content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
"""
Reverse Polish Notation(RPN) is a mathematical notation where every operator follows all of its operands. For instance,
to add three and four, one would write "3 4 +" rather than "3 + 4". If there are multiple operations, the operator is
given immediately after its second operand; so the expression written "3 ? 4 + 5" would be written "3 4 ? 5 +" first
subtract 4 from 3, then add 5 to that.
Transform the algebraic expression with brackets into RPN form.
You can assume that for the test cases below only single letters will be used, brackets [ ] will not be used and each
expression has only one RPN form (no expressions like abc)
Test Input:
(a+(b*c))
((a+b)*(z+x))
((a+t)*((b+(a+c))^(c+d)))
Test Output:
abc*+
ab+zx+*
at+bac++cd+ ^ *
"""
import re
inp = '((a+t)*((b+(a+c))^(c+d)))'
print(inp)
parenth = re.compile(r"(?<=\()[^()]*(?=\))", re.DOTALL)
symbol = re.compile(r"[+\-*/^](?=\w)", re.DOTALL)
while True:
# Find expression between two parens without parens inbetween. End loop if not found
txt = parenth.search(inp)
if txt is None:
break
# find operator and its location in found expression
sym = symbol.search(txt.group())
# rearrange expression
new = txt.group()[:sym.span()[0]] + txt.group()[sym.span()[1]:] + sym.group()
# update rearranged expression
inp = inp[:txt.span()[0]-1] + new + inp[txt.span()[1]+1:]
print(inp)
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] | 2.695568 | 519 |
from corehq.util.urlvalidate.urlvalidate import (
PossibleSSRFAttempt,
validate_user_input_url,
)
from corehq.apps.sms.models import SMSBase
from corehq.util.metrics import metrics_counter
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] | 2.739726 | 73 |
# Origins Project Choice Lists, Vocabularies, Ontologies
# choice lists and vocabularies are defined with the following design template:
# variable_label1 = value # variable_labels are lowercase, values can be strings or numbers or codes
# variable_label2 = value
# CHOICES = (
# (variable_label1, 'string_representation')
# (variable_label2, 'string_representation')
# The design allows use of the variable_labels in code. Changes to the value applies automatically then in code and
# in what is written to database.
# Continents of the World
africa = 'Africa'
antarctica = 'Antarctica'
asia = 'Asia'
australia = 'Australia'
europe = 'Europe'
north_america = 'North America'
south_america = 'South America'
CONTINENT_CHOICES = (
(africa, 'Africa'),
(antarctica, 'Antarctica'),
(asia, 'Asia'),
(australia, 'Australia'),
(europe, 'Europe'),
(north_america, 'North America'),
(south_america, 'South America')
)
# Type Specimens Choices
# Definitions copied from ICZN online http://code.iczn.org
allotype = 'allotype' # A term, not regulated by the Code, for a designated specimen of opposite sex to the holotype
cotype = 'cotype' # A term not recognized by the Code, formerly used for either syntype or paratype, but that should
# not now be used in zoological nomenclature
genotype = 'genotype' # A term not recognized by the Code, formerly used for type species, but that should not
# now be used in zoological nomenclature
hapanotype = 'hapanotype' # One or more preparations consisting of directly related individuals representing distinct
# stages in the life cycle, which together form the name-bearing type in an extant species of protistan.
holotype = 'holotype' # The single specimen (except in the case of a hapantotype, q.v.) designated or otherwise fixed
# as the name-bearing type of a nominal species or subspecies when the nominal taxon is established.
isotype = 'isotype' # A duplicate specimen of the holotype.
isosyntype = 'isosyntype' # A duplicate of a syntype.
paratype = 'paratype' # A specimen not formally designated as a type but cited along with the type collection in the
# original description of a taxon.
lectotype = 'lectotype' # A syntype designated as the single name-bearing type specimen subsequent to the establishment
# of a nominal species or subspecies
neotype = 'neotype' # The single specimen designated as the name-bearing type of a nominal species or subspecies
# when there is a need to define the nominal taxon objectively and no name-bearing type is believed to be extant.
# If stability and universality are threatened, because an existing name-bearing type is either taxonomically
# inadequate or not in accord with the prevailing usage of a name, the Commission may use its plenary power
# to set aside that type and designate a neotype.
paralectotype = 'paralectotype' # Each specimen of a former syntype series remaining after the designation
# of a lectotype
syntype = 'syntype' # Each specimen of a type series (q.v.) from which neither a holotype nor a lectotype has
# been designated. The syntypes collectively constitute the name-bearing type.
topotype = 'topotype' # A term, not regulated by the Code, for a specimen originating from the type locality of the
# species or subspecies to which it is thought to belong, whether or not the specimen is part of the type series.
# Using a select set of terms recognized by ICZN.
TYPE_CHOICES = (
(holotype, 'Holotype'),
(paratype, 'Paratype'),
(lectotype, 'Lectotype'),
(neotype, 'Neotype'),
(syntype, 'Syntype'),
)
# Nomenclatural Code Choices
iczn = 'ICZN'
icbn = 'ICBN'
NOMENCLATURAL_CODE_CHOICES = (
(iczn, 'ICZN'),
(icbn, 'ICBN')
)
# Nomenclatural Status Choices
valid = 'valid'
invalid_gh = 'invalid_gh' # Generic homonym
invalid_ga = 'invalid_ga' # Genus nomen nudum before 1931
invalid_gb = 'invalid_gb' # Genus nomen nudum after 1930
invalid_sh = 'invalid_sh' # Specific homonym
invalid_sm = 'invalid_sm' # Specific nomen nudum before 1931
invalid_sn = 'invalid_sn' # Specific nomen nudum after 1930
invalid_so = 'invalid_so' # Specific nomen nudum - proposed conditionally
suppressed = 'suppressed' # Name suppressed by ICZN decision.
NOMENCLATURAL_STATUS_CHOICES = (
(valid, 'Valid'),
(invalid_gh, 'Invalid GH'),
(invalid_ga, 'Invalid GA'),
(invalid_gb, 'Invalid GB'),
(invalid_sh, 'Inavlid SH'),
(invalid_sm, 'Invalid SM'),
(invalid_sn, 'Invalid SN'),
(invalid_so, 'Inavlid SO'),
(suppressed, 'Supressed')
)
# Classification Status Choices
accepted = 'accepted'
junior_synonym = 'junior_synonym'
deprecated = 'deprecated'
# supressed defined above for Nomenclatural status choices
CLASSIFICATION_STATUS_CHOICES = (
(accepted, 'Accepted'),
(junior_synonym, 'Junior Synonym'),
(deprecated, 'Deprecated')
)
# helper functions
def choices2list(choices_tuple):
"""
Helper function that returns a choice list tuple as a simple list of stored values
:param choices_tuple:
:return:
"""
return [c[0] for c in choices_tuple]
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if last_question[bot.message.chat_id] == '%block_name%' and not got_answer:
%get_answer%
%next_blocks%
got_answer = True
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# Copyright (c) 2013, Carnegie Mellon University. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# Redistributions in binary form must reproduce the above copyright notice, this
# list of conditions and the following disclaimer in the documentation and/or
# other materials provided with the distribution.
#
# Neither the name of the Carnegie Mellon University nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Helpers for core CAP Collector module."""
import copy
from datetime import datetime
import logging
import lxml
import os
import re
import uuid
from bs4 import BeautifulSoup
from core import models
from dateutil import parser
from django.conf import settings
from django.core.urlresolvers import reverse
from django.template.loader import render_to_string
from django.utils import timezone
from django.utils.translation import ugettext
import pytz
try:
import xmlsec
XMLSEC_DEFINED = True
except ImportError:
# This module is not available on AppEngine.
# https://code.google.com/p/googleappengine/issues/detail?id=1034
XMLSEC_DEFINED = False
def GetCurrentDate():
"""The current date helper."""
return datetime.now(pytz.utc)
def GenerateFeed(feed_type="xml"):
"""Generates XML for alert feed based on active alert files.
Args:
feed_type: (string) Either xml of html.
Returns:
String. Ready to serve XML feed content.
"""
# Build feed header.
now = timezone.now().isoformat()
time_str, tz_str = now.split("+")
feed_updated = "%s+%s" % (time_str.split(".")[0], tz_str)
feed_url = settings.SITE_URL + reverse("feed", args=[feed_type])
entries = []
# For each unexpired message, get the necessary values and add it to the feed.
for alert in models.Alert.objects.filter(
updated=False,
expires_at__gt=GetCurrentDate()).order_by("-created_at"):
entries.append(ParseAlert(alert.content, feed_type, alert.uuid))
feed_dict = {
"entries": entries,
"feed_url": feed_url,
"updated": feed_updated,
"version": settings.VERSION,
}
feed_template = "core/feed." + feed_type + ".tmpl"
return BeautifulSoup(
render_to_string(feed_template, feed_dict), feed_type).prettify()
def ParseAlert(xml_string, feed_type, alert_uuid):
"""Parses select fields from the CAP XML file at file_name.
Primary use is intended for populating a feed <entry>.
Note:
- This code assumes the input alert XML has only one <info>.
- The parsed XML does not contain all fields in the CAP specification.
- The code accepts both complete and partial CAP messages.
Args:
xml_string: (string) Alert XML string.
feed_type: (string) Alert feed representation (XML or HTML).
alert_uuid: (string) Alert UUID.
Returns:
Dictionary.
Keys/values corresponding to alert XML attributes or empty dictionary.
"""
def GetFirstText(xml_element):
"""Returns the first text item from an XML element."""
if xml_element and len(xml_element):
return xml_element[0].text
return ""
def GetAllText(xml_element):
"""Returns an array of text items from multiple elements."""
if xml_element and len(xml_element):
return [item.text for item in xml_element]
return []
def GetNameValuePairs(xml_elements):
"""Returns a list of dictionaries for paired elements."""
pair_list = []
for xml_element in xml_elements:
name_element, value_element = xml_element.getchildren()
pair_list.append({
"name": name_element.text,
"value": value_element.text})
return pair_list
def GetCapElement(element_name, xml_tree):
"""Extracts elements from CAP XML tree."""
element = "//p:" + element_name
finder = lxml.etree.XPath(element, namespaces={"p": settings.CAP_NS})
return finder(xml_tree)
alert_dict = {}
try:
xml_tree = lxml.etree.fromstring(xml_string)
expires_str = GetFirstText(GetCapElement("expires", xml_tree))
# Extract the other needed values from the CAP XML.
sender = GetFirstText(GetCapElement("sender", xml_tree))
sender_name = GetFirstText(GetCapElement("senderName", xml_tree))
name = sender
if sender_name:
name = name + ": " + sender_name
title = GetFirstText(GetCapElement("headline", xml_tree))
if not title:
title = ugettext("Alert Message") # Force a default.
link = "%s%s" % (settings.SITE_URL,
reverse("alert", args=[alert_uuid, feed_type]))
expires = parser.parse(expires_str) if expires_str else None
sent_str = GetFirstText(GetCapElement("sent", xml_tree))
sent = parser.parse(sent_str) if sent_str else None
alert_dict = {
"title": title,
"event": GetFirstText(GetCapElement("event", xml_tree)),
"link": link,
"web": GetFirstText(GetCapElement("web", xml_tree)),
"name": name,
"sender": sender,
"sender_name": sender_name,
"expires": expires,
"msg_type": GetFirstText(GetCapElement("msgType", xml_tree)),
"references": GetFirstText(GetCapElement("references", xml_tree)),
"alert_id": GetFirstText(GetCapElement("identifier", xml_tree)),
"category": GetFirstText(GetCapElement("category", xml_tree)),
"response_type": GetFirstText(GetCapElement("responseType", xml_tree)),
"sent": sent,
"description": GetFirstText(GetCapElement("description", xml_tree)),
"instruction": GetFirstText(GetCapElement("instruction", xml_tree)),
"urgency": GetFirstText(GetCapElement("urgency", xml_tree)),
"severity": GetFirstText(GetCapElement("severity", xml_tree)),
"certainty": GetFirstText(GetCapElement("certainty", xml_tree)),
"language": GetFirstText(GetCapElement("language", xml_tree)),
"parameters": GetNameValuePairs(GetCapElement("parameter", xml_tree)),
"event_codes": GetNameValuePairs(GetCapElement("eventCode", xml_tree)),
"area_desc": GetFirstText(GetCapElement("areaDesc", xml_tree)),
"geocodes": GetNameValuePairs(GetCapElement("geocode", xml_tree)),
"circles": GetAllText(GetCapElement("circle", xml_tree)),
"polys": GetAllText(GetCapElement("polygon", xml_tree)),
}
# Non-CAP-compliant fields used for message templates.
expiresDurationMinutes = GetFirstText(
GetCapElement("expiresDurationMinutes", xml_tree))
if expiresDurationMinutes:
alert_dict["expiresDurationMinutes"] = expiresDurationMinutes
# We don't expect any invalid XML alerts.
except lxml.etree.XMLSyntaxError as e:
logging.exception(e)
return alert_dict
def SignAlert(xml_tree, username):
"""Sign XML with user key/certificate.
Args:
xml_tree: (string) Alert XML tree.
username: (string) Username of the alert author.
Returns:
String.
Signed alert XML tree if your has key/certificate pair
Unchanged XML tree otherwise.
"""
if not XMLSEC_DEFINED:
return xml_tree
key_path = os.path.join(settings.CREDENTIALS_DIR, username + ".key")
cert_path = os.path.join(settings.CREDENTIALS_DIR, username + ".cert")
try:
signed_xml_tree = copy.deepcopy(xml_tree)
xmlsec.add_enveloped_signature(signed_xml_tree, pos=-1)
xmlsec.sign(signed_xml_tree, key_path, cert_path)
return signed_xml_tree
except (IOError, xmlsec.exceptions.XMLSigException):
return xml_tree
def CreateAlert(xml_string, username):
"""Creates alert signed by userame from provided XML string.
Args:
xml_string: (string) XML content.
username: (string) Username of the alert author.
Returns:
A tuple of (msg_id, valid, error) where:
msg_id: (string) Unique alert ID (UUID)
valid: (bool) Whether alert has valid XML or not.
error: (string) Error message in case XML is invalid.
"""
msg_id = None
valid = False
try:
# Clean up the XML format a bit.
xml_string = re.sub("> +<", "><", xml_string)
# Now parse into etree and validate.
xml_tree = lxml.etree.fromstring(xml_string)
with open(os.path.join(settings.SCHEMA_DIR,
settings.CAP_SCHEMA_FILE), "r") as schema_file:
schema_string = schema_file.read()
xml_schema = lxml.etree.XMLSchema(lxml.etree.fromstring(schema_string))
valid = xml_schema.validate(xml_tree)
error = xml_schema.error_log.last_error
except lxml.etree.XMLSyntaxError as e:
error = "Malformed XML: %s" % e
if valid:
msg_id = str(uuid.uuid4())
# Assign <identifier> and <sender> values.
find_identifier = lxml.etree.XPath("//p:identifier",
namespaces={"p": settings.CAP_NS})
identifier = find_identifier(xml_tree)[0]
identifier.text = msg_id
# Set default <web> field if one was not filled by user.
find_web = lxml.etree.XPath("//p:info/p:web",
namespaces={"p": settings.CAP_NS})
web = find_web(xml_tree)[0]
if web.text == "pending":
web.text = "%s%s" % (settings.SITE_URL,
reverse("alert", args=[msg_id, "html"]))
find_sender = lxml.etree.XPath("//p:sender",
namespaces={"p": settings.CAP_NS})
sender = find_sender(xml_tree)[0]
sender.text = username + "@" + settings.SITE_DOMAIN
find_sent = lxml.etree.XPath("//p:sent",
namespaces={"p": settings.CAP_NS})
sent = find_sent(xml_tree)[0]
find_expires = lxml.etree.XPath("//p:expires",
namespaces={"p": settings.CAP_NS})
expires = find_expires(xml_tree)[0]
find_references = lxml.etree.XPath("//p:references",
namespaces={"p": settings.CAP_NS})
has_references = len(find_references(xml_tree)) != 0
# Sign the XML tree.
xml_tree = SignAlert(xml_tree, username)
# Re-serialize as string.
signed_xml_string = lxml.etree.tostring(xml_tree, pretty_print=False)
alert_obj = models.Alert()
alert_obj.uuid = msg_id
alert_obj.created_at = sent.text
alert_obj.expires_at = expires.text
alert_obj.content = signed_xml_string
alert_obj.save()
if has_references:
for element in find_references(xml_tree):
updated_alert_uuid = element.text.split(",")[1]
models.Alert.objects.filter(
uuid=updated_alert_uuid).update(updated=True)
return (msg_id, valid, error)
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4972,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
5420,
4972,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
44598,
62,
312,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
738,
7483,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
22872,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
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22872,
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62,
21048,
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198,
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220,
220,
220,
220,
220,
220,
366,
26209,
62,
4906,
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7,
3855,
15610,
20180,
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26209,
6030,
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35555,
62,
21048,
36911,
198,
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220,
220,
220,
220,
220,
220,
366,
34086,
1298,
1908,
11,
198,
220,
220,
220,
220,
220,
220,
220,
366,
11213,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
11213,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
8625,
2762,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
8625,
2762,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
3686,
1387,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
3686,
1387,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
28116,
414,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
28116,
414,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
39239,
774,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
39239,
774,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
16129,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
16129,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
17143,
7307,
1298,
3497,
5376,
11395,
47,
3468,
7,
3855,
15610,
20180,
7203,
17143,
2357,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
15596,
62,
40148,
1298,
3497,
5376,
11395,
47,
3468,
7,
3855,
15610,
20180,
7203,
15596,
10669,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
20337,
62,
20147,
1298,
3497,
5962,
8206,
7,
3855,
15610,
20180,
7203,
20337,
24564,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
469,
420,
4147,
1298,
3497,
5376,
11395,
47,
3468,
7,
3855,
15610,
20180,
7203,
469,
420,
1098,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
66,
343,
5427,
1298,
3497,
3237,
8206,
7,
3855,
15610,
20180,
7203,
45597,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
220,
220,
220,
220,
366,
35428,
82,
1298,
3497,
3237,
8206,
7,
3855,
15610,
20180,
7203,
35428,
14520,
1600,
35555,
62,
21048,
36911,
198,
220,
220,
220,
1782,
198,
220,
220,
220,
1303,
8504,
12,
33177,
12,
23855,
3014,
7032,
973,
329,
3275,
24019,
13,
198,
220,
220,
220,
27396,
26054,
9452,
1769,
796,
3497,
5962,
8206,
7,
198,
220,
220,
220,
220,
220,
220,
220,
3497,
15610,
20180,
7203,
11201,
2387,
26054,
9452,
1769,
1600,
35555,
62,
21048,
4008,
198,
220,
220,
220,
611,
27396,
26054,
9452,
1769,
25,
198,
220,
220,
220,
220,
220,
7995,
62,
11600,
14692,
11201,
2387,
26054,
9452,
1769,
8973,
796,
27396,
26054,
9452,
1769,
198,
220,
1303,
775,
836,
470,
1607,
597,
12515,
23735,
21675,
13,
198,
220,
2845,
300,
19875,
13,
316,
631,
13,
55,
5805,
13940,
41641,
12331,
355,
304,
25,
198,
220,
220,
220,
18931,
13,
1069,
4516,
7,
68,
8,
198,
220,
1441,
7995,
62,
11600,
628,
198,
4299,
5865,
36420,
7,
19875,
62,
21048,
11,
20579,
2599,
198,
220,
37227,
11712,
23735,
351,
2836,
1994,
14,
22583,
22460,
13,
628,
220,
943,
14542,
25,
198,
220,
220,
220,
35555,
62,
21048,
25,
357,
8841,
8,
23276,
23735,
5509,
13,
198,
220,
220,
220,
20579,
25,
357,
8841,
8,
50069,
286,
262,
7995,
1772,
13,
628,
220,
16409,
25,
198,
220,
220,
220,
10903,
13,
198,
220,
220,
220,
36215,
7995,
23735,
5509,
611,
534,
468,
1994,
14,
22583,
22460,
5166,
198,
220,
220,
220,
34867,
5102,
23735,
5509,
4306,
13,
198,
220,
37227,
628,
220,
611,
407,
23735,
23683,
62,
7206,
20032,
1961,
25,
198,
220,
220,
220,
1441,
35555,
62,
21048,
628,
220,
1994,
62,
6978,
796,
28686,
13,
6978,
13,
22179,
7,
33692,
13,
9419,
1961,
3525,
12576,
50,
62,
34720,
11,
20579,
1343,
27071,
2539,
4943,
198,
220,
5051,
62,
6978,
796,
28686,
13,
6978,
13,
22179,
7,
33692,
13,
9419,
1961,
3525,
12576,
50,
62,
34720,
11,
20579,
1343,
27071,
22583,
4943,
628,
220,
1949,
25,
198,
220,
220,
220,
4488,
62,
19875,
62,
21048,
796,
4866,
13,
22089,
30073,
7,
19875,
62,
21048,
8,
198,
220,
220,
220,
35555,
2363,
13,
2860,
62,
268,
1091,
276,
62,
12683,
1300,
7,
32696,
62,
19875,
62,
21048,
11,
1426,
10779,
16,
8,
198,
220,
220,
220,
35555,
2363,
13,
12683,
7,
32696,
62,
19875,
62,
21048,
11,
1994,
62,
6978,
11,
5051,
62,
6978,
8,
198,
220,
220,
220,
1441,
4488,
62,
19875,
62,
21048,
198,
220,
2845,
357,
9399,
12331,
11,
35555,
2363,
13,
1069,
11755,
13,
55,
5805,
50,
328,
16922,
2599,
198,
220,
220,
220,
1441,
35555,
62,
21048,
628,
198,
4299,
13610,
36420,
7,
19875,
62,
8841,
11,
20579,
2599,
198,
220,
37227,
16719,
274,
7995,
4488,
416,
2836,
480,
422,
2810,
23735,
4731,
13,
628,
220,
943,
14542,
25,
198,
220,
220,
220,
35555,
62,
8841,
25,
357,
8841,
8,
23735,
2695,
13,
198,
220,
220,
220,
20579,
25,
357,
8841,
8,
50069,
286,
262,
7995,
1772,
13,
628,
220,
16409,
25,
198,
220,
220,
220,
317,
46545,
286,
357,
19662,
62,
312,
11,
4938,
11,
4049,
8,
810,
25,
198,
220,
220,
220,
220,
220,
31456,
62,
312,
25,
357,
8841,
8,
30015,
7995,
4522,
357,
52,
27586,
8,
198,
220,
220,
220,
220,
220,
4938,
25,
357,
30388,
8,
10127,
7995,
468,
4938,
23735,
393,
407,
13,
198,
220,
220,
220,
220,
220,
4049,
25,
357,
8841,
8,
13047,
3275,
287,
1339,
23735,
318,
12515,
13,
198,
220,
37227,
628,
220,
31456,
62,
312,
796,
6045,
198,
220,
4938,
796,
10352,
628,
220,
1949,
25,
198,
220,
220,
220,
1303,
5985,
510,
262,
23735,
5794,
257,
1643,
13,
198,
220,
220,
220,
35555,
62,
8841,
796,
302,
13,
7266,
7,
5320,
1343,
27,
1600,
366,
6927,
1600,
35555,
62,
8841,
8,
198,
220,
220,
220,
1303,
2735,
21136,
656,
2123,
631,
290,
26571,
13,
198,
220,
220,
220,
35555,
62,
21048,
796,
300,
19875,
13,
316,
631,
13,
6738,
8841,
7,
19875,
62,
8841,
8,
628,
220,
220,
220,
351,
1280,
7,
418,
13,
6978,
13,
22179,
7,
33692,
13,
50,
3398,
27630,
62,
34720,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6460,
13,
33177,
62,
50,
3398,
27630,
62,
25664,
828,
366,
81,
4943,
355,
32815,
62,
7753,
25,
198,
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220,
220,
220,
32815,
62,
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32815,
62,
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13,
961,
3419,
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13,
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2845,
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13,
55,
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13940,
41641,
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355,
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25,
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220,
4049,
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366,
15029,
12214,
23735,
25,
4064,
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1,
4064,
304,
628,
220,
611,
4938,
25,
198,
220,
220,
220,
31456,
62,
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796,
965,
7,
12303,
312,
13,
12303,
312,
19,
28955,
198,
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220,
220,
1303,
2195,
570,
1279,
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29,
290,
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29,
3815,
13,
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1064,
62,
738,
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796,
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13,
316,
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13,
55,
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1003,
79,
25,
738,
7483,
1600,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3891,
43076,
28,
4895,
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1298,
6460,
13,
33177,
62,
8035,
30072,
198,
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27421,
796,
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7,
19875,
62,
21048,
38381,
15,
60,
198,
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27421,
13,
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31456,
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1303,
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14,
79,
25,
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220,
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3891,
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8035,
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3992,
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3992,
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36521,
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9575,
7203,
44598,
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26498,
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8973,
4008,
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3891,
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6460,
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29788,
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29788,
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20579,
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44212,
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220,
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220,
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220,
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4981,
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7,
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357,
19662,
62,
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11,
4938,
11,
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8,
198
] | 2.699111 | 4,274 |
from .generate_eval_jobs import generate_eval_jobs
from .generate_plane_jobs import generate_plane_data
| [
6738,
764,
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378,
62,
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62,
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7716,
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198
] | 3.466667 | 30 |
import logging
import os
import urllib
from time import time
from json import loads
from rain_api_core.general_util import log_context, return_timing_object, duration
from rain_api_core.view_util import make_set_cookie_headers_jwt, get_exp_time, JWT_COOKIE_NAME
from rain_api_core.aws_util import retrieve_secret
log = logging.getLogger(__name__)
def get_urs_creds():
"""
Fetches URS creds from secrets manager.
:return: looks like:
{
"UrsId": "stringofseeminglyrandomcharacters",
"UrsAuth": "verymuchlongerstringofseeminglyrandomcharacters"
}
:type: dict
"""
secret_name = os.getenv('URS_CREDS_SECRET_NAME', None)
if not secret_name:
log.error('URS_CREDS_SECRET_NAME not set')
return {}
secret = retrieve_secret(secret_name)
if not ('UrsId' in secret and 'UrsAuth' in secret):
log.error('AWS secret {} does not contain required keys "UrsId" and "UrsAuth"'.format(secret_name))
return secret
# This do_login() is mainly for chalice clients.
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62,
38235,
3419,
318,
8384,
329,
442,
282,
501,
7534,
13,
198
] | 2.494226 | 433 |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import logging
import os
import uuid
import requests
import json
from copy import copy
from datetime import datetime
from crhelper import CfnResource
from util.solution_metrics import send_metrics
logger = logging.getLogger(__name__)
helper = CfnResource(json_logging=True, log_level="INFO")
@helper.create
@helper.update
@helper.delete
| [
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198,
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] | 3.230216 | 139 |
from solutions.FIZ import fizz_buzz_solution
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62,
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62,
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] | 1.596154 | 52 |
#!/usr/bin/env python
"""Classes to mock components for use in system testing.
The main class is the `Component`, which is intended to be used as the base
class for an object that plays the part of a separately running process.
However, a component is actually executed under the control of the
`cleversheep3.Test.PollManager`. This allows its behaviour to be tightly
controlled and tests to be fairly deterministic.
Actually this has become suitably general purpose as to be put somewhere else.
But what I will probably do is keep Component as a mocking class and make it
inherit from some other class, which will contain the bulk of current
Component.
"""
from __future__ import print_function
__docformat__ = "restructuredtext"
import itertools
from cleversheep3.Test.Tester import Logging
from . import Comms
class Component:
"""The base class for a mock component in a test scenario.
The intention for this class is that it should provide most of the
functionality required to emulate a process. At least well enough for most
test purposes.
:Ivariables:
pollManager
The ``PollManager`` that is managing the component.
connections
One or more active connections managed by the component.
listeners
A dict of active listeners being manager by the component. The key is
the name of the peer that is expected to connect and the value of the
``Listener`` managing the listen socket.
pendingConnections
A dict of pending outgoing connections. The key is the name of the peer
that is expected to connect and the value of the ``Connector`` trying
to connect to the peer.
"""
_name = "UNKNOWN"
peerCounter = itertools.count(1)
#{ Construction
def __init__(self, pollManager, log=None):
"""Constructor:
:Parameters:
pollManager
This is typically a `PollManager` instance, but could be something
that provides the same interface.
log
A standard ``logging`` object. If omitted, a default logging object
is used.
This is likely to disappear, so it is best not to use it. Currently
the log object is stored, but not used.
"""
self.log = log or Logging.getLogger(self._name).info
# self.log = log or getLog(self._name).info
self.pollManager = pollManager
self.connections = {}
self.listeners = {}
self.pendingConnections = {}
#{ Connection establishment
def listenFor(self, listenName, bindAddr):
"""Listen for connection from a peer.
Arranges to start listening for connection from a peer.
A SOCK_STREAM socket is created and added to a list of listenting
sockets. Each call to this methods sets up a new listener.
When a connection request occurs a socket for the connection is
created (by accepting the request). Then the `onIncomingConnect` method
is invoked as ``self.onIncomingConnect(s, peerName)``, where ``s`` is
the new connection socket and ``peerName`` is the ``listenName``
passed to this ``listenFor`` method.
:Param listenName:
The name by which the listener should be known, which is normally
name of the peer that is expected to try to connect.
:Param bindAddr:
The address to bind to for listening. This is a tuple of
``(ipAddr, port)`` and the ``ipAddr`` is often an empty string,
meaning accept connection from any address.
"""
listenSocket = Comms.Listener(self.pollManager, listenName, bindAddr,
onConnect=self._onIncomingConnect)
self.listeners[listenName] = listenSocket
return listenSocket
addListener = listenFor
def openDgramSocket(self, peerName, bindAddr=None, peerAddr=None):
"""Open a datagram socket.
:Param peerName:
A symbolic name for the communicating peer.
:Param bindAddr:
The Taddress to bind to for receiving packets.
"""
p = Comms.Dgram(self.pollManager, peerName, bindAddr=bindAddr,
peerAddr=peerAddr, onReceive=self._onReceive)
self.connections[peerName] = p
def openInputPipe(self, pipePath, peerName, native=False):
"""Open a named pipe for reading from.
:Param pipePath:
The path name of the PIPE.
"""
p = Comms.PipeConn(False, pipePath, peerName, self.pollManager,
onReceive=self._onReceive, onClose=self._onClose,
onError=self._onError, native=native)
self.connections[peerName] = p
def openOutputPipe(self, pipePath, peerName, native=False):
"""Open a named pipe for reading from.
:Param pipePath:
The path name of the PIPE.
"""
p = Comms.PipeConn(True, pipePath, peerName, self.pollManager,
onReceive=self._onReceive, onClose=self._onClose,
onError=self._onError, native=native)
self.connections[peerName] = p
def connectTo(self, peerName, peerAddr, connectTimes=(0.0, 0.5),
bindAddress=None):
"""Start trying to connect to a peer.
:Param peerName:
The name by which the connection to the peer should be known,
which is normally the peer's name.
:Param peerAddr:
The internet address, i.e. a tuple of ``(ipAddr, port)``, of the
peer. The ``ipAddr`` may be an empty string, meaning
``localhost``.
:Param connectTimes:
A tuple of ``(firstDelay, retryDelay)``, each is floating point
value representing seconds. The first connection attempt will
be made after ``firstDelay`` seconds. If that fails then
connection attempts will be made every ``retryDelay`` seconds.
"""
c = Comms.Connecter(self.pollManager, peerName, peerAddr,
connectTimes, self._onOutgoingConnect,
bindAddress=bindAddress)
self.pendingConnections[peerName] = c
return c
def sendTo(self, peerName, bytes, count=None):
"""Send bytes to a named peer.
:Param peerName:
The name by which the connection to the peer is known, which is
normally the peer's name.
:Param bytes, count:
The string of bytes to write and how many of those bytes to send.
The `count` is normally omitted (or ``None``), in which case the
entire string is sent.
"""
conn = self.connections.get(peerName)
if not conn:
return
conn.send(bytes, count=count)
#{ The Component protocol methods.
def onIncomingConnect(self, s, peerName):
"""Invoked when accepting a connection from a peer."""
def onOutgoingConnect(self, peerName):
"""Invoked when an outgoing connection is established."""
def onReceive(self, conn):
"""Invoked when a connection has received bytes."""
def onClose(self, conn):
"""Invoked when a connection closes."""
def onError(self, conn, exc):
"""Invoked when a connection has an abnormal error."""
# TODO: Get a better name.
def getConnName(self, listenName, peerAddr):
"""Map an incoming connection to a peer name.
If you listen for multiple clients connecting to a single port then
you need to over-ride this so that each new connection gets given
a new name.
"""
return listenName
#{ Handling of socket activity.
def _onIncomingConnect(self, s, listenName, peerAddr, source):
"""This is invoked by a `Listener`, for a new connection."""
peerName = self.getConnName(listenName, peerAddr)
if peerName.endswith('%d'):
peerName = peerName % next(self.peerCounter)
conn = self.connections[peerName] = Comms.SocketConn(s,
peerName, self.pollManager,
onReceive=self._onReceive, onClose=self._onClose,
onError=self._onError, addr=peerAddr,
isSSL=source.isSslConnection())
self.onIncomingConnect(s, peerName)
return conn
def _onOutgoingConnect(self, peerName, source):
"""This is invoked by a `Connecter`, for a new connection."""
# TODO: Make logging controllable.
# logComms(self.log, self._name, peerName, "<CONNECT>")
pending = self.pendingConnections.pop(peerName)
conn = self.connections[peerName] = Comms.SocketConn(pending.s,
peerName, self.pollManager,
onReceive=self._onReceive, onClose=self._onClose,
onError=self._onError,
isSSL=source.isSslConnection())
self.onOutgoingConnect(peerName)
| [
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] | 2.57295 | 3,475 |
from django.db import connection
from django.db.models.signals import post_save
from django.dispatch import receiver
# TODO(mkurek): make this working as a decorator, example:
# @post_commit(MyModel)
# def my_handler(instance):
# ...
def post_commit(func, model, signal=post_save, single_call=True):
"""
Post commit signal for specific model.
It's better than Django's post_save, because:
* it handles transaction rollback (transaction could be rolled back
after calling post_save)
* it handles M2M relations (post_save is (usually) called when main model
is saved, before related M2M instances are saved)
Writing tests:
Remember to make your TestCase inheriting from one of:
- TransactionTestCase (Django)
- APITransactionTestCase (Django Rest Framework)
Unless `on_commit` signal won't be called.
Requirements:
* you have to use database supporting transactions (ex. MySQL)
* you have to use django-transaction-hooks
(https://github.com/carljm/django-transaction-hooks) for Django<=1.8
(it was merged into Django 1.9)
Notice that this feature will work whether or not you're using transactions
in your code. Possible scenarios are as follows:
* `ATOMIC_REQUESTS` is set to True in settings - then every request is
wrapped in transaction - at the end of processing each (saving) request,
this hook will be processed (for models which were saved)
* view is decorated using `transaction.atomic` - at the end of processing
the view, this hook will be called (if any of registered models was saved)
* if transaction is not started for current request, then this hook will
behave as post_save (will be called immediately)
"""
@receiver(signal, sender=model, weak=False)
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] | 3.128425 | 584 |
# Copyright 2014 Google Inc. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from multiprocessing import *
import sys
range4 = range(4)
job_table = {}
table = {}
# vim:sw=4:expandtab:softtabstop=4
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] | 2.927835 | 97 |
import numpy as np
from scipy.linalg import eig
from nengo.params import IntParam, NumberParam
from nengo.neurons import NeuronTypeParam
from nengo.synapses import SynapseParam
import nengo
from nengolib import Network
from nengolib.neurons import Tanh
from nengolib.networks.reservoir import Reservoir
__all__ = ['EchoState']
class EchoState(Network, Reservoir):
"""An Echo State Network (ESN) within a Nengo Reservoir.
This creates a standard Echo State Network (ENS) as a Nengo network,
defaulting to the standard set of assumptions of non-spiking Tanh units
and a random recurrent weight matrix [1]_. This is based on the
minimalist Python implementation from [2]_.
The network takes some arbitrary time-varying vector as input, encodes it
randomly, and filters it using nonlinear units and a random recurrent
weight matrix normalized by its spectral radius.
This class also inherits ``nengolib.networks.Reservoir``, and thus the
optimal linear readout is solved for in the same way: the network is
simulated on a test signal, and then a solver is used to optimize the
decoding connection weights.
References:
[1] http://www.scholarpedia.org/article/Echo_state_network
[2] http://minds.jacobs-university.de/mantas/code
"""
n_neurons = IntParam('n_neurons', default=None, low=1)
dimensions = IntParam('dimensions', default=None, low=1)
dt = NumberParam('dt', low=0, low_open=True)
recurrent_synapse = SynapseParam('recurrent_synapse')
gain = NumberParam('gain', low=0, low_open=True)
neuron_type = NeuronTypeParam('neuron_type')
def __init__(self, n_neurons, dimensions, recurrent_synapse=0.005,
readout_synapse=None, radii=1.0, gain=1.25, rng=None,
neuron_type=Tanh(), include_bias=True, ens_seed=None,
label=None, seed=None, add_to_container=None, **ens_kwargs):
"""Initializes the Echo State Network.
Parameters
----------
n_neurons : int
The number of neurons to use in the reservoir.
dimensions : int
The dimensionality of the input signal.
recurrent_synapse : nengo.synapses.Synapse (Default: ``0.005``)
Synapse used to filter the recurrent connection.
readout_synapse : nengo.synapses.Synapse (Default: ``None``)
Optional synapse to filter all of the outputs before solving
for the linear readout. This is included in the connection to the
``output`` Node created within the network.
radii : scalar or array_like, optional (Default: ``1``)
The radius of each dimension of the input signal, used to normalize
the incoming connection weights.
gain : scalar, optional (Default: ``1.25``)
A scalar gain on the recurrent connection weight matrix.
rng : ``numpy.random.RandomState``, optional (Default: ``None``)
Random state used to initialize all weights.
neuron_type : ``nengo.neurons.NeuronType`` optional \
(Default: ``Tanh()``)
Neuron model to use within the reservoir.
include_bias : ``bool`` (Default: ``True``)
Whether to include a bias current to the neural nonlinearity.
This should be ``False`` if the neuron model already has a bias,
e.g., ``LIF`` or ``LIFRate``.
ens_seed : int, optional (Default: ``None``)
Seed passed to the ensemble of neurons.
"""
Network.__init__(self, label, seed, add_to_container)
self.n_neurons = n_neurons
self.dimensions = dimensions
self.recurrent_synapse = recurrent_synapse
self.radii = radii # TODO: make array or scalar parameter?
self.gain = gain
self.rng = np.random if rng is None else rng
self.neuron_type = neuron_type
self.include_bias = include_bias
self.W_in = (
self.rng.rand(self.n_neurons, self.dimensions) - 0.5) / self.radii
if self.include_bias:
self.W_bias = self.rng.rand(self.n_neurons, 1) - 0.5
else:
self.W_bias = np.zeros((self.n_neurons, 1))
self.W = self.rng.rand(self.n_neurons, self.n_neurons) - 0.5
self.W *= self.gain / max(abs(eig(self.W)[0]))
with self:
self.ensemble = nengo.Ensemble(
self.n_neurons, 1, neuron_type=self.neuron_type, seed=ens_seed,
**ens_kwargs)
self.input = nengo.Node(size_in=self.dimensions)
pool = self.ensemble.neurons
nengo.Connection(
self.input, pool, transform=self.W_in, synapse=None)
nengo.Connection( # note the bias will be active during training
nengo.Node(output=1, label="bias"), pool,
transform=self.W_bias, synapse=None)
nengo.Connection(
self.ensemble.neurons, pool, transform=self.W,
synapse=self.recurrent_synapse)
Reservoir.__init__(
self, self.input, pool, readout_synapse=readout_synapse,
network=self)
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3127,
28,
944,
8,
198
] | 2.375516 | 2,181 |
# -*- coding: utf-8 -*-
#
# Copyright Contributors to the Conu project.
# SPDX-License-Identifier: MIT
#
"""
Tests for Kubernetes backend
"""
from conu.backend.k8s.utils import k8s_ports_to_metadata_ports, metadata_ports_to_k8s_ports
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62,
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62,
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82,
62,
3742,
628
] | 2.576087 | 92 |
import matplotlib.pyplot as plt
import yaml
from datetime import datetime
from collections import defaultdict
from copy import deepcopy
import itertools
import numpy as np
import torch as th
from torch.optim import Adam
import time
import numpy as np
import scipy.signal
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions.normal import Normal
from research.rl.buffers import OGRB, ReplayBuffer, PPOBuffer
from research.rl.pponets import ActorCritic
from research.define_config import env_fn
from boxLCD import env_map
import boxLCD
from research import utils
from research import wrappers
#from research.nets.flat_everything import FlatEverything
from jax.tree_util import tree_multimap, tree_map
from ._base import RLAlgo, TN | [
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62,
8899,
198,
6738,
47540,
8692,
1330,
45715,
2348,
2188,
11,
29025
] | 3.578704 | 216 |
import asyncio
from datetime import datetime
from decimal import Decimal
from tortoise import Tortoise
import pytest
@pytest.fixture(autouse=True)
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] | 3.454545 | 44 |
'''input
10
10
76
4
3
10
'''
# -*- coding: utf-8 -*-
# AtCoder Beginner Contest
# Problem B
if __name__ == '__main__':
operation_count = int(input())
incremental_value = int(input())
candidates = list()
for i in range(operation_count + 1):
result = 2 ** i + (operation_count - i) * incremental_value
candidates.append(result)
print(min(candidates))
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7,
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7,
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201,
198
] | 2.274725 | 182 |
# -*- coding: utf-8 -*-
"""These test the utils.py functions."""
import pytest
from hypothesis import given
from hypothesis.strategies import binary
from natsort.ns_enum import NSType, ns
from natsort.utils import BytesTransformer, parse_bytes_factory
@pytest.mark.parametrize(
"alg, example_func",
[
(ns.DEFAULT, lambda x: (x,)),
(ns.IGNORECASE, lambda x: (x.lower(),)),
# With PATH, it becomes a tested tuple.
(ns.PATH, lambda x: ((x,),)),
(ns.PATH | ns.IGNORECASE, lambda x: ((x.lower(),),)),
],
)
@given(x=binary())
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220,
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220,
220,
220,
220,
220,
357,
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930,
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87,
13,
21037,
22784,
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198,
220,
220,
220,
16589,
198,
8,
198,
31,
35569,
7,
87,
28,
39491,
28955,
198
] | 2.415966 | 238 |
"""
*minor 7th*
The minor 7th diatonic interval.
"""
from dataclasses import dataclass
from fivear.musical.scale import Diatonic
from ...simple import SimpleInterval
__all__ = ["MinorSeventh"]
@dataclass
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# isort:skip_file
# flake8: noqa
from .html import TEMPLATE_HTML
from .apps import TEMPLATE_APPS
from .urls import TEMPLATE_URLS
from .views import TEMPLATE_VIEWS
from .tests import TEMPLATE_TESTS
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import string
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] | 4.666667 | 3 |
from typing import Dict, Optional
from pydantic import BaseModel, validator
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#!/usr/bin/env python3
# run in python 3.5 and after
import fire
import subprocess
import os
import re
import signal
import time
import sys
class unity(object):
"""An enhanced unity cli."""
if __name__ == '__main__':
fire.Fire(unity)
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#
"""
Various position embedders.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import tensorflow as tf
from texar.modules.embedders.embedder_base import EmbedderBase
from texar.modules.embedders import embedder_utils
from texar.utils.mode import is_train_mode
from texar.utils.shapes import mask_sequences
# pylint: disable=arguments-differ, invalid-name
__all__ = [
"PositionEmbedder",
"SinusoidsPositionEmbedder",
]
class PositionEmbedder(EmbedderBase):
"""Simple position embedder that maps position indexes into embeddings
via lookup.
Either :attr:`init_value` or :attr:`position_size` is required. If both are
given, :attr:`init_value.shape[0]` must equal :attr:`position_size`.
Args:
init_value (optional): A `Tensor` or numpy array that contains the
initial value of embeddings. It is typically of shape
`[position_size, embedding dim]`
If `None`, embedding is initialized as specified in
:attr:`hparams["initializer"]`. Otherwise, the
:attr:`"initializer"` and :attr:`"dim"`
hyperparameters in :attr:`hparams` are ignored.
position_size (int, optional): The number of possible positions, e.g.,
the maximum sequence length. Required if :attr:`init_value` is
not given.
hparams (dict, optional): Embedder hyperparameters. If it is not
specified, the default hyperparameter setting is used. See
:attr:`default_hparams` for the sturcture and default values.
"""
@staticmethod
def default_hparams():
"""Returns a dictionary of hyperparameters with default values.
Returns:
A dictionary with the following structure and values.
.. code-block:: python
{
"name": "position_embedder",
"dim": 100,
"initializer": {
"type": "random_uniform_initializer",
"kwargs": {
"minval": -0.1,
"maxval": 0.1,
"seed": None
}
},
"regularizer": {
"type": "L1L2",
"kwargs": {
"l1": 0.,
"l2": 0.
}
},
"dropout_rate": 0,
"trainable": True,
}
See :func:`~texar.modules.default_embedding_hparams` for more
details.
"""
hparams = embedder_utils.default_embedding_hparams()
hparams["name"] = "position_embedder"
return hparams
def _build(self, positions=None, sequence_length=None, mode=None, **kwargs):
"""Embeds with look-up.
Either :attr:`position` or :attr:`sequence_length` is required:
- If both are given, :attr:`sequence_length` is used to mask out \
embeddings of those time steps beyond the respective sequence \
lengths.
- If only :attr:`sequence_length` is given, then positions \
from 0 to sequence length - 1 are embedded.
Args:
positions (optional): An integer tensor containing the position
ids to embed.
sequence_length (optional): An integer tensor of shape
`[batch_size]`. Time steps beyond
the respective sequence lengths will have zero-valued
embeddings.
mode (optional): A tensor taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
`TRAIN`, `EVAL`, and `PREDICT`. If `None`, dropout will be
controlled by :func:`texar.context.global_mode`.
kwargs: Additional keyword arguments for
:tf_main:`tf.nn.embedding_lookup <nn/embedding_lookup>` besides
:attr:`params` and :attr:`ids`.
Returns:
A `Tensor` of shape `shape(inputs) + embedding dimension`.
"""
# Gets embedder inputs
inputs = positions
if positions is None:
if sequence_length is None:
raise ValueError(
'Either `positions` or `sequence_length` is required.')
max_length = tf.reduce_max(sequence_length)
single_inputs = tf.range(start=0, limit=max_length, dtype=tf.int32)
# Expands `single_inputs` to have shape [batch_size, max_length]
expander = tf.expand_dims(tf.ones_like(sequence_length), -1)
inputs = expander * tf.expand_dims(single_inputs, 0)
ids_rank = len(inputs.shape.dims)
embedding = self._embedding
is_training = is_train_mode(mode)
# Gets dropout strategy
st = self._hparams.dropout_strategy
if positions is None and st == 'item':
# If `inputs` is based on `sequence_length`, then dropout
# strategies 'item' and 'item_type' have the same effect, we
# use 'item_type' to avoid unknown noise_shape in the 'item'
# strategy
st = 'item_type'
# Dropouts as 'item_type' before embedding
if st == 'item_type':
dropout_layer = self._get_dropout_layer(
self._hparams, dropout_strategy=st)
if dropout_layer:
embedding = dropout_layer.apply(inputs=embedding,
training=is_training)
# Embeds
outputs = tf.nn.embedding_lookup(embedding, inputs, **kwargs)
# Dropouts as 'item' or 'elements' after embedding
if st != 'item_type':
dropout_layer = self._get_dropout_layer(
self._hparams, ids_rank=ids_rank, dropout_input=outputs,
dropout_strategy=st)
if dropout_layer:
outputs = dropout_layer.apply(inputs=outputs,
training=is_training)
# Optionally masks
if sequence_length is not None:
outputs = mask_sequences(
outputs, sequence_length,
tensor_rank=len(inputs.shape.dims) + self._dim_rank)
return outputs
@property
def embedding(self):
"""The embedding tensor.
"""
return self._embedding
@property
def dim(self):
"""The embedding dimension.
"""
return self._dim
@property
def position_size(self):
"""The position size, i.e., maximum number of positions.
"""
return self._position_size
class SinusoidsPositionEmbedder(EmbedderBase):
"""Sinusoid position embedder that maps position indexes into embeddings
via sinusoid calculation.
Each channel of the input Tensor is incremented by a sinusoid of a
different frequency and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query
and thememory inputs to attention.
The use of relative position is possible because sin(x+y) and
cos(x+y) can be experessed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
"""
def default_hparams(self):
"""returns a dictionary of hyperparameters with default values
We use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels/2.
"""
hparams = {
'name':'sinusoid_posisiton_embedder',
'min_timescale': 1.0,
'max_timescale': 1.0e4,
'trainable': False,
}
return hparams
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] | 2.204521 | 3,760 |
#!/usr/bin/python3
# Converts Prom1.x rule format into Prom2.x while keeping formatting
# and comments. This does not work in general. Some valid Prom1 rules
# files might not get converted properly.
import glob
import re
for rules_file in glob.iglob('*.rules'):
name = re.match(r'(.*)\.rules', rules_file)[1]
with open(name + '.yml', mode='w') as yaml:
print('groups:', file=yaml)
print('- name:', name, file=yaml)
print(' rules:', file=yaml)
with open(rules_file) as rules:
convert(rules, yaml)
| [
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] | 2.579439 | 214 |
from tkinter import*
from tkinter import ttk
#===================FUNCTION DECLARATION==============================================================================
if __name__ == '__main__':
root=Tk()
obj=ChatBot(root)
root.mainloop()
| [
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201,
198
] | 1.983516 | 182 |
# Copyright (c) 2010-2012 OpenStack, LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
# TODO: More tests
import mock
import httplib
import socket
import StringIO
import testtools
import warnings
from urlparse import urlparse
# TODO: mock http connection class with more control over headers
from .utils import fake_http_connect, fake_get_keystoneclient_2_0
from swiftclient import client as c
from swiftclient import utils as u
# TODO: following tests are placeholders, need more tests, better coverage
if __name__ == '__main__':
testtools.main()
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] | 3.620805 | 298 |
import numpy as np
import os
from utils.load_data import load_c3d_file
# declare variables
path = 'D:/MoCap_Data/David/NewSession_labeled/'
file = 'NeutralTrail14.c3d'
save_folder = 'data/'
save_name = 'David_neutral_pose'
neutral_frame = 900
template_labels = ['LeftBrow1', 'LeftBrow2', 'LeftBrow3', 'LeftBrow4', 'RightBrow1', 'RightBrow2', 'RightBrow3',
'RightBrow4', 'Nose1', 'Nose2', 'Nose3', 'Nose4', 'Nose5', 'Nose6', 'Nose7', 'Nose8',
'UpperMouth1', 'UpperMouth2', 'UpperMouth3', 'UpperMouth4', 'UpperMouth5', 'LowerMouth1',
'LowerMouth2', 'LowerMouth3', 'LowerMouth4', 'LeftOrbi1', 'LeftOrbi2', 'RightOrbi1', 'RightOrbi2',
'LeftCheek1', 'LeftCheek2', 'LeftCheek3', 'RightCheek1', 'RightCheek2', 'RightCheek3',
'LeftJaw1', 'LeftJaw2', 'RightJaw1', 'RightJaw2', 'LeftEye1', 'RightEye1', 'Head1', 'Head2',
'Head3', 'Head4']
# load sequence
data, labels = load_c3d_file(os.path.join(path, file),
template_labels=template_labels,
get_labels=True,
verbose=True)
print("labels", len(labels))
print(labels)
print("shape data[neutral_frame]", np.shape(data[neutral_frame]))
print(data[neutral_frame])
# save
np.save(os.path.join(save_folder, save_name), data[neutral_frame]) | [
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62,
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828,
1366,
58,
29797,
62,
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12962
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from senseHAT.BaseTest import SenseHATBaseTest
from random import randint
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198,
6738,
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600,
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] | 3.7 | 20 |
import pytest
import os
from jikken.database.config import get_config, write_default_config, JikkenConfig, read_config
@pytest.fixture()
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] | 3.155556 | 45 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
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from corehq.apps.cleanup.management.commands.populate_sql_model_from_couch_model import PopulateSQLCommand
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from OpenGLCffi.GL import params
@params(api='gl', prms=[])
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from pudzu.charts import *
df = pd.read_csv("datasets/flagstriband.csv")
df = pd.concat([pd.DataFrame(df.colours.apply(list).tolist(), columns=list("TMB")), df], axis=1).set_index("colours")
FONT, SIZE = calibri, 24
fg, bg = "black", "#EEEEEE"
default_img = "https://s-media-cache-ak0.pinimg.com/736x/0d/36/e7/0d36e7a476b06333d9fe9960572b66b9.jpg"
COLORS = { "W": "white", "Y": "yellow", "R": "red", "G": "green", "B": "blue", "K": "black", }
W, H = 320, 200
PAD = 100
grids = list(generate_batches([grid(c) for c in COLORS], 2))
grid = Image.from_array(grids, padding=(PAD,PAD//2), bg=bg)
title = Image.from_column([
Image.from_text_bounded("From Austria to Zanzibar".upper(), grid.size, 360, partial(FONT, bold=True), fg=fg, bg=bg, padding=(PAD,20)),
Image.from_text_bounded("a catalog of horizontal triband flags".upper(), grid.size, 240, partial(FONT, bold=True), fg=fg, bg=bg, padding=(PAD,20)),
], padding=0)
img = Image.from_column([title, grid], bg=bg, padding=(20,0)).pad(10, bg)
img.place(Image.from_text("/u/Udzu", FONT(48), fg=fg, bg=bg, padding=10).pad((2,2,0,0), fg), align=1, padding=10, copy=False)
img.save("output/flagstriband.png")
img.resize_fixed_aspect(scale=0.5).save("output/flagstriband2.png")
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14,
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822,
392,
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198
] | 2.314607 | 534 |
# Copyright 2020 by Aaron Baker.
# All rights reserved.
# This file is part of the Nightcap Project,
# and is released under the "MIT License Agreement". Please see the LICENSE
# file that should have been included as part of this package.
# region Imports
import tempfile
import shutil
from nightcapcore import Printer
from nightcappackages import *
# endregion | [
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import socket
from tkinter import *
ServerIP='127.0.0.1'
port = 4500
thisClient=FTPClient()
thisClient.run() | [
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# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: jet-to-python.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='jet-to-python.proto',
package='jet_to_python',
syntax='proto3',
serialized_options=_b('\n\035com.hazelcast.jet.python.grpcB\023JetToPythonTopLevelP\001'),
serialized_pb=_b('\n\x13jet-to-python.proto\x12\rjet_to_python\"\"\n\x0cInputMessage\x12\x12\n\ninputValue\x18\x01 \x03(\t\"$\n\rOutputMessage\x12\x13\n\x0boutputValue\x18\x01 \x03(\t2_\n\x0bJetToPython\x12P\n\rstreamingCall\x12\x1b.jet_to_python.InputMessage\x1a\x1c.jet_to_python.OutputMessage\"\x00(\x01\x30\x01\x42\x36\n\x1d\x63om.hazelcast.jet.python.grpcB\x13JetToPythonTopLevelP\x01\x62\x06proto3')
)
_INPUTMESSAGE = _descriptor.Descriptor(
name='InputMessage',
full_name='jet_to_python.InputMessage',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='inputValue', full_name='jet_to_python.InputMessage.inputValue', index=0,
number=1, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=38,
serialized_end=72,
)
_OUTPUTMESSAGE = _descriptor.Descriptor(
name='OutputMessage',
full_name='jet_to_python.OutputMessage',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='outputValue', full_name='jet_to_python.OutputMessage.outputValue', index=0,
number=1, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=74,
serialized_end=110,
)
DESCRIPTOR.message_types_by_name['InputMessage'] = _INPUTMESSAGE
DESCRIPTOR.message_types_by_name['OutputMessage'] = _OUTPUTMESSAGE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
InputMessage = _reflection.GeneratedProtocolMessageType('InputMessage', (_message.Message,), {
'DESCRIPTOR' : _INPUTMESSAGE,
'__module__' : 'jet_to_python_pb2'
# @@protoc_insertion_point(class_scope:jet_to_python.InputMessage)
})
_sym_db.RegisterMessage(InputMessage)
OutputMessage = _reflection.GeneratedProtocolMessageType('OutputMessage', (_message.Message,), {
'DESCRIPTOR' : _OUTPUTMESSAGE,
'__module__' : 'jet_to_python_pb2'
# @@protoc_insertion_point(class_scope:jet_to_python.OutputMessage)
})
_sym_db.RegisterMessage(OutputMessage)
DESCRIPTOR._options = None
_JETTOPYTHON = _descriptor.ServiceDescriptor(
name='JetToPython',
full_name='jet_to_python.JetToPython',
file=DESCRIPTOR,
index=0,
serialized_options=None,
serialized_start=112,
serialized_end=207,
methods=[
_descriptor.MethodDescriptor(
name='streamingCall',
full_name='jet_to_python.JetToPython.streamingCall',
index=0,
containing_service=None,
input_type=_INPUTMESSAGE,
output_type=_OUTPUTMESSAGE,
serialized_options=None,
),
])
_sym_db.RegisterServiceDescriptor(_JETTOPYTHON)
DESCRIPTOR.services_by_name['JetToPython'] = _JETTOPYTHON
# @@protoc_insertion_point(module_scope)
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] | 2.498153 | 1,624 |
from setuptools import setup
setup(name='hackathon',
install_requires=['pandas'],
extras_require={'test': ['pytest'],},
packages=['hackathon'])
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] | 2.65 | 60 |
#
# author: Paul Galatic
#
# This program is JUST for drawing a rounded rectangle.
#
import pdb
from PIL import Image, ImageDraw
from extern import *
def sub_rectangle(draw, xy, corner_radius=25, fill=(255, 255, 255)):
'''
Source: https://stackoverflow.com/questions/7787375/python-imaging-library-pil-drawing-rounded-rectangle-with-gradient
'''
upper_left_point = xy[0]
bottom_right_point = xy[1]
draw.rectangle(
[
(upper_left_point[0], upper_left_point[1] + corner_radius),
(bottom_right_point[0], bottom_right_point[1] - corner_radius)
],
fill=fill,
)
draw.rectangle(
[
(upper_left_point[0] + corner_radius, upper_left_point[1]),
(bottom_right_point[0] - corner_radius, bottom_right_point[1])
],
fill=fill,
)
draw.pieslice([upper_left_point, (upper_left_point[0] + corner_radius * 2, upper_left_point[1] + corner_radius * 2)],
180,
270,
fill=fill,
)
draw.pieslice([(bottom_right_point[0] - corner_radius * 2, bottom_right_point[1] - corner_radius * 2), bottom_right_point],
0,
90,
fill=fill,
)
draw.pieslice([(upper_left_point[0], bottom_right_point[1] - corner_radius * 2), (upper_left_point[0] + corner_radius * 2, bottom_right_point[1])],
90,
180,
fill=fill,
)
draw.pieslice([(bottom_right_point[0] - corner_radius * 2, upper_left_point[1]), (bottom_right_point[0], upper_left_point[1] + corner_radius * 2)],
270,
360,
fill=fill,
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] | 2.14324 | 747 |
#%%
# utilities
import subprocess
import os
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from numpy import linalg
import m8r as sf
from scipy.ndimage.filters import gaussian_filter
from scipy.ndimage.interpolation import map_coordinates
from tensorflow.python.ops.image_ops_impl import _random_flip
from skimage.transform import resize
class _const():
"""Default settings for modeling and inversion
"""
dx = 50
dt = 0.005
T_max = 7
nt = int(T_max / dt + 1)
central_freq = 7
jgx = 2
jsx = jgx
jdt = 4
sxbeg = 5000//dx
gxbeg = 1000//dx
szbeg = 2
jlogz = 2
trmodel = "marmvel.hh"
random_state_number = 314
random_model_repeat = 100
# upsample for plotting
ups_plot = 4
# one can stretch training models horizontally
stretch_X_train = 1
const = _const()
#%%
def tf_random_flip_channels(image, seed=None):
"""
With a 1 in 2 chance, outputs the contents of `image` flipped along the
third dimension, which is `channels`. Otherwise output the image as-is.
Args:
image: 4-D Tensor of shape `[batch, height, width, channels]` or
3-D Tensor of shape `[height, width, channels]`.
seed: A Python integer. Used to create a random seed. See
`tf.set_random_seed`
for behavior.
Returns:
A tensor of the same type and shape as `image`.
Raises:
ValueError: if the shape of `image` not supported.
"""
return _random_flip(image, 2, seed, 'random_flip_channels')
def np_to_rsf(vel, model_output, d1 = const.dx, d2 = const.dx):
''' Write 2D numpy array vel to rsf file model_output '''
yy = sf.Output(model_output)
yy.put('n1',np.shape(vel)[1])
yy.put('n2',np.shape(vel)[0])
yy.put('d1',d1)
yy.put('d2',d2)
yy.put('o1',0)
yy.put('o2',0)
yy.write(vel)
yy.close()
def merge_dict(dict1, dict2):
''' Merge dictionaries with same keys'''
dict3 = dict1.copy()
for key, value in dict1.items():
dict3[key] = np.concatenate((value, dict2[key]), axis=0)
return dict3
def cmd(command):
"""Run command and pipe what you would see in terminal into the output cell
"""
process = subprocess.Popen(command, stderr=subprocess.PIPE, stdout=subprocess.PIPE, shell=True)
while True:
output = process.stderr.readline().decode('utf-8')
if output == '' and process.poll() is not None:
# this prints the stdout in the end
output2 = process.stdout.read().decode('utf-8')
print(output2.strip())
break
if output:
print(output.strip())
rc = process.poll()
return rc
class cd:
"""Context manager for changing the current working directory"""
# to distort the model
def elastic_transform(image, alpha, sigma, random_state_number=None, v_dx=const.dx, plot_name=None):
"""Elastic deformation of images as described in [Simard2003]_.
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
"""
random_state = np.random.RandomState(random_state_number)
shape = image.shape
#print(shape)
# with our velocities dx is vertical shift
dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), (sigma, sigma/10, 1), mode="constant", cval=0) * 4 * alpha
# with our velocities dy is horizontal
dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), (sigma, sigma/10, 1), mode="constant", cval=0) * alpha
dz = np.zeros_like(dx)
x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))
distorted_image = map_coordinates(image, indices, order=1, mode='reflect', prefilter=False)
distorted_image = distorted_image.reshape(image.shape)
if plot_name != None:
plt_nb_T(v_dx * np.squeeze(dx[:,:]), fname=f"VerticalShifts_{alpha}", title="Vertical shifts (km)")
dq_x = 100
dq_z = 17
M = np.hypot(dy.squeeze()[::dq_x,::dq_z].T, dx.squeeze()[::dq_x,::dq_z].T)
M = dx.squeeze()[::dq_x,::dq_z].T
M = np.squeeze(image)[::dq_x,::dq_z].T
if 1:
fig1, ax1 = plt.subplots(figsize=(16,9))
ax1.set_title('Guiding model')
plt.imshow(1e-3*np.squeeze(image.T), extent=(0, v_dx * dx.shape[0] * 1e-3, v_dx * dx.shape[1] *1e-3, 0))
plt.axis("tight")
plt.xlabel("Distance (km)")
plt.ylabel("Depth (km)")
plt.colorbar()
Q = ax1.quiver(
1e-3*v_dx *y.squeeze()[::dq_x,::dq_z].T, 1e-3*v_dx *x.squeeze()[::dq_x,::dq_z].T,
np.abs(1e-4*v_dx*dx.squeeze()[::dq_x,::dq_z].T), 1e-3*v_dx*dx.squeeze()[::dq_x,::dq_z].T,
scale_units='xy', scale=1, pivot='tip')
plt.savefig(f"../latex/Fig/shiftsVectors", bbox_inches='tight')
plt_show_proceed()
fig1, ax1 = plt.subplots(figsize=(16,9))
ax1.set_title('Distorted model')
plt.imshow(1e-3*np.squeeze(distorted_image.T), extent=(0, v_dx * dx.shape[0] * 1e-3, v_dx * dx.shape[1] *1e-3, 0))
plt.axis("tight")
plt.xlabel("Distance (km)")
plt.ylabel("Depth (km)")
plt.colorbar()
Q = ax1.quiver(
1e-3*v_dx *y.squeeze()[::dq_x,::dq_z].T, 1e-3*v_dx *x.squeeze()[::dq_x,::dq_z].T,
np.abs(1e-4*v_dx*dx.squeeze()[::dq_x,::dq_z].T), 1e-3*v_dx*dx.squeeze()[::dq_x,::dq_z].T,
scale_units='xy', scale=1, pivot='tip')
plt.savefig(f"../latex/Fig/deformedModel{plot_name}", bbox_inches='tight')
plt_show_proceed()
return distorted_image | [
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331,
88,
796,
264,
69,
13,
26410,
7,
19849,
62,
22915,
8,
198,
220,
220,
220,
331,
88,
13,
1996,
10786,
77,
16,
3256,
37659,
13,
43358,
7,
626,
38381,
16,
12962,
198,
220,
220,
220,
331,
88,
13,
1996,
10786,
77,
17,
3256,
37659,
13,
43358,
7,
626,
38381,
15,
12962,
198,
220,
220,
220,
331,
88,
13,
1996,
10786,
67,
16,
3256,
67,
16,
8,
198,
220,
220,
220,
331,
88,
13,
1996,
10786,
67,
17,
3256,
67,
17,
8,
198,
220,
220,
220,
331,
88,
13,
1996,
10786,
78,
16,
3256,
15,
8,
198,
220,
220,
220,
331,
88,
13,
1996,
10786,
78,
17,
3256,
15,
8,
198,
220,
220,
220,
331,
88,
13,
13564,
7,
626,
8,
198,
220,
220,
220,
331,
88,
13,
19836,
3419,
198,
220,
220,
220,
220,
198,
4299,
20121,
62,
11600,
7,
11600,
16,
11,
8633,
17,
2599,
198,
220,
220,
220,
705,
7061,
39407,
48589,
3166,
351,
976,
8251,
7061,
6,
198,
220,
220,
220,
8633,
18,
796,
8633,
16,
13,
30073,
3419,
198,
220,
220,
220,
329,
1994,
11,
1988,
287,
8633,
16,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
8633,
18,
58,
2539,
60,
796,
45941,
13,
1102,
9246,
268,
378,
19510,
8367,
11,
8633,
17,
58,
2539,
46570,
16488,
28,
15,
8,
198,
220,
220,
220,
1441,
8633,
18,
198,
198,
4299,
23991,
7,
21812,
2599,
198,
220,
220,
220,
37227,
10987,
3141,
290,
12656,
644,
345,
561,
766,
287,
12094,
656,
262,
5072,
2685,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1429,
796,
850,
14681,
13,
47,
9654,
7,
21812,
11,
336,
1082,
81,
28,
7266,
14681,
13,
47,
4061,
36,
11,
14367,
448,
28,
7266,
14681,
13,
47,
4061,
36,
11,
7582,
28,
17821,
8,
198,
220,
220,
220,
981,
6407,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5072,
796,
1429,
13,
301,
1082,
81,
13,
961,
1370,
22446,
12501,
1098,
10786,
40477,
12,
23,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5072,
6624,
10148,
290,
1429,
13,
30393,
3419,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
428,
20842,
262,
14367,
448,
287,
262,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5072,
17,
796,
1429,
13,
19282,
448,
13,
961,
22446,
12501,
1098,
10786,
40477,
12,
23,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
22915,
17,
13,
36311,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5072,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
22915,
13,
36311,
28955,
198,
220,
220,
220,
48321,
796,
1429,
13,
30393,
3419,
198,
220,
220,
220,
1441,
48321,
198,
198,
4871,
22927,
25,
198,
220,
220,
220,
37227,
21947,
4706,
329,
5609,
262,
1459,
1762,
8619,
37811,
198,
198,
2,
284,
30867,
262,
2746,
198,
4299,
27468,
62,
35636,
7,
9060,
11,
17130,
11,
264,
13495,
11,
4738,
62,
5219,
62,
17618,
28,
14202,
11,
410,
62,
34350,
28,
9979,
13,
34350,
11,
7110,
62,
3672,
28,
14202,
2599,
198,
220,
220,
220,
37227,
9527,
3477,
390,
1161,
286,
4263,
355,
3417,
287,
685,
8890,
446,
16088,
60,
44807,
198,
220,
220,
220,
11485,
685,
8890,
446,
16088,
60,
3184,
446,
11,
2441,
676,
430,
385,
290,
1345,
1078,
11,
366,
13014,
42134,
329,
198,
220,
220,
220,
220,
220,
220,
34872,
2122,
282,
47986,
27862,
5625,
284,
15612,
16854,
14691,
1600,
287,
198,
220,
220,
220,
220,
220,
220,
31345,
13,
286,
262,
4037,
8785,
319,
16854,
14691,
290,
198,
220,
220,
220,
220,
220,
220,
31517,
653,
11,
5816,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
198,
220,
220,
220,
4738,
62,
5219,
796,
45941,
13,
25120,
13,
29531,
9012,
7,
25120,
62,
5219,
62,
17618,
8,
628,
220,
220,
220,
5485,
796,
2939,
13,
43358,
198,
220,
220,
220,
1303,
4798,
7,
43358,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
351,
674,
11555,
420,
871,
44332,
318,
11723,
6482,
198,
220,
220,
220,
44332,
796,
31986,
31562,
62,
24455,
19510,
25120,
62,
5219,
13,
25192,
46491,
43358,
8,
1635,
362,
532,
352,
828,
357,
82,
13495,
11,
264,
13495,
14,
940,
11,
352,
828,
4235,
2625,
9979,
415,
1600,
269,
2100,
28,
15,
8,
1635,
604,
1635,
17130,
198,
220,
220,
220,
220,
198,
220,
220,
220,
1303,
351,
674,
11555,
420,
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20268,
318,
16021,
198,
220,
220,
220,
20268,
796,
31986,
31562,
62,
24455,
19510,
25120,
62,
5219,
13,
25192,
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43358,
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1635,
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264,
13495,
14,
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2100,
28,
15,
8,
1635,
17130,
198,
220,
220,
220,
288,
89,
796,
45941,
13,
9107,
418,
62,
2339,
7,
34350,
8,
198,
220,
220,
220,
220,
198,
220,
220,
220,
2124,
11,
331,
11,
1976,
796,
45941,
13,
76,
5069,
25928,
7,
37659,
13,
283,
858,
7,
43358,
58,
16,
46570,
45941,
13,
283,
858,
7,
43358,
58,
15,
46570,
45941,
13,
283,
858,
7,
43358,
58,
17,
60,
4008,
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220,
36525,
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3447,
1758,
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88,
10,
9892,
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13841,
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11,
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3447,
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34350,
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36911,
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3447,
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13841,
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11,
352,
4008,
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220,
220,
220,
26987,
62,
9060,
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3975,
62,
37652,
17540,
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9060,
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36525,
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1502,
28,
16,
11,
4235,
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35051,
3256,
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353,
28,
25101,
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198,
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26987,
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43358,
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198,
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7110,
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6045,
25,
198,
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220,
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220,
220,
458,
83,
62,
46803,
62,
51,
7,
85,
62,
34350,
1635,
45941,
13,
16485,
1453,
2736,
7,
34350,
58,
45299,
25,
46570,
277,
3672,
28,
69,
1,
42369,
605,
2484,
19265,
23330,
26591,
92,
1600,
3670,
2625,
42369,
605,
15381,
357,
13276,
8,
4943,
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288,
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628,
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220,
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1441,
26987,
62,
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] | 2.156791 | 2,717 |
"""Test the analog.main module and CLI."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
try:
from unittest import mock
except ImportError:
import mock
import pytest
import analog
@pytest.fixture
def tmp_logfile(tmpdir):
"""Fixture creating a temporary logfile.
:returns: local tempfile object.
"""
log_name = 'logmock.log'
logfile = tmpdir.join(log_name)
logfile.write("log entry #1")
return logfile
def test_help(capsys):
"""analog --help prints help and describes arguments."""
with pytest.raises(SystemExit):
analog.main(['analog', '--help'])
out, err = capsys.readouterr()
# main docstring is used as help description
assert analog.main.__doc__ in out
# analog arguments are listed
assert '--config' in out
assert '--version' in out
assert '--format' in out
assert '--regex' in out
assert '--max-age' in out
assert '--print-stats' in out
assert '--print-path-stats' in out
def test_format_or_regex_required(capsys, tmp_logfile):
"""analog requires log --format or pattern --regex."""
with pytest.raises(SystemExit) as exit:
analog.main(['analog', str(tmp_logfile)])
assert exit.errisinstance(analog.MissingFormatError)
@mock.patch('analog.analyze', return_value=analog.Report([], []))
def test_paths(mock_analyze, capsys, tmp_logfile):
"""analog --path specifies paths to monitor."""
with pytest.raises(SystemExit):
# the --path argument can be specified multiple times, also as -p
analog.main(['analog',
'--format', 'nginx',
'--config', '/foo/bar',
str(tmp_logfile)])
mock_analyze.assert_called_once_with(
log=mock.ANY,
format='nginx',
config='/foo/bar',
max_age=10,
print_stats=False,
print_path_stats=False)
| [
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] | 2.318442 | 873 |
'''Global Helpers'''
# pylint: disable=invalid-name,superfluous-parens,missing-docstring,wildcard-import,unused-wildcard-import
from sys import platform
import os
import sys
import re
import traceback
import webbrowser
import subprocess
from shutil import copytree, rmtree
from platform import python_version
from configparser import ConfigParser
from threading import Timer
import cchardet
from PySide2 import QtGui, QtCore, __version__
from PySide2.QtWidgets import QFileDialog, QMessageBox, QWidget
from src.globals import data
from src.globals.constants import *
from src.gui.file_dialog import FileDialog
from src.gui.alerts import MessageCouldntOpenFile, MessageNotConfigured, MessageUnsupportedOS
def copyFolder(src, dst):
'''Copy folder from src to dst'''
dst = os.path.normpath(dst)
src = os.path.normpath(src)
print(
f'copying from {src} to {dst} (exists: {os.path.isdir(os.path.normpath(dst))})')
rmtree(dst, ignore_errors=True)
while os.path.isdir(dst):
pass
copytree(src, dst)
def restartProgram():
'''Restarts the program'''
data.config.write()
python = sys.executable
os.execl(python, python, *sys.argv)
def getFile(directory="", extensions="", title="Select Files or Folders"):
'''Opens custom dialog for selecting multiple folders or files'''
return FileDialog(None, title, str(directory), str(extensions)).selectedFiles
def getSize(start_path='.'):
'''Calculates the size of the selected folder'''
total_size = 0
for dirpath, _, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
total_size += os.path.getsize(fp)
return total_size
def getIcon(filename):
'''Gets icon from the res folder'''
icon = QtGui.QIcon()
icon.addFile(getProgramRootFolder() + '/res/' + filename)
return icon
def getKey(item):
'''Helper function for the mod list'''
return item[1]
def isData(name):
'''Checks if given name represents correct mod folder or not'''
return re.match(r"^(~|)mod.+$", name)
def fixUserSettingsDuplicateBrackets():
'''Fix invalid section names in user.settings'''
try:
config = ConfigParser(strict=False)
config.optionxform = str
config.read(data.config.settings + "/user.settings",
encoding=detectEncoding(data.config.settings + "/user.settings"))
for section in config.sections():
newSection = section
while newSection[:1] == "[":
newSection = newSection[1:]
while newSection[-1:] == "]":
newSection = newSection[:-1]
if newSection != section:
items = config.items(section)
if not config.has_section(newSection):
config.add_section(newSection)
for item in items:
config.set(newSection, item[0], item[1])
config.remove_section(section)
with open(data.config.settings+"/user.settings", 'w', encoding="utf-8") as userfile:
config.write(userfile, space_around_delimiters=False)
except:
print("fixing duplicate brackets failed")
def throttle(ms: int):
"""Decorator ensures function that can only be called once every `ms` milliseconds"""
from datetime import datetime, timedelta
return decorate
def debounce(ms: int):
"""Debounce a functions execution by {ms} milliseconds"""
return decorator
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] | 2.553145 | 1,383 |
from __future__ import absolute_import
from .alert import Alert
from .domain import URI, Domain
from .file import File, FileOf
from .ip_address import IPAddress
from .node import Node
from .process import Process, SysMonProc
from .registry import RegistryKey
__all__ = [
"Node",
"URI",
"Domain",
"File",
"FileOf",
"IPAddress",
"SysMonProc",
"Process",
"RegistryKey",
"Alert",
]
| [
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import enum
__all__ = ["UpdateMode"]
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] | 2.857143 | 14 |
# -*- coding: utf-8 -*-
#
# Convert list of Office files (.docx, .xslx, .pptx) files from
# old text encoding to Unicode.
import os
import re
import sys
import convertOffice
import osageConversion
import convertUtil
if __name__ == "__main__":
main(sys.argv)
| [
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] | 2.8 | 95 |
#-*- coding:utf-8 -*-
import time
from bs4 import BeautifulSoup
from user_agents import agents
import requests
import random
import re
def get_article(url):
'''
:param url: 指定日期的链接
:return content: 指定url的正文内容
'''
agent = random.choice(agents)
header = {'User-Agent': agent}
res = requests.get(url, headers=header)
time.sleep(2)
res.encoding = 'utf-8'
soup = BeautifulSoup(res.text, 'html.parser')
newsArticle = soup.select('.articleText')
pattern = re.compile(r'<[^>]+>', re.S)
for item in (str(newsArticle[0]).split('<strong>')):
new_item = item.split('</strong>')
if len(new_item) > 1:
contents = pattern.sub('', str(new_item))
content_list = contents.split('\'')
content = ''.join(content_list)
return content | [
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4458,
22179,
7,
11299,
62,
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8,
198,
220,
220,
220,
1441,
2695
] | 2.227642 | 369 |
# Generated by Django 3.1.4 on 2021-01-13 17:40
from django.db import migrations
| [
2,
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] | 2.766667 | 30 |
import os, json
import hdefereval
import sdm.houdini
from sdm.houdini.dialog import checkForUpdates
from sdm.houdini.shelves import addShelf
from sdm.houdini.node import applyDefaultShapesAndColors
hdefereval.executeDeferred(checkUpdates)
hdefereval.executeDeferred(addShelf)
hdefereval.executeDeferred(applyDefaultShapesAndColors)
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8,
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71,
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13,
41049,
7469,
17436,
7,
39014,
19463,
2484,
7916,
1870,
5216,
669,
8,
198
] | 2.854701 | 117 |
'''
@ Author: Tiexin
@ email: [email protected]
@Data: 2019-8-14
'''
from engine.configs.parser import BaseOptions
# import engine.fsl_trainer as trainer
import engine.ssl_trainer as trainer
import sys
sys.dont_write_bytecode = True
try:
from itertools import izip as zip
except ImportError: # will be 3.x series
pass
if __name__ == '__main__':
# Load experiment setting
opts = BaseOptions().opts
trainer = trainer.Trainer(opts)
trainer.train()
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8,
198,
220,
220,
220,
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13,
27432,
3419,
628,
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] | 2.710227 | 176 |
#!/usr/bin/env python3
import lib
n=1
d=1
i=0
N=1000
count = 0
while i < N+1:
#print(i,str(n)+'/'+str(d),n/d)
if (lib.num_digits(n) >lib.num_digits(d)):
count += 1
#term' = 1 + 1/(1+term)
n+= d #1 + term
n,d=d,n #1/(1+term)
n+= d #1+1/(1+term)
i+=1
print(count)
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] | 1.775 | 160 |
import csv
from bs4 import BeautifulSoup
import requests
# function to scrape smartybro
# Code to scrape Anycouponcode.com
# function to scrape BuzzUdemy.com
# function to scrape Comidoc.com
# function to scrape coupontry.com
# function to scrape udemycoupon.learnviral
# function to scrape Udemycoupon.club
# function that sorts the function to be used
# Main driver Program
listFile2 = open('output.csv', 'w')
listFile2.close()
with open('input.txt') as openfileobject:
for line in openfileobject:
page_link = line
checker(page_link)
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2198,
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] | 3.005236 | 191 |
import threading
| [
11748,
4704,
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] | 4 | 5 |
# -*- coding: utf-8 -*-
'''
default network class
给神经网络类的接口格式定义，神经网络具体需要自行添加
'''
import pickle as pkl
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] | 1.095745 | 94 |
import json
import pprint
######### OPEN AND READ THE DATA FILE ###########
inFile = open("data/scf_data.json","r")
scf_data = json.load(inFile)
# print(scf_data)
inFile.close()
############ DATA EXPLORATION #############
# dataType = str(type(scf_data))
# print("type of data: " + dataType)
# print("dictionary keys: " + str(scf_data.keys()))
# issues_data_type = str(type(scf_data["issues"]))
# print("data type of the 'issues' value: " + issues_data_type )
# print("first element of 'issues' list:")
# print(scf_data["issues"][0])
## print data variables
# pp = pprint.PrettyPrinter(indent=4)
# print("first data entry:")
# pp.pprint(scf_data["issues"][0])
############ DATA MODIFICATION #############
new_scf_data = []
variables = ["address","created_at","summary","description","lng","lat","rating"]
for old_entry in scf_data["issues"]:
new_entry={}
for variable in variables:
new_entry[variable] = old_entry[variable]
# print(new_entry)
new_scf_data.append(new_entry)
### OUTPUTTING THE NEW DATA TO A NEW FILE ###
outfile = open("data/scf_output_data.json","w")
json.dump(new_scf_data, outfile, indent=4)
outfile.close()
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28,
19,
8,
198,
448,
7753,
13,
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3419,
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] | 2.745843 | 421 |
# -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-03-24 11:51
from __future__ import unicode_literals
from django.db import migrations, models
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] | 2.719298 | 57 |
from time import sleep
import pytest
from ...utils import assert_finished, assert_obniz, assert_send, receive_json
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from flask_restplus import fields
from polylogyx.blueprints.v1.external_api import api
# Node Wrappers
node_info_wrapper = api.model('node_info_wrapper', {
'computer_name': fields.String(),
'hardware_model': fields.String(),
'hardware_serial': fields.String(),
'hardware_vendor': fields.String(),
'physical_memory': fields.String(),
'cpu_physical_cores': fields.String()
})
nodewrapper = api.model('nodewrapper', {
'id':fields.Integer(),
'host_identifier': fields.String(),
'node_key': fields.String(),
'last_ip': fields.String(),
'platform': fields.String(),
'os_info': fields.Raw(),
'node_info': fields.Nested(node_info_wrapper, default=None),
'network_info': fields.Raw(),
'host_details': fields.Raw(),
'last_checkin': fields.DateTime(default = None),
'enrolled_on': fields.DateTime(default = None),
'last_status': fields.DateTime(default = None),
'last_result': fields.DateTime(default = None),
'last_config': fields.DateTime(default = None),
'last_query_read': fields.DateTime(default = None),
'last_query_write': fields.DateTime(default = None),
})
node_tag_wrapper = api.model('node_tag_wrapper', {
'host_identifier': fields.String(),
'node_key': fields.String()
})
node_status_log_wrapper = api.model('node_status_log_wrapper', {
'line': fields.Integer(),
'message': fields.String(),
'severity': fields.Integer(),
'filename': fields.String(),
'created': fields.DateTime(),
'version': fields.String(),
}) | [
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198,
220,
220,
220,
705,
9641,
10354,
7032,
13,
10100,
22784,
198,
30072
] | 2.770758 | 554 |
"""
Entradas
(X,M)-->int-->valores
Salida
Nueva experiencia Monster-->int-->E
"""
#Caja negra
while True:
#Entrada
valores=input("")
(X,M)=valores.split(" ")
X=int(X)
M=int(M)
#Caja negra
if (X==0) and M==0:
break
else:
E=X*M
#Salida
print(E) | [
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] | 1.741758 | 182 |
"""
Print out the nodes of a taxonomy
Usage:
print_nodes (-h | help)
print_nodes <taxon>... [--debug]
Options:
-h --help Prints this documentation
<taxon> A yaml file containing partial or whole taxonomy. Multiple
files will be merged.
-d --debug Print log information while running
"""
import logging
import sys
import docopt
import yamlconf
ENWIKI_HOST = 'https://en.wikipedia.org'
logger = logging.getLogger(__name__)
if __name__ == "__main__":
sys.exit(main())
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198
] | 2.588235 | 204 |
#! -*- coding: utf-8 -*-
'''DeepLearning学習処理の実装サンプル
引数に指定する設定ファイルで指定されたパラメータに従い，DeepLearningモデルの学習を実行する実装サンプル．
設定ファイルで指定するパラメータ:
* env: 環境設定
* fifo: 学習制御用のFIFOパス
* result_dir: 結果を格納するディレクトリ
* dataset: データセット関連の設定
* dataset_name: データセット名(Preset: MNIST, CIFAR-10)
* dataset_dir: データセットを格納したディレクトリ
* norm: 正規化方式(max, max-min, z-score)
* data_augmentation: DataAugmentation関連の設定
* rotation_range: 画像の回転[deg]
* width_shift_range: 水平方向の画像幅に対するシフト率[0.0-1.0]
* height_shift_range: 垂直方向の画像高さに対するシフト率[0.0-1.0]
* zoom_range: 拡大率[%]
* channel_shift_range: チャネル(RGB)のシフト率[0.0-1.0]
* horizontal_flip: 水平方向反転有無(True or False)
* model: 学習するモデル関連の設定
* model_type: モデル種別(MLP, SimpleCNN, DeepCNN, SimpleResNet, DeepResNet)
* training_parameter: ハイパーパラメータ
* optimizer: 最適化方式(momentum, adam, sgd, adam_lrs, sgd, lrs)
* batch_size: バッチサイズ
* epochs: EPOCH数
* initializer: 重みの初期化アルゴリズム
glrot_uniform: Xavierの一様分布
glrot_normal: Xavierの正規分布
he_uniform: Heの一様分布
he_normal: Heの正規分布
* droptout_rate: ドロップアウトによる欠落率[0.0-1.0]
* loss_func: 損失関数(tf.keras.lossesのメンバを指定)
'''
#---------------------------------
# モジュールのインポート
#---------------------------------
import os
import json
import argparse
import numpy as np
import pandas as pd
import pickle
from machine_learning.lib.data_loader.data_loader import DataLoaderMNIST
from machine_learning.lib.data_loader.data_loader import DataLoaderCIFAR10
from machine_learning.lib.trainer.trainer import TrainerMLP, TrainerCNN, TrainerResNet
#---------------------------------
# 定数定義
#---------------------------------
#---------------------------------
# 関数
#---------------------------------
#---------------------------------
# メイン処理
#---------------------------------
if __name__ == '__main__':
main()
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834,
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834,
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197,
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] | 1.712442 | 1,085 |
import streamlit as slt
from sklearn.svm import SVC,SVR
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import plot_confusion_matrix
from matplotlib.colors import ListedColormap
from sklearn.cluster import KMeans, AgglomerativeClustering
from sklearn.metrics import precision_score, recall_score,mean_squared_error
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import scipy.cluster.hierarchy as sch
if __name__ == '__main__':
main()
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] | 3.303571 | 224 |
##############################################################################
#copyright 2012, Hamid MEDJAHED ([email protected]) Prologue #
#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. #
##############################################################################
#!/usr/bin/env python
# -*- coding: latin-1 -*-
import sys
import pypacksrc
srcdirectory=pypacksrc.srcpydir+"/pyaccords"
sys.path.append(srcdirectory)
from amazonEc2Action import *
from actionClass import *
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] | 2.201667 | 600 |
#!/usr/bin/env bash
#!/bin/bash
#!/bin/sh
#!/bin/sh -
from vk_api.utils import get_random_id
from vk_api import VkUpload
from vk_api.bot_longpoll import VkBotLongPoll, VkBotEventType
from vk_api.keyboard import VkKeyboard, VkKeyboardColor
import random, requests, vk_api, os, bs4
from google_images_download import google_images_download
from lxml import html
import urllib.parse
dict = [".", ",", "!", "?", ")", "(", ":", ";", "'", ']', '[', '"']
dictan = [")", "(", ":", ";", "'", ']', '[', '"', '\\', 'n', '&', 'q', 'u', 'o', 't']
dict7 = {'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5, 'June': 6, 'July': 7, 'August': 8,
'September': 9, 'October': 10, 'November': 11, 'December': 12}
dict8 = {'овен':'aries','телец':'taurus' ,'близнецы':'gemini' ,'рак':'cancer' ,'лев':'leo' ,'дева':'virgo' ,'весы':'libra' ,'скорпион':'scorpio' ,'стрелец':'sagittarius','козерог':'capricorn' ,'водолей':'aquarius' ,'рыбы':'pisces'}
kolresp = 0
attachments = []
chand = 0
flagtime = False
fltm1 = False
fltm2 = False
flaggoroscop=True
#защита от пидарасов
f=open('/root/bot_herobot_ls/token.txt','r')
token=f.read()
f.close()
session = requests.Session()
vk_session = vk_api.VkApi(token=token)
longpoll = VkBotLongPoll(vk_session, '178949259')
vk = vk_session.get_api()
upload = VkUpload(vk_session) # Для загрузки изображений
keyboardgor = VkKeyboard(one_time=False)
keyboardgor.add_button('Овен', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Телец', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Близнецы', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Рак', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_line() # Переход на вторую строку
keyboardgor.add_button('Лев', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Дева', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Весы', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Скорпион', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_line() # Переход на вторую строку
keyboardgor.add_button('Стрелец', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Козерог', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Водолей', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_button('Рыбы', color=VkKeyboardColor.PRIMARY)
keyboardgor.add_line() # Переход на вторую строку
keyboardgor.add_button('Убери гороскоп', color=VkKeyboardColor.NEGATIVE)
keyboardosn = VkKeyboard(one_time=False)
keyboardosn.add_button('Мысль', color=VkKeyboardColor.PRIMARY)
keyboardosn.add_button('Цитата', color=VkKeyboardColor.PRIMARY)
keyboardosn.add_button('Факт', color=VkKeyboardColor.PRIMARY)
keyboardosn.add_button('Анекдот', color=VkKeyboardColor.PRIMARY)
#keyboardosn.add_line() # Переход на вторую строку
#keyboardosn.add_button('Анекдот', color=VkKeyboardColor.PRIMARY)
'''
print(keyboardgor.get_keyboard())
vk.messages.send(
user_id=195310233,
random_id=get_random_id(),
keyboard=keyboardgor.get_keyboard(),
message="Я перезагружен!"
)
'''
iscl=["легкое", "сложное", "среднее", "❓ что это такое", "♻ другое слово", "!рестарт крокодил"]
mainfunc()
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] | 1.963717 | 1,571 |
from environment_models.base import BaseEnv
from airobot_utils.pusher_simulator import PusherSimulator
import numpy as np
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] | 3.378378 | 37 |
import heckguide.wsgi
from whitenoise import WhiteNoise
application = heckguide.wsgi.application
application = WhiteNoise(application, root='/home/heckkciy/dev.heckguide.com/static') | [
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# function call to the transformation functions of relevance for the hpsModel
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../transformations/'))
import hpsModel as HPS
import hpsTransformations as HPST
import harmonicTransformations as HT
import utilFunctions as UF
def analysis(inputFile='../../sounds/sax-phrase-short.wav', window='blackman', M=601, N=1024, t=-100,
minSineDur=0.1, nH=100, minf0=350, maxf0=700, f0et=5, harmDevSlope=0.01, stocf=0.1):
"""
Analyze a sound with the harmonic plus stochastic model
inputFile: input sound file (monophonic with sampling rate of 44100)
window: analysis window type (rectangular, hanning, hamming, blackman, blackmanharris)
M: analysis window size
N: fft size (power of two, bigger or equal than M)
t: magnitude threshold of spectral peaks
minSineDur: minimum duration of sinusoidal tracks
nH: maximum number of harmonics
minf0: minimum fundamental frequency in sound
maxf0: maximum fundamental frequency in sound
f0et: maximum error accepted in f0 detection algorithm
harmDevSlope: allowed deviation of harmonic tracks, higher harmonics have higher allowed deviation
stocf: decimation factor used for the stochastic approximation
returns inputFile: input file name; fs: sampling rate of input file,
hfreq, hmag: harmonic frequencies, magnitude; mYst: stochastic residual
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# read input sound
(fs, x) = UF.wavread(inputFile)
# compute analysis window
w = get_window(window, M)
# compute the harmonic plus stochastic model of the whole sound
hfreq, hmag, hphase, mYst = HPS.hpsModelAnal(x, fs, w, N, H, t, nH, minf0, maxf0, f0et, harmDevSlope, minSineDur, Ns, stocf)
# synthesize the harmonic plus stochastic model without original phases
y, yh, yst = HPS.hpsModelSynth(hfreq, hmag, np.array([]), mYst, Ns, H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_hpsModel.wav'
UF.wavwrite(y,fs, outputFile)
# create figure to plot
plt.figure(figsize=(9, 6))
# frequency range to plot
maxplotfreq = 15000.0
# plot the input sound
plt.subplot(3,1,1)
plt.plot(np.arange(x.size)/float(fs), x)
plt.axis([0, x.size/float(fs), min(x), max(x)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('input sound: x')
# plot spectrogram stochastic compoment
plt.subplot(3,1,2)
numFrames = int(mYst[:,0].size)
sizeEnv = int(mYst[0,:].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = (.5*fs)*np.arange(sizeEnv*maxplotfreq/(.5*fs))/sizeEnv
plt.pcolormesh(frmTime, binFreq, np.transpose(mYst[:,:int(sizeEnv*maxplotfreq/(.5*fs))+1]))
plt.autoscale(tight=True)
# plot harmonic on top of stochastic spectrogram
if (hfreq.shape[1] > 0):
harms = hfreq*np.less(hfreq,maxplotfreq)
harms[harms==0] = np.nan
numFrames = int(harms[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, harms, color='k', ms=3, alpha=1)
plt.xlabel('time (sec)')
plt.ylabel('frequency (Hz)')
plt.autoscale(tight=True)
plt.title('harmonics + stochastic spectrogram')
# plot the output sound
plt.subplot(3,1,3)
plt.plot(np.arange(y.size)/float(fs), y)
plt.axis([0, y.size/float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show(block=False)
return inputFile, fs, hfreq, hmag, mYst
def transformation_synthesis(inputFile, fs, hfreq, hmag, mYst, freqScaling = np.array([0, 1.2, 2.01, 1.2, 2.679, .7, 3.146, .7]),
freqStretching = np.array([0, 1, 2.01, 1, 2.679, 1.5, 3.146, 1.5]), timbrePreservation = 1,
timeScaling = np.array([0, 0, 2.138, 2.138-1.0, 3.146, 3.146])):
"""
transform the analysis values returned by the analysis function and synthesize the sound
inputFile: name of input file
fs: sampling rate of input file
hfreq, hmag: harmonic frequencies and magnitudes
mYst: stochastic residual
freqScaling: frequency scaling factors, in time-value pairs (value of 1 no scaling)
freqStretching: frequency stretching factors, in time-value pairs (value of 1 no stretching)
timbrePreservation: 1 preserves original timbre, 0 it does not
timeScaling: time scaling factors, in time-value pairs
"""
# size of fft used in synthesis
Ns = 512
# hop size (has to be 1/4 of Ns)
H = 128
# frequency scaling of the harmonics
hfreqt, hmagt = HT.harmonicFreqScaling(hfreq, hmag, freqScaling, freqStretching, timbrePreservation, fs)
# time scaling the sound
yhfreq, yhmag, ystocEnv = HPST.hpsTimeScale(hfreqt, hmagt, mYst, timeScaling)
# synthesis from the trasformed hps representation
y, yh, yst = HPS.hpsModelSynth(yhfreq, yhmag, np.array([]), ystocEnv, Ns, H, fs)
# write output sound
outputFile = 'output_sounds/' + os.path.basename(inputFile)[:-4] + '_hpsModelTransformation.wav'
UF.wavwrite(y,fs, outputFile)
# create figure to plot
plt.figure(figsize=(12, 6))
# frequency range to plot
maxplotfreq = 15000.0
# plot spectrogram of transformed stochastic compoment
plt.subplot(2,1,1)
numFrames = int(ystocEnv[:,0].size)
sizeEnv = int(ystocEnv[0,:].size)
frmTime = H*np.arange(numFrames)/float(fs)
binFreq = (.5*fs)*np.arange(sizeEnv*maxplotfreq/(.5*fs))/sizeEnv
plt.pcolormesh(frmTime, binFreq, np.transpose(ystocEnv[:,:int(sizeEnv*maxplotfreq/(.5*fs))+1]))
plt.autoscale(tight=True)
# plot transformed harmonic on top of stochastic spectrogram
if (yhfreq.shape[1] > 0):
harms = yhfreq*np.less(yhfreq,maxplotfreq)
harms[harms==0] = np.nan
numFrames = int(harms[:,0].size)
frmTime = H*np.arange(numFrames)/float(fs)
plt.plot(frmTime, harms, color='k', ms=3, alpha=1)
plt.xlabel('time (sec)')
plt.ylabel('frequency (Hz)')
plt.autoscale(tight=True)
plt.title('harmonics + stochastic spectrogram')
# plot the output sound
plt.subplot(2,1,2)
plt.plot(np.arange(y.size)/float(fs), y)
plt.axis([0, y.size/float(fs), min(y), max(y)])
plt.ylabel('amplitude')
plt.xlabel('time (sec)')
plt.title('output sound: y')
plt.tight_layout()
plt.show()
if __name__ == "__main__":
# analysis
inputFile, fs, hfreq, hmag, mYst = analysis()
# transformation and synthesis
transformation_synthesis(inputFile, fs, hfreq, hmag, mYst)
plt.show()
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2,
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80,
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11,
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56,
301,
796,
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628,
197,
2,
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290,
21263,
198,
197,
7645,
1161,
62,
1837,
429,
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7,
15414,
8979,
11,
43458,
11,
289,
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80,
11,
289,
19726,
11,
285,
56,
301,
8,
628,
197,
489,
83,
13,
12860,
3419,
198
] | 2.432562 | 2,721 |
#!/usr/bin/python
with open('works.txt', 'r') as f:
data1 = f.read(22)
print(data1)
f.seek(0, 0)
data2 = f.read(22)
print(data2)
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] | 1.936709 | 79 |
import decimal
import uuid
from django.conf import settings
from django.db.backends.base.operations import BaseDatabaseOperations
from django.utils import timezone
| [
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198,
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] | 3.772727 | 44 |
teststring ="hello"
print(teststring) | [
9288,
8841,
796,
1,
31373,
1,
198,
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7,
9288,
8841,
8
] | 3.083333 | 12 |
from insights.parsers.nfs_exports import NFSExports, NFSExportsD
from insights.tests import context_wrap
EXPORTS = """
/home/utcs/shared/ro @rhtttttttttttt(ro,sync) ins1.example.com(rw,sync,no_root_squash) ins2.example.com(rw,sync,no_root_squash)
/home/insights/shared/rw @rhtttttttttttt(rw,sync) ins1.example.com(rw,sync,no_root_squash) ins2.example.com(ro,sync,no_root_squash)
/home/insights/shared/special/all/mail @rhtttttttttttt(rw,sync,no_root_squash)
/home/insights/ins/special/all/config @rhtttttttttttt(ro,sync,no_root_squash) ins1.example.com(rw,sync,no_root_squash)
#/home/insights ins1.example.com(rw,sync,no_root_squash)
/home/example @rhtttttttttttt(rw,sync,root_squash) ins1.example.com(rw,sync,no_root_squash) ins2.example.com(rw,sync,no_root_squash)
/home/example ins3.example.com(rw,sync,no_root_squash)
""".strip()
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7,
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11,
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11,
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62,
15763,
62,
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1077,
8,
198,
14,
11195,
14,
20688,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1035,
18,
13,
20688,
13,
785,
7,
31653,
11,
27261,
11,
3919,
62,
15763,
62,
16485,
1077,
8,
198,
15931,
1911,
36311,
3419,
628,
628,
628,
198
] | 2.248062 | 387 |
#!/usr/bin/env python
#bbfreeze setup file for PEAT_DB distribution on Windows
#Damien Farrell, #October 2009
"""
This script can be used to create a standalone executable for
either windows or linux. It must be run on the target platform.
You will need to install bbfreeze, see http://pypi.python.org/pypi/bbfreeze/
"""
from bbfreeze import Freezer
import sys, os, shutil
shutil.rmtree('peatdb', ignore_errors=True)
path=os.path.abspath('../../..')
peatpath=os.path.abspath('../../../PEATDB')
version = '2.0'
f = Freezer('peatdb', includes=("numpy",),excludes=("wx",))
f.addScript(os.path.join(peatpath, "PEATApp.py"))
f.addScript(os.path.join(peatpath, "Ekin/Ekin_main.py"))
f.addScript(os.path.join(peatpath, "DNAtool/DNAtool.py"))
m=f.mf
f() # runs the freezing process
'''post freeze'''
#mpl data
import matplotlib
mpldir = matplotlib.get_data_path()
datadir = 'peatdb/mpl-data'
shutil.copytree(mpldir, datadir)
#add peat resource files
resources = ['PEATDB/DNAtool/restriction_enzymes.DAT',
'PEATDB/data/AA_masses.txt',
'PEATDB/App.ico',
'PEATDB/DNAtool/DNAtool.ico',
'Protool/AA.DAT',
'Protool/bbdep02.May.sortlib']
for r in resources:
shutil.copy(os.path.join(path, r), 'peatdb')
#set icon?
#make zip archive
import zipfile
f = zipfile.ZipFile("peatdb-2.0.zip", "w")
for dirpath, dirnames, filenames in os.walk('peatdb'):
for fname in filenames:
fullname = os.path.join(dirpath, fname)
f.write(fullname)
f.close()
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] | 2.216667 | 720 |
import collections
import random
from math import sqrt
import numpy as np
import torch
from gym.wrappers import LazyFrames
from common.utils import prep_observation_for_qnet
class PrioritizedReplayBuffer:
""" based on https://nn.labml.ai/rl/dqn, supports n-step bootstrapping and parallel environments,
removed alpha hyperparameter like google/dopamine
"""
@staticmethod
def find_prefix_sum_idx(self, prefix_sum):
""" find the largest i such that the sum of the leaves from 1 to i is <= prefix sum"""
idx = 1
while idx < self.capacity:
if self.priority_sum[idx * 2] > prefix_sum:
idx = 2 * idx
else:
prefix_sum -= self.priority_sum[idx * 2]
idx = 2 * idx + 1
return idx - self.capacity
@property
@property
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220,
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26745,
628,
220,
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220,
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628
] | 2.468023 | 344 |
import logging
import aiohttp
from typing import Tuple
from controller.data.VM import VM
from controller.connectors.Connector import Connector
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from .models import Business,Profile
from django import forms
class BusinessForm(forms.ModelForm):
"""
class BusinessForm to enable a user to register their businesses
with the application
"""
class ProfileForm(forms.ModelForm):
"""
class BusinessForm to enable a user to register their businesses
with the application
"""
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] | 3.254386 | 114 |
"""
Pre-processing for mb_graph_batch.py of oriented membranes from TomoSegMemTV output
Input: - STAR file with 3 columns:
+ _rlnMicrographName: tomogram original
+ _rlnImageName: TomoSegMemTV density map output
+ _psSegLabel: (optional) label for membrane segmentation
+ _psSegImage: (optional) binary mask to focus the segmentation analysis
+ _mtMtubesCsv: (optional) a .csv file with microtubule center lines
- Setting for segmenting the membranes from TomoSegMemTV density map:
+ Density threshold: (optional) required if _psSegLabel not defined
+ Size threshold: (optional) required if _psSegLabel not defined
- Sub-volume splitting settings
Output: - A STAR file with 3 columns:
+ _rlnMicrographName: tomogram original
+ _rlnImageName: sub-volumes
+ _psSegImage: Un-oriented membrane segmentations for each subvolume
+ Columns for localizing the sub-volumes within each original tomogram
"""
################# Package import
import argparse
import gc
import os
import sys
import math
import time
import pyseg as ps
import scipy as sp
import skimage as sk
import numpy as np
from pyseg.globals import signed_distance_2d
###### Global variables
__author__ = 'Antonio Martinez-Sanchez'
MB_LBL, MB_NEIGH = 1, 2
MB_NEIGH_INT, MB_NEIGH_EXT = 2, 3
########################################################################################
# PARAMETERS
########################################################################################
ROOT_PATH = '/fs/pool/pool-ruben/antonio/shiwei'
# Input STAR file
in_star = ROOT_PATH + '/pre/in/mb_seg_single_oriented.star'
# Output directory
out_dir = ROOT_PATH + '/pre/mbo_nosplit'
# Subvolume splitting settings
sp_split = None # (2, 2, 1)
sp_off_voxels = 30 # vox
# Membrane segmentation
sg_res = 0.52 # nm/voxel
sg_th = None # 8
sg_sz = None # 3e3
sg_mb_thick = 4 # nm
sg_mb_neigh = 15 # nm
# CSV file pre-processing
cv_coords_cools = (1, 2, 3)
cv_id_col = 4
# Microtubule settings
mt_rad = 30 # nm
mt_swap_xy = False
########################################################################################
# MAIN ROUTINE
########################################################################################
# Get them from the command line if they were passed through it
parser = argparse.ArgumentParser()
parser.add_argument('--inStar', default=in_star, help='Input star file.')
parser.add_argument('--outDir', default=out_dir, help='Output directory.')
parser.add_argument('--spSplit', nargs='+', type=int, default=sp_split, help='Number of splits (X, Y, Z).')
parser.add_argument('--spOffVoxels', type=int, default=sp_off_voxels, help='Offset voxels.')
parser.add_argument('--sgVoxelSize', default=sg_res, type=float, help='Voxel size (nm/voxel).')
parser.add_argument('--sgThreshold', type=int, default=sg_th, help='Density threshold.')
parser.add_argument('--sgSizeThreshold', type=int, default=sg_sz, help='Size threshold (voxels).')
parser.add_argument('--sgMembThk', default=sg_mb_thick, type=float, help='Segmented membrane thickness (nm)')
parser.add_argument('--sgMembNeigh', default=sg_mb_neigh, type=float, help='Segmented membrane neighbours (nm)')
args = parser.parse_args()
in_star = args.inStar
out_dir = args.outDir
sp_split = None if args.spSplit == [-1] else args.spSplit
sp_off_voxels = args.spOffVoxels
sg_res = args.sgVoxelSize
sg_th = None if args.sgThreshold == -1 else args.sgThreshold
sg_sz = None if args.sgSizeThreshold == -1 else args.sgSizeThreshold
sg_mb_thick = args.sgMembThk
sg_mb_neigh = args.sgMembNeigh
########## Print initial message
print('Pre-processing for SEG analysis of un-oriented membranes from TomoSegMemTV output.')
print('\tAuthor: ' + __author__)
print('\tDate: ' + time.strftime("%c") + '\n')
print('Options:')
print('\tOutput directory: ' + str(out_dir))
print('\tInput STAR file: ' + str(in_star))
print('\tData resolution: ' + str(sg_res) + ' nm/vx')
if sg_th is not None:
print('\tSegmentation settings: ')
print('\t\t-Density threshold: ' + str(sg_th))
print('\t\t-Size threshold: ' + str(sg_sz) + ' vx')
print('\tSub-volume splitting settings: ')
print('\t\t-Number of splits (X, Y, Z): ' + str(sp_split))
print('\t\t-Offset voxels: ' + str(sp_off_voxels))
print('\tMicrotubule settings:')
print('\t\t-Microtube luminal radius: ' + str(mt_rad) + ' nm')
print('\tCSV pre-processing: ')
print('\t\t-Columns for samples coordinates (X, Y, Z): ' + str(cv_coords_cools))
print('\t\t-Column for microtubule ID: ' + str(cv_id_col))
print('')
######### Process
print('Parsing input parameters...')
sp_res, mt_rad, sp_off_voxels = float(sg_res), float(mt_rad), int(sp_off_voxels)
out_stem = os.path.splitext(os.path.split(in_star)[1])[0]
conn_mask = np.ones(shape=(3,3,3))
out_seg_dir = out_dir + '/segs'
if not os.path.isdir(out_seg_dir):
os.makedirs(out_seg_dir)
print('Loading input STAR file...')
gl_star = ps.sub.Star()
try:
gl_star.load(in_star)
except ps.pexceptions.PySegInputError as e:
print('ERROR: input STAR file could not be loaded because of "' + e.get_message() + '"')
print('Terminated. (' + time.strftime("%c") + ')')
sys.exit(-1)
star = ps.sub.Star()
star.add_column(key='_rlnMicrographName')
star.add_column(key='_rlnImageName')
star.add_column(key='_psSegImage')
star.add_column(key='_psSegRot')
star.add_column(key='_psSegTilt')
star.add_column(key='_psSegPsi')
star.add_column(key='_psSegOffX')
star.add_column(key='_psSegOffY')
star.add_column(key='_psSegOffZ')
mode_oriented = False
if gl_star.has_column('_rlnOriginX') and gl_star.has_column('_rlnOriginY') and gl_star.has_column('_rlnOriginZ'):
print('\t-Segmentation origin found, oriented membrane segmentation activated!')
mode_oriented = True
print('Main Routine: tomograms loop')
tomo_id = 0
for row in range(gl_star.get_nrows()):
in_ref = gl_star.get_element('_rlnMicrographName', row)
print('\tProcessing tomogram: ' + in_ref)
out_ref_stem = os.path.splitext(os.path.split(in_ref)[1])[0]
in_mb = gl_star.get_element('_rlnImageName', row)
print('\t\t-Loading membrane segmentation: ' + in_mb)
tomo_mb = ps.disperse_io.load_tomo(in_mb)
tomo_ref = ps.disperse_io.load_tomo(in_ref, mmap=True)
off_mask_min_x, off_mask_max_x = 0, tomo_ref.shape[0]
off_mask_min_y, off_mask_max_y = 0, tomo_ref.shape[1]
off_mask_min_z, off_mask_max_z = 0, tomo_ref.shape[2]
wide_x = off_mask_max_x - off_mask_min_x
wide_y = off_mask_max_y - off_mask_min_y
wide_z = off_mask_max_z - off_mask_min_z
mt_mask = None
if gl_star.has_column('_mtMtubesCsv'):
in_csv = gl_star.get_element('_mtMtubesCsv', row)
print('\tReading input CSV file: ' + in_csv)
mt_dic = ps.globals.read_csv_mts(in_csv, cv_coords_cools, cv_id_col, swap_xy=mt_swap_xy)
mts_points = list()
for mt_id, mt_samps in zip(iter(mt_dic.keys()), iter(mt_dic.values())):
mts_points += mt_samps
mts_points = np.asarray(mts_points, dtype=np.float32) * (1./sg_res)
print('\tSegmenting the microtubules...')
mt_mask = ps.globals.points_to_mask(mts_points, tomo_mb.shape, inv=True)
mt_mask = sp.ndimage.morphology.distance_transform_edt(mt_mask, sampling=sg_res, return_indices=False)
mt_mask = mt_mask > mt_rad
mb_lbl = 0
if sg_th is None:
if gl_star.has_column('_psSegLabel'):
mb_lbl = gl_star.get_element('_psSegLabel', row)
print('\t\t\t+Segmenting membranes with label: ' + str(mb_lbl))
if mb_lbl > 0:
tomo_mb = tomo_mb == mb_lbl
else:
tomo_mb = tomo_mb > 0
else:
tomo_mb = tomo_mb > 0
else:
tomo_mb = tomo_mb >= sg_th
if gl_star.has_column('_mtMtubesCsv'):
tomo_mb *= mt_mask
del mt_mask
if gl_star.has_column('_psSegImage'):
print('\tApplying the mask...')
hold_mask = ps.disperse_io.load_tomo(gl_star.get_element('_psSegImage', row))
if mb_lbl > 0:
hold_mask = hold_mask == mb_lbl
else:
hold_mask = hold_mask > 0
tomo_mb *= hold_mask
ids_mask = np.where(hold_mask)
off_mask_min_x, off_mask_max_x = ids_mask[0].min()-sp_off_voxels, ids_mask[0].max()+sp_off_voxels
if off_mask_min_x < 0:
off_mask_min_x = 0
if off_mask_max_x > hold_mask.shape[0]:
off_mask_max_x = hold_mask.shape[0]
off_mask_min_y, off_mask_max_y = ids_mask[1].min()-sp_off_voxels, ids_mask[1].max()+sp_off_voxels
if off_mask_min_y < 0:
off_mask_min_y = 0
if off_mask_max_y > hold_mask.shape[1]:
off_mask_max_y = hold_mask.shape[1]
off_mask_min_z, off_mask_max_z = ids_mask[2].min()-sp_off_voxels, ids_mask[2].max()+sp_off_voxels
if off_mask_min_z < 0:
off_mask_min_z = 0
if off_mask_max_z > hold_mask.shape[2]:
off_mask_max_z = hold_mask.shape[2]
del hold_mask
del ids_mask
# ps.disperse_io.save_numpy(tomo_mb, out_dir + '/hold.mrc')
if sg_th is not None:
print('\tMembrane thresholding...')
tomo_sz = ps.globals.global_analysis(tomo_mb, 0.5, c=26)
tomo_mb = tomo_sz > sg_sz
del tomo_sz
seg_center = None
if mode_oriented:
seg_center = np.asarray((gl_star.get_element('_rlnOriginX', row),
gl_star.get_element('_rlnOriginY', row),
gl_star.get_element('_rlnOriginZ', row)))
seg_center[0] -= off_mask_min_x
seg_center[1] -= off_mask_min_y
seg_center[2] -= off_mask_min_z
print('\tSegmenting the membranes...')
if sp_split is None:
svol_mb = tomo_mb[off_mask_min_x:off_mask_max_x, off_mask_min_y:off_mask_max_y, off_mask_min_z:off_mask_max_z]
svol = tomo_ref[off_mask_min_x:off_mask_max_x, off_mask_min_y:off_mask_max_y, off_mask_min_z:off_mask_max_z]
svol_dst = sp.ndimage.morphology.distance_transform_edt(np.invert(svol_mb), sampling=sg_res,
return_indices=False)
svol_seg = np.zeros(shape=svol.shape, dtype=np.float32)
if not mode_oriented:
svol_seg[svol_dst < sg_mb_neigh + sg_mb_thick] = MB_NEIGH
svol_seg[svol_dst < sg_mb_thick] = MB_LBL
else:
svol_dst = signed_distance_2d(svol_mb, res=1, del_b=True, mode_2d=True, set_point=seg_center)
svol_seg[(svol_dst > 0) & (svol_dst < sg_mb_neigh + sg_mb_thick)] = MB_NEIGH_INT
svol_seg[(svol_dst < 0) & (svol_dst > -1. * (sg_mb_neigh + sg_mb_thick))] = MB_NEIGH_EXT
svol_seg[np.absolute(svol_dst) < sg_mb_thick] = MB_LBL
svol_seg[svol_dst == 0] = 0
svol_seg[svol_mb > 0] = MB_LBL
out_svol = out_seg_dir + '/' + out_ref_stem + '_tid_' + str(tomo_id) + '.mrc'
out_seg = out_seg_dir + '/' + out_ref_stem + '_tid_' + str(tomo_id) + '_seg.mrc'
ps.disperse_io.save_numpy(svol, out_svol)
ps.disperse_io.save_numpy(svol_seg, out_seg)
del svol_seg
del svol_dst
row_dic = dict()
row_dic['_rlnMicrographName'] = in_ref
row_dic['_rlnImageName'] = out_svol
row_dic['_psSegImage'] = out_seg
row_dic['_psSegRot'] = 0
row_dic['_psSegTilt'] = 0
row_dic['_psSegPsi'] = 0
row_dic['_psSegOffX'] = off_mask_min_x # 0
row_dic['_psSegOffY'] = off_mask_min_y # 0
row_dic['_psSegOffZ'] = off_mask_min_z
star.add_row(**row_dic)
else:
print('\tSplitting into subvolumes:')
if sp_split[0] > 1:
hold_wide = int(math.ceil(wide_x / sp_split[0]))
hold_pad = int(math.ceil((off_mask_max_x - off_mask_min_x) / sp_split[0]))
hold_split = int(sp_split[0] * math.ceil(float(hold_pad)/hold_wide))
offs_x = list()
pad_x = off_mask_min_x + int(math.ceil((off_mask_max_x-off_mask_min_x) / hold_split))
offs_x.append((off_mask_min_x, pad_x+sp_off_voxels))
lock = False
while not lock:
hold = offs_x[-1][1] + pad_x
if hold >= off_mask_max_x:
offs_x.append((offs_x[-1][1] - sp_off_voxels, off_mask_max_x))
lock = True
else:
offs_x.append((offs_x[-1][1]-sp_off_voxels, offs_x[-1][1]+pad_x+sp_off_voxels))
else:
offs_x = [(off_mask_min_x, off_mask_max_x),]
if sp_split[1] > 1:
hold_wide = int(math.ceil(wide_y / sp_split[1]))
hold_pad = int(math.ceil((off_mask_max_y - off_mask_min_y) / sp_split[1]))
hold_split = int(sp_split[1] * math.ceil(float(hold_pad) / hold_wide))
offs_y = list()
pad_y = off_mask_min_y + int(math.ceil((off_mask_max_y-off_mask_min_y) / hold_split))
offs_y.append((off_mask_min_x, pad_y + sp_off_voxels))
lock = False
while not lock:
hold = offs_y[-1][1] + pad_y
if hold >= off_mask_max_y:
offs_y.append((offs_y[-1][1] - sp_off_voxels, off_mask_max_y))
lock = True
else:
offs_y.append((offs_y[-1][1] - sp_off_voxels, offs_y[-1][1] + pad_y + sp_off_voxels))
else:
offs_y = [(off_mask_min_x, off_mask_max_x),]
if sp_split[2] > 1:
hold_wide = int(math.ceil(wide_z / sp_split[2]))
hold_pad = int(math.ceil((off_mask_max_z - off_mask_min_z) / sp_split[2]))
hold_split = int(sp_split[2] * math.ceil(float(hold_pad) / hold_wide))
offs_z = list()
pad_z = off_mask_min_z + int(math.ceil((off_mask_max_z-off_mask_min_z) / hold_split))
offs_z.append((off_mask_min_z, pad_z + sp_off_voxels))
lock = False
while not lock:
hold = offs_z[-1][1] + pad_z
if hold >= off_mask_max_z:
offs_z.append((offs_z[-1][1] - sp_off_voxels, off_mask_max_z))
lock = True
else:
offs_z.append((offs_z[-1][1] - sp_off_voxels, offs_z[-1][1] + pad_z + sp_off_voxels))
else:
offs_z = [(off_mask_min_z, off_mask_max_z),]
split_id = 1
for off_x in offs_x:
for off_y in offs_y:
for off_z in offs_z:
print('\t\t-Splitting subvolume: [' + str(off_x) + ', ' + str(off_y) + ', ' + str(off_z) +']')
svol_mb = tomo_mb[off_x[0]:off_x[1], off_y[0]:off_y[1], off_z[0]:off_z[1]]
svol = tomo_ref[off_x[0]:off_x[1], off_y[0]:off_y[1], off_z[0]:off_z[1]]
svol_seg = np.zeros(shape=svol.shape, dtype=np.float32)
if not mode_oriented:
svol_dst = sp.ndimage.morphology.distance_transform_edt(np.invert(svol_mb), sampling=sg_res,
return_indices=False)
svol_seg[svol_dst < sg_mb_neigh + sg_mb_thick] = MB_NEIGH
svol_seg[svol_dst < sg_mb_thick] = MB_LBL
else:
seg_off_center = seg_center - np.asarray((off_x[0], off_y[0], off_z[0]))
svol_dst = signed_distance_2d(svol_mb, res=1, del_b=True, mode_2d=True,
set_point=seg_off_center)
svol_seg[(svol_dst > 0) & (svol_dst < sg_mb_neigh + sg_mb_thick)] = MB_NEIGH_INT
svol_seg[(svol_dst < 0) & (svol_dst > -1. * (sg_mb_neigh + sg_mb_thick))] = MB_NEIGH_EXT
svol_seg[np.absolute(svol_dst) < sg_mb_thick] = MB_LBL
svol_seg[svol_dst == 0] = 0
svol_seg[svol_mb > 0] = MB_LBL
out_svol = out_seg_dir + '/' + out_ref_stem + '_id_' + str(tomo_id) + '_split_' + str(split_id) + '.mrc'
out_seg = out_seg_dir + '/' + out_ref_stem + '_id_' + str(tomo_id) + '_split_' + str(split_id) + '_mb.mrc'
ps.disperse_io.save_numpy(svol, out_svol)
ps.disperse_io.save_numpy(svol_seg, out_seg)
del svol_seg
del svol_dst
split_id += 1
row_dic = dict()
row_dic['_rlnMicrographName'] = in_ref
row_dic['_rlnImageName'] = out_svol
row_dic['_psSegImage'] = out_seg
row_dic['_psSegRot'] = 0
row_dic['_psSegTilt'] = 0
row_dic['_psSegPsi'] = 0
row_dic['_psSegOffX'] = off_x[0]
row_dic['_psSegOffY'] = off_y[0]
row_dic['_psSegOffZ'] = off_z[0]
star.add_row(**row_dic)
# Prepare next iteration
gc.collect()
tomo_id += 1
out_star = out_dir + '/' + out_stem + '_pre.star'
print('\tStoring output STAR file in: ' + out_star)
star.store(out_star)
print('Terminated. (' + time.strftime("%c") + ')') | [
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7,
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62,
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8,
198,
198,
4798,
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13,
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1343,
640,
13,
2536,
31387,
7203,
4,
66,
4943,
1343,
705,
8,
11537
] | 1.945526 | 8,940 |
from tkinter import *
from tkinter import ttk
from tkinter import messagebox
import sqlite3
from sqlite3 import Error
Sea().mainloop() | [
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# pylint: disable=C0111,R0902,R0913
# Smartsheet Python SDK.
#
# Copyright 2016 Smartsheet.com, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"): you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import collections
import importlib
import json
import logging
import six
from datetime import datetime
from dateutil.parser import parse
from enum import Enum
try:
from collections import MutableSequence
except ImportError:
from collections.abc import MutableSequence
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220,
422,
17268,
13,
39305,
1330,
13859,
540,
44015,
594,
628,
628,
628,
628
] | 3.616279 | 258 |
import os
from urllib.request import urlretrieve
url = "https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage"
orca = '/usr/local/bin/orca'
urlretrieve(url, orca)
os.chmod(orca, 0o755)
os.system("apt install xvfb libgconf-2-4") | [
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] | 2.265487 | 113 |
from grafo_adj import *
g = Grafo([],[])
for i in ['9','8','7','2','11','5','3', '10']:
g.adiciona_vertice(i)
for i in ['7-11', '5-8', '3-11', '7-8', '8-9','11-10','11-2', '5-10']:
g.adiciona_aresta(i)
print(g)
print(g.dfs('7'))
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] | 1.861538 | 130 |
from collections import Counter
import nltk
from nltk.corpus import stopwords
stopwords = set(stopwords.words('english'))
# read sentence
lines = []
for line in open('building_global_community.txt'):
# delete the blank and line feed at the begining and end
line = line.strip()
# add processed line text into list 'lines'
lines.append(line)
# do Counter,
# wordCounter : all words
# wordCounter_Noun : noun words
# wordCounter_Adj : Adj words
# wordCounter_verb : Verb words
# wordCounter_Other : other POS words
wordCounter = Counter()
wordCounter_verb =Counter()
wordCounter_Adj = Counter()
wordCounter_Noun = Counter()
wordCounter_Other = Counter()
wordCounter_adv = Counter()
word_punc_tokenizer = nltk.WordPunctTokenizer()
for sen in lines:
# split sentence into words
tokens = word_punc_tokenizer.tokenize(sen)
#tokens = [word for word in nltk.word_tokenize(sen)]
#tokens= filter(lambda word: word not in '[.,\/#!$%\^&\*;:{}-=\_`~()]', tokens)
tmp_list = list()
for token in tokens:
if (token.isdigit()==False) and (token.isalpha()==True) and (token.lower() not in stopwords) :
tmp_list.append(token.lower())
for element in tmp_list:
get_pos = nltk.pos_tag(element.split())
word,pos = get_pos[0]
if pos.startswith('NN'):
wordCounter_Noun.update(word.split())
elif pos.startswith('JJ'):
wordCounter_Adj.update(word.split())
elif pos.startswith('VB'):
wordCounter_verb.update(word.split())
elif pos.startswith('RB'):
wordCounter_adv.update(word.split())
else:
wordCounter_Other.update(word.split())
wordCounter.update(tmp_list)
# show the occurance of all words
print '## All wordcount TOP-20: '
#print wordCounter.most_common(20)
for word, count in wordCounter.most_common(20):
print('{0}: {1}'.format(word, count))
# show the occurance of Noun words
print '## Noun words TOP-10: '
#print wordCounter_Noun.most_common(10)
for word, count in wordCounter_Noun.most_common(10):
print('{0}: {1}'.format(word, count))
# show the occurance of Adj words
print '## Adj words TOP-10: '
#print wordCounter_Adj.most_common(10)
for word, count in wordCounter_Adj.most_common(10):
print('{0}: {1}'.format(word, count))
# show the occurance of Adv words
print '## Adv Words TOP-10: '
#print wordCounter_adv.most_common(10)
for word, count in wordCounter_adv.most_common(10):
print('{0}: {1}'.format(word, count))
# show the occurance of Other POS words
print '## Other POS words TOP-10: '
#print wordCounter_Other.most_common(10)
for word, count in wordCounter_Other.most_common(10):
print('{0}: {1}'.format(word, count))
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] | 2.60076 | 1,052 |
# -*- coding: utf-8 -*-
# Copyright (C) 2006-2016 Mag. Christian Tanzer. All rights reserved
# Glasauergasse 32, A--1130 Wien, Austria. [email protected]
# ****************************************************************************
#
# This module is licensed under the terms of the BSD 3-Clause License
# <http://www.c-tanzer.at/license/bsd_3c.html>.
# ****************************************************************************
#
#++
# Name
# TFL.Recordifier
#
# Purpose
# Provide classes supporting the conversion of formatted strings to records
#
# Revision Dates
# 17-Sep-2006 (CT) Creation
# 23-Dec-2010 (CT) Use `_print` for doctest (`%s` instead of `%r` for `v`)
# 9-Oct-2016 (CT) Move to Package_Namespace `TFL`
# 9-Oct-2016 (CT) Fix Python 3 compatibility
# ««revision-date»»···
#--
from _TFL import TFL
from _TFL.pyk import pyk
from _TFL.Regexp import re
import _TFL.Caller
import _TFL.Record
import _TFL._Meta.Object
# end def _print
# end def __init__
# end def __call__
# end class _Recordifier_
class By_Regexp (_Recordifier_) :
"""Convert strings via regexp to records.
>>> br = By_Regexp (
... TFL.Regexp
... (r"(?P<dt> (?P<y> \d{4})-(?P<m> \d{2})(?:-(?P<d> \d{2}))?)"
... r" \s+ (?P<M> \d+) \s+ (?P<w> \d+\.\d*)", re.X)
... , M = int, weight = float, y = int, m = int, d = int)
>>> _print (br ("2006-06-01 6 96.4 1.20 93.5 98.1"))
(M = 6, d = 1, dt = 2006-06-01, m = 6, w = 96.4, y = 2006)
>>> _print (br ("2006-06 6 96.4 1.20 93.5 98.1"))
(M = 6, dt = 2006-06, m = 6, w = 96.4, y = 2006)
"""
field_pat = TFL.Regexp \
( r"\(\?P< (?P<name> [a-zA-Z_][a-zA-Z0-9_]*) >"
, flags = re.VERBOSE
)
# end def __init__
# end def _field_iter
# end class By_Regexp
class By_Separator (_Recordifier_) :
"""Convert strings by splitting on whitespace into records.
>>> bw = By_Separator (
... "d", ("m", int), "avg", "err", "min", "max",
... _default_converter = float, d = str)
>>> _print (bw ("2006-06-01 6 96.4 1.20 93.5 98.1"))
(avg = 96.4, d = 2006-06-01, err = 1.2, m = 6, max = 98.1, min = 93.5)
>>> _print (bw ("2006-06-01 6 96.4 1.20 93.5"))
(avg = 96.4, d = 2006-06-01, err = 1.2, m = 6, min = 93.5)
>>> _print (bw ("2006-06-01 6 96.4 1.20 93.5 98.1 42"))
(avg = 96.4, d = 2006-06-01, err = 1.2, m = 6, max = 98.1, min = 93.5)
"""
_separator = None
_default_converter = str
# end def __init__
# end def _field_iter
# end class By_Separator
if __name__ == "__main__" :
TFL._Export_Module ()
### __END__ TFL.Recordifier
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"""Various helper functions to set the dtypes."""
# ----------------------------------------------------
# Name : df2onehot.py
# Author : E.Taskesen
# Contact : [email protected]
# github : https://github.com/erdogant/df2onehot
# Licence : MIT
# ----------------------------------------------------
# %% Libraries
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
from tqdm import tqdm
# %% Set dtypes
def set_dtypes(df, dtypes='pandas', deep_extract=False, perc_min_num=None, num_if_decimal=True, verbose=3):
"""Set the dtypes of the dataframe.
Parameters
----------
df : pd.DataFrame()
Input dataframe for which the rows are the features, and colums are the samples.
dtypes : list of str or 'pandas', optional
Representation of the columns in the form of ['cat','num']. By default the dtype is determiend based on the pandas dataframe.
deep_extract : bool [False, True] (default : False)
True: Extract information from a vector that contains a list/array/dict.
False: converted to a string and treated as catagorical ['cat'].
perc_min_num : float [None, 0..1], optional
Force column (int or float) to be numerical if unique non-zero values are above percentage. The default is None. Alternative can be 0.8
num_if_decimal : bool [False, True], optional
Force column to be numerical if column with original dtype (int or float) show values with one or more decimals. The default is True.
verbose : int, optional
Print message to screen. The default is 3.
0: (default), 1: ERROR, 2: WARN, 3: INFO, 4: DEBUG, 5: TRACE
Returns
-------
tuple containing dataframe and dtypes.
"""
config = {}
config['dtypes'] = dtypes
config['deep_extract'] = deep_extract
config['perc_min_num'] = perc_min_num
config['num_if_decimal'] = num_if_decimal
config['verbose'] = verbose
# Determine dtypes for columns
config['dtypes'] = _auto_dtypes(df, config['dtypes'], deep_extract=config['deep_extract'], perc_min_num=config['perc_min_num'], num_if_decimal=config['num_if_decimal'], verbose=config['verbose'])
# Setup dtypes in columns
df = _set_types(df.copy(), config['dtypes'], verbose=config['verbose'])
# return
return(df, config['dtypes'])
# %% Setup columns in correct dtypes
# %% Setup columns in correct dtypes
# %% Set y
def set_y(y, y_min=None, numeric=False, verbose=3):
"""Group labels if required.
Parameters
----------
y : list
input labels.
y_min : int, optional
If unique y-labels are less then absolute y_min, labels are grouped into the _other_ group. The default is None.
numeric : bool [True, False], optional
Convert to numeric labels. The default is False.
verbose : int, optional
Print message to screen. The default is 3.
0: (default), 1: ERROR, 2: WARN, 3: INFO, 4: DEBUG, 5: TRACE
Returns
-------
list of labels.
"""
y = y.astype(str)
if not isinstance(y_min, type(None)):
if verbose>=3: print('[df2onehot] >Group [y] labels that contains less then %d occurences are grouped under one single name [_other_]' %(y_min))
[uiy, ycounts] = np.unique(y, return_counts=True)
labx = uiy[ycounts<y_min]
y = y.astype('O')
y[np.isin(y, labx)] = '_other_' # Note that this text is captured in compute_significance! Do not change or also change it over there!
y = y.astype(str)
if numeric:
y = label_encoder.fit_transform(y).astype(int)
return(y)
# %% function to remove non-ASCII
# %% Convert to pandas dataframe
def is_DataFrame(data, verbose=3):
"""Convert data into dataframe.
Parameters
----------
data : array-like
Array-like data matrix.
verbose : int, optional
Print message to screen. The default is 3.
0: (default), 1: ERROR, 2: WARN, 3: INFO, 4: DEBUG, 5: TRACE
Returns
-------
pd.dataframe()
"""
if isinstance(data, list):
data = pd.DataFrame(data)
elif isinstance(data, np.ndarray):
data = pd.DataFrame(data)
elif isinstance(data, pd.DataFrame):
pass
else:
if verbose>=3: print('Typing should be pd.DataFrame()!')
data=None
return(data)
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] | 2.649038 | 1,664 |
import random
import struct
import json
import flask
import time
import numpy as np
from collections import defaultdict
from threading import Thread
from Queue import Queue
from StringIO import StringIO
from sqlalchemy import create_engine
from flask import Flask, render_template, Response, request, stream_with_context
from py.ds import *
from py.manager import *
app = Flask(__name__)
#
# Global variables
#
flask.DEBUG = False
flask.val = 0
flask.dist = []
flask.dist_update_time = None
flask.queries = {}
flask.db = create_engine("postgresql://localhost/test")
flask.manager = Manager()
@app.route("/")
@app.route("/attr/stats", methods=["post", "get"])
def table_stats():
"""
Used by client to get the domain of the x and y axis expressions/attributes
opts: {
table: <table name>
attrs: {
<attrname>: <data type> ("continuous" | "discrete")
}
}
"""
discq = "SELECT DISTINCT %s FROM %s ORDER BY %s"
contq = "SELECT min(%s), max(%s) FROM %s"
opts = json.loads(request.data)
table = opts['table']
contattrs = []
ret = {}
for attr, typ in opts.get("attrs", {}).items():
if typ == "discrete":
q = discq % (attr, table, attr)
ret[attr] = zip(*flask.db.execute(q).fetchall())[0]
else:
q = contq % (attr, attr, table)
ret[attr] = list(flask.db.execute(q).fetchone())
return Response(json.dumps(ret))
@app.route("/register/querytemplate", methods=["post"])
def register_qtemplate():
"""
Registers a query template. Uses the query template name to instantiate (if possible)
the corresponding data structure based on those in ds_klasses
"""
template = json.loads(request.data)
flask.queries[template["tid"]] = template
tid = template['tid']
if flask.manager.has_data_structure(tid):
return Response("ok", mimetype="application/wu")
for ds_klass in ds_klasses:
if ds_klass.can_answer(template):
try:
ds = ds_klass(None, template)
ds.id = tid
flask.manager.add_data_structure(ds)
except Exception as e:
print e
continue
return Response("ok", mimetype="application/wu")
@app.route("/distribution/set", methods=["post"])
def dist_set():
"""
Set the current query distribution
A distribution is currently defined as a list of [query, probability]
where query is a dictionary: {
template: <output of js template's .toWire()>
data: { paramname: val }
}
The corresponding client files are in js/dist.js
"""
flask.dist = json.loads(request.data)
flask.dist_update_time = time.time()
if flask.DEBUG:
print "got query distribution"
return Response("ok", mimetype="application/wu")
@app.route("/data")
def data():
"""
This API opens the data stream and starts sending data via the Manager object.
The current implementation doesn't take advantage of the streaming nature and simply implements:
1. waits for a new query distribution,
2. picks the highest non-zero probability query
3. sends the cached data to the client
In effect, this implements a basic request-response model of interaction.
Details:
The data stream has a simple encoding:
[length of payload (32 bits)][encoding id (32 bits)][payload (a byte array)]
The payload is encoded based on the particular data structure
"""
return Response(flask.manager(), mimetype="test/event-stream")
@app.route("/fakedata")
if __name__ == '__main__':
import psycopg2
DEC2FLOAT = psycopg2.extensions.new_type(
psycopg2.extensions.DECIMAL.values,
'DEC2FLOAT',
lambda value, curs: float(value) if value is not None else None)
psycopg2.extensions.register_type(DEC2FLOAT)
app.run(host="localhost", port=5000, debug=0, threaded=1)# | [
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] | 2.893963 | 1,292 |
import maya.cmds as mc
createVisualizerNodes()
| [
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] | 2.833333 | 18 |
import kivy
from kivy.app import App
from kivy.uix.widget import Widget
from kivy.properties import ObjectProperty, StringProperty
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.gridlayout import GridLayout
from kivy.uix.image import Image, AsyncImage
from kivy.uix.textinput import TextInput
from kivy.config import Config
from kivy.loader import Loader
from math import sin
import wikipedia
import matplotlib
from kivy.garden.matplotlib.backend_kivyagg import FigureCanvasKivyAgg,\
NavigationToolbar2Kivy
from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
import numpy as np
import numpy as np2
import matplotlib.pyplot as plt
matplotlib.rcParams.update({'font.size': 8})
app = WikipediaComparatorApp()
app.run() | [
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] | 2.672241 | 299 |
# -*- coding: utf-8 -*-
"""Tests for internals.py"""
from asyncio.tasks import Task
import os
import json
import sys
import unittest
import asyncio
import logging
from io import StringIO
from importlib import reload
from unittest.mock import AsyncMock, Mock, call, patch
from aiohttp.client_exceptions import ClientConnectorError
from google.cloud.storage.blob import Blob
from google.cloud.storage.bucket import Bucket
from slack_bolt.app.async_app import AsyncApp
from slack_sdk.errors import SlackApiError
from slack_sdk.web.async_client import AsyncWebClient
from slack_sdk.web.async_slack_response import AsyncSlackResponse
from multi_reaction_add.internals import check_env, setup_logger, build_home_tab_view, user_data_key,\
delete_users_data, EmojiOperator
# pylint: disable=attribute-defined-outside-init
class TestCheckEnv(unittest.TestCase):
"""Test env vars checker"""
def setUp(self):
"""Setup tests"""
self.env_keys = ["SLACK_CLIENT_ID", "SLACK_CLIENT_SECRET", "SLACK_SIGNING_SECRET",
"SLACK_INSTALLATION_GOOGLE_BUCKET_NAME", "SLACK_STATE_GOOGLE_BUCKET_NAME", "USER_DATA_BUCKET_NAME"]
def test_checkenv_ok(self):
"""Test checkenv success"""
for key in self.env_keys:
os.environ[key] = ""
check_env()
for key in self.env_keys:
del os.environ[key]
@unittest.expectedFailure
def test_checkenv_missing(self):
"""Test checkenv throws error"""
# pylint: disable=no-self-use
check_env()
class TestCloudLogging(unittest.TestCase):
"""Test logger class"""
def tearDown(self):
"""Cleanup tests"""
logging.shutdown()
reload(logging)
def test_log_format(self):
"""Test logger has correct format"""
with StringIO() as stream:
logger = setup_logger(stream=stream)
logger.info("a message")
self.assertRegex(stream.getvalue(), r'{"timestamp": "\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2},\d{3}", '
'"severity": "INFO", "funcName": "test_log_format", '
'"component": "root", "message": "a message"}')
def test_log_level_set(self):
"""Test log level can be set from env"""
os.environ["LOG_LEVEL"] = "WARNING"
with StringIO() as stream:
logger = setup_logger(stream=stream)
del os.environ["LOG_LEVEL"]
logger.info("a message")
logger.warning("some message")
logger.error("another message")
output = stream.getvalue()
self.assertTrue(all([
"a message" not in output,
"some message" in output,
"another message" in output
]), msg="Cannot set log level")
class TestInternals(unittest.TestCase):
"""Test light methods"""
def test_build_home_tab(self):
"""Test build_home_tab method"""
# check home tab with no urls
home_tab_dict = build_home_tab_view()
home_tab_json = json.dumps(home_tab_dict, separators=(",", ":"))
self.assertEqual(home_tab_json, '{"type":"home","blocks":[{"type":"header","text":{"type":"plain_text","text":'
'"Setting emojis :floppy_disk:","emoji":true}},{"type":"section","text":{"type"'
':"mrkdwn","text":"Type `/multireact <list of emojis>` in any chat to set a'
' list of emojis for later usage."}},{"type":"section","text":{"type":"mrkdwn",'
'"text":"You can view what you saved any moment by typing `/multireact` in'
' any chat."}},{"type":"divider"},{"type":"header","text":{"type":"plain_text",'
'"text":"Adding Reactions :star-struck:","emoji":true}},{"type":"section",'
'"text":{"type":"mrkdwn","text":"Go to a message, click `More Actions`, then'
' click on `Multireact` to react with the saved emojis to the message.\\n\\nIf'
' you can\'t see `Multireact`, click `More message shortcuts...`'
' to find it."}}]}')
# check home tab with urls
home_tab_dict = build_home_tab_view(app_url="localhost")
home_tab_json = json.dumps(home_tab_dict, separators=(",", ":"))
self.assertEqual(home_tab_json, '{"type":"home","blocks":[{"type":"header","text":{"type":"plain_text","text":'
'"Setting emojis :floppy_disk:","emoji":true}},{"type":"section","text":{"type"'
':"mrkdwn","text":"Type `/multireact <list of emojis>` in any chat to set a'
' list of emojis for later usage."}},{"type":"image","image_url":'
'"localhost/img/reaction-write-emojis.png?w=1024&ssl=1","alt_text":'
'"write emojis"},{"type":"image","image_url":'
'"localhost/img/reaction-save.png?w=1024&ssl=1","alt_text":'
'"saved emojis"},{"type":"section","text":{"type":"mrkdwn","text":'
'"You can view what you saved any moment by typing `/multireact` in any'
' chat."}},{"type":"image","image_url":'
'"localhost/img/reaction-write-nothing.png?w=1024&ssl=1","alt_text":'
'"view emojis"},{"type":"image","image_url":'
'"localhost/img/reaction-view.png?w=1024&ssl=1","alt_text":"view emojis"},'
'{"type":"divider"},{"type":"header","text":{"type":"plain_text","text":'
'"Adding Reactions :star-struck:","emoji":true}},{"type":"section","text":'
'{"type":"mrkdwn","text":"Go to a message, click `More Actions`, then click on'
' `Multireact` to react with the saved emojis to the message.\\n\\nIf you'
' can\'t see `Multireact`, click `More message shortcuts...` to find it."}},'
'{"type":"image","image_url":'
'"localhost/img/reaction-none.png?w=1024&ssl=1","alt_text":"message with no'
' reactions"},{"type":"image","image_url":'
'"localhost/img/reaction-menu.png?w=1024&ssl=1","alt_text":"message menu"},'
'{"type":"image","image_url":'
'"localhost/img/reaction-add.png?w=1024&ssl=1","alt_text":'
'"message with reactions"}]}')
def test_user_data_key(self):
"""Test user_data_key method"""
self.assertEqual(
user_data_key("client_id", "enter_id", "team_id", "user_id"),
"client_id/enter_id-team_id/user_id")
self.assertEqual(
user_data_key("client_id", None, "team_id", "user_id"),
"client_id/none-team_id/user_id")
class TestDeleteUserData(unittest.IsolatedAsyncioTestCase):
"""Test user data deletion"""
async def asyncSetUp(self):
"""Setup tests"""
self.bucket = Mock(spec=Bucket)
self.blob = Blob(name="name", bucket=self.bucket)
self.blob.delete = Mock()
self.bucket.blob = Mock(return_value=self.blob)
@classmethod
def setUpClass(cls):
"""Setup tests once"""
if sys.platform.startswith("win"):
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
@patch("multi_reaction_add.internals.user_data_key")
async def test_delete_users_data(self, mock_user_data_key: Mock):
"""Test delete_users_data method"""
# test user data exists
self.blob.exists = Mock(return_value=True)
await delete_users_data(self.bucket, "client_id", "enter_id", "team_id", ["user_id"])
self.blob.exists.assert_called_once()
self.blob.delete.assert_called_once()
self.blob.delete.reset_mock()
# test user data doesn't exist
self.blob.exists = Mock(return_value=False)
await delete_users_data(self.bucket, "client_id", "enter_id", "team_id", ["user_id"])
self.blob.exists.assert_called_once()
self.blob.delete.assert_not_called()
# test multiple user data
await delete_users_data(self.bucket, "client_id", "enter_id", "team_id", ["user_id1", "user_id2"])
mock_user_data_key.assert_has_calls([call(slack_client_id="client_id",
enterprise_id="enter_id",
team_id="team_id",
user_id="user_id1"),
call(slack_client_id="client_id",
enterprise_id="enter_id",
team_id="team_id",
user_id="user_id2")])
class TestEmojiOperator(unittest.IsolatedAsyncioTestCase):
"""Test EmojiOperator class"""
# pylint: disable=protected-access
async def asyncSetUp(self):
"""Setup tests"""
self.client = AsyncMock(AsyncWebClient)
self.client.token = None
self.http_args = {"client": self.client, "http_verb": "POST", "api_url": "some-api", "req_args": {},
"headers": {}, "status_code": 200}
self.app = AsyncMock(AsyncApp)
self.app.client = self.client
self.logger = logging.getLogger()
self.logger.handlers = []
@classmethod
def setUpClass(cls):
"""Setup tests once"""
if sys.platform.startswith("win"):
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
async def test_get_user_reactions(self):
"""Test get_user_reactions method"""
# check no reactions
response = AsyncSlackResponse(**{**self.http_args, **{"data": {"type": "message", "message": {}}} })
self.client.reactions_get.return_value = response
emojis = await EmojiOperator.get_user_reactions(client=self.client,
channel_id="channel_id",
message_ts="message_ts",
user_id="user_id")
self.assertEqual(emojis, [])
# sample response: https://api.slack.com/methods/reactions.get
# check reactions on message
response = AsyncSlackResponse(**{**self.http_args, **{"data": {"type": "message", "message": {
"reactions": [{
"name": "smile",
"users": [ "user_id1", "user_id2" ]
}, {
"name": "wink",
"users": [ "user_id2", "user_id3" ]
}]
}}}})
self.client.reactions_get.return_value = response
emojis = await EmojiOperator.get_user_reactions(client=self.client,
channel_id="channel_id",
message_ts="message_ts",
user_id="user_id2")
self.assertEqual(emojis, ["smile", "wink"])
# check reactions on file
response = AsyncSlackResponse(**{**self.http_args, **{"data": {"type": "file", "file": {
"reactions": [{
"name": "laugh",
"users": [ "user_id1", "user_id2" ]
}]
}}}})
self.client.reactions_get.return_value = response
emojis = await EmojiOperator.get_user_reactions(client=self.client,
channel_id="channel_id",
message_ts="message_ts",
user_id="user_id1")
self.assertEqual(emojis, ["laugh"])
# check reactions on file_comment
response = AsyncSlackResponse(**{**self.http_args, **{"data": {"type": "file_comment", "comment": {
"reactions": [{
"name": "heart",
"users": [ "user_id1", "user_id2" ]
}]
}}}})
self.client.reactions_get.return_value = response
emojis = await EmojiOperator.get_user_reactions(client=self.client,
channel_id="channel_id",
message_ts="message_ts",
user_id="user_id2")
self.assertEqual(emojis, ["heart"])
@patch("aiohttp.ClientSession.get")
async def test_get_reactions_in_team(self, get: AsyncMock):
"""Test get_reactions_in_team method"""
mock_context_manager: AsyncMock = get.return_value.__aenter__.return_value
mock_context_manager.status = 200
mock_context_manager.text.return_value = \
'[{"base":"anguished"}, {"base":"sad_face"}, {"base":"clap"}]'
# sample response: https://api.slack.com/methods/emoji.list
slack_response = AsyncSlackResponse(**{**self.http_args, **{"data": {
"emoji": {
"longcat": "some url",
"doge": "alias",
"partyparrot": "some url",
},
"categories": [ {
"name": "faces",
"emoji_names": ["smile", "wink"]
}, {
"name": "flags",
"emoji_names": ["flag1", "flag2", "flag3"]
}
]
}}})
self.client.emoji_list.return_value = slack_response
# test standard emojis response ok
emojis = await EmojiOperator._get_reactions_in_team(client=self.client, logger=self.logger)
self.client.emoji_list.assert_awaited_once_with(include_categories=True)
# session.get.assert_called_once_with("https://www.emojidex.com/api/v1/utf_emoji")
mock_context_manager.text.assert_awaited_once_with(encoding="utf-8")
self.assertEqual(set(emojis),
set(["longcat", "doge", "partyparrot", "smile", "wink", "flag1", "flag2", "flag3",
"anguished", "sad_face", "clap"]),
msg="Could not parse all emojis")
mock_context_manager.reset_mock()
get.reset_mock()
# test standard emojis response not ok
get.return_value.__aenter__.return_value.status = 500
emojis = await EmojiOperator._get_reactions_in_team(client=self.client, logger=self.logger)
mock_context_manager.text.assert_not_awaited()
self.assertEqual(set(emojis),
set(["longcat", "doge", "partyparrot", "smile", "wink", "flag1", "flag2", "flag3"]),
msg="Should not return standard emojis when invalid http request")
mock_context_manager.reset_mock()
get.reset_mock()
# test standard emojis response exception
get.return_value.__aenter__.side_effect = ClientConnectorError(None, Mock())
emojis = await EmojiOperator._get_reactions_in_team(client=self.client, logger=self.logger)
mock_context_manager.text.assert_not_awaited()
self.assertEqual(set(emojis),
set(["longcat", "doge", "partyparrot", "smile", "wink", "flag1", "flag2", "flag3"]),
msg="Should not return standard emojis when connection error")
@patch("multi_reaction_add.internals.EmojiOperator._get_reactions_in_team")
async def test_update_emoji_list(self, get_reactions: AsyncMock):
"""Test update_emoji_list method"""
get_reactions.return_value = ["some", "emojis"]
emoji_operator = EmojiOperator()
self.client.token = "old token"
# test normal execution
try:
await asyncio.wait_for(
emoji_operator._update_emoji_list(
app=self.app,
token="new token",
logger=self.logger,
sleep=1),
timeout=1.5)
except asyncio.TimeoutError:
pass
get_reactions.assert_awaited_once_with(self.client, self.logger)
self.assertEqual(emoji_operator._all_emojis, ["some", "emojis"])
self.assertEqual(self.client.token, "old token")
# test all_emojis left unchanged on slack api error
get_reactions.side_effect = SlackApiError(None, None)
try:
await asyncio.wait_for(
emoji_operator._update_emoji_list(
app=self.app,
token="new token",
logger=self.logger,
sleep=1),
timeout=1.5)
except asyncio.TimeoutError:
pass
self.assertEqual(emoji_operator._all_emojis, ["some", "emojis"])
self.assertEqual(self.client.token, "old token")
# test all_emojis unset on slack api exception
emoji_operator._all_emojis = None
get_reactions.side_effect = SlackApiError(None, None)
try:
await asyncio.wait_for(
emoji_operator._update_emoji_list(
app=self.app,
token="new token",
logger=self.logger,
sleep=1),
timeout=1.5)
except asyncio.TimeoutError:
pass
self.assertEqual(emoji_operator._all_emojis, None)
self.assertEqual(self.client.token, "old token")
async def test_stop_emoji_thread(self):
"""Test stop_emoji_thread method"""
emoji_operator = EmojiOperator()
emoji_operator._emoji_task = asyncio.create_task(some_method())
await emoji_operator.stop_emoji_update()
await asyncio.sleep(0.1) # task will be canceled when it will be scheduled in the event loop
self.assertTrue(emoji_operator._emoji_task.done())
@patch("multi_reaction_add.internals.EmojiOperator._get_reactions_in_team")
async def test_get_valid_reactions(self, get_reactions: AsyncMock):
"""Test get_valid_reactions method"""
emoji_operator = EmojiOperator()
emoji_operator._emoji_task = Mock(spec=Task)
emoji_operator._emoji_task.done.return_value = False
emoji_operator._update_emoji_list = AsyncMock()
emoji_operator._all_emojis = ["smile", "wink", "face", "laugh", "some-emoji", "-emj-", "_emj_", "some_emoji",
"+one", "'quote'", "54"]
# check empty input
emojis = await emoji_operator.get_valid_reactions(text="",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, [])
# check no emojis in input
emojis = await emoji_operator.get_valid_reactions(text="some text",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, [])
# check no valid emojis
emojis = await emoji_operator.get_valid_reactions(text="::::",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, [])
# check valid input
emojis = await emoji_operator.get_valid_reactions(text=":smile: :wink:",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["smile", "wink"])
# check emojis special characters
emojis = await emoji_operator.get_valid_reactions(
text=":some-emoji: :-emj-: :_emj_: :some_emoji: :+one: :'quote': :54:",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["some-emoji", "-emj-", "_emj_", "some_emoji", "+one", "'quote'", "54"])
# check remove duplicates
emojis = await emoji_operator.get_valid_reactions(text=":smile: :wink: :smile:",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["smile", "wink"])
# check emoji with modifier
emojis = await emoji_operator.get_valid_reactions(text=":face::skin-tone-2:",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["face::skin-tone-2"])
# check no space in input
emojis = await emoji_operator.get_valid_reactions(
text=":smile::wink::face::skin-tone-2::face::skin-tone-3::laugh:",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["smile", "wink", "face::skin-tone-2", "face::skin-tone-3", "laugh"])
# check text and emojis
emojis = await emoji_operator.get_valid_reactions(
text="sometext:smile:anothertext:wink:moretext:laugh:endoftext",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["smile", "wink", "laugh"])
# check invalid emoji
emojis = await emoji_operator.get_valid_reactions(text=":smile: :invalid:",
client=self.client,
app=self.app,
logger=self.logger)
self.assertEqual(emojis, ["smile"])
# check emoji_task is started when finished
get_reactions.return_value = ["joy"]
emoji_operator._emoji_task.done.return_value = True
emojis = await emoji_operator.get_valid_reactions(text=":joy:",
client=self.client,
app=self.app,
logger=self.logger)
get_reactions.assert_awaited_once_with(self.client, self.logger)
self.assertEqual(emojis, ["joy"])
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] | 1.994553 | 11,016 |
from flask import Flask
from flask_restful import Api
from multiprocessing import Process
from heartbit import heartbit
from api_health import HealthEndPoint
from api_manifest import ManifestEndPoint
from api_metrics import MetricsEndPoint
app = Flask(__name__)
api = Api(app)
async_process = Process(
target=heartbit,
daemon=True
)
async_process.start()
health_check_routes = ['/', '/health', '/health/', '/v1', '/v1/', '/v1/health', '/v1/health/']
manifest_routes = ['/manifest', '/manifest/', '/v1/manifest', '/v1/manifest/']
disk_routes = ['/metrics', '/metrics/', '/v1/metrics', '/v1/metrics/']
api.add_resource(HealthEndPoint, *health_check_routes)
api.add_resource(ManifestEndPoint, *manifest_routes)
api.add_resource(MetricsEndPoint, *disk_routes)
if __name__ == '__main__':
app.run()
| [
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] | 2.697674 | 301 |
from Field_D_SupportingClasses import *
ProgramID = "DF Word Score Sonifier v1.0"
WorkTitle = "Untitled Sonification"
Lyricist = ""
Input = DF_TextInput()
WorkTitle = Input.provideTitle()
Lyricist = Input.provideLyricist()
verses = Input.provideVerses()
positions = Input.providePositions()
scores = Input.provideScrabbleScores()
Planner = DF_SongPlanner(verses, positions, scores)
verseKeys = Planner.getVerseKeys()
Planner.getBassPart(Planner.homeKey)
Planner.getTenorPart(Planner.homeKey)
Planner.getAltoPart(Planner.homeKey)
Planner.getSopPart(Planner.homeKey)
X = DF_MusicXML(WorkTitle, ProgramID, Lyricist)
basNotes = Planner.bassNotes
basDurations = Planner.bassRhythms
basLyric = Planner.bassWords
basPos = Planner.bassPositions
basTies = Planner.bassTies
tenNotes = Planner.tenNotes
tenDurations = Planner.tenRhythms
tenLyric = Planner.tenWords
tenPos = Planner.tenPositions
tenTies = Planner.tenTies
altoNotes = Planner.altoNotes
altoDurations = Planner.altoRhythms
altoLyric = Planner.altoWords
altoPos = Planner.altoPositions
altoTies = Planner.altoTies
sopNotes = Planner.sopNotes
sopDurations = Planner.sopRhythms
sopLyric = Planner.sopWords
sopPos = Planner.sopPositions
sopTies = Planner.sopTies
X.writeSop(sopNotes, sopDurations, sopLyric, sopPos, sopTies)
X.writeAlto(altoNotes, altoDurations, altoLyric, altoPos, altoTies)
X.writeTenor(tenNotes, tenDurations, tenLyric, tenPos, tenTies)
X.writeBass(basNotes, basDurations, basLyric, basPos, basTies)
X.endXMLFile() | [
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] | 2.509836 | 610 |
#!/usr/bin/env python3
import gzip
import io
import os
import time
import git
from broker.config import env, logging
from broker.libs.ipfs import decrypt_using_gpg
from broker.utils import cd, is_gzip_file_empty, log, path_leaf, run
# from subprocess import CalledProcessError
def initialize_check(path):
""".git/ folder should exist within the target folder"""
with cd(path):
if not is_initialized(path):
try:
run(["git", "init", "--initial-branch=master"])
add_all()
except Exception as error:
logging.error(f"E: {error}")
return False
return True
def diff_patch(path, source_code_hash, index, target_path):
"""
* "git diff HEAD" for detecting all the changes:
* Shows all the changes between the working directory and HEAD (which includes changes in the index).
* This shows all the changes since the last commit, whether or not they have been staged for commit
* or not.
"""
sep = "*" # separator in between the string infos
is_file_empty = False
with cd(path):
log(f"==> Navigate to {path}")
"""TODO
if not is_initialized(path):
upload everything, changed files!
"""
repo = git.Repo(".", search_parent_directories=True)
try:
repo.git.config("core.fileMode", "false") # git config core.fileMode false
# first ignore deleted files not to be added into git
run(["bash", f"{env.EBLOCPATH}/broker/bash_scripts/git_ignore_deleted.sh"])
head_commit_id = repo.rev_parse("HEAD")
patch_name = f"patch{sep}{head_commit_id}{sep}{source_code_hash}{sep}{index}.diff"
except:
return False
patch_upload_name = f"{patch_name}.gz" # file to be uploaded as zip
patch_file = f"{target_path}/{patch_upload_name}"
logging.info(f"patch_path={patch_upload_name}")
try:
repo.git.add(A=True)
diff_and_gzip(patch_file)
except:
return False
time.sleep(0.25)
if is_gzip_file_empty(patch_file):
log("==> Created patch file is empty, nothing to upload")
os.remove(patch_file)
is_file_empty = True
return patch_upload_name, patch_file, is_file_empty
def apply_patch(git_folder, patch_file, is_gpg=False):
"""Apply git patch.
https://stackoverflow.com/a/15375869/2402577
"""
if is_gpg:
decrypt_using_gpg(patch_file)
with cd(git_folder):
base_name = path_leaf(patch_file)
log(f"==> {base_name}")
# folder_name = base_name_split[2]
try:
# base_name_split = base_name.split("_")
# git_hash = base_name_split[1]
# run(["git", "checkout", git_hash])
# run(["git", "reset", "--hard"])
# run(["git", "clean", "-f"])
# echo "\n" >> patch_file.txt seems like fixing it
with open(patch_file, "a") as myfile:
myfile.write("\n")
# output = repo.git.apply("--reject", "--whitespace=fix", patch_file)
run(["git", "apply", "--reject", "--whitespace=fix", "--verbose", patch_file])
return True
except:
return False
def generate_git_repo(folders):
"""Create git repositories in the given folders if it does not exist."""
if isinstance(folders, list):
for folder in folders:
_generate_git_repo(folder)
else: # if string given "/home/user/folder" retreive string instead of "/" with for above
_generate_git_repo(folders)
# def extract_gzip():
# pass
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10352,
628,
628,
198,
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7716,
62,
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62,
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7,
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364,
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220,
220,
220,
37227,
16447,
17606,
38072,
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262,
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340,
857,
407,
2152,
526,
15931,
198,
220,
220,
220,
611,
318,
39098,
7,
11379,
364,
11,
1351,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
329,
9483,
287,
24512,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4808,
8612,
378,
62,
18300,
62,
260,
7501,
7,
43551,
8,
198,
220,
220,
220,
2073,
25,
220,
1303,
611,
4731,
1813,
12813,
11195,
14,
7220,
14,
43551,
1,
1005,
260,
425,
4731,
2427,
286,
12813,
1,
351,
329,
2029,
198,
220,
220,
220,
220,
220,
220,
220,
4808,
8612,
378,
62,
18300,
62,
260,
7501,
7,
11379,
364,
8,
628,
198,
2,
825,
7925,
62,
70,
13344,
33529,
198,
2,
220,
220,
220,
220,
1208,
198
] | 2.26055 | 1,635 |
# -*- coding: utf-8 -*-
"""Tag Matching Module
This module mirrors the Tag Matching API. It allows the user to search for tag id matches.
https://doc.cognitedata.com/0.5/#Cognite-API-Tag-Matching
"""
import cognite._utils as _utils
import cognite.config as config
from cognite.v05.dto import TagMatchingResponse
def tag_matching(tag_ids, fuzzy_threshold=0, platform=None, **kwargs):
"""Returns a TagMatchingObject containing a list of matched tags for the given query.
This method takes an arbitrary string as argument and performs fuzzy matching with a user defined threshold
toward tag ids in the system.
Args:
tag_ids (list): The tag_ids to retrieve matches for.
fuzzy_threshold (int): The threshold to use when searching for matches. A fuzzy threshold of 0 means you only
want to accept perfect matches. Must be >= 0.
platform (str): The platform to search on.
Keyword Args:
api_key (str): Your api-key.
project (str): Project name.
Returns:
v05.dto.TagMatchingResponse: A data object containing the requested data with several getter methods with different
output formats.
"""
api_key, project = config.get_config_variables(kwargs.get("api_key"), kwargs.get("project"))
url = config.get_base_url(api_version=0.5) + "/projects/{}/tagmatching".format(project)
body = {"tagIds": tag_ids, "metadata": {"fuzzyThreshold": fuzzy_threshold, "platform": platform}}
headers = {"api-key": api_key, "content-type": "*/*", "accept": "application/json"}
res = _utils.post_request(url=url, body=body, headers=headers, cookies=config.get_cookies())
return TagMatchingResponse(res.json())
| [
2,
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410,
2713,
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25,
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2134,
7268,
262,
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1366,
351,
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651,
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5050,
351,
1180,
198,
220,
220,
220,
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5072,
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62,
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86,
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62,
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62,
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62,
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28,
15,
13,
20,
8,
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90,
92,
14,
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278,
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7,
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8,
198,
220,
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220,
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796,
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12985,
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82,
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7621,
62,
2340,
11,
366,
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1298,
19779,
69,
4715,
88,
817,
10126,
1298,
34669,
62,
400,
10126,
11,
366,
24254,
1298,
3859,
11709,
198,
220,
220,
220,
24697,
796,
19779,
15042,
12,
2539,
1298,
40391,
62,
2539,
11,
366,
11299,
12,
4906,
1298,
366,
9,
15211,
1600,
366,
13635,
1298,
366,
31438,
14,
17752,
20662,
198,
220,
220,
220,
581,
796,
4808,
26791,
13,
7353,
62,
25927,
7,
6371,
28,
6371,
11,
1767,
28,
2618,
11,
24697,
28,
50145,
11,
14746,
28,
11250,
13,
1136,
62,
27916,
444,
28955,
198,
220,
220,
220,
1441,
17467,
44,
19775,
31077,
7,
411,
13,
17752,
28955,
198
] | 2.802862 | 629 |
import StringIO
import json
import logging
import random
import urllib
import urllib2
from xml.dom import minidom
# for sending images
from PIL import Image
import multipart
# standard app engine imports
from google.appengine.api import urlfetch
from google.appengine.ext import ndb
import webapp2
TOKEN = '119152358:AAFvnvYU_5sxfTInk0LNQ55a_U5FMY3pyUo'
BASE_URL = 'https://api.telegram.org/bot' + TOKEN + '/'
# ================================
# ================================
# ================================
app = webapp2.WSGIApplication([
('/me', MeHandler),
('/updates', GetUpdatesHandler),
('/set_webhook', SetWebhookHandler),
('/webhook', WebhookHandler),
], debug=True)
| [
11748,
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571,
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299,
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3992,
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198,
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705,
16315,
1314,
1954,
3365,
25,
38540,
85,
48005,
56,
52,
62,
20,
82,
26152,
51,
818,
74,
15,
43,
45,
48,
2816,
64,
62,
52,
20,
23264,
56,
18,
9078,
52,
78,
6,
198,
198,
33,
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62,
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796,
705,
5450,
1378,
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13,
660,
30536,
13,
2398,
14,
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6,
1343,
5390,
43959,
1343,
31051,
6,
628,
198,
2,
36658,
25609,
18604,
628,
198,
2,
36658,
25609,
18604,
628,
198,
2,
36658,
25609,
18604,
628,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
628,
220,
220,
220,
220,
220,
220,
220,
628,
198,
1324,
796,
3992,
1324,
17,
13,
19416,
38,
3539,
381,
10142,
26933,
198,
220,
220,
220,
19203,
14,
1326,
3256,
2185,
25060,
828,
198,
220,
220,
220,
19203,
14,
929,
19581,
3256,
3497,
4933,
19581,
25060,
828,
198,
220,
220,
220,
19203,
14,
2617,
62,
12384,
25480,
3256,
5345,
13908,
25480,
25060,
828,
198,
220,
220,
220,
19203,
14,
12384,
25480,
3256,
5313,
25480,
25060,
828,
198,
4357,
14257,
28,
17821,
8,
198
] | 2.805344 | 262 |
#Faça um Programa que leia um vetor de 5 números inteiros e mostre-os.
lista=[]
for i in range(1, 6):
lista.append(int(input('Digite um número: ')))
print(lista)
| [
2,
50110,
50041,
23781,
6118,
64,
8358,
443,
544,
23781,
1569,
13165,
390,
642,
299,
21356,
647,
418,
493,
20295,
4951,
304,
749,
260,
12,
418,
13,
198,
198,
4868,
64,
28,
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198,
1640,
1312,
287,
2837,
7,
16,
11,
718,
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198,
220,
220,
220,
1351,
64,
13,
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7,
600,
7,
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19511,
578,
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299,
21356,
647,
78,
25,
705,
22305,
198,
4798,
7,
4868,
64,
8,
198
] | 2.287671 | 73 |
from django.utils.text import slugify
from soil import DownloadBase
from corehq.apps.hqmedia.tasks import build_application_zip
from corehq.util.view_utils import absolute_reverse, json_error
from corehq.apps.domain.models import Domain
from dimagi.utils.web import json_response
from corehq.apps.domain.decorators import (
login_or_digest_or_basic,
)
from corehq.apps.app_manager.dbaccessors import get_app
@json_error
@login_or_digest_or_basic()
@json_error
| [
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198,
8,
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6738,
4755,
71,
80,
13,
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13,
1324,
62,
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13,
9945,
15526,
669,
1330,
651,
62,
1324,
628,
198,
31,
17752,
62,
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198,
31,
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62,
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395,
62,
273,
62,
35487,
3419,
628,
198,
31,
17752,
62,
18224,
198
] | 2.968354 | 158 |
import re
import datetime
from ingestion import datasource as ds
# Provides access to coinmarketcap.com data, using the API when available,
# or web scraping when there is no public API
class CoinList(ds.DataSource):
"""Used to get a list of all the coins on coinmarketcap"""
class Ticker(CoinList):
"""Used to get current price/marketcap/volume data for all coins"""
class CoinLinks(ds.DataSource):
"""Used to get social media links for a coin (subreddit, twitter, btctalk)"""
class HistoricalPrices(ds.DataSource):
"""Used to get historical price data for a coin
This requires scraping the site, because there is no API for this data
This is only used for the initial data import, and after that we can just periodically get the ticker
"""
| [
11748,
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198,
220,
220,
220,
37227,
38052,
284,
651,
6754,
2756,
1366,
329,
257,
10752,
198,
220,
220,
220,
770,
4433,
46743,
262,
2524,
11,
780,
612,
318,
645,
7824,
329,
428,
1366,
198,
220,
220,
220,
770,
318,
691,
973,
329,
262,
4238,
1366,
1330,
11,
290,
706,
326,
356,
460,
655,
26034,
651,
262,
4378,
263,
198,
220,
220,
220,
37227,
198
] | 3.611111 | 216 |
#create file myaperture.dat needed for source optimization
f = open("myaperture.dat",'w')
f.write(" 50.0 -0.002 0.002 -0.002 0.002")
f.close()
print("File written to disk: myaperture.dat")
| [
2,
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2393,
616,
499,
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495,
13,
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329,
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3194,
284,
11898,
25,
616,
499,
861,
495,
13,
19608,
4943,
198
] | 2.426829 | 82 |
print(">>>>>> import schemas_invitation.py > Invitation ...")
from typing import List, Optional, Any
import datetime
from pydantic import BaseModel, EmailStr
# from uuid import UUID
from .schemas_choices import ItemType, InvitationStatus, InviteeType, InvitationStatusAction
from .schemas_auths import AuthsInfosBasics
from .schemas_user import User, UserInDBBaseLight
# print("=== SCH-schemas_invitation > InvitationBase : ", InvitationBase)
# class InvitationList(Invitation):
# pass
| [
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7,
19904,
3780,
2599,
198,
220,
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1208,
628
] | 3.245161 | 155 |
import pytest
from signal_ocean import VesselClassFilter
from .builders import create_vessel_class
@pytest.mark.parametrize(
'name_like',
[
'matching name', 'matching', 'name', 'mat', 'me', 'ing na',
'MATCHING NAME', 'MATCHING', 'NAME', 'MAT', 'ME', 'ING NA',
'mAtchiNG NamE', 'Matching', 'nAME', 'MaT', 'mE', 'INg nA',
' '
]
)
| [
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8,
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] | 2.19883 | 171 |
# from transformers import pipeline
# generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')
# generator("EleutherAI has", do_sample=True, min_length=50)
# [{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}]
from transformers import GPT2Tokenizer, GPT2Model
model_name = "microsoft/CodeGPT-small-java-adaptedGPT2"
# model_name = "./CodeGPT-small-java-adaptedGPT2"
tokenizer = GPT2Tokenizer.from_pretrained(model_name) # CodeGPT-small-java-adaptedGPT2
model = GPT2Model.from_pretrained(model_name)
# tokenizer.save_pretrained(f"./{model_name}")
# model.save_pretrained(f"./{model_name}")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
print(model)
output = model(**encoded_input, output_hidden_states=True)
print(len(output["hidden_states"]))
print(output["hidden_states"][0].shape) | [
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] | 2.903125 | 320 |
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from datetime import datetime
from pprint import pprint
import pytz
import locale
import sys
import process
sys.path.insert(0,'./process.py')
#set locale
locale.setlocale(locale.LC_TIME, 'id_ID.UTF-8')
#Set up credentials
scope = ["https://spreadsheets.google.com/feeds",'https://www.googleapis.com/auth/spreadsheets',"https://www.googleapis.com/auth/drive.file","https://www.googleapis.com/auth/drive"]
creds = ServiceAccountCredentials.from_json_keyfile_name("creds.json", scope)
client = gspread.authorize(creds)
#Open gsheet
sa = client.open("Copy of Absensi CBP 2022")
now = datetime.now()
localtz = pytz.timezone('Asia/Jakarta')
date_jkt = int(localtz.localize(now).strftime("%d"))
month_jkt = localtz.localize(now).strftime("%B")
wks = sa.worksheet(month_jkt)
#Get all data
values=wks.get_all_values()
absen=values[2:]
hasil = process.yang_masuk(absen) | [
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62,
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8
] | 2.758017 | 343 |
# Copyright 2019-2020 ETH Zurich and the DaCe authors. All rights reserved.
""" Tests for half-precision syntax quirks. """
import dace
import math
import numpy as np
from dace.transformation.dataflow import MapFusion, Vectorization
from dace.transformation.optimizer import Optimizer
N = dace.symbol('N')
def _test_half(veclen):
""" Tests a set of elementwise operations on a vector half type. """
@dace.program
A = np.random.rand(24).astype(np.float16)
B = np.random.rand(24).astype(np.float16)
sdfg = halftest.to_sdfg()
sdfg.apply_strict_transformations()
sdfg.apply_gpu_transformations()
# Apply vectorization on each map and count applied
applied = 0
for xform in Optimizer(sdfg).get_pattern_matches(patterns=[Vectorization]):
xform.vector_len = veclen
xform.postamble = False
xform.apply(sdfg)
applied += 1
assert applied == 2
out = sdfg(A=A, B=B, N=24)
assert np.allclose(out, A * B + A)
def test_half4():
""" Tests a set of elementwise operations on half with vector length 4. """
_test_half(4)
def test_half8():
""" Tests a set of elementwise operations on half with vector length 8. """
_test_half(8)
def test_exp_vec():
""" Tests an exp operator on a vector half type. """
@dace.program
A = np.random.rand(24).astype(np.float16)
sdfg = halftest.to_sdfg()
sdfg.apply_gpu_transformations()
assert sdfg.apply_transformations(Vectorization, dict(vector_len=8)) == 1
out = sdfg(A=A, N=24)
assert np.allclose(out, np.exp(A))
def test_relu_vec():
""" Tests a ReLU operator on a vector half type. """
@dace.program
A = np.random.rand(24).astype(np.float16)
sdfg = halftest.to_sdfg()
sdfg.apply_gpu_transformations()
assert sdfg.apply_transformations(Vectorization, dict(vector_len=8)) == 1
out = sdfg(A=A, N=24)
assert np.allclose(out, np.maximum(A, 0))
def test_dropout_vec():
""" Tests a dropout operator on a vector half type. """
@dace.program
A = np.random.rand(24).astype(np.float16)
mask = np.random.randint(0, 2, size=[24]).astype(np.float16)
sdfg: dace.SDFG = halftest.to_sdfg()
sdfg.apply_gpu_transformations()
assert sdfg.apply_transformations(Vectorization, dict(vector_len=8)) == 1
out = sdfg(A=A, mask=mask, N=24)
assert np.allclose(out, A * mask)
def test_gelu_vec():
""" Tests a GELU operator on a vector half type. """
s2pi = math.sqrt(2.0 / math.pi)
@dace.program
A = np.random.rand(24).astype(np.float16)
sdfg = halftest.to_sdfg()
sdfg.apply_gpu_transformations()
assert sdfg.apply_transformations(Vectorization, dict(vector_len=4)) == 1
out = sdfg(A=A, N=24)
expected = 0.5 * A * (
1 + np.tanh(math.sqrt(2.0 / math.pi) * (A + 0.044715 * (A**3))))
assert np.allclose(out, expected, rtol=1e-2, atol=1e-4)
if __name__ == '__main__':
# Prerequisite for test: CUDA compute capability >= 6.0
dace.Config.set('compiler', 'cuda', 'cuda_arch', value='60')
test_half4()
test_half8()
test_exp_vec()
test_relu_vec()
test_dropout_vec()
test_gelu_vec()
| [
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] | 2.397126 | 1,322 |
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