code stringlengths 22 1.05M | apis listlengths 1 3.31k | extract_api stringlengths 75 3.25M |
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# Generated by Django 3.1.7 on 2021-03-24 17:18
import datetime
from django.db import migrations, models
import django.db.models.deletion
import uuid
class Migration(migrations.Migration):
dependencies = [
('xmpt', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='BusinessCategory',
fields=[
('business_category_id', models.IntegerField(primary_key=True, serialize=False)),
('business_category_code', models.CharField(max_length=254)),
('business_category_title', models.CharField(max_length=254)),
('version_year', models.CharField(default='2017', max_length=10)),
],
options={
'verbose_name_plural': 'Business Categories',
},
),
migrations.CreateModel(
name='Community',
fields=[
('community_guid', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('community_code', models.CharField(default='empty', max_length=254)),
('community_name', models.CharField(default='empty', max_length=254)),
('email', models.EmailField(blank=True, max_length=254)),
('phone_number', models.CharField(blank=True, max_length=20)),
('phone_type', models.CharField(blank=True, choices=[('M', 'Mobile'), ('O', 'Office')], max_length=1)),
('street_address_1', models.CharField(default='empty', max_length=254)),
('street_address_2', models.CharField(blank=True, max_length=254)),
('apt_suit', models.CharField(blank=True, max_length=50)),
('postal_code', models.CharField(default='00000', max_length=20)),
('postal_code_9', models.CharField(blank=True, max_length=20)),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('is_community_owner', models.BooleanField(default=False)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('disabled_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('business_category', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.businesscategory')),
],
options={
'verbose_name_plural': 'Communities',
},
),
migrations.CreateModel(
name='CommunityHierarchy',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('parentCommunityGUID', models.CharField(max_length=36)),
('childCommunityGUID', models.CharField(max_length=36)),
('relationshipType', models.IntegerField(blank=True, choices=[(1, 'Parent_Child'), (2, 'Sibling_Sibling')], default=1)),
('can_share', models.BooleanField(default=True)),
('can_view', models.BooleanField(default=True)),
('can_manage', models.BooleanField(default=False)),
('can_report', models.BooleanField(default=True)),
('can_analyze', models.BooleanField(default=True)),
('can_audit', models.BooleanField(default=True)),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('deleted_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
],
options={
'verbose_name_plural': 'Community Hierarchy',
},
),
migrations.CreateModel(
name='Country',
fields=[
('country_id', models.IntegerField(primary_key=True, serialize=False)),
('country_alpha2', models.CharField(max_length=2)),
('country_alpha3', models.CharField(max_length=3)),
('country_name', models.CharField(max_length=254)),
],
options={
'verbose_name_plural': 'Countries',
},
),
migrations.CreateModel(
name='TaxForm',
fields=[
('tax_form_id', models.IntegerField(primary_key=True, serialize=False)),
('tax_form_name', models.CharField(blank=True, max_length=254)),
('jurisdiction_name', models.CharField(blank=True, max_length=254)),
],
options={
'verbose_name': 'Tax Form',
'verbose_name_plural': 'Tax Forms',
},
),
migrations.CreateModel(
name='TIN',
fields=[
('tax_code_id', models.AutoField(primary_key=True, serialize=False)),
('tin_type', models.CharField(choices=[('EIN', 'EIN'), ('TIN', 'TIN'), ('SSN', 'SSN'), ('STN', 'STID')], max_length=3)),
('tin_code', models.CharField(max_length=254)),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('disabled_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('country_id', models.ForeignKey(default=840, on_delete=django.db.models.deletion.CASCADE, to='xmpt.country')),
],
options={
'verbose_name_plural': 'Tax Identifiers',
},
),
migrations.CreateModel(
name='USCounty',
fields=[
('county_id', models.IntegerField(primary_key=True, serialize=False)),
('state_alpha2', models.CharField(default='ZZ', max_length=2)),
('state_fips_code', models.CharField(blank=True, max_length=10)),
('county_fips_code', models.CharField(blank=True, max_length=10)),
('county_subdivision_fips_code', models.CharField(blank=True, max_length=10)),
('county_name', models.CharField(max_length=254)),
('jurisdiction_level_code', models.CharField(blank=True, max_length=10)),
('country_alpha2', models.CharField(default='US', max_length=2)),
('country_alpha3', models.CharField(default='USA', max_length=3)),
('country_name', models.CharField(default='United States of America', max_length=254)),
('country_id', models.ForeignKey(default=840, on_delete=django.db.models.deletion.CASCADE, to='xmpt.country')),
],
options={
'verbose_name_plural': 'US Counties',
},
),
migrations.CreateModel(
name='User',
fields=[
('user_guid', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('user_name', models.CharField(default='empty', max_length=254)),
('first_name', models.CharField(blank=True, max_length=254)),
('middle_name', models.CharField(blank=True, max_length=100)),
('last_name', models.CharField(blank=True, max_length=254)),
('email', models.EmailField(blank=True, max_length=254)),
('phone_number', models.CharField(blank=True, max_length=20)),
('phone_type', models.CharField(blank=True, choices=[('M', 'Mobile'), ('O', 'Office')], max_length=1)),
('street_address_1', models.CharField(default='empty', max_length=254)),
('street_address_2', models.CharField(blank=True, max_length=254)),
('apt_suit', models.CharField(blank=True, max_length=50)),
('postal_code', models.CharField(default='00000', max_length=20)),
('postal_code_9', models.CharField(blank=True, max_length=20)),
('signature_name', models.CharField(blank=True, max_length=254)),
('signature_title', models.CharField(blank=True, max_length=254)),
('signature_date', models.DateTimeField(blank=True, default=datetime.datetime.now)),
('drivers_license_number', models.CharField(default='empty', max_length=20)),
('drivers_license_issue_date', models.DateField(default='9999-12-31')),
('drivers_license_exp_date', models.DateField(default='9999-12-31')),
('drivers_license_front', models.FileField(blank=True, upload_to='', verbose_name='Picture DL front')),
('drivers_license_back', models.FileField(blank=True, upload_to='', verbose_name='Picture DL back')),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('is_community_owner', models.BooleanField(default=False)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True)),
('disabled_timestamp', models.DateTimeField(blank=True)),
('business_category', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.businesscategory')),
('country_name', models.ForeignKey(default=840, on_delete=django.db.models.deletion.CASCADE, to='xmpt.country')),
('county_name', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.uscounty')),
],
),
migrations.CreateModel(
name='USState',
fields=[
('state_id', models.IntegerField(default=0, primary_key=True, serialize=False)),
('state_alpha2', models.CharField(default='ZZ', max_length=2)),
('state_name', models.CharField(default='ZZ', max_length=254)),
('jurisdiction_level_code', models.CharField(blank=True, max_length=10)),
('state_fips_code', models.CharField(blank=True, max_length=10)),
('country_alpha2', models.CharField(default='US', max_length=2)),
('country_alpha3', models.CharField(default='USA', max_length=3)),
('country_name', models.CharField(default='United States of America', max_length=254)),
('country_id', models.ForeignKey(default=840, on_delete=django.db.models.deletion.CASCADE, to='xmpt.country')),
],
options={
'verbose_name': 'US State',
'verbose_name_plural': 'US States',
},
),
migrations.CreateModel(
name='XmptCertificate',
fields=[
('certificate_guid', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('buyer_user_guid', models.CharField(max_length=36)),
('seller_user_guid', models.CharField(max_length=36)),
('certificate_qr_code', models.ImageField(height_field=200, upload_to='', width_field=200)),
('certificate_description', models.TextField()),
('certificate_type', models.CharField(blank=True, max_length=254)),
('is_active', models.BooleanField(default=True)),
('is_valid', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('expired_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('tax_form_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.taxform')),
],
),
migrations.CreateModel(
name='USTown',
fields=[
('town_id', models.IntegerField(primary_key=True, serialize=False)),
('state_fips_code', models.CharField(blank=True, max_length=10)),
('county_fips_code', models.CharField(blank=True, max_length=10)),
('county_subdivision_fips_code', models.CharField(blank=True, max_length=10)),
('place_fips_code', models.CharField(blank=True, max_length=10)),
('city_fips_code', models.CharField(blank=True, max_length=10)),
('town_name', models.CharField(max_length=254)),
('jurisdiction_level_code', models.CharField(blank=True, max_length=10)),
('state_alpha2', models.CharField(default='ZZ', max_length=2)),
('country_alpha2', models.CharField(default='US', max_length=2)),
('country_alpha3', models.CharField(default='USA', max_length=3)),
('country_name', models.CharField(default='United States of America', max_length=254)),
('country_id', models.ForeignKey(default=840, on_delete=django.db.models.deletion.CASCADE, to='xmpt.country')),
('state_id', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.usstate')),
],
options={
'verbose_name_plural': 'US Towns',
},
),
migrations.CreateModel(
name='UserTINs',
fields=[
('user_tin_id', models.AutoField(primary_key=True, serialize=False)),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('disabled_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('tin_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.tin')),
('user_guid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.user')),
],
options={
'verbose_name_plural': 'User TINs',
},
),
migrations.AddField(
model_name='user',
name='state_name',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.usstate'),
),
migrations.AddField(
model_name='user',
name='town_name',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.ustown'),
),
migrations.AddField(
model_name='uscounty',
name='state_id',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.usstate'),
),
migrations.AddField(
model_name='tin',
name='state_id',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.usstate'),
),
migrations.CreateModel(
name='TaxAuthority',
fields=[
('tax_authority_guid', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)),
('state_name', models.CharField(max_length=50)),
('community_guid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.community')),
('state_id', models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.usstate')),
],
options={
'verbose_name_plural': 'Tax Authorities',
},
),
migrations.CreateModel(
name='Membership',
fields=[
('membership_id', models.IntegerField(primary_key=True, serialize=False)),
('roleCode', models.SmallIntegerField(choices=[(0, 'Member'), (1, 'Owner'), (2, 'Tax Authority'), (3, 'XMPT')], default=0)),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('disabled_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('community_guid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.community')),
('user_guid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.user')),
],
),
migrations.CreateModel(
name='CommunityTINs',
fields=[
('community_tin_id', models.AutoField(primary_key=True, serialize=False)),
('is_active', models.BooleanField(default=True)),
('is_enabled', models.BooleanField(default=True)),
('created_timestamp', models.DateTimeField(auto_now=True)),
('deactivated_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('disabled_timestamp', models.DateTimeField(blank=True, default='9999-12-31 00:00')),
('community_guid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.community')),
('tin_id', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='xmpt.tin')),
],
options={
'verbose_name_plural': 'Community TINs',
},
),
migrations.AddField(
model_name='community',
name='country_name',
field=models.ForeignKey(default=840, on_delete=django.db.models.deletion.CASCADE, to='xmpt.country'),
),
migrations.AddField(
model_name='community',
name='county_name',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.uscounty'),
),
migrations.AddField(
model_name='community',
name='state_name',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.usstate'),
),
migrations.AddField(
model_name='community',
name='town_name',
field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='xmpt.ustown'),
),
]
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"django.db.models.BooleanField",
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# INFO
__author__ = "<NAME>"
__date__ = "26 Mar 2022"
__license__ = "MIT"
__version__ = "1.0"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Definitive version"
__copyright__ = "© 2022"
# SCRIPT
import numpy as np
from logistic_regression import *
from other_functions import *
print('\033[92m\033[1m\n\n------------------ 1. TRAINING RESULTS --------------------\033[0m')
# Load the training data
train_X, train_Y = load_file("../titanic-data/titanic-train.txt")
''' Understand the best learning rate value '''
# Which is a good value for the learning rate?
learning_rate_accuracy = {}
learning_rate_values = 1e-4, 0.5e-4, 1e-3, 0.5e-3, 1e-2, 0.05e-1
for lr in learning_rate_values:
w, b, iteration_loss = logreg_train(train_X, train_Y, lr)
P = logreg_inference(train_X, w, b)
Y_hat_train = (P >= 0.5)
accuracy = (train_Y == Y_hat_train).mean() * 100
learning_rate_accuracy[lr] = accuracy
# Show how the accuracy goes for specific learning rate values
display_lr_vs_accuracy(learning_rate_accuracy)
# Find the best learning rate value, such that it maximizes the accuracy
best_lr_value, max_accuracy = find_best_lr_value(learning_rate_accuracy)
# What is the training accuracy of the trained model?
print('\nBest learning rate value:', best_lr_value, ' Accuracy:', round(max_accuracy, 2))
''' Once understood the best learning rate, now lets understand which is the best value for the iterations '''
w, b, iteration_loss = logreg_train(train_X, train_Y, best_lr_value)
# Show how the loss goes for specific iterations value
display_iteration_vs_loss(iteration_loss)
# Find the best iterations value, such that it minimizes the loss function
best_iterations_value, min_loss = find_best_iterations_value(iteration_loss)
# How many iterations are required to converge?
print('\nBest iterations value:', best_iterations_value, ' Loss:', round(min_loss, 2))
''' Extra: Load the obtained parameters into an external .csv file '''
# Obtain the parameters considering the "best model", using the previously obtained values
# Why 60000 as iteration value? Time-performance trade-off...by increasing the number of iterations over
# 60000 the loss function doesn't decreases so much and the time spent it's not worth it
w, b, iteration_loss = logreg_train(train_X, train_Y, best_lr_value, 60000)
# Load the parameters into the 'parameter.csv' file
np.savetxt( '../parameters.csv', np.append(w, b))
# END
print('\n') | [
"numpy.append"
] | [((2421, 2436), 'numpy.append', 'np.append', (['w', 'b'], {}), '(w, b)\n', (2430, 2436), True, 'import numpy as np\n')] |
import requests
import urllib
class ResolveCoreference:
def __init__(self):
self.resolved_sentece = []
self.input_sentence = None
def process(self, corenlp_output):
self.input_sentence = corenlp_output
for coref in self.input_sentence['corefs']:
mentions = self.input_sentence['corefs'][coref]
antecedent = mentions[0] # first mention in the coreference chain
for j in range(1, len(mentions)):
mention = mentions[j]
if mention['type'] == 'PRONOMINAL':
# get the attributes of the target mention in the corresponding sentence
target_sentence = mention['sentNum']
target_token = mention['startIndex'] - 1
# transfer the antecedent's word form to the appropriate token in the sentence
self.input_sentence['sentences'][target_sentence - 1]['tokens'][target_token]['word'] = antecedent['text']
def get_resolved(self):
possessives = {'hers', 'his', 'their', 'theirs'}
for sentence in self.input_sentence['sentences']:
for token in sentence['tokens']:
output_word = token['word']
# check lemmas as well as tags for possessive pronouns in case of tagging errors
if token['lemma'] in possessives or token['pos'] == 'PRP$':
output_word += "'s" # add the possessive morpheme
output_word += token['after']
self.resolved_sentece.append(output_word)
resolvedStr = ''.join(self.resolved_sentece)
return resolvedStr
def ResolveCoreferenceDriver(data):
text = "Tom and Jane are good friends. They are cool. He knows a lot of things and so does she. His car is red, but " \
"hers is blue. It is older than hers. The big cat ate its dinner."
url = 'http://localhost:9000?properties='
params = '\"annotators": \"dcoref\", \"outputFormat\": \"json\"'
#print(params)
queryString = urllib.parse.quote(params)
#data = 'John is teacher. He loves coding'
#data = "You pick up the fork and plate. Then move them to another table."
#data = 'You move the green ball on red block. Now pick it up again and put it on yellow base'
#data = 'push the box to left of table and put fork on it.'
#print(url+queryString)
response = requests.post(url+queryString,data)
resolveObj = ResolveCoreference()
resolveObj.process(response.json())
return resolveObj.get_resolved()
if __name__ == "__main__":
print(ResolveCoreferenceDriver(input()))
| [
"requests.post",
"urllib.parse.quote"
] | [((2063, 2089), 'urllib.parse.quote', 'urllib.parse.quote', (['params'], {}), '(params)\n', (2081, 2089), False, 'import urllib\n'), ((2422, 2460), 'requests.post', 'requests.post', (['(url + queryString)', 'data'], {}), '(url + queryString, data)\n', (2435, 2460), False, 'import requests\n')] |
#!/usr/bin/python3.8
"""
Genetic Algorithm to maximize surveillance over a population for AI Assignment.
Author: Sam (eremus-dev)
Repo: https://github.com/eremus-dev
"""
import math
from collections import Counter
from typing import List, Dict
import numpy as np
import matplotlib.pyplot as plt
from test_pop import test_pop
"""
GENETIC ALGORITHM CONFIGURATION VARIABLES
"""
# Genetic Algorithm and Camera Config
genetic_pop = 100 # number different genetic strains
generation = 100 # number of generations to maximize coverage
view_radius = 15 # how far the cameras see
citizens = 200 # how many people we need to surveil
cam_count = 4 # how many cams we have to surveil them with
mutation_chance = 10 # percentage chance mutation occurs
threshold = 100 # stop at this result or generation
test_number = 10 # number of tests to run, set to zero if no tests
Coord = List[int] # Type of co-ordinates
def gen_randpop(size: int) -> List[Coord]:
"""
Function to generate randomly distributed population
to surveil
"""
obs = [] # [x,y] of size number of people
for _ in range(1, size + 1):
xy = [] # x, y co-ords of each person
x = np.random.randint(1, 100)
xy.append(x)
y = np.random.randint(1, 100)
xy.append(y)
obs.append(xy)
return np.array(obs, copy=True)
def rate_gen(cams: Dict[int, List[Coord]], pop: List[Coord]) -> Dict[int, int]:
"""
Function to get the best of the population to breed, mutate and to survive
"""
scores = {}
for n in cams:
scores[n] = fitness_function(cams[n], pop)
return scores
def fitness_function(cams: List[Coord], pop: List[Coord]) -> int:
"""
Function to calculate number of surveilled citizens.
Check if all the cameras can see them, if any can score increases
"""
score = []
for cit in pop:
test = False
for cam in cams:
if (
math.sqrt(((cam[0] - cit[0]) ** 2) + ((cam[1] - cit[1]) ** 2))
<= view_radius
):
test = True
score.append(test)
return score.count(True)
def select_from_pop(
cams: Dict[int, List[Coord]], total_scores
) -> Dict[int, List[Coord]]:
"""
Function that takes a dict of camera positions and a dict of scores and breeds the strongest
returns new population of cameras
"""
top_scores = {}
new_pop = {}
selection = int(len(total_scores) / 2)
scores = sorted(total_scores, key=total_scores.get, reverse=True)[:selection]
assert len(scores) == selection
for i in scores:
top_scores[i] = total_scores[i]
new_pop[i] = cams[i]
assert len(new_pop) == selection
return breed_strongest(top_scores, new_pop)
def breed_strongest(
top_scores: Dict[int, int], new_pop: Dict[int, List[Coord]]
) -> Dict[int, List[Coord]]:
"""
Function to breed 25 best positions.
Strongest always remains unchanged.
"""
count = 0
full_pop = {}
keys = list(new_pop.keys())
for i in keys:
dad = []
child = []
mum = []
mum = np.copy(new_pop[i])
child = dad = np.copy(
new_pop[np.random.choice(keys)]
) # randomly select breeding mate
child[0] = mum[np.random.randint(0, 3)]
child[1] = mum[np.random.randint(0, 3)]
full_pop[count] = mum # save mum
count += 1
full_pop[count] = child # add random child
count += 1
full_pop = mutate(full_pop, top_scores)
assert len(full_pop) == genetic_pop
return full_pop
def mutate(
full_pop: Dict[int, List[Coord]], top_scores: Dict[int, int]
) -> Dict[int, List[Coord]]:
"""
Function to mutate population, 10% chance they will mutate
"""
for i in full_pop:
if np.random.randint(0, 100) > (100 - mutation_chance):
temp = full_pop[i]
xmod, ymod = [
np.random.randint(-20, 20),
np.random.randint(-20, 20),
] # pick random mutation
camera_num = np.random.randint(0, 3)
camera = temp[camera_num] # cameras to mod
camera[0] = (camera[0] + xmod) % 100
camera[1] = (camera[1] + ymod) % 100
temp[camera_num] = camera
full_pop[i] = temp
return full_pop
def plot_pop(pop: List[Coord], cams: List[Coord], top_score: int, gen: int, run: int) -> None:
"""
Function to plot placement of cams and population on graph
"""
plt.cla() # clears graph
plt.gcf().canvas.mpl_connect( # allows exit key to quit qraph
"key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
)
plt.axis("equal")
plt.grid(True)
plt.plot(pop[:, 0], pop[:, 1], "ok")
plt.plot(cams[:, 0], cams[:, 1], "*")
for i in range(len(cams)): # plots camera view range
circle = plt.Circle(
(cams[i][0], cams[i][1]), view_radius, color="r", fill=False
)
ax = plt.gca()
ax.add_artist(circle)
ax = plt.gca()
ax.set_xlabel("City Terrain X") # sets up all labels
ax.set_ylabel("City Terrain Y")
ax.set_title(f"Visualisation of Cameras and Population\nSurveilled Population {max_seen} in Generation {gen}")
plt.pause(0.01)
plt.draw() # draws graph
if gen == 199:
plt.savefig(f'./results/last_gen_test{run}.png')
def plot_final_results(generational_record: Dict[int, int], run: int, max_seen: int) -> None:
'''
Produces final plot of the progression of the GA across a single generational run
'''
plt.cla()
plt.grid(True)
lists = sorted(generational_record.items())
x, y = zip(*lists)
plt.xlim(-2, generation+2)
plt.ylim(50, 120)
plt.plot(x, y, label="Pop Surveilled", linestyle="--", marker='o')
ax = plt.gca()
ax.set_xlabel("Generations")
ax.set_ylabel("Number of Population Surveilled")
ax.set_title(f"Population Surveilled Over Generations\nMax Population Surveilled {max_seen}")
plt.savefig(f'./results/final_results_test{run}.png')
if test_number > 0:
plt.pause(0.5)
plt.draw()
else:
plt.show()
def plot_aggregate_results(aggregate_results: Dict[int, int], ) -> None:
'''
Produces plot of aggregate results for test runs of GA
'''
# Graph aggregate results and average of test runs
plt.cla()
plt.grid(True)
lists = sorted(aggregate_results.items())
x,y = zip(*lists)
avg = [sum(y) / len(y)] * len(x)
mean = np.mean(y)
std_dev = format(np.std(y), '.3f')
maximum = max(y)
plt.scatter(x, y, label="Pop Surveilled", color="r")
ax = plt.gca()
ax.plot(x, avg, label='Mean', linestyle='--')
ax.set_title(f"Population Surveilled Over Tests using Genetic Algorithm\nPopulation Surveilled Mean: {mean}, Max {maximum}, Stdev {std_dev}")
ax.legend(loc='upper left')
ax.set_xlabel("Test Number")
ax.set_ylabel("Number of Population Surveilled")
plt.savefig(f'./results/aggregate_result_GA_test_run.png')
plt.show()
if __name__ == "__main__":
aggregate_results = {} # collect each tests results
# run the GA for test_number times and graph results
for run in range(0, test_number):
generational_record = {} # record to graph at end
cams = {} # dictionary of genetic population
#citpop = gen_randpop(citizens) # a numpy array of citizens randomly distributed
citpop = np.array(test_pop)
for i in range(genetic_pop): # generate genetic population
cams[i] = gen_randpop(cam_count) # a numpy array of cams randomly distributed
# Main Genetic Algorithm Loop
gen = 0
max_seen = 0
while (gen < generation) & (
max_seen < threshold
): # evolve for number of generations
if gen != 0: # do nothing first time through loop
cams = select_from_pop(cams, total_scores)
total_scores = rate_gen(cams, citpop)
best_cam = max(total_scores, key=total_scores.get)
max_seen = total_scores[best_cam]
print(f"We surveilled {max_seen} in generation {gen}, best is {best_cam}")
plot_pop(citpop, cams[best_cam], max_seen, gen, run) # print best fit for each generation
generational_record[gen] = max_seen # to graph at end of process
gen += 1
# Graph Results of Genetic Algorithm over generations
plot_final_results(generational_record, run, max_seen)
aggregate_results[run] = max_seen
# Graph aggregate results and average of test runs
plot_aggregate_results(aggregate_results)
| [
"matplotlib.pyplot.grid",
"math.sqrt",
"numpy.array",
"numpy.mean",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.scatter",
"matplotlib.pyplot.axis",
"matplotlib.pyplot.ylim",
"matplotlib.pyplot.cla",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.Circle",
"numpy.random.choice",
"matplotlib.... | [((1327, 1351), 'numpy.array', 'np.array', (['obs'], {'copy': '(True)'}), '(obs, copy=True)\n', (1335, 1351), True, 'import numpy as np\n'), ((4549, 4558), 'matplotlib.pyplot.cla', 'plt.cla', ([], {}), '()\n', (4556, 4558), True, 'import matplotlib.pyplot as plt\n'), ((4776, 4793), 'matplotlib.pyplot.axis', 'plt.axis', (['"""equal"""'], {}), "('equal')\n", (4784, 4793), True, 'import matplotlib.pyplot as plt\n'), ((4821, 4835), 'matplotlib.pyplot.grid', 'plt.grid', (['(True)'], {}), '(True)\n', (4829, 4835), True, 'import matplotlib.pyplot as plt\n'), ((4840, 4876), 'matplotlib.pyplot.plot', 'plt.plot', (['pop[:, 0]', 'pop[:, 1]', '"""ok"""'], {}), "(pop[:, 0], pop[:, 1], 'ok')\n", (4848, 4876), True, 'import matplotlib.pyplot as plt\n'), ((4881, 4918), 'matplotlib.pyplot.plot', 'plt.plot', (['cams[:, 0]', 'cams[:, 1]', '"""*"""'], {}), "(cams[:, 0], cams[:, 1], '*')\n", (4889, 4918), True, 'import matplotlib.pyplot as plt\n'), ((5156, 5165), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (5163, 5165), True, 'import matplotlib.pyplot as plt\n'), ((5382, 5397), 'matplotlib.pyplot.pause', 'plt.pause', (['(0.01)'], {}), '(0.01)\n', (5391, 5397), True, 'import matplotlib.pyplot as plt\n'), ((5402, 5412), 'matplotlib.pyplot.draw', 'plt.draw', ([], {}), '()\n', (5410, 5412), True, 'import matplotlib.pyplot as plt\n'), ((5731, 5740), 'matplotlib.pyplot.cla', 'plt.cla', ([], {}), '()\n', (5738, 5740), True, 'import matplotlib.pyplot as plt\n'), ((5745, 5759), 'matplotlib.pyplot.grid', 'plt.grid', (['(True)'], {}), '(True)\n', (5753, 5759), True, 'import matplotlib.pyplot as plt\n'), ((5835, 5863), 'matplotlib.pyplot.xlim', 'plt.xlim', (['(-2)', '(generation + 2)'], {}), '(-2, generation + 2)\n', (5843, 5863), True, 'import matplotlib.pyplot as plt\n'), ((5866, 5883), 'matplotlib.pyplot.ylim', 'plt.ylim', (['(50)', '(120)'], {}), '(50, 120)\n', (5874, 5883), True, 'import matplotlib.pyplot as plt\n'), ((5888, 5954), 'matplotlib.pyplot.plot', 'plt.plot', (['x', 'y'], {'label': '"""Pop Surveilled"""', 'linestyle': '"""--"""', 'marker': '"""o"""'}), "(x, y, label='Pop Surveilled', linestyle='--', marker='o')\n", (5896, 5954), True, 'import matplotlib.pyplot as plt\n'), ((5964, 5973), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (5971, 5973), True, 'import matplotlib.pyplot as plt\n'), ((6162, 6215), 'matplotlib.pyplot.savefig', 'plt.savefig', (['f"""./results/final_results_test{run}.png"""'], {}), "(f'./results/final_results_test{run}.png')\n", (6173, 6215), True, 'import matplotlib.pyplot as plt\n'), ((6529, 6538), 'matplotlib.pyplot.cla', 'plt.cla', ([], {}), '()\n', (6536, 6538), True, 'import matplotlib.pyplot as plt\n'), ((6543, 6557), 'matplotlib.pyplot.grid', 'plt.grid', (['(True)'], {}), '(True)\n', (6551, 6557), True, 'import matplotlib.pyplot as plt\n'), ((6674, 6684), 'numpy.mean', 'np.mean', (['y'], {}), '(y)\n', (6681, 6684), True, 'import numpy as np\n'), ((6749, 6801), 'matplotlib.pyplot.scatter', 'plt.scatter', (['x', 'y'], {'label': '"""Pop Surveilled"""', 'color': '"""r"""'}), "(x, y, label='Pop Surveilled', color='r')\n", (6760, 6801), True, 'import matplotlib.pyplot as plt\n'), ((6811, 6820), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (6818, 6820), True, 'import matplotlib.pyplot as plt\n'), ((7139, 7197), 'matplotlib.pyplot.savefig', 'plt.savefig', (['f"""./results/aggregate_result_GA_test_run.png"""'], {}), "(f'./results/aggregate_result_GA_test_run.png')\n", (7150, 7197), True, 'import matplotlib.pyplot as plt\n'), ((7202, 7212), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (7210, 7212), True, 'import matplotlib.pyplot as plt\n'), ((1185, 1210), 'numpy.random.randint', 'np.random.randint', (['(1)', '(100)'], {}), '(1, 100)\n', (1202, 1210), True, 'import numpy as np\n'), ((1245, 1270), 'numpy.random.randint', 'np.random.randint', (['(1)', '(100)'], {}), '(1, 100)\n', (1262, 1270), True, 'import numpy as np\n'), ((3145, 3164), 'numpy.copy', 'np.copy', (['new_pop[i]'], {}), '(new_pop[i])\n', (3152, 3164), True, 'import numpy as np\n'), ((4999, 5071), 'matplotlib.pyplot.Circle', 'plt.Circle', (['(cams[i][0], cams[i][1])', 'view_radius'], {'color': '"""r"""', 'fill': '(False)'}), "((cams[i][0], cams[i][1]), view_radius, color='r', fill=False)\n", (5009, 5071), True, 'import matplotlib.pyplot as plt\n'), ((5107, 5116), 'matplotlib.pyplot.gca', 'plt.gca', ([], {}), '()\n', (5114, 5116), True, 'import matplotlib.pyplot as plt\n'), ((5480, 5528), 'matplotlib.pyplot.savefig', 'plt.savefig', (['f"""./results/last_gen_test{run}.png"""'], {}), "(f'./results/last_gen_test{run}.png')\n", (5491, 5528), True, 'import matplotlib.pyplot as plt\n'), ((6253, 6267), 'matplotlib.pyplot.pause', 'plt.pause', (['(0.5)'], {}), '(0.5)\n', (6262, 6267), True, 'import matplotlib.pyplot as plt\n'), ((6276, 6286), 'matplotlib.pyplot.draw', 'plt.draw', ([], {}), '()\n', (6284, 6286), True, 'import matplotlib.pyplot as plt\n'), ((6305, 6315), 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{}), '((cam[0] - cit[0]) ** 2 + (cam[1] - cit[1]) ** 2)\n', (1969, 2018), False, 'import math\n'), ((3216, 3238), 'numpy.random.choice', 'np.random.choice', (['keys'], {}), '(keys)\n', (3232, 3238), True, 'import numpy as np\n'), ((3966, 3992), 'numpy.random.randint', 'np.random.randint', (['(-20)', '(20)'], {}), '(-20, 20)\n', (3983, 3992), True, 'import numpy as np\n'), ((4010, 4036), 'numpy.random.randint', 'np.random.randint', (['(-20)', '(20)'], {}), '(-20, 20)\n', (4027, 4036), True, 'import numpy as np\n'), ((4607, 4616), 'matplotlib.pyplot.gcf', 'plt.gcf', ([], {}), '()\n', (4614, 4616), True, 'import matplotlib.pyplot as plt\n')] |
####################################################################
# Animus AI Developed by <NAME>ated 13th March 2018 #
####################################################################
'''
THIS PROGRAM IS INTENDED FOR USE UNDER SUPERVISION OF HUMAN
INTERVENTION. THE SKILL ADDS CAPABILITY OF SCANNING AND ACCESSING
DEVICES ON THE NETWORK. NEITHER THE CREATOR OR THE DEVELOPERS OF
ANIMUS AI ARE RESPONSIBLE FOR MISUSE OF THIS CODE FOR MALITIOUS
PURPOSES. ANIMUS AI HAS THE CAPABILITY TO SCAN FOR DEVICES ON THE
NETWORK TO ENABLE IT TO INTERACT WITH OTHER DEVICES. PLEASE USE
WISELY AND AT YOUR OWN DISCRETION.
'''
#GoogleSkill.py
#from Summarize import search_shorten
from .EchoSkill import echo
def google(search):
echo("Sir, I am Searching Google for " + search)
from googlesearch.googlesearch import GoogleSearch
response = GoogleSearch().search(search, num_results = 2)
for result in response.results:
echo("Title: " + result.title)
# search_shorten(result.getText())
# echo("Content: " + result.getText())
return result.getText()
######################################################################
#System wide google search implementation through the google skill in#
#Animus AI #
#Copyright <NAME> #
######################################################################
| [
"googlesearch.googlesearch.GoogleSearch"
] | [((874, 888), 'googlesearch.googlesearch.GoogleSearch', 'GoogleSearch', ([], {}), '()\n', (886, 888), False, 'from googlesearch.googlesearch import GoogleSearch\n')] |
'''
Copyright (c) The Dojo Foundation 2011. All Rights Reserved.
Copyright (c) IBM Corporation 2008, 2011. All Rights Reserved.
'''
# tornado
import tornado.ioloop
# std lib
import logging
import time
# coweb
from .session import BayeuxSession
from .connection import BayeuxConnection
from .channel import BayeuxChannel
log = logging.getLogger('bayeux.server')
class BayeuxManager(object):
'''Base class for a Bayeux manager tracking known clients.'''
def __init__(self, purgeInterval=15, deadAfter=60, exts=[],
client_cls=BayeuxSession, connection_cls=BayeuxConnection):
self.deadAfter = deadAfter
self.timeout = int(self.deadAfter/2.0)
self._clientCls = client_cls
self._connectionCls = connection_cls
self._root = BayeuxChannel('/')
self._clients = {}
self._flushable = set()
self._ioloop = tornado.ioloop.IOLoop.instance()
self._purgeGen = None
if purgeInterval is None:
self._timer = None
else:
self._timer = tornado.ioloop.PeriodicCallback(self.purge_clients,
purgeInterval*1000)
self._timer.start()
self._exts = exts
self._willFlush = False
def destroy(self):
'''Destroyes the Bayeux manager and deletes its known clients'''
# stop the purge timer
if self._timer is not None:
# do after in case we're in the callback to prevent re-registration
# due to tornado bug
self._ioloop.add_callback(self._timer.stop)
# force the callback reference to None, because stop doesn't
# remove the reference from the ioloop, else gc impeded until
# next callback occurs, even though stopped
self._timer.callback = None
self._timer = None
self._purgeGen = None
# purge all clients
cids = list(self._clients.keys())
for cid in cids:
cl = self.delete_client(cid)
cl.destroy()
self._clients = {}
def flush(self):
'''Flushes all outgoing client messages immediately.'''
self._willFlush = False
leftover = set()
while self._flushable:
cl = self._flushable.pop()
if not cl.on_flush():
leftover.add(cl)
# don't reschedule flush now, next connection will do it
self._flushable = leftover
def should_flush(self, cl):
'''
Marks a client as having outgoing messages that should be flushed.
'''
self._flushable.add(cl)
# schedule the flush if not one already pending
if not self._willFlush:
self._ioloop.add_callback(self.flush)
self._willFlush = True
def publish(self, msg):
'''Publishes a message to all clients subscribed to its channel.'''
# strip off client ID before sending out to other clients
try:
senderId = msg['clientId']
del msg['clientId']
except KeyError:
senderId = None
try:
del msg['id']
except KeyError:
pass
ch = msg.get('channel', None)
# find all subscribed clients
clients = set()
self._root.collect_clients(clients, BayeuxChannel.split(ch))
for cl in clients:
cl.add_message(msg, senderId)
def get_root_channel(self):
'''Gets the root / BayeuxChannel instance.'''
return self._root
def build_connection(self, handler):
'''
Builds a BayeuxConnection instance to represent a single connection
over a negotiated transport from a client.
'''
return self._connectionCls(handler, self)
def new_client(self):
'''
Builds a new BayeuxSession instance to represent a continuing session
with a client.'''
c = self._clientCls(self, exts=self._exts)
self._clients[c.clientId] = c
return c
def is_client(self, cid):
'''Gets if the given client ID is one for a known client.'''
return cid in self._clients
def get_client(self, cid):
'''Gets the client associated with the given client ID.'''
return self._clients[cid]
def delete_client(self, cid):
'''Deletes the client assocaited with the given client ID.'''
cl = self._clients[cid]
del self._clients[cid]
try:
self.on_purging_client(cid, cl)
except Exception:
log.exception('purge delegate')
return cl
def purge_clients(self):
'''
Purges clients that have not performed any action within the
configured self.deadAfter interval in seconds.
'''
if not self._purgeGen:
# build a new purge generator
self._purgeGen = self._purge_clients()
try:
# iterate the generator
next(self._purgeGen)
except StopIteration:
# purge complete, toss the generator
self._purgeGen = None
else:
# purge incomplete, schedule for immediately continuation
self._ioloop.add_callback(self.purge_clients)
def _purge_clients(self, timeout=2, blockSize=100):
# snapshot the clients in the dict
cids = list(self._clients.keys())
# get the current time
now = time.time()
for i, cid in enumerate(cids):
cl = self._clients[cid]
if cl.lastSeen is None:
self.delete_client(cid)
cl.destroy()
continue
dt = now - cl.lastSeen
if dt > self.deadAfter:
cl = self.delete_client(cid)
cl.destroy()
if i % blockSize == 0 and time.time() - now > timeout:
# we're taking too long, yield
yield
def on_purging_client(self, cid, cl):
'''
Called after this manager stops tracking a client but before the
BayeuxSession instace for the client is destroyed (i.e., it's
final messages flushed, sockets closed, etc.) No expected return
value.
'''
pass
| [
"logging.getLogger",
"time.time"
] | [((327, 361), 'logging.getLogger', 'logging.getLogger', (['"""bayeux.server"""'], {}), "('bayeux.server')\n", (344, 361), False, 'import logging\n'), ((5462, 5473), 'time.time', 'time.time', ([], {}), '()\n', (5471, 5473), False, 'import time\n'), ((5862, 5873), 'time.time', 'time.time', ([], {}), '()\n', (5871, 5873), False, 'import time\n')] |
"""Consumer responsible for writing to stackdriver and associated helpers."""
import logging
from datetime import tzinfo, timedelta, datetime
class _FixedOffsetTimeZone(tzinfo):
"""Hack for dealing w/ lack of %z in 2.7 strptime.
See https://docs.python.org/2/library/datetime.html#datetime.tzinfo.fromutc
"""
def __init__(self, offset):
self.__offset = timedelta(seconds=offset)
self.__name = 'UTC%+is' % (offset)
def utcoffset(self, dt):
_ = dt
return self.__offset
def tzname(self, dt):
_ = dt
return self.__name
def dst(self, dt):
_ = dt
return timedelta(0)
class NginxAccessLogConsumer(object):
"""Consumes nginx log lines and exports to custom stackdriver metrics.
Currently only supports exporting request counts by status code.
"""
NGINX_BASE_TIMESTAMP_FORMAT = '%d/%b/%Y:%H:%M:%S'
def __init__(self, client, resource, http_response_metric_name):
"""Initialize NginxAccessLogConsumer.
Args:
client: cloud monitoring client.
resource: resource object identifying the monitored instance.
"""
self._client = client
self._resource = resource
self._reset_time_utc = datetime.utcnow()
self._reset_time_utc_offset = self._reset_time_utc.replace(
tzinfo=_FixedOffsetTimeZone(0))
self._response_codes = {}
self._response_code_metrics = {}
self._has_delta = False
self._http_response_metric_name = http_response_metric_name
def _parse_nginx_timestamp(self, ts_str):
"""Parse the provided timestamp string.
Args:
ts_str: nginx format timestamp string '02/Jul/2017:00:00:00 +0000'
Returns:
datetime object or None if the timestamp could not be parsed.
"""
try:
base, offset = ts_str.split(' ')
base_dt = datetime.strptime(base, self.NGINX_BASE_TIMESTAMP_FORMAT)
except ValueError:
return None
return base_dt.replace(tzinfo=_FixedOffsetTimeZone(int(offset)))
def reset_time_utc(self):
"""Returns the time relative to which metric counters are registered.
Returns:
datetime object relative to UTC (no tzinfo).
"""
return self._reset_time_utc
def record(self, parsed_groups):
"""Record supported metrics from the parsed log line.
Args:
parsed_groups: dict of str => str elements from an nginx access log
line; only relevant fields are datetime and statuscode.
"""
log_time = self._parse_nginx_timestamp(parsed_groups['datetime'])
if log_time is None:
logging.warn('Could not parse datetime: "%s"',
parsed_groups['datetime'])
return
if log_time < self._reset_time_utc_offset:
return
try:
code = int(parsed_groups['statuscode'])
except ValueError:
logging.warn('Could not parse statuscode: "%s"',
parsed_groups['statuscode'])
return
if code not in self._response_codes:
self._response_codes[code] = 0
self._response_codes[code] += 1
self._has_delta = True
def commit(self):
"""Write the supported metrics to cloud monitoring."""
if self._has_delta:
logging.info('Writing updated counters to %s: %s',
self._http_response_metric_name,
str(self._response_codes))
for code, count in self._response_codes.iteritems():
if code not in self._response_code_metrics:
self._response_code_metrics[code] = self._client.metric(
type_=self._http_response_metric_name,
labels={'response_code': str(code)})
self._client.write_point(
self._response_code_metrics[code],
self._resource,
count,
start_time=self._reset_time_utc)
self._has_delta = False
| [
"datetime.datetime.strptime",
"datetime.timedelta",
"logging.warn",
"datetime.datetime.utcnow"
] | [((382, 407), 'datetime.timedelta', 'timedelta', ([], {'seconds': 'offset'}), '(seconds=offset)\n', (391, 407), False, 'from datetime import tzinfo, timedelta, datetime\n'), ((648, 660), 'datetime.timedelta', 'timedelta', (['(0)'], {}), '(0)\n', (657, 660), False, 'from datetime import tzinfo, timedelta, datetime\n'), ((1262, 1279), 'datetime.datetime.utcnow', 'datetime.utcnow', ([], {}), '()\n', (1277, 1279), False, 'from datetime import tzinfo, timedelta, datetime\n'), ((1936, 1993), 'datetime.datetime.strptime', 'datetime.strptime', (['base', 'self.NGINX_BASE_TIMESTAMP_FORMAT'], {}), '(base, self.NGINX_BASE_TIMESTAMP_FORMAT)\n', (1953, 1993), False, 'from datetime import tzinfo, timedelta, datetime\n'), ((2736, 2809), 'logging.warn', 'logging.warn', (['"""Could not parse datetime: "%s\\""""', "parsed_groups['datetime']"], {}), '(\'Could not parse datetime: "%s"\', parsed_groups[\'datetime\'])\n', (2748, 2809), False, 'import logging\n'), ((3028, 3105), 'logging.warn', 'logging.warn', (['"""Could not parse statuscode: "%s\\""""', "parsed_groups['statuscode']"], {}), '(\'Could not parse statuscode: "%s"\', parsed_groups[\'statuscode\'])\n', (3040, 3105), False, 'import logging\n')] |
#! /usr/bin/env python3
"""
%(prog)s takes a fault injection executable and executes it
Usage: %(prog)s --CLI/--GUI <fault injection executable> <the same options that you use to run the excutable before>
%(prog)s --help(-h): show help information
Prerequisite:
0. You need to specify --CLI or --GUI depending on whether you're invoking it from the command line or from the GUI.
1. You need to be at the parent directory of the <fault injection executable> to invoke %(prog)s. This is to make it easier for LLFI to track the outputs generated by <fault injection executable>
2. (prog)s only checks recursively at the current directory for possible outputs, if your output is not under current directory, you need to store that output by yourself
3. You need to put your input files (if any) under current directory
4. You need to have 'input.yaml' under your current directory, which contains appropriate options for LLFI.
"""
# This script injects faults the program and produces output
# This script should be run after the profiling step
import sys, os, subprocess
import yaml
import time
import random
import shutil
from subprocess import TimeoutExpired
runOverride = False
optionlist = []
defaultTimeout = 500
fi_max_multiple_default = 100
# basedir is assigned in parseArgs(args)
basedir = ""
prog = os.path.basename(sys.argv[0])
fi_exe = ""
options = {
"verbose": False,
}
def usage(msg = None):
retval = 0
if msg is not None:
retval = 1
msg = "ERROR: " + msg
print(msg, file=sys.stderr)
print(__doc__ % globals(), file=sys.stderr)
sys.exit(retval)
def parseArgs(args):
global optionlist, fi_exe
if args[0] == "--help" or args[0] == "-h":
usage()
fi_exe = os.path.realpath(args[0])
basedir = basedir = os.path.abspath(os.path.dirname(os.path.dirname(fi_exe)))
optionlist = args[1:]
# remove the directory prefix for input files, this is to make it easier for the program
# to take a snapshot
for index, opt in enumerate(optionlist):
if os.path.isfile(opt):
if os.path.realpath(os.path.dirname(opt)) != basedir:
usage("File %s passed through option is not under current directory" % opt)
else:
optionlist[index] = os.path.basename(opt)
if basedir != os.getcwd():
print("Change directory to:", basedir)
os.chdir(basedir)
def checkInputYaml():
global doc
global defaultTimeout
#Check for input.yaml's presence
yamldir = os.path.dirname(os.path.dirname(fi_exe))
try:
f = open(os.path.join(basedir, 'input.yaml'),'r')
except:
usage("No input.yaml file in the parent directory of fault injection executable")
exit(1)
#Check for input.yaml's correct formmating
try:
doc = yaml.load(f)
except:
f.close()
usage("input.yaml is not formatted in proper YAML (reminder: use spaces, not tabs)")
exit(1)
finally:
f.close()
if "kernelOption" in doc:
for opt in doc["kernelOption"]:
if opt=="forceRun":
runOverride = True
print("Kernel: Forcing run")
if "defaultTimeout" in doc:
defaultTimeout = int(doc["defaultTimeout"])
assert defaultTimeout > 0, "The timeOut option must be greater than 0"
else:
print("Default timeout is set to " + str(defaultTimeout) + " by default.")
def print_progressbar(idx, nruns):
pct = (float(idx) / float(nruns))
WIDTH = 50
bar = "=" * int(pct * WIDTH)
bar += ">"
bar += "-" * (WIDTH - int(pct * WIDTH))
print(("\r[%s] %.1f%% (%d / %d)" % (bar, pct * 100, idx, nruns)), end='\n')
sys.stdout.flush()
################################################################################
def config():
global inputdir, outputdir, errordir, stddir, llfi_stat_dir
# config
llfi_dir = os.path.dirname(fi_exe)
inputdir = os.path.join(llfi_dir, "prog_input")
outputdir = os.path.join(llfi_dir, "prog_output")
errordir = os.path.join(llfi_dir, "error_output")
stddir = os.path.join(llfi_dir, "std_output")
llfi_stat_dir = os.path.join(llfi_dir, "llfi_stat_output")
if not os.path.isdir(outputdir):
os.mkdir(outputdir)
if not os.path.isdir(errordir):
os.mkdir(errordir)
if not os.path.isdir(inputdir):
os.mkdir(inputdir)
if not os.path.isdir(stddir):
os.mkdir(stddir)
if not os.path.isdir(llfi_stat_dir):
os.mkdir(llfi_stat_dir)
################################################################################
def execute( execlist, timeout):
global outputfile
global return_codes
print(' '.join(execlist))
#get state of directory
dirSnapshot()
print(execlist)
p = subprocess.Popen(execlist, stdout = subprocess.PIPE)
outputFile = open(outputfile, "wb")
program_timed_out = False
start_time = 0
elapsetime = 0
#communicate() will block until program exits or until timeout is reached
try:
start_time = time.time()
(p_stdout,p_stderr) = p.communicate(timeout=timeout)
elapsetime = int(time.time() - start_time + 1)
except TimeoutExpired: #Child process timed out
p.kill() #Need to kill the process and then clean up commmunication
(p_stdout,p_stderr) = p.communicate(timeout=timeout)
program_timed_out = True
moveOutput()
if program_timed_out:
print("\tParent : Child timed out. Cleaning up ... ")
else:
print("\t program finish", p.returncode)
print("\t time taken", elapsetime,"\n")
outputFile = open(outputfile, "wb")
if program_timed_out:
outputFile.write(
bytes("\n\n ### Process killed by LLFI for timing out ###\n","UTF-8"))
outputFile.write(p_stdout)
if program_timed_out:
outputFile.write(
bytes("\n\n ### Process killed by LLFI for timing out ###\n","UTF-8"))
outputFile.close()
replenishInput() #for cases where program deletes input or alters them each run
# Keep a dict of all return codes received.
if program_timed_out:
if "TO" in return_codes:
return_codes["TO"] += 1
else:
return_codes["TO"] = 1
else:
if p.returncode in return_codes:
return_codes[p.returncode] += 1
else:
return_codes[p.returncode] = 1
if program_timed_out:
return "timed-out"
else:
return str(p.returncode)
################################################################################
def storeInputFiles():
global inputList
inputList=[]
##========Consider comma as separator of arguments ==================================
temp_optionlist = []
for item in optionlist:
if item.count(',') == 0:
temp_optionlist.append(item)
else:
temp_optionlist.extend(item.split(','))
##===================================================================================
for opt in temp_optionlist:
if os.path.isfile(opt):#stores all files in inputList and copy over to inputdir
shutil.copy2(opt, os.path.join(inputdir, opt))
inputList.append(opt)
################################################################################
def replenishInput():#TODO make condition to skip this if input is present
for each in inputList:
if not os.path.isfile(each):#copy deleted inputfiles back to basedir
shutil.copy2(os.path.join(inputdir, each), each)
################################################################################
def moveOutput():
#move all newly created files
newfiles = [_file for _file in os.listdir(".")]
for each in newfiles:
if each not in dirBefore:
fileSize = os.stat(each).st_size
if fileSize == 0 and each.startswith("llfi"):
#empty library output, can delete
print(each+ " is going to be deleted for having size of " + str(fileSize))
os.remove(each)
else:
flds = each.split(".")
newName = '.'.join(flds[0:-1])
newName+='.'+run_id+'.'+flds[-1]
if newName.startswith("llfi"):
os.rename(each, os.path.join(llfi_stat_dir, newName))
else:
os.rename(each, os.path.join(outputdir, newName))
################################################################################
def dirSnapshot():
#snapshot of directory before each execute() is performed
global dirBefore
dirBefore = [_file for _file in os.listdir(".")]
################################################################################
def readCycles():
global totalcycles
profinput= open("llfi.stat.prof.txt","r")
while 1:
line = profinput.readline()
if line.strip():
if line[0] == 't':
label, totalcycles = line.split("=")
break
profinput.close()
################################################################################
def checkValues(key, val, var1 = None,var2 = None,var3 = None,var4 = None):
#preliminary input checking for fi options
#also checks for fi_bit usage by non-kernel users
#optional var# are used for fi_bit's case only
if key =='run_number':
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val)>0, key+" must be greater than 0 in input.yaml"
elif key == 'fi_type':
pass
##======== Add number of corrupted bits QINING @MAR 13th========
elif key == 'fi_num_bits':
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >=1, key+" must be greater than or equal to 1 in input.yaml"
##==============================================================
##======== Add second corrupted regs QINING @MAR 27th===========
elif key == "window_len":
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >=0, key+" must be greater than or equal to zero in input.yaml"
##==================================================================
##BEHROOZ: Add max number of target locations
elif key == "fi_max_multiple":
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >1, key+" must be greater than one in input.yaml"
assert int(val) <=int(fi_max_multiple_default), key+" must be smaller than or equal to "+str(fi_max_multiple_default)+ " in input.yaml"
##==============================================================
##BEHROOZ: Add multiple corrupted regs
elif key == "window_len_multiple":
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >0, key+" must be greater than zero in input.yaml"
elif key == "window_len_multiple_startindex":
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >0, key+" must be greater than zero in input.yaml"
elif key == "window_len_multiple_endindex":
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >0, key+" must be greater than zero in input.yaml"
##==============================================================
elif key == 'fi_cycle':
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
##BEHROOZ: I changed the below line to the current one to fix the fi_cycle
assert int(val) > 0, key+" must be greater than 0 in input.yaml"
#assert int(val) >= 0, key+" must be greater than or equal to 0 in input.yaml"
assert int(val) <= int(totalcycles), key +" must be less than or equal to "+totalcycles.strip()+" in input.yaml"
elif key == 'fi_reg_index':
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >= 0, key+" must be greater than or equal to 0 in input.yaml"
elif key == 'fi_bit':
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >= 0, key+" must be greater than or equal to 0 in input.yaml"
if runOverride:
pass
elif var1 != None and var1 > 1 and (var2 or var3) and var4:
user_input = input("\nWARNING: Injecting into the same cycle(index), bit multiple times "+
"is redundant as it would yield the same result."+
"\nTo turn off this warning, please see Readme "+
"for kernel mode.\nDo you wish to continue anyway? (Y/N)\n ")
if user_input.upper() =="Y":
pass
else:
exit(1)
elif key == 'fi_random_seed':
assert isinstance(val, int)==True, key+" must be an integer in input.yaml"
assert int(val) >= 0, key+" must be greater than or equal to 0 in input.yaml"
################################################################################
def main(args):
global optionlist, outputfile, totalcycles,run_id, return_codes
global defaultTimeout
fi_indexs=[]
parseArgs(args)
checkInputYaml()
config()
# get total num of cycles
readCycles()
storeInputFiles()
#Set up each config file and its corresponding run_number
try:
rOpt = doc["runOption"]
except:
print("ERROR: Please include runOption in input.yaml.")
exit(1)
if not os.path.isfile(fi_exe):
print("ERROR: The executable "+ fi_exe+" does not exist.")
print("Please build the executables with create-executables.\n")
exit(1)
else:
print("======Fault Injection======")
for ii, run in enumerate(rOpt):
# Maintain a dict of all return codes received and print summary at end
return_codes = {}
# Put an empty line between configs
if ii > 0:
print("")
print("---FI Config #"+str(ii)+"---")
if "numOfRuns" not in run["run"]:
print("ERROR: Must include a run number per fi config in input.yaml.")
exit(1)
if "timeOut" in run["run"]:
timeout = int(run["run"]["timeOut"])
assert timeout > 0, "The timeOut option must be greater than 0"
else:
timeout = defaultTimeout
print("Run with default timeout " + str(timeout))
run_number=run["run"]["numOfRuns"]
checkValues("run_number", run_number)
# check for verbosity option, set at the FI run level
if "verbose" in run["run"]:
options["verbose"] = run["run"]["verbose"]
# reset all configurations
if 'fi_type' in locals():
del fi_type
if 'loop_number' in locals():
del loop_number
if 'loop_start' in locals():
del loop_start
if 'fi_cycle' in locals():
del fi_cycle
if 'fi_index' in locals():
del fi_index
if 'fi_reg_index' in locals():
del fi_reg_index
if 'fi_bit' in locals():
del fi_bit
##======== Add number of corrupted bits QINING @MAR 13th========
if 'fi_num_bits' in locals():
del fi_num_bits
##==============================================================
##======== Add second corrupted regs QINING @MAR 27th===========
if 'window_len' in locals():
del window_len
##==============================================================
if 'fi_random_seed' in locals():
del fi_random_seed
##==============================================================
##BEHROOZ: Add max number of target locations
if 'fi_max_multiple' in locals():
del fi_max_multiple
##==============================================================
##BEHROOZ: Add multiple corrupted regs
if 'window_len_multiple' in locals():
del window_len_multiple
if 'window_len_multiple_startindex' in locals():
del window_len_multiple_startindex
if 'window_len_multiple_endindex' in locals():
del window_len_multiple_endindex
##==============================================================
#write new fi config file according to input.yaml
num_of_data=1
if "num_of_data" in run["run"]:
num_of_data=run["run"]["num_of_data"]
if "fi_type" in run["run"]:
fi_type=run["run"]["fi_type"]
if fi_type == "SoftwareFault" or fi_type == "AutoInjection" or fi_type == "Automated":
try:
cOpt = doc["compileOption"]
injectorname = cOpt["instSelMethod"][0]["customInstselector"]["include"][0]
except:
print("\n\nERROR: Cannot extract fi_type from instSelMethod. Please check the customInstselector field in input.yaml\n")
else:
fi_type = injectorname
checkValues("fi_type",fi_type)
##======== Add number of corrupted bits QINING @MAR 13th========
if "fi_num_bits" in run["run"]:
fi_num_bits=run["run"]["fi_num_bits"]
checkValues("fi_num_bits", fi_num_bits)
##==============================================================
##======== Add second corrupted regs QINING @MAR 27th===========
if 'window_len' in run["run"]:
window_len=run["run"]["window_len"]
checkValues("window_len", window_len)
##==============================================================
##BEHROOZ: Add max number of target locations
if 'fi_max_multiple' in run["run"]:
fi_max_multiple=run["run"]["fi_max_multiple"]
checkValues("fi_max_multiple", fi_max_multiple)
if ('fi_max_multiple' in locals()) and 'window_len' in locals():
print(("\nERROR: window_len and fi_max_multiple cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
##==============================================================
##BEHROOZ: Add multiple corrupted regs
if 'window_len_multiple' in run["run"]:
window_len_multiple=run["run"]["window_len_multiple"]
checkValues("window_len_multiple", window_len_multiple)
if ('window_len_multiple' in locals()):
if ('window_len' in run["run"]):
print(("\nERROR: window_len and window_len_multiple cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
elif ('window_len_multiple_startindex' in run["run"]):
print(("\nERROR: window_len_multiple_startindex and window_len_multiple cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
elif ('window_len_multiple_endindex' in run["run"]):
print(("\nERROR: window_len_multiple_endindex and window_len_multiple cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
if 'window_len_multiple_startindex' in run["run"]:
window_len_multiple_startindex=run["run"]["window_len_multiple_startindex"]
checkValues("window_len_multiple_startindex", window_len_multiple_startindex)
if ('window_len_multiple_startindex' in locals()):
if ('window_len' in run["run"]):
print(("\nERROR: window_len and window_len_multiple_startindex cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
elif ('window_len_multiple' in run["run"]):
print(("\nERROR: window_len_multiple_startindex and window_len_multiple cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
elif ('window_len_multiple_endindex' not in run["run"]):
print(("\nERROR: window_len_multiple_startindex should come with window_len_multiple_endindex."
" Please specify both."))
exit(1)
if 'window_len_multiple_endindex' in run["run"]:
window_len_multiple_endindex=run["run"]["window_len_multiple_endindex"]
checkValues("window_len_multiple_endindex", window_len_multiple_endindex)
if ('window_len_multiple_endindex' in locals()):
if('window_len' in run["run"]):
print(("\nERROR: window_len and window_len_multiple_endindex cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
elif('window_len_multiple' in run["run"]):
print(("\nERROR: window_len_multiple_endindex and window_len_multiple cannot be specified"
" at the same time in the input.yaml file. Please choose one."))
exit(1)
elif('window_len_multiple_startindex' not in run["run"]):
print(("\nERROR: window_len_multiple_endindex should come with window_len_multiple_startindex."
" Please specify both."))
exit(1)
##==============================================================
if "fi_cycle" in run["run"]:
fi_cycle=run["run"]["fi_cycle"]
checkValues("fi_cycle",fi_cycle)
##================UPDATE of LCFI================================
if "fi_index" in run["run"] and num_of_data == 1:
fi_index=run["run"]["fi_index"][0]
checkValues("fi_index",fi_index)
if "loop_start" in run["run"]:
loop_start=run["run"]["loop_start"]
checkValues("loop_start",loop_start)
if "loop_number" in run["run"]:
loop_number=run["run"]["loop_number"]
checkValues("loop_start",loop_number)
if num_of_data >= 1:
fi_index=run["run"]["fi_index"][0]
for i in range(num_of_data):
fi_indexs.append(run["run"]["fi_index"][i])
##================UPDATE of LCFI================================
if "fi_reg_index" in run["run"]:
fi_reg_index=run["run"]["fi_reg_index"]
checkValues("fi_reg_index",fi_reg_index)
if "fi_bit" in run["run"]:
fi_bit=run["run"]["fi_bit"]
checkValues("fi_bit",fi_bit,run_number,fi_cycle,fi_index,fi_reg_index)
if "fi_random_seed" in run["run"]:
fi_random_seed=run["run"]["fi_random_seed"]
checkValues("fi_random_seed",fi_random_seed)
if ('fi_cycle' not in locals()) and 'fi_index' in locals():
print(("\nINFO: You choose to inject faults based on LLFI index, "
"this will inject into every runtime instruction whose LLFI "
"index is %d\n" % fi_index))
##BEHROOZ:
if ('window_len_multiple' in locals() or 'window_len_multiple_startindex' in locals() or 'window_len_multiple_endindex' in locals()):
if('fi_max_multiple' not in locals()):
print(("\nINFO: You choose a window length for multiple bit-flip injection, "
"however you have not specified the maximum number of locations."
" Thus, the maximum number of locations will be chosen as " +str(fi_max_multiple_default)+ ".\n"))
fi_max_multiple = int(fi_max_multiple_default)
if ('window_len_multiple' not in locals() and 'window_len_multiple_startindex' not in locals()) and 'fi_max_multiple' in locals():
print(("\nINFO: You choose the maximum number of multiple bit injection, "
"however you have not specified the window length for multiple bit-flip injection."
" Thus, the window size will be chosen equal to the total number of cycles-1= "
+ str(int(totalcycles)-1)+ ".\n"))
window_len_multiple = int(totalcycles) - 1
##======================================================
need_to_calc_fi_cycle = True
if ('fi_cycle' in locals()) or 'fi_index' in locals():
need_to_calc_fi_cycle = False
# fault injection
for index in range(0, run_number):
run_id = str(ii)+"-"+str(index)
outputfile = stddir + "/std_outputfile-" + "run-"+run_id
errorfile = errordir + "/errorfile-" + "run-"+run_id
execlist = [fi_exe]
if('fi_cycle' not in locals() and 'fi_random_seed' in locals()):
random.seed(fi_random_seed)
if need_to_calc_fi_cycle:
##BEHROOZ: I changed the below line to the current one to fix the fi_cycle
fi_cycle = random.randint(1, int(totalcycles))
##fi_cycle = random.randint(0, int(totalcycles) - 1)
ficonfig_File = open("llfi.config.runtime.txt", 'w')
if 'fi_cycle' in locals():
ficonfig_File.write("fi_cycle="+str(fi_cycle)+'\n')
##================UPDATE of LCFI================================
#elif 'fi_index' in locals() and num_of_data == 1:
# ficonfig_File.write("fi_index="+str(fi_index)+'\n')
elif 'fi_index' in locals():
#print(num_of_data)
for i in range(num_of_data):
ficonfig_File.write("fi_index="+str(fi_indexs[i])+'\n')
if 'loop_start' in locals():
ficonfig_File.write("loop_start="+str(loop_start)+'\n')
if 'loop_number' in locals():
ficonfig_File.write("loop_number="+str(loop_number)+'\n')
##================UPDATE of LCFI================================
if 'fi_type' in locals():
ficonfig_File.write("fi_type="+fi_type+'\n')
if 'fi_reg_index' in locals():
ficonfig_File.write("fi_reg_index="+str(fi_reg_index)+'\n')
if 'fi_bit' in locals():
ficonfig_File.write("fi_bit="+str(fi_bit)+'\n')
##======== Add number of corrupted bits QINING @MAR 13th========
if 'fi_num_bits' in locals():
ficonfig_File.write("fi_num_bits="+str(fi_num_bits)+'\n')
##==============================================================
##======== Add second corrupted regs QINING @MAR 27th===========
if 'window_len' in locals():
##BEHROOZ: I changed the below line to the current one to fix the fi_cycle
fi_second_cycle = min(fi_cycle + random.randint(1, int(window_len)), int(totalcycles))
#fi_second_cycle = min(fi_cycle + random.randint(1, int(window_len)), int(totalcycles) - 1)
ficonfig_File.write("fi_second_cycle="+str(fi_second_cycle)+'\n')
##==================================================================
##BEHROOZ: Add max number of target locations
if ('fi_max_multiple' in locals()):
win_start_index = 1
win_end_index = 1
if('window_len_multiple' in locals()):
win_end_index = int(window_len_multiple)
elif('window_len_multiple_startindex' in locals() and 'window_len_multiple_endindex' in locals()):
win_start_index = window_len_multiple_startindex
win_end_index = window_len_multiple_endindex
if(win_start_index > win_end_index):
print(("\nERROR: In the yaml file, the window_len_multiple_startindex cannot be bigger than window_len_multiple_endindex!"))
exit(1)
#The line below has been substituted with the one below it. This way the maximum number injection is not selected randomly and is
#equal to the value specified by the user
##selected_num_of_injection = random.randint(1, int(fi_max_multiple))
ficonfig_File.write("fi_max_multiple="+str(fi_max_multiple)+'\n')
selected_num_of_injection = fi_max_multiple
##======The -1 here is because we have already selected the first location by choosing the fi-cycle
##===== and here we are looking for the remaining cycles.=================
fi_next_cycle = fi_cycle
for index_multiple in range(1, int(selected_num_of_injection)):
fi_next_cycle = min(fi_next_cycle + random.randint(win_start_index, win_end_index), int(totalcycles))
ficonfig_File.write("fi_next_cycle="+str(fi_next_cycle)+'\n')
if fi_next_cycle == int(totalcycles):
break
##==================================================================
ficonfig_File.close()
# print run index before executing. Comma removes newline for prettier
# formatting
execlist.extend(optionlist)
ret = execute(execlist, timeout)
if ret == "timed-out":
error_File = open(errorfile, 'w')
error_File.write("Program hang\n")
error_File.close()
elif int(ret) < 0:
error_File = open(errorfile, 'w')
error_File.write("Program crashed, terminated by the system, return code " + ret + '\n')
error_File.close()
elif int(ret) > 0:
error_File = open(errorfile, 'w')
error_File.write("Program crashed, terminated by itself, return code " + ret + '\n')
error_File.close()
# Print updates, print the number of injections finished
print_progressbar(index+1, run_number)
#print_progressbar(run_number, run_number)
print("") # progress bar needs a newline after 100% reached
# Print summary
if options["verbose"]:
print("========== SUMMARY ==========")
print("Return codes: (code:\toccurance)")
for r in list(return_codes.keys()):
print((" %3s: %5d" % (str(r), return_codes[r])))
################################################################################
if __name__=="__main__":
if len(sys.argv) == 1:
usage('Must provide the fault injection executable and its options')
exit(1)
main(sys.argv[1:])
| [
"yaml.load",
"sys.exit",
"os.remove",
"os.listdir",
"subprocess.Popen",
"os.path.isdir",
"os.mkdir",
"sys.stdout.flush",
"random.randint",
"os.path.isfile",
"os.path.dirname",
"time.time",
"os.path.join",
"random.seed",
"os.getcwd",
"os.path.realpath",
"os.chdir",
"os.path.basename... | [((1322, 1351), 'os.path.basename', 'os.path.basename', (['sys.argv[0]'], {}), '(sys.argv[0])\n', (1338, 1351), False, 'import sys, os, subprocess\n'), ((1579, 1595), 'sys.exit', 'sys.exit', (['retval'], {}), '(retval)\n', (1587, 1595), False, 'import sys, os, subprocess\n'), ((1715, 1740), 'os.path.realpath', 'os.path.realpath', (['args[0]'], {}), '(args[0])\n', (1731, 1740), False, 'import sys, os, subprocess\n'), ((3524, 3542), 'sys.stdout.flush', 'sys.stdout.flush', ([], {}), '()\n', (3540, 3542), False, 'import sys, os, subprocess\n'), ((3726, 3749), 'os.path.dirname', 'os.path.dirname', (['fi_exe'], {}), '(fi_exe)\n', (3741, 3749), False, 'import sys, os, subprocess\n'), ((3763, 3799), 'os.path.join', 'os.path.join', (['llfi_dir', '"""prog_input"""'], {}), "(llfi_dir, 'prog_input')\n", (3775, 3799), False, 'import sys, os, subprocess\n'), ((3814, 3851), 'os.path.join', 'os.path.join', (['llfi_dir', '"""prog_output"""'], {}), "(llfi_dir, 'prog_output')\n", 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import os
from pathlib import Path
import pytest
from jubox import JupyterNotebook, CodeCell
from nbformat.notebooknode import NotebookNode
def test_creation_from_file(notebook_file_simple):
#file = f"{notebook_folder}/nb_simple.ipynb"
file = notebook_file_simple
nb = JupyterNotebook(file)
assert nb.file == file
assert isinstance(nb.node, NotebookNode)
assert nb.node.cells[0].source == "# This is simple Jupyter Notebook"
def test_creation_from_pathlib_file(notebook_file_simple):
file = Path(notebook_file_simple)
nb = JupyterNotebook(file)
assert nb.file == file
assert isinstance(nb.node, NotebookNode)
assert nb.node.cells[0].source == "# This is simple Jupyter Notebook"
def test_creation_empty():
nb = JupyterNotebook()
assert isinstance(nb.node, NotebookNode)
def test_creation_list_of_cells():
nb = JupyterNotebook([
CodeCell("1 cell"),
CodeCell("2 cell"),
CodeCell("3 cell"),
])
assert isinstance(nb.node, NotebookNode)
assert len(nb.node.cells) == 3
assert isinstance(nb.node.cells[0], NotebookNode)
assert "1 cell" == nb.node.cells[0]["source"]
assert "2 cell" == nb.node.cells[1]["source"]
assert "3 cell" == nb.node.cells[2]["source"]
def test_creation_list_of_empty_cells():
nb = JupyterNotebook([
CodeCell(),
CodeCell(),
CodeCell(),
])
assert isinstance(nb.node, NotebookNode)
assert len(nb.node.cells) == 3
assert isinstance(nb.node.cells[0], NotebookNode)
def test_creation_another_notebook():
nb_orig = JupyterNotebook([
CodeCell("1 cell"),
CodeCell("2 cell"),
CodeCell("3 cell"),
])
nb = JupyterNotebook(nb_orig)
assert isinstance(nb.node, NotebookNode)
assert len(nb.node.cells) == 3
assert isinstance(nb.node.cells[0], NotebookNode)
assert nb.node is nb_orig.node | [
"jubox.JupyterNotebook",
"jubox.CodeCell",
"pathlib.Path"
] | [((284, 305), 'jubox.JupyterNotebook', 'JupyterNotebook', (['file'], {}), '(file)\n', (299, 305), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((523, 549), 'pathlib.Path', 'Path', (['notebook_file_simple'], {}), '(notebook_file_simple)\n', (527, 549), False, 'from pathlib import Path\n'), ((559, 580), 'jubox.JupyterNotebook', 'JupyterNotebook', (['file'], {}), '(file)\n', (574, 580), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((764, 781), 'jubox.JupyterNotebook', 'JupyterNotebook', ([], {}), '()\n', (779, 781), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1708, 1732), 'jubox.JupyterNotebook', 'JupyterNotebook', (['nb_orig'], {}), '(nb_orig)\n', (1723, 1732), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((898, 916), 'jubox.CodeCell', 'CodeCell', (['"""1 cell"""'], {}), "('1 cell')\n", (906, 916), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((926, 944), 'jubox.CodeCell', 'CodeCell', (['"""2 cell"""'], {}), "('2 cell')\n", (934, 944), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((954, 972), 'jubox.CodeCell', 'CodeCell', (['"""3 cell"""'], {}), "('3 cell')\n", (962, 972), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1343, 1353), 'jubox.CodeCell', 'CodeCell', ([], {}), '()\n', (1351, 1353), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1363, 1373), 'jubox.CodeCell', 'CodeCell', ([], {}), '()\n', (1371, 1373), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1383, 1393), 'jubox.CodeCell', 'CodeCell', ([], {}), '()\n', (1391, 1393), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1615, 1633), 'jubox.CodeCell', 'CodeCell', (['"""1 cell"""'], {}), "('1 cell')\n", (1623, 1633), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1643, 1661), 'jubox.CodeCell', 'CodeCell', (['"""2 cell"""'], {}), "('2 cell')\n", (1651, 1661), False, 'from jubox import JupyterNotebook, CodeCell\n'), ((1671, 1689), 'jubox.CodeCell', 'CodeCell', (['"""3 cell"""'], {}), "('3 cell')\n", (1679, 1689), False, 'from jubox import JupyterNotebook, CodeCell\n')] |
import spot
from crome_logic.specification.temporal import LTL
from crome_logic.tools.crome_io import save_to_file
from crome_logic.typelement.robotic import BooleanAction, BooleanSensor
from crome_logic.typeset import Typeset
from crome_synthesis.controller import Controller
from crome_synthesis.controller.synthesis import generate_controller
def example() -> None:
a = LTL(
_init_formula="G(F(sens))",
_typeset=Typeset({BooleanSensor(name="sens")}),
)
g = LTL(
_init_formula="G(sens -> act)",
_typeset=Typeset({BooleanAction(name="act")}),
)
controller = Controller(assumptions=a, guarantees=g)
controller.save("dot")
controller.save("lbtt")
def example_1() -> None:
a1: str = "G(F(a1))"
g1: str = "G(a1 <-> (b1 | c1))"
i1: str = "a1"
o1: str = "b1, c1"
realizable1, controller1, time1 = generate_controller(a1, g1, i1, o1)
print(f"\n\n{controller1}")
file_path = save_to_file(controller1, "controller_1")
automaton = spot.automaton(file_path)
dotfile = automaton.to_str(format="dot")
print(f"\n\n{dotfile}")
lbtt = automaton.to_str(format="lbtt")
print(f"\n\n{lbtt}")
sens: str = "G(F(sens))"
g2: str = "G(sens -> act)"
i2: str = "sens"
o2: str = "act"
realizable2, controller2, time2 = generate_controller(sens, g2, i2, o2)
print(f"\n\n{controller2}")
if __name__ == "__main__":
example()
| [
"crome_synthesis.controller.synthesis.generate_controller",
"crome_logic.typelement.robotic.BooleanSensor",
"crome_synthesis.controller.Controller",
"crome_logic.tools.crome_io.save_to_file",
"crome_logic.typelement.robotic.BooleanAction",
"spot.automaton"
] | [((614, 653), 'crome_synthesis.controller.Controller', 'Controller', ([], {'assumptions': 'a', 'guarantees': 'g'}), '(assumptions=a, guarantees=g)\n', (624, 653), False, 'from crome_synthesis.controller import Controller\n'), ((878, 913), 'crome_synthesis.controller.synthesis.generate_controller', 'generate_controller', (['a1', 'g1', 'i1', 'o1'], {}), '(a1, g1, i1, o1)\n', (897, 913), False, 'from crome_synthesis.controller.synthesis import generate_controller\n'), ((963, 1004), 'crome_logic.tools.crome_io.save_to_file', 'save_to_file', (['controller1', '"""controller_1"""'], {}), "(controller1, 'controller_1')\n", (975, 1004), False, 'from crome_logic.tools.crome_io import save_to_file\n'), ((1022, 1047), 'spot.automaton', 'spot.automaton', (['file_path'], {}), '(file_path)\n', (1036, 1047), False, 'import spot\n'), ((1331, 1368), 'crome_synthesis.controller.synthesis.generate_controller', 'generate_controller', (['sens', 'g2', 'i2', 'o2'], {}), '(sens, g2, i2, o2)\n', (1350, 1368), False, 'from crome_synthesis.controller.synthesis import generate_controller\n'), ((447, 473), 'crome_logic.typelement.robotic.BooleanSensor', 'BooleanSensor', ([], {'name': '"""sens"""'}), "(name='sens')\n", (460, 473), False, 'from crome_logic.typelement.robotic import BooleanAction, BooleanSensor\n'), ((562, 587), 'crome_logic.typelement.robotic.BooleanAction', 'BooleanAction', ([], {'name': '"""act"""'}), "(name='act')\n", (575, 587), False, 'from crome_logic.typelement.robotic import BooleanAction, BooleanSensor\n')] |
from . import db
from werkzeug.security import generate_password_hash,check_password_hash
from datetime import datetime
import time
from flask_login import UserMixin
from . import login_manager
@login_manager.user_loader
def load_user(user_id):
return User.query.get(int(user_id))
# @login_manager.user_loader
# def load_user(seller_id):
# return Seller.query.get(int(seller_id))
class User(db.Model,UserMixin):
__tablename__='users'
id=db.Column(db.Integer, primary_key=True)
username=db.Column(db.String)
email=db.Column(db.String)
pass_secure = db.Column(db.String(255))
phone=db.Column(db.String)
profile_picture_path=db.Column(db.String)
orders =db.relationship("Orders", backref="users", lazy="dynamic")
cart =db.relationship("Cart", backref="users", lazy="dynamic")
@property
def password(self):
raise AttributeError('You cannot read the password attribute')
@password.setter
def password(self,password):
self.pass_secure = generate_password_hash(password)
def verify_password(self,password):
return check_password_hash(self.pass_secure,password)
def __repr__(self):
return f'User{self.username}'
class Seller(db.Model,UserMixin):
__tablename__='sellers'
id=db.Column(db.Integer, primary_key=True)
username=db.Column(db.String)
bio=db.Column(db.String)
profile_pic_path=db.Column(db.String)
email=db.Column(db.String)
pass_secure=db.Column(db.String)
phone=db.Column(db.String)
products=db.relationship("Product",backref="sellers", lazy="dynamic")
orders =db.relationship("Orders", backref="sellers", lazy="dynamic")
@property
def password(self):
raise AttributeError('You cannot read the password attribute')
@password.setter
def password(self,password):
self.pass_secure = generate_password_hash(password)
def verify_password(self,password):
return check_password_hash(self.pass_secure,password)
def __repr__(self):
return f'Seller{self.username}'
class Product(db.Model):
__tablename__="products"
id=db.Column(db.Integer, primary_key=True)
product_name=db.Column(db.String)
product_picture=db.Column(db.String)
description=db.Column(db.String)
seller_id=db.Column(db.Integer, db.ForeignKey('sellers.id',ondelete='SET NULL'),nullable = True)
orders=db.relationship("Orders", backref="products", lazy="dynamic")
cart=db.relationship("Cart", backref="products", lazy="dynamic")
def save_new_product(self):
db.session.add(self)
db.session.commit()
def __repr__(self):
return f'Product{self.product}'
class Orders(db.Model):
__tablename__="orders"
id=db.Column(db.Integer, primary_key=True)
pizza_name=db.Column(db.String)
pizza_size=db.Column(db.String)
price=db.Column(db.Integer)
isAccepted=db.Column(db.Boolean,default=False, server_default="false")
product_id=db.Column(db.Integer, db.ForeignKey('products.id',ondelete='SET NULL'),nullable = True)
time=db.Column(db.DateTime(),default=datetime.utcnow)
user_id=db.Column(db.Integer, db.ForeignKey("users.id",ondelete='SET NULL'),nullable = True)
seller_id=db.Column(db.Integer, db.ForeignKey('sellers.id',ondelete='SET NULL'),nullable = True)
def add_order(self):
db.session.add(self)
db.session.commit()
def __repr__(self):
return f'Orders{self.pizza_name}'
class Cart(db.Model):
__tablename__="cart"
id=db.Column(db.Integer, primary_key=True)
product=db.Column(db.String)
product_picture=db.Column(db.String)
product_price = db.Column(db.Integer)
amount = db.Column(db.Integer,default=0)
size=db.Column(db.String)
product_cost = db.Column(db.Integer,default=0)
product_id=db.Column(db.Integer, db.ForeignKey('products.id',ondelete='SET NULL'),nullable = True)
user_id=db.Column(db.Integer, db.ForeignKey("users.id",ondelete='SET NULL'),nullable = True)
def add_item_to_cart(self):
db.session.add(self)
db.session.commit()
def __repr__(self):
return f'Cart{self.product}'
| [
"werkzeug.security.generate_password_hash",
"werkzeug.security.check_password_hash"
] | [((1023, 1055), 'werkzeug.security.generate_password_hash', 'generate_password_hash', (['password'], {}), '(password)\n', (1045, 1055), False, 'from werkzeug.security import generate_password_hash, check_password_hash\n'), ((1120, 1167), 'werkzeug.security.check_password_hash', 'check_password_hash', (['self.pass_secure', 'password'], {}), '(self.pass_secure, password)\n', (1139, 1167), False, 'from werkzeug.security import generate_password_hash, check_password_hash\n'), ((1893, 1925), 'werkzeug.security.generate_password_hash', 'generate_password_hash', (['password'], {}), '(password)\n', (1915, 1925), False, 'from werkzeug.security import generate_password_hash, check_password_hash\n'), ((1990, 2037), 'werkzeug.security.check_password_hash', 'check_password_hash', (['self.pass_secure', 'password'], {}), '(self.pass_secure, password)\n', (2009, 2037), False, 'from werkzeug.security import generate_password_hash, check_password_hash\n')] |
import logging
from django.db import connection
from prometheus_client import Gauge
from zentral.utils.prometheus import BasePrometheusMetricsView
from .models import Status
logger = logging.getLogger("zentral.core.compliance_checks.metrics_views")
class MetricsView(BasePrometheusMetricsView):
def add_compliance_checks_gauge(self):
g = Gauge('zentral_compliance_checks', 'Zentral compliance checks',
['model'], registry=self.registry)
query = (
"select model, count(*) "
"from compliance_checks_compliancecheck "
"group by model"
)
with connection.cursor() as cursor:
cursor.execute(query)
for model, count in cursor.fetchall():
g.labels(model=model).set(count)
def add_machine_statuses_gauge(self):
g = Gauge('zentral_compliance_checks_statuses_bucket', 'Zentral compliance checks statuses',
['model', 'name', 'status', 'le'],
registry=self.registry)
query = (
"with machine_statuses as ("
" select compliance_check_id, compliance_check_version, status,"
" date_part('days', now() - status_time) as age"
" from compliance_checks_machinestatus"
") select "
"cc.model, cc.name, ms.status,"
'count(*) filter (where ms.age < 1) as "1",'
'count(*) filter (where ms.age < 7) as "7",'
'count(*) filter (where ms.age < 14) as "14",'
'count(*) filter (where ms.age < 30) as "30",'
'count(*) filter (where ms.age < 45) as "45",'
'count(*) filter (where ms.age < 90) as "90",'
'count(*) as "+Inf" '
"from compliance_checks_compliancecheck as cc "
"join machine_statuses as ms on "
"(ms.compliance_check_id = cc.id and ms.compliance_check_version = cc.version) "
"group by cc.model, cc.name, ms.status"
)
with connection.cursor() as cursor:
cursor.execute(query)
columns = [c.name for c in cursor.description]
for row in cursor.fetchall():
row_d = dict(zip(columns, row))
for le in ("1", "7", "14", "30", "45", "90", "+Inf"):
g.labels(
model=row_d["model"],
name=row_d["name"],
status=Status(row_d["status"]).name,
le=le
).set(
row_d[le]
)
def populate_registry(self):
self.add_compliance_checks_gauge()
self.add_machine_statuses_gauge()
| [
"logging.getLogger",
"django.db.connection.cursor",
"prometheus_client.Gauge"
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import click
import ast
import requests
import pandas as pd
import numpy as np
from pysradb.sraweb import SRAweb
import requests
import pysam
import re
import pyfastx
import pkg_resources # part of setuptools
import json
import warnings
import sys
import os
from .run_workflow import make_snakes
# Allows passing strings to CLI and eval as python objects
# From https://stackoverflow.com/questions/47631914/how-to-pass-several-list-of-arguments-to-click-option
# Had to add the "str(value)" part or default would throw ValueErrors.
class PythonLiteralOption(click.Option):
def type_cast_value(self, ctx, value):
try:
return ast.literal_eval(str(value))
except ValueError:
raise click.BadParameter(value)
# Constants
AVAILABLE_MODES = [
"DRIP",
"DRIPc",
"qDRIP",
"sDRIP",
"ssDRIP",
"R-ChIP",
"RR-ChIP",
"RDIP",
"S1-DRIP",
"DRIVE",
"RNH-CnR",
"MapR",
"RNA-Seq"
]
SRA_URL = "https://www.ncbi.nlm.nih.gov/sra/"
SRA_COLS = [
"experiment",
"study_accession",
"experiment_title",
"experiment_accession",
"organism_taxid ",
"run_accession",
"library_layout",
"run_total_bases",
"run_total_spots",
]
redict = {
"fastq": "^.+\\.[fastq]+[\\.gz]*$|^.+\\.[fastq]+[\\.gz]*\\~.+\\.[fastq]+[\\.gz]*$",
"bam": "^.+\\.bam$",
"public": "^GSM[0-9]+$|^SRX[0-9]+$",
}
# __file__=os.path.abspath("../RLPipes/rlpipes/cli.py")
this_dir = os.path.dirname(__file__)
DATA_PATH = os.path.abspath(
os.path.join(this_dir, "src", "data", "available_genomes.tsv.xz")
)
GENSIZE_PATH = os.path.abspath(
os.path.join(this_dir, "src", "data", "eff_gen_size.tsv.xz")
)
SRC_DIR = os.path.abspath(os.path.join(this_dir, "src"))
N_BAM_READS_CHECK = 1000
# Set verion
__version__ = pkg_resources.require("rlpipes")[0].version
# Help text
snakeHelp = """
Dict of arguments passed to the snakemake python API. Default: "{'use_conda': True}".
Read the snakemake API reference for the full list of options.
"""
modeHelp = """
The type of sequencing (e.g., "DRIP"). The available options are currently:
DRIP, DRIPc, qDRIP, sDRIP, ssDRIP, R-ChIP, RR-ChIP, RDIP, S1-DRIP, DRIVE, RNH-CnR, and MapR
"""
groupHelp = """
Column(s) which identify biologically-meaningful grouping(s) of samples (i.e., conditions).
Can be any column name from the `samples` CSV file. If using public data accessions,
it may also include "study". NOTE: If --groupby is set and there R-loop-mapping and expression
samples within groups, expression-matched analysis will be run. This can be disabled with the --noexp flag.\n
Example #1: "RSeqCLI build outdir/ samples.csv --groupcols tissue"\n
samples.csv:\n
experiment, mode, tissue\n
\tGSM1720615, DRIP, NT2\n
\tGSM1720616, DRIP, NT2\n
\tGSM1720619, DRIP, K562\n
\n
Example #2: "RSeqCLI build outdir/ samples.csv --groupby tissue"\n
samples.csv:\n
experiment, mode, tissue\n
\tGSM1720615, DRIP, NT2\n
\tGSM1720616, DRIP, NT2\n
\tGSM1720613, DRIPc, NT2\n
\tGSM1720614, DRIPc, NT2\n
\tGSM1720622, RNA-seq, NT2\n
\tGSM1720623, RNA-seq, NT2\n
\n
"""
expHelp = """
If set, no expression-matched analysis will be performed.
"""
# Get the shared options
# From https://stackoverflow.com/questions/40182157/shared-options-and-flags-between-commands
verify_run_options = [
click.option(
"--smargs",
"-s",
cls=PythonLiteralOption,
help=snakeHelp,
default="{'use_conda': True}",
),
click.option(
"--threads", "-t", help="Number of threads to use. Default: 1", default=1
),
click.option(
"--bwamem2",
is_flag=True,
help="Align with BWA-MEM2 instead of BWA. BWA MEM2 Needs > 70GB RAM avaialble to build index, but shows > 3x speed increase. Default: False.",
default=False,
),
click.option(
"--macs3",
help="Call peaks using macs3 instead of macs2",
is_flag=True,
default=False,
),
click.option(
"--groupby",
"-G",
help=groupHelp
),
click.option(
"--noexp",
help=expHelp,
is_flag=True,
default=False
),
click.option(
"--noreport",
help="If set, RSeq reports will not be generated.",
is_flag=True,
default=False
),
click.option(
"--debug",
is_flag=True,
help="Run pipeline on subsampled number of reads (for testing).",
default=False,
),
click.option(
"--tsv",
is_flag=True,
help="Obtain config from config.tsv file instead of config.json.",
default=False,
),
click.option(
"--useaws",
is_flag=True,
help="If set, prefetch from SRA tools will be used to download any public SRA data instead of AWS S3.",
default=False,
)
]
# Function for addint these options to the click command
def add_options(options):
def _add_options(func):
for option in reversed(options):
func = option(func)
return func
return _add_options
def validate_genome(ctx, param, value):
"""Validate genome input"""
if value is not None:
available_genomes = pd.read_table(DATA_PATH)
try:
assert value in available_genomes.UCSC_orgID.to_list()
except AssertionError:
raise click.BadParameter(
"'" + value + "' is not a valid UCSC genome ID (e.g., 'hg38' is valid)."
)
return value
def validate_mode(ctx, param, value):
"""Validate mode input"""
if value is not None:
try:
assert value in AVAILABLE_MODES
except AssertionError:
raise click.BadParameter(
"'"
+ value
+ "' is not a valid mode. (RSeqCLI build --help for more info)"
)
return value
def validate_run_dir(ctx, param, value):
try:
os.makedirs(value, exist_ok=True)
except FileNotFoundError:
raise click.BadParameter(
"'"
+ value
+ "' could not be created using `os.makedirs("
+ value
+ ", exist_ok=True)` please re-check this path."
)
except FileExistsError:
raise click.BadParameter(
"RUN_DIR must be a directory. User supplied '" + value + "'"
)
return os.path.abspath(value)
def validate_run_dir_prepped(ctx, param, value):
try:
assert os.path.exists(value) and os.path.exists(
os.path.join(value, "config.json")
)
except AssertionError:
raise click.BadParameter(
"Configuration file '"
+ os.path.join(value, "config.json")
+ "' is not found. Have you run 'RSeqCLI build' yet?"
)
return os.path.abspath(value)
def bam_info(bamfile, n_bam_reads_check=1000):
"""Tests whether bam file is paired end and checks read length. Requires pysam."""
save = pysam.set_verbosity(0)
samfile = pysam.AlignmentFile(bamfile, "rb")
pysam.set_verbosity(save)
numPair = sum([x.is_paired for x in samfile.head(n=n_bam_reads_check)])
read_len = (
sum([x.infer_read_length() for x in samfile.head(n=n_bam_reads_check)])
// n_bam_reads_check
)
return {"paired_end": numPair > n_bam_reads_check / 2, "read_len": read_len}
def validate_samples(ctx, param, value):
"""Validate and wrangle sampels input"""
# value = "../RLPipes/tests/test_data/fq_test_samples_1.csv"
samps = pd.read_csv(value)
# First, check for matching pattern
exp = samps.experiment[0]
try:
samptype = [key for key, val in redict.items() if re.match(val, exp)][0]
except IndexError:
raise click.BadParameter(
message="Unable to detect data format for file " + exp
)
samps["file_type"] = samptype
# Wrangle controls if provided
if "control" in samps.columns:
controls = True
samps = pd.concat(
[
samps,
samps.assign(experiment=samps.control).assign(condition="Input").assign(control=pd.NA).dropna(subset=["experiment"]),
]
)
samps = samps.assign(
control=samps.control.apply(lambda x: pd.NA if pd.isna(x) else x)
).drop_duplicates()
else:
controls = False
samps["control"] = ""
if samptype == "public":
# Init SRAdb
db = SRAweb(os.environ.get("NCBI_API_KEY", None))
def getsra(x):
"""Except for unreachable accessions in SRA"""
try:
data=db.sra_metadata(x)
data['experiment'] = x
except SystemExit:
data=pd.DataFrame({
'experiment': x,
'study_accession': pd.NA,
'experiment_title': pd.NA,
'experiment_accession': pd.NA,
'organism_taxid ': pd.NA,
'run_accession': pd.NA,
'library_layout': pd.NA,
'run_total_bases': pd.NA,
'run_total_spots': pd.NA
}, index=[0])
return data
# Query the SRAdb and wrangle with original data
newSamps = pd.concat(
samps.experiment.progress_apply(lambda x: getsra(x)).values.tolist()
)[SRA_COLS]
# Drop NaNs
newSamps.dropna(subset=["experiment_accession"], inplace=True)
newSamps.dropna(subset=["run_total_bases"], inplace=True)
# Remove samples which have been retracted
newSamps = newSamps[newSamps['run_total_bases'] != '']
# Get the read length
newSamps = newSamps.astype(
{"run_total_bases": "int64", "run_total_spots": "int64"}
)
newSamps["read_length"] = newSamps.run_total_bases // newSamps.run_total_spots
# Get the latest genomes.
# From https://stackoverflow.com/questions/15705630/get-the-rows-which-have-the-max-value-in-groups-using-groupby
available_genome = pd.read_table(DATA_PATH)
latest_genomes = available_genome[
available_genome.groupby(axis=0, by=["taxId"])["year"].transform(max)
== available_genome["year"]
]
latest_genomes = latest_genomes.rename(columns={"taxId": "organism_taxid "})
newSamps["organism_taxid "] = newSamps["organism_taxid "].astype(np.int64)
# Necessary to avoid taxid conflict between sacCer2/3
newSamps.loc[newSamps["organism_taxid "] == 4932, "organism_taxid "] = 559292
newSamps = newSamps.set_index("organism_taxid ")
latest_genomes = latest_genomes.set_index("organism_taxid ")
newSamps = newSamps.join(latest_genomes, how="left")
newSamps = (
newSamps[
[
"experiment",
"study_accession",
"experiment_title",
"experiment_accession",
"run_accession",
"library_layout",
"UCSC_orgID",
"read_length",
]
]
.rename(
columns={
"experiment": "experiment_original",
"study_accession": "study",
"experiment_title": "name",
"library_layout": "paired_end",
"experiment_accession": "experiment",
"run_accession": "run",
"UCSC_orgID": "genome",
}
)
)
# Set paired end
newSamps["paired_end"] = newSamps["paired_end"] == "PAIRED"
# Get srx to orig mapping
srx_to_orig=newSamps[['experiment', 'experiment_original']]
# Set index as exp orig
newSamps=newSamps.set_index("experiment_original")
if "genome" in samps.columns:
newSamps = newSamps.drop("genome", axis=1)
# Finally, join the dataframes by experiment...
samps['experiment_original'] = samps['experiment']
samps['control_original'] = samps['control']
samps=samps.drop('experiment', axis=1).drop('control', axis=1)
samps=pd.merge(
samps,
newSamps,
on = "experiment_original"
).drop_duplicates()
# And control
srx_to_origctr=srx_to_orig.rename(
columns={
"experiment": "control",
"experiment_original": "control_original",
}
)
samps=pd.merge(
samps,
srx_to_origctr,
how="left",
on = "control_original"
).drop_duplicates()
samps = samps.assign(
control=samps.control.apply(lambda x: pd.NA if pd.isna(x) else x)
).drop_duplicates()
else:
if samptype == "bam":
# Check which are paired-end
samps["paired_end"] = [
bam_info(bam, N_BAM_READS_CHECK)["paired_end"]
for bam in samps["experiment"]
]
samps["read_length"] = [
bam_info(bam, N_BAM_READS_CHECK)["read_len"] for bam in samps["experiment"]
]
samps["name"] = [
os.path.splitext(os.path.basename(exp))[0] for exp in samps["experiment"]
]
if controls:
samps["control"] = [
os.path.splitext(os.path.basename(exp))[0] if not pd.isna(exp) else exp
for exp in samps["control"]
]
else:
samps["control"] = ""
samps["run"] = [os.path.abspath(bam) for bam in samps["experiment"]]
elif samptype == "fastq":
# Check which are paired-end
samps["paired_end"] = [bool(re.match(".+\\~.+", exp)) for exp in samps["experiment"]]
def get_readlen(fq, lines=500):
"""
Get Read Length from a fastq file
params:
fq: Path to a FASTQ file
line: Number of lines to scan. Default: 500
"""
seqlst=[]
for name,seq,qual in pyfastx.Fastq(fq, build_index=False):
seqlst.append(seq)
if len(seqlst) > lines:
break
toavg = [len(x) for x in seqlst]
return round(sum(toavg) / len(toavg))
samps["read_length"] = [
get_readlen(re.sub('\\~.+', "", exp )) for exp in samps["experiment"]
]
def get_samplename(fq):
splt = os.path.splitext(os.path.basename(fq))
if splt[1] == ".gz":
nm=os.path.splitext(splt[0])[0]
else:
nm=splt[0]
return re.sub('[\\._]{1}[R1-2]+$', "", nm)
samps["name"] = [
get_samplename(re.sub('\\~.+', "", exp)) for exp in samps["experiment"]
]
if controls:
samps["control"] = [
get_samplename(re.sub('\\~.+', "", exp)) if not pd.isna(exp) else exp
for exp in samps["control"]
]
else:
samps["control"] = ""
def get_fq_path(fq, pe):
if pe:
fq1=os.path.abspath(re.sub('\\~.+', "", fq))
fq2=os.path.abspath(re.sub('.+\\~', "", fq))
return fq1 + "~" + fq2
else:
return os.path.abspath(fq)
samps["run"] = [get_fq_path(fq, pe) for idx, fq, pe in samps[["experiment", "paired_end"]].itertuples()]
samps["experiment"] = samps["name"]
return samps
@click.group()
@click.version_option(__version__)
@click.pass_context
def cli(ctx, **kwargs):
"""
RLPipes: A standardized R-loop-mapping pipeline.
"""
pass
@cli.command("build")
@click.argument("run_dir", type=click.Path(), callback=validate_run_dir)
@click.argument("samples", type=click.File("rb"), callback=validate_samples)
@click.option("--mode", "-m", callback=validate_mode, help=modeHelp)
@click.option(
"--genome",
"-g",
callback=validate_genome,
help="UCSC genome for samples (e.g., 'hg38'). Not required if providing public data accessions.",
)
@click.option(
"--name",
"-n",
help="Sample names for use in output report. By default, inferred from inputs.",
)
@click.pass_context
def build(ctx, samples, mode, genome, run_dir, name):
"""
Configure an RLPipes workflow.
RUN_DIR: Directory for RLPipes Execution. Will be created if it does not exist.
SAMPLES: A CSV file with at least one column "experiment" that provides the
path to either local fastq files, bam files, or public sample accessions (SRX or GSM).
Input controls should be in the "control" column. \n
If providing paired-end fastq files, enter: "exp_1.fastq~exp_2.fastq".\n
Columns may also include "genome" and "mode" columns. These will
override the -g, -m, and -n options.\n
"genome" (-g/--genome) is not required if providing public data accessions.\n
\n
Example #1: "RLPipes build -m DRIP outdir/ samples.csv"\n
samples.csv:\n
\texperiment\n
\tSRX113812\n
\tSRX113813\n
\n
Example #2: "RLPipes build outdir/ samples.csv"\n
samples.csv:\n
\texperiment, control, genome, mode\n
\tqDRIP_siGL3_1.fq~qDRIP_siGL3_2.fq, , hg38, qDRIP\n
\tDRIPc_3T3.fq, Input_3T3.fq, mm10, DRIPc\n
"""
# Add in potentially, missing columns
if not "mode" in samples.columns:
samples["mode"] = mode
if not "genome" in samples.columns:
try:
assert genome is not None
except AssertionError:
raise click.BadParameter(
message="Genome cannot be missing when running local files."
)
samples["genome"] = genome
if not "name" in samples.columns:
samples["name"] = name
# Get the effective genome sizes
eff_gen_size = pd.read_table(GENSIZE_PATH)
sizes = eff_gen_size["read_length"].unique()
samples["read_length"] = [
min(sizes, key=lambda x: abs(x - sizeCheck))
for sizeCheck in samples["read_length"]
]
samples = pd.merge(
samples, eff_gen_size.rename(columns={"UCSC_orgID": "genome"}), how="inner"
)
# Compile to json for snakemake
outtsv = os.path.join(run_dir, "config.tsv")
outdf = samples.fillna("").reset_index().drop("index", axis=1)
outdf.to_csv(outtsv, sep="\t", index=False)
outjson = os.path.join(run_dir, "config.json")
outdict = samples.fillna("").reset_index().drop("index", axis=1).to_dict("list")
with open(outjson, "w") as f:
json.dump(outdict, f, ensure_ascii=False)
print("\nSuccess! RLPipes has been initialized at the specified directory: " + run_dir)
print("\nRun 'RLPipes check " + run_dir + "' to verify the configuration.\n")
@cli.command("check")
@click.argument("run_dir", type=click.Path(), callback=validate_run_dir_prepped)
@add_options(verify_run_options)
def check(run_dir, threads, debug, bwamem2, macs3, groupby, noexp, noreport, tsv, useaws, **kwargs):
"""
Validate an RLPipes workflow.
RUN_DIR: Directory configured with `RLPipes build` and ready for checking and execution.
"""
smargs = kwargs["smargs"]
dagfile = make_snakes(
snake_args=smargs,
run_dir=run_dir,
src_dir=SRC_DIR,
threads=threads,
bwamem2=bwamem2,
macs3=macs3,
groupby=groupby,
noexp=noexp,
noreport=noreport,
debug=debug,
tsv=tsv,
useaws=useaws,
verify=True,
)
print(
"\nSuccess! The DAG has been generated successfully. You can view it here: "
+ dagfile
)
print("\nRun 'RLPipes run " + run_dir + "' to execute the workflow.\n")
@cli.command("run")
@click.argument("run_dir", type=click.Path(), callback=validate_run_dir_prepped)
@add_options(verify_run_options)
def run(run_dir, threads, debug, bwamem2, macs3, groupby, noexp, noreport, tsv, useaws, **kwargs):
"""
Execute an RLPipes workflow.
RUN_DIR: Directory configured with `RLPipes build` and ready for checking and execution.
"""
smargs = kwargs["smargs"]
exitcode = make_snakes(
snake_args=smargs,
run_dir=run_dir,
src_dir=SRC_DIR,
threads=threads,
bwamem2=bwamem2,
macs3=macs3,
groupby=groupby,
noexp=noexp,
noreport=noreport,
debug=debug,
tsv=tsv,
useaws=useaws,
verify=False,
)
print(exitcode)
print("Success! RLPipes will now close.")
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"""
Copyright (c) 2015 Red Hat, Inc
All rights reserved.
This software may be modified and distributed under the terms
of the BSD license. See the LICENSE file for details.
"""
from __future__ import print_function, unicode_literals
import logging
import os
import sys
import pytest
from atomic_reactor.buildimage import BuildImageBuilder
from atomic_reactor.core import DockerTasker
import atomic_reactor.cli.main
from tests.fixtures import is_registry_running, temp_image_name, get_uuid
from tests.constants import LOCALHOST_REGISTRY, DOCKERFILE_GIT, DOCKERFILE_OK_PATH, FILES, MOCK
if MOCK:
from tests.docker_mock import mock_docker
PRIV_BUILD_IMAGE = None
DH_BUILD_IMAGE = None
logger = logging.getLogger('atomic_reactor.tests')
if MOCK:
mock_docker()
dt = DockerTasker()
reactor_root = os.path.dirname(os.path.dirname(__file__))
with_all_sources = pytest.mark.parametrize('source_provider, uri', [
('git', DOCKERFILE_GIT),
('path', DOCKERFILE_OK_PATH),
])
# TEST-SUITE SETUP
def setup_module(module):
global PRIV_BUILD_IMAGE, DH_BUILD_IMAGE
PRIV_BUILD_IMAGE = get_uuid()
DH_BUILD_IMAGE = get_uuid()
if MOCK:
return
b = BuildImageBuilder(reactor_local_path=reactor_root)
b.create_image(os.path.join(reactor_root, 'images', 'privileged-builder'),
PRIV_BUILD_IMAGE, use_cache=True)
b2 = BuildImageBuilder(reactor_local_path=reactor_root)
b2.create_image(os.path.join(reactor_root, 'images', 'dockerhost-builder'),
DH_BUILD_IMAGE, use_cache=True)
def teardown_module(module):
if MOCK:
return
dt.remove_image(PRIV_BUILD_IMAGE, force=True)
dt.remove_image(DH_BUILD_IMAGE, force=True)
# TESTS
class TestCLISuite(object):
def exec_cli(self, command):
saved_args = sys.argv
sys.argv = command
atomic_reactor.cli.main.run()
sys.argv = saved_args
@with_all_sources
def test_simple_privileged_build(self, is_registry_running, temp_image_name,
source_provider, uri):
if MOCK:
mock_docker()
temp_image = temp_image_name
command = [
"main.py",
"--verbose",
"build",
source_provider,
"--method", "privileged",
"--build-image", PRIV_BUILD_IMAGE,
"--image", temp_image.to_str(),
"--uri", uri,
]
if is_registry_running:
logger.info("registry is running")
command += ["--source-registry", LOCALHOST_REGISTRY]
else:
logger.info("registry is NOT running")
with pytest.raises(SystemExit) as excinfo:
self.exec_cli(command)
assert excinfo.value.code == 0
@with_all_sources
def test_simple_dh_build(self, is_registry_running, temp_image_name, source_provider, uri):
if MOCK:
mock_docker()
temp_image = temp_image_name
command = [
"main.py",
"--verbose",
"build",
source_provider,
"--method", "hostdocker",
"--build-image", DH_BUILD_IMAGE,
"--image", temp_image.to_str(),
"--uri", uri,
]
if is_registry_running:
logger.info("registry is running")
command += ["--source-registry", LOCALHOST_REGISTRY]
else:
logger.info("registry is NOT running")
with pytest.raises(SystemExit) as excinfo:
self.exec_cli(command)
assert excinfo.value.code == 0
dt.remove_image(temp_image, noprune=True)
def test_building_from_json_source_provider(self, is_registry_running, temp_image_name):
if MOCK:
mock_docker()
temp_image = temp_image_name
command = [
"main.py",
"--verbose",
"build",
"json",
"--method", "hostdocker",
"--build-image", DH_BUILD_IMAGE,
os.path.join(FILES, 'example-build.json'),
"--substitute", "image={0}".format(temp_image),
"source.uri={0}".format(DOCKERFILE_OK_PATH)
]
if is_registry_running:
logger.info("registry is running")
command += ["--source-registry", LOCALHOST_REGISTRY]
else:
logger.info("registry is NOT running")
with pytest.raises(SystemExit) as excinfo:
self.exec_cli(command)
assert excinfo.value.code == 0
dt.remove_image(temp_image, noprune=True)
def test_create_build_image(self, temp_image_name):
if MOCK:
mock_docker()
temp_image = temp_image_name
priv_builder_path = os.path.join(reactor_root, 'images', 'privileged-builder')
command = [
"main.py",
"--verbose",
"create-build-image",
"--reactor-local-path", reactor_root,
priv_builder_path,
temp_image.to_str(),
]
with pytest.raises(SystemExit) as excinfo:
self.exec_cli(command)
assert excinfo.value.code == 0
dt.remove_image(temp_image, noprune=True)
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# Copyright (c) 2017-2018 Wind River Systems, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from django.conf.urls import url
from rest_framework.urlpatterns import format_suffix_patterns
from titanium_cloud.swagger.views import SwaggerJsonViewDepreciated
from titanium_cloud.swagger.views import APIv1SwaggerJsonViewDepreciated
from titanium_cloud.swagger.views import SwaggerJsonView
from titanium_cloud.swagger.views import APIv1SwaggerJsonView
URLPATTERNS = [
# API v0, depreciated
url(r'^api/multicloud-titanium_cloud/v0/swagger.json$', SwaggerJsonViewDepreciated.as_view()),
# API v1, depreciated
url(r'^api/multicloud-titanium_cloud/v1/swagger.json$', APIv1SwaggerJsonViewDepreciated.as_view()),
# API v0, new namespace: MULTICLOUD-335
url(r'^api/multicloud-titaniumcloud/v0/swagger.json$', SwaggerJsonView.as_view()),
# API v1, new namespace: MULTICLOUD-335
url(r'^api/multicloud-titaniumcloud/v1/swagger.json$', APIv1SwaggerJsonView.as_view()),
]
urlpatterns = format_suffix_patterns(URLPATTERNS)
| [
"titanium_cloud.swagger.views.APIv1SwaggerJsonView.as_view",
"titanium_cloud.swagger.views.APIv1SwaggerJsonViewDepreciated.as_view",
"titanium_cloud.swagger.views.SwaggerJsonViewDepreciated.as_view",
"titanium_cloud.swagger.views.SwaggerJsonView.as_view",
"rest_framework.urlpatterns.format_suffix_patterns"
... | [((1518, 1553), 'rest_framework.urlpatterns.format_suffix_patterns', 'format_suffix_patterns', (['URLPATTERNS'], {}), '(URLPATTERNS)\n', (1540, 1553), False, 'from rest_framework.urlpatterns import format_suffix_patterns\n'), ((1061, 1097), 'titanium_cloud.swagger.views.SwaggerJsonViewDepreciated.as_view', 'SwaggerJsonViewDepreciated.as_view', ([], {}), '()\n', (1095, 1097), False, 'from titanium_cloud.swagger.views import SwaggerJsonViewDepreciated\n'), ((1187, 1228), 'titanium_cloud.swagger.views.APIv1SwaggerJsonViewDepreciated.as_view', 'APIv1SwaggerJsonViewDepreciated.as_view', ([], {}), '()\n', (1226, 1228), False, 'from titanium_cloud.swagger.views import APIv1SwaggerJsonViewDepreciated\n'), ((1335, 1360), 'titanium_cloud.swagger.views.SwaggerJsonView.as_view', 'SwaggerJsonView.as_view', ([], {}), '()\n', (1358, 1360), False, 'from titanium_cloud.swagger.views import SwaggerJsonView\n'), ((1467, 1497), 'titanium_cloud.swagger.views.APIv1SwaggerJsonView.as_view', 'APIv1SwaggerJsonView.as_view', ([], {}), '()\n', (1495, 1497), False, 'from titanium_cloud.swagger.views import APIv1SwaggerJsonView\n')] |
from ofptHeader import ofptHeader
def ofptBarrierReq():
header = ofptHeader(20)
return header | [
"ofptHeader.ofptHeader"
] | [((67, 81), 'ofptHeader.ofptHeader', 'ofptHeader', (['(20)'], {}), '(20)\n', (77, 81), False, 'from ofptHeader import ofptHeader\n')] |
import pytest
from whylogs.core.metrics.nlp_metrics import NLPMetrics
from whylogs.proto import NLPMetricsMessage
def test_nlp_metrics():
nlp_metric = NLPMetrics()
assert nlp_metric.mer is not None
assert nlp_metric.mer.count == 0
nlp_metric.update("hello", "hello")
assert nlp_metric.mer.histogram.get_min_value() == 0.0
assert nlp_metric.mer.histogram.get_max_value() == 0.0
assert nlp_metric.mer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.0, 0.0, 0.0]
assert nlp_metric.mer.count == 1
assert nlp_metric.wer.histogram.get_min_value() == 0.0
assert nlp_metric.wer.histogram.get_max_value() == 0.0
assert nlp_metric.wer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.0, 0.0, 0.0]
assert nlp_metric.wer.count == 1
assert nlp_metric.wil.histogram.get_min_value() == 0.0
assert nlp_metric.wil.histogram.get_max_value() == 0.0
assert nlp_metric.wil.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.0, 0.0, 0.0]
assert nlp_metric.wil.count == 1
def test_nlp_metrics_message():
nlp_metric = NLPMetrics()
assert nlp_metric.mer is not None
assert nlp_metric.mer.count == 0
nlp_metric.update(["hello brother"], ["hello sister"])
nlp_metric.update(["bye brother"], ["bye sister"])
nlp_metric.update(["what up brother"], ["what up ii sister"])
assert nlp_metric.mer.histogram.get_min_value() == 0.5
assert nlp_metric.mer.histogram.get_max_value() == 0.5
assert nlp_metric.mer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.5, 0.5, 0.5000]
assert nlp_metric.mer.count == 3
assert nlp_metric.wer.histogram.get_min_value() == 0.5
assert nlp_metric.wer.histogram.get_max_value() == 0.5
assert nlp_metric.wer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.5, 0.5, 0.5000]
assert nlp_metric.wer.count == 3
assert pytest.approx(nlp_metric.wil.histogram.get_min_value(), 0.001) == 0.6666
assert nlp_metric.wil.histogram.get_max_value() == 0.75
assert pytest.approx(nlp_metric.wil.histogram.get_quantiles([0.25, 0.5, 0.75]), 0.001) == [0.666666, 0.75, 0.75]
assert nlp_metric.wil.count == 3
nlp_message = nlp_metric.to_protobuf()
nlp_metric_new = NLPMetrics.from_protobuf(nlp_message)
assert nlp_metric_new.wil.histogram.get_min_value() == nlp_metric.wil.histogram.get_min_value()
assert nlp_metric_new.wil.histogram.get_max_value() == nlp_metric.wil.histogram.get_max_value()
assert nlp_metric_new.wil.histogram.get_quantiles([0.25, 0.5, 0.75]) == nlp_metric.wil.histogram.get_quantiles([0.25, 0.5, 0.75])
assert nlp_metric_new.wil.count == nlp_metric.wil.count
assert nlp_metric_new.mer.histogram.get_min_value() == nlp_metric.mer.histogram.get_min_value()
assert nlp_metric_new.mer.histogram.get_max_value() == nlp_metric.mer.histogram.get_max_value()
assert nlp_metric_new.mer.histogram.get_quantiles([0.25, 0.5, 0.75]) == nlp_metric.mer.histogram.get_quantiles([0.25, 0.5, 0.75])
assert nlp_metric_new.mer.count == nlp_metric.mer.count
assert nlp_metric_new.wer.histogram.get_min_value() == nlp_metric.wer.histogram.get_min_value()
assert nlp_metric_new.wer.histogram.get_max_value() == nlp_metric.wer.histogram.get_max_value()
assert nlp_metric_new.wer.histogram.get_quantiles([0.25, 0.5, 0.75]) == nlp_metric.wer.histogram.get_quantiles([0.25, 0.5, 0.75])
assert nlp_metric_new.wer.count == nlp_metric.wer.count
def test_nlp_merge():
nlp_metric = NLPMetrics()
nlp_metric.update("hello", "hello")
nlp_initial_test = None
nlp_merge_metrics = nlp_metric.merge(nlp_initial_test)
assert nlp_merge_metrics.mer.histogram.get_min_value() == 0.0
assert nlp_merge_metrics.mer.histogram.get_max_value() == 0.0
assert nlp_merge_metrics.mer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.0, 0.0, 0.0]
assert nlp_merge_metrics.mer.count == 1
assert nlp_merge_metrics.wer.histogram.get_min_value() == 0.0
assert nlp_merge_metrics.wer.histogram.get_max_value() == 0.0
assert nlp_merge_metrics.wer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.0, 0.0, 0.0]
assert nlp_merge_metrics.wer.count == 1
assert nlp_merge_metrics.wil.histogram.get_min_value() == 0.0
assert nlp_merge_metrics.wil.histogram.get_max_value() == 0.0
assert nlp_merge_metrics.wil.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.0, 0.0, 0.0]
assert nlp_merge_metrics.wil.count == 1
nlp_metric_2 = NLPMetrics()
nlp_metric_2.update(["hello brother"], ["hello sister"])
nlp_metric_2.update(["bye brother"], ["bye sister"])
nlp_metric_2.update(["what up brother"], ["what up ii sister"])
nlp_merge_metrics = nlp_merge_metrics.merge(nlp_metric_2)
assert nlp_merge_metrics.mer.histogram.get_min_value() == 0.0
assert nlp_merge_metrics.mer.histogram.get_max_value() == 0.5
assert nlp_merge_metrics.mer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.5, 0.5, 0.5000]
assert nlp_merge_metrics.mer.count == 4
assert nlp_merge_metrics.wer.histogram.get_min_value() == 0.0
assert nlp_merge_metrics.wer.histogram.get_max_value() == 0.5
assert nlp_merge_metrics.wer.histogram.get_quantiles([0.25, 0.5, 0.75]) == [0.5, 0.5, 0.5000]
assert nlp_merge_metrics.wer.count == 4
assert pytest.approx(nlp_merge_metrics.wil.histogram.get_min_value(), 0.001) == 0.0
assert nlp_merge_metrics.wil.histogram.get_max_value() == 0.75
assert pytest.approx(nlp_merge_metrics.wil.histogram.get_quantiles([0.25, 0.5, 0.75]), 0.001) == [0.666666, 0.75, 0.75]
assert nlp_merge_metrics.wil.count == 4
| [
"whylogs.core.metrics.nlp_metrics.NLPMetrics",
"whylogs.core.metrics.nlp_metrics.NLPMetrics.from_protobuf"
] | [((158, 170), 'whylogs.core.metrics.nlp_metrics.NLPMetrics', 'NLPMetrics', ([], {}), '()\n', (168, 170), False, 'from whylogs.core.metrics.nlp_metrics import NLPMetrics\n'), ((1069, 1081), 'whylogs.core.metrics.nlp_metrics.NLPMetrics', 'NLPMetrics', ([], {}), '()\n', (1079, 1081), False, 'from whylogs.core.metrics.nlp_metrics import NLPMetrics\n'), ((2195, 2232), 'whylogs.core.metrics.nlp_metrics.NLPMetrics.from_protobuf', 'NLPMetrics.from_protobuf', (['nlp_message'], {}), '(nlp_message)\n', (2219, 2232), False, 'from whylogs.core.metrics.nlp_metrics import NLPMetrics\n'), ((3459, 3471), 'whylogs.core.metrics.nlp_metrics.NLPMetrics', 'NLPMetrics', ([], {}), '()\n', (3469, 3471), False, 'from whylogs.core.metrics.nlp_metrics import NLPMetrics\n'), ((4435, 4447), 'whylogs.core.metrics.nlp_metrics.NLPMetrics', 'NLPMetrics', ([], {}), '()\n', (4445, 4447), False, 'from whylogs.core.metrics.nlp_metrics import NLPMetrics\n')] |
# https://leetcode.com/problems/ransom-note/
from collections import Counter
class Solution(object):
def canConstruct(self, ransomNote, magazine):
"""
:type ransomNote: str
:type magazine: str
:rtype: bool
"""
available = Counter(magazine)
for c in ransomNote:
if available[c] <= 0: return False
available[c] -= 1
return True
| [
"collections.Counter"
] | [((278, 295), 'collections.Counter', 'Counter', (['magazine'], {}), '(magazine)\n', (285, 295), False, 'from collections import Counter\n')] |
# InteractionSubmission:
import tensorflow as tf
import numpy as np
from waymo_open_dataset.protos import motion_submission_pb2
def eval_and_generate_submission(model, eval_dataset, num_modes = 6):
submission = motion_submission_pb2.MotionChallengeSubmission()
submission.submission_type = 2
submission.affiliation = ''
submission.description = ''
submission.method_link = ''
current_scenario_id = ""
for i, batch in enumerate(eval_dataset):
if i % 100 == 0:
print(i)
trajectories, confidences = model.predict_step(batch) # (B, K**2, 2, 16, 2), (B, K**2)
trajectories = trajectories.numpy()
confidences = confidences.numpy()
for example_index in range(len(trajectories)):
example_scenario_id = batch['scenario_id'][example_index, 0].numpy()
if example_scenario_id != current_scenario_id:
current_scenario_id = example_scenario_id
current_scenario_prediction = submission.scenario_predictions.add()
current_scenario_prediction.scenario_id = example_scenario_id
# prediction is of type SingleObjectPrediction
for mode in range(num_modes):
joint_trajectory = current_scenario_prediction.joint_prediction.joint_trajectories.add()
joint_trajectory.confidence = confidences[example_index, mode]
for j in batch['indices'][example_index]:
object_trajectory = joint_trajectory.trajectories.add()
object_trajectory.object_id = batch['object_id'][example_index, j].numpy().astype(np.int32)
center_x = []
center_y = []
for t in range(16):
x = trajectories[example_index, mode, j, t, 0]
y = trajectories[example_index, mode, j, t, 1]
center_x.append(x)
center_y.append(y)
trajectory = object_trajectory.trajectory
trajectory.center_x.extend(center_x)
trajectory.center_y.extend(center_y)
return submission
def write_submission_to_file(submission, output_filename):
f = open(output_filename, "wb")
f.write(submission.SerializeToString())
f.close() | [
"waymo_open_dataset.protos.motion_submission_pb2.MotionChallengeSubmission"
] | [((214, 263), 'waymo_open_dataset.protos.motion_submission_pb2.MotionChallengeSubmission', 'motion_submission_pb2.MotionChallengeSubmission', ([], {}), '()\n', (261, 263), False, 'from waymo_open_dataset.protos import motion_submission_pb2\n')] |
from django.conf import settings
from rest_framework.permissions import IsAdminUser
from rest_framework import status, viewsets, decorators
from quser.permissions import CURDPermissionsOrReadOnly
from rest_framework.response import Response
from . import models, serializers
from .filters import FileFilter
class TagViewSet(viewsets.ModelViewSet):
queryset = models.Tag.objects.all()
serializer_class = serializers.TagSerializer
permission_classes = (IsAdminUser,)
class FileViewSet(viewsets.ModelViewSet):
queryset = models.File.objects.filter(active=True).order_by("-id")
serializer_class = serializers.FileSerializer
permission_classes = (CURDPermissionsOrReadOnly,)
filterset_class = FileFilter
def perform_destroy(self, instance):
instance.active = False
instance.save()
@decorators.action(methods=['delete'], detail=False, serializer_class=serializers.BulkDestroySerializer,
permission_classes=(IsAdminUser,))
def bulk_destroy(self, request, *args, **kwargs):
serializer = self.serializer_class(data=request.data, context=dict(request=request))
serializer.is_valid(raise_exception=True)
serializer.save()
return Response(status=status.HTTP_204_NO_CONTENT)
@decorators.action(methods=['post',], detail=False, serializer_class=serializers.BulkUploadSerializer,
permission_classes=(IsAdminUser,))
def bulk_upload(self, request, *args, **kwargs):
serializer = self.serializer_class(data=request.data, context=dict(request=request))
serializer.is_valid(raise_exception=True)
res = serializer.save()
return Response(res, status=status.HTTP_201_CREATED) | [
"rest_framework.response.Response",
"rest_framework.decorators.action"
] | [((836, 979), 'rest_framework.decorators.action', 'decorators.action', ([], {'methods': "['delete']", 'detail': '(False)', 'serializer_class': 'serializers.BulkDestroySerializer', 'permission_classes': '(IsAdminUser,)'}), "(methods=['delete'], detail=False, serializer_class=\n serializers.BulkDestroySerializer, permission_classes=(IsAdminUser,))\n", (853, 979), False, 'from rest_framework import status, viewsets, decorators\n'), ((1286, 1426), 'rest_framework.decorators.action', 'decorators.action', ([], {'methods': "['post']", 'detail': '(False)', 'serializer_class': 'serializers.BulkUploadSerializer', 'permission_classes': '(IsAdminUser,)'}), "(methods=['post'], detail=False, serializer_class=\n serializers.BulkUploadSerializer, permission_classes=(IsAdminUser,))\n", (1303, 1426), False, 'from rest_framework import status, viewsets, decorators\n'), ((1236, 1279), 'rest_framework.response.Response', 'Response', ([], {'status': 'status.HTTP_204_NO_CONTENT'}), '(status=status.HTTP_204_NO_CONTENT)\n', (1244, 1279), False, 'from rest_framework.response import Response\n'), ((1689, 1734), 'rest_framework.response.Response', 'Response', (['res'], {'status': 'status.HTTP_201_CREATED'}), '(res, status=status.HTTP_201_CREATED)\n', (1697, 1734), False, 'from rest_framework.response import Response\n')] |
""" GitFeederController tests """
from django.test import TestCase
from django.contrib.auth.models import User
from app.logic.gitfeeder.models.FeedModel import FeedEntry
from app.logic.gitrepo.models.GitProjectModel import GitProjectEntry
from app.logic.gitrepo.models.GitUserModel import GitUserEntry
from app.logic.bluesteelworker.models.WorkerModel import WorkerEntry
from app.logic.commandrepo.models.CommandGroupModel import CommandGroupEntry
class FeedModelTestCase(TestCase):
def setUp(self):
self.user1 = User.objects.create_user('<EMAIL>', '<EMAIL>', '<PASSWORD>')
self.worker1 = WorkerEntry.objects.create(
name='worker-name-1',
uuid='uuid-worker-1',
operative_system='osx',
description='long-description-1',
user=self.user1,
git_feeder=False
)
self.git_project1 = GitProjectEntry.objects.create(url='http://test/')
self.git_user1 = GitUserEntry.objects.create(
project=self.git_project1,
name='user1',
email='<EMAIL>'
)
def tearDown(self):
pass
def test_feed_is_removed_if_git_project_is_deleted(self):
command_group = CommandGroupEntry.objects.create(user=self.user1)
FeedEntry.objects.create(command_group=command_group, worker=self.worker1, git_project=self.git_project1)
self.assertEqual(1, FeedEntry.objects.all().count())
self.git_project1.delete()
self.assertEqual(0, FeedEntry.objects.all().count())
def test_feed_deletion_also_deletes_command_group(self):
command_group = CommandGroupEntry.objects.create(user=self.user1)
feed_entry = FeedEntry.objects.create(command_group=command_group, worker=self.worker1, git_project=self.git_project1)
self.assertEqual(1, FeedEntry.objects.all().count())
self.assertEqual(1, CommandGroupEntry.objects.all().count())
feed_entry.delete()
self.assertEqual(0, FeedEntry.objects.all().count())
self.assertEqual(0, CommandGroupEntry.objects.all().count())
| [
"app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.all",
"app.logic.gitrepo.models.GitUserModel.GitUserEntry.objects.create",
"app.logic.commandrepo.models.CommandGroupModel.CommandGroupEntry.objects.create",
"app.logic.commandrepo.models.CommandGroupModel.CommandGroupEntry.objects.all",
"app.logic.git... | [((528, 588), 'django.contrib.auth.models.User.objects.create_user', 'User.objects.create_user', (['"""<EMAIL>"""', '"""<EMAIL>"""', '"""<PASSWORD>"""'], {}), "('<EMAIL>', '<EMAIL>', '<PASSWORD>')\n", (552, 588), False, 'from django.contrib.auth.models import User\n'), ((613, 785), 'app.logic.bluesteelworker.models.WorkerModel.WorkerEntry.objects.create', 'WorkerEntry.objects.create', ([], {'name': '"""worker-name-1"""', 'uuid': '"""uuid-worker-1"""', 'operative_system': '"""osx"""', 'description': '"""long-description-1"""', 'user': 'self.user1', 'git_feeder': '(False)'}), "(name='worker-name-1', uuid='uuid-worker-1',\n operative_system='osx', description='long-description-1', user=self.\n user1, git_feeder=False)\n", (639, 785), False, 'from app.logic.bluesteelworker.models.WorkerModel import WorkerEntry\n'), ((888, 938), 'app.logic.gitrepo.models.GitProjectModel.GitProjectEntry.objects.create', 'GitProjectEntry.objects.create', ([], {'url': '"""http://test/"""'}), "(url='http://test/')\n", (918, 938), False, 'from app.logic.gitrepo.models.GitProjectModel import GitProjectEntry\n'), ((964, 1054), 'app.logic.gitrepo.models.GitUserModel.GitUserEntry.objects.create', 'GitUserEntry.objects.create', ([], {'project': 'self.git_project1', 'name': '"""user1"""', 'email': '"""<EMAIL>"""'}), "(project=self.git_project1, name='user1', email=\n '<EMAIL>')\n", (991, 1054), False, 'from app.logic.gitrepo.models.GitUserModel import GitUserEntry\n'), ((1221, 1270), 'app.logic.commandrepo.models.CommandGroupModel.CommandGroupEntry.objects.create', 'CommandGroupEntry.objects.create', ([], {'user': 'self.user1'}), '(user=self.user1)\n', (1253, 1270), False, 'from app.logic.commandrepo.models.CommandGroupModel import CommandGroupEntry\n'), ((1280, 1389), 'app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.create', 'FeedEntry.objects.create', ([], {'command_group': 'command_group', 'worker': 'self.worker1', 'git_project': 'self.git_project1'}), '(command_group=command_group, worker=self.worker1,\n git_project=self.git_project1)\n', (1304, 1389), False, 'from app.logic.gitfeeder.models.FeedModel import FeedEntry\n'), ((1632, 1681), 'app.logic.commandrepo.models.CommandGroupModel.CommandGroupEntry.objects.create', 'CommandGroupEntry.objects.create', ([], {'user': 'self.user1'}), '(user=self.user1)\n', (1664, 1681), False, 'from app.logic.commandrepo.models.CommandGroupModel import CommandGroupEntry\n'), ((1703, 1812), 'app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.create', 'FeedEntry.objects.create', ([], {'command_group': 'command_group', 'worker': 'self.worker1', 'git_project': 'self.git_project1'}), '(command_group=command_group, worker=self.worker1,\n git_project=self.git_project1)\n', (1727, 1812), False, 'from app.logic.gitfeeder.models.FeedModel import FeedEntry\n'), ((1415, 1438), 'app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.all', 'FeedEntry.objects.all', ([], {}), '()\n', (1436, 1438), False, 'from app.logic.gitfeeder.models.FeedModel import FeedEntry\n'), ((1513, 1536), 'app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.all', 'FeedEntry.objects.all', ([], {}), '()\n', (1534, 1536), False, 'from app.logic.gitfeeder.models.FeedModel import FeedEntry\n'), ((1838, 1861), 'app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.all', 'FeedEntry.objects.all', ([], {}), '()\n', (1859, 1861), False, 'from app.logic.gitfeeder.models.FeedModel import FeedEntry\n'), ((1899, 1930), 'app.logic.commandrepo.models.CommandGroupModel.CommandGroupEntry.objects.all', 'CommandGroupEntry.objects.all', ([], {}), '()\n', (1928, 1930), False, 'from app.logic.commandrepo.models.CommandGroupModel import CommandGroupEntry\n'), ((1998, 2021), 'app.logic.gitfeeder.models.FeedModel.FeedEntry.objects.all', 'FeedEntry.objects.all', ([], {}), '()\n', (2019, 2021), False, 'from app.logic.gitfeeder.models.FeedModel import FeedEntry\n'), ((2059, 2090), 'app.logic.commandrepo.models.CommandGroupModel.CommandGroupEntry.objects.all', 'CommandGroupEntry.objects.all', ([], {}), '()\n', (2088, 2090), False, 'from app.logic.commandrepo.models.CommandGroupModel import CommandGroupEntry\n')] |
from django.conf.urls import patterns, url
from irods_browser_app import views
urlpatterns = patterns('',
url(r'^login/$',views.login, name='irods_login'),
url(r'^store/$',views.store, name='irods_store'),
url(r'^upload/$',views.upload, name='irods_upload'),
url(r'^upload_add/$',views.upload_add, name='irods_upload_add'),
)
| [
"django.conf.urls.url"
] | [((111, 159), 'django.conf.urls.url', 'url', (['"""^login/$"""', 'views.login'], {'name': '"""irods_login"""'}), "('^login/$', views.login, name='irods_login')\n", (114, 159), False, 'from django.conf.urls import patterns, url\n'), ((165, 213), 'django.conf.urls.url', 'url', (['"""^store/$"""', 'views.store'], {'name': '"""irods_store"""'}), "('^store/$', views.store, name='irods_store')\n", (168, 213), False, 'from django.conf.urls import patterns, url\n'), ((219, 270), 'django.conf.urls.url', 'url', (['"""^upload/$"""', 'views.upload'], {'name': '"""irods_upload"""'}), "('^upload/$', views.upload, name='irods_upload')\n", (222, 270), False, 'from django.conf.urls import patterns, url\n'), ((276, 339), 'django.conf.urls.url', 'url', (['"""^upload_add/$"""', 'views.upload_add'], {'name': '"""irods_upload_add"""'}), "('^upload_add/$', views.upload_add, name='irods_upload_add')\n", (279, 339), False, 'from django.conf.urls import patterns, url\n')] |
#!/usr/bin/python
"""
Script to initialise CROP database.
"""
import sys
from crop.constants import SQL_CONNECTION_STRING, SQL_DBNAME
from crop.db import create_database
def confirm(question):
"""
Ask user to enter Y or N (case-insensitive).
:return: True if the answer is Y.
"""
answer = ""
while answer not in ["y", "n"]:
answer = input("{0} [Y/N]? ".format(question)).lower()
return answer == "y"
if __name__ == "__main__":
if confirm("Create DB?"):
create_database(SQL_CONNECTION_STRING, SQL_DBNAME)
print("Finished.") | [
"crop.db.create_database"
] | [((518, 568), 'crop.db.create_database', 'create_database', (['SQL_CONNECTION_STRING', 'SQL_DBNAME'], {}), '(SQL_CONNECTION_STRING, SQL_DBNAME)\n', (533, 568), False, 'from crop.db import create_database\n')] |
import rules
# Projectroles dependency
from projectroles import rules as pr_rules # To access common predicates
# Predicates -------------------------------------------------------------
# TODO: If we need to assign new predicates, we do it here
# Rules ------------------------------------------------------------------
# TODO: Rules should not be needed, use permissions for user rights
# Permissions ------------------------------------------------------------
# Allow viewing the sample sheet of the project
rules.add_perm(
'samplesheets.view_sheet',
pr_rules.is_project_owner
| pr_rules.is_project_delegate
| pr_rules.is_project_contributor
| pr_rules.is_project_guest,
)
# Allow creating, importing or modifying the project's sample sheet
rules.add_perm(
'samplesheets.edit_sheet',
pr_rules.is_project_owner
| pr_rules.is_project_delegate
| pr_rules.is_project_contributor,
)
# Allow managing sample sheet configuration and editing
rules.add_perm(
'samplesheets.manage_sheet',
pr_rules.is_project_owner | pr_rules.is_project_delegate,
)
# Allow exporting a sample sheet from project
rules.add_perm(
'samplesheets.export_sheet',
pr_rules.is_project_owner
| pr_rules.is_project_delegate
| pr_rules.is_project_contributor
| pr_rules.is_project_guest,
)
# Allow viewing sample sheet versions
rules.add_perm(
'samplesheets.view_versions',
pr_rules.is_project_owner
| pr_rules.is_project_delegate
| pr_rules.is_project_contributor
| pr_rules.is_project_guest,
)
# Allow creating collection structure in iRODS
rules.add_perm(
'samplesheets.create_colls',
pr_rules.is_project_owner
| pr_rules.is_project_delegate
| pr_rules.is_project_contributor,
)
# Allow deleting the project sample sheet
rules.add_perm(
'samplesheets.delete_sheet',
pr_rules.is_project_owner
| pr_rules.is_project_delegate
| pr_rules.is_project_contributor,
)
| [
"rules.add_perm"
] | [((526, 695), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.view_sheet"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate | pr_rules.\n is_project_contributor | pr_rules.is_project_guest)'], {}), "('samplesheets.view_sheet', pr_rules.is_project_owner |\n pr_rules.is_project_delegate | pr_rules.is_project_contributor |\n pr_rules.is_project_guest)\n", (540, 695), False, 'import rules\n'), ((780, 917), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.edit_sheet"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate | pr_rules.\n is_project_contributor)'], {}), "('samplesheets.edit_sheet', pr_rules.is_project_owner |\n pr_rules.is_project_delegate | pr_rules.is_project_contributor)\n", (794, 917), False, 'import rules\n'), ((990, 1095), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.manage_sheet"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate)'], {}), "('samplesheets.manage_sheet', pr_rules.is_project_owner |\n pr_rules.is_project_delegate)\n", (1004, 1095), False, 'import rules\n'), ((1150, 1321), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.export_sheet"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate | pr_rules.\n is_project_contributor | pr_rules.is_project_guest)'], {}), "('samplesheets.export_sheet', pr_rules.is_project_owner |\n pr_rules.is_project_delegate | pr_rules.is_project_contributor |\n pr_rules.is_project_guest)\n", (1164, 1321), False, 'import rules\n'), ((1376, 1548), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.view_versions"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate | pr_rules.\n is_project_contributor | pr_rules.is_project_guest)'], {}), "('samplesheets.view_versions', pr_rules.is_project_owner |\n pr_rules.is_project_delegate | pr_rules.is_project_contributor |\n pr_rules.is_project_guest)\n", (1390, 1548), False, 'import rules\n'), ((1612, 1751), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.create_colls"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate | pr_rules.\n is_project_contributor)'], {}), "('samplesheets.create_colls', pr_rules.is_project_owner |\n pr_rules.is_project_delegate | pr_rules.is_project_contributor)\n", (1626, 1751), False, 'import rules\n'), ((1810, 1949), 'rules.add_perm', 'rules.add_perm', (['"""samplesheets.delete_sheet"""', '(pr_rules.is_project_owner | pr_rules.is_project_delegate | pr_rules.\n is_project_contributor)'], {}), "('samplesheets.delete_sheet', pr_rules.is_project_owner |\n pr_rules.is_project_delegate | pr_rules.is_project_contributor)\n", (1824, 1949), False, 'import rules\n')] |
from django.conf.urls.defaults import patterns, url, include
from pycash.controllers import TaxController as controller
urlpatterns = patterns('',
(r'^upcomingList$', controller.upcomingList),
(r'^upcoming$', controller.upcoming),
url(r'^pay$', controller.pay, name="tax_pay"),
(r'^list$', controller.list),
url(r'^save$', controller.save_or_update, name="tax_save"),
(r'^update$', controller.save_or_update),
url(r'^delete$', controller.delete, name="tax_delete"),
(r'^$', controller.index)
)
| [
"django.conf.urls.defaults.url"
] | [((244, 288), 'django.conf.urls.defaults.url', 'url', (['"""^pay$"""', 'controller.pay'], {'name': '"""tax_pay"""'}), "('^pay$', controller.pay, name='tax_pay')\n", (247, 288), False, 'from django.conf.urls.defaults import patterns, url, include\n'), ((329, 386), 'django.conf.urls.defaults.url', 'url', (['"""^save$"""', 'controller.save_or_update'], {'name': '"""tax_save"""'}), "('^save$', controller.save_or_update, name='tax_save')\n", (332, 386), False, 'from django.conf.urls.defaults import patterns, url, include\n'), ((439, 492), 'django.conf.urls.defaults.url', 'url', (['"""^delete$"""', 'controller.delete'], {'name': '"""tax_delete"""'}), "('^delete$', controller.delete, name='tax_delete')\n", (442, 492), False, 'from django.conf.urls.defaults import patterns, url, include\n')] |
from abc import abstractmethod
from functools import wraps
from dd.api.workflow.utils import normalize_columns
class CallableBuilder(object):
# TODO : remove reference to dataset
# replace with lighter reference to dataset.output_table
def __init__(self, context, dataset=None):
self.context = context
self.dataset = dataset
@abstractmethod
def build(self):
raise NotImplementedError("Callable Builder is an Abstract Class")
class TransformCallableBuilder(CallableBuilder):
def __init__(self, context, dataset=None, transformation=None,
function_args=None, function_kwargs=None, write_options=None):
super(TransformCallableBuilder, self).__init__(context, dataset)
self.write_options = write_options or {}
self.transformation = transformation
self.function_kwargs = function_kwargs or {}
self.function_args = function_args or []
def bind_to(self, dataset):
return TransformCallableBuilder(self.context,
dataset,
transformation=self.transformation,
function_args=self.function_args,
function_kwargs=self.function_kwargs,
write_options=self.write_options)
def build(self):
@wraps(self.transformation)
def run():
function_args = [p.collect() for p in
self.dataset.predecessors
if hasattr(p, 'collect')]
function_args += self.function_args
new_df = self.transformation(*function_args,
**self.function_kwargs)
if type(new_df) is tuple:
if type(self.dataset.output_table) is not tuple and len(self.dataset.output_table) != len(new_df):
raise ValueError("Invalid argument output_table: output table must be specified when function"
" returns multiple dataframes")
gen_write_options = []
if type(self.write_options) is list and len(new_df) != len(self.write_options):
raise ValueError("Invalid argument write_options: if a list of write_options is specified, this"
" list must be of the same size than the number of dataframes")
elif type(self.write_options) is not list:
for i in range(len(self.dataset.output_table)):
gen_write_options.append(self.write_options)
for i in range(len(self.dataset.output_table)):
df = new_df[i]
name = self.dataset.output_table[i]
opts = gen_write_options[i]
self.context._save_dataframe(df, name, **opts)
else:
self.context._save_dataframe(new_df,
self.dataset.output_table,
**self.write_options)
return new_df
return run
def with_transformation(self, transformation):
return TransformCallableBuilder(self.context,
self.dataset,
transformation=transformation,
function_args=self.function_args,
function_kwargs=self.function_kwargs,
write_options=self.write_options)
def with_kwargs(self, **kwargs):
return TransformCallableBuilder(self.context,
self.dataset,
transformation=self.transformation,
function_args=self.function_args,
function_kwargs=kwargs,
write_options=self.write_options)
def with_write_options(self, **write_options):
return TransformCallableBuilder(self.context,
self.dataset,
transformation=self.transformation,
function_args=self.function_args,
function_kwargs=self.function_kwargs,
write_options=write_options)
def with_args(self, *args):
return TransformCallableBuilder(self.context,
self.dataset,
transformation=self.transformation,
function_args=args,
function_kwargs=self.function_kwargs,
write_options=self.write_options)
class LoadFileCallableBuilder(CallableBuilder):
def __init__(self, context, dataset=None, path=None,
reader=None, write_options=None, normalization=False):
super(LoadFileCallableBuilder, self).__init__(context, dataset)
self.path = path
self.reader = reader
self.write_options = write_options or {}
self.normalization = normalization
def bind_to(self, dataset):
return LoadFileCallableBuilder(self.context,
dataset,
path=self.path,
reader=self.reader,
write_options=self.write_options,
normalization=self.normalization)
def build(self):
def run():
df = self.reader.read(self.path)
if self.normalization:
df.columns = normalize_columns(df.columns)
self.context._save_dataframe(df,
self.dataset.output_table,
**self.write_options)
return df
return run
def load(self):
raise NotImplementedError
def with_path(self, path):
return LoadFileCallableBuilder(self.context,
self.dataset,
path=path,
reader=self.reader,
write_options=self.write_options,
normalization=self.normalization)
def with_reader(self, reader):
return LoadFileCallableBuilder(self.context,
self.dataset,
path=self.path,
reader=reader,
write_options=self.write_options,
normalization=self.normalization)
def with_write_options(self, **write_options):
return LoadFileCallableBuilder(self.context,
self.dataset,
path=self.path,
reader=self.reader,
write_options=write_options,
normalization=self.normalization)
def with_normalization(self, boolean):
return LoadFileCallableBuilder(self.context,
self.dataset,
path=self.path,
reader=self.reader,
write_options=self.write_options,
normalization=boolean)
class ModelFitCallableBuilder(CallableBuilder):
def __init__(self, context, model=None, model_address=None,
dataset=None, target=None,
columns=None, write_options=None):
super(ModelFitCallableBuilder, self).__init__(context, dataset)
self.model = model
self.model_address = model_address
self.target = target
self._columns = columns
self.write_options = write_options or {}
def bind_to(self, dataset):
return ModelFitCallableBuilder(context=self.context,
model=self.model,
model_address=self.model_address,
dataset=dataset,
target=self.target,
columns=self._columns,
write_options=self.write_options)
def build(self):
def run():
train_set = self.dataset.collect()
model = self.model.collect()
model_address = self.model.model_address
columns = train_set.columns if self._columns is None \
else self._columns
if self.target is not None:
X = train_set[columns].drop(self.target, axis=1)
y = train_set[self.target]
model.fit(X, y)
else:
X = train_set[columns]
model.fit(X)
self.context._save_model(model, model_address,
**self.write_options)
return model
return run
def with_columns(self, columns):
return ModelFitCallableBuilder(context=self.context,
model=self.model,
model_address=self.model_address,
dataset=self.dataset,
target=self.target,
columns=columns,
write_options=self.write_options)
def with_model(self, model):
return ModelFitCallableBuilder(context=self.context,
model=model,
model_address=self.model_address,
dataset=self.dataset,
target=self.target,
columns=self._columns,
write_options=self.write_options)
def with_target(self, target):
return ModelFitCallableBuilder(context=self.context,
model=self.model,
model_address=self.model_address,
dataset=self.dataset,
target=target,
columns=self._columns,
write_options=self.write_options)
def with_write_options(self, **write_options):
return ModelFitCallableBuilder(context=self.context,
model=self.model,
model_address=self.model_address,
dataset=self.dataset,
target=self.target,
columns=self._columns,
write_options=write_options)
class QueryCallableBuilder(CallableBuilder):
def __init__(self, context, query=None, dataset=None, create_table=False,
if_exists='replace'):
super(QueryCallableBuilder, self).__init__(context)
self.query = query
self.dataset = dataset
self.create_table = create_table
self.if_exists = if_exists
def bind_to(self, dataset):
return QueryCallableBuilder(context=self.context,
query=self.query,
dataset=dataset,
create_table=self.create_table,
if_exists=self.if_exists)
def build(self):
def query():
if not self.create_table:
dataframe = self.context._query(self.query)
self.context._save_dataframe(dataframe,
self.dataset.output_table,
index=False,
if_exists=self.if_exists)
return dataframe
else:
query = ""
if self.if_exists == 'replace':
query = "DROP TABLE IF EXISTS {output_table};".format(
output_table=self.dataset.output_table)
query += "CREATE TABLE {output_table} AS {query};".format(
output_table=self.dataset.output_table,
query=self.query)
self.context._execute(query)
return query
def with_query(self, query):
return QueryCallableBuilder(context=self.context,
query=query,
dataset=self.dataset,
create_table=self.create_table,
if_exists=self.if_exists)
class ActionCallableBuilder(CallableBuilder):
def __init__(self, context, dataset=None, operation=None,
operation_args=None, operation_kwargs=None):
super(ActionCallableBuilder, self).__init__(context, dataset)
self.operation = operation
self.operation_args = operation_args or []
self.operation_kwargs = operation_kwargs or {}
def bind_to(self, dataset):
return ActionCallableBuilder(context=self.context,
dataset=dataset,
operation=self.operation,
operation_args=self.operation_args,
operation_kwargs=self.operation_kwargs)
def build(self):
@wraps(self.operation)
def run():
operation_args = [p.collect() for p in
self.dataset.predecessors
if hasattr(p, 'collect')]
operation_args += self.operation_args
self.operation(*operation_args, **self.operation_kwargs)
return run
def with_operation(self, operation):
return ActionCallableBuilder(context=self.context,
dataset=self.dataset,
operation=operation,
operation_args=self.operation_args,
operation_kwargs=self.operation_kwargs)
def with_kwargs(self, **kwargs):
return ActionCallableBuilder(context=self.context,
dataset=self.dataset,
operation=self.operation,
operation_args=self.operation_args,
operation_kwargs=kwargs)
def with_args(self, *args):
return ActionCallableBuilder(context=self.context,
dataset=self.dataset,
operation=self.operation,
operation_args=args,
operation_kwargs=self.operation_kwargs)
class LoadDataFrameCallableBuilder(CallableBuilder):
def __init__(self, context, dataset=None, dataframe=None, write_options=None):
super(LoadDataFrameCallableBuilder, self).__init__(context, dataset)
self.dataframe = dataframe
self.write_options = write_options or {}
def build(self):
def run():
self.context._save_dataframe(self.dataframe,
self.dataset.output_table,
**self.write_options)
return self.dataframe
return run
def with_dataframe(self, dataframe):
return LoadDataFrameCallableBuilder(context=self.context,
dataset=self.dataset,
dataframe=dataframe,
write_options=self.write_options)
def with_write_options(self, **write_options):
return LoadDataFrameCallableBuilder(context=self.context,
dataset=self.dataset,
dataframe=self.dataframe,
write_options=write_options)
def bind_to(self, dataset):
return LoadDataFrameCallableBuilder(context=self.context,
dataset=dataset,
dataframe=self.dataframe,
write_options=self.write_options)
operators_map = {"transform": TransformCallableBuilder,
"load": LoadFileCallableBuilder,
"model_fit": ModelFitCallableBuilder,
"query": QueryCallableBuilder}
| [
"dd.api.workflow.utils.normalize_columns",
"functools.wraps"
] | [((1409, 1435), 'functools.wraps', 'wraps', (['self.transformation'], {}), '(self.transformation)\n', (1414, 1435), False, 'from functools import wraps\n'), ((14081, 14102), 'functools.wraps', 'wraps', (['self.operation'], {}), '(self.operation)\n', (14086, 14102), False, 'from functools import wraps\n'), ((5911, 5940), 'dd.api.workflow.utils.normalize_columns', 'normalize_columns', (['df.columns'], {}), '(df.columns)\n', (5928, 5940), False, 'from dd.api.workflow.utils import normalize_columns\n')] |
# Generated by Django 2.1.15 on 2021-05-21 08:24
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Company',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=255, verbose_name='title')),
],
),
migrations.CreateModel(
name='Person',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(max_length=255, verbose_name='title')),
('last_name', models.CharField(max_length=255, verbose_name='title')),
],
),
migrations.CreateModel(
name='Staff',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('position', models.CharField(max_length=255, verbose_name='position')),
('order', models.IntegerField(default=0, editable=False, verbose_name='order')),
('company', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='staff', to='sortables.Company')),
('person', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='companies', to='sortables.Person')),
],
),
]
| [
"django.db.models.ForeignKey",
"django.db.models.AutoField",
"django.db.models.CharField",
"django.db.models.IntegerField"
] | [((337, 430), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (353, 430), False, 'from django.db import migrations, models\n'), ((455, 509), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(255)', 'verbose_name': '"""title"""'}), "(max_length=255, verbose_name='title')\n", (471, 509), False, 'from django.db import migrations, models\n'), ((641, 734), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (657, 734), False, 'from django.db import migrations, models\n'), ((764, 818), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(255)', 'verbose_name': '"""title"""'}), "(max_length=255, verbose_name='title')\n", (780, 818), False, 'from django.db import migrations, models\n'), ((851, 905), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(255)', 'verbose_name': '"""title"""'}), "(max_length=255, verbose_name='title')\n", (867, 905), False, 'from django.db import migrations, models\n'), ((1036, 1129), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (1052, 1129), False, 'from django.db import migrations, models\n'), ((1157, 1214), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(255)', 'verbose_name': '"""position"""'}), "(max_length=255, verbose_name='position')\n", (1173, 1214), False, 'from django.db import migrations, models\n'), ((1243, 1311), 'django.db.models.IntegerField', 'models.IntegerField', ([], {'default': '(0)', 'editable': '(False)', 'verbose_name': '"""order"""'}), "(default=0, editable=False, verbose_name='order')\n", (1262, 1311), False, 'from django.db import migrations, models\n'), ((1342, 1455), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""staff"""', 'to': '"""sortables.Company"""'}), "(on_delete=django.db.models.deletion.CASCADE, related_name\n ='staff', to='sortables.Company')\n", (1359, 1455), False, 'from django.db import migrations, models\n'), ((1480, 1596), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""companies"""', 'to': '"""sortables.Person"""'}), "(on_delete=django.db.models.deletion.CASCADE, related_name\n ='companies', to='sortables.Person')\n", (1497, 1596), False, 'from django.db import migrations, models\n')] |
r"""
Echelle Spectrum
----------------
An abstract base class for a high resolution spectrum, for some echelle order :math:`m \in ` out of :math:`M` total orders, each with vectors for wavelength, flux, and uncertainty, e.g. :math:`F_m(\lambda)`. This class is a subclass of specutils' Spectrum1D and is intended to have its methods inherited by specific instrument classes.
EchelleSpectrum
###############
"""
import warnings
import logging
import numpy as np
import astropy
import pandas as pd
from astropy.io import fits
from astropy import units as u
from astropy.units import Quantity
from astropy.wcs import WCS, FITSFixedWarning
from astropy.nddata import StdDevUncertainty
from scipy.stats import median_abs_deviation
from scipy.interpolate import InterpolatedUnivariateSpline
from specutils.analysis import equivalent_width
from scipy.interpolate import UnivariateSpline, interp1d
from scipy.signal import savgol_filter
from astropy.constants import R_jup, R_sun, G, M_jup, R_earth, c
from astropy.modeling.physical_models import BlackBody
import specutils
from muler.utilities import apply_numpy_mask, resample_list
# from barycorrpy import get_BC_vel
from astropy.coordinates import SkyCoord, EarthLocation
from astropy.time import Time
from scipy.optimize import minimize
import matplotlib.pyplot as plt
import os
import copy
from specutils.spectra.spectral_region import SpectralRegion
from specutils.analysis import equivalent_width
log = logging.getLogger(__name__)
from astropy.io.fits.verify import VerifyWarning
warnings.simplefilter("ignore", category=VerifyWarning)
# See Issue: https://github.com/astropy/specutils/issues/779
warnings.filterwarnings(
"ignore", category=astropy.utils.exceptions.AstropyDeprecationWarning
)
warnings.filterwarnings("ignore", category=FITSFixedWarning)
# See Issue: https://github.com/astropy/specutils/issues/800
warnings.filterwarnings("ignore", category=RuntimeWarning)
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
from specutils import Spectrum1D
from specutils import SpectrumList
class EchelleSpectrum(Spectrum1D):
r"""
An abstract base class to provide common methods that will be inherited by instrument-specific classes
"""
def __init__(self, *args, **kwargs):
self.ancillary_spectra = None
super().__init__(*args, **kwargs)
@property
def snr(self):
"""The Signal-to-Noise Ratio :math:`\frac{S}{N}`, the flux divided by the uncertainty
The spectrum should have an input uncertainty, otherwise returns NaNs
"""
if self.uncertainty is not None:
if self.uncertainty.uncertainty_type == "std":
snr_estimate = self.flux / self.uncertainty.quantity
elif self.uncertainty.uncertainty_type == "ivar":
snr_estimate = self.flux * np.sqrt(self.uncertainty.quantity)
else:
message = "SNR only supports standard deviation and inverse variance uncertainty"
raise NotImplementedError(message)
else:
snr_estimate = np.repeat(np.NaN, len(self.flux)) * u.dimensionless_unscaled
return snr_estimate
@property
def available_ancillary_spectra(self):
"""The list of available ancillary spectra"""
output = []
if hasattr(self, "ancillary_spectra"):
if self.ancillary_spectra is not None:
output = [
ancillary_spectrum
for ancillary_spectrum in self.ancillary_spectra
if ancillary_spectrum in self.meta.keys()
]
return output
def estimate_barycorr(self):
"""Estimate the Barycentric Correction from the Date and Target Coordinates
Returns
-------
barycentric_corrections : float
Barycentric correction for targets in units of m/s
"""
obstime = self.astropy_time
loc = EarthLocation.of_site(self.site_name)
sc = SkyCoord(ra=self.RA, dec=self.DEC)
barycorr = sc.radial_velocity_correction(obstime=obstime, location=loc)
return barycorr
def measure_ew(self, lower=None, upper=None):
"""Measure the equivalent width of a given spectrum
Parameters
----------
lower : AstroPy Quantity or float
The short wavelength limit at which to define the EW lower bound.
If the value is a float, it assume Angstrom units.
upper : AstroPy Quantity or float
The long wavelength limit at which to define the EW upper bound.
If the value is a float, it assume Angstrom units.
Returns
-------
equivalent width : (scalar)
"""
if type(lower) is not u.Quantity:
# Assume it's Angstroms
lower = lower * u.Angstrom
upper = upper * u.Angstrom
ew = equivalent_width(self, regions=SpectralRegion(lower, upper))
return ew
def normalize(self):
"""Normalize spectrum by its median value
Returns
-------
normalized_spec : (KeckNIRSPECSpectrum)
Normalized Spectrum
"""
spec = self._copy(
spectral_axis=self.wavelength.value * self.wavelength.unit, wcs=None
)
median_flux = np.nanmedian(spec.flux.value)
# Each ancillary spectrum (e.g. sky) should also be normalized
meta_out = copy.deepcopy(spec.meta)
for ancillary_spectrum in self.available_ancillary_spectra:
meta_out[ancillary_spectrum] = meta_out[ancillary_spectrum].divide(
median_flux * spec.flux.unit, handle_meta="ff"
)
# spec.meta = meta_out
return spec.divide(
median_flux * spec.flux.unit, handle_meta="first_found"
)._copy(meta=meta_out)
def flatten_by_black_body(self, Teff):
"""Flatten the spectrum by a scaled black body, usually after deblazing
Note: This method applies mostly to high-bandwidth stellar spectra.
Parameters
----------
Teff : float
The effective temperature of the black body in Kelvin units
"""
blackbody = BlackBody(temperature=Teff * u.K)(self.wavelength)
blackbody = blackbody / np.mean(blackbody)
wl_scaled = self.wavelength
wl_scaled = wl_scaled / np.median(wl_scaled)
try:
return self.divide(blackbody / wl_scaled ** 2, handle_meta="first_found")
except u.UnitConversionError:
return self.divide(
blackbody / wl_scaled ** 2 * self.unit, handle_meta="first_found"
)
def flatten(
self,
window_length=101,
polyorder=2,
return_trend=False,
break_tolerance=5,
niters=3,
sigma=3,
mask=None,
**kwargs,
):
"""Removes the low frequency trend using scipy's Savitzky-Golay filter.
This method wraps `scipy.signal.savgol_filter`. Abridged from the
`lightkurve` method with the same name for flux time series.
Parameters
----------
window_length : int
The length of the filter window (i.e. the number of coefficients).
``window_length`` must be a positive odd integer.
polyorder : int
The order of the polynomial used to fit the samples. ``polyorder``
must be less than window_length.
return_trend : bool
If `True`, the method will return a tuple of two elements
(flattened_spec, trend_spec) where trend_spec is the removed trend.
break_tolerance : int
If there are large gaps in wavelength, flatten will split the flux into
several sub-spectra and apply `savgol_filter` to each
individually. A gap is defined as a region in wavelength larger than
`break_tolerance` times the median gap. To disable this feature,
set `break_tolerance` to None.
niters : int
Number of iterations to iteratively sigma clip and flatten. If more than one, will
perform the flatten several times, removing outliers each time.
sigma : int
Number of sigma above which to remove outliers from the flatten
mask : boolean array with length of self.wavelength
Boolean array to mask data with before flattening. Flux values where
mask is True will not be used to flatten the data. An interpolated
result will be provided for these points. Use this mask to remove
data you want to preserve, e.g. spectral regions of interest.
**kwargs : dict
Dictionary of arguments to be passed to `scipy.signal.savgol_filter`.
Returns
-------
flatten_spec : `EchelleSpectrum`
New light curve object with long-term trends removed.
If ``return_trend`` is set to ``True``, this method will also return:
trend_spec : `EchelleSpectrum`
New light curve object containing the trend that was removed.
"""
if mask is None:
mask = np.ones(len(self.wavelength), dtype=bool)
else:
# Deep copy ensures we don't change the original.
mask = copy.deepcopy(~mask)
# No NaNs
mask &= np.isfinite(self.flux)
# No outliers
mask &= np.nan_to_num(np.abs(self.flux - np.nanmedian(self.flux))) <= (
np.nanstd(self.flux) * sigma
)
for iter in np.arange(0, niters):
if break_tolerance is None:
break_tolerance = np.nan
if polyorder >= window_length:
polyorder = window_length - 1
log.warning(
"polyorder must be smaller than window_length, "
"using polyorder={}.".format(polyorder)
)
# Split the lightcurve into segments by finding large gaps in time
dlam = self.wavelength.value[mask][1:] - self.wavelength.value[mask][0:-1]
with warnings.catch_warnings(): # Ignore warnings due to NaNs
warnings.simplefilter("ignore", RuntimeWarning)
cut = np.where(dlam > break_tolerance * np.nanmedian(dlam))[0] + 1
low = np.append([0], cut)
high = np.append(cut, len(self.wavelength[mask]))
# Then, apply the savgol_filter to each segment separately
trend_signal = Quantity(
np.zeros(len(self.wavelength[mask])), unit=self.flux.unit
)
for l, h in zip(low, high):
# Reduce `window_length` and `polyorder` for short segments;
# this prevents `savgol_filter` from raising an exception
# If the segment is too short, just take the median
if np.any([window_length > (h - l), (h - l) < break_tolerance]):
trend_signal[l:h] = np.nanmedian(self.flux[mask][l:h])
else:
# Scipy outputs a warning here that is not useful, will be fixed in version 1.2
with warnings.catch_warnings():
warnings.simplefilter("ignore", FutureWarning)
trsig = savgol_filter(
x=self.flux.value[mask][l:h],
window_length=window_length,
polyorder=polyorder,
**kwargs,
)
trend_signal[l:h] = Quantity(trsig, trend_signal.unit)
# Ignore outliers;
# Note that it's possible numerical noise can cause outliers...
# If this happens you can add `1e-14` below to avoid detecting
# outliers which are merely caused by numerical noise.
mask1 = np.nan_to_num(np.abs(self.flux[mask] - trend_signal)) < (
np.nanstd(self.flux[mask] - trend_signal)
* sigma
# + Quantity(1e-14, self.flux.unit)
)
f = interp1d(
self.wavelength.value[mask][mask1],
trend_signal[mask1],
fill_value="extrapolate",
)
trend_signal = Quantity(f(self.wavelength.value), self.flux.unit)
mask[mask] &= mask1
flatten_spec = copy.deepcopy(self)
trend_spec = self._copy(flux=trend_signal)
with warnings.catch_warnings():
# ignore invalid division warnings
warnings.simplefilter("ignore", RuntimeWarning)
flatten_spec = flatten_spec.divide(trend_spec, handle_meta="ff")
if return_trend:
return flatten_spec, trend_spec
else:
return flatten_spec
def deblaze(self, method="spline"):
"""Remove blaze function from spectrum by interpolating a spline function
Note: It is recommended to remove NaNs before running this operation,
otherwise edge effects can be appear from zero-padded edges.
Returns
-------
blaze corrrected spectrum
"""
if method == "spline":
if np.any(np.isnan(self.flux)):
log.warning(
"your spectrum contains NaNs, "
"it is highly recommended to run `.remove_nans()` before deblazing"
)
spline = UnivariateSpline(self.wavelength, np.nan_to_num(self.flux), k=5)
interp_spline = spline(self.wavelength) * self.flux.unit
no_blaze = self.divide(interp_spline, handle_meta="first_found")
if "sky" in self.meta.keys():
new_sky = self.sky.divide(interp_spline, handle_meta="first_found")
no_blaze.meta["sky"] = new_sky
return no_blaze
else:
raise NotImplementedError
def barycentric_correct(self):
"""shift spectrum by barycenter velocity
Returns
-------
barycenter corrected Spectrum : (KeckNIRSPECSpectrum)
"""
bcRV = +1.0 * self.estimate_barycorr()
return self.rv_shift(bcRV)
def rv_shift(self, velocity):
"""
Shift velocity of spectrum in astropy units (or km/s if input velocity is just a float)
"""
if (
type(velocity) == float
): # If supplied velocity is not using astropy units, default to km/s
velocity = velocity * (u.km / u.s)
try:
self.radial_velocity = velocity
return self._copy(
spectral_axis=self.wavelength.value * self.wavelength.unit,
wcs=None,
)
except:
log.error(
"rv shift requires specutils version >= 1.2, you have: {}".format(
specutils.__version__
)
)
raise
def remove_nans(self):
"""Remove data points that have NaN fluxes
By default the method removes NaN's from target, sky, and lfc fibers.
Returns
-------
finite_spec : (KeckNIRSPECSpectrum)
Spectrum with NaNs removed
"""
keep_indices = (self.mask == False) & (self.flux == self.flux)
return self.apply_boolean_mask(keep_indices)
def smooth_spectrum(
self, return_model=False, optimize_kernel=False, bandwidth=150.0
):
"""Smooth the spectrum using Gaussian Process regression
Parameters
-------
return_model : (bool)
Whether or not to return the gp model, which takes a wavelength axis
as input and outputs the smooth trend
optimize_kernel : (bool)
Whether to optimize the GP hyperparameters: correlation scale and amplitude
bandwidth : (float)
The smoothing bandwidth in Angstroms. Defaults to 150 Angstrom lengthscale.
Returns
-------
smoothed_spec : (EchelleSpectrum)
Smooth version of input Spectrum
"""
try:
from celerite2 import terms
import celerite2
except ImportError:
raise ImportError(
"You need to install celerite2 to use the smoothing='celerite' method."
)
if self.uncertainty is not None:
unc = self.uncertainty.array
else:
unc = np.repeat(np.nanmedian(self.flux.value) / 100.0, len(self.flux))
# TODO: change rho to depend on the bandwidth
kernel = terms.SHOTerm(sigma=0.01, rho=bandwidth, Q=0.25)
gp = celerite2.GaussianProcess(kernel, mean=0.0)
gp.compute(self.wavelength, yerr=unc)
if optimize_kernel:
# Construct the GP model with celerite
def set_params(params, gp):
gp.mean = params[0]
theta = np.exp(params[1:])
gp.kernel = terms.SHOTerm(sigma=theta[0], rho=theta[1], Q=0.5)
gp.compute(self.wavelength.value, yerr=unc + theta[2], quiet=True)
return gp
def neg_log_like(params, gp):
gp = set_params(params, gp)
return -gp.log_likelihood(self.flux.value)
initial_params = [np.log(1), np.log(0.001), np.log(5.0), np.log(0.01)]
soln = minimize(neg_log_like, initial_params, method="L-BFGS-B", args=(gp,))
opt_gp = set_params(soln.x, gp)
else:
opt_gp = gp
mean_model = opt_gp.predict(self.flux.value, t=self.wavelength.value)
smoothed_spectrum = self.__class__(
spectral_axis=self.wavelength.value * self.wavelength.unit,
flux=mean_model * self.flux.unit,
uncertainty=None,
mask=np.zeros_like(mean_model, dtype=np.bool),
meta=copy.deepcopy(self.meta),
wcs=None,
)
if return_model:
gp_model = lambda wl: opt_gp.predict(self.flux.value, t=wl)
return (smoothed_spectrum, gp_model)
else:
return smoothed_spectrum
def plot(self, ax=None, ylo=0.6, yhi=1.2, figsize=(10, 4), **kwargs):
"""Plot a quick look of the spectrum"
Parameters
----------
ax : `~matplotlib.axes.Axes`
A matplotlib axes object to plot into. If no axes is provided,
a new one will be generated.
ylo : scalar
Lower limit of the y axis
yhi : scalar
Upper limit of the y axis
figsize : tuple
The figure size for the plot
label : str
The legend label to for plt.legend()
Returns
-------
ax : (`~matplotlib.axes.Axes`)
The axis to display and/or modify
"""
if ax is None:
fig, ax = plt.subplots(1, figsize=figsize)
ax.set_ylim(ylo, yhi)
ax.set_xlabel("$\lambda \;(\AA)$")
ax.set_ylabel("Flux")
if hasattr(self, "spectrographname"):
ax.set_title(self.spectrographname + " Spectrum")
ax.step(self.wavelength, self.flux, **kwargs, where="mid")
else:
ax.step(self.wavelength, self.flux, **kwargs, where="mid")
return ax
def remove_outliers(self, threshold=5):
"""Remove outliers above threshold
Parameters
----------
threshold : float
The sigma-clipping threshold (in units of sigma)
Returns
-------
clean_spec : (KeckNIRSPECSpectrum)
Cleaned version of input Spectrum
"""
residual = self.flux - self.smooth_spectrum().flux
mad = median_abs_deviation(residual.value, nan_policy="omit")
keep_indices = (np.abs(residual.value) < (threshold * mad)) == True
return self.apply_boolean_mask(keep_indices)
def trim_edges(self, limits=None):
"""Trim the order edges, which falloff in SNR
This method applies limits on absolute x pixel values, regardless
of the order of previous destructive operations, which may not
be the intended behavior in some applications.
Parameters
----------
limits : tuple
The index bounds (lo, hi) for trimming the order
Returns
-------
trimmed_spec : (EchelleSpectrum)
Trimmed version of input Spectrum
"""
if limits is None:
limits = self.noisy_edges
lo, hi = limits
if self.meta is not None:
if "x_values" in self.meta.keys():
x_values = self.meta["x_values"]
else:
log.warn(
"The spectrum metadata is missing its native pixel location labels. "
"Proceeding by assuming contiguous pixel labels, which may not be what you want."
)
x_values = np.arange(len(self.wavelength))
keep_indices = (x_values > lo) & (x_values < hi)
return self.apply_boolean_mask(keep_indices)
def estimate_uncertainty(self):
"""Estimate the uncertainty based on residual after smoothing
Returns
-------
uncertainty : (np.float)
Typical uncertainty
"""
residual = self.flux - self.smooth_spectrum().flux
return median_abs_deviation(residual.value)
def to_HDF5(self, path, file_basename):
"""Export to spectral order to HDF5 file format
This format is required for per-order Starfish input
Parameters
----------
path : str
The directory destination for the HDF5 file
file_basename : str
The basename of the file to which the order number and extension
are appended. Typically source name that matches a database entry.
"""
try:
import h5py
except ImportError:
raise ImportError("You need to install h5py to export to the HDF5 format.")
grating_order = self.meta["m"]
out_path = path + "/" + file_basename + "_m{:03d}.hdf5".format(grating_order)
# The mask should be ones everywhere
mask_out = np.ones(len(self.wavelength), dtype=int)
f_new = h5py.File(out_path, "w")
f_new.create_dataset("fls", data=self.flux.value)
f_new.create_dataset("wls", data=self.wavelength.to(u.Angstrom).value)
f_new.create_dataset("sigmas", data=self.uncertainty.array)
f_new.create_dataset("masks", data=mask_out)
f_new.close()
def resample_list(self, specList, **kwargs):
"""
Resample a single EchelleSpectrum object into a EchelleSpectrumList object.
Useful for converting models into echelle spectra with multiple orders.
"""
return resample_list(self, specList, **kwargs)
def apply_boolean_mask(self, mask):
"""Apply a boolean mask to the spectrum and any available ancillary spectra
Parameters
----------
mask: boolean mask, typically a numpy array
The boolean mask with numpy-style masking: True means "keep" that index and
False means discard that index
"""
spec = apply_numpy_mask(self, mask)
for ancillary_spectrum in self.available_ancillary_spectra:
spec.meta[ancillary_spectrum] = apply_numpy_mask(
spec.meta[ancillary_spectrum], mask
)
return spec
class EchelleSpectrumList(SpectrumList):
r"""
An enhanced container for a list of Echelle spectral orders
"""
def __init__(self, *args, **kwargs):
self.normalization_order_index = 0
super().__init__(*args, **kwargs)
def normalize(self, order_index=0):
"""Normalize all orders to one of the other orders"""
index = self.normalization_order_index
median_flux = copy.deepcopy(np.nanmedian(self[index].flux))
for i in range(len(self)):
self[i] = self[i].divide(median_flux, handle_meta="first_found")
return self
def remove_nans(self):
"""Remove all the NaNs"""
# TODO: is this in-place overriding of self allowed?
# May have unintended consequences?
# Consider making a copy instead...
for i in range(len(self)):
self[i] = self[i].remove_nans()
return self
def remove_outliers(self, threshold=5):
"""Remove all the outliers
Parameters
----------
threshold : float
The sigma-clipping threshold (in units of sigma)
"""
for i in range(len(self)):
self[i] = self[i].remove_outliers(threshold=threshold)
return self
def trim_edges(self, limits=None):
"""Trim all the edges"""
for i in range(len(self)):
self[i] = self[i].trim_edges(limits)
return self
def deblaze(self, method="spline"):
"""Remove blaze function from all orders by interpolating a spline function
Note: It is recommended to remove NaNs before running this operation,
otherwise effects can be appear from zero-padded edges.
"""
spec_out = copy.deepcopy(self)
for i in range(len(self)):
spec_out[i] = self[i].deblaze(method=method)
return spec_out
def flatten_by_black_body(self, Teff):
"""Flatten by black body"""
spec_out = copy.deepcopy(self)
index = self.normalization_order_index
median_wl = copy.deepcopy(np.nanmedian(self[index].wavelength))
blackbody_func = BlackBody(temperature=Teff * u.K)
blackbody_ref = blackbody_func(median_wl)
for i in range(len(spec_out)):
blackbody = (
blackbody_func(spec_out[i].wavelength)
/ blackbody_ref
/ (spec_out[i].wavelength / median_wl) ** 2
)
try:
spec_out[i] = spec_out[i].divide(blackbody, handle_meta="first_found")
except u.UnitConversionError:
spec_out[i] = spec_out[i].divide(
blackbody * self.unit, handle_meta="first_found"
)
return spec_out
def to_HDF5(self, path, file_basename):
"""Save all spectral orders to the HDF5 file format"""
for i in range(len(self)):
self[i].to_HDF5(path, file_basename)
def stitch(self):
"""Stitch all the spectra together, assuming zero overlap in wavelength."""
spec = copy.deepcopy(self)
wls = (
np.hstack([spec[i].wavelength.value for i in range(len(spec))])
* spec[0].wavelength.unit
)
fluxes = (
np.hstack([spec[i].flux.value for i in range(len(spec))])
* spec[0].flux.unit
)
if spec[0].uncertainty is not None:
# HACK We assume if one order has it, they all do, and that it's StdDev
unc = np.hstack([spec[i].uncertainty.array for i in range(len(self))])
unc_out = StdDevUncertainty(unc)
else:
unc_out = None
# Stack the x_values:
x_values = np.hstack([spec[i].meta["x_values"] for i in range(len(spec))])
meta_out = copy.deepcopy(spec[0].meta)
meta_out["x_values"] = x_values
for ancillary_spectrum in spec[0].available_ancillary_spectra:
if spec[0].meta[ancillary_spectrum].meta is not None:
meta_of_meta = spec[0].meta[ancillary_spectrum].meta
x_values = np.hstack(
[
spec[i].meta[ancillary_spectrum].meta["x_values"]
for i in range(len(spec))
]
)
meta_of_meta["x_values"] = x_values
else:
meta_of_meta = None
wls_anc = np.hstack(
[spec[i].meta[ancillary_spectrum].wavelength for i in range(len(spec))]
)
fluxes_anc = np.hstack(
[spec[i].meta[ancillary_spectrum].flux for i in range(len(spec))]
)
meta_out[ancillary_spectrum] = spec[0].__class__(
spectral_axis=wls_anc, flux=fluxes_anc, meta=meta_of_meta
)
return spec[0].__class__(
spectral_axis=wls, flux=fluxes, uncertainty=unc_out, meta=meta_out, wcs=None
)
def plot(self, **kwargs):
"""Plot the entire spectrum list"""
if not "ax" in kwargs:
ax = self[0].plot(figsize=(25, 4), **kwargs)
for i in range(1, len(self)):
self[i].plot(ax=ax, **kwargs)
return ax
else:
for i in range(1, len(self)):
self[i].plot(**kwargs)
def __add__(self, other):
"""Bandmath addition"""
spec_out = copy.deepcopy(self)
for i in range(len(self)):
spec_out[i] = self[i] + other[i]
return spec_out
def __sub__(self, other):
"""Bandmath subtraction"""
spec_out = copy.deepcopy(self)
for i in range(len(self)):
spec_out[i] = self[i] - other[i]
return spec_out
def __mul__(self, other):
"""Bandmath multiplication"""
spec_out = copy.deepcopy(self)
for i in range(len(self)):
spec_out[i] = self[i] * other[i]
return spec_out
def __truediv__(self, other):
"""Bandmath division"""
spec_out = copy.deepcopy(self)
for i in range(len(self)):
spec_out[i] = self[i] / other[i]
return spec_out
def rv_shift(self, velocity):
"""
Shift velocity of spectrum in km s^-1
"""
spec_out = copy.deepcopy(self)
for i in range(len(self)):
spec_out[i] = self[i].rv_shift(velocity)
return spec_out
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'numpy.nanmedian', 'np.nanmedian', (['dlam'], {}), '(dlam)\n', (10346, 10352), True, 'import numpy as np\n')] |
import nltk
import string
import requests
import json
from io import StringIO
from html.parser import HTMLParser
import os
import time
from sys import platform
path = ""
if platform == "linux" or platform == "linux2":
path = os.path.dirname(os.path.realpath(__file__)).replace("test","data/nltk")
elif platform == "darwin":
path = os.path.dirname(os.path.realpath(__file__)).replace("test","data/nltk")
elif platform == "win32":
path = os.path.dirname(os.path.realpath(__file__)).replace("test","data\\nltk")
nltk.data.path.append(path)
def nltk_download(name,find):
try:
print(nltk.data.find(find))
except LookupError:
nltk.download(name, download_dir = path)
def main():
nltk_download('punkt','tokenizers/punkt')
nltk_download('wordnet','corpora/wordnet')
nltk_download('omw-1.4','corpora/omw-1.4')
print("Ready")
if __name__ == "__main__":
main() | [
"os.path.realpath",
"nltk.data.find",
"nltk.data.path.append",
"nltk.download"
] | [((541, 568), 'nltk.data.path.append', 'nltk.data.path.append', (['path'], {}), '(path)\n', (562, 568), False, 'import nltk\n'), ((627, 647), 'nltk.data.find', 'nltk.data.find', (['find'], {}), '(find)\n', (641, 647), False, 'import nltk\n'), ((683, 721), 'nltk.download', 'nltk.download', (['name'], {'download_dir': 'path'}), '(name, download_dir=path)\n', (696, 721), False, 'import nltk\n'), ((258, 284), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (274, 284), False, 'import os\n'), ((370, 396), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (386, 396), False, 'import os\n'), ((481, 507), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (497, 507), False, 'import os\n')] |
#!/usr/bin/env python
# coding=utf-8
from db import Model
__doc__ = "记录数据库层封装"
recordModel = Model('record')
#类添加函数
# join查找 | [
"db.Model"
] | [((96, 111), 'db.Model', 'Model', (['"""record"""'], {}), "('record')\n", (101, 111), False, 'from db import Model\n')] |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Hillas shower parametrization.
TODO:
-----
- Should have a separate function or option to compute 3rd order
moments + asymmetry (which are not always needed)
- remove alpha calculation (which is only about (0,0), and make a get
alpha function that does it from an arbitrary point given a
pre-computed list of parameters
"""
import numpy as np
from astropy.units import Quantity
from collections import namedtuple
__all__ = [
'MomentParameters',
'HighOrderMomentParameters',
'hillas_parameters',
]
MomentParameters = namedtuple(
"MomentParameters",
"size,cen_x,cen_y,length,width,r,phi,psi,miss"
)
"""Shower moment parameters up to second order.
See also
--------
HighOrderMomentParameters, hillas_parameters, hillas_parameters_2
"""
HighOrderMomentParameters = namedtuple(
"HighOrderMomentParameters",
"skewness,kurtosis,asymmetry"
)
"""Shower moment parameters of third order.
See also
--------
MomentParameters, hillas_parameters, hillas_parameters_2
"""
def hillas_parameters_1(pix_x, pix_y, image):
"""Compute Hillas parameters for a given shower image.
Reference: Appendix of the Whipple Crab paper Weekes et al. (1998)
http://adsabs.harvard.edu/abs/1989ApJ...342..379W
(corrected for some obvious typos)
Parameters
----------
pix_x : array_like
Pixel x-coordinate
pix_y : array_like
Pixel y-coordinate
image : array_like
Pixel values corresponding
Returns
-------
hillas_parameters : `MomentParameters`
"""
pix_x = Quantity(np.asanyarray(pix_x, dtype=np.float64)).value
pix_y = Quantity(np.asanyarray(pix_y, dtype=np.float64)).value
image = np.asanyarray(image, dtype=np.float64)
assert pix_x.shape == image.shape
assert pix_y.shape == image.shape
# Compute image moments
_s = np.sum(image)
m_x = np.sum(image * pix_x) / _s
m_y = np.sum(image * pix_y) / _s
m_xx = np.sum(image * pix_x * pix_x) / _s # note: typo in paper
m_yy = np.sum(image * pix_y * pix_y) / _s
m_xy = np.sum(image * pix_x * pix_y) / _s # note: typo in paper
# Compute major axis line representation y = a * x + b
S_xx = m_xx - m_x * m_x
S_yy = m_yy - m_y * m_y
S_xy = m_xy - m_x * m_y
d = S_yy - S_xx
temp = d * d + 4 * S_xy * S_xy
a = (d + np.sqrt(temp)) / (2 * S_xy)
b = m_y - a * m_x
# Compute Hillas parameters
width_2 = (S_yy + a * a * S_xx - 2 * a * S_xy) / (1 + a * a)
width = np.sqrt(width_2)
length_2 = (S_xx + a * a * S_yy + 2 * a * S_xy) / (1 + a * a)
length = np.sqrt(length_2)
miss = np.abs(b / (1 + a * a))
r = np.sqrt(m_x * m_x + m_y * m_y)
phi = np.arctan2(m_y, m_x)
# Compute azwidth by transforming to (p, q) coordinates
sin_theta = m_y / r
cos_theta = m_x / r
q = (m_x - pix_x) * sin_theta + (pix_y - m_y) * cos_theta
m_q = np.sum(image * q) / _s
m_qq = np.sum(image * q * q) / _s
azwidth_2 = m_qq - m_q * m_q
azwidth = np.sqrt(azwidth_2)
return MomentParameters(size=_s, cen_x=m_x, cen_y=m_y, length=length,
width=width, r=r, phi=phi, psi=None, miss=miss)
def hillas_parameters_2(pix_x, pix_y, image):
"""Compute Hillas parameters for a given shower image.
Alternate implementation of `hillas_parameters` ...
in the end we'll just keep one, but we're using Hilllas parameter
computation as an example for performance checks.
Parameters
----------
pix_x : array_like
Pixel x-coordinate
pix_y : array_like
Pixel y-coordinate
image : array_like
Pixel values corresponding
Returns
-------
hillas_parameters : `MomentParameters`
"""
pix_x = Quantity(np.asanyarray(pix_x, dtype=np.float64)).value
pix_y = Quantity(np.asanyarray(pix_y, dtype=np.float64)).value
image = np.asanyarray(image, dtype=np.float64)
assert pix_x.shape == image.shape
assert pix_y.shape == image.shape
# Compute image moments (done in a bit faster way, but putting all
# into one 2D array, where each row will be summed to calculate a
# moment) However, this doesn't avoid a temporary created for the
# 2D array
size = image.sum()
momdata = np.row_stack([pix_x,
pix_y,
pix_x * pix_x,
pix_y * pix_y,
pix_x * pix_y]) * image
moms = momdata.sum(axis=1) / size
# calculate variances
vx2 = moms[2] - moms[0] ** 2
vy2 = moms[3] - moms[1] ** 2
vxy = moms[4] - moms[0] * moms[1]
# common factors:
dd = vy2 - vx2
zz = np.sqrt(dd ** 2 + 4.0 * vxy ** 2)
# miss
uu = 1.0 + dd / zz
vv = 2.0 - uu
miss = np.sqrt((uu * moms[0] ** 2 + vv * moms[1] ** 2) / 2.0
- moms[0] * moms[1] * 2.0 * vxy / zz)
# shower shape parameters
width = np.sqrt(vx2 + vy2 - zz)
length = np.sqrt(vx2 + vy2 + zz)
azwidth = np.sqrt(moms[2] + moms[3] - zz)
# rotation angle of ellipse relative to centroid
tanpsi_numer = (dd + zz) * moms[1] + 2.0 * vxy * moms[0]
tanpsi_denom = (2 * vxy * moms[1]) - (dd - zz) * moms[0]
psi = np.pi / 2.0 + np.arctan2(tanpsi_numer, tanpsi_denom)
# polar coordinates of centroid
rr = np.hypot(moms[0], moms[1])
phi = np.arctan2(moms[1], moms[0])
return MomentParameters(size=size, cen_x=moms[0], cen_y=moms[1],
length=length, width=width, r=rr, phi=phi,
psi=psi, miss=miss)
# use the 2 version by default
hillas_parameters = hillas_parameters_2
| [
"numpy.abs",
"collections.namedtuple",
"numpy.sqrt",
"numpy.asanyarray",
"numpy.sum",
"numpy.arctan2",
"numpy.row_stack",
"numpy.hypot"
] | [((608, 686), 'collections.namedtuple', 'namedtuple', (['"""MomentParameters"""', '"""size,cen_x,cen_y,length,width,r,phi,psi,miss"""'], {}), "('MomentParameters', 'size,cen_x,cen_y,length,width,r,phi,psi,miss')\n", (618, 686), False, 'from collections import namedtuple\n'), ((863, 933), 'collections.namedtuple', 'namedtuple', (['"""HighOrderMomentParameters"""', '"""skewness,kurtosis,asymmetry"""'], {}), "('HighOrderMomentParameters', 'skewness,kurtosis,asymmetry')\n", (873, 933), False, 'from collections import namedtuple\n'), ((1751, 1789), 'numpy.asanyarray', 'np.asanyarray', (['image'], {'dtype': 'np.float64'}), '(image, dtype=np.float64)\n', (1764, 1789), True, 'import numpy as np\n'), ((1904, 1917), 'numpy.sum', 'np.sum', (['image'], {}), '(image)\n', (1910, 1917), True, 'import numpy as np\n'), ((2548, 2564), 'numpy.sqrt', 'np.sqrt', (['width_2'], {}), '(width_2)\n', (2555, 2564), True, 'import numpy as np\n'), ((2644, 2661), 'numpy.sqrt', 'np.sqrt', (['length_2'], {}), '(length_2)\n', (2651, 2661), True, 'import numpy as np\n'), ((2673, 2696), 'numpy.abs', 'np.abs', (['(b / (1 + a * a))'], {}), '(b / (1 + a * a))\n', (2679, 2696), True, 'import numpy as np\n'), ((2705, 2735), 'numpy.sqrt', 'np.sqrt', (['(m_x * m_x + m_y * m_y)'], {}), '(m_x * m_x + m_y * m_y)\n', (2712, 2735), True, 'import numpy as np\n'), ((2746, 2766), 'numpy.arctan2', 'np.arctan2', (['m_y', 'm_x'], {}), '(m_y, m_x)\n', (2756, 2766), True, 'import numpy as np\n'), ((3056, 3074), 'numpy.sqrt', 'np.sqrt', (['azwidth_2'], {}), '(azwidth_2)\n', (3063, 3074), True, 'import numpy as np\n'), ((3925, 3963), 'numpy.asanyarray', 'np.asanyarray', (['image'], {'dtype': 'np.float64'}), '(image, dtype=np.float64)\n', (3938, 3963), True, 'import numpy as np\n'), ((4722, 4755), 'numpy.sqrt', 'np.sqrt', (['(dd ** 2 + 4.0 * vxy ** 2)'], {}), '(dd ** 2 + 4.0 * vxy ** 2)\n', (4729, 4755), True, 'import numpy as np\n'), ((4821, 4916), 'numpy.sqrt', 'np.sqrt', (['((uu * moms[0] ** 2 + vv * moms[1] ** 2) / 2.0 - moms[0] * moms[1] * 2.0 *\n vxy / zz)'], {}), '((uu * moms[0] ** 2 + vv * moms[1] ** 2) / 2.0 - moms[0] * moms[1] *\n 2.0 * vxy / zz)\n', (4828, 4916), True, 'import numpy as np\n'), ((4976, 4999), 'numpy.sqrt', 'np.sqrt', (['(vx2 + vy2 - zz)'], {}), '(vx2 + vy2 - zz)\n', (4983, 4999), True, 'import numpy as np\n'), ((5013, 5036), 'numpy.sqrt', 'np.sqrt', (['(vx2 + vy2 + zz)'], {}), '(vx2 + vy2 + zz)\n', (5020, 5036), True, 'import numpy as np\n'), ((5051, 5082), 'numpy.sqrt', 'np.sqrt', (['(moms[2] + moms[3] - zz)'], {}), '(moms[2] + moms[3] - zz)\n', (5058, 5082), True, 'import numpy as np\n'), ((5370, 5396), 'numpy.hypot', 'np.hypot', (['moms[0]', 'moms[1]'], {}), '(moms[0], moms[1])\n', (5378, 5396), True, 'import numpy as np\n'), ((5407, 5435), 'numpy.arctan2', 'np.arctan2', (['moms[1]', 'moms[0]'], {}), '(moms[1], moms[0])\n', (5417, 5435), True, 'import numpy as np\n'), ((1928, 1949), 'numpy.sum', 'np.sum', (['(image * pix_x)'], {}), '(image * pix_x)\n', (1934, 1949), True, 'import numpy as np\n'), ((1965, 1986), 'numpy.sum', 'np.sum', (['(image * pix_y)'], {}), '(image * pix_y)\n', (1971, 1986), True, 'import numpy as np\n'), ((2003, 2032), 'numpy.sum', 'np.sum', (['(image * pix_x * pix_x)'], {}), '(image * pix_x * pix_x)\n', (2009, 2032), True, 'import numpy as np\n'), ((2072, 2101), 'numpy.sum', 'np.sum', (['(image * pix_y * pix_y)'], {}), '(image * pix_y * pix_y)\n', (2078, 2101), True, 'import numpy as np\n'), ((2118, 2147), 'numpy.sum', 'np.sum', (['(image * pix_x * pix_y)'], {}), '(image * pix_x * pix_y)\n', (2124, 2147), True, 'import numpy as np\n'), ((2948, 2965), 'numpy.sum', 'np.sum', (['(image * q)'], {}), '(image * q)\n', (2954, 2965), True, 'import numpy as np\n'), ((2982, 3003), 'numpy.sum', 'np.sum', (['(image * q * q)'], {}), '(image * q * q)\n', (2988, 3003), True, 'import numpy as np\n'), ((4305, 4378), 'numpy.row_stack', 'np.row_stack', (['[pix_x, pix_y, pix_x * pix_x, pix_y * pix_y, pix_x * pix_y]'], {}), '([pix_x, pix_y, pix_x * pix_x, pix_y * pix_y, pix_x * pix_y])\n', (4317, 4378), True, 'import numpy as np\n'), ((5284, 5322), 'numpy.arctan2', 'np.arctan2', (['tanpsi_numer', 'tanpsi_denom'], {}), '(tanpsi_numer, tanpsi_denom)\n', (5294, 5322), True, 'import numpy as np\n'), ((1626, 1664), 'numpy.asanyarray', 'np.asanyarray', (['pix_x'], {'dtype': 'np.float64'}), '(pix_x, dtype=np.float64)\n', (1639, 1664), True, 'import numpy as np\n'), ((1693, 1731), 'numpy.asanyarray', 'np.asanyarray', (['pix_y'], {'dtype': 'np.float64'}), '(pix_y, dtype=np.float64)\n', (1706, 1731), True, 'import numpy as np\n'), ((2388, 2401), 'numpy.sqrt', 'np.sqrt', (['temp'], {}), '(temp)\n', (2395, 2401), True, 'import numpy as np\n'), ((3800, 3838), 'numpy.asanyarray', 'np.asanyarray', (['pix_x'], {'dtype': 'np.float64'}), '(pix_x, dtype=np.float64)\n', (3813, 3838), True, 'import numpy as np\n'), ((3867, 3905), 'numpy.asanyarray', 'np.asanyarray', (['pix_y'], {'dtype': 'np.float64'}), '(pix_y, dtype=np.float64)\n', (3880, 3905), True, 'import numpy as np\n')] |
# Generated by Django 4.0.1 on 2022-01-12 23:44
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('points', '0003_spend_alter_transaction_timestamp'),
]
operations = [
migrations.AddField(
model_name='spend',
name='receipt',
field=models.TextField(blank=True, null=True),
),
]
| [
"django.db.models.TextField"
] | [((348, 387), 'django.db.models.TextField', 'models.TextField', ([], {'blank': '(True)', 'null': '(True)'}), '(blank=True, null=True)\n', (364, 387), False, 'from django.db import migrations, models\n')] |
import json
from multiprocessing import Pool, cpu_count
from catti.IO.portfolio import loadPortfolioInfo
from catti.IO.general import *
from catti.indicators.calculations import indicatorsCalculator
from catti.indicators.signals import signalsCalculator
from catti.indicators.signalsValidation import signalsValidator
from catti.indicators.reporters import signalsReport, validatedSignalsReport
import tqdm
def calculate(target, chunkPercentage=2, skipCalculated=True, extraArgs=1, verbose=False):
# Each target has its own way of processing the data.
functionSelection = {
'indicators': indicatorsCalculator,
'signals': signalsCalculator,
'validated signals': signalsValidator,
'signals report': signalsReport,
'validated signals report': validatedSignalsReport
}
selectedFunction = functionSelection[target]
# We load portfolio info, to grab the securities' ticker names.
portfolio = loadPortfolioInfo()
securitiesToCalculate = portfolio['securities']
# We skip already calculated target data if requested.
if skipCalculated:
securitiesToCalculate = skip(target, portfolio['securities'], True)
# The new size of all the tickers after skipping calculated.
size = len(securitiesToCalculate)
# A percentage of the total data to be calculated per chunk.
chunkSize = int(size * chunkPercentage/100)
# If the chunkPercentage is relatively too small, the chunkSize becomes zero.
# In that case, the user basically wants to calculate 1 security at a time,
# which is actually futile. Nevertheless, we need to avoid 0.
if chunkSize == 0:
chunkSize = 1
print('Calculating at most {} securites per chunk, which is ~{}% of the total ({})'.format(chunkSize, chunkPercentage, size))
# We create a list of lists that consists of the chunks.
chunks = splitter(securitiesToCalculate, chunkSize)
# We need the number of splits to track the progress.
splits = size // chunkSize
# Initialize the results of all the chunks, to generate reports later.
chunkResults = []
for chunk in tqdm.tqdm(chunks, desc='Chunk completion', total=splits):
# For each chunk, we load whatever is required and
# we pass the arguments to the corresponding function.
args = parameters(target, chunk, extraArgs)
# The chunksize=1 parameter, basically gives 1 security to work on per thread.
# This is intented because the calculations are already intensive. Feel free to tweak.
with Pool(processes=cpu_count()) as pool:
if 'report' in target:
results = pool.starmap(selectedFunction, tqdm.tqdm(args, desc='Calculating {}'.format(target)), chunksize=1)
chunkResults.extend(results)
else:
pool.starmap(selectedFunction, tqdm.tqdm(args, desc='Calculating {}'.format(target)), chunksize=1)
# Aggregate results of indicators' data for whole portfolio.
if 'report' in target:
manageReports(target, chunkResults, verbose)
def parameters(target, chunk, extraArgs, verbose=False):
# Each target we calculate depends on other data to be calculated.
targetRequirement = {
'indicators': 'historical',
'signals': 'indicators',
'validated signals': 'signals',
'signals report': 'signals',
'validated signals report': 'validated signals'
}
requiredTarget = targetRequirement[target]
# NOTE: Cyclomatic complexity: HIGH.
# We either want to generate reports...
if 'report' in target:
# We always need to load the data required for the target's function if we proceed below.
targetData = loadChunkParallel(requiredTarget, chunk)
if target == 'signals report':
# The function header is "signalsReport(securityName, signals, verbose)"
return [(security, targetData[security], verbose) for security in targetData]
elif target == 'validated signals report':
# The function header is "signalsReport(securityName, validatedSignals, verbose)"
return [(security, targetData[security], verbose) for security in targetData]
# ...or we want to either calculate indicator signals or to validate them.
else:
# We always need to load historical data for comparisons.
historicalData = loadChunkParallel('historical', chunk)
# If we calculate indicators, our target data is only historical.
if target == 'indicators':
# The function header is "indicatorsCalculator(securityName, security, verbose)"
return [(security, historicalData[security], verbose) for security in historicalData]
# We always need to load the data required for the target's function if we proceed below.
targetData = loadChunkParallel(requiredTarget, chunk)
if target == 'signals':
# The function header is "signalsCalculator(securityName, security, indicators)"
return [(security, historicalData[security], targetData[security]) for security in historicalData]
elif target == 'validated signals':
# The function header is "signalsValidator(securityName, security, signals, days)"
return [(security, historicalData[security], targetData[security], extraArgs) for security in historicalData]
def manageReports(target, chunkResults, verbose=False):
aggregatedResults = {}
checkCreateFolder(target, 'indicators')
# Initialize the percentages for each indicator
for security in chunkResults:
for indicator in security:
aggregatedResults[indicator] = {}
for key in security[indicator]:
aggregatedResults[indicator][key] = 0.0
# Aggregate them...
for security in chunkResults:
for indicator in security:
for key in security[indicator]:
# We divide with the number of total loaded securities, because they might be less than those
# included in portfolio. Those with insufficient data are ignored.
aggregatedResults[indicator][key] += security[indicator][key] / len(chunkResults) # might carry errors
# Save them individually...
for indicator in aggregatedResults:
subFolder = os.path.join(folders[target], 'indicators')
fileName = os.path.join(subFolder, indicator + '.json')
with open(fileName, 'w') as f:
if verbose:
print('[+] Saving aggregated report for {}'.format(indicator))
json.dump(aggregatedResults[indicator], f, indent=1)
# ...and all together
allTogetherFilename = os.path.join(subFolder, 'combined.json')
with open(allTogetherFilename, 'w') as f:
if verbose:
print('[+] Saving aggregated report for all indicators')
json.dump(aggregatedResults, f, indent=1) | [
"multiprocessing.cpu_count",
"catti.IO.portfolio.loadPortfolioInfo",
"tqdm.tqdm",
"json.dump"
] | [((976, 995), 'catti.IO.portfolio.loadPortfolioInfo', 'loadPortfolioInfo', ([], {}), '()\n', (993, 995), False, 'from catti.IO.portfolio import loadPortfolioInfo\n'), ((2184, 2240), 'tqdm.tqdm', 'tqdm.tqdm', (['chunks'], {'desc': '"""Chunk completion"""', 'total': 'splits'}), "(chunks, desc='Chunk completion', total=splits)\n", (2193, 2240), False, 'import tqdm\n'), ((7098, 7139), 'json.dump', 'json.dump', (['aggregatedResults', 'f'], {'indent': '(1)'}), '(aggregatedResults, f, indent=1)\n', (7107, 7139), False, 'import json\n'), ((6797, 6849), 'json.dump', 'json.dump', (['aggregatedResults[indicator]', 'f'], {'indent': '(1)'}), '(aggregatedResults[indicator], f, indent=1)\n', (6806, 6849), False, 'import json\n'), ((2636, 2647), 'multiprocessing.cpu_count', 'cpu_count', ([], {}), '()\n', (2645, 2647), False, 'from multiprocessing import Pool, cpu_count\n')] |
import indicoio
from celery import Celery
from flask import Flask
from flask_bootstrap import Bootstrap
from flask_googlemaps import GoogleMaps
from flask_mail import Mail
from flask_mongoengine import MongoEngine
from app.celery.factory import init_celery
from config import config, DEVELOPMENT_CONFIG_NAME
celery = Celery('app')
mail = Mail()
db = MongoEngine()
bootstrap = Bootstrap()
def create_app(config_name=DEVELOPMENT_CONFIG_NAME):
app = Flask(__name__)
app.config.from_object(config[config_name])
register_extensions(app)
register_blueprints(app)
init_celery(app, celery)
GoogleMaps(app)
set_indico_key(config[config_name])
return app
def set_indico_key(cfg):
indicoio.config.api_key = cfg.INDICO_KEY
def register_extensions(app):
mail.init_app(app)
db.init_app(app)
bootstrap.init_app(app)
def register_blueprints(app):
from app.blueprints.main import main
from app.blueprints.auth import auth
from app.blueprints.account import account
from app.blueprints.errors import errors
app.register_blueprint(main)
app.register_blueprint(auth)
app.register_blueprint(account)
app.register_blueprint(errors)
| [
"flask_mail.Mail",
"flask.Flask",
"celery.Celery",
"flask_googlemaps.GoogleMaps",
"app.celery.factory.init_celery",
"flask_bootstrap.Bootstrap",
"flask_mongoengine.MongoEngine"
] | [((319, 332), 'celery.Celery', 'Celery', (['"""app"""'], {}), "('app')\n", (325, 332), False, 'from celery import Celery\n'), ((340, 346), 'flask_mail.Mail', 'Mail', ([], {}), '()\n', (344, 346), False, 'from flask_mail import Mail\n'), ((352, 365), 'flask_mongoengine.MongoEngine', 'MongoEngine', ([], {}), '()\n', (363, 365), False, 'from flask_mongoengine import MongoEngine\n'), ((378, 389), 'flask_bootstrap.Bootstrap', 'Bootstrap', ([], {}), '()\n', (387, 389), False, 'from flask_bootstrap import Bootstrap\n'), ((455, 470), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (460, 470), False, 'from flask import Flask\n'), ((582, 606), 'app.celery.factory.init_celery', 'init_celery', (['app', 'celery'], {}), '(app, celery)\n', (593, 606), False, 'from app.celery.factory import init_celery\n'), ((612, 627), 'flask_googlemaps.GoogleMaps', 'GoogleMaps', (['app'], {}), '(app)\n', (622, 627), False, 'from flask_googlemaps import GoogleMaps\n')] |
# -*- coding:utf-8 -*-
import logging
import stores
release = "0.1.1"
logging.getLogger("dbsync")
class DBSync():
def __init__(self):
pass
def syncer(self, syncer):
return self
def validator(self, validator):
return self
def serializer(self, serializer):
return self
def notifier(self, notifier):
return self
def start(self):
pass
def stop(self):
pass
class Engine():
def __init__(self):
pass
| [
"logging.getLogger"
] | [((72, 99), 'logging.getLogger', 'logging.getLogger', (['"""dbsync"""'], {}), "('dbsync')\n", (89, 99), False, 'import logging\n')] |
# -*- coding: utf-8 -*-
import asyncio
from config import (CHECK_SERVER_INTERVAL, CHECK_SERVER_INTERVAL_MAX,
CRON_LOOP_INTERVAL)
from discord import Activity, ActivityType
from discord.errors import Forbidden, NotFound
from discord.ext import commands, tasks
from modules.db import Servers
from modules.logging import logger
from modules.utils import embed_generator, get_server_info, stop_server
class ServersCron(commands.Cog):
def __init__(self, bot: commands.Bot):
self.bot = bot
self.loop = asyncio.get_event_loop()
self.crontab.start()
logger.info("Cron started")
@tasks.loop(seconds=CRON_LOOP_INTERVAL, reconnect=True)
async def crontab(self):
await self.bot.wait_until_ready()
channels = await Servers.filter(worked=True).group_by("channel").values_list("channel", flat=True)
for channel_id in channels:
self.loop.create_task(self.for_channels(channel_id))
servers_count = await Servers.filter(worked=True).count()
await self.bot.change_presence(activity=Activity(type=ActivityType.watching,
name=f"Use !help | {servers_count} game servers"))
async def for_channels(self, channel_id):
servers_ids = await Servers.filter(channel=channel_id, worked=True).values_list("id", flat=True)
channel = self.bot.get_channel(channel_id)
if channel is None:
await Servers.filter(channel=channel_id).update(worked=False)
return
sleep = CHECK_SERVER_INTERVAL_MAX if len(servers_ids) > 3 else CHECK_SERVER_INTERVAL
for id in servers_ids:
await self.for_id(channel, id)
await asyncio.sleep(sleep)
async def for_id(self, channel, id):
instance = await Servers.filter(id=id).first()
server_info, players = await get_server_info(instance.ip, instance.port)
if server_info:
await Servers.filter(id=id).update(name=server_info.server_name, game=server_info.game)
try:
msg = await channel.fetch_message(instance.message)
embed = await embed_generator(server_info, players, instance)
await msg.edit(embed=embed)
except (NotFound, Forbidden) as e:
user = await self.bot.fetch_user(instance.author)
if isinstance(e, Forbidden):
await user.send(f"I don't have permission to edit {instance.ip}:{instance.port} in #{channel.name}\n"
"Please give me permission to edit messages in this channel.\n"
"To resume the server, react with 🔄")
elif isinstance(e, NotFound):
await user.send(f"Server {instance.ip}:{instance.port} in #{channel.name} has been deleted")
#await user.send(f"Server {instance.ip}:{instance.port} in channel <#{instance.channel}> is off if you not delete bot message, check bot permissions")
await stop_server(instance.message)
def setup(bot: commands.Bot):
bot.add_cog(ServersCron(bot))
| [
"modules.utils.embed_generator",
"asyncio.get_event_loop",
"modules.utils.get_server_info",
"modules.utils.stop_server",
"discord.Activity",
"asyncio.sleep",
"modules.logging.logger.info",
"discord.ext.tasks.loop",
"modules.db.Servers.filter"
] | [((635, 689), 'discord.ext.tasks.loop', 'tasks.loop', ([], {'seconds': 'CRON_LOOP_INTERVAL', 'reconnect': '(True)'}), '(seconds=CRON_LOOP_INTERVAL, reconnect=True)\n', (645, 689), False, 'from discord.ext import commands, tasks\n'), ((539, 563), 'asyncio.get_event_loop', 'asyncio.get_event_loop', ([], {}), '()\n', (561, 563), False, 'import asyncio\n'), ((601, 628), 'modules.logging.logger.info', 'logger.info', (['"""Cron started"""'], {}), "('Cron started')\n", (612, 628), False, 'from modules.logging import logger\n'), ((1893, 1936), 'modules.utils.get_server_info', 'get_server_info', (['instance.ip', 'instance.port'], {}), '(instance.ip, instance.port)\n', (1908, 1936), False, 'from modules.utils import embed_generator, get_server_info, stop_server\n'), ((1738, 1758), 'asyncio.sleep', 'asyncio.sleep', (['sleep'], {}), '(sleep)\n', (1751, 1758), False, 'import asyncio\n'), ((2164, 2211), 'modules.utils.embed_generator', 'embed_generator', (['server_info', 'players', 'instance'], {}), '(server_info, players, instance)\n', (2179, 2211), False, 'from modules.utils import embed_generator, get_server_info, stop_server\n'), ((999, 1026), 'modules.db.Servers.filter', 'Servers.filter', ([], {'worked': '(True)'}), '(worked=True)\n', (1013, 1026), False, 'from modules.db import Servers\n'), ((1083, 1174), 'discord.Activity', 'Activity', ([], {'type': 'ActivityType.watching', 'name': 'f"""Use !help | {servers_count} game servers"""'}), "(type=ActivityType.watching, name=\n f'Use !help | {servers_count} game servers')\n", (1091, 1174), False, 'from discord import Activity, ActivityType\n'), ((1303, 1350), 'modules.db.Servers.filter', 'Servers.filter', ([], {'channel': 'channel_id', 'worked': '(True)'}), '(channel=channel_id, worked=True)\n', (1317, 1350), False, 'from modules.db import Servers\n'), ((1826, 1847), 'modules.db.Servers.filter', 'Servers.filter', ([], {'id': 'id'}), '(id=id)\n', (1840, 1847), False, 'from modules.db import Servers\n'), ((3014, 3043), 'modules.utils.stop_server', 'stop_server', (['instance.message'], {}), '(instance.message)\n', (3025, 3043), False, 'from modules.utils import embed_generator, get_server_info, stop_server\n'), ((1478, 1512), 'modules.db.Servers.filter', 'Servers.filter', ([], {'channel': 'channel_id'}), '(channel=channel_id)\n', (1492, 1512), False, 'from modules.db import Servers\n'), ((1979, 2000), 'modules.db.Servers.filter', 'Servers.filter', ([], {'id': 'id'}), '(id=id)\n', (1993, 2000), False, 'from modules.db import Servers\n'), ((786, 813), 'modules.db.Servers.filter', 'Servers.filter', ([], {'worked': '(True)'}), '(worked=True)\n', (800, 813), False, 'from modules.db import Servers\n')] |
from django.urls import path
from dashboards.views import DashboardView
urlpatterns = [
path(
"<slug:organisation>/dashboards/<slug:dashboard>/",
DashboardView.as_view(),
name="dashboard_detail",
),
]
| [
"dashboards.views.DashboardView.as_view"
] | [((168, 191), 'dashboards.views.DashboardView.as_view', 'DashboardView.as_view', ([], {}), '()\n', (189, 191), False, 'from dashboards.views import DashboardView\n')] |
from datetime import date
from django.http import JsonResponse
from rest_framework import viewsets
from rest_framework.permissions import IsAdminUser
from .models import CampPlan
from .serializers import CampPlanSerializer
# ===========================
# Camp Plan ViewSet
# ===========================
class CampPlanViewSet(viewsets.ModelViewSet):
# Only admin can do CUD (from CRUD) operations
permission_classes_by_action = {
'create': [IsAdminUser],
'update': [IsAdminUser],
'destroy': [IsAdminUser],
'partial_update': [IsAdminUser]
}
queryset = CampPlan.objects.all().order_by('name')
serializer_class = CampPlanSerializer
# overriding create method to validate camp start & end dates
def create(self, request, *agrs, **kwargs):
serializer = self.get_serializer(data=request.data)
if serializer.is_valid():
# validating camp start & end dates
start_date = serializer.validated_data.get('start_date')
end_date = serializer.validated_data.get('end_date')
if start_date > end_date:
return JsonResponse({'error': 'Camp start date should be less than camp end date'})
if date.today() > start_date:
return JsonResponse({'error': 'Camp start date should be more than current date'})
if date.today() > end_date:
return JsonResponse({'error': 'Camp end date should be more than current date'})
serializer.save()
return JsonResponse({'success': 'Successfully created camp plan'})
else:
return JsonResponse({'error': serializer.errors})
# handle permissions
def get_permissions(self):
try:
return [permission() for permission in self.permission_classes_by_action[self.action]]
except:
return [permission() for permission in self.permission_classes]
| [
"datetime.date.today",
"django.http.JsonResponse"
] | [((1544, 1603), 'django.http.JsonResponse', 'JsonResponse', (["{'success': 'Successfully created camp plan'}"], {}), "({'success': 'Successfully created camp plan'})\n", (1556, 1603), False, 'from django.http import JsonResponse\n'), ((1637, 1679), 'django.http.JsonResponse', 'JsonResponse', (["{'error': serializer.errors}"], {}), "({'error': serializer.errors})\n", (1649, 1679), False, 'from django.http import JsonResponse\n'), ((1139, 1215), 'django.http.JsonResponse', 'JsonResponse', (["{'error': 'Camp start date should be less than camp end date'}"], {}), "({'error': 'Camp start date should be less than camp end date'})\n", (1151, 1215), False, 'from django.http import JsonResponse\n'), ((1231, 1243), 'datetime.date.today', 'date.today', ([], {}), '()\n', (1241, 1243), False, 'from datetime import date\n'), ((1281, 1356), 'django.http.JsonResponse', 'JsonResponse', (["{'error': 'Camp start date should be more than current date'}"], {}), "({'error': 'Camp start date should be more than current date'})\n", (1293, 1356), False, 'from django.http import JsonResponse\n'), ((1372, 1384), 'datetime.date.today', 'date.today', ([], {}), '()\n', (1382, 1384), False, 'from datetime import date\n'), ((1420, 1493), 'django.http.JsonResponse', 'JsonResponse', (["{'error': 'Camp end date should be more than current date'}"], {}), "({'error': 'Camp end date should be more than current date'})\n", (1432, 1493), False, 'from django.http import JsonResponse\n')] |
# coding=utf-8
# 导入自己的函数包d2lzh_pytorch，注意要先将目标包的父路径添加到系统路径中
import sys
sys.path.append(r".")
from d2lzh_pytorch import data_process, plot, train, rnn
import torch
import time
import torchvision
from PIL import Image
from torch import nn,optim
from torch.utils.data import Dataset,DataLoader
"""
这一节介绍了如何对图像数据集进行增广以获得更多的可用数据
"""
# 加上这个限定才能支持多线程读取
if __name__ == "__main__":
# 设置计算设备，让计算在GPU上进行
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 先尝试显示一下猫图
plot.set_figsize()
img = Image.open(r"./Datasets"+'/Img/cat1.jpg')
# plot.plt.imshow(img)
# # 记得要显示时需要show一下
# plot.plt.show()
print('————————————————————————————')
# 翻转和裁剪是最常见的增广方法
# 写一个增广图片的函数，其中aug是对应的增广操作函数，让目标图像增广多次然后显示出来
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
# 在循环内对图像执行num_rows * num_cols次操作并组合，然后下面显示出来
Y = [aug(img) for _ in range(num_rows * num_cols)]
# 这里保存了一个在一图中显示图片集的函数show_images，需要代入网格尺寸和缩放
plot.show_images(Y, num_rows, num_cols, scale)
# # 此处应用了随机水平翻转的库函数，翻转是最方便的增广方法，效果有限
# apply(img, torchvision.transforms.RandomHorizontalFlip())
# # 这里是更少用的垂直翻转
# apply(img, torchvision.transforms.RandomVerticalFlip())
# # 然后为了降低网络对图像目标所在位置的敏感性，可进行随机裁剪
# # 这里的参数随机裁剪出面积0.1~1，宽高比0.5~2的区域，然后缩放到200像素来运用
# apply(img, torchvision.transforms.RandomResizedCrop(
# 200, scale=(0.1, 1), ratio=(0.5, 2)))
print('————————————————————————————')
# # 可从亮度，对比度，饱和度，色调，四个方面调整图像
# # 可以同时设置
# apply(img, torchvision.transforms.ColorJitter(
# brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5))
print('————————————————————————————')
# # 可以将多种图像增广结合起来用，使用Compose函数组合
# apply(img, torchvision.transforms.Compose([
# torchvision.transforms.RandomHorizontalFlip(0.5),
# torchvision.transforms.ColorJitter(
# brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5),
# torchvision.transforms.RandomResizedCrop(
# 200, scale=(0.1, 1), ratio=(0.5, 2))
# ]))
print('————————————————————————————')
# 这里实际测试一下增广的方法，使用CIFAR10数据集，读取后用循环来显示
all_imgs = torchvision.datasets.CIFAR10(
train=True, root=r"./Datasets/CIFAR", download=True)
# plot.show_images([all_imgs[i][0] for i in range(32)], 4, 8, scale=0.8)
# 有处理的增广函数，进行了水平翻转
flip_aug = torchvision.transforms.Compose([
# 这个组合增广进行了随机水平翻转，然后用ToTensor将图像转为Pytorch可用的格式
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor()
])
# 无处理的增广函数
no_aug = torchvision.transforms.Compose([
torchvision.transforms.ToTensor()
])
num_workers = 4
# 一个简单的图像读取函数
def load_cifar10(is_train, augs, batch_size, root=r"./Datasets/CIFAR"):
# 读取数据集，augs控制想要应用在数据集读取时的增广函数
dataset = torchvision.datasets.CIFAR10(
root=root, train=is_train, transform=augs, download=True)
# 返回对应的读取器
return DataLoader(dataset, batch_size=batch_size, shuffle=is_train,
num_workers=num_workers)
# 然后把保存一个基本的训练函数train
# 接着再写一个包含了读取，增广，训练全过程的接口函数来调用
def train_with_data_aug(train_augs, test_augs, lr=0.001):
batch_size, net = 256, rnn.resnet18(10)
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
loss = torch.nn.CrossEntropyLoss()
train_iter = load_cifar10(True, train_augs, batch_size)
test_iter = load_cifar10(False, test_augs, batch_size)
train.train(train_iter, test_iter, net, loss,
optimizer, device, num_epochs=10)
# 调用接口函数进行训练，其中测试集不要进行增广保证结果的稳定
train_with_data_aug(flip_aug, no_aug)
print('————————————————————————————')
| [
"PIL.Image.open",
"torch.nn.CrossEntropyLoss",
"torch.utils.data.DataLoader",
"torchvision.transforms.RandomHorizontalFlip",
"d2lzh_pytorch.plot.set_figsize",
"torch.cuda.is_available",
"d2lzh_pytorch.rnn.resnet18",
"torchvision.datasets.CIFAR10",
"d2lzh_pytorch.plot.show_images",
"torchvision.tra... | [((72, 92), 'sys.path.append', 'sys.path.append', (['"""."""'], {}), "('.')\n", (87, 92), False, 'import sys\n'), ((496, 514), 'd2lzh_pytorch.plot.set_figsize', 'plot.set_figsize', ([], {}), '()\n', (512, 514), False, 'from d2lzh_pytorch import data_process, plot, train, rnn\n'), ((525, 567), 'PIL.Image.open', 'Image.open', (["('./Datasets' + '/Img/cat1.jpg')"], {}), "('./Datasets' + '/Img/cat1.jpg')\n", (535, 567), False, 'from PIL import Image\n'), ((2148, 2233), 'torchvision.datasets.CIFAR10', 'torchvision.datasets.CIFAR10', ([], {'train': '(True)', 'root': '"""./Datasets/CIFAR"""', 'download': '(True)'}), "(train=True, root='./Datasets/CIFAR', download=True\n )\n", (2176, 2233), False, 'import torchvision\n'), ((986, 1032), 'd2lzh_pytorch.plot.show_images', 'plot.show_images', (['Y', 'num_rows', 'num_cols', 'scale'], {}), '(Y, num_rows, num_cols, scale)\n', (1002, 1032), False, 'from d2lzh_pytorch import data_process, plot, train, rnn\n'), ((2829, 2919), 'torchvision.datasets.CIFAR10', 'torchvision.datasets.CIFAR10', ([], {'root': 'root', 'train': 'is_train', 'transform': 'augs', 'download': '(True)'}), '(root=root, train=is_train, transform=augs,\n download=True)\n', (2857, 2919), False, 'import torchvision\n'), ((2963, 3053), 'torch.utils.data.DataLoader', 'DataLoader', (['dataset'], {'batch_size': 'batch_size', 'shuffle': 'is_train', 'num_workers': 'num_workers'}), '(dataset, batch_size=batch_size, shuffle=is_train, num_workers=\n num_workers)\n', (2973, 3053), False, 'from torch.utils.data import Dataset, DataLoader\n'), ((3324, 3351), 'torch.nn.CrossEntropyLoss', 'torch.nn.CrossEntropyLoss', ([], {}), '()\n', (3349, 3351), False, 'import torch\n'), ((3487, 3566), 'd2lzh_pytorch.train.train', 'train.train', (['train_iter', 'test_iter', 'net', 'loss', 'optimizer', 'device'], {'num_epochs': '(10)'}), '(train_iter, test_iter, net, loss, optimizer, device, num_epochs=10)\n', (3498, 3566), False, 'from d2lzh_pytorch import data_process, plot, train, rnn\n'), ((437, 462), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (460, 462), False, 'import torch\n'), ((2450, 2495), 'torchvision.transforms.RandomHorizontalFlip', 'torchvision.transforms.RandomHorizontalFlip', ([], {}), '()\n', (2493, 2495), False, 'import torchvision\n'), ((2505, 2538), 'torchvision.transforms.ToTensor', 'torchvision.transforms.ToTensor', ([], {}), '()\n', (2536, 2538), False, 'import torchvision\n'), ((2615, 2648), 'torchvision.transforms.ToTensor', 'torchvision.transforms.ToTensor', ([], {}), '()\n', (2646, 2648), False, 'import torchvision\n'), ((3230, 3246), 'd2lzh_pytorch.rnn.resnet18', 'rnn.resnet18', (['(10)'], {}), '(10)\n', (3242, 3246), False, 'from d2lzh_pytorch import data_process, plot, train, rnn\n')] |
import sphinx_rtd_theme
project = 'Bitcoin DCA'
copyright = '2021, <NAME>'
author = '<NAME>'
extensions = []
templates_path = ['_templates']
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
html_theme = 'sphinx_rtd_theme'
html_static_path = ['_static']
pygments_style = 'sphinx'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
html_theme_options = {
'navigation_depth': 4,
}
master_doc = 'index'
html_logo = '../resources/images/logo-white.png'
# I use a privacy focussed service https://usefathom.com/ to track how the documentation
# is being used. This allows me to improve its contents.
html_js_files = [('https://krill.jorijn.com/script.js', {'data-site': 'MXGDAIWO'})]
| [
"sphinx_rtd_theme.get_html_theme_path"
] | [((306, 344), 'sphinx_rtd_theme.get_html_theme_path', 'sphinx_rtd_theme.get_html_theme_path', ([], {}), '()\n', (342, 344), False, 'import sphinx_rtd_theme\n')] |
from django.contrib import admin
from app_restaurantes.models import Restaurante
# Register your models here.
admin.site.register(Restaurante)
| [
"django.contrib.admin.site.register"
] | [((112, 144), 'django.contrib.admin.site.register', 'admin.site.register', (['Restaurante'], {}), '(Restaurante)\n', (131, 144), False, 'from django.contrib import admin\n')] |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.9 on 2016-09-09 15:44
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('wagtailcore', '0029_unicode_slugfield_dj19'),
('people', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='Group',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100)),
],
options={
'ordering': ('name',),
},
),
migrations.CreateModel(
name='Membership',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('group', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='people.Group')),
],
),
migrations.CreateModel(
name='PersonIndexPage',
fields=[
('page_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='wagtailcore.Page')),
('groups', models.ManyToManyField(to='people.Group')),
],
options={
'abstract': False,
},
bases=('wagtailcore.page',),
),
migrations.AlterModelOptions(
name='person',
options={'ordering': ('last_name',)},
),
migrations.AddField(
model_name='person',
name='title',
field=models.CharField(blank=True, max_length=100, null=True),
),
migrations.AddField(
model_name='membership',
name='person',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='groups', to='people.Person'),
),
migrations.AddField(
model_name='group',
name='people',
field=models.ManyToManyField(blank=True, through='people.Membership', to='people.Person'),
),
migrations.AlterUniqueTogether(
name='membership',
unique_together=set([('group', 'person')]),
),
]
| [
"django.db.models.OneToOneField",
"django.db.models.ForeignKey",
"django.db.models.ManyToManyField",
"django.db.migrations.AlterModelOptions",
"django.db.models.AutoField",
"django.db.models.CharField"
] | [((1558, 1644), 'django.db.migrations.AlterModelOptions', 'migrations.AlterModelOptions', ([], {'name': '"""person"""', 'options': "{'ordering': ('last_name',)}"}), "(name='person', options={'ordering': (\n 'last_name',)})\n", (1586, 1644), False, 'from django.db import migrations, models\n'), ((1782, 1837), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'max_length': '(100)', 'null': '(True)'}), '(blank=True, max_length=100, null=True)\n', (1798, 1837), False, 'from django.db import migrations, models\n'), ((1961, 2071), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""groups"""', 'to': '"""people.Person"""'}), "(on_delete=django.db.models.deletion.CASCADE, related_name\n ='groups', to='people.Person')\n", (1978, 2071), False, 'from django.db import migrations, models\n'), ((2185, 2273), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'blank': '(True)', 'through': '"""people.Membership"""', 'to': '"""people.Person"""'}), "(blank=True, through='people.Membership', to=\n 'people.Person')\n", (2207, 2273), False, 'from django.db import migrations, models\n'), ((470, 563), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (486, 563), False, 'from django.db import migrations, models\n'), ((587, 619), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(100)'}), '(max_length=100)\n', (603, 619), False, 'from django.db import migrations, models\n'), ((831, 924), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (847, 924), False, 'from django.db import migrations, models\n'), ((949, 1035), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'to': '"""people.Group"""'}), "(on_delete=django.db.models.deletion.CASCADE, to=\n 'people.Group')\n", (966, 1035), False, 'from django.db import migrations, models\n'), ((1177, 1347), 'django.db.models.OneToOneField', 'models.OneToOneField', ([], {'auto_created': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'parent_link': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'to': '"""wagtailcore.Page"""'}), "(auto_created=True, on_delete=django.db.models.deletion\n .CASCADE, parent_link=True, primary_key=True, serialize=False, to=\n 'wagtailcore.Page')\n", (1197, 1347), False, 'from django.db import migrations, models\n'), ((1367, 1408), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'to': '"""people.Group"""'}), "(to='people.Group')\n", (1389, 1408), False, 'from django.db import migrations, models\n')] |
import pandas as pd
from matplotlib import pyplot as plt
from scipy.interpolate import spline
from scipy.ndimage.filters import gaussian_filter1d
import numpy as np
#%%
# TZ numbers via https://www.fangraphs.com/leaders.aspx?pos=3b&stats=fld&lg=all&qual=y&type=0&season=2017&month=0&season1=1961&ind=1&team=0&rost=0&age=0&filter=&players=0
df = pd.read_csv(r'TZ.csv')
df = df[['Season','Name','TZ','playerid']]
# For Total Zone delete anything past 2002
df = df.loc[df.Season < 2002]
# UZR numbers via https://www.fangraphs.com/leaders.aspx?pos=3b&stats=fld&lg=all&qual=y&type=1&season=2017&month=0&season1=2002&ind=1&team=0&rost=0&age=0&filter=&players=0
df2 = pd.read_csv(r'UZR.csv')
df2 = df2[['Season','Name','UZR','playerid']]
df2.columns = ['Season','Name','TZ','playerid']
df = pd.concat([df,df2])
df = df.sort_values(by='Season')
#%%
# Figure out which names to use
# by looking at max TZ for each year
gb = df.groupby(['Season'])['TZ'].max()
gb = gb.reset_index()
gb = gb.sort_values(by='Season')
df3 = df.merge(gb)
df3 = df3.sort_values(by='Season')
df3 = df3.loc[df3['TZ'] > 15]
names = ['<NAME>', '<NAME>', '<NAME>', '<NAME>',
'<NAME>', '<NAME>', '<NAME>',
'<NAME>', '<NAME>', 'Evan Longoria', '<NAME>']
colors = ['blue','lightblue','lightblue','blue','lightblue','lightblue','blue','lightblue','gold','lightblue','lightblue']
fig = plt.figure()
SPLINE_FACTOR = 1.6
# Go through and plot each player
for i,name in enumerate(names):
#print(name)
col = colors[i]
linewidth = 1
if col != 'lightblue':
linewidth=2
df_name = df.loc[df['Name'] == name]
df_name.index = range(len(df_name))
xnew = np.linspace(df_name.Season.min(),df_name.Season.max(),300) #300 represents number of points to make between T.min and T.max
#tz_smooth = spline(df_name.Season,df_name.TZ,xnew)
tz_smooth = spline(df_name.Season,gaussian_filter1d(df_name.TZ,SPLINE_FACTOR),xnew)
tz_convolved = np.convolve(df_name.TZ,np.ones(3,)/3,mode='same')
tz_smooth2 = spline(df_name.Season,gaussian_filter1d(tz_convolved,SPLINE_FACTOR),xnew)
#plt.plot(xnew,tz_smooth,label=name)#,color=col)
#plt.plot(df_name['Season'],df_name['TZ'],label=name)#,color=col)
#plt.plot(df_name['Season'],tz_convolved,label=name)#,color=col)
plt.plot(xnew,tz_smooth2,label=name,color=col,linewidth=linewidth)
#print df_name['TZ']
# Axis labeling and such
#plt.legend(fontsize=8,loc='lower left')
plt.ylabel('TZ/UZR')
plt.xlabel('Year')
plt.xlim((1960,2017))
plt.title('Third Base Defense')
plt.tight_layout()
# Save the plot as PNG
filename = '3b_defense.png'
fig.savefig(filename,dpi=400)
| [
"numpy.ones",
"pandas.read_csv",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.figure",
"scipy.ndimage.filters.gaussian_filter1d",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.title",
"matplotlib.pyplot.xlim",
"pandas.concat"
] | [((347, 368), 'pandas.read_csv', 'pd.read_csv', (['"""TZ.csv"""'], {}), "('TZ.csv')\n", (358, 368), True, 'import pandas as pd\n'), ((666, 688), 'pandas.read_csv', 'pd.read_csv', (['"""UZR.csv"""'], {}), "('UZR.csv')\n", (677, 688), True, 'import pandas as pd\n'), ((791, 811), 'pandas.concat', 'pd.concat', (['[df, df2]'], {}), '([df, df2])\n', (800, 811), True, 'import pandas as pd\n'), ((1373, 1385), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1383, 1385), True, 'from matplotlib import pyplot as plt\n'), ((2468, 2488), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['"""TZ/UZR"""'], {}), "('TZ/UZR')\n", (2478, 2488), True, 'from matplotlib import pyplot as plt\n'), ((2489, 2507), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['"""Year"""'], {}), "('Year')\n", (2499, 2507), True, 'from matplotlib import pyplot as plt\n'), ((2508, 2530), 'matplotlib.pyplot.xlim', 'plt.xlim', (['(1960, 2017)'], {}), '((1960, 2017))\n', (2516, 2530), True, 'from matplotlib import pyplot as plt\n'), ((2530, 2561), 'matplotlib.pyplot.title', 'plt.title', (['"""Third Base Defense"""'], {}), "('Third Base Defense')\n", (2539, 2561), True, 'from matplotlib import pyplot as plt\n'), ((2562, 2580), 'matplotlib.pyplot.tight_layout', 'plt.tight_layout', ([], {}), '()\n', (2578, 2580), True, 'from matplotlib import pyplot as plt\n'), ((2305, 2375), 'matplotlib.pyplot.plot', 'plt.plot', (['xnew', 'tz_smooth2'], {'label': 'name', 'color': 'col', 'linewidth': 'linewidth'}), '(xnew, tz_smooth2, label=name, color=col, linewidth=linewidth)\n', (2313, 2375), True, 'from matplotlib import pyplot as plt\n'), ((1895, 1939), 'scipy.ndimage.filters.gaussian_filter1d', 'gaussian_filter1d', (['df_name.TZ', 'SPLINE_FACTOR'], {}), '(df_name.TZ, SPLINE_FACTOR)\n', (1912, 1939), False, 'from scipy.ndimage.filters import gaussian_filter1d\n'), ((2053, 2099), 'scipy.ndimage.filters.gaussian_filter1d', 'gaussian_filter1d', (['tz_convolved', 'SPLINE_FACTOR'], {}), '(tz_convolved, SPLINE_FACTOR)\n', (2070, 2099), False, 'from scipy.ndimage.filters import gaussian_filter1d\n'), ((1987, 1997), 'numpy.ones', 'np.ones', (['(3)'], {}), '(3)\n', (1994, 1997), True, 'import numpy as np\n')] |
import pytest
import numpy as np
import pandas as pd
from endaq.calc import rotation
@pytest.mark.parametrize(
'quat, euler',
[
((0., 0., 0., 1.), (0., 0., 0.)),
((0., 0., 0., -1.), (0., 0., 0.)),
((1., 0., 0., 0.), (np.pi, 0., 0.)),
((0., 1., 0., 0.), (np.pi, 0., np.pi)),
((0., 0., 1., 0.), (0., 0., np.pi)),
]
)
def test_quat_to_euler_data(quat, euler):
df = pd.DataFrame([quat], index=[0], columns=['X', 'Y', 'Z', 'W'])
target = pd.DataFrame([euler], index=[0], columns=['x', 'y', 'z'])
pd.testing.assert_frame_equal(rotation.quaternion_to_euler(df), target)
@pytest.mark.parametrize(
'mode, columns, raises',
[
('xyz', ['x', 'y', 'z'], None),
('roll-pitch-yaw', ['roll', 'pitch', 'yaw'], None),
('snap-crackle-pop', ['snap', 'crackle', 'pop'], pytest.raises(ValueError)),
]
)
def test_quat_to_euler_modes(mode, columns, raises):
df = pd.DataFrame([(0., 0., 0., 1.)], index=[0], columns=['X', 'Y', 'Z', 'W'])
target = pd.DataFrame([(0., 0., 0.)], index=[0], columns=columns)
if raises is None:
pd.testing.assert_frame_equal(rotation.quaternion_to_euler(df, mode=mode), target)
else:
with raises:
pd.testing.assert_frame_equal(rotation.quaternion_to_euler(df, mode=mode), target) | [
"pandas.DataFrame",
"pytest.mark.parametrize",
"endaq.calc.rotation.quaternion_to_euler",
"pytest.raises"
] | [((90, 357), 'pytest.mark.parametrize', 'pytest.mark.parametrize', (['"""quat, euler"""', '[((0.0, 0.0, 0.0, 1.0), (0.0, 0.0, 0.0)), ((0.0, 0.0, 0.0, -1.0), (0.0, 0.0,\n 0.0)), ((1.0, 0.0, 0.0, 0.0), (np.pi, 0.0, 0.0)), ((0.0, 1.0, 0.0, 0.0),\n (np.pi, 0.0, np.pi)), ((0.0, 0.0, 1.0, 0.0), (0.0, 0.0, np.pi))]'], {}), "('quat, euler', [((0.0, 0.0, 0.0, 1.0), (0.0, 0.0, \n 0.0)), ((0.0, 0.0, 0.0, -1.0), (0.0, 0.0, 0.0)), ((1.0, 0.0, 0.0, 0.0),\n (np.pi, 0.0, 0.0)), ((0.0, 1.0, 0.0, 0.0), (np.pi, 0.0, np.pi)), ((0.0,\n 0.0, 1.0, 0.0), (0.0, 0.0, np.pi))])\n", (113, 357), False, 'import pytest\n'), ((494, 555), 'pandas.DataFrame', 'pd.DataFrame', (['[quat]'], {'index': '[0]', 'columns': "['X', 'Y', 'Z', 'W']"}), "([quat], index=[0], columns=['X', 'Y', 'Z', 'W'])\n", (506, 555), True, 'import pandas as pd\n'), ((569, 626), 'pandas.DataFrame', 'pd.DataFrame', (['[euler]'], {'index': '[0]', 'columns': "['x', 'y', 'z']"}), "([euler], index=[0], columns=['x', 'y', 'z'])\n", (581, 626), True, 'import pandas as pd\n'), ((1058, 1135), 'pandas.DataFrame', 'pd.DataFrame', (['[(0.0, 0.0, 0.0, 1.0)]'], {'index': '[0]', 'columns': "['X', 'Y', 'Z', 'W']"}), "([(0.0, 0.0, 0.0, 1.0)], index=[0], columns=['X', 'Y', 'Z', 'W'])\n", (1070, 1135), True, 'import pandas as pd\n'), ((1145, 1204), 'pandas.DataFrame', 'pd.DataFrame', (['[(0.0, 0.0, 0.0)]'], {'index': '[0]', 'columns': 'columns'}), '([(0.0, 0.0, 0.0)], index=[0], columns=columns)\n', (1157, 1204), True, 'import pandas as pd\n'), ((662, 694), 'endaq.calc.rotation.quaternion_to_euler', 'rotation.quaternion_to_euler', (['df'], {}), '(df)\n', (690, 694), False, 'from endaq.calc import rotation\n'), ((1270, 1313), 'endaq.calc.rotation.quaternion_to_euler', 'rotation.quaternion_to_euler', (['df'], {'mode': 'mode'}), '(df, mode=mode)\n', (1298, 1313), False, 'from endaq.calc import rotation\n'), ((944, 969), 'pytest.raises', 'pytest.raises', (['ValueError'], {}), '(ValueError)\n', (957, 969), False, 'import pytest\n'), ((1396, 1439), 'endaq.calc.rotation.quaternion_to_euler', 'rotation.quaternion_to_euler', (['df'], {'mode': 'mode'}), '(df, mode=mode)\n', (1424, 1439), False, 'from endaq.calc import rotation\n')] |
# Copyright 2021 <NAME>
# 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 toshiba_ac.http_api import ToshibaAcHttpApi
from toshiba_ac.amqp_api import ToshibaAcAmqpApi
from toshiba_ac.device import ToshibaAcDevice
import asyncio
class ToshibaAcDeviceManager:
def __init__(self, user, password):
self.http_api = ToshibaAcHttpApi(user, password)
self.reg_info = None
self.amqp_api = None
self.devices = {}
async def connect(self):
await self.http_api.connect()
self.reg_info = await self.http_api.register_client()
self.amqp_api = ToshibaAcAmqpApi(self.reg_info.sas_token)
self.amqp_api.register_command_handler('CMD_FCU_FROM_AC', self.handle_cmd_fcu_from_ac)
self.amqp_api.register_command_handler('CMD_HEARTBEAT', self.handle_cmd_heartbeat)
await self.amqp_api.connect()
async def get_devices(self, refresh=False):
# TODO handle refresh
if not self.devices:
devices_info = await self.http_api.get_devices()
for device_info in devices_info:
device = ToshibaAcDevice(device_info.ac_name,
self.reg_info.device_id,
device_info.ac_id,
device_info.ac_unique_id,
self.amqp_api,self.http_api
)
await device.connect()
await self.http_api.get_device_state(device_info.ac_id)
self.devices[device.ac_unique_id] = device
return list(self.devices.values())
def handle_cmd_fcu_from_ac(self, source_id, message_id, target_id, payload, timestamp):
self.devices[source_id].handle_cmd_fcu_from_ac(payload)
def handle_cmd_heartbeat(self, source_id, message_id, target_id, payload, timestamp):
self.devices[source_id].handle_cmd_heartbeat(payload)
async def shutdown(self):
await asyncio.gather(*[device.shutdown() for device in self.devices.values()])
if self.amqp_api:
await self.amqp_api.shutdown()
| [
"toshiba_ac.amqp_api.ToshibaAcAmqpApi",
"toshiba_ac.device.ToshibaAcDevice",
"toshiba_ac.http_api.ToshibaAcHttpApi"
] | [((823, 855), 'toshiba_ac.http_api.ToshibaAcHttpApi', 'ToshibaAcHttpApi', (['user', 'password'], {}), '(user, password)\n', (839, 855), False, 'from toshiba_ac.http_api import ToshibaAcHttpApi\n'), ((1096, 1137), 'toshiba_ac.amqp_api.ToshibaAcAmqpApi', 'ToshibaAcAmqpApi', (['self.reg_info.sas_token'], {}), '(self.reg_info.sas_token)\n', (1112, 1137), False, 'from toshiba_ac.amqp_api import ToshibaAcAmqpApi\n'), ((1601, 1742), 'toshiba_ac.device.ToshibaAcDevice', 'ToshibaAcDevice', (['device_info.ac_name', 'self.reg_info.device_id', 'device_info.ac_id', 'device_info.ac_unique_id', 'self.amqp_api', 'self.http_api'], {}), '(device_info.ac_name, self.reg_info.device_id, device_info.\n ac_id, device_info.ac_unique_id, self.amqp_api, self.http_api)\n', (1616, 1742), False, 'from toshiba_ac.device import ToshibaAcDevice\n')] |
"""JSON-LD utilities.
"""
__all__ = ('encode_jsonld', 'JsonLdEncoder', 'decode_jsonld')
import datetime
import json
def encode_jsonld(jsonld_dataset, **kwargs):
"""Encode a JSON-LD dataset into a string.
Parameters
----------
jsonld_dataset : `dict`
A JSON-LD dataset.
kwargs
Keyword argument passed to the encoder. See `json.JSONEncoder`.
Returns
-------
encoded_dataset : `str`
The JSON-LD dataset encoded as a string.
"""
encoder = JsonLdEncoder(**kwargs)
return encoder.encode(jsonld_dataset)
class JsonLdEncoder(json.JSONEncoder):
"""Customized JSON encoder (replaces `json.JSONEncoder`) that supports
datetime encoding.
"""
def default(self, obj):
"""Encode values as JSON strings.
This method overrides the default implementation from
`json.JSONEncoder`.
"""
if isinstance(obj, datetime.datetime):
return self._encode_datetime(obj)
# Fallback to the default encoding
return json.JSONEncoder.default(self, obj)
def _encode_datetime(self, dt):
"""Encode a datetime in the format '%Y-%m-%dT%H:%M:%SZ'.
The datetime can be naieve (doesn't have timezone info) or aware
(it does have a tzinfo attribute set). Regardless, the datetime
is transformed into UTC.
"""
if dt.tzinfo is None:
# Force it to be a UTC datetime
dt = dt.replace(tzinfo=datetime.timezone.utc)
# Convert to UTC (no matter what)
dt = dt.astimezone(datetime.timezone.utc)
return dt.strftime('%Y-%m-%dT%H:%M:%SZ')
def decode_jsonld(jsonld_text):
"""Decode a JSON-LD dataset, including decoding datetime
strings into `datetime.datetime` objects.
Parameters
----------
encoded_dataset : `str`
The JSON-LD dataset encoded as a string.
Returns
-------
jsonld_dataset : `dict`
A JSON-LD dataset.
Examples
--------
>>> doc = '{"dt": "2018-01-01T12:00:00Z"}'
>>> decode_jsonld(doc)
{'dt': datetime.datetime(2018, 1, 1, 12, 0, tzinfo=datetime.timezone.utc)}
"""
decoder = json.JSONDecoder(object_pairs_hook=_decode_object_pairs)
return decoder.decode(jsonld_text)
def _decode_object_pairs(pairs):
doc = {}
for key, value in pairs:
if isinstance(value, str):
# attempt to parse as a datetime
try:
value = datetime.datetime.strptime(value, '%Y-%m-%dT%H:%M:%SZ')
# ensure timezone is UTC
if value.tzinfo is None:
value = value.replace(tzinfo=datetime.timezone.utc)
value = value.astimezone(datetime.timezone.utc)
except ValueError:
pass
doc[key] = value
return doc
| [
"datetime.datetime.strptime",
"json.JSONDecoder",
"json.JSONEncoder.default"
] | [((2180, 2236), 'json.JSONDecoder', 'json.JSONDecoder', ([], {'object_pairs_hook': '_decode_object_pairs'}), '(object_pairs_hook=_decode_object_pairs)\n', (2196, 2236), False, 'import json\n'), ((1044, 1079), 'json.JSONEncoder.default', 'json.JSONEncoder.default', (['self', 'obj'], {}), '(self, obj)\n', (1068, 1079), False, 'import json\n'), ((2474, 2529), 'datetime.datetime.strptime', 'datetime.datetime.strptime', (['value', '"""%Y-%m-%dT%H:%M:%SZ"""'], {}), "(value, '%Y-%m-%dT%H:%M:%SZ')\n", (2500, 2529), False, 'import datetime\n')] |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def loss2(logits, labels, num_classes, scope, head=None):
with tf.name_scope(scope):
logits = tf.reshape(logits, (-1, num_classes))
softmax = tf.nn.softmax(logits) + 1e-4
labels = tf.to_float(tf.one_hot(tf.reshape(labels, [-1]), num_classes))
eps = 1e-2
labels = (1-eps)*tf.to_float(tf.reshape(labels, (-1, num_classes))) + eps/num_classes
if head is not None:
cross_entropy = -tf.reduce_sum(tf.multiply(labels * tf.log(softmax),
head), reduction_indices=[1])
else:
cross_entropy = -tf.reduce_sum(
labels * tf.log(softmax), reduction_indices=[1])
return tf.reduce_mean(cross_entropy)
| [
"tensorflow.name_scope",
"tensorflow.nn.softmax",
"tensorflow.reshape",
"tensorflow.reduce_mean",
"tensorflow.log"
] | [((838, 867), 'tensorflow.reduce_mean', 'tf.reduce_mean', (['cross_entropy'], {}), '(cross_entropy)\n', (852, 867), True, 'import tensorflow as tf\n'), ((201, 221), 'tensorflow.name_scope', 'tf.name_scope', (['scope'], {}), '(scope)\n', (214, 221), True, 'import tensorflow as tf\n'), ((240, 277), 'tensorflow.reshape', 'tf.reshape', (['logits', '(-1, num_classes)'], {}), '(logits, (-1, num_classes))\n', (250, 277), True, 'import tensorflow as tf\n'), ((296, 317), 'tensorflow.nn.softmax', 'tf.nn.softmax', (['logits'], {}), '(logits)\n', (309, 317), True, 'import tensorflow as tf\n'), ((366, 390), 'tensorflow.reshape', 'tf.reshape', (['labels', '[-1]'], {}), '(labels, [-1])\n', (376, 390), True, 'import tensorflow as tf\n'), ((462, 499), 'tensorflow.reshape', 'tf.reshape', (['labels', '(-1, num_classes)'], {}), '(labels, (-1, num_classes))\n', (472, 499), True, 'import tensorflow as tf\n'), ((786, 801), 'tensorflow.log', 'tf.log', (['softmax'], {}), '(softmax)\n', (792, 801), True, 'import tensorflow as tf\n'), ((613, 628), 'tensorflow.log', 'tf.log', (['softmax'], {}), '(softmax)\n', (619, 628), True, 'import tensorflow as tf\n')] |
import pytest
from pretalx.event.models import Event
@pytest.mark.django_db
def test_shred_used_event(resource, answered_choice_question, personal_answer, rejected_submission, deleted_submission, mail, sent_mail, room_availability, slot, unreleased_slot, past_slot, feedback, canceled_talk, review, information, other_event):
assert Event.objects.count() == 2
rejected_submission.event.organiser.shred()
assert Event.objects.count() == 1
| [
"pretalx.event.models.Event.objects.count"
] | [((340, 361), 'pretalx.event.models.Event.objects.count', 'Event.objects.count', ([], {}), '()\n', (359, 361), False, 'from pretalx.event.models import Event\n'), ((426, 447), 'pretalx.event.models.Event.objects.count', 'Event.objects.count', ([], {}), '()\n', (445, 447), False, 'from pretalx.event.models import Event\n')] |
# -*- coding: utf-8 -*-
""" Setup for pytrafik """
from distutils.core import setup
setup(
name='pytrafik',
version='0.2.1',
description='PyTrafik',
long_description='Wrapper for Västtrafik public API.',
url='https://github.com/axelniklasson/PyTrafik',
download_url = 'https://github.com/axelniklasson/PyTrafik/tarball/0.2',
author='<NAME>',
author_email='<EMAIL>',
license='MIT',
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Topic :: Home Automation',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
],
keywords='vasttrafik västtrafik',
install_requires=['requests>=2.9.1'],
packages=['pytrafik'],
zip_safe=True)
| [
"distutils.core.setup"
] | [((86, 901), 'distutils.core.setup', 'setup', ([], {'name': '"""pytrafik"""', 'version': '"""0.2.1"""', 'description': '"""PyTrafik"""', 'long_description': '"""Wrapper for Västtrafik public API."""', 'url': '"""https://github.com/axelniklasson/PyTrafik"""', 'download_url': '"""https://github.com/axelniklasson/PyTrafik/tarball/0.2"""', 'author': '"""<NAME>"""', 'author_email': '"""<EMAIL>"""', 'license': '"""MIT"""', 'classifiers': "['Development Status :: 4 - Beta', 'Intended Audience :: Developers',\n 'Topic :: Home Automation', 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5']", 'keywords': '"""vasttrafik västtrafik"""', 'install_requires': "['requests>=2.9.1']", 'packages': "['pytrafik']", 'zip_safe': '(True)'}), "(name='pytrafik', version='0.2.1', description='PyTrafik',\n long_description='Wrapper for Västtrafik public API.', url=\n 'https://github.com/axelniklasson/PyTrafik', download_url=\n 'https://github.com/axelniklasson/PyTrafik/tarball/0.2', author=\n '<NAME>', author_email='<EMAIL>', license='MIT', classifiers=[\n 'Development Status :: 4 - Beta', 'Intended Audience :: Developers',\n 'Topic :: Home Automation', 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5'], keywords=\n 'vasttrafik västtrafik', install_requires=['requests>=2.9.1'], packages\n =['pytrafik'], zip_safe=True)\n", (91, 901), False, 'from distutils.core import setup\n')] |
#coding=utf-8
'''
Created on 2015-10-23
@author: Devuser
'''
from doraemon.ci.models import CITask,CITaskHistory
from gatesidelib.common.simplelogger import SimpleLogger
from django.contrib.admin.models import DELETION,CHANGE,ADDITION
from doraemon.project.models import Product,Project,Tag
from business.project.project_service import ProjectService
from business.project.version_service import VersionService
from business.ci.ci_task_parameter_service import CITaskParameterService
from business.ci.ci_task_config_service import CITaskConfigService
from business.ci.ci_task_history_service import CITaskHistoryService
from business.business_service import BusinessService
from business.common.redis_service import RedisService
from business.common.mongodb_service import MongoDBService
from doraemon.api.ci.mongo_models import PackgeMongoFile
from doraemon.home.models import TaskQueue
from gatesidelib.datetimehelper import DateTimeHelper
from business.common.system_config_service import SystemConfigService
from doraemon.ci.datamodels.task_queue_command_enum import TaskQueueCommandTypeEnum
from doraemon.ci.datamodels.task_queue_status_enum import TaskQueueStatusEnum
import uuid
from business.common.file_info_service import FileInfoService
class CITaskService(BusinessService):
'''
classdocs
'''
@staticmethod
def get_products_include_me(request):
my_projects=ProjectService.get_projects_include_me(request)
prodcut_ids=list()
for project in my_projects:
if project.Product not in prodcut_ids:
prodcut_ids.append(project.Product)
return Product.objects.all().filter(id__in=prodcut_ids)
@staticmethod
def get_ci_tasks_recently_build(request,task_type,product_id):
result = list()
try:
result=CITaskService.get_product_ci_tasks(request, task_type, product_id).filter(LastHistory__exclude=(0,))
except Exception as ex:
SimpleLogger.exception(ex)
return result
@staticmethod
def get_my_ci_tasks(request,task_type,product_id):
prodcut_tasks=CITaskService.get_product_ci_tasks(request,task_type,product_id)
return prodcut_tasks
@staticmethod
def get_latest_history(task_id):
result=None
historys=CITaskHistory.objects.all().filter(CITaskID=task_id).order_by('-id')
if historys:
result=historys[0]
return result
@staticmethod
def get_product_ci_tasks(request,task_type,product_id):
result = list()
try:
my_projects=ProjectService.get_projects_include_me(request)
my_project_ids=[project.id for project in my_projects]
if product_id.upper()=="ALL":
if task_type!=0:
result=CITask.objects.all().filter(TaskType=task_type).filter(Project__in=my_project_ids).order_by('-LastHistory')
else:
result=CITask.objects.all().filter(Project__in=my_project_ids).order_by('-LastHistory')
else:
product_projects=Project.objects.all().filter(Product=int(product_id)).filter(id__in=my_project_ids)
result=CITask.objects.all().filter(Project__in=product_projects).filter(TaskType=task_type).order_by('-LastHistory')
except Exception as ex:
SimpleLogger.error(ex)
return result
@staticmethod
def search_tasks(request):
result=list()
keyword=request.POST.get('keyword','all') #get search keyword
task_type=int(request.POST.get('task_type',0)) #get task type
product_id=request.POST.get('product_id','all') # get product type
# all my tasks include the built task recently and the tasks belong to product
built_tasks=CITaskService.get_ci_tasks_recently_build(request,task_type,product_id)
product_tasks=CITaskService.get_product_ci_tasks(request,task_type,product_id)
# if keyword is all,then return all my tasks
# else filter with keyword and return the rest tasks
if keyword.upper() =='ALL':
result.extend(built_tasks)
for task in product_tasks:
if task not in result:
result.append(task)
else:
built_tasks=[task for task in built_tasks if keyword.upper() in task.TaskName.upper()]
product_task=product_tasks.filter(TaskName__icontains=keyword)
result.extend(built_tasks)
for task in product_task:
if task not in result:
result.append(task)
return result
@staticmethod
def filter_tasks(request):
result=CITaskService.search_tasks(request)
project_ids=request.POST.get('project_id')
tag_ids=request.POST.get('tag_id')
if project_ids!="":
result=[task for task in result if task.Project in eval(project_ids)]
match_tag_task=list()
if tag_ids!="":
for task in result:
for tag in eval(tag_ids):
if task.Tags and tag in eval(task.Tags):
match_tag_task.append(task)
break;
result=match_tag_task
return result
@staticmethod
def create_ci_task(validate_data,user):
task_type=int(validate_data.get('TaskType'))
ci_task=CITask()
if task_type:
ci_task=CITask()
ci_task=CITaskService.init_ci_task(validate_data, ci_task)
ci_task.IsActive=1
ci_task.Creator=user.id
ci_task.TaskConfig=CITaskConfigService.create_config()
ci_task.save()
else:
ci_task=CITaskService.copy_ci_task(validate_data,user)
CITaskService.log_create_activity(user, ci_task)
return ci_task
@staticmethod
def save_task_config(request,ci_taskid):
ci_task=CITask.objects.get(int(ci_taskid))
CITaskConfigService.save_task_config(request, ci_task)
CITaskService.log_change_activity(request.user, ci_task)
@staticmethod
def copy_ci_task(validate_data,user):
task_id=int(validate_data.get('CopyTaskID'))
ci_task=CITask.objects.get(int(task_id))
ci_task.TaskName=validate_data.get('TaskName')
ci_task.Project=validate_data.get('Project')
ci_task.BuildVersion=0
ci_task.id=None
ci_task.Creator=user.id
ci_task.save()
new_config=CITaskConfigService.copy_config(ci_task.TaskConfig,ci_task.id,ci_task.TaskName)
ci_task.TaskConfig=new_config
ci_task.save()
CITaskParameterService.copy_parameter_group_form_task(task_id,ci_task.id)
CITaskService.log_create_activity(user, ci_task)
return ci_task
@staticmethod
def delete_ci_task(request,task_id):
ci_task=CITask.objects.get(int(task_id))
CITaskConfigService.delete_config(ci_task.TaskConfig)
CITaskHistoryService.clean_all_history(task_id,True)
ci_task.IsActive=0
ci_task.save()
CITaskService.log_delete_activity(request.user, ci_task)
@staticmethod
def clean_task_history(request,task_id):
ci_task=CITask.objects.get(int(task_id))
CITaskHistoryService.clean_all_history(task_id,False)
CITaskService.log_clean_activity(request.user, ci_task)
@staticmethod
def start_ci_task(request,task_id,parameter_group_id,project_version):
ci_task=CITask.objects.get(int(task_id))
queuetask=TaskQueue()
queuetask.EnqueueTime=DateTimeHelper.get_local_now()
queuetask.TaskType=ci_task.TaskType
queuetask.Command=TaskQueueCommandTypeEnum.TaskQueueCommandType_Start
queuetask.Priority=2
queuetask.Status =TaskQueueStatusEnum.QueueTaskStatus_New
queuetask.TaskID=int(task_id)
queuetask.TaskUUID=uuid.uuid1()
if parameter_group_id:
queuetask.BuildParameterID=parameter_group_id
else:
queuetask.BuildParameterID=CITaskParameterService.default_parameter_group(int(task_id))
queuetask.save()
ci_task.BuildVersion=ci_task.BuildVersion+1
if str(project_version) == '0':
project_version = VersionService.get_latest_version(ci_task.Project)
if project_version:
project_version = project_version.id
else:
project_version = 0
ci_task.LastHistory = CITaskService.save_ci_taskhistory(request,queuetask,ci_task,project_version)
ci_task.save(update_fields=['BuildVersion','LastHistory'])
message="任务ID为:"+str(task_id)+"的执行指令已经下发，请耐心等待。"
user_id=0
if request.user.id:
user_id=request.user.id
CITaskService.log_build_activity(user_id, ci_task)
CITaskService.send_task_enqueue_message()
return [queuetask.id,message,queuetask.TaskUUID]
@staticmethod
def save_ci_taskhistory(request,task_queue,ci_task,project_version):
if not project_version:
project_latest_version=CITaskService.get_project_latest_version(ci_task.Project)
else:
project_latest_version=int(project_version)
task_history=CITaskHistory()
task_history.CITaskID=ci_task.id
task_history.TaskUUID = task_queue.TaskUUID
task_history.TaskQueueID=task_queue.id
task_history.IsActive=1
task_history.BuildStatus=0
if request.user.id!=None:
task_history.StartedBy=request.user.id
else:
task_history.StartedBy=0
task_history.BuildVersion=ci_task.BuildVersion
task_history.ProjectVersion=project_latest_version
task_history.BuildParameterID=task_queue.BuildParameterID
task_history.save()
return task_history.id
@staticmethod
def update_ci_taskhistory(tq_id,file_id,file_type):
task_history=CITaskHistory.objects.get_by_tqid(tq_id)
if file_type=='1':
if task_history.PackageID:
task_history.PackageID=task_history.PackageID+str(file_id)+','
else:
task_history.PackageID=str(file_id)+','
if file_type=='2':
if task_history.LogFileID:
task_history.LogFileID=task_history.LogFileID+str(file_id)+','
else:
task_history.LogFileID=str(file_id)+','
task_history.save()
@staticmethod
def stop_ci_task(request,task_id):
command_type=TaskQueueCommandTypeEnum.TaskQueueCommandType_Stop
tq_uuid=request.GET.get("TaskUUID","")
tq_tasks = TaskQueue.objects.all().filter(TaskUUID=tq_uuid).filter(Command=command_type)
if not tq_tasks.exists():
ci_task=CITask.objects.get(int(task_id))
queuetask=TaskQueue()
queuetask.EnqueueTime=DateTimeHelper.get_local_now()
queuetask.TaskType=ci_task.TaskType
queuetask.Command=command_type
queuetask.Priority=7
queuetask.Status =TaskQueueStatusEnum.QueueTaskStatus_New
queuetask.TaskID=int(task_id)
queuetask.TaskUUID=tq_uuid
queuetask.save()
else:
queuetask = tq_tasks[0]
message="任务ID为:"+str(task_id)+"的取消执行指令已经下发，请耐心等待。"
CITaskService.send_task_enqueue_message()
return [queuetask.id,message,queuetask.TaskUUID]
@staticmethod
def send_task_enqueue_message():
RedisService.websocket_publish_message("TASKSTATUSCHANGE",'Task enqueue now!')
@staticmethod
def init_ci_task(formdata,ci_task):
tmp_ci_task=ci_task
tmp_ci_task.TaskName=formdata.get('TaskName')
tmp_ci_task.Project=formdata.get('Project')
tmp_ci_task.TaskType=formdata.get('TaskType')
historyCleanStrategy=formdata.get('HistoryCleanStrategy',5)
if historyCleanStrategy=="":
historyCleanStrategy=5
tmp_ci_task.HistoryCleanStrategy=historyCleanStrategy
tmp_ci_task.Schedule=formdata.get('time_trigger',"")
if int(tmp_ci_task.TaskType)==3:
tmp_ci_task.DeployService=formdata.get('DeployService',0)
tmp_ci_task.Description=formdata.get('Description',"")
return tmp_ci_task
@staticmethod
def upload_package(request):
result=0
tq_id=request.POST.get('tq_id','')
file_type=request.POST.get('file_type','')
upload_file=request.FILES['upload_file']
max_file_size=SystemConfigService.get_upload_file_maxsize()
file_wihte_list=SystemConfigService.get_file_type_white_list()
if CITaskService.validate_upload_file(upload_file,max_file_size,file_wihte_list):
mongo_fileid=MongoDBService.save_file(upload_file,PackgeMongoFile)
result=FileInfoService.add_file(0,mongo_fileid, upload_file.name,1,0,upload_file.size)
CITaskService.update_ci_taskhistory(tq_id,result,file_type)
return result
@staticmethod
def download_package(request,file_path):
return PackgeMongoFile.objects.get(file_path)
@staticmethod
def update_property(request,taskid):
task=CITask.objects.get(taskid)
task.Tags=request.POST.get("Tags")
update_fields=list()
for field in request.POST:
update_fields.append(field)
task.save(update_fields=update_fields)
CITaskService.log_change_activity(request.user,task)
@staticmethod
def get_avalible_menu_tags(menu_type):
'''
menu_type: 1: ci_task,2:task_history,3:agent
'''
tag_types=[1]
if str(menu_type)=="1":
tag_types=[1,4]
if str(menu_type)=="2":
tag_types=[2]
if str(menu_type)=="3":
tag_types=[3]
if str(menu_type)=="4":
tag_types=[4]
return Tag.objects.all().filter(TagType__in=tag_types)
@staticmethod
def get_agent_filter__tags():
return Tag.objects.all().filter(TagType__in=[3])
@staticmethod
def log_create_activity(user,ci_task):
CITask.objects.log_action(user.id,ci_task.id,ci_task.TaskName,ADDITION,"创建了CI任务",ci_task.Project,CITaskService.ActionLogType.CI)
@staticmethod
def log_delete_activity(user,ci_task):
CITask.objects.log_action(user.id,ci_task.id,ci_task.TaskName,DELETION,"删除了CI任务",ci_task.Project,CITaskService.ActionLogType.CI)
@staticmethod
def log_clean_activity(user,ci_task):
CITask.objects.log_action(user.id,ci_task.id,ci_task.TaskName,DELETION,"删除了历史记录",ci_task.Project,CITaskService.ActionLogType.CI)
@staticmethod
def log_change_activity(user,ci_task):
CITask.objects.log_action(user.id,ci_task.id,ci_task.TaskName,CHANGE,"修改了CI任务",ci_task.Project,CITaskService.ActionLogType.CI)
@staticmethod
def log_build_activity(user_id,ci_task):
CITask.objects.log_action(user_id,ci_task.id,ci_task.TaskName,CHANGE,"构建了CI任务",ci_task.Project,CITaskService.ActionLogType.CI)
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import struct
from pox.lib.addresses import EthAddr, IPAddr
"""
Utility to convert MAC and IP to/from integers.
"""
def eth_to_int(addr):
if not isinstance(addr, EthAddr):
return None
value = 0
raw = addr.toRaw()
for i in range(len(raw)):
byte_shift = 5 - i
byte = raw[i]
byte_value = struct.unpack("B", byte)[0]
value += (byte_value << (8 * byte_shift))
return value
def int_to_eth(addr):
addr = long(addr)
val = []
for _ in range(6):
val.insert(0, struct.pack("B", (addr & 0xFF)))
addr >>= 8
return EthAddr(''.join(val))
def ip_to_uint(addr):
if isinstance(addr, tuple):
addr = addr[0]
if not isinstance(addr, IPAddr):
return None
return addr.toUnsigned()
def uint_to_ip(addr):
return IPAddr(addr)
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"struct.pack"
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import os
import json
import glob
from rcnn.utils import get_path_with_annotation,get_path_with_annotation_ratio
from rcnn.utils import get_weight_path
__disease__ = ['Covid-Seg','Lung_Tumor']
__net__ = ['rcnn_unet']
__mode__ = ['cls','seg','mtl']
json_path = {
'Cervical':'/staff/shijun/torch_projects/Med_Seg/converter/dcm_converter/static_files/Cervical_Oar.json',
'Nasopharynx':'/staff/shijun/torch_projects/Med_Seg/converter/dcm_converter/static_files/Nasopharynx_Oar.json',
'Structseg_HaN':'/staff/shijun/torch_projects/Med_Seg/converter/nii_converter/static_files/Structseg_HaN.json',
'Structseg_THOR':'/staff/shijun/torch_projects/Med_Seg/converter/nii_converter/static_files/Structseg_THOR.json',
'SegTHOR':'/staff/shijun/torch_projects/Med_Seg/converter/nii_converter/static_files/SegTHOR.json',
'Covid-Seg':'/staff/shijun/torch_projects/Med_Seg/converter/nii_converter/static_files/Covid-Seg.json', # competition
'Lung':'/staff/shijun/torch_projects/Med_Seg/converter/dcm_converter/static_files/Lung_Oar.json',
'Lung_Tumor':'/staff/shijun/torch_projects/Med_Seg/converter/dcm_converter/static_files/Lung_Tumor.json',
'EGFR':'/staff/shijun/torch_projects/Med_Seg/converter/dcm_converter/static_files/EGFR.json',
}
DISEASE = 'Lung'
MODE = 'seg'
NET_NAME = 'rcnn_unet'
VERSION = 'v1.3-3x8x512'
with open(json_path[DISEASE], 'r') as fp:
info = json.load(fp)
DEVICE = '0,1'
# Must be True when pre-training and inference
PRE_TRAINED = False
CKPT_POINT = False
# 1,2,...,8
CURRENT_FOLD = 1
GPU_NUM = len(DEVICE.split(','))
FOLD_NUM = 5
# Arguments for trainer initialization
#--------------------------------- single or multiple
ROI_NUMBER = 4# or 0,1,2,3,4,5,6
NUM_CLASSES = info['annotation_num'] + 1 # 2 for binary, more for multiple classes
if ROI_NUMBER is not None:
NUM_CLASSES = 2
ROI_NAME = info['annotation_list'][ROI_NUMBER - 1]
else:
ROI_NAME = 'All'
SCALE = info['scale'][ROI_NAME]
#---------------------------------
#--------------------------------- mode and data path setting
#all
PATH_LIST = glob.glob(os.path.join(info['npy_path'],'*.hdf5'))
# PATH_LIST.extend(glob.glob(os.path.join('/staff/shijun/dataset/Med_Seg/EGFR/2d_data','*.hdf5')))
# PATH_LIST.extend(glob.glob(os.path.join('/staff/shijun/dataset/Med_Seg/Covid-Seg/2d_data','*.hdf5')))
#--------------------------------- others
SEQ_LEN = 3
INPUT_SHAPE = (512,512)
BATCH_SIZE = 8
# CKPT_PATH = './ckpt/{}/{}/{}/{}/fold{}'.format(DISEASE, 'seg', 'v8.3-zero', ROI_NAME, str(1))
CKPT_PATH = './ckpt/{}/{}/{}/{}/fold{}'.format(DISEASE,MODE,VERSION,ROI_NAME,str(CURRENT_FOLD))
WEIGHT_PATH = get_weight_path(CKPT_PATH)
print(WEIGHT_PATH)
INIT_TRAINER = {
'net_name':NET_NAME,
'lr':1e-3,
'n_epoch':200,
'channels':1,
'num_classes':NUM_CLASSES,
'roi_number':ROI_NUMBER,
'scale':SCALE,
'input_shape':INPUT_SHAPE,
'seq_len':SEQ_LEN,
'crop':0,
'batch_size':BATCH_SIZE,
'num_workers':2,
'device':DEVICE,
'pre_trained':PRE_TRAINED,
'ckpt_point':CKPT_POINT,
'weight_path':WEIGHT_PATH,
'weight_decay': 0.0001,
'momentum': 0.99,
'gamma': 0.1,
'milestones': [40,80],
'T_max':5,
'mode':MODE,
'topk':20,
'freeze':None,
}
#---------------------------------
__seg_loss__ = ['DiceLoss','TverskyLoss','FocalTverskyLoss','TopkCEPlusDice','DynamicTopkCEPlusDice','TopkCEPlusTopkShiftDice','TopkCEPlusShiftDice','PowDiceLoss','Cross_Entropy','TopkDiceLoss','DynamicTopKLoss','TopKLoss','CEPlusDice','TopkCEPlusDice','CEPlusTopkDice','TopkCEPlusTopkDice']
__cls_loss__ = ['BCEWithLogitsLoss']
__mtl_loss__ = ['BCEPlusDice']
# Arguments when perform the trainer
if MODE == 'cls':
LOSS_FUN = 'BCEWithLogitsLoss'
elif MODE == 'seg' :
LOSS_FUN = 'TopkCEPlusDice'
else:
LOSS_FUN = 'BCEPlusDice'
SETUP_TRAINER = {
'output_dir':'./ckpt/{}/{}/{}/{}'.format(DISEASE,MODE,VERSION,ROI_NAME),
'log_dir':'./log/{}/{}/{}/{}'.format(DISEASE,MODE,VERSION,ROI_NAME),
'optimizer':'Adam',
'loss_fun':LOSS_FUN,
'class_weight':None, #[1,4]
'lr_scheduler':None, #'CosineAnnealingLR'
}
#---------------------------------
TEST_PATH = None | [
"json.load",
"rcnn.utils.get_weight_path",
"os.path.join"
] | [((2639, 2665), 'rcnn.utils.get_weight_path', 'get_weight_path', (['CKPT_PATH'], {}), '(CKPT_PATH)\n', (2654, 2665), False, 'from rcnn.utils import get_weight_path\n'), ((1402, 1415), 'json.load', 'json.load', (['fp'], {}), '(fp)\n', (1411, 1415), False, 'import json\n'), ((2092, 2132), 'os.path.join', 'os.path.join', (["info['npy_path']", '"""*.hdf5"""'], {}), "(info['npy_path'], '*.hdf5')\n", (2104, 2132), False, 'import os\n')] |
# -*- python -*-
import math
import numpy
import Shadow
from Shadow.ShadowPreprocessorsXraylib import prerefl, pre_mlayer, bragg
from srxraylib.sources import srfunc
from sirepo.template import transfer_mat_bl
from pykern.pkcollections import PKDict
from pykern import pkjson
sigmax = 0.0045000000000000005
sigdix = 2.913e-05
sigmaz = 0.0045000000000000005
sigdiz = 2.913e-05
beam_stats = []
epsilon = 1e-06
beam = transfer_mat_bl.create_mat_rays(epsilon)
sigma_mat = numpy.matrix([
[sigmax ** 2, 0, 0, 0],
[0, sigdix ** 2, 0, 0],
[0, 0, sigmaz ** 2, 0],
[0, 0, 0, sigdiz ** 2],
])
alpha = 0
def calculate_stats(pos, oe):
global alpha
Tmat, x_prop_cen, xp_prop_cen, z_prop_cen, zp_prop_cen = transfer_mat_bl.tmat_calc(beam.duplicate(), epsilon)
res = Tmat * sigma_mat * numpy.transpose(Tmat)
pos += (oe.T_SOURCE if oe else 0)
if oe:
# oe.ALPHA is in radians after traceOE()
alpha = int(alpha + 180 / math.pi * oe.ALPHA) % 360
beam_stats.append(PKDict(
isRotated=True if alpha == 90 or alpha == 270 else False,
s=pos * 1e-2,
x=x_prop_cen,
xp=xp_prop_cen,
z=z_prop_cen,
zp=zp_prop_cen,
matrix=Tmat.tolist(),
sigmax=math.sqrt(res[0, 0]) * 1e-2,
sigdix=math.sqrt(res[1, 1]),
sigmaz=math.sqrt(res[2, 2]) * 1e-2,
sigdiz=math.sqrt(res[3, 3]),
))
return pos
pos = calculate_stats(0, None)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 1)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 2)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 3)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 4)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 5)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 6)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 7)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 8)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 9)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 10)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 11)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 12)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 13)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 14)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 15)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 16)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 17)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 18)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 19)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 20)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 21)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 22)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 23)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 24)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 25)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 26)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 27)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 28)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 29)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 30)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 31)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 32)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 33)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 34)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 35)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 36)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 37)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 38)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 39)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 40)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 41)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 42)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 43)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 44)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 45)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 46)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 47)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 48)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 49)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 50)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 51)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 52)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 53)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 54)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 55)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 56)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 57)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 58)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 59)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 60)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 61)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 62)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 63)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 64)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 65)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 66)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 67)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 68)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 69)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 70)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 71)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 72)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 73)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 74)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 75)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 76)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 77)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 78)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 79)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 80)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 81)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 82)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 83)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 84)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 85)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 86)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 87)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 88)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 89)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 90)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 91)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 92)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 93)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 94)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 95)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 96)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 97)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 98)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 99)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.FMIRR = 2
oe.ALPHA = 0
oe.FHIT_C = 0
oe.F_EXT = 0
oe.F_DEFAULT = 0
oe.SSOUR = 2900.0
oe.SIMAG = 1000.0
oe.THETA = 2.0002
oe.F_CONVEX = 0
oe.FCYL = 1
oe.CIL_ANG = 90.0
oe.T_INCIDENCE = 2.0
oe.T_REFLECTION = 2.0
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 28.5
beam.traceOE(oe, 100)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 101)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 102)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 103)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 104)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 105)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 106)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 107)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 108)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 109)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 110)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 111)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 112)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 113)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 114)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 115)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 116)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 117)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 118)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 119)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 120)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 121)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 122)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 123)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 124)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 125)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 126)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 127)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 128)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 129)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 130)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 131)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 132)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 133)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 134)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 135)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 136)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 137)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 138)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 139)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 140)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 141)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 142)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 143)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 144)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 145)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 146)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 147)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 148)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 149)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 150)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 151)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 152)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 153)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 154)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 155)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 156)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 157)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 158)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 159)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 160)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 161)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 162)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 163)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 164)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 165)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 166)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 167)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 168)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 169)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 170)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 171)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 172)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 173)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 174)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 175)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 176)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 177)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 178)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 179)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 180)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 181)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 182)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 183)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 184)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 185)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 186)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 187)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 188)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 189)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 190)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 191)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 192)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 193)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 194)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 195)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 196)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 197)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 198)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 199)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.FMIRR = 2
oe.ALPHA = 0
oe.FHIT_C = 1
oe.F_EXT = 0
oe.F_DEFAULT = 0
oe.SSOUR = 3000.0
oe.SIMAG = 900.0
oe.THETA = 2.0002
oe.F_CONVEX = 0
oe.FCYL = 1
oe.CIL_ANG = 0.0
oe.FSHAPE = 2
oe.RWIDX2 = 15.0
oe.RLEN2 = 25.0
oe.F_MOVE = 1
oe.OFFX = 1.0
oe.T_INCIDENCE = 2.0
oe.T_REFLECTION = 2.0
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.0
beam.traceOE(oe, 200)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 201)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 202)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 203)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 204)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 205)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 206)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 207)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 208)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 209)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 210)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 211)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 212)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 213)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 214)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 215)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 216)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 217)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 218)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 219)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 220)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 221)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 222)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 223)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 224)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 225)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 226)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 227)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 228)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 229)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 230)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 231)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 232)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 233)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 234)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 235)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 236)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 237)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 238)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 239)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 240)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 241)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 242)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 243)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 244)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 245)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 246)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 247)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 248)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 249)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 250)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 251)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 252)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 253)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 254)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 255)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 256)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 257)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 258)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 259)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 260)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 261)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 262)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 263)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 264)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 265)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 266)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 267)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 268)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 269)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 270)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 271)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 272)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 273)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 274)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 275)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 276)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 277)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 278)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 279)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 280)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 281)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 282)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 283)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 284)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 285)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 286)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 287)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 288)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 289)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 290)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 291)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 292)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 293)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 294)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 295)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 296)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 297)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 298)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.050000000000182
beam.traceOE(oe, 299)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty()
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 9.049999999981537
beam.traceOE(oe, 300)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 301)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 302)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 303)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 304)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 305)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 306)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 307)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 308)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 309)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 310)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 311)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 312)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 313)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 314)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 315)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 316)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 317)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 318)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 319)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 320)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 321)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 322)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 323)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 324)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 325)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 326)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 327)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 328)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 329)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 330)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 331)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 332)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 333)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 334)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 335)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 336)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 337)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 338)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 339)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 340)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 341)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 342)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 343)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 344)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 345)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 346)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 347)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 348)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 349)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 350)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 351)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 352)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 353)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 354)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 355)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 356)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 357)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 358)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 359)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 360)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 361)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 362)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 363)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 364)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 365)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 366)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 367)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 368)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 369)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 370)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 371)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 372)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 373)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 374)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 375)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 376)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 377)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 378)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 379)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 380)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 381)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 382)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 383)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 384)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 385)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 386)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 387)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 388)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 389)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 390)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 391)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 392)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 393)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 394)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 395)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 396)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 397)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 398)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 399)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty(ALPHA=0)
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 0.9499999999998181
beam.traceOE(oe, 400)
pos = calculate_stats(pos, oe)
oe = Shadow.OE()
oe.DUMMY = 1.0
oe.set_empty()
oe.FWRITE = 3
oe.T_IMAGE = 0.0
oe.T_SOURCE = 1.864464138634503e-11
beam.traceOE(oe, 401)
pos = calculate_stats(pos, oe)
pkjson.dump_pretty(beam_stats, filename='beam_stats.json')
import Shadow.ShadowTools
Shadow.ShadowTools.plotxy(beam, 1, 3, nbins=100, nolost=1)
| [
"pykern.pkjson.dump_pretty",
"Shadow.OE",
"Shadow.ShadowTools.plotxy",
"sirepo.template.transfer_mat_bl.create_mat_rays",
"math.sqrt",
"numpy.matrix",
"numpy.transpose"
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import json
import logging
import os
import re
from collections import namedtuple
from copy import deepcopy
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
import spacy
from scirex_utilities.analyse_pwc_entity_results import *
from scirex_utilities.entity_utils import *
from spacy.tokens import Doc
from tqdm import tqdm
tqdm.pandas()
LabelSpan = namedtuple("Span", ["start", "end", "token_start", "token_end", "entity", "links", "modified"])
logging.basicConfig(level=logging.INFO)
class WhitespaceTokenizer(object):
def __init__(self, vocab):
self.vocab = vocab
def __call__(self, text):
words = text.split()
# All tokens 'own' a subsequent space character in this tokenizer
spaces = [True] * len(words)
return Doc(self.vocab, words=words, spaces=spaces)
nlp = spacy.load("en")
nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
def process_folder(folder: str) -> Tuple[dict, str]:
span_labels = {}
map_T_to_span = {}
if not os.path.isdir(folder) or "document.txt" not in os.listdir(folder):
print(folder, " have not document")
return None
doc_text = open(os.path.join(folder, "document.txt")).read()
ann_file = open(os.path.join(folder, "document.ann")).read().strip()
annotations = [x.split("\t", 1) for x in ann_file.split("\n")]
annotations = sorted(annotations, key=lambda x: 0 if x[0] == "T" else 1)
for ann_type, ann in annotations:
if ann_type[0] == "T":
ann, ann_text = ann.split("\t")
if ";" in ann:
continue
else:
enttype, span_start, span_end = ann.split()
span_start, span_end = int(span_start), int(span_end)
if (span_start, span_end) in span_labels:
assert "Span already present"
else:
span_labels[(span_start, span_end)] = {"E": enttype, "A": set(), "T": ann_text}
map_T_to_span[ann_type] = (span_start, span_end)
if ann_type[0] == "A":
ann, ann_T = ann.split()
if ann_T in map_T_to_span:
span_labels[map_T_to_span[ann_T]]["A"].add(ann)
else:
print("Attribute before Trigger")
return span_labels, doc_text
def get_all_document_annotations(brat_folder: str) -> Dict[str, Tuple[dict, str]]:
map_id_to_ann = {}
for f in tqdm(os.listdir(brat_folder)):
try:
map_id_to_ann[f] = process_folder(os.path.join(brat_folder, f))
except Exception as e:
print(f)
return map_id_to_ann
def process_back_to_dataframe(span_labels: Dict[Tuple[int, int], dict], doc_text: str):
sentences = doc_text.split("\n ")
assert sentences[-1] == ""
sentences = [x + "\n " for x in sentences[:-1]]
sentence_limits = np.cumsum([len(x) for x in sentences])
sentence_limits = list(zip([0] + list(sentence_limits)[:-1], sentence_limits))
for s, e in sentence_limits:
assert doc_text[e - 2 : e] == "\n "
assert doc_text[s] != " "
span_labels = list(map(lambda x: [list(x[0]), x[1]], sorted(span_labels.items(), key=lambda x: x[0][0])))
sl_ix = 0
map_sentence_limits_to_spans = {}
for ss, se in sentence_limits:
map_sentence_limits_to_spans[(ss, se)] = []
while sl_ix < len(span_labels) and span_labels[sl_ix][0][0] >= ss and span_labels[sl_ix][0][1] <= se:
map_sentence_limits_to_spans[(ss, se)].append(span_labels[sl_ix])
sl_ix += 1
spans_in_l = 0
for k, v in map_sentence_limits_to_spans.items():
for span, _ in v:
assert k[0] <= span[0] and k[1] >= span[1]
spans_in_l += 1
assert span[1] < k[1] - 1
assert spans_in_l == len(span_labels)
for k, v in map_sentence_limits_to_spans.items():
for span, _ in v:
span[0] -= k[0]
span[1] -= k[0]
df = []
for sent_id, ((ss, se), st) in enumerate(zip(sentence_limits, sentences)):
for span, d in map_sentence_limits_to_spans[(ss, se)]:
assert st[-2:] == "\n ", st[-2:]
assert span[1] < len(st) - 2
assert st[span[0] : span[1]] == d["T"] and len(d["T"]) > 0, (st[span[0] : span[1]], d["T"])
df.append({"sentence": st, "spans": map_sentence_limits_to_spans[(ss, se)], "sentence_id": sent_id})
assert df[4]["sentence"].strip() == "", breakpoint()
df = df[5:]
df = pd.DataFrame(df)
return df
def get_dataframe_from_folder(brat_folder):
logging.info("Generating DataFrame ...")
map_changes = get_all_document_annotations(brat_folder)
logging.info("Done generating DataFrame")
doc_df = []
for k in tqdm(map_changes):
if map_changes[k] is None:
continue
df = process_back_to_dataframe(*map_changes[k])
df["doc_id"] = k
doc_df.append(df)
doc_df = pd.concat(doc_df)
return doc_df
def overlap(span_1, span_2):
if span_1[0] >= span_2[1] or span_2[0] >= span_1[1]:
return False
return True
def process_cluster(cluster):
stats = {
"new_spans": len([x for x in cluster if "pre" not in x[1]]),
"old_spans": len([x for x in cluster if "pre" in x[1]]),
"type_change": 0,
"change_attributes": 0,
}
old_spans = [x for x in cluster if "pre" in x[1]]
new_spans = [x for x in cluster if "pre" not in x[1]]
old_spans_modified, old_spans_unmodified = [], []
for span, info in old_spans:
if [info[k] for k in ["E", "T", "A"]] == [info["pre"][k] for k in ["E", "T", "A"]]:
del info["pre"]
if any(overlap(span, n_span) for n_span, _ in new_spans):
continue
old_spans_unmodified.append((span, info))
else:
del info["pre"]
if any(overlap(span, n_span) for n_span, _ in new_spans):
continue
old_spans_modified.append((span, info))
assert all((si == sj or not overlap(si[0], sj[0])) for si in new_spans for sj in new_spans), breakpoint()
assert len(old_spans_unmodified) == 0 or len(old_spans_modified) == 0, breakpoint()
assert all(
(not overlap(ospan, nspan)) for ospan, _ in old_spans_modified for nspan, _ in new_spans
), breakpoint()
assert all(
(not overlap(ospan, nspan)) for ospan, _ in old_spans_unmodified for nspan, _ in new_spans
), breakpoint()
if len(old_spans_modified + old_spans_unmodified) > 0 and len(new_spans) > 0:
breakpoint()
new_spans = [
LabelSpan(
start=x[0][0],
end=x[0][1],
entity=x[1]["E"],
links=x[1]["A"],
token_start=None,
token_end=None,
modified=True,
)._asdict()
for x in new_spans + old_spans_modified
]
new_spans += [
LabelSpan(
start=x[0][0],
end=x[0][1],
entity=x[1]["E"],
links=x[1]["A"],
token_start=None,
token_end=None,
modified=False,
)._asdict()
for x in old_spans_unmodified
]
stats["spans_kept"] = len(new_spans)
return new_spans, stats
# Cases 1 : Pre entity have labels / post don't -> copy labels / delete pre entity
# Cases 2 : Pre entity have labels / post also have labels -> don't copy labels / delete pre entity
# Cases 3 : If post entity have different type than pre entity, remove pre entity
def normalize_spans(row):
span_list_1, span_list_2 = row["spans_old"], row["spans_new"]
map_1_span_to_ix = {tuple(k): v for k, v in span_list_1}
if len(span_list_2) == 0:
return [], None
spans = [tuple(x[0]) for x in span_list_2]
if len(spans) != len(set(spans)):
assert "Duplicate spans", span_list_2
span_list_2 = sorted(span_list_2, key=lambda x: x[0])
stats = []
clusters = []
curr_cluster = []
cstart, cend = -1, -1
for (start, end), span_info in span_list_2:
cspan = ((start, end), span_info)
if (start, end) in map_1_span_to_ix:
span_info["pre"] = map_1_span_to_ix[(start, end)]
if cstart == -1: # (Start First Cluster)
curr_cluster.append(cspan)
cstart, cend = start, end
elif start < cend: # Append to current cluster
curr_cluster.append(cspan)
cend = max(cend, end)
else: # Start new cluster
curr_cluster, cluster_stats = process_cluster(curr_cluster)
stats.append(cluster_stats)
clusters.append(curr_cluster)
curr_cluster = [cspan]
cstart, cend = start, end
curr_cluster, cluster_stats = process_cluster(curr_cluster)
stats.append(cluster_stats)
clusters.append(curr_cluster)
clusters = sorted([z for x in clusters for z in x], key=lambda x: (x["start"], x["end"]))
for i in range(len(clusters) - 1):
if clusters[i]["end"] > clusters[i + 1]["start"]:
breakpoint()
stats_reduced = {}
for s in stats:
for k, v in s.items():
if k not in stats_reduced:
stats_reduced[k] = v
else:
stats_reduced[k] += v
return clusters, stats_reduced
def add_token_index(row):
if len(row["cluster"]) == 0:
return []
sentence = row["sentence_old"]
words = row["words"]
word_indices = row["word_indices"]
sentence_start = row["sentence_start"]
starts, ends = list(zip(*word_indices))
for i, (start, end) in enumerate(zip(starts, ends)):
assert sentence[start:end] == words[i], breakpoint()
new_cluster = []
cluster = row["cluster"]
for i, span in enumerate(cluster):
assert "start" in span, breakpoint()
assert "end" in span, breakpoint()
if not (span["start"] in starts):
if sentence[span["start"]].strip() == "":
span["start"] += 1
else:
span["start"] = min(
starts, key=lambda x: abs(x - span["start"]) if x < span["start"] else float("inf")
)
if not (span["end"] in ends):
if sentence[span["end"] - 1].strip() == "":
span["end"] -= 1
else:
span["end"] = min(
ends, key=lambda x: abs(x - span["end"]) if x > span["end"] else float("inf")
)
span["token_start"] = starts.index(span["start"]) + sentence_start - len(words)
span["token_end"] = ends.index(span["end"]) + 1 + sentence_start - len(words)
for cleaned_span in new_cluster:
if overlap(
(span["token_start"], span["token_end"]),
(cleaned_span["token_start"], cleaned_span["token_end"]),
):
print(row["doc_id"])
print(" ".join(row["words"]))
print("=" * 20)
new_cluster.append(span)
return new_cluster
def generate_token_and_indices(sentence):
words = sorted(
[(m.group(0), (m.start(), m.end())) for m in re.finditer(r"[^\s\+\-/\(\)&\[\],]+", sentence)]
+ [(m.group(0), (m.start(), m.end())) for m in re.finditer(r"[\+\-/\(\)&\[\],]+", sentence)]
+ [(m.group(0), (m.start(), m.end())) for m in re.finditer(r"\s+", sentence)],
key=lambda x: x[1],
)
if len(words) == 0 or sentence.strip() == "":
return [], []
try:
words, indices = list(zip(*[(t, i) for t, i in words if t.strip() != ""]))
except:
breakpoint()
return words, indices
def compare_brat_annotations(ann_old_df, ann_new_df):
df_merged = ann_old_df.merge(ann_new_df, on=["doc_id", "sentence_id"], suffixes=("_old", "_new"))
logging.info("Applying Normalize Spans ...")
output = df_merged.progress_apply(normalize_spans, axis=1)
df_merged["cluster"], df_merged["stats"] = list(zip(*output))
df_merged = df_merged.sort_values(["doc_id", "sentence_id"]).reset_index(drop=True)
logging.info("Applying Add Token Index ...")
df_merged["words"], df_merged["word_indices"] = list(
zip(*df_merged["sentence_old"].progress_apply(generate_token_and_indices))
)
df_merged["num_words"] = df_merged["words"].progress_apply(len)
df_merged["sentence_start"] = df_merged.groupby("doc_id")["num_words"].cumsum()
df_merged["entities"] = df_merged.apply(add_token_index, axis=1)
df_merged = (
df_merged.sort_values(["doc_id", "sentence_id"])
.reset_index(drop=True)
.drop(columns=["spans_old", "spans_new", "sentence_new", "cluster"])
.rename(columns={"sentence_old": "sentence"})
)
return df_merged
def generate_relations_in_pwc_df(pwc_df):
pwc_df_keep = pwc_df[["s2_paper_id"] + true_entities + ["score"]].rename(
columns=map_true_entity_to_available
)
pwc_df_keep = (
pwc_df_keep[(~pwc_df_keep.duplicated()) & (pwc_df_keep.s2_paper_id != "not_found")]
.sort_values(["s2_paper_id"] + used_entities + ["score"])
.reset_index(drop=True)
)
# pwc_df_keep[used_entities] = pwc_df_keep[used_entities].applymap(lambda x: re.sub(r"[^\w-]", "_", x))
pwc_df_keep = (
pwc_df_keep.groupby("s2_paper_id")
.apply(lambda x: list(x[used_entities + ["score"]].itertuples(index=False, name="Relation")))
.reset_index()
.rename(columns={0: "Relations"})
)
return pwc_df_keep
def combine_brat_to_original_data(
pwc_doc_file,
pwc_sentence_file,
pwc_prediction_file,
original_brat_anno_folder,
annotated_brat_anno_folder,
):
logging.info("Loading pwc docs ... ")
pwc_df = load_pwc_full_text(pwc_doc_file)
pwc_grouped = (
pwc_df.groupby("s2_paper_id")[["dataset", "task", "model_name", "metric"]]
.aggregate(lambda x: list(set(tuple(x))))
.reset_index()
)
pwc_df_relations = generate_relations_in_pwc_df(pwc_df)
pwc_df_relations = pwc_df_relations.rename(columns={"s2_paper_id": "doc_id"})[["doc_id", "Relations"]]
pwc_df_relations.index = pwc_df_relations.doc_id
pwc_df_relations = pwc_df_relations.drop(columns=["doc_id"])
pwc_df_relations: Dict[str, Relation] = pwc_df_relations.to_dict()["Relations"]
method_breaks = {
d: {
clean_name(rel.Method): [(i, clean_name(x)) for i, x in chunk_string(rel.Method)]
for rel in relations
}
for d, relations in pwc_df_relations.items()
}
pwc_df_relations = {
d: [{k: clean_name(x) if k != "score" else x for k, x in rel._asdict().items()} for rel in relations]
for d, relations in pwc_df_relations.items()
}
logging.info("Loading PwC Sentence Predictions ... ")
pwc_sentences = load_pwc_sentence_predictions(pwc_sentence_file, pwc_prediction_file)
pwc_sentences = pwc_sentences.merge(pwc_grouped, left_on="doc_id", right_on="s2_paper_id")
pwc_sentences = pwc_sentences.sort_values(
by=["doc_id", "section_id", "para_id", "sentence_id"]
).reset_index(drop=True)
pwc_sentences["words"] = pwc_sentences["words"].progress_apply(
lambda x: generate_token_and_indices(" ".join(x))[0]
)
df_changed = get_dataframe_from_folder(annotated_brat_anno_folder)
df_original = get_dataframe_from_folder(original_brat_anno_folder)
df_merged = compare_brat_annotations(df_original, df_changed)
assert (
pwc_sentences.groupby("doc_id")["words"].agg(lambda words: [x for y in words for x in y])
!= df_merged.groupby("doc_id")["words"].agg(lambda words: [x for y in words for x in y])
).sum() == 0, breakpoint()
def add_nums(rows, columns, name):
rows[name] = list(rows.groupby(columns).grouper.group_info[0])
return rows
pwc_sentences["para_num"] = None
pwc_sentences["sentence_num"] = None
pwc_sentences = pwc_sentences.groupby("doc_id").progress_apply(
lambda x: add_nums(x, ["section_id", "para_id"], "para_num")
)
pwc_sentences = pwc_sentences.groupby("doc_id").progress_apply(
lambda x: add_nums(x, ["section_id", "para_id", "sentence_id"], "sentence_num")
)
words: Dict[str, List[str]] = pwc_sentences.groupby("doc_id")["words"].agg(
lambda words: [x for y in words for x in y]
).to_dict()
pwc_sentences["num_words"] = pwc_sentences["words"].apply(len)
sentences = pwc_sentences.groupby(["doc_id", "sentence_num"])["num_words"].agg(sum)
sections = pwc_sentences.groupby(["doc_id", "section_id"])["num_words"].agg(sum)
sections: Dict[str, Dict[int, int]] = {
level: sections.xs(level).to_dict() for level in sections.index.levels[0]
}
sentences: Dict[str, Dict[int, int]] = {
level: sentences.xs(level).to_dict() for level in sentences.index.levels[0]
}
words_merged = (
df_merged.groupby("doc_id")["words"].agg(lambda words: [x for y in words for x in y]).to_dict()
)
entities = (
df_merged.groupby("doc_id")["entities"].agg(lambda ents: [x for y in ents for x in y]).to_dict()
)
def compute_start_end(cards):
ends = list(np.cumsum(cards))
starts = [0] + ends
return list(zip([int(x) for x in starts], [int(x) for x in ends]))
combined_information = {}
for d in words:
assert words[d] == words_merged[d], breakpoint()
assert list(sentences[d].keys()) == list(range(max(sentences[d].keys()) + 1)), breakpoint()
assert list(sections[d].keys()) == list(range(max(sections[d].keys()) + 1)), breakpoint()
sent = compute_start_end([sentences[d][i] for i in range(len(sentences[d]))])
sec = compute_start_end([sections[d][i] for i in range(len(sections[d]))])
for e in entities[d]:
del e["start"]
del e["end"]
combined_information[d] = {
"words": words[d],
"sentences": sent,
"sections": sec,
"relations": pwc_df_relations[d],
"entities": entities[d],
"doc_id": d,
"method_subrelations": method_breaks[d],
}
return combined_information
def _annotation_to_dict(dc):
# convenience method
if isinstance(dc, dict):
ret = dict()
for k, v in dc.items():
k = _annotation_to_dict(k)
v = _annotation_to_dict(v)
ret[k] = v
return ret
elif isinstance(dc, str):
return dc
elif isinstance(dc, (set, frozenset, list, tuple)):
ret = []
for x in dc:
ret.append(_annotation_to_dict(x))
return tuple(ret)
else:
return dc
def annotations_to_jsonl(annotations, output_file, key="doc_id"):
with open(output_file, "w") as of:
for ann in sorted(annotations, key=lambda x: x[key]):
as_json = _annotation_to_dict(ann)
as_str = json.dumps(as_json, sort_keys=True)
of.write(as_str)
of.write("\n")
def propagate_annotations(data_dict: Dict[str, Any]):
words = data_dict["words"]
entities = data_dict["entities"]
entities = {(e["token_start"], e["token_end"]): e for e in entities}
assert not any(e != f and overlap(e, f) for e in entities for f in entities), breakpoint()
new_entities = {}
for (s, e) in entities:
if entities[(s, e)]["modified"] == True:
span_text = words[s:e]
possible_matches = [
(i, i + len(span_text))
for i in range(len(words))
if words[i : i + len(span_text)] == span_text
]
for match in possible_matches:
add_match = False
if match in entities:
if entities[match].get("proped", False):
continue
if entities[match]["modified"] == False: # Propagate the changes
for k in ["entity", "links", "modified"]:
entities[match][k] = deepcopy(entities[(s, e)][k])
elif entities[match]["entity"] != entities[(s, e)]["entity"]:
if match > (s, e):
for k in ["entity", "links", "modified"]:
entities[match][k] = deepcopy(entities[(s, e)][k])
elif set(entities[match]["links"]) != set(
entities[(s, e)]["links"]
): # Two entities with same text have different annotations. BAD !!!
merged_links = set(entities[match]["links"]) | set(entities[(s, e)]["links"])
entities[match]["links"] = deepcopy(list(merged_links))
entities[(s, e)]["links"] = deepcopy(list(merged_links))
entities[match]["proped"] = True
add_match = False
else:
for span in entities:
if overlap(span, match):
if entities[span]["modified"] == True:
add_match = False
if entities[span]["entity"] != entities[(s, e)]["entity"]:
break
elif set(entities[span]["links"]) != set(entities[(s, e)]["links"]):
diff_links = set(entities[(s, e)]["links"]) ^ set(entities[span]["links"])
canon_name = set(["Canonical_Name"])
if (
diff_links != canon_name
and set(entities[(s, e)]["links"]) != canon_name
and set(entities[span]["links"]) != canon_name
):
break
else:
merged_links = set(entities[(s, e)]["links"]) | set(
entities[span]["links"]
)
entities[(s, e)]["links"] = deepcopy(list(merged_links))
entities[span]["links"] = deepcopy(list(merged_links))
break
break
else:
add_match = True
if match in new_entities:
if new_entities[match]["entity"] != entities[(s, e)]["entity"]:
breakpoint()
elif set(new_entities[match]["links"]) != set(
entities[(s, e)]["links"]
): # Two entities with same text have different annotations. BAD !!!
diff_links = set(new_entities[match]["links"]) & set(entities[(s, e)]["links"])
if (
len(diff_links) == 0
and len(set(new_entities[match]["links"])) > 0
and len(set(entities[(s, e)]["links"])) > 0
):
breakpoint()
else:
merged_links = set(new_entities[match]["links"] + entities[(s, e)]["links"])
entities[(s, e)]["links"] = deepcopy(list(merged_links))
new_entities[match]["links"] = deepcopy(list(merged_links))
else:
add_match = False
if add_match:
new_entities[match] = {
k: deepcopy(entities[(s, e)][k]) for k in ["entity", "links", "modified"]
}
new_entities[match]["token_start"] = match[0]
new_entities[match]["token_end"] = match[1]
for match in list(new_entities.keys()):
for span in list(entities.keys()):
if overlap(match, span):
assert entities[span]["modified"] == False or entities[span]["proped"], breakpoint()
if entities[span].get("proped", False):
if match in new_entities:
del new_entities[match]
elif not entities[span]["modified"]:
del entities[span]
new_entities = sorted(list(new_entities.items()), key=lambda x: x[0][1])
disjoint_new_entities = []
for e in new_entities:
if len(disjoint_new_entities) == 0:
disjoint_new_entities.append(e)
else:
if e[0][0] >= disjoint_new_entities[-1][0][1]:
disjoint_new_entities.append(e)
assert not any(
e[0] != f[0] and overlap(e[0], f[0]) for e in disjoint_new_entities for f in disjoint_new_entities
)
disjoint_new_entities = dict(disjoint_new_entities)
assert not any(overlap(e, f) for e in disjoint_new_entities for f in entities), breakpoint()
entities.update(disjoint_new_entities)
assert not any(e != f and overlap(e, f) for e in entities for f in entities), breakpoint()
assert all(v["token_start"] == s and v["token_end"] == e for (s, e), v in entities.items()), breakpoint()
data_dict["entities"] = [x for x in entities.values()]
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--annotator")
if __name__ == "__main__":
args = parser.parse_args()
annotations_dict = combine_brat_to_original_data(
"data/pwc_s2_cleaned_text_v2.jsonl",
"data/pwc_s2_cleaned_text_v2_sentences.jsonl",
"outputs/pwc_s2_cleaned_text_v2_sentences_predictions.jsonl.clean",
"/home/sarthakj/brat/brat/data/result_extraction/outputs/second_phase_annotations_"
+ args.annotator
+ "/",
"/home/sarthakj/brat/brat/data/result_extraction/outputs/second_phase_annotations_original/",
)
annotations_to_jsonl(list(annotations_dict.values()), "model_data/all_data_" + args.annotator + ".jsonl")
data = [json.loads(line) for line in open("model_data/all_data_" + args.annotator + ".jsonl")]
for d in tqdm(data):
names = [v for rel in d["relations"] for k, v in rel.items() if k != "score"]
names += [n for m, subm in d["method_subrelations"].items() for idx, n in subm]
names = set(names)
propagate_annotations(d)
coreference = {n: [] for n in names}
ner = []
for e in d["entities"]:
e["links"] = set(e["links"])
e["canon"] = "Canonical_Name" in e["links"]
if e["canon"]:
e["links"].remove("Canonical_Name")
if "proped" in e:
del e["proped"]
del e["modified"]
e["links"] = e["links"] & names
for l in e["links"]:
coreference[l].append([e["token_start"], e["token_end"]])
ner.append((e["token_start"], e["token_end"], e["entity"]))
del d["entities"]
d["n_ary_relations"] = d["relations"]
del d["relations"]
d["coref"] = coreference
d["ner"] = ner
assert d["sentences"][-1][-1] == len(d["words"]), breakpoint()
assert d["sections"][-1][-1] == len(d["words"]), breakpoint()
annotations_to_jsonl(data, "model_data/all_data_" + args.annotator + "_propagated.jsonl")
| [
"logging.basicConfig",
"json.loads",
"collections.namedtuple",
"os.listdir",
"argparse.ArgumentParser",
"spacy.load",
"tqdm.tqdm",
"json.dumps",
"spacy.tokens.Doc",
"os.path.join",
"os.path.isdir",
"pandas.concat",
"re.finditer",
"copy.deepcopy",
"pandas.DataFrame",
"numpy.cumsum",
"... | [((357, 370), 'tqdm.tqdm.pandas', 'tqdm.pandas', ([], {}), '()\n', (368, 370), False, 'from tqdm import tqdm\n'), ((383, 482), 'collections.namedtuple', 'namedtuple', (['"""Span"""', "['start', 'end', 'token_start', 'token_end', 'entity', 'links', 'modified']"], {}), "('Span', ['start', 'end', 'token_start', 'token_end', 'entity',\n 'links', 'modified'])\n", (393, 482), False, 'from collections import namedtuple\n'), ((479, 518), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO'}), '(level=logging.INFO)\n', (498, 518), False, 'import logging\n'), ((852, 868), 'spacy.load', 'spacy.load', (['"""en"""'], {}), "('en')\n", (862, 868), False, 'import spacy\n'), ((25462, 25487), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (25485, 25487), False, 'import argparse\n'), ((4482, 4498), 'pandas.DataFrame', 'pd.DataFrame', (['df'], {}), '(df)\n', (4494, 4498), True, 'import pandas as pd\n'), ((4564, 4604), 'logging.info', 'logging.info', (['"""Generating DataFrame ..."""'], {}), "('Generating DataFrame ...')\n", (4576, 4604), False, 'import logging\n'), ((4670, 4711), 'logging.info', 'logging.info', (['"""Done generating DataFrame"""'], {}), "('Done generating DataFrame')\n", (4682, 4711), False, 'import logging\n'), ((4741, 4758), 'tqdm.tqdm', 'tqdm', (['map_changes'], {}), '(map_changes)\n', (4745, 4758), False, 'from tqdm import tqdm\n'), ((4937, 4954), 'pandas.concat', 'pd.concat', (['doc_df'], {}), '(doc_df)\n', (4946, 4954), True, 'import pandas as pd\n'), ((11806, 11850), 'logging.info', 'logging.info', (['"""Applying Normalize Spans ..."""'], {}), "('Applying Normalize Spans ...')\n", (11818, 11850), False, 'import logging\n'), ((12074, 12118), 'logging.info', 'logging.info', (['"""Applying Add Token Index ..."""'], {}), "('Applying Add Token Index ...')\n", (12086, 12118), False, 'import logging\n'), ((13686, 13723), 'logging.info', 'logging.info', (['"""Loading pwc docs ... """'], {}), "('Loading pwc docs ... ')\n", (13698, 13723), False, 'import logging\n'), ((14754, 14807), 'logging.info', 'logging.info', (['"""Loading PwC Sentence Predictions ... """'], {}), "('Loading PwC Sentence Predictions ... ')\n", (14766, 14807), False, 'import logging\n'), ((26277, 26287), 'tqdm.tqdm', 'tqdm', (['data'], {}), '(data)\n', (26281, 26287), False, 'from tqdm import tqdm\n'), ((800, 843), 'spacy.tokens.Doc', 'Doc', (['self.vocab'], {'words': 'words', 'spaces': 'spaces'}), '(self.vocab, words=words, spaces=spaces)\n', (803, 843), False, 'from spacy.tokens import Doc\n'), ((2426, 2449), 'os.listdir', 'os.listdir', (['brat_folder'], {}), '(brat_folder)\n', (2436, 2449), False, 'import os\n'), ((26177, 26193), 'json.loads', 'json.loads', (['line'], {}), '(line)\n', (26187, 26193), False, 'import json\n'), ((1026, 1047), 'os.path.isdir', 'os.path.isdir', (['folder'], {}), '(folder)\n', (1039, 1047), False, 'import os\n'), ((1073, 1091), 'os.listdir', 'os.listdir', (['folder'], {}), '(folder)\n', (1083, 1091), False, 'import os\n'), ((17206, 17222), 'numpy.cumsum', 'np.cumsum', (['cards'], {}), '(cards)\n', (17215, 17222), True, 'import numpy as np\n'), ((18956, 18991), 'json.dumps', 'json.dumps', (['as_json'], {'sort_keys': '(True)'}), '(as_json, sort_keys=True)\n', (18966, 18991), False, 'import json\n'), ((1177, 1213), 'os.path.join', 'os.path.join', (['folder', '"""document.txt"""'], {}), "(folder, 'document.txt')\n", (1189, 1213), False, 'import os\n'), ((2511, 2539), 'os.path.join', 'os.path.join', (['brat_folder', 'f'], {}), '(brat_folder, f)\n', (2523, 2539), False, 'import os\n'), ((11352, 11381), 're.finditer', 're.finditer', (['"""\\\\s+"""', 'sentence'], {}), "('\\\\s+', sentence)\n", (11363, 11381), False, 'import re\n'), ((1242, 1278), 'os.path.join', 'os.path.join', (['folder', '"""document.ann"""'], {}), "(folder, 'document.ann')\n", (1254, 1278), False, 'import os\n'), ((11147, 11200), 're.finditer', 're.finditer', (['"""[^\\\\s\\\\+\\\\-/\\\\(\\\\)&\\\\[\\\\],]+"""', 'sentence'], {}), "('[^\\\\s\\\\+\\\\-/\\\\(\\\\)&\\\\[\\\\],]+', sentence)\n", (11158, 11200), False, 'import re\n'), ((11251, 11300), 're.finditer', 're.finditer', (['"""[\\\\+\\\\-/\\\\(\\\\)&\\\\[\\\\],]+"""', 'sentence'], {}), "('[\\\\+\\\\-/\\\\(\\\\)&\\\\[\\\\],]+', sentence)\n", (11262, 11300), False, 'import re\n'), ((23801, 23828), 'copy.deepcopy', 'deepcopy', (['entities[s, e][k]'], {}), '(entities[s, e][k])\n', (23809, 23828), False, 'from copy import deepcopy\n'), ((20079, 20106), 'copy.deepcopy', 'deepcopy', (['entities[s, e][k]'], {}), '(entities[s, e][k])\n', (20087, 20106), False, 'from copy import deepcopy\n'), ((20357, 20384), 'copy.deepcopy', 'deepcopy', (['entities[s, e][k]'], {}), '(entities[s, e][k])\n', (20365, 20384), False, 'from copy import deepcopy\n')] |
# Copyright (C) 2018 Innoviz Technologies
# All rights reserved.
#
# This software may be modified and distributed under the terms
# of the BSD 3-Clause license. See the LICENSE file for details.
import pandas as pd
import os
import numpy as np
from utilities.math_utils import RotationTranslationData
from visualizations.vis import pcshow
from utilities import data_utils
if __name__ == '__main__':
base_dir = os.path.dirname(os.getcwd())
video_dir = os.path.join(base_dir, 'data_examples', 'test_video')
agg_point_cloud_list = []
max_frames_to_keep = 10
min_idx = 0
decimate = 1
max_dist = 100
for idx in data_utils.enumerate_frames(video_dir):
if idx < min_idx or idx % decimate != 0:
continue
pc_file = data_utils.frame_to_filename(video_dir, idx, 'pointcloud')
pc, ego, label = data_utils.read_all_data(video_dir, idx)
ego_rt = RotationTranslationData(vecs=(ego[:3], ego[3:]))
ego_pc = ego_rt.apply_transform(pc[:, :3])
ego_pc = np.concatenate((ego_pc, pc[:, 3:4]), -1)
labeled_pc = np.concatenate((ego_pc, label), -1)
agg_point_cloud_list.append(labeled_pc)
if len(agg_point_cloud_list) > max_frames_to_keep:
agg_point_cloud_list = agg_point_cloud_list[1:]
agg_point_cloud = np.concatenate(agg_point_cloud_list, 0)
pc2disp = ego_rt.inverse().apply_transform(agg_point_cloud[:, :3])
pc2disp = np.concatenate((pc2disp, agg_point_cloud[:, 3:]), -1)
pc2disp = pc2disp[np.linalg.norm(pc2disp[:, :3], axis=1) < max_dist]
pcshow(pc2disp, on_screen_text=pc_file, max_points=32000 * max_frames_to_keep)
| [
"utilities.data_utils.enumerate_frames",
"os.path.join",
"os.getcwd",
"utilities.math_utils.RotationTranslationData",
"numpy.concatenate",
"numpy.linalg.norm",
"utilities.data_utils.read_all_data",
"utilities.data_utils.frame_to_filename",
"visualizations.vis.pcshow"
] | [((463, 516), 'os.path.join', 'os.path.join', (['base_dir', '"""data_examples"""', '"""test_video"""'], {}), "(base_dir, 'data_examples', 'test_video')\n", (475, 516), False, 'import os\n'), ((642, 680), 'utilities.data_utils.enumerate_frames', 'data_utils.enumerate_frames', (['video_dir'], {}), '(video_dir)\n', (669, 680), False, 'from utilities import data_utils\n'), ((434, 445), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (443, 445), False, 'import os\n'), ((770, 828), 'utilities.data_utils.frame_to_filename', 'data_utils.frame_to_filename', (['video_dir', 'idx', '"""pointcloud"""'], {}), "(video_dir, idx, 'pointcloud')\n", (798, 828), False, 'from utilities import data_utils\n'), ((854, 894), 'utilities.data_utils.read_all_data', 'data_utils.read_all_data', (['video_dir', 'idx'], {}), '(video_dir, idx)\n', (878, 894), False, 'from utilities import data_utils\n'), ((912, 960), 'utilities.math_utils.RotationTranslationData', 'RotationTranslationData', ([], {'vecs': '(ego[:3], ego[3:])'}), '(vecs=(ego[:3], ego[3:]))\n', (935, 960), False, 'from utilities.math_utils import RotationTranslationData\n'), ((1029, 1069), 'numpy.concatenate', 'np.concatenate', (['(ego_pc, pc[:, 3:4])', '(-1)'], {}), '((ego_pc, pc[:, 3:4]), -1)\n', (1043, 1069), True, 'import numpy as np\n'), ((1092, 1127), 'numpy.concatenate', 'np.concatenate', (['(ego_pc, label)', '(-1)'], {}), '((ego_pc, label), -1)\n', (1106, 1127), True, 'import numpy as np\n'), ((1321, 1360), 'numpy.concatenate', 'np.concatenate', (['agg_point_cloud_list', '(0)'], {}), '(agg_point_cloud_list, 0)\n', (1335, 1360), True, 'import numpy as np\n'), ((1454, 1507), 'numpy.concatenate', 'np.concatenate', (['(pc2disp, agg_point_cloud[:, 3:])', '(-1)'], {}), '((pc2disp, agg_point_cloud[:, 3:]), -1)\n', (1468, 1507), True, 'import numpy as np\n'), ((1593, 1671), 'visualizations.vis.pcshow', 'pcshow', (['pc2disp'], {'on_screen_text': 'pc_file', 'max_points': '(32000 * max_frames_to_keep)'}), '(pc2disp, on_screen_text=pc_file, max_points=32000 * max_frames_to_keep)\n', (1599, 1671), False, 'from visualizations.vis import pcshow\n'), ((1534, 1572), 'numpy.linalg.norm', 'np.linalg.norm', (['pc2disp[:, :3]'], {'axis': '(1)'}), '(pc2disp[:, :3], axis=1)\n', (1548, 1572), True, 'import numpy as np\n')] |
from typing import Sequence, Optional
import pandas as pd
import numpy as np
def formatted_corr_df(df: pd.DataFrame, cols: Optional[Sequence[str]] = None) -> pd.DataFrame:
"""
Calculates correlations on a DataFrame and displays only the lower triangular of the
resulting correlation DataFrame.
:param df:
:param cols: subset of column names on which to calculate correlations
:return:
"""
if not cols:
use_cols = list(df.columns)
else:
use_cols = list(cols)
corr_df = df[use_cols].corr()
corr_df = _lower_triangular_of_df(corr_df)
return corr_df.applymap(lambda x: f'{x:.2f}' if not isinstance(x, str) else x)
def _lower_triangular_of_df(df):
return pd.DataFrame(np.tril(df), index=df.index, columns=df.columns).replace(0, '')
| [
"numpy.tril"
] | [((738, 749), 'numpy.tril', 'np.tril', (['df'], {}), '(df)\n', (745, 749), True, 'import numpy as np\n')] |
# -*- coding: utf-8 -*-
__doc__="返回选择物体的类型"
import rpw
from rpw import revit, DB, UI,db,doc
#from System.Collections.Generic import List
#import json
#from scriptutils import this_script
#from scriptutils.userinput import CommandSwitchWindow
#import subprocess as sp
#Change Selected Grid From 3D to 2D
#selection = rpw.ui.Selection().elements
#Grid=selection[0]
#Grid= rpw.db.Element(Grid).parameters.all
#print(Grid)
#Change Grid buble visibility
selection = rpw.ui.Selection()
Grid=selection
endpoint=DB.DatumEnds.End0
startpoint=DB.DatumEnds.End1
CurrentView=doc.ActiveView
DatumExtentType2D=DB.DatumExtentType.ViewSpecific
@rpw.db.Transaction.ensure('Hide_Grid_Bubble')
def DatumExtentType3DTo2D(_Grid,points,CurrentView):
for i in points:
_Grid.SetDatumExtentType(i,CurrentView,DatumExtentType2D)
for i in Grid:
DatumExtentType3DTo2D(i,[endpoint,startpoint],CurrentView)
print("Good")
| [
"rpw.ui.Selection",
"rpw.db.Transaction.ensure"
] | [((465, 483), 'rpw.ui.Selection', 'rpw.ui.Selection', ([], {}), '()\n', (481, 483), False, 'import rpw\n'), ((635, 680), 'rpw.db.Transaction.ensure', 'rpw.db.Transaction.ensure', (['"""Hide_Grid_Bubble"""'], {}), "('Hide_Grid_Bubble')\n", (660, 680), False, 'import rpw\n')] |
import json
import urllib.request
import urllib.parse
import csv
import login
class Action:
def __init__(self, tenantId, appId, appSecret, body, url, filename, column):
self.tenantId = tenantId
self.appId = appId
self.appSecret = appSecret
self.body = body
self.url = url
self.filename = filename
self.column = column
def action(self):
login_MDEcli = login.Login(self.tenantId, self.appId, self.appSecret)
aadToken = login_MDEcli.login()
with open(self.filename, newline='') as csvfile:
data = csv.DictReader(csvfile)
for item in data:
try:
url = f"https://api.securitycenter.microsoft.com/api/machines/{item[self.column].strip()}/{self.url}"
headers = {
'Content-Type' : 'application/json',
'Accept' : 'application/json',
'Authorization' : "Bearer " + aadToken
}
data = str(json.dumps(self.body)).encode("utf-8")
req = urllib.request.Request(url=url, data=data, headers=headers)
response = urllib.request.urlopen(req)
jsonResponse = json.loads(response.read())
computerDnsName = jsonResponse['computerDnsName']
print(f'{self.url} started on {computerDnsName}')
except Exception as e:
print(f'Someting went wrong:{e}') | [
"login.Login",
"csv.DictReader",
"json.dumps"
] | [((426, 480), 'login.Login', 'login.Login', (['self.tenantId', 'self.appId', 'self.appSecret'], {}), '(self.tenantId, self.appId, self.appSecret)\n', (437, 480), False, 'import login\n'), ((598, 621), 'csv.DictReader', 'csv.DictReader', (['csvfile'], {}), '(csvfile)\n', (612, 621), False, 'import csv\n'), ((1061, 1082), 'json.dumps', 'json.dumps', (['self.body'], {}), '(self.body)\n', (1071, 1082), False, 'import json\n')] |
from flask import Flask
from flask_login import LoginManager
from flask_wtf import CsrfProtect
login_manager = LoginManager()
def create_app():
app = Flask(__name__)
app.config.from_pyfile("config/setting.py")
csrf = CsrfProtect()
csrf.init_app(app)
register_blueprint(app)
create_db(app)
login_manager.init_app(app)
login_manager.login_view = "auth.login"
login_manager.login_message = "Please login first!"
return app
def register_blueprint(app):
from .manager.blueprint import auth, web
app.register_blueprint(web)
app.register_blueprint(auth)
def create_db(app):
from .models.base import db
db.init_app(app)
db.create_all(app=app)
| [
"flask_login.LoginManager",
"flask_wtf.CsrfProtect",
"flask.Flask"
] | [((112, 126), 'flask_login.LoginManager', 'LoginManager', ([], {}), '()\n', (124, 126), False, 'from flask_login import LoginManager\n'), ((157, 172), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (162, 172), False, 'from flask import Flask\n'), ((232, 245), 'flask_wtf.CsrfProtect', 'CsrfProtect', ([], {}), '()\n', (243, 245), False, 'from flask_wtf import CsrfProtect\n')] |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""
parse_bib_file.py: Convert raw bibtex file to yml format and md pages.
"""
import os
import yaml
import bibtexparser as bb
def get_link(entry):
if 'url' in entry.keys():
return entry['url']
elif 'doi' in entry.keys():
return f"https://doi.org/{entry['doi']}"
else:
print('Warning: no link in', entry['ID'])
return ''
def get_journal(entry):
if entry.get('ENTRYTYPE') == 'article':
return entry.get('journal')
elif entry.get('ENTRYTYPE') == 'inproceedings':
return entry.get('booktitle')
else:
print('Warning: unknown entry type in', entry)
return ''
def clean(list_of_string):
replace = [
['\\"{u}}', 'ü'],
['{', ''],
['}', ''],
]
if not isinstance(list_of_string, list):
list_of_string = [list_of_string]
clean_list = []
for a in list_of_string:
for r in replace:
a = a.replace(*r)
clean_list.append(a)
return clean_list
def check_for_overwrite(fname):
answer = ""
if os.path.exists(fname):
while answer not in ["y", "n"]:
answer = input(f"{fname} exists, overwrite? (y/[n])") or "n"
if answer == "n":
return False
return True
if __name__ == "__main__":
import sys
in_name_bib = "_data/mendeley-export.bib"
out_name_data = "_data/mendeley-export.yml"
out_folder_pages = "_publications"
with open(in_name_bib, 'r') as f:
bib_db = bb.load(f)
general_dict = {
'layout': 'publication',
'ref-code': '',
'ref-link': '',
'ref-video': ''
}
categories = {'inproceedings': 'Conference', 'article': 'Journal'}
all_dicts = []
for entry in bib_db.entries:
author_tuples = [a.split(', ') for a in entry['author'].split(' and ')]
author_names = [f"{a[1].strip()[0]}. {a[0]}" for a in author_tuples]
author_names = clean(author_names)
dict_to_write = {}
dict_to_write['ref-authors'] = author_names
dict_to_write['title'] = clean(entry['title'])[0]
dict_to_write['ref-year'] = int(entry['year'])
dict_to_write['ref-journal'] = get_journal(entry)
dict_to_write['ref-link'] = get_link(entry)
dict_to_write['categories'] = categories.get(entry['ENTRYTYPE'],
'Other')
all_dicts.append(dict_to_write)
fname = os.path.join(out_folder_pages, entry['ID'] + '.md')
if not check_for_overwrite(fname):
continue
with open(fname, 'w') as f:
f.write('---\n')
f.write('layout: publication\n')
f.write(yaml.dump(dict_to_write))
f.write('---\n\n\n')
f.write(entry.get('abstract', ''))
f.write('\n')
print('wrote', fname)
if not check_for_overwrite(out_name_data):
sys.exit(0) # successful termination
with open(out_name_data, 'w') as f:
f.write(yaml.dump(all_dicts))
print('wrote', out_name_data)
| [
"os.path.exists",
"yaml.dump",
"os.path.join",
"sys.exit",
"bibtexparser.load"
] | [((1113, 1134), 'os.path.exists', 'os.path.exists', (['fname'], {}), '(fname)\n', (1127, 1134), False, 'import os\n'), ((1545, 1555), 'bibtexparser.load', 'bb.load', (['f'], {}), '(f)\n', (1552, 1555), True, 'import bibtexparser as bb\n'), ((2510, 2561), 'os.path.join', 'os.path.join', (['out_folder_pages', "(entry['ID'] + '.md')"], {}), "(out_folder_pages, entry['ID'] + '.md')\n", (2522, 2561), False, 'import os\n'), ((2977, 2988), 'sys.exit', 'sys.exit', (['(0)'], {}), '(0)\n', (2985, 2988), False, 'import sys\n'), ((3071, 3091), 'yaml.dump', 'yaml.dump', (['all_dicts'], {}), '(all_dicts)\n', (3080, 3091), False, 'import yaml\n'), ((2758, 2782), 'yaml.dump', 'yaml.dump', (['dict_to_write'], {}), '(dict_to_write)\n', (2767, 2782), False, 'import yaml\n')] |
# Generated by Django 2.2.13 on 2020-07-22 12:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('apis_vocabularies', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='texttype',
name='lang',
field=models.CharField(blank=True, default='deu', help_text="The ISO 639-3 (or 2) code for the label's language.", max_length=3, null=True, verbose_name='ISO Code'),
),
]
| [
"django.db.models.CharField"
] | [((334, 501), 'django.db.models.CharField', 'models.CharField', ([], {'blank': '(True)', 'default': '"""deu"""', 'help_text': '"""The ISO 639-3 (or 2) code for the label\'s language."""', 'max_length': '(3)', 'null': '(True)', 'verbose_name': '"""ISO Code"""'}), '(blank=True, default=\'deu\', help_text=\n "The ISO 639-3 (or 2) code for the label\'s language.", max_length=3,\n null=True, verbose_name=\'ISO Code\')\n', (350, 501), False, 'from django.db import migrations, models\n')] |
import torch
import get_data
import numpy as np
import torchaudio
def number_of_correct(pred, target):
return pred.squeeze().eq(target).sum().item()
def get_likely_index(tensor):
return tensor.argmax(dim=-1)
def compute_accuracy(model, data_loader, device):
model.eval()
correct = 0
for data, target in data_loader:
data = data.to(device)
target = target.to(device)
pred = model(data)
pred = get_likely_index(pred)
correct += number_of_correct(pred, target)
score = correct / len(data_loader.dataset)
return score
def apply_to_wav(model, waveform: torch.Tensor, sample_rate: float, device: str):
model.eval()
mel_spec = get_data.prepare_wav(waveform, sample_rate)
mel_spec = torch.unsqueeze(mel_spec, dim=0).to(device)
res = model(mel_spec)
probs = torch.nn.Softmax(dim=-1)(res).cpu().detach().numpy()
predictions = []
for idx in np.argsort(-probs):
label = get_data.idx_to_label(idx)
predictions.append((label, probs[idx]))
return predictions
def apply_to_file(model, wav_file: str, device: str):
waveform, sample_rate = torchaudio.load(wav_file)
return apply_to_wav(model, waveform, sample_rate, device)
| [
"torch.nn.Softmax",
"get_data.prepare_wav",
"torchaudio.load",
"torch.unsqueeze",
"get_data.idx_to_label",
"numpy.argsort"
] | [((707, 750), 'get_data.prepare_wav', 'get_data.prepare_wav', (['waveform', 'sample_rate'], {}), '(waveform, sample_rate)\n', (727, 750), False, 'import get_data\n'), ((938, 956), 'numpy.argsort', 'np.argsort', (['(-probs)'], {}), '(-probs)\n', (948, 956), True, 'import numpy as np\n'), ((1156, 1181), 'torchaudio.load', 'torchaudio.load', (['wav_file'], {}), '(wav_file)\n', (1171, 1181), False, 'import torchaudio\n'), ((974, 1000), 'get_data.idx_to_label', 'get_data.idx_to_label', (['idx'], {}), '(idx)\n', (995, 1000), False, 'import get_data\n'), ((766, 798), 'torch.unsqueeze', 'torch.unsqueeze', (['mel_spec'], {'dim': '(0)'}), '(mel_spec, dim=0)\n', (781, 798), False, 'import torch\n'), ((849, 873), 'torch.nn.Softmax', 'torch.nn.Softmax', ([], {'dim': '(-1)'}), '(dim=-1)\n', (865, 873), False, 'import torch\n')] |
import os
import sys
import numpy as np
from sklearn import linear_model
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from scipy.sparse import csr_matrix
import csv
def ReadCsv(fileName, mode):
in_file = open(fileName)
reader = csv.reader(in_file, delimiter='\t', quotechar='"')
if(mode == 'input'):
data = [[]]
for row in reader:
data.append(row)
data.pop(0)
elif(mode == 'output'):
column = 1
data = []
for row in reader:
data.append(int(row[column]))
return data
# in_filepath = sys.argv[1]
in_filepath = "/home/rostunov/workspace/neuro/maltparser/malt/data/x.txt"
X = np.asarray(ReadCsv(in_filepath, 'input'), 'int')
# A = csr_matrix(X)
# print (A)
| [
"csv.reader"
] | [((287, 337), 'csv.reader', 'csv.reader', (['in_file'], {'delimiter': '"""\t"""', 'quotechar': '"""\\""""'}), '(in_file, delimiter=\'\\t\', quotechar=\'"\')\n', (297, 337), False, 'import csv\n')] |
import string
from src import config
def password_generator():
alphabet = string.digits
alphabet += string.ascii_lowercase
base = len(alphabet)
shift = 0
length = int(config.get('minimal_password_length'))
while True:
password = ''
temp = shift
while temp > 0:
k = temp // base
rest = temp % base
password = alphabet[rest] + password
temp = k
while len(password) < length:
password = alphabet[0] + password
yield password
if alphabet[-1] * length == password:
length += 1
shift = 0
else:
shift += 1
| [
"src.config.get"
] | [((191, 228), 'src.config.get', 'config.get', (['"""minimal_password_length"""'], {}), "('minimal_password_length')\n", (201, 228), False, 'from src import config\n')] |
from django.db import models
from django.contrib.auth import get_user_model
class TextAnswer(models.Model):
"""Model definition for TextAnswer."""
user = models.ForeignKey(get_user_model(), on_delete=models.CASCADE)
question = models.ForeignKey('Question', on_delete=models.CASCADE)
answer = models.CharField('Answer', max_length=100)
class Meta:
"""Meta definition for TextAnswer."""
verbose_name = 'Text Answer'
verbose_name_plural = 'Text Answers'
def __str__(self):
"""Unicode representation of TextAnswer."""
return f'{self.answer}' | [
"django.contrib.auth.get_user_model",
"django.db.models.CharField",
"django.db.models.ForeignKey"
] | [((242, 297), 'django.db.models.ForeignKey', 'models.ForeignKey', (['"""Question"""'], {'on_delete': 'models.CASCADE'}), "('Question', on_delete=models.CASCADE)\n", (259, 297), False, 'from django.db import models\n'), ((311, 353), 'django.db.models.CharField', 'models.CharField', (['"""Answer"""'], {'max_length': '(100)'}), "('Answer', max_length=100)\n", (327, 353), False, 'from django.db import models\n'), ((183, 199), 'django.contrib.auth.get_user_model', 'get_user_model', ([], {}), '()\n', (197, 199), False, 'from django.contrib.auth import get_user_model\n')] |
from django.conf.urls import patterns, url
from qs import views
urlpatterns = patterns('',
url(r'^$', views.index, name='index'),
url(r'^sendqs/$', views.sendqs, name='sendqs'),
url(r'^show/$', views.showall, name='showall'),
)
| [
"django.conf.urls.url"
] | [((97, 133), 'django.conf.urls.url', 'url', (['"""^$"""', 'views.index'], {'name': '"""index"""'}), "('^$', views.index, name='index')\n", (100, 133), False, 'from django.conf.urls import patterns, url\n'), ((140, 185), 'django.conf.urls.url', 'url', (['"""^sendqs/$"""', 'views.sendqs'], {'name': '"""sendqs"""'}), "('^sendqs/$', views.sendqs, name='sendqs')\n", (143, 185), False, 'from django.conf.urls import patterns, url\n'), ((192, 237), 'django.conf.urls.url', 'url', (['"""^show/$"""', 'views.showall'], {'name': '"""showall"""'}), "('^show/$', views.showall, name='showall')\n", (195, 237), False, 'from django.conf.urls import patterns, url\n')] |
import cv2
import numpy as np
from plantcv.plantcv.transform import nonuniform_illumination
def test_nonuniform_illumination_rgb(transform_test_data):
"""Test for PlantCV."""
# Load rgb image
rgb_img = cv2.imread(transform_test_data.small_rgb_img)
corrected = nonuniform_illumination(img=rgb_img, ksize=11)
assert np.mean(corrected) < np.mean(rgb_img)
def test_nonuniform_illumination_gray(transform_test_data):
"""Test for PlantCV."""
# Load rgb image
gray_img = cv2.imread(transform_test_data.small_gray_img, -1)
corrected = nonuniform_illumination(img=gray_img, ksize=11)
assert corrected.shape == gray_img.shape
| [
"numpy.mean",
"plantcv.plantcv.transform.nonuniform_illumination",
"cv2.imread"
] | [((216, 261), 'cv2.imread', 'cv2.imread', (['transform_test_data.small_rgb_img'], {}), '(transform_test_data.small_rgb_img)\n', (226, 261), False, 'import cv2\n'), ((278, 324), 'plantcv.plantcv.transform.nonuniform_illumination', 'nonuniform_illumination', ([], {'img': 'rgb_img', 'ksize': '(11)'}), '(img=rgb_img, ksize=11)\n', (301, 324), False, 'from plantcv.plantcv.transform import nonuniform_illumination\n'), ((500, 550), 'cv2.imread', 'cv2.imread', (['transform_test_data.small_gray_img', '(-1)'], {}), '(transform_test_data.small_gray_img, -1)\n', (510, 550), False, 'import cv2\n'), ((567, 614), 'plantcv.plantcv.transform.nonuniform_illumination', 'nonuniform_illumination', ([], {'img': 'gray_img', 'ksize': '(11)'}), '(img=gray_img, ksize=11)\n', (590, 614), False, 'from plantcv.plantcv.transform import nonuniform_illumination\n'), ((336, 354), 'numpy.mean', 'np.mean', (['corrected'], {}), '(corrected)\n', (343, 354), True, 'import numpy as np\n'), ((357, 373), 'numpy.mean', 'np.mean', (['rgb_img'], {}), '(rgb_img)\n', (364, 373), True, 'import numpy as np\n')] |
def main():
import numpy as np
import matplotlib.pyplot as plt
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import pickle
from random import randint, randrange
import sys
from tqdm import tqdm
import cv2
print("CUDA available: {}".format(torch.cuda.is_available()))
location = "ncc"
# Import model architectures
from models.DSCLRCN_OldContext import DSCLRCN
from models.CoSADUV import CoSADUV
from models.CoSADUV_NoTemporal import CoSADUV_NoTemporal
# Prepare settings and get the datasets
from util.data_utils import get_SALICON_datasets, get_video_datasets
### Data options ###
dataset_root_dir = "Dataset/UAV123" # Dataset/[SALICON, UAV123, UAV123_LIKE_MISC]
mean_image_name = (
"mean_image.npy"
) # Must be located at dataset_root_dir/mean_image_name
img_size = (
480,
640,
) # height, width - original: 480, 640, reimplementation: 96, 128
duration = (
300
) # Length of sequences loaded from each video, if a video dataset is used
from util import loss_functions
from util.solver import Solver
### Testing options ###
# Minibatchsize: Determines how many images are processed at a time on the GPU
minibatchsize = 2 # Recommended: 4 for 480x640 for >12GB mem, 2 for <12GB mem.
########## PREPARE DATASETS ##########
### Prepare datasets and loaders ###
if "SALICON" in dataset_root_dir:
train_data, val_data, test_data, mean_image = get_SALICON_datasets(
dataset_root_dir, mean_image_name, img_size
)
train_loader = [
torch.utils.data.DataLoader(
train_data,
batch_size=minibatchsize,
shuffle=True,
num_workers=8,
pin_memory=True,
)
]
val_loader = [
torch.utils.data.DataLoader(
val_data,
batch_size=minibatchsize,
shuffle=True,
num_workers=8,
pin_memory=True,
)
]
# Load test loader using val_data as SALICON does not give GT for its test set
test_loader = [
torch.utils.data.DataLoader(
val_data,
batch_size=minibatchsize,
shuffle=True,
num_workers=8,
pin_memory=True,
)
]
elif "UAV123" in dataset_root_dir:
train_loader, val_loader, test_loader, mean_image = get_video_datasets(
dataset_root_dir,
mean_image_name,
duration=duration,
img_size=img_size,
shuffle=False,
loader_settings={
"batch_size": minibatchsize,
"num_workers": 8,
"pin_memory": False,
},
)
########## LOADING MODELS ##########
# Loading a model from the saved state that produced
# the lowest validation loss during training:
# Requires the model classes be loaded
# Assumes the model uses models.CoSADUV_NoTemporal architecture.
# If not, this method will fail
def load_model_from_checkpoint(model_name):
filename = "trained_models/" + model_name + ".pth"
if torch.cuda.is_available():
checkpoint = torch.load(filename)
else:
# Load GPU model on CPU
checkpoint = torch.load(filename, map_location="cpu")
start_epoch = checkpoint["epoch"]
best_accuracy = checkpoint["best_accuracy"]
if "DSCLRCN" in model_name:
model = DSCLRCN(input_dim=img_size, local_feats_net="Seg")
elif "CoSADUV_NoTemporal" in model_name:
model = CoSADUV_NoTemporal(input_dim=img_size, local_feats_net="Seg")
elif "CoSADUV" in model_name:
model = CoSADUV(input_dim=img_size, local_feats_net="Seg")
else:
tqdm.write("Error: no model name found in filename: {}".format(model_name))
return
# Ignore extra parameters ('.num_batches_tracked'
# that are added on NCC due to different pytorch version)
model.load_state_dict(checkpoint["state_dict"], strict=False)
tqdm.write(
"=> loaded model checkpoint '{}' (trained for {} epochs)\n with architecture {}".format(
model_name, checkpoint["epoch"], type(model).__name__
)
)
if torch.cuda.is_available():
model = model.cuda()
tqdm.write(" loaded to cuda")
model.eval()
return model
def load_model(model_name):
model = torch.load("trained_models/" + model_name, map_location="cpu")
print("=> loaded model '{}'".format(model_name))
if torch.cuda.is_available():
model = model.cuda()
print(" loaded to cuda")
model.eval()
return model
########## LOAD THE MODELS ##########
models = []
model_names = []
# Loading some pretrained models to test them on the images:
# DSCLRCN models
## Trained on SALICON
### NSS_loss
# model_names.append("DSCLRCN/SALICON NSS -1.62NSS val best and last/best_model_DSCLRCN_NSS_loss_batch20_epoch5")
## Trained on UAV123
### NSS_alt loss func
# model_names.append("DSCLRCN/UAV123 NSS_alt 1.38last 3.15best testing/best_model_DSCLRCN_NSS_alt_batch20_epoch5")
# CoSADUV_NoTemporal models
## Trained on UAV123
### DoM loss func
# model_names.append(
# "CoSADUV_NoTemporal/DoM SGD 0.01lr - 3.16 NSS_alt/best_model_CoSADUV_NoTemporal_DoM_batch20_epoch6"
# )
### NSS_alt loss func
# model_names.append("CoSADUV_NoTemporal/NSS_alt Adam lr 1e-4 - 1.36/best_model_CoSADUV_NoTemporal_NSS_alt_batch20_epoch5")
### CE_MAE loss func
# model_names.append("CoSADUV_NoTemporal/best_model_CoSADUV_NoTemporal_CE_MAE_loss_batch20_epoch10")
# CoSADUV models (CoSADUV2)
## Trained on UAV123
### NSS_alt loss func
#### 1 Frame backpropagation
#### Kernel size 1
# model_names.append("CoSADUV/NSS_alt Adam 0.001lr 1frame backprop size1 kernel -2train -0.7val 1epoch/best_model_CoSADUV_NSS_alt_batch20_epoch5")
#### Kernel size 3
model_names.append(
"CoSADUV/NSS_alt Adam 0.01lr 1frame backprop size3 kernel/best_model_CoSADUV_NSS_alt_batch20_epoch5"
)
#### 2 Frame backpropagation
#### Kernel size 3
#model_names.append(
# "CoSADUV/NSS_alt Adam 0.01lr 2frame backprop size3 kernel - 6.56 NSS_alt val/best_model_CoSADUV_NSS_alt_batch20_epoch5"
#)
### DoM loss func
# Only very poor results achieved
### CE_MAE loss func
# Only very poor results achieved
max_name_len = max([len(name) for name in model_names])
# Load the models specified above
iterable = model_names
# for i, name in enumerate(iterable):
# if "best_model" in name:
# models.append(load_model_from_checkpoint(name))
# else:
# models.append(load_model(name))
print()
print("Loaded all specified models")
########## TEST THE MODEL ##########
# Define a function for testing a model
# Output is resized to the size of the data_source
def test_model(model, data_loader, loss_fns=[loss_functions.MAE_loss]):
loss_sums = []
loss_counts = []
for i, loss_fn in enumerate(loss_fns):
if loss_fn != loss_functions.NSS_alt:
loss_sums.append(0)
loss_counts.append(0)
else:
loss_sums.append([0, 0])
loss_counts.append([0, 0])
loop1 = data_loader
if location != "ncc":
loop1 = tqdm(loop1)
for video_loader in loop1:
# Reset temporal state if model is temporal
if model.temporal:
model.clear_temporal_state()
loop2 = video_loader
if location != "ncc":
loop2 = tqdm(loop2)
for data in loop2:
inputs, labels = data
if torch.cuda.is_available():
inputs = inputs.cuda()
labels = labels.cuda()
# Produce the output
outputs = model(inputs).squeeze(1)
# if model is temporal detach its state
if model.temporal:
model.detach_temporal_state()
# Move the output to the CPU so we can process it using numpy
outputs = outputs.cpu().data.numpy()
# Threshold output if model is temporal
if model.temporal:
outputs[outputs >= 0.50001] = 1
outputs[outputs < 0.50001] = 0
# If outputs contains a single image, insert
# a singleton batchsize dimension at index 0
if len(outputs.shape) == 2:
outputs = np.expand_dims(outputs, 0)
# Resize the images to input size
outputs = np.array(
[
cv2.resize(output, (labels.shape[2], labels.shape[1]))
for output in outputs
]
)
outputs = torch.from_numpy(outputs)
if torch.cuda.is_available():
outputs = outputs.cuda()
labels = labels.cuda()
# Apply each loss function, add results to corresponding entry in loss_sums and loss_counts
for i, loss_fn in enumerate(loss_fns):
# If loss fn is NSS_alt, manually add std_dev() if the target is all-0
if loss_fn == loss_functions.NSS_alt:
for i in range(len(labels)):
if labels[i].sum() == 0:
loss_sums[i][1] += outputs[i].std().item()
loss_counts[i][1] += 1
else:
loss_sums[i][0] += loss_fn(outputs[i], labels[i]).item()
loss_counts[i][0] += 1
else:
loss_sums[i] += loss_fn(outputs, labels).item()
loss_counts[i] += 1
return loss_sums, loss_counts
# Obtaining loss values on the test set for different models:
loop3 = model_names
if location != "ncc":
loop3 = tqdm(loop3)
for i, model_name in enumerate(loop3):
if location != "ncc":
tqdm.write("model name: {}".format(model_name))
else:
print("model name: {}".format(model_name))
if "best_model" in model_name:
model = load_model_from_checkpoint(model_name)
else:
model = load_model(model_name)
loss_fns = [
loss_functions.NSS_alt,
loss_functions.CE_loss,
loss_functions.MAE_loss,
loss_functions.DoM,
]
test_losses, test_counts = test_model(model, test_loader, loss_fns=loss_fns)
# Print out the result
tqdm.write("[{}] Model: ".format(i, model_names[i]))
for i, func in enumerate(loss_fns):
if func == loss_functions.NSS_alt:
tqdm.write(
("{:25} : {:6f}").format(
"NSS_alt (+ve imgs)", test_losses[i][0] / max(test_counts[i][0], 1)
)
)
tqdm.write(
("{:25} : {:6f}").format(
"NSS_alt (-ve imgs)", test_losses[i][1] / max(test_counts[i][1], 1)
)
)
else:
tqdm.write(
("{:25} : {:6f}").format(
func.__name__, test_losses[i] / test_counts[i]
)
)
del model
if __name__ == "__main__":
import torch
torch.multiprocessing.set_start_method("forkserver") # spawn, forkserver, or fork
# Use CuDNN with benchmarking for performance improvement:
# from 1.05 batch20/s to 1.55 batch20/s on Quadro P4000
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
main()
| [
"models.DSCLRCN_OldContext.DSCLRCN",
"cv2.resize",
"tqdm.tqdm.write",
"torch.load",
"util.data_utils.get_SALICON_datasets",
"tqdm.tqdm",
"models.CoSADUV_NoTemporal.CoSADUV_NoTemporal",
"util.data_utils.get_video_datasets",
"torch.from_numpy",
"models.CoSADUV.CoSADUV",
"torch.cuda.is_available",
... | [((12055, 12107), 'torch.multiprocessing.set_start_method', 'torch.multiprocessing.set_start_method', (['"""forkserver"""'], {}), "('forkserver')\n", (12093, 12107), False, 'import torch\n'), ((1562, 1627), 'util.data_utils.get_SALICON_datasets', 'get_SALICON_datasets', (['dataset_root_dir', 'mean_image_name', 'img_size'], {}), '(dataset_root_dir, mean_image_name, img_size)\n', (1582, 1627), False, 'from util.data_utils import get_SALICON_datasets, get_video_datasets\n'), ((3348, 3373), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (3371, 3373), False, 'import torch\n'), ((4524, 4549), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (4547, 4549), False, 'import torch\n'), ((4719, 4781), 'torch.load', 'torch.load', (["('trained_models/' + model_name)"], {'map_location': '"""cpu"""'}), "('trained_models/' + model_name, map_location='cpu')\n", (4729, 4781), False, 'import torch\n'), ((4850, 4875), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (4873, 4875), False, 'import torch\n'), ((10555, 10566), 'tqdm.tqdm', 'tqdm', (['loop3'], {}), '(loop3)\n', (10559, 10566), False, 'from tqdm import tqdm\n'), ((315, 340), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (338, 340), False, 'import torch\n'), ((1687, 1803), 'torch.utils.data.DataLoader', 'torch.utils.data.DataLoader', (['train_data'], {'batch_size': 'minibatchsize', 'shuffle': '(True)', 'num_workers': '(8)', 'pin_memory': '(True)'}), '(train_data, batch_size=minibatchsize, shuffle=\n True, num_workers=8, pin_memory=True)\n', (1714, 1803), False, 'import torch\n'), ((1939, 2053), 'torch.utils.data.DataLoader', 'torch.utils.data.DataLoader', (['val_data'], {'batch_size': 'minibatchsize', 'shuffle': '(True)', 'num_workers': '(8)', 'pin_memory': '(True)'}), '(val_data, batch_size=minibatchsize, shuffle=\n True, num_workers=8, pin_memory=True)\n', (1966, 2053), False, 'import torch\n'), ((2277, 2391), 'torch.utils.data.DataLoader', 'torch.utils.data.DataLoader', (['val_data'], {'batch_size': 'minibatchsize', 'shuffle': '(True)', 'num_workers': '(8)', 'pin_memory': '(True)'}), '(val_data, batch_size=minibatchsize, shuffle=\n True, num_workers=8, pin_memory=True)\n', (2304, 2391), False, 'import torch\n'), ((2591, 2791), 'util.data_utils.get_video_datasets', 'get_video_datasets', (['dataset_root_dir', 'mean_image_name'], {'duration': 'duration', 'img_size': 'img_size', 'shuffle': '(False)', 'loader_settings': "{'batch_size': minibatchsize, 'num_workers': 8, 'pin_memory': False}"}), "(dataset_root_dir, mean_image_name, duration=duration,\n img_size=img_size, shuffle=False, loader_settings={'batch_size':\n minibatchsize, 'num_workers': 8, 'pin_memory': False})\n", (2609, 2791), False, 'from util.data_utils import get_SALICON_datasets, get_video_datasets\n'), ((3400, 3420), 'torch.load', 'torch.load', (['filename'], {}), '(filename)\n', (3410, 3420), False, 'import torch\n'), ((3496, 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(['loop2'], {}), '(loop2)\n', (8063, 8070), False, 'from tqdm import tqdm\n'), ((8160, 8185), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (8183, 8185), False, 'import torch\n'), ((9354, 9379), 'torch.from_numpy', 'torch.from_numpy', (['outputs'], {}), '(outputs)\n', (9370, 9379), False, 'import torch\n'), ((9400, 9425), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (9423, 9425), False, 'import torch\n'), ((3928, 3978), 'models.CoSADUV.CoSADUV', 'CoSADUV', ([], {'input_dim': 'img_size', 'local_feats_net': '"""Seg"""'}), "(input_dim=img_size, local_feats_net='Seg')\n", (3935, 3978), False, 'from models.CoSADUV import CoSADUV\n'), ((9026, 9052), 'numpy.expand_dims', 'np.expand_dims', (['outputs', '(0)'], {}), '(outputs, 0)\n', (9040, 9052), True, 'import numpy as np\n'), ((9186, 9240), 'cv2.resize', 'cv2.resize', (['output', '(labels.shape[2], labels.shape[1])'], {}), '(output, (labels.shape[2], labels.shape[1]))\n', (9196, 9240), False, 'import cv2\n')] |
#!/usr/bin/env python3
from pyglet.gl import *
from pyglet.window import NoSuchConfigException
from rubiks import CubeController, CubeView
if __name__ == '__main__':
controller = CubeController()
platform = pyglet.window.get_platform()
display = platform.get_default_display()
screen = display.get_default_screen()
try:
template = pyglet.gl.Config(depth_size=24, sample_buffers=1, samples=4)
config = screen.get_best_config(template)
except NoSuchConfigException:
config = screen.get_best_config()
window = pyglet.window.Window(width=1024, height=768, caption="The Rubik's Cube", resizable=True, config=config)
window.push_handlers(controller.on_key_press)
view = CubeView(controller, window)
pyglet.app.run()
| [
"rubiks.CubeController",
"rubiks.CubeView"
] | [((186, 202), 'rubiks.CubeController', 'CubeController', ([], {}), '()\n', (200, 202), False, 'from rubiks import CubeController, CubeView\n'), ((729, 757), 'rubiks.CubeView', 'CubeView', (['controller', 'window'], {}), '(controller, window)\n', (737, 757), False, 'from rubiks import CubeController, CubeView\n')] |
import sys
import os
from PyQt5.QtWidgets import (QTabWidget, QMessageBox)
from codeeditor import CodeEditor
from widgets import MessageBox
class TabWidget(QTabWidget):
def __init__(self, parent=None):
super().__init__()
self.mainWindow = parent
self.setStyleSheet(
'''
background-color: #2c2c2c;
color: white;
alternate-background-color: #FFFFFF;
selection-background-color: #3b5784;
''')
self.setStyleSheet('''
QTabBar::tab:selected {background: darkgreen;}
''')
self.setMovable(True)
self.setTabsClosable(True)
# signals
self.tabCloseRequested.connect(self.closeTab)
self.currentChanged.connect(self.changeTab)
self.textPad = None
self.codeView = None
def newTab(self, editor=None, codeView=None):
if not editor:
editor = CodeEditor(parent=self.mainWindow)
self.addTab(editor, "noname")
editor.filename = None
if self.mainWindow:
self.codeView = self.mainWindow.codeView
else:
if editor.filename == None:
self.addTab(editor, "noname")
else:
self.addTab(editor, os.path.basename(editor.filename))
x = self.count() - 1
self.setTabToolTip(x, editor.filename)
self.codeView = self.mainWindow.codeView
def closeTab(self, index):
x = self.currentIndex()
if x != index:
self.setCurrentIndex(index)
tabText = self.tabText(index)
if '*' in tabText:
q = MessageBox(QMessageBox.Warning, 'Warning',
'File not saved\n\nSave now ?',
QMessageBox.Yes | QMessageBox.No)
if (q.exec_() == QMessageBox.Yes):
self.mainWindow.save()
self.removeTab(index)
else:
self.removeTab(index)
else:
self.removeTab(index)
x = self.currentIndex()
self.setCurrentIndex(x)
if x == -1:
self.refreshCodeView('')
self.mainWindow.setWindowTitle('CrossCobra - Python IDE')
def changeTab(self, index):
x = self.count()
y = x - 1
if y >= 0:
self.setCurrentIndex(index)
textPad = self.currentWidget()
self.textPad = textPad
text = self.textPad.text()
if self.codeView:
self.refreshCodeView(text)
else:
self.codeView = self.mainWindow.codeView
self.refreshCodeView(text)
if self.textPad:
self.mainWindow.refresh(self.textPad)
def refreshCodeView(self, text=None):
text = text
codeViewDict = self.codeView.makeDictForCodeView(text)
self.codeView.updateCodeView(codeViewDict)
def getCurrentTextPad(self):
textPad = self.currentWidget()
return textPad
| [
"os.path.basename",
"codeeditor.CodeEditor",
"widgets.MessageBox"
] | [((997, 1031), 'codeeditor.CodeEditor', 'CodeEditor', ([], {'parent': 'self.mainWindow'}), '(parent=self.mainWindow)\n', (1007, 1031), False, 'from codeeditor import CodeEditor\n'), ((1811, 1925), 'widgets.MessageBox', 'MessageBox', (['QMessageBox.Warning', '"""Warning"""', '"""File not saved\n\nSave now ?"""', '(QMessageBox.Yes | QMessageBox.No)'], {}), '(QMessageBox.Warning, \'Warning\', """File not saved\n\nSave now ?""",\n QMessageBox.Yes | QMessageBox.No)\n', (1821, 1925), False, 'from widgets import MessageBox\n'), ((1391, 1424), 'os.path.basename', 'os.path.basename', (['editor.filename'], {}), '(editor.filename)\n', (1407, 1424), False, 'import os\n')] |
#!/usr/bin/python3
#Title: headers.py
#Author: ApexPredator
#License: MIT
#Github: https://github.com/ApexPredator-InfoSec/header_check
#Description: This script take a URL or list or URLs as arguments and tests for the headers: 'Strict-Transport-Security', 'Content-Security-Policy', 'X-Frame-Options', and 'Server'
import requests
import argparse
import socket
from urllib3.exceptions import InsecureRequestWarning
parser = argparse.ArgumentParser(prog='headers.py', usage='python3 -t <target> -f <file contianing target list> -d\npython3 headers.py -t https://securityheaders.com -d\npython3 headers.py -f urls.txt') #build argument list
parser.add_argument('-t', '--target', help='Target URL', required=False)
parser.add_argument('-f', '--file', help='File Containing Target URLs', required=False)
parser.add_argument('-d','--debug', help='Debug with proxy', required=False, action = 'store_const', const = True)
args = parser.parse_args()
s = requests.session()
requests.packages.urllib3.disable_warnings(category=InsecureRequestWarning) #disable SSL verification warning to cleanup output
http_proxy = 'http://127.0.0.1:8080' #define proxy address to enable using BURP or ZAP
proxyDict = { #define proxy dictionary to enable using BURP or ZAP
"http" : http_proxy,
"https" : http_proxy
}
if args.debug:
proxy = proxyDict #enable proxy if -d or --debug is present
else:
proxy = False #disable proxy is -d or --debug is not present
def test_url(target):
print("[+] Sending get request to target and checking header....")
res = s.get(target, verify=False, proxies=proxy) #perform get request on url, disble SSL verification to prevent error for sites with invalid certs, proxy if proxy is enabled
if 'https://' in target: #test for https url
test_headers(target, res, 'Strict-Transport-Security') #test for Strict-Transport-Security header
test_headers(target, res, 'Content-Security-Policy') #test for Content-Security-Policy
test_headers(target, res, 'X-Frame-Options') #test for X-Frame-Options header
test_headers(target, res, 'Server') #test for Server header
elif 'http://' in target: #test for http url
test_header(target, res, 'Content-Security-Policy') #test for Content-Security-Policy
test_header(target, res, 'X-Frame-Options') #test for X-Frame-Options header
test_header(target, res, 'Server') #test for Server header
else:
print("%s is an invalid URL" %target) #print invalid url if not https or http
def test_headers(target, res, header):
print("[+]Testing headers for %s" %target + ' IP: ' + socket.gethostbyname(target[8:])) #print URL being tested and its IP
if header in res.headers: #test if the header passed to test_headers is in the get response headers
print("[+] %s is enabled" %header) #print header is enabled
print("[+] Value of %s header is: %s" %(header,res.headers[header])) #print value of header
else:
print("[-] !!!! %s is not enabled on %s" %(header,target) + ' IP: ' + socket.gethostbyname(target[8:])) #print header is not enabled if it is not present, display URL and IP
def test_header(target, res, header):
print("[+]Testing headers for %s" %target + ' IP: ' + socket.gethostbyname(target[7:])) #print URL being tested and its IP
if header in res.headers: #test if header passed to test_header is in the get response headers
print("[+] %s is enabled" %header) #print the header is enabled
print("[+] Value of %s header is: %s" %(header,res.headers[header])) #print the vlaue of the header
else:
print("[-] !!!! %s is not enabled on %s" %(header,target) + ' IP: ' + socket.gethostbyname(target[7:])) #print header is not enabled if header is not found in get response header, display URL and IP
def main():
if args.target: #test it -t or --target were passed and set target with value passed
target = args.target
test_url(target)
elif args.file: #test if -f or --file were passed and set target with file named passed
file = args.file
with open(file, 'r') as target_list: #open file passed
for line in target_list.readlines(): #read the lines in
target = line.strip() #set target
print("\n[+]Fetching URL from file.......\n")
test_url(target) #test the url currently set in target
else:
print("[-]Either -t or -f arguments are required\nusage: python3 headers.py -t <target> -f <file contianing target list> -d\npython3 headers.py -t https://securityheaders.com -d\npython3 headers.py -f urls.txt") #print help message if neither -t or -f is passed
if __name__ == '__main__':
main()
| [
"socket.gethostbyname",
"requests.session",
"requests.packages.urllib3.disable_warnings",
"argparse.ArgumentParser"
] | [((427, 632), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'prog': '"""headers.py"""', 'usage': '"""python3 -t <target> -f <file contianing target list> -d\npython3 headers.py -t https://securityheaders.com -d\npython3 headers.py -f urls.txt"""'}), '(prog=\'headers.py\', usage=\n """python3 -t <target> -f <file contianing target list> -d\npython3 headers.py -t https://securityheaders.com -d\npython3 headers.py -f urls.txt"""\n )\n', (450, 632), False, 'import argparse\n'), ((950, 968), 'requests.session', 'requests.session', ([], {}), '()\n', (966, 968), False, 'import requests\n'), ((969, 1044), 'requests.packages.urllib3.disable_warnings', 'requests.packages.urllib3.disable_warnings', ([], {'category': 'InsecureRequestWarning'}), '(category=InsecureRequestWarning)\n', (1011, 1044), False, 'import requests\n'), ((2638, 2670), 'socket.gethostbyname', 'socket.gethostbyname', (['target[8:]'], {}), '(target[8:])\n', (2658, 2670), False, 'import socket\n'), ((3269, 3301), 'socket.gethostbyname', 'socket.gethostbyname', (['target[7:]'], {}), '(target[7:])\n', (3289, 3301), False, 'import socket\n'), ((3068, 3100), 'socket.gethostbyname', 'socket.gethostbyname', (['target[8:]'], {}), '(target[8:])\n', (3088, 3100), False, 'import socket\n'), ((3706, 3738), 'socket.gethostbyname', 'socket.gethostbyname', (['target[7:]'], {}), '(target[7:])\n', (3726, 3738), False, 'import socket\n')] |
# Generated by Django 2.1 on 2018-08-06 02:11
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('sites', '0002_alter_domain_unique'),
]
operations = [
migrations.CreateModel(
name='Feature',
fields=[
('created', models.DateTimeField(auto_now_add=True)),
('last_modified', models.DateTimeField(auto_now=True)),
('name', models.CharField(max_length=32)),
('identifier', models.SlugField(blank=True, primary_key=True, serialize=False, unique=True)),
],
options={
'ordering': ('created', 'last_modified'),
'abstract': False,
},
),
migrations.CreateModel(
name='FeatureUsage',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', models.DateTimeField(auto_now_add=True)),
('last_modified', models.DateTimeField(auto_now=True)),
('feature', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='usage', to='features.Feature')),
('sites', models.ManyToManyField(to='sites.Site')),
],
options={
'ordering': ('created', 'last_modified'),
'abstract': False,
},
),
migrations.CreateModel(
name='FeatureValue',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created', models.DateTimeField(auto_now_add=True)),
('last_modified', models.DateTimeField(auto_now=True)),
],
options={
'ordering': ('created', 'last_modified'),
'abstract': False,
},
),
migrations.CreateModel(
name='BooleanFeatureValue',
fields=[
('featurevalue_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='features.FeatureValue')),
('value', models.BooleanField()),
],
options={
'ordering': ('created', 'last_modified'),
'abstract': False,
},
bases=('features.featurevalue',),
),
migrations.CreateModel(
name='NumericFeatureValue',
fields=[
('featurevalue_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='features.FeatureValue')),
('value', models.DecimalField(decimal_places=3, max_digits=11)),
],
options={
'ordering': ('created', 'last_modified'),
'abstract': False,
},
bases=('features.featurevalue',),
),
migrations.CreateModel(
name='StringFeatureValue',
fields=[
('featurevalue_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='features.FeatureValue')),
('value', models.TextField()),
],
options={
'ordering': ('created', 'last_modified'),
'abstract': False,
},
bases=('features.featurevalue',),
),
migrations.AddField(
model_name='featureusage',
name='value',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='usage', to='features.FeatureValue'),
),
]
| [
"django.db.models.OneToOneField",
"django.db.models.TextField",
"django.db.models.ForeignKey",
"django.db.models.ManyToManyField",
"django.db.models.BooleanField",
"django.db.models.SlugField",
"django.db.models.AutoField",
"django.db.models.DateTimeField",
"django.db.models.DecimalField",
"django... | [((3818, 3935), 'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""usage"""', 'to': '"""features.FeatureValue"""'}), "(on_delete=django.db.models.deletion.CASCADE, related_name\n ='usage', to='features.FeatureValue')\n", (3835, 3935), False, 'from django.db import migrations, models\n'), ((386, 425), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)'}), '(auto_now_add=True)\n', (406, 425), False, 'from django.db import migrations, models\n'), ((462, 497), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now': '(True)'}), '(auto_now=True)\n', (482, 497), False, 'from django.db import migrations, models\n'), ((525, 556), 'django.db.models.CharField', 'models.CharField', ([], {'max_length': '(32)'}), '(max_length=32)\n', (541, 556), False, 'from django.db import migrations, models\n'), ((590, 666), 'django.db.models.SlugField', 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'django.db.models.ForeignKey', 'models.ForeignKey', ([], {'on_delete': 'django.db.models.deletion.CASCADE', 'related_name': '"""usage"""', 'to': '"""features.Feature"""'}), "(on_delete=django.db.models.deletion.CASCADE, related_name\n ='usage', to='features.Feature')\n", (1213, 1308), False, 'from django.db import migrations, models\n'), ((1332, 1371), 'django.db.models.ManyToManyField', 'models.ManyToManyField', ([], {'to': '"""sites.Site"""'}), "(to='sites.Site')\n", (1354, 1371), False, 'from django.db import migrations, models\n'), ((1639, 1732), 'django.db.models.AutoField', 'models.AutoField', ([], {'auto_created': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'verbose_name': '"""ID"""'}), "(auto_created=True, primary_key=True, serialize=False,\n verbose_name='ID')\n", (1655, 1732), False, 'from django.db import migrations, models\n'), ((1759, 1798), 'django.db.models.DateTimeField', 'models.DateTimeField', ([], {'auto_now_add': '(True)'}), '(auto_now_add=True)\n', 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{'auto_created': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'parent_link': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'to': '"""features.FeatureValue"""'}), "(auto_created=True, on_delete=django.db.models.deletion\n .CASCADE, parent_link=True, primary_key=True, serialize=False, to=\n 'features.FeatureValue')\n", (2729, 2884), False, 'from django.db import migrations, models\n'), ((2903, 2955), 'django.db.models.DecimalField', 'models.DecimalField', ([], {'decimal_places': '(3)', 'max_digits': '(11)'}), '(decimal_places=3, max_digits=11)\n', (2922, 2955), False, 'from django.db import migrations, models\n'), ((3289, 3464), 'django.db.models.OneToOneField', 'models.OneToOneField', ([], {'auto_created': '(True)', 'on_delete': 'django.db.models.deletion.CASCADE', 'parent_link': '(True)', 'primary_key': '(True)', 'serialize': '(False)', 'to': '"""features.FeatureValue"""'}), "(auto_created=True, on_delete=django.db.models.deletion\n .CASCADE, parent_link=True, primary_key=True, serialize=False, to=\n 'features.FeatureValue')\n", (3309, 3464), False, 'from django.db import migrations, models\n'), ((3483, 3501), 'django.db.models.TextField', 'models.TextField', ([], {}), '()\n', (3499, 3501), False, 'from django.db import migrations, models\n')] |
import argparse
import os
from os.path import join
import sys
import joblib
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('TkAgg')
sys.path.append('.')
from project.models.common import get_errors, get_model_details_for_algorithm, get_color, init_scale_from_train_set
from project.models.details import get_model_filepath, ModelDetails
from project.models.scale import transform_x, inverse_transform_y
from project.utils.app_ids import app_name_to_id
from project.utils.logger import logger
from project.definitions import ROOT_DIR
from project.models.data import (
get_data_frame,
DataFrameColumns,
)
parser = argparse.ArgumentParser(description='Model training and validation.')
parser.add_argument('--app_name', required=True, type=str, help='app name')
parser.add_argument('--alg', required=True, type=str, help='algorithm')
if __name__ == "__main__":
args = parser.parse_args()
logger.info(args)
app_id = app_name_to_id.get(args.app_name, None)
if app_id is None:
raise ValueError(f'missing app "{args.app_name}" from app map={str(app_name_to_id)}')
results_filepath = join(ROOT_DIR, '..', 'execution_results/results.csv')
results_test_filepath = os.path.join(ROOT_DIR, '..', 'execution_results/results_test.csv')
results_train_filepath = os.path.join(ROOT_DIR, '..', 'execution_results/results_train.csv')
df, df_err = get_data_frame(results_filepath, app_id)
df_test, df_test_err = get_data_frame(results_test_filepath, app_id)
df_train, df_train_err = get_data_frame(results_train_filepath, app_id)
if df_err is not None or df_test_err is not None or df_train_err is not None:
raise ValueError(f'data frame load err')
x_origin = df.loc[:, df.columns != DataFrameColumns.EXECUTION_TIME]
x_test = df_test.loc[:, df_test.columns != DataFrameColumns.EXECUTION_TIME]
x_train = df_train.loc[:, df_train.columns != DataFrameColumns.EXECUTION_TIME]
y = df.loc[:, df.columns == DataFrameColumns.EXECUTION_TIME]
y_test = df_test.loc[:, df_test.columns == DataFrameColumns.EXECUTION_TIME]
y_train = df_train.loc[:, df_train.columns == DataFrameColumns.EXECUTION_TIME]
x_plot_train = x_train[DataFrameColumns.OVERALL_SIZE]
y_plot_train = x_train[DataFrameColumns.CPUS]
z_plot_train = y_train[DataFrameColumns.EXECUTION_TIME]
x_plot_test = x_test[DataFrameColumns.OVERALL_SIZE]
y_plot_test = x_test[DataFrameColumns.CPUS]
z_plot_test = y_test[DataFrameColumns.EXECUTION_TIME]
# plot data points
ax = plt.axes(projection='3d')
ax.set_xlabel('over', linespacing=0.1, labelpad=-12)
ax.set_ylabel('cpus', linespacing=0.1, labelpad=-12)
ax.set_zlabel('t', linespacing=0.1, labelpad=-15)
ax.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False,
left=False, # ticks along the bottom edge are off
right=False,
labelbottom=False,
labeltop=False,
labelright=False,
labelleft=False
)
ax.dist = 8
ax.scatter(x_plot_train, y_plot_train, z_plot_train, c='#2ca02c', alpha=1, label='training points')
ax.scatter(x_plot_test, y_plot_test, z_plot_test, label='test points', c='#cc0000', alpha=1)
# Load model details
model_details = get_model_details_for_algorithm(args.app_name, args.alg)
if model_details.scale:
init_scale_from_train_set(model_details, app_id)
x_test = pd.DataFrame(transform_x(x_test), columns=x_test.columns)
x = pd.DataFrame(transform_x(x_origin), columns=x_origin.columns)
# Load model
model_filepath, err = get_model_filepath(args.alg, model_details)
if err is not None:
raise ValueError(err)
model = joblib.load(model_filepath)
z_all = model.predict(x)
# Efficiency
z_test = model.predict(x_test)
z_test_inverse = inverse_transform_y(z_test)
y_test_list = list(y_test[DataFrameColumns.EXECUTION_TIME])
y_train_list = list(y_train[DataFrameColumns.EXECUTION_TIME])
errors, errors_rel = get_errors(y_test_list, z_test_inverse)
logger.info('############### SUMMARY ##################')
logger.info('avg time [s] = %s' % str(sum(y_test_list) / len(y_test_list)))
logger.info('avg error [s] = %s' % str(sum(errors) / len(errors)))
logger.info('avg error relative [percentage] = %s' % str(sum(errors_rel) / len(errors_rel)))
logger.info(f'best params: {str(model.get_params())}')
# Plot prediction surface
z_inverse = inverse_transform_y(z_all)
x_plot = x_origin[DataFrameColumns.OVERALL_SIZE].to_numpy()
y_plot = x_origin[DataFrameColumns.CPUS].to_numpy()
ax.plot_trisurf(x_plot, y_plot, z_inverse, alpha=0.5, color=get_color(args.alg))
fake_legend_point = matplotlib.lines.Line2D([0], [0], linestyle="solid", c=get_color(args.alg))
plt.margins()
plt.gcf().autofmt_xdate()
handles, labels = ax.get_legend_handles_labels()
handles.append(fake_legend_point)
labels.append(args.alg)
ax.legend(handles, labels, loc='upper left')
ax.view_init(elev=20., azim=140)
model_scheme = ModelDetails(args.app_name, 1.0, True, False)
fig_path = os.path.join(ROOT_DIR, 'models', 'figures', '_'.join([args.alg, args.app_name, 'surf.png']))
plt.savefig(fig_path, bbox_inches='tight', pad_inches=0)
| [
"project.utils.logger.logger.info",
"project.models.common.init_scale_from_train_set",
"project.models.details.ModelDetails",
"project.models.details.get_model_filepath",
"sys.path.append",
"matplotlib.pyplot.margins",
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import copy as _copy
import math as _math
import os as _os
import cv2 as _cv2
import numpy as _np
from PIL import Image as _IMG
from easytorch.utils.logger import *
"""
##################################################################################################
Very useful image related utilities
##################################################################################################
"""
def _same_file(x):
return x
class Image:
def __init__(self, dtype=_np.uint8):
self.dir = None
self.file = None
self.array = None
self.mask = None
self.ground_truth = None
self.extras = {}
self.dtype = dtype
def load(self, dir, file):
try:
self.dir = dir
self.file = file
self.array = _np.array(_IMG.open(self.path), dtype=self.dtype)
except Exception as e:
error('Fail to load file: ' + self.file + ': ' + str(e))
def load_mask(self, mask_dir=None, fget_mask=_same_file):
if fget_mask is None:
fget_mask = _same_file
try:
mask_file = fget_mask(self.file)
self.mask = _np.array(_IMG.open(_os.path.join(mask_dir, mask_file)), dtype=self.dtype)
except Exception as e:
error('Fail to load mask: ' + str(e))
def load_ground_truth(self, gt_dir=None, fget_ground_truth=_same_file):
if fget_ground_truth is None:
fget_ground_truth = _same_file
try:
gt_file = fget_ground_truth(self.file)
self.ground_truth = _np.array(_IMG.open(_os.path.join(gt_dir, gt_file)), dtype=self.dtype)
except Exception as e:
error('Fail to load ground truth: ' + str(e))
def get_array(self, dir='', getter=_same_file, file=None):
if getter is None:
getter = _same_file
if not file:
file = self.file
arr = _np.array(_IMG.open(_os.path.join(dir, getter(file))), dtype=self.dtype)
return arr
def apply_mask(self):
if self.mask is not None:
self.array[self.mask == 0] = 0
def apply_clahe(self, clip_limit=2.0, tile_shape=(8, 8)):
enhancer = _cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_shape)
if len(self.array.shape) == 2:
self.array = enhancer.apply(self.array)
elif len(self.array.shape) == 3:
self.array[:, :, 0] = enhancer.apply(self.array[:, :, 0])
self.array[:, :, 1] = enhancer.apply(self.array[:, :, 1])
self.array[:, :, 2] = enhancer.apply(self.array[:, :, 2])
else:
error('More than three channels')
def __copy__(self):
copy_obj = Image()
copy_obj.file = _copy.copy(self.file)
copy_obj.array = _copy.copy(self.array)
copy_obj.mask = _copy.copy(self.mask)
copy_obj.ground_truth = _copy.copy(self.ground_truth)
copy_obj.extras = _copy.deepcopy(self.extras)
copy_obj.dtype = _copy.deepcopy(self.dtype)
return copy_obj
@property
def path(self):
return _os.path.join(self.dir, self.file)
def get_rgb_scores(arr_2d=None, truth=None):
"""
Returns a rgb image of pixelwise separation between ground truth and arr_2d
(predicted image) with different color codes
Easy when needed to inspect segmentation result against ground truth.
:param arr_2d:
:param truth:
:return:
"""
arr_rgb = _np.zeros([arr_2d.shape[0], arr_2d.shape[1], 3], dtype=_np.uint8)
x = arr_2d.copy()
y = truth.copy()
x[x == 255] = 1
y[y == 255] = 1
xy = x + (y * 2)
arr_rgb[xy == 3] = [255, 255, 255]
arr_rgb[xy == 1] = [0, 255, 0]
arr_rgb[xy == 2] = [255, 0, 0]
arr_rgb[xy == 0] = [0, 0, 0]
return arr_rgb
def get_praf1(arr_2d=None, truth=None):
"""
Returns precision, recall, f1 and accuracy score between two binary arrays upto five precision.
:param arr_2d:
:param truth:
:return:
"""
x = arr_2d.copy()
y = truth.copy()
x[x == 255] = 1
y[y == 255] = 1
xy = x + (y * 2)
tp = xy[xy == 3].shape[0]
fp = xy[xy == 1].shape[0]
tn = xy[xy == 0].shape[0]
fn = xy[xy == 2].shape[0]
try:
p = tp / (tp + fp)
except ZeroDivisionError:
p = 0
try:
r = tp / (tp + fn)
except ZeroDivisionError:
r = 0
try:
a = (tp + tn) / (tp + fp + fn + tn)
except ZeroDivisionError:
a = 0
try:
f1 = 2 * p * r / (p + r)
except ZeroDivisionError:
f1 = 0
return {
'Precision': round(p, 5),
'Recall': round(r, 5),
'Accuracy': round(a, 5),
'F1': round(f1, 5)
}
def rescale2d(arr):
m = _np.max(arr)
n = _np.min(arr)
return (arr - n) / (m - n)
def rescale3d(arrays):
return list(rescale2d(arr) for arr in arrays)
def get_signed_diff_int8(image_arr1=None, image_arr2=None):
signed_diff = _np.array(image_arr1 - image_arr2, dtype=_np.int8)
fx = _np.array(signed_diff - _np.min(signed_diff), _np.uint8)
fx = rescale2d(fx)
return _np.array(fx * 255, _np.uint8)
def whiten_image2d(img_arr2d=None):
img_arr2d = img_arr2d.copy()
img_arr2d = (img_arr2d - img_arr2d.mean()) / img_arr2d.std()
return _np.array(rescale2d(img_arr2d) * 255, dtype=_np.uint8)
def get_chunk_indexes(img_shape=(0, 0), chunk_shape=(0, 0), offset_row_col=None):
"""
Returns a generator for four corners of each patch within image as specified.
:param img_shape: Shape of the original image
:param chunk_shape: Shape of desired patch
:param offset_row_col: Offset for each patch on both x, y directions
:return:
"""
img_rows, img_cols = img_shape
chunk_row, chunk_col = chunk_shape
offset_row, offset_col = offset_row_col
row_end = False
for i in range(0, img_rows, offset_row):
if row_end:
continue
row_from, row_to = i, i + chunk_row
if row_to > img_rows:
row_to = img_rows
row_from = img_rows - chunk_row
row_end = True
col_end = False
for j in range(0, img_cols, offset_col):
if col_end:
continue
col_from, col_to = j, j + chunk_col
if col_to > img_cols:
col_to = img_cols
col_from = img_cols - chunk_col
col_end = True
yield [int(row_from), int(row_to), int(col_from), int(col_to)]
def get_chunk_indices_by_index(img_shape=(0, 0), chunk_shape=(0, 0), indices=None):
x, y = chunk_shape
ix = []
for (c1, c2) in indices:
w, h = img_shape
p, q, r, s = c1 - x // 2, c1 + x // 2, c2 - y // 2, c2 + y // 2
if p < 0:
p, q = 0, x
if q > w:
p, q = w - x, w
if r < 0:
r, s = 0, y
if s > h:
r, s = h - y, h
ix.append([int(p), int(q), int(r), int(s)])
return ix
def merge_patches(patches=None, image_size=(0, 0), patch_size=(0, 0), offset_row_col=None):
"""
Merge different pieces of image to form a full image. Overlapped regions are averaged.
:param patches: List of all patches to merge in order (left to right).
:param image_size: Full image size
:param patch_size: A patch size(Patches must be uniform in size to be able to merge)
:param offset_row_col: Offset used to chunk the patches.
:return:
"""
padded_sum = _np.zeros([image_size[0], image_size[1]])
non_zero_count = _np.zeros_like(padded_sum)
for i, chunk_ix in enumerate(get_chunk_indexes(image_size, patch_size, offset_row_col)):
row_from, row_to, col_from, col_to = chunk_ix
patch = _np.array(patches[i, :, :]).squeeze()
padded = _np.pad(patch, [(row_from, image_size[0] - row_to), (col_from, image_size[1] - col_to)],
'constant')
padded_sum = padded + padded_sum
non_zero_count = non_zero_count + _np.array(padded > 0).astype(int)
non_zero_count[non_zero_count == 0] = 1
return _np.array(padded_sum / non_zero_count, dtype=_np.uint8)
def expand_and_mirror_patch(full_img_shape=None, orig_patch_indices=None, expand_by=None):
"""
Given a patch within an image, this function select a speciified region around it if present, else mirros it.
It is useful in neuralnetworks like u-net which look for wide range of area than the actual input image.
:param full_img_shape: Full image shape
:param orig_patch_indices: Four cornets of the actual patch
:param expand_by: Expand by (x, y ) in each dimension
:return:
"""
i, j = int(expand_by[0] / 2), int(expand_by[1] / 2)
p, q, r, s = orig_patch_indices
a, b, c, d = p - i, q + i, r - j, s + j
pad_a, pad_b, pad_c, pad_d = [0] * 4
if a < 0:
pad_a = i - p
a = 0
if b > full_img_shape[0]:
pad_b = b - full_img_shape[0]
b = full_img_shape[0]
if c < 0:
pad_c = j - r
c = 0
if d > full_img_shape[1]:
pad_d = d - full_img_shape[1]
d = full_img_shape[1]
return a, b, c, d, [(pad_a, pad_b), (pad_c, pad_d)]
def largest_cc(binary_arr=None):
from skimage.measure import label
labels = label(binary_arr)
if labels.max() != 0: # assume at least 1 CC
largest = labels == _np.argmax(_np.bincount(labels.flat)[1:]) + 1
return largest
def map_img_to_img2d(map_to, img):
arr = map_to.copy()
rgb = arr.copy()
if len(arr.shape) == 2:
rgb = _np.zeros((arr.shape[0], arr.shape[1], 3), dtype=_np.uint8)
rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2] = arr, arr, arr
rgb[:, :, 0][img == 255] = 255
rgb[:, :, 1][img == 255] = 0
rgb[:, :, 2][img == 255] = 0
return rgb
def remove_connected_comp(segmented_img, connected_comp_diam_limit=20):
"""
Remove connected components of a binary image that are less than smaller than specified diameter.
:param segmented_img: Binary image.
:param connected_comp_diam_limit: Diameter limit
:return:
"""
from scipy.ndimage.measurements import label
img = segmented_img.copy()
structure = _np.ones((3, 3), dtype=_np.int)
labeled, n_components = label(img, structure)
for i in range(n_components):
ixy = _np.array(list(zip(*_np.where(labeled == i))))
x1, y1 = ixy[0]
x2, y2 = ixy[-1]
dst = _math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
if dst < connected_comp_diam_limit:
for u, v in ixy:
img[u, v] = 0
return img
def get_pix_neigh(i, j, eight=False):
"""
Get four/ eight neighbors of an image.
:param i: x position of pixel
:param j: y position of pixel
:param eight: Eight neighbors? Else four
:return:
"""
n1 = (i - 1, j - 1)
n2 = (i - 1, j)
n3 = (i - 1, j + 1)
n4 = (i, j - 1)
n5 = (i, j + 1)
n6 = (i + 1, j - 1)
n7 = (i + 1, j)
n8 = (i + 1, j + 1)
if eight:
return [n1, n2, n3, n4, n5, n6, n7, n8]
else:
return [n2, n5, n7, n4]
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"copy.copy",
"numpy.zeros_like"
] | [((3470, 3535), 'numpy.zeros', '_np.zeros', (['[arr_2d.shape[0], arr_2d.shape[1], 3]'], {'dtype': '_np.uint8'}), '([arr_2d.shape[0], arr_2d.shape[1], 3], dtype=_np.uint8)\n', (3479, 3535), True, 'import numpy as _np\n'), ((4755, 4767), 'numpy.max', '_np.max', (['arr'], {}), '(arr)\n', (4762, 4767), True, 'import numpy as _np\n'), ((4776, 4788), 'numpy.min', '_np.min', (['arr'], {}), '(arr)\n', (4783, 4788), True, 'import numpy as _np\n'), ((4975, 5025), 'numpy.array', '_np.array', (['(image_arr1 - image_arr2)'], {'dtype': '_np.int8'}), '(image_arr1 - image_arr2, dtype=_np.int8)\n', (4984, 5025), True, 'import numpy as _np\n'), ((5126, 5156), 'numpy.array', '_np.array', (['(fx * 255)', '_np.uint8'], {}), '(fx * 255, _np.uint8)\n', (5135, 5156), True, 'import numpy as _np\n'), ((7501, 7542), 'numpy.zeros', '_np.zeros', (['[image_size[0], image_size[1]]'], {}), '([image_size[0], image_size[1]])\n', (7510, 7542), True, 'import numpy as _np\n'), ((7564, 7590), 'numpy.zeros_like', '_np.zeros_like', (['padded_sum'], {}), '(padded_sum)\n', (7578, 7590), True, 'import numpy as _np\n'), ((8109, 8164), 'numpy.array', '_np.array', (['(padded_sum / non_zero_count)'], {'dtype': '_np.uint8'}), '(padded_sum / non_zero_count, dtype=_np.uint8)\n', (8118, 8164), True, 'import numpy as _np\n'), ((9292, 9309), 'scipy.ndimage.measurements.label', 'label', (['binary_arr'], {}), '(binary_arr)\n', (9297, 9309), False, 'from scipy.ndimage.measurements import label\n'), ((10220, 10251), 'numpy.ones', '_np.ones', (['(3, 3)'], {'dtype': '_np.int'}), '((3, 3), dtype=_np.int)\n', (10228, 10251), True, 'import numpy as _np\n'), ((10280, 10301), 'scipy.ndimage.measurements.label', 'label', (['img', 'structure'], {}), '(img, structure)\n', (10285, 10301), False, 'from scipy.ndimage.measurements import label\n'), ((2205, 2268), 'cv2.createCLAHE', '_cv2.createCLAHE', ([], {'clipLimit': 'clip_limit', 'tileGridSize': 'tile_shape'}), '(clipLimit=clip_limit, tileGridSize=tile_shape)\n', (2221, 2268), True, 'import cv2 as _cv2\n'), ((2747, 2768), 'copy.copy', '_copy.copy', (['self.file'], {}), '(self.file)\n', (2757, 2768), True, 'import copy as _copy\n'), ((2794, 2816), 'copy.copy', '_copy.copy', (['self.array'], {}), '(self.array)\n', (2804, 2816), True, 'import copy as _copy\n'), ((2841, 2862), 'copy.copy', '_copy.copy', (['self.mask'], {}), '(self.mask)\n', (2851, 2862), True, 'import copy as _copy\n'), ((2895, 2924), 'copy.copy', '_copy.copy', (['self.ground_truth'], {}), '(self.ground_truth)\n', (2905, 2924), True, 'import copy as _copy\n'), ((2951, 2978), 'copy.deepcopy', '_copy.deepcopy', (['self.extras'], {}), '(self.extras)\n', (2965, 2978), True, 'import copy as _copy\n'), ((3004, 3030), 'copy.deepcopy', '_copy.deepcopy', (['self.dtype'], {}), '(self.dtype)\n', (3018, 3030), True, 'import copy as _copy\n'), ((3105, 3139), 'os.path.join', '_os.path.join', (['self.dir', 'self.file'], {}), '(self.dir, self.file)\n', (3118, 3139), True, 'import os as _os\n'), ((7811, 7916), 'numpy.pad', '_np.pad', (['patch', '[(row_from, image_size[0] - row_to), (col_from, image_size[1] - col_to)]', '"""constant"""'], {}), "(patch, [(row_from, image_size[0] - row_to), (col_from, image_size[1\n ] - col_to)], 'constant')\n", (7818, 7916), True, 'import numpy as _np\n'), ((9582, 9641), 'numpy.zeros', '_np.zeros', (['(arr.shape[0], arr.shape[1], 3)'], {'dtype': '_np.uint8'}), '((arr.shape[0], arr.shape[1], 3), dtype=_np.uint8)\n', (9591, 9641), True, 'import numpy as _np\n'), ((10460, 10503), 'math.sqrt', '_math.sqrt', (['((x2 - x1) ** 2 + (y2 - y1) ** 2)'], {}), '((x2 - x1) ** 2 + (y2 - y1) ** 2)\n', (10470, 10503), True, 'import math as _math\n'), ((5059, 5079), 'numpy.min', '_np.min', (['signed_diff'], {}), '(signed_diff)\n', (5066, 5079), True, 'import numpy as _np\n'), ((820, 840), 'PIL.Image.open', '_IMG.open', (['self.path'], {}), '(self.path)\n', (829, 840), True, 'from PIL import Image as _IMG\n'), ((7755, 7782), 'numpy.array', '_np.array', (['patches[i, :, :]'], {}), '(patches[i, :, :])\n', (7764, 7782), True, 'import numpy as _np\n'), ((1190, 1224), 'os.path.join', '_os.path.join', (['mask_dir', 'mask_file'], {}), '(mask_dir, mask_file)\n', (1203, 1224), True, 'import os as _os\n'), ((1600, 1630), 'os.path.join', '_os.path.join', (['gt_dir', 'gt_file'], {}), '(gt_dir, gt_file)\n', (1613, 1630), True, 'import os as _os\n'), ((8020, 8041), 'numpy.array', '_np.array', (['(padded > 0)'], {}), '(padded > 0)\n', (8029, 8041), True, 'import numpy as _np\n'), ((9399, 9424), 'numpy.bincount', '_np.bincount', (['labels.flat'], {}), '(labels.flat)\n', (9411, 9424), True, 'import numpy as _np\n'), ((10370, 10393), 'numpy.where', '_np.where', (['(labeled == i)'], {}), '(labeled == i)\n', (10379, 10393), True, 'import numpy as _np\n')] |
from django.test import TestCase
from django.test import TransactionTestCase
from main import tasks
from celery.contrib.testing.worker import start_worker
from main.models import User
from src.celery import app
class UserModelTests(TestCase):
@classmethod
def setUpTestData(cls):
cls.user = User.objects.create_user('<EMAIL>', '<PASSWORD>')
def test_user_str(self):
email = str(self.user)
self.assertEqual(email, '<EMAIL>')
| [
"main.models.User.objects.create_user"
] | [((309, 358), 'main.models.User.objects.create_user', 'User.objects.create_user', (['"""<EMAIL>"""', '"""<PASSWORD>"""'], {}), "('<EMAIL>', '<PASSWORD>')\n", (333, 358), False, 'from main.models import User\n')] |
from pexpect import pxssh
s = pxssh.pxssh()
s.force_password=True
if not s.login ('192.168.127.12', 'root', '<PASSWORD>!'):
print ("SSH session failed on login.")
else:
print ("SSH session login successful")
s.sendline ('cp /root/some.txt /root/some2.txt')
s.prompt() # match the prompt
print(s.before.decode('utf-8')) # print everything before the prompt.
s.logout()
| [
"pexpect.pxssh.pxssh"
] | [((30, 43), 'pexpect.pxssh.pxssh', 'pxssh.pxssh', ([], {}), '()\n', (41, 43), False, 'from pexpect import pxssh\n')] |
from zeus.config import db
from zeus.db.mixins import ApiTokenMixin, RepositoryMixin, StandardAttributes
from zeus.db.utils import model_repr
class RepositoryApiToken(StandardAttributes, RepositoryMixin, ApiTokenMixin, db.Model):
"""
An API token associated to a repository.
"""
__tablename__ = "repository_api_token"
__repr__ = model_repr("repository_id", "key")
def get_token_key(self):
return "r"
| [
"zeus.db.utils.model_repr"
] | [((352, 386), 'zeus.db.utils.model_repr', 'model_repr', (['"""repository_id"""', '"""key"""'], {}), "('repository_id', 'key')\n", (362, 386), False, 'from zeus.db.utils import model_repr\n')] |
import numpy as np
from PySide import QtGui, QtCore
import sharppy.sharptab as tab
from sharppy.sharptab.constants import *
## Written by <NAME> - OU School of Meteorology
## and <NAME> - CIMMS
__all__ = ['backgroundWatch', 'plotWatch']
class backgroundWatch(QtGui.QFrame):
'''
Draw the background frame and lines for the watch plot frame
'''
def __init__(self):
super(backgroundWatch, self).__init__()
self.initUI()
def initUI(self):
## window configuration settings,
## sich as padding, width, height, and
## min/max plot axes
self.lpad = 0; self.rpad = 0
self.tpad = 0; self.bpad = 20
self.wid = self.size().width() - self.rpad
self.hgt = self.size().height() - self.bpad
self.tlx = self.rpad; self.tly = self.tpad
self.brx = self.wid; self.bry = self.hgt
if self.physicalDpiX() > 75:
fsize = 10
else:
fsize = 12
self.title_font = QtGui.QFont('Helvetica', fsize)
self.plot_font = QtGui.QFont('Helvetica', fsize)
self.title_metrics = QtGui.QFontMetrics( self.title_font )
self.plot_metrics = QtGui.QFontMetrics( self.plot_font )
self.title_height = self.title_metrics.height()
self.plot_height = self.plot_metrics.height()
self.plotBitMap = QtGui.QPixmap(self.width(), self.height())
self.plotBitMap.fill(QtCore.Qt.black)
self.plotBackground()
def resizeEvent(self, e):
'''
Handles the event the window is resized
'''
self.initUI()
def draw_frame(self, qp):
'''
Draw the background frame.
qp: QtGui.QPainter object
'''
## set a new pen to draw with
pen = QtGui.QPen(QtCore.Qt.white, 2, QtCore.Qt.SolidLine)
qp.setPen(pen)
qp.setFont(self.title_font)
## draw the borders in white
qp.drawLine(self.tlx, self.tly, self.brx, self.tly)
qp.drawLine(self.brx, self.tly, self.brx, self.bry)
qp.drawLine(self.brx, self.bry, self.tlx, self.bry)
qp.drawLine(self.tlx, self.bry, self.tlx, self.tly)
y1 = self.bry / 13.
pad = self.bry / 100.
rect0 = QtCore.QRect(0, pad*4, self.brx, self.title_height)
qp.drawText(rect0, QtCore.Qt.TextDontClip | QtCore.Qt.AlignCenter, 'Psbl Haz. Type')
pen = QtGui.QPen(QtCore.Qt.white, 1, QtCore.Qt.SolidLine)
qp.setPen(pen)
qp.drawLine(0, pad*4 + (self.title_height + 3), self.brx, pad*4 + (self.title_height + 3))
def plotBackground(self):
qp = QtGui.QPainter()
qp.begin(self.plotBitMap)
#qp.setRenderHint(qp.Antialiasing)
#qp.setRenderHint(qp.TextAntialiasing)
## draw the frame
self.draw_frame(qp)
qp.end()
class plotWatch(backgroundWatch):
'''
Plot the data on the frame. Inherits the background class that
plots the frame.
'''
def __init__(self):
super(plotWatch, self).__init__()
self.prof = None
def setProf(self, prof):
self.prof = prof
self.watch_type = self.prof.watch_type
self.watch_type_color = self.prof.watch_type_color
self.clearData()
self.plotBackground()
self.plotData()
self.update()
def resizeEvent(self, e):
'''
Handles when the window is resized
'''
super(plotWatch, self).resizeEvent(e)
self.plotData()
def paintEvent(self, e):
'''
Handles painting on the frame
'''
## this function handles painting the plot
super(plotWatch, self).paintEvent(e)
## create a new painter obkect
qp = QtGui.QPainter()
qp.begin(self)
## end the painter
qp.drawPixmap(0,0,self.plotBitMap)
qp.end()
def clearData(self):
'''
Handles the clearing of the pixmap
in the frame.
'''
self.plotBitMap = QtGui.QPixmap(self.width(), self.height())
self.plotBitMap.fill(QtCore.Qt.black)
def plotData(self):
if self.prof is None:
return
qp = QtGui.QPainter()
qp.begin(self.plotBitMap)
qp.setRenderHint(qp.Antialiasing)
qp.setRenderHint(qp.TextAntialiasing)
pen = QtGui.QPen(QtGui.QColor(self.watch_type_color), 1, QtCore.Qt.SolidLine)
qp.setPen(pen)
qp.setFont(self.plot_font)
centery = self.bry / 2.
rect0 = QtCore.QRect(0, centery, self.brx, self.title_height)
qp.drawText(rect0, QtCore.Qt.TextDontClip | QtCore.Qt.AlignCenter, self.watch_type)
qp.end()
| [
"PySide.QtGui.QFont",
"PySide.QtGui.QFontMetrics",
"PySide.QtGui.QColor",
"PySide.QtGui.QPen",
"PySide.QtGui.QPainter",
"PySide.QtCore.QRect"
] | [((996, 1027), 'PySide.QtGui.QFont', 'QtGui.QFont', (['"""Helvetica"""', 'fsize'], {}), "('Helvetica', fsize)\n", (1007, 1027), False, 'from PySide import QtGui, QtCore\n'), ((1053, 1084), 'PySide.QtGui.QFont', 'QtGui.QFont', (['"""Helvetica"""', 'fsize'], {}), "('Helvetica', fsize)\n", (1064, 1084), False, 'from PySide import QtGui, QtCore\n'), ((1114, 1149), 'PySide.QtGui.QFontMetrics', 'QtGui.QFontMetrics', (['self.title_font'], {}), '(self.title_font)\n', (1132, 1149), False, 'from PySide import QtGui, QtCore\n'), ((1180, 1214), 'PySide.QtGui.QFontMetrics', 'QtGui.QFontMetrics', (['self.plot_font'], {}), '(self.plot_font)\n', (1198, 1214), False, 'from PySide import QtGui, QtCore\n'), ((1775, 1826), 'PySide.QtGui.QPen', 'QtGui.QPen', (['QtCore.Qt.white', '(2)', 'QtCore.Qt.SolidLine'], {}), '(QtCore.Qt.white, 2, QtCore.Qt.SolidLine)\n', (1785, 1826), False, 'from PySide import QtGui, QtCore\n'), ((2247, 2300), 'PySide.QtCore.QRect', 'QtCore.QRect', (['(0)', '(pad * 4)', 'self.brx', 'self.title_height'], {}), '(0, pad * 4, self.brx, self.title_height)\n', (2259, 2300), False, 'from PySide import QtGui, QtCore\n'), ((2406, 2457), 'PySide.QtGui.QPen', 'QtGui.QPen', (['QtCore.Qt.white', '(1)', 'QtCore.Qt.SolidLine'], {}), '(QtCore.Qt.white, 1, QtCore.Qt.SolidLine)\n', (2416, 2457), False, 'from PySide import QtGui, QtCore\n'), ((2628, 2644), 'PySide.QtGui.QPainter', 'QtGui.QPainter', ([], {}), '()\n', (2642, 2644), False, 'from PySide import QtGui, QtCore\n'), ((3746, 3762), 'PySide.QtGui.QPainter', 'QtGui.QPainter', ([], {}), '()\n', (3760, 3762), False, 'from PySide import QtGui, QtCore\n'), ((4191, 4207), 'PySide.QtGui.QPainter', 'QtGui.QPainter', ([], {}), '()\n', (4205, 4207), False, 'from PySide import QtGui, QtCore\n'), ((4530, 4583), 'PySide.QtCore.QRect', 'QtCore.QRect', (['(0)', 'centery', 'self.brx', 'self.title_height'], {}), '(0, centery, self.brx, self.title_height)\n', (4542, 4583), False, 'from PySide import QtGui, QtCore\n'), ((4355, 4390), 'PySide.QtGui.QColor', 'QtGui.QColor', (['self.watch_type_color'], {}), '(self.watch_type_color)\n', (4367, 4390), False, 'from PySide import QtGui, QtCore\n')] |
#!/usr/bin/python
# Copyright (c) 2011 GeometryFactory Sarl (France)
#
# $URL$
# $Id$
# SPDX-License-Identifier: LGPL-3.0-or-later OR LicenseRef-Commercial
#
# Author(s) : <NAME>
import sys
import os
import gdb
sys.path.insert(0, os.getcwd() + '/python')
import CGAL.printers
| [
"os.getcwd"
] | [((239, 250), 'os.getcwd', 'os.getcwd', ([], {}), '()\n', (248, 250), False, 'import os\n')] |
import os
import dill
from copy import deepcopy
from itertools import tee
from abc import ABCMeta, abstractmethod
from malemba import ModelBase
from malemba.ds_tools import ArrayHandler, group_array
class ModelDualStage(ModelBase):
class Concatenator(object, metaclass=ABCMeta):
@abstractmethod
def concatenate(self, X, Y):
return
def __init__(self, model1, model2, concatenator, params1=None, params2=None, **kwargs):
"""
:param model1: Boosting model object inherited from malemba.ArrayModelBase that is the first to be applied
:param model2: Boosting model object inherited from malemba.ArrayModelBase that is the to be applied over model1
:param concatenator: the object inherited from Concatenator class to be used to concatinate X and
model1 prediction for model2 input
:param params1: parameters dict for model1
:param params2: parameters dict for model2
:param kwargs:
"""
self.model1 = model1
self.model2 = model2
assert isinstance(self.model1, ModelBase), \
"Error: a value for model1 argument must be an object inherited from malemba.ModelBase"
assert isinstance(self.model2, ModelBase), \
"Error: a value for model2 argument must be an object inherited from malemba.ModelBase"
self._model1_class = self.model1.__class__
self._model2_class = self.model2.__class__
self.concatenator = concatenator
assert isinstance(concatenator, ModelDualStage.Concatenator), \
"Error: a value for 'concatenator' argument must be an object inherited from ModelDualStage.Concatenator"
self.params1 = params1
self.params2 = params2
super(ModelDualStage, self).__init__(params=None, **kwargs)
self.aggr_level1 = kwargs.get("aggr_level1", 0)
self.aggr_level2 = kwargs.get("aggr_level2", 0)
self._data_l = 0
self._model1_fract = 0.0
def fit(self, X, Y, model1_fract=0.0, aggr_level1=None, aggr_level2=None, dump_scheme_path=None, **kwargs):
"""
:param X: features data
:param Y: labels data
:param model1_fract: the fraction of data is to be used for model1 training
:param aggr_level1: number of aggregation steps to turn the input data into appropriate for model1 input
:param aggr_level2: number of aggregation steps to turn the input data into appropriate for model2 input
:param dump_scheme_path: The path for models dump
:param kwargs:
:return:
"""
super(ModelDualStage, self).fit(X=X, Y=Y, **kwargs)
if aggr_level1 is not None:
self.aggr_level1 = aggr_level1
if aggr_level2 is not None:
self.aggr_level2 = aggr_level2
if model1_fract > 0.0:
self._model1_fract = model1_fract
self._data_l = kwargs.get("data_l", 0)
if self._data_l <= 0:
Y, Y_l = tee(Y)
for y in Y_l:
self._data_l += 1
X_1 = ModelDualStage._data_part(X, int(float(self._data_l) * self._model1_fract))
Y_1 = ModelDualStage._data_part(Y, int(float(self._data_l) * self._model1_fract))
if self.aggr_level1 > 0:
data1_handler = ArrayHandler()
Y_1 = data1_handler.aggregate(Y_1, aggr_level=self.aggr_level1)
data1_handler.group_lims = None
X_1 = data1_handler.aggregate(X_1, aggr_level=self.aggr_level1)
self.model1.fit(X=X_1, Y=Y_1)
if dump_scheme_path is not None:
try:
self.model1.dump(scheme_path=os.path.join(dump_scheme_path, "model1"))
except:
print("WARNING: model1 dump failed")
if self._model1_fract >= 1.0:
print("WARNING: model1 fraction >= 1.0 - model2 is model1")
self.model2 = self.model1
else:
X = self._get_model2_data(X=X, aggr_level=self.aggr_level1)
if self.aggr_level2 > 0:
data2_handler= ArrayHandler()
Y = data2_handler.aggregate(Y, aggr_level=self.aggr_level2)
data2_handler.group_lims = None
X = data2_handler.aggregate(X, aggr_level=self.aggr_level2)
self.model2.fit(X=X, Y=Y)
if dump_scheme_path is not None:
try:
self.model2.dump(scheme_path=os.path.join(dump_scheme_path, "model2"))
except:
print("WARNING: model2 dump failed")
self.dump(scheme_path=dump_scheme_path, only_meta=True)
@staticmethod
def _data_part(data, part_l):
l = 0
for d in data:
if l >= part_l:
break
l += 1
yield d
def predict(self, X, aggr_level1=None, aggr_level2=None, **kwargs):
"""
:param X: features data
:param concat_method: the function to be used to concatinate X and model1 prediction for model2 input
:param aggr_level1: number of aggregation steps to turn the input data into one dimensional array
:param kwargs:
:return:
"""
if aggr_level1 is not None:
self.aggr_level1 = aggr_level1
if aggr_level2 is not None:
self.aggr_level2 = aggr_level2
X = self._get_model2_data(X=X, aggr_level=self.aggr_level1)
if self.aggr_level2 > 0:
X_handler = ArrayHandler()
X = X_handler.aggregate(X, aggr_level=self.aggr_level2)
return group_array(self.model2.predict(X=X), group_lims=X_handler.group_lims)
else:
return self.model2.predict(X=X)
def _get_model2_data(self, X, aggr_level=0):
X, X_1 = tee(X)
if aggr_level > 0:
X_1_handler = ArrayHandler()
X_1 = X_1_handler.aggregate(X_1, aggr_level=aggr_level)
model1_pred = group_array(self.model1.predict(X=X_1), group_lims=X_1_handler.group_lims)
else:
model1_pred = self.model1.predict(X=X_1)
return self.concatenator.concatenate(X, model1_pred)
@staticmethod
def _convert_str_to_factors():
return False
def str_to_factors(self, X):
pass
def validate(self, X_test, Y_test, labels_to_remove=None, aggr_level1=None, aggr_level2=None):
if aggr_level1 is not None:
self.aggr_level1 = aggr_level1
if aggr_level2 is not None:
self.aggr_level2 = aggr_level2
X_test = self._get_model2_data(X=X_test, aggr_level=self.aggr_level1)
if self.aggr_level1 > 0:
data_handler = ArrayHandler()
Y_test = data_handler.aggregate(Y_test, aggr_level=self.aggr_level2)
data_handler.group_lims = None
X_test = data_handler.aggregate(X_test, aggr_level=self.aggr_level2)
return self.model2.validate(X_test=X_test, Y_test=Y_test, labels_to_remove=labels_to_remove)
def validate_model1(self, X_test, Y_test, labels_to_remove=None, aggr_level=None):
if aggr_level is not None:
self.aggr_level1 = aggr_level
if self.aggr_level1 > 0:
data_handler = ArrayHandler()
Y_test = data_handler.aggregate(Y_test, aggr_level=self.aggr_level1)
data_handler.group_lims = None
X_test = data_handler.aggregate(X_test, aggr_level=self.aggr_level1)
return self.model1.validate(X_test=X_test, Y_test=Y_test, labels_to_remove=labels_to_remove)
def validate_model2(self, X_test, Y_test, labels_to_remove=None, aggr_level1=None, aggr_level2=None):
return self.validate(X_test=X_test,
Y_test=Y_test,
labels_to_remove=labels_to_remove,
aggr_level1=aggr_level1,
aggr_level2=aggr_level2)
def dump(self, scheme_path, **kwargs):
if not os.path.exists(scheme_path):
os.makedirs(scheme_path)
model1 = self.__dict__.pop("model1")
model2 = self.__dict__.pop("model2")
meta_f = open(os.path.join(scheme_path, "meta.m"), "wb")
dill.dump(self.__dict__, meta_f)
meta_f.close()
self.model1 = model1
self.model2 = model2
if kwargs.get("only_meta", False):
return
self.model1.dump(scheme_path=os.path.join(scheme_path, "model1"), **kwargs)
self.model2.dump(scheme_path=os.path.join(scheme_path, "model2"), **kwargs)
@classmethod
def load(cls, scheme_path, params1=None, params2=None, **kwargs):
with open(os.path.join(scheme_path, "meta.m"), "rb") as meta_f:
meta_dict = dill.load(meta_f)
if params1 is not None:
if meta_dict["params1"] is not None:
meta_dict["params1"].update(params1)
else:
meta_dict["params1"] = params1
if params2 is not None:
if meta_dict["params2"] is not None:
meta_dict["params2"].update(params2)
else:
meta_dict["params2"] = params2
model1 = meta_dict["_model1_class"].load(scheme_path=os.path.join(scheme_path, "model1"),
params=meta_dict["params1"],
**kwargs)
model2 = meta_dict["_model2_class"].load(scheme_path=os.path.join(scheme_path, "model2"),
params=meta_dict["params2"],
**kwargs)
model_dual_stage = cls(model1=model1,
model2=model2,
concatenator=meta_dict["concatenator"],
params1=params1,
params2=params2)
model_dual_stage.__dict__.update(meta_dict)
return model_dual_stage
@property
def num_threads(self):
return self.model2.num_threads
def get_features(self, X):
pass
@property
def features(self):
return self.model1_features
@property
def model1_features(self):
return self.model1.features
@property
def model2_features(self):
return self.model2.features
@property
def feature_types(self):
return self.model1_feature_types
@property
def model1_feature_types(self):
return self.model1.feature_types
@property
def model2_feature_types(self):
return self.model2.feature_types
@property
def labels(self):
return self.model1.labels
@property
def label_freqs(self):
if not self._label_freqs:
for label in self.model1.label_freqs:
self._label_freqs[label] = self.model1.label_freqs[label] * self._model1_fract
for label in self.model2.label_freqs:
self._label_freqs[label] += self.model2.label_freqs[label] * (1.0-self._model1_fract)
return self._label_freqs
| [
"os.path.exists",
"os.makedirs",
"os.path.join",
"malemba.ds_tools.ArrayHandler",
"itertools.tee",
"dill.dump",
"dill.load"
] | [((5840, 5846), 'itertools.tee', 'tee', (['X'], {}), '(X)\n', (5843, 5846), False, 'from itertools import tee\n'), ((8255, 8287), 'dill.dump', 'dill.dump', (['self.__dict__', 'meta_f'], {}), '(self.__dict__, meta_f)\n', (8264, 8287), False, 'import dill\n'), ((5542, 5556), 'malemba.ds_tools.ArrayHandler', 'ArrayHandler', ([], {}), '()\n', (5554, 5556), False, 'from malemba.ds_tools import ArrayHandler, group_array\n'), ((5900, 5914), 'malemba.ds_tools.ArrayHandler', 'ArrayHandler', ([], {}), '()\n', (5912, 5914), False, 'from malemba.ds_tools import ArrayHandler, group_array\n'), ((6732, 6746), 'malemba.ds_tools.ArrayHandler', 'ArrayHandler', ([], {}), '()\n', (6744, 6746), False, 'from malemba.ds_tools import ArrayHandler, group_array\n'), ((7279, 7293), 'malemba.ds_tools.ArrayHandler', 'ArrayHandler', ([], {}), '()\n', (7291, 7293), False, 'from malemba.ds_tools import ArrayHandler, group_array\n'), ((8026, 8053), 'os.path.exists', 'os.path.exists', (['scheme_path'], {}), '(scheme_path)\n', (8040, 8053), False, 'import os\n'), ((8067, 8091), 'os.makedirs', 'os.makedirs', (['scheme_path'], {}), '(scheme_path)\n', (8078, 8091), False, 'import os\n'), ((8204, 8239), 'os.path.join', 'os.path.join', (['scheme_path', '"""meta.m"""'], {}), "(scheme_path, 'meta.m')\n", (8216, 8239), False, 'import os\n'), ((8784, 8801), 'dill.load', 'dill.load', (['meta_f'], {}), '(meta_f)\n', (8793, 8801), False, 'import dill\n'), ((3014, 3020), 'itertools.tee', 'tee', (['Y'], {}), '(Y)\n', (3017, 3020), False, 'from itertools import tee\n'), ((3347, 3361), 'malemba.ds_tools.ArrayHandler', 'ArrayHandler', ([], {}), '()\n', (3359, 3361), False, 'from malemba.ds_tools import ArrayHandler, group_array\n'), ((4153, 4167), 'malemba.ds_tools.ArrayHandler', 'ArrayHandler', ([], {}), '()\n', (4165, 4167), False, 'from malemba.ds_tools import ArrayHandler, group_array\n'), ((8469, 8504), 'os.path.join', 'os.path.join', (['scheme_path', '"""model1"""'], {}), "(scheme_path, 'model1')\n", (8481, 8504), False, 'import os\n'), ((8553, 8588), 'os.path.join', 'os.path.join', (['scheme_path', '"""model2"""'], {}), "(scheme_path, 'model2')\n", (8565, 8588), False, 'import os\n'), ((8706, 8741), 'os.path.join', 'os.path.join', (['scheme_path', '"""meta.m"""'], {}), "(scheme_path, 'meta.m')\n", (8718, 8741), False, 'import os\n'), ((9261, 9296), 'os.path.join', 'os.path.join', (['scheme_path', '"""model1"""'], {}), "(scheme_path, 'model1')\n", (9273, 9296), False, 'import os\n'), ((9496, 9531), 'os.path.join', 'os.path.join', (['scheme_path', '"""model2"""'], {}), "(scheme_path, 'model2')\n", (9508, 9531), False, 'import os\n'), ((4510, 4550), 'os.path.join', 'os.path.join', (['dump_scheme_path', '"""model2"""'], {}), "(dump_scheme_path, 'model2')\n", (4522, 4550), False, 'import os\n'), ((3727, 3767), 'os.path.join', 'os.path.join', (['dump_scheme_path', '"""model1"""'], {}), "(dump_scheme_path, 'model1')\n", (3739, 3767), False, 'import os\n')] |
import xml.etree.ElementTree as ET
from typing import NewType
MethodCall = NewType("MethodCall", ET.Element)
ConfigurableMethodTerminal = NewType("ConfigurableMethodTerminal", ET.Element)
MethodTerminal = NewType("MethodTerminal", ET.Element)
WhileLoop = NewType("WhileLoop", ET.Element)
Terminal = NewType("Terminal", ET.Element)
PairedConfigurableMethodCall = NewType("PairedConfigurableMethodCall",ET.Element)
SequenceNode = NewType("PairedConfigurableMethodCall",ET.Element) | [
"typing.NewType"
] | [((76, 109), 'typing.NewType', 'NewType', (['"""MethodCall"""', 'ET.Element'], {}), "('MethodCall', ET.Element)\n", (83, 109), False, 'from typing import NewType\n'), ((139, 188), 'typing.NewType', 'NewType', (['"""ConfigurableMethodTerminal"""', 'ET.Element'], {}), "('ConfigurableMethodTerminal', ET.Element)\n", (146, 188), False, 'from typing import NewType\n'), ((206, 243), 'typing.NewType', 'NewType', (['"""MethodTerminal"""', 'ET.Element'], {}), "('MethodTerminal', ET.Element)\n", (213, 243), False, 'from typing import NewType\n'), ((256, 288), 'typing.NewType', 'NewType', (['"""WhileLoop"""', 'ET.Element'], {}), "('WhileLoop', ET.Element)\n", (263, 288), False, 'from typing import NewType\n'), ((300, 331), 'typing.NewType', 'NewType', (['"""Terminal"""', 'ET.Element'], {}), "('Terminal', ET.Element)\n", (307, 331), False, 'from typing import NewType\n'), ((363, 414), 'typing.NewType', 'NewType', (['"""PairedConfigurableMethodCall"""', 'ET.Element'], {}), "('PairedConfigurableMethodCall', ET.Element)\n", (370, 414), False, 'from typing import NewType\n'), ((429, 480), 'typing.NewType', 'NewType', (['"""PairedConfigurableMethodCall"""', 'ET.Element'], {}), "('PairedConfigurableMethodCall', ET.Element)\n", (436, 480), False, 'from typing import NewType\n')] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from pylookyloo import Lookyloo
import json
# lookyloo_url = "https://lookyloo.circl.lu/"
lookyloo_url = "http://0.0.0.0:5100"
lookyloo = Lookyloo(lookyloo_url)
lookyloo.init_apikey(username='admin', password='<PASSWORD>')
event = lookyloo.misp_push('6ae2afdc-4d90-41ce-9cae-510daf1e6577')
print(json.dumps(event, indent=2))
| [
"json.dumps",
"pylookyloo.Lookyloo"
] | [((188, 210), 'pylookyloo.Lookyloo', 'Lookyloo', (['lookyloo_url'], {}), '(lookyloo_url)\n', (196, 210), False, 'from pylookyloo import Lookyloo\n'), ((347, 374), 'json.dumps', 'json.dumps', (['event'], {'indent': '(2)'}), '(event, indent=2)\n', (357, 374), False, 'import json\n')] |
# Copyright (C) 2015-2019 <NAME>
# SPDX-License-Identifier: Apache-2.0
# Check for presence of FEniCS etc
from .verify_environment import verify_env
verify_env()
__version__ = '2019.0.2'
# This should potentially be made local to the mesh creation routines
import dolfin
dolfin.parameters['ghost_mode'] = 'shared_vertex'
del dolfin
def get_version():
"""
Return the version number of Ocellaris
"""
return __version__
def get_detailed_version():
"""
Return the version number of Ocellaris including
source control commit revision information
"""
import os
import subprocess
this_dir = os.path.dirname(os.path.abspath(__file__))
proj_dir = os.path.abspath(os.path.join(this_dir, '..'))
if os.path.isdir(os.path.join(proj_dir, '.git')):
cmd = ['git', 'describe', '--always']
version = subprocess.check_output(cmd, cwd=proj_dir)
local_version = '+git.' + version.decode('utf8').strip()
else:
local_version = ''
return get_version() + local_version
# Convenience imports for scripting
from .simulation import Simulation
from .run import setup_simulation, run_simulation
| [
"os.path.abspath",
"subprocess.check_output",
"os.path.join"
] | [((656, 681), 'os.path.abspath', 'os.path.abspath', (['__file__'], {}), '(__file__)\n', (671, 681), False, 'import os\n'), ((714, 742), 'os.path.join', 'os.path.join', (['this_dir', '""".."""'], {}), "(this_dir, '..')\n", (726, 742), False, 'import os\n'), ((765, 795), 'os.path.join', 'os.path.join', (['proj_dir', '""".git"""'], {}), "(proj_dir, '.git')\n", (777, 795), False, 'import os\n'), ((862, 904), 'subprocess.check_output', 'subprocess.check_output', (['cmd'], {'cwd': 'proj_dir'}), '(cmd, cwd=proj_dir)\n', (885, 904), False, 'import subprocess\n')] |
'''
Created on July 7, 2019
@author: Terry
@email：<EMAIL>
'''
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree
# Parameter
n_classes = 3
plot_colors = "ryb"
plot_step = 0.02
# Load data
iris = load_iris()
for pairidx, pair in enumerate([[0,1],[0,2],[0,3],
[1,2],[1,3],[2,3]]):
# We only take two corresponding features
X = iris.data[:,pair]
y = iris.target
# Train
clf = DecisionTreeClassifier().fit(X,y)
# Plot the descision boundary
plt.subplot(2,3,pairidx + 1)
x_min, x_max = X[:,0].min() - 1, X[:,0].max() - 1
y_min, y_max = X[:,1].min() - 1, X[:,1].max() - 1
xx, yy = np.meshgrid(np.arange(x_min,x_max,plot_step),
np.arange(y_min,y_max,plot_step))
plt.tight_layout(h_pad=0.5,w_pad=0.5,pad=2.5)
Z = clf.predict(np.c_[xx.ravel(),yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.RdYlBu)
plt.xlabel(iris.feature_names[pair[0]])
plt.ylabel(iris.feature_names[pair[1]])
# Plot the training points
for i, color in zip(range(n_classes),plot_colors):
idx = np.where(y == i)
plt.scatter(X[idx,0],X[idx,1],c=color,label=iris.target_names[i],
cmap=plt.cm.RdYlBu,edgecolors='black',s=15)
plt.suptitle("Decision surface of a decision tree using paired features")
plt.legend(loc='lower right', borderpad=0, handletextpad=0)
plt.axis("tight")
plt.figure()
clf = DecisionTreeClassifier().fit(iris.data, iris.target)
plot_tree(clf, filled=True)
plt.show() | [
"sklearn.datasets.load_iris",
"matplotlib.pyplot.contourf",
"matplotlib.pyplot.ylabel",
"numpy.arange",
"numpy.where",
"matplotlib.pyplot.xlabel",
"sklearn.tree.DecisionTreeClassifier",
"matplotlib.pyplot.figure",
"sklearn.tree.plot_tree",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.sca... | [((315, 326), 'sklearn.datasets.load_iris', 'load_iris', ([], {}), '()\n', (324, 326), False, 'from sklearn.datasets import load_iris\n'), ((1404, 1477), 'matplotlib.pyplot.suptitle', 'plt.suptitle', (['"""Decision surface of a decision tree using paired features"""'], {}), "('Decision surface of a decision tree using paired features')\n", (1416, 1477), True, 'import matplotlib.pyplot as plt\n'), ((1478, 1537), 'matplotlib.pyplot.legend', 'plt.legend', ([], {'loc': '"""lower right"""', 'borderpad': '(0)', 'handletextpad': '(0)'}), "(loc='lower right', borderpad=0, handletextpad=0)\n", (1488, 1537), True, 'import matplotlib.pyplot as plt\n'), ((1538, 1555), 'matplotlib.pyplot.axis', 'plt.axis', (['"""tight"""'], {}), "('tight')\n", (1546, 1555), True, 'import matplotlib.pyplot as plt\n'), ((1557, 1569), 'matplotlib.pyplot.figure', 'plt.figure', ([], {}), '()\n', (1567, 1569), True, 'import matplotlib.pyplot as plt\n'), ((1629, 1656), 'sklearn.tree.plot_tree', 'plot_tree', (['clf'], {'filled': '(True)'}), '(clf, filled=True)\n', (1638, 1656), False, 'from sklearn.tree import DecisionTreeClassifier, plot_tree\n'), ((1657, 1667), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (1665, 1667), True, 'import matplotlib.pyplot as plt\n'), ((620, 650), 'matplotlib.pyplot.subplot', 'plt.subplot', (['(2)', '(3)', '(pairidx + 1)'], {}), '(2, 3, pairidx + 1)\n', (631, 650), True, 'import matplotlib.pyplot as plt\n'), ((878, 925), 'matplotlib.pyplot.tight_layout', 'plt.tight_layout', ([], {'h_pad': '(0.5)', 'w_pad': '(0.5)', 'pad': '(2.5)'}), '(h_pad=0.5, w_pad=0.5, pad=2.5)\n', (894, 925), True, 'import matplotlib.pyplot as plt\n'), ((1012, 1055), 'matplotlib.pyplot.contourf', 'plt.contourf', (['xx', 'yy', 'Z'], {'cmap': 'plt.cm.RdYlBu'}), '(xx, yy, Z, cmap=plt.cm.RdYlBu)\n', (1024, 1055), True, 'import matplotlib.pyplot as plt\n'), ((1060, 1099), 'matplotlib.pyplot.xlabel', 'plt.xlabel', (['iris.feature_names[pair[0]]'], {}), '(iris.feature_names[pair[0]])\n', (1070, 1099), True, 'import matplotlib.pyplot as plt\n'), ((1104, 1143), 'matplotlib.pyplot.ylabel', 'plt.ylabel', (['iris.feature_names[pair[1]]'], {}), '(iris.feature_names[pair[1]])\n', (1114, 1143), True, 'import matplotlib.pyplot as plt\n'), ((782, 816), 'numpy.arange', 'np.arange', (['x_min', 'x_max', 'plot_step'], {}), '(x_min, x_max, plot_step)\n', (791, 816), True, 'import numpy as np\n'), ((840, 874), 'numpy.arange', 'np.arange', (['y_min', 'y_max', 'plot_step'], {}), '(y_min, y_max, plot_step)\n', (849, 874), True, 'import numpy as np\n'), ((1249, 1265), 'numpy.where', 'np.where', (['(y == i)'], {}), '(y == i)\n', (1257, 1265), True, 'import numpy as np\n'), ((1274, 1395), 'matplotlib.pyplot.scatter', 'plt.scatter', (['X[idx, 0]', 'X[idx, 1]'], {'c': 'color', 'label': 'iris.target_names[i]', 'cmap': 'plt.cm.RdYlBu', 'edgecolors': '"""black"""', 's': '(15)'}), "(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i], cmap\n =plt.cm.RdYlBu, edgecolors='black', s=15)\n", (1285, 1395), True, 'import matplotlib.pyplot as plt\n'), ((1576, 1600), 'sklearn.tree.DecisionTreeClassifier', 'DecisionTreeClassifier', ([], {}), '()\n', (1598, 1600), False, 'from sklearn.tree import DecisionTreeClassifier, plot_tree\n'), ((547, 571), 'sklearn.tree.DecisionTreeClassifier', 'DecisionTreeClassifier', ([], {}), '()\n', (569, 571), False, 'from sklearn.tree import DecisionTreeClassifier, plot_tree\n')] |
# encoding: utf-8
from application import static_manager
from static_bundle import (JsBundle,
CssBundle)
css1 = CssBundle("css")
css1.add_file("example1.less")
css1.add_file("example2.css")
js1 = JsBundle("js")
js1.add_file("vendors/example1.js")
js1.add_file("vendors/example2.js")
js2 = JsBundle("js/include")
# modules depends on app.js
js2.add_file("app.js")
js2.add_directory("modules")
builder = static_manager.create_builder()
builder.create_asset("Styles").add_bundle(css1)
builder.create_asset("Vendors", minify=True).add_bundle(js1)
builder.create_asset("Application", minify=True).add_bundle(js2) | [
"application.static_manager.create_builder",
"static_bundle.CssBundle",
"static_bundle.JsBundle"
] | [((141, 157), 'static_bundle.CssBundle', 'CssBundle', (['"""css"""'], {}), "('css')\n", (150, 157), False, 'from static_bundle import JsBundle, CssBundle\n'), ((226, 240), 'static_bundle.JsBundle', 'JsBundle', (['"""js"""'], {}), "('js')\n", (234, 240), False, 'from static_bundle import JsBundle, CssBundle\n'), ((320, 342), 'static_bundle.JsBundle', 'JsBundle', (['"""js/include"""'], {}), "('js/include')\n", (328, 342), False, 'from static_bundle import JsBundle, CssBundle\n'), ((434, 465), 'application.static_manager.create_builder', 'static_manager.create_builder', ([], {}), '()\n', (463, 465), False, 'from application import static_manager\n')] |
""" Intermediate Factors
@author: <NAME>
This module computes the interpolated features between the principal vectors -- the one
linking source to target following the geodesics on the Grassmannian. We use the
equivalent formulation derived in [1] and represent this geodesics for each pair
of principal components.
Example
-------
Examples are given in the vignettes.
Notes
-------
Examples are given in the vignette
References
-------
[1] <NAME>., <NAME>., <NAME>., "TO CHANGE"
"""
import numpy as np
import pandas as pd
from pathlib import Path
from joblib import Parallel, delayed
from precise.principal_vectors import PVComputation
class IntermediateFactors:
"""
Handle the intermediate representations between
Attributes
-------
source_components_ : numpy.ndarray, shape (n_components, n_features)
Loadings of the source factors, be them already aligned to target or not.
target_components : numpy.ndarray, shape (n_components, n_features)
Loadings of the target factors, be them already aligned to source or not.
intermediate_factors_ : numpy.ndarray, shape (n_representations, n_components, n_features)
Loadings of intermediate factors along the geodesic path. Components are ordered
by similarity, i.e. first components correspond to path between first PVs, etc.
n_representations: int
Number of representations along the geodesic path. If -1, means that the Geodesic Flow Kernel
has been used instead.
geodesic_matrix_: numpy.ndarray, shape (n_features, n_features)
Geodesic Matrix for geodesic flow kernel.
geodesic_flow_: method float:numpy.array
Method that computes geodesic flow at a certain position.
"""
def __init__(self, n_representations, n_jobs=1):
"""
Parameters
-------
n_representations : int
Number of representations to pick between source and target.
n_jobs: int (optional, default to 1)
Number of jobs for computation.
"""
self.n_representations = n_representations
self.intermediate_factors_ = None
self.source_components_ = None
self.target_components_ = None
self.n_jobs = 1
def _compute_principal_vectors(self):
n_pv = np.min([self.source_components_.shape[0],
self.target_components_.shape[0]])
n_factors = {
'source': self.source_components_.shape[0],
'target': self.target_components_.shape[0]
}
self.principal_vectors_ = PVComputation(n_factors, n_pv)
self.principal_vectors_.compute_principal_vectors(self.source_components_,
self.target_components_)
def _compute_flow_time(t, principal_vectors):
Pi = np.sin( (1-t) * principal_vectors.angles_)\
/np.sin(principal_vectors.angles_)
Pi[np.isnan(Pi)] = 1-t # Asymptotic value of sin/sin in 0
Xi = np.sin( t * principal_vectors.angles_)\
/ np.sin(principal_vectors.angles_)
Xi[np.isnan(Xi)] = t # Asymptotic value of sin/sin in 0
return (principal_vectors.source_components_.T*Pi \
+ principal_vectors.target_components_.T*Xi).T
def sample_flow(self, source_components, target_components, already_aligned=False):
"""
Sample intermediate subspaces (i.e. set of factors) uniformely along the geodesic flow.
IMPORTANT: Same genes have to be given for source and target, and in same order
Parameters
-------
source_components : np.ndarray, shape (n_components, n_features)
Source factors
target_components : np.ndarray, shape (n_components, n_features)
Target factors
already_aligned : boolean (optional, default to False)
Whether the components are already aligned (i.e. are they PV or not).
Return values
-------
Intermediate subspace, numpy.ndarray of shape (n_representations + 1, n_components, n_features).
"""
self.source_components_ = source_components
self.target_components_ = target_components
# Compute the principal vectors
if not already_aligned:
self._compute_principal_vectors()
else:
self.principal_vectors_.source_components_ = self.source_components_
self.principal_vectors_.target_components = self.target_components_
# Sample at different uniformly distributed time points
if self.n_representations == -1:
t_sample = np.array([1])
else:
t_sample = np.linspace(0, 1, self.n_representations + 1)
if self.n_jobs >= 2:
return np.array(
Parallel(n_jobs=self.n_jobs)\
(delayed(IntermediateFactors._compute_flow_time)(t, self.principal_vectors_)\
for t in t_sample)
)
else:
return np.array([IntermediateFactors._compute_flow_time(t, self.principal_vectors_) for t in t_sample])
def compute_geodesic_matrix(self, source_components, target_components):
"""
Return method for computing the domain-invariant kernel of Geodesic Flow Kernel.
Parameters
-------
source_components : np.ndarray, shape (n_components, n_features)
Source factors
target_components : np.ndarray, shape (n_components, n_features)
Target factors
Return values
-------
Method that takes two p-dimensional vector and returns their domain-invariant
scalar product.
"""
self.source_components_ = source_components
self.target_components_ = target_components
self._compute_principal_vectors()
diag_term = (self.principal_vectors_.angles_ - np.cos(self.principal_vectors_.angles_)*np.sin(self.principal_vectors_.angles_)) \
/ 2 / self.principal_vectors_.angles_ / np.power(np.sin(self.principal_vectors_.angles_), 2)
off_diag_term = (np.sin(self.principal_vectors_.angles_) - np.cos(self.principal_vectors_.angles_)*self.principal_vectors_.angles_) \
/ 2 / np.power(np.sin(self.principal_vectors_.angles_),2) / self.principal_vectors_.angles_
# Correct for extreme case when theta = 0
diag_term[np.isnan(diag_term)] = 1./3.
diag_term[np.isinf(diag_term)] = 1./3.
off_diag_term[np.isnan(off_diag_term)] = 1./6.
off_diag_term[np.isinf(off_diag_term)] = 1./6.
diag_term = np.diag(diag_term)
off_diag_term = np.diag(off_diag_term)
self.G_matrix = np.block([
[diag_term, off_diag_term],
[off_diag_term, diag_term]
])
self.projection = np.block([self.principal_vectors_.source_components_.transpose(), self.principal_vectors_.target_components_.transpose()])
return self.G_matrix
#return lambda x,y: IntermediateFactors._compute_domain_invariant_scalar_product(x, y, self.projection, self.G_matrix)
def _compute_domain_invariant_scalar_product(x, y, projection, G_matrix):
x_p = x.dot(projection)
y_p = y.dot(projection)
return x_p.dot(G_matrix).dot(y_p.transpose())
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import logging
import os
import hydra
import pytorch_lightning as pl
from hydra.utils import instantiate
from omegaconf import DictConfig, OmegaConf
from deep_learning_template import BaseDataModule, BaseTask
from deep_learning_template.core import initialization as init
from deep_learning_template.utils.config import freeze_config
os.environ["HYDRA_FULL_ERROR"] = "1"
log = logging.getLogger(__name__)
@hydra.main(config_path="conf", config_name="config")
def main(cfg: DictConfig) -> None:
"""Hydra application's main function, that builds the model and trains/tests it on the dataset.
Args:
cfg: Application-wide configuration generated by Hydra.
"""
freeze_config(cfg)
log.info(f"Hydra app running with config: \n{OmegaConf.to_yaml(cfg)}")
init.validate_cfg(cfg)
trainer: pl.Trainer = instantiate(cfg.trainer.trainer, logger=init.initialize_loggers(cfg))
data_module: BaseDataModule = instantiate(cfg.datamodule.datamodule)
task: BaseTask = init.initialize_task(cfg, data_module)
if cfg.trainer.train:
trainer.fit(task, datamodule=data_module)
log.info("Training complete.")
if cfg.trainer.test:
trainer.test(task, datamodule=data_module)
log.info("Testing complete.")
if __name__ == "__main__":
main()
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"deep_learning_template.core.initialization.validate_cfg",
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import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.cross_validation import train_test_split
import theanets
import climate
climate.enable_default_logging()
X_orig = np.load('/Users/bzamecnik/Documents/music-processing/music-processing-experiments/c-scale-piano_spectrogram_2048_hamming.npy')
sample_count, feature_count = X_orig.shape
X = MinMaxScaler().fit_transform(X_orig)
X = X.astype(np.float32)
X_train, X_test = train_test_split(X, test_size=0.4, random_state=42)
X_val, X_test = train_test_split(X_test, test_size=0.5, random_state=42)
# (np.maximum(0, 44100/512*np.arange(13)-2)).astype('int')
#blocks = [0, 84, 170, 256, 342, 428, 514, 600, 687, 773, 859, 945, 1031, 1205]
blocks = [0, 48, 98, 148, 198, 248, 298, 348, 398, 448, 498, 548, 598, 700]
def make_labels(blocks):
label_count = len(blocks) - 1
labels = np.zeros(blocks[-1])
for i in range(label_count):
labels[blocks[i]:blocks[i+1]] = i
return labels
y = make_labels(blocks)
def score(exp, Xs):
X_train, X_val, X_test = Xs
def sc(exp, X):
return r2_score(X, exp.network.predict(X))
print("training: ", sc(exp, X_train))
# NOTE: only optimize to validation dataset's score!
print("validation:", sc(exp, X_val))
print("test: ", sc(exp, X_test))
exp1 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1)
exp1.train(X_train, X_val, optimize='nag', learning_rate=1e-3, momentum=0.9)
exp2 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1)
exp2.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
# gives quite nice prediction, trains slow
exp3 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1, hidden_activation='relu')
exp3.train(X_train, X_val, optimize='nag', learning_rate=1e-3, momentum=0.9)
exp4 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1, input_dropout=0.3)
exp4.train(X_train, X_val, optimize='nag', learning_rate=1e-3, momentum=0.9)
# rmsprop - converges faster in this case than nag
exp5 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1)
exp5.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# tied weighs - work good, much lower loss function values
# r2: 0.75037549551862703
exp6 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1, tied_weights=True)
exp6.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# higher hidden L1 penalty - worse
exp7 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.7, tied_weights=True)
exp7.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# hidden L2 penalty - a bit worse
exp8 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
hidden_l1=0.1, hidden_l2=0.1, tied_weights=True)
exp8.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# no regularization - in this case better
# r2: 0.82211329411744094
exp10 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
tied_weights=True)
exp10.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# layerwise autoencoder training
exp11 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 500, feature_count), tied_weights=True)
exp11.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
# wow - this actually is able to to a 2D visualization
exp12 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 100, 10, 2, 10, 100, feature_count),
tied_weights=True)
exp12.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
def compute_middle_layer(X, model):
X_pred_ff = model.feed_forward(X)
middle = int(len(X_pred_ff)/2)
X_middle = X_pred_ff[middle]
return X_middle
def visualize_2d(X, y=None):
colors = y/max(y) if y is not None else np.linspace(0,1,len(X))
scatter(X[:,0], X[:,1],
c=colors, alpha=0.2, edgecolors='none', cmap='rainbow')
# same visualization, a little bit better r2
exp13 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 256, 64, 16, 2, 16, 64, 256, feature_count),
tied_weights=True)
exp13.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
# contractive - better than without
# r2: 0.82820148664941162
exp14 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
tied_weights=True, contractive=0.8)
exp14.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# tanh - bad
exp15 = theanets.Experiment( theanets.Autoencoder,
layers=(feature_count, 500, feature_count),
tied_weights=True, hidden_activation='tanh')
exp15.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# relu, contractive
exp16 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 128, 16, 2, 16, 128, feature_count),
tied_weights=True, hidden_activation='relu', contractive=0.5)
exp16.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
exp17 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 128, 16, 2, 16, 128, feature_count),
tied_weights=True, contractive=0.8)
exp17.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
exp18 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.8)
exp18.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
# r2: 0.83371355062803953
exp19 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.8, hidden_dropout=0.8)
exp19.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
exp20 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.9, hidden_dropout=0.9)
exp20.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
# -----------------
# animate the 2D point movement
import matplotlib.animation as animation
def export_animation(X_2d, y, filename):
fig = plt.figure()
# 854x480 px (480p) in inches, note that 8.54 gives 853px width :/
fig.set_size_inches(8.545, 4.80)
plt.axis('equal')
# plt.tight_layout()
# plt.xlim(-0.1, 1.1)
# plt.ylim(-0.1, 1.1)
images = []
im1 = scatter(X_2d[:, 0], X_2d[:, 1], c=y/max(y), cmap='rainbow', alpha=0.2)
for i in range(len(X_2d)):
im2 = scatter(X_2d[i, 0], X_2d[i, 1], c=y[i]/max(y), cmap='rainbow')
images.append([im1, im2])
ani = animation.ArtistAnimation(fig, images,
interval=20, blit=False, repeat=False)
writer = animation.writers['ffmpeg'](fps=50, bitrate=5000)
ani.save(filename, writer=writer, dpi=100)
export_animation(X_tsne, y, 'piano-tsne.mp4')
#----------------------
exp21 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.3, hidden_dropout=0.5,
batch_size=len(X_train))
exp21.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
exp22 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.3, hidden_dropout=0.5)
exp22.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
exp23 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, 256, 128, 64, 32, 16, 8, 4, 2,
4, 8, 16, 32, 64, 128, 256, 512, feature_count),
tied_weights=True, input_dropout=0.3, hidden_dropout=0.5)
exp23.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
exp24 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.3, hidden_dropout=0.5,
hidden_activation='linear')
exp24.train(X_train, X_val, optimize='rmsprop', learning_rate=1e-3, momentum=0.9)
# r2: 0.833454635805
exp25 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp25.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.9)
# r2: 0.731835366439
exp26 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp26.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.1)
# r2: 0.854741515141 (*)
exp27 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp27.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# r2: 0.84260338122
exp28 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp28.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.7)
exp29 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp29.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp30 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, input_dropout=0.9)
exp30.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp31 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 100, feature_count),
tied_weights=True)
exp31.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp32 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 200, 20, 2, 20, 200, feature_count),
tied_weights=True, input_dropout=0.5, hidden_dropout=0.5)
exp32.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# bad - makes a single curve
exp33 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 200, 20, 2, 20, 200, feature_count),
tied_weights=True, hidden_l1=0.1)
exp33.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# bad - makes a non-discriminative curve
exp34 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 200, 20, 2, 20, 200, feature_count),
tied_weights=True, input_dropout=0.5)
exp34.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp35 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 200, 20, 2, 20, 200, feature_count),
tied_weights=True, hidden_dropout=0.5)
exp35.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp36 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 200, 20, 2, 20, 200, feature_count),
tied_weights=True)
exp36.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp33 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, 256, 128, 64, 32, 16, 8, 4, 2,
4, 8, 16, 32, 64, 128, 256, 512, feature_count),
tied_weights=True)
exp33.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
X_zca_train, X_zca_test = train_test_split(X_zca, test_size=0.4, random_state=42)
X_zca_val, X_zca_test = train_test_split(X_zca_test, test_size=0.5, random_state=42)
exp34 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp34.train(X_zca_train, X_zca_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp35 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, 256, 128, 64, 32, 16, 8, 4, 2,
4, 8, 16, 32, 64, 128, 256, 512, feature_count),
tied_weights=True, hidden_activation='relu')
exp35.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# - try tanh and relu for deeper networks
# - try other normalization (mean-std instead od min-max)
X_ms = StandardScaler().fit_transform(X_orig).astype(np.float32)
X_ms_train, X_ms_test = train_test_split(X_ms, test_size=0.4, random_state=42)
X_ms_val, X_ms_test = train_test_split(X_ms_test, test_size=0.5, random_state=42)
exp36 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp36.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp37 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='tanh')
exp37.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp38 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp38.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
X_orig_train, X_orig_test = train_test_split(X_orig.astype('float32'), test_size=0.4, random_state=42)
X_orig_val, X_orig_test = train_test_split(X_orig_test, test_size=0.5, random_state=42)
exp39 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True)
exp39.train(X_orig_train, X_orig_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp40 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='linear', hidden_l1=0.5)
exp40.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp41 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='relu', hidden_l1=0.5)
exp41.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp42 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='relu', weight_l1=0.5)
exp42.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# bad
exp43 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='relu', contractive=0.9)
exp43.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# not bad
exp44 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='relu')
exp45.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp45 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='relu', contractive=0.5)
exp45.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
# r2: 0.849283267068
exp46 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='linear', contractive=0.5)
exp46.train(X_ms_train, X_ms_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
exp47 = theanets.Experiment(theanets.Autoencoder,
layers=(feature_count, 512, feature_count),
tied_weights=True, hidden_activation='linear', contractive=0.5)
exp47.train(X_train, X_val, optimize='layerwise', learning_rate=1e-3, momentum=0.5)
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"numpy.zeros",
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"numpy.load",
"sklearn.preprocessing.MinMaxScaler"
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'theanets.Experiment', (['theanets.Autoencoder'], {'layers': '(feature_count, 512, feature_count)', 'tied_weights': '(True)', 'hidden_activation': '"""linear"""', 'contractive': '(0.5)'}), "(theanets.Autoencoder, layers=(feature_count, 512,\n feature_count), tied_weights=True, hidden_activation='linear',\n contractive=0.5)\n", (15469, 15607), False, 'import theanets\n'), ((880, 900), 'numpy.zeros', 'np.zeros', (['blocks[-1]'], {}), '(blocks[-1])\n', (888, 900), True, 'import numpy as np\n'), ((7106, 7183), 'matplotlib.animation.ArtistAnimation', 'animation.ArtistAnimation', (['fig', 'images'], {'interval': '(20)', 'blit': '(False)', 'repeat': '(False)'}), '(fig, images, interval=20, blit=False, repeat=False)\n', (7131, 7183), True, 'import matplotlib.animation as animation\n'), ((385, 399), 'sklearn.preprocessing.MinMaxScaler', 'MinMaxScaler', ([], {}), '()\n', (397, 399), False, 'from sklearn.preprocessing import MinMaxScaler, StandardScaler\n'), ((12378, 12394), 'sklearn.preprocessing.StandardScaler', 'StandardScaler', ([], {}), '()\n', (12392, 12394), False, 'from sklearn.preprocessing import MinMaxScaler, StandardScaler\n')] |
import os
import glob
import argparse
import numpy as np
def parse_args():
parser = argparse.ArgumentParser(description='Display datas')
parser.add_argument('--data-dir', default='C:/Users/junya/Documents/plant_segmentation_data',
help='dataset directory')
parser.add_argument('--val-rate', default=0.1, type=float,
help='Number of validation rate')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
phase = 'train'
train_dir = os.path.join(args.data_dir, phase)
all_set = np.array(glob.glob(os.path.join(train_dir, '*.jpg')))
all_idx = np.random.choice(all_set.shape[0], all_set.shape[0], replace=False)
train_num = int(all_set.shape[0] * (1 - args.val_rate))
train_idx = all_idx[0:train_num]
val_idx = all_idx[train_num:all_set.shape[0]]
train_set = all_set[train_idx]
val_set = all_set[val_idx]
np.savetxt("segmentation/train.txt", train_set, fmt='%s', delimiter=',')
np.savetxt("segmentation/val.txt", val_set, fmt='%s', delimiter=',')
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"numpy.savetxt",
"os.path.join",
"argparse.ArgumentParser"
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''' This file gets the cities from the Deutscher Wetterdienst
website file (a file of the name
'TU_Stundenwerte_Beschreibung_Stationen.txt' must already be in the
current folder) and saves it into the file 'current_cityfile.dump'.'''
import pickle
import re # regular expressions package
import csv
#### FUNCTION DEFINITIONS #####################################################
def get_cities(filename):
''' Reads cities and ids from textfile '''
print('Reading Deutscher Wetterdienst - File and extract cities...')
citylist = []
# Attention! The textfile given by the DWD is encoded in Latin-1.
# Python3 uses utf-8 by default, so we have to specify it here.
# In Python2 none of this will work, the open() function doesn't
# even accept encode= as a parameter.
with open(filename, 'rt', encoding='Latin-1') as text_file:
# Read the first two lines, which we don't need.
text_file.readline()
text_file.readline()
lines = text_file.readlines()
for idx, textline in enumerate(lines):
wordlist = re.sub("[^\w]", " ", textline).split()
# The city is the 8th. word in each line
try:
citylist.append([wordlist[0], wordlist[8], wordlist[9]])
except IndexError:
# check if this IndexError was raised in the last line:
if len(lines) == idx+1:
print("Checking exited cleanly at the end of the file.")
else:
print('There was an indexerror while reading from' \
'the DW text file in line ' + str(idx))
list_sorted = sorted(citylist, key= lambda line:line[1])
return list_sorted, text_file
def delete_multiples(citylist):
''' Removes duplicate items from a list. A duplicate is if
there are several weather stations in the same city'''
print('Deleting cities that occur multiple times in the city list ' + \
'because the DW has several stations within one city...')
duplicates = 0
new_citylist = []
citiesonly = [] # this list is used to temporarilty store
# the cities and check whether it occured already
for element in citylist:
# element is of the form ['ID', 'city', 'part_of_city']
if element[1] not in citiesonly:
new_citylist.append(element)
citiesonly.append(element[1])
else:
duplicates+=1
print("Deleted " + str(duplicates) + " duplicates.")
return new_citylist
#### IMPLEMENTATION ###########################################################
citylist, text_file = get_cities('TU_Stundenwerte_Beschreibung_Stationen.txt')
citylist = delete_multiples(citylist)
with open('citylist.txt', 'w', encoding='utf-8') as myfile:
myfile.writelines(["%s\n" % item1 for item in citylist for item1 in item])
pickle.dump(citylist, open('current_cityfile.dump','wb')) | [
"re.sub"
] | [((1146, 1177), 're.sub', 're.sub', (['"""[^\\\\w]"""', '""" """', 'textline'], {}), "('[^\\\\w]', ' ', textline)\n", (1152, 1177), False, 'import re\n')] |
# python3
# Copyright 2021 InstaDeep Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Wraps a PettingZoo MARL environment to be used as a dm_env environment."""
import copy
from typing import Any, Dict, Iterator, List, Optional, Union
import dm_env
import gym
import numpy as np
from acme import specs
from acme.wrappers.gym_wrapper import _convert_to_spec
from pettingzoo.utils.env import AECEnv, ParallelEnv
from supersuit import black_death_v1
from mava import types
from mava.utils.wrapper_utils import (
apply_env_wrapper_preprocessers,
convert_np_type,
parameterized_restart,
)
from mava.wrappers.env_wrappers import ParallelEnvWrapper, SequentialEnvWrapper
class PettingZooAECEnvWrapper(SequentialEnvWrapper):
"""Environment wrapper for PettingZoo MARL environments."""
def __init__(
self,
environment: AECEnv,
env_preprocess_wrappers: Optional[List] = [
# (env_preprocessor, dict_with_preprocessor_params)
(black_death_v1, None),
],
):
self._environment = environment
self._reset_next_step = True
if env_preprocess_wrappers:
self._environment = apply_env_wrapper_preprocessers(
self._environment, env_preprocess_wrappers
)
self.correct_agent_name()
self.last_turn_agent = None
def reset(self) -> dm_env.TimeStep:
"""Resets the episode."""
self._reset_next_step = False
self._environment.reset()
self._step_types = {
agent: dm_env.StepType.FIRST for agent in self.possible_agents
}
self._first_step_performed = {agent: False for agent in self.possible_agents}
observe, _, done, _ = self._environment.last()
agent = self.current_agent
observation = self._convert_observation(agent, observe, done)
self._discount = convert_np_type(self.discount_spec()[agent].dtype, 1)
reward = convert_np_type(self.reward_spec()[agent].dtype, 0)
return parameterized_restart(reward, self._discount, observation)
def step( # type: ignore[override]
self, action: Union[int, float]
) -> dm_env.TimeStep:
"""Steps the environment."""
if self._reset_next_step:
return self.reset()
_, _, done, _ = self._environment.last()
# If current agent is done
if done:
self._environment.step(None)
else:
self._environment.step(action)
agent = self.current_agent
# Reset if all agents are done
if self.env_done():
self._reset_next_step = True
reward = convert_np_type(self.reward_spec()[agent].dtype, 0)
observation = self._convert_observation(
agent, self._environment.observe(agent), done
)
else:
# observation for next agent
observe, reward, done, info = self._environment.last()
# Convert rewards to match spec
reward = convert_np_type(self.reward_spec()[agent].dtype, reward)
observation = self._convert_observation(agent, observe, done)
step_type = dm_env.StepType.LAST if done else dm_env.StepType.MID
return dm_env.TimeStep(
observation=observation,
reward=reward,
discount=self._discount,
step_type=step_type,
)
def env_done(self) -> bool:
return not self.agents
def agent_iter(self, max_iter: int = 2 ** 63) -> Iterator:
return self._environment.agent_iter(max_iter)
# Convert PettingZoo observation so it's dm_env compatible. Also, the list
# of legal actions must be converted to a legal actions mask.
def _convert_observation( # type: ignore[override]
self, agent: str, observe: Union[dict, np.ndarray], done: bool
) -> types.OLT:
legals: np.ndarray = None
observation: np.ndarray = None
if isinstance(observe, dict) and "action_mask" in observe:
legals = observe["action_mask"]
observation = observe["observation"]
else:
legals = np.ones(
_convert_to_spec(self._environment.action_spaces[agent]).shape,
dtype=self._environment.action_spaces[agent].dtype,
)
observation = observe
if observation.dtype == np.int8:
observation = np.dtype(np.float32).type(
observation
) # observation is not expected to be int8
if legals.dtype == np.int8:
legals = np.dtype(np.int64).type(legals)
observation = types.OLT(
observation=observation,
legal_actions=legals,
terminal=np.asarray([done], dtype=np.float32),
)
return observation
def correct_agent_name(self) -> None:
self._environment.reset()
if "tictactoe" in self._environment.metadata["name"]:
corrected_names = ["player_0", "player_1"]
self._environment.unwrapped.possible_agents = corrected_names
self._environment.unwrapped.agents = corrected_names
self._environment.possible_agents = corrected_names
self._environment.agents = corrected_names
previous_names = list(self.observation_spaces.keys())
for corrected_name, prev_name in zip(corrected_names, previous_names):
self.observation_spaces[corrected_name] = self.observation_spaces[
prev_name
]
self.action_spaces[corrected_name] = self.action_spaces[prev_name]
self.rewards[corrected_name] = self.rewards[prev_name]
self.dones[corrected_name] = self.dones[prev_name]
self.infos[corrected_name] = self.infos[prev_name]
del self.observation_spaces[prev_name]
del self.action_spaces[prev_name]
del self.rewards[prev_name]
del self.dones[prev_name]
del self.infos[prev_name]
def observation_spec(self) -> types.Observation:
observation_specs = {}
for agent in self._environment.possible_agents:
if isinstance(self._environment.observation_spaces[agent], gym.spaces.Dict):
obs_space = copy.deepcopy(
self._environment.observation_spaces[agent]["observation"]
)
legal_actions_space = copy.deepcopy(
self._environment.observation_spaces[agent]["action_mask"]
)
else:
obs_space = copy.deepcopy(self._environment.observation_spaces[agent])
legal_actions_space = copy.deepcopy(
self._environment.action_spaces[agent]
)
if obs_space.dtype == np.int8:
obs_space.dtype = np.dtype(np.float32)
if legal_actions_space.dtype == np.int8:
legal_actions_space.dtype = np.dtype(np.int64)
observation_specs[agent] = types.OLT(
observation=_convert_to_spec(obs_space),
legal_actions=_convert_to_spec(legal_actions_space),
terminal=specs.Array((1,), np.float32),
)
return observation_specs
def action_spec(self) -> Dict[str, specs.DiscreteArray]:
action_specs = {}
for agent in self.possible_agents:
action_specs[agent] = _convert_to_spec(
self._environment.action_spaces[agent]
)
return action_specs
def reward_spec(self) -> Dict[str, specs.Array]:
reward_specs = {}
for agent in self.possible_agents:
reward_specs[agent] = specs.Array((), np.float32)
return reward_specs
def discount_spec(self) -> Dict[str, specs.BoundedArray]:
discount_specs = {}
for agent in self.possible_agents:
discount_specs[agent] = specs.BoundedArray(
(), np.float32, minimum=0, maximum=1.0
)
return discount_specs
def extra_spec(self) -> Dict[str, specs.BoundedArray]:
return {}
@property
def agents(self) -> List:
return self._environment.agents
@property
def possible_agents(self) -> List:
return self._environment.possible_agents
@property
def environment(self) -> AECEnv:
"""Returns the wrapped environment."""
return self._environment
@property
def current_agent(self) -> Any:
return self._environment.agent_selection
@property
def num_agents(self) -> int:
return self._environment.num_agents
def __getattr__(self, name: str) -> Any:
"""Expose any other attributes of the underlying environment."""
if hasattr(self.__class__, name):
return self.__getattribute__(name)
else:
return getattr(self._environment, name)
class PettingZooParallelEnvWrapper(ParallelEnvWrapper):
"""Environment wrapper for PettingZoo MARL environments."""
def __init__(
self,
environment: ParallelEnv,
env_preprocess_wrappers: Optional[List] = [
# (env_preprocessor, dict_with_preprocessor_params)
(black_death_v1, None),
],
):
self._environment = environment
self._reset_next_step = True
if env_preprocess_wrappers:
self._environment = apply_env_wrapper_preprocessers(
self._environment, env_preprocess_wrappers
)
def reset(self) -> dm_env.TimeStep:
"""Resets the episode."""
self._reset_next_step = False
self._step_type = dm_env.StepType.FIRST
discount_spec = self.discount_spec()
observe = self._environment.reset()
self._discounts = {
agent: convert_np_type(discount_spec[agent].dtype, 1)
for agent in self.possible_agents
}
if type(observe) == tuple:
observe, env_extras = observe
else:
env_extras = {}
observations = self._convert_observations(
observe, {agent: False for agent in self.possible_agents}
)
rewards_spec = self.reward_spec()
rewards = {
agent: convert_np_type(rewards_spec[agent].dtype, 0)
for agent in self.possible_agents
}
return parameterized_restart(rewards, self._discounts, observations), env_extras
def step(self, actions: Dict[str, np.ndarray]) -> dm_env.TimeStep:
"""Steps the environment."""
if self._reset_next_step:
return self.reset()
observations, rewards, dones, infos = self._environment.step(actions)
rewards_spec = self.reward_spec()
# Handle empty rewards
if not rewards:
rewards = {
agent: convert_np_type(rewards_spec[agent].dtype, 0)
for agent in self.possible_agents
}
else:
rewards = {
agent: convert_np_type(rewards_spec[agent].dtype, reward)
for agent, reward in rewards.items()
}
if observations:
observations = self._convert_observations(observations, dones)
if self.env_done():
self._step_type = dm_env.StepType.LAST
self._reset_next_step = True
else:
self._step_type = dm_env.StepType.MID
return dm_env.TimeStep(
observation=observations,
reward=rewards,
discount=self._discounts,
step_type=self._step_type,
)
def env_done(self) -> bool:
return not self.agents
# Convert PettingZoo observation so it's dm_env compatible. Also, the list
# of legal actions must be converted to a legal actions mask.
def _convert_observations(
self, observes: Dict[str, np.ndarray], dones: Dict[str, bool]
) -> types.Observation:
observations: Dict[str, types.OLT] = {}
for agent, observation in observes.items():
if isinstance(observation, dict) and "action_mask" in observation:
legals = observation["action_mask"]
observation = observation["observation"]
else:
# TODO Handle legal actions better for continous envs,
# maybe have min and max for each action and clip the agents actions
# accordingly
legals = np.ones(
_convert_to_spec(self._environment.action_spaces[agent]).shape,
dtype=self._environment.action_spaces[agent].dtype,
)
observations[agent] = types.OLT(
observation=observation,
legal_actions=legals,
terminal=np.asarray([dones[agent]], dtype=np.float32),
)
return observations
def observation_spec(self) -> types.Observation:
observation_specs = {}
for agent in self.possible_agents:
observation_specs[agent] = types.OLT(
observation=_convert_to_spec(
self._environment.observation_spaces[agent]
),
legal_actions=_convert_to_spec(self._environment.action_spaces[agent]),
terminal=specs.Array((1,), np.float32),
)
return observation_specs
def action_spec(self) -> Dict[str, Union[specs.DiscreteArray, specs.BoundedArray]]:
action_specs = {}
action_spaces = self._environment.action_spaces
for agent in self.possible_agents:
action_specs[agent] = _convert_to_spec(action_spaces[agent])
return action_specs
def reward_spec(self) -> Dict[str, specs.Array]:
reward_specs = {}
for agent in self.possible_agents:
reward_specs[agent] = specs.Array((), np.float32)
return reward_specs
def discount_spec(self) -> Dict[str, specs.BoundedArray]:
discount_specs = {}
for agent in self.possible_agents:
discount_specs[agent] = specs.BoundedArray(
(), np.float32, minimum=0, maximum=1.0
)
return discount_specs
def extra_spec(self) -> Dict[str, specs.BoundedArray]:
return {}
@property
def agents(self) -> List:
return self._environment.agents
@property
def possible_agents(self) -> List:
return self._environment.possible_agents
@property
def environment(self) -> ParallelEnv:
"""Returns the wrapped environment."""
return self._environment
@property
def current_agent(self) -> Any:
return self._environment.agent_selection
def __getattr__(self, name: str) -> Any:
"""Expose any other attributes of the underlying environment."""
if hasattr(self.__class__, name):
return self.__getattribute__(name)
else:
return getattr(self._environment, name)
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(7453, 7465), True, 'import numpy as np\n'), ((7563, 7581), 'numpy.dtype', 'np.dtype', (['np.int64'], {}), '(np.int64)\n', (7571, 7581), True, 'import numpy as np\n'), ((11446, 11491), 'mava.utils.wrapper_utils.convert_np_type', 'convert_np_type', (['rewards_spec[agent].dtype', '(0)'], {}), '(rewards_spec[agent].dtype, 0)\n', (11461, 11491), False, 'from mava.utils.wrapper_utils import apply_env_wrapper_preprocessers, convert_np_type, parameterized_restart\n'), ((11617, 11667), 'mava.utils.wrapper_utils.convert_np_type', 'convert_np_type', (['rewards_spec[agent].dtype', 'reward'], {}), '(rewards_spec[agent].dtype, reward)\n', (11632, 11667), False, 'from mava.utils.wrapper_utils import apply_env_wrapper_preprocessers, convert_np_type, parameterized_restart\n'), ((4710, 4766), 'acme.wrappers.gym_wrapper._convert_to_spec', '_convert_to_spec', (['self._environment.action_spaces[agent]'], {}), '(self._environment.action_spaces[agent])\n', (4726, 4766), False, 'from acme.wrappers.gym_wrapper import _convert_to_spec\n'), ((4957, 4977), 'numpy.dtype', 'np.dtype', (['np.float32'], {}), '(np.float32)\n', (4965, 4977), True, 'import numpy as np\n'), ((5125, 5143), 'numpy.dtype', 'np.dtype', (['np.int64'], {}), '(np.int64)\n', (5133, 5143), True, 'import numpy as np\n'), ((7660, 7687), 'acme.wrappers.gym_wrapper._convert_to_spec', '_convert_to_spec', (['obs_space'], {}), '(obs_space)\n', (7676, 7687), False, 'from acme.wrappers.gym_wrapper import _convert_to_spec\n'), ((7719, 7756), 'acme.wrappers.gym_wrapper._convert_to_spec', '_convert_to_spec', (['legal_actions_space'], {}), '(legal_actions_space)\n', (7735, 7756), False, 'from acme.wrappers.gym_wrapper import _convert_to_spec\n'), ((7783, 7812), 'acme.specs.Array', 'specs.Array', (['(1,)', 'np.float32'], {}), '((1,), np.float32)\n', (7794, 7812), False, 'from acme import specs\n'), ((13398, 13442), 'numpy.asarray', 'np.asarray', (['[dones[agent]]'], {'dtype': 'np.float32'}), '([dones[agent]], dtype=np.float32)\n', (13408, 13442), True, 'import numpy as np\n'), ((13693, 13754), 'acme.wrappers.gym_wrapper._convert_to_spec', '_convert_to_spec', (['self._environment.observation_spaces[agent]'], {}), '(self._environment.observation_spaces[agent])\n', (13709, 13754), False, 'from acme.wrappers.gym_wrapper import _convert_to_spec\n'), ((13824, 13880), 'acme.wrappers.gym_wrapper._convert_to_spec', '_convert_to_spec', (['self._environment.action_spaces[agent]'], {}), '(self._environment.action_spaces[agent])\n', (13840, 13880), False, 'from acme.wrappers.gym_wrapper import _convert_to_spec\n'), ((13907, 13936), 'acme.specs.Array', 'specs.Array', (['(1,)', 'np.float32'], {}), '((1,), np.float32)\n', (13918, 13936), False, 'from acme import specs\n'), ((13094, 13150), 'acme.wrappers.gym_wrapper._convert_to_spec', '_convert_to_spec', (['self._environment.action_spaces[agent]'], {}), '(self._environment.action_spaces[agent])\n', (13110, 13150), False, 'from acme.wrappers.gym_wrapper import _convert_to_spec\n')] |
import os
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits import mplot3d
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import IncrementalPCA
## set paths
inputpath = './input/'
## Toggle PCA for better visualization of clusters
pca_flag = int(input("\nPerform PCA for cluster visualization?:" \
+ " press 1 if YES, and 0 if NO \n"))
# number of PCA components
N = 3
#import data set
print("\n\n")
print("\nLoading data from " + os.path.dirname(os.path.realpath(__file__)) +
inputpath[1:])
print("\nPlease be patient...this can take a while for large files...")
print("\n\n")
with open(inputpath + 'X.txt') as file:
X = np.array([[float(digit) for digit in line.split()] for line in file])
# import clustering labels but check to see if files exist first
if not os.path.exists(inputpath + 'kx.txt'):
kx = np.ones((np.shape(X)[0],))
else:
kx = np.genfromtxt(inputpath + 'kx.txt')
if not os.path.exists(inputpath + 'cx.txt'):
cx = np.ones((len(kx),))
else:
cx = np.genfromtxt(inputpath + 'cx.txt')
# create index for plotting
indx = np.vstack((kx.astype(int), cx.astype(int)))
# get number of clustering instances
K = len(np.unique(indx[0,:]))
if pca_flag:
# batch size for incremental PCA
batchsz = 10
# perform PCA for visualization of clusters
pca = IncrementalPCA(n_components = N, batch_size = batchsz)
X = pca.fit_transform(X)
# main loop
notdone = True
while notdone:
instance = input("\nWhat is the clustering instance you wish to plot? ")
instance = int(instance)
print('\nProcessing...\n')
# project onto 3D axes
plt.figure(figsize=(10,8))
ax = plt.axes(projection='3d')
title = "Cluster plot: instance %d" % instance
kindx = np.asarray(np.where(indx[0,:] == instance))
cindx = np.unique(indx[1,kindx])
#for i, target_name in zip(range(Nc), iris.target_names):
for i in cindx:
ax.scatter3D(X[kindx[indx[1,kindx] == i], 0],
X[kindx[indx[1,kindx] == i], 1],
X[kindx[indx[1,kindx] == i], 2],
label = str(i), s = 4)
#print(np.std(X[kindx[indx[1,kindx] == i],:], axis = 0))
plt.title(title + " of %d" % K)
#if display_clusternum:
# ax.text2D(1, 1, r'y ='+str(y[instance-1]), fontsize=10, transform=ax.transAxes)
plt.legend(loc="upper left", shadow=False, scatterpoints=1)
plt.show()
getuserinput = input("Want to continue?: press 1 if YES," \
+ " and 0 to EXIT \n\n")
if(eval(getuserinput) == 0):
notdone = False
print('\nExiting...\n\n')
| [
"os.path.exists",
"numpy.shape",
"numpy.unique",
"numpy.where",
"os.path.realpath",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.axes",
"matplotlib.pyplot.title",
"sklearn.decomposition.IncrementalPCA",
"numpy.genfromtxt",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.show"
] | [((935, 971), 'os.path.exists', 'os.path.exists', (["(inputpath + 'kx.txt')"], {}), "(inputpath + 'kx.txt')\n", (949, 971), False, 'import os\n'), ((1027, 1062), 'numpy.genfromtxt', 'np.genfromtxt', (["(inputpath + 'kx.txt')"], {}), "(inputpath + 'kx.txt')\n", (1040, 1062), True, 'import numpy as np\n'), ((1071, 1107), 'os.path.exists', 'os.path.exists', (["(inputpath + 'cx.txt')"], {}), "(inputpath + 'cx.txt')\n", (1085, 1107), False, 'import os\n'), ((1156, 1191), 'numpy.genfromtxt', 'np.genfromtxt', (["(inputpath + 'cx.txt')"], {}), "(inputpath + 'cx.txt')\n", (1169, 1191), True, 'import numpy as np\n'), ((1324, 1345), 'numpy.unique', 'np.unique', (['indx[0, :]'], {}), '(indx[0, :])\n', (1333, 1345), True, 'import numpy as np\n'), ((1478, 1528), 'sklearn.decomposition.IncrementalPCA', 'IncrementalPCA', ([], {'n_components': 'N', 'batch_size': 'batchsz'}), '(n_components=N, batch_size=batchsz)\n', (1492, 1528), False, 'from sklearn.decomposition import IncrementalPCA\n'), ((1783, 1810), 'matplotlib.pyplot.figure', 'plt.figure', ([], {'figsize': '(10, 8)'}), '(figsize=(10, 8))\n', (1793, 1810), True, 'from matplotlib import pyplot as plt\n'), ((1820, 1845), 'matplotlib.pyplot.axes', 'plt.axes', ([], {'projection': '"""3d"""'}), "(projection='3d')\n", (1828, 1845), True, 'from matplotlib import pyplot as plt\n'), ((1968, 1993), 'numpy.unique', 'np.unique', (['indx[1, kindx]'], {}), '(indx[1, kindx])\n', (1977, 1993), True, 'import numpy as np\n'), ((2379, 2410), 'matplotlib.pyplot.title', 'plt.title', (["(title + ' of %d' % K)"], {}), "(title + ' of %d' % K)\n", (2388, 2410), True, 'from matplotlib import pyplot as plt\n'), ((2535, 2594), 'matplotlib.pyplot.legend', 'plt.legend', ([], {'loc': '"""upper left"""', 'shadow': '(False)', 'scatterpoints': '(1)'}), "(loc='upper left', shadow=False, scatterpoints=1)\n", (2545, 2594), True, 'from matplotlib import pyplot as plt\n'), ((2600, 2610), 'matplotlib.pyplot.show', 'plt.show', ([], {}), '()\n', (2608, 2610), True, 'from matplotlib import pyplot as plt\n'), ((1922, 1954), 'numpy.where', 'np.where', (['(indx[0, :] == instance)'], {}), '(indx[0, :] == instance)\n', (1930, 1954), True, 'import numpy as np\n'), ((541, 567), 'os.path.realpath', 'os.path.realpath', (['__file__'], {}), '(__file__)\n', (557, 567), False, 'import os\n'), ((992, 1003), 'numpy.shape', 'np.shape', (['X'], {}), '(X)\n', (1000, 1003), True, 'import numpy as np\n')] |
import unittest
from database import Database
from search_database import SearchDatabase
class SearchDatabaseTestCase(unittest.TestCase):
def test_update(self):
database = Database('purplePolitics', 'events')
events = database.get_events(False)
search_database = SearchDatabase()
search_database.clear()
search_database.update(events)
if __name__ == '__main__':
unittest.main()
| [
"unittest.main",
"search_database.SearchDatabase",
"database.Database"
] | [((415, 430), 'unittest.main', 'unittest.main', ([], {}), '()\n', (428, 430), False, 'import unittest\n'), ((187, 223), 'database.Database', 'Database', (['"""purplePolitics"""', '"""events"""'], {}), "('purplePolitics', 'events')\n", (195, 223), False, 'from database import Database\n'), ((294, 310), 'search_database.SearchDatabase', 'SearchDatabase', ([], {}), '()\n', (308, 310), False, 'from search_database import SearchDatabase\n')] |
from sys import stdin
def main_s():
print("---------------------------------------------------------------------")
print("Bienvenido al simulador de Pac-Man")
print("OPCIONES:")
print("1. Aleatorio")
print("2. Manual")
val = str(stdin.readline().strip())
print("---------------------------------------------------------------------")
if val == "2":
from Español.No_aleatorio import no_aleatorio
no_aleatorio()
elif val == "1":
from Español.aleatorio import aleatorio
aleatorio()
main_s()
| [
"sys.stdin.readline",
"Español.No_aleatorio.no_aleatorio",
"Español.aleatorio.aleatorio"
] | [((445, 459), 'Español.No_aleatorio.no_aleatorio', 'no_aleatorio', ([], {}), '()\n', (457, 459), False, 'from Español.No_aleatorio import no_aleatorio\n'), ((537, 548), 'Español.aleatorio.aleatorio', 'aleatorio', ([], {}), '()\n', (546, 548), False, 'from Español.aleatorio import aleatorio\n'), ((255, 271), 'sys.stdin.readline', 'stdin.readline', ([], {}), '()\n', (269, 271), False, 'from sys import stdin\n')] |
import discord
from bs4 import BeautifulSoup
import asyncio
import datetime
from io import BytesIO
class Archiver:
def __init__(self, bot):
self.bot = bot
async def on_message(self, message):
await self.archive_message(message)
async def on_message_edit(self, before, after):
await self.archive_message(after, True, before)
async def archive_message(self, message, edit: bool = False, before=None):
# if message not in whitelisted server
if message.guild not in (self.bot.whitelisted_servers if self.bot.whitelisted_servers else []): # (or bot still booting up)
return
# if the message is in an archive channel, ignore
if(await self.is_in_archive_channel(message)):
return
# sending an message with an embed somehow triggers on_message_edit - this should prevent double messages
if(len(message.embeds) > 0 and edit):
return
# generate archive channel name
archive_channel_name = f"ar-{message.channel.name}"
# if generated channel doesn't exist in either server, default to ar-other
if(discord.utils.get(self.bot.backup_server.channels, name=archive_channel_name) == None or discord.utils.get(self.bot.main_server.channels, name=archive_channel_name) == None):
archive_channel_name = "ar-other"
# get channels for both main and backup servers
mainserver_archive_channel = discord.utils.get(self.bot.main_server.channels, name=archive_channel_name)
backupserver_archive_channel = discord.utils.get(self.bot.backup_server.channels, name=archive_channel_name)
text = None
# if the message has an embed anyway (likely bot), send that instead
if(len(message.embeds) > 0):
archive_embed = message.embeds[0]
text = f"{message.author.name}#{message.author.discriminator} sent the following embed at {self.format_time(message.created_at)}:"
# else send the message in an embed
else:
if(len(message.clean_content) > 0):
archive_embed = await self.generate_archive_embed(message, edit, before)
# if message is empty (likely attachment only), send no embed
else:
archive_embed = None
# if message text is empty, add extra message
if(len(message.attachments) > 0):
if(len(message.clean_content) == 0):
text = f"{message.author.name}#{message.author.discriminator} sent the following attachment(s) at {self.format_time(message.created_at)}:"
else:
text = "\n---\nAttachments:"
# send messages to both servers
await mainserver_archive_channel.send(content=text, embed=archive_embed, files=(await self.filify(message.attachments) if message.attachments else None))
await backupserver_archive_channel.send(content=text, embed=archive_embed, files=(await self.filify(message.attachments) if message.attachments else None))
# checks if a message is sent in an archive channel
async def is_in_archive_channel(self, message):
if(message.channel.name.startswith("ar-")):
return True
return False
async def generate_archive_embed(self, message, edit: bool = False, before = None):
embed = discord.Embed()
embed.set_author(name=f"{message.author.name}#{message.author.discriminator}", icon_url=message.author.avatar_url)
footer_text = f"#{message.channel.name} | Sent: {self.format_time(message.created_at)}"
if(edit):
embed.title = "Edited message"
embed.add_field(
name="Before",
value=before.clean_content
)
embed.add_field(
name="After",
value=message.clean_content
)
footer_text += f" | Edited: {self.format_time(message.edited_at)}"
else:
embed.add_field(
name="Message",
value=message.clean_content
)
if(len(message.attachments) > 0):
embed.add_field(
name="Attachments included",
value="See above"
)
embed.set_footer(text=footer_text)
return embed
def format_time(self, time):
return time.strftime("%Y-%m-%d %H:%M:%S")
@staticmethod
async def filify(attachments):
"""Converts attachments of an instance of `discord.Message` to a list of instances of `discord.File`."""
ret = []
for file in attachments:
byte = BytesIO()
await file.save(byte)
byte.seek(0)
ret.append(discord.File(byte, filename=file.filename))
return ret
def setup(bot):
bot.add_cog(Archiver(bot))
| [
"discord.utils.get",
"discord.Embed",
"io.BytesIO",
"discord.File"
] | [((1493, 1568), 'discord.utils.get', 'discord.utils.get', (['self.bot.main_server.channels'], {'name': 'archive_channel_name'}), '(self.bot.main_server.channels, name=archive_channel_name)\n', (1510, 1568), False, 'import discord\n'), ((1608, 1685), 'discord.utils.get', 'discord.utils.get', (['self.bot.backup_server.channels'], {'name': 'archive_channel_name'}), '(self.bot.backup_server.channels, name=archive_channel_name)\n', (1625, 1685), False, 'import discord\n'), ((3386, 3401), 'discord.Embed', 'discord.Embed', ([], {}), '()\n', (3399, 3401), False, 'import discord\n'), ((4699, 4708), 'io.BytesIO', 'BytesIO', ([], {}), '()\n', (4706, 4708), False, 'from io import BytesIO\n'), ((1178, 1255), 'discord.utils.get', 'discord.utils.get', (['self.bot.backup_server.channels'], {'name': 'archive_channel_name'}), '(self.bot.backup_server.channels, name=archive_channel_name)\n', (1195, 1255), False, 'import discord\n'), ((1267, 1342), 'discord.utils.get', 'discord.utils.get', (['self.bot.main_server.channels'], {'name': 'archive_channel_name'}), '(self.bot.main_server.channels, name=archive_channel_name)\n', (1284, 1342), False, 'import discord\n'), ((4791, 4833), 'discord.File', 'discord.File', (['byte'], {'filename': 'file.filename'}), '(byte, filename=file.filename)\n', (4803, 4833), False, 'import discord\n')] |
# Copyright (c) 2012-2018 SoftBank Robotics. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the COPYING file.
from qisrc.test.conftest import git_server, qisrc_action # pylint: disable=unused-import
# pylint: disable=redefined-outer-name
def test_info(qibuild_action, qisrc_action, git_server, record_messages):
git_server.create_repo("foo.git")
qisrc_action("init", git_server.manifest_url)
foo_project = qibuild_action.create_project("foo")
record_messages.reset()
qibuild_action("info", "foo")
assert record_messages.find("src: foo")
assert record_messages.find("repo: foo.git")
qibuild_action.chdir(foo_project.path)
record_messages.reset()
qibuild_action("info")
assert record_messages.find("Build project: foo")
| [
"qisrc.test.conftest.qisrc_action",
"qisrc.test.conftest.git_server.create_repo"
] | [((379, 412), 'qisrc.test.conftest.git_server.create_repo', 'git_server.create_repo', (['"""foo.git"""'], {}), "('foo.git')\n", (401, 412), False, 'from qisrc.test.conftest import git_server, qisrc_action\n'), ((417, 462), 'qisrc.test.conftest.qisrc_action', 'qisrc_action', (['"""init"""', 'git_server.manifest_url'], {}), "('init', git_server.manifest_url)\n", (429, 462), False, 'from qisrc.test.conftest import git_server, qisrc_action\n')] |
import sqlite3
mem_db = sqlite3.connect(':memory:')
db = sqlite3.connect('db/mydb')
cr = db.cursor()
"""
Syntax or Purpose
------ --------- ---------------------- ------------
SELECT DISTINCT Exclude duplicate records for fields selected.
SELECT DISTINCT CASE WHEN expression Conditional expression
SELECT DISTINCT THEN expression
SELECT DISTINCT ELSE expression END
SELECT FROM table_name Multiple table names are separated by commas.
SELECT WHERE Row level filtering
SELECT WHERE expression AND expression
SELECT WHERE expression OR expression
SELECT WHERE IN Comma delimited list enclosed in parenthesis
SELECT WHERE NOT IN Comma delimited list enclosed in parenthesis
SELECT WHERE BETWEEN Select records within the specified numeric range
SELECT WHERE NOT BETWEEN Select records outside of the specified numeric range
SELECT WHERE LIKE String with wildcard (%) enclosed in parenthesis
SELECT WHERE NOT LIKE String with wildcard (%) enclosed in parenthesis
SELECT GROUP BY
SELECT HAVING ORDER BY Sorting of the output using a comma delimited list of column names
SELECT HAVING LIMIT Limit the number of rows returned
"""
db.close() | [
"sqlite3.connect"
] | [((25, 52), 'sqlite3.connect', 'sqlite3.connect', (['""":memory:"""'], {}), "(':memory:')\n", (40, 52), False, 'import sqlite3\n'), ((58, 84), 'sqlite3.connect', 'sqlite3.connect', (['"""db/mydb"""'], {}), "('db/mydb')\n", (73, 84), False, 'import sqlite3\n')] |
#!/usr/bin/python
# ref: https://gist.github.com/bruienne/f81ea88253629abaf5f9
import objc
import plistlib
class attrdict(dict):
__getattr__ = dict.__getitem__
__setattr__ = dict.__setitem__
ServerInformation = attrdict()
ServerInformation_bundle = objc.loadBundle('ServerInformation', ServerInformation, \
bundle_path='/System/Library/PrivateFrameworks/ServerInformation.framework')
platformsupport = plistlib.readPlist('/System/Library/CoreServices/PlatformSupport.plist')
#
disabledsystems = platformsupport.get('SupportedBoardIds')
#
print('------------------------------------------------------------\n%i Board IDs in list\n------------------------------------------------------------\n' % len(disabledsystems))
unmatchedboardids = []
for system in disabledsystems:
for modelid in ServerInformation.ServerInformationComputerModelInfo.modelPropertiesForBoardIDs_([system]):
if system not in modelid:
print('Board ID: %s = System ID: %s' % (system, modelid))
else:
unmatchedboardids.append(system)
if len(unmatchedboardids) > 0:
print('------------------------------------------------------------')
for boardid in unmatchedboardids:
print('-- No match for Board ID %s --' % boardid)
print('------------------------------------------------------------\n')
| [
"plistlib.readPlist",
"objc.loadBundle"
] | [((260, 397), 'objc.loadBundle', 'objc.loadBundle', (['"""ServerInformation"""', 'ServerInformation'], {'bundle_path': '"""/System/Library/PrivateFrameworks/ServerInformation.framework"""'}), "('ServerInformation', ServerInformation, bundle_path=\n '/System/Library/PrivateFrameworks/ServerInformation.framework')\n", (275, 397), False, 'import objc\n'), ((418, 490), 'plistlib.readPlist', 'plistlib.readPlist', (['"""/System/Library/CoreServices/PlatformSupport.plist"""'], {}), "('/System/Library/CoreServices/PlatformSupport.plist')\n", (436, 490), False, 'import plistlib\n')] |
import numpy as np
import matplotlib.pyplot as plt
import pickle
import pandas as pd
import cv2 as cv
import os
def read_pkl(file_path):
obj = pd.read_pickle(file_path)
return obj
def show_results(obj , folder):
for im_num in obj.keys():
data = obj[im_num]
file = data['fileName'][12:]
file_path = os.path.join(folder, file)
im = cv.imread(file_path)#[:,:,::-1]
detections = data['detections']
for detection in detections:
type = detection[0]
roi = detection[2]
roi_tl = [roi[0] - roi[2]/2, roi[1] - roi[3]/2, roi[2], roi[3]]
roi_ = np.asarray(roi_tl).astype(int)
im = cv.rectangle(img=im, rec=roi_, color=(255, 0, 0), thickness=3)
plt.imshow(im[:, :, ::-1])
plt.pause(0.0001)
a=1
def track_objects(obj , folder):
for im_num in obj.keys():
data = obj[im_num]
file = data['fileName'][12:]
file_path = os.path.join(folder, file)
im = cv.imread(file_path)#[:,:,::-1]
detections = data['detections']
for detection in detections:
# type = detection[0]
roi = detection[2]
cen = [roi[0], roi[1]]
if cen[1] > 500 and cen[0] > 500 and cen[1] < 1300:
roi_tl = [roi[0] - roi[2] / 2, roi[1] - roi[3] / 2, roi[2], roi[3]]
roi_ = np.asarray(roi_tl).astype(int)
im = cv.rectangle(img=im, rec=roi_, color=(0, 0, 255), thickness=3)
continue
out_path = os.path.join(folder, 'warning', file)
cv.imwrite(out_path, im)
a=1
if __name__ == '__main__':
folder = r'E:\rafi\got_your_back\data\results_files\res\temp_dir - Copy (9)'
file_path = os.path.join(folder, r"YoloV3_res\res_pkl.pkl")
obj = read_pkl(file_path)
track_objects(obj, folder)
# show_results(obj, folder) | [
"pandas.read_pickle",
"cv2.imwrite",
"cv2.rectangle",
"matplotlib.pyplot.imshow",
"os.path.join",
"numpy.asarray",
"matplotlib.pyplot.pause",
"cv2.imread"
] | [((149, 174), 'pandas.read_pickle', 'pd.read_pickle', (['file_path'], {}), '(file_path)\n', (163, 174), True, 'import pandas as pd\n'), ((1781, 1828), 'os.path.join', 'os.path.join', (['folder', '"""YoloV3_res\\\\res_pkl.pkl"""'], {}), "(folder, 'YoloV3_res\\\\res_pkl.pkl')\n", (1793, 1828), False, 'import os\n'), ((337, 363), 'os.path.join', 'os.path.join', (['folder', 'file'], {}), '(folder, file)\n', (349, 363), False, 'import os\n'), ((377, 397), 'cv2.imread', 'cv.imread', (['file_path'], {}), '(file_path)\n', (386, 397), True, 'import cv2 as cv\n'), ((991, 1017), 'os.path.join', 'os.path.join', (['folder', 'file'], {}), '(folder, file)\n', (1003, 1017), False, 'import os\n'), ((1031, 1051), 'cv2.imread', 'cv.imread', (['file_path'], {}), '(file_path)\n', (1040, 1051), True, 'import cv2 as cv\n'), ((1573, 1610), 'os.path.join', 'os.path.join', (['folder', '"""warning"""', 'file'], {}), "(folder, 'warning', file)\n", (1585, 1610), False, 'import os\n'), ((1619, 1643), 'cv2.imwrite', 'cv.imwrite', (['out_path', 'im'], {}), '(out_path, im)\n', (1629, 1643), True, 'import cv2 as cv\n'), ((693, 755), 'cv2.rectangle', 'cv.rectangle', ([], {'img': 'im', 'rec': 'roi_', 'color': '(255, 0, 0)', 'thickness': '(3)'}), '(img=im, rec=roi_, color=(255, 0, 0), thickness=3)\n', (705, 755), True, 'import cv2 as cv\n'), ((770, 796), 'matplotlib.pyplot.imshow', 'plt.imshow', (['im[:, :, ::-1]'], {}), '(im[:, :, ::-1])\n', (780, 796), True, 'import matplotlib.pyplot as plt\n'), ((809, 826), 'matplotlib.pyplot.pause', 'plt.pause', (['(0.0001)'], {}), '(0.0001)\n', (818, 826), True, 'import matplotlib.pyplot as plt\n'), ((1464, 1526), 'cv2.rectangle', 'cv.rectangle', ([], {'img': 'im', 'rec': 'roi_', 'color': '(0, 0, 255)', 'thickness': '(3)'}), '(img=im, rec=roi_, color=(0, 0, 255), thickness=3)\n', (1476, 1526), True, 'import cv2 as cv\n'), ((644, 662), 'numpy.asarray', 'np.asarray', (['roi_tl'], {}), '(roi_tl)\n', (654, 662), True, 'import numpy as np\n'), ((1412, 1430), 'numpy.asarray', 'np.asarray', (['roi_tl'], {}), '(roi_tl)\n', (1422, 1430), True, 'import numpy as np\n')] |
from os import path
import sqlite3 as sql
ROOT = path.dirname(path.relpath(__file__))
dbname = 'database.sqlite'
def create_post(user, post, parent, location, item, tags, ats, created, status):
con = sql.connect(path.join(ROOT, dbname))
cur = con.cursor()
cur.execute(
"""insert into post
(user, post, parent, location, item, tags, ats, created, status)
values (?,?,?,?,?,?,?,?,?)
"""
, (user, post, parent, location, item, tags, ats, created, status)
)
con.commit()
con.close()
def get_posts():
con = sql.connect(path.join(ROOT, dbname))
cur = con.cursor()
posts = cur.execute('select * from post')
con.commit()
con.close()
return posts
def make_db():
schema = open(path.join(ROOT,'schema.sql')).read()
con = sql.connect(path.join(ROOT, dbname))
con.execute(schema)
con.commit()
con.close()
| [
"os.path.join",
"os.path.relpath"
] | [((63, 85), 'os.path.relpath', 'path.relpath', (['__file__'], {}), '(__file__)\n', (75, 85), False, 'from os import path\n'), ((218, 241), 'os.path.join', 'path.join', (['ROOT', 'dbname'], {}), '(ROOT, dbname)\n', (227, 241), False, 'from os import path\n'), ((605, 628), 'os.path.join', 'path.join', (['ROOT', 'dbname'], {}), '(ROOT, dbname)\n', (614, 628), False, 'from os import path\n'), ((843, 866), 'os.path.join', 'path.join', (['ROOT', 'dbname'], {}), '(ROOT, dbname)\n', (852, 866), False, 'from os import path\n'), ((784, 813), 'os.path.join', 'path.join', (['ROOT', '"""schema.sql"""'], {}), "(ROOT, 'schema.sql')\n", (793, 813), False, 'from os import path\n')] |