content stringlengths 1 1.05M | input_ids listlengths 1 883k | ratio_char_token float64 1 22.9 | token_count int64 1 883k |
|---|---|---|---|
'''
HDF-saving features
'''
import time
import tempfile
import random
import traceback
import numpy as np
import fnmatch
import os, sys
import subprocess
from riglib import calibrations, bmi
from riglib.bmi import extractor
from riglib.experiment import traits
import hdfwriter
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#
# Copyright (c) 2013-2018 Quarkslab.
# This file is part of IRMA project.
#
# 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 in the top-level directory
# of this distribution and at:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# No part of the project, including this file, may be copied,
# modified, propagated, or distributed except according to the
# terms contained in the LICENSE file.
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1... | 3.417722 | 158 |
import time
import os
import math
import numpy as np
from libs import utils
from libs.img_dataset import ImgDataset
from nets.crnn import CRNN
from nets.cnn.paper_cnn import PaperCNN
import shutil
def calculate_accuracy(predicts, labels):
"""
:param predicts: encoded predict result
:param labels: ground true label
:return: accuracy
"""
assert len(predicts) == len(labels)
correct_count = 0
for i, p_label in enumerate(predicts):
if p_label == labels[i]:
correct_count += 1
acc = correct_count / len(predicts)
return acc, correct_count
def calculate_edit_distance_mean(edit_distences):
"""
edit_distance == 0
:param edit_distences:
:return:
"""
data = np.array(edit_distences)
data = data[data != 0]
if len(data) == 0:
return 0
return np.mean(data)
def validation(sess, feeds, fetches, dataset, converter, result_dir, name,
step=None, print_batch_info=False, copy_failed=False):
"""
Save file name: {acc}_{step}.txt
:param sess: tensorflow session
:param model: crnn network
:param result_dir:
:param name: val, test, infer. used to create sub dir in result_dir
:return:
"""
sess.run(dataset.init_op)
img_paths = []
predicts = []
trimed_predicts = []
labels = []
trimed_labels = []
edit_distances = []
total_batch_time = 0
for batch in range(dataset.num_batches):
img_batch, widths, label_batch, batch_labels, batch_img_paths = dataset.get_next_batch(sess)
if len(batch_labels) == 0:
continue
batch_start_time = time.time()
feed = {feeds['inputs']: img_batch,
feeds['labels']: label_batch,
feeds['sequence_length']: PaperCNN.get_sequence_lengths(widths),
feeds['is_training']: False}
try:
batch_predicts, edit_distance, batch_edit_distances = sess.run(fetches, feed)
except Exception:
print(batch_labels)
continue
batch_predicts = [converter.decode(p, CRNN.CTC_INVALID_INDEX) for p in batch_predicts]
trimed_batch_predicts = [utils.remove_all_symbols(txt) for txt in batch_predicts]
trimed_batch_labels = [utils.remove_all_symbols(txt) for txt in batch_labels]
img_paths.extend(batch_img_paths)
predicts.extend(batch_predicts)
labels.extend(batch_labels)
trimed_predicts.extend(trimed_batch_predicts)
trimed_labels.extend(trimed_batch_labels)
edit_distances.extend(batch_edit_distances)
acc, correct_count = calculate_accuracy(batch_predicts, batch_labels)
trimed_acc, trimed_correct_count = calculate_accuracy(trimed_batch_predicts, trimed_batch_labels)
batch_time = time.time() - batch_start_time
total_batch_time += batch_time
if print_batch_info:
print("{:.03f}s [{}/{}] acc: {:.03f}({}/{}), edit_distance: {:.03f}, trim_acc {:.03f}({}/{})"
.format(batch_time, batch, dataset.num_batches,
acc, correct_count, dataset.batch_size,
edit_distance,
trimed_acc, trimed_correct_count, dataset.batch_size))
acc, correct_count = calculate_accuracy(predicts, labels)
trimed_acc, trimed_correct_count = calculate_accuracy(trimed_predicts, trimed_labels)
edit_distance_mean = calculate_edit_distance_mean(edit_distances)
total_edit_distance = sum(edit_distances)
acc_str = "Accuracy: {:.03f} ({}/{}), Trimed Accuracy: {:.03f} ({}/{})" \
"Total edit distance: {:.03f}, " \
"Average edit distance: {:.03f}, Average batch time: {:.03f}" \
.format(acc, correct_count, dataset.size,
trimed_acc, trimed_correct_count, dataset.size,
total_edit_distance, edit_distance_mean, total_batch_time / dataset.num_batches)
print(acc_str)
save_dir = os.path.join(result_dir, name)
utils.check_dir_exist(save_dir)
result_file_path = save_txt_result(save_dir, acc, step, labels, predicts, 'acc',
edit_distances, acc_str)
save_txt_result(save_dir, acc, step, labels, predicts, 'acc', edit_distances,
acc_str, only_failed=True)
save_txt_result(save_dir, trimed_acc, step, trimed_labels, trimed_predicts, 'tacc',
edit_distances)
save_txt_result(save_dir, trimed_acc, step, trimed_labels, trimed_predicts, 'tacc',
edit_distances, only_failed=True)
save_txt_4_analyze(save_dir, labels, predicts, 'acc', step)
save_txt_4_analyze(save_dir, trimed_labels, trimed_predicts, 'tacc', step)
# Copy image not all match to a dir
# TODO: we will only save failed imgs for acc
if copy_failed:
failed_infer_img_dir = result_file_path[:-4] + "_failed"
if os.path.exists(failed_infer_img_dir) and os.path.isdir(failed_infer_img_dir):
shutil.rmtree(failed_infer_img_dir)
utils.check_dir_exist(failed_infer_img_dir)
failed_image_indices = []
for i, val in enumerate(edit_distances):
if val != 0:
failed_image_indices.append(i)
for i in failed_image_indices:
img_path = img_paths[i]
img_name = img_path.split("/")[-1]
dst_path = os.path.join(failed_infer_img_dir, img_name)
shutil.copyfile(img_path, dst_path)
failed_infer_result_file_path = os.path.join(failed_infer_img_dir, "result.txt")
with open(failed_infer_result_file_path, 'w', encoding='utf-8') as f:
for i in failed_image_indices:
p_label = predicts[i]
t_label = labels[i]
f.write("{}\n".format(img_paths[i]))
f.write("input: {:17s} length: {}\n".format(t_label, len(t_label)))
f.write("predict: {:17s} length: {}\n".format(p_label, len(p_label)))
f.write("edit distance: {}\n".format(edit_distances[i]))
f.write('-' * 30 + '\n')
return acc, trimed_acc, edit_distance_mean, total_edit_distance, correct_count, trimed_correct_count
def save_txt_4_analyze(save_dir, labels, predicts, acc_type, step):
"""
txt
"""
txt_path = os.path.join(save_dir, '%d_%s_gt_and_pred.txt' % (step, acc_type))
with open(txt_path, 'w', encoding='utf-8') as f:
for i, p_label in enumerate(predicts):
t_label = labels[i]
f.write("{}__$__{}\n".format(t_label, p_label))
def save_txt_result(save_dir, acc, step, labels, predicts, acc_type,
edit_distances=None, acc_str=None, only_failed=False):
"""
:param acc_type: 'acc' or 'tacc'
:return:
"""
failed_suffix = ''
if only_failed:
failed_suffix = 'failed'
if step is not None:
txt_path = os.path.join(save_dir, '%d_%s_%.3f_%s.txt' % (step, acc_type, acc, failed_suffix))
else:
txt_path = os.path.join(save_dir, '%s_%.3f_%s.txt' % (acc_type, acc, failed_suffix))
print("Write result to %s" % txt_path)
with open(txt_path, 'w', encoding='utf-8') as f:
for i, p_label in enumerate(predicts):
t_label = labels[i]
all_match = (t_label == p_label)
if only_failed and all_match:
continue
# f.write("{}\n".format(img_paths[i]))
f.write("input: {:17s} length: {}\n".format(t_label, len(t_label)))
f.write("predict: {:17s} length: {}\n".format(p_label, len(p_label)))
f.write("all match: {}\n".format(1 if all_match else 0))
if edit_distances:
f.write("edit distance: {}\n".format(edit_distances[i]))
f.write('-' * 30 + '\n')
if acc_str:
f.write(acc_str + "\n")
return txt_path
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317... | 2.127882 | 3,730 |
#!/bin/python3
import math
import os
import random
import re
import sys
# Complete the solve function below.
if __name__ == '__main__':
meal_cost = float(input())
tip_percent = int(input())
tax_percent = int(input())
solve(meal_cost, tip_percent, tax_percent)
# Time complexity: O(1)
# Space complexity: O(1)
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19... | 2.503546 | 141 |
# -*- coding: utf-8 -*-
#
# Helper Script for Mass-Invitation of Participant Organisations
#
# RLPPTM Template Version 1.0
#
# Execute in web2py folder after code upgrade like:
# python web2py.py -S eden -M -R applications/eden/modules/templates/RLPPTM/tools/mis.py
#
import os
import sys
from core import s3_format_datetime
from templates.RLPPTM.config import SCHOOLS
from templates.RLPPTM.helpers import InviteUserOrg
# Batch limit (set to False to disable)
BATCH_LIMIT = 250
# Override auth (disables all permission checks)
auth.override = True
# Failed-flag
failed = False
# Info
log = None
# Load models for tables
otable = s3db.org_organisation
gtable = s3db.org_group
mtable = s3db.org_group_membership
utable = s3db.auth_user
oltable = s3db.org_organisation_user
pltable = s3db.pr_person_user
ctable = s3db.pr_contact
timestmp = s3_format_datetime(dtfmt="%Y%m%d%H%M%S")
LOGFILE = os.path.join(request.folder, "private", "mis_%s.log" % timestmp)
# -----------------------------------------------------------------------------
# Invite organisations
#
if not failed:
try:
with open(LOGFILE, "w", encoding="utf-8") as logfile:
log = logfile
join = [mtable.on((mtable.organisation_id == otable.id) & \
(mtable.deleted == False)),
gtable.on((gtable.id == mtable.group_id) & \
(gtable.name == SCHOOLS) & \
(gtable.deleted == False)),
]
query = (otable.deleted == False)
organisations = db(query).select(otable.id,
otable.pe_id,
otable.name,
join = join,
orderby = otable.id,
)
total = len(organisations)
infoln("Total: %s Organisations" % total)
infoln("")
skipped = sent = failures = 0
invite_org = InviteUserOrg.invite_account
for organisation in organisations:
info("%s..." % organisation.name)
# Get all accounts that are linked to this org
organisation_id = organisation.id
join = oltable.on((oltable.user_id == utable.id) & \
(oltable.deleted == False))
left = pltable.on((pltable.user_id == utable.id) & \
(pltable.deleted == False))
query = (oltable.organisation_id == organisation_id)
rows = db(query).select(utable.id,
utable.email,
utable.registration_key,
pltable.pe_id,
join = join,
left = left,
)
if rows:
# There are already accounts linked to this organisation
invited, registered = [], []
for row in rows:
username = row.auth_user.email
if row.pr_person_user.pe_id:
registered.append(username)
else:
invited.append(username)
if registered:
infoln("already registered (%s)." % ", ".join(registered))
else:
infoln("already invited (%s)." % ", ".join(invited))
skipped += 1
continue
# Find email address
query = (ctable.pe_id == organisation.pe_id) & \
(ctable.contact_method == "EMAIL") & \
(ctable.deleted == False)
contact = db(query).select(ctable.value,
orderby = ctable.priority,
limitby = (0, 1),
).first()
if contact:
email = contact.value
info("(%s)..." % email)
else:
infoln("no email address.")
skipped += 1
continue
error = invite_org(organisation, email, account=None)
if not error:
sent += 1
infoln("invited.")
db.commit()
else:
failures += 1
infoln("invitation failed (%s)." % error)
if BATCH_LIMIT and sent >= BATCH_LIMIT:
infoln("Batch limit (%s) reached" % BATCH_LIMIT)
skipped = total - (sent + failures)
break
infoln("")
infoln("%s invitations sent" % sent)
infoln("%s invitations failed" % failures)
infoln("%s organisations skipped" % skipped)
log = None
except IOError:
infoln("...failed (could not create logfile)")
failed = True
# -----------------------------------------------------------------------------
# Finishing up
#
if failed:
db.rollback()
infoln("PROCESS FAILED - Action rolled back.")
else:
db.commit()
infoln("PROCESS SUCCESSFUL.")
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839... | 1.816387 | 3,039 |
def save_form(form, actor=None):
"""Allows storing a form with a passed actor. Normally, Form.save() does not accept an actor, but if you require
this to be passed (is not handled by middleware), you can use this to replace form.save().
Requires you to use the audit.Model model as the actor is passed to the object's save method.
"""
obj = form.save(commit=False)
obj.save(actor=actor)
form.save_m2m()
return obj
#def intermediate_save(instance, actor=None):
# """Allows saving of an instance, without storing the changes, but keeping the history. This allows you to perform
# intermediate saves:
#
# obj.value1 = 1
# intermediate_save(obj)
# obj.value2 = 2
# obj.save()
# <value 1 and value 2 are both stored in the database>
# """
# if hasattr(instance, '_audit_changes'):
# tmp = instance._audit_changes
# if actor:
# instance.save(actor=actor)
# else:
# instance.save()
# instance._audit_changes = tmp
# else:
# if actor:
# instance.save(actor=actor)
# else:
# instance.save()
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... | 2.569161 | 441 |
# coding = utf-8
# Create date: 2018-11-05
# Author :Hailong
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import json
import logging
from django.core.management.base import BaseCommand
from django.db import transaction
from paprika_sync.core.models import PaprikaAccount
from paprika_sync.core.serializers import RecipeSerializer, CategorySerializer
from paprika_sync.core.utils import log_start_end
logger = logging.getLogger(__name__)
| [
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2... | 3.612903 | 93 |
#!/usr/bin/env python
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import mica.archive.asp_l1
mica.archive.asp_l1.main()
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from time import sleep
from proto9x.usb import usb
from proto9x.tls import tls
from proto9x.flash import read_flash
from proto9x.init_flash import init_flash
from proto9x.upload_fwext import upload_fwext
from proto9x.calibrate import calibrate
from proto9x.init_db import init_db
#usb.trace_enabled=True
#tls.trace_enabled=True
usb.open()
print('Initializing flash...')
init_flash()
restart()
print('Uploading firmware...')
upload_fwext()
restart()
print('Calibrating...')
calibrate()
print('Init database...')
init_db()
print('That\'s it, pairing\'s finished')
| [
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... | 2.893401 | 197 |
from dataclasses import dataclass, field
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628,
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import json
import os
from collections import OrderedDict
from copy import deepcopy
import SimpleITK as sitk
from batchgenerators.augmentations.utils import resize_segmentation # resize_softmax_output
from skimage.transform import resize
from torch.optim import lr_scheduler
from torch import nn
import numpy as np
import torch
from scipy.ndimage import binary_fill_holes
'''
This code is not intended to be looked at by anyone. It is messy. It is undocumented.
And the entire training pipeline is missing.
'''
max_num_filters_3d = 320
max_num_filters_2d = 480
join = os.path.join
def softmax_helper(x):
rpt = [1 for _ in range(len(x.size()))]
rpt[1] = x.size(1)
x_max = x.max(1, keepdim=True)[0].repeat(*rpt)
e_x = torch.exp(x - x_max)
return e_x / e_x.sum(1, keepdim=True).repeat(*rpt)
def soft_dice(net_output, gt, smooth=1., smooth_in_nom=1.):
axes = tuple(range(2, len(net_output.size())))
intersect = sum_tensor(net_output * gt, axes, keepdim=False)
denom = sum_tensor(net_output + gt, axes, keepdim=False)
result = (- ((2 * intersect + smooth_in_nom) / (denom + smooth))).mean()
return result
def sum_tensor(input, axes, keepdim=False):
axes = np.unique(axes)
if keepdim:
for ax in axes:
input = input.sum(ax, keepdim=True)
else:
for ax in sorted(axes, reverse=True):
input = input.sum(ax)
return input
def resize_softmax_output(softmax_output, new_shape, order=3):
'''
Resizes softmax output. Resizes each channel in c separately and fuses results back together
:param softmax_output: c x x x y x z
:param new_shape: x x y x z
:param order:
:return:
'''
tpe = softmax_output.dtype
new_shp = [softmax_output.shape[0]] + list(new_shape)
result = np.zeros(new_shp, dtype=softmax_output.dtype)
for i in range(softmax_output.shape[0]):
result[i] = resize(softmax_output[i].astype(float), new_shape, order, "constant", 0, True)
return result.astype(tpe)
def save_segmentation_nifti_softmax(softmax_output, dct, out_fname, order=3, region_class_order=None):
'''
segmentation must have the same spacing as the original nifti (for now). segmentation may have been cropped out
of the original image
:param segmentation:
:param dct:
:param out_fname:
:return:
'''
old_size = dct.get('size_before_cropping')
bbox = dct.get('brain_bbox')
if bbox is not None:
seg_old_size = np.zeros([softmax_output.shape[0]] + list(old_size))
for c in range(3):
bbox[c][1] = np.min((bbox[c][0] + softmax_output.shape[c+1], old_size[c]))
seg_old_size[:, bbox[0][0]:bbox[0][1],
bbox[1][0]:bbox[1][1],
bbox[2][0]:bbox[2][1]] = softmax_output
else:
seg_old_size = softmax_output
segmentation = resize_softmax_output(seg_old_size, np.array(dct['size'])[[2, 1, 0]], order=order)
if region_class_order is None:
segmentation = segmentation.argmax(0)
else:
seg_old_spacing_final = np.zeros(segmentation.shape[1:])
for i, c in enumerate(region_class_order):
seg_old_spacing_final[segmentation[i] > 0.5] = c
segmentation = seg_old_spacing_final
return segmentation.astype(np.uint8)
def subfiles(folder, join=True, prefix=None, suffix=None, sort=True):
if join:
l = os.path.join
else:
l = lambda x, y: y
res = [l(folder, i) for i in os.listdir(folder) if os.path.isfile(os.path.join(folder, i))
and (prefix is None or i.startswith(prefix))
and (suffix is None or i.endswith(suffix))]
if sort:
res.sort()
return res
def maybe_mkdir_p(directory):
splits = directory.split("/")[1:]
for i in range(0, len(splits)):
if not os.path.isdir(os.path.join("/", *splits[:i+1])):
os.mkdir(os.path.join("/", *splits[:i+1]))
def preprocess_image(itk_image, is_seg=False, spacing_target=(1, 0.5, 0.5), brain_mask=None, cval=0):
"""
brain mask must be a numpy array that has the same shape as itk_image's pixel array. This function is not ideal but
gets the job done
:param itk_image:
:param is_seg:
:param spacing_target:
:param brain_mask:
:return:
"""
spacing = np.array(itk_image.GetSpacing())[[2, 1, 0]]
image = sitk.GetArrayFromImage(itk_image).astype(float)
if not is_seg:
if brain_mask is None:
brain_mask = (image!=image[0,0,0]).astype(float)
if np.any([[i!=j] for i, j in zip(spacing, spacing_target)]):
image = resize_image(image, spacing, spacing_target, 3, cval).astype(np.float32)
brain_mask = resize_image(brain_mask.astype(float), spacing, spacing_target, order=0).astype(int)
image[brain_mask==0] = 0
#subtract mean, divide by std. use heuristic masking
image[brain_mask!=0] -= image[brain_mask!=0].mean()
image[brain_mask!=0] /= image[brain_mask!=0].std()
else:
new_shape = (int(np.round(spacing[0] / spacing_target[0] * float(image.shape[0]))),
int(np.round(spacing[1] / spacing_target[1] * float(image.shape[1]))),
int(np.round(spacing[2] / spacing_target[2] * float(image.shape[2]))))
image = resize_segmentation(image, new_shape, 1, cval)
return image
def create_brain_masks(data):
"""
data must be (b, c, x, y, z), brain mask is hole filled binary mask where all sequences are 0 (this is a heuristic
to recover a brain mask form brain extracted mri sequences, not an actual brain ectraction)
:param data:
:return:
"""
shp = list(data.shape)
brain_mask = np.zeros(shp, dtype=np.float32)
for b in range(data.shape[0]):
for c in range(data.shape[1]):
this_mask = data[b, c] != 0
this_mask = binary_fill_holes(this_mask)
brain_mask[b, c] = this_mask
return brain_mask
def segment(t1_file, t1ce_file, t2_file, flair_file, netLoc):
"""
Segments the passed files
"""
trainer = NetworkTrainerBraTS2018Baseline2RegionsCotrainingBraTSDecSDCE()
trainer.initialize(False)
all_data, dct = load_and_preprocess(t1_file, t1ce_file, t2_file, flair_file, None, None,
True, None)
all_softmax = []
for fold in range(5):
trainer.output_folder = join(netLoc, "%d" % fold)
trainer.load_best_checkpoint(False)
trainer.network.infer(True)
trainer.network.test_return_output = 0
softmax = trainer.predict_preprocessed_data_return_softmax(all_data[:4], True, 1, False, 1, (2, 3, 4), False,
None, None, trainer.patch_size, True)
all_softmax.append(softmax[None])
softmax_consolidated = np.vstack(all_softmax).mean(0)
output = save_segmentation_nifti_softmax(softmax_consolidated, dct,
"tumor_isen2018_class.nii.gz", 1,
trainer.regions_class_order)
return output
| [
11748,
33918,
198,
11748,
28686,
198,
6738,
17268,
1330,
14230,
1068,
35,
713,
198,
6738,
4866,
1330,
2769,
30073,
198,
11748,
17427,
2043,
42,
355,
1650,
74,
198,
6738,
15458,
8612,
2024,
13,
559,
5154,
602,
13,
26791,
1330,
47558,
62,... | 2.230265 | 3,205 |
import os
basepath = '/home/archit/scratch/cartpoles/data/hyperparam/cartpole/offline_learning/esarsa-adam/'
dirs = os.listdir(basepath)
string = ''''''
for dir in dirs:
print(dir)
subbasepath = basepath + dir + '/'
subdirs = os.listdir(subbasepath)
for subdir in subdirs:
print(subdir)
subsubbasepath = subbasepath + subdir + '/'
subsubdirs = os.listdir(subsubbasepath)
string += subsubbasepath + '\n'
content = []
for i in range(0,len(subsubdirs)-1):
for j in range(i+1, len(subsubdirs)):
a = os.system('diff ' + subsubbasepath + subsubdirs[i] + '/log_json.txt ' + subsubbasepath + subsubdirs[j] + '/log_json.txt')
content.append([a, subsubdirs[i], subsubdirs[j]])
filteredcontent = [i for i in content if i[0] == 0]
for i in range(len(filteredcontent)):
string += ' and '.join(filteredcontent[i][1:])
if i != len(filteredcontent) - 1:
string += ', '
string += '\n\n'
f = open('offlinelearningerrors.txt','w')
f.write(string)
f.close()
| [
11748,
28686,
198,
12093,
538,
776,
796,
31051,
11195,
14,
998,
270,
14,
1416,
36722,
14,
26674,
79,
4316,
14,
7890,
14,
49229,
17143,
14,
26674,
36869,
14,
2364,
1370,
62,
40684,
14,
274,
945,
64,
12,
324,
321,
14,
6,
198,
15908,
... | 2.316038 | 424 |
import os
import shutil
import tempfile
import zipfile
def archive_write(archivepath, data, filename, compression, compressionlevel):
"""
Create a file named filename in the archive and write data to it
:param archivepath: The path to the zip-archive
:type archivepath: str
:param data: The data to be written to the file
:type data: str
:param filename: The filename for the newly created file
:type filename: str
:param compression: The desired compression for the zip-archive
:type compression: int
:param compressionlevel: The desired compression level for the zip-archive
:type compressionlevel: int
:return: void
"""
archive = zipfile.ZipFile(archivepath, mode='a',
compression=compression,
compresslevel=compressionlevel)
archive.writestr(filename, data)
archive.close()
def create_archive(archivepath, filedict, compression, compressionlevel):
"""
Write filedict to zip-archive data subdirectory. Will check wether archive
at archivepath exists before writing. If file exists will raise a
FileExistsError.
:param archivepath: the path to the file
:param filedict: dictionary containing the filepath, filename key-value
pairs
:param compression: desired compression methods (see zipfile documentation)
:param compressionlevel: compression level (see zipfile documentation)
:return: void
"""
if os.path.isfile(archivepath):
raise FileExistsError("Specified file already exists")
else:
archive = zipfile.ZipFile(archivepath, mode='x',
compression=compression,
compresslevel=compressionlevel)
for filepath, filename in filedict.items():
archive.write(filepath, arcname="data/" + filename)
archive.close()
def extract_archdata(archivepath, filename, destination):
"""
Extract a file from a archive and write it to the destination. If the
destination path already exists extract_archdata will not overwrite but
will throw a "FileExists" error.
:param archivepath: The path to the archive containing the file
:type archivepath: str
:param filename: The archive name of the desired file.
:type filename: str
:param destination: The path at which the extracted file is to be placed.
:type destination: str
:return: void
:rtype: None
"""
# check if destination path already exists
if os.path.exists(destination):
raise FileExistsError("The specified destination is already in use")
archive = zipfile.ZipFile(archivepath, mode='r')
with tempfile.TemporaryDirectory() as tmpdir:
archive.extract(filename, path=tmpdir)
# create directories for the destination
os.makedirs(os.path.dirname(destination), exist_ok=True)
shutil.copy(os.path.abspath(tmpdir + "/" + filename), destination)
def read_bin(archivepath, filelist):
"""
Read a list of files from an archive and return the file data as a
dictionary of filename, data key-value pairs.
:param archivepath: the path to the archive
:param filelist: list of filenames to read
:return: dictionary with filename, data key-value pairs
:rtype: dict
"""
datadict = dict()
if os.path.isfile(archivepath):
archive = zipfile.ZipFile(archivepath, mode='r')
else:
raise FileNotFoundError("Specified file does not exist")
for filename in filelist:
try:
file = archive.open(filename)
datadict[filename] = file.read().decode()
file.close()
except KeyError:
datadict[filename] = None
archive.close()
return datadict
def read_diff_log(archivepath):
"""
Read the diff-log.csv from a given archive file.
:param archivepath: The path to the zip-archive
:type archivepath: str
:return: The diff-log.csv contents in ascii string form.
:rtype: str
"""
arch = zipfile.ZipFile(archivepath, mode='r')
diff_log_file = arch.open("diff-log.csv")
diff_log_bin = diff_log_file.read()
diff_log = diff_log_bin.decode()
diff_log_file.close()
arch.close()
return diff_log
def zip_extract(archivepath, filelist, extractpath):
"""
Extract a list of files to a specific location
:param archivepath: the path to the zip-archive
:param filelist: list of member filenames to extract
:param extractpath: path for the extracted files
:return: void
"""
if os.path.isfile(archivepath):
archive = zipfile.ZipFile(archivepath, mode='r')
else:
raise FileNotFoundError("Specified file does not exist")
archive.extractall(path=extractpath, members=filelist)
archive.close()
| [
11748,
28686,
198,
11748,
4423,
346,
198,
11748,
20218,
7753,
198,
11748,
19974,
7753,
628,
198,
4299,
15424,
62,
13564,
7,
17474,
6978,
11,
1366,
11,
29472,
11,
19794,
11,
19794,
5715,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
... | 2.815331 | 1,722 |
"""Abstract class for all the scan planners
https://www.postgresql.org/docs/9.1/using-explain.html
https://www.postgresql.org/docs/9.5/runtime-config-query.html
"""
from src.query_planner.abstract_plan import AbstractPlan
from typing import List
| [
37811,
23839,
1398,
329,
477,
262,
9367,
33596,
198,
5450,
1378,
2503,
13,
7353,
34239,
13976,
13,
2398,
14,
31628,
14,
24,
13,
16,
14,
3500,
12,
20676,
391,
13,
6494,
198,
5450,
1378,
2503,
13,
7353,
34239,
13976,
13,
2398,
14,
316... | 3.126582 | 79 |
# !usr/bin/python
# coding:utf-8
import time
import socket
if __name__ == "__main__" :
main()
| [
2,
5145,
14629,
14,
8800,
14,
29412,
198,
2,
19617,
25,
40477,
12,
23,
198,
198,
11748,
640,
198,
11748,
17802,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1,
1058,
198,
220,
220,
220,
1388,
3419,
198
] | 2.439024 | 41 |
import argparse
import sys
from cliquet.scripts import cliquet
from pyramid.scripts import pserve
from pyramid.paster import bootstrap
def main(args=None):
"""The main routine."""
if args is None:
args = sys.argv[1:]
parser = argparse.ArgumentParser(description="Kinto commands")
subparsers = parser.add_subparsers(title='subcommands',
description='valid subcommands',
help='init/start/migrate')
parser_init = subparsers.add_parser('init')
parser_init.set_defaults(which='init')
parser_init.add_argument('--config_file', required=False,
help='Config file may be passed as argument')
parser_migrate = subparsers.add_parser('migrate')
parser_migrate.set_defaults(which='migrate')
parser_start = subparsers.add_parser('start')
parser_start.set_defaults(which='start')
args = vars(parser.parse_args())
if args['which'] == 'init':
if(args['config_file'] is None):
env = bootstrap('config/kinto.ini')
else:
config_file = format(args['config_file'])
env = bootstrap(config_file)
elif args['which'] == 'migrate':
env = bootstrap('config/kinto.ini')
cliquet.init_schema(env)
elif args['which'] == 'start':
pserve_argv = ['pserve', 'config/kinto.ini', '--reload']
pserve.main(pserve_argv)
if __name__ == "__main__":
main()
| [
11748,
1822,
29572,
198,
11748,
25064,
198,
6738,
537,
1557,
316,
13,
46521,
1330,
537,
1557,
316,
198,
6738,
27944,
13,
46521,
1330,
279,
2655,
303,
198,
6738,
27944,
13,
79,
1603,
1330,
6297,
26418,
628,
198,
4299,
1388,
7,
22046,
2... | 2.034696 | 807 |
from django.contrib import admin
from django.contrib.auth.models import User
from .models import Vegetable, Harvest, Transaction, Merchandise, MerchandisePrice
from .models import PurchasedItem, UserProfile, VegetablePrice, StockedVegetable
from .models import MerchandisePhotos
admin.site.register(Vegetable)
admin.site.register(StockedVegetable)
admin.site.register(Harvest)
admin.site.register(VegetablePrice)
admin.site.register(PurchasedItem)
admin.site.register(Transaction)
admin.site.register(UserProfile)
admin.site.register(Merchandise)
admin.site.register(MerchandisePrice)
admin.site.register(MerchandisePhotos)
| [
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
198,
6738,
42625,
14208,
13,
3642,
822,
13,
18439,
13,
27530,
1330,
11787,
198,
6738,
764,
27530,
1330,
23892,
540,
11,
26149,
11,
45389,
11,
34414,
18888,
11,
34414,
18888,
18124,
198,
67... | 3.434066 | 182 |
from ctypes import sizeof
from io import BytesIO
import unittest
from pyglet.media.synthesis import *
local_dir = os.path.dirname(__file__)
test_data_path = os.path.abspath(os.path.join(local_dir, '..', '..', 'data'))
del local_dir
def get_test_data_file(*file_parts):
"""Get a file from the test data directory in an OS independent way.
Supply relative file name as you would in os.path.join().
"""
return os.path.join(test_data_path, *file_parts)
| [
6738,
269,
19199,
1330,
39364,
198,
6738,
33245,
1330,
2750,
4879,
9399,
198,
11748,
555,
715,
395,
198,
198,
6738,
12972,
70,
1616,
13,
11431,
13,
1837,
429,
8497,
1330,
1635,
628,
198,
12001,
62,
15908,
796,
28686,
13,
6978,
13,
159... | 2.784884 | 172 |
#Datos de entrada
num=int(input("Ingrese un numero: "))
# Proceso
if num==10:
print("Calificacion: A")
elif num==9:
print("Calificacion: B")
elif num==8:
print("Calificacion: C")
elif num==7 and num==6:
print("Calificacion: D")
elif num<=5 and num>=0:
print("Calificacion: F")
| [
2,
27354,
418,
390,
24481,
4763,
198,
22510,
28,
600,
7,
15414,
7203,
27682,
260,
325,
555,
997,
3529,
25,
366,
4008,
198,
2,
1041,
728,
78,
198,
361,
997,
855,
940,
25,
198,
3601,
7203,
9771,
811,
49443,
25,
317,
4943,
198,
417,
... | 2.410256 | 117 |
ribbon_needed = 0
with open("input.txt", "r") as puzzle_input:
for line in puzzle_input:
length, width, height = [int(item) for item in line.split("x")]
dimensions = [length, width, height]
smallest_side = min(dimensions)
dimensions.remove(smallest_side)
second_smallest_side = min(dimensions)
ribbon_needed += 2*smallest_side + 2*second_smallest_side + length*width*height
print(ribbon_needed)
| [
822,
4189,
62,
27938,
796,
657,
198,
198,
4480,
1280,
7203,
15414,
13,
14116,
1600,
366,
81,
4943,
355,
15027,
62,
15414,
25,
198,
197,
1640,
1627,
287,
15027,
62,
15414,
25,
198,
197,
197,
13664,
11,
9647,
11,
6001,
796,
685,
600,
... | 2.690323 | 155 |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# 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 clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Algorithm.Framework")
from System import *
from QuantConnect import *
from QuantConnect.Orders.Fees import ConstantFeeModel
from QuantConnect.Data.UniverseSelection import *
from QuantConnect.Indicators import *
from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
from datetime import timedelta, datetime
from math import ceil
from itertools import chain
#
# This alpha picks stocks according to Joel Greenblatt's Magic Formula.
# First, each stock is ranked depending on the relative value of the ratio EV/EBITDA. For example, a stock
# that has the lowest EV/EBITDA ratio in the security universe receives a score of one while a stock that has
# the tenth lowest EV/EBITDA score would be assigned 10 points.
#
# Then, each stock is ranked and given a score for the second valuation ratio, Return on Capital (ROC).
# Similarly, a stock that has the highest ROC value in the universe gets one score point.
# The stocks that receive the lowest combined score are chosen for insights.
#
# Source: Greenblatt, J. (2010) The Little Book That Beats the Market
#
# This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
# sourced so the community and client funds can see an example of an alpha.
#
class GreenBlattMagicFormulaUniverseSelectionModel(FundamentalUniverseSelectionModel):
'''Defines a universe according to Joel Greenblatt's Magic Formula, as a universe selection model for the framework algorithm.
From the universe QC500, stocks are ranked using the valuation ratios, Enterprise Value to EBITDA (EV/EBITDA) and Return on Assets (ROA).
'''
def __init__(self,
filterFineData = True,
universeSettings = None,
securityInitializer = None):
'''Initializes a new default instance of the MagicFormulaUniverseSelectionModel'''
super().__init__(filterFineData, universeSettings, securityInitializer)
# Number of stocks in Coarse Universe
self.NumberOfSymbolsCoarse = 500
# Number of sorted stocks in the fine selection subset using the valuation ratio, EV to EBITDA (EV/EBITDA)
self.NumberOfSymbolsFine = 20
# Final number of stocks in security list, after sorted by the valuation ratio, Return on Assets (ROA)
self.NumberOfSymbolsInPortfolio = 10
self.lastMonth = -1
self.dollarVolumeBySymbol = {}
self.symbols = []
def SelectCoarse(self, algorithm, coarse):
'''Performs coarse selection for constituents.
The stocks must have fundamental data
The stock must have positive previous-day close price
The stock must have positive volume on the previous trading day'''
month = algorithm.Time.month
if month == self.lastMonth:
return self.symbols
self.lastMonth = month
# The stocks must have fundamental data
# The stock must have positive previous-day close price
# The stock must have positive volume on the previous trading day
filtered = [x for x in coarse if x.HasFundamentalData
and x.Volume > 0
and x.Price > 0]
# sort the stocks by dollar volume and take the top 1000
top = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)[:self.NumberOfSymbolsCoarse]
self.dollarVolumeBySymbol = { i.Symbol: i.DollarVolume for i in top }
self.symbols = list(self.dollarVolumeBySymbol.keys())
return self.symbols
def SelectFine(self, algorithm, fine):
'''QC500: Performs fine selection for the coarse selection constituents
The company's headquarter must in the U.S.
The stock must be traded on either the NYSE or NASDAQ
At least half a year since its initial public offering
The stock's market cap must be greater than 500 million
Magic Formula: Rank stocks by Enterprise Value to EBITDA (EV/EBITDA)
Rank subset of previously ranked stocks (EV/EBITDA), using the valuation ratio Return on Assets (ROA)'''
# QC500:
## The company's headquarter must in the U.S.
## The stock must be traded on either the NYSE or NASDAQ
## At least half a year since its initial public offering
## The stock's market cap must be greater than 500 million
filteredFine = [x for x in fine if x.CompanyReference.CountryId == "USA"
and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS")
and (algorithm.Time - x.SecurityReference.IPODate).days > 180
and x.EarningReports.BasicAverageShares.ThreeMonths * x.EarningReports.BasicEPS.TwelveMonths * x.ValuationRatios.PERatio > 5e8]
count = len(filteredFine)
if count == 0: return []
myDict = dict()
percent = float(self.NumberOfSymbolsFine / count)
# select stocks with top dollar volume in every single sector
for key in ["N", "M", "U", "T", "B", "I"]:
value = [x for x in filteredFine if x.CompanyReference.IndustryTemplateCode == key]
value = sorted(value, key=lambda x: self.dollarVolumeBySymbol[x.Symbol], reverse = True)
myDict[key] = value[:ceil(len(value) * percent)]
# stocks in QC500 universe
topFine = list(chain.from_iterable(myDict.values()))[:self.NumberOfSymbolsCoarse]
# Magic Formula:
## Rank stocks by Enterprise Value to EBITDA (EV/EBITDA)
## Rank subset of previously ranked stocks (EV/EBITDA), using the valuation ratio Return on Assets (ROA)
# sort stocks in the security universe of QC500 based on Enterprise Value to EBITDA valuation ratio
sortedByEVToEBITDA = sorted(topFine, key=lambda x: x.ValuationRatios.EVToEBITDA , reverse=True)
# sort subset of stocks that have been sorted by Enterprise Value to EBITDA, based on the valuation ratio Return on Assets (ROA)
sortedByROA = sorted(sortedByEVToEBITDA[:self.NumberOfSymbolsFine], key=lambda x: x.ValuationRatios.ForwardROA, reverse=False)
# retrieve list of securites in portfolio
top = sortedByROA[:self.NumberOfSymbolsInPortfolio]
self.symbols = [f.Symbol for f in top]
return self.symbols
| [
2,
19604,
1565,
4825,
1340,
48842,
13,
9858,
532,
9755,
2890,
15007,
11,
2295,
6477,
278,
34884,
13,
198,
2,
45661,
978,
7727,
9383,
25469,
7117,
410,
17,
13,
15,
13,
15069,
1946,
16972,
13313,
10501,
13,
198,
2,
198,
2,
49962,
739,... | 2.84441 | 2,603 |
import numpy as np
import torch
import torch.nn.functional as F
def content_loss(content_weight, content_current, content_target):
"""
Compute the content loss for style transfer.
Inputs:
- content_weight: Scalar giving the weighting for the content loss.
- content_current: features of the current image; this is a PyTorch Tensor of shape
(1, C_l, H_l, W_l).
- content_target: features of the content image, Tensor with shape (1, C_l, H_l, W_l).
Returns:
- scalar content loss
"""
##############################################################################
# YOUR CODE HERE #
##############################################################################
_, C, H, W = content_current.shape
current_features = content_current.view(C, H*W)
target_features = content_target.view(C, H*W)
loss = content_weight * torch.sum(torch.square(current_features - target_features))
return loss
##############################################################################
# END OF YOUR CODE #
##############################################################################
def gram_matrix(features, normalize=True):
"""
Compute the Gram matrix from features.
Inputs:
- features: PyTorch Variable of shape (N, C, H, W) giving features for
a batch of N images.
- normalize: optional, whether to normalize the Gram matrix
If True, divide the Gram matrix by the number of neurons (H * W * C)
Returns:
- gram: PyTorch Variable of shape (N, C, C) giving the
(optionally normalized) Gram matrices for the N input images.
"""
##############################################################################
# YOUR CODE HERE #
##############################################################################
C, H, W = features.shape[-3], features.shape[-2], features.shape[-1]
reshaped = features.view(-1, C, H*W)
G = reshaped @ reshaped.transpose(dim0=1, dim1=2)
if normalize:
G = G / (H*W*C)
return G
##############################################################################
# END OF YOUR CODE #
##############################################################################
def style_loss(feats, style_layers, style_targets, style_weights):
"""
Computes the style loss at a set of layers.
Inputs:
- feats: list of the features at every layer of the current image, as produced by
the extract_features function.
- style_layers: List of layer indices into feats giving the layers to include in the
style loss.
- style_targets: List of the same length as style_layers, where style_targets[i] is
a PyTorch Variable giving the Gram matrix the source style image computed at
layer style_layers[i].
- style_weights: List of the same length as style_layers, where style_weights[i]
is a scalar giving the weight for the style loss at layer style_layers[i].
Returns:
- style_loss: A PyTorch Variable holding a scalar giving the style loss.
"""
# Hint: you can do this with one for loop over the style layers, and should
# not be very much code (~5 lines). You will need to use your gram_matrix function.
##############################################################################
# YOUR CODE HERE #
##############################################################################
loss = 0
for i, l in enumerate(style_layers):
A, G = style_targets[i], gram_matrix(feats[l])
loss += style_weights[i] * torch.sum(torch.square(G - A))
return loss
##############################################################################
# END OF YOUR CODE #
##############################################################################
def tv_loss(img, tv_weight):
"""
Compute total variation loss.
Inputs:
- img: PyTorch Variable of shape (1, 3, H, W) holding an input image.
- tv_weight: Scalar giving the weight w_t to use for the TV loss.
Returns:
- loss: PyTorch Variable holding a scalar giving the total variation loss
for img weighted by tv_weight.
"""
# Your implementation should be vectorized and not require any loops!
##############################################################################
# YOUR CODE HERE #
##############################################################################
tv = torch.square(img[..., 1:, :-1] - img[..., :-1, :-1]) + torch.square(img[..., :-1, 1:] - img[..., :-1, :-1])
return tv_weight * torch.sum(tv)
##############################################################################
# END OF YOUR CODE #
##############################################################################
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13,
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13,
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355,
376,
628,
198,
4299,
2695,
62,
22462,
7,
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62,
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2599,
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220,
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37227,
... | 2.781761 | 1,897 |
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
import xml.sax.saxutils
from Peach.transformer import Transformer
| [
2,
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6127,
5178,
318,
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284,
262,
2846,
286,
262,
29258,
5094,
198,
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11,
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11,
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460,
7330,
530,
379,
... | 3.3375 | 80 |
# From: http://williams.best.vwh.net/avform.htm#GCF
import math
EPS = 0.0001
d2r = math.pi / 180.0
r2d = 180.0 / math.pi
rad2nm = (180.0 * 60.0) / math.pi
nm2rad = 1.0 / rad2nm
nm2meter = 1852
meter2nm = 1.0 / nm2meter
# p1 = (lat1(deg), lon1(deg))
# p2 = (lat2(deg), lon2(deg))
| [
2,
3574,
25,
2638,
1378,
10594,
1789,
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13,
13466,
13,
85,
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13,
3262,
14,
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687,
13,
19211,
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657,
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81,
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1... | 1.958333 | 144 |
import connexion, os
from connexion.resolver import RestyResolver
from flask import json
from flask_sqlalchemy import SQLAlchemy
from flask_marshmallow import Marshmallow
# Globally accessible libraries
db = SQLAlchemy()
mm = Marshmallow()
def init_app():
"""Initialize the Connexion application."""
BASE_DIR = os.path.abspath(os.path.dirname(__file__))
openapi_path = os.path.join(BASE_DIR, "../")
conn_app = connexion.FlaskApp(
__name__, specification_dir=openapi_path, options={
"swagger_ui": True,
"serve_spec": True
}
)
conn_app.add_api("openapi.yaml", resolver=RestyResolver('run'),
strict_validation=True)
# Flask app and getting into app_context
app = conn_app.app
# Load application config
app.config.from_object('config.ProdConfig')
app.json_encoder = json.JSONEncoder
# Initialize Plugins
db.init_app(app)
mm.init_app(app)
with app.app_context():
# Include our Routes/views
import run
# Register Blueprints
# app.register_blueprint(auth.auth_bp)
# app.register_blueprint(admin.admin_bp)
return app
| [
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369,
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295,
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5406... | 2.419028 | 494 |
'''
Summary: Program that implements a routing deamon based on the
RIP version 2 protocol from RFC2453.
Usage: python3 Router.py <router_config_file>
Configuration File:
The user supplies a router configuration file of the format:
[Settings]
router-id = <router_number>
input-ports = <input> [, <input>, ...]
outputs = <output>-<metric>-<destination_router>
[, <output>-<metric>-<destination_router>, ...]
where,
router_number: ID of router between 1 - 64000.
input: port number between 1024 - 64000.
output: port number between 1024 - 6400,
not equal to any inputs.
metric: metric of output between 1 - 16.
destination_router: ID of destination router.
Description:
This program implements a basic RIPv2 routing protocol from RFC2453
for routing computations in computer networks. It takes a configuration
file as shown above and sets up a router with a new socket for each
input-port.
The RIPv2 protocol uses a routing table to keep track of all reachable
routers on the network along with their metric/cost and the direct
next hop router ID along the route to that destination router. However,
it can only send messages to the direct neighbours specified in outputs.
The protocol uses the Bellman-Ford distance vector algorithm to compute
the lowest cost route to each router in the network. If the metric is
16 or greater, the router is considered unreachable.
The routing table initially starts with a single route entry (RTE) for
itself with a metric of zero. The routing table is periodically
transmitted too each of its direct output ports via an unsolicited
response message as defined in RFC2453 section 3.9.2 and 4. This is
performed on a separate thread so it does not interfere with other
operations
The receives messages from other routers by using the python select()
function which blocks until a message is ready to be read. Once a
message is received the header and contents are validated.
If the message is valid each RTE is processed according to RFC2453
section 3.9.2.
If a new router is found the RTE is added
to the routing table, adding the cost to the metric for the output
the message was received on.
If the RTE already exists, but the metric is smaller, the metric
is updated to the lower metric.
If the lower metric is from a different next hop router, change the
next hop.
If nothing has changed, restart the timeout timer.
If RTE metric >= max metric of 16, mark the entry for
garbage collection and update the metric in the table.
If any change has occurred in the routing table as a result of a
received message, a triggered update (RFC2453 section 3.10.1) is sent
to all outputs with the updated entries. Triggered updates are sent with
a random delay between 1 - 5 seconds to prevent synchronized updates.
Request messages are not implemented in this program.
Timers (all timers are on separate threads) (RFC2453 section 3.8):
Update timer - Periodic unsolicited response message sent to all
outputs. The period is adjusted each time to a random value
between 0.8 * BASE_TIMER and 1.2 * BASE_TIMER to prevent
synchronized updates.
Timeout - used to check the routing table for RTEs which have
have not been updated within the ROUTE_TIMEOUT interval. If
a router has not been heard from within this time, then set the
metric to the max metric of 16 and start the garbage collection
timer.
Garbage timer - used to check the routing table for RTEs set
for garbage collection. If the timeout >= DELETE_TIMEOUT,
mark the RTE for deletion.
Garbage Collection - used to check the routing table for RTEs
marked for deletion, and removes those entries from the table.
'''
import configparser
import select
import socket
import sys
import time
import threading
import struct
import datetime
from random import randint, randrange
DEBUG = False
HOST = '127.0.0.1' # localhost
BASE_TIMER = 5
MAX_METRIC = 16
ROUTE_TIMEOUT = BASE_TIMER * 6
DELETE_TIMEOUT = BASE_TIMER * 4
AF_INET = 2
# ===========================================================================
# TRANSITIONS
# ===========================================================================
# STATES
# ===========================================================================
# FINITE STATE MACHINE
# ===========================================================================
# IMPLEMENTATION
# RUN THE PROGRAM
def print_message(message):
'''Print the given message with the current time before it'''
if DEBUG:
print("[" + time.strftime("%H:%M:%S") + "]: " + message)
def main():
'''Main function to run the program.'''
if __name__ == "__main__":
router = Router(str(sys.argv[-1]))
router.start_timers()
router.main_loop()
main()
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... | 2.481792 | 2,389 |
from ibllib.io import spikeglx
import numpy as np
import ibllib.dsp as dsp
from scipy import signal
from ibllib.misc import print_progress
from pathlib import Path
import alf.io as aio
import logging
import ibllib.ephys.ephysqc as ephysqc
from phylib.io import alf
_logger = logging.getLogger('ibllib')
RMS_WIN_LENGTH_SECS = 3
WELCH_WIN_LENGTH_SAMPLES = 1024
def rmsmap(fbin, spectra=True):
"""
Computes RMS map in time domain and spectra for each channel of Neuropixel probe
:param fbin: binary file in spike glx format (will look for attached metatdata)
:type fbin: str or pathlib.Path
:param spectra: whether to compute the power spectrum (only need for lfp data)
:type: bool
:return: a dictionary with amplitudes in channeltime space, channelfrequency space, time
and frequency scales
"""
if not isinstance(fbin, spikeglx.Reader):
sglx = spikeglx.Reader(fbin)
rms_win_length_samples = 2 ** np.ceil(np.log2(sglx.fs * RMS_WIN_LENGTH_SECS))
# the window generator will generates window indices
wingen = dsp.WindowGenerator(ns=sglx.ns, nswin=rms_win_length_samples, overlap=0)
# pre-allocate output dictionary of numpy arrays
win = {'TRMS': np.zeros((wingen.nwin, sglx.nc)),
'nsamples': np.zeros((wingen.nwin,)),
'fscale': dsp.fscale(WELCH_WIN_LENGTH_SAMPLES, 1 / sglx.fs, one_sided=True),
'tscale': wingen.tscale(fs=sglx.fs)}
win['spectral_density'] = np.zeros((len(win['fscale']), sglx.nc))
# loop through the whole session
for first, last in wingen.firstlast:
D = sglx.read_samples(first_sample=first, last_sample=last)[0].transpose()
# remove low frequency noise below 1 Hz
D = dsp.hp(D, 1 / sglx.fs, [0, 1])
iw = wingen.iw
win['TRMS'][iw, :] = dsp.rms(D)
win['nsamples'][iw] = D.shape[1]
if spectra:
# the last window may be smaller than what is needed for welch
if last - first < WELCH_WIN_LENGTH_SAMPLES:
continue
# compute a smoothed spectrum using welch method
_, w = signal.welch(D, fs=sglx.fs, window='hanning', nperseg=WELCH_WIN_LENGTH_SAMPLES,
detrend='constant', return_onesided=True, scaling='density',
axis=-1)
win['spectral_density'] += w.T
# print at least every 20 windows
if (iw % min(20, max(int(np.floor(wingen.nwin / 75)), 1))) == 0:
print_progress(iw, wingen.nwin)
return win
def extract_rmsmap(fbin, out_folder=None, spectra=True):
"""
Wrapper for rmsmap that outputs _ibl_ephysRmsMap and _ibl_ephysSpectra ALF files
:param fbin: binary file in spike glx format (will look for attached metatdata)
:param out_folder: folder in which to store output ALF files. Default uses the folder in which
the `fbin` file lives.
:param spectra: whether to compute the power spectrum (only need for lfp data)
:type: bool
:return: None
"""
_logger.info(f"Computing QC for {fbin}")
sglx = spikeglx.Reader(fbin)
# check if output ALF files exist already:
if out_folder is None:
out_folder = Path(fbin).parent
else:
out_folder = Path(out_folder)
alf_object_time = f'_iblqc_ephysTimeRms{sglx.type.upper()}'
alf_object_freq = f'_iblqc_ephysSpectralDensity{sglx.type.upper()}'
# crunch numbers
rms = rmsmap(fbin, spectra=spectra)
# output ALF files, single precision with the optional label as suffix before extension
if not out_folder.exists():
out_folder.mkdir()
tdict = {'rms': rms['TRMS'].astype(np.single), 'timestamps': rms['tscale'].astype(np.single)}
aio.save_object_npy(out_folder, object=alf_object_time, dico=tdict)
if spectra:
fdict = {'power': rms['spectral_density'].astype(np.single),
'freqs': rms['fscale'].astype(np.single)}
aio.save_object_npy(out_folder, object=alf_object_freq, dico=fdict)
def _sample2v(ap_file):
"""
Convert raw ephys data to Volts
"""
md = spikeglx.read_meta_data(ap_file.with_suffix('.meta'))
s2v = spikeglx._conversion_sample2v_from_meta(md)
return s2v['ap'][0]
def ks2_to_alf(ks_path, bin_path, out_path, bin_file=None, ampfactor=1, label=None, force=True):
"""
Convert Kilosort 2 output to ALF dataset for single probe data
:param ks_path:
:param bin_path: path of raw data
:param out_path:
:return:
"""
m = ephysqc.phy_model_from_ks2_path(ks2_path=ks_path, bin_path=bin_path, bin_file=bin_file)
ephysqc.spike_sorting_metrics_ks2(ks_path, m, save=True)
ac = alf.EphysAlfCreator(m)
ac.convert(out_path, label=label, force=force, ampfactor=ampfactor)
# if __name__ == '__main__':
#
# ephys_path = Path('C:/Users/Mayo/Downloads/raw_ephys_data')
# ks_path = Path('C:/Users/Mayo/Downloads/KS2')
# out_path = Path('C:/Users/Mayo/Downloads/alf')
# extract_data(ks_path, ephys_path, out_path)
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57... | 2.224287 | 2,314 |
from django.db import models
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1... | 1.211009 | 109 |
# -*- coding: utf-8 -*-
# vispy: gallery 10
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
import sys
import numpy as np
from vispy import app, gloo, visuals
from vispy.visuals.filters import Clipper, ColorFilter
from vispy.visuals.shaders import MultiProgram
from vispy.visuals.collections import PointCollection
from vispy.visuals.transforms import STTransform
from vispy.scene import SceneCanvas
from vispy.scene.visuals import create_visual_node
canvas = app.Canvas(keys='interactive', size=(900, 600), show=True,
title="Visual Canvas")
pos = np.random.normal(size=(1000, 2), loc=0, scale=50).astype('float32')
pos[0] = [0, 0]
# Make a line visual
line = LineVisual(pos=pos)
line.transforms.canvas = canvas
line.transform = STTransform(scale=(2, 1), translate=(20, 20))
panzoom = PanZoomTransform(canvas)
line.transforms.scene_transform = panzoom
panzoom.changed.connect(lambda ev: canvas.update())
# Attach color filter to all views (current and future) of the visual
line.attach(ColorFilter((1, 1, 0.5, 0.7)))
# Attach a clipper just to this view. The Clipper filter requires a
# transform that maps from the framebuffer coordinate system to the
# clipping coordinates.
tr = line.transforms.get_transform('framebuffer', 'canvas')
line.attach(Clipper((20, 20, 260, 260), transform=tr), view=line)
# Make a view of the line that will draw its shadow
shadow = line.view()
shadow.transforms.canvas = canvas
shadow.transform = STTransform(scale=(2, 1), translate=(25, 25))
shadow.transforms.scene_transform = panzoom
shadow.attach(ColorFilter((0, 0, 0, 0.6)), view=shadow)
tr = shadow.transforms.get_transform('framebuffer', 'canvas')
shadow.attach(Clipper((20, 20, 260, 260), transform=tr), view=shadow)
# And make a second view of the line with different clipping bounds
view = line.view()
view.transforms.canvas = canvas
view.transform = STTransform(scale=(2, 0.5), translate=(450, 150))
tr = view.transforms.get_transform('framebuffer', 'canvas')
view.attach(Clipper((320, 20, 260, 260), transform=tr), view=view)
# Make a compound visual
plot = PlotLineVisual(pos, (0.5, 1, 0.5, 0.2), (0.5, 1, 1, 0.3))
plot.transforms.canvas = canvas
plot.transform = STTransform(translate=(80, 450), scale=(1.5, 1))
tr = plot.transforms.get_transform('framebuffer', 'canvas')
plot.attach(Clipper((20, 320, 260, 260), transform=tr), view=plot)
# And make a view on the compound
view2 = plot.view()
view2.transforms.canvas = canvas
view2.transform = STTransform(scale=(1.5, 1), translate=(450, 400))
tr = view2.transforms.get_transform('framebuffer', 'canvas')
view2.attach(Clipper((320, 320, 260, 260), transform=tr), view=view2)
# And a shadow for the view
shadow2 = plot.view()
shadow2.transforms.canvas = canvas
shadow2.transform = STTransform(scale=(1.5, 1), translate=(455, 405))
shadow2.attach(ColorFilter((0, 0, 0, 0.6)), view=shadow2)
tr = shadow2.transforms.get_transform('framebuffer', 'canvas')
shadow2.attach(Clipper((320, 320, 260, 260), transform=tr), view=shadow2)
# Example of a collection visual
collection = PointCollectionVisual()
collection.transforms.canvas = canvas
collection.transform = STTransform(translate=(750, 150))
collection.append(np.random.normal(loc=0, scale=20, size=(10000, 3)),
itemsize=5000)
collection.color = (1, 0.5, 0.5, 1), (0.5, 0.5, 1, 1)
shadow3 = collection.view()
shadow3.transforms.canvas = canvas
shadow3.transform = STTransform(scale=(1, 1), translate=(752, 152))
shadow3.attach(ColorFilter((0, 0, 0, 0.6)), view=shadow3)
# tr = shadow3.transforms.get_transform('framebuffer', 'canvas')
# shadow3.attach(Clipper((320, 320, 260, 260), transform=tr), view=shadow2)
order = [shadow, line, view, plot, shadow2, view2, shadow3, collection]
canvas.events.resize.connect(on_resize)
on_resize(None)
Line = create_visual_node(LineVisual)
canvas2 = SceneCanvas(keys='interactive', title='Scene Canvas', show=True)
v = canvas2.central_widget.add_view(margin=10)
v.border_color = (1, 1, 1, 1)
v.bgcolor = (0.3, 0.3, 0.3, 1)
v.camera = 'panzoom'
line2 = Line(pos, parent=v.scene)
v.events.mouse_press.connect(mouse)
if __name__ == '__main__':
if sys.flags.interactive != 1:
app.run()
| [
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12,
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25,
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69,
12,
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532,
9,
12,
198,
2,
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25,
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838,
198,
2,
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... | 2.892568 | 1,480 |
from unittest import TestCase
from datetime import datetime
import pyarrow as pa
import numpy as np
import pandas as pd
from h1st.schema import SchemaInferrer
| [
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13,
15952,
2611,
... | 3.333333 | 48 |
"""
# refactoring
Refactoring is the key to successfull projects.
Refactor:
1) annuity_factor such that:
conversion to integer is handled,
no extra printing
2) policy_book into a class such that:
a function generates the book and the premium
stats and visualizations functions are avalaible
3) book_report such that:
it uses all the previous improvements
""" | [
37811,
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... | 3.453704 | 108 |
"""Auxiliary methods."""
import os
import json
from errno import EEXIST
import numpy as np
import seaborn as sns
import cPickle as pickle
import matplotlib.pyplot as plt
sns.set()
DEFAULT_LOG_DIR = 'log'
ATOB_WEIGHTS_FILE = 'atob_weights.h5'
D_WEIGHTS_FILE = 'd_weights.h5'
def convert_to_rgb(img, is_binary=False):
"""Given an image, make sure it has 3 channels and that it is between 0 and 1."""
if len(img.shape) != 3:
raise Exception("""Image must have 3 dimensions (channels x height x width). """
"""Given {0}""".format(len(img.shape)))
img_ch, _, _ = img.shape
if img_ch != 3 and img_ch != 1:
raise Exception("""Unsupported number of channels. """
"""Must be 1 or 3, given {0}.""".format(img_ch))
imgp = img
if img_ch == 1:
imgp = np.repeat(img, 3, axis=0)
if not is_binary:
imgp = imgp * 127.5 + 127.5
imgp /= 255.
return np.clip(imgp.transpose((1, 2, 0)), 0, 1)
def compose_imgs(a, b, is_a_binary=True, is_b_binary=False):
"""Place a and b side by side to be plotted."""
ap = convert_to_rgb(a, is_binary=is_a_binary)
bp = convert_to_rgb(b, is_binary=is_b_binary)
if ap.shape != bp.shape:
raise Exception("""A and B must have the same size. """
"""{0} != {1}""".format(ap.shape, bp.shape))
# ap.shape and bp.shape must have the same size here
h, w, ch = ap.shape
composed = np.zeros((h, 2*w, ch))
composed[:, :w, :] = ap
composed[:, w:, :] = bp
return composed
def get_log_dir(log_dir, expt_name):
"""Compose the log_dir with the experiment name."""
if log_dir is None:
raise Exception('log_dir can not be None.')
if expt_name is not None:
return os.path.join(log_dir, expt_name)
return log_dir
def mkdir(mypath):
"""Create a directory if it does not exist."""
try:
os.makedirs(mypath)
except OSError as exc:
if exc.errno == EEXIST and os.path.isdir(mypath):
pass
else:
raise
def create_expt_dir(params):
"""Create the experiment directory and return it."""
expt_dir = get_log_dir(params.log_dir, params.expt_name)
# Create directories if they do not exist
mkdir(params.log_dir)
mkdir(expt_dir)
# Save the parameters
json.dump(params, open(os.path.join(expt_dir, 'params.json'), 'wb'),
indent=4, sort_keys=True)
return expt_dir
def plot_loss(loss, label, filename, log_dir):
"""Plot a loss function and save it in a file."""
plt.figure(figsize=(5, 4))
plt.plot(loss, label=label)
plt.legend()
plt.savefig(os.path.join(log_dir, filename))
plt.clf()
def log(losses, atob, it_val, N=4, log_dir=DEFAULT_LOG_DIR, expt_name=None,
is_a_binary=True, is_b_binary=False):
"""Log losses and atob results."""
log_dir = get_log_dir(log_dir, expt_name)
# Save the losses for further inspection
pickle.dump(losses, open(os.path.join(log_dir, 'losses.pkl'), 'wb'))
###########################################################################
# PLOT THE LOSSES #
###########################################################################
plot_loss(losses['d'], 'discriminator', 'd_loss.png', log_dir)
plot_loss(losses['d_val'], 'discriminator validation', 'd_val_loss.png', log_dir)
plot_loss(losses['p2p'], 'Pix2Pix', 'p2p_loss.png', log_dir)
plot_loss(losses['p2p_val'], 'Pix2Pix validation', 'p2p_val_loss.png', log_dir)
###########################################################################
# PLOT THE A->B RESULTS #
###########################################################################
plt.figure(figsize=(10, 6))
for i in range(N*N):
a, _ = next(it_val)
bp = atob.predict(a)
img = compose_imgs(a[0], bp[0], is_a_binary=is_a_binary, is_b_binary=is_b_binary)
plt.subplot(N, N, i+1)
plt.imshow(img)
plt.axis('off')
plt.savefig(os.path.join(log_dir, 'atob.png'))
plt.clf()
# Make sure all the figures are closed.
plt.close('all')
def save_weights(models, log_dir=DEFAULT_LOG_DIR, expt_name=None):
"""Save the weights of the models into a file."""
log_dir = get_log_dir(log_dir, expt_name)
models.atob.save_weights(os.path.join(log_dir, ATOB_WEIGHTS_FILE), overwrite=True)
models.d.save_weights(os.path.join(log_dir, D_WEIGHTS_FILE), overwrite=True)
def load_weights(atob, d, log_dir=DEFAULT_LOG_DIR, expt_name=None):
"""Load the weights into the corresponding models."""
log_dir = get_log_dir(log_dir, expt_name)
atob.load_weights(os.path.join(log_dir, ATOB_WEIGHTS_FILE))
d.load_weights(os.path.join(log_dir, D_WEIGHTS_FILE))
def load_weights_of(m, weights_file, log_dir=DEFAULT_LOG_DIR, expt_name=None):
"""Load the weights of the model m."""
log_dir = get_log_dir(log_dir, expt_name)
m.load_weights(os.path.join(log_dir, weights_file))
def load_losses(log_dir=DEFAULT_LOG_DIR, expt_name=None):
"""Load the losses of the given experiment."""
log_dir = get_log_dir(log_dir, expt_name)
losses = pickle.load(open(os.path.join(log_dir, 'losses.pkl'), 'rb'))
return losses
def load_params(params):
"""
Load the parameters of an experiment and return them.
The params passed as argument will be merged with the new params dict.
If there is a conflict with a key, the params passed as argument prevails.
"""
expt_dir = get_log_dir(params.log_dir, params.expt_name)
expt_params = json.load(open(os.path.join(expt_dir, 'params.json'), 'rb'))
# Update the loaded parameters with the current parameters. This will
# override conflicting keys as expected.
expt_params.update(params)
return expt_params
| [
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198,
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384,
397,
1211,
355,
3013,
82,
198,
11748,
269,
31686,
2... | 2.358304 | 2,523 |
from github import Github
# parsedUrl = parseGithubURL('https://github.com/CakeCrusher/restock_emailer')
# filePaths = fetchRepoFiles(parsedUrl['owner'], parsedUrl['repo'])
# files = [path.split('/')[-1] for path in filePaths]
# print(files)
| [
6738,
33084,
1330,
38994,
198,
198,
2,
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28165,
796,
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38,
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2,
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15235,
82,
796,
21207,
6207,
78,
2587... | 2.793103 | 87 |
#-*- coding: utf-8 -*-
import codecs
import random
from utils.global_names import GlobalNames, get_file_path
| [
2,
12,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
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198,
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14933,
1330,
8060,
36690,
11,
651,
62,
7753,
62,
6978,
628,
198
] | 2.825 | 40 |
from graphgallery.functional import device
import tensorflow as tf
import torch
if __name__ == "__main__":
test_device()
| [
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4823,
24460,
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6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220... | 2.745098 | 51 |
# -*- coding: utf-8 -*-
"""
A card (duh).
"""
import random
import uuid
from enum import Enum
from typing import List
from py_hanabi.settings import CARD_DECK_DISTRIBUTION
__author__ = "Jakrin Juangbhanich"
__email__ = "juangbhanich.k@gmail.com"
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
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646,
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198,
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334,
27112,
198,
6738,
33829,
1330,
2039,
388,
198,
6738,
19720,
1330,
7343,... | 2.571429 | 98 |
import types
import django.test.testcases
from django.conf import settings
from facetools.models import TestUser
from facetools.common import _create_signed_request
from facetools.test import TestUserNotLoaded
from facetools.signals import sync_facebook_test_user, setup_facebook_test_client
from facetools.common import _get_facetools_test_fixture_name
def get_app_name_from_test_case(module_path_string):
"""
Gets thet Django app from the __class__ attribute of a TestCase in a Django app.
class_string should look something like this: 'facetools_tests.tests.test_test_module'
"""
packages = module_path_string.split(".")
try:
tests_location = packages.index("tests")
except ValueError:
raise ValueError("Couldn't find tests module in %s (are you running this test from tests.py or a tests package in your Django app?)" % module_path_string)
if tests_location == 0:
raise ValueError("Facetools doesn't support Django app's with a name of 'tests', or it failed to find the Django app name out of %s" % module_path_string)
app_name = packages[tests_location - 1]
if app_name not in settings.INSTALLED_APPS:
raise ValueError("Facetools didn't find %s among INSTALLED_APPS. (app name pulled from %s)" % (app_name, module_path_string))
return app_name
# -----------------------------------------------------------------------------
# Test Cases
# -----------------------------------------------------------------------------
if 'LiveServerTestCase' in dir(django.test.testcases):
| [
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198,
6738,
39144,
10141,
13,
11321,
1330,
4808,
17953,
62,
32696... | 3.389978 | 459 |
import setuptools
import distpickymodel
setuptools.setup(
name="distpickymodel",
version=distpickymodel.__version__,
author="Dan G",
author_email="daniel.garcia@d2garcia.com",
description="A shared Mongoengine-based model library",
long_description=get_long_desc(),
url="https://github.com/d2gex/distpickymodel",
# Exclude 'tests' and 'docs'
packages=['distpickymodel'],
python_requires='>=3.6',
install_requires=['pymongo>=3.7.2', 'mongoengine>=0.17.0', 'six'],
tests_require=['pytest>=4.4.0', 'PyYAML>=5.1'],
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Web Environment',
'Environment :: Console',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: Implementation :: CPython',
'Topic :: Software Development :: Libraries :: Python Modules',
]
)
| [
11748,
900,
37623,
10141,
198,
11748,
1233,
79,
17479,
19849,
628,
198,
198,
2617,
37623,
10141,
13,
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7,
198,
220,
220,
220,
1438,
2625,
17080,
79,
17479,
19849,
1600,
198,
220,
220,
220,
2196,
28,
17080,
79,
17479,
19849,
13,
8... | 2.598109 | 423 |
import unittest
from password import Credentials
def test_init(self):
"""
Test for correct initialization
"""
self.assertEqual(self.new_credentials.account_name,"Github")
self.assertEqual(self.new_credentials.username,"tinatasga")
self.assertEqual(self.new_credentials.password,"@#tinatasha")
def test_save_credentials(self):
"""
Test to check whether app saves account credentials
"""
self.new_credentials.save_credentials()
self.assertEqual(len(Credentials.credentials_list),1)
"""
Test for saving multiple credentials
"""
self.new_credentials.save_credentials()
test_credentials = Credentials("AllFootball","Kibet","messithegoat")
test_credentials.save_credentials()
self.assertEqual(len(Credentials.credentials_list),2)
def test_view_credentials(self):
"""
Test to view an account credential
"""
self.assertEqual(Credentials.display_credentials(),Credentials.credentials_list)
def test_delete_credentials(self):
"""
Test to delete account credentials
"""
self.new_credentials.save_credentials()
test_credentials = Credentials("i","love","cats")
test_credentials.save_credentials()
self.new_credentials.delete_credentials()
self.assertEqual(len(Credentials.credentials_list),1)
if __name__ == '__main__':
unittest.main() | [
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220,
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3376,
37588,
198,
220,
... | 2.349684 | 632 |
# populacao = [[0,0],[-3,1]]
# calc_fitness(pop=populacao)
# print(populacao)
| [
198,
2,
16595,
330,
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2,
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377,
330,
5488,
8,
628
] | 2 | 40 |
# -*- coding: utf-8 -*-
from oopschool.school import Student,Tesla,SpecialStudent,Teacher
from oopschool.newschool import Test | [
2,
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12,
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1251,
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1251,
1330,
6208
] | 3.047619 | 42 |
import logging
from rdl.data_sources.MsSqlDataSource import MsSqlDataSource
from rdl.data_sources.AWSLambdaDataSource import AWSLambdaDataSource
| [
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6814,
6601,
7416,
1330,
14356,
863... | 3.106383 | 47 |
# ---------------------------------
# Prepare the data etc.
# ----------------------------------
import numpy as np
import pandas as pd
# train_x is the training data, train_y is the target values, and test_x is the test data
# stored in pandas DataFrames and Series (numpy arrays also used)
train = pd.read_csv('../input/sample-data/train_preprocessed.csv')
train_x = train.drop(['target'], axis=1)
train_y = train['target']
test_x = pd.read_csv('../input/sample-data/test_preprocessed.csv')
# As time-series data assume a period variable is set that changes with time
train_x['period'] = np.arange(0, len(train_x)) // (len(train_x) // 4)
train_x['period'] = np.clip(train_x['period'], 0, 3)
test_x['period'] = 4
# -----------------------------------
# Hold-out method for time-series data
# -----------------------------------
# Partition using the period variable as the basis (0 to 3 are the training data, 4 is the test data)
# Here for within the training data period 3 is used for validation and periods 0 to 2 are used for training
is_tr = train_x['period'] < 3
is_va = train_x['period'] == 3
tr_x, va_x = train_x[is_tr], train_x[is_va]
tr_y, va_y = train_y[is_tr], train_y[is_va]
# -----------------------------------
# Cross validation for time-series data (use method that follows time)
# -----------------------------------
# Partition using the period variable as the basis (0 to 3 are the training data, 4 is the test data)
# Periods 1, 2 and 3 are each used for cross-validation, and the preceding periods are used for training
va_period_list = [1, 2, 3]
for va_period in va_period_list:
is_tr = train_x['period'] < va_period
is_va = train_x['period'] == va_period
tr_x, va_x = train_x[is_tr], train_x[is_va]
tr_y, va_y = train_y[is_tr], train_y[is_va]
# (For reference) Using TimeSeriesSplit() function is difficult as only the order of the data can be used
from sklearn.model_selection import TimeSeriesSplit
tss = TimeSeriesSplit(n_splits=4)
for tr_idx, va_idx in tss.split(train_x):
tr_x, va_x = train_x.iloc[tr_idx], train_x.iloc[va_idx]
tr_y, va_y = train_y.iloc[tr_idx], train_y.iloc[va_idx]
# -----------------------------------
# Cross validation for time-series data (method to simply partition by time)
# -----------------------------------
# Partition using the period variable as the basis (0 to 3 are the training data, 4 is the test data)
# Periods 1, 2 and 3 are each used for cross-validation, and the preceding periods are used for training
va_period_list = [0, 1, 2, 3]
for va_period in va_period_list:
is_tr = train_x['period'] != va_period
is_va = train_x['period'] == va_period
tr_x, va_x = train_x[is_tr], train_x[is_va]
tr_y, va_y = train_y[is_tr], train_y[is_va]
| [
2,
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12,
198,
2,
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318,
262,
2496,
3... | 2.995647 | 919 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import wit
import json
if __name__ == "__main__":
print "You ran the Wit client, nothing will happen. Exiting..." | [
2,
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366,
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834,
1298,
198,
197,... | 2.704918 | 61 |
# Problem Statement: https://www.hackerrank.com/challenges/itertools-combinations-with-replacement/problem
from itertools import combinations_with_replacement
S, k = input().split()
for comb in combinations_with_replacement(sorted(S), int(k)):
print(''.join(comb)) | [
2,
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62,
4480,
62,
35666,
5... | 3.190476 | 84 |
"""
Visual Genome Python API wrapper, models
"""
| [
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462,
11361,
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29908,
11,
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198,
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628,
628,
628,
628,
628
] | 3.411765 | 17 |
import csv
import matplotlib.pyplot as plt
import time
PLOT_PER_WINDOW = False
WINDOW_LENGTH = 60000
BINS = 1000
delay_store = {}
perwindow_delay_store = {}
plotting_delay_store = {}
filename = "output-large.csv"
# filename = "output.csv"
# filename = "output-medium.csv"
# filename = "output-small.csv"
# filename = "output-tiny.csv"
with open(filename, "rU") as dataFile:
csvreader = csv.reader(dataFile)
for row in csvreader:
if len(row) > 2 and str(row[0]).isdigit():
delay_store[long(row[1])] = long(row[2])
window_begin = min(delay_store.keys())
window_end = max(delay_store.keys())
if PLOT_PER_WINDOW:
window_end = window_begin + WINDOW_LENGTH
# find the time delays that are within the window of choice
for (tapp, delay) in delay_store.iteritems():
if window_begin <= tapp <= window_end:
perwindow_delay_store[tapp] = delay
plotting_delay_store = perwindow_delay_store
else:
plotting_delay_store = delay_store
# the histogram of the data
n, bins, patches = plt.hist(plotting_delay_store.values(), BINS, histtype='stepfilled',
normed=True, cumulative=False, facecolor='blue', alpha=0.9)
# plt.axhline(y=0.95, color='red', label='0.95')
max_delay = max(plotting_delay_store.values())
min_delay = min(plotting_delay_store.values())
count = len(plotting_delay_store.values())
# format epoch time to date time to be shown in the plot figure
window_begin_in_datetime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(window_begin / 1000))
window_end_in_datetime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(window_end / 1000))
title = "Window begin: %s\n" % window_begin_in_datetime
title += "Window end: %s\n" % window_end_in_datetime
# title += "Window length: %dms\n" % WINDOW_LENGTH
title += "Window length: ~%dmins\n" % ((window_end - window_begin)/60000)
title += "Maximum delay: %dms\n" % max_delay
title += "Minimum delay: %dms\n" % min_delay
title += "Count: %d" % count
# start plotting
plt.xlabel('Delay (ms)')
plt.ylabel('Probability')
plt.grid(True)
plt.legend()
plt.suptitle(title)
plt.subplots_adjust(top=0.8)
plt.show()
| [
11748,
269,
21370,
198,
11748,
2603,
29487,
8019,
13,
9078,
29487,
355,
458,
83,
198,
11748,
640,
198,
198,
6489,
2394,
62,
18973,
62,
28929,
3913,
796,
10352,
198,
28929,
3913,
62,
43,
49494,
796,
718,
2388,
198,
33,
20913,
796,
8576... | 2.511085 | 857 |
#!/usr/bin/env python
# author: Peter Thorpe September 2015. The James Hutton Insitute, Dundee, UK.
# title rename single copy busco genes
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio import SeqIO
import os
from sys import stdin,argv
import sys
from optparse import OptionParser
########################################################################
# functions
def parse_busco_file(busco):
"""this is a function to open busco full ouput
and get a list of duplicated genes. This list is required
so we can ignore these genes later. Takes file,
return list"""
duplicated_list = []
with open(busco) as handle:
for line in handle:
if not line.strip():
continue # if the last line is blank
if line.startswith("#"):
continue
if not line:
print ("your file is empty")
return False
line_info = line.rstrip().split("\t")
# first element
Busco_name = line_info[0]
# second element
status = line_info[1]
if status == "Duplicated" or status == "Fragmented":
duplicated_list.append(Busco_name)
return duplicated_list
def reformat_as_fasta(filename,prefix,outfile):
"this function re-write a file as a fasta file"
f= open(outfile, 'w')
fas = open(filename, "r")
for line in fas:
if not line.strip():
continue # if the last line is blank
if line.startswith("#"):
continue
if not line:
return False
if not line.startswith(">"):
seq = line
title = ">" + prefix + "_" + filename.replace("BUSCOa", "").split(".fas")[0]
data = "%s\n%s\n" %(title, seq)
f.write(data)
f.close()
if "-v" in sys.argv or "--version" in sys.argv:
print "v0.0.1"
sys.exit(0)
usage = """Use as follows:
converts
$ python renaem....py -p Mce -b full_table_BUSCO_output
script to walk through all files in a folder and rename the seq id
to start with Prefix.
Used for Busco output.
give it the busco full ouput table. The script will only return
complete single copy gene. Duplicate gene will be ignored.
"""
parser = OptionParser(usage=usage)
parser.add_option("-p", "--prefix", dest="prefix",
default=None,
help="Output filename",
metavar="FILE")
parser.add_option("-b", "--busco", dest="busco",
default=None,
help="full_table_*_BUSCO output from BUSCO",
metavar="FILE")
(options, args) = parser.parse_args()
prefix = options.prefix
busco = options.busco
# Run as script
if __name__ == '__main__':
#call function to get a list of dupicated gene.
#these genes will be ignored
duplicated_list = parse_busco_file(busco)
#iterate through the dir
for filename in os.listdir("."):
count = 1
if not filename.endswith(".fas"):
continue
#filter out the ones we dont want
if filename.split(".fa")[0] in duplicated_list:
continue
out_file = "../"+prefix+filename
out_file = out_file.replace("BUSCOa", "")
#out_file = "../"+filename
try:
#print filename
reformat_as_fasta(filename, prefix, out_file)
except:
ValueError
continue
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
2,
1772,
25,
5613,
13002,
431,
2693,
1853,
13,
383,
3700,
367,
21115,
7088,
3678,
11,
37921,
1453,
11,
3482,
13,
198,
2,
3670,
36265,
2060,
4866,
1323,
1073,
10812,
198,
198,
6738,
1... | 2.288728 | 1,517 |
# Download the Python helper library from twilio.com/docs/python/install
from twilio.rest import Client
# Your Account Sid and Auth Token from twilio.com/console
api_key_sid = 'SKXXXX'
api_key_secret = 'your_api_key_secret'
client = Client(api_key_sid, api_key_secret)
did_delete = client.video\
.compositionHooks('HKXXXX')\
.delete()
if(did_delete):
print('Composition removed')
| [
2,
10472,
262,
11361,
31904,
5888,
422,
665,
346,
952,
13,
785,
14,
31628,
14,
29412,
14,
17350,
198,
6738,
665,
346,
952,
13,
2118,
1330,
20985,
198,
198,
2,
3406,
10781,
15686,
290,
26828,
29130,
422,
665,
346,
952,
13,
785,
14,
... | 2.511765 | 170 |
from time import sleep
debug_mode = False
time_to_exit = False
exiting = False
exit_code = 0
| [
6738,
640,
1330,
3993,
198,
198,
24442,
62,
14171,
796,
10352,
198,
198,
2435,
62,
1462,
62,
37023,
796,
10352,
198,
1069,
1780,
796,
10352,
198,
37023,
62,
8189,
796,
657,
628,
198
] | 2.939394 | 33 |
from __future__ import absoulte_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from data import data_utils
data = data_utils
if __name__ == "__main__":
tf.test.main()
| [
6738,
11593,
37443,
834,
1330,
2352,
2852,
660,
62,
11748,
198,
6738,
11593,
37443,
834,
1330,
7297,
198,
6738,
11593,
37443,
834,
1330,
3601,
62,
8818,
628,
198,
11748,
11192,
273,
11125,
355,
48700,
198,
198,
6738,
1366,
1330,
1366,
6... | 3.191781 | 73 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628
] | 3.888889 | 9 |
import argparse
import numpy as np
import matlab.engine
from scipy.io import savemat
import os
from time import time
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--form',
default='neuron',
const='neuron',
nargs='?',
choices=('neuron', 'network', 'layer', 'network-rand', 'network-dec-vars'),
help='LipSDP formulation to use')
parser.add_argument('-v', '--verbose',
action='store_true',
help='prints CVX output from solve if supplied')
parser.add_argument('--alpha',
type=float,
default=0,
nargs=1,
help='lower bound for slope restriction bound')
parser.add_argument('--beta',
type=float,
default=1,
nargs=1,
help='lower bound for slope restriction bound')
parser.add_argument('--num-neurons',
type=int,
default=100,
nargs=1,
help='number of neurons to couple for LipSDP-Network-rand formulation')
parser.add_argument('--split',
action='store_true',
help='splits network into subnetworks for more efficient solving if supplied')
parser.add_argument('--parallel',
action='store_true',
help='parallelizes solving for split formulations if supplied')
parser.add_argument('--split-size',
type=int,
default=2,
nargs=1,
help='number of layers in each subnetwork for splitting formulations')
parser.add_argument('--num-workers',
type=int,
default=0,
nargs=1,
help='number of workers for parallelization of splitting formulations')
parser.add_argument('--num-decision-vars',
type=int,
default=10,
nargs=1,
help='specify number of decision variables to be used for LipSDP')
parser.add_argument('--weight-path',
type=str,
required=True,
nargs=1,
help='path of weights corresponding to trained neural network model')
args = parser.parse_args()
if args.parallel is True and args.num_workers[0] < 1:
raise ValueError('When you use --parallel, --num-workers must be an integer >= 1.')
if args.split is True and args.split_size[0] < 1:
raise ValueError('When you use --split, --split-size must be an integer >= 1.')
main(args)
| [
11748,
1822,
29572,
198,
11748,
299,
32152,
355,
45941,
198,
11748,
2603,
23912,
13,
18392,
198,
6738,
629,
541,
88,
13,
952,
1330,
3613,
6759,
198,
11748,
28686,
198,
6738,
640,
1330,
640,
628,
198,
361,
11593,
3672,
834,
6624,
705,
... | 2.487342 | 948 |
from stockprophet.cli import entry_point
from stockprophet.crawler import (
init_stock_type, init_stock_category
)
from stockprophet.db import init_db
from .utils import read_db_settings
| [
6738,
4283,
22930,
3202,
13,
44506,
1330,
5726,
62,
4122,
198,
6738,
4283,
22930,
3202,
13,
66,
39464,
1330,
357,
198,
220,
220,
220,
2315,
62,
13578,
62,
4906,
11,
2315,
62,
13578,
62,
22872,
198,
8,
198,
6738,
4283,
22930,
3202,
1... | 3.180328 | 61 |
import aoc_helper
RAW = aoc_helper.day(25)
print(RAW)
DATA = parse_raw()
aoc_helper.submit(25, part_one)
aoc_helper.submit(25, part_two)
| [
11748,
257,
420,
62,
2978,
525,
198,
198,
20530,
796,
257,
420,
62,
2978,
525,
13,
820,
7,
1495,
8,
198,
4798,
7,
20530,
8,
198,
198,
26947,
796,
21136,
62,
1831,
3419,
198,
198,
64,
420,
62,
2978,
525,
13,
46002,
7,
1495,
11,
... | 2.153846 | 65 |
groupList = []
tempGroup = []
with open("./6/input.txt") as inputFile:
for line in inputFile:
line = line.replace("\n","")
if len(line) > 0:
tempGroup.append(set(line))
else:
groupList.append(tempGroup)
tempGroup = []
if len(tempGroup) > 0:
groupList.append(tempGroup)
groupList = list(map(setIntersectionCount,groupList))
print("{} common options in groups".format(sum(groupList))) | [
198,
8094,
8053,
796,
17635,
198,
29510,
13247,
796,
17635,
198,
4480,
1280,
7,
1911,
14,
21,
14,
15414,
13,
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4943,
355,
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25,
198,
197,
1640,
1627,
287,
5128,
8979,
25,
198,
197,
197,
1370,
796,
1627,
13,
33491,
720... | 2.640523 | 153 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import copy
import numpy as np
from paddleslim.nas import GPNAS
# GP-NAS[CVPR 2021 NAS](https://www.cvpr21-nas.com/competition) Track2 demo
# [CVPR 2021 NASTrack2 studio](https://aistudio.baidu.com/aistudio/competition/detail/71?lang=en)
# [AI studio GP-NAS demo](https://aistudio.baidu.com/aistudio/projectdetail/1824958)
# demo paddleslimNASGP-NAS:Gaussian Process based Neural Architecture Search
# demo
if __name__ == '__main__':
stage1_file = './datasets/Track2_stage1_trainning.json'
stage2_file = './datasets/Track2_stage2_few_show_trainning.json'
X_train_stage1, Y_train_stage1, X_test_stage1, Y_test_stage1 = preprare_trainning_data(
stage1_file, 1)
X_train_stage2, Y_train_stage2, X_test_stage2, Y_test_stage2 = preprare_trainning_data(
stage2_file, 2)
gpnas = GPNAS()
w = gpnas.get_initial_mean(X_test_stage1, Y_test_stage1)
init_cov = gpnas.get_initial_cov(X_train_stage1)
error_list = np.array(
Y_test_stage2.reshape(len(Y_test_stage2), 1) - gpnas.get_predict(
X_test_stage2))
print('RMSE trainning on stage1 testing on stage2:',
np.sqrt(np.dot(error_list.T, error_list) / len(error_list)))
gpnas.get_posterior_mean(X_train_stage2[0::3], Y_train_stage2[0::3])
gpnas.get_posterior_mean(X_train_stage2[1::3], Y_train_stage2[1::3])
gpnas.get_posterior_cov(X_train_stage2[1::3], Y_train_stage2[1::3])
error_list = np.array(
Y_test_stage2.reshape(len(Y_test_stage2), 1) - gpnas.get_predict_jiont(
X_test_stage2, X_train_stage2[::1], Y_train_stage2[::1]))
print('RMSE using stage1 as prior:',
np.sqrt(np.dot(error_list.T, error_list) / len(error_list)))
| [
2,
15069,
357,
66,
8,
33448,
220,
350,
37382,
47,
37382,
46665,
13,
1439,
6923,
33876,
13,
198,
2,
198,
2,
49962,
739,
262,
24843,
13789,
11,
10628,
362,
13,
15,
357,
1169,
366,
34156,
1,
198,
2,
345,
743,
407,
779,
428,
2393,
2... | 2.448312 | 948 |
# Generated by Django 2.0.6 on 2018-11-02 09:44
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
362,
13,
15,
13,
21,
319,
2864,
12,
1157,
12,
2999,
7769,
25,
2598,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
from collections import OrderedDict
from random import Random
from typing import Set
from .._types import Dataset, Split, LabelIndices
from .._util import per_label
from ._RandomSplitter import RandomSplitter
from ._Splitter import Splitter
| [
6738,
17268,
1330,
14230,
1068,
35,
713,
198,
6738,
4738,
1330,
14534,
198,
6738,
19720,
1330,
5345,
198,
198,
6738,
11485,
62,
19199,
1330,
16092,
292,
316,
11,
27758,
11,
36052,
5497,
1063,
198,
6738,
11485,
62,
22602,
1330,
583,
62,
... | 3.983607 | 61 |
from enum import Enum
| [
6738,
33829,
1330,
2039,
388,
628
] | 3.833333 | 6 |
from __future__ import absolute_import, division, print_function, unicode_literals
import torch.nn.qat as nnqat
import torch.nn.intrinsic
import torch.nn.functional as F
| [
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
11,
7297,
11,
3601,
62,
8818,
11,
28000,
1098,
62,
17201,
874,
201,
198,
11748,
28034,
13,
20471,
13,
80,
265,
355,
299,
77,
80,
265,
201,
198,
11748,
28034,
13,
20471,
13,
600,
81,
... | 3.052632 | 57 |
'''OpenGL extension EXT.draw_buffers2
This module customises the behaviour of the
OpenGL.raw.GL.EXT.draw_buffers2 to provide a more
Python-friendly API
Overview (from the spec)
This extension builds upon the ARB_draw_buffers extension and provides
separate blend enables and color write masks for each color output. In
ARB_draw_buffers (part of OpenGL 2.0), separate values can be written to
each color buffer, but the blend enable and color write mask are global
and apply to all color outputs.
While this extension does provide separate blend enables, it does not
provide separate blend functions or blend equations per color output.
The official definition of this extension is available here:
http://www.opengl.org/registry/specs/EXT/draw_buffers2.txt
'''
from OpenGL import platform, constants, constant, arrays
from OpenGL import extensions, wrapper
from OpenGL.GL import glget
import ctypes
from OpenGL.raw.GL.EXT.draw_buffers2 import *
### END AUTOGENERATED SECTION | [
7061,
6,
11505,
8763,
7552,
27489,
13,
19334,
62,
36873,
364,
17,
198,
198,
1212,
8265,
2183,
2696,
262,
9172,
286,
262,
220,
198,
11505,
8763,
13,
1831,
13,
8763,
13,
13918,
13,
19334,
62,
36873,
364,
17,
284,
2148,
257,
517,
220,
... | 3.701493 | 268 |
""" Module containing the RetryingClient wrapper class. """
from time import sleep
def _ensure_tuple_argument(argument_name, argument_value):
"""
Helper function to ensure the given arguments are tuples of Exceptions (or
subclasses), or can at least be converted to such.
Args:
argument_name: str, name of the argument we're checking, only used for
raising meaningful exceptions.
argument: any, the argument itself.
Returns:
tuple[Exception]: A tuple with the elements from the argument if they are
valid.
Exceptions:
ValueError: If the argument was not None, tuple or Iterable.
ValueError: If any of the elements of the argument is not a subclass of
Exception.
"""
# Ensure the argument is a tuple, set or list.
if argument_value is None:
return tuple()
elif not isinstance(argument_value, (tuple, set, list)):
raise ValueError("%s must be either a tuple, a set or a list." % argument_name)
# Convert the argument before checking contents.
argument_tuple = tuple(argument_value)
# Check that all the elements are actually inherited from Exception.
# (Catchable)
if not all([issubclass(arg, Exception) for arg in argument_tuple]):
raise ValueError(
"%s is only allowed to contain elements that are subclasses of "
"Exception." % argument_name
)
return argument_tuple
| [
37811,
19937,
7268,
262,
4990,
14992,
11792,
29908,
1398,
13,
37227,
198,
198,
6738,
640,
1330,
3993,
628,
198,
4299,
4808,
641,
495,
62,
83,
29291,
62,
49140,
7,
49140,
62,
3672,
11,
4578,
62,
8367,
2599,
198,
220,
220,
220,
37227,
... | 3.012448 | 482 |
import numpy as np
import numpy.linalg as lg
A_mat = np.matrix([
[0, 1, 1, 1, 0],
[1, 0, 0, 0, 1],
[1, 0, 0, 1, 1],
[1, 0, 1, 0, 1],
[0, 1, 1, 1, 0]
])
eigen = lg.eig(A_mat) # return Arr[5] with 5 different linear independent eigen values
vec = eigen[1][:, 0] # the column (eigen vector) with the largest eigen value
value = eigen[0][0] # the largest eigen value
print(vec)
print(A_mat * vec)
print(value * vec)
| [
11748,
299,
32152,
355,
45941,
201,
198,
11748,
299,
32152,
13,
75,
1292,
70,
355,
300,
70,
201,
198,
201,
198,
32,
62,
6759,
796,
45941,
13,
6759,
8609,
26933,
201,
198,
220,
220,
220,
685,
15,
11,
352,
11,
352,
11,
352,
11,
65... | 2.058036 | 224 |
# Generated by Django 2.2.2 on 2019-08-25 09:29
from django.db import migrations, models
| [
2,
2980,
515,
416,
37770,
362,
13,
17,
13,
17,
319,
13130,
12,
2919,
12,
1495,
7769,
25,
1959,
198,
198,
6738,
42625,
14208,
13,
9945,
1330,
15720,
602,
11,
4981,
628
] | 2.84375 | 32 |
from jumbo_api.objects.store import Store
| [
6738,
474,
29309,
62,
15042,
13,
48205,
13,
8095,
1330,
9363,
628
] | 3.583333 | 12 |
#!/usr/bin/env python
import os
import sys
import time
import errno
import stat
import datetime
import socket
import struct
import atexit
import logging
#from lru import LRUCacheDict
from logging import handlers
from task_manager import Job, taskManage
from ctypes import *
from urlparse import *
from multiprocessing import Process,Lock
from log_obj import CLog
from parse_conf import cConfParser
log_file = "timelog.log"
log_fmt = '%(asctime)s: %(message)s'
config_file = 'test.config'
domain_white_dict = {}
pps_ip_list = []
pps_port = 0
domain_sfx_err_count = 0
domain_sfx_err_rate = 0
ats_ip = ''
once_flag = 0
txn_idx = 0
d1 = {}
mutex = Lock()
version_message = '1.0.1'
#1.0.1: Add conf obj; Add log obj
#1.0.2: More pps. add tool config
if __name__ == '__main__':
help_message = 'Usage: python %s' % sys.argv[0]
if len(sys.argv) == 2 and (sys.argv[1] in '--version'):
print version_message
exit(1)
if len(sys.argv) == 2 and (sys.argv[1] in '--help'):
print help_message
exit(1)
if len(sys.argv) != 1:
print help_message
exit(1)
cp = config_parse()
get_domain_white(cp.get('common', 'domain_white_list'))
loger = CLog(log_file, log_fmt, 12, 5, cp.get('common', 'debug'))
print 'Start ok'
daemonize()
tmg = taskManage()
tmg.run()
pull_pps_job = Job(txn_idx, period_check_task, time.time(), int(cp.get('common', 'interval')), '', '', callback_routine, '', '')
tmg.task_add(pull_pps_job)
l = LogWatcher("/opt/ats/var/log/trafficserver", callback)
l.loop()
#https://docs.python.org/2/library/ctypes.html
#https://blog.csdn.net/u012611644/article/details/80529746
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
198,
198,
11748,
28686,
198,
11748,
25064,
198,
11748,
640,
198,
11748,
11454,
3919,
198,
11748,
1185,
198,
11748,
4818,
8079,
198,
11748,
17802,
198,
11748,
2878,
198,
11748,
379,
37023,
198,
... | 2.439528 | 678 |
# -*- encoding: utf-8 -*-
from flask import request
from lazyblacksmith.utils.request import is_xhr
import logging
logger = logging.getLogger('lb.ajax')
def is_not_ajax():
"""
Return True if request is not ajax
This function is used in @cache annotation
to not cache direct call (http 403)
"""
return not is_xhr(request)
| [
2,
532,
9,
12,
21004,
25,
3384,
69,
12,
23,
532,
9,
12,
201,
198,
6738,
42903,
1330,
2581,
201,
198,
6738,
16931,
2436,
4595,
22947,
13,
26791,
13,
25927,
1330,
318,
62,
87,
11840,
201,
198,
201,
198,
11748,
18931,
201,
198,
201,
... | 2.570423 | 142 |
import os
| [
11748,
28686,
198
] | 3.333333 | 3 |
import django
from django.conf import settings
from django.conf.urls import include, url
from django.contrib import admin
from django.contrib.staticfiles.urls import staticfiles_urlpatterns
if django.VERSION[:2] > (1, 9):
from django.views.i18n import JavaScriptCatalog
else:
from django.views.i18n import javascript_catalog
from django_comments_xtd import LatestCommentFeed
from django_comments_xtd.views import XtdCommentListView
from comp import views
admin.autodiscover()
urlpatterns = [
url(r'^$', views.HomepageView.as_view(), name='homepage'),
url(r'^i18n/', include('django.conf.urls.i18n')),
url(r'^admin/', include(admin.site.urls)),
url(r'^articles/', include('comp.articles.urls')),
url(r'^quotes/', include('comp.quotes.urls')),
url(r'^comments/', include('django_comments_xtd.urls')),
url(r'^comments/$', XtdCommentListView.as_view(
content_types=["articles.article", "quotes.quote"],
paginate_by=10, page_range=5),
name='comments-xtd-list'),
url(r'^feeds/comments/$', LatestCommentFeed(), name='comments-feed'),
url(r'^api-auth/', include('rest_framework.urls',
namespace='rest_framework')),
]
if django.VERSION[:2] > (1, 9):
urlpatterns.append(
url(r'^jsi18n/$', JavaScriptCatalog.as_view(),
name='javascript-catalog')
)
else:
js_info_dict = {
'packages': ('django_comments_xtd',)
}
urlpatterns.append(
url(r'^jsi18n/$', javascript_catalog, js_info_dict,
name='javascript-catalog')
)
if settings.DEBUG:
urlpatterns += staticfiles_urlpatterns()
if 'rosetta' in settings.INSTALLED_APPS:
urlpatterns += [url(r'^rosetta/', include('rosetta.urls'))]
| [
11748,
42625,
14208,
198,
6738,
42625,
14208,
13,
10414,
1330,
6460,
198,
6738,
42625,
14208,
13,
10414,
13,
6371,
82,
1330,
2291,
11,
19016,
198,
6738,
42625,
14208,
13,
3642,
822,
1330,
13169,
198,
6738,
42625,
14208,
13,
3642,
822,
1... | 2.382114 | 738 |
import math
import time
if __name__ == "__main__":
primes = []
t1 = time.time()
# 100109100129100151 big prime
# http://primes.utm.edu/curios/page.php/100109100129100151.html
# number_range = xrange(100109100129100153, 100109100129101238, 2)
number_range = range(100109100129101237, 100109100129201238, 2)
# new expensive near-primes
# [(95362951, (100109100129100369, 7.254560947418213))
# (171656941, (100109100129101027, 13.052711009979248))
# (121344023, (100109100129101291, 8.994053840637207)
# note these two lines of timings look really wrong, they're about 4sec
# each really
# [(265687139, (100109100129102047, 19.642582178115845)), (219609683, (100109100129102277, 16.178056001663208)), (121344023, (100109100129101291, 8.994053840637207))]
# [(316096873, (100109100129126653, 23.480671882629395)), (313994287, (100109100129111617, 23.262380123138428)), (307151363, (100109100129140177, 22.80288815498352))]
# primes
# 100109100129162907
# 100109100129162947
highest_factors = {}
for possible_prime in number_range:
t2 = time.time()
is_prime, factor = check_prime(possible_prime)
if is_prime:
primes.append(possible_prime)
print("GOT NEW PRIME", possible_prime)
else:
highest_factors[factor] = (possible_prime, time.time() - t2)
hf = highest_factors.items()
hf = sorted(hf, reverse=True)
print(hf[:3])
print("Took:", time.time() - t1)
print(len(primes), primes[:10], primes[-10:])
| [
11748,
10688,
198,
11748,
640,
628,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
778,
999,
796,
17635,
198,
220,
220,
220,
256,
16,
796,
640,
13,
2435,
3419,
628,
220,
220,
220,
1303,
220,
... | 2.20111 | 721 |
import pytest
from katana.dynamic_bitset import DynamicBitset
__all__ = []
SIZE = 50
| [
11748,
12972,
9288,
198,
198,
6738,
479,
43777,
13,
67,
28995,
62,
9895,
316,
1330,
26977,
33,
896,
316,
198,
198,
834,
439,
834,
796,
17635,
198,
198,
33489,
796,
2026,
628,
628,
628,
628,
628,
628,
628
] | 2.657895 | 38 |
import unittest
import astar
if __name__ == '__main__':
unittest.main()
| [
11748,
555,
715,
395,
198,
11748,
6468,
283,
628,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 2.516129 | 31 |
#!/bin/env python3
import csv
effects = {}
ingredients = {}
print("Formulating formulas")
with open('ingredients.csv') as csvfile:
aff = csv.reader(csvfile, delimiter=',')
for row in aff:
if row[0] not in effects.keys():
effects[row[0]] = row[1]
with open('skyrim-ingredients.csv', newline='') as csvfile:
ingre = csv.reader(csvfile, delimiter=',')
for row in ingre:
if row[0] not in ingredients.keys():
ingredients[row[0]] = [row[1],row[2],row[3],row[4]]
multieffects = {}
for ce in effects:
curing = []
for ing in ingredients:
if ce in ingredients[ing]:
curing.append(ing)
for k,curi in enumerate(curing):
for i in range(k+1,len(curing)):
cureff = intersect(ingredients[curi],ingredients[curing[i]])
cureff.sort()
if len(cureff)>1:
if curi>curing[i]:
curname = curing[i] + ':' + curi
else:
curname = curi + ':' + curing[i]
multieffects[curname] = cureff
finallist = {}
for me in multieffects:
curing = me.split(":")
for ing in ingredients:
if ing!=curing[0] and ing!=curing[1]:
eff1 = intersect(ingredients[curing[0]],ingredients[ing])
eff2 = intersect(ingredients[curing[1]],ingredients[ing])
if len(eff1)>0 or len(eff2)>0:
tmpname = [ val for val in curing ]
tmpname.append(ing)
tmpname.sort()
finalname = ":".join(tmpname)
finallist[finalname] = list(set(multieffects[me] + eff1 + eff2))
finallist[finalname].sort()
with open('formulas.csv',mode='w') as formula_file:
formula_writer = csv.writer(formula_file, delimiter=',')
formula_writer.writerow(['Category','Ingredient 1','Ingredient 2','Ingredient 3','Effect 1','Effect 2','Effect 3','Effect 4','Effect 5'])
for fl in finallist:
formula_writer.writerow([category(finallist[fl],effects)] + fl.split(":") + finallist[fl])
for fl in multieffects:
formula_writer.writerow([category(multieffects[fl],effects)] + fl.split(":") + [''] + multieffects[fl])
| [
2,
48443,
8800,
14,
24330,
21015,
18,
198,
198,
11748,
269,
21370,
198,
198,
34435,
796,
23884,
198,
278,
23320,
796,
23884,
198,
4798,
7203,
8479,
8306,
32126,
4943,
628,
198,
4480,
1280,
10786,
278,
23320,
13,
40664,
11537,
355,
269,
... | 2.116412 | 1,048 |
# coding: utf-8
"""
Control-M Services
Provides access to BMC Control-M Services # noqa: E501
OpenAPI spec version: 9.20.215
Contact: customer_support@bmc.com
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
from clients.ctm_api_client.configuration import Configuration
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, UserAdditionalProperties):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, UserAdditionalProperties):
return True
return self.to_dict() != other.to_dict()
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
198,
37811,
198,
220,
220,
220,
6779,
12,
44,
6168,
628,
220,
220,
220,
47081,
1895,
284,
40714,
6779,
12,
44,
6168,
220,
1303,
645,
20402,
25,
412,
33548,
628,
220,
220,
220,
4946,
17614,
1020... | 2.590452 | 398 |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
from unittest import TestCase
from pyleecan.Classes.CellMat import CellMat
from pyleecan.Classes.MeshSolution import MeshSolution
from pyleecan.Classes.PointMat import PointMat
from pyleecan.Classes.MeshMat import MeshMat
from pyleecan.Classes.ScalarProductL2 import ScalarProductL2
from pyleecan.Classes.Interpolation import Interpolation
from pyleecan.Classes.RefSegmentP1 import RefSegmentP1
from pyleecan.Classes.FPGNSeg import FPGNSeg
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
198,
11748,
12972,
9288,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
555,
715,
395,
1330,
6208,
20448,
198,
198,
6738,
279,
2349,
721,
272,
13,
9487,
274,
13,
28780,
... | 2.890173 | 173 |
# -*- coding: utf-8 -*-
"""
Pytorch models
__author__ = 'Jamie (krikit@naver.com)'
__copyright__ = 'No copyright. Just copyleft!'
"""
# pylint: disable=no-member
# pylint: disable=invalid-name
###########
# imports #
###########
import torch
import torch.nn as nn
from embedder import Embedder
from pos_models import PosTagger, FnnTagger, CnnTagger # pylint: disable=unused-import
#############
# Ner Class #
#############
#################
# Encoder Class #
#################
#################
# Decoder Class #
#################
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
628,
198,
37811,
198,
20519,
13165,
354,
4981,
198,
834,
9800,
834,
796,
705,
48337,
357,
74,
12602,
270,
31,
2616,
332,
13,
785,
33047,
198,
834,
22163,
4766,
834,
796,
70... | 2.983696 | 184 |
# -*- coding: utf-8 -*-
"""Top-level package for pyseqlogo."""
__author__ = """Saket Choudhary"""
__email__ = 'saketkc@gmail.com'
__version__ = '0.1.0'
from .pyseqlogo import draw_logo
from .pyseqlogo import setup_axis
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
37811,
9126,
12,
5715,
5301,
329,
12972,
41068,
6404,
78,
526,
15931,
198,
198,
834,
9800,
834,
796,
37227,
50,
461,
316,
609,
2778,
71,
560,
37811,
198,
834,
12888,
8... | 2.351064 | 94 |
# coding: utf-8
import os.path
try:
from setuptools import setup
extras = dict(zip_safe=False, test_suite='nose.collector', tests_require=['nose'])
except ImportError:
from distutils.core import setup
extras = {}
import apscheduler
here = os.path.dirname(__file__)
readme_path = os.path.join(here, 'README.rst')
readme = open(readme_path).read()
setup(
name='APScheduler',
version=apscheduler.release,
description='In-process task scheduler with Cron-like capabilities',
long_description=readme,
author='Alex Gronholm',
author_email='apscheduler@nextday.fi',
url='http://pypi.python.org/pypi/APScheduler/',
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python',
'Programming Language :: Python :: 2.6',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3'
],
keywords='scheduling cron',
license='MIT',
packages=('apscheduler', 'apscheduler.jobstores', 'apscheduler.triggers', 'apscheduler.triggers.cron'),
)
| [
2,
19617,
25,
3384,
69,
12,
23,
198,
11748,
28686,
13,
6978,
198,
198,
28311,
25,
198,
220,
220,
220,
422,
900,
37623,
10141,
1330,
9058,
628,
220,
220,
220,
33849,
796,
8633,
7,
13344,
62,
21230,
28,
25101,
11,
1332,
62,
2385,
57... | 2.605317 | 489 |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Travis Yates
"""Tests for object_detection.export_inference_graph."""
import os
import mock
import numpy as np
import tensorflow as tf
from object_detection import exporter
from object_detection.builders import model_builder
from object_detection.core import model
from object_detection.protos import pipeline_pb2
if __name__ == '__main__':
tf.test.main()
| [
2,
15069,
2177,
383,
309,
22854,
37535,
46665,
13,
1439,
6923,
33876,
13,
198,
2,
198,
2,
19804,
34916,
198,
198,
37811,
51,
3558,
329,
2134,
62,
15255,
3213,
13,
39344,
62,
259,
4288,
62,
34960,
526,
15931,
198,
11748,
28686,
198,
... | 3.459677 | 124 |
import os
import sys
import random
main()
| [
11748,
28686,
198,
11748,
25064,
198,
11748,
4738,
628,
198,
12417,
3419,
628,
198
] | 3.285714 | 14 |
"""Automated CI tools to run with Nox"""
import nox
from nox import Session
locations = "src", "noxfile.py", "docs/conf.py"
nox.options.sessions = "lint", "tests"
package = "hypermodern_python"
| [
37811,
38062,
515,
14514,
4899,
284,
1057,
351,
399,
1140,
37811,
198,
11748,
645,
87,
198,
6738,
645,
87,
1330,
23575,
198,
198,
17946,
602,
796,
366,
10677,
1600,
366,
35420,
7753,
13,
9078,
1600,
366,
31628,
14,
10414,
13,
9078,
1,... | 2.873239 | 71 |
import cocotb_test.simulator
# For partial back compatibility
| [
198,
11748,
8954,
313,
65,
62,
9288,
13,
14323,
8927,
628,
198,
2,
1114,
13027,
736,
17764,
198
] | 3.611111 | 18 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
18211,
1330,
2034,
16934,
628,
198
] | 3.6 | 10 |
"""
~ Tracing ~
This modules containes functions and classes
related to the console debug long or trace.
"""
from enum import Enum, auto
import time
__all__ = (
"TraceLEVELS",
"trace"
)
m_use_debug = None
def trace(message: str,
level: TraceLEVELS = TraceLEVELS.NORMAL):
""""
Name : trace
Param:
- message : str = Trace message
- level : TraceLEVELS = Level of the trace
"""
if m_use_debug:
timestruct = time.localtime()
timestamp = "Date: {:02d}.{:02d}.{:04d} Time:{:02d}:{:02d}"
timestamp = timestamp.format(timestruct.tm_mday,
timestruct.tm_mon,
timestruct.tm_year,
timestruct.tm_hour,
timestruct.tm_min)
l_trace = f"{timestamp}\nTrace level: {level.name}\nMessage: {message}\n"
print(l_trace)
| [
37811,
198,
220,
220,
220,
5299,
220,
220,
833,
4092,
220,
220,
220,
5299,
198,
220,
220,
220,
770,
13103,
3994,
274,
5499,
290,
6097,
198,
220,
220,
220,
3519,
284,
262,
8624,
14257,
890,
393,
12854,
13,
198,
37811,
198,
6738,
3382... | 1.910891 | 505 |
"""
sunkit-image
============
A image processing toolbox for Solar Physics.
* Homepage: https://sunpy.org
* Documentation: https://sunkit-image.readthedocs.io/en/latest/
"""
import sys
from .version import version as __version__ # NOQA
# Enforce Python version check during package import.
__minimum_python_version__ = "3.7"
if sys.version_info < tuple(int(val) for val in __minimum_python_version__.split(".")):
# This has to be .format to keep backwards compatibly.
raise UnsupportedPythonError(
"sunkit_image does not support Python < {}".format(__minimum_python_version__)
)
__all__ = []
| [
37811,
198,
82,
2954,
270,
12,
9060,
198,
25609,
198,
198,
32,
2939,
7587,
2891,
3524,
329,
12347,
23123,
13,
198,
198,
9,
5995,
7700,
25,
3740,
1378,
19155,
9078,
13,
2398,
198,
9,
43925,
25,
3740,
1378,
82,
2954,
270,
12,
9060,
... | 3.029268 | 205 |
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd | [
11748,
7110,
306,
13,
34960,
62,
48205,
355,
467,
198,
11748,
7110,
306,
13,
42712,
355,
279,
87,
198,
11748,
19798,
292,
355,
279,
67
] | 3.24 | 25 |
# Exercise 4.11
# Author: Noah Waterfield Price
import sys
g = 9.81 # acceleration due to gravity
try:
# initial velocity (convert to m/s)
v0 = (1000. / 3600) * float(sys.argv[1])
mu = float(sys.argv[2]) # coefficient of friction
except IndexError:
print 'Both v0 (in km/s) and mu must be supplied on the command line'
v0 = (1000. / 3600) * float(raw_input('v0 = ?\n'))
mu = float(raw_input('mu = ?\n'))
except ValueError:
print 'v0 and mu must be pure numbers'
sys.exit(1)
d = 0.5 * v0 ** 2 / mu / g
print d
"""
Sample run:
python stopping_length.py 120 0.3
188.771850342
python stopping_length.py 50 0.3
32.7728906843
"""
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1851... | 2.488722 | 266 |
# Testing code to check update status on demand
from socketIO_client import SocketIO, LoggingNamespace
from threading import Thread
socketIO = SocketIO('localhost', 3000)
status = 'pause'
receive_thread = Thread(target=_receive_thread, daemon=True)
receive_thread.start()
socketIO.on('pushState', on_push_state)
# issue this and the socketIO.wait in the background will push the reply
socketIO.emit('getState', '', on_push_state) | [
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... | 3.382813 | 128 |
__version__ = "2.1.1"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when present.
# see: https://github.com/abseil/abseil-py/issues/99
# and: https://github.com/tensorflow/tensorflow/issues/26691#issuecomment-500369493
try:
import absl.logging
absl.logging.set_verbosity('info')
absl.logging.set_stderrthreshold('info')
absl.logging._warn_preinit_stderr = False
except:
pass
import logging
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
# Files and general utilities
from .file_utils import (TRANSFORMERS_CACHE, PYTORCH_TRANSFORMERS_CACHE, PYTORCH_PRETRAINED_BERT_CACHE,
cached_path, add_start_docstrings, add_end_docstrings,
WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, CONFIG_NAME,
is_tf_available, is_torch_available)
# Tokenizers
from .tokenization_utils import (PreTrainedTokenizer)
from .tokenization_auto import AutoTokenizer
from .tokenization_bert import BertTokenizer, BasicTokenizer, WordpieceTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_transfo_xl import (TransfoXLTokenizer, TransfoXLCorpus)
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_xlnet import XLNetTokenizer, SPIECE_UNDERLINE
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
# Configurations
from .configuration_utils import PretrainedConfig
from .configuration_auto import AutoConfig
from .configuration_bert import BertConfig, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_openai import OpenAIGPTConfig, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_transfo_xl import TransfoXLConfig, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_gpt2 import GPT2Config, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlnet import XLNetConfig, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_ctrl import CTRLConfig, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_xlm import XLMConfig, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_roberta import RobertaConfig, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
from .configuration_distilbert import DistilBertConfig, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
# Modeling
if is_torch_available():
from .modeling_utils import (PreTrainedModel, prune_layer, Conv1D)
from .modeling_auto import (AutoModel, AutoModelForSequenceClassification, AutoModelForQuestionAnswering,
AutoModelWithLMHead)
from .modeling_bert import (BertPreTrainedModel, BertModel, BertForPreTraining,
BertForMaskedLM, BertForNextSentencePrediction,
BertForSequenceClassification, BertForMultipleChoice,
BertForTokenClassification, BertForQuestionAnswering,
load_tf_weights_in_bert, BERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_openai import (OpenAIGPTPreTrainedModel, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel,
load_tf_weights_in_openai_gpt, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_transfo_xl import (TransfoXLPreTrainedModel, TransfoXLModel, TransfoXLLMHeadModel,
load_tf_weights_in_transfo_xl, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_gpt2 import (GPT2PreTrainedModel, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel,
load_tf_weights_in_gpt2, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_ctrl import (CTRLPreTrainedModel, CTRLModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlnet import (XLNetPreTrainedModel, XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForMultipleChoice,
XLNetForQuestionAnsweringSimple, XLNetForQuestionAnswering,
load_tf_weights_in_xlnet, XLNET_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_xlm import (XLMPreTrainedModel , XLMModel,
XLMWithLMHeadModel, XLMForSequenceClassification,
XLMForQuestionAnswering, XLMForQuestionAnsweringSimple,
XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_roberta import (RobertaForMaskedLM, RobertaModel,
RobertaForSequenceClassification, RobertaForMultipleChoice,
ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
from .modeling_albert import AlbertForSequenceClassification
# Optimization
from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,
WarmupCosineWithHardRestartsSchedule, WarmupLinearSchedule)
if not is_tf_available() and not is_torch_available():
logger.warning("Neither PyTorch nor TensorFlow >= 2.0 have been found."
"Models won't be available and only tokenizers, configuration"
"and file/data utilities can be used.")
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2... | 2.26811 | 2,540 |
from __future__ import absolute_import, division, print_function
data = r"""cdials_array_family_flex_ext
shoebox
p1
(tRp2
(cscitbx_array_family_flex_ext
grid
p3
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tb."""
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17,... | 1.316733 | 69,437 |
#!/usr/bin/env python
"""
Info: This script loads the model trained in the cnn-asl.py script and enables the user to use it for classifying unseen ASL letters. It also visualizes the feature map of the last convolutional layer of the network to enable the user to get an insight into exactly which parts of the original image that the model is paying attention to when classifying the image.
Parameters:
(optional) model_name: str <name-of-the-model-to-load>, default = "saved_model.json"
(optional) train_data: str <name-of-training-data>, default = "asl_alphabet_train_subset"
(optional) unseen_image: str <name-of-unseen-image>, default = "unseen_img_test1.png"
Usage:
$ python use-model.py
Output:
- unseen_image_superimposed_heatmap.png: superimposed heatmap on unseen image.
- unseen_image_prediction.txt: model prediction of unseen image.
"""
### DEPENDENCIES ###
# Core libraries
import os
import sys
sys.path.append(os.path.join(".."))
# Matplotlib, numpy, OpenCV
import matplotlib.pyplot as plt
import numpy as np
import cv2
# TensorFlow
import tensorflow as tf
from tensorflow.keras.preprocessing.image import (load_img, img_to_array)
from tensorflow.keras.applications.resnet import preprocess_input
from tensorflow.keras.models import model_from_json
from tensorflow.keras import backend as K
# argparse
import argparse
### MAIN FUNCTION ###
# Creating classifier class
# Define behaviour when called from command line
if __name__=="__main__":
main() | [
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7... | 3.123457 | 486 |
from Algorithmia import ADK
# API calls will begin at the apply() method, with the request body passed as 'input'
# For more details, see algorithmia.com/developers/algorithm-development/languages
# This turns your library code into an algorithm that can run on the platform.
# If you intend to use loading operations, remember to pass a `load` function as a second variable.
algorithm = ADK(apply)
# The 'init()' function actually starts the algorithm, you can follow along in the source code
# to see how everything works.
algorithm.init("Algorithmia")
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14,... | 3.964539 | 141 |
# Copyright 2018 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
import logging
from typing import Callable, Optional, List, Tuple
import pandas as pd
from autogluon.tabular import TabularPredictor as AutogluonTabularPredictor
from gluonts.core.component import validated
from gluonts.dataset.common import Dataset
from gluonts.dataset.util import to_pandas
from gluonts.model.estimator import Estimator
from gluonts.time_feature import (
TimeFeature,
get_lags_for_frequency,
time_features_from_frequency_str,
)
from .predictor import (
TabularPredictor,
mean_abs_scaling,
get_features_dataframe,
)
logger = logging.getLogger(__name__)
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... | 3.181572 | 369 |
"""Errors module."""
__all__ = [
'Error',
'AddressError',
'AuthenticationError',
'TransportError',
'ValidationError',
'RegisterError',
'MessageError',
'DBusError',
'SignatureError',
'TooLongError',
]
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8... | 2.367925 | 106 |
from XDR_iocs import *
import pytest
from freezegun import freeze_time
Client.severity = 'INFO'
client = Client({'url': 'test'})
def test_create_file_sync_all_types(self, mocker):
"""
Given:
- Sync command
When:
- iocs as all types
Then:
- Verify sync file data.
"""
all_iocs, expected_data = self.get_all_iocs(self.data_test_create_file_sync, 'txt')
mocker.patch.object(demisto, 'searchIndicators', return_value=all_iocs)
create_file_sync(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
assert data == expected_data, f'create_file_sync with all iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
data_test_create_file_with_empty_indicators = [
{},
{'value': '11.11.11.11'},
{'indicator_type': 'IP'}
]
def test_create_file_iocs_to_keep_without_iocs(self, mocker):
"""
Given:
- iocs to keep command
When:
- there is no iocs
Then:
- Verify iocs to keep file data.
"""
mocker.patch.object(demisto, 'searchIndicators', return_value={})
create_file_iocs_to_keep(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
expected_data = ''
assert data == expected_data, f'create_file_iocs_to_keep with no iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
def test_create_file_iocs_to_keep_all_types(self, mocker):
"""
Given:
- iocs to keep command
When:
- iocs as all types
Then:
- Verify iocs to keep file data.
"""
all_iocs, expected_data = self.get_all_iocs(self.data_test_create_file_iocs_to_keep, 'txt')
mocker.patch.object(demisto, 'searchIndicators', return_value=all_iocs)
create_file_iocs_to_keep(TestCreateFile.path)
data = self.get_file(TestCreateFile.path)
assert data == expected_data, f'create_file_iocs_to_keep with all iocs\n\tcreates: {data}\n\tinstead: {expected_data}'
class TestDemistoIOCToXDR:
data_test_demisto_expiration_to_xdr = [
(None, -1),
('', -1),
('0001-01-01T00:00:00Z', -1),
('2020-06-03T00:00:00Z', 1591142400000)
]
data_test_demisto_reliability_to_xdr = [
(None, 'F'),
('A - Completely reliable', 'A'),
('B - Usually reliable', 'B'),
('C - Fairly reliable', 'C'),
('D - Not usually reliable', 'D'),
('E - Unreliable', 'E'),
('F - Reliability cannot be judged', 'F')
]
data_test_demisto_types_to_xdr = [
('File', 'HASH'),
('IP', 'IP'),
('Domain', 'DOMAIN_NAME')
]
data_test_demisto_vendors_to_xdr = [
(
{'moduleID': {'sourceBrand': 'test', 'reliability': 'A - Completely reliable', 'score': 2}},
{'vendor_name': 'test', 'reputation': 'SUSPICIOUS', 'reliability': 'A'}
),
(
{'moduleID': {'reliability': 'A - Completely reliable', 'score': 2}},
{'vendor_name': 'moduleID', 'reputation': 'SUSPICIOUS', 'reliability': 'A'}
),
(
{'moduleID': {'sourceBrand': 'test', 'score': 2}},
{'vendor_name': 'test', 'reputation': 'SUSPICIOUS', 'reliability': 'F'}
),
(
{'moduleID': {'reliability': 'A - Completely reliable', 'score': 0}},
{'vendor_name': 'moduleID', 'reputation': 'UNKNOWN', 'reliability': 'A'}
)
]
data_test_demisto_ioc_to_xdr = [
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO',
'type': 'IP'}
),
(
{'value': '11.11.11.11', 'indicator_type': 100, 'score': 2},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'SUSPICIOUS', 'severity': 'INFO', 'type': '100'}
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP'},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP'}
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'expiration': '2020-06-03T00:00:00Z'},
{'expiration_date': 1591142400000, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'comments': [{'type': 'IndicatorCommentTimeLine', 'content': 'test'}]}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP'}
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'comments': [{'type': 'IndicatorCommentRegular', 'content': 'test'}]}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'comment': 'test'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'comments': [{'type': 'IndicatorCommentRegular', 'content': 'test'}, {'type': 'IndicatorCommentRegular', 'content': 'this is the comment'}]}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'comment': 'this is the comment'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'aggregatedReliability': 'A - Completely reliable'},
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'reliability': 'A'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'CustomFields': {'threattypes': {'threatcategory': 'Malware'}}}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'class': 'Malware'} # noqa: E501
),
(
{'value': '11.11.11.11', 'indicator_type': 'IP', 'moduleToFeedMap': {'module': {'sourceBrand': 'test', 'score': 2}}}, # noqa: E501
{'expiration_date': -1, 'indicator': '11.11.11.11', 'reputation': 'UNKNOWN', 'severity': 'INFO', 'type': 'IP', 'vendors': [{'vendor_name': 'test', 'reputation': 'SUSPICIOUS', 'reliability': 'F'}]} # noqa: E501
)
]
class TestXDRIOCToDemisto:
data_test_xdr_expiration_to_demisto = [
(-1, 'Never'),
(1591142400000, '2020-06-03T00:00:00Z'),
(1592142400000, '2020-06-14T13:46:40Z')
]
data_test_xdr_ioc_to_demisto = [
(
{
'RULE_ID': 863, 'RULE_INSERT_TIME': 1591165763753, 'RULE_MODIFY_TIME': 1591166095668,
'RULE_SEVERITY': 'SEV_010_INFO', 'NUMBER_OF_HITS': 0, 'RULE_SOURCE': 'XSOAR TIM', 'RULE_COMMENT': '',
'RULE_STATUS': 'DISABLED', 'BS_STATUS': 'DONE', 'BS_TS': 1591165801230, 'BS_RETRIES': 1,
'RULE_EXPIRATION_TIME': -1, 'IOC_TYPE': 'HASH',
'RULE_INDICATOR': 'fa66f1e0e318b6d7b595b6cee580dc0d8e4ac38fbc8dbfcac6ad66dbe282832e', 'REPUTATION': 'GOOD', # noqa: E501
'RELIABILITY': None, 'VENDORS': None, 'KLASS': None, 'IS_DEFAULT_TTL': False, 'RULE_TTL': -1,
'MARKED_DELETED': 0
},
{
'value': 'fa66f1e0e318b6d7b595b6cee580dc0d8e4ac38fbc8dbfcac6ad66dbe282832e',
'type': 'File',
'score': 1,
'fields': {
'expirationdate': 'Never',
'tags': 'Cortex XDR',
'xdrstatus': 'disabled'
}
}
),
(
{
'RULE_ID': 861, 'RULE_INSERT_TIME': 1591165763753, 'RULE_MODIFY_TIME': 1591166095668,
'RULE_SEVERITY': 'SEV_010_INFO', 'NUMBER_OF_HITS': 0, 'RULE_SOURCE': 'XSOAR TIM', 'RULE_COMMENT': '',
'RULE_STATUS': 'DISABLED', 'BS_STATUS': 'DONE', 'BS_TS': 1591165801784, 'BS_RETRIES': 1,
'RULE_EXPIRATION_TIME': -1, 'IOC_TYPE': 'DOMAIN_NAME', 'RULE_INDICATOR': 'test.com', 'REPUTATION': 'GOOD', # noqa: E501
'RELIABILITY': None, 'VENDORS': None, 'KLASS': None, 'IS_DEFAULT_TTL': False, 'RULE_TTL': -1,
'MARKED_DELETED': 0
},
{
'value': 'test.com',
'type': 'Domain',
'score': 1,
'fields': {
'expirationdate': 'Never',
'tags': 'Cortex XDR',
'xdrstatus': 'disabled'
}
}
),
(
{
'RULE_ID': 862, 'RULE_INSERT_TIME': 1591165763753, 'RULE_MODIFY_TIME': 1591166095668,
'RULE_SEVERITY': 'SEV_010_INFO', 'NUMBER_OF_HITS': 0, 'RULE_SOURCE': 'XSOAR TIM', 'RULE_COMMENT': '',
'RULE_STATUS': 'ENABLED', 'BS_STATUS': 'DONE', 'BS_TS': 1591165801784, 'BS_RETRIES': 1,
'RULE_EXPIRATION_TIME': -1, 'IOC_TYPE': 'DOMAIN_NAME', 'RULE_INDICATOR': 'test.co.il',
'REPUTATION': 'SUSPICIOUS', 'RELIABILITY': 'A',
'VENDORS': [{'vendor_name': 'Cortex XDR - IOC', 'reputation': 'SUSPICIOUS', 'reliability': 'A'}],
'KLASS': None,
'IS_DEFAULT_TTL': False, 'RULE_TTL': -1, 'MARKED_DELETED': 0
},
{
'value': 'test.co.il',
'type': 'Domain',
'score': 2,
'fields': {
'expirationdate': 'Never',
'tags': 'Cortex XDR',
'xdrstatus': 'enabled'
}
}
)
]
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... | 1.851783 | 5,411 |
from django.contrib.auth.models import AbstractUser
from django.db.models import CharField
from django.urls import reverse
from django.utils.translation import ugettext_lazy as _
from django.db import models
from PIL import Image
| [
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415... | 3.553846 | 65 |
# Copyright (c) 2015-2020 Cloudify Platform 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.
from cloudify_common_sdk import exceptions
from cloudify_terminal_sdk import base_connection
# final of any package
NETCONF_1_0_END = "]]>]]>"
# base level of communication
NETCONF_1_0_CAPABILITY = 'urn:ietf:params:netconf:base:1.0'
# package based communication
NETCONF_1_1_CAPABILITY = 'urn:ietf:params:netconf:base:1.1'
| [
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2... | 3.355872 | 281 |
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 19 17:48:13 2018
@author: Sediment
"""
# -*- coding: utf-8 -*-
'''
Keras implementation of deep embedder to improve clustering, inspired by:
"Unsupervised Deep Embedding for Clustering Analysis" (Xie et al, ICML 2016)
Definition can accept somewhat custom neural networks. Defaults are from paper.
'''
import sys
import numpy as np
import pandas as pd
import keras.backend as K
from keras.initializers import RandomNormal
from keras.engine.topology import Layer, InputSpec
from keras.models import Model, Sequential
from keras.layers import Dense, Dropout, Input, Conv1D, MaxPooling1D, BatchNormalization, Activation, Flatten, UpSampling1D, Reshape
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Nadam
from keras.regularizers import l2
from sklearn.preprocessing import normalize
from keras.callbacks import LearningRateScheduler
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from sklearn import manifold
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from matplotlib import pyplot as plt
if (sys.version[0] == 2):
import cPickle as pickle
else:
import pickle
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