file_name large_stringlengths 4 140 | prefix large_stringlengths 0 39k | suffix large_stringlengths 0 36.1k | middle large_stringlengths 0 29.4k | fim_type large_stringclasses 4
values |
|---|---|---|---|---|
main.go | package main
import (
"code.google.com/p/go.net/websocket"
"code.google.com/p/goauth2/oauth"
"code.google.com/p/google-api-go-client/mirror/v1"
"code.google.com/p/google-api-go-client/oauth2/v2"
"encoding/json"
"fmt"
picarus "github.com/bwhite/picarus/go"
"github.com/gorilla/pat"
"github.com/ugorji/go-msgpack... |
resp, err := trans.RoundTrip(req)
if err != nil {
LogPrintf("getattachment: content")
return nil, err
}
defer resp.Body.Close()
imageData, err := ioutil.ReadAll(resp.Body)
if err != nil {
LogPrintf("getattachment: body")
return nil, err
}
return imageData, nil
}
func notifyOpenGlass(conn *picarus.Conn... | {
LogPrintf("getattachment: http")
return nil, err
} | conditional_block |
main.go | package main
import (
"code.google.com/p/go.net/websocket"
"code.google.com/p/goauth2/oauth"
"code.google.com/p/google-api-go-client/mirror/v1"
"code.google.com/p/google-api-go-client/oauth2/v2"
"encoding/json"
"fmt"
picarus "github.com/bwhite/picarus/go"
"github.com/gorilla/pat"
"github.com/ugorji/go-msgpack... | w.WriteHeader(500)
LogPrintf("oauth: json marshal")
return
}
storeCredential(userId, tok, string(userSer))
http.Redirect(w, r, fullUrl, http.StatusFound)
}
func SetupHandler(w http.ResponseWriter, r *http.Request) {
userId, err := userID(r)
if err != nil || userId == "" {
w.WriteHeader(400)
LogPrintf("s... | }
userSer, err := json.Marshal(u)
if err != nil { | random_line_split |
main.go | package main
import (
"code.google.com/p/go.net/websocket"
"code.google.com/p/goauth2/oauth"
"code.google.com/p/google-api-go-client/mirror/v1"
"code.google.com/p/google-api-go-client/oauth2/v2"
"encoding/json"
"fmt"
picarus "github.com/bwhite/picarus/go"
"github.com/gorilla/pat"
"github.com/ugorji/go-msgpack... | (conn *picarus.Conn, svc *mirror.Service, trans *oauth.Transport, t *mirror.TimelineItem) ([]byte, error) {
a, err := svc.Timeline.Attachments.Get(t.Id, t.Attachments[0].Id).Do()
if err != nil {
LogPrintf("getattachment: metadata")
return nil, err
}
req, err := http.NewRequest("GET", a.ContentUrl, nil)
if err ... | getImageAttachment | identifier_name |
snapshots.go | /*
Copyright The containerd Authors.
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... | Flags: []cli.Flag{
cli.StringFlag{
Name: "target, t",
Usage: "Mount target path, will print mount, if provided",
},
cli.BoolFlag{
Name: "mounts",
Usage: "Print out snapshot mounts as JSON",
},
},
Action: func(context *cli.Context) error {
if narg := context.NArg(); narg < 1 || narg > 2 {
r... | ArgsUsage: "[flags] <key> [<parent>]", | random_line_split |
snapshots.go | /*
Copyright The containerd Authors.
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... | {
// FIXME: This is specific to Unix
for _, m := range mounts {
fmt.Printf("mount -t %s %s %s -o %s\n", m.Type, m.Source, filepath.Join(target, m.Target), strings.Join(m.Options, ","))
}
} | identifier_body | |
snapshots.go | /*
Copyright The containerd Authors.
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... | (target string, mounts []mount.Mount) {
// FIXME: This is specific to Unix
for _, m := range mounts {
fmt.Printf("mount -t %s %s %s -o %s\n", m.Type, m.Source, filepath.Join(target, m.Target), strings.Join(m.Options, ","))
}
}
| printMounts | identifier_name |
snapshots.go | /*
Copyright The containerd Authors.
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... |
defer cancel()
snapshotter := client.SnapshotService(context.GlobalString("snapshotter"))
info, err := snapshotter.Stat(ctx, key)
if err != nil {
return err
}
commands.PrintAsJSON(info)
return nil
},
}
var setLabelCommand = cli.Command{
Name: "label",
Usage: "Add labels to content",... | {
return err
} | conditional_block |
server.py | # -*- coding: utf-8 -*-
# 简易http 与 websocket 服务端
import logging
import socket
import base64
import hashlib
import struct
import os
import binascii
import json
from select import select
logging.basicConfig(level=logging.DEBUG)
def md5_for_file(f, block_size=2 ** 20):
md5 = hashlib.md5()
w... | v):
key = base64.b64encode(hashlib.sha1(v + '258EAFA5-E914-47DA-95CA-C5AB0DC85B11').digest())
response = 'HTTP/1.1 101 Switching Protocols\r\n' \
'Upgrade: websocket\r\n' \
'Connection: Upgrade\r\n' \
'Sec-WebSocket-Accept:' + key + '\r\n\... | self, conn, | identifier_name |
server.py | # -*- coding: utf-8 -*-
# 简易http 与 websocket 服务端
import logging
import socket
import base64
import hashlib
import struct
import os
import binascii
import json
from select import select
logging.basicConfig(level=logging.DEBUG)
def md5_for_file(f, block_size=2 ** 20):
md5 = hashlib.md5()
w... | length = len_flag
ret = ''
for cnt, d in enumerate(raw):
ret += chr(ord(d) ^ ord(mask[cnt % 4]))
if not ret:
pass
# logging.debug("frame info FIN %d Opcode %d mask %d length %d " % (FIN, Opcode, is_mask, length))
# hexstr = binas... | raw = data[14:]
length = reduce(lambda y, z: y * 256 + z, map(lambda x: ord(x), data[2:9]))
else:
mask = data[2:6]
raw = data[6:]
| random_line_split |
server.py | # -*- coding: utf-8 -*-
# 简易http 与 websocket 服务端
import logging
import socket
import base64
import hashlib
import struct
import os
import binascii
import json
from select import select
logging.basicConfig(level=logging.DEBUG)
def md5_for_file(f, block_size=2 ** 20):
md5 = hashlib.md5()
w... | ey]['no']))
elif msg['a'] == 'f':
logging.info('a %s s %d e %d n %d' % (msg['a'], msg['s'], msg['e'], msg['n']))
start, end = msg['s'], msg['e']
length = end - start
if msg['n'] != session['no']:
if msg['n'] < session[... | self.session[sesskey]['filebuffer'] = []
self.session[sesskey]['no'] = 0
self.session[sesskey]['file'] = open(
os.path.join(os.path.dirname(__file__), 'upload', msg['name']), 'ab')
elif msg['a'] == 'ping':
self.ws_send(conn, "ok:%d"... | conditional_block |
server.py | # -*- coding: utf-8 -*-
# 简易http 与 websocket 服务端
import logging
import socket
import base64
import hashlib
import struct
import os
import binascii
import json
from select import select
logging.basicConfig(level=logging.DEBUG)
def md5_for_file(f, block_size=2 ** 20):
md5 = hashli | Server:
socket = None
socket_list = set()
port = 7000
buffersize = 1024*1024
timeout = 20
content = dict()
session = dict()
def __init__(self):
filelist = ['test.html', 'upload.js', 'spark-md5.min.js']
for i in filelist:
with open(i, 'r') as f:
... | b.md5()
while True:
data = f.read(block_size)
if not data:
break
md5.update(data)
return md5.hexdigest()
class | identifier_body |
rol_common.js | // 防止事件冒泡
function stopBubble(e) {
// If an event object is provided, then this is a non-IE browser
if ( e && e.stopPropagation )
// and therefore it supports the W3C stopPropagation() method
e.stopPropagation();
else
// Otherwise, we need to use the Internet Explorer
// way of cancelling event bubbling
window... | function show_msg_tips_newdiv(type,msg,id){
if(!type || !msg)
return;
var time=3000;
if('success'==type || 'yes'==type)
$.scmtips.show("success",msg,null,time);
if('warn'==type || 'warning'==type)
$.scmtips.show("warn",msg,null,time);
if('error'==type || 'wrong'==type)
$.scmtips.show("error",msg,null,time)... |
//在弹出框中显示提示信息 | random_line_split |
rol_common.js | // 防止事件冒泡
function stopBubble(e) {
// If an event object is provided, then this is a non-IE browser
if ( e && e.stopPropagation )
// and therefore it supports the W3C stopPropagation() method
e.stopPropagation();
else
// Otherwise, we need to use the Internet Explorer
// way of cancelling event bubbling
window... | nav_open");
}
}
//左边菜单展开，关闭替换图标
function replaceImg(obj){
var img = $(obj).find("img");
var src = img.attr("src");
if(src.indexOf('open')>0){
img.attr("src",src.replace("open","close"));
}else{
img.attr("src",src.replace("close","open"));
}
}
//打开左侧菜单，根据设置的.info样式
function open_left_nav(){
$("#left_nav"... | $(obj).css("zIndex",1000);
$(obj).css("height",$(obj).find(".float_div_content").height()+70)
},100);
}else{
$(obj).hide();
}
}
//左菜单展开，关闭
function switch_left_nav(obj){
var div = $(obj).parent();
if($(obj).hasClass("left_nav_open"))
{
div.find(">div:not(div:first-child)").hide();
$(obj).removeClass("le... | conditional_block |
rol_common.js | // 防止事件冒泡
function stopBubble(e) {
// If an event object is provided, then this is a non-IE browser
if ( e && e.stopPropagation )
// and therefore it supports the W3C stopPropagation() method
e.stopPropagation();
else
// Otherwise, we need to use the Internet Explorer
// way of cancelling event bubbling
window... | rror",msg,null,time);
}
function show_msg_tips(type,msg,width){
if(!type || !msg)
return;
var time=1000;
if('success'==type || 'yes'==type)
$.scmtips.show("success",msg, width,time);
if('warn'==type || 'warning'==type)
$.scmtips.show("warn",msg, width,time);
if('error'==type || 'wrong'==type)
$.scmtips.sh... | cmtips.show("warn",msg,null,time);
if('error'==type || 'wrong'==type)
$.scmtips.show("e | identifier_body |
rol_common.js | // 防止事件冒泡
function stopBubble(e) {
// If an event object is provided, then this is a non-IE browser
if ( e && e.stopPropagation )
// and therefore it supports the W3C stopPropagation() method
e.stopPropagation();
else
// Otherwise, we need to use the Internet Explorer
// way of cancelling event bubbling
window... | }
}
if(flag)
return true;
}
var patrn = /^[^<]*(<(.|\s)+>)[^>]*$|^#$/;
if (!patrn.exec(s)) return false ;
return true ;
}
//字符串真实的长度
function getStrRealLength(str){
return str.replace(/[^\x00-\xff]/gi, "--").replace(/[wW]/gi, "--").length;
}
//字符串缩略
function subStrBreviary(str,maxLength){... | break;
| identifier_name |
ranker_ltr.py | """
Uses Learning to Rank to rank entities
@author: Faegheh Hasibi
"""
from datetime import datetime
import pickle
from nordlys.erd.features.query_sim_feat import QuerySimFeat
from nordlys.ml.cross_validation import CrossValidation
from nordlys.erd.features.entity_feat import EntityFeat
from nordlys.erd.features.men... |
return self.ml.apply_model(inss, model)
def rank_queries(self, queries, time_log_file=None): # commonness_th, filter=True,
"""
Ranks entities for the given queries using the trained model.
:param queries: a dictionary, {q_id: q_content, ...}
:param time_log_file: file nam... | model = self.model | conditional_block |
ranker_ltr.py | """
Uses Learning to Rank to rank entities
@author: Faegheh Hasibi
"""
from datetime import datetime
import pickle
from nordlys.erd.features.query_sim_feat import QuerySimFeat
from nordlys.ml.cross_validation import CrossValidation
from nordlys.erd.features.entity_feat import EntityFeat
from nordlys.erd.features.men... | (self, queries, time_log_file=None): # commonness_th, filter=True,
"""
Ranks entities for the given queries using the trained model.
:param queries: a dictionary, {q_id: q_content, ...}
:param time_log_file: file name to save time log
:return erd.ml.CERInstances, Ranked instanc... | rank_queries | identifier_name |
ranker_ltr.py | """
Uses Learning to Rank to rank entities
@author: Faegheh Hasibi
"""
from datetime import datetime
import pickle
from nordlys.erd.features.query_sim_feat import QuerySimFeat
from nordlys.ml.cross_validation import CrossValidation
from nordlys.erd.features.entity_feat import EntityFeat
from nordlys.erd.features.men... |
def rank_query(self, query):
"""
Generates ranking score for entities related to the given query.
:param query: query.Query
:return erd.ml.CERInstances
"""
q_inss = CERInstances.gen_instances(query, self.commonness_th, sf_source=self.sf_source, filter=self.filter)
... | """
Ranks entities for the given queries using the trained model.
:param queries: a dictionary, {q_id: q_content, ...}
:param time_log_file: file name to save time log
:return erd.ml.CERInstances, Ranked instances
"""
print "Ranking queries ..."
total_time = 0.0
... | identifier_body |
ranker_ltr.py | """
Uses Learning to Rank to rank entities
@author: Faegheh Hasibi
"""
from datetime import datetime
import pickle
from nordlys.erd.features.query_sim_feat import QuerySimFeat
from nordlys.ml.cross_validation import CrossValidation
from nordlys.erd.features.entity_feat import EntityFeat
from nordlys.erd.features.men... | model: the trained model
"""
def __init__(self, commonness_th=None, sf_source=None, filter=True, model=None, config={}):
self.commonness_th = commonness_th
self.sf_source = sf_source
self.filter = filter
self.config = config
self.model = model
self.ml = ML... | random_line_split | |
opentuna-stack.ts | import * as cdk from '@aws-cdk/core';
import * as cloudfront from '@aws-cdk/aws-cloudfront';
import * as route53 from '@aws-cdk/aws-route53';
import * as route53targets from '@aws-cdk/aws-route53-targets';
import * as acm from '@aws-cdk/aws-certificatemanager';
import * as cloudwatch from '@aws-cdk/aws-cloudwatch';
imp... | extends cdk.Stack {
constructor(scope: cdk.Construct, id: string, props: OpenTunaStackProps) {
super(scope, id, props);
const stack = cdk.Stack.of(this);
const domainName = this.node.tryGetContext('domainName');
const domainZoneName = this.node.tryGetContext('domainZone');
const iamCertId = thi... | OpentunaStack | identifier_name |
opentuna-stack.ts | import * as cdk from '@aws-cdk/core';
import * as cloudfront from '@aws-cdk/aws-cloudfront';
import * as route53 from '@aws-cdk/aws-route53';
import * as route53targets from '@aws-cdk/aws-route53-targets';
import * as acm from '@aws-cdk/aws-certificatemanager';
import * as cloudwatch from '@aws-cdk/aws-cloudwatch';
imp... | else if (typeof cloudfrontCert === "string") {
// IAM cert
cloudfrontProps = {
viewerCertificate: cloudfront.ViewerCertificate.fromIamCertificate(
cloudfrontCert,
{
aliases: [domainName],
securityPolicy: cloudfront.SecurityPolicyProtocol.TLS... | {
// ACM cert
cloudfrontProps = {
aliasConfiguration: {
acmCertRef: cloudfrontCert.certificateArn,
names: [domainName],
},
...cloudfrontProps
}
} | conditional_block |
opentuna-stack.ts | import * as cdk from '@aws-cdk/core';
import * as cloudfront from '@aws-cdk/aws-cloudfront';
import * as route53 from '@aws-cdk/aws-route53';
import * as route53targets from '@aws-cdk/aws-route53-targets';
import * as acm from '@aws-cdk/aws-certificatemanager';
import * as cloudwatch from '@aws-cdk/aws-cloudwatch';
imp... |
}
| {
super(scope, id, props);
const stack = cdk.Stack.of(this);
const domainName = this.node.tryGetContext('domainName');
const domainZoneName = this.node.tryGetContext('domainZone');
const iamCertId = this.node.tryGetContext('iamCertId');
let useHTTPS = false;
let domainZone: r53.IHostedZone... | identifier_body |
opentuna-stack.ts | import * as cdk from '@aws-cdk/core';
import * as cloudfront from '@aws-cdk/aws-cloudfront';
import * as route53 from '@aws-cdk/aws-route53';
import * as route53targets from '@aws-cdk/aws-route53-targets';
import * as acm from '@aws-cdk/aws-certificatemanager';
import * as cloudwatch from '@aws-cdk/aws-cloudwatch';
imp... | allowAllOutbound: true,
});
const tunaManagerALBSG = new ec2.SecurityGroup(this, "TunaManagerALBSG", {
vpc,
description: "SG of ALB of Tuna Manager",
allowAllOutbound: false,
});
const tunaWorkerSG = new ec2.SecurityGroup(this, "TunaWorkerSG", {
vpc,
description: "SG ... |
const tunaManagerSG = new ec2.SecurityGroup(this, "TunaManagerSG", {
vpc,
description: "SG of Tuna Manager", | random_line_split |
L.IM_RoutingControl.js | /**
* L.Control.RoutingControl control de routing basado en el leaflet-routing-machine
*/
L.Control.RoutingControl = L.Control.extend({
includes: L.Mixin.Events,
options: {
position: 'topleft',
lang: 'ca',
id: 'dv_bt_Routing',
className: 'leaflet-bar btn btn-default btn-sm grisfort',
title: 'Routing',
... | else {
return L.marker(wp.latLng, {
draggable: true,
icon: puntIntermig
});
}
};
this._plan = new this._reversablePlan([], {
geocoder: L.Control.Geocoder.icgc(),
routeWhileDragging: true,
language: lang,
createMarker: createM... | return L.marker(wp.latLng, {
draggable: true,
icon: puntDesti
});
}
| conditional_block |
L.IM_RoutingControl.js | /**
* L.Control.RoutingControl control de routing basado en el leaflet-routing-machine
*/
L.Control.RoutingControl = L.Control.extend({
includes: L.Mixin.Events,
options: {
position: 'topleft',
lang: 'ca',
id: 'dv_bt_Routing',
className: 'leaflet-bar btn btn-default btn-sm grisfort',
title: 'Routing',
... | '<i class="t-square-rounded" style="-webkit-transform:scale(1.25) scale(0.65) rotate(45deg);-moz-transform:scale(1.25) scale(0.65) rotate(45deg);transform:scale(1.25) scale(0.65) rotate(45deg)"></i>'+
'<i class="t-turn-90-l t-c-white" style="-webkit-transform:scale(-1.3, 1.3);-moz-transform:scale(-1.3, 1.3);transfo... | random_line_split | |
views.py | from django.shortcuts import render, redirect, get_object_or_404
from django.core.urlresolvers import reverse_lazy
from django.views.generic.list import ListView
from django.views.generic.detail import DetailView
from django.views.generic.base import TemplateResponseMixin, View
from django.views.generic.edit import Cre... | lass CourseUpdateView(PermissionRequiredMixin, OwnerCourseEditMixin, UpdateView):
"""
Используется для изменения Course
"""
# PermissionRequiredMixin проверяет если у пользователя указанный permission_required
permission_required = "courses.change_course"
class CourseDeleteView(PermissionRequiredMixin, OwnerCou... | age_course_list')
template_name = "courses/manage/course/form.html"
class ManageCourseListView(OwnerCourseMixin, ListView):
"""
Используя наследование от OwnerCourseMixin, ListView
этот класс также будет содержать все поля и методы из
OwnerCourseMixin, ListView, OwnerMixin
"""
template_name = "courses/manage/c... | identifier_body |
views.py | from django.shortcuts import render, redirect, get_object_or_404
from django.core.urlresolvers import reverse_lazy
from django.views.generic.list import ListView
from django.views.generic.detail import DetailView
from django.views.generic.base import TemplateResponseMixin, View
from django.views.generic.edit import Cre... | ved': 'OK'})
class CourseListView(TemplateResponseMixin, View):
"""
Список всех курсов
"""
model = Course
template_name = "courses/course/list.html"
def get(self, request, subject=None):
# возвращаем все subjects с количеством курсов для subject
subjects = Subject.objects.annotate(total_courses=Count('cour... | ериализирует response
"""
def post(self, request):
for id, order in self.request_json.items():
print('id', id, ' -- ', order)
for id, order in self.request_json.items():
Content.objects.filter(id=id, module__course__owner=request.user).update(order=order)
return self.render_json_response({'sa | conditional_block |
views.py | from django.shortcuts import render, redirect, get_object_or_404
from django.core.urlresolvers import reverse_lazy
from django.views.generic.list import ListView
from django.views.generic.detail import DetailView
from django.views.generic.base import TemplateResponseMixin, View
from django.views.generic.edit import Cre... | content.content_object.delete()
content.delete()
# возвращаемся к списку контента модуля
return redirect('module_content_list', module.id)
class ModuleContentListView(TemplateResponseMixin, View):
template_name = "courses/manage/module/content_list.html"
def get(self, request, module_id):
module = get_ob... | id=id,
module__course__owner=request.user)
module = content.module | random_line_split |
views.py | from django.shortcuts import render, redirect, get_object_or_404
from django.core.urlresolvers import reverse_lazy
from django.views.generic.list import ListView
from django.views.generic.detail import DetailView
from django.views.generic.base import TemplateResponseMixin, View
from django.views.generic.edit import Cre... | ы)
задается владелец этого объекта.
"""
form.instance.owner = self.request.user
return super(OwnerEditMixin, self).form_valid(form)
class OwnerCourseMixin(OwnerMixin, LoginRequiredMixin):
"""
Указание модели для queryset во всех дочерних классах
"""
model = Course
class OwnerCourseEditMixin(OwnerCourseM... | верждение форм | identifier_name |
CKEditor_media_tab.js | /**
* Add a media tab to the image properties dialog.
*
* This process as to be executed in a precise time frame;
* After CKEditor is loaded, but before it's executed.
*
* It tooks me some time to find out a stable way to define this.
* There is how I understand the loading process:
*
* Drupal Page:
* ... | else {
inputEl.setAttribute('readonly', true);
inputEl.addClass('disabled');
}
};
var imageStyles = [
['Original', 'media_original'],
['Link', 'media_link'],
['Preview', 'media_preview'],
['Large', 'media_large']
];
// NOTE: Drupal.settings.eatlas_media_frame_filter.drupal_custom_image... | {
inputEl.removeAttribute('readonly');
inputEl.removeClass('disabled');
} | conditional_block |
CKEditor_media_tab.js | /**
* Add a media tab to the image properties dialog.
*
* This process as to be executed in a precise time frame;
* After CKEditor is loaded, but before it's executed.
*
* It tooks me some time to find out a stable way to define this.
* There is how I understand the loading process:
*
* Drupal Page:
* ... |
// Remove previous 'image style' class and find the image ID
var newClasses = [];
for (var i=0, len=classes.length; i<len; i++) {
if (classes[i].substring(0, IMAGESTYLE_CLASS_PREFIX.length) !== IMAGESTYLE_CLASS_PREFIX) {
newClasses.push(classes[i]);
}
}
// Add new 'image style' class
ne... | // API: dialog.getValueOf(pageId, elementId);
var classes = dialog.getValueOf('advanced', 'txtGenClass');
classes = classes ? classes.split(/\s+/) : []; | random_line_split |
CKEditor_media_tab.js | /**
* Add a media tab to the image properties dialog.
*
* This process as to be executed in a precise time frame;
* After CKEditor is loaded, but before it's executed.
*
* It tooks me some time to find out a stable way to define this.
* There is how I understand the loading process:
*
* Drupal Page:
* ... |
/**
* element: CKEDITOR.dom.element
* The file ID is store in the class of the element:
* class="... img__fid__12 ...";
*/
function _eatlas_media_frame_ckeditor_get_fid(element) {
var classesStr = element.getAttribute('class');
var fidClassPrefix = 'img__fid__';
if (classesStr) {
var ... | {
return $('<div/>').html(str).text();
} | identifier_body |
CKEditor_media_tab.js | /**
* Add a media tab to the image properties dialog.
*
* This process as to be executed in a precise time frame;
* After CKEditor is loaded, but before it's executed.
*
* It tooks me some time to find out a stable way to define this.
* There is how I understand the loading process:
*
* Drupal Page:
* ... | (str) {
return $('<div/>').html(str).text();
}
/**
* element: CKEDITOR.dom.element
* The file ID is store in the class of the element:
* class="... img__fid__12 ...";
*/
function _eatlas_media_frame_ckeditor_get_fid(element) {
var classesStr = element.getAttribute('class');
var fidClassP... | _decode | identifier_name |
lib.rs | // This file is part of Substrate.
// Copyright (C) Parity Technologies (UK) Ltd.
// SPDX-License-Identifier: Apache-2.0
// 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.a... |
}
#[pallet::hooks]
impl<T: Config> Hooks<BlockNumberFor<T>> for Pallet<T> {
fn integrity_test() {
// If you hit this error, you need to try to `Box` big dispatchable parameters.
assert!(
sp_std::mem::size_of::<<T as Config>::RuntimeCall>() as u32 <= CALL_ALIGN,
"Call enum size should be smaller tha... | {
let allocator_limit = sp_core::MAX_POSSIBLE_ALLOCATION;
let call_size = ((sp_std::mem::size_of::<<T as Config>::RuntimeCall>() as u32 +
CALL_ALIGN - 1) / CALL_ALIGN) *
CALL_ALIGN;
// The margin to take into account vec doubling capacity.
let margin_factor = 3;
allocator_limit / margin_factor /... | identifier_body |
lib.rs | // This file is part of Substrate.
// Copyright (C) Parity Technologies (UK) Ltd.
// SPDX-License-Identifier: Apache-2.0
// 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.a... | () {
// If you hit this error, you need to try to `Box` big dispatchable parameters.
assert!(
sp_std::mem::size_of::<<T as Config>::RuntimeCall>() as u32 <= CALL_ALIGN,
"Call enum size should be smaller than {} bytes.",
CALL_ALIGN,
);
}
}
#[pallet::error]
pub enum Error<T> {
/// Too many ca... | integrity_test | identifier_name |
lib.rs | // This file is part of Substrate.
// Copyright (C) Parity Technologies (UK) Ltd.
// SPDX-License-Identifier: Apache-2.0
// 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.a... |
let is_root = ensure_root(origin.clone()).is_ok();
let calls_len = calls.len();
ensure!(calls_len <= Self::batched_calls_limit() as usize, Error::<T>::TooManyCalls);
// Track the actual weight of each of the batch calls.
let mut weight = Weight::zero();
for (index, call) in calls.into_iter().enumer... | {
return Err(BadOrigin.into())
} | conditional_block |
lib.rs | // This file is part of Substrate.
// Copyright (C) Parity Technologies (UK) Ltd.
// SPDX-License-Identifier: Apache-2.0
// 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.a... | /// event is deposited. If a call failed and the batch was interrupted, then the
/// `BatchInterrupted` event is deposited, along with the number of successful calls made
/// and the error of the failed call. If all were successful, then the `BatchCompleted`
/// event is deposited.
#[pallet::call_index(0)]
... | /// - O(C) where C is the number of calls to be batched.
///
/// This will return `Ok` in all circumstances. To determine the success of the batch, an | random_line_split |
main.py | import argparse
import os
import random
import shutil
import time
import warnings
import numpy as np
from torchvision import datasets
from functions import *
from imagepreprocess import *
from model_init import *
from src.representation import *
import torch
import torch.nn as nn
import torch.nn.parallel
import torc... | (self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
class Learning_rate_generater(ob... | __init__ | identifier_name |
main.py | import argparse
import os
import random
import shutil
import time
import warnings
import numpy as np
from torchvision import datasets
from functions import *
from imagepreprocess import *
from model_init import *
from src.representation import *
import torch
import torch.nn as nn
import torch.nn.parallel
import torc... |
else:
raise KeyError("=> undefined learning rate method '{}'" .format(method))
self.lr_factor = lr_factor
self.lr = lr
def step(self, params, total_epoch):
decrease_until = decode_params(params)
decrease_num = len(decrease_until)
base_factor = 0.1
... | lr_factor, lr = self.log(params, total_epoch) | conditional_block |
main.py | import argparse
import os
import random
import shutil
import time
import warnings
import numpy as np
from torchvision import datasets
from functions import *
from imagepreprocess import *
from model_init import *
from src.representation import *
import torch
import torch.nn as nn
import torch.nn.parallel
import torc... |
def validate(val_loader, model, criterion):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
... | batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.upd... | identifier_body |
main.py | import argparse
import os
import random
import shutil
import time
import warnings
import numpy as np
from torchvision import datasets
from functions import *
from imagepreprocess import *
from model_init import *
from src.representation import *
import torch
import torch.nn as nn
import torch.nn.parallel
import torc... | help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gp... | parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, | random_line_split |
shlex.go | /*
Copyright 2012 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in ... |
func (classifier *TokenClassifier) ClassifyRune(rune int32) RuneTokenType {
return classifier.typeMap[rune]
}
/*
A type for turning an input stream in to a sequence of strings. Whitespace and
comments are skipped.
*/
type Lexer struct {
tokenizer *Tokenizer
}
/*
Create a new lexer.
*/
func NewLexer(r io.Reader) (... | {
typeMap := map[int32]RuneTokenType{}
addRuneClass(&typeMap, RUNE_CHAR, RUNETOKEN_CHAR)
addRuneClass(&typeMap, RUNE_SPACE, RUNETOKEN_SPACE)
addRuneClass(&typeMap, RUNE_ESCAPING_QUOTE, RUNETOKEN_ESCAPING_QUOTE)
addRuneClass(&typeMap, RUNE_NONESCAPING_QUOTE, RUNETOKEN_NONESCAPING_QUOTE)
addRuneClass(&typeMap, RUNE... | identifier_body |
shlex.go | /*
Copyright 2012 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in ... | never equal another token.
*/
func (a *Token) Equal(b *Token) bool {
if a == nil || b == nil {
return false
}
if a.tokenType != b.tokenType {
return false
}
return a.value == b.value
}
const (
RUNE_CHAR string = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789._-,/@$*()+=><:;&^%~|... | random_line_split | |
shlex.go | /*
Copyright 2012 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in ... | (r io.Reader) (*Lexer, error) {
tokenizer, err := NewTokenizer(r)
if err != nil {
return nil, err
}
lexer := &Lexer{tokenizer: tokenizer}
return lexer, nil
}
/*
Return the next word, and an error value. If there are no more words, the error
will be io.EOF.
*/
func (l *Lexer) NextWord() (string, error) {
var t... | NewLexer | identifier_name |
shlex.go | /*
Copyright 2012 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in ... |
return subStrings, nil
}
| {
word, err := l.NextWord()
if err != nil {
if err == io.EOF {
return subStrings, nil
}
return subStrings, err
}
subStrings = append(subStrings, word)
} | conditional_block |
mod.rs | mod console_tests;
mod iterator;
use std::borrow::Cow;
use ansi_term::Style;
use itertools::Itertools;
use unicode_segmentation::UnicodeSegmentation;
use unicode_width::UnicodeWidthStr;
use iterator::{AnsiElementIterator, Element};
pub const ANSI_CSI_CLEAR_TO_EOL: &str = "\x1b[0K";
pub const ANSI_CSI_CLEAR_TO_BOL: ... | asure_text_width_osc_hyperlink_non_ascii() {
assert_eq!(measure_text_width("\x1b[38;5;4m\x1b]8;;file:///Users/dan/src/delta/src/ansi/mod.rs\x1b\\src/ansi/modバー.rs\x1b]8;;\x1b\\\x1b[0m"),
measure_text_width("src/ansi/modバー.rs"));
}
#[test]
fn test_parse_first_style() {
let... | _text_width("\x1b[38;5;4m\x1b]8;;file:///Users/dan/src/delta/src/ansi/mod.rs\x1b\\src/ansi/mod.rs\x1b]8;;\x1b\\\x1b[0m"),
measure_text_width("src/ansi/mod.rs"));
}
#[test]
fn test_me | identifier_body |
mod.rs | mod console_tests;
mod iterator;
use std::borrow::Cow;
use ansi_term::Style;
use itertools::Itertools;
use unicode_segmentation::UnicodeSegmentation;
use unicode_width::UnicodeWidthStr;
use iterator::{AnsiElementIterator, Element};
pub const ANSI_CSI_CLEAR_TO_EOL: &str = "\x1b[0K";
pub const ANSI_CSI_CLEAR_TO_BOL: ... | ure_text_width("\x1b[38;5;4m\x1b]8;;file:///Users/dan/src/delta/src/ansi/mod.rs\x1b\\src/ansi/modバー.rs\x1b]8;;\x1b\\\x1b[0m"),
measure_text_width("src/ansi/modバー.rs"));
}
#[test]
fn test_parse_first_style() {
let minus_line_from_unconfigured_git = "\x1b[31m-____\x1b[m\n";
... | hyperlink_non_ascii() {
assert_eq!(meas | identifier_name |
mod.rs | mod console_tests;
mod iterator;
use std::borrow::Cow;
use ansi_term::Style;
use itertools::Itertools;
use unicode_segmentation::UnicodeSegmentation;
use unicode_width::UnicodeWidthStr;
use iterator::{AnsiElementIterator, Element};
pub const ANSI_CSI_CLEAR_TO_EOL: &str = "\x1b[0K";
pub const ANSI_CSI_CLEAR_TO_BOL: ... |
}
Cow::from(format!("{result}{result_tail}"))
}
pub fn parse_style_sections(s: &str) -> Vec<(ansi_term::Style, &str)> {
let mut sections = Vec::new();
let mut curr_style = Style::default();
for element in AnsiElementIterator::new(s) {
match element {
Element::Text(start, end) ... | {
result.push_str(t);
} | conditional_block |
mod.rs | mod console_tests;
mod iterator;
use std::borrow::Cow;
use ansi_term::Style;
use itertools::Itertools;
use unicode_segmentation::UnicodeSegmentation;
use unicode_width::UnicodeWidthStr;
use iterator::{AnsiElementIterator, Element};
pub const ANSI_CSI_CLEAR_TO_EOL: &str = "\x1b[0K";
pub const ANSI_CSI_CLEAR_TO_BOL: ... | measure_text_width("src/ansi/mod.rs"));
}
#[test]
fn test_measure_text_width_osc_hyperlink_non_ascii() {
assert_eq!(measure_text_width("\x1b[38;5;4m\x1b]8;;file:///Users/dan/src/delta/src/ansi/mod.rs\x1b\\src/ansi/modバー.rs\x1b]8;;\x1b\\\x1b[0m"),
measure_text_w... | random_line_split | |
main.rs | #![no_std]
#![no_main]
#![feature(asm)]
#![feature(collections)]
extern crate stm32f7_discovery as stm32f7;
extern crate collections;
extern crate r0;
pub mod plot;
pub mod model;
pub mod temp_sensor;
pub mod time;
pub mod util;
pub mod pid;
pub mod ramp;
pub mod state_button;
mod leak;
use stm32f7::{system_clock,b... | let drag_color = Color::from_hex(0x000000);
let grid_color = Color::from_hex(0x444444);
// lcd controller
let mut lcd = lcd::init(ltdc, rcc, &mut gpio);
touch::check_family_id(&mut i2c_3).unwrap();
loop {
SYSCLOCK.reset();
lcd.clear_screen();
lcd.set_background_color(Color::from_h... | gpio::Resistor::NoPull)
.expect("Could not configure pwm pin");
let axis_color = Color::from_hex(0xffffff); | random_line_split |
main.rs | #![no_std]
#![no_main]
#![feature(asm)]
#![feature(collections)]
extern crate stm32f7_discovery as stm32f7;
extern crate collections;
extern crate r0;
pub mod plot;
pub mod model;
pub mod temp_sensor;
pub mod time;
pub mod util;
pub mod pid;
pub mod ramp;
pub mod state_button;
mod leak;
use stm32f7::{system_clock,b... | (hw: board::Hardware) -> ! {
let board::Hardware {
rcc,
pwr,
flash,
fmc,
ltdc,
gpio_a,
gpio_b,
gpio_c,
gpio_d,
gpio_e,
gpio_f,
gpio_g,
gpio_h,
gpio_i,
gpio_j,
gpio_k,
spi_2,
... | main | identifier_name |
main.rs | #![no_std]
#![no_main]
#![feature(asm)]
#![feature(collections)]
extern crate stm32f7_discovery as stm32f7;
extern crate collections;
extern crate r0;
pub mod plot;
pub mod model;
pub mod temp_sensor;
pub mod time;
pub mod util;
pub mod pid;
pub mod ramp;
pub mod state_button;
mod leak;
use stm32f7::{system_clock,b... |
// WORKAROUND: rust compiler will inline & reorder fp instructions into
#[inline(never)] // reset() before the FPU is initialized
fn main(hw: board::Hardware) -> ! {
let board::Hardware {
rcc,
pwr,
flash,
fmc,
ltdc,
gpio_a,
... | {
extern "C" {
static __DATA_LOAD: u32;
static __DATA_END: u32;
static mut __DATA_START: u32;
static mut __BSS_START: u32;
static mut __BSS_END: u32;
}
let data_load = &__DATA_LOAD;
let data_start = &mut __DATA_START;
let data_end = &__DATA_END;
let bss_s... | identifier_body |
navtreeindex22.js | var NAVTREEINDEX22 =
{
"token-manufacturing_8h_source.htm":[4,0,71],
"token-stack_8h.htm":[4,0,72],
"token-stack_8h.htm#ga006255fff5ca38bbdfdb2989a565b178":[4,0,72,7],
"token-stack_8h.htm#ga01aefbb730a37c874113d90b1a0f9af9":[4,0,72,32],
"token-stack_8h.htm#ga022fc57c48683ed95b65884daa186bf2":[4,0,72,8],
"token-stack_8h... | "zigbee-device-host_8h.htm#ga2f364bcec311543f51855d7409e31c10":[4,0,77,1],
"zigbee-device-host_8h.htm#ga49488e5cefe8a5080dadef88b758b116":[4,0,77,2],
"zigbee-device-host_8h.htm#ga82b8033689722e322fe47584188735b3":[4,0,77,3],
"zigbee-device-host_8h.htm#gac234f5a3c0960f5deda51f70b8116282":[4,0,77,4],
"zigbee-device-host_... | "zigbee-device-common_8h.htm#gaf8e641f05f5b8359571fa677ccb8c4b3":[4,0,76,0],
"zigbee-device-common_8h_source.htm":[4,0,76],
"zigbee-device-host_8h.htm":[4,0,77], | random_line_split |
index.ts | import Chunk from '../Chunk';
import { optimizeChunks } from '../chunk-optimization';
import Graph from '../Graph';
import { createAddons } from '../utils/addons';
import { createAssetPluginHooks, finaliseAsset } from '../utils/assetHooks';
import commondir from '../utils/commondir';
import { Deprecation } from '../uti... | utOptions: InputOptions, plugin: Plugin) {
if (plugin.options) return plugin.options(inputOptions) || inputOptions;
return inputOptions;
}
function getInputOptions(rawInputOptions: GenericConfigObject): any {
if (!rawInputOptions) {
throw new Error('You must supply an options object to rollup');
}
// inputOpti... | yOptionHook(inp | identifier_name |
index.ts | import Chunk from '../Chunk';
import { optimizeChunks } from '../chunk-optimization';
import Graph from '../Graph';
import { createAddons } from '../utils/addons';
import { createAssetPluginHooks, finaliseAsset } from '../utils/assetHooks';
import commondir from '../utils/commondir';
import { Deprecation } from '../uti... | .then(addons => {
// pre-render all chunks
for (const chunk of chunks) {
if (!inputOptions.experimentalPreserveModules)
chunk.generateInternalExports(outputOptions);
if (chunk.isEntryModuleFacade)
chunk.exportMode = getExportMode(chunk, outputOptions);
}
... | random_line_split | |
index.ts | import Chunk from '../Chunk';
import { optimizeChunks } from '../chunk-optimization';
import Graph from '../Graph';
import { createAddons } from '../utils/addons';
import { createAssetPluginHooks, finaliseAsset } from '../utils/assetHooks';
import commondir from '../utils/commondir';
import { Deprecation } from '../uti... | function checkInputOptions(options: InputOptions) {
if (options.transform || options.load || options.resolveId || options.resolveExternal) {
throw new Error(
'The `transform`, `load`, `resolveId` and `resolveExternal` options are deprecated in favour of a unified plugin API. See https://rollupjs.org/guide/en#plug... | {
const message = `The following options have been renamed — please update your config: ${deprecations
.map(option => `${option.old} -> ${option.new}`)
.join(', ')}`;
warn({
code: 'DEPRECATED_OPTIONS',
message,
deprecations
});
}
| identifier_body |
index.ts | import Chunk from '../Chunk';
import { optimizeChunks } from '../chunk-optimization';
import Graph from '../Graph';
import { createAddons } from '../utils/addons';
import { createAssetPluginHooks, finaliseAsset } from '../utils/assetHooks';
import commondir from '../utils/commondir';
import { Deprecation } from '../uti... |
function checkOutputOptions(options: OutputOptions) {
if (<string>options.format === 'es6') {
error({
message: 'The `es6` output format is deprecated – use `es` instead',
url: `https://rollupjs.org/guide/en#output-format-f-format`
});
}
if (!options.format) {
error({
message: `You must specify outp... | throw new Error(
'The `transform`, `load`, `resolveId` and `resolveExternal` options are deprecated in favour of a unified plugin API. See https://rollupjs.org/guide/en#plugins'
);
}
} | conditional_block |
model_probabilistic.py | #!/usr/bin/env python
import os
import pickle
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tf_bijs = tfp.bijectors
tf_dist = tfp.distributions
tf_mean_field = tfp.layers.default_mean_field_normal_fn
#==============================================================
class... | with self.sess.as_default():
output_scaled = []
for _ in range(self.NUM_SAMPLES):
output_scaled.append(self.sess.run(self.net_out, feed_dict = {self.x_ph: input_scaled, self.is_training: False}))
output_scaled = np.array(output_scaled)
output_raw = self.get_raw_targets(output_scaled)
output_raw_... |
input_scaled = self.get_scaled_features(input_raw)
| random_line_split |
model_probabilistic.py | #!/usr/bin/env python
import os
import pickle
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tf_bijs = tfp.bijectors
tf_dist = tfp.distributions
tf_mean_field = tfp.layers.default_mean_field_normal_fn
#==============================================================
class... |
return raw
def set_hyperparameters(self, hyperparam_dict):
for key, value in hyperparam_dict.items():
setattr(self, key, value)
def construct_graph(self):
act_funcs = {
'linear': lambda y: y,
'leaky_relu': lambda y: tf.nn.leaky_relu(y, 0.2),
'relu': lambda y: tf.nn.relu(y),
'so... | raw = targets | conditional_block |
model_probabilistic.py | #!/usr/bin/env python
import os
import pickle
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tf_bijs = tfp.bijectors
tf_dist = tfp.distributions
tf_mean_field = tfp.layers.default_mean_field_normal_fn
#==============================================================
class... |
def predict(self, input_raw):
input_scaled = self.get_scaled_features(input_raw)
with self.sess.as_default():
output_scaled = []
for _ in range(self.NUM_SAMPLES):
output_scaled.append(self.sess.run(self.net_out, feed_dict = {self.x_ph: input_scaled, self.is_training: False}))
output_scaled = np.a... | if not self.is_graph_constructed: self.construct_inference()
self.sess = tf.compat.v1.Session(graph = self.graph)
self.saver = tf.compat.v1.train.Saver()
try:
self.saver.restore(self.sess, model_path)
return True
except AttributeError:
return False | identifier_body |
model_probabilistic.py | #!/usr/bin/env python
import os
import pickle
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tf_bijs = tfp.bijectors
tf_dist = tfp.distributions
tf_mean_field = tfp.layers.default_mean_field_normal_fn
#==============================================================
class... | (self, graph, dataset_details, config, scope, batch_size, max_iter = 10**8):
self.graph = graph
self.scope = scope
self.config = config
self.batch_size = batch_size
self.dataset_details = dataset_details
self.max_iter = max_iter
self.is_graph_constructed = False
... | __init__ | identifier_name |
walk.py | import os
import platform
import shutil
from datetime import datetime
from functools import reduce
from hashlib import md5
from math import inf
from multiprocessing import cpu_count
from multiprocessing.pool import Pool
from operator import itemgetter
from pathlib import Path
from looptools import Timer
from dirutili... | (path_list):
"""Pool process file hashing."""
return pool_process(md5_tuple, path_list, 'MD5 hashing')
def remover(file_path):
"""Delete a file or directory path only if it exists."""
if os.path.isfile(file_path):
os.remove(file_path)
return True
elif os.path.isdir(file_path):
... | pool_hash | identifier_name |
walk.py | import os
import platform
import shutil
from datetime import datetime
from functools import reduce
from hashlib import md5
from math import inf
from multiprocessing import cpu_count
from multiprocessing.pool import Pool
from operator import itemgetter
from pathlib import Path
from looptools import Timer
from dirutili... |
class DirPaths:
def __init__(self,
directory,
full_paths=False,
topdown=True,
to_include=None,
to_exclude=None,
min_level=0,
max_level=inf,
filters=None,
non_e... | """Pool process file creation dates."""
return pool_process(creation_date_tuple, path_list, 'File creation dates') | identifier_body |
walk.py | import os
import platform
import shutil
from datetime import datetime
from functools import reduce
from hashlib import md5
from math import inf
from multiprocessing import cpu_count
from multiprocessing.pool import Pool
from operator import itemgetter
from pathlib import Path
from looptools import Timer
from dirutili... |
else:
self.filters = False
self.console_output = console_output
self.console_stream = console_stream
self._hash_files = hash_files
self._printer = Printer(console_output, console_stream).printer
self._printer('DIRPATHS')
# Check that parallelizatio... | self.filters = PathFilters(to_include, to_exclude, min_level, max_level, filters, non_empty_folders) | conditional_block |
walk.py | import os
import platform
import shutil
from datetime import datetime
from functools import reduce
from hashlib import md5
from math import inf
from multiprocessing import cpu_count
from multiprocessing.pool import Pool
from operator import itemgetter
from pathlib import Path
from looptools import Timer
from dirutili... | return str(self.tree_dict)
@property
def dict(self):
return self.tree_dict
def _filter(self, folders, folder_or_file):
for index in range(0, len(folders)):
filters = self.branches[index][folder_or_file]
if filters:
exclude = filters.get
... | def __iter__(self):
return iter(self.tree_dict.items())
def __str__(self): | random_line_split |
utils.py | import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import rankdata
import time
import logging
logger = logging.getLogger(__name__)
def plot_lear... | pred_proba_samples: array of predicted probability samples with shape
(n_mc_samples, n_examples, n_classes)/(n_mc_samples, n_examples)
for multiclass/binary classification. (This is the shape returned by BNN_Classifier.predict).
labels: array of one-hot encoded labels with shape (n_examples, n_clas... | Get the sampled accuracies over the entire test set from logit samples.
Args: | random_line_split |
utils.py | import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import rankdata
import time
import logging
logger = logging.getLogger(__name__)
def plot_lear... |
return roc_curve_df
def load_mnist(fashion, onehot_encode=True, flatten_x=False, crop_x=0, classes=None):
"""
Load the MNIST dataset
Args:
onehot_encode: Boolean indicating whether to one-hot encode training
and test labels (default True)
flatten_x: Boolean indicating whether to flatten the training... | for repeat_idx in range(np.amax(variable_importances["repeat_idx"].unique()+1)):
df = variable_importances.loc[
(variable_importances["method"]==method) &
(variable_importances["repeat_idx"]==repeat_idx) &
(variable_importances[... | conditional_block |
utils.py | import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import rankdata
import time
import logging
logger = logging.getLogger(__name__)
def plot_lear... | (pvals, SNPs):
"""
Compute the power for identifying causal predictors.
Args:
Ps: list of causal predictors
Output: matrix with dimension (num. predictors, 2), where columns are FPR, TPR
"""
nsnps = len(pvals)
all_snps = np.arange(0, nsnps)
pos = SNPs
negs = list(set(all_snps) - set(SNPs))
pvals_rank = ran... | compute_power | identifier_name |
utils.py | import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import rankdata
import time
import logging
logger = logging.getLogger(__name__)
def plot_lear... |
if crop_x > 0:
x_train = crop(x_train, crop_x)
x_test = crop(x_test, crop_x)
# Flatten to 2d arrays (each example 1d)
def flatten_image(X):
return X.reshape(X.shape[0], X.shape[1]*X.shape[1])
if flatten_x:
x_train = flatten_image(x_train)
x_test = flatten_image(x_test)
if onehot_encode:
y_train ... | assert crop_x < X.shape[1]/2
assert crop_x < X.shape[2]/2
return X[:,crop_size:-crop_size,crop_size:-crop_size] | identifier_body |
analysis.py | import itertools
import pandas as pd
import pingouin as pg
import numpy as np
from scipy.stats import wilcoxon
from sklearn.decomposition import PCA
from sklearn.covariance import EllipticEnvelope
def preprocess_data(users, blocks, trials):
""" Clean data.
:param users: Data from users table
:type u... |
return vectors
def get_pca_vectors_by(dataframe, by=None):
""" Get principal components for each group as vectors. Vectors can then be used to annotate graphs.
:param dataframe: Data holding 'df1' and 'df2' values as columns.
:type dataframe: pandas.DataFrame
:param by: Column to group data... | v = row[['x', 'y']].values * np.sqrt(row['var_expl']) * 3 # Scale up for better visibility.
mean = row[['meanx', 'meany']].values
mean_offset = (mean, mean + v)
vectors.append(mean_offset) | conditional_block |
analysis.py | import itertools
import pandas as pd
import pingouin as pg
import numpy as np
from scipy.stats import wilcoxon
from sklearn.decomposition import PCA
from sklearn.covariance import EllipticEnvelope
def preprocess_data(users, blocks, trials):
""" Clean data.
:param users: Data from users table
:type u... |
def posthoc_ttests(dataframe, var_='dVz'):
""" Pairwise posthoc t-tests on a variable in a mixed design. Between factor is 'condition', within factor is
'block'.
:param dataframe: Aggregated data containing Fisher-z-transformed synergy index in long format.
:type dataframe: pandas.DataFrame
:p... | " 3 x (3) Two-way split-plot ANOVA with between-factor condition and within-factor block.
:param dataframe: Aggregated data containing Fisher-z-transformed synergy index.
:type dataframe: pandas.DataFrame
:return: mixed-design ANOVA results.
:rtype: pandas.DataFrame
"""
if dataframe['condit... | identifier_body |
analysis.py | import itertools
import pandas as pd
import pingouin as pg
import numpy as np
from scipy.stats import wilcoxon
from sklearn.decomposition import PCA
from sklearn.covariance import EllipticEnvelope
def preprocess_data(users, blocks, trials):
""" Clean data.
:param users: Data from users table
:type u... | / variances[['parallel', 'orthogonal']].sum(axis='columns')
except KeyError:
synergy_indices = pd.DataFrame(columns=["dV", "dVz"])
else:
dVz = 0.5 * np.log((n/d + dV)/(n/(n-d) - dV))
synergy_indices = pd.DataFrame({"dV": dV, "dVz": dVz})
return synergy_indices
def get_... | try:
dV = n * (variances['parallel']/(n-d) - variances['orthogonal']/d) \ | random_line_split |
analysis.py | import itertools
import pandas as pd
import pingouin as pg
import numpy as np
from scipy.stats import wilcoxon
from sklearn.decomposition import PCA
from sklearn.covariance import EllipticEnvelope
def preprocess_data(users, blocks, trials):
""" Clean data.
:param users: Data from users table
:type u... | (dataframe):
""" Get principal components as vectors. Vectors can then be used to annotate graphs.
:param dataframe: Tabular PCA data.
:type dataframe: pandas.DataFrame
:return: Principal components as vector pairs in input space with mean as origin first and offset second.
:rtype: list
"""... | get_pca_vectors | identifier_name |
system_information.rs | use crate::{SMBiosStruct, UndefinedStruct};
use serde::{ser::SerializeStruct, Serialize, Serializer};
use core::{
array::TryFromSliceError,
convert::{TryFrom, TryInto},
fmt,
ops::Deref,
any
};
#[cfg(feature = "no_std")]
use alloc::{string::String, format};
/// # System Information (Type 1)
///
/// ... | fn parts(&self) -> &'a UndefinedStruct {
self.parts
}
}
impl<'a> SMBiosSystemInformation<'a> {
/// Manufacturer
pub fn manufacturer(&self) -> Option<String> {
self.parts.get_field_string(0x04)
}
/// Product name
pub fn product_name(&self) -> Option<String> {
self.pa... | Self { parts }
}
| identifier_body |
system_information.rs | use crate::{SMBiosStruct, UndefinedStruct};
use serde::{ser::SerializeStruct, Serialize, Serializer};
use core::{
array::TryFromSliceError,
convert::{TryFrom, TryInto},
fmt,
ops::Deref,
any
};
#[cfg(feature = "no_std")]
use alloc::{string::String, format};
/// # System Information (Type 1)
///
/// ... | /// Raw value
///
/// _raw_ is most useful when _value_ is None.
/// This is most likely to occur when the standard was updated but
/// this library code has not been updated to match the current
/// standard.
pub raw: u8,
/// The contained [SystemWakeUpType] value
pub value: SystemW... |
/// # System - Wake-up Type Data
pub struct SystemWakeUpTypeData { | random_line_split |
system_information.rs | use crate::{SMBiosStruct, UndefinedStruct};
use serde::{ser::SerializeStruct, Serialize, Serializer};
use core::{
array::TryFromSliceError,
convert::{TryFrom, TryInto},
fmt,
ops::Deref,
any
};
#[cfg(feature = "no_std")]
use alloc::{string::String, format};
/// # System Information (Type 1)
///
/// ... | >(&self, serializer: S) -> Result<S::Ok, S::Error>
where
S: Serializer,
{
serializer.serialize_str(format!("{}", self).as_str())
}
}
/// # System - Wake-up Type Data
pub struct SystemWakeUpTypeData {
/// Raw value
///
/// _raw_ is most useful when _value_ is None.
/// This i... | rialize<S | identifier_name |
loader.rs | extern crate xml;
use std;
use std::fs::File;
use std::path::{Path, PathBuf};
use std::ffi::OsStr;
use std::io::{BufReader, Cursor, Read};
use std::sync::mpsc::Sender;
use std::collections::HashMap;
use zip::read::{ZipArchive, ZipFile};
use loader::xml::reader::{EventReader, XmlEvent};
use rodio::source::Source;
... | println!("Unknown song field {}", name.local_name);
xml_skip_tag(&mut reader).unwrap();
State::Song(None)
}
},
XmlEvent::EndElement { .. } => {
if song_rhythm.is_empty() {
// TODO: be graceful
panic!("Empty rhythm");
}
let song = SongData {
name: song_nam... | "buildup" => State::Song(Some(SongField::Buildup)),
"buildupRhythm" => State::Song(Some(SongField::BuildupRhythm)),
_ => { | random_line_split |
loader.rs | extern crate xml;
use std;
use std::fs::File;
use std::path::{Path, PathBuf};
use std::ffi::OsStr;
use std::io::{BufReader, Cursor, Read};
use std::sync::mpsc::Sender;
use std::collections::HashMap;
use zip::read::{ZipArchive, ZipFile};
use loader::xml::reader::{EventReader, XmlEvent};
use rodio::source::Source;
... | {
pub info: PackInfo,
pub images: Vec<ImageLoader>,
pub songs: Vec<Song>,
}
pub struct ImageLoader {
//data: SurfaceContext
pub name: String,
pub fullname: Option<String>,
pub data: Surface,
pub source: Option<String>,
pub source_other: Option<String>,
}
pub struct SongData {
pub name: String,
pub title: ... | ResPack | identifier_name |
lib.rs | ///////////////////////////////////////////////////////////////////////////////
//
// Copyright 2018-2021 Robonomics Network <research@robonomics.network>
//
// 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 cop... | (
uri: &str,
technics: &TechnicsFor<Runtime>,
economics: &EconomicsFor<Runtime>,
) -> (AccountId32, MultiSignature) {
let pair = sr25519::Pair::from_string(uri, None).unwrap();
let sender = <MultiSignature as Verify>::Signer::from(pair.public()).into_account();
let si... | get_params_proof | identifier_name |
lib.rs | ///////////////////////////////////////////////////////////////////////////////
//
// Copyright 2018-2021 Robonomics Network <research@robonomics.network>
//
// 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 cop... |
/// How to report of agreement execution.
type Report: dispatch::Parameter + Report<Self::Index, Self::AccountId>;
/// The overarching event type.
type Event: From<Event<Self>> + IsType<<Self as frame_system::Config>::Event>;
}
pub type TechnicsFor<T> =
<<T as Config>:... | /// How to make and process agreement between two parties.
type Agreement: dispatch::Parameter + Processing + Agreement<Self::AccountId>; | random_line_split |
vrf.go | //
// Copyright 2017-2019 Nippon Telegraph and Telephone Corporation.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless requi... |
// Forward inbound packets to L3 Tunnel
fts := createFiveTuples(ra, t.local, t.IPProto(), PortRange{})
for i, ft := range fts {
if err := v.router.connect(vif.Inbound(), Match5Tuple, ft); err != nil {
vif.disconnect(MatchIPv4Dst, &ra[0])
for _, addedFt := range fts[0:i] {
v.router.disconnect(Match5Tupl... | {
return fmt.Errorf("Adding a rule to %v failed for L3 tunnel: %v", vif, err)
} | conditional_block |
vrf.go | //
// Copyright 2017-2019 Nippon Telegraph and Telephone Corporation.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless requi... | (name string) *VRF {
vrfMgr.mutex.Lock()
defer vrfMgr.mutex.Unlock()
return vrfMgr.byName[name]
}
// GetVRFByIndex returns a VRF with the given index.
func GetVRFByIndex(index VRFIndex) *VRF {
vrfMgr.mutex.Lock()
defer vrfMgr.mutex.Unlock()
return vrfMgr.byIndex[int(index)]
}
| GetVRFByName | identifier_name |
vrf.go | //
// Copyright 2017-2019 Nippon Telegraph and Telephone Corporation.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless requi... |
// should be called with lock held
func (vm *vrfManager) releaseIndex(vrf *VRF) {
vm.byIndex[int(vrf.index)] = nil
}
// NewVRF creates a VRF instance.
func NewVRF(name string) (*VRF, error) {
if !vrfMgr.re.MatchString(name) {
return nil, fmt.Errorf("Invalid VRF name: '%v'", name)
}
vrfMgr.mutex.Lock()
defer ... | {
// try from the nextIndex to the end
if vm.findSlot(vrf, vm.nextIndex, len(vm.byIndex)) {
return true
}
// try from the head to the nextIndex
return vm.findSlot(vrf, 0, vm.nextIndex)
} | identifier_body |
vrf.go | //
// Copyright 2017-2019 Nippon Telegraph and Telephone Corporation.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless requi... |
vif.disconnect(MatchIPv4Dst, &(t.RemoteAddresses()[0]))
}
func createVxLANs(remotes []net.IP, local net.IP, dstPort uint16, vni uint32) []*VxLAN {
vxlans := make([]*VxLAN, len(remotes))
for i, remote := range remotes {
vxlan := &VxLAN{
Src: remote,
Dst: local,
DstPort: dstPort,
VNI: vni,
... | if t.Security() == SecurityIPSec {
for _, nat := range createFiveTuples(t.remotes, t.local, IPP_UDP, PortRange{Start: 4500}) {
v.router.disconnect(Match5Tuple, nat)
}
} | random_line_split |
brush.rs | // Copyright © 2020 Cormac O'Brien.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish,... | let texture =
state.create_texture(None, lightmap.width(), lightmap.height(), &lightmap_data);
let id = self.lightmaps.len();
self.lightmaps.push(texture);
//self.lightmap_views
//.push(self.lightmaps[id].create_view(&Default::default()));
... | lightmap: Cow::Borrowed(lightmap.data()),
});
| random_line_split |
brush.rs | // Copyright © 2020 Cormac O'Brien.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish,... |
pub fn pipeline(&self) -> &wgpu::RenderPipeline {
&self.pipeline
}
pub fn bind_group_layouts(&self) -> &[wgpu::BindGroupLayout] {
&self.bind_group_layouts
}
pub fn bind_group_layout(&self, id: BindGroupLayoutId) -> &wgpu::BindGroupLayout {
assert!(id as usize >= BindGroupL... |
let layout_refs: Vec<_> = world_bind_group_layouts
.iter()
.chain(self.bind_group_layouts.iter())
.collect();
self.pipeline = BrushPipeline::recreate(device, compiler, &layout_refs, sample_count);
}
| identifier_body |
brush.rs | // Copyright © 2020 Cormac O'Brien.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish,... |
BspTextureKind::Static(bsp_tex) => {
BrushTexture::Static(self.create_brush_texture_frame(
state,
bsp_tex.mipmap(BspTextureMipmap::Full),
tex.width(),
tex.height(),
tex.name(),
... |
let primary_frames: Vec<_> = primary
.iter()
.map(|f| {
self.create_brush_texture_frame(
state,
f.mipmap(BspTextureMipmap::Full),
width,
... | conditional_block |
brush.rs | // Copyright © 2020 Cormac O'Brien.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish,... | &self) -> &[wgpu::BindGroupLayout] {
&self.bind_group_layouts
}
pub fn bind_group_layout(&self, id: BindGroupLayoutId) -> &wgpu::BindGroupLayout {
assert!(id as usize >= BindGroupLayoutId::PerTexture as usize);
&self.bind_group_layouts[id as usize - BindGroupLayoutId::PerTexture as usiz... | ind_group_layouts( | identifier_name |
glsl3.rs | use std::mem::size_of;
use std::ptr;
use crossfont::RasterizedGlyph;
use log::info;
use alacritty_terminal::term::cell::Flags;
use crate::display::content::RenderableCell;
use crate::display::SizeInfo;
use crate::gl;
use crate::gl::types::*;
use crate::renderer::shader::{ShaderProgram, ShaderVersion};
use crate::ren... |
unsafe {
self.program.set_rendering_pass(RenderingPass::Background);
gl::DrawElementsInstanced(
gl::TRIANGLES,
6,
gl::UNSIGNED_INT,
ptr::null(),
self.batch.len() as GLsizei,
);
self.... | {
unsafe {
gl::BindTexture(gl::TEXTURE_2D, self.batch.tex());
}
*self.active_tex = self.batch.tex();
} | conditional_block |
glsl3.rs | use std::mem::size_of;
use std::ptr;
use crossfont::RasterizedGlyph;
use log::info;
use alacritty_terminal::term::cell::Flags;
use crate::display::content::RenderableCell;
use crate::display::SizeInfo;
use crate::gl;
use crate::gl::types::*;
use crate::renderer::shader::{ShaderProgram, ShaderVersion};
use crate::ren... |
impl TextShaderProgram {
pub fn new(shader_version: ShaderVersion) -> Result<TextShaderProgram, Error> {
let program = ShaderProgram::new(shader_version, None, TEXT_SHADER_V, TEXT_SHADER_F)?;
Ok(Self {
u_projection: program.get_uniform_location(cstr!("projection"))?,
u_cell_... | /// Rendering is split into two passes; one for backgrounds, and one for text.
u_rendering_pass: GLint,
} | random_line_split |
glsl3.rs | use std::mem::size_of;
use std::ptr;
use crossfont::RasterizedGlyph;
use log::info;
use alacritty_terminal::term::cell::Flags;
use crate::display::content::RenderableCell;
use crate::display::SizeInfo;
use crate::gl;
use crate::gl::types::*;
use crate::renderer::shader::{ShaderProgram, ShaderVersion};
use crate::ren... | <'a> {
active_tex: &'a mut GLuint,
batch: &'a mut Batch,
atlas: &'a mut Vec<Atlas>,
current_atlas: &'a mut usize,
program: &'a mut TextShaderProgram,
}
impl<'a> TextRenderApi<Batch> for RenderApi<'a> {
fn batch(&mut self) -> &mut Batch {
self.batch
}
fn render_batch(&mut self) ... | RenderApi | identifier_name |
glsl3.rs | use std::mem::size_of;
use std::ptr;
use crossfont::RasterizedGlyph;
use log::info;
use alacritty_terminal::term::cell::Flags;
use crate::display::content::RenderableCell;
use crate::display::SizeInfo;
use crate::gl;
use crate::gl::types::*;
use crate::renderer::shader::{ShaderProgram, ShaderVersion};
use crate::ren... |
#[inline]
pub fn capacity(&self) -> usize {
BATCH_MAX
}
#[inline]
pub fn size(&self) -> usize {
self.len() * size_of::<InstanceData>()
}
pub fn clear(&mut self) {
self.tex = 0;
self.instances.clear();
}
}
/// Text drawing program.
///
/// Uniforms are... | {
self.instances.len()
} | identifier_body |
service.rs | use crate::{
common::client::{ClientId, Credentials, Token},
coordinator,
};
use bytes::Bytes;
use derive_more::From;
use futures::{ready, stream::Stream};
use std::{
collections::HashMap,
error::Error,
future::Future,
pin::Pin,
task::{Context, Poll},
};
use tarpc::context::current as rpc_co... | /// A future that orchestrates the entire aggregator service.
// TODO: maybe add a HashSet or HashMap of clients who already
// uploaded their weights to prevent a client from uploading weights
// multiple times. Or we could just remove that ID from the
// `allowed_ids` map.
// TODO: maybe add a HashSet for clients th... | random_line_split | |
service.rs | use crate::{
common::client::{ClientId, Credentials, Token},
coordinator,
};
use bytes::Bytes;
use derive_more::From;
use futures::{ready, stream::Stream};
use std::{
collections::HashMap,
error::Error,
future::Future,
pin::Pin,
task::{Context, Poll},
};
use tarpc::context::current as rpc_co... |
}
impl<A> ServiceHandle<A>
where
A: Aggregator + 'static,
{
pub fn new() -> (Self, ServiceRequests<A>) {
let (upload_tx, upload_rx) = unbounded_channel::<UploadRequest>();
let (download_tx, download_rx) = unbounded_channel::<DownloadRequest>();
let (aggregate_tx, aggregate_rx) = unboun... | {
Self {
upload: self.upload.clone(),
download: self.download.clone(),
aggregate: self.aggregate.clone(),
select: self.select.clone(),
}
} | identifier_body |
service.rs | use crate::{
common::client::{ClientId, Credentials, Token},
coordinator,
};
use bytes::Bytes;
use derive_more::From;
use futures::{ready, stream::Stream};
use std::{
collections::HashMap,
error::Error,
future::Future,
pin::Pin,
task::{Context, Poll},
};
use tarpc::context::current as rpc_co... | (&mut self, request: SelectRequest<A>) {
info!("handling select request");
let SelectRequest {
credentials,
response_tx,
} = request;
let (id, token) = credentials.into_parts();
self.allowed_ids.insert(id, token);
if response_tx.send(Ok(())).is_err... | handle_select_request | identifier_name |
service.rs | use crate::{
common::client::{ClientId, Credentials, Token},
coordinator,
};
use bytes::Bytes;
use derive_more::From;
use futures::{ready, stream::Stream};
use std::{
collections::HashMap,
error::Error,
future::Future,
pin::Pin,
task::{Context, Poll},
};
use tarpc::context::current as rpc_co... |
pin.poll_aggregation(cx);
Poll::Pending
}
}
pub struct ServiceRequests<A>(Pin<Box<dyn Stream<Item = Request<A>> + Send>>)
where
A: Aggregator;
impl<A> Stream for ServiceRequests<A>
where
A: Aggregator,
{
type Item = Request<A>;
fn poll_next(mut self: Pin<&mut Self>, cx: &mut Co... | {
return Poll::Ready(());
} | conditional_block |
utils.py | from mpi4py import MPI
import matplotlib
from tmm import coh_tmm
import pandas as pd
import os
from numpy import pi
from scipy.interpolate import interp1d
from joblib import Parallel, delayed
import numpy as np
import glob
import matplotlib.pyplot as plt
import pickle as pkl
import seaborn as sns
from scipy.optimize im... |
class TMM_sim():
def __init__(self, mats=['Ge'], wavelength=np.arange(0.38, 0.805, 0.01), substrate='Cr', substrate_thick=500):
'''
This class returns the spectrum given the designed structures.
'''
self.mats = mats
# include substrate
self.all_mats = mats + [substr... | for i in range(len(progress)):
print(progress[i], 0)
progress[i] = ['|'.join([l + ' ' + str(d) + ' nm' for l, d in zip(progress[i][0], progress[i][1])]), progress[i][2]]
return progress | identifier_body |
utils.py | from mpi4py import MPI
import matplotlib
from tmm import coh_tmm
import pandas as pd
import os
from numpy import pi
from scipy.interpolate import interp1d
from joblib import Parallel, delayed
import numpy as np
import glob
import matplotlib.pyplot as plt
import pickle as pkl
import seaborn as sns
from scipy.optimize im... |
res = Parallel(n_jobs=num_workers)(delayed(spectrum)(args)
for args in
zip(names_list, thickness_list))
res = np.array(res)
Rs, Ts, As = res[:, 0, :], res[:, 1, :], res[:, 2, :]
return Rs, Ts, As
def merge_layers(categorie... | random_line_split | |
utils.py | from mpi4py import MPI
import matplotlib
from tmm import coh_tmm
import pandas as pd
import os
from numpy import pi
from scipy.interpolate import interp1d
from joblib import Parallel, delayed
import numpy as np
import glob
import matplotlib.pyplot as plt
import pickle as pkl
import seaborn as sns
from scipy.optimize im... |
return progress
class TMM_sim():
def __init__(self, mats=['Ge'], wavelength=np.arange(0.38, 0.805, 0.01), substrate='Cr', substrate_thick=500):
'''
This class returns the spectrum given the designed structures.
'''
self.mats = mats
# include substrate
self.all_... | print(progress[i], 0)
progress[i] = ['|'.join([l + ' ' + str(d) + ' nm' for l, d in zip(progress[i][0], progress[i][1])]), progress[i][2]] | conditional_block |
utils.py | from mpi4py import MPI
import matplotlib
from tmm import coh_tmm
import pandas as pd
import os
from numpy import pi
from scipy.interpolate import interp1d
from joblib import Parallel, delayed
import numpy as np
import glob
import matplotlib.pyplot as plt
import pickle as pkl
import seaborn as sns
from scipy.optimize im... | (categories, thicknesses):
'''
Merges consecutive layers with the same material types.
'''
thicknesses = thicknesses[1:-1]
c_output = [categories[0]]
t_output = [thicknesses[0]]
for i, (c, d) in enumerate(zip(categories[1:], thicknesses[1:])):
if c == c_output[-1]:
t_ou... | merge_layers | identifier_name |
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