Starting from version 0.0.6 (and above), additionnal includes can be manually set up by users in jenkins configuration screen.
This page is intended to share common includes which are not yet hard coded in scm-sync-configuration synchronization strategies.
Feel free to add yours.
Working includes
Include |
Short description |
Needed plugin |
Notes |
|---|---|---|---|
users/*/config.xml |
Synchronize users informations |
jenkins-core |
|
*/jobs//promotions/*/config.xml |
Synchronize configuration for promotion job steps |
|
|
jenkins.plugins.publish_over_ssh.BapSshPublisherPlugin.xml |
Synchronize ssh servers |
|
|
org.jenkinsci.plugins.*.xml |
Synchronize various Jenkins plugin configurations |
|
|
log/*.xml |
Synchronize log configuration |
jenkins-core |
|
jenkins.model.JenkinsLocationConfiguration.xml |
Synchronize location configuration |
jenkins-core |
|
com.cloudbees*.xml |
cloudbees-free-plugins data |
|
|
nodeMonitors.xml |
Slave nodes monitors |
jenkins-core |
|
proxy.xml |
Install plugins http proxy |
jenkins-core |
|
ownership.xml |
Ownership global properties |
|
|
ru.yandex.jenkins.plugins.debuilder.*.xml |
Debian package create and publish config |
|
|
log/*.xml |
Custom logs recorders |
jenkins-core |
|
*/jobs//config.xml |
Jobs inside folders |
Now this is default, not required. |
|
email-templates/* |
Email Templates |
Email-ext plugin |
|
nodes/*/config.xml |
Synchronize "slave" node configuration |
|
|
Non-working includes
Include |
Short description |
Needed plugin |
Notes |
|---|---|---|---|
jobs/*/nextBuildNumber |
Will allow better backup when restoring things from repository |
jenkins-core |
Doesn't work because nextBuildNumber is not corresponding to a Saveable. Nevertheless, watch out : if it would have worked, by adding this, every time a build would end, a commit would be done on your repository, slowing down jenkins instance |
credentials.xml |
Credentials for jenkins |
Do not update the git repo after changes |


About
Introduce yourself here… so people will know who you are and what you do.. blah..blah..blah..
© 2018 گزارش ا.پ. ― قدرت گرفته توسط Jekyll و Textlog theme
Introduce yourself here… so people will know who you are and what you do.. blah..blah..blah..
© 2018 گزارش ا.پ. ― قدرت گرفته توسط Jekyll و Textlog theme


Instructions to install and configure Laravel in a Nginx / Arch environment.
Install and configure Laravel with Nginx - Arch Linux
Difficulty: 3
Time: 20 minutes
Laravel is a framework used to create web applications. It is assumed you are installing on top of a LEMP stack.
Update and Install Dependencies
- Update
packages
sudo pacman -Su
- Install git
sudo pacman -Syu git
- Install php-related items
sudo pacman -Syu php-mcrypt
Configure php
php.ini
- Edit php initialization file. Open /etc/php.ini for editing
sudo vim /etc/php/php.ini
- Find the cgi.fix_pathinfo directive under a section called Paths and Directories. Verify / modify the cgi.fix_pathinf directive as shown below:
cgi.fix_pathinfo=0
- Further down in the file, under a section titled "Dynamic Extensions" remove the comment(;) from the following lines so they look like this:
extension=mcrypt.so
extension=mysqli.so
extension=mysql.so
extension=mcrypt.so
extension=openssl.so
- Save and close.
Configure Nginx
Nginx.conf
- Create a physical location for the Laravel server
sudo mkdir -p /var/www/laravel
- Open the nginx configuration file for editing
sudo vim /etc/nginx/nginx.conf
- Edit the /etc/nginx/nginx.conf file. In the default server context, comment out the root directive
and add a new one for laravel
# server context
server {
listen 80 default_server;
server_name localhost;
# root /usr/share/nginx/html;
root /var/www/laravel/public;
#charset koi8-r;
#access_log /var/log/nginx/host.access.log main;
# Load configuration files for the default server block.
include /etc/nginx/default.d/*.conf;
- Edit the php location context as shown below. (Changes / adds are underlined italic):
#location context
location ~ \.php$ {
try_files $uri /index.php =404;
fastcgi_split_path_info ^(.+\.php)(/.+)$;
fastcgi_pass unix:/var/run/php-fpm.sock;
fastcgi_index index.php;
fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
include fastcgi_params;
}
- Save and close.
- Restart Nginx
sudo systemctl restart nginx
Create a swap file
In the event your server does not have sufficient memory ( > 2GB) to handle the requirements of Composer and Laravel in memory, you will probably need to create a swap file.
- Create a 1GB file
sudo fallocate -l 1G /myswapfile
- Format it as swap space
sudo mkswap /myswapfile
- Change permissions
sudo chmod 0600 /myswapfile
- Turn it on
sudo swapon /myswapfile
Install Composer
Composer is a tool we will use to manage the Laravel implementation.
- Navigate to your home directory and run the Composer install script.
cd ~
curl -sS https://getcomposer.org/installer | php
- You will see
All settings correct for using Composer
Downloading...
Composer successfully installed to: /home/randomcode/composer.phar
Use it: php composer.phar
- Change the name of the newly-created composer.phar file to "composer"
sudo mv composer.phar /usr/local/bin/composer
Install Laravel
- Use composer to retrieve and install Laravel into the directory we created earlier
sudo /usr/local/bin/composer create-project laravel/laravel /var/www/laravel 4.2
- Change the owner to "http".
sudo chown -R :nginx /var/www/laravel
- Change the permissions to allow the http user to write to the app/storage directory
sudo chmod -R 775 /var/www/laravel/app/storage
- Verify success by navigating to yourcloudserver
http://yourcloudserver.com
Next steps
You have arrived. Now you can use the Laravel framework to make beautiful apps.


Forms
Default Form
To quickly create a default form.
Email
Password
File
Remember me
Submit
Email
Password
File
Remember me
Submit
Basic Form
To quickly create a basic form.
Remember me
Submit
Remember me
Submit
Aligned Form
To quickly create a aligned form.
Email:
Password:
Remember me
Submit
Email:
Password:
Remember me
Submit


| v | x | y | z | {% for v in inputstructure_cell_vectors %}
|---|---|---|---|
| v{{ v[0] }} | {{ "%.10f" % v[1]|float }} |
{{ "%.10f" % v[2]|float }} |
{{ "%.10f" % v[3]|float }} |
| Element | r1 | r2 | r3 | {% for b in inputstructure_atoms_scaled %}
|---|---|---|---|
| {{ b[0] }} | {{ "%.10f" % b[1]|float }} |
{{ "%.10f" % b[2]|float }} |
{{ "%.10f" % b[3]|float }} |
| Element | x | y | z | {% for b in inputstructure_atoms_cartesian %}
|---|---|---|---|
| {{ b[0] }} | {{ "%.10f" % b[1]|float }} |
{{ "%.10f" % b[2]|float }} |
{{ "%.10f" % b[3]|float }} |
We are sorry, but your browser does not seem to support WebGL, so we cannot show 3D objects like the crystal structure.
You may want to visit this page to get a list of recent browsers that support WebGL.
Drag to rotate, scroll to zoom, double-click to enable/disable interaction
{{ warning|safe }}
| Metal Site | Prediction | Confidence Rating [?] | Predicted Probability | Predictions of Base Models [?] | Closest structures in the training set [?] |
|---|---|---|---|---|---|
| {{ prediction[0] }} | {{ prediction[1] }} |
low
high
|
{{ "%.3f" % prediction[2]|float }} | {{ prediction[3] }} | {{ prediction[6]|safe }} |
Click on the header to open a section
Note that the feature importance scores in this app are calculated with a crude estimate. The base value is the average prediction of our model, i.e. the prediction the model would give with uninformative features. The plot then shows the most important features that shift the prediction for a particular metal site away from the base value. The colorcoding indicates the direction into which the feature moves the prediction and the area indicates the importance.
{% for k, v in featurization_output.items() %}| feature | value (percentile position)[?] | category |
|---|---|---|
| {{ name }} | {{ "%.3f" % value[0]|float }} ({{ value[1] }}) | {{ value[3] }} |
Technical info: Time spent to compute quantities (featurization, prediction and interpretation): {{ '%.3f' % compute_time }} s. Model version: {{ model_version }}.
More information about the features and the model can be found in the preprint on Chemrxiv (DOI: 10.26434/chemrxiv.11604129.v1).


Trucks
id
Model
License plate
Produced year
Capacity
Average speed (in km)
Type of gas
Create
Ships/Argosy
id
Model
Serial number/Name
Produced year
Capacity
Average speed (in nm)
Count of team
Create
Cost of delivery
Transport Model
Cost (by 1 kg of cargo)
Cost (by 1 km of distance)
Create
Transports
Costs


import networkx as nx
import matplotlib.pyplot as plt
import warnings
from custom import load_data as cf
from itertools import combinations
warnings.filterwarnings('ignore')
%matplotlib inline
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = 'retina'
As usual, let's start by loading some network data. This time round, we have a physician trust network, but slightly modified such that it is undirected rather than directed.
This directed network captures innovation spread among 246 physicians in for towns in Illinois, Peoria, Bloomington, Quincy and Galesburg. The data was collected in 1966. A node represents a physician and an edge between two physicians shows that the left physician told that the righ physician is his friend or that he turns to the right physician if he needs advice or is interested in a discussion. There always only exists one edge between two nodes even if more than one of the listed conditions are true.
# Load the network. This network, while in reality is a directed graph,
# is intentionally converted to an undirected one for simplification.
G = cf.load_physicians_network()
# Make a Circos plot of the graph
from nxviz import CircosPlot
c = CircosPlot(G)
c.draw()
What can you infer about the structure of the graph from the Circos plot?
My answer: The structure is interesting. The graph looks like the physician trust network is comprised of discrete subnetworks.
We can leverage what we have learned in the previous notebook to identify special structures in a graph.
In a network, cliques are one of these special structures.
In a social network, cliques are groups of people in which everybody knows everybody.
Questions:
Let's try implementing a simple algorithm that finds out whether a node is present in a simple complex clique.
# Example code.
def in_triangle(G, node):
"""
Returns whether a given node is present in a triangle relationship or not.
"""
# Then, iterate over every pair of the node's neighbors.
for nbr1, nbr2 in combinations(G.neighbors(node), 2):
# Check to see if there is an edge between the node's neighbors.
# If there is an edge, then the given node is present in a triangle.
if G.has_edge(nbr1, nbr2):
# We return because any triangle that is present automatically
# satisfies the problem requirements.
return True
return False
in_triangle(G, 3)
In reality, NetworkX already has a function that counts the number of triangles that any given node is involved in. This is probably more useful than knowing whether a node is present in a triangle or not, but the above code was simply for practice.
nx.triangles(G, 3)
Can you write a function that takes in one node and its associated graph as an input, and returns a list or set of itself + all other nodes that it is in a triangle relationship with? Do not return the triplets, but the set
/list
of nodes. (5 min.)
Possible Implementation: If I check every pair of my neighbors, any pair that are also connected in the graph are in a triangle relationship with me.
Hint: Python's itertools
module has a combinations
function that may be useful.
Hint: NetworkX graphs have a .has_edge(node1, node2)
function that checks whether an edge exists between two nodes.
Verify your answer by drawing out the subgraph composed of those nodes.
# Possible answer
def get_triangles(G, node):
neighbors1 = set(G.neighbors(node))
triangle_nodes = set()
triangle_nodes.add(node)
"""
Fill in the rest of the code below.
"""
for nbr1, nbr2 in combinations(neighbors1, 2):
if G.has_edge(nbr1, nbr2):
triangle_nodes.add(nbr1)
triangle_nodes.add(nbr2)
return triangle_nodes
# Verify your answer with the following funciton call. Should return something of the form:
# {3, 9, 11, 41, 42, 67}
get_triangles(G, 3)
# Then, draw out those nodes.
nx.draw(G.subgraph(get_triangles(G, 3)), with_labels=True)
# Compare for yourself that those are the only triangles that node 3 is involved in.
neighbors3 = list(G.neighbors(3))
neighbors3.append(3)
nx.draw(G.subgraph(neighbors3), with_labels=True)
Now that we have some code that identifies closed triangles, we might want to see if we can do some friend recommendations by looking for open triangles.
Open triangles are like those that we described earlier on - A knows B and B knows C, but C's relationship with A isn't captured in the graph.
What are the two general scenarios for finding open triangles that a given node is involved in?
Can you write a function that identifies, for a given node, the other two nodes that it is involved with in an open triangle, if there is one? (5 min.)
Note: For this exercise, only consider the case when the node of interest is the centre node.
Possible Implementation: Check every pair of my neighbors, and if they are not connected to one another, then we are in an open triangle relationship.
def get_open_triangles(G, node):
"""
There are many ways to represent this. One may choose to represent
only the nodes involved in an open triangle; this is not the
approach taken here.
Rather, we have a code that explicitly enumrates every open triangle present.
"""
open_triangle_nodes = []
neighbors = set(G.neighbors(node))
for n1, n2 in combinations(neighbors, 2):
if not G.has_edge(n1, n2):
open_triangle_nodes.append([n1, node, n2])
return open_triangle_nodes
# # Uncomment the following code if you want to draw out each of the triplets.
# nodes = get_open_triangles(G, 2)
# for i, triplet in enumerate(nodes):
# fig = plt.figure(i)
# nx.draw(G.subgraph(triplet), with_labels=True)
print(get_open_triangles(G, 3))
len(get_open_triangles(G, 3))
Triangle closure is also the core idea behind social networks' friend recommendation systems; of course, it's definitely more complicated than what we've implemented here.
We have figured out how to find triangles. Now, let's find out what cliques are present in the network. Recall: what is the definition of a clique?
n
include all cliques of size < n
list(nx.find_cliques(G))[0:20]
Try writing a function maximal_cliques_of_size(size, G)
that implements a search for all maximal cliques of a given size. (3 min.)
def maximal_cliqes_of_size(size, G):
# Defensive programming check.
assert isinstance(size, int), "size has to be an integer"
assert size >= 2, "cliques are of size 2 or greater."
return [i for i in list(nx.find_cliques(G)) if len(i) == size]
maximal_cliqes_of_size(2, G)[0:20]
From Wikipedia:
In graph theory, a connected component (or just component) of an undirected graph is a subgraph in which any two vertices are connected to each other by paths, and which is connected to no additional vertices in the supergraph.
NetworkX also implements a function that identifies connected component subgraphs.
Remember how based on the Circos plot above, we had this hypothesis that the physician trust network may be divided into subgraphs. Let's check that, and see if we can redraw the Circos visualization.
ccsubgraphs = list(nx.connected_component_subgraphs(G))
ccsubgraphs
Draw a circos plot of the graph, but now colour and order the nodes by their connected component subgraph. (5 min.)
Recall Circos API:
c = CircosPlot(G, node_order='...', node_color='...')
c.draw()
plt.show() # or plt.savefig(...)
# Start by labelling each node in the master graph G by some number
# that represents the subgraph that contains the node.
for i, g in enumerate(ccsubgraphs):
for n in g.nodes():
G.node[n]['subgraph'] = i
c = CircosPlot(G, node_color='subgraph', node_order='subgraph')
c.draw()
plt.savefig('images/physicians.png', dpi=300)
And "admire" the division of the US congress over the years...


Protracker and FastTracker compatible: load, edit and save .mod and .xm files
Allows up to 32 channels
Has a built-in sample editor
Midi support
Imports 8-bit WAV files, 8-Bit Sampled Voice (8SVX) files, and RAW samples (as well as any other format the AudioContext interface can read, such as .mp3 and .ogg)
Exports to .wav and .mp3 formats
Includes the historic ST-01 and ST-02 sample disks, released in 1987 (as well as a bunch of others)
Uses the modarchive API to give access to several thousands of music files
Uses the modules.pl API to give access to even more music files
Can connect to your Dropbox account to directly read and write your own files
Includes a lightweight player library which you can use in your own projects
If words like Trackers, Samples, MOD and XM don't sound familiar and you're interested in the roots of home-produced digital music, read the "What is a tracker" section, or watch this
highly entertaining video to get a feel of what's it all about.


This section contains current and archived manuals for PostgreSQL users. You can read the release notes, and view a listing of books written about PostgreSQL.


---
layout: post
title: Dancing feet Storyboards frames 20 29
date: '2017-07-06T10:30:32+02:00'
categories:
- Storyboards
- Sketches
picture_url: https://film-Storyboards.tumblr.com/post/162670997469/dancing-feet-Storyboards-frames-20-29
---
Dancing Feet Storyboards — frames 20 to 29.
Concept: Daniel Cloud Campos
Production: QUAD


{{ title }}
{% for beverage in kitchen.beverages %}
{{ beverage }} queue:
{% for brew in kitchen.beverage_queue(beverage) %}
{{ brew.person }} : {{ brew.status }}
{% else %}
Nothing brewing.
{% endfor %}
{% endfor %}


Urbo is a platform that allows people to find and connect with local business, events and offers.
A platform designed to track a topic in social media (twitter, facebook) and your organization users impact on that topic.
Online store - custom CRM with booking capabilities, client management and multiple stores domain
A custom made real estate CRM with properties mapping to a image diagram, prospects management, multi-tenant real state developments, sellers management, etc.
A project for the local justice bureau, that aimed to help in the representation of the crime records in the Pachuca metropolitan area.
A custom CRM for a truck assembler company. Modules included international logistics, storage, sells, assembly line production, accountability and customer service.
SleepyLand was a mobile app which introduced memorable and fun characters to kids, and let them call their favourite one via a 'call center' like service directly from the app.
Euphorye is an accountant software designed to comply with the Mexican laws, it made the managing of invoices simple, yet flexible enoguh to adapt to any organization.
A Job Bank for the Miguel Hidalgo Municipality in Mexico City.