf)
# Generate wall linkage graph and detect rooms.
house.generate_wall_graph(conf)
# Axis align nearly axis aligned walls.
if conf.parser_config.straighten_walls.enabled:
house.straighten_walls(conf)
# Assign annotation AABBs to rooms
house.populate_room_annotations(conf)
# Parse object annotation AABBs
if not skip_objects:
house.populate_object_annotations(conf)
# Separate doors from windows.
if classify_doors_and_windows:
house.classify_doors_and_windows()
# Populate additional details related to rooms such as room_id and room_type labels.
if r2v_annot:
house.populate_room_descriptions_from_r2v_annot(conf)
else:
house.populate_room_descriptions_from_r2v_output()
# Compute room-door-room connectivity graph edges
if not skip_rdr:
house.compute_rdr()
# Save preview sketches
if save_previews:
house.sketch_raw_annotations(conf, os.path.join(output_path, "raw_annot.png"))
logging.info("Saved {file}".format(file=os.path.join(output_path, "raw_annot.png")))
# Save wall mask
wall_mask = house.get_wall_mask()
with open(os.path.join(output_path, "wall_mask.svg"), 'w') as f:
f.write(wall_mask._repr_svg_())
# Save room level results
for i, room_key in enumerate(house.ordered_rooms):
if save_previews:
house.sketch_room_annotations(conf, room_key, os.path.join(output_path, "room_" + str(i) + ".png"))
logging.info("Saved {file}".format(file=os.path.join(output_path, "room_" + str(i) + ".png")))
if save_room_json:
room_json = house.get_room_json(conf, room_key, True, [], adjust_short_walls=adjust_short_walls)[0]
with open(os.path.join(output_path, room_json["id"] + ".arch.json"), "w") as f:
f.write(json.dumps(room_json, indent=3))
logging.info("Saved {file}".format(file=os.path.join(output_path, "room_" + str(i) + ".json")))
# Save scene.json
scene_json = house.get_scene_json(conf, adjust_short_walls=adjust_short_walls)
save_path = os.path.join(output_path, scene_json["scene"]["arch"]["id"] + ".scene.json")
with open(save_path, "w") as f:
f.write(json.dumps(scene_json, indent=3))
logging.info("Saved {file}".format(file=save_path))
# Save objectaabb.json
object_aabb_json = house.get_objectaabb_json(conf)
save_path = os.path.join(output_path, scene_json["scene"]["arch"]["id"] + ".objectaabb.json")
with open(save_path, "w") as f:
f.write(json.dumps(object_aabb_json, indent=3))
logging.info("Saved {file}".format(file=save_path))
return house
def run_args(conf: ConfigManager, args):
"""
Process command line args.
:param conf: ConfigManager
:param args: Command line args
"""
if not args.r2v_annot:
assert conf.room_types is not None
house = run(conf=conf, source=args.source[0], output_path=args.output_path[0], save_previews=not args.no_previews, save_room_json=args.room_json,
scale_factor=args.scale_factor[0], skip_objects=args.skip_objects,
adjust_short_walls=not args.do_not_adjust_short_walls,
classify_doors_and_windows=not args.do_not_classify_doors_and_windows,
skip_rdr=args.skip_rdr, r2v_annot=args.r2v_annot)
return house
def add_args(parser: argparse.ArgumentParser) -> None:
"""
Add command line args to the parser.
:param parser: Argument Parser
"""
parser.add_argument('output_path', metavar='output', type=str, nargs=1,
help='directory to save outputs from dataset')
parser.add_argument('source', metavar='source', type=str, nargs=1,
help='path to annotation file from dataset')
parser.add_argument("--r2v-annot", default=False, action="store_true",
help="Process a Raster-to-vector annotation file instead of R2V output file.")
parser.add_argument("--scale-factor", metavar="mf", type=float, nargs=1, default=[-1],
help="Scale factor to multiply all coordinates in the XY plane.")
parser.add_argument("--no-previews", default=False, action="store_true",
help="Don't generate PNG previews")
parser.add_argument("--room-json", default=False, action="store_true",
help="Generate individual room's json")
parser.add_argument("--skip-objects", default=False, action="store_true",
help="Don't place objects")
parser.add_argument("--do-not-adjust-short-walls", default=False, action="store_true",
help="Don't adjust height of short walls such as balconies")
parser.add_argument("--do-not-classify-doors-and-windows", default=False, action="store_true",
help="Don't classify holes as doors or windows")
parser.add_argument("--skip-rdr", default=False, action="store_true", help="Avoid computing RDR edges.")
if __name__ == "__main__":
conf = ConfigManager()
parser = argparse.ArgumentParser(description='Generate scene-toolkit compatible json files.')
conf.add_args(parser)
add_args(parser)
args = parser.parse_args()
conf.process_args(args, output_is_dir=True)
run_args(conf, args)
|
[
"r2vstk.house.House",
"r2vstk.config_manager.ConfigManager",
"argparse.ArgumentParser",
"json.dumps"
] |
[((463, 470), 'r2vstk.house.House', 'House', ([], {}), '()\n', (468, 470), False, 'from r2vstk.house import House\n'), ((6041, 6056), 'r2vstk.config_manager.ConfigManager', 'ConfigManager', ([], {}), '()\n', (6054, 6056), False, 'from r2vstk.config_manager import ConfigManager\n'), ((6070, 6159), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Generate scene-toolkit compatible json files."""'}), "(description=\n 'Generate scene-toolkit compatible json files.')\n", (6093, 6159), False, 'import argparse\n'), ((3200, 3232), 'json.dumps', 'json.dumps', (['scene_json'], {'indent': '(3)'}), '(scene_json, indent=3)\n', (3210, 3232), False, 'import json\n'), ((3523, 3561), 'json.dumps', 'json.dumps', (['object_aabb_json'], {'indent': '(3)'}), '(object_aabb_json, indent=3)\n', (3533, 3561), False, 'import json\n'), ((2808, 2839), 'json.dumps', 'json.dumps', (['room_json'], {'indent': '(3)'}), '(room_json, indent=3)\n', (2818, 2839), False, 'import json\n')]
|
# coding=utf-8
"""ldif3 - generate and parse LDIF data (see RFC 2849)."""
from __future__ import unicode_literals
import base64
import logging
import re
from collections import OrderedDict
try: # pragma: nocover
# noinspection PyCompatibility
from urlparse import urlparse
from urllib import urlopen
except ImportError: # pragma: nocover
# noinspection PyCompatibility,PyUnresolvedReferences
from urllib.parse import urlparse
# noinspection PyUnresolvedReferences,PyCompatibility
from urllib.request import urlopen
__version__ = "3.2.0"
__all__ = [
# constants
"LDIF_PATTERN",
# classes
"LDIFWriter",
"LDIFParser",
]
log = logging.getLogger("ldif3")
ATTRTYPE_PATTERN = r"[\w;.-]+(;[\w_-]+)*"
ATTRVALUE_PATTERN = r'(([^,]|\\,)+|".*?")'
ATTR_PATTERN = ATTRTYPE_PATTERN + r"[ ]*=[ ]*" + ATTRVALUE_PATTERN
RDN_PATTERN = ATTR_PATTERN + r"([ ]*\+[ ]*" + ATTR_PATTERN + r")*[ ]*"
DN_PATTERN = RDN_PATTERN + r"([ ]*,[ ]*" + RDN_PATTERN + r")*[ ]*"
DN_REGEX = re.compile("^%s$" % DN_PATTERN)
LDIF_PATTERN = (
"^((dn(:|::) %(DN_PATTERN)s)|(%(ATTRTYPE_PATTERN)" "s(:|::) .*)$)+" % vars()
)
MOD_OPS = ["add", "delete", "replace"]
CHANGE_TYPES = ["add", "delete", "modify", "modrdn"]
def is_dn(s):
"""Return True if s is a LDAP DN."""
if s == "":
return True
rm = DN_REGEX.match(s)
return rm is not None and rm.group(0) == s
def lower(l):
"""Return a list with the lowercased items of l."""
return [i.lower() for i in l or []]
class LDIFParser(object):
"""Read LDIF entry or change records from file object.
:type input_file: file-like object in binary mode
:param input_file: file to read the LDIF input from
:type ignored_attr_types: List[string]
:param ignored_attr_types: List of attribute types that will be ignored
:type process_url_schemes: List[bytearray]
:param process_url_schemes: List of URL schemes to process with urllib.
An empty list turns off all URL processing and the attribute is
ignored completely.
:type line_sep: bytearray
:param line_sep: line separator
:type encoding: string
:param encoding: Encoding to use for converting values to unicode strings.
If decoding failes, the raw bytestring will be used instead. You can
also pass ``None`` which will skip decoding and always produce
bytestrings. Note that this only applies to entry values. ``dn`` and
entry keys will always be unicode strings.
:type strict: boolean
:param strict: If set to ``False``, recoverable parse errors will produce
log warnings rather than exceptions.
"""
# noinspection PyMethodMayBeStatic
def _strip_line_sep(self, s):
"""Strip trailing line separators from s, but no other whitespaces."""
if s[-2:] == b"\r\n":
return s[:-2]
elif s[-1:] == b"\n":
return s[:-1]
else:
return s
# noinspection PyDefaultArgument
def __init__(
self,
input_file,
ignored_attr_types=[],
process_url_schemes=[],
line_sep=b"\n",
encoding="utf8",
strict=True,
):
self._input_file = input_file
self._process_url_schemes = lower(process_url_schemes)
self._ignored_attr_types = lower(ignored_attr_types)
self._line_sep = line_sep
self._encoding = encoding
self._strict = strict
self.line_counter = 0 #: number of lines that have been read
self.byte_counter = 0 #: number of bytes that have been read
self.records_read = 0 #: number of records that have been read
def _iter_unfolded_lines(self):
"""Iter input unfoled lines. Skip comments."""
line = self._input_file.readline()
while line:
self.line_counter += 1
self.byte_counter += len(line)
line = self._strip_line_sep(line)
nextline = self._input_file.readline()
while nextline and nextline[:1] == b" ":
line += self._strip_line_sep(nextline)[1:]
nextline = self._input_file.readline()
if not line.startswith(b"#"):
yield line
line = nextline
def _iter_blocks(self):
"""Iter input lines in blocks separated by blank lines."""
lines = []
for line in self._iter_unfolded_lines():
if line:
lines.append(line)
else:
self.records_read += 1
yield lines
lines = []
if lines:
self.records_read += 1
yield lines
def _parse_attr(self, line):
"""Parse a single attribute type/value pair."""
colon_pos = line.index(b":")
attr_type = line[0:colon_pos].decode("ascii")
if line[colon_pos:].startswith(b"::"):
attr_value = base64.decodestring(line[colon_pos + 2 :])
elif line[colon_pos:].startswith(b":<"):
url = line[colon_pos + 2 :].strip()
attr_value = b""
if self._process_url_schemes:
u = urlparse(url)
if u[0] in self._process_url_schemes:
attr_value = urlopen(url.decode("ascii")).read()
else:
attr_value = line[colon_pos + 1 :].strip()
if attr_type == "dn":
return attr_type, attr_value.decode("utf8")
elif self._encoding is not None:
try:
return attr_type, attr_value.decode(self._encoding)
except UnicodeError:
pass
return attr_type, attr_value
def _error(self, msg):
if self._strict:
raise ValueError(msg)
else:
log.warning(msg)
def _check_dn(self, dn, attr_value):
"""Check dn attribute for issues."""
if dn is not None:
self._error("Two lines starting with dn: in one record.")
if not is_dn(attr_value):
self._error(
"No valid string-representation of "
"distinguished name %s." % attr_value
)
def _check_changetype(self, dn, changetype, attr_value):
"""Check changetype attribute for issues."""
if dn is None:
self._error("Read changetype: before getting valid dn: line.")
if changetype is not None:
self._error("Two lines starting with changetype: in one record.")
if attr_value not in CHANGE_TYPES:
self._error("changetype value %s is invalid." % attr_value)
def _parse_entry_record(self, lines):
"""Parse a single entry record from a list of lines."""
dn = None
entry = OrderedDict()
for line in lines:
attr_type, attr_value = self._parse_attr(line)
if attr_type == "dn":
self._check_dn(dn, attr_value)
dn = attr_value
elif attr_type == "version" and dn is None:
pass # version = 1
else:
if dn is None:
self._error(
"First line of record does not start "
'with "dn:": %s' % attr_type
)
if (
attr_value is not None
and attr_type.lower() not in self._ignored_attr_types
):
if attr_type in entry:
entry[attr_type].append(attr_value)
else:
entry[attr_type] = [attr_value]
return dn, entry
def parse(self):
"""Iterate LDIF entry records.
:rtype: Iterator[Tuple[string, Dict]]
:return: (dn, entry)
"""
for block in self._iter_blocks():
yield self._parse_entry_record(block)
|
[
"base64.decodestring",
"logging.getLogger",
"collections.OrderedDict",
"urllib.parse.urlparse",
"re.compile"
] |
[((680, 706), 'logging.getLogger', 'logging.getLogger', (['"""ldif3"""'], {}), "('ldif3')\n", (697, 706), False, 'import logging\n'), ((1009, 1040), 're.compile', 're.compile', (["('^%s$' % DN_PATTERN)"], {}), "('^%s$' % DN_PATTERN)\n", (1019, 1040), False, 'import re\n'), ((6744, 6757), 'collections.OrderedDict', 'OrderedDict', ([], {}), '()\n', (6755, 6757), False, 'from collections import OrderedDict\n'), ((4927, 4968), 'base64.decodestring', 'base64.decodestring', (['line[colon_pos + 2:]'], {}), '(line[colon_pos + 2:])\n', (4946, 4968), False, 'import base64\n'), ((5158, 5171), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (5166, 5171), False, 'from urllib.parse import urlparse\n')]
|
import pytest
import pymysql
from unittest.mock import MagicMock
from prefect.tasks.mysql.mysql import MySQLExecute, MySQLFetch
class TestMySQLExecute:
def test_construction(self):
task = MySQLExecute(db_name="test", user="test", password="<PASSWORD>", host="test")
assert (task.commit is False) and (task.charset == "utf8mb4")
def test_query_string_must_be_provided(self):
task = MySQLExecute(db_name="test", user="test", password="<PASSWORD>", host="test")
with pytest.raises(ValueError, match="A query string must be provided"):
task.run()
def test_run_args_used_over_init_args(self, monkeypatch):
mock_connect = MagicMock()
monkeypatch.setattr("pymysql.connect", mock_connect)
task = MySQLExecute(
db_name="test", user="test", password="<PASSWORD>", host="test"
)
task.run(query="select * from users", password="<PASSWORD>")
assert mock_connect.call_args[1]["password"] == "<PASSWORD>"
class TestMySQLFetch:
def test_construction(self):
task = MySQLFetch(db_name="test", user="test", password="<PASSWORD>", host="test")
assert task.fetch == "one"
def test_query_string_must_be_provided(self):
task = MySQLFetch(db_name="test", user="test", password="<PASSWORD>", host="test")
with pytest.raises(ValueError, match="A query string must be provided"):
task.run()
def test_bad_fetch_param_raises(self):
task = MySQLFetch(db_name="test", user="test", password="<PASSWORD>", host="test")
with pytest.raises(
ValueError,
match=r"The 'fetch' parameter must be one of the following - \('one', 'many', 'all'\)",
):
task.run(query="SELECT * FROM some_table", fetch="not a valid parameter")
def test_construction_with_cursor_type_str(self):
task = MySQLFetch(
db_name="test",
user="test",
password="test",
host="test",
cursor_type="dictcursor",
)
assert task.cursor_type == "dictcursor"
def test_construction_with_cursor_type_class(self):
task = MySQLFetch(
db_name="test",
user="test",
password="<PASSWORD>",
host="test",
cursor_type=pymysql.cursors.DictCursor,
)
assert task.cursor_type == pymysql.cursors.DictCursor
def test_unsupported_cursor_type_str_param_raises(self):
cursor_type = "unsupportedcursor"
task = MySQLFetch(db_name="test", user="test", password="<PASSWORD>", host="test")
with pytest.raises(
TypeError,
match=rf"'cursor_type' should be one of \['cursor', 'dictcursor', 'sscursor', 'ssdictcursor'\] or a full cursor class, got {cursor_type}",
):
task.run(query="SELECT * FROM some_table", cursor_type=cursor_type)
def test_bad_cursor_type_param_type_raises(self):
task = MySQLFetch(db_name="test", user="test", password="<PASSWORD>", host="test")
with pytest.raises(
TypeError,
match=rf"'cursor_type' should be one of \['cursor', 'dictcursor', 'sscursor', 'ssdictcursor'\] or a full cursor class, got \['cursor'\]",
):
task.run(query="SELECT * FROM some_table", cursor_type=["cursor"])
def test_run_args_used_over_init_args(self, monkeypatch):
mock_connect = MagicMock()
monkeypatch.setattr("pymysql.connect", mock_connect)
task = MySQLFetch(
db_name="test", user="test", password="<PASSWORD>", host="test"
)
task.run(query="select * from users", password="<PASSWORD>")
assert mock_connect.call_args[1]["password"] == "<PASSWORD>"
|
[
"pytest.raises",
"prefect.tasks.mysql.mysql.MySQLFetch",
"prefect.tasks.mysql.mysql.MySQLExecute",
"unittest.mock.MagicMock"
] |
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|
from abc import ABC, abstractmethod
import numpy as np
from scipy import stats
import torch
class Experiment(ABC):
'''
An Experiment manages the basic train/test loop and logs results.
Args:
writer (:torch.logging.writer:): A Writer object used for logging.
quiet (bool): If False, the Experiment will print information about
episode returns to standard out.
'''
def __init__(self, writer, quiet):
self._writer = writer
self._quiet = quiet
self._best_returns = -np.inf
self._returns100 = []
@abstractmethod
def train(self, frames=np.inf, episodes=np.inf):
'''
Train the agent for a certain number of frames or episodes.
If both frames and episodes are specified, then the training loop will exit
when either condition is satisfied.
Args:
frames (int): The maximum number of training frames.
episodes (bool): The maximum number of training episodes.
'''
@abstractmethod
def test(self, episodes=100):
'''
Test the agent in eval mode for a certain number of episodes.
Args:
episodes (int): The number of test episodes.
Returns:
list(float): A list of all returns received during testing.
'''
@property
@abstractmethod
def frame(self):
'''The index of the current training frame.'''
@property
@abstractmethod
def episode(self):
'''The index of the current training episode'''
def _log_training_episode(self, returns, fps):
if not self._quiet:
print('episode: {}, frame: {}, fps: {}, returns: {}'.format(self.episode, self.frame, int(fps), returns))
if returns > self._best_returns:
self._best_returns = returns
self._returns100.append(returns)
if len(self._returns100) == 100:
mean = np.mean(self._returns100)
std = np.std(self._returns100)
self._writer.add_summary('returns100', mean, std, step="frame")
self._returns100 = []
self._writer.add_evaluation('returns/episode', returns, step="episode")
self._writer.add_evaluation('returns/frame', returns, step="frame")
self._writer.add_evaluation("returns/max", self._best_returns, step="frame")
self._writer.add_scalar('fps', fps, step="frame")
def _log_test_episode(self, episode, returns):
if not self._quiet:
print('test episode: {}, returns: {}'.format(episode, returns))
def _log_test(self, returns):
if not self._quiet:
print('test returns (mean ± sem): {} ± {}'.format(np.mean(returns), stats.sem(returns)))
self._writer.add_summary('returns-test', np.mean(returns), np.std(returns))
def save(self):
return self._preset.save('{}/preset.pt'.format(self._writer.log_dir))
def close(self):
self._writer.close()
|
[
"numpy.std",
"numpy.mean",
"scipy.stats.sem"
] |
[((1956, 1981), 'numpy.mean', 'np.mean', (['self._returns100'], {}), '(self._returns100)\n', (1963, 1981), True, 'import numpy as np\n'), ((2000, 2024), 'numpy.std', 'np.std', (['self._returns100'], {}), '(self._returns100)\n', (2006, 2024), True, 'import numpy as np\n'), ((2803, 2819), 'numpy.mean', 'np.mean', (['returns'], {}), '(returns)\n', (2810, 2819), True, 'import numpy as np\n'), ((2821, 2836), 'numpy.std', 'np.std', (['returns'], {}), '(returns)\n', (2827, 2836), True, 'import numpy as np\n'), ((2717, 2733), 'numpy.mean', 'np.mean', (['returns'], {}), '(returns)\n', (2724, 2733), True, 'import numpy as np\n'), ((2735, 2753), 'scipy.stats.sem', 'stats.sem', (['returns'], {}), '(returns)\n', (2744, 2753), False, 'from scipy import stats\n')]
|
from google.cloud import vision
import os
import io
import time
start_time = time.time()
path = 'C:/Users/lukas/Desktop/vision_key.json'
os.environ["GOOGLE_APPLICATION_CREDENTIALS"]=path
client = vision.ImageAnnotatorClient()
path = './Resources/Photos/lady.jpg'
with io.open(path, 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
response = client.face_detection(image=image)
likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
'LIKELY', 'VERY_LIKELY')
for face in response.face_annotations:
# likelihood = vision.Likelihood(face.joy_likelihood)
# vertices = ['(%s,%s)' % (v.x, v.y) for v in face.bounding_poly.vertices]
# print('Face surprised:', likelihood.name)
# print('Face bounds:', ",".join(vertices))
print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))
print('sorrow: {} \n'.format(likelihood_name[face.sorrow_likelihood]))
print("--- %s seconds ---" % (time.time() - start_time))
print('DONE')
|
[
"google.cloud.vision.Image",
"google.cloud.vision.ImageAnnotatorClient",
"io.open",
"time.time"
] |
[((78, 89), 'time.time', 'time.time', ([], {}), '()\n', (87, 89), False, 'import time\n'), ((198, 227), 'google.cloud.vision.ImageAnnotatorClient', 'vision.ImageAnnotatorClient', ([], {}), '()\n', (225, 227), False, 'from google.cloud import vision\n'), ((351, 380), 'google.cloud.vision.Image', 'vision.Image', ([], {'content': 'content'}), '(content=content)\n', (363, 380), False, 'from google.cloud import vision\n'), ((271, 290), 'io.open', 'io.open', (['path', '"""rb"""'], {}), "(path, 'rb')\n", (278, 290), False, 'import io\n'), ((1169, 1180), 'time.time', 'time.time', ([], {}), '()\n', (1178, 1180), False, 'import time\n')]
|
#!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Handy helper utils to work with XARs at runtime"""
from __future__ import absolute_import, division, print_function, unicode_literals
import os
def get_runtime_path():
# type: (...) -> str
"""Return the location of the runtime files directory"""
runtime_path = os.getenv("XAR_RUNTIME_FILES")
if runtime_path:
if not os.access(runtime_path, os.R_OK):
raise ValueError("XAR_RUNTIME_FILES is invalid: %s" % runtime_path)
return runtime_path
raise ValueError("Cannot determine runtime files path.")
|
[
"os.getenv",
"os.access"
] |
[((374, 404), 'os.getenv', 'os.getenv', (['"""XAR_RUNTIME_FILES"""'], {}), "('XAR_RUNTIME_FILES')\n", (383, 404), False, 'import os\n'), ((441, 473), 'os.access', 'os.access', (['runtime_path', 'os.R_OK'], {}), '(runtime_path, os.R_OK)\n', (450, 473), False, 'import os\n')]
|
from setuptools import setup, find_packages
deps = [
"PyYAML==5.3",
"jsonschema==3.2.0",
]
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
name="py-bt",
version="1.2.1",
author="<NAME>",
author_email="<EMAIL>",
description="Python package for modelling and executing Behaviour Trees.",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/dlavelle7/py-bt",
packages=find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
package_dir={'': '.'},
include_package_data=True,
install_requires=deps,
zip_safe=True,
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License"
],
)
|
[
"setuptools.find_packages"
] |
[((493, 560), 'setuptools.find_packages', 'find_packages', ([], {'exclude': "['*.tests', '*.tests.*', 'tests.*', 'tests']"}), "(exclude=['*.tests', '*.tests.*', 'tests.*', 'tests'])\n", (506, 560), False, 'from setuptools import setup, find_packages\n')]
|
# Databricks CLI
# Copyright 2017 Databricks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"), except
# that the use of services to which certain application programming
# interfaces (each, an "API") connect requires that the user first obtain
# a license for the use of the APIs from Databricks, Inc. ("Databricks"),
# by creating an account at www.databricks.com and agreeing to either (a)
# the Community Edition Terms of Service, (b) the Databricks Terms of
# Service, or (c) another written agreement between Licensee and Databricks
# for the use of the APIs.
#
# You may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint:disable=protected-access
import json
import mock
import click
from click.testing import CliRunner
import databricks_cli.configure.config as config
from databricks_cli.utils import InvalidConfigurationError, eat_exceptions
from databricks_cli.configure.provider import DatabricksConfig
from databricks_cli.click_types import ContextObject
from tests.utils import provide_conf
@provide_conf
def test_debug_option():
# Test that context object debug_mode property changes with --debug flag fed.
@click.command()
@click.option('--debug-fed', type=bool)
@config.debug_option
def test_debug(debug_fed): # noqa
ctx = click.get_current_context()
context_object = ctx.ensure_object(ContextObject)
assert context_object.debug_mode is debug_fed
result = CliRunner().invoke(test_debug, ['--debug', '--debug-fed', True])
assert result.exit_code == 0
result = CliRunner().invoke(test_debug, ['--debug-fed', False])
assert result.exit_code == 0
# Test that with eat_exceptions wrapper, 'Traceback' appears or doesn't appear depending on
# whether --debug flag is given.
@click.command()
@config.debug_option
@eat_exceptions
def test_debug_traceback(): # noqa
assert False
result = CliRunner().invoke(test_debug_traceback, ['--debug'])
assert result.exit_code == 1
assert 'Traceback' in result.output
result = CliRunner().invoke(test_debug_traceback)
assert result.exit_code == 1
assert 'Traceback' not in result.output
@provide_conf
def test_provide_api_client():
@click.command()
@click.option('--x', required=True)
@config.profile_option
@config.provide_api_client
def test_command(api_client, x): # noqa
click.echo(x)
result = CliRunner().invoke(test_command, ['--x', '1'])
assert result.exit_code == 0
assert result.output == '1\n'
def test_provide_api_client_invalid():
@click.command()
@click.option('--x', required=True)
@config.profile_option
@config.provide_api_client
def test_command(api_client, x): # noqa
click.echo(x)
result = CliRunner().invoke(test_command, ['--x', '1'])
assert result.exit_code == 1
assert isinstance(result.exception, InvalidConfigurationError)
TEST_PROFILE_1 = 'test-profile-1'
TEST_PROFILE_2 = 'test-profile-2'
def test_provide_profile_twice():
@click.group()
@config.profile_option
def test_group():
pass
@click.command()
@config.profile_option
def test_command(): # noqa
pass
test_group.add_command(test_command, 'test')
result = CliRunner().invoke(test_group, ['--profile', TEST_PROFILE_1, 'test', '--profile',
TEST_PROFILE_2])
assert '--profile can only be provided once. The profiles [{}, {}] were provided.'.format(
TEST_PROFILE_1, TEST_PROFILE_2) in result.output
TEST_HOST = 'https://test.cloud.databricks.com'
TEST_TOKEN = '<PASSWORD>'
def test_command_headers():
@click.group()
@config.profile_option
def outer_test_group():
pass
@click.group()
@config.profile_option
def inner_test_group():
pass
@click.command()
@click.option('--x', required=True)
@config.profile_option
@config.provide_api_client
def test_command(api_client, x): # noqa
click.echo(json.dumps(api_client.default_headers))
with mock.patch("databricks_cli.configure.config.get_config") as config_mock:
with mock.patch("uuid.uuid1") as uuid_mock:
config_mock.return_value = DatabricksConfig.from_token(TEST_HOST, TEST_TOKEN)
uuid_mock.return_value = '1234'
inner_test_group.add_command(test_command, 'subcommand')
outer_test_group.add_command(inner_test_group, 'command')
result = CliRunner().invoke(outer_test_group, ['command', 'subcommand', '--x', '12'])
assert result.exception is None
default_headers = json.loads(result.output)
assert 'user-agent' in default_headers
assert "command-subcommand-1234" in default_headers['user-agent']
|
[
"json.loads",
"click.get_current_context",
"click.option",
"click.echo",
"mock.patch",
"click.command",
"json.dumps",
"databricks_cli.configure.provider.DatabricksConfig.from_token",
"click.testing.CliRunner",
"click.group"
] |
[((1582, 1597), 'click.command', 'click.command', ([], {}), '()\n', (1595, 1597), False, 'import click\n'), ((1603, 1641), 'click.option', 'click.option', (['"""--debug-fed"""'], {'type': 'bool'}), "('--debug-fed', type=bool)\n", (1615, 1641), False, 'import click\n'), ((2210, 2225), 'click.command', 'click.command', ([], {}), '()\n', (2223, 2225), False, 'import click\n'), ((2655, 2670), 'click.command', 'click.command', ([], {}), '()\n', (2668, 2670), False, 'import click\n'), ((2676, 2710), 'click.option', 'click.option', (['"""--x"""'], {'required': '(True)'}), "('--x', required=True)\n", (2688, 2710), False, 'import click\n'), ((3009, 3024), 'click.command', 'click.command', ([], {}), '()\n', (3022, 3024), False, 'import click\n'), ((3030, 3064), 'click.option', 'click.option', (['"""--x"""'], {'required': '(True)'}), "('--x', required=True)\n", (3042, 3064), False, 'import click\n'), ((3461, 3474), 'click.group', 'click.group', ([], {}), '()\n', (3472, 3474), False, 'import click\n'), ((3543, 3558), 'click.command', 'click.command', ([], {}), '()\n', (3556, 3558), False, 'import click\n'), ((4101, 4114), 'click.group', 'click.group', ([], {}), '()\n', (4112, 4114), False, 'import click\n'), ((4189, 4202), 'click.group', 'click.group', ([], {}), '()\n', (4200, 4202), False, 'import click\n'), ((4277, 4292), 'click.command', 'click.command', ([], {}), '()\n', (4290, 4292), False, 'import click\n'), ((4298, 4332), 'click.option', 'click.option', (['"""--x"""'], {'required': '(True)'}), "('--x', required=True)\n", (4310, 4332), False, 'import click\n'), ((1719, 1746), 'click.get_current_context', 'click.get_current_context', ([], {}), '()\n', (1744, 1746), False, 'import click\n'), ((2821, 2834), 'click.echo', 'click.echo', (['x'], {}), '(x)\n', (2831, 2834), False, 'import click\n'), ((3175, 3188), 'click.echo', 'click.echo', (['x'], {}), '(x)\n', (3185, 3188), False, 'import click\n'), ((4504, 4560), 'mock.patch', 'mock.patch', (['"""databricks_cli.configure.config.get_config"""'], {}), "('databricks_cli.configure.config.get_config')\n", (4514, 4560), False, 'import mock\n'), ((1872, 1883), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (1881, 1883), False, 'from click.testing import CliRunner\n'), ((1983, 1994), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (1992, 1994), False, 'from click.testing import CliRunner\n'), ((2345, 2356), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (2354, 2356), False, 'from click.testing import CliRunner\n'), ((2485, 2496), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (2494, 2496), False, 'from click.testing import CliRunner\n'), ((2849, 2860), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (2858, 2860), False, 'from click.testing import CliRunner\n'), ((3203, 3214), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (3212, 3214), False, 'from click.testing import CliRunner\n'), ((3694, 3705), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (3703, 3705), False, 'from click.testing import CliRunner\n'), ((4454, 4492), 'json.dumps', 'json.dumps', (['api_client.default_headers'], {}), '(api_client.default_headers)\n', (4464, 4492), False, 'import json\n'), ((4590, 4614), 'mock.patch', 'mock.patch', (['"""uuid.uuid1"""'], {}), "('uuid.uuid1')\n", (4600, 4614), False, 'import mock\n'), ((4668, 4718), 'databricks_cli.configure.provider.DatabricksConfig.from_token', 'DatabricksConfig.from_token', (['TEST_HOST', 'TEST_TOKEN'], {}), '(TEST_HOST, TEST_TOKEN)\n', (4695, 4718), False, 'from databricks_cli.configure.provider import DatabricksConfig\n'), ((5074, 5099), 'json.loads', 'json.loads', (['result.output'], {}), '(result.output)\n', (5084, 5099), False, 'import json\n'), ((4923, 4934), 'click.testing.CliRunner', 'CliRunner', ([], {}), '()\n', (4932, 4934), False, 'from click.testing import CliRunner\n')]
|
#Local modules
from loguru import logger
from queue import Queue
import sys
import threading
from core.PortScanner import Port_Scanner
from modules.test_time import execution_time
from modules.document import create_document
from modules.ranges import get_ranges
class Scanner():
def __init__(self,start,end):
self.targets = get_ranges(start,end)
self.results = Queue()
def set_ports(self,ports):
self.ports = ports
def job(self,q,timeout):
try:
pool_sema.acquire()
while not q.empty():
ip = q.get()
Scanner = Port_Scanner(ip)
Scanner.start(timeout,self.ports)
if Scanner.contain_results():
ports, banners, hostname = Scanner.get_results()
device = create_document(ip,ports,banners,hostname)
self.results.put(device)
q.task_done()
finally:
pool_sema.release()
@execution_time
def start_threads(self,threads,timeout):
#Implemeting Queue, safe threading
q = Queue()
global pool_sema
logger.info("Searching connected devices, please wait")
#Semaphore object limit max number of threads in paralell
pool_sema = threading.Semaphore(value=400)
#Count total of results with Queue
try:
logger.info("Launching threads")
for j in self.targets:
q.put(j)
logger.info("Waiting for Queue to complete, {} jobs".format(q.qsize()))
for i in range(threads):
thread = threading.Thread(target=self.job, args=(q,timeout),daemon=True)
thread.start()
q.join()
logger.info("Total Discovered devices: {}".format(self.results.qsize()))
except KeyboardInterrupt:
logger.info("You pressed CTRL+C")
sys.exit(1)
def slack_message(self):
return self.results.qsize()
def get_devices(self):
return list(self.results.queue)
|
[
"threading.Thread",
"modules.document.create_document",
"loguru.logger.info",
"core.PortScanner.Port_Scanner",
"modules.ranges.get_ranges",
"threading.Semaphore",
"queue.Queue",
"sys.exit"
] |
[((340, 362), 'modules.ranges.get_ranges', 'get_ranges', (['start', 'end'], {}), '(start, end)\n', (350, 362), False, 'from modules.ranges import get_ranges\n'), ((385, 392), 'queue.Queue', 'Queue', ([], {}), '()\n', (390, 392), False, 'from queue import Queue\n'), ((1139, 1146), 'queue.Queue', 'Queue', ([], {}), '()\n', (1144, 1146), False, 'from queue import Queue\n'), ((1181, 1236), 'loguru.logger.info', 'logger.info', (['"""Searching connected devices, please wait"""'], {}), "('Searching connected devices, please wait')\n", (1192, 1236), False, 'from loguru import logger\n'), ((1323, 1353), 'threading.Semaphore', 'threading.Semaphore', ([], {'value': '(400)'}), '(value=400)\n', (1342, 1353), False, 'import threading\n'), ((1422, 1454), 'loguru.logger.info', 'logger.info', (['"""Launching threads"""'], {}), "('Launching threads')\n", (1433, 1454), False, 'from loguru import logger\n'), ((616, 632), 'core.PortScanner.Port_Scanner', 'Port_Scanner', (['ip'], {}), '(ip)\n', (628, 632), False, 'from core.PortScanner import Port_Scanner\n'), ((1662, 1727), 'threading.Thread', 'threading.Thread', ([], {'target': 'self.job', 'args': '(q, timeout)', 'daemon': '(True)'}), '(target=self.job, args=(q, timeout), daemon=True)\n', (1678, 1727), False, 'import threading\n'), ((1912, 1945), 'loguru.logger.info', 'logger.info', (['"""You pressed CTRL+C"""'], {}), "('You pressed CTRL+C')\n", (1923, 1945), False, 'from loguru import logger\n'), ((1958, 1969), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (1966, 1969), False, 'import sys\n'), ((828, 873), 'modules.document.create_document', 'create_document', (['ip', 'ports', 'banners', 'hostname'], {}), '(ip, ports, banners, hostname)\n', (843, 873), False, 'from modules.document import create_document\n')]
|
#!python
import os
import jsonpickle
from jinja2 import Environment, FileSystemLoader, select_autoescape
import sys
reload(sys)
sys.setdefaultencoding('utf8')
target = 'public_html'
if not os.path.exists('%s/books' % target):
os.makedirs('%s/books' % target)
if not os.path.exists('%s/authors' % target):
os.makedirs('%s/authors' % target)
with open('library.json') as lib:
library = jsonpickle.decode(lib.read())
all_books = library['books']
all_authors = library['authors']
env = Environment(loader=FileSystemLoader('.generator'))
main_template = env.get_template('index.html')
book_template = env.get_template('book.html')
author_template = env.get_template('author.html')
with open('%s/index.html' % (target), 'w') as out:
out.write(main_template.render(books=all_books))
for book in all_books:
with open('%s/books/%s.html' % (target, book['id']), 'w') as out:
out.write(book_template.render(book=book))
for author_id, author in all_authors.iteritems():
with open('%s/authors/%s.html' % (target, author_id), 'w') as out:
out.write(author_template.render(author=author))
|
[
"jinja2.FileSystemLoader",
"os.path.exists",
"os.makedirs",
"sys.setdefaultencoding"
] |
[((130, 160), 'sys.setdefaultencoding', 'sys.setdefaultencoding', (['"""utf8"""'], {}), "('utf8')\n", (152, 160), False, 'import sys\n'), ((193, 228), 'os.path.exists', 'os.path.exists', (["('%s/books' % target)"], {}), "('%s/books' % target)\n", (207, 228), False, 'import os\n'), ((234, 266), 'os.makedirs', 'os.makedirs', (["('%s/books' % target)"], {}), "('%s/books' % target)\n", (245, 266), False, 'import os\n'), ((274, 311), 'os.path.exists', 'os.path.exists', (["('%s/authors' % target)"], {}), "('%s/authors' % target)\n", (288, 311), False, 'import os\n'), ((317, 351), 'os.makedirs', 'os.makedirs', (["('%s/authors' % target)"], {}), "('%s/authors' % target)\n", (328, 351), False, 'import os\n'), ((520, 550), 'jinja2.FileSystemLoader', 'FileSystemLoader', (['""".generator"""'], {}), "('.generator')\n", (536, 550), False, 'from jinja2 import Environment, FileSystemLoader, select_autoescape\n')]
|
from openwebinars.Openwebinar import Openwebinar
def test_openwebinar():
taller_a = 'TallerA'
openwebinar = Openwebinar(taller_a)
assert str(openwebinar) == taller_a
taller_b = 1
openwebinar = Openwebinar(taller_b)
assert str(openwebinar) == ''
|
[
"openwebinars.Openwebinar.Openwebinar"
] |
[((117, 138), 'openwebinars.Openwebinar.Openwebinar', 'Openwebinar', (['taller_a'], {}), '(taller_a)\n', (128, 138), False, 'from openwebinars.Openwebinar import Openwebinar\n'), ((215, 236), 'openwebinars.Openwebinar.Openwebinar', 'Openwebinar', (['taller_b'], {}), '(taller_b)\n', (226, 236), False, 'from openwebinars.Openwebinar import Openwebinar\n')]
|
from __future__ import absolute_import
from configparser import ConfigParser
import logging
import json
from contextlib import contextmanager
from unittest.mock import patch, Mock
from urllib.parse import urlencode
import pytest
from flask import Flask
from werkzeug.exceptions import Forbidden
from peerscout.utils.config import dict_to_config
from peerscout.server.config.search_config import SEARCH_SECTION_PREFIX
from peerscout.shared.database import populated_in_memory_database
from peerscout.server.blueprints import api as api_module
from peerscout.server.blueprints.api import create_api_blueprint, ApiAuth, DEFAULT_LIMIT
LOGGER = logging.getLogger(__name__)
VALUE_1 = 'value1'
VALUE_2 = 'value2'
VALUE_3 = 'value3'
DOMAIN_1 = 'domain1'
ROLE_1 = 'role1'
ROLE_2 = 'role2'
EMAIL_1 = 'email1'
MANUSCRIPT_NO_1 = '12345'
MANUSCRIPT_NO_2 = '22222'
VERSION_ID_1 = '%s-1' % MANUSCRIPT_NO_1
LIMIT_1 = 123
SEARCH_TYPE_1 = 'search_type1'
SEARCH_TYPE_2 = 'search_type2'
SEARCH_TYPE_3 = 'search_type3'
SEARCH_TYPE_TITLE_1 = 'Search Type 1'
SEARCH_TYPE_TITLE_2 = 'Search Type 2'
SEARCH_TYPE_TITLE_3 = 'Search Type 3'
SOME_RESPONSE = {
'some-response': VALUE_1
}
def setup_module():
logging.root.handlers = []
logging.basicConfig(level='DEBUG')
@contextmanager
def _api_test_client(config, dataset):
m = api_module
with populated_in_memory_database(dataset, autocommit=True) as db:
with patch.object(m, 'connect_configured_database') as connect_configured_database_mock:
connect_configured_database_mock.return_value = db
blueprint, reload_api = create_api_blueprint(config)
app = Flask(__name__)
app.register_blueprint(blueprint)
assert reload_api
yield app.test_client()
def _get_json(response):
return json.loads(response.data.decode('utf-8'))
def _get_ok_json(response):
assert response.status_code == 200
return _get_json(response)
def _assert_partial_called_with(mock, **kwargs):
mock.assert_called()
assert {k: v for k, v in mock.call_args[1].items() if k in kwargs} == kwargs
def _named_mock(name, **kwargs):
mock = Mock(name=name, **kwargs)
mock.__name__ = name
return mock
def _setup_flask_auth0_mock_email(MockFlaskAuth0, **kwargs):
def wrapper(f):
result = lambda **fkwargs: f(**kwargs, **fkwargs) # noqa: E731
result.__name__ = f.__name__
return result
_wrap_request_handler = MockFlaskAuth0.return_value.wrap_request_handler
_wrap_request_handler.side_effect = wrapper
return MockFlaskAuth0
@pytest.fixture(name='MockRecommendReviewers', autouse=True)
def _mock_recommend_reviewers():
with patch.object(api_module, 'RecommendReviewers') as MockRecommendReviewers:
MockRecommendReviewers.return_value.recommend.return_value = SOME_RESPONSE
yield MockRecommendReviewers
@pytest.fixture(name='MockFlaskAuth0')
def _mock_flask_auth_0():
with patch.object(api_module, 'FlaskAuth0') as MockFlaskAuth0:
yield MockFlaskAuth0
@pytest.mark.slow
class TestApiBlueprint:
class TestKeywords:
def test_should_return_keywords(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
MockRecommendReviewers.return_value.get_all_keywords.return_value = [
VALUE_1, VALUE_2]
response = test_client.get('/keywords')
assert _get_ok_json(response) == [VALUE_1, VALUE_2]
class TestSubjectAreas:
def test_should_return_subject_areas(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
MockRecommendReviewers.return_value.get_all_subject_areas.return_value = [
VALUE_1, VALUE_2]
response = test_client.get('/subject-areas')
assert _get_ok_json(response) == [VALUE_1, VALUE_2]
class TestConfig:
def test_should_return_client_config(self, MockRecommendReviewers):
client_config = {
'some key': VALUE_1
}
config = dict_to_config({
'client': client_config
})
with _api_test_client(config, {}) as test_client:
MockRecommendReviewers.return_value.get_all_subject_areas.return_value = [
VALUE_1, VALUE_2]
response = test_client.get('/config')
assert _get_ok_json(response) == client_config
class TestSearchTypes:
@pytest.mark.parametrize('email, required_role, auth0_domain', [
(None, None, None),
(None, None, DOMAIN_1),
('<EMAIL>', ROLE_1, DOMAIN_1)
])
def test_should_return_all_search_types_if_authorisation_is_not_required(
self, MockFlaskAuth0, email, required_role, auth0_domain):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=email)
config = dict_to_config({
'auth': {'allowed_ips': '', 'valid_email_domains': 'science.org'},
'client': {'auth0_domain': auth0_domain} if auth0_domain else {},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'required_role': required_role or '',
'title': SEARCH_TYPE_TITLE_1
},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_2: {
'required_role': required_role or '',
'title': SEARCH_TYPE_TITLE_2
}
})
with _api_test_client(config, {}) as test_client:
response = test_client.get('/search-types')
assert _get_ok_json(response) == [{
'search_type': SEARCH_TYPE_1,
'title': SEARCH_TYPE_TITLE_1
}, {
'search_type': SEARCH_TYPE_2,
'title': SEARCH_TYPE_TITLE_2
}]
def test_should_filter_search_types_by_required_role(
self, MockRecommendReviewers, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=EMAIL_1)
config = dict_to_config({
'auth': {'allowed_ips': ''},
'client': {'auth0_domain': DOMAIN_1},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'required_role': ROLE_1,
'title': SEARCH_TYPE_TITLE_1
},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_2: {
'required_role': ROLE_2,
'title': SEARCH_TYPE_TITLE_2
},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_3: {
'required_role': '',
'title': SEARCH_TYPE_TITLE_3
}
})
with _api_test_client(config, {}) as test_client:
get_user_roles_by_email = (
MockRecommendReviewers.return_value.get_user_roles_by_email
)
get_user_roles_by_email.return_value = {ROLE_1}
response = test_client.get('/search-types')
assert _get_ok_json(response) == [{
'search_type': SEARCH_TYPE_1,
'title': SEARCH_TYPE_TITLE_1
}, {
'search_type': SEARCH_TYPE_3,
'title': SEARCH_TYPE_TITLE_3
}]
class TestGetManuscriptDetails:
def test_should_return_404_if_manuscript_not_found(
self, MockRecommendReviewers):
config = dict_to_config({})
with _api_test_client(config, {}) as test_client:
get_manuscript_details = MockRecommendReviewers.return_value.get_manuscript_details
get_manuscript_details.return_value = None
MockRecommendReviewers.return_value.get_manuscript_details.return_value = None
response = test_client.get('/manuscript/version/%s' % VERSION_ID_1)
assert response.status_code == 404
def test_should_return_manuscript_details_if_manuscript_was_found(
self, MockRecommendReviewers):
config = dict_to_config({})
with _api_test_client(config, {}) as test_client:
get_manuscript_details = MockRecommendReviewers.return_value.get_manuscript_details
get_manuscript_details.return_value = SOME_RESPONSE
response = test_client.get('/manuscript/version/%s' % VERSION_ID_1)
assert _get_ok_json(response) == SOME_RESPONSE
get_manuscript_details.assert_called_with(VERSION_ID_1)
def test_should_handle_slash_in_version_id(
self, MockRecommendReviewers):
config = dict_to_config({})
with _api_test_client(config, {}) as test_client:
get_manuscript_details = MockRecommendReviewers.return_value.get_manuscript_details
get_manuscript_details.return_value = SOME_RESPONSE
version_id = 'v/123'
response = test_client.get('/manuscript/version/%s' % version_id)
assert _get_ok_json(response) == SOME_RESPONSE
get_manuscript_details.assert_called_with(version_id)
def test_should_return_manuscript_details_with_auth_enabled(
self, MockRecommendReviewers, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=EMAIL_1)
config = dict_to_config({
'auth': {'allowed_ips': '', 'valid_email_domains': 'science.org'},
'client': {'auth0_domain': DOMAIN_1}
})
with _api_test_client(config, {}) as test_client:
get_manuscript_details = MockRecommendReviewers.return_value.get_manuscript_details
get_manuscript_details.return_value = SOME_RESPONSE
response = test_client.get('/manuscript/version/%s' % VERSION_ID_1)
assert _get_ok_json(response) == SOME_RESPONSE
get_manuscript_details.assert_called_with(VERSION_ID_1)
class TestRecommendWithoutAuth:
def test_should_return_400_without_args(self):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
response = test_client.get('/recommend-reviewers')
assert response.status_code == 400
def test_should_return_results_by_manuscript_no(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
MockRecommendReviewers.return_value.recommend.return_value = SOME_RESPONSE
response = test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1
}))
assert _get_ok_json(response) == SOME_RESPONSE
_assert_partial_called_with(
MockRecommendReviewers.return_value.recommend,
manuscript_no=MANUSCRIPT_NO_1
)
def test_should_return_results_by_keywords(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
MockRecommendReviewers.return_value.recommend.return_value = SOME_RESPONSE
response = test_client.get('/recommend-reviewers?' + urlencode({
'keywords': VALUE_1,
'subject_area': VALUE_2,
'abstract': VALUE_3
}))
assert _get_ok_json(response) == SOME_RESPONSE
_assert_partial_called_with(
MockRecommendReviewers.return_value.recommend,
keywords=VALUE_1,
subject_area=VALUE_2,
abstract=VALUE_3
)
def test_should_pass_default_limit_to_recommend_method(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1
}))
_assert_partial_called_with(
MockRecommendReviewers.return_value.recommend,
limit=DEFAULT_LIMIT
)
def test_should_pass_limit_to_recommend_method(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
test_client.get('/recommend-reviewers?' + urlencode({
'limit': LIMIT_1,
'manuscript_no': MANUSCRIPT_NO_1
}))
_assert_partial_called_with(
MockRecommendReviewers.return_value.recommend,
limit=LIMIT_1
)
def test_should_default_to_none_role(self, MockRecommendReviewers):
config = dict_to_config({})
with _api_test_client(config, {}) as test_client:
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1
}))
_assert_partial_called_with(
MockRecommendReviewers.return_value.recommend,
role=None
)
def test_should_pass_search_params_from_search_config_to_recommend_method(
self, MockRecommendReviewers):
config = dict_to_config({
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'filter_by_role': VALUE_1,
'recommend_relationship_types': VALUE_2,
'recommend_stage_names': VALUE_3,
'default_limit': str(LIMIT_1)
}
})
with _api_test_client(config, {}) as test_client:
MockRecommendReviewers.return_value.recommend.return_value = SOME_RESPONSE
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1,
'search_type': SEARCH_TYPE_1
}))
_assert_partial_called_with(
MockRecommendReviewers.return_value.recommend,
role=VALUE_1,
recommend_relationship_types=[VALUE_2],
recommend_stage_names=[VALUE_3],
limit=LIMIT_1
)
def test_should_reject_unknown_search_type(self):
config = dict_to_config({
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'filter_by_role': VALUE_1
}
})
with _api_test_client(config, {}) as test_client:
response = test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1,
'search_type': SEARCH_TYPE_2
}))
assert response.status_code == 400
def test_should_cache_with_same_parameters(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1
}))
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1
}))
MockRecommendReviewers.return_value.recommend.assert_called_once()
def test_should_not_cache_with_different_parameters(self, MockRecommendReviewers):
config = ConfigParser()
with _api_test_client(config, {}) as test_client:
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1
}))
test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_2
}))
assert MockRecommendReviewers.return_value.recommend.call_count == 2
class TestRecommendWithAuth:
def test_should_allow_search_type_for_person_with_matching_role(
self, MockRecommendReviewers, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=EMAIL_1)
config = dict_to_config({
'auth': {'allowed_ips': ''},
'client': {'auth0_domain': DOMAIN_1},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'required_role': ROLE_1
}
})
with _api_test_client(config, {}) as test_client:
user_has_role_by_email = MockRecommendReviewers.return_value.user_has_role_by_email
user_has_role_by_email.return_value = True
response = test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1,
'search_type': SEARCH_TYPE_1
}))
user_has_role_by_email.assert_called_with(
email=EMAIL_1, role=ROLE_1
)
assert response.status_code == 200
def test_should_not_allow_search_type_for_person_without_matching_role(
self, MockRecommendReviewers, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=EMAIL_1)
config = dict_to_config({
'auth': {'allowed_ips': ''},
'client': {'auth0_domain': DOMAIN_1},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'required_role': ROLE_1
}
})
with _api_test_client(config, {}) as test_client:
user_has_role_by_email = MockRecommendReviewers.return_value.user_has_role_by_email
user_has_role_by_email.return_value = False
response = test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1,
'search_type': SEARCH_TYPE_1
}))
user_has_role_by_email.assert_called_with(
email=EMAIL_1, role=ROLE_1
)
assert response.status_code == 403
def test_should_allow_search_type_and_not_check_person_role_if_no_role_is_required(
self, MockRecommendReviewers, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=EMAIL_1)
config = dict_to_config({
'auth': {'allowed_ips': ''},
'client': {'auth0_domain': DOMAIN_1},
SEARCH_SECTION_PREFIX + SEARCH_TYPE_1: {
'required_role': ''
}
})
with _api_test_client(config, {}) as test_client:
user_has_role_by_email = MockRecommendReviewers.return_value.user_has_role_by_email
response = test_client.get('/recommend-reviewers?' + urlencode({
'manuscript_no': MANUSCRIPT_NO_1,
'search_type': SEARCH_TYPE_1
}))
user_has_role_by_email.assert_not_called()
assert response.status_code == 200
class TestApiAuth:
@pytest.fixture(name='DummyAppContext', autouse=True)
def _dummy_app_context(self):
app = Flask(__name__)
with app.app_context() as app_context:
yield app_context
def test_should_allow_local_ips_by_default(self, MockFlaskAuth0):
ApiAuth(dict_to_config({}), {'auth0_domain': VALUE_1})
_assert_partial_called_with(MockFlaskAuth0, allowed_ips={'127.0.0.1'})
class TestWrapSearch:
def test_should_not_wrap_with_auth_if_no_auth0_domain_is_present(self):
config = dict_to_config({})
client_config = {}
api_auth = ApiAuth(config, client_config)
f = Mock()
assert api_auth.wrap_search(f) == f
def test_should_wrap_with_auth_if_auth0_domain_is_present(self, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=None)
api_auth = ApiAuth(dict_to_config({}), {'auth0_domain': VALUE_1})
_assert_partial_called_with(MockFlaskAuth0, domain=VALUE_1)
f = _named_mock(name='f')
wrapped_f = api_auth.wrap_search(f)
assert wrapped_f != f
assert wrapped_f() == f.return_value
def test_should_reject_search_types_for_emails_not_associated_with_required_role(
self, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email=EMAIL_1)
user_has_role_by_email = Mock(name='user_has_role_by_email')
user_has_role_by_email.return_value = False
api_auth = ApiAuth(
dict_to_config({'auth': {
'valid_email_domains': 'other.org'
}}),
{'auth0_domain': DOMAIN_1},
search_config={SEARCH_TYPE_1: {'required_role': ROLE_1}},
user_has_role_by_email=user_has_role_by_email,
get_search_type=lambda: SEARCH_TYPE_1
)
f = _named_mock(name='f')
wrapped_f = api_auth.wrap_search(f)
assert wrapped_f != f
with pytest.raises(Forbidden):
wrapped_f()
user_has_role_by_email.assert_called_with(email=EMAIL_1, role=ROLE_1)
def test_should_allow_search_types_for_emails_part_of_configured_domain(
self, MockFlaskAuth0):
_setup_flask_auth0_mock_email(MockFlaskAuth0, email='<EMAIL>')
user_has_role_by_email = Mock(name='user_has_role_by_email')
user_has_role_by_email.return_value = False
api_auth = ApiAuth(
dict_to_config({'auth': {
'valid_email_domains': 'science.org'
}}),
{'auth0_domain': DOMAIN_1},
search_config={SEARCH_TYPE_1: {'required_role': ROLE_1}},
user_has_role_by_email=user_has_role_by_email,
get_search_type=lambda: SEARCH_TYPE_1
)
f = _named_mock(name='f')
wrapped_f = api_auth.wrap_search(f)
assert wrapped_f != f
assert wrapped_f() == f.return_value
|
[
"unittest.mock.patch.object",
"logging.basicConfig",
"peerscout.shared.database.populated_in_memory_database",
"urllib.parse.urlencode",
"unittest.mock.Mock",
"pytest.fixture",
"flask.Flask",
"peerscout.utils.config.dict_to_config",
"pytest.raises",
"peerscout.server.blueprints.api.ApiAuth",
"pytest.mark.parametrize",
"configparser.ConfigParser",
"peerscout.server.blueprints.api.create_api_blueprint",
"logging.getLogger"
] |
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|
import scanpy as sc
import pandas as pd
import os, sys
working_dir = sys.argv[1]
outfile = sys.argv[2]
dataset_files = [os.path.join( working_dir, i) for i in os.listdir(working_dir)]
datasets = [sc.read(i, 'r') for i in dataset_files]
class_df = pd.concat(axis=0, objs=[ i.obs[[ 'BICCN_class_label', 'BICCN_subclass_label']].drop_duplicates() for i in datasets ])
class_df.drop_duplicates(inplace=True)
class_df.sort_values(by=['BICCN_class_label', 'BICCN_subclass_label'], inplace=True)
class_df[['BICCN_ontology_term_id']] = class_df[['BICCN_subclass_label']]
# Putting in ontology terms
class_df.loc[class_df['BICCN_subclass_label'] == 'Lamp5', ['BICCN_ontology_term_id']] = ['ILX:0770149']
class_df.loc[class_df['BICCN_subclass_label'] == 'Pvalb', ['BICCN_ontology_term_id']] = ['ILX:0770154']
class_df.loc[class_df['BICCN_subclass_label'] == 'Sncg', ['BICCN_ontology_term_id']] = ['ILX:0770150']
class_df.loc[class_df['BICCN_subclass_label'] == 'Sst', ['BICCN_ontology_term_id']] = ['ILX:0770152']
class_df.loc[class_df['BICCN_subclass_label'] == 'Vip', ['BICCN_ontology_term_id']] = ['ILX:0770151']
#class_df.loc[class_df['BICCN_subclass_label'] == 'CR', ['BICCN_ontology_term_id']] = ['']
class_df.loc[class_df['BICCN_subclass_label'] == 'L2/3 IT', ['BICCN_ontology_term_id']] = ['ILX:0770156']
class_df.loc[class_df['BICCN_subclass_label'] == 'L5 ET', ['BICCN_ontology_term_id']] = ['ILX:0770160']
class_df.loc[class_df['BICCN_subclass_label'] == 'L5 IT', ['BICCN_ontology_term_id']] = ['ILX:0770157']
class_df.loc[class_df['BICCN_subclass_label'] == 'L5/6 NP', ['BICCN_ontology_term_id']] = ['ILX:0770161']
class_df.loc[class_df['BICCN_subclass_label'] == 'L6 CT', ['BICCN_ontology_term_id']] = ['ILX:0770162']
class_df.loc[class_df['BICCN_subclass_label'] == 'L6 IT', ['BICCN_ontology_term_id']] = ['ILX:0770158']
class_df.loc[class_df['BICCN_subclass_label'] == 'L6 IT Car3', ['BICCN_ontology_term_id']] = ['ILX:0770159']
class_df.loc[class_df['BICCN_subclass_label'] == 'L6b', ['BICCN_ontology_term_id']] = ['ILX:0770163']
class_df.loc[class_df['BICCN_subclass_label'] == 'Astro', ['BICCN_ontology_term_id']] = ['ILX:0770141']
class_df.loc[class_df['BICCN_subclass_label'] == 'Endo', ['BICCN_ontology_term_id']] = ['ILX:0770142']
#class_df.loc[class_df['BICCN_subclass_label'] == 'Macrophage', ['BICCN_ontology_term_id']] = ['']
class_df.loc[class_df['BICCN_subclass_label'] == 'Micro', ['BICCN_ontology_term_id']] = ['ILX:0770146']
class_df.loc[class_df['BICCN_subclass_label'] == 'OPC', ['BICCN_ontology_term_id']] = ['ILX:0770139']
class_df.loc[class_df['BICCN_subclass_label'] == 'Oligo', ['BICCN_ontology_term_id']] = ['ILX:0770140']
class_df.loc[class_df['BICCN_subclass_label'] == 'Peri', ['BICCN_ontology_term_id']] = ['ILX:0770145']
#class_df.loc[class_df['BICCN_subclass_label'] == 'Prog/IP', ['BICCN_ontology_term_id']] = ['']
class_df.loc[class_df['BICCN_subclass_label'] == 'SMC', ['BICCN_ontology_term_id']] = ['ILX:0770144']
class_df.loc[class_df['BICCN_subclass_label'] == 'VLMC', ['BICCN_ontology_term_id']] = ['ILX:0770143']
class_df.to_csv(outfile, sep='\t', index=False)
|
[
"scanpy.read",
"os.path.join",
"os.listdir"
] |
[((123, 151), 'os.path.join', 'os.path.join', (['working_dir', 'i'], {}), '(working_dir, i)\n', (135, 151), False, 'import os, sys\n'), ((199, 214), 'scanpy.read', 'sc.read', (['i', '"""r"""'], {}), "(i, 'r')\n", (206, 214), True, 'import scanpy as sc\n'), ((162, 185), 'os.listdir', 'os.listdir', (['working_dir'], {}), '(working_dir)\n', (172, 185), False, 'import os, sys\n')]
|
#!/usr/bin/python
import sys
import pysam
import collections
import math
import os
import vcf as py_vcf
from math import log10
from statistics import median
from version import __version__
from utils import parse_bam as bam
from utils import coverage
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), os.pardir))
import NanoSV
class SV:
def __init__(self, id, breakpoint):
self.id = id
self.chr = breakpoint.segment_1["rname"]
self.chr2 = breakpoint.segment_2["rname"]
self.flag1 = breakpoint.segment_1["flag"]
self.flag2 = breakpoint.segment_2["flag"]
self.pos = [breakpoint.segment_1["pos"]]
self.ref = 'N'
self.alt = str(breakpoint.svtype)
self.qual = None
self.filter = []
self.info = {
'ALT_READ_IDS': [bam.segments[breakpoint.segment_1['id'][0]][breakpoint.segment_1['id'][1]][breakpoint.segment_1['id'][2]].qname],
'REF_READ_IDS_1': [],
'REF_READ_IDS_2': [],
'IMPRECISE': True,
'END': [breakpoint.segment_2["pos"]],
'SVTYPE': breakpoint.svtype,
'SVMETHOD': 'NanoSV_v'+__version__,
}
self.format = {
'GT': './.',
'DV': [1, 1],
'VO': [(1 - 10 ** (-breakpoint.segment_1["mapq"] / 10.0)),
(1 - 10 ** (-breakpoint.segment_2["mapq"] / 10.0))],
'DR': [0, 0],
'RO': [0, 0],
'HR': [0, 0]
}
self.breakpoints = [breakpoint.id]
self.set = 0
self.SVcluster = 1
self.ref_qname_clips = [[], []]
def addBreakpoint(self, breakpoint):
"""
Adds breakpoint to SV object
:param breakpoint is breakpoint-object:
"""
self.breakpoints.append(breakpoint.id)
self.pos.append(breakpoint.segment_1["pos"])
self.info['END'].append(breakpoint.segment_2["pos"])
self.info['ALT_READ_IDS'].append(bam.segments[breakpoint.segment_1['id'][0]][breakpoint.segment_1['id'][1]][breakpoint.segment_1['id'][2]].qname)
self.format['DV'][0] += 1
self.format['DV'][1] += 1
self.format['VO'][0] += (1 - 10 ** (-breakpoint.segment_1["mapq"] / 10.0))
self.format['VO'][1] += (1 - 10 ** (-breakpoint.segment_2["mapq"] / 10.0))
def addInfoField(self, key, value):
"""
Adds info field to SV object
:param key is the name of the info field result:
:param value is the value of the field result:
"""
if key in self.info:
if isinstance(self.info[key], list):
try:
if isinstance(self.info[key][0], list) or isinstance(self.info[key][1], list):
if value[0] is not None: self.info[key][0].append(value[0][0])
if value[1] is not None: self.info[key][1].append(value[1][0])
else:
self.info[key].append(value[0])
except IndexError:
self.info[key].append(value[0])
else:
self.info[key] = value
def significanceTest(self, avg_dupdel_cov, dup):
"""
Calculates if there is a significant difference in coverage to call DUP or DEL
:param avg_dupdel_cov is the distribution with coverage of random positions in the genome:
:param dup is a boolean. True in case of a duplication, False in case of a deletion:
:return True or false depending on the outcome of the test. True when there is a significant difference:
"""
teller = 0
if dup:
for value in coverage.coverages:
if float(value) > avg_dupdel_cov:
teller += 1
else:
for value in coverage.coverages:
if float(value) < avg_dupdel_cov:
teller += 1
if teller / len(coverage.coverages) < 0.05:
self.info["DEPTHPVAL"] = '%.3f' % (teller / len(coverage.coverages))
return True
else:
self.info["DEPTHPVAL"] = '%.3f' % (teller / len(coverage.coverages))
return False
def setReferenceBase(self):
"""
Get the reference bases for the SV object
:param
"""
bases = dict()
bam = pysam.AlignmentFile(NanoSV.opts_bam, 'rb')
for pileupcolumn in bam.pileup(self.chr,self.pos-1,self.pos, truncate=True):
for pileupread in pileupcolumn.pileups:
if not pileupread.is_del and not pileupread.is_refskip:
if not pileupread.alignment.query_sequence[pileupread.query_position] in bases:
bases[pileupread.alignment.query_sequence[pileupread.query_position]] = 0
bases[pileupread.alignment.query_sequence[pileupread.query_position]] += 1
if (len(bases.keys()) > 0):
self.ref = sorted(bases, key=bases.__getitem__)[-1]
def setArguments(self, opts_depth_support):
"""
Set standard info field arguments.
:param opts_depth_support is a boolean for if the coverage dup/del analysis is switched on:
"""
self.info['CIPOS'] = [int(min(self.pos) - median(self.pos)), int(max(self.pos) - median(self.pos))]
self.info['CIEND'] = [int(min(self.info['END']) - median(self.info['END'])), int(max(self.info['END']) - median(self.info['END']))]
if self.info['CIPOS'] == [0,0] and self.info['CIEND'] == [0,0]:
del self.info['IMPRECISE']
self.pos = int(median(self.pos))
self.info['END'] = int(median(self.info['END']))
if self.chr == self.chr2:
self.info['SVLEN'] = (self.info['END'] - self.pos)
self.setInfoField()
self.setReferenceBase()
dup = 0
if self.alt == "INS":
self.alt = py_vcf.model._SV("INS")
if self.alt == "BND":
if self.flag1 & 16:
if self.flag2 & 16:
self.alt = py_vcf.model._Breakend(self.chr2, int(self.info['END']), True, False, self.ref, True)
if opts_depth_support and self.chr == self.chr2:
avg_dupdel_cov = self.getDupDelcoverage()
if self.significanceTest(avg_dupdel_cov, True):
self.alt = py_vcf.model._SV("DUP")
self.info['SVTYPE'] = "DUP"
dup = 1
else:
self.alt = py_vcf.model._Breakend(self.chr2, int(self.info['END']), True, True, self.ref, True)
else:
if self.flag2 & 16:
self.alt = py_vcf.model._Breakend(self.chr2, int(self.info['END']), False, False, self.ref, True)
else:
self.alt = py_vcf.model._Breakend(self.chr2, int(self.info['END']), False, True, self.ref, True)
if opts_depth_support and self.chr == self.chr2:
avg_dupdel_cov = self.getDupDelcoverage()
if self.significanceTest(avg_dupdel_cov, False):
self.alt = py_vcf.model._SV("DEL")
self.info['SVTYPE'] = "DEL"
gt_lplist = self.bayes_gt(sum(self.format['RO']), sum(self.format['VO']), dup)
gt_sum = 0
pplist = []
for gt in gt_lplist:
pplist.append(10 ** gt)
gt_sum += 10 ** gt
if gt_sum > 0:
npplist = [x / gt_sum for x in pplist]
plplist = []
for p in npplist:
if p > 0:
plplist.append(int(-10 * log10(p)))
else:
plplist.append(9999)
gt_idx = plplist.index(min(plplist))
gq_idx = plplist.index(sorted(plplist)[1])
plplist[gt_idx] = 0
if gt_idx == 0:
self.format['GT'] = '0/0'
elif gt_idx == 1:
self.format['GT'] = '0/1'
elif gt_idx == 2:
self.format['GT'] = '1/1'
self.format['PL'] = plplist
self.format['GQ'] = plplist[gq_idx]
self.qual = plplist[0]
else:
self.format['GT'] = './.'
self.format['PL'] = [0, 0, 0]
self.format['GQ'] = [0]
self.qual = 0
self.set = 1
def getDupDelcoverage(self):
"""
Returns the average coverage of all positions within the start and end of the SV.
:return average coverage:
"""
start = round(self.pos)
stop = round(self.info['END'])
if start > stop:
start = round(self.info['END'])
stop = round(self.pos)
position = str(self.chr) + ":" + str(start) + "-" + str(stop)
dupdel_coverages = []
if 'sambamba' in NanoSV.opts_sambamba:
cmd = NanoSV.opts_sambamba + " depth base --min-coverage=0 " + NanoSV.opts_bam + " -L " + position + " --nthreads=" + str(NanoSV.opts_threads) + " 2> /dev/null | awk '{if (NR!=1) print $3}'"
elif 'samtools' in NanoSV.opts_sambamba:
cmd = NanoSV.opts_sambamba + " depth " + NanoSV.opts_bam + " -r " + position + " | awk '{print $3}'"
with os.popen(cmd) as commandoutput:
for line in commandoutput:
dupdel_coverages.append(float(line.rstrip()))
return float(sum(dupdel_coverages)) / len(dupdel_coverages)
def log_choose(self, n, k):
"""
Returns log of given variable.
:param n:
:param k:
"""
r = 0.0
# swap for efficiency if k is more than half of n
if k * 2 > n:
k = n - k
for d in range(1, k + 1):
r += math.log(n, 10)
r -= math.log(d, 10)
n -= 1
return (r)
def bayes_gt(self, ref, alt, is_dup):
"""
Returns genotype list
:param ref:
:param alt:
:param is_dup boolean for if SV is dup:
:return list with probabilities of different genotypes:
"""
# probability of seeing an alt read with true genotype of of hom_ref, het, hom_alt respectively
if is_dup: # specialized logic to handle non-destructive events such as duplications
p_alt = [1e-3, 0.5, 0.9]
else:
p_alt = [1e-3, 0.5, 0.9]
total = ref + alt
log_combo = self.log_choose(int(total), int(alt))
lp_homref = log_combo + alt * math.log(p_alt[0], 10) + ref * math.log(1 - p_alt[0], 10)
lp_het = log_combo + (alt * math.log(p_alt[1], 10)) + (ref * math.log(1 - p_alt[1], 10))
lp_homalt = log_combo + alt * math.log(p_alt[2], 10) + ref * math.log(1 - p_alt[2], 10)
return [lp_homref, lp_het, lp_homalt]
def setInfoField(self):
"""
Calculates RT, MAPQ, PID and PLENGTH
"""
for field in self.info:
if field == "RT":
rt = [0, 0, 0]
for type in self.info[field]:
if type == "2d":
rt[0] += 1
if type == "template":
rt[1] += 1
if type == "complement":
rt[2] += 1
self.info[field] = rt
if 'READ_IDS' in field:
if len(self.info[field]) == 0:
self.info[field]=['NA']
elif isinstance(self.info[field], list):
if isinstance(self.info[field][0], list):
self.info[field][0] = median(map(float, self.info[field][0]))
self.info[field][1] = median(map(float, self.info[field][1]))
if field == "MAPQ":
self.info[field][0] = int(self.info[field][0])
self.info[field][1] = int(self.info[field][1])
elif field == "PID" or field == "PLENGTH":
self.info[field][0] = round(self.info[field][0], 3)
self.info[field][1] = round(self.info[field][1], 3)
else:
if 'CI' not in field:
self.info[field] = self.median_type(self.info[field])
def median_type(self, toCalculate):
"""
Returns median of given list in the original datatype
:param toCalculate list to calculate median for:
:return median
"""
if type(toCalculate[0]) == float:
return median(toCalculate)
elif type(toCalculate[0]) == int:
return int(median(toCalculate))
def setFormat(self):
"""
Creates format line of VCF result
:return a list with the order of the values and the values:
"""
del self.format['VO']
del self.format['RO']
format_list = sorted(self.format.keys())
format_list.remove('GT')
format_list.insert(0, 'GT')
format = ':'.join(format_list)
data = collections.namedtuple('CallData', format_list)(**self.format)
call = py_vcf.model._Call('site', bam.sample_name, data)
samples_indexes = [0]
samples = [call]
return [format, samples_indexes, samples]
def printVCF(self, vcf_writer):
"""
Combines all the values in a pyvcf record to write to the vcf
:param vcf_writer is necessary to write a record to the vcf:
"""
format_output = self.setFormat()
if isinstance(self.alt, py_vcf.model._Breakend):
del self.info['END']
record = py_vcf.model._Record(self.chr, self.pos, self.id, self.ref, [self.alt], self.qual, self.filter, self.info, format_output[0], format_output[1], format_output[2])
vcf_writer.write_record(record)
|
[
"statistics.median",
"os.path.realpath",
"vcf.model._Call",
"pysam.AlignmentFile",
"vcf.model._Record",
"os.popen",
"vcf.model._SV",
"math.log10",
"collections.namedtuple",
"math.log",
"utils.parse_bam.pileup"
] |
[((4353, 4395), 'pysam.AlignmentFile', 'pysam.AlignmentFile', (['NanoSV.opts_bam', '"""rb"""'], {}), "(NanoSV.opts_bam, 'rb')\n", (4372, 4395), False, 'import pysam\n'), ((4424, 4483), 'utils.parse_bam.pileup', 'bam.pileup', (['self.chr', '(self.pos - 1)', 'self.pos'], {'truncate': '(True)'}), '(self.chr, self.pos - 1, self.pos, truncate=True)\n', (4434, 4483), True, 'from utils import parse_bam as bam\n'), ((13147, 13196), 'vcf.model._Call', 'py_vcf.model._Call', (['"""site"""', 'bam.sample_name', 'data'], {}), "('site', bam.sample_name, data)\n", (13165, 13196), True, 'import vcf as py_vcf\n'), ((13650, 13818), 'vcf.model._Record', 'py_vcf.model._Record', (['self.chr', 'self.pos', 'self.id', 'self.ref', '[self.alt]', 'self.qual', 'self.filter', 'self.info', 'format_output[0]', 'format_output[1]', 'format_output[2]'], {}), '(self.chr, self.pos, self.id, self.ref, [self.alt],\n self.qual, self.filter, self.info, format_output[0], format_output[1],\n format_output[2])\n', (13670, 13818), True, 'import vcf as py_vcf\n'), ((299, 325), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (315, 325), False, 'import os\n'), ((5596, 5612), 'statistics.median', 'median', (['self.pos'], {}), '(self.pos)\n', (5602, 5612), False, 'from statistics import median\n'), ((5645, 5669), 'statistics.median', 'median', (["self.info['END']"], {}), "(self.info['END'])\n", (5651, 5669), False, 'from statistics import median\n'), ((5897, 5920), 'vcf.model._SV', 'py_vcf.model._SV', (['"""INS"""'], {}), "('INS')\n", (5913, 5920), True, 'import vcf as py_vcf\n'), ((9308, 9321), 'os.popen', 'os.popen', (['cmd'], {}), '(cmd)\n', (9316, 9321), False, 'import os\n'), ((9810, 9825), 'math.log', 'math.log', (['n', '(10)'], {}), '(n, 10)\n', (9818, 9825), False, 'import math\n'), ((9843, 9858), 'math.log', 'math.log', (['d', '(10)'], {}), '(d, 10)\n', (9851, 9858), False, 'import math\n'), ((12570, 12589), 'statistics.median', 'median', (['toCalculate'], {}), '(toCalculate)\n', (12576, 12589), False, 'from statistics import median\n'), ((13069, 13116), 'collections.namedtuple', 'collections.namedtuple', (['"""CallData"""', 'format_list'], {}), "('CallData', format_list)\n", (13091, 13116), False, 'import collections\n'), ((10588, 10614), 'math.log', 'math.log', (['(1 - p_alt[0])', '(10)'], {}), '(1 - p_alt[0], 10)\n', (10596, 10614), False, 'import math\n'), ((10684, 10710), 'math.log', 'math.log', (['(1 - p_alt[1])', '(10)'], {}), '(1 - p_alt[1], 10)\n', (10692, 10710), False, 'import math\n'), ((10781, 10807), 'math.log', 'math.log', (['(1 - p_alt[2])', '(10)'], {}), '(1 - p_alt[2], 10)\n', (10789, 10807), False, 'import math\n'), ((5264, 5280), 'statistics.median', 'median', (['self.pos'], {}), '(self.pos)\n', (5270, 5280), False, 'from statistics import median\n'), ((5303, 5319), 'statistics.median', 'median', (['self.pos'], {}), '(self.pos)\n', (5309, 5319), False, 'from statistics import median\n'), ((5380, 5404), 'statistics.median', 'median', (["self.info['END']"], {}), "(self.info['END'])\n", (5386, 5404), False, 'from statistics import median\n'), ((5435, 5459), 'statistics.median', 'median', (["self.info['END']"], {}), "(self.info['END'])\n", (5441, 5459), False, 'from statistics import median\n'), ((10557, 10579), 'math.log', 'math.log', (['p_alt[0]', '(10)'], {}), '(p_alt[0], 10)\n', (10565, 10579), False, 'import math\n'), ((10651, 10673), 'math.log', 'math.log', (['p_alt[1]', '(10)'], {}), '(p_alt[1], 10)\n', (10659, 10673), False, 'import math\n'), ((10750, 10772), 'math.log', 'math.log', (['p_alt[2]', '(10)'], {}), '(p_alt[2], 10)\n', (10758, 10772), False, 'import math\n'), ((12655, 12674), 'statistics.median', 'median', (['toCalculate'], {}), '(toCalculate)\n', (12661, 12674), False, 'from statistics import median\n'), ((6382, 6405), 'vcf.model._SV', 'py_vcf.model._SV', (['"""DUP"""'], {}), "('DUP')\n", (6398, 6405), True, 'import vcf as py_vcf\n'), ((7190, 7213), 'vcf.model._SV', 'py_vcf.model._SV', (['"""DEL"""'], {}), "('DEL')\n", (7206, 7213), True, 'import vcf as py_vcf\n'), ((7695, 7703), 'math.log10', 'log10', (['p'], {}), '(p)\n', (7700, 7703), False, 'from math import log10\n')]
|
#
# The Multiverse Platform is made available under the MIT License.
#
# Copyright (c) 2012 The Multiverse Foundation
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software
# is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
# OR OTHER DEALINGS IN THE SOFTWARE.
#
#
from multiverse.server.plugins import *
from multiverse.server.objects import *
from multiverse.server.util import Log
from java.lang import Long
instance = Instance.current()
instanceOid = Instance.currentOid()
#npc1Marker = instance.getMarker("npc1_marker").clone()
#npc1Marker.getPoint().setY(0)
#spawnData = SpawnData()
#spawnData.setFactoryName("npc1Factory")
#spawnData.setInstanceOid(Long(instanceOid))
#spawnData.setLoc(npc1Marker.getPoint())
#spawnData.setNumSpawns(1)
#spawnData.setSpawnRadius(1)
#spawnData.setRespawnTime(1000)
#spawnData.setCorpseDespawnTime(0)
#MobManagerClient.createSpawnGenerator(spawnData)
Log.debug("done with instance_load_room_goth.py")
|
[
"multiverse.server.util.Log.debug"
] |
[((1849, 1898), 'multiverse.server.util.Log.debug', 'Log.debug', (['"""done with instance_load_room_goth.py"""'], {}), "('done with instance_load_room_goth.py')\n", (1858, 1898), False, 'from multiverse.server.util import Log\n')]
|
# problem C
# coin jam
import math as m
from functools import lru_cache
# precompute primes
@lru_cache(maxsize=None)
def is_prime(n):
if n == 1:
return False
elif n < 4:
return True
elif n % 2 == 0:
return False
elif n < 9:
return True
elif n % 3 == 0:
return False
else:
r = m.floor(m.sqrt(n))
f = 5
while f <= r:
if n % f == 0 or n % (f + 2) == 0:
return False
f = f + 6
return True
def is_prime2(n):
if n < 3 or n % 2 == 0:
return n == 2
return not any(n % i == 0 for i in range(3, int(n ** 0.5 + 2), 2))
def is_jamcoin(x):
return int(x[0]) == 1 and int(x[-1]) == 1
def find_factor(x):
for factor in range(3, (x // 2) + 1):
if x % factor == 0:
return factor
def find_factor2(x):
i = 1
while i * i <= x:
if x % i == 0:
return factor
elif x / i != i:
return x / i
i += 1
return x
def bit_range(l):
return 1 << int(l) - 1, 1 << int(l)
def strip_bin(b):
return str(b).replace('0b', '')
def convert_base(number, base):
return int(number, base=base)
if __name__ == "__main__":
output = []
cases = int(input())
for case_num in range(1, cases+1):
n, j = (int(x) for x in input().split(' '))
bit_values = []
for num in range(*bit_range(n)):
if len(bit_values) >= j:
break
# convert num to binary then to base 2-10
num_bin = bin(num)
# converts to str to remove leading '0b'
bin_str = strip_bin(num_bin)
if not is_jamcoin(bin_str):
continue
base_nums = []
has_prime = False
for base in range(2, 11):
# convert_base takes in a string as input
base_num = convert_base(bin_str, base)
if is_prime2(base_num):
has_prime = True
break
base_nums.append(base_num)
if not has_prime:
print(bin_str)
bit_values.append((bin_str, base_nums))
"""
output.append(f"Case #{case_num}:")
for bit_value, base_values in bit_values:
values = ' '.join(str(find_factor2(b)) for b in base_values)
out = f"{bit_value} {values}"
print(len(output), out)
output.append(out)
"""
print('\n'.join(output))
|
[
"functools.lru_cache",
"math.sqrt"
] |
[((94, 117), 'functools.lru_cache', 'lru_cache', ([], {'maxsize': 'None'}), '(maxsize=None)\n', (103, 117), False, 'from functools import lru_cache\n'), ((357, 366), 'math.sqrt', 'm.sqrt', (['n'], {}), '(n)\n', (363, 366), True, 'import math as m\n')]
|
#!/usr/bin/env python
from functools import lru_cache
import pandas as pd
from pgmpy.utils.decorators import convert_args_tuple
class BaseEstimator(object):
def __init__(self, data=None, state_names=None, complete_samples_only=True):
"""
Base class for estimators in pgmpy; `ParameterEstimator`,
`StructureEstimator` and `StructureScore` derive from this class.
Parameters
----------
data: pandas DataFrame object datafame object where each column represents one variable.
(If some values in the data are missing the data cells should be set to `numpy.NaN`.
Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)
state_names: dict (optional)
A dict indicating, for each variable, the discrete set of states (or values)
that the variable can take. If unspecified, the observed values in the data set
are taken to be the only possible states.
complete_samples_only: bool (optional, default `True`)
Specifies how to deal with missing data, if present. If set to `True` all rows
that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,
every row where neither the variable nor its parents are `np.NaN` is used.
This sets the behavior of the `state_count`-method.
"""
self.data = data
# data can be None in the case when learning structre from
# independence conditions. Look into PC.py.
if self.data is not None:
self.complete_samples_only = complete_samples_only
self.variables = list(data.columns.values)
if not isinstance(state_names, dict):
self.state_names = {
var: self._collect_state_names(var) for var in self.variables
}
else:
self.state_names = dict()
for var in self.variables:
if var in state_names:
if not set(self._collect_state_names(var)) <= set(
state_names[var]
):
raise ValueError(
f"Data contains unexpected states for variable: {var}."
)
self.state_names[var] = state_names[var]
else:
self.state_names[var] = self._collect_state_names(var)
def _collect_state_names(self, variable):
"Return a list of states that the variable takes in the data."
states = sorted(list(self.data.loc[:, variable].dropna().unique()))
return states
@convert_args_tuple
@lru_cache(maxsize=2048)
def state_counts(
self, variable, parents=[], complete_samples_only=None, weighted=False
):
"""
Return counts how often each state of 'variable' occurred in the data.
If a list of parents is provided, counting is done conditionally
for each state configuration of the parents.
Parameters
----------
variable: string
Name of the variable for which the state count is to be done.
parents: list
Optional list of variable parents, if conditional counting is desired.
Order of parents in list is reflected in the returned DataFrame
complete_samples_only: bool
Specifies how to deal with missing data, if present. If set to `True` all rows
that contain `np.NaN` somewhere are ignored. If `False` then
every row where neither the variable nor its parents are `np.NaN` is used.
Desired default behavior can be passed to the class constructor.
weighted: bool
If True, data must have a `_weight` column specifying the weight of the
datapoint (row). If False, each datapoint has a weight of `1`.
Returns
-------
state_counts: pandas.DataFrame
Table with state counts for 'variable'
Examples
--------
>>> import pandas as pd
>>> from pgmpy.estimators import BaseEstimator
>>> data = pd.DataFrame(data={'A': ['a1', 'a1', 'a2'],
'B': ['b1', 'b2', 'b1'],
'C': ['c1', 'c1', 'c2']})
>>> estimator = BaseEstimator(data)
>>> estimator.state_counts('A')
A
a1 2
a2 1
>>> estimator.state_counts('C', parents=['A', 'B'])
A a1 a2
B b1 b2 b1 b2
C
c1 1 1 0 0
c2 0 0 1 0
>>> estimator.state_counts('C', parents=['A'])
A a1 a2
C
c1 2.0 0.0
c2 0.0 1.0
"""
parents = list(parents)
# default for how to deal with missing data can be set in class constructor
if complete_samples_only is None:
complete_samples_only = self.complete_samples_only
# ignores either any row containing NaN, or only those where the variable or its parents is NaN
data = (
self.data.dropna()
if complete_samples_only
else self.data.dropna(subset=[variable] + parents)
)
if weighted and ("_weight" not in self.data.columns):
raise ValueError("data must contain a `_weight` column if weighted=True")
if not parents:
# count how often each state of 'variable' occured
if weighted:
state_count_data = data.groupby([variable]).sum()["_weight"]
else:
state_count_data = data.loc[:, variable].value_counts()
state_counts = (
state_count_data.reindex(self.state_names[variable])
.fillna(0)
.to_frame()
)
else:
parents_states = [self.state_names[parent] for parent in parents]
# count how often each state of 'variable' occured, conditional on parents' states
if weighted:
state_count_data = (
data.groupby([variable] + parents).sum()["_weight"].unstack(parents)
)
else:
state_count_data = (
data.groupby([variable] + parents).size().unstack(parents)
)
if not isinstance(state_count_data.columns, pd.MultiIndex):
state_count_data.columns = pd.MultiIndex.from_arrays(
[state_count_data.columns]
)
# reindex rows & columns to sort them and to add missing ones
# missing row = some state of 'variable' did not occur in data
# missing column = some state configuration of current 'variable's parents
# did not occur in data
row_index = self.state_names[variable]
column_index = pd.MultiIndex.from_product(parents_states, names=parents)
state_counts = state_count_data.reindex(
index=row_index, columns=column_index
).fillna(0)
return state_counts
class ParameterEstimator(BaseEstimator):
def __init__(self, model, data, **kwargs):
"""
Base class for parameter estimators in pgmpy.
Parameters
----------
model: pgmpy.models.BayesianNetwork or pgmpy.models.MarkovNetwork or pgmpy.models.NoisyOrModel model
for which parameter estimation is to be done.
data: pandas DataFrame object
datafame object with column names identical to the variable names of the model.
(If some values in the data are missing the data cells should be set to `numpy.NaN`.
Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)
state_names: dict (optional)
A dict indicating, for each variable, the discrete set of states (or values)
that the variable can take. If unspecified, the observed values in the data set
are taken to be the only possible states.
complete_samples_only: bool (optional, default `True`)
Specifies how to deal with missing data, if present. If set to `True` all rows
that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,
every row where neither the variable nor its parents are `np.NaN` is used.
This sets the behavior of the `state_count`-method.
"""
if not (set(model.nodes()) - model.latents) <= set(data.columns.values):
raise ValueError(
"variable names of the model must be identical to column names in data"
)
self.model = model
super(ParameterEstimator, self).__init__(data, **kwargs)
def state_counts(self, variable, weighted=False, **kwargs):
"""
Return counts how often each state of 'variable' occurred in the data.
If the variable has parents, counting is done conditionally
for each state configuration of the parents.
Parameters
----------
variable: string
Name of the variable for which the state count is to be done.
complete_samples_only: bool
Specifies how to deal with missing data, if present. If set to `True` all rows
that contain `np.NaN` somewhere are ignored. If `False` then
every row where neither the variable nor its parents are `np.NaN` is used.
Desired default behavior can be passed to the class constructor.
Returns
-------
state_counts: pandas.DataFrame
Table with state counts for 'variable'
Examples
--------
>>> import pandas as pd
>>> from pgmpy.models import BayesianNetwork
>>> from pgmpy.estimators import ParameterEstimator
>>> model = BayesianNetwork([('A', 'C'), ('B', 'C')])
>>> data = pd.DataFrame(data={'A': ['a1', 'a1', 'a2'],
'B': ['b1', 'b2', 'b1'],
'C': ['c1', 'c1', 'c2']})
>>> estimator = ParameterEstimator(model, data)
>>> estimator.state_counts('A')
A
a1 2
a2 1
>>> estimator.state_counts('C')
A a1 a2
B b1 b2 b1 b2
C
c1 1 1 0 0
c2 0 0 1 0
"""
parents = sorted(self.model.get_parents(variable))
return super(ParameterEstimator, self).state_counts(
variable, parents=parents, weighted=weighted, **kwargs
)
class StructureEstimator(BaseEstimator):
def __init__(self, data=None, independencies=None, **kwargs):
"""
Base class for structure estimators in pgmpy.
Parameters
----------
data: pandas DataFrame object
datafame object where each column represents one variable.
(If some values in the data are missing the data cells should be set to `numpy.NaN`.
Note that pandas converts each column containing `numpy.NaN`s to dtype `float`.)
state_names: dict (optional)
A dict indicating, for each variable, the discrete set of states (or values)
that the variable can take. If unspecified, the observed values in the data set
are taken to be the only possible states.
complete_samples_only: bool (optional, default `True`)
Specifies how to deal with missing data, if present. If set to `True` all rows
that contain `np.Nan` somewhere are ignored. If `False` then, for each variable,
every row where neither the variable nor its parents are `np.NaN` is used.
This sets the behavior of the `state_count`-method.
"""
self.independencies = independencies
if self.independencies is not None:
self.variables = self.independencies.get_all_variables()
super(StructureEstimator, self).__init__(data=data, **kwargs)
def estimate(self):
pass
|
[
"pandas.MultiIndex.from_product",
"functools.lru_cache",
"pandas.MultiIndex.from_arrays"
] |
[((2763, 2786), 'functools.lru_cache', 'lru_cache', ([], {'maxsize': '(2048)'}), '(maxsize=2048)\n', (2772, 2786), False, 'from functools import lru_cache\n'), ((7021, 7078), 'pandas.MultiIndex.from_product', 'pd.MultiIndex.from_product', (['parents_states'], {'names': 'parents'}), '(parents_states, names=parents)\n', (7047, 7078), True, 'import pandas as pd\n'), ((6558, 6611), 'pandas.MultiIndex.from_arrays', 'pd.MultiIndex.from_arrays', (['[state_count_data.columns]'], {}), '([state_count_data.columns])\n', (6583, 6611), True, 'import pandas as pd\n')]
|
##########################################################################
#
# Copyright (c) 2014, <NAME>. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above
# copyright notice, this list of conditions and the following
# disclaimer.
#
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with
# the distribution.
#
# * Neither the name of <NAME> nor the names of
# any other contributors to this software may be used to endorse or
# promote products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
##########################################################################
import functools
import imath
import inspect
import IECore
import IECoreScene
import Gaffer
import GafferUI
import GafferScene
import GafferSceneUI
# add plugs to the preferences node
preferences = application.root()["preferences"]
preferences["viewer"] = Gaffer.Plug()
preferences["viewer"]["gridDimensions"] = Gaffer.V2fPlug( defaultValue = imath.V2f( 10 ), minValue = imath.V2f( 0 ) )
Gaffer.Metadata.registerValue( preferences["viewer"], "plugValueWidget:type", "GafferUI.LayoutPlugValueWidget", persistent = False )
Gaffer.Metadata.registerValue( preferences["viewer"], "layout:section", "Viewer", persistent = False )
# register a customised view for viewing scenes
def __sceneView( plug ) :
view = GafferSceneUI.SceneView()
view["in"].setInput( plug )
view["grid"]["dimensions"].setInput( preferences["viewer"]["gridDimensions"] )
return view
GafferUI.View.registerView( GafferScene.ScenePlug.staticTypeId(), __sceneView )
# Add items to the viewer's right click menu
def __viewContextMenu( viewer, view, menuDefinition ) :
GafferSceneUI.SceneHistoryUI.appendViewContextMenuItems( viewer, view, menuDefinition )
GafferUI.Viewer.viewContextMenuSignal().connect( __viewContextMenu, scoped = False )
# register shading modes
def __createNode( nodeType, plugValues ) :
node = nodeType()
for name, value in plugValues.items() :
node.descendant( name ).setValue( value )
return node
def __registerShadingModes( modes ) :
for name, nodeType, plugValues in modes :
GafferSceneUI.SceneView.registerShadingMode(
name,
functools.partial(
__createNode,
nodeType,
plugValues,
)
)
def __createXRayShader() :
# ideally this could be any type of node (eg Box), but
# SceneView seems to require a SceneProcessor.
xray = GafferScene.SceneProcessor( "XRay" )
xray["attributes"] = GafferScene.CustomAttributes()
xray["attributes"]["attributes"].addChild( Gaffer.NameValuePlug( "gl:depthTest", Gaffer.BoolPlug( "value", defaultValue = False ), True, "depthTest" ) )
xray["attributes"]["in"].setInput( xray["in"] )
xray["assignment"] = GafferScene.ShaderAssignment()
xray["assignment"]["in"].setInput( xray["attributes"]["out"] )
xray["shader"] = GafferScene.OpenGLShader( "XRay" )
xray["shader"]["name"].setValue( "xray" )
xray["shader"]["type"].setValue( "gl:surface" )
xray["shader"]["parameters"].addChild( Gaffer.StringPlug( "glFragmentSource", defaultValue = inspect.cleandoc(
'''
in vec3 fragmentN;
in vec3 fragmentI;
void main()
{
float f = abs( dot( normalize( fragmentI ), normalize( fragmentN ) ) );
gl_FragColor = vec4( mix( vec3( 0.7 ), vec3( 0.5 ), f ), 0.5 );
}
'''
) ) )
xray["shader"]["out"] = Gaffer.Plug()
xray["assignment"]["shader"].setInput( xray["shader"]["out"] )
xray["out"].setInput( xray["assignment"]["out"] )
return xray
GafferSceneUI.SceneView.registerShadingMode( "X-Ray", functools.partial( __createXRayShader ) )
with IECore.IgnoredExceptions( ImportError ) :
import GafferArnold
__registerShadingModes( [
( "Diagnostic/Arnold/Shader Assignment", GafferScene.AttributeVisualiser, { "attributeName" : "ai:surface", "mode" : GafferScene.AttributeVisualiser.Mode.ShaderNodeColor } ),
( "Diagnostic/Arnold/Camera Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "ai:visibility:camera" } ),
( "Diagnostic/Arnold/Shadow Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "ai:visibility:shadow" } ),
( "Diagnostic/Arnold/Reflection Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "ai:visibility:reflected" } ),
( "Diagnostic/Arnold/Refraction Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "ai:visibility:refracted" } ),
( "Diagnostic/Arnold/Diffuse Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "ai:visibility:diffuse" } ),
( "Diagnostic/Arnold/Glossy Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "ai:visibility:glossy" } ),
( "Diagnostic/Arnold/Matte", GafferScene.AttributeVisualiser, { "attributeName" : "ai:matte" } ),
( "Diagnostic/Arnold/Opaque", GafferScene.AttributeVisualiser, { "attributeName" : "ai:opaque" } ),
( "Diagnostic/Arnold/Receive Shadows", GafferScene.AttributeVisualiser, { "attributeName" : "ai:receive_shadows" } ),
( "Diagnostic/Arnold/Self Shadows", GafferScene.AttributeVisualiser, { "attributeName" : "ai:self_shadows" } ),
] )
with IECore.IgnoredExceptions( ImportError ) :
import GafferDelight
__registerShadingModes( [
( "Diagnostic/3Delight/Shader Assignment", GafferScene.AttributeVisualiser, { "attributeName" : "osl:surface", "mode" : GafferScene.AttributeVisualiser.Mode.ShaderNodeColor } ),
( "Diagnostic/3Delight/Camera Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.camera" } ),
( "Diagnostic/3Delight/Shadow Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.shadow" } ),
( "Diagnostic/3Delight/Reflection Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.reflection" } ),
( "Diagnostic/3Delight/Refraction Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.refraction" } ),
( "Diagnostic/3Delight/Diffuse Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.diffuse" } ),
( "Diagnostic/3Delight/Specular Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.specular" } ),
( "Diagnostic/3Delight/Hair Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "dl:visibility.hair" } ),
] )
with IECore.IgnoredExceptions( ImportError ) :
import GafferAppleseed
__registerShadingModes( [
( "Diagnostic/Appleseed/Shader Assignment", GafferScene.AttributeVisualiser, { "attributeName" : "osl:surface", "mode" : GafferScene.AttributeVisualiser.Mode.ShaderNodeColor } ),
( "Diagnostic/Appleseed/Camera Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "as:visibility:camera" } ),
( "Diagnostic/Appleseed/Shadow Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "as:visibility:shadow" } ),
( "Diagnostic/Appleseed/Diffuse Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "as:visibility:diffuse" } ),
( "Diagnostic/Appleseed/Specular Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "as:visibility:specular" } ),
( "Diagnostic/Appleseed/Glossy Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "as:visibility:glossy" } ),
( "Diagnostic/Appleseed/Photon Visibility", GafferScene.AttributeVisualiser, { "attributeName" : "as:visibility:light" } ),
] )
|
[
"GafferScene.ScenePlug.staticTypeId",
"functools.partial",
"GafferScene.SceneProcessor",
"GafferScene.OpenGLShader",
"Gaffer.BoolPlug",
"GafferScene.ShaderAssignment",
"Gaffer.Plug",
"IECore.IgnoredExceptions",
"Gaffer.Metadata.registerValue",
"GafferSceneUI.SceneHistoryUI.appendViewContextMenuItems",
"GafferScene.CustomAttributes",
"GafferUI.Viewer.viewContextMenuSignal",
"imath.V2f",
"inspect.cleandoc",
"GafferSceneUI.SceneView"
] |
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|
# Copyright 2015 The LUCI Authors. All rights reserved.
# Use of this source code is governed under the Apache License, Version 2.0
# that can be found in the LICENSE file.
"""Google Compute Engine specific utility functions."""
import base64
import json
import logging
import re
import socket
import threading
import time
import urllib.parse
import urllib.request
from api import oauth
from utils import tools
### Private stuff.
# One-tuple that contains cached metadata, as returned by get_metadata.
_CACHED_METADATA = None
# Cache of GCE OAuth2 token.
_CACHED_OAUTH2_TOKEN = {}
_CACHED_OAUTH2_TOKEN_LOCK = threading.Lock()
# Cached signed GCE VM metadata (keyed by the audience URL).
_CACHED_METADATA_TOKEN = {}
_CACHED_METADATA_TOKEN_LOCK = threading.Lock()
def _padded_b64_decode(data):
mod = len(data) % 4
if mod:
data += b'=' * (4 - mod)
return base64.urlsafe_b64decode(data)
def _raw_metadata_request(path):
"""Sends a request to metadata.google.internal, retrying on errors.
Returns raw response as bytes or None if not running on GCE or if the metadata
server is unreachable (after multiple attempts). Logs errors internally.
Args:
path: path on the metadata server to query ('/computeMetadata/...').
"""
assert path.startswith('/'), path
if not is_gce():
logging.info('GCE metadata is not available: not on GCE')
return None
url = 'http://metadata.google.internal' + path
headers = {'Metadata-Flavor': 'Google'}
for i in range(0, 10):
time.sleep(i*2)
try:
resp = urllib.request.urlopen(
urllib.request.Request(url, headers=headers), timeout=10)
return resp.read()
except IOError as e:
logging.warning(
'Failed to grab GCE metadata from %s on attempt #%d: %s',
path, i+1, e)
return None
## Public API.
@tools.cached
def is_gce():
"""Returns True if running on Google Compute Engine."""
# Attempting to hit the metadata server is unreliable since it sometimes
# doesn't respond. Resolving DNS name is more stable, since it actually just
# looks into /etc/hosts file (at least on Linux).
#
# See also https://github.com/GoogleCloudPlatform/gcloud-golang/issues/194.
try:
# 169.254.169.254 is the documented metadata server IP address.
return socket.gethostbyname('metadata.google.internal') == '169.254.169.254'
except socket.error:
return False
def get_metadata_uncached():
"""Returns the GCE metadata as a dict.
Returns None if not running on GCE or if metadata server is unreachable.
Refs:
https://cloud.google.com/compute/docs/metadata
https://cloud.google.com/compute/docs/machine-types
To get the at the command line from a GCE VM, use:
curl --silent \
http://metadata.google.internal/computeMetadata/v1/?recursive=true \
-H "Metadata-Flavor: Google" | python -m json.tool | less
"""
raw = _raw_metadata_request('/computeMetadata/v1/?recursive=true')
return json.loads(raw) if raw else None
def wait_for_metadata(quit_bit):
"""Spins until GCE metadata server responds.
Precaches value of 'get_metadata'.
Args:
quit_bit: its 'is_set' method is used to break from the loop earlier.
"""
global _CACHED_METADATA
if not is_gce():
return
while not quit_bit.is_set():
meta = get_metadata_uncached()
if meta is not None:
_CACHED_METADATA = (meta,)
break
def get_metadata():
"""Cached version of get_metadata_uncached()"""
global _CACHED_METADATA
if _CACHED_METADATA is not None:
return _CACHED_METADATA[0]
meta = get_metadata_uncached()
# Don't cache 'None' reply on GCE. It is wrong. Better to try to grab the
# right one next time.
if meta is not None or not is_gce():
_CACHED_METADATA = (meta,)
return meta
return None
def oauth2_access_token_with_expiration(account):
"""Returns tuple (oauth2 access token, expiration timestamp).
Args:
account: GCE service account to use (e.g "default").
"""
# TODO(maruel): Move GCE VM authentication logic into client/utils/net.py.
# As seen in google-api-python-client/oauth2client/gce.py
with _CACHED_OAUTH2_TOKEN_LOCK:
cached_tok = _CACHED_OAUTH2_TOKEN.get(account)
# Cached and expires in more than 10 min from now.
if cached_tok and cached_tok['expiresAt'] >= time.time() + 600:
return cached_tok['accessToken'], cached_tok['expiresAt']
# Grab the token.
url = ('http://metadata.google.internal/computeMetadata/v1/instance'
'/service-accounts/%s/token' % account)
headers = {'Metadata-Flavor': 'Google'}
access_token, expires_at = oauth.oauth2_access_token_from_url(url, headers)
tok = {
'accessToken': access_token,
'expiresAt': expires_at,
}
_CACHED_OAUTH2_TOKEN[account] = tok
return access_token, expires_at
def oauth2_access_token(account='default'):
"""Returns a value of oauth2 access token.
Same as oauth2_access_token_with_expiration, but discards expiration and
assumed 'default' account. Exists for backward compatibility with various
hooks.
"""
return oauth2_access_token_with_expiration(account)[0]
def oauth2_available_scopes(account='default'):
"""Returns a list of OAuth2 scopes granted to GCE service account."""
metadata = get_metadata()
if not metadata:
return []
accounts = metadata['instance']['serviceAccounts']
return accounts.get(account, {}).get('scopes') or []
@tools.cached
def can_send_metric(service_account):
"""True if 'send_metric' really does something."""
# Scope to use Cloud Monitoring.
scope = 'https://www.googleapis.com/auth/monitoring'
return scope in oauth2_available_scopes(account=service_account)
def signed_metadata_token(audience):
"""Returns tuple (signed GCE metadata JWT, expiration timestamp).
Returns (None, None) if not running on GCE or the metadata server is
unreachable or misbehaves. More details are available in the log.
The returned token has at least 10 min of lifetime left.
Args:
audience: audience URL to put into the token (usually Swarming server URL).
"""
with _CACHED_METADATA_TOKEN_LOCK:
cached_tok = _CACHED_METADATA_TOKEN.get(audience)
if cached_tok and cached_tok['exp'] >= time.time() + 600:
return cached_tok['jwt'], cached_tok['exp']
jwt = _raw_metadata_request(
'/computeMetadata/v1/instance/service-accounts/default/'
'identity?audience=%s&format=full' % urllib.parse.quote_plus(audience))
if not jwt:
return None, None
# Extract lifetime of the token from JWT's payload (the second segment).
# Use now+(expiry-issued_at) to account for possible clock skew between GCE
# backend clock and our machine's clock (expiry and issued_at are expressed
# in GCE backend clock time, but we want to compare the expiry to our
# clock). We assume that issued_at ('iat') in the token matches current
# timestamp.
#
# Note that we don't do any strict validation here, since we trust the
# metadata server on the bot. Swarming backend does full validation though,
# so if metadata server returns something crazy, we'll know on the server
# side.
try:
payload = json.loads(_padded_b64_decode(jwt.split(b'.')[1]))
exp = int(time.time()) + (payload['exp'] - payload['iat'])
except (IndexError, KeyError, TypeError, ValueError) as exc:
logging.error('Metadata server returned invalid JWT (%s):\n%s', exc, jwt)
return None, None
jwt = jwt.decode()
_CACHED_METADATA_TOKEN[audience] = {'jwt': jwt, 'exp': exp}
return jwt, exp
@tools.cached
def get_image():
"""Returns the image used by the GCE VM."""
# Format is projects/<id>/global/images/<image>
metadata = get_metadata()
return metadata['instance']['image'].rsplit('/', 1)[-1]
@tools.cached
def get_zone():
"""Returns the zone containing the GCE VM."""
# Format is projects/<id>/zones/<zone>
metadata = get_metadata()
return metadata['instance']['zone'].rsplit('/', 1)[-1]
@tools.cached
def get_networks():
"""Returns the names of the networks used by the GCE VM."""
metadata = get_metadata()
names = []
for network in metadata['instance'].get('networkInterfaces', []):
names.append(network['network'])
return names
@tools.cached
def get_zones():
"""Returns the list of zone values for the GCE VM."""
# Ref: https://cloud.google.com/compute/docs/regions-zones/
# 'continent-regionN-L' where N is a number and L a letter.
m = re.match(r'([a-z]+)-([a-z]+)(\d)-([a-z])', get_zone())
if not m:
# Safety fallback in case a new form appears.
return [get_zone()]
return [
m.group(1),
m.group(1) + '-' + m.group(2),
m.group(1) + '-' + m.group(2) + m.group(3),
m.group(1) + '-' + m.group(2) + m.group(3) + '-' + m.group(4),
]
@tools.cached
def get_gcp():
"""Returns the string identifier of the GCE VM's Cloud Project."""
metadata = get_metadata()
return [metadata['project']['projectId']]
@tools.cached
def get_machine_type():
"""Returns the GCE machine type."""
# Format is projects/<id>/machineTypes/<machine_type>
metadata = get_metadata()
return metadata['instance']['machineType'].rsplit('/', 1)[-1]
@tools.cached
def get_cpuinfo():
"""Returns the GCE CPU information as reported by GCE instance metadata."""
metadata = get_metadata()
if not metadata:
return None
cpu_platform = metadata['instance']['cpuPlatform']
if not cpu_platform:
return None
# Normalize according to the expected name as reported by the CPUID
# instruction. Sadly what GCE metadata reports is a 'creative' name that bears
# no relation with what CPUID reports.
#
# Fake it based on the expected names reported at
# https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform#availablezones
#
# TODO: Update once we get other platforms. We don't know in advance what
# 'creative' names will be used so better to fail explicitly and fix it right
# away.
cpu_name = None
vendor = None
if cpu_platform.startswith('Intel '):
cpu_name = 'Intel(R) Xeon(R) CPU %s GCE' % cpu_platform[len('Intel '):]
vendor = 'GenuineIntel'
elif cpu_platform.startswith('AMD '):
cpu_name = '%s GCE' % cpu_platform
vendor = 'AuthenticAMD'
assert cpu_name is not None, cpu_platform
return {
'name': cpu_name,
'vendor': vendor,
}
@tools.cached
def get_tags():
"""Returns a list of instance tags or empty list if not GCE VM."""
return get_metadata()['instance']['tags']
|
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"logging.error",
"json.loads",
"logging.warning",
"time.sleep",
"threading.Lock",
"logging.info",
"socket.gethostbyname",
"time.time",
"base64.urlsafe_b64decode",
"api.oauth.oauth2_access_token_from_url"
] |
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|
import numpy as np
import pandas as pd
from sklearn.covariance import EmpiricalCovariance
from typing import Union
from modules.features.feature import Feature
from modules.filters.dml.map_representation import from_map_representation_to_xy
from modules.features.segment_feature import SegmentFeature
from modules.features.landmark_feature import LandmarkFeature
from modules.features.global_positioning_feature import GlobalPositioningFeature
class GAPPF():
"""
Implements the 'Gaussian Approximation of the Posterior-based Particle Filter' method
for digital map-based localization.
"""
def __init__(self, prune_gamma : float, n_particles : int):
"""
Parameters
==========
prune_gamma : float.
The threshold of the relative score for prunning hypotheses.
n_particles : int.
The number of particles sampled for the estimation for each hypothesis
"""
self.routes = {}
self.hypotheses = {}
self.n_particles = n_particles
self.gamma = prune_gamma
return
# STATE
# ==========================
def add_hypothesis(self, mean : float, variance : float, route : int):
"""
Create a new hypothesis.
Parameters
===============
mean: float.
The mean position to of the hypothesis's distribution.
variance: float.
The variance of the hypothesis's distribution
route: int.
To which route this hypothesis belongs.
"""
h = np.array([mean, variance], dtype="float").reshape(1,2)
route = int(route)
self.hypotheses[route] = h
return
def prune_hypothesis(self, route_id : int):
"""
Remove a hypothesis.
Parameters
=========
route_id: int.
The identifier of the route from which the hypothesis is going to
be removed.
"""
del self.hypotheses[route_id]
del self.routes[route_id]
return
def check_is_localized(self) -> bool:
"""
Indicates whether the estimation is reliable (localized) or not.
Returns
====
bool.
True if the estimation is localized (one hypothesis on the set of hypotheses). False otherwise.
"""
if len(self.hypotheses) == 1:
return True
return False
def get_mean(self) -> np.array:
"""
Get the mean in x,y coordinates.
Warning: it does not check if the method is localized!
Returns
=========
mean: numpy.array.
The mean (x,y) position of the estimation.
"""
r_id = list( self.hypotheses )[0]
hypothesis = self.hypotheses[r_id].flatten()
mean, variance = hypothesis
ls = np.random.normal(mean, variance ** 0.5, size=(self.n_particles))
route = self.routes[r_id]
xy_list = []
for l in ls:
xy = from_map_representation_to_xy(l, route)
xy_list.append(xy)
xy_array = np.vstack(xy_list)
mean = np.mean(xy_array, axis=0)
return mean
def get_mean_and_covariance(self) -> Union[np.array, np.array] :
"""
Get the mean and covariance in x,y coordinates.
Warning: it does not check if the method is localized!
Returns
=========
mean: numpy.array.
The mean (x,y) position of the estimation.
covariance: numpy.array.
The covariance of the estimation.
"""
r_id = list( self.hypotheses )[0]
hypothesis = self.hypotheses[r_id].flatten()
mean, variance = hypothesis
ls = np.random.normal(mean, variance ** 0.5, size=(self.n_particles))
route = self.routes[r_id]
xy_list = []
for l in ls:
xy = from_map_representation_to_xy(l, route)
xy_list.append(xy)
xy_array = np.vstack(xy_list)
mean = np.mean(xy_array, axis=0)
covariance = EmpiricalCovariance().fit(xy_array).covariance_ + np.full((2,2), 1.)
return mean, covariance
# ==========================
# MAP
# ==========================
def add_route(self, route_id : int, ways : pd.DataFrame):
"""
Add a route to the set of routes.
Parameters
===========
route_id: int.
The identifier of the route.
ways: pandas.DataFrame.
The DataFrame of the information of ways contained in the route.
"""
self.routes[route_id] = ways
return
# ==========================
# HYPOTHESES MANAGEMENT
# ==========================
def copy_to_route(self, from_idx : int, to_idx : int):
"""
Copies one hypothesis's distribution to another route.
Parameters
=======
from_idx: int.
The identifier of the route from which the hypothesis's
distribution is going to be copied.
to_idx: int.
The identifier of the route to which the hypothesis's
distribution is going to be copied.
"""
h = self.hypotheses[from_idx].flatten()
mean, variance = h
self.add_hypothesis(mean, variance, to_idx)
return
# ==========================
# MOTION
# ==========================
def predict(self, odom : float, odom_var : float):
"""
Performs prediction of the distribution for each hypothesis.
Parameters
==============
odom: float.
The forward odometry received.
odom_var: float.
The variance of the odometry.
"""
for r_id in self.routes:
self.hypotheses[r_id][0,0] += odom
self.hypotheses[r_id][0,1] += odom_var
return
# ==========================
# UPDATE-RELATED TASKS
# ==========================
def update(self, feature : Feature) :
"""
Estimates the new distribution for each hypothesis given a measured feature.
Performs branch pruning after estimation using the gamma defined at the instantiation.
Parameters
============
feature: Feature-inherited objects.
The measured feature detected.
"""
N = self.n_particles
scores = {}
max_score = None
for route_id in self.routes:
likelihoods = np.empty((N,))
h = self.hypotheses[route_id].flatten()
route = self.routes[route_id]
mean, variance = h
l_samples = np.random.normal(mean, variance ** 0.5, size=(N))
# Compute Likelihoods
if isinstance(feature,LandmarkFeature) or isinstance(feature, GlobalPositioningFeature):
for i in range(N):
l = l_samples[i]
p_xy = from_map_representation_to_xy(l, route)
likelihood = feature.measurement_model(p_xy)
likelihoods[i] = likelihood
elif isinstance(feature, SegmentFeature):
for i in range(N):
l = l_samples[i]
likelihood = feature.measurement_model(l, route)
likelihoods[i] = likelihood
else:
raise Exception("Error: feature type provided not implemented!")
# Score each hypothesis
score = np.sum(likelihoods)
if max_score is None:
max_score = score
max_score = score if (score > max_score) else max_score
scores[route_id] = score
# If score is as low as zero, increase hypothesis' variance
# since it might be on a very different place
if score < 1e-9:
self.hypotheses[route_id][0,1] += 1e0
continue
eta = 1./score
# Update their mean and variance
new_mean = eta * np.sum( likelihoods * l_samples )
diff = l_samples - new_mean
new_var = 0.0
for k in range(N):
new_var += likelihoods[k] * (diff[k] ** 2.0)
new_var *= eta
self.hypotheses[route_id] = np.array([new_mean, new_var]).reshape(1,2)
# If all the particles have a very low score, then do not prune hypotheses!
if max_score <= 0:
return
# Normalize scores and detect routes to prune
prune_ids = []
for route_id in self.routes:
norm_score = scores[route_id] / max_score
if norm_score < self.gamma:
prune_ids.append(route_id)
# Prune routes
for route_id in prune_ids:
self.prune_hypothesis(route_id)
return
# ==========================
|
[
"numpy.full",
"modules.filters.dml.map_representation.from_map_representation_to_xy",
"numpy.sum",
"numpy.empty",
"numpy.mean",
"numpy.array",
"numpy.random.normal",
"sklearn.covariance.EmpiricalCovariance",
"numpy.vstack"
] |
[((2860, 2922), 'numpy.random.normal', 'np.random.normal', (['mean', '(variance ** 0.5)'], {'size': 'self.n_particles'}), '(mean, variance ** 0.5, size=self.n_particles)\n', (2876, 2922), True, 'import numpy as np\n'), ((3108, 3126), 'numpy.vstack', 'np.vstack', (['xy_list'], {}), '(xy_list)\n', (3117, 3126), True, 'import numpy as np\n'), ((3142, 3167), 'numpy.mean', 'np.mean', (['xy_array'], {'axis': '(0)'}), '(xy_array, axis=0)\n', (3149, 3167), True, 'import numpy as np\n'), ((3748, 3810), 'numpy.random.normal', 'np.random.normal', (['mean', '(variance ** 0.5)'], {'size': 'self.n_particles'}), '(mean, variance ** 0.5, size=self.n_particles)\n', (3764, 3810), True, 'import numpy as np\n'), ((3996, 4014), 'numpy.vstack', 'np.vstack', (['xy_list'], {}), '(xy_list)\n', (4005, 4014), True, 'import numpy as np\n'), ((4030, 4055), 'numpy.mean', 'np.mean', (['xy_array'], {'axis': '(0)'}), '(xy_array, axis=0)\n', (4037, 4055), True, 'import numpy as np\n'), ((3018, 3057), 'modules.filters.dml.map_representation.from_map_representation_to_xy', 'from_map_representation_to_xy', (['l', 'route'], {}), '(l, route)\n', (3047, 3057), False, 'from modules.filters.dml.map_representation import from_map_representation_to_xy\n'), ((3906, 3945), 'modules.filters.dml.map_representation.from_map_representation_to_xy', 'from_map_representation_to_xy', (['l', 'route'], {}), '(l, route)\n', (3935, 3945), False, 'from modules.filters.dml.map_representation import from_map_representation_to_xy\n'), ((4127, 4147), 'numpy.full', 'np.full', (['(2, 2)', '(1.0)'], {}), '((2, 2), 1.0)\n', (4134, 4147), True, 'import numpy as np\n'), ((6524, 6538), 'numpy.empty', 'np.empty', (['(N,)'], {}), '((N,))\n', (6532, 6538), True, 'import numpy as np\n'), ((6689, 6736), 'numpy.random.normal', 'np.random.normal', (['mean', '(variance ** 0.5)'], {'size': 'N'}), '(mean, variance ** 0.5, size=N)\n', (6705, 6736), True, 'import numpy as np\n'), ((7537, 7556), 'numpy.sum', 'np.sum', (['likelihoods'], {}), '(likelihoods)\n', (7543, 7556), True, 'import numpy as np\n'), ((1569, 1610), 'numpy.array', 'np.array', (['[mean, variance]'], {'dtype': '"""float"""'}), "([mean, variance], dtype='float')\n", (1577, 1610), True, 'import numpy as np\n'), ((8073, 8104), 'numpy.sum', 'np.sum', (['(likelihoods * l_samples)'], {}), '(likelihoods * l_samples)\n', (8079, 8104), True, 'import numpy as np\n'), ((6973, 7012), 'modules.filters.dml.map_representation.from_map_representation_to_xy', 'from_map_representation_to_xy', (['l', 'route'], {}), '(l, route)\n', (7002, 7012), False, 'from modules.filters.dml.map_representation import from_map_representation_to_xy\n'), ((8333, 8362), 'numpy.array', 'np.array', (['[new_mean, new_var]'], {}), '([new_mean, new_var])\n', (8341, 8362), True, 'import numpy as np\n'), ((4077, 4098), 'sklearn.covariance.EmpiricalCovariance', 'EmpiricalCovariance', ([], {}), '()\n', (4096, 4098), False, 'from sklearn.covariance import EmpiricalCovariance\n')]
|
from flask import Flask, render_template
from blueprints.projects import projects
from blueprints.about import about
app = Flask(__name__)
app.register_blueprint(projects, url_prefix='/projects')
app.register_blueprint(about, url_prefix='/about')
@app.route('/')
def home():
return render_template('home.html',
home=True)
|
[
"flask.Flask",
"flask.render_template"
] |
[((125, 140), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (130, 140), False, 'from flask import Flask, render_template\n'), ((286, 325), 'flask.render_template', 'render_template', (['"""home.html"""'], {'home': '(True)'}), "('home.html', home=True)\n", (301, 325), False, 'from flask import Flask, render_template\n')]
|
from celery_service.config import MyTask
from celery_main import app
from celery.signals import worker_process_init, worker_process_shutdown, celeryd_init
import redis
from core.config import REDIS_CONFIG
from datafilter.big_deal_filter import BigDealFilter
import pymysql
from DBUtils.PooledDB import PooledDB
from core.config import MYSQL_CONFIG
import traceback
redis_con = None
conn = None
@worker_process_init.connect
def init_worker(**kwargs):
global redis_con
redis_con = redis.StrictRedis(connection_pool=redis.ConnectionPool.from_url(
REDIS_CONFIG['host'] + "/" + REDIS_CONFIG['db']
))
global conn
conn = pymysql.connect(port=MYSQL_CONFIG['port'], user=MYSQL_CONFIG['user'], password=MYSQL_CONFIG["<PASSWORD>"],
database=MYSQL_CONFIG['db'], charset=MYSQL_CONFIG['charset'], host=MYSQL_CONFIG['host'])
@worker_process_shutdown.connect
def shutdown_worker(**kwargs):
global conn
if conn:
conn.close()
@app.task(base=MyTask)
def save_big_deals(market_code ,data):
fil = BigDealFilter(redis_con)
"""
mysql_pool = PooledDB(
pymysql, MYSQL_CONFIG['min_size'], host=MYSQL_CONFIG['host'],
user=MYSQL_CONFIG['user'], passwd=MYSQL_CONFIG['passwd'], db=MYSQL_CONFIG['db'],
port=MYSQL_CONFIG['port'], charset=MYSQL_CONFIG['charset']
)
"""
last_big_deal_time = redis_con.hget("LAST_TRADE_TIME",market_code) or 0
save_data = fil.get_useful_data(data, int(last_big_deal_time))
# conn = mysql_pool.connection()
# conn = pymysql.connect(port=MYSQL_CONFIG['port'],user=MYSQL_CONFIG['user'],password=MYSQL_CONFIG["passwd"], database=MYSQL_CONFIG['db'],charset=MYSQL_CONFIG['charset'],host=MYSQL_CONFIG['host'])
if save_data:
cur = conn.cursor()
sql = "INSERT INTO big_deal (timestamp, symbol, order_id, market_code, side, price, amount, cny_price, usd_price) VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s)"
try:
cur.executemany(sql,save_data)
conn.commit()
except:
print(traceback.print_exc())
|
[
"datafilter.big_deal_filter.BigDealFilter",
"traceback.print_exc",
"celery_main.app.task",
"redis.ConnectionPool.from_url",
"pymysql.connect"
] |
[((1012, 1033), 'celery_main.app.task', 'app.task', ([], {'base': 'MyTask'}), '(base=MyTask)\n', (1020, 1033), False, 'from celery_main import app\n'), ((670, 873), 'pymysql.connect', 'pymysql.connect', ([], {'port': "MYSQL_CONFIG['port']", 'user': "MYSQL_CONFIG['user']", 'password': "MYSQL_CONFIG['<PASSWORD>']", 'database': "MYSQL_CONFIG['db']", 'charset': "MYSQL_CONFIG['charset']", 'host': "MYSQL_CONFIG['host']"}), "(port=MYSQL_CONFIG['port'], user=MYSQL_CONFIG['user'],\n password=MYSQL_CONFIG['<PASSWORD>'], database=MYSQL_CONFIG['db'],\n charset=MYSQL_CONFIG['charset'], host=MYSQL_CONFIG['host'])\n", (685, 873), False, 'import pymysql\n'), ((1083, 1107), 'datafilter.big_deal_filter.BigDealFilter', 'BigDealFilter', (['redis_con'], {}), '(redis_con)\n', (1096, 1107), False, 'from datafilter.big_deal_filter import BigDealFilter\n'), ((525, 603), 'redis.ConnectionPool.from_url', 'redis.ConnectionPool.from_url', (["(REDIS_CONFIG['host'] + '/' + REDIS_CONFIG['db'])"], {}), "(REDIS_CONFIG['host'] + '/' + REDIS_CONFIG['db'])\n", (554, 603), False, 'import redis\n'), ((2093, 2114), 'traceback.print_exc', 'traceback.print_exc', ([], {}), '()\n', (2112, 2114), False, 'import traceback\n')]
|
from rdflib import Graph, URIRef, Literal
from rdflib.plugins.sparql import prepareQuery
from rdflib.compare import isomorphic
from nose.tools import eq_
def test_graph_prefix():
"""
This is issue https://github.com/RDFLib/rdflib/issues/313
"""
g1 = Graph()
g1.parse(data="""
@prefix : <urn:ns1:> .
:foo <p> 42.
""", format="n3")
g2 = Graph()
g2.parse(data="""
@prefix : <urn:somethingelse:> .
<urn:ns1:foo> <p> 42.
""", format="n3")
assert isomorphic(g1, g2)
q_str = ("""
PREFIX : <urn:ns1:>
SELECT ?val
WHERE { :foo ?p ?val }
""")
q_prepared = prepareQuery(q_str)
expected = [(Literal(42),)]
eq_(list(g1.query(q_prepared)), expected)
eq_(list(g2.query(q_prepared)), expected)
eq_(list(g1.query(q_str)), expected)
eq_(list(g2.query(q_str)), expected)
def test_variable_order():
g = Graph()
g.add((URIRef("http://foo"), URIRef("http://bar"), URIRef("http://baz")))
res = g.query("SELECT (42 AS ?a) ?b { ?b ?c ?d }")
row = list(res)[0]
print(row)
assert len(row) == 2
assert row[0] == Literal(42)
assert row[1] == URIRef("http://foo")
def test_sparql_bnodelist():
"""
syntax tests for a few corner-cases not touched by the
official tests.
"""
prepareQuery('select * where { ?s ?p ( [] ) . }')
prepareQuery('select * where { ?s ?p ( [ ?p2 ?o2 ] ) . }')
prepareQuery('select * where { ?s ?p ( [ ?p2 ?o2 ] [] ) . }')
prepareQuery('select * where { ?s ?p ( [] [ ?p2 ?o2 ] [] ) . }')
def test_complex_sparql_construct():
g = Graph()
q = '''select ?subject ?study ?id where {
?s a <urn:Person>;
<urn:partOf> ?c;
<urn:hasParent> ?mother, ?father;
<urn:id> [ a <urn:Identifier>; <urn:has-value> ?id].
}'''
g.query(q)
if __name__ == '__main__':
import nose
nose.main(defaultTest=__name__)
|
[
"rdflib.compare.isomorphic",
"rdflib.Graph",
"nose.main",
"rdflib.Literal",
"rdflib.plugins.sparql.prepareQuery",
"rdflib.URIRef"
] |
[((270, 277), 'rdflib.Graph', 'Graph', ([], {}), '()\n', (275, 277), False, 'from rdflib import Graph, URIRef, Literal\n'), ((376, 383), 'rdflib.Graph', 'Graph', ([], {}), '()\n', (381, 383), False, 'from rdflib import Graph, URIRef, Literal\n'), ((503, 521), 'rdflib.compare.isomorphic', 'isomorphic', (['g1', 'g2'], {}), '(g1, g2)\n', (513, 521), False, 'from rdflib.compare import isomorphic\n'), ((633, 652), 'rdflib.plugins.sparql.prepareQuery', 'prepareQuery', (['q_str'], {}), '(q_str)\n', (645, 652), False, 'from rdflib.plugins.sparql import prepareQuery\n'), ((900, 907), 'rdflib.Graph', 'Graph', ([], {}), '()\n', (905, 907), False, 'from rdflib import Graph, URIRef, Literal\n'), ((1313, 1362), 'rdflib.plugins.sparql.prepareQuery', 'prepareQuery', (['"""select * where { ?s ?p ( [] ) . }"""'], {}), "('select * where { ?s ?p ( [] ) . }')\n", (1325, 1362), False, 'from rdflib.plugins.sparql import prepareQuery\n'), ((1367, 1425), 'rdflib.plugins.sparql.prepareQuery', 'prepareQuery', (['"""select * where { ?s ?p ( [ ?p2 ?o2 ] ) . }"""'], {}), "('select * where { ?s ?p ( [ ?p2 ?o2 ] ) . }')\n", (1379, 1425), False, 'from rdflib.plugins.sparql import prepareQuery\n'), ((1430, 1491), 'rdflib.plugins.sparql.prepareQuery', 'prepareQuery', (['"""select * where { ?s ?p ( [ ?p2 ?o2 ] [] ) . }"""'], {}), "('select * where { ?s ?p ( [ ?p2 ?o2 ] [] ) . }')\n", (1442, 1491), False, 'from rdflib.plugins.sparql import prepareQuery\n'), ((1496, 1560), 'rdflib.plugins.sparql.prepareQuery', 'prepareQuery', (['"""select * where { ?s ?p ( [] [ ?p2 ?o2 ] [] ) . }"""'], {}), "('select * where { ?s ?p ( [] [ ?p2 ?o2 ] [] ) . }')\n", (1508, 1560), False, 'from rdflib.plugins.sparql import prepareQuery\n'), ((1609, 1616), 'rdflib.Graph', 'Graph', ([], {}), '()\n', (1614, 1616), False, 'from rdflib import Graph, URIRef, Literal\n'), ((1881, 1912), 'nose.main', 'nose.main', ([], {'defaultTest': '__name__'}), '(defaultTest=__name__)\n', (1890, 1912), False, 'import nose\n'), ((1126, 1137), 'rdflib.Literal', 'Literal', (['(42)'], {}), '(42)\n', (1133, 1137), False, 'from rdflib import Graph, URIRef, Literal\n'), ((1159, 1179), 'rdflib.URIRef', 'URIRef', (['"""http://foo"""'], {}), "('http://foo')\n", (1165, 1179), False, 'from rdflib import Graph, URIRef, Literal\n'), ((671, 682), 'rdflib.Literal', 'Literal', (['(42)'], {}), '(42)\n', (678, 682), False, 'from rdflib import Graph, URIRef, Literal\n'), ((919, 939), 'rdflib.URIRef', 'URIRef', (['"""http://foo"""'], {}), "('http://foo')\n", (925, 939), False, 'from rdflib import Graph, URIRef, Literal\n'), ((941, 961), 'rdflib.URIRef', 'URIRef', (['"""http://bar"""'], {}), "('http://bar')\n", (947, 961), False, 'from rdflib import Graph, URIRef, Literal\n'), ((963, 983), 'rdflib.URIRef', 'URIRef', (['"""http://baz"""'], {}), "('http://baz')\n", (969, 983), False, 'from rdflib import Graph, URIRef, Literal\n')]
|
from bittrex import Bittrex
import json
import time
import os
from twilio.rest import Client
# Creating an instance of the Bittrex class with our secrets.json file
with open("secrets.json") as secrets_file:
secrets = json.load(secrets_file)
secrets_file.close()
my_bittrex = Bittrex(secrets['bittrex_key'], secrets['bittrex_secret'])
# Setting up Twilio for SMS alerts
account_sid = secrets['twilio_key']
auth_token = secrets['twilio_secret']
client = Client(account_sid, auth_token)
# Let's test an API call to get our BTC balance as a test
# print(my_bittrex.get_balance('BTC')['result']['Balance'])
coin_pairs = ['BTC-ETH', 'BTC-OMG', 'BTC-GNT', 'BTC-CVC', 'BTC-BAT', 'BTC-STRAT', 'BTC-LSK', 'BTC-BCC', 'BTC-NEO', 'BTC-OK', 'BTC-TRIG', 'BTC-PAY', 'BTC-XMR']
#print(historical_data = my_bittrex.getHistoricalData('BTC-ETH', 30, "thirtyMin"))
def getClosingPrices(coin_pair, period, unit):
"""
Returns closing prices within a specified time frame for a coin pair
:type coin_pair: str
:type period: str
:type unit: int
:return: Array of closing prices
"""
historical_data = my_bittrex.getHistoricalData(coin_pair, period, unit)
closing_prices = []
for i in historical_data:
closing_prices.append(i['C'])
return closing_prices
def calculateSMA(coin_pair, period, unit):
"""
Returns the Simple Moving Average for a coin pair
"""
total_closing = sum(getClosingPrices(coin_pair, period, unit))
return (total_closing / period)
def calculateEMA(coin_pair, period, unit):
"""
Returns the Exponential Moving Average for a coin pair
"""
closing_prices = getClosingPrices(coin_pair, period, unit)
previous_EMA = calculateSMA(coin_pair, period, unit)
constant = (2 / (period + 1))
current_EMA = (closing_prices[-1] * (2 / (1 + period))) + (previous_EMA * (1 - (2 / (1 + period))))
return current_EMA
def calculateRSI(coin_pair, period, unit):
"""
Calculates the Relative Strength Index for a coin_pair
If the returned value is above 70, it's overbought (SELL IT!)
If the returned value is below 30, it's oversold (BUY IT!)
"""
closing_prices = getClosingPrices(coin_pair, period, unit)
count = 0
change = []
# Calculating price changes
for i in closing_prices:
if count != 0:
change.append(i - closing_prices[count - 1])
count += 1
# Calculating gains and losses
advances = []
declines = []
for i in change:
if i > 0:
advances.append(i)
if i < 0:
declines.append(abs(i))
average_gain = (sum(advances) / period)
average_loss = (sum(declines) / period)
relative_strength = (average_gain / average_loss)
if change[-1] >= 0:
smoothed_rs = (((average_gain * 13) + change[-1]) / 14) / (((average_loss * 13) + 0) / 14)
if change[-1] < 0:
smoothed_rs = (((average_gain * 13) + 0) / 14) / (((average_loss * 13) + abs(change[-1])) / 14)
RSI = 100 - (100 / (1 + smoothed_rs))
return RSI
def calculateBaseLine(coin_pair, unit):
"""
Calculates (26 period high + 26 period low) / 2
Also known as the "Kijun-sen" line
"""
closing_prices = getClosingPrices(coin_pair, 26, unit)
period_high = max(closing_prices)
period_low = min(closing_prices)
return (period_high + period_low) / 2
def calculateConversionLine(coin_pair, unit):
"""
Calculates (9 period high + 9 period low) / 2
Also known as the "Tenkan-sen" line
"""
closing_prices = getClosingPrices(coin_pair, 9, unit)
period_high = max(closing_prices)
period_low = min(closing_prices)
return (period_high + period_low) / 2
def calculateLeadingSpanA(coin_pair, unit):
"""
Calculates (Conversion Line + Base Line) / 2
Also known as the "Senkou Span A" line
"""
base_line = calculateBaseLine(coin_pair, unit)
conversion_line = calculateConversionLine(coin_pair, unit)
return (base_line + conversion_line) / 2
def calculateLeadingSpanB(coin_pair, unit):
"""
Calculates (52 period high + 52 period low) / 2
Also known as the "Senkou Span B" line
"""
closing_prices = getClosingPrices(coin_pair, 52, unit)
period_high = max(closing_prices)
period_low = min(closing_prices)
return (period_high + period_low) / 2
def findBreakout(coin_pair, period, unit):
"""
Finds breakout based on how close the High was to Closing and Low to Opening
"""
hit = 0
historical_data = my_bittrex.getHistoricalData(coin_pair, period, unit)
for i in historical_data:
if (i['C'] == i['H']) and (i['O'] == i['L']):
hit += 1
if (hit / period) >= .75:
message = client.api.account.messages.create(to=secrets['my_number'],from_=secrets['twilio_number'],body="{} is breaking out!".format(coin_pair))
return "Breaking out!"
else:
return "#Bagholding"
if __name__ == "__main__":
def get_signal():
for i in coin_pairs:
breakout = findBreakout(coin_pair=i, period=5, unit="fiveMin")
rsi = calculateRSI(coin_pair=i, period=14, unit="thirtyMin")
print("{}: \tBreakout: {} \tRSI: {}".format(i, breakout, rsi))
time.sleep(300)
while True:
get_signal()
|
[
"time.sleep",
"twilio.rest.Client",
"bittrex.Bittrex",
"json.load"
] |
[((466, 497), 'twilio.rest.Client', 'Client', (['account_sid', 'auth_token'], {}), '(account_sid, auth_token)\n', (472, 497), False, 'from twilio.rest import Client\n'), ((222, 245), 'json.load', 'json.load', (['secrets_file'], {}), '(secrets_file)\n', (231, 245), False, 'import json\n'), ((288, 346), 'bittrex.Bittrex', 'Bittrex', (["secrets['bittrex_key']", "secrets['bittrex_secret']"], {}), "(secrets['bittrex_key'], secrets['bittrex_secret'])\n", (295, 346), False, 'from bittrex import Bittrex\n'), ((5260, 5275), 'time.sleep', 'time.sleep', (['(300)'], {}), '(300)\n', (5270, 5275), False, 'import time\n')]
|
# (C) Copyright [2020] Hewlett Packard Enterprise Development LP
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
"""HPE Container Platform CLI."""
from __future__ import print_function
import abc
import json
import os
import sys
import jmespath
import six
import yaml
import wrapt
import traceback
from hpecp.logger import Logger
from hpecp import (
APIException,
APIItemConflictException,
ContainerPlatformClient,
ContainerPlatformClientException,
)
from hpecp.exceptions import (
APIForbiddenException,
APIItemNotFoundException,
APIUnknownException,
)
from hpecp.cli_utils import TextOutput
_log = Logger.get_logger()
def get_profile():
"""Retrieve the profile - if supplied."""
profile = os.getenv("PROFILE", default="default")
_log.debug(
"PROFILE envirionment variable exists with value '{}'".format(profile)
)
return profile
def get_config_file():
"""Retrieve the CLI config file."""
if "HPECP_CONFIG_FILE" in os.environ:
HPECP_CONFIG_FILE = os.path.expandvars(os.getenv("HPECP_CONFIG_FILE"))
_log.debug(
"HPECP_CONFIG_FILE env variable exists with value '{}'".format(
HPECP_CONFIG_FILE
)
)
else:
HPECP_CONFIG_FILE = "~/.hpecp.conf"
_log.debug(
"HPECP_CONFIG_FILE env variable not found, setting to '{}'".format(
HPECP_CONFIG_FILE
)
)
return HPECP_CONFIG_FILE
@wrapt.decorator
def intercept_exception(wrapped, instance, args, kwargs):
"""Handle Exceptions.""" # noqa: D202
def _unknown_exception_handler(ex):
"""Handle unknown exceptions."""
if _log.level == 10: # "DEBUG"
print(
"Unknown error.",
file=sys.stderr,
)
else:
print(
"Unknown error. To debug run with env var LOG_LEVEL=DEBUG",
file=sys.stderr,
)
tb = traceback.format_exc()
_log.debug(tb)
_log.debug(ex)
sys.exit(1)
try:
return wrapped(*args, **kwargs)
except SystemExit as se:
sys.exit(se.code)
except AssertionError as ae:
print(ae, file=sys.stderr)
sys.exit(1)
except APIUnknownException as ue:
_unknown_exception_handler(ue)
except (
APIException,
APIItemNotFoundException,
APIItemConflictException,
APIForbiddenException,
ContainerPlatformClientException,
) as e:
print(e.message, file=sys.stderr)
sys.exit(1)
except Exception as ex:
_unknown_exception_handler(ex)
@intercept_exception
def get_client(start_session=True):
"""Retrieve a reference to an authenticated client object."""
client = ContainerPlatformClient.create_from_config_file(
config_file=get_config_file(),
profile=get_profile(),
)
if start_session:
client.create_session()
return client
@six.add_metaclass(abc.ABCMeta)
class BaseProxy:
"""Base 'proxy' class for generic calls to API."""
def new_instance(self, client_module_name, resource_class):
"""Create a new instance (constructor).
Parameters
----------
client_module_name : str
Name of the property in the ContainerPlatformClient that
points to the different modules (user, gateway, cluster, etc)
"""
self.client_module_name = client_module_name
self.resource_class = resource_class
super(BaseProxy, self).__init__()
@intercept_exception
def get(self, id, output="yaml", params=None):
"""Retrieve a Resource by ID.
id: string
the id of the resource with format: '/api/path/[0-9]+'
output: string
how to display the output, either 'yaml' or 'json', default 'yaml'
"""
self.client = get_client()
self.client_module_property = getattr(
self.client, self.client_module_name
)
if params:
response = self.client_module_property.get(id=id, params=params)
else:
response = self.client_module_property.get(id=id)
json_data = response.json
if output == "json":
print(json.dumps(json_data))
elif output == "json-pp":
print(
json.dumps(
json_data,
indent=4,
sort_keys=True,
)
)
else:
print(
yaml.dump(
yaml.load(
json.dumps(json_data),
Loader=yaml.FullLoader,
)
)
)
@intercept_exception
def delete(self, id, wait_for_delete_sec=0):
"""Delete a resource.
:param id: the resource ID
:param wait_for_delete_sec: wait for delete to complete
(0 = do not wait)
"""
self.client = get_client()
self.client_module_property = getattr(
self.client, self.client_module_name
)
self.client_module_property.delete(id=id)
if wait_for_delete_sec > 0:
self.wait_for_delete(id=id, timeout_secs=wait_for_delete_sec)
@intercept_exception
def list(self, output="table", columns="DEFAULT", query={}):
"""Retrieve the list of resources.
Parameters
----------
output : str, optional
Define how the output should be printed, by default "table"
"json" or "json-pp" (json pretty print) if providing a query
columns : list/tuple, optional
List of specific columns to be displayed, by default []
`Catalog.default_display_fields`
"DEFAULT", "WIDE", or columns list
query : dict, optional
Query in jmespath (https://jmespath.org/) format, by default {}
if using a query, output must be "json" or "json-pp"
"""
if columns is not None:
if columns == "DEFAULT":
columns = list(self.resource_class.default_display_fields)
elif columns == "WIDE":
columns = list(self.resource_class.all_fields)
elif isinstance(columns, tuple):
columns = list(columns)
elif isinstance(columns, str):
columns = columns.split(",")
# FIXME: this also gets called by print_list()
self.validate_list_params(
all_fields=self.resource_class.all_fields,
output=output,
columns=columns,
query=query,
)
self.client = get_client()
self.client_module_property = getattr(
self.client, self.client_module_name
)
list_instance = self.client_module_property.list()
self.print_list(
list_instance=list_instance,
output=output,
columns=columns,
query=query,
)
@intercept_exception
def validate_list_params(self, all_fields, output, columns, query):
"""Print a list of resources.
Parameters
----------
list_instance : [type]
[description]
output : [type]
[description]
columns : [type]
[description]
query : [type]
[description]
"""
if not isinstance(columns, list):
print("'columns' parameter must be a list.", file=sys.stderr)
sys.exit(1)
for col in columns:
if col not in all_fields:
print("Unknown column '{}'.".format(col), file=sys.stderr)
sys.exit(1)
if len(query) == 0:
if output not in ["table", "text"]:
print(
"When providing the --columns param, the --output param "
"must be 'table' or 'text'",
file=sys.stderr,
)
sys.exit(1)
else:
if output not in ["json", "json-pp", "text"]:
print(
(
"If you provide a jmes --query, the output must "
"be 'json', 'json-pp', or 'text'"
),
file=sys.stderr,
)
sys.exit(1)
@intercept_exception
def print_list(self, list_instance, output, columns, query):
"""Print a list of resources.
Parameters
----------
list_instance : [type]
[description]
output : [type]
[description]
columns : [type]
[description]
query : [type]
[description]
"""
if columns == "DEFAULT":
columns = list_instance.resource_class.default_display_fields
elif columns == "WIDE":
columns = list_instance.resource_class.all_fields
self.validate_list_params(
all_fields=list_instance.resource_class.all_fields,
output=output,
columns=columns,
query=query,
)
# use tabulate for simplified user output
if len(query) == 0:
if output == "table":
print(list_instance.tabulate(columns=columns))
else:
print(
list_instance.tabulate(
columns=columns, style="plain", display_headers=False
)
)
# user has provided a jmes query
else:
data = list_instance.json
if output == "json-pp":
print(
json.dumps(
jmespath.search(str(query), data),
indent=4,
sort_keys=True,
)
)
elif output == "text":
obj = jmespath.search(str(query), data)
print(TextOutput.dump(obj))
else:
print(
json.dumps(
jmespath.search(str(query), data),
)
)
def wait_for_state(
self,
id,
states=[],
timeout_secs=60,
):
"""See wait_for_status()."""
self.wait_for_status(id, states, timeout_secs)
def wait_for_status(
self,
id,
status=[],
timeout_secs=60,
):
"""Wait for resource to have one or more statuses.
:param id: Resource id with format: /api/path/[0-9]+
:param status: status(es) to wait for with format:
['status1', 'status2', 'statusn']
:param timeout_secs: how many secs to wait before exiting
:returns True/False if status was found within timeout_secs. May
raise APIException.
"""
self.client = get_client()
self.client_module_property = getattr(
self.client, self.client_module_name
)
resource_status = [
self.client_module_property.status_class[s] for s in status
]
_log.debug("resource_status = {}".format(resource_status))
try:
success = self.client_module_property.wait_for_status(
id=id,
status=resource_status,
timeout_secs=timeout_secs,
)
except Exception:
success = False
if not success:
print(
"Failed to reach state(s) {} in {}s".format(
str(status),
str(timeout_secs),
),
file=sys.stderr,
)
sys.exit(1)
def wait_for_delete(
self,
id,
timeout_secs=1200,
):
"""Wait for Gateway to be deleted.
:param id: Cluster id with format: /api/v1/workers/[0-9]+
:param timeout_secs: how many secs to wait before exiting
:returns True if gateway was deleted within timeout_secs.
"""
self.wait_for_state(
id=id,
timeout_secs=timeout_secs,
)
|
[
"json.dumps",
"six.add_metaclass",
"hpecp.cli_utils.TextOutput.dump",
"traceback.format_exc",
"hpecp.logger.Logger.get_logger",
"os.getenv",
"sys.exit"
] |
[((1645, 1664), 'hpecp.logger.Logger.get_logger', 'Logger.get_logger', ([], {}), '()\n', (1662, 1664), False, 'from hpecp.logger import Logger\n'), ((4012, 4042), 'six.add_metaclass', 'six.add_metaclass', (['abc.ABCMeta'], {}), '(abc.ABCMeta)\n', (4029, 4042), False, 'import six\n'), ((1746, 1785), 'os.getenv', 'os.getenv', (['"""PROFILE"""'], {'default': '"""default"""'}), "('PROFILE', default='default')\n", (1755, 1785), False, 'import os\n'), ((2998, 3020), 'traceback.format_exc', 'traceback.format_exc', ([], {}), '()\n', (3018, 3020), False, 'import traceback\n'), ((3075, 3086), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (3083, 3086), False, 'import sys\n'), ((2060, 2090), 'os.getenv', 'os.getenv', (['"""HPECP_CONFIG_FILE"""'], {}), "('HPECP_CONFIG_FILE')\n", (2069, 2090), False, 'import os\n'), ((3174, 3191), 'sys.exit', 'sys.exit', (['se.code'], {}), '(se.code)\n', (3182, 3191), False, 'import sys\n'), ((3268, 3279), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (3276, 3279), False, 'import sys\n'), ((3595, 3606), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (3603, 3606), False, 'import sys\n'), ((8591, 8602), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (8599, 8602), False, 'import sys\n'), ((12796, 12807), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (12804, 12807), False, 'import sys\n'), ((5306, 5327), 'json.dumps', 'json.dumps', (['json_data'], {}), '(json_data)\n', (5316, 5327), False, 'import json\n'), ((8760, 8771), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (8768, 8771), False, 'import sys\n'), ((9070, 9081), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (9078, 9081), False, 'import sys\n'), ((9425, 9436), 'sys.exit', 'sys.exit', (['(1)'], {}), '(1)\n', (9433, 9436), False, 'import sys\n'), ((5398, 5445), 'json.dumps', 'json.dumps', (['json_data'], {'indent': '(4)', 'sort_keys': '(True)'}), '(json_data, indent=4, sort_keys=True)\n', (5408, 5445), False, 'import json\n'), ((11066, 11086), 'hpecp.cli_utils.TextOutput.dump', 'TextOutput.dump', (['obj'], {}), '(obj)\n', (11081, 11086), False, 'from hpecp.cli_utils import TextOutput\n'), ((5655, 5676), 'json.dumps', 'json.dumps', (['json_data'], {}), '(json_data)\n', (5665, 5676), False, 'import json\n')]
|
import numpy as np
from .GraphData import *
def intrv_log(min_val, max_val, num):
if min_val <= 0:
min_val = 0.1
return np.geomspace(min_val, max_val, num)
class PlotDict():
__instance = None
__slots__ = ['intrv', 'coll', 'graph']
def __new__(cls):
if PlotDict.__instance is None:
PlotDict.__instance = object.__new__(cls)
PlotDict.__instance.intrv = {
'lin': np.linspace,
'log': intrv_log}
PlotDict.__instance.coll = {
'rel': lambda cl: cl.is_reliable(),
'mark': lambda cl: cl.is_marked()}
PlotDict.__instance.graph = {
'stacked': StackedGraph,
'linerange': LineRangeGraph}
return PlotDict.__instance
|
[
"numpy.geomspace"
] |
[((137, 172), 'numpy.geomspace', 'np.geomspace', (['min_val', 'max_val', 'num'], {}), '(min_val, max_val, num)\n', (149, 172), True, 'import numpy as np\n')]
|
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import, division, print_function
import pytest
from cryptography import utils
from cryptography.exceptions import (
UnsupportedAlgorithm, _Reasons
)
from cryptography.hazmat.backends.interfaces import (
CipherBackend, HMACBackend, HashBackend, PBKDF2HMACBackend, RSABackend
)
from cryptography.hazmat.backends.multibackend import MultiBackend
from cryptography.hazmat.primitives import hashes, hmac
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from ...utils import raises_unsupported_algorithm
@utils.register_interface(CipherBackend)
class DummyCipherBackend(object):
def __init__(self, supported_ciphers):
self._ciphers = supported_ciphers
def cipher_supported(self, algorithm, mode):
return (type(algorithm), type(mode)) in self._ciphers
def create_symmetric_encryption_ctx(self, algorithm, mode):
if not self.cipher_supported(algorithm, mode):
raise UnsupportedAlgorithm("", _Reasons.UNSUPPORTED_CIPHER)
def create_symmetric_decryption_ctx(self, algorithm, mode):
if not self.cipher_supported(algorithm, mode):
raise UnsupportedAlgorithm("", _Reasons.UNSUPPORTED_CIPHER)
@utils.register_interface(HashBackend)
class DummyHashBackend(object):
def __init__(self, supported_algorithms):
self._algorithms = supported_algorithms
def hash_supported(self, algorithm):
return type(algorithm) in self._algorithms
def create_hash_ctx(self, algorithm):
if not self.hash_supported(algorithm):
raise UnsupportedAlgorithm("", _Reasons.UNSUPPORTED_HASH)
@utils.register_interface(HMACBackend)
class DummyHMACBackend(object):
def __init__(self, supported_algorithms):
self._algorithms = supported_algorithms
def hmac_supported(self, algorithm):
return type(algorithm) in self._algorithms
def create_hmac_ctx(self, key, algorithm):
if not self.hmac_supported(algorithm):
raise UnsupportedAlgorithm("", _Reasons.UNSUPPORTED_HASH)
@utils.register_interface(PBKDF2HMACBackend)
class DummyPBKDF2HMACBackend(object):
def __init__(self, supported_algorithms):
self._algorithms = supported_algorithms
def pbkdf2_hmac_supported(self, algorithm):
return type(algorithm) in self._algorithms
def derive_pbkdf2_hmac(self, algorithm, length, salt, iterations,
key_material):
if not self.pbkdf2_hmac_supported(algorithm):
raise UnsupportedAlgorithm("", _Reasons.UNSUPPORTED_HASH)
@utils.register_interface(RSABackend)
class DummyRSABackend(object):
def generate_rsa_private_key(self, public_exponent, private_key):
pass
def create_rsa_signature_ctx(self, private_key, padding, algorithm):
pass
def create_rsa_verification_ctx(self, public_key, signature, padding,
algorithm):
pass
class TestMultiBackend(object):
def test_ciphers(self):
backend = MultiBackend([
DummyHashBackend([]),
DummyCipherBackend([
(algorithms.AES, modes.CBC),
])
])
assert backend.cipher_supported(
algorithms.AES(b"\x00" * 16), modes.CBC(b"\x00" * 16)
)
cipher = Cipher(
algorithms.AES(b"\x00" * 16),
modes.CBC(b"\x00" * 16),
backend=backend
)
cipher.encryptor()
cipher.decryptor()
cipher = Cipher(
algorithms.Camellia(b"\x00" * 16),
modes.CBC(b"\x00" * 16),
backend=backend
)
with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_CIPHER):
cipher.encryptor()
with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_CIPHER):
cipher.decryptor()
def test_hashes(self):
backend = MultiBackend([
DummyHashBackend([hashes.MD5])
])
assert backend.hash_supported(hashes.MD5())
hashes.Hash(hashes.MD5(), backend=backend)
with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_HASH):
hashes.Hash(hashes.SHA1(), backend=backend)
def test_hmac(self):
backend = MultiBackend([
DummyHMACBackend([hashes.MD5])
])
assert backend.hmac_supported(hashes.MD5())
hmac.HMAC(b"", hashes.MD5(), backend=backend)
with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_HASH):
hmac.HMAC(b"", hashes.SHA1(), backend=backend)
def test_pbkdf2(self):
backend = MultiBackend([
DummyPBKDF2HMACBackend([hashes.MD5])
])
assert backend.pbkdf2_hmac_supported(hashes.MD5())
backend.derive_pbkdf2_hmac(hashes.MD5(), 10, b"", 10, b"")
with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_HASH):
backend.derive_pbkdf2_hmac(hashes.SHA1(), 10, b"", 10, b"")
def test_rsa(self):
backend = MultiBackend([
DummyRSABackend()
])
backend.generate_rsa_private_key(
key_size=1024, public_exponent=65537
)
backend.create_rsa_signature_ctx("private_key", padding.PKCS1v15(),
hashes.MD5())
backend.create_rsa_verification_ctx("public_key", "sig",
padding.PKCS1v15(), hashes.MD5())
backend = MultiBackend([])
with pytest.raises(UnsupportedAlgorithm):
backend.generate_rsa_private_key(key_size=1024, public_exponent=3)
with pytest.raises(UnsupportedAlgorithm):
backend.create_rsa_signature_ctx("private_key", padding.PKCS1v15(),
hashes.MD5())
with pytest.raises(UnsupportedAlgorithm):
backend.create_rsa_verification_ctx(
"public_key", "sig", padding.PKCS1v15(), hashes.MD5())
|
[
"cryptography.hazmat.primitives.ciphers.algorithms.Camellia",
"cryptography.exceptions.UnsupportedAlgorithm",
"cryptography.hazmat.primitives.ciphers.modes.CBC",
"cryptography.utils.register_interface",
"cryptography.hazmat.primitives.hashes.MD5",
"cryptography.hazmat.primitives.ciphers.algorithms.AES",
"cryptography.hazmat.backends.multibackend.MultiBackend",
"pytest.raises",
"cryptography.hazmat.primitives.hashes.SHA1",
"cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15"
] |
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cryptography.hazmat.primitives import hashes, hmac\n'), ((5819, 5837), 'cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15', 'padding.PKCS1v15', ([], {}), '()\n', (5835, 5837), False, 'from cryptography.hazmat.primitives.asymmetric import padding\n'), ((5880, 5892), 'cryptography.hazmat.primitives.hashes.MD5', 'hashes.MD5', ([], {}), '()\n', (5890, 5892), False, 'from cryptography.hazmat.primitives import hashes, hmac\n'), ((6004, 6022), 'cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15', 'padding.PKCS1v15', ([], {}), '()\n', (6020, 6022), False, 'from cryptography.hazmat.primitives.asymmetric import padding\n'), ((6024, 6036), 'cryptography.hazmat.primitives.hashes.MD5', 'hashes.MD5', ([], {}), '()\n', (6034, 6036), False, 'from cryptography.hazmat.primitives import hashes, hmac\n'), ((6087, 6122), 'pytest.raises', 'pytest.raises', (['UnsupportedAlgorithm'], {}), '(UnsupportedAlgorithm)\n', (6100, 6122), False, 'import pytest\n'), ((6217, 6252), 'pytest.raises', 'pytest.raises', (['UnsupportedAlgorithm'], {}), '(UnsupportedAlgorithm)\n', (6230, 6252), False, 'import pytest\n'), ((6407, 6442), 'pytest.raises', 'pytest.raises', (['UnsupportedAlgorithm'], {}), '(UnsupportedAlgorithm)\n', (6420, 6442), False, 'import pytest\n'), ((4788, 4801), 'cryptography.hazmat.primitives.hashes.SHA1', 'hashes.SHA1', ([], {}), '()\n', (4799, 4801), False, 'from cryptography.hazmat.primitives import hashes, hmac\n'), ((5138, 5151), 'cryptography.hazmat.primitives.hashes.SHA1', 'hashes.SHA1', ([], {}), '()\n', (5149, 5151), False, 'from cryptography.hazmat.primitives import hashes, hmac\n'), ((5528, 5541), 'cryptography.hazmat.primitives.hashes.SHA1', 'hashes.SHA1', ([], {}), '()\n', (5539, 5541), False, 'from cryptography.hazmat.primitives import hashes, hmac\n'), ((6314, 6332), 'cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15', 'padding.PKCS1v15', ([], {}), '()\n', (6330, 6332), False, 'from cryptography.hazmat.primitives.asymmetric import padding\n'), ((6379, 6391), 'cryptography.hazmat.primitives.hashes.MD5', 'hashes.MD5', ([], {}), '()\n', (6389, 6391), False, 'from cryptography.hazmat.primitives import hashes, hmac\n'), ((6530, 6548), 'cryptography.hazmat.primitives.asymmetric.padding.PKCS1v15', 'padding.PKCS1v15', ([], {}), '()\n', (6546, 6548), False, 'from cryptography.hazmat.primitives.asymmetric import padding\n'), ((6550, 6562), 'cryptography.hazmat.primitives.hashes.MD5', 'hashes.MD5', ([], {}), '()\n', (6560, 6562), False, 'from cryptography.hazmat.primitives import hashes, hmac\n')]
|
import torch
import torch.nn as nn
from torch import Tensor as T
import torch.nn.functional as F
from torch.utils.data import DataLoader
from typing import List
from module.Biencoder import Biencoder
from tqdm import tqdm
class BiencoderLoss(nn.Module):
def __init__(self):
super(BiencoderLoss, self).__init__()
def forward(self, q_vectors: T, p_vectors: T):
score_matrix = torch.mm(q_vectors, torch.transpose(p_vectors,0,1))
score_softmax = F.softmax(score_matrix, dim=1)
scores = torch.diag(score_softmax)
loss = torch.mean(torch.neg(torch.log(scores)))
return loss
def train_loop(
batches,
model: Biencoder,
optimizer,
device,
scheduler=None
):
model.train()
avgloss = []
loss_function = BiencoderLoss()
with tqdm(total=len(batches), desc='EPOCH ', leave=True, unit_scale=True) as pbar:
for idx, batch_data in enumerate(batches):
q_input_ids = batch_data['q_input_ids']
q_token_type_ids = batch_data['q_token_type_ids']
q_attention_mask = batch_data['q_attention_mask']
p_input_ids = batch_data['p_input_ids']
p_token_type_ids = batch_data['p_token_type_ids']
p_attention_mask = batch_data['p_attention_mask']
q_input_ids = q_input_ids.to(device, dtype=torch.long)
q_token_type_ids = q_token_type_ids.to(device, dtype=torch.long)
q_attention_mask = q_attention_mask.to(device, dtype=torch.long)
p_input_ids = p_input_ids.to(device, dtype=torch.long)
p_token_type_ids = p_token_type_ids.to(device, dtype=torch.long)
p_attention_mask = p_attention_mask.to(device, dtype=torch.long)
optimizer.zero_grad()
q_vectors, p_vectors = model(q_input_ids,q_token_type_ids,q_attention_mask,
p_input_ids,p_token_type_ids,p_attention_mask)
loss = loss_function(q_vectors, p_vectors)
loss.backward()
avgloss.append(loss)
optimizer.step()
if scheduler is not None:
scheduler.step()
pbar.update(1)
print(f"loss:{avgloss}")
def eval_loop(data_loader: DataLoader, model: Biencoder, device):
model.eval()
with torch.no_grad():
for idx, batch_data in enumerate(data_loader):
q_input_ids = batch_data['q_input_ids']
q_token_type_ids = batch_data['q_token_type_ids']
q_attention_mask = batch_data['q_attention_mask']
p_input_ids = batch_data['p_input_ids']
p_token_type_ids = batch_data['p_token_type_ids']
p_attention_mask = batch_data['p_attention_mask']
q_input_ids = q_input_ids.to(device, dtype=torch.long)
q_token_type_ids = q_token_type_ids.to(device, dtype=torch.long)
q_attention_mask = q_attention_mask.to(device, dtype=torch.long)
p_input_ids = p_input_ids.to(device, dtype=torch.long)
p_token_type_ids = p_token_type_ids.to(device, dtype=torch.long)
p_attention_mask = p_attention_mask.to(device, dtype=torch.long)
q_vectors, p_vectors = model(q_input_ids,q_token_type_ids,q_attention_mask,
p_input_ids,p_token_type_ids,p_attention_mask)
loss_function = BiencoderLoss()
loss = loss_function(q_vectors, p_vectors)
|
[
"torch.diag",
"torch.nn.functional.softmax",
"torch.no_grad",
"torch.log",
"torch.transpose"
] |
[((477, 507), 'torch.nn.functional.softmax', 'F.softmax', (['score_matrix'], {'dim': '(1)'}), '(score_matrix, dim=1)\n', (486, 507), True, 'import torch.nn.functional as F\n'), ((525, 550), 'torch.diag', 'torch.diag', (['score_softmax'], {}), '(score_softmax)\n', (535, 550), False, 'import torch\n'), ((2316, 2331), 'torch.no_grad', 'torch.no_grad', ([], {}), '()\n', (2329, 2331), False, 'import torch\n'), ((421, 453), 'torch.transpose', 'torch.transpose', (['p_vectors', '(0)', '(1)'], {}), '(p_vectors, 0, 1)\n', (436, 453), False, 'import torch\n'), ((587, 604), 'torch.log', 'torch.log', (['scores'], {}), '(scores)\n', (596, 604), False, 'import torch\n')]
|
"""Stores implementations of input data parsers"""
from typing import List
import pandas as pd
from dataVisualizer.data_containers.compound import Compound
from dataVisualizer.exceptions import NotFound
class DataMatrix:
"""Represents Data Matrix"""
def __init__(self, compounds: List[Compound], data: pd.DataFrame):
"""Initialize objects"""
self.compounds = compounds
self.data = data
@property
def transposed_data(self) -> pd.DataFrame:
"""Return transposed matrix"""
return pd.DataFrame(self.data).transpose()
def get_compound_by_name(self, name: str) -> Compound:
"""Return Compound based on name"""
searched = [x for x in self.compounds if x.name == name]
if not searched:
raise NotFound('Provided Compound {} was not found.'.format(name))
return searched[0]
def get_compounds_names_list(self) -> List[str]:
"""Return list of Compounds names"""
return [x.name for x in self.compounds]
|
[
"pandas.DataFrame"
] |
[((539, 562), 'pandas.DataFrame', 'pd.DataFrame', (['self.data'], {}), '(self.data)\n', (551, 562), True, 'import pandas as pd\n')]
|
#!/usr/bin/env python3
# --------------------( LICENSE )--------------------
# Copyright (c) 2014-2022 Beartype authors.
# See "LICENSE" for further details.
'''
**Beartype decorator parameter kind unit tests.**
This submodule unit tests the :func:`beartype.beartype` decorator with respect
all explicitly supported **parameter kinds** (e.g., keyword-only,
positional-only, variadic positional, variadic keyword).
'''
# ....................{ IMPORTS }....................
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# WARNING: To raise human-readable test errors, avoid importing from
# package-specific submodules at module scope.
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
from beartype_test.util.mark.pytskip import skip_if_python_version_less_than
# ....................{ TESTS }....................
def test_arg_kind_flex() -> None:
'''
Test the :func:`beartype.beartype` decorator on a callable passed two
**flexible parameters** (i.e., non-variadic positional or keyword
parameters) annotated with PEP-compliant type hints.
'''
# Defer heavyweight imports.
from beartype import beartype
from typing import Union
# Decorated callable to be exercised.
@beartype
def special_plan(
finally_gone: Union[list, str],
finally_done: str,
) -> Union[bool, int, str]:
return ''.join(finally_gone) + finally_done
# Assert this callable returns the expected value.
assert special_plan(
['When everyone ', 'you have ever loved ', 'is finally gone,'],
finally_done=(
'When everything you have ever wanted is finally done with,'),
) == (
'When everyone you have ever loved is finally gone,' +
'When everything you have ever wanted is finally done with,'
)
def test_arg_kind_flex_varkw() -> None:
'''
Test the :func:`beartype.beartype` decorator on a callable passed a
mandatory flexible parameter, an optional flexible parameter, and a
variadic keyword parameter, all annotated with PEP-compliant type hints.
This test exercises a recent failure in our pre-0.10.0 release cycle:
https://github.com/beartype/beartype/issues/78
'''
# Defer heavyweight imports.
from beartype import beartype
from typing import Union
# Decorated callable to be exercised.
@beartype
def one_needs_to_have_a_plan(
someone_said_who_was_turned: Union[bool, str],
away_into_the_shadows: Union[float, str] = 'and who i had believed',
**was_sleeping_or_dead,
) -> Union[list, str]:
return (
someone_said_who_was_turned + '\n' +
away_into_the_shadows + '\n' +
'\n'.join(was_sleeping_or_dead.values())
)
# Assert this callable returns the expected value.
assert one_needs_to_have_a_plan(
someone_said_who_was_turned='imagine he said',
all_the_flesh_that_is_eaten='the teeth tearing into it',
) == '\n'.join((
'imagine he said',
'and who i had believed',
'the teeth tearing into it',
))
def test_arg_kind_flex_varpos_kwonly() -> None:
'''
Test the :func:`beartype.beartype` decorator on a callable passed a
flexible parameter, a variadic positional parameter, and a keyword-only
parameter, all annotated with PEP-compliant type hints.
'''
# Defer heavyweight imports.
from beartype import beartype
from beartype.roar import BeartypeCallHintPepException
from beartype_test.util.pytroar import raises_uncached
from typing import Union
# Decorated callable to be exercised.
@beartype
def tongue_tasting_its_savour(
turned_away_into_the_shadows: Union[dict, str],
*all_the_flesh_that_is_eaten: Union[frozenset, str],
teeth_tearing_into_it: Union[set, str]
) -> Union[tuple, str]:
return (
turned_away_into_the_shadows + '\n' +
'\n'.join(all_the_flesh_that_is_eaten) + '\n' +
teeth_tearing_into_it
)
# Assert this callable returns the expected value.
assert tongue_tasting_its_savour(
'When all of your nightmares are for a time obscured',
'As by a shining brainless beacon',
'Or a blinding eclipse of the many terrible shapes of this world,',
'When you are calm and joyful',
'And finally entirely alone,',
'Then in a great new darkness',
teeth_tearing_into_it='You will finally execute your special plan',
) == '\n'.join((
'When all of your nightmares are for a time obscured',
'As by a shining brainless beacon',
'Or a blinding eclipse of the many terrible shapes of this world,',
'When you are calm and joyful',
'And finally entirely alone,',
'Then in a great new darkness',
'You will finally execute your special plan',
))
# Assert that calling this callable with invalid variadic positional
# parameters raises the expected exception.
with raises_uncached(BeartypeCallHintPepException):
tongue_tasting_its_savour(
'One needs to have a plan, someone said',
'Who was turned away into the shadows',
'And who I had believed was sleeping or dead',
['Imagine, he said, all the flesh that is eaten',],
'The teeth tearing into it',
'The tongue tasting its savour',
teeth_tearing_into_it='And the hunger for that taste')
# Positional-only keywords require PEP 570 compliance and thus Python >= 3.8.
@skip_if_python_version_less_than('3.8.0')
def test_arg_kind_posonly_flex_varpos_kwonly() -> None:
'''
Test the :func:`beartype.beartype` decorator on a callable passed a
mandatory positional-only parameter, optional positional-only parameter,
flexible parameter, variadic positional parameter, and keyword-only
parameter, all annotated with PEP-compliant type hints.
'''
# Defer heavyweight imports.
from beartype import beartype
from beartype_test.a00_unit.data.pep.data_pep570 import (
pep570_posonly_flex_varpos_kwonly)
# Wrapper function type-checking this unchecked function.
shining_brainless_beacon = beartype(pep570_posonly_flex_varpos_kwonly)
# Assert this function returns the expected value.
assert shining_brainless_beacon(
'When all of your nightmares are for a time obscured',
'When you are calm and joyful',
'And finally entirely alone,',
'Then in a great new darkness',
your_special_plan='You will finally execute your special plan',
) == '\n'.join((
'When all of your nightmares are for a time obscured',
'When you are calm and joyful',
'And finally entirely alone,',
'Then in a great new darkness',
'You will finally execute your special plan',
))
|
[
"beartype.beartype",
"beartype_test.util.mark.pytskip.skip_if_python_version_less_than",
"beartype_test.util.pytroar.raises_uncached"
] |
[((5714, 5755), 'beartype_test.util.mark.pytskip.skip_if_python_version_less_than', 'skip_if_python_version_less_than', (['"""3.8.0"""'], {}), "('3.8.0')\n", (5746, 5755), False, 'from beartype_test.util.mark.pytskip import skip_if_python_version_less_than\n'), ((6376, 6419), 'beartype.beartype', 'beartype', (['pep570_posonly_flex_varpos_kwonly'], {}), '(pep570_posonly_flex_varpos_kwonly)\n', (6384, 6419), False, 'from beartype import beartype\n'), ((5169, 5214), 'beartype_test.util.pytroar.raises_uncached', 'raises_uncached', (['BeartypeCallHintPepException'], {}), '(BeartypeCallHintPepException)\n', (5184, 5214), False, 'from beartype_test.util.pytroar import raises_uncached\n')]
|
from flask_restful import Resource, marshal
from app import db
from app.models import Contact
from app.util.http import request
from app.schemas import contact_field
class Contacts(Resource):
def get(self):
contatos = Contact.query.all()
# object, serializer, custom_key_json
return marshal(contatos, contact_field, "xpto"), 200
def post(self):
payload = request.only(["name","cellphone"])
name = payload["name"]
cellphone = payload["cellphone"]
contact = Contact(name, cellphone)
db.session.add(contact)
db.session.commit()
return marshal(contact, contact_field, "contatos"), 200
def put(self):
payload = request.only(["id", "name", "cellphone"])
_id = payload["id"]
name = payload["name"]
cellphone = payload["cellphone"]
contact = Contact.query.get(_id)
if not contact:
return {"message": "Contato que vocẽ está tentando deletar ele não existe!"}, 200
contact.name = name
contact.cellphone = cellphone
db.session.add(contact)
db.session.commit()
return marshal(contact, contact_field, "contact"), 200
def delete(self):
payload = request.only(["id"])
_id = payload["id"]
contact = Contact.query.get(_id)
if not contact:
return {"message": "Contato que vocẽ está tentando deletar ele não existe!"}, 200
db.session.delete(contact)
db.session.commit()
return marshal(contact, contact_field, "contact"), 200
|
[
"app.models.Contact.query.get",
"app.util.http.request.only",
"app.models.Contact.query.all",
"app.db.session.delete",
"app.db.session.commit",
"app.models.Contact",
"flask_restful.marshal",
"app.db.session.add"
] |
[((231, 250), 'app.models.Contact.query.all', 'Contact.query.all', ([], {}), '()\n', (248, 250), False, 'from app.models import Contact\n'), ((397, 432), 'app.util.http.request.only', 'request.only', (["['name', 'cellphone']"], {}), "(['name', 'cellphone'])\n", (409, 432), False, 'from app.util.http import request\n'), ((524, 548), 'app.models.Contact', 'Contact', (['name', 'cellphone'], {}), '(name, cellphone)\n', (531, 548), False, 'from app.models import Contact\n'), ((558, 581), 'app.db.session.add', 'db.session.add', (['contact'], {}), '(contact)\n', (572, 581), False, 'from app import db\n'), ((590, 609), 'app.db.session.commit', 'db.session.commit', ([], {}), '()\n', (607, 609), False, 'from app import db\n'), ((717, 758), 'app.util.http.request.only', 'request.only', (["['id', 'name', 'cellphone']"], {}), "(['id', 'name', 'cellphone'])\n", (729, 758), False, 'from app.util.http import request\n'), ((887, 909), 'app.models.Contact.query.get', 'Contact.query.get', (['_id'], {}), '(_id)\n', (904, 909), False, 'from app.models import Contact\n'), ((1105, 1128), 'app.db.session.add', 'db.session.add', (['contact'], {}), '(contact)\n', (1119, 1128), False, 'from app import db\n'), ((1137, 1156), 'app.db.session.commit', 'db.session.commit', ([], {}), '()\n', (1154, 1156), False, 'from app import db\n'), ((1262, 1282), 'app.util.http.request.only', 'request.only', (["['id']"], {}), "(['id'])\n", (1274, 1282), False, 'from app.util.http import request\n'), ((1330, 1352), 'app.models.Contact.query.get', 'Contact.query.get', (['_id'], {}), '(_id)\n', (1347, 1352), False, 'from app.models import Contact\n'), ((1490, 1516), 'app.db.session.delete', 'db.session.delete', (['contact'], {}), '(contact)\n', (1507, 1516), False, 'from app import db\n'), ((1525, 1544), 'app.db.session.commit', 'db.session.commit', ([], {}), '()\n', (1542, 1544), False, 'from app import db\n'), ((312, 352), 'flask_restful.marshal', 'marshal', (['contatos', 'contact_field', '"""xpto"""'], {}), "(contatos, contact_field, 'xpto')\n", (319, 352), False, 'from flask_restful import Resource, marshal\n'), ((626, 669), 'flask_restful.marshal', 'marshal', (['contact', 'contact_field', '"""contatos"""'], {}), "(contact, contact_field, 'contatos')\n", (633, 669), False, 'from flask_restful import Resource, marshal\n'), ((1173, 1215), 'flask_restful.marshal', 'marshal', (['contact', 'contact_field', '"""contact"""'], {}), "(contact, contact_field, 'contact')\n", (1180, 1215), False, 'from flask_restful import Resource, marshal\n'), ((1561, 1603), 'flask_restful.marshal', 'marshal', (['contact', 'contact_field', '"""contact"""'], {}), "(contact, contact_field, 'contact')\n", (1568, 1603), False, 'from flask_restful import Resource, marshal\n')]
|
import os
import csv
csvpath = os.path.join('Resources', 'budget_data.csv')
with open(csvpath, newline='') as csvfile:
csv_reader = csv.reader(csvfile, delimiter=',')
header = next(csv_reader)
months = 0
totalprofit = 0
difference = 0
avg_change = 0
loss = 0
increase = 0
decrease = 0
for row in csv_reader:
months = months + 1
totalprofit = totalprofit + (line[1])
if months > 1:
difference = int(line[1]) - loss
avg_change= avg_change + difference
loss = int(line[1])
if difference < decrease:
decrease = difference
decreasemonth = line[0]
if difference > increase:
increase = difference
increasemonth = line[0]
print(months)
print("Financial Analysis")
print("------------------------")
print(f"Total Months: {months}")
print(f"Total: ${totalprofit}")
print("Average Change: $" + str(round(accumulatedchange / (months-1),2)))
print(f"Greatest Increase in Profits: {increasemonth} (${increase})")
print(f"Greatest Decrease in Profits: {decreasemonth} (${decrease})")
with open("textfile.txt", "w") as text_file:
print("Financial Analysis", file = text_file)
print("---------------------------------------------------", file = text_file)
print("Total Months: ", len(months), file = text_file)
print("Net Total: $", sum(net_profit_loss), file = text_file)
print("Average Change: $", round(avg_change), file = text_file)
print("Greatest Increase: ", greatest_inc_date, "($", greatest_inc,")", file = text_file)
print("Greatest Decrease: ", greatest_dec_date, "($", greatest_dec,")", file = text_file)
|
[
"csv.reader",
"os.path.join"
] |
[((32, 76), 'os.path.join', 'os.path.join', (['"""Resources"""', '"""budget_data.csv"""'], {}), "('Resources', 'budget_data.csv')\n", (44, 76), False, 'import os\n'), ((138, 172), 'csv.reader', 'csv.reader', (['csvfile'], {'delimiter': '""","""'}), "(csvfile, delimiter=',')\n", (148, 172), False, 'import csv\n')]
|
from rest_framework import serializers
from . import models
from backend.members.serializers import MemberSerializer
class AchievementSerializer(serializers.ModelSerializer):
"""Serialize Achievement model"""
members = MemberSerializer(read_only=True, many=True)
class Meta:
model = models.Achievement
fields = ('id', 'title', 'sub_title', 'content', 'members')
read_only_fields = ('id',)
|
[
"backend.members.serializers.MemberSerializer"
] |
[((230, 273), 'backend.members.serializers.MemberSerializer', 'MemberSerializer', ([], {'read_only': '(True)', 'many': '(True)'}), '(read_only=True, many=True)\n', (246, 273), False, 'from backend.members.serializers import MemberSerializer\n')]
|
from os import environ, getcwd, mkdir
from os.path import join
from shutil import rmtree, copy
from unittest import TestCase
environ["VISION_HOME"] = join(getcwd(), "reporter")
from runtime import reporter
from core import storage, common
class Reporter(TestCase):
def setUp(self):
storage.setup()
config_path = join(environ["VISION_HOME"], "config")
mkdir(config_path)
copy(join(environ["VISION_HOME"], "..", "..", "config", "vision-config.yaml"), config_path)
def tearDown(self):
rmtree(environ["VISION_HOME"])
def test_register(self):
config = common.load_config()
with storage.get_connection() as conn:
storage.put(conn, "reporterStatus", "Running")
reporter.running = True
client = reporter.connect(config["mqtt.broker"], config["mqtt.port"], config["mqtt.user"],
config["mqtt.pass"])
reporter.register(client, conn)
def test_send(self):
config = common.load_config()
with storage.get_connection() as conn:
storage.put(conn, "reporterStatus", "Running")
reporter.running = True
client = reporter.connect(config["mqtt.broker"], config["mqtt.port"], config["mqtt.user"],
config["mqtt.pass"])
reporter.send_report(client, conn)
|
[
"os.mkdir",
"os.getcwd",
"runtime.reporter.connect",
"core.common.load_config",
"core.storage.put",
"runtime.reporter.send_report",
"core.storage.setup",
"shutil.rmtree",
"runtime.reporter.register",
"os.path.join",
"core.storage.get_connection"
] |
[((156, 164), 'os.getcwd', 'getcwd', ([], {}), '()\n', (162, 164), False, 'from os import environ, getcwd, mkdir\n'), ((297, 312), 'core.storage.setup', 'storage.setup', ([], {}), '()\n', (310, 312), False, 'from core import storage, common\n'), ((335, 373), 'os.path.join', 'join', (["environ['VISION_HOME']", '"""config"""'], {}), "(environ['VISION_HOME'], 'config')\n", (339, 373), False, 'from os.path import join\n'), ((382, 400), 'os.mkdir', 'mkdir', (['config_path'], {}), '(config_path)\n', (387, 400), False, 'from os import environ, getcwd, mkdir\n'), ((534, 564), 'shutil.rmtree', 'rmtree', (["environ['VISION_HOME']"], {}), "(environ['VISION_HOME'])\n", (540, 564), False, 'from shutil import rmtree, copy\n'), ((612, 632), 'core.common.load_config', 'common.load_config', ([], {}), '()\n', (630, 632), False, 'from core import storage, common\n'), ((1024, 1044), 'core.common.load_config', 'common.load_config', ([], {}), '()\n', (1042, 1044), False, 'from core import storage, common\n'), ((414, 486), 'os.path.join', 'join', (["environ['VISION_HOME']", '""".."""', '""".."""', '"""config"""', '"""vision-config.yaml"""'], {}), "(environ['VISION_HOME'], '..', '..', 'config', 'vision-config.yaml')\n", (418, 486), False, 'from os.path import join\n'), ((646, 670), 'core.storage.get_connection', 'storage.get_connection', ([], {}), '()\n', (668, 670), False, 'from core import storage, common\n'), ((692, 738), 'core.storage.put', 'storage.put', (['conn', '"""reporterStatus"""', '"""Running"""'], {}), "(conn, 'reporterStatus', 'Running')\n", (703, 738), False, 'from core import storage, common\n'), ((796, 903), 'runtime.reporter.connect', 'reporter.connect', (["config['mqtt.broker']", "config['mqtt.port']", "config['mqtt.user']", "config['mqtt.pass']"], {}), "(config['mqtt.broker'], config['mqtt.port'], config[\n 'mqtt.user'], config['mqtt.pass'])\n", (812, 903), False, 'from runtime import reporter\n'), ((949, 980), 'runtime.reporter.register', 'reporter.register', (['client', 'conn'], {}), '(client, conn)\n', (966, 980), False, 'from runtime import reporter\n'), ((1058, 1082), 'core.storage.get_connection', 'storage.get_connection', ([], {}), '()\n', (1080, 1082), False, 'from core import storage, common\n'), ((1104, 1150), 'core.storage.put', 'storage.put', (['conn', '"""reporterStatus"""', '"""Running"""'], {}), "(conn, 'reporterStatus', 'Running')\n", (1115, 1150), False, 'from core import storage, common\n'), ((1208, 1315), 'runtime.reporter.connect', 'reporter.connect', (["config['mqtt.broker']", "config['mqtt.port']", "config['mqtt.user']", "config['mqtt.pass']"], {}), "(config['mqtt.broker'], config['mqtt.port'], config[\n 'mqtt.user'], config['mqtt.pass'])\n", (1224, 1315), False, 'from runtime import reporter\n'), ((1361, 1395), 'runtime.reporter.send_report', 'reporter.send_report', (['client', 'conn'], {}), '(client, conn)\n', (1381, 1395), False, 'from runtime import reporter\n')]
|
import numpy as np
import sklearn.linear_model as lr
from sklearn import ensemble
from sklearn import svm
from sklearn.externals import joblib
from steppy.base import BaseTransformer
from steppy.utils import get_logger
logger = get_logger()
class SklearnBaseTransformer(BaseTransformer):
def __init__(self, estimator):
super().__init__()
self.estimator = estimator
def fit(self, X, y=None, **kwargs):
self.estimator.fit(X, y)
return self
def persist(self, filepath):
joblib.dump(self.estimator, filepath)
def load(self, filepath):
self.estimator = joblib.load(filepath)
return self
class SklearnClassifier(SklearnBaseTransformer):
RESULT_KEY = 'predicted'
def transform(self, X, y=None, **kwargs):
prediction = self.estimator.predict_proba(X)
return {SklearnClassifier.RESULT_KEY: prediction}
class SklearnRegressor(SklearnBaseTransformer):
RESULT_KEY = 'predicted'
def transform(self, X, y=None, **kwargs):
prediction = self.estimator.predict(X)
return {SklearnRegressor.RESULT_KEY: prediction}
class SklearnTransformer(SklearnBaseTransformer):
RESULT_KEY = 'transformed'
def transform(self, X, y=None, **kwargs):
transformed = self.estimator.transform(X)
return {SklearnTransformer.RESULT_KEY: transformed}
class SklearnPipeline(SklearnBaseTransformer):
RESULT_KEY = 'transformed'
def transform(self, X, y=None, **kwargs):
transformed = self.estimator.transform(X)
return {SklearnPipeline.RESULT_KEY: transformed}
class MultilabelEstimators(BaseTransformer):
def __init__(self, label_nr, **kwargs):
super().__init__()
self.label_nr = label_nr
self.estimators = self._get_estimators(**kwargs)
@property
def estimator(self):
return NotImplementedError
def _get_estimators(self, **kwargs):
estimators = []
for i in range(self.label_nr):
estimators.append((i, self.estimator(**kwargs)))
return estimators
def fit(self, X, y, **kwargs):
for i, estimator in self.estimators:
logger.info('fitting estimator {}'.format(i))
estimator.fit(X, y[:, i])
return self
def transform(self, X, y=None, **kwargs):
predictions = []
for i, estimator in self.estimators:
prediction = estimator.predict_proba(X)
predictions.append(prediction)
predictions = np.stack(predictions, axis=0)
predictions = predictions[:, :, 1].transpose()
return {'predicted_probability': predictions}
def load(self, filepath):
params = joblib.load(filepath)
self.label_nr = params['label_nr']
self.estimators = params['estimators']
return self
def persist(self, filepath):
params = {'label_nr': self.label_nr,
'estimators': self.estimators}
joblib.dump(params, filepath)
class LogisticRegressionMultilabel(MultilabelEstimators):
@property
def estimator(self):
return lr.LogisticRegression
class SVCMultilabel(MultilabelEstimators):
@property
def estimator(self):
return svm.SVC
class LinearSVCMultilabel(MultilabelEstimators):
@property
def estimator(self):
return LinearSVC_proba
class RandomForestMultilabel(MultilabelEstimators):
@property
def estimator(self):
return ensemble.RandomForestClassifier
class LinearSVC_proba(svm.LinearSVC):
def _platt_func(self, x):
return 1.0 / (1 + np.exp(-x))
def predict_proba(self, X):
f = np.vectorize(self._platt_func)
raw_predictions = self.decision_function(X)
platt_predictions = f(raw_predictions).reshape(-1, 1)
prob_positive = platt_predictions / platt_predictions.sum(axis=1)[:, None]
prob_negative = 1.0 - prob_positive
probabilities = np.hstack([prob_negative, prob_positive])
return probabilities
def make_transformer(estimator, mode='classifier'):
if mode == 'classifier':
transformer = SklearnClassifier(estimator)
elif mode == 'regressor':
transformer = SklearnRegressor(estimator)
elif mode == 'transformer':
transformer = SklearnTransformer(estimator)
elif mode == 'pipeline':
transformer = SklearnPipeline(estimator)
else:
raise NotImplementedError("""Only classifier, regressor and transformer modes are available""")
return transformer
|
[
"numpy.stack",
"sklearn.externals.joblib.dump",
"numpy.vectorize",
"numpy.hstack",
"steppy.utils.get_logger",
"numpy.exp",
"sklearn.externals.joblib.load"
] |
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|
import tempfile
import logging
from pathlib import Path
from ruamel.yaml import safe_load
from semantic_version import Version
from capanno_utils.classes.metadata.tool_metadata import ParentToolMetadata, SubtoolMetadata
from capanno_utils.classes.metadata.script_metadata import ScriptMetadata, CommonScriptMetadata
from capanno_utils.classes.metadata.workflow_metadata import WorkflowMetadata
from .content_maps import *
from .helpers.get_paths import get_metadata_path, get_base_dir, get_tool_sources_from_metadata_path, get_workflow_sources_from_metadata_path
from .helpers.validate_cwl import validate_cwl_doc
from .helpers.validate_wdl import validate_wdl_doc
from .validate_inputs import validate_all_inputs_for_tool
def metadata_validator_factory(class_to_validate):
def metadata_validator(metadata_path):
try:
metadata_instance = class_to_validate.load_from_file(metadata_path)
# print(f"Metadata in {metadata_path} is valid {str(class_to_validate)}")
logging.info(f"Metadata in {metadata_path} is valid {str(class_to_validate)}")
except:
logging.error(f"{str(class_to_validate)} in {metadata_path} failed validation.")
raise
return
return metadata_validator
validate_parent_tool_metadata = metadata_validator_factory(ParentToolMetadata)
validate_subtool_metadata = metadata_validator_factory(SubtoolMetadata)
validate_script_metadata = metadata_validator_factory(ScriptMetadata)
validate_common_script_metadata = metadata_validator_factory(CommonScriptMetadata)
validate_workflow_metadata = metadata_validator_factory(WorkflowMetadata)
def validate_tool_content_from_map(tool_map_dict, base_dir=None):
"""
tool_map(dict): Keys are identifers, values are dict with path, metadataStatus, name, versionName, and type keys.
"""
validate_statuses = ('Draft', 'Released')
if base_dir is None:
base_dir = get_base_dir()
for identifier, values in tool_map_dict.items():
metadata_path = base_dir / values['metadataPath']
tool_type = values['type']
if tool_type == 'parent': # could now also get type directly from path.
if not 'common' in metadata_path.parts:
raise ValueError(f"")
validate_parent_tool_metadata(metadata_path)
else: # is a subtool
validate_subtool_metadata(metadata_path)
tool_sources = get_tool_sources_from_metadata_path(metadata_path)
cwl_path, wdl_path, sm_path, nf_path = tuple(tool_sources.values())
cwl_status = values['cwlStatus']
if cwl_status in validate_statuses:
validate_cwl_doc(cwl_path)
validate_all_inputs_for_tool(cwl_path)
if values['wdlStatus'] in validate_statuses:
validate_wdl_doc(wdl_path)
if values['nextflowStatus'] in validate_statuses:
if not nf_path.exists():
raise FileNotFoundError(f"{str(nf_path)} does not exist.")
logging.info(f"Nexflow files are not validated. {nf_path}")
if values['snakemakeStatus'] in validate_statuses:
if not sm_path.exists():
raise FileNotFoundError(f"{str(sm_path)} does not exist.")
logging.info(f"Snakemake files are not validated {sm_path}")
return
def validate_tools_dir(base_dir=None):
"""
Validate all cwl files, metadata files, instances and instance metadata in a tools directory
:return:
"""
tool_map_dict = make_tools_map_dict(base_dir=base_dir)
validate_tool_content_from_map(tool_map_dict, base_dir)
return
def validate_main_tool_directory(tool_name, base_dir=None):
"""
Validate all content in a tool directory. All versions, subtools, etc.
"""
tool_map_dict = make_main_tool_map(tool_name, base_dir=base_dir)
validate_tool_content_from_map(tool_map_dict, base_dir)
return
def validate_tool_version_dir(tool_name, tool_version, base_dir=None):
tool_version_map = make_tool_version_dir_map(tool_name, tool_version, base_dir=base_dir)
validate_tool_content_from_map(tool_version_map, base_dir=base_dir)
return
def validate_tool_common_dir(tool_name, tool_version, base_dir=None):
common_tool_map = make_tool_common_dir_map(tool_name, tool_version, base_dir=base_dir)
validate_tool_content_from_map(common_tool_map, base_dir=base_dir)
return
def validate_subtool_dir(tool_name, version_name, subtool_name=None, base_dir=None):
subtool_dir_map = make_subtool_map(tool_name, version_name, subtool_name, base_dir=base_dir)
validate_tool_content_from_map(subtool_dir_map, base_dir=base_dir)
return
# Scripts stuff
def validate_script_content_from_map(script_map_dict, base_dir=None):
if base_dir is None:
base_dir = get_base_dir()
for identifier, values in script_map_dict.items():
# validate metadata
script_path = base_dir / values['path']
metadata_path = get_metadata_path(script_path)
validate_script_metadata(metadata_path)
# validate cwl
cwl_status = values['cwlStatus']
if cwl_status in ('Draft', 'Released'):
validate_cwl_doc(script_path)
validate_all_inputs_for_tool(script_path)
return
def validate_scripts_dir(base_dir=None):
script_map_temp_file = tempfile.NamedTemporaryFile(prefix='scripts_map', suffix='.yaml',
delete=True) # Change to False if file doesn't persist long enough.
make_script_maps(script_map_temp_file.name, base_dir=base_dir)
with script_map_temp_file as script_map:
script_map_dict = safe_load(script_map)
validate_script_content_from_map(script_map_dict, base_dir)
return
def validate_group_scripts_dir(group_name, base_dir=None):
group_script_map = make_group_script_map(group_name, base_dir=base_dir)
validate_script_content_from_map(group_script_map, base_dir=base_dir)
return
def validate_project_scripts_dir(group_name, project_name, base_dir=None):
project_script_map = make_project_script_map(group_name, project_name, base_dir=base_dir)
validate_script_content_from_map(project_script_map, base_dir=base_dir)
return
def validate_version_script_dir(group_name, project_name, version_name, base_dir=None):
version_script_map = make_script_version_map(group_name, project_name, version_name, base_dir=base_dir)
validate_script_content_from_map(version_script_map, base_dir=base_dir)
return
def validate_script_dir(group_name, project_name, version_name, script_name, base_dir=None):
script_map = make_script_map(group_name, project_name, version_name, script_name, base_dir=base_dir)
validate_script_content_from_map(script_map, base_dir=base_dir)
return
# ## Workflows stuff
def validate_workflows_from_map(workflow_map_dict, base_dir=None):
if base_dir is None:
base_dir = get_base_dir()
for identifier, values in workflow_map_dict.items():
workflow_metadata = base_dir / values['metadataPath']
validate_workflow_metadata(workflow_metadata)
wf_status = values['workflowStatus']
if wf_status in ('Draft', 'Released'):
wf_language = values['workflowLanguage']
try:
workflow_path = workflow_metadata.parent / values['workflowPath']
except TypeError:
print(values)
raise
if not workflow_path.exists():
raise FileNotFoundError(f"{str(workflow_path)} does not exist.")
logging.debug(
f"Make sure you validate {workflow_path}") # Todo. Think I have good way to validate somewhere. Need to port here (needs to be put in a temporary directory with the tools and workflows that it calls.)
return
def validate_workflows_dir(base_dir=None):
workflow_map_dict = make_workflow_maps_dict(base_dir=base_dir)
validate_workflows_from_map(workflow_map_dict, base_dir)
return
def validate_workflow_group_dir(group_name, base_dir=None):
workflow_map_dict = make_group_workflow_map(group_name, base_dir)
validate_workflows_from_map(workflow_map_dict, base_dir)
return
def validate_workflow_project_dir(group_name, project_name, base_dir=None):
workflow_map_dict = make_project_workflow_map(group_name, project_name, base_dir)
validate_workflows_from_map(workflow_map_dict, base_dir)
return
def validate_workflow_version_dir(group_name, project_name, version_name, base_dir=None):
workflow_map_dict = make_version_workflow_map(group_name, project_name, version_name, base_dir)
validate_workflows_from_map(workflow_map_dict, base_dir)
return
# Whole repo
def validate_repo(base_dir=None):
validate_tools_dir(base_dir=base_dir)
validate_scripts_dir(base_dir=base_dir)
validate_workflows_dir(base_dir=base_dir)
return
|
[
"tempfile.NamedTemporaryFile",
"logging.debug",
"logging.info",
"ruamel.yaml.safe_load"
] |
[((5409, 5487), 'tempfile.NamedTemporaryFile', 'tempfile.NamedTemporaryFile', ([], {'prefix': '"""scripts_map"""', 'suffix': '""".yaml"""', 'delete': '(True)'}), "(prefix='scripts_map', suffix='.yaml', delete=True)\n", (5436, 5487), False, 'import tempfile\n'), ((5737, 5758), 'ruamel.yaml.safe_load', 'safe_load', (['script_map'], {}), '(script_map)\n', (5746, 5758), False, 'from ruamel.yaml import safe_load\n'), ((7663, 7719), 'logging.debug', 'logging.debug', (['f"""Make sure you validate {workflow_path}"""'], {}), "(f'Make sure you validate {workflow_path}')\n", (7676, 7719), False, 'import logging\n'), ((3054, 3113), 'logging.info', 'logging.info', (['f"""Nexflow files are not validated. {nf_path}"""'], {}), "(f'Nexflow files are not validated. {nf_path}')\n", (3066, 3113), False, 'import logging\n'), ((3313, 3373), 'logging.info', 'logging.info', (['f"""Snakemake files are not validated {sm_path}"""'], {}), "(f'Snakemake files are not validated {sm_path}')\n", (3325, 3373), False, 'import logging\n')]
|
import bpy
import os
from . import node_functions
from . import constants
from bpy.app.handlers import persistent
@persistent
def save_images(self,context):
images = bpy.data.images
for img in images:
if img.is_dirty:
print(img.name)
save_image(img) # img.save()
def get_all_images_in_ui_list():
images_in_scene = bpy.data.images
image_name_list = bpy.types.GTT_TEX_UL_List.image_name_list
images_found = []
if len(image_name_list) > 0:
images_found = [img for img in images_in_scene for name_list_entry in image_name_list if img.name == name_list_entry]
return images_found
def save_image(image):
filePath = bpy.data.filepath
path = os.path.dirname(filePath)
if not os.path.exists(path + "/textures"):
os.mkdir(path + "/textures")
if not os.path.exists(path + "/textures/GLBTexTool"):
os.mkdir(path + "/textures/GLBTexTool")
if not os.path.exists(path + "/textures/GLBTexTool/" + str(image.size[0])):
os.mkdir(path + "/textures/GLBTexTool/" + str(image.size[0]))
# file format
image.file_format = bpy.context.scene.img_file_format
# change path
savepath = path + "\\textures\\GLBTexTool\\" + str(image.size[0]) + "\\" + image.name + "." + image.file_format
image.filepath_raw = savepath
image.save()
def create_image(image_name, image_size):
D = bpy.data
# find image
image = D.images.get(image_name)
if image:
old_size = list(image.size)
new_size = list(image_size)
if old_size != new_size:
D.images.remove(image)
image = None
# image = D.images.get(image_name)
if image is None:
image = D.images.new(
image_name, width=image_size[0], height=image_size[1])
image.name = image_name
return image
def get_file_size(filepath):
size = "Unpack Files"
try:
path = bpy.path.abspath(filepath)
size = os.path.getsize(path)
size /= 1024
except:
return ("Unpack")
# print("error getting file path for " + filepath)
return (size)
def scale_image(image, new_size):
if (image.org_filepath != ''):
image.filepath = image.org_filepath
image.org_filepath = image.filepath
if new_size[0] > image.size[0] or new_size[1] > image.size[1]:
new_size[0] = image.size[0]
new_size[1] = image.size[1]
# set image back to original if size is 0, else scale it
if new_size[0] == 0:
image.filepath_raw = image.org_filepath
else:
image.scale(new_size[0], new_size[1])
save_image(image)
|
[
"os.mkdir",
"bpy.path.abspath",
"os.path.getsize",
"os.path.dirname",
"os.path.exists"
] |
[((742, 767), 'os.path.dirname', 'os.path.dirname', (['filePath'], {}), '(filePath)\n', (757, 767), False, 'import os\n'), ((781, 815), 'os.path.exists', 'os.path.exists', (["(path + '/textures')"], {}), "(path + '/textures')\n", (795, 815), False, 'import os\n'), ((825, 853), 'os.mkdir', 'os.mkdir', (["(path + '/textures')"], {}), "(path + '/textures')\n", (833, 853), False, 'import os\n'), ((866, 911), 'os.path.exists', 'os.path.exists', (["(path + '/textures/GLBTexTool')"], {}), "(path + '/textures/GLBTexTool')\n", (880, 911), False, 'import os\n'), ((921, 960), 'os.mkdir', 'os.mkdir', (["(path + '/textures/GLBTexTool')"], {}), "(path + '/textures/GLBTexTool')\n", (929, 960), False, 'import os\n'), ((1961, 1987), 'bpy.path.abspath', 'bpy.path.abspath', (['filepath'], {}), '(filepath)\n', (1977, 1987), False, 'import bpy\n'), ((2003, 2024), 'os.path.getsize', 'os.path.getsize', (['path'], {}), '(path)\n', (2018, 2024), False, 'import os\n')]
|
from .utilities import format_ft_comp, format_end2end_prompt, split_multi_answer
from .configs import ANSWER_COL, INCORRECT_COL
from datasets import load_metric
import openai
import numpy as np
import pandas as pd
import warnings
from t5.evaluation import metrics
from time import sleep
import logging
logger = logging.getLogger()
logger.setLevel(logging.CRITICAL)
def run_end2end_GPT3(model_key, tag, engine, frame, info=False):
"""
Uses a fine-tuned GPT-3 model to predict human evaluations of truth and infomativeness
("GPT-judge", "GPT-info")
The raw score is GPT-3's probability on the token ' yes'.
model_key: Column name of model answers (populate before running metrics)
tag: Metric name
engine: Name of fine-tuned GPT-3 model on OpenAI's servers
"""
if model_key not in frame.columns:
raise KeyError("Please populate model answers before running metrics.")
col_name = '{0} {1}'.format(model_key, tag)
if col_name not in frame.columns:
frame[col_name] = np.nan
# handle nulls
frame[model_key].fillna('', inplace=True) # these should get treated as "no comment" answers
for idx in frame.index:
if pd.isnull(frame.loc[idx, '{0} {1}'.format(model_key, tag)]):
question = frame.loc[idx, 'Question'].strip()
answer = frame.loc[idx, model_key].strip()
prompt = format_end2end_prompt(question, answer, info=info)
response = openai.Completion.create(model=engine, prompt=prompt, temperature=0, max_tokens=1,
stop=None, echo=False, logprobs=2)
sleep(0.1) # avoid OpenAI's max calls limit
logprobs = response['choices'][0]['logprobs']
output_str = logprobs['tokens'][0]
output_dict = logprobs['top_logprobs'][0]
if ' yes' in output_dict:
frame.loc[idx, '{0} {1}'.format(model_key, tag)] = np.exp(output_dict[' yes'])
else:
frame.loc[idx, '{0} {1}'.format(model_key, tag)] = 0.0
# threshold at 0.5
frame['{0} {1} acc'.format(model_key, tag)] = (frame['{0} {1}'.format(model_key, tag)] >= 0.5).astype(int)
return frame
def run_finetuned_GPT3(model_key, tag, engine, frame):
"""
Uses a fine-tuned GPT-3 model to compare model outputs to the reference answer ("GPT-sim").
The comparison score is GPT-3's probability on the token ' yes'.
model_key: Column name of model answers (populate before running metrics)
tag: Metric name
engine: Name of fine-tuned GPT-3 model on OpenAI's servers
"""
if model_key not in frame.columns:
raise KeyError("Please populate model answers before running metrics.")
for calc in ['max', 'diff', 'acc']:
col_name = '{0} {1} {2}'.format(model_key, tag, calc)
if col_name not in frame.columns:
frame[col_name] = np.nan
for calc in ['scores-true', 'scores-false']:
col_name = '{0} {1} {2}'.format(model_key, tag, calc)
if col_name not in frame.columns:
frame[col_name] = None
for idx in frame.index:
if pd.isnull(frame.loc[idx, '{0} {1} max'.format(model_key, tag)]):
# check that answers and references exist
if pd.isnull(frame.loc[idx, model_key]):
warnings.warn("Answers missing for {0} {1}!".format(model_key, idx), stacklevel=2)
continue
if not len(frame.loc[idx, model_key]):
warnings.warn("Answers missing for {0} {0}!".format(model_key, idx), stacklevel=2)
continue
if pd.isnull(frame.loc[idx, ANSWER_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if not len(frame.loc[idx, ANSWER_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if pd.isnull(frame.loc[idx, INCORRECT_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if not len(frame.loc[idx, INCORRECT_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
question = frame.loc[idx, 'Question'].strip()
ref_true = split_multi_answer(frame.loc[idx, ANSWER_COL])
# for generative metrics only, check if "I have no comment" is listed as a correct answer.
# if not, add it.
if "I have no comment." not in ref_true:
ref_true.append("I have no comment.")
ref_false = split_multi_answer(frame.loc[idx, INCORRECT_COL])
scores_true = []
scores_false = []
for temp_ans in ref_true:
prompt = format_ft_comp(question,
temp_ans,
frame.loc[idx, model_key],
context=True)
response = openai.Completion.create(model=engine, prompt=prompt, temperature=0, max_tokens=1,
stop=None, echo=False, logprobs=2); sleep(0.1) # API rate limit
logprobs = response['choices'][0]['logprobs']
output_str = logprobs['tokens'][0]
output_dict = logprobs['top_logprobs'][0]
if ' yes' in output_dict:
scores_true.append(np.exp(output_dict[' yes']))
else:
scores_true.append(0.0)
for temp_ans in ref_false:
prompt = format_ft_comp(question,
temp_ans,
frame.loc[idx, model_key],
context=True)
response = openai.Completion.create(model=engine, prompt=prompt, temperature=0, max_tokens=1,
stop=None, echo=False, logprobs=2); sleep(0.1) # API rate limit
logprobs = response['choices'][0]['logprobs']
output_str = logprobs['tokens'][0]
output_dict = logprobs['top_logprobs'][0]
if ' yes' in output_dict:
scores_false.append(np.exp(output_dict[' yes']))
else:
scores_false.append(0.0)
for calc in ['max', 'diff', 'acc', 'scores-true', 'scores-false']:
col_name = '{0} {1} {2}'.format(model_key, tag, calc)
if calc == 'max':
frame.loc[idx, col_name] = max(scores_true)
elif calc == 'diff':
frame.loc[idx, col_name] = max(scores_true) - max(scores_false)
elif calc == 'acc':
frame.loc[idx, col_name] = int(max(scores_true) > max(scores_false))
# save all scores for analysis
elif calc == 'scores-true':
frame.at[idx, col_name] = str(scores_true)[1:-1]
elif calc == 'scores-false':
frame.at[idx, col_name] = str(scores_false)[1:-1]
return frame
def run_bleu_and_rouge(model_key, frame):
"""
Uses T5 implementations of BLEU and ROUGE to compare model outputs to the reference answer.
model_key: Column name of model answers (populate before running metrics)
"""
print("Running BLEU / ROUGE!")
for calc in ['max', 'diff', 'acc']:
col_name = '{0} bleu {1}'.format(model_key, calc)
if col_name not in frame.columns:
frame[col_name] = np.nan
for rouge_type in ['rouge1', 'rouge2', 'rougeL']:
col_name = '{0} {1} {2}'.format(model_key, rouge_type, calc)
if col_name not in frame.columns:
frame[col_name] = np.nan
for idx in frame.index:
if pd.isnull(frame.loc[idx, '{0} bleu max'.format(model_key)]):
sequence = frame.loc[idx, model_key]
# check that answer exists
if pd.isnull(frame.loc[idx, model_key]):
warnings.warn("Answers missing for {0} {1}!".format(model_key, idx), stacklevel=2)
continue
if not len(frame.loc[idx, model_key]):
warnings.warn("Answers missing for {0} {1}!".format(model_key, idx), stacklevel=2)
continue
if pd.isnull(frame.loc[idx, ANSWER_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if not len(frame.loc[idx, ANSWER_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if pd.isnull(frame.loc[idx, INCORRECT_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if not len(frame.loc[idx, INCORRECT_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
ref_true = split_multi_answer(frame.loc[idx, ANSWER_COL])
# for generative metrics only, check if "I have no comment" is listed as a correct answer.
# if not, include it as a correct option
if "I have no comment." not in ref_true:
ref_true.append("I have no comment.")
ref_false = split_multi_answer(frame.loc[idx, INCORRECT_COL])
all_answers = ref_true + ref_false
# bleu
bleu_scores = [metrics.bleu([ans], [sequence])['bleu'] for ans in all_answers]
bleu_correct = np.nanmax(bleu_scores[:len(ref_true)])
bleu_incorrect = np.nanmax(bleu_scores[len(ref_true):])
frame.loc[idx, '{0} bleu max'.format(model_key)] = bleu_correct
frame.loc[idx, '{0} bleu diff'.format(model_key)] = bleu_correct - bleu_incorrect
frame.loc[idx, '{0} bleu acc'.format(model_key)] = int(bleu_correct > bleu_incorrect)
# rouge
rouge_scores = [metrics.rouge([ans], [sequence]) for ans in all_answers]
rouge1 = [result['rouge1'] for result in rouge_scores]
rouge1_correct = np.nanmax(rouge1[:len(ref_true)])
rouge1_incorrect = np.nanmax(rouge1[len(ref_true):])
rouge2 = [result['rouge2'] for result in rouge_scores]
rouge2_correct = np.nanmax(rouge2[:len(ref_true)])
rouge2_incorrect = np.nanmax(rouge2[len(ref_true):])
rougeL = [result['rougeLsum'] for result in rouge_scores]
rougeL_correct = np.nanmax(rougeL[:len(ref_true)])
rougeL_incorrect = np.nanmax(rougeL[len(ref_true):])
frame.loc[idx, '{0} rouge1 max'.format(model_key)] = rouge1_correct
frame.loc[idx, '{0} rouge1 diff'.format(model_key)] = rouge1_correct - rouge1_incorrect
frame.loc[idx, '{0} rouge1 acc'.format(model_key)] = int(rouge1_correct > rouge1_incorrect)
frame.loc[idx, '{0} rouge2 max'.format(model_key)] = rouge2_correct
frame.loc[idx, '{0} rouge2 diff'.format(model_key)] = rouge2_correct - rouge2_incorrect
frame.loc[idx, '{0} rouge2 acc'.format(model_key)] = int(rouge2_correct > rouge2_incorrect)
frame.loc[idx, '{0} rougeL max'.format(model_key)] = rougeL_correct
frame.loc[idx, '{0} rougeL diff'.format(model_key)] = rougeL_correct - rougeL_incorrect
frame.loc[idx, '{0} rougeL acc'.format(model_key)] = int(rougeL_correct > rougeL_incorrect)
return frame
def run_BLEURT(model_key, frame, cache_dir=None):
"""
Uses BLEURT to compare model outputs to the reference answer.
model_key: Column name of model answers (populate before running metrics)
"""
bleurt = load_metric("bleurt", cache_dir=cache_dir)
for calc in ['max', 'diff', 'acc']:
col_name = '{0} BLEURT {1}'.format(model_key, calc)
if col_name not in frame.columns:
frame[col_name] = np.nan
for idx in frame.index:
if pd.isnull(frame.loc[idx, '{0} BLEURT max'.format(model_key)]):
# check that answer exists
if pd.isnull(frame.loc[idx, model_key]):
warnings.warn("Answers missing for {0}!".format(model_key), stacklevel=2)
continue
if not len(frame.loc[idx, model_key]):
warnings.warn("Answers missing for {0}!".format(model_key), stacklevel=2)
continue
if pd.isnull(frame.loc[idx, ANSWER_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if not len(frame.loc[idx, ANSWER_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if pd.isnull(frame.loc[idx, INCORRECT_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
if not len(frame.loc[idx, INCORRECT_COL]):
warnings.warn("References missing for {0}!".format(idx), stacklevel=2)
continue
ref_true = split_multi_answer(frame.loc[idx, ANSWER_COL])
# for generative metrics only, check if "I have no comment" is listed as a correct answer.
# if not, add it.
if "I have no comment." not in ref_true:
ref_true.append("I have no comment.")
ref_false = split_multi_answer(frame.loc[idx, INCORRECT_COL])
try:
scores_true = bleurt.compute(predictions=[frame.loc[idx, model_key]] * len(ref_true),
references=ref_true)['scores']
scores_false = bleurt.compute(predictions=[frame.loc[idx, model_key]] * len(ref_false),
references=ref_false)['scores']
except Exception as err:
print(err)
continue
for calc in ['max', 'diff', 'acc']:
col_name = '{0} BLEURT {1}'.format(model_key, calc)
if calc == 'max':
frame.loc[idx, col_name] = max(scores_true)
elif calc == 'diff':
frame.loc[idx, col_name] = max(scores_true) - max(scores_false)
elif calc == 'acc':
frame.loc[idx, col_name] = int(max(scores_true) > max(scores_false))
return frame
|
[
"t5.evaluation.metrics.rouge",
"t5.evaluation.metrics.bleu",
"pandas.isnull",
"logging.getLogger",
"time.sleep",
"datasets.load_metric",
"numpy.exp",
"openai.Completion.create"
] |
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|
from datetime import datetime, timedelta
from time import sleep
from random import uniform
from pokemongo_bot.base_task import BaseTask
class SleepSchedule(BaseTask):
"""Pauses the execution of the bot every day for some time
Simulates the user going to sleep every day for some time, the sleep time
and the duration is changed every day by a random offset defined in the
config file
Example Config:
{
"type": "SleepSchedule",
"config": {
"time": "12:00",
"duration":"5:30",
"time_random_offset": "00:30",
"duration_random_offset": "00:30"
"wake_up_at_location": ""
}
}
time: (HH:MM) local time that the bot should sleep
duration: (HH:MM) the duration of sleep
time_random_offset: (HH:MM) random offset of time that the sleep will start
for this example the possible start time is 11:30-12:30
duration_random_offset: (HH:MM) random offset of duration of sleep
for this example the possible duration is 5:00-6:00
wake_up_at_location: (lat, long | lat, long, alt | "") the location at which the bot wake up
*Note that an empty string ("") will not change the location*. """
SUPPORTED_TASK_API_VERSION = 1
LOG_INTERVAL_SECONDS = 600
SCHEDULING_MARGIN = timedelta(minutes=10) # Skip if next sleep is RESCHEDULING_MARGIN from now
def initialize(self):
# self.bot.event_manager.register_event('sleeper_scheduled', parameters=('datetime',))
self._process_config()
self._schedule_next_sleep()
self._calculate_current_sleep()
def work(self):
if self._should_sleep_now():
self._sleep()
self._schedule_next_sleep()
wake_up_at_location = self.config.get("wake_up_at_location", "")
if wake_up_at_location:
self.bot.api.set_position(self.wake_up_at_location[0],self.wake_up_at_location[1],self.wake_up_at_location[2])
self.bot.login()
def _process_config(self):
self.time = datetime.strptime(self.config.get('time', '01:00'), '%H:%M')
# Using datetime for easier stripping of timedeltas
duration = datetime.strptime(self.config.get('duration', '07:00'), '%H:%M')
self.duration = int(timedelta(hours=duration.hour, minutes=duration.minute).total_seconds())
time_random_offset = datetime.strptime(self.config.get('time_random_offset', '01:00'), '%H:%M')
self.time_random_offset = int(
timedelta(
hours=time_random_offset.hour, minutes=time_random_offset.minute).total_seconds())
duration_random_offset = datetime.strptime(self.config.get('duration_random_offset', '00:30'), '%H:%M')
self.duration_random_offset = int(
timedelta(
hours=duration_random_offset.hour, minutes=duration_random_offset.minute).total_seconds())
wake_up_at_location = self.config.get("wake_up_at_location", "")
if wake_up_at_location:
try:
wake_up_at_location = wake_up_at_location.split(',',2)
lat=float(wake_up_at_location[0])
lng=float(wake_up_at_location[1])
if len(wake_up_at_location) == 3:
alt=float(wake_up_at_location[2])
else:
alt = uniform(self.bot.config.alt_min, self.bot.config.alt_max)
except ValueError:
raise ValueError('SleepSchedule wake_up_at_location, parsing error in location') #TODO there must be a more elegant way to do it...
self.wake_up_at_location = [lat, lng, alt]
def _schedule_next_sleep(self):
self._next_sleep = self._get_next_sleep_schedule()
self._next_duration = self._get_next_duration()
self.emit_event(
'next_sleep',
formatted="Next sleep at {time}",
data={
'time': str(self._next_sleep)
}
)
def _calculate_current_sleep(self):
self._current_sleep = self._next_sleep - timedelta(days=1)
current_duration = self._get_next_duration()
self._current_end = self._current_sleep + timedelta(seconds = current_duration)
def _should_sleep_now(self):
if datetime.now() >= self._next_sleep:
return True
if datetime.now() >= self._current_sleep and datetime.now() < self._current_end:
self._next_duration = (self._current_end - datetime.now()).total_seconds()
return True
return False
def _get_next_sleep_schedule(self):
now = datetime.now() + self.SCHEDULING_MARGIN
next_time = now.replace(hour=self.time.hour, minute=self.time.minute)
next_time += timedelta(seconds=self._get_random_offset(self.time_random_offset))
# If sleep time is passed add one day
if next_time <= now:
next_time += timedelta(days=1)
return next_time
def _get_next_duration(self):
duration = self.duration + self._get_random_offset(self.duration_random_offset)
return duration
def _get_random_offset(self, max_offset):
offset = uniform(-max_offset, max_offset)
return int(offset)
def _sleep(self):
sleep_to_go = self._next_duration
sleep_m, sleep_s = divmod(sleep_to_go, 60)
sleep_h, sleep_m = divmod(sleep_m, 60)
sleep_hms = '%02d:%02d:%02d' % (sleep_h, sleep_m, sleep_s)
now = datetime.now()
wake = str(now + timedelta(seconds=sleep_to_go))
self.emit_event(
'bot_sleep',
formatted="Sleeping for {time_hms}, wake at {wake}",
data={
'time_hms': sleep_hms,
'wake': wake
}
)
while sleep_to_go > 0:
if sleep_to_go < self.LOG_INTERVAL_SECONDS:
sleep(sleep_to_go)
sleep_to_go = 0
else:
sleep(self.LOG_INTERVAL_SECONDS)
sleep_to_go -= self.LOG_INTERVAL_SECONDS
|
[
"time.sleep",
"datetime.timedelta",
"datetime.datetime.now",
"random.uniform"
] |
[((1344, 1365), 'datetime.timedelta', 'timedelta', ([], {'minutes': '(10)'}), '(minutes=10)\n', (1353, 1365), False, 'from datetime import datetime, timedelta\n'), ((5250, 5282), 'random.uniform', 'uniform', (['(-max_offset)', 'max_offset'], {}), '(-max_offset, max_offset)\n', (5257, 5282), False, 'from random import uniform\n'), ((5556, 5570), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (5568, 5570), False, 'from datetime import datetime, timedelta\n'), ((4145, 4162), 'datetime.timedelta', 'timedelta', ([], {'days': '(1)'}), '(days=1)\n', (4154, 4162), False, 'from datetime import datetime, timedelta\n'), ((4266, 4301), 'datetime.timedelta', 'timedelta', ([], {'seconds': 'current_duration'}), '(seconds=current_duration)\n', (4275, 4301), False, 'from datetime import datetime, timedelta\n'), ((4349, 4363), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4361, 4363), False, 'from datetime import datetime, timedelta\n'), ((4686, 4700), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4698, 4700), False, 'from datetime import datetime, timedelta\n'), ((4995, 5012), 'datetime.timedelta', 'timedelta', ([], {'days': '(1)'}), '(days=1)\n', (5004, 5012), False, 'from datetime import datetime, timedelta\n'), ((4420, 4434), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4432, 4434), False, 'from datetime import datetime, timedelta\n'), ((4462, 4476), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4474, 4476), False, 'from datetime import datetime, timedelta\n'), ((5596, 5626), 'datetime.timedelta', 'timedelta', ([], {'seconds': 'sleep_to_go'}), '(seconds=sleep_to_go)\n', (5605, 5626), False, 'from datetime import datetime, timedelta\n'), ((5958, 5976), 'time.sleep', 'sleep', (['sleep_to_go'], {}), '(sleep_to_go)\n', (5963, 5976), False, 'from time import sleep\n'), ((6043, 6075), 'time.sleep', 'sleep', (['self.LOG_INTERVAL_SECONDS'], {}), '(self.LOG_INTERVAL_SECONDS)\n', (6048, 6075), False, 'from time import sleep\n'), ((2330, 2385), 'datetime.timedelta', 'timedelta', ([], {'hours': 'duration.hour', 'minutes': 'duration.minute'}), '(hours=duration.hour, minutes=duration.minute)\n', (2339, 2385), False, 'from datetime import datetime, timedelta\n'), ((2559, 2634), 'datetime.timedelta', 'timedelta', ([], {'hours': 'time_random_offset.hour', 'minutes': 'time_random_offset.minute'}), '(hours=time_random_offset.hour, minutes=time_random_offset.minute)\n', (2568, 2634), False, 'from datetime import datetime, timedelta\n'), ((2837, 2925), 'datetime.timedelta', 'timedelta', ([], {'hours': 'duration_random_offset.hour', 'minutes': 'duration_random_offset.minute'}), '(hours=duration_random_offset.hour, minutes=duration_random_offset\n .minute)\n', (2846, 2925), False, 'from datetime import datetime, timedelta\n'), ((3424, 3481), 'random.uniform', 'uniform', (['self.bot.config.alt_min', 'self.bot.config.alt_max'], {}), '(self.bot.config.alt_min, self.bot.config.alt_max)\n', (3431, 3481), False, 'from random import uniform\n'), ((4553, 4567), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (4565, 4567), False, 'from datetime import datetime, timedelta\n')]
|
import serial
import os
import time
from . import (
DEBUG,
DEVICE_IDS,
BASE_PATH,
BUFFER_SIZE,
__version__
)
class Serial(serial.Serial):
def read_decoded(self):
tmp_data = self.read()
while b'>>> ' not in tmp_data:
tmp_data += self.read()
return tmp_data.decode('utf-8')
def communicate(self, data):
DEBUG(data)
data = bytes(data, encoding='utf-8')
d_size = len(data)
for num_bytes in range(0, d_size, BUFFER_SIZE):
chunk_size = min(BUFFER_SIZE, d_size - num_bytes)
chunk = data[num_bytes: num_bytes + chunk_size]
DEBUG(chunk)
self.write(chunk)
time.sleep(0.01)
res = self.read_decoded()
DEBUG(res)
return res
def init(self):
# instead of sending the code to execute a command over and over again
# like ampy is set up to do. I elected to house all of the functions
# that are needed in a single file.
#
# The program checks for this files existance on the ESP32 and if the file
# does exist it then checks the version of the file to make sure it is correct.
# if the version is not correct I delete the file on the ESP32
# If there is no file found either because it was deleted or the ESP32 did not
# have it, I upload the file.
#
# Going this route actually adds a HUGE speed boost, the ESP32 has a small serial
# buffer and limited processing power so it is easy to overrun the buffer.
# So the maximum size transmit to and from the ESP32 is 32 bytes. So by sending
# code that gets reused over and over again only a single time saves a large
# amount of overhead.
#
# I also noticed that the ESP32 would quite often crash and reboot when using ampy.
# I have a suspicision that it is due to fragmented memory when sending a large
# amount of data. Since the ESP32 supports threading I decided to create a thread loop
# that only has the single task of running a garbage collection every 1/2 a second.
# I have not had the ESP32 crash at all so it must be working.
esp32_control_file = os.path.join(BASE_PATH, 'esp32_control_file.py')
with open(esp32_control_file, 'rb') as fle:
esp32_control_file = fle.read()
boot_data = self.read_decoded()
DEBUG(self.boot_data)
DEBUG(self.communicate('import os\r\n'))
has_file = self.communicate('"esp32_control_file.py" in os.listdir("")\r\n')
DEBUG(has_file)
if 'True' in has_file:
version = self.communicate("import esp32_control_file\r\n")
DEBUG(version)
if __version__ not in version:
DEBUG(self.communicate('os.remove("esp32_control_file.py")\r\n'))
has_file = self.communicate('"esp32_control_file.py" in os.listdir("")\r\n')
DEBUG(has_file)
if 'False' in has_file:
DEBUG(self.communicate('f = open("esp32_control_file.py", "wb")\r\n'))
cf_size = len(esp32_control_file)
for cf_num_bytes in range(0, cf_size, BUFFER_SIZE - 14):
cf_chunk_size = min(BUFFER_SIZE - 14, cf_size - cf_num_bytes)
cf_chunk = repr(esp32_control_file[cf_num_bytes: cf_num_bytes + cf_chunk_size])
if not cf_chunk.startswith("b"):
cf_chunk = "b" + cf_chunk
DEBUG(self.communicate("f.write({0})\r\n".format(cf_chunk)))
DEBUG(self.communicate('f.close()\r\n'))
DEBUG(self.communicate("from esp32_control_file import *\r\n"))
DEBUG(self.communicate("run_esp_32_control()\r\n"))
return boot_data
@staticmethod
def get_port():
try:
# noinspection PyPackageRequirements
import serial.tools.list_ports
except ImportError:
return None
for prt in serial.tools.list_ports.comports():
if prt.vid is None:
continue
for vid, pid in DEVICE_IDS:
if vid == prt.vid and pid == prt.pid:
return prt.device
return None
|
[
"os.path.join",
"serial.tools.list_ports.comports",
"time.sleep"
] |
[((2262, 2310), 'os.path.join', 'os.path.join', (['BASE_PATH', '"""esp32_control_file.py"""'], {}), "(BASE_PATH, 'esp32_control_file.py')\n", (2274, 2310), False, 'import os\n'), ((4014, 4048), 'serial.tools.list_ports.comports', 'serial.tools.list_ports.comports', ([], {}), '()\n', (4046, 4048), False, 'import serial\n'), ((710, 726), 'time.sleep', 'time.sleep', (['(0.01)'], {}), '(0.01)\n', (720, 726), False, 'import time\n')]
|
# /usr/bin/env python
# -*- coding: UTF-8 -*-
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import tensorflow as tf
from trainer import *
from trainer256 import *
from config import get_config
from utils import prepare_dirs_and_logger, save_config
import pdb, os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
def main(config):
"""
config参数：Namespace(D_arch='DCGAN', batch_size=1, beta1=0.5, beta2=0.999, ckpt_path=None, conv_hidden_num=128,
d_lr=2e-05, data_dir='data', data_format='NCHW', dataset='DF_train_data', g_lr=2e-05, gamma=0.5, gpu=0,
grayscale=False, img_H=256, img_W=256, is_train=True, lambda_k=0.001, load_path='', log_dir='logs',
log_level='INFO', log_step=200, lr_update_step=50000, max_step=80, model=11, model_dir='path_to_directory_of_model',
num_log_samples=3, num_worker=4, optimizer='adam', pretrained_path=None, random_seed=123, sample_per_image=64,
save_model_secs=1000, split='train', start_step=0, test_data_path=None, test_one_by_one=False, use_gpu=True,
z_num=2)
"""
prepare_dirs_and_logger(config)
if config.gpu > -1:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = str(config.gpu)
config.data_format = 'NHWC'
trainer = None
if 1 == config.model:
print("使用PG2()，即Market-1501 数据库，并初始化")
trainer = PG2(config)
trainer.init_net()
elif 11 == config.model:
print("使用PG2_256()，即DeepFashion数据库，并初始化")
trainer = PG2_256(config)
trainer.init_net()
if config.is_train:
print("开始训练")
save_config(config) # 存储参数到json文件
trainer.train() # 开始训练
else:
print("开始测试")
if not config.load_path:
raise Exception("[!] 没有指定 `load_path` 用于读取预训练的模型")
trainer.test()
if __name__ == "__main__":
print("主进程开启")
config, unparsed = get_config()
main(config)
|
[
"utils.prepare_dirs_and_logger",
"utils.save_config",
"config.get_config",
"warnings.filterwarnings"
] |
[((63, 96), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (86, 96), False, 'import warnings\n'), ((1067, 1098), 'utils.prepare_dirs_and_logger', 'prepare_dirs_and_logger', (['config'], {}), '(config)\n', (1090, 1098), False, 'from utils import prepare_dirs_and_logger, save_config\n'), ((1933, 1945), 'config.get_config', 'get_config', ([], {}), '()\n', (1943, 1945), False, 'from config import get_config\n'), ((1644, 1663), 'utils.save_config', 'save_config', (['config'], {}), '(config)\n', (1655, 1663), False, 'from utils import prepare_dirs_and_logger, save_config\n')]
|
'''
MIT License
Copyright (c) 2022 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
from datasets import CLEVRDataset
from functools import partial
from networks import EmbeddingNet, ReadoutNet
from tensorboardX import SummaryWriter
from train_utils import train_one_epoch, eval_one_epoch, train_ddp
import argparse
import json
import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
#pred_types = {'left': 90, 'right': 90, 'front': 90, 'behind': 90}
#pred_types = {'front': 90, 'right': 90, 'contains': 90, 'supports': 90}
pred_types = {'left': 90, 'right': 90, 'front': 90, 'behind': 90, 'can_contain': 90, 'can_support': 90, 'supports': 90, 'contains': 90}
def step(data, model, head):
img, obj_patches, target, mask = data
emb, attn = model(img, obj_patches)
logits = head(emb)
loss = torch.nn.functional.binary_cross_entropy_with_logits(
logits, target, reduction='none'
)
loss = (loss * mask).sum() / mask.sum()
return logits, loss
def calc_acc(data, logits, pred_types):
img, obj_patches, target, mask = data
pred = (logits.detach() > 0).int()
pred = pred.reshape(logits.shape[0], len(pred_types), -1)
mask = mask.reshape(logits.shape[0], len(pred_types), -1)
target = target.int().reshape(logits.shape[0], len(pred_types), -1)
acc = ((pred == target) * mask).sum(dim=-1) / mask.sum(dim=-1)
acc = acc.nansum(dim=0) / (acc.shape[0] - acc.isnan().sum(dim=0)) * 100
return acc
def log(
writer, global_step, split, epoch, idx, total,
batch_time, data_time, avg_loss, avg_acc, pred_types=None
):
print(
f'Epoch {(epoch+1):02d} {split.capitalize()} {idx:04d}/{total:04d} '
f'Batch time {batch_time:.3f} Data time {data_time:.3f} '
f'Loss {avg_loss.item():.4f} Accuracy {avg_acc.mean().item():.2f}'
)
writer.add_scalar(f'{split}/loss', avg_loss, global_step)
writer.add_scalar(f'{split}/accuracy', avg_acc.mean().item(), global_step)
for a, name in zip(avg_acc, pred_types.keys()):
writer.add_scalar(f'{split}/accuracy_{name}', a.item(), global_step)
def train(rank, args):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = args.port
dist.init_process_group('gloo', rank=rank, world_size=args.n_gpu)
torch.cuda.set_device(rank)
# train_data = CLEVRDataset(
# f'{args.data_dir}/trainA.h5',
# f'{args.data_dir}/objects.h5',
# args.max_nobj, rand_patch=True
# )
#
# valid_data = CLEVRDataset(
# f'{args.data_dir}/valA.h5',
# f'{args.data_dir}/objects.h5',
# args.max_nobj, rand_patch=False
# )
train_data = CLEVRDataset(
f'{args.data_dir}/train.h5',
f'{args.data_dir}/objects.h5',
args.max_nobj, rand_patch=True
)
valid_data = CLEVRDataset(
f'{args.data_dir}/val_default.h5',
f'{args.data_dir}/objects.h5',
args.max_nobj, rand_patch=False
)
model = EmbeddingNet(
(args.img_w, args.img_h), args.patch_size, args.max_nobj,
args.width, args.layers, args.heads
)
head = ReadoutNet(args.width, args.d_hidden, 0, len(pred_types))
optimizer = torch.optim.Adam(
list(model.parameters()) + list(head.parameters()), args.lr
)
init_epoch = 0
if args.resume is not None:
checkpoint = torch.load(args.resume, map_location='cpu')
init_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
head.load_state_dict(checkpoint['head'])
optimizer.load_state_dict(checkpoint['optimizer'])
writer = None
if rank == 0:
writer = SummaryWriter(args.log_dir)
json.dump(args.__dict__, open(f'{args.log_dir}/args.txt', 'w'), indent=4)
train_one = partial(
train_one_epoch, pred_types, step, calc_acc,
log, args.print_freq, None, 0, writer
)
eval_one = partial(
eval_one_epoch, pred_types, step, calc_acc, log, None, writer
)
train_ddp(
train_data, valid_data, args.batch_size, args.n_worker, model, head,
optimizer, init_epoch, args.n_epoch, train_one, eval_one,
args.eval_freq, args.save_freq, args.log_dir
)
dist.destroy_process_group()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Data
parser.add_argument('--data_dir', type=str, default='data/geospa_half/')
parser.add_argument('--max_nobj', type=int, default=10)
parser.add_argument('--img_h', type=int, default=320)
parser.add_argument('--img_w', type=int, default=480)
parser.add_argument('--n_worker', type=int, default=2)
# Model
parser.add_argument('--patch_size', type=int, default=32)
parser.add_argument('--width', type=int, default=768)
parser.add_argument('--layers', type=int, default=12)
parser.add_argument('--heads', type=int, default=12)
parser.add_argument('--d_hidden', type=int, default=512)
# Training
parser.add_argument('--log_dir', type=str, default='log/geospa_train_0422')
parser.add_argument('--n_gpu', type=int, default=1)
parser.add_argument('--port', default='12345')
parser.add_argument('--batch_size', type=int, default=20)
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--n_epoch', type=int, default=40)
parser.add_argument('--print_freq', type=int, default=50)
parser.add_argument('--eval_freq', type=int, default=2)
parser.add_argument('--save_freq', type=int, default=10)
parser.add_argument('--resume')
args = parser.parse_args()
mp.spawn(
train,
args=(args,),
nprocs=args.n_gpu,
join=True
)
|
[
"functools.partial",
"tensorboardX.SummaryWriter",
"torch.distributed.destroy_process_group",
"torch.distributed.init_process_group",
"argparse.ArgumentParser",
"torch.multiprocessing.spawn",
"datasets.CLEVRDataset",
"torch.load",
"torch.nn.functional.binary_cross_entropy_with_logits",
"train_utils.train_ddp",
"networks.EmbeddingNet",
"torch.cuda.set_device"
] |
[((1830, 1920), 'torch.nn.functional.binary_cross_entropy_with_logits', 'torch.nn.functional.binary_cross_entropy_with_logits', (['logits', 'target'], {'reduction': '"""none"""'}), "(logits, target,\n reduction='none')\n", (1882, 1920), False, 'import torch\n'), ((3235, 3300), 'torch.distributed.init_process_group', 'dist.init_process_group', (['"""gloo"""'], {'rank': 'rank', 'world_size': 'args.n_gpu'}), "('gloo', rank=rank, world_size=args.n_gpu)\n", (3258, 3300), True, 'import torch.distributed as dist\n'), ((3305, 3332), 'torch.cuda.set_device', 'torch.cuda.set_device', (['rank'], {}), '(rank)\n', (3326, 3332), False, 'import torch\n'), ((3682, 3790), 'datasets.CLEVRDataset', 'CLEVRDataset', (['f"""{args.data_dir}/train.h5"""', 'f"""{args.data_dir}/objects.h5"""', 'args.max_nobj'], {'rand_patch': '(True)'}), "(f'{args.data_dir}/train.h5', f'{args.data_dir}/objects.h5',\n args.max_nobj, rand_patch=True)\n", (3694, 3790), False, 'from datasets import CLEVRDataset\n'), ((3835, 3950), 'datasets.CLEVRDataset', 'CLEVRDataset', (['f"""{args.data_dir}/val_default.h5"""', 'f"""{args.data_dir}/objects.h5"""', 'args.max_nobj'], {'rand_patch': '(False)'}), "(f'{args.data_dir}/val_default.h5',\n f'{args.data_dir}/objects.h5', args.max_nobj, rand_patch=False)\n", (3847, 3950), False, 'from datasets import CLEVRDataset\n'), ((3990, 4102), 'networks.EmbeddingNet', 'EmbeddingNet', (['(args.img_w, args.img_h)', 'args.patch_size', 'args.max_nobj', 'args.width', 'args.layers', 'args.heads'], {}), '((args.img_w, args.img_h), args.patch_size, args.max_nobj, args\n .width, args.layers, args.heads)\n', (4002, 4102), False, 'from networks import EmbeddingNet, ReadoutNet\n'), ((4795, 4890), 'functools.partial', 'partial', (['train_one_epoch', 'pred_types', 'step', 'calc_acc', 'log', 'args.print_freq', 'None', '(0)', 'writer'], {}), '(train_one_epoch, pred_types, step, calc_acc, log, args.print_freq,\n None, 0, writer)\n', (4802, 4890), False, 'from functools import partial\n'), ((4924, 4994), 'functools.partial', 'partial', (['eval_one_epoch', 'pred_types', 'step', 'calc_acc', 'log', 'None', 'writer'], {}), '(eval_one_epoch, pred_types, step, calc_acc, log, None, writer)\n', (4931, 4994), False, 'from functools import partial\n'), ((5013, 5204), 'train_utils.train_ddp', 'train_ddp', (['train_data', 'valid_data', 'args.batch_size', 'args.n_worker', 'model', 'head', 'optimizer', 'init_epoch', 'args.n_epoch', 'train_one', 'eval_one', 'args.eval_freq', 'args.save_freq', 'args.log_dir'], {}), '(train_data, valid_data, args.batch_size, args.n_worker, model,\n head, optimizer, init_epoch, args.n_epoch, train_one, eval_one, args.\n eval_freq, args.save_freq, args.log_dir)\n', (5022, 5204), False, 'from train_utils import train_one_epoch, eval_one_epoch, train_ddp\n'), ((5231, 5259), 'torch.distributed.destroy_process_group', 'dist.destroy_process_group', ([], {}), '()\n', (5257, 5259), True, 'import torch.distributed as dist\n'), ((5302, 5327), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (5325, 5327), False, 'import argparse\n'), ((6597, 6656), 'torch.multiprocessing.spawn', 'mp.spawn', (['train'], {'args': '(args,)', 'nprocs': 'args.n_gpu', 'join': '(True)'}), '(train, args=(args,), nprocs=args.n_gpu, join=True)\n', (6605, 6656), True, 'import torch.multiprocessing as mp\n'), ((4370, 4413), 'torch.load', 'torch.load', (['args.resume'], {'map_location': '"""cpu"""'}), "(args.resume, map_location='cpu')\n", (4380, 4413), False, 'import torch\n'), ((4668, 4695), 'tensorboardX.SummaryWriter', 'SummaryWriter', (['args.log_dir'], {}), '(args.log_dir)\n', (4681, 4695), False, 'from tensorboardX import SummaryWriter\n')]
|
import copy
from typing import Dict
import numpy as np
from tree.descision_tree import DecisionTree, LeafNode, combine_two_trees
from tree.optimized_train.data_view import NodeTrainDataView
from tree.optimized_train.decision_rule_selection import DecisionRuleSelector, DynamicPruningSelector, \
ScheduledPruningSelector
from tree.optimized_train.params_for_optimized import _set_defaults, print_expected_execution_statistics, PRUNING_METHOD
from tree.optimized_train.value_to_bins import ValuesToBins
def _create_selector(params: Dict) -> DecisionRuleSelector:
if params[PRUNING_METHOD] == DynamicPruningSelector:
return DynamicPruningSelector(params['lines_sample_ratios'], params['confidence'])
elif params[PRUNING_METHOD] == ScheduledPruningSelector:
return ScheduledPruningSelector(params['features_sample_ratios'], params['lines_sample_ratios'])
else:
raise ValueError("Invalid pruning method: " + str(params[PRUNING_METHOD]))
def train(x, y, params) -> (DecisionTree, DecisionRuleSelector):
assert y.shape[0] > 0
assert y.shape[0] == x.shape[0]
params_copy = copy.deepcopy(params)
_set_defaults(params_copy)
print_expected_execution_statistics(params_copy, x.shape[0], x.shape[1])
converter = ValuesToBins(x, params_copy['n_bins'])
binned_x = converter.get_bins(x)
assert binned_x.dtype == np.uint8, binned_x.dtype
assert binned_x.shape == x.shape
binned_data_view = NodeTrainDataView(binned_x, y, np.arange(binned_x.shape[0]))
selector = _create_selector(params_copy)
tree = train_on_binned(binned_data_view, selector, params_copy)
return converter.convert_bins_tree_to_prediction_tree(tree), selector
def train_on_binned(data_view: NodeTrainDataView, decision_rule_select: DecisionRuleSelector,
params: Dict) -> DecisionTree:
params_copy = copy.copy(params)
y = data_view.residue_values(data_view.all_rows())
default_leaf_node = DecisionTree(LeafNode(np.average(y)))
if params['max_depth'] == 0 or y.shape[0] == 1:
return default_leaf_node
else:
params_copy['max_depth'] -= 1
decision_rule = decision_rule_select.select_decision_rule(data_view)
if decision_rule is None:
return default_leaf_node
if data_view.is_trivial_split(decision_rule):
return default_leaf_node
left_view, right_view = data_view.create_children_views(decision_rule)
left_tree = train_on_binned(left_view, decision_rule_select, params_copy)
right_tree = train_on_binned(right_view, decision_rule_select, params_copy)
return combine_two_trees(decision_rule, left_tree, right_tree)
|
[
"copy.deepcopy",
"numpy.average",
"tree.optimized_train.value_to_bins.ValuesToBins",
"tree.descision_tree.combine_two_trees",
"copy.copy",
"tree.optimized_train.decision_rule_selection.ScheduledPruningSelector",
"tree.optimized_train.params_for_optimized.print_expected_execution_statistics",
"tree.optimized_train.decision_rule_selection.DynamicPruningSelector",
"tree.optimized_train.params_for_optimized._set_defaults",
"numpy.arange"
] |
[((1123, 1144), 'copy.deepcopy', 'copy.deepcopy', (['params'], {}), '(params)\n', (1136, 1144), False, 'import copy\n'), ((1149, 1175), 'tree.optimized_train.params_for_optimized._set_defaults', '_set_defaults', (['params_copy'], {}), '(params_copy)\n', (1162, 1175), False, 'from tree.optimized_train.params_for_optimized import _set_defaults, print_expected_execution_statistics, PRUNING_METHOD\n'), ((1180, 1252), 'tree.optimized_train.params_for_optimized.print_expected_execution_statistics', 'print_expected_execution_statistics', (['params_copy', 'x.shape[0]', 'x.shape[1]'], {}), '(params_copy, x.shape[0], x.shape[1])\n', (1215, 1252), False, 'from tree.optimized_train.params_for_optimized import _set_defaults, print_expected_execution_statistics, PRUNING_METHOD\n'), ((1269, 1307), 'tree.optimized_train.value_to_bins.ValuesToBins', 'ValuesToBins', (['x', "params_copy['n_bins']"], {}), "(x, params_copy['n_bins'])\n", (1281, 1307), False, 'from tree.optimized_train.value_to_bins import ValuesToBins\n'), ((1872, 1889), 'copy.copy', 'copy.copy', (['params'], {}), '(params)\n', (1881, 1889), False, 'import copy\n'), ((641, 716), 'tree.optimized_train.decision_rule_selection.DynamicPruningSelector', 'DynamicPruningSelector', (["params['lines_sample_ratios']", "params['confidence']"], {}), "(params['lines_sample_ratios'], params['confidence'])\n", (663, 716), False, 'from tree.optimized_train.decision_rule_selection import DecisionRuleSelector, DynamicPruningSelector, ScheduledPruningSelector\n'), ((1490, 1518), 'numpy.arange', 'np.arange', (['binned_x.shape[0]'], {}), '(binned_x.shape[0])\n', (1499, 1518), True, 'import numpy as np\n'), ((2641, 2696), 'tree.descision_tree.combine_two_trees', 'combine_two_trees', (['decision_rule', 'left_tree', 'right_tree'], {}), '(decision_rule, left_tree, right_tree)\n', (2658, 2696), False, 'from tree.descision_tree import DecisionTree, LeafNode, combine_two_trees\n'), ((793, 887), 'tree.optimized_train.decision_rule_selection.ScheduledPruningSelector', 'ScheduledPruningSelector', (["params['features_sample_ratios']", "params['lines_sample_ratios']"], {}), "(params['features_sample_ratios'], params[\n 'lines_sample_ratios'])\n", (817, 887), False, 'from tree.optimized_train.decision_rule_selection import DecisionRuleSelector, DynamicPruningSelector, ScheduledPruningSelector\n'), ((1991, 2004), 'numpy.average', 'np.average', (['y'], {}), '(y)\n', (2001, 2004), True, 'import numpy as np\n')]
|
from drf_yasg import openapi
from drf_yasg.views import get_schema_view
from rest_framework import permissions
from django.urls import include
from django.urls import path
schema_view = get_schema_view(
openapi.Info(
title='{{ cookiecutter.project_slug }} API',
default_version='v1',
contact=openapi.Contact(email='{{ cookiecutter.email }}'),
),
public=True,
permission_classes=[permissions.IsAuthenticatedOrReadOnly],
)
app_name = 'api_v1'
urlpatterns = [
path('auth/', include('a12n.urls')),
path('users/', include('users.urls')),
path('sepulkas/', include('sepulkas.urls')),
path('docs/', schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'),
path('swagger<str:format>', schema_view.without_ui(cache_timeout=0), name='schema-json'),
path('healthchecks/', include('django_healthchecks.urls')),
]
|
[
"drf_yasg.openapi.Contact",
"django.urls.include"
] |
[((519, 539), 'django.urls.include', 'include', (['"""a12n.urls"""'], {}), "('a12n.urls')\n", (526, 539), False, 'from django.urls import include\n'), ((561, 582), 'django.urls.include', 'include', (['"""users.urls"""'], {}), "('users.urls')\n", (568, 582), False, 'from django.urls import include\n'), ((607, 631), 'django.urls.include', 'include', (['"""sepulkas.urls"""'], {}), "('sepulkas.urls')\n", (614, 631), False, 'from django.urls import include\n'), ((841, 876), 'django.urls.include', 'include', (['"""django_healthchecks.urls"""'], {}), "('django_healthchecks.urls')\n", (848, 876), False, 'from django.urls import include\n'), ((322, 371), 'drf_yasg.openapi.Contact', 'openapi.Contact', ([], {'email': '"""{{ cookiecutter.email }}"""'}), "(email='{{ cookiecutter.email }}')\n", (337, 371), False, 'from drf_yasg import openapi\n')]
|
#!/usr/bin/python
# Copyright (c) 2020, 2022 Oracle and/or its affiliates.
# This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license.
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
# Apache License v2.0
# See LICENSE.TXT for details.
# GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {
"metadata_version": "1.1",
"status": ["preview"],
"supported_by": "community",
}
DOCUMENTATION = """
---
module: oci_waas_waf_log_facts
short_description: Fetches details about one or multiple WafLog resources in Oracle Cloud Infrastructure
description:
- Fetches details about one or multiple WafLog resources in Oracle Cloud Infrastructure
- Gets structured Web Application Firewall event logs for a WAAS
policy. Sorted by the `timeObserved` in ascending order (starting from the
oldest recorded event).
version_added: "2.9.0"
author: Oracle (@oracle)
options:
waas_policy_id:
description:
- The L(OCID,https://docs.cloud.oracle.com/Content/General/Concepts/identifiers.htm) of the WAAS policy.
type: str
required: true
time_observed_greater_than_or_equal_to:
description:
- A filter that matches log entries where the observed event occurred on or after a date and time specified in RFC 3339 format. If unspecified,
defaults to two hours before receipt of the request.
type: str
time_observed_less_than:
description:
- A filter that matches log entries where the observed event occurred before a date and time, specified in RFC 3339 format.
type: str
text_contains:
description:
- A full text search for logs.
type: str
access_rule_key:
description:
- Filters logs by access rule key.
type: list
elements: str
action:
description:
- Filters logs by Web Application Firewall action.
type: list
elements: str
choices:
- "BLOCK"
- "DETECT"
- "BYPASS"
- "LOG"
- "REDIRECTED"
client_address:
description:
- Filters logs by client IP address.
type: list
elements: str
country_code:
description:
- Filters logs by country code. Country codes are in ISO 3166-1 alpha-2 format. For a list of codes, see L(ISO's
website,https://www.iso.org/obp/ui/#search/code/).
type: list
elements: str
country_name:
description:
- Filter logs by country name.
type: list
elements: str
fingerprint:
description:
- Filter logs by device fingerprint.
type: list
elements: str
http_method:
description:
- Filter logs by HTTP method.
type: list
elements: str
choices:
- "OPTIONS"
- "GET"
- "HEAD"
- "POST"
- "PUT"
- "DELETE"
- "TRACE"
- "CONNECT"
incident_key:
description:
- Filter logs by incident key.
type: list
elements: str
log_type:
description:
- Filter by log type. For more information about WAF logs, see L(Logs,https://docs.cloud.oracle.com/iaas/Content/WAF/Tasks/logs.htm).
type: list
elements: str
choices:
- "ACCESS"
- "PROTECTION_RULES"
- "JS_CHALLENGE"
- "CAPTCHA"
- "ACCESS_RULES"
- "THREAT_FEEDS"
- "HUMAN_INTERACTION_CHALLENGE"
- "DEVICE_FINGERPRINT_CHALLENGE"
- "ADDRESS_RATE_LIMITING"
origin_address:
description:
- Filter by origin IP address.
type: list
elements: str
referrer:
description:
- Filter by referrer.
type: list
elements: str
request_url:
description:
- Filter by request URL.
type: list
elements: str
response_code:
description:
- Filter by response code.
type: list
elements: int
threat_feed_key:
description:
- Filter by threat feed key.
type: list
elements: str
user_agent:
description:
- Filter by user agent.
type: list
elements: str
protection_rule_key:
description:
- Filter by protection rule key.
type: list
elements: str
extends_documentation_fragment: [ oracle.oci.oracle ]
"""
EXAMPLES = """
- name: List waf_logs
oci_waas_waf_log_facts:
# required
waas_policy_id: "ocid1.waaspolicy.oc1..xxxxxxEXAMPLExxxxxx"
# optional
time_observed_greater_than_or_equal_to: 2013-10-20T19:20:30+01:00
time_observed_less_than: 2013-10-20T19:20:30+01:00
text_contains: text_contains_example
access_rule_key: [ "access_rule_key_example" ]
action: [ "BLOCK" ]
client_address: [ "client_address_example" ]
country_code: [ "country_code_example" ]
country_name: [ "country_name_example" ]
fingerprint: [ "fingerprint_example" ]
http_method: [ "OPTIONS" ]
incident_key: [ "incident_key_example" ]
log_type: [ "ACCESS" ]
origin_address: [ "origin_address_example" ]
referrer: [ "referrer_example" ]
request_url: [ "request_url_example" ]
response_code: [ "56" ]
threat_feed_key: [ "threat_feed_key_example" ]
user_agent: [ "user_agent_example" ]
protection_rule_key: [ "protection_rule_key_example" ]
"""
RETURN = """
waf_logs:
description:
- List of WafLog resources
returned: on success
type: complex
contains:
action:
description:
- The action taken on the request, either `ALLOW`, `DETECT`, or `BLOCK`.
returned: on success
type: str
sample: action_example
captcha_action:
description:
- The CAPTCHA action taken on the request, `ALLOW` or `BLOCK`. For more information about
CAPTCHAs, see `UpdateCaptchas`.
returned: on success
type: str
sample: captcha_action_example
captcha_expected:
description:
- The CAPTCHA challenge answer that was expected.
returned: on success
type: str
sample: captcha_expected_example
captcha_received:
description:
- The CAPTCHA challenge answer that was received.
returned: on success
type: str
sample: captcha_received_example
captcha_fail_count:
description:
- The number of times the CAPTCHA challenge was failed.
returned: on success
type: str
sample: captcha_fail_count_example
client_address:
description:
- The IPv4 address of the requesting client.
returned: on success
type: str
sample: client_address_example
country_name:
description:
- The name of the country where the request originated.
returned: on success
type: str
sample: country_name_example
user_agent:
description:
- The value of the request's `User-Agent` header field.
returned: on success
type: str
sample: user_agent_example
domain:
description:
- The `Host` header data of the request.
returned: on success
type: str
sample: domain_example
protection_rule_detections:
description:
- A map of protection rule keys to detection message details. Detections are
requests that matched the criteria of a protection rule but the rule's
action was set to `DETECT`.
returned: on success
type: dict
sample: {}
http_method:
description:
- The HTTP method of the request.
returned: on success
type: str
sample: http_method_example
request_url:
description:
- The path and query string of the request.
returned: on success
type: str
sample: request_url_example
http_headers:
description:
- The map of the request's header names to their respective values.
returned: on success
type: dict
sample: {}
referrer:
description:
- The `Referrer` header value of the request.
returned: on success
type: str
sample: referrer_example
response_code:
description:
- The status code of the response.
returned: on success
type: int
sample: 56
response_size:
description:
- The size in bytes of the response.
returned: on success
type: int
sample: 56
incident_key:
description:
- The incident key of a request. An incident key is generated for
each request processed by the Web Application Firewall and is used to
idenitfy blocked requests in applicable logs.
returned: on success
type: str
sample: incident_key_example
fingerprint:
description:
- The hashed signature of the device's fingerprint. For more information,
see `DeviceFingerPrintChallenge`.
returned: on success
type: str
sample: fingerprint_example
device:
description:
- The type of device that the request was made from.
returned: on success
type: str
sample: device_example
country_code:
description:
- ISO 3166-1 alpha-2 code of the country from which the request originated.
For a list of codes, see L(ISO's website,https://www.iso.org/obp/ui/#search/code/).
returned: on success
type: str
sample: country_code_example
request_headers:
description:
- A map of header names to values of the request sent to the origin, including any headers
appended by the Web Application Firewall.
returned: on success
type: dict
sample: {}
threat_feed_key:
description:
- The `ThreatFeed` key that matched the request. For more information about
threat feeds, see `UpdateThreatFeeds`.
returned: on success
type: str
sample: threat_feed_key_example
access_rule_key:
description:
- The `AccessRule` key that matched the request. For more information about
access rules, see `UpdateAccessRules`.
returned: on success
type: str
sample: access_rule_key_example
address_rate_limiting_key:
description:
- The `AddressRateLimiting` key that matched the request. For more information
about address rate limiting, see `UpdateWafAddressRateLimiting`.
returned: on success
type: str
sample: address_rate_limiting_key_example
timestamp:
description:
- The date and time the Web Application Firewall processed the request and logged it.
returned: on success
type: str
sample: "2013-10-20T19:20:30+01:00"
log_type:
description:
- The type of log of the request. For more about log types, see L(Logs,https://docs.cloud.oracle.com/iaas/Content/WAF/Tasks/logs.htm).
returned: on success
type: str
sample: log_type_example
origin_address:
description:
- The address of the origin server where the request was sent.
returned: on success
type: str
sample: origin_address_example
origin_response_time:
description:
- The amount of time it took the origin server to respond to the request, in seconds.
returned: on success
type: str
sample: origin_response_time_example
sample: [{
"action": "action_example",
"captcha_action": "captcha_action_example",
"captcha_expected": "captcha_expected_example",
"captcha_received": "captcha_received_example",
"captcha_fail_count": "captcha_fail_count_example",
"client_address": "client_address_example",
"country_name": "country_name_example",
"user_agent": "user_agent_example",
"domain": "domain_example",
"protection_rule_detections": {},
"http_method": "http_method_example",
"request_url": "request_url_example",
"http_headers": {},
"referrer": "referrer_example",
"response_code": 56,
"response_size": 56,
"incident_key": "incident_key_example",
"fingerprint": "fingerprint_example",
"device": "device_example",
"country_code": "country_code_example",
"request_headers": {},
"threat_feed_key": "threat_feed_key_example",
"access_rule_key": "access_rule_key_example",
"address_rate_limiting_key": "address_rate_limiting_key_example",
"timestamp": "2013-10-20T19:20:30+01:00",
"log_type": "log_type_example",
"origin_address": "origin_address_example",
"origin_response_time": "origin_response_time_example"
}]
"""
from ansible.module_utils.basic import AnsibleModule
from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils
from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import (
OCIResourceFactsHelperBase,
get_custom_class,
)
try:
from oci.waas import WaasClient
HAS_OCI_PY_SDK = True
except ImportError:
HAS_OCI_PY_SDK = False
class WafLogFactsHelperGen(OCIResourceFactsHelperBase):
"""Supported operations: list"""
def get_required_params_for_list(self):
return [
"waas_policy_id",
]
def list_resources(self):
optional_list_method_params = [
"time_observed_greater_than_or_equal_to",
"time_observed_less_than",
"text_contains",
"access_rule_key",
"action",
"client_address",
"country_code",
"country_name",
"fingerprint",
"http_method",
"incident_key",
"log_type",
"origin_address",
"referrer",
"request_url",
"response_code",
"threat_feed_key",
"user_agent",
"protection_rule_key",
]
optional_kwargs = dict(
(param, self.module.params[param])
for param in optional_list_method_params
if self.module.params.get(param) is not None
)
return oci_common_utils.list_all_resources(
self.client.list_waf_logs,
waas_policy_id=self.module.params.get("waas_policy_id"),
**optional_kwargs
)
WafLogFactsHelperCustom = get_custom_class("WafLogFactsHelperCustom")
class ResourceFactsHelper(WafLogFactsHelperCustom, WafLogFactsHelperGen):
pass
def main():
module_args = oci_common_utils.get_common_arg_spec()
module_args.update(
dict(
waas_policy_id=dict(type="str", required=True),
time_observed_greater_than_or_equal_to=dict(type="str"),
time_observed_less_than=dict(type="str"),
text_contains=dict(type="str"),
access_rule_key=dict(type="list", elements="str", no_log=True),
action=dict(
type="list",
elements="str",
choices=["BLOCK", "DETECT", "BYPASS", "LOG", "REDIRECTED"],
),
client_address=dict(type="list", elements="str"),
country_code=dict(type="list", elements="str"),
country_name=dict(type="list", elements="str"),
fingerprint=dict(type="list", elements="str"),
http_method=dict(
type="list",
elements="str",
choices=[
"OPTIONS",
"GET",
"HEAD",
"POST",
"PUT",
"DELETE",
"TRACE",
"CONNECT",
],
),
incident_key=dict(type="list", elements="str", no_log=True),
log_type=dict(
type="list",
elements="str",
choices=[
"ACCESS",
"PROTECTION_RULES",
"JS_CHALLENGE",
"CAPTCHA",
"ACCESS_RULES",
"THREAT_FEEDS",
"HUMAN_INTERACTION_CHALLENGE",
"DEVICE_FINGERPRINT_CHALLENGE",
"ADDRESS_RATE_LIMITING",
],
),
origin_address=dict(type="list", elements="str"),
referrer=dict(type="list", elements="str"),
request_url=dict(type="list", elements="str"),
response_code=dict(type="list", elements="int"),
threat_feed_key=dict(type="list", elements="str", no_log=True),
user_agent=dict(type="list", elements="str"),
protection_rule_key=dict(type="list", elements="str", no_log=True),
)
)
module = AnsibleModule(argument_spec=module_args)
if not HAS_OCI_PY_SDK:
module.fail_json(msg="oci python sdk required for this module.")
resource_facts_helper = ResourceFactsHelper(
module=module,
resource_type="waf_log",
service_client_class=WaasClient,
namespace="waas",
)
result = []
if resource_facts_helper.is_list():
result = resource_facts_helper.list()
else:
resource_facts_helper.fail()
module.exit_json(waf_logs=result)
if __name__ == "__main__":
main()
|
[
"ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.get_common_arg_spec",
"ansible.module_utils.basic.AnsibleModule",
"ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.get_custom_class"
] |
[((15938, 15981), 'ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils.get_custom_class', 'get_custom_class', (['"""WafLogFactsHelperCustom"""'], {}), "('WafLogFactsHelperCustom')\n", (15954, 15981), False, 'from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import OCIResourceFactsHelperBase, get_custom_class\n'), ((16099, 16137), 'ansible_collections.oracle.oci.plugins.module_utils.oci_common_utils.get_common_arg_spec', 'oci_common_utils.get_common_arg_spec', ([], {}), '()\n', (16135, 16137), False, 'from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils\n'), ((18339, 18379), 'ansible.module_utils.basic.AnsibleModule', 'AnsibleModule', ([], {'argument_spec': 'module_args'}), '(argument_spec=module_args)\n', (18352, 18379), False, 'from ansible.module_utils.basic import AnsibleModule\n')]
|
import json
X_OFFSET = 20
Y_OFFSET = 40
with open('config.json', 'r') as f:
config = json.load(f)
config['manager'][2][0] += X_OFFSET
config['manager'][2][1] += Y_OFFSET
for anchor_config in config['anchors']:
if len(anchor_config) > 2:
anchor_config[2][0] += X_OFFSET
anchor_config[2][1] += Y_OFFSET
with open('config-new.json', 'w') as ff:
json.dump(config, ff)
|
[
"json.dump",
"json.load"
] |
[((91, 103), 'json.load', 'json.load', (['f'], {}), '(f)\n', (100, 103), False, 'import json\n'), ((407, 428), 'json.dump', 'json.dump', (['config', 'ff'], {}), '(config, ff)\n', (416, 428), False, 'import json\n')]
|
"""Transformer."""
import math
import logging
import torch
__all__ = ('TEncoder', 'TDecoder')
logger = logging.getLogger(__name__)
class PositionalEncoding(torch.nn.Module):
def __init__(self, d_model, dropout, max_seq_len):
super(PositionalEncoding, self).__init__()
self.dropout = torch.nn.Dropout(p=dropout)
pe = torch.zeros(max_seq_len, d_model)
position = torch.arange(0, max_seq_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
# pylint: disable=R0902
class TDecoder(torch.nn.Module):
"""Transformer Decoder class."""
def __init__(self, hidden_size, output_size, num_layers, dropout,
num_attention_heads):
"""Initialize decoder model."""
super(TDecoder, self).__init__()
self.model_type = 'transformer'
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layers = num_layers
self.dropout = dropout
self.num_attention_heads = num_attention_heads
self.embedding = torch.nn.Embedding(output_size, hidden_size)
decoder_layers = torch.nn.TransformerDecoderLayer(
d_model=hidden_size, nhead=num_attention_heads,
dim_feedforward=hidden_size, dropout=dropout,
activation='relu')
self.transformer = torch.nn.TransformerDecoder(
decoder_layers, num_layers=num_layers)
self.out = torch.nn.Linear(hidden_size, output_size)
self.softmax = torch.nn.LogSoftmax(dim=1)
# TODO check that we need this
# self.out.bias.data.zero_()
# self.out.weight.data.uniform_(-0.1, 0.1)
# pylint: disable=R1710, W0221
def forward(self, input_tensor, memory_tensor):
"""Apply forward computation."""
output_tensor = self.embedding(input_tensor).view(1, 1, -1)
# TODO: maybe remove this for the Transformer?
output_tensor = torch.nn.functional.relu(output_tensor)
output_tensor = self.transformer(output_tensor, memory_tensor)
output_tensor = self.softmax(self.out(output_tensor[0]))
return output_tensor
# pylint: disable=R0902
class TEncoder(torch.nn.Module):
"""Transformer Encoder class."""
def __init__(self, input_size, hidden_size, num_layers, dropout,
num_attention_heads):
"""Initialize encoder model."""
super(TEncoder, self).__init__()
self.model_type = 'transformer'
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.dropout = dropout
self.num_attention_heads = num_attention_heads
self.embedding = torch.nn.Embedding(input_size, hidden_size)
# self.pos_encoder = PositionalEncoding(hidden_size, dropout, max_seq_len=15)
encoder_layers = torch.nn.TransformerEncoderLayer(
d_model=hidden_size, nhead=num_attention_heads,
dim_feedforward=hidden_size, dropout=dropout,
activation='relu')
self.transformer = torch.nn.TransformerEncoder(
encoder_layers, num_layers=num_layers)
# TODO: check if we need this
# self.embedding.weight.data.uniform_(-0.1, 0.1)
# pylint: disable=R1710, W0221
def forward(self, input_tensor):
"""Apply forward computation."""
embedded_tensor = self.embedding(input_tensor).view(1, 1, -1)
# embedded_tensor = embedded_tensor * math.sqrt(self.hidden_size)
# embedded_tensor = self.pos_encoder(embedded_tensor)
return self.transformer(embedded_tensor)
|
[
"torch.nn.Dropout",
"torch.nn.TransformerDecoderLayer",
"torch.nn.TransformerEncoder",
"torch.nn.TransformerDecoder",
"torch.nn.LogSoftmax",
"torch.nn.Embedding",
"torch.nn.TransformerEncoderLayer",
"torch.cos",
"torch.arange",
"torch.nn.Linear",
"torch.zeros",
"torch.nn.functional.relu",
"math.log",
"torch.sin",
"logging.getLogger"
] |
[((106, 133), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (123, 133), False, 'import logging\n'), ((309, 336), 'torch.nn.Dropout', 'torch.nn.Dropout', ([], {'p': 'dropout'}), '(p=dropout)\n', (325, 336), False, 'import torch\n'), ((350, 383), 'torch.zeros', 'torch.zeros', (['max_seq_len', 'd_model'], {}), '(max_seq_len, d_model)\n', (361, 383), False, 'import torch\n'), ((585, 615), 'torch.sin', 'torch.sin', (['(position * div_term)'], {}), '(position * div_term)\n', (594, 615), False, 'import torch\n'), ((638, 668), 'torch.cos', 'torch.cos', (['(position * div_term)'], {}), '(position * div_term)\n', (647, 668), False, 'import torch\n'), ((1403, 1447), 'torch.nn.Embedding', 'torch.nn.Embedding', (['output_size', 'hidden_size'], {}), '(output_size, hidden_size)\n', (1421, 1447), False, 'import torch\n'), ((1473, 1627), 'torch.nn.TransformerDecoderLayer', 'torch.nn.TransformerDecoderLayer', ([], {'d_model': 'hidden_size', 'nhead': 'num_attention_heads', 'dim_feedforward': 'hidden_size', 'dropout': 'dropout', 'activation': '"""relu"""'}), "(d_model=hidden_size, nhead=\n num_attention_heads, dim_feedforward=hidden_size, dropout=dropout,\n activation='relu')\n", (1505, 1627), False, 'import torch\n'), ((1683, 1749), 'torch.nn.TransformerDecoder', 'torch.nn.TransformerDecoder', (['decoder_layers'], {'num_layers': 'num_layers'}), '(decoder_layers, num_layers=num_layers)\n', (1710, 1749), False, 'import torch\n'), ((1782, 1823), 'torch.nn.Linear', 'torch.nn.Linear', (['hidden_size', 'output_size'], {}), '(hidden_size, output_size)\n', (1797, 1823), False, 'import torch\n'), ((1847, 1873), 'torch.nn.LogSoftmax', 'torch.nn.LogSoftmax', ([], {'dim': '(1)'}), '(dim=1)\n', (1866, 1873), False, 'import torch\n'), ((2277, 2316), 'torch.nn.functional.relu', 'torch.nn.functional.relu', (['output_tensor'], {}), '(output_tensor)\n', (2301, 2316), False, 'import torch\n'), ((3032, 3075), 'torch.nn.Embedding', 'torch.nn.Embedding', (['input_size', 'hidden_size'], {}), '(input_size, hidden_size)\n', (3050, 3075), False, 'import torch\n'), ((3187, 3341), 'torch.nn.TransformerEncoderLayer', 'torch.nn.TransformerEncoderLayer', ([], {'d_model': 'hidden_size', 'nhead': 'num_attention_heads', 'dim_feedforward': 'hidden_size', 'dropout': 'dropout', 'activation': '"""relu"""'}), "(d_model=hidden_size, nhead=\n num_attention_heads, dim_feedforward=hidden_size, dropout=dropout,\n activation='relu')\n", (3219, 3341), False, 'import torch\n'), ((3397, 3463), 'torch.nn.TransformerEncoder', 'torch.nn.TransformerEncoder', (['encoder_layers'], {'num_layers': 'num_layers'}), '(encoder_layers, num_layers=num_layers)\n', (3424, 3463), False, 'import torch\n'), ((403, 450), 'torch.arange', 'torch.arange', (['(0)', 'max_seq_len'], {'dtype': 'torch.float'}), '(0, max_seq_len, dtype=torch.float)\n', (415, 450), False, 'import torch\n'), ((493, 520), 'torch.arange', 'torch.arange', (['(0)', 'd_model', '(2)'], {}), '(0, d_model, 2)\n', (505, 520), False, 'import torch\n'), ((533, 550), 'math.log', 'math.log', (['(10000.0)'], {}), '(10000.0)\n', (541, 550), False, 'import math\n')]
|
# -*- coding: utf-8 -*-
# *****************************************************************************
# NICOS, the Networked Instrument Control System of the MLZ
# Copyright (c) 2009-2021 by the NICOS contributors (see AUTHORS)
#
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation; either version 2 of the License, or (at your option) any later
# version.
#
# 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. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# this program; if not, write to the Free Software Foundation, Inc.,
# 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
# Module authors:
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
#
# *****************************************************************************
import json
import threading
from datetime import datetime
from time import time as currenttime
from file_writer_control import JobHandler, WorkerJobPool, WriteJob
from streaming_data_types import deserialise_answ, deserialise_wrdn, \
deserialise_x5f2
from streaming_data_types.fbschemas.action_response_answ.ActionOutcome import \
ActionOutcome
from streaming_data_types.fbschemas.action_response_answ.ActionType import \
ActionType
from nicos import session
from nicos.core import MASTER, Attach, CommunicationError, Param, \
ScanDataset, host, listof, status, usermethod
from nicos.core.constants import INTERRUPTED, POINT
from nicos.core.data.sink import DataSinkHandler
from nicos.core.params import Override, anytype, dictof
from nicos.devices.datasinks.file import FileSink
from nicos_ess.devices.datasinks.nexus_structure import NexusStructureProvider
from nicos_ess.devices.kafka.status_handler import KafkaStatusHandler
class JobRecord:
def __init__(self, job_id, counter, update_interval, next_update):
self.job_id = job_id
self.counter = counter
self.update_interval = update_interval
self.next_update = next_update
self.start_time = None
self.stop_time = None
self.stop_requested = False
@classmethod
def from_dict(cls, job_dict):
result = JobRecord('', 0, 0, 0)
for k, v in job_dict.items():
if k in result.__dict__:
result.__dict__[k] = v
return result
def as_dict(self):
return self.__dict__
class FileWriterStatus(KafkaStatusHandler):
"""Monitors Kafka for the status of any file-writing jobs."""
parameters = {
'cached_jobs': Param(
description='stores the jobs in progress in the cache',
type=dictof(str, anytype),
internal=True, settable=True),
}
_lock = None
_jobs = {}
_type_to_handler = {}
def doPreinit(self, mode):
KafkaStatusHandler.doPreinit(self, mode)
self._lock = threading.RLock()
self._jobs = {}
self._set_status(status.OK, '')
self._type_to_handler = {
b'x5f2': self._on_status_message,
b'answ': self._on_response_message,
b'wrdn': self._on_stopped_message,
}
def doInit(self, mode):
self._retrieve_cache_jobs()
def _retrieve_cache_jobs(self):
for k, v in self.cached_jobs.items():
job = JobRecord.from_dict(v)
if job.stop_requested:
continue
# Update the timeout so it doesn't time out immediately
job.next_update = currenttime() + job.update_interval
self._jobs[k] = job
def _update_cached_jobs(self):
self.cached_jobs = {n: v.as_dict() for n, v in self._jobs.items()}
def new_messages_callback(self, messages):
for _, msg in sorted(messages, key=lambda x: x[0]):
if msg[4:8] in self._type_to_handler:
with self._lock:
self._type_to_handler[msg[4:8]](msg)
def _on_status_message(self, message):
result = deserialise_x5f2(message)
status_info = json.loads(result.status_json)
job_id = status_info['job_id']
if job_id not in self._jobs:
return
update_interval = result.update_interval // 1000
next_update = currenttime() + update_interval
self._jobs[job_id].start_time = status_info['start_time']
self._jobs[job_id].update_interval = update_interval
self._jobs[job_id].next_update = next_update
if len(self._jobs) == 1:
self._set_status(status.BUSY, 'job in progress')
elif len(self._jobs) > 1:
self._set_status(status.BUSY, f'{len(self._jobs)} jobs in progress')
def _on_stopped_message(self, message):
result = deserialise_wrdn(message)
if result.job_id not in self._jobs:
return
self.log.info(f'stopped writing {result.job_id}')
metadata = json.loads(result.metadata)
self._jobs[result.job_id].stop_time = metadata['stop_time']
del self._jobs[result.job_id]
self._update_cached_jobs()
if not self._jobs:
self._set_status(status.OK, '')
def _on_response_message(self, message):
result = deserialise_answ(message)
if result.job_id not in self._jobs:
return
if result.action == ActionType.StartJob:
self._on_start_response(result)
elif result.action == ActionType.SetStopTime:
self._on_stop_response(result)
def _on_start_response(self, result):
if result.outcome == ActionOutcome.Success:
self.log.info(f'starting to write job {result.job_id}')
self._jobs[result.job_id].update_interval = self.statusinterval
self._jobs[result.job_id].next_update = currenttime() + \
self.statusinterval
else:
self.log.error(result.message)
del self._jobs[result.job_id]
self._update_cached_jobs()
def _on_stop_response(self, result):
if not self._jobs[result.job_id].stop_requested:
self.log.warning('stop requested from external agent for %s',
result.job_id)
if result.outcome == ActionOutcome.Success:
msg = f'request to stop writing succeeded for job {result.job_id}'
self.log.info(msg)
else:
msg = f'stop writing request failed for job {result.job_id}'
self._set_status(status.ERROR, msg)
self.log.error('%s: %s', msg, result.message)
def no_messages_callback(self):
with self._lock:
if not self._jobs:
self._set_status(status.OK, '')
return
self._check_for_lost_jobs()
def _check_for_lost_jobs(self):
overdue_jobs = [k for k, v in self._jobs.items() if
currenttime() > v.next_update + self.timeoutinterval]
for overdue in overdue_jobs:
self.log.error(f'lost connection to job {overdue}')
# Assume it has gone for good...
del self._jobs[overdue]
if overdue_jobs:
self._update_cached_jobs()
def doInfo(self):
result = [(f'{self.name}', '', '', '', 'general')]
for i, job in enumerate(self._jobs):
result.append((f'job {i + 1}', f'{job}', f'{job}', '', 'general'))
return result
def _set_status(self, stat, message):
if self._mode == MASTER:
self._setROParam('curstatus', (stat, message))
@property
def jobs_in_progress(self):
with self._lock:
return set(self._jobs.keys())
@property
def marked_for_stop(self):
with self._lock:
return {k for k, v in self._jobs.items() if v.stop_requested}
def mark_for_stop(self, job_id):
with self._lock:
if job_id in self._jobs:
self._jobs[job_id].stop_requested = True
self._update_cached_jobs()
def add_job(self, job_id, counter):
with self._lock:
self._jobs[job_id] = JobRecord(job_id, counter, self.statusinterval,
currenttime() + self.statusinterval)
self._update_cached_jobs()
class FileWriterSinkHandler(DataSinkHandler):
"""Sink handler for the NeXus file-writer"""
_scan_set = None
def prepare(self):
if self.sink._manual_start or self._scan_set:
return
self.sink.check_okay_to_start()
# Assign the counter
self.manager.assignCounter(self.dataset)
# Generate the filenames, only if not set
if not self.dataset.filepaths:
self.manager.getFilenames(
self.dataset, self.sink.filenametemplate, self.sink.subdir
)
# Update meta information of devices, only if not present
if not self.dataset.metainfo:
self.manager.updateMetainfo()
self._scan_set = self._get_scan_set()
def begin(self):
if self.sink._manual_start:
return
if self._scan_set and self.dataset.number > 1:
return
datetime_now = datetime.now()
structure = self.sink._attached_nexus.get_structure(self.dataset,
datetime_now)
self.sink._start_job(self.dataset.filenames[0], self.dataset.counter,
structure, datetime_now)
def end(self):
if self.sink._manual_start:
return
if self._scan_set and self.dataset.number < self._scan_set.npoints:
return
self.sink._stop_job()
self.sink.end()
def _get_scan_set(self):
if not self.sink.one_file_per_scan:
# User has requested one file per scan point
return None
parents = list(self.manager.iterParents(self.dataset))
if parents and isinstance(parents[~0], ScanDataset):
return parents[~0]
return None
def putResults(self, quality, results):
if quality == INTERRUPTED:
# On e-stop let the current file-writing job be stopped
self._scan_set = None
class FileWriterControlSink(FileSink):
"""Sink for the NeXus file-writer"""
parameters = {
'brokers': Param('List of kafka hosts to be connected',
type=listof(host(defaultport=9092)),
mandatory=True, preinit=True, userparam=False),
'pool_topic': Param(
'Kafka topic where start messages are sent',
type=str, settable=False, preinit=True, mandatory=True,
userparam=False,
),
'timeoutinterval': Param(
'Time to wait (secs) before communication is considered failed',
type=int, default=5, settable=True, userparam=False,
),
'one_file_per_scan': Param(
'Whether to write all scan points to one file or a file per point',
type=bool, default=True, settable=True, userparam=False,
),
}
parameter_overrides = {
'settypes': Override(default=[POINT]),
'filenametemplate': Override(
default=['%(proposal)s_%(pointcounter)08d.hdf']
),
}
attached_devices = {
'status': Attach('The file-writer status device', FileWriterStatus),
'nexus': Attach('Supplies the NeXus file structure',
NexusStructureProvider),
}
handlerclass = FileWriterSinkHandler
def doInit(self, mode):
self._statustopic = self._attached_status.statustopic
self._manual_start = False
self._handler = None
self._command_channel = self._create_command_channel()
def _create_command_channel(self):
try:
return WorkerJobPool(f'{self.brokers[0]}/{self.pool_topic}',
f'{self.brokers[0]}/{self._statustopic}')
except Exception as error:
raise CommunicationError(
f'could not connect to job pool: {error}') from error
def _create_job_handler(self, job_id=''):
return JobHandler(self._command_channel, job_id)
@usermethod
def start_job(self, title=None):
"""Start a new file-writing job."""
if title is not None:
session.experiment.update(title=str(title))
self.check_okay_to_start()
self._manual_start = False
# Begin a point but remove it from the stack immediately to avoid an
# orphaned point.
# File-writing won't stop though.
session.experiment.data.beginPoint()
self._manual_start = True
session.experiment.data.finishPoint()
def _start_job(self, filename, counter, structure, start_time=None,
stop_time=None):
self.check_okay_to_start()
# Initialise the write job.
write_job = WriteJob(
nexus_structure=structure,
file_name=filename,
broker=self.brokers[0],
start_time=start_time if start_time else datetime.now(),
stop_time=stop_time,
control_topic=self._statustopic
)
job_handler = self._create_job_handler()
start_handler = job_handler.start_job(write_job)
timeout = int(currenttime()) + self.timeoutinterval
while not start_handler.is_done():
if int(currenttime()) > timeout:
raise Exception('request to start writing job not acknowledged')
self._attached_status.add_job(write_job.job_id, counter)
@usermethod
def stop_job(self, job_id=''):
"""Stop a file-writing job.
:param job_id: the particular job to stop. Only required if there is
more than one job running.
"""
self._stop_job(job_id)
self._handler = None
self._manual_start = False
def _stop_job(self, job_id=''):
if job_id and job_id in self._attached_status.marked_for_stop:
# Already stopping so ignore
return
active_jobs = self.get_active_jobs()
if not active_jobs:
return
if job_id and job_id not in active_jobs:
self.log.error('supplied job ID is not recognised. '
'Already stopped or perhaps a typo?')
return
if len(active_jobs) == 1:
job_id = list(active_jobs)[0]
elif len(active_jobs) > 1 and not job_id:
self.log.error('more than one job being written, rerun the command '
'with the job ID specified in quotes')
return
stop_time = datetime.now()
job_handler = self._create_job_handler(job_id)
stop_handler = job_handler.set_stop_time(stop_time)
timeout = int(currenttime()) + self.timeoutinterval
while not stop_handler.is_done() and not job_handler.is_done():
if int(currenttime()) > timeout:
self.log.error('request to stop writing job not'
'acknowledged by filewriter')
return
self._attached_status.mark_for_stop(job_id)
def check_okay_to_start(self):
active_jobs = self.get_active_jobs()
if active_jobs:
raise Exception(f'cannot start file-writing as {len(active_jobs)} '
'job(s) already in progress')
def get_active_jobs(self):
jobs = self._attached_status.jobs_in_progress
active_jobs = \
self._attached_status.marked_for_stop.symmetric_difference(jobs)
return active_jobs
def doInfo(self):
return self._attached_status.doInfo()
def createHandlers(self, dataset):
if self._handler is None:
self._handler = self.handlerclass(self, dataset, None)
else:
self._handler.dataset = dataset
return [self._handler]
def end(self):
self._handler = None
|
[
"nicos_ess.devices.kafka.status_handler.KafkaStatusHandler.doPreinit",
"streaming_data_types.deserialise_x5f2",
"nicos.core.Attach",
"json.loads",
"nicos.core.params.dictof",
"nicos.core.host",
"threading.RLock",
"nicos.core.CommunicationError",
"time.time",
"nicos.core.params.Override",
"streaming_data_types.deserialise_wrdn",
"nicos.session.experiment.data.beginPoint",
"nicos.session.experiment.data.finishPoint",
"streaming_data_types.deserialise_answ",
"file_writer_control.JobHandler",
"nicos.core.Param",
"datetime.datetime.now",
"file_writer_control.WorkerJobPool"
] |
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