Source code for apache_beam.utils.profiler
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
#
"""A profiler context manager based on cProfile.Profile objects."""
import cProfile
import logging
import os
import pstats
import StringIO
import tempfile
import time
import warnings
from threading import Timer
[docs]class Profile(object):
"""cProfile wrapper context for saving and logging profiler results."""
SORTBY = 'cumulative'
def __init__(self, profile_id, profile_location=None, log_results=False,
file_copy_fn=None):
self.stats = None
self.profile_id = str(profile_id)
self.profile_location = profile_location
self.log_results = log_results
self.file_copy_fn = file_copy_fn
def __enter__(self):
logging.info('Start profiling: %s', self.profile_id)
self.profile = cProfile.Profile()
self.profile.enable()
return self
def __exit__(self, *args):
self.profile.disable()
logging.info('Stop profiling: %s', self.profile_id)
if self.profile_location and self.file_copy_fn:
dump_location = os.path.join(
self.profile_location, 'profile',
('%s-%s' % (time.strftime('%Y-%m-%d_%H_%M_%S'), self.profile_id)))
fd, filename = tempfile.mkstemp()
self.profile.dump_stats(filename)
logging.info('Copying profiler data to: [%s]', dump_location)
self.file_copy_fn(filename, dump_location) # pylint: disable=protected-access
os.close(fd)
os.remove(filename)
if self.log_results:
s = StringIO.StringIO()
self.stats = pstats.Stats(
self.profile, stream=s).sort_stats(Profile.SORTBY)
self.stats.print_stats()
logging.info('Profiler data: [%s]', s.getvalue())
[docs]class MemoryReporter(object):
"""A memory reporter that reports the memory usage and heap profile.
Usage:::
mr = MemoryReporter(interval_second=30.0)
mr.start()
while ...
<do something>
# this will report continuously with 30 seconds between reports.
mr.stop()
NOTE: A reporter with start() should always stop(), or the parent process can
never finish.
Or simply the following which does star() and stop():
with MemoryReporter(interval_second=100):
while ...
<do some thing>
Also it could report on demand without continuous reporting.::
mr = MemoryReporter() # default interval 60s but not started.
<do something>
mr.report_once()
"""
def __init__(self, interval_second=60.0):
# guppy might not have installed. http://pypi.python.org/pypi/guppy/0.1.10
# The reporter can be set up only when guppy is installed (and guppy cannot
# be added to the required packages in setup.py, since it's not available
# in all platforms).
try:
from guppy import hpy # pylint: disable=import-error
self._hpy = hpy
self._interval_second = interval_second
self._timer = None
except ImportError:
warnings.warn('guppy is not installed; MemoryReporter not available.')
self._hpy = None
self._enabled = False
def __enter__(self):
self.start()
return self
def __exit__(self, *args):
self.stop()
[docs] def start(self):
if self._enabled or not self._hpy:
return
self._enabled = True
def report_with_interval():
if not self._enabled:
return
self.report_once()
self._timer = Timer(self._interval_second, report_with_interval)
self._timer.start()
self._timer = Timer(self._interval_second, report_with_interval)
self._timer.start()
[docs] def stop(self):
if not self._enabled:
return
self._timer.cancel()
self._enabled = False
[docs] def report_once(self):
if not self._hpy:
return
report_start_time = time.time()
heap_profile = self._hpy().heap()
logging.info('*** MemoryReport Heap:\n %s\n MemoryReport took %.1f seconds',
heap_profile, time.time() - report_start_time)


There are many kinds of file systems, varying widely both in their superficial syntactic details, and in their underlying power and structure. The facilities provided by Common Lisp for referring to and manipulating files has been chosen to be compatible with many kinds of file systems, while at the same time minimizing the program-visible differences between kinds of file systems.
Since file systems vary in their conventions for naming files, there are two distinct ways to represent filenames: as namestrings and as pathnames.
| • Namestrings as Filenames: | ||
| • Pathnames as Filenames: | ||
| • Parsing Namestrings Into Pathnames: |


Keyboard Controls
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Geological Time
0 Million years ago


Looking for a place to drink?
Find a brewery
or plan a pub crawl.
Search your local area for the nearest brew pub or find the new brewery that has created a lot of buzz.
Map out your night to find all the breweries in one area.
Tour a brewery while on vacation. Or plan your vacation around your brewrey tours.
Bottoms Up.
Looking for something specific?
Be sure to check the boxes to make your night
perfect.
Click on a brewery and map out your night.


Zhouxing Liao, Shaffan Mustafa, Madelyn Onweller, Xiaoran Wang Geog 5201 Group Project Instructor: Morteza Karimzadeh


--- title: Binary Segmentation keywords: fastai sidebar: home_sidebar nb_path: "nbs/course2020/vision/07_Binary_Segmentation.ipynb" ---
file = "https://drive.google.com/uc?id=18xM3jU2dSp1DiDqEM6PVXattNMZvsX4z"
!gdown {file}
We'll unzip the data
from zipfile import ZipFile
with ZipFile('Portrait.zip', 'r') as zip_ref:
zip_ref.extractall('')
from fastai.vision.all import *
And grab our ground truth labels and files
path = Path('')
lbl_names = get_image_files(path/'GT_png')
fnames = get_image_files(path/'images_data_crop')
img_fn = fnames[10]; img_fn
lbl_names[10]
fn = '00013.jpg'
im = PILImage.create(f'images_data_crop/{fn}')
msk = PILMask.create(f'GT_png/00013_mask.png')
Now, our mask isn't set up how fastai
expects, in which the mask points are not all in a row. We need to change this:
len(np.unique(msk))
np.unique(msk)
We'll do this through an n_codes
function. What this will do is run through our masks and build a set based on the unique values present in our masks. From there we will build a dictionary that will replace our points once we load in the image
def n_codes(fnames, is_partial=True):
"Gather the codes from a list of `fnames`"
vals = set()
if is_partial:
random.shuffle(fnames)
fnames = fnames[:10]
for fname in fnames:
msk = np.array(PILMask.create(fname))
for val in np.unique(msk):
if val not in vals:
vals.add(val)
vals = list(vals)
p2c = dict()
for i,val in enumerate(vals):
p2c[i] = vals[i]
return p2c
p2c = n_codes(lbl_names)
So p2c
in this case is anywhere that is 255 in our mask should be replaced to one
p2c
So now let's build a get_msk
function that will modify our mask we get based on this dictionary and override those values
def get_msk(fn, pix2class):
"Grab a mask from a `filename` and adjust the pixels based on `pix2class`"
fn = path/'GT_png'/f'{fn.stem}_mask.png'
msk = np.array(PILMask.create(fn))
mx = np.max(msk)
for i, val in enumerate(p2c):
msk[msk==p2c[i]] = val
return PILMask.create(msk)
codes = ['Background', 'Face']
Now we can build a get_y
and a DataBlock
!
get_y = lambda o: get_msk(o, p2c)
binary = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),
get_items=get_image_files,
splitter=RandomSplitter(),
get_y=get_y,
item_tfms=Resize(224),
batch_tfms=[Normalize.from_stats(*imagenet_stats)])
dls = binary.dataloaders(path/'images_data_crop', bs=8)
We can look at how our masks look by adjusting the colormap and the vmin and max
dls.show_batch(cmap='Blues', vmin=0, vmax=1)
And now we can train!
learn = unet_learner(dls, resnet34)
learn.fit(1)
And we're good :)
learn.show_results(cmap='Blues', vmin=0, vmax=1)
If we want to examine it further we can do:
preds = learn.get_preds()
preds[0][0].shape
p = preds[0][0]
plt.imshow(p[1])
plt.imshow(p[0])


{% extends "User/layout.html" %} {% block User_content %}
| Campaign ID | Campaign Name | Creation date | Voting system | Formula system |
|---|---|---|---|---|
| {{campaign.id}} | {{campaign.name}} | {{campaign.create_date}} | {{campaign.voting_system}} | {{campaign.formula.id}} - {{campaign.formula.name}} |


Strategy
Because a great website is more than just pretty pictures and fancy words. We work with you to determine how to create a successful website that reaches your target users and accomplishes your goals.
UX/UI Design
A websites nees structure and a flow. We create the information architecture to present and organize your content effectively. Then it is on to burn the midnight oil pushing pixels to ensure the design best represents your brand.
Web Development
We develop cutting edge web sites and web applications using the latest standards. We also incorporate emerging techonogies to ensure great performance now and in the future. We also can develop for mobile devices.
CMS Integration
If you would like to update your site yourself, we can do that too! If you already have a CMS that you like, great! If not we can put you site into a content management system so you can add/edit content with ease.


| DiscreteWavelets Toolbox |
Plot a one-dimensional wavelet transfomration
WaveletVectorPlot(wt,its)
WaveletVectorPlot(wt,its,options)
WaveletVectorPlot(wt,its) takes a vector wt, presumably obtained by computing the wavelet transformation of some vector, and a number of iterations its, and plots wt with each portion of the transformation separated by grid lines and illustrated with a different color.
WaveletVectorPlot(wt,its,options) allows the user to set several parameters. See Optional Arguments for more details.
There are eight optional arguments that can be set for WaveletVectorPlot. The order of the parameters does not matter in the function call, but you must follow the standard convention 'Option', 'Value' when invoking the parameters.
'Region' can be set to either 'All' (default), 'LowPass', or 'HighPass'. In the last case, the routine looks for the value of 'Iteration' (see below) and then plots the selected portion of wt.
'Iteration' can be set to 'All' (default) or any positive integer 1,2,...,its. This value is used if 'Region' has been set to 'HighPass' to plot that particular region. If 'Region' is set to 'All' and 'Iteration' is set to i, then the routine plots the lowpass portion and highpass portions k=i,...,its.
'DivideLines' can be set to 'True' (default) or 'False' in order to show or hide, respectively, gridlines dividing the different portions of the transformation.
'DivideLinesColor' is a length three vector whose entries are in the interval [0,1] that serves as the color of the gridlines. The default color of 'DivideLinesColor' is [.3 .3 .3] (dark gray).
'DivideLinesThickness' can be set to a nonnegative integer (default is 1) that tells the routine how thick to make the gridlines that separate the different portions of the transformation.
The 'UseColors' parameter is set to either 'True' (default) to show the different portions of the transformation with different color. Setting the parameter to 'False' to use the default color of blue for the entire plot.
If 'UseColors' has been set to 'True', the function looks for values in 'ColorList'. This input must be an n x 3 matrix whose entries are numbers in the interval [0,1]. If n < its+1, then the n x 3 matrix is augmented with enough rows so that the number of rows is >=its+1. In this way, the routine has enough colors to plot each portion of the transformation. A default color list (6 colors) is provided with the routine.
A plot title can be added using the 'Title' option. For example, WaveletVectorPlot(wt,its,'Title','Here is a Picture') adds a title to the plot.
First, we create a vector and compute four iterations of its wavelet transformation. Different examples will illustrate the different parameters that can be set with WaveletVectorPlot.
snd=AudioNames(); %Get all audio files included with the toolbox v=wavread(snd{2}); %Read an audio file v=ChopVector(v,4); %Make sure the length is divisible by 16 plot(v); %Plot the vector wt=WT1D(v,Daub(6),4); %Compute four iterations of the transformation
WaveletVectorPlot(wt,4,'Title','Four Iterations Using Daub(6)');
Plot only iterations 3 and 4:
WaveletVectorPlot(wt,4,'Region','All','Iteration',3);
Plot the highpass portion of iteration 1
WaveletVectorPlot(wt,4,'Region','HighPass','Iteration',1);
Change the divide lines color and thickness
WaveletVectorPlot(wt,4,'DivideLinesColor',[.5 .2 1],'DivideLinesThickness',3);
Provide your own colors.
WaveletVectorPlot(wt,4,'ColorList',[1 0 0; 0 1 0; 0 0 1; 1 0 1; 0 1 1; 1 1 0]);
Plot the lowpass portion in green and provide a title.
WaveletVectorPlot(wt,4,'ColorList',[0 1 0],'Region','LowPass','Title','The Lowpass Portion');
BWT1D, HWT1D, LWT1D, WT1D, WaveletDensityPlot
© 2007-2008 Patrick Van Fleet


Authentication
This app supports two types of authentication:
- Using the Personal Access Token
- OAuth authentication
Using the Personal Access Token
To use this method navigate to developer site in a browser and log in with your credentials. Copy the token and paste it into the Personal Access Token field in asset configuration. Click save and run test connectivity.
OAuth authentication
To enable OAuth and to retrieve Client ID and Client Secret, we need to follow the steps which are mentioned in the developer document.
-
Steps
- Log in to the Cisco Webex app page.
- Click "Create a New App" then "Create an Integration" to start the wizard.
- You'll need to provide some basic information like your integration's name, description, and logo.
- After successful registration, you'll be taken to a different screen containing your integration's newly created Client ID and Client Secret.
- The Client Secret will only be shown once so please copy and keep it safe!
- Give the required scope which defines the level of access that your integration requires.
While creating the asset for this authentication method, provide the Client ID and Client Secret generated during previous steps in client_id and client_secret field of asset configuration and click on Save.
A new field called POST incoming for Cisco Webex to this location will appear in the Asset Settings tab. Take the URL found in this field and place it in the Redirect URI(s) field of your registered app. To this URL, add /result at the end. After doing so the URL should look something like:
https://<phantom_host>/rest/handler/ciscowebex_34624d1a-f0ae-47d6-a731-8499d5617cf7/<asset_name>/result
Method to run Test Connectivity
-
Using the Personal Access Token
- For the Personal Access Token method of authentication, you just need to click the TEST CONNECTIVITY button.
-
OAuth authentication
- After setting up the asset, click the 'TEST CONNECTIVITY' button. A pop-up window will be displayed with appropriate test connectivity logs. It will also display a specific URL on that pop-up window.
- Open this URL in a separate browser tab. This new tab will redirect to the Cisco Webex login page to complete the login process to grant the permissions to the app.
- Log in using the same Cisco Webex account that was used to configure the integration and the application on the Cisco Webex page.
- This will display a successful message of 'Code received. Please close this window, the action will continue to get new token.' on the browser tab.
- Finally, close the browser tab and come back to the 'Test Connectivity' browser tab. The pop-up window should display a 'Test Connectivity Passed' message.
Explanation of Test Connectivity Workflow for Interactive auth
- This app uses (version 1.0) OAUTH 2.0 authorization code workflow APIs for generating the [access_token] and [refresh_token] pairs if the authentication method is interactive else [Personal access token] if the authentication method is non-interactive is used for all the API calls to the Cisco Webex instance.
- An interactive authentication mechanism is a user-context-based workflow and the permissions of the user also matter along with the API permissions set to define the scope and permissions of the generated tokens. For more information visit the link mentioned here for the OAUTH 2.0 AUTH CODE.
- The step-by-step process for the entire authentication mechanism is explained below.
- The first step is to get an application created on the Cisco Webex developer portal. Generate the [client_secret] for the configured application. The detailed steps have been mentioned in the earlier section.
- Run the test connectivity action for the Interactive method.
- Configure the Cisco Webex app's asset with appropriate values for [client_id] and [client_secret] configuration parameters.
- Internally, the connectivity creates a URL for hitting the /v1/authorize endpoint for the generation of the authorization code and displays it on the connectivity pop-up window. The user is requested to hit this URL in a browser new tab and complete the authorization request successfully resulting in the generation of an authorization code.
- The authorization code generated in the above step is used by the connectivity to make the next API call to generate the [access_token] and [refresh_token] pair. The generated authorization code, [access_token], and [refresh_token] are stored in the state file of the app on the Phantom server.
- The authorization code can be used only once to generate the pair of [access_token] and [refresh_token]. If the [access_token] expires, then the [refresh_token] is used internally automatically by the application to re-generate the [access_token] by making the corresponding API call. This entire autonomous workflow will seamlessly work until the [refresh_token] does not get expired. Once the [refresh_token] expires, the user will have to run the test connectivity action once again to generate the authorization code followed by the generation of an entirely fresh pair of [access_token] and [refresh_token].
- The successful run of the Test Connectivity ensures that a valid pair of [access_token] and [refresh_token] has been generated and stored in the app's state file. These tokens will be used in all the actions' execution flow to authorize their API calls to the Cisco Webex instance.
State file permissions
Please check the permissions for the state file as mentioned below.
State file path
- For Non-NRI instance: /opt/phantom/local_data/app_states/<appid>/<asset_id>_state.json
- For NRI instance: /<PHANTOM_HOME_DIRECTORY>/local_data/app_states/<appid>/<asset_id>_state.json
State file permissions
- File rights: rw-rw-r-- (664) (The phantom user should have read and write access for the state file)
- File owner: Appropriate phantom user
Notes:
- All the asset configuration parameters are optional, but you need to provide the configuration parameters based on the authentication method you want to use. Otherwise, the actions will return an error.
- The Personal Access Token is valid for only 12 hours.
- If you have provided all the configuration parameters, the priority of the authentication method will be Personal Access Token
The app is configured and ready to be used now.
LDAP Ports Requirements (Based on Standard Guidelines of IANA ORG)
- Webex(service) TCP(transport protocol)
- Webex(service) UDP(transport protocol)


A non-empty array A consisting of N integers is given.
The array represents one round of a game played by N people. The players stand in a circle, such that:
Each player chooses an integer between 0 and M in secret from the other players. Each element A[K] represents the number chosen by player K. Once each player has chosen a number, all the numbers are revealed simultaneously. The game is then scored as follows for each player K:
For example, given:
A[0] = 3 A[1] = 3 A[2] = 1 A[3] = 2Players 0 and 1 would get 0 points, because they both picked 3. Player 2 would 1 point, because they chose a number that's less than the one chosen by player 3. Player 3 would get 2 points, because they chose a number that's both less than the one chosen by player 0, and greater than the one chosen by player 2.
Write a function:
def solution(A)
that, given a non-empty array A of N integers, returns the biggest score achieved by any player in that round of the game.
For example, given:
A[0] = 3 A[1] = 3 A[2] = 1 A[3] = 2the function should return 2, since player 3 achieved a score of 2, as explained above.
Assume that:
Complexity:


---
name: Line styles
layout: layout-container
description: Line styles provide guidance on how to bound, divide, and highlight content and maintain visual hierarchy.
---
Line styles
SKY UX uses various line styles to bound, divide, and highlight content. The lines are designed to maintain visual hierarchy regardless of how complex pages become or how many nested containers they include.
As with other types of formatting or decoration in SKY UX, use lines minimally. Remember that white space and positioning can group or highlight content without extra subdivisions or boundaries.
SKY UX uses 3 generalized line styles that you can apply with a SASS mixin.
Line styles
Usage
The light separator is a very light gray, single-pixel line that subdivides content within a container. Do not use it at the edge of a container. Only use it when it is adjacent to white space on both sides. SKY UX uses this style for:
- Subsection dividers within containers such as tiles
- Top boundaries for action bars on modals
Code
@include bb-border(light)
Styling
- 1px solid #e2e3e4
- When this line style subdivides content within a container, use a 15px margin on both ends.
Usage
The dark separator is a moderate gray, single-pixel line. This heavier stroke holds up between two different colors, which makes it ideal for borders and boundaries. It also frequently separates active content from passive content. For example, it can separate a toolbar from the content that the toolbar acts on. SKY UX uses this style for:
- Borders on buttons
- Top and bottom boundaries for toolbars in areas such as tiles and grids
- Column dividers for header rows in grids
- Borders for cards
- Built-in data visualization axes
Code
@include bb-border(dark)
Styling
1px solid #cdcfd2
Usage
This line style separates members of a homogenous set. It separates the rows in
grids or
repeaters. The dotted style lowers its visual weight, and the moderate gray allows it to work with or without row striping on the list rows. It stretches to the full width of the rows (not the width of the container that holds the rows).
Code
@include bb-border(row)
Styling
1px dotted #cdcfd2
Corners
In general, the component should handle the style of corners. If you need to directly specify the corners of an element, the rule of thumb is to use rounded corners for interactive elements and square corners for containers.
Usage
Rounded corners are primarily for interactive elements such as
buttons to distinguish them from non-interactive elements.
Styling
border-radius: 3px
Shadows
In general, the component should handle the use of shadows. Interface designs should be flat aside from these components and use other techniques to create visual separation.
Usage
Shadows provide visual offsets for elements such as
dropdowns that float over other elements.
Styling
box-shadow: 0 0 5px 0 rgba(0, 0, 0, 0.3)


data-theme="${Theme}"#if>>
data-theme="${Theme}"#if>>
Master-detail application
Home
data-theme="${Theme}"#if>>
Introduction
This template is for a master-detail structured application.
Left bars to navigate details (master)
Right content pane (detail)
data-theme="${Theme}" data-content-theme="${Theme}"#if>>
More in this section
data-theme="${Theme}" data-dividertheme="${Theme}"#if>>
Details
Introduction
Section 1
Section 2


A common used technique to increase sales
Hard to achieve in offline shops due to limited possibilities for real-time interaction with customer
If only it could recognize what's inside and display real-time recommendations for customers
Model size
Insufficient computing power
Large power needs
Low efficiency of simple models
This tiny, fanless, deep learning device allows you to learn AI programming at the edge.
An ultra-low power design
Unique design for vision and AI workloads
A small-area footprint
Supported frameworks: TensorFlow*, Caffe*
Real-time on device inference – Cloud connectivity not required
The ability to run multiple devices on the same platform to scale performance
The original image
The image is splitted into a grid
Each cell products anchor boxes and the likelihood P(Object)
Each cell products anchor boxes and the likelihood P(Object)
Each cell products anchor boxes and the likelihood P(Object)
Each cell products anchor boxes and the likelihood P(Object)
Each cell products anchor boxes and the likelihood P(Object)
Each cell products anchor boxes and the likelihood P(Object)
Each cell also predicts a probability of belonging to a given class
Then we multiply anchor boxes times class predictions
In the last step, the threshold is applied
Allows devices interact with cloud environment
Supports bi-directional communication
Support protocols: MQTT, HTTP, MQTT + WebSocket
Serverless
AWS IoT Device SDK for Python
class Communication:
def connect(self):
self.myAWSIoTMQTTClient.connect()
def subscribe(self, topic, callback):
self.myAWSIoTMQTTClient.subscribe(topic, 1, callback)
def publish(self, topic, message):
self.myAWSIoTMQTTClient.publish(topic, message, 1)
{
"RaspberryPI":{
"product_type":"productA"
}
}
Run code without servers
Triggers computation immediately
Executes code only when needed
Integration with Tensorflow
{
"AWS":{
"recommendation":"productB"
}
}
Train YOLO with more different products
Rebuild and improve the recommendation algorithm
Add payment terminal to the cart
Marcin Stachowiak
Michał Dura


{% include analytics.html %}
Bryce Mueller |
Software Engineer
Toggle Navigation
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{{ content }}


{% extends "base/home.html" %} {% block content %} BrandToggle navigation Home Projects CV Hire me Hire Me Subject This is item 1 This is item 2 This is item 3 Email Message Date Hire Me {% endblock %}


Particles³:
Gravity:
Particle size:
Force:
Controls:
Left Mouse Button: Scene rotation
Mouse Wheel: Zoom in/out
Space: Activate force
F11: Toggle fullscreen
W: Move Force -Z
A: Move Force -X
S: Move Force +Z
D: Move Force +X
Page Up: Move Force +Y
Page Down: Move Force -Y
Stats:
Frame time ms/frame:
Particle count:


Buy and hold
TOMAS
Enter your investment strategy to create the perfect portfolio using proprietary advanced AI and the blockchain.
$ticker
$name
$exchange
$allocation%


Water Bottle Collection Competition
The first class to collect 100 lbs of bottles wins a pizza party!
Total bottles collected: 221.9 lbs
34.3 lbs
Mr. Loeschen
Kindergarten
52.1 lbs
Ms. Thomas
1st Grade
29.7 lbs
Mr. Medellin
2nd Grade
32.1 lbs
Ms. Dukes
3rd Grade
25.9 lbs
Ms. Ervin
4th Grade
47.8 lbs
Mr. Hasler
5th Grade


MyClass::myMethod
and myObject::myMethod
,
and suggests replacing them with an equivalent lambda expression.
Lambda expressions can be easier to modify than method references.
Example:
System.out::println
After the quick-fix is applied:
s -> System.out.println(s)
By default, this inspection does not highlight the code in the editor, but only provides a quick-fix.


---
layout: default
---
{% include header.html %}
Page Not Found
Perhaps you were looking for one of the following:
Essays
{% assign essay_pages = site.pages | where: "type", "essay" | sort: "date" | reverse %}
{% for page in essay_pages %}
- {{ page.title }}
{% endfor %}
Projects
{% assign project_pages = site.pages | where: "type", "project" | sort: "title" %}
{% for page in project_pages %}
- {{ page.title }}
{% endfor %}
{% include footer.html %}


--- layout: default title: Documentation permalink: /documentation --- Documentation Browse documentation for everything in the Jupyter universe. General Project-wide Documentation Contributing to Jupyter User Interfaces JupyterLab Jupyter Notebook Jupyter Console Qt Console IPython IPython IPyParallel Traitlets Kernels IPython IRkernel IJulia Community maintained kernels Widgets IPyWidgets widget-cookiecutter Notebook Documents nbconvert nbformat nbviewer JupyterHub JupyterHub Zero to JupyterHub nbgrader All JupyterHub Projects… Deployment Docker Stacks Kernel Gateway Foundations Jupyter Client Jupyter Core Jupyter Alabaster Theme
Browse documentation for everything in the Jupyter universe.


--- layout: page title: Pages header: Pages group: navigation comments: false sharing: false --- {% include setup %}


"x" "1" "ALEJANDRE-DURAN,E, CLARAMUNT,RM, SANZ,D, VILAPLANA,MJ, MOLINA,P ANDPUEYO,C, STUDY ON THE MUTAGENICITY OF NIFURTIMOX AND EIGHT DERIVATIVES WITH THEL-ARABINOSE RESISTANCE TEST OF SALMONELLA TYPHIMURIUM, __ MUTAT. RES. 206(2):193-200, 1988"


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Gateway
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[telegram] <TonyStarkBR> Using small amounts, I've lost 250 USD at my first try doing it, last year... Invested in an exit scam...  I buy a masternode (if value is low enough) or shares of it on a pool, wait until it give me ROI (which can happen in days), take the returns out to something else and leave the node (or shares) running while it can pay for it's hosting (re @JohDio: Currently not brave enough to strongly trade micro caps but should give it a try)


🤹🏻♂ Usage Examples¶
Import Functions¶
There is a separate function for each of the Synoptic Mesonet API services as described in the Synotpic documentation.
Import all the functions with
import synoptic.services as ss
or import a specific function like
from synoptic.services import stations_timeseries
📝 Jupyter Notebook Examples¶
I have provided a number of notebooks on GitHub that contain some practical examples for importing and using these functions to get and show station data.
🧭 Function Parameters¶
Function arguments are stitched together to create a web query. The parameters you can use to filter the data depend on the API service. Synoptic’s API Explorer can help you determine what parameters can be used for each service.
If the Synoptic API is new to you, I recommend you become familiar with the Station Selector arguments first. These parameters key in on specific stations or a set of stations within an area of interest (stid
, radius
, vars
, state
, etc.).
Some things you should know when specifying a parameter:
All lists are joined together into a comma separated string. For instance, if you are requesting three stations, you could do
stid=['WBB', 'KSLC', 'KMRY']
, and that will be converted to a comma separated liststid='WBB,KSLC,KMRY'
required for the API request URL. Both inputs are accepted by the functions.Any input that is a datetime object (any datetime that can be parsed with f-string,
f'{DATE:%Y%m%d%H%M}'
) will be converted to a string required by the API. For example,start=datetime(2020,1,1)
will be converted tostart='YYYYmmddHHMM'
when the query is made. Both inputs are accepted by the functions.For services that requires the
within
orrecent
arguments, the API required these given in minutes. You may give integers for those arguments, but converting time to minutes is done automatically by the function if you input adatetime.timedelta
or apandas timedelta
. For example, if you setwithin=timedelta(hours=1)
orrecent=pd.to_timedelta('1d')
, the function will convert the value to minutes for you.
❓ What if I don’t know a station’s ID?
MesoWest is your friend if you don’t know what stations are available or what they are named: https://mesowest.utah.edu/.
Query Timeseries Data¶
To get a time series of air temperature and wind speed for the last 10 hours for the William Browning Building (WBB) you can do…
from datetime import timedelta
from synotpic.services import stations_timeseries
df = stations_timeseries(stid='WBB',
vars=['air_temp', 'wind_speed'],
recent=timedelta(hours=10))
Query Latest Data¶
To get the latest air temperature and wind speed data for WBB (University of Utah) and KRMY (Monterey, CA airport) within one hour, we can also set the minutes as an integer instead as a timedelta.
from synotpic.services import stations_latest
df = stations_latest(stid=['WBB', 'KMRY'],
vars=['air_temp', 'wind_speed'],
within=60)
Parameters may be given as a list/datetime/timedelta, or as a string/integer interpreted by the Synoptic API. Thus,
stations_latest(stid='WBB,KMRY',
vars='air_temp,wind_speed',
within=60)
is equivalent to the above example.
Query Nearesttime Data¶
To get the air temperature and wind speed for WBB and KMRY nearest 00:00 UTC Jan 1, 2020 within one hour…
from datetime import datetime
from synotpic.services import stations_nearesttime
df = stations_nearesttime(stid=['WBB', 'KMRY'],
vars=['air_temp', 'wind_speed'],
attime=datetime(2020,1,1),
within=60)
Note: the string/integer alternative to the above example is
stations_nearesttime(stid='WBB,KMRY',
vars='air_temp,wind_speed',
attime='2020010100',
within=60)
Use whichever is more convenient for you. I often use both methods. It depends on what I am doing.
♻ Returned Data: Variable Names¶
The raw data retrieved from the Synoptic API is converted from JSON to a Pandas DataFrame.
If you look at the raw JSON returned, you will see that the observation values are returned as “sets” and “values”, (e.g., air_temp_set_1
, pressure_set_1d
, wind_speed_value_1
, etc.). This is because some stations have more than one sensor for a variable (e.g., wind at more than one level at a single site) or is reported at more than one interval (e.g., ozone at 1 hr and 15 min intervals). Time series requests return “sets” and nearest time requests return “values”.
I don’t really like dealing with the set and value labels. Almost always, I want the set or value with the most data or the most recent observation. My functions, by default, will strip the set_1
and value_1
from the labels on the returned data. If there are more than one set or value, however, then the “set” and “value” labels will be retained for those extra sets.
If a query returns
air_temp_set_1
andair_temp_set_2
, then the labels are renamedair_temp
andair_temp_set_2
.If a query returns
pressure_set_1
andpressure_set_1d
, then the labels are renamedpressure_set_1
andpressure
if set_1d has more observations than set_1.If a query returns
dew_point_temperature_value_1
at 00:00 UTC anddew_point_temperature_value_1d
at 00:15 UTC are both returned, then the labels are renameddew_point_temperature_value_1
anddew_point_temperature
because the derived quantity is the most recent observation available.
In short, all sets and values are always returned, but column labels are simplified for the columns that I am most likely to use.
For the renamed columns, it is up to the user to know if the data is a derived quantity and which set/value it is. To find out, look for attributes “SENSOR_VARIABLES” and “RENAME” in the DataFrame attributes (df.attrs
), or look at the raw JSON.
Doing this makes sense to me, but if you are confused and don’t trust what I’m doing, you can turn this “relabeling” off with rename_set_1=False
and rename_value_1=False
(for the appropriate function).
🌐 Latitude and Longitude¶
I should mention, LATITUDE
and LONGITUDE
in the raw JSON is renamed to latitude
and longitude
(lowercase) to match CF convention.
💨 U and V Wind Components¶
If the returned data contains variables for both wind_speed
and wind_direction
, then the DataFrame will compute and return the U and V wind components as wind_u
and wind_v
.
⏲ Timezone¶
The default timezone the data is returned is in UTC time. You may change the time to local time with the parameter obtimezone=local
. Pandas will return the data with a timezone-aware index. However, I found that matplotlib plotting functions convert this time back to UTC. To plot by local time, you need to use the tz_localize(None)
method to make it unaware of timezone and plot local time correctly. For example, compare the two plots created with the following:
import matplotlib.pyplot as plt
from synoptic.services import stations_timeseries
df = stations_timeseries(stid='KSLC',
recent=1000,
obtimezone='local',
vars='air_temp')
plt.plot(df.index, df.air_temp, label='tz aware (plots in UTC)')
plt.plot(df.index.tz_localize(None), df.air_temp, label='tz unaware (as local time)')
plt.legend()
✅ Quality Control Checks¶
By default, only basic QC range checks are applied to the data before it is returned by the API. These basic checks remove physically implausible data like removing a 300 degree temperature instead of returning the value.
You can add additional QC checks that more stringently remove “bad” data that might not be representative of the area or caused by a faulty sensor. However, you can’t expect every bad observation will be removed (or every good observation will be retained).
Some tips:
You can turn on more QC checks by Synoptic with the parameter
qc_checks='synopticlabs'
You can turn all QC checks on (includes synopiclab, mesowest, and madis checks) with the parameter
qc_checks='all'
.You can see the number of data point removed in the QC summary in the DataFrame attributes
df.attrs['QC_SUMMARY']
.Specific checks can be turned on (read the docs for more details).
For example:
df = stations_timeseries(stid='UKBKB', recent=60, qc_checks='synopticlabs')
or
df = stations_timeseries(stid='UKBKB', recent=60, qc_checks='all')
Look at the QC_SUMMARY in the DataFrame attributes to see some info about what each QC check means and how many are flagged…
df.attrs['QC_SUMMARY']
# Contains the following
{'QC_SHORTNAMES': {'18': 'ma_stat_cons_check', '16': 'ma_temp_cons_check'},
'QC_CHECKS_APPLIED': ['all'],
'PERCENT_OF_TOTAL_OBSERVATIONS_FLAGGED': 2.03,
'QC_SOURCENAMES': {'18': 'MADIS', '16': 'MADIS'},
'TOTAL_OBSERVATIONS_FLAGGED': 750.0,
'QC_NAMES': {'18': 'MADIS Spatial Consistency Check', '16': 'MADIS Temporal Consistency Check'}}
You might be able to find a better way to mask out those QC’ed values, but here is one method for the QC check for wind_speed_set_1:
# Identify which ones "passed" the QC checks (these have None in the QC array)
qc_mask = np.array([x is None for x in df.attrs['QC']['wind_speed_set_1']])
df = df.loc[qc_mask]
📈 plots.py
These are a work in progress¶
Some helpers for plotting data from the Synoptic API.
# Import all functions
import synoptic.plots as sp
or
# Import individual functions
from synoptic.plots import plot_timeseries


Table of Contents
Cheap, powerful computing and networking have created countless new applications that could not have existed a decade ago. The advent of the World-Wide Web, and its influence in driving the Internet into homes and businesses, is one obvious example. Equally important, though, is the shift from large, general-purpose desktop and server computers toward smaller, special-purpose devices with built-in processing and communications services.
As computer hardware has spread into virtually every corner of our lives, of course, software has followed. Software developers today are building applications not just for conventional desktop and server environments, but also for handheld computers, home appliances, networking hardware, cars and trucks, factory floor automation systems, cellphones, and more.
While these operating environments are diverse, the problems that software engineers must solve in them are often strikingly similar. Most systems must deal with the outside world, whether that means communicating with users or controlling machinery. As a result, most need some sort of I/O system. Even a simple, single-function system generally needs to handle multiple tasks, and so needs some kind of operating system to schedule and manage control threads. Also, many computer systems must store and retrieve data to track history, record configuration settings, or manage access.
Data management can be very simple. In some cases, just recording configuration in a flat text file is enough. More often, though, programs need to store and search a large amount of data, or structurally complex data. Database management systems are tools that programmers can use to do this work quickly and efficiently using off-the-shelf software.
Of course, database management systems have been around for a long time. Data storage is a problem dating back to the earliest days of computing. Software developers can choose from hundreds of good, commercially-available database systems. The problem is selecting the one that best solves the problems that their applications face.


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2 - Locate
Get To See Where the desired house Is Located By Clicking On
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Directions From Anywhere In The Country
3 - Apply
Apply For Your Interview To The House Of Your Choice. Click On
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FAQ
Have a question or problem?
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# Tools
* [GitHub](http://www.github.com)
* [Slack](http://www.slack.com)
* [ShareLatex](http://www.sharelatex.com)
## git vs GitHub
* ```git``` is an open-source distributed version control system
* developed for Linux kernel development
* GitHub is a (commercial) service built around ```git```
## Important git Features
* flexible branching model
* distributed
* supports staging
## Key git Commands
* ```init``` and ```clone```
* ```status``` and ```diff```
* ```add``` and ```commit```
* ```push```
* ```pull```
* ```branch```
# git init
```init``` initializes a new repository. I usually do this on GitHub.
# git clone
```clone``` copies a repository into a new working directory.
```
> git clone git@github.com:ngehlenborg/collaboration-tutorial.git
Cloning into 'collaboration-tutorial'...
remote: Counting objects: 5, done.
remote: Compressing objects: 100% (4/4), done.
remote: Total 5 (delta 0), reused 0 (delta 0), pack-reused 0
Receiving objects: 100% (5/5), done.
Checking connectivity... done.
```
## git status
```status``` shows the current status of the working directory with respect to the repository.
```
> git status
On branch master
Your branch is up-to-date with 'origin/master'.
nothing to commit, working directory clean
```
## git status (cont'd)
Then I added some files to the directory ...
```
> git status
On branch master
Your branch is up-to-date with 'origin/master'.
nothing to commit, working directory clean
octothorpe:collaboration-tutorial nils$ git status
On branch master
Your branch is up-to-date with 'origin/master'.
Untracked files:
(use "git add ..." to include in what will be committed)
.DS_Store
css/
index.html
js/
lib/
plugin/
nothing added to commit but untracked files present (use "git add" to track)
```
## git add
```add``` stages changes before they are committed to the repository.
```
> git status
On branch master
Your branch is up-to-date with 'origin/master'.
nothing to commit, working directory clean
```


@* * Copyright 2023 HM Revenue & Customs * * 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 config.ViewConfig @import model.des._ @import model._ @import formaters.CommonFormatter @this( mainTemplate: views.html.main_template, viewsHelpers: views.ViewsHelpers, viewConfig: ViewConfig ) @( vrn: Vrn, viewProgress: ViewProgress, showEstimatedRepaymentDat: Boolean, amountDescription: String, pageTitle: String, showHistoryUrl: Boolean, showPayUrl: Boolean, amountInPence: Long )( implicit request: Request[_], messages: Messages ) @mainTemplate(title = pageTitle, backButtonUrl = Some(Call("GET", viewConfig.showResultsUrl))) {
£@{ CommonFormatter.formatAmount(viewProgress.amount) }
@{ if(showHistoryUrl) { } } @{ if(showPayUrl) { } }{CommonFormatter.formatDate(viewProgress.estimatedRepaymentDate)}
@Html(whsf.description)


{% extends "admin_base.html" %} {% block bodycontent %} {% if contacts %}
Contacts
{% if is_admin %} {% for contact in contacts %}{{contact.contact.first_name}}
{% endfor %} {% else %} {% for contact in contacts %} {% endfor %} {% endif %} {% endif %}Incident: {{ organization.incident.name }}
Name: {{ organization.name }}
Email: {{ organization.email }}
Phone: {{ organization.phone }}
URL: {{ organization.url }}
Twitter: {{ organization.twitter }}
Facebook: {{ organization.facebook }}
Organization: {{ organization.name }}
Address: {{ organization.address }}
City: {{ organization.city }}
State: {{ organization.state }}
Zip Code: {{ organization.zip_code }}
Physical Presence: {{ organization.physical_presence }}
Work Area: {{ organization.work_area }}
Number Of Volunteers: {{ organization.number_volunteers }}
Voad Member: {{ organization.voad_member }}
Voad Member urls: {{ organization.voad_member_url }}
Voad Referrals: {{ organization.voad_referral }}
{# phase fields #} {% for phase_field_name in organization.get_phase_boolean_names() %}{{ phase_field_name }}: {{ organization[phase_field_name] }}
{% endfor %}Password: {{ organization.password }}
{% endblock %}


There are a few things you might consider when running your AngularJS application in production.
By default AngularJS attaches information about binding and scopes to DOM nodes, and adds CSS classes to data-bound elements:
As a result of ngBind
, ngBindHtml
or {{...}}
interpolations, binding data and CSS class
ng-binding
are attached to the corresponding element.
Where the compiler has created a new scope, the scope and either ng-scope
or ng-isolated-scope
CSS class are attached to the corresponding element. These scope references can then be accessed via
element.scope()
and element.isolateScope()
.
Placeholder comments for structural directives will contain information about what directive
and binding caused the placeholder. E.g. <!-- ngIf: shouldShow() -->
.
Tools like Protractor and Batarang need this information to run, but you can disable this in production for a significant performance boost with:
myApp.config(['$compileProvider', function ($compileProvider) {
$compileProvider.debugInfoEnabled(false);
}]);
If you wish to debug an application with this information then you should open up a debug console in the browser then call this method directly in this console:
angular.reloadWithDebugInfo();
The page should reload and the debug information should now be available.
For more see the docs pages on $compileProvider
and angular.reloadWithDebugInfo
.
Using strict di mode in your production application will throw errors when an injectable function is not annotated explicitly. Strict di mode is intended to help you make sure that your code will work when minified. However, it also will force you to make sure that your injectable functions are explicitly annotated which will improve angular's performance when injecting dependencies in your injectable functions because it doesn't have to dynamically discover a function's dependencies. It is recommended to automate the explicit annotation via a tool like ng-annotate when you deploy to production (and enable strict di mode)
To enable strict di mode, you have two options:
<div ng-app="myApp" ng-strict-di>
<!-- your app here -->
</div>
or
angular.bootstrap(document, ['myApp'], {
strictDi: true
});
For more information, see the DI Guide.
By default AngularJS compiles and executes all directives inside comments and element classes. In order to perform this task, the AngularJS compiler must look for directives by:
Parse all your application element classes.
Parse all your application html comments.
Nowadays most of the AngularJS projects are using only element and attribute directives, and in such projects there is no need to compile comments and classes.
If you are sure that your project only uses element and attribute directives, and you are not using any 3rd party library that uses directives inside element classes or html comments, you can disable the compilation of directives on element classes and comments for the whole application. This results in a compilation performance gain, as the compiler does not have to check comments and element classes looking for directives.
To disable comment and css class directives use the $compileProvider
:
$compileProvider.commentDirectivesEnabled(false);
$compileProvider.cssClassDirectivesEnabled(false);
For more see the docs pages on
$compileProvider.commentDirectivesEnabled
and
$compileProvider.cssClassDirectivesEnabled
.


Trait Router
Class Router
Direct Known Users
Deimos\Request\RequestExtension
Namespace: Deimos\Request\Adapter
Package: Deimos\Request\Adapter
Located at Request/Adapter/Router.php
Package: Deimos\Request\Adapter
Located at Request/Adapter/Router.php
public
|
|
public
mixed
|
|
public
array
|
|
public
mixed
|
public
integer
|
|
public
float
|
|
public
boolean
|
|
public
string
|
|
public
string
|
|
public
string
|
|
public
mixed
|


New questions
for knot diagrams
Steady states for crossing changes in knot diagrams
Harrison Chapman
Colorado State University
hchapman.org/talks/birs_steadystate
The Topology of Nucleic Acids:
Research at the Interface
of Low-Dimensional Topology,
Polymer Physics and Molecular Biology
Banff International Research Station
Banff, Alberta
March 24th 2019
Knot Diagrams
Crossings are the primary components of knot diagram models
Crossings as Self-contacts
Crossings may be viewed as self-contacts where enzymes may act to change DNA topology
Strand passage action, e.g. topo-IV
Coherent smoothing action, e.g. XerCD-dif-FtsK complex
A Prior Study
Hua, Nguyen, Raghavan, Arsuaga, Vasquez (2007)
Calculated transition probabilities between
knot types and steady-state probabilities of DNA
molecules under the action of topo-IV.
Strategy of Hua et al.
Issue
Action of topo-IV is easily expressed diagrammatically,
but complex to represent with self-avoiding polygons (SAPs) alone
Strategy
Sample SAPs, then transfer to diagrams to study action of topo-IV
Strategy of Hua et al.
For each prime knot type \(K\), sample diagrams representing \(K\) from a weighted distribution:
- Sample SAPs representing \(K\) uniformly of desired sizes, using BFACF
- Randomly project to 50–100 diagrams
- Result is a list of diagrams for \(K\), weighted by occurrence: sample from this list uniformly
Idea
Crossings in diagrams drawn under weighted distribution
likely represent crossings close in original SAPs
Dependence on Distribution?
Question
How tied are the results of Hua et al. to the
projection-weighted distribution on diagrams?
Aim to answer this by applying the same techniques, now
using uniform distribution on knotted diagrams
Diagram MCMC
One step of a diagram Markov chain takes as input a knot diagram, performs with some probability a (Reidemeister) transition, and returns the resulting knot diagram
Shadow Markov Chain
Explore all knot shadows by ignoring crossing signs; get all knot diagrams by adding crossing information
Fixed Knot Type Markov Chain
Explore all diagrams of fixed knot type by respecting crossing signs (c.f. BFACF and lattice polygons)
Stationary Distribution
C–Rechnitzer
The Wang-Landau Markov chain on diagrams of knot type \(K\) has stationary distribution where the probability that an \(n\)-crossing diagram \(D\) representing \(K\) is sampled is,
\[ \pi(D) \propto \frac{1}{g_n(K)} \approx \frac{1}{k_n(K)}. \]
Knot diagrams for \(K\) are sampled
- uniformly for any given size, and
- approximately each size is equally likely
Under the (Sampling) Hood
- Maximum diagram size considered: \(70\) crossings
- Enumeration step ran with flatness parameter
\(\Delta = 0.99\) until update parameter \(f \le 10^{-6}\)
- Enumeration step took approximately 1d to tune all 36 knot types
- Samples drawn every \(1.5 \times 10^{5}\) attempted transitions
- \(100,000\) diagrams sampled for each knot type (approx. \(1,500\) samples per diagram size)
- Data for each knot type gathered in approximately 25m, totalling 15h
Approximate Enumeration: \(6_1\)
| \(n\) |
Rooted |
Rooted/\(4n\) |
Exact (CCM '16) |
| 6 |
12 |
.5 |
1 |
| 7 |
911.68 |
32.56 |
34 |
| 8 |
40 207.17 |
1 256.47 |
1 267 |
| 9 |
1 370 185.44 |
38 060.71 |
38 199 |
| 10 |
40 659 767.57 |
1 016 494.19 |
1 015 996 |
| 11 |
1 103 328 823.05 |
25 075 655.07 |
N/A |
Exact enumeration data from Cantarella-C-Mastin '16
Diagram Sizes Sampled over Time: \(6_1\)
Strategy for Uniformly Sampled Diagrams
Now we just:
- For each prime knot type \(K\), sample diagrams representing \(K\) from the uniform distribution of a desired size range, using fixed-knot type diagram MCMC
Then, we apply the same analysis as in Hua et al.
Steady States for Topo-IV Action
In either study, after sampling a diagram of type \(K\):
- Reduce the diagram fully by \(\mathrm{RI}^{-}\) and \(\mathrm{RII}^-\) moves
(except in the case of the unknot)
A Random, Non-Reduced \(6_1\)
Reduced Diagram Size: \(6_1\)
Reduced Diagram Size: \(6_1\)
Steady States for Topo-IV Action
In either study, after sampling a diagram of type \(K\):
- Reduce the diagram fully by \(\mathrm{RI}^{-}\) and \(\mathrm{RII}^-\) moves
(except in the case of the unknot)
- Pick a crossing uniformly at random and make a crossing change
- If the resulting knot type is sufficiently small (\(cr(L) \le 8\)) and prime, record it
After normalizing, result is a vector of transition probabilities for knot type \(K\)
Topo-IV Transition Probabilities: \(6_1\)
Topo-IV Transition Probabilities: \(6_1\)
Steady States for Topo-IV Action
Gathering data for each prime knot type \(K\) of up to 8 crossings yields a square stochastic matrix \(M\)
The eigenvector associated to the largest eigenvalue of \(M\) is the steady state of the system.
Topo-IV Steady State Distributions
Topo-IV Steady State Distributions
Topo-IV Steady State Distributions
Rescaling the \(x\)-axis?
The expected average crossing number over all projections of a \(M\)-edge Gaussian random polygon is \(O(M \ln M)\)
Can rescale \(x\)-axis by \(e^{W_0(n)}\)
where \(W_0\) is the main branch of the Lambert \(W\) function (satisfying \(z = W(ze^z)\))
Topo-IV Steady State Distributions
Questions and Take-aways
- Do these data show a key difference between diagrams and space polygons?
- Uniform diagram sampler allows new tests for null
hypotheses in many other previous experiments (e.g. Stolz et al. '17 work on XerCD-dif-FtsK in E. coli)
- Wang-Landau approximate enumeration provides new enumeration of numbers of diagrams for given knot types


Date: {{event.date}}
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Address:
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{{event.location.city}}, {{event.location.province}}
Sessions
-
{{session.name}}
{{session.creator}}
Duration: {{session.duration}}
Level: {{session.level}}
{{session.abstract}}


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| Class and Description |
|---|
| ConstrainedMap
This class represents map with constrained entries.
|
| ConstrainedMap.Entry
This map entry holds metadata information and provides advanced
operations.
|
| ConstrainedMap.Transaction
This class allows to perform modification operations of this map on a
snapshot that is created at the moment of construction of the
transaction.
|
| Validation
The root interface in the validation hierarchy.
|
| ValidationContext
This context holds current validation state.
|
| ValidationDeclarationException
Thrown to indicate that declaration of
Validation annotation
is not legal. |
| ValidationTarget
Defines value part to which validation should be applied.
|
| Validator
This class allows to perform validation of entities, properties, etc.
|
| Validator.Context
This context holds current validation state.
|
| Validator.ContextBuilder
This builder allows to perform validation operations with custom
parameters.
|
| ValidatorFactory
Defines a factory that allows to create
Validator instances. |
| ViolationException
Thrown to indicate that validation of a single element fails.
|
| Class and Description |
|---|
| AbstractValidation
This class provides base implementation of the
Validation
interface. |
| Validation
The root interface in the validation hierarchy.
|
| ValidationContext
This context holds current validation state.
|
| ValidationDefaults
Defines default validation properties for the annotated bean.
|
| ViolationException
Thrown to indicate that validation of a single element fails.
|
| Class and Description |
|---|
| AbstractValidation
This class provides base implementation of the
Validation
interface. |
| Validation
The root interface in the validation hierarchy.
|
| ValidationContext
This context holds current validation state.
|
| ViolationException
Thrown to indicate that validation of a single element fails.
|
| Class and Description |
|---|
| Validation
The root interface in the validation hierarchy.
|
| ValidationContext
This context holds current validation state.
|
| Class and Description |
|---|
| ValidationException
Thrown to indicate that validation of a multiple elements fails.
|
| ViolationException
Thrown to indicate that validation of a single element fails.
|
| Class and Description |
|---|
| Validation
The root interface in the validation hierarchy.
|
| ValidationTarget
Defines value part to which validation should be applied.
|
Copyright © 2011–2020 FoxLabs. All rights reserved.


User Study Document of GPVQL
1、Task one
Drag the border of Karl_Marx, then double click the edge, and input predicate 'influencedBy'.
After this you will get a list of result. you can choose expand result while you right click the result ring.
2、Task two
Add a new node through the top button, double click the new node and input 'Bob_Black'.
Drag the border of Bob_Black and drag to the last result node, as shown in the following figure.
Double click the new edge, and input predicate 'influencedBy'. The number of the result node is change.
3、Task three
Query the people who influenced Karl_Marx and Bob_Black, influenced by who.
4、Task four
Add a filter operation on the result of task three.
Right click the result and choose filter operation, then input 'birthDate>1750'
Then you will find that the results have changed.


WITNESS LIST Transportation Committee April 7, 1998-10:30A General On: Allison, Jim (Co. Judges & Comm. Ass.) Armstrong, Betty (Co. Judges & Comm. Ass.) Barbour, Donald J. (Bell Helicopter Textron) Bartosowiecz, John (Ft. Worth Trans. System) Broyles, Stephen D. (City of Muenster) Chron, Rick (TX Towing & Storage) Dillard, Don (Hunt Petroleum) Foster, Irby (P.U.S.H.) Gadbois, Glenn (TX Bicycle Coalition) Hubbard, James (Wise County) Jones, Joe Paul (NCTCOG/Partners in Mob.) Lewis, Bill (M.A.D.D.) Melton, W.J. "Bud" (TX Trails Network) Miller, Jack (NCTCOG/Partners in Mob.) Morris, Michael (NCTCOG/Partners in Mob) Moseley, Jeff (N. America Superhwy. Co) Murphy, John (NCTCOG/Partners in Mob.) Nichols, Robert (TX Transportation Comm.) Poinsatte, Chris (DART) Reed, James B. (NCSL) Sherman, E. C. (Dept. of Public Safety) Slagel, Gary (City of Richardson) Smith, Stephen A. (Walnut Hill Wrecker) Snoble, Roger (DART) Stanton, Norma (DART) Sullivan, Patty (Rusk County) Wageman, Paul (NCTCOG/Partners in Mob) Registering, but not testifying: On: Currie, Bob (TX Logging Council) Davis, Tommy (Dept. of Public Safety) Martin, Jerry (TX Railroad Commission) McCarley, James (N.TX Tollway Authority) Wood, Paul (Wise County)


c4 - Connect Four
c4 - Connect Four
Welcome to connect four game!
Please refer to
Wikipedia article on Connect Four
for how to play.
Mode chooser
Offline: Human player vs AI player
Offline: Human player vs human player
Start game
OK


Yet another React state manager
Simple, low-impact state manager for smaller React applications.
npm install react-concise-state
import createStoreContext from "react-concise-state"
// 1⃣ create a store context providing initial state and an actions
const [context, Provider] = createStoreContext({
counter: 0
}, ({ state, setState }) => ({
// 👇 actions modify state using provided `setState`
incrementBy: (increment: number) => {
const newValue = state.counter + increment
setState({ counter: newValue })
},
reset: () => setState({ counter: 0 })
}))
// 2⃣ wrap component in created provider
const App = props => {
return <Provider>
<CounterComponent />
</Provider>
}
// 3⃣ hook context in consumer to use generated store
const CounterComponent: React.FC = props => {
const store = React.useContext(context)
// 👇 generated store contains both the state and actions to call
const onIncrement = () => store.incrementBy(1)
const onDecrement = () => store.incrementBy(-1)
return

Counter: {store.counter}

<button onClick={onIncrement}>Increment</button>
<button onClick={onDecrement}>Decrement</button>
<button onClick={store.reset}>Reset</button>

}
react-concise-state
born in frustration and fatigue caused by "modern" React state management. Writing hundreds of boilerplate redux code just to support basic feature gets boring quickly. Newer React features such as context and hooks are there to make state simpler, and this package uses it to make state managing extremely easy, concise and fun. Reducing boilerplate code to zero is the core concept.
Yarn:
yarn add react-concise-state
NPM:
npm install react-concise-state
Make sure you are using recent React version (>=16.8.0) because it works best with it.
support of React >= 16.3.0 is possible. Should it be implemented?
If you are using TypeScript, some of the types might default to any
on version <3.2 because of a bug with tuple types.
⭐Click here to see usage examples⭐
Below you can find an introduction to the core core concepts of react-concise-state
. You will find basic step-by-step walkthrough how to use this package.
createStoreContext
- creating store contextApplication or application part state can be represented as a plain JavaScript object. For example Counter component state can be defined with such object.
const state = {
counter: 0
}
Now you create a store context.
import createStoreContext from 'react-concise-state'
const [context, Provider] = createStoreContext(state)
context
and Provider
are created which you can use in your application to access created store.
context
- consuming created contextcontext
is React.Context
, so you can use its Consumer
property as you would normally use React Consumer, or instead you can use hooks API.
Component API
const Counter = props => <context.Consumer> {store =>

Current counter: {store.counter}

} </context.Consumer>
Hooks API
const Counter = props => {
const store = React.useContext(context)
return
Current counter: {store.counter}

}
Provider
is a context provider which you should wrap your store consuming components into.
const App = props => {
return <Provider>
<Counter />
</Provider>
}
Note that there should only be one provider for 1 instance of state and consumers might not be the first or only descenders of provider.
const App = props => {
return <Provider>

<Counter />

<OtherCounterWithSameState />

</Provider>
}
actions
- modifying stateUsually having plain state does not make any sense. There should be some way to modify it. React provides powerfull setState
to do that, however using setState
for common state on many child components is dangerous and is generally a bad idea. Flux architecture (redux) solves it by defining actions - a contracts telling how it is possible to mutate state, and then defining reducers, sagas, thunks, middleware etc. to actually mutate it. In react-concise-state
all those concepts are combined into one in a terse and fluent way.
actions
in react-concise-state
are plain JavaScript methods which you can call from consumer components to modify current state. Those actions
setState
To create store action in react-concise-state
provide a second argument to createStoreContext
- an object where object keys are action names and values are actions themselves.
Basic examples:
// Imperative
const actions = ({state, setState}) => ({
someAction: (payload) => {
const newState = ... // do something
setState(newState)
}
})
// Functional
const actions = ({setState}) => ({
someAction: (payload) => setState(prev => { ..prev, /* do something */})
})
// Create store
createStoreContext(state, actions)
Those actions will be transformed to store actions which you can call from consumers. In consumers only payload
is required argument. Calling this store action will execute the action. {state, setState}
wil have real values from provider.
Basic usage examples:
const store = React.useContext(context)
store.someAction('this is a payload string')
Payload for actions is optional. There could be any amount of payload arguments.
const [context, Provider] = createStoreContext({counter: 0}, ({state, setState}) => ({
// No payload
increment: () => setState({counter: ++state.counter}),
// If you are using functional style you can also get current state inside `setState` using callback function
decrement: () => setState(state => ({counter: --state.counter})),
// With payload
setValue: (value) => setState({counter: value}),
setValueIfMoreThan: (value, limit) => {
if(state.counter > limit)
setState({counter: value})
}
}))
...
// Usage
const store = React.useContext(context)
store.increment()
// > store.counter is 1
store.decrement()
// > store.counter is 0
store.setValue(10)
// > store.counter is 10
store.setValueIfMoreThan(9, 1)
// > store.counter is 1
You can get return value from actions. It also enabled awaiting async actions.
const [context, Provider] = createStoreContext({todos: []}, ({setState}) => ({
// Returning a value
addTodo: (todo) => {
const result = Api.addTodo(todo)
return result
},
// Getting todos asynchronously
getTodos: async () => {
const todos = await Api.getTodos()
setState({todos})
}
}))
...
// Usage
const store = React.useContext(context)
const result = store.addTodo('buy milk')
await store.getTodos()
You can call actions from other actions.
const [context, Provider] = createStoreContext({todos: []}, ({setState}) => ({
addTodo(todo) {
const result = Api.addTodo(todo)
this.getTodos()
},
getTodos: async () => {
const todos = await Api.getTodos()
setState({todos})
}
}))
...
// Usage
const store = React.useContext(context)
const result = store.addTodo('buy milk')
// await store.getTodos() - don't need to call it. `.addTodo` will call it
contexts
- calling other storesSometimes you would like to call other store action from an action. You can't use React.useContext
because of specific hook rules in React. Hook amount should never change during runtime and only way to supply that is to initialize all dependency contexts before bootstraping actions.
You can call actions in other stores by providing dependency contexts in a 3rd parameter to createStoreContext
. Those contexts will be mapped to corresponding stores internally and will be available in stores
object in {setState, action, stores}
argument of action creator.
Example:
const [todoContext, Provider] = createStoreContext({todos: []}, ({state, setState}) => ({
addTodo: (todo) => {
setState({todos: [...state.todos, todo]})
},
}))
const [mainContext, Provider] = createStoreContext({message: ''}, ({setState, stores}) => ({
someAction: (name) => {
const { todos } = stores.todoContext // stores.todoContext is a "todo store" ({todos: [], addTodo: (todo) => void})
const newMessage = `Hello, ${name}, you have ${todos.length} todos!`
setState({message: newMessage})
},
}), { contexts: { todoContext })
...
// Usage
const todoStore = React.useContext(todoContext)
const mainStore = React.useContext(mainContext)
todoStore.addTodo('buy milk')
todoStore.addTodo('learn typescript')
mainStore.someAction('Dmitrijs')
// mainStore.message is "Hello, Dmitrijs, you have 2 todos!"
Actions are just a functions which modify state and/or return some values. Actions may be just plain state reducers, or they can contain some complex logic with API calls and data manipulation. In any case you will usually run into such situation, that all actions of the store need to do something the same way. E.g. log input values, handle errors the same way, etc... Middleware is there for that exact reason.
You can provide any middleware to store creation in a 3rd parameter to createStoreContext
. That middleware will be executed every time you call any store action, just after you call it and just before it actually executes.
Example:
// Without middleware
const [todoContext, Provider] = createStoreContext({todos: []}, ({state, setState}) => ({
addTodo: (todo) => {
// Logging
console.log('Calling addTodo with argument ' + todo)
// Error handling
try {
Api.addTodo(todo)
} catch (ex) {
console.log(ex)
}
},
getAll: () => {
// Logging
console.log('Calling getAll')
// Error handling
// Notice how at this point we are writing same stuff over and over again
try {
const todos = Api.getAll()
setState({todos})
} catch (ex) {
console.log(ex)
}
}
}))
// With middleware
import { Middleware } from 'react-concise-state'
// Error handling middleware
const errorHandling: Middleware = (next, args, meta) => {
try {
// try calling next executable function in the flow (either next middleware or aciton itself)
// Don't forget to pass arguments
next(args)
} catch (ex) {
// If it fails (action or other middleware) log an error
console.log(ex)
}
}
// Logging middleware
const logging: Middleware = (next, args, meta) => {
// Log to console action key (name) and it's arguments
console.log(`Calling ${meta.actionName} with arguments ${args}`)
// Don't forget to call `next(args)`!
next(args)
}
const [todoContext, Provider] = createStoreContext({todos: []}, ({state, setState}) => ({
addTodo: (todo) => Api.addTodo(todo),
getAll: () => {
const todos = Api.getAll()
setState({todos})
}
}), { middleware: [errorHandling, logging]}) // Provide middleware to the store
createMiddleware
- creating custom store-bind middlewareMiddleware is a useful pattern, which you can use to streamline store actions, make stores more generic and have almost perfect reusability across contexts.
Default and the easiest way to create a middleware for your store is to make a new function of type Middleware
.
However, what if you want to save every exception into some store and then display those errors in some other components nicely? You may inject stores into middleware by using
createMiddleware
helper. After injecting stores you may access store state and actions inside middleware. Error handling middleware example:
import { createMiddleware, createStoreContext } from 'react-concise-state'
// Creating errors store
const [context, Provider] = createStateContext({
latestError: null as Error | null,
errorLog: [] as Error[]
}, ({setState}) => {
// Set latestError and push it to error log
handleError: (error: Error) => {
setState(prev => ({...prev, latestError: error, errorLog: [...prev.errorLog, error]}))
}
})
// Creating error handling middleware with injected errors store
const errorHandling = createMiddleware((next, args, meta) => {
try {
await next(args)
} catch (ex) {
// Call error store to save error in it
meta.stores.errors.handleError(ex)
}
}, { errors: context })
meta
- store metadataAny settings or additional store information which might be needed can be stored in special meta
option of the store creator. This meta information is unchanged through store lifetime, is available for every action and middleware. Meta
type is a dictionary of user-defined values. The most common usage for metadata is providing API url/token, dev/prod flags or anything else which is static through application lifetime. Example:
// This file will export correct baseUrl and headers for authentication base on the environment (DEV/TEST/PROD)
import { baseUrl, authHeaders } from './config'
const [context, Provider] = createStateContext({
todos: []
}, ({setState, meta}) => {
getAll: async () => {
// Use provided meta data. It is alternative way of using those values from global scope
const res = await fetch(meta.baseUrl, { headers: meta.headers })
const todos = await res.json()
setState({todos})
}
}. {
// Provide values through meta option
meta: {
baseUrl: baseUrl,
headers: authHeaders,
// Flag to tell logging middleware that this store must not be logged
shouldNotLog: true
},
middleware: [logging]
})
// Logging middleware
const logging: Middleware = (next, args, meta) => {
// Check meta data to see if this store should not be logged
if (meta.shouldNotLog) return next(args)
console.log(`Calling ${meta.actionName} with arguments ${args}`)
next(args)
}
This library is written in TypeScript and leverages its type system to the fullest. One of the main goals of this library is to provide type-safe state management with minimum (almost zero) boilerplate code.
TypeScript is really powerful. It's type system is so flexible yet so smart ([turing-complete smart](https://github.com/Microsoft/TypeScript/issues/14833)) that it is a shame very few developers and libraries use it to the fullest.
TypeScript is able to infer and calculate most of the types itself, yet libraries still require developers to write interfaces, implement contracts, provide types for every single bit of functionality. TypeScript should guide towards correct implementation, not hinder from incorrect one.
You can use this library without writing any type and you will still have perfect type-safety and type-correctness. Types will be automatically resolved and given to you so you are safe about your implementation.
initialState
When creating a new store context initial state could be anything. Resulting state will be infered from provided initialState
You can also provide TState
type to constrain initial state or narrow state types.
actions
When creating store actions you will be provided with correct types for current state
, setState
method and stores
and meta
objects.
After describing your store with createStoreContext
you will be possible to resolve store using React.useContext
hook. Resulting store will be an intersection of state
and mapped actions.
You can set additional action arguments and return any value. Mapped action will infer all of that and provide it to you.
📖 Read full api reference and docs by clicking here 📖
MIT License Copyright (C) Dmitrijs Minajevs<EMAIL_ADDRESS>Generated using TypeDoc


| DOSE: T+ 0:00 |
13 mg |
oral |
Amphetamines |
(liquid) |
| T+ 1:40 |
13 mg |
oral |
Amphetamines |
(liquid) |
| T+ 3:20 |
2 tablets |
oral |
Pharms - Escitalopram |
(pill / tablet) |
| T+ 3:20 |
6.0 mg |
oral |
Amphetamines |
(liquid) |
| T+ 4:10 |
1 tablet |
oral |
Pharms - Escitalopram |
(pill / tablet) |
| T+ 6:00 |
8.0 mg |
oral |
Amphetamines |
(liquid) |
| T+ 6:00 |
20 mg |
oral |
Amphetamines |
(capsule) |
| T+ 6:00 |
1 bowl |
smoked |
Cannabis |
(plant material) |
| T+ 11:00 |
20 mg |
oral |
Amphetamines |
(capsule) |
I am currently a seventeen-year-old high school student who had recently traded a quantity of home-extracted salvia to a friend for a number of pills, noticeably XR (extended-release) Adderall and Lexapro. I had done Adderall once before when I had taken three 20-milligram XR pills, an experience which piqued my curiosity in the substance, but nevertheless felt that the extended-release mechanism significantly detracted from the experience, due to the lack of rushing as the amphetamines hit since the active chemicals were released much more slowly. I had been waiting for a few days to try out this combination, keeping a bowl of very good bud to try and combine with Adderall, not only to see the reactions between the two but because cannabis in general makes me tired and lethargic, an effect I wished to offset by dosing with amphetamines beforehand.
Preparations: The night beforehand, I got a bottle of half ginger ale, half Coke and opened a 20-mg Adderall pill into it. To my dismay, a few of the little pellets fell on the floor, though not a significant quantity, so I prevented any further loss by simply dropping the second pill right in the bottle. I let it sit overnight in my backpack for the next day, hoping to have made a much more quickly absorbed version of the amphetamines than would have been allowed by simply popping the pills.
Experience: The day was a Friday, and like usual by the end of the week, I was rather jaded and tired of the same old bullshit routine. I was excited to try out this combination, however, due to expectations that the effects of combining of these three drugs may be interesting.
As a note, I donít take any of these pharmaceuticals myself, so thereís no reason for me to have built a tolerance to them. I had never taken Lexapro before, and didnít really think much would happen because of it, due to the fact that I had taken antidepressants before and only gotten a mildly relaxed mindset combined with dilated pupils and (the most noticeable effect) an intensified orgasm.
It was beginning of the first period of the day, and I was in the gym locker room. I took out the laced bottle of soda and began drinking it, downing about one-third of it in a few seconds. The taste was normal, but nevertheless the XR capsules had not fully dissolved, and I could see little bubbles of actives in the drink. I didnít worry, however, since I do believe I had sped up the absorption of the drug significantly, even if the extended-release mechanism was not completely destroyed. By the end of the period, I began to feel what I thought were threshold effects, with a generally better over-all mood being present. Nevertheless, I couldnít be sure, and at that stage would not have completely discredited the placebo effect.
Second period passed by, and with the passing of time, my mood in general continued to improve. By this point, the Adderall felt like nothing more than a mild caffeine buzz, so at the beginning of third period, I downed another one-third of the drink.
Third period was a rather work-intensive honors class, and as the teacher went over the subjects, I could tell the amphetamines were definitely beginning to take hold. My notes were taken quickly and neatly, and my concentration level seemed much higher than normal. My heartbeat seemed quickened, and I never hesitated to ask questions, mainly because I needed to in order to prevent the teacher from calling on me to answer a question on the homework I had not yet done.
Fourth period, the effects continued to rise, though they were nothing special yet.  My entire body felt light and I had an abundance of energy, almost as if I was ready to take on any task required of me.
In the few minutes of free time between fourth and fifth period, I thought it would be a good idea to try out the antidepressants. I took two Lexapro pills out the bag of three my friend had given me and popped them in my mouth. They dissolved quickly, leaving a rather disgusting taste behind that I washed down with more of the amphetamine-laced soda. By now I probably only had one-fifth of the bottle left, and was most definitely feeling more than a typical caffeine buzz or feeling of natural energy. My confidence was boosted in myself to a great degree, and not only my actions but also my thoughts worked at a much accelerated rate.
We had a quiz fifth period, which I completed quickly, remembering information I donít think I wouldíve remember without the amphetamines there to improve both my memory and thought. My pace and heartbeat were both increasingly rapid, though not too much above normal; my heart rate seemed nothing more than it would be on a quick walk, not like the ridiculous frenzied pounding my heart had done on other drugs like high doses of DXM. By the end of the period, nausea and regret over taking the antidepressants was setting in. With ten minutes left of class, I could feel an increasing upset flutter in my stomach, and quickly went to the bathroom, where I vomited a minor, almost insignificant, amount. I place these effects entirely on either the antidepressants or their reactions with the amphetamines, since Adderall had never given me any nausea before at much higher doses.
Next period was lunch, where I finished the last antidepressant pill. I wouldnít finish off the soda until school was over, but the next two periods nevertheless passed by with my thoughts running at a million miles a minute. Adderall makes many users into ideal students, in my experience, possibly why itís so highly prescribed.
At the end of the day, I was talking to a friend of mine, extremely sped up by now from the nearly 40 milligrams of Adderall I had taken, and he asked me what I was eating. The answer was, of course, nothing; my teeth were gnashing each other just like the stereotype of a methamphetamines user. Funny how they fight a War on Drugs against methamphetamines, when the amphetamines doctors prescribe to the youngest of our children every day produce the same effects as the street drug.
After school, I drank the rest of the soda the soda, and popped another 20 milligram pill of Adderall. My confidence was flying by now, and I completely understood why people get addicted to methamphetamines and other stimulants. I could approach anyone, talk about anything, and still feel on top of the world. On the way home, I passed through the local park, where I packed a bowl of cannabis into a simple pipe I had made out of a Coke can. The day was windy, but I felt no cold or discomfort as I sat there, smoking some of the best bud I had sampled in months. It was a light-green color, smelling like a skunk and smoked very smoothly.
Now there were some interesting effects afterwards I cannot fully describe. I was pretty damn high at this point, having the old surreal thought patterns from smoking cannabis, yet the Adderall and cannabis combination was perfect; the high was the most energetic I had ever felt. There were no munchies, no nodding off, no lethargy- all the good side effects of cannabis without any of the bad ones.
As soon as I got home, I sat down and began working on a novel. Pages flew by, and by the time I looked up again, four hours had passed, simply like that. It was amazing how much time simply vaporized into nothingness while working under the influence of amphetamines.
I took my last 20-milligram Adderall around 7:30 at night, chewing it a bit first. I had been tweaking all day by this point, and, still riding a long and strange plateau of energy, went to walk our dog with my sister, who told me I should go to my room because the effects of the amphetamines were apparent. My voice was frenzied and I spoke at a ridiculously rapid rate, as if I needed to get all of my words out as fast as I could. Nevertheless, I had told her what I was on, and my parents didnít notice, so itís possible that she only knew because she was listening for it. I could have acted totally sober and slowed my words to a normal rate if I wanted to.
As for the antidepressants, I noticed they had taken hold because my pupils were completely dilated, until there was only about a millimeter of iris around the black center.
The antidepressants seemed to impair my sexual drive, however, as it took me nearly an hour of frenzied activity to get off. The next day, I would find that I literally rubbed my skin raw in my amphetamine-paced activity, yet nevertheless, the finale was really something else. The antidepressants and Adderall combination made it one of the most intense I have ever experienced, lasting at least a few times longer than a normal one and feeling much more insanely good.
 I got about two hours of sleep before being woken up to my work with my father for the day, and still felt mild feelings of amphetamine rushes throughout the day, though by the next night I was completely exhausted and passed out for around thirteen hours.
Adderall is an aide whose usefulness extends beyond description for someone who wants to actually get things done, and is a perfect precursor to smoking cannabis if one wants a combination to remember. The Lexapro, however, had only borderline usefulness, and even that seemed only in relation to its functions on the sexual drive of any users.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


---
layout: base.html
title: The super main page
author: Mike Zamansky
var: a new variable
l:
- one
- two
- three
block: content
---
{{title}}
stuff between the headings
more stuff in {{ var }} between
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This is the index page
more stuff inserted


traceroute - manual page for traceroute 1.31.1
traceroute [-46FIlnrv] [-f 1ST_TTL] [-m MAXTTL]
[-q PROBES] [-p PORT]
BusyBox v1.31.1 (2020-04-30 13:38:01 EDT) multi-call binary.
- [-t TOS] [-w WAIT_SEC] [-s SRC_IP] [-i IFACE] [-z PAUSE_MSEC] HOST
[BYTES]
Trace the route to HOST
- -4,-6
- Force IP or IPv6 name resolution
- -F
- Set don't fragment bit
- -I
- Use ICMP ECHO instead of UDP datagrams
- -l
- Display TTL value of the returned packet
- -n
- Print numeric addresses
- -r
- Bypass routing tables, send directly to HOST
- -v
- Verbose
- -f N
- First number of hops (default 1)
- -m N
- Max number of hops
- -q N
- Number of probes per hop (default 3)
- -p N
- Base UDP port number used in probes (default 33434)
- -s IP
- Source address
-i IFACE Source interface
- -t N
- Type-of-service in probe packets (default 0)
- -w SEC
- Time to wait for a response (default 3)
- -g IP
- Loose source route gateway (8 max)


Test whether or not a network estimates can be considered structurally dependent, i.e. a function of the network structure. By rewiring the graph and calculating a particular statistic \(t\), the test compares the observed mean of \(t\) against the empirical distribution of it obtained from rewiring the network.
n_rewires(graph, p = c(20L, rep(0.1, nslices(graph) - 1))) struct_test(graph, statistic, R, rewire.args = list(), ...) # S3 method for diffnet_struct_test c(..., recursive = FALSE) # S3 method for diffnet_struct_test print(x, ...) # S3 method for diffnet_struct_test hist(x, main = "Empirical Distribution of Statistic", xlab = expression(Values ~ of ~ t), breaks = 20, annotated = TRUE, b0 = expression(atop(plain("") %up% plain("")), t[0]), b = expression(atop(plain("") %up% plain("")), t[]), ask = TRUE, ...) struct_test_asymp(graph, Y, statistic_name = "distance", p = 2, ...)
| graph | A |
|---|---|
| p | Either a Numeric scalar or vector of length |
| statistic | A function that returns either a scalar or a vector. |
| R | Integer scalar. Number of repetitions. |
| rewire.args | List. Arguments to be passed to |
| ... | Further arguments passed to the method (see details). |
| recursive | Ignored |
| x | A |
| main | Character scalar. Title of the histogram. |
| xlab | Character scalar. x-axis label. |
| breaks | Passed to |
| annotated | Logical scalar. When TRUE marks the observed data average and the simulated data average. |
| b0 | Character scalar. When |
| b | Character scalar. When |
| ask | Logical scalar. When |
| Y | Numeric vector of length \(n\). |
| statistic_name | Character scalar. Name of the metric to compute. Currently
this can be either |
A list of class diffnet_struct_test
containing the following:
The graph passed to struct_test
.
The resulting p-value of the test (see details).
The observed value of the statistic.
The average value of the statistic applied to the simulated networks.
Number of simulations.
The function statistic
passed to struct_test
.
A boot
class object as return from the call to boot
.
The list rewire.args
passed to struct_test
.
struct_test
computes the test by generating the null distribution using
Monte Carlo simulations (rewiring). struct_test_asymp
computes the
test using an asymptotic approximation. While available, we do not recommend
using the asymptotic approximation since it has not shown good results when
compared to the MC approximation. Furthermore, the asymptotic version has only
been implemented for graph
as static graph.
The output from the hist
method is the same as hist.default
.
struct_test
is a wrapper for the function boot
from the
boot package. Instead of resampling data--vertices or edges--in each iteration the function
rewires the original graph using rewire_graph
and applies
the function defined by the user in statistic
.
The default values to rewire_graph
via rewire.args
are:
p | |
| Number or Integer with default | |
n_rewires(graph) | . |
undirected | |
| Logical scalar with default | |
getOption("diffnet.undirected", FALSE) | . |
copy.first | |
| Logical scalar with | |
TRUE | . |
algorithm | |
| Character scalar with default | |
"swap" | . |
In struct_test
…
are passed to boot
, otherwise are passed
to the corresponding method (hist
for instance).
From the print
method, p-value for the null of the statistic been
equal between graph and its rewired versions is computed as follows
$$% p(\tau)=2\times\min\left(\mbox{Pr}(t\leq\tau), \mbox{Pr}(t\geq\tau)\right) % $$
Where \(\mbox{Pr}\{\cdot\}\) is approximated using the Empirical Distribution Function retrieved from the simulations.
For the case of the asymptotic approximation, under the null we have
$$% \sqrt{n}\left(\hat\beta(Y,G)-\mu_\beta\right)\sim^d\mbox{N}\left(0,\sigma_\beta^2\right) $$
The test is actually on development by Vega Yon and Valente. A copy of the working paper can be distributed upon request to<EMAIL_ADDRESS>The function n_rewires
proposes a vector of number of rewirings that
are performed in each iteration.
Vega Yon, George G. and Valente, Thomas W. (On development).
Davidson, R., & MacKinnon, J. G. (2004). Econometric Theory and Methods. New York: Oxford University Press.
# Creating a random graph set.seed(881) diffnet <- rdiffnet(100, 5, seed.graph="small-world") # Testing structure-dependency of threshold res <- struct_test(diffnet, function(g) mean(threshold(g), na.rm=TRUE), R=100) res#> #> Structure dependence test #> # Simulations : 100 #> # nodes : 100 #> # of time periods : 5 #> -------------------------------------------------------------------------------- #> H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency) #> observed expected p.val #> 0.5323 0.2015 0.0000hist(res)# Adding a legend legend("topright", bty="n", legend=c( expression(t[0]:~Baseline), expression(t:~Rewired~average) ) )#> #> Structure dependence test #> # Simulations : 200 #> # nodes : 100 #> # of time periods : 5 #> -------------------------------------------------------------------------------- #> H0: E[beta(Y,G)|G] - E[beta(Y,G)] = 0 (no structure dependency) #> observed expected p.val #> 0.5323 0.2015 0.0000# Running in parallel fashion# NOT RUN { res <- struct_test(diffnet, function(g) mean(threshold(g), na.rm=TRUE), R=100, ncpus=4, parallel="multicore") res hist(res) # }


Class ObjectMixin
Orm\Object behaviour mixin.
Final
Namespace: Orm
Package: Orm
Subpackage: Common
Copyright: Nette Framework
Author: David Grudl
Author: Petr Procházka
Located at Common/ObjectMixin.php
Namespace: Orm
Package: Orm
Subpackage: Common
Copyright: Nette Framework
Author: David Grudl
Author: Petr Procházka
Located at Common/ObjectMixin.php
public static
mixed
|
|
public static
mixed
|
|
public static
mixed
&
|
|
public static
|
|
public static
|
|
public static
boolean
|


Now that you have created a buyer, a user, and generated a successful API Access Claim giving your buyer-users the ability to authenticate on the client-site, you are ready to sign in as a buyer-user and truly begin your journey in OrderCloud. The goal if this page is for you to get the current authenticated users basic information. This is possible through the use of the 'Me' service, which will be one of your most powerful tools when building the buyer-site.
To get to the point where you can use the 'Me' service, you first need to set your clientID to the ID in the Access Claim you just created a moment ago. To do so, you will need to open the context selection form in the bottom right corner ( icon), and fill out the form with your new clientID, along with your new buyer-users username and password.
Once you have successfully authenticated as this user, you are ready to run your first Me service request. You do not have to add anything yourself, as the Me service is meant to be a very easy and straightforward way of getting a users information and objects they have access to (products, categories, addresses, etc.).


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This is the last of the tips.
The "Tip of the Day" window will not appear again until a software update adds new functionality and new tips.