Visual Dashboard for Real-Time Analysis of Social Media
School of Computing and Information Systems, Singapore Management University
Detecting abnormal events, such as disaster or crisis, from microblog social media has become a trend, as social media has played a pervasive role in the way people behave and think. Nowadays, people are also using time-stamped, geo -located data to share live information about what’s happening in their surroundings, which enables the public, government and researchers to identify abnormal events in community more quickly and take immediate actions.
To better analyzing and visualizing social media texts, several text analytics techniques can be applied, such as word cloud, topic modeling, network analysis, geospatial analysis and so on. In real-world practices, researchers have built various social media text visualization using different techniques, but their solutions lack an integrated way of working.
In order to build a comprehend visualization dashboard with an interactive user interface, we built the application based on R shiny - a web application framework to create interactive web applications - and text analytical R packages.
Our research is motivated by the lack of integrated and comprehensive real-time social-media dashboard. We aim to apply appropriate text analytics method and visually driven data analysis techniques in R language and R shiny to provide a handy analytic tool to help users understand the social-media posting through social media post analytics approaches.
VAST Challenge 2021 Mini-Challenge 3 poses a social media and text analysis challenge to detect the meaningful event reports, evaluate risk level, public influence and suggest team of first response based on location-stamped posts. It provides the collection of microblogs and emergency calls from the event day and geospatial map of the corresponding areas.
| No | Dataset | Information |
|---|---|---|
| 1 | Aliba.shp | Aliba geospatial map file with geometry information. |
| 2 | csv-1700-1830,csv-1831-2000,csv-2001-2131 | Call Center and Blog information during time period from 1700 to 2131 on January 23,2014, with 5 variables – date, author, message, hotspot location, latitude and longitude |
Summary of datasets at glance
• Temporal Pattern: Temporal pattern distribution of the number of posts from various source such as microblog posts vs. call center messages, tweets vs. retweets given the specific time period.
• Word Cloud based on frequency and cooccurrence rate: straightforward text analysis provides a overview of content being discussed and help to highlight past events that occurred from various sources
Customized parameters for LDA topic modeling
By selecting time range and LDA topic modeling parameters, the text modeling will perform dynamically in the backend. With the LDA results, users can futher explores top meaningful words in each topic, identify the peaks of key major events at certain point of time through topic trend distribution and profile users based on their posts contributions to the topics.
With this VisNetwork object, we visuliaze the retweets network and draw the histograms of right skewed centrality distribution graph indicating patterns of the information dissemination. A variety of insights would be derived through different centrality measurements. For instance, most popular and active users could be identified by in-degree and out-degree centrality. And most influential users can also be detected by closeness and eigenvector centrality.
To visually display the social media contents，we first plotted location-stamped text and call data on the leaflet map (via extraction and st_intersection of cross junctions). Then, we extracted hexagon binning map by using MMQGIS grid layer function in QGIS application.Subsequently, we identified the location-stamped posts intersection with the hexagon and counted the number of posts within each hexagon areas. By plotting the hexagon layer with color gradient over Aliba map, we then visualized the hexagon binning distribution map.
From initial exploratory analysis, we observe from the spikes that the frequency of microblogs and calls received are in synced. The number of retweets and tweets also corresponds to each other, most likely by people affected during that period of time.
The top trending hashtags observed are “#POKRally” and “#KronosStar”, while “ #DancingDolphinFire” and “ #standoff” interestingly appears towards the end of the timeline. A deeper analysis of tagged user shows that top users such as “@ centralbulletin” and “@ newsonlinetoday”, which denotes the official accounts of Abila media outlets that are frequently tagged or retweeted by public.
Topic Modeling using microblog data and with 10 topics of interest across the entire evening shows that the main events occurred were: 1.Fire at Dancing Dolphin building (from 6:40pm to about 8pm) 2.Black Van incident and Shooting Standoff at gelato galore (from 7:40pm onwards) 3. POK Rally event (from 5pm to 6pm).
We can tell from the in-degree distribution graph that the top 3 users which are being retweeted a lot by other users are news media AbilaPost, KronosStar and HomelandIlluminations.
From out-degree centrality, WiseWords, BlueVelvet, SaraJane and rnbwBrite are the most active users. Interestingly, for the eigenvector centrality distribution, the 6 out of top 10 tweet users are the same as that in in-degree centrality distribution. Only BlueVelvet, rnbwBrite and wiseWords are different.
The final event of the evening denotes another serious incident, given the high number of messages (both microblog and calls) received within the same area. We also noticed that the black van hit-and-run incident seems to be connected to the shooting as well, given the call messages locations tracked along the streets until around 7:39pm. Given the call message for SWAT team at 7:44pm, and that the estimated start time for this event is around 7:40pm, we noticed a slight lag in time but nonetheless almost in-synced.
In this work we presented an integrated visual analytics dashboard for public and government analysis of social media data. We applied multiple text mining and visualization te chniques for social events response based on text content vitalization, network exploration and spatio-temporal analysis. For abnormal event response, we demonstrated a analyze flow to verify and examine the dashboard application. For future work, we will research and include more text mining techniques and provide more functional extension to the dashboard, and we hope to apply the concept of the framework design for more real-world text data analysis in response to time, entity relationship, and location.
Visual Dashboard for Real-Time Analysis of Social Media


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The Geometry Defined¶
Ground-floor newbie or penthouse-suite expert, sometimes we could all use a refresher. If you need info on planet angles or spins, go here.
-
reflectdirect.
Geometry_Reference
(ref_save=False, _active=False, incD=85, oblD=0, solD=0, ratRO=10.0, phaseD=[0], ph_colors=['k'], name='NONE', reference=True)¶ Makes a reference diagram about exoplanetary systems.
For example, this shows how important angles are defined. See Appendix A of Schwartz et al. (2016).
- Args:
- ref_save (bool):
- Save the diagram as “geometry_reference.pdf” in the current working directory. Default is False.
Note
Starting with
_active
, ignore the remaining arguments. These are used by the classDirectImaging_Planet
for the interactive functionSandbox_Reflection()
.


sa-nginx-container
Helper role to be executed with ansible-container
aiming to build nginx based service for your application. Role is based on sa-nginx
role,
your docker image might be any of ubuntu (14.04 LTS / 16.04 LTS), CentOS 7+, Fedora 25+, Alpine (3.4. 3.5 +)
Role is best used in conjuntion with previously applied https://github.com/softasap/sa-container-bootstrap role.
Please check sa-container-bootstrap
role for options.
Role can be used on a standalone basis, including dumb-init support out of the box.
Variables
User inside container used to run
container_user: nginx
Container's nginx user id
container_uid: 1000
Container init system used in base image. Can be unset (you are using smth on your own control)
container_init: "dumb-init" # phusion-init
dumb_init_version: "1.2.0"
Directory to place nginx pid
nginx_pid_dir: /run/nginx
Interoperability with other containers - where to place static content, and which directories need to be rsynced inside container.
nginx_static_dir: /static
# directories to be copied inside docker container
nginx_asset_dirs: []
#directories already mapped inside docker container during build
nginx_container_asset_dirs: []
In case if you don't provide your own nginx conf, container will use sa-nginx
scheme: site configs to be placed to /etc/nginx/sites-enabled
, default nginx.conf
might be adjusted by specifying list of corrections in nginx_conf_properties
nginx_conf_properties:
- {
regexp: "^daemon *",
line: "daemon off;",
insertbefore: "BOF"
}
- {
regexp: "^worker_processes *",
line: "worker_processes auto;",
insertbefore: "BOF"
}
- {
regexp: "^pid *",
line: "pid {{nginx_pid_dir}}/nginx.pid;",
insertbefore: "BOF"
}
Code in action
container.yml
we install nginx container role, and configure our application by applying some project specific role in order to provide needed nginx tuning.
version: "2"
settings:
conductor_base: ubuntu:16.04
volumes:
- temp-space:/tmp # Used to copy static content between containers
services:
www:
from: ubuntu:16.04
container_name: www
roles:
- {
role: "softasap.sa-nginx-container"
}
- {
role: "../standalone-fallback"
}
volumes:
temp-space:
docker: {}
See box-example for the standalone working example. It will configure application image that will display 'OK' on connect - check it out:
Usage with sa-container-bootstrap
2BD
Temporary hints
(1)
at a moment ansible-container is highly under development. You might spot issues, that are fixed in develop branch only.
In that case you might need to install ansible-container from source, i.e.
git clone https://github.com/ansible/ansible-container.git
cd ansible-container
git checkout develop
pip install -e .[docker,openshift]
If for some reason install is messed (manual packages updates, removals, etc) - try pip install with --ignore-installed
flag.
later, when issue fix is released - to uninstall package installed in that way from source:
At {virtualenv}/lib/python2.7/site-packages/ (if not using virtualenv then {system_dir, like /usr/local}/lib/python2.7/dist-packages/)
remove the egg file (e.g. ansible-container.egg-link) if there is any;
from file easy-install.pth, remove the corresponding line (it should be a path to the source directory or of an egg file).
(2)
When using box-example, pay attention to container.yml
, in particular, conductor_base
should be derived
from the same distribution as you're building your target containers with, check list of currently supported base systems:
(3) If your system services are derived from different OS base images, than ... ?
Copyright and license
Code licensed under the [BSD 3 clause] (https://opensource.org/licenses/BSD-3-Clause) or the [MIT License] (http://opensource.org/licenses/MIT).
Subscribe for roles updates at [FB] (https://www.facebook.com/SoftAsap/)
Join gitter discussion channel at Gitter


General Protection Fault
Garfield Espanol
Garfield
Sinfest
Least I Could Do
Glasbergen
Dilbert
Blic Strip
Menage a 3
Looking For Group
Up to Four players
Cheer Up Emo Kid


Real-Time MARTA Train Departures
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**Departures at terminal stations and starred times are based on the schedule and not real-time data. Real-time data represents arrival times. This data is used to fill gaps not supplied by real-time data. Trains may hold at terminal stations for scheduled departures.
Scheduled and real-time data supplied by MARTA. Actual arrivals and departures may vary. We are not responsible for your commute. Thank you for using NavDash!


I have a degree in informatics engineering and I focus mostly on web and mobile development.
I'm very familiar with the most common web tools and I'm quick to learn and adapt.
I have experience with:
- React
- HTML5 & CSS3
- Javascript & JQuery
- ASP.NET MVC
- .Net Framework
- Xamarin Platform


“One thing you can’t get or replicate in any club or group is the close bond as brothers we share. I love the fact that no matter the time or place, there will always be a Brother selfless enough to help.”
– Jing “Nexus” Bao
“I saw an opportunity to develop not only myself but something that was bigger than any one individual. I wanted to be able to look back at my college career and say I took full advantage of what Greek Life had to offer and have a brotherhood, a family, that would last me way beyond just college.”
– Max “Zuko” Kohutich
“Being a brother of Nu Alpha Phi means that you can talk to any brother, present or past, without hesitation or awkwardness. There is a world of connections in different veins of the world present in Nu Alpha Phi, and they are all within your grasp."
– Karun “Avatar” Sekhar
We are the Theta Chapter of Nu Alpha Phi Fraternity Inc. founded here on September 21st, 2008. We are an Asian interest but not an Asian exclusive Brotherhood. We stand proudly honoring the 7 principles of our fraternity including Brotherhood, Discipline, Leadership, Pride, Professionalism, Service, and Unity. Though we are steadily increasing, we do not pride ourselves shallowly on numbers, but in remaining true to the fundamentals that the Founding Fathers have passed down to us, being exclusive to those who truly deserve to belong to such an outstanding group of men, those who are accurate representations of our Seven Principles, and those who really have a powerful urge to don and represent the Greek letters that we deem sacred. Don’t be shy, meet our brothers, come out to our Rush events and see if you are meant to be a part of an entity that will go beyond your college years, a genuine Brotherhood for life.
Learn more about what makes Nu Alpha Phi the tighest brotherhood.
Get to know the brothers of the active house!
Meet, network, and mingle with the sisters of an all-inclusive Greek life!
On March 18th, 1994 eight great men founded the first chapter of Nu Alpha Phi Fraternity Inc. at SUNY Albany.
On September 21st, 2008, eight bold men founded the eighth chapter of Nu Alpha Phi Fraternity Inc. at Stevens Institute of Technology.
The fraternity professes to create a bond of brotherhood passed down from generation to generation, including lasting relationships between alumni and currently active members.


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In this project we are going to do predict car price prediction based on some of the features like Fuel, Year and Transmission type and so on.
In this project we are going to do predict Flight Fare prediction based on some of the features like Airlince Name, Date of journey Destination, Arrival.
In this project we are going to do classifying Heart Disease based on some of the conditions like BP and Diabetes, BMI and Weight and so on.
In this project we are going to do predict wheather a person will got diabetes or not based on some of the conditions like BMI Insulin level, glucose level and so on.
In this project we are going to do predict Cricket Score based on some of the features like Overs, Wickets, Runs below the 5 overs and Runs after 15 overs and so on.
In this project we are going to do classifying or Detecting the car brands based on some of the car images using CNN algorithms and, used on some of the state-of-the-art-algorithms.
In this project we are going to do classifying or Detecting various kind of fashion things like T-shirt, sneakers, coat & trouser using CNN algorithms and, used some of the state-of-the-art-algorithms.
In this project we are going to do classifying various real-world objects using trained by ImageNet dataset. As well over here I was used CNN & some Transfer learning techniques for classifying particular object.
In this project I was implemented Customer churn model for predicting a particular customer will exit from the bank or not. Over here I was using ANN for predicting the churn model.
In this project we are going to do classifying or Detecting wheather a particular cotton leaf was affected by any plant diseases or not using CNN algorithms and, used on some of the state-of-the-art-algorithms.
In this project we are going to do classifying or Detecting the malaria affected cell images. Over here we go to dected a particular cell image is parasite or unaffected image using CNN algorithms and, used on some of the state-of-the-art-algorithms.
In this project we are going Predict stock price in the marketing domain using LSTM RNN deep learning method.
In this project we are going detect wheather a particular image is cat or dog using CNN.
In this project we are going to do crerate a movie Recommandation Engine with sentiment Analysis. Using the Movie dataset from kaggle and download the data from MVDB and API Key Generation.
In this project we are going classify wheather a particular statement of news is spam or not spam or evan we can say its real or fake news using LSTM RNN in Natural Language Processing.
In this project we are going to do make some fun using one of the most computer vision library called OpenCV. It's just hinding our clothes based on matrix color code.
In this project we are going to do make a AI system for find out wheather a particular person wear a mask or not based on trained the multiple mask and without mask images in CNN Network in Deep Learning and Computer Vision.
In this Hand Gesture Recognition project, we’ve built a hand gesture recognizer using OpenCV and python. We’ve used MediaPipe and Tensorflow framework for the detection and gesture recognition respectively. Here we’ve learned about the basics of the Neural Network, File handling, some common image processing techniques, etc.
In this project we are going to learn how to use Gesture Control to change the volume of a computer. We first look into hand tracking and then we will use the hand landmarks to find gesture of our hand to change the volume.
In this Project we are going to Implement how to count fingers. We first look into hand tracking and then we will use the hand landmarks to count of the fingers. And all of this will be happening in Realtime and requires close to no installations and configurations.


The global link list right under the navigation offers the ability to add links to your project. This can be done by clicking on the '+' button in the top left, via drag and drop or via pasting a previously copied link while the application has focus.
Links can be edited and deleted via the buttons that appear when hovering over a link.
Files can also be linked as can custom commands (be careful with this!).
Here's an example to see what everything does.


#! /usr/bin/python
# -*- coding: utf8 -*-
import tensorflow as tf
import numpy as np
from six.moves import xrange
[docs]def discount_episode_rewards(rewards, gamma=0.99):
""" Take 1D float array of rewards and compute discounted rewards for an
episode. When encount a non-zero value, consider as the end a of an episode.
Parameters
----------
rewards : numpy list
a list of rewards
gamma : float
discounted factor
Examples
----------
>>> rewards = np.asarray([0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1])
>>> gamma = 0.9
>>> discount_rewards = tl.rein.discount_episode_rewards(rewards, gamma)
>>> print(discount_rewards)
... [ 0.72899997 0.81 0.89999998 1. 0.72899997 0.81
... 0.89999998 1. 0.72899997 0.81 0.89999998 1. ]
"""
discounted_r = np.zeros_like(rewards, dtype=np.float32)
running_add = 0
for t in reversed(xrange(0, rewards.size)):
if rewards[t] != 0: running_add = 0
running_add = running_add * gamma + rewards[t]
discounted_r[t] = running_add
return discounted_r
[docs]def cross_entropy_reward_loss(logits, actions, rewards):
""" Calculate the loss for Policy Gradient Network.
Parameters
----------
logits : tensor
The network outputs without softmax. This function implements softmax
inside.
actions : tensor/ placeholder
The agent actions.
rewards : tensor/ placeholder
The rewards.
Examples
----------
>>> states_batch_pl = tf.placeholder(tf.float32, shape=[None, D]) # observation for training
>>> network = tl.layers.InputLayer(states_batch_pl, name='input_layer')
>>> network = tl.layers.DenseLayer(network, n_units=H, act = tf.nn.relu, name='relu1')
>>> network = tl.layers.DenseLayer(network, n_units=3, act = tl.activation.identity, name='output_layer')
>>> probs = network.outputs
>>> sampling_prob = tf.nn.softmax(probs)
>>> actions_batch_pl = tf.placeholder(tf.int32, shape=[None])
>>> discount_rewards_batch_pl = tf.placeholder(tf.float32, shape=[None])
>>> loss = cross_entropy_reward_loss(probs, actions_batch_pl, discount_rewards_batch_pl)
>>> train_op = tf.train.RMSPropOptimizer(learning_rate, decay_rate).minimize(loss)
"""
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, actions)
loss = tf.reduce_sum(tf.mul(cross_entropy, rewards)) # element-wise mul
return loss


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object --+ | RedisBase
Base helper class for redis communication
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Binds lookup keys to a string to query in redis. Examples: __set_key_string('user', 'id', email') returns 'user:id:email' __set_key_string('user', 'id') returns 'user:id'
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Sets a database lock with a timeout in case the user does not unlock it
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Releases a lock if it still exists
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Adds the value broken down into 1st letter, 1st + 2nd, 1st + 2nd + ... Should not be called directly, only used in another method adding a user
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Kodak Black Gets Bad News: Two More Gun Charges Could Mean 30 Years In Jail Each
by Alexis StoneKodak Black is definitely not living his best life these days. Just after he’s been sentenced to about four years in federal prison, he has been hit with two more weapon charges that could cost him even more time.
The Shade Room cited the info from Miami Herald and reported that ‘the Miami-Dade State Attorney’s office formally charged Kodak with two counts of possession of a firearm by a convicted felon today.’
TSR continued and said that ‘If convicted on these new charges, Kodak could serve up to 30 years in prison on each count as a “habitual violent offender.”’
It’s been also revealed that according to court documents, prosecutors filed one count for a gun purchased by Kodak in January as you can see in TSR’s post below.

Another follower said: ‘😂 rappers be having millions but won’t pay a couple hundred on a gun license lmaooo!!’
Someone posted: ‘When all he had to do,… was stay away from guns??!!!! How tha fk hard is that for real????’
 SHOOTING & KILLING HIM? White privilege is REAL.’
A person said: ‘That’s Strong, but he should’ve respected his freedom 🤷🏾♀ minded his business and made his music.’
Not too long ago, attorney Lee Pearlman chatted with HollywoodLife, revealing that the rapper has few chances to be released earlier on.
Pearlman explained that the 46 months he will have to remain in prison is ‘a reasonable amount’ of time.
But since then, as you read above, two more charges came along. Stay tuned for more info about Kodak’s case.


Berlin airport to open in 2020 after nine-year delay
by AFP Relax NewsBerlin's new international airport is set to open on October 31, 2020, its operating company said Friday, after an embarrassing nine-year delay owing to structural problems and corruption.
"The BER will open on 31.10.2020," Flughafen Berlin Brandenburg (FBB) said on Twitter, using the international code for the airport.
FBB said chief executive Engelbert Luetke Daldrup had informed the airport operator's board of the new date.
Mismanagement of the project in the Brandenburg region that surrounds Berlin has dented Germany's pride in its engineering prowess and reputation for honesty in business.
The airport was planned in the 1990s and construction began in 2006.
It was originally to open in 2011 but the date has been repeatedly pushed back over a series of issues, including fire safety.
In 2016, a former manager was jailed for accepting a bribe.
Meanwhile, the airport's initial budget of 1.7 billion euros ($1.9 billion) has more than tripled as the delay dragged on.
Brandenburg regional chief Dietmar Woidke hailed Friday's announcement, saying "this time it's going to work."
The airport is intended to eventually replace Berlin's two existing airports -- Schoenefeld and Tegel.
But, in a referendum in 2017, Berliners voted to keep Tegel, arguing that with the growing number of arrivals the new airport may already be too small when it opens.


# RecipeBook
| Ingredients | Quantity |
|---|---|
| Banana | 4 medium ripe |
| Chocolate | 16 ounces |
| Popsicle Sticks | 4 wooden |
Step 1: Peel the four bananas.
Step 2: Melt chocolate on low heat in medium sauce pan.
Step 3: Put popsicle stick inside banana, leaving enough out to make a handle.
Step 4: Dip banana in chocolate. Coat completely.
Step 5: Place chocolate coated banana on a cookie sheet.
Step 6: Place in freeze for one hour or until frozen.
| Ingredients | Quantity |
|---|---|
| Cream Cheese | 2 16oz packages |
| White Sugar | 1 cup |
| Graham Cracker | 1 pie crust |
| Cherry Pie Filling | 16 ounce |
Step 1: Soften Cream Cheese by leaving on counter for 3 hours.
Step 2: Mix Cream Cheese and White Sugar in a bowl until a smooth consistency.
Step 3: Place mixture in Graham Cracker Pie.
Step 4: Pour Cherry Pie Filling on top of mixture.
Step 5: Put Pie in the refrigerator to let set for 3 hours.
Step 6: Take Pie out, cut into 8 pieces, and eat!
| Ingredients | Quantity |
|---|---|
| Apples | 10 |
| Brown Sugar | 1 Cup |
| Oatmeal | 3 Cups |
| Butter | 8 Ounces |
Step 1: Soften butter by microwaving 1 minute or until soft.
Step 2: Mix Brown Sugar, Oatmeal, Butter in a bowl until crumbly. Set aside.
Step 3: Wash and cut apples into 8 slices.
Step 4: Place apples into a greased 8 x 10 baking pan.
Step 5: Evenly sprinkle the mixture you made on top of the apple slices.
Step 6: Bake in preheated 350 degree oven for 1 hour
Step 7: Take out of oven. Let cool for 10 minutes. Cut into 6 even portions.
Source: Taste of Home more recipes


About Me
Hello my name is Jimmy Zebroski.  I work at a pizza restraunt called "Sahara Pizza." In my free time I like to Golf, Fish, Play video games and hangout with my friends.
Watch this video, How to write a short professional bio Good Day! My name is Jimmy Zebroski. I'm currently a student at the Year Up program who is eager to learn more and do my best in the technical field. Before I attended the Year Up program I completed a full 6 month Web/Software development boot camp in downtown Seattle. Here I learned a lot of new skills HTML,CSS,JavaScript,Git,React,C# and more. As for the program itself I'm currently reviewing some of the things I learned at my boot camp which are HTML,CSS, Git, and JavaScript. While learning new skills like Excel and Business Analytics. All while educating myself to operate like a professional in the tech field. Once completing the program I hope to acquire a job where I can continue to grow as a professional and continue my education into becoming a well rounded Full-Stack Developer.
Page Origin
The HTML/CSS comes from Sara Newman's WEB110 class as taught at Seattle Central. This page provides a basis for students to build a responsive portal page.
Copyright [2014-2020] [Sara Newman]. Licensed under the Apache License, Version 2.0


 I didn't know that I... 'til I tasted you
I didn't know that I...  I didn't know that I... 'til I tasted you
I didn't know that I... I didn't know that I... 'til I tasted you
By the way, right away you do things to my body
I didn't know that I was starving 'til I tasted you
You, yeah, 'til I tasted you
I didn't know that I... I didn't know that I... 'til I tasted you
By the way,right away you do things to my body
I didn't know that I was starving 'til I tasted you
The more that I know you, the more I want to
Something inside me's changed
I was so much younger yesterday


This project was created for my module CMP304 at university. This module looks at AI technologies which are being used or could be used in the future to enhance intelligent game systems.
This involves looking at traditional AI systems such as pathfinding, rule based system, finite state machines etc. Then researching technology such as ML and neural networks.
The assesment for this project was broken into two parts. One part for a comparision of two AI techniques/technologies and the other for ML based emotion recognition.
The technologies that I chose were RL and GA. To test these I created a game where the AI had to throw a ball into a number of cups (think of beer pong).
The source code for the GA & RL can be found on Github here
The source code for the emotion recognition can be found on Github here
GA video:
Emotion recognition video:


An Open Collective is a group of people who collects money to do something together in full transparency: Everyone who puts money in the collective can follow the money and see all the transactions.
If you have an organization, you can use OpenCollective to empower local chapters to raise money locally and have their own budget. See OpenCollective For Organizations.
OpenCollective is ideal for meetups, open source projects, parent associations, neighborhood associations, clubs, unions, movements, non-profits, business incubators, etc.
Because you want to easily collect money online for a group and you want to give visibility to everyone involved on how the money collected will be used.
All those crowdfunding platforms are awesome to get money from the crowd for well defined products or for a "one-off" cause that is limited in time. OpenCollective on the other hand is meant to collect money from the crowd (or from members) to finance the activities of a group that has a well defined purpose.
Because on OpenCollective backers don't expect to receive goods, we give them full visibility on how their money is being used by the collective. Their money doesn't go into a black box. Everyone who puts money in a collective can "follow the money".
When you create a collective, you connect it to your PayPal account. The money collected goes directly to your account. Whenever you –or a member of the collective– wants to use the money, they submit an expense via the mobile app. Once you approve it, the person who submitted the expense is automatically reimbursed on their personal PayPal account. The budget and all the expenses are visible to everyone who put money in the collective.
We are currently in private beta. Contact us for pricing details.
Right now, only US based organizations and individuals can create an open collective, but there is no restriction on members and donors.
But fear not, as soon as we figure things out here, we will very quickly expand to new territories based on where the demand is the highest (so let us know!).
OpenCollective For Organizations lets you empower local chapters to raise money and have their own budget without having to create a legal entity and a bank account. No more asking a local sponsor to directly pay for the pizzas!
OpenCollective sits on top of the PayPal account of your organization. It creates virtual groups for each of your chapters. You can define the budget for each of them. You can also choose to let them have their own dedicated page to raise money locally to increase their budget (and optionally keep a percentage for the main organization). Legally speaking, this is the equivalent of acting as a "Fiscal Sponsor".
We are currently in private beta. Contact us and we'll work with you.
We did a first pre-seed round of $500k in October 2015 (SAFE, $5M cap) with:
We want to empower people to create the organizations and institutions of tomorrow.
After Storify (my first startup in Silicon Valley), one of my options was to go back to Belgium (where I’m originally from) and start a new political party or a “union 2.0”. Why? Because 90%+ of the young people there don’t feel represented by existing political parties or unions (and I’m pretty sure it’s the same in your country). All those institutions were founded 100+ years ago at a time when paper was the only two-way communication channel. They lack feedback loops, they lack transparency and as a result, they are increasingly disconnected from their base. We need new institutions for this Internet era.
So I could spend the next 5-10 years of my life doing that. But who cares about Belgium? It doesn’t scale. Instead, I wondered: “How can I empower others to go build those political parties and unions of tomorrow?”; “What tools do people need to create those organizations?”.
While the Internet has been really good so far at helping people get together and do things together, it has been quite bad at dealing with money. As a result, we’ve seen a lot of movements popping up here and there that ended up disappearing as quickly. The harsh reality of this world is that without money, you can’t sustain a group over time.
The biggest notable exception is Podemos, the new political party in Spain that started in 2014 and who quickly became the second largest political party just a year later. Their secret? They were able to get 285,000+ people to become members and pay a minimum of 5 euros (~$6) per month to finance and sustain the movement.
Unfortunately, it’s still too complicated for a group to quickly collect money for their cause.
I experienced it first hand when I lead the Startup Manifesto in Belgium.  At some point, we wanted to print stickers for a conference but we didn’t have money. Plenty of people were willing to support our cause and make a donation. But in order to accept that money, we would have to create a bank account, which requires first to create a legal entity. We had better things to do. Not to mention that the money would have been collected in a black box where all supporters would have zero visibility. That was against our values. So we just gave up on the idea of collecting money. And an existing organization simply paid the stickers for us.
That’s why I’m building OpenCollective.
I want to empower people to do things together with money. I want to empower people to go build the organizations, movements, associations and institutions of tomorrow in a bottom-up fashion where transparency is built in by design.
– Xavier, founder


DBC Reflections
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Ristretto is looking for talented event baristas, and hospitality pros as Freelancers and for contracts in 2017.
-
- A1 Standart
- UK/EU drivers’ licence
- Available for nationwide
events
- Superb hospitality skills
- Work well as part of our
team
- Are we testing this right?
-
- B1 Standart
- UK/EU drivers’ licence
- Available for nationwide
events
- Available for short term contract
-
- Right to work in the UK
- Experience of driving heavy vehicles in London
- Fluent in English
- Clean driver's licence


What is ICSE 2014? As you all know that ICSE is one of the greatest conferences on software engieering, I would like to know details of the conference this year.
ICSE, the International Conference on Software Engineering,(R) is the premier software engineering conference, providing a forum for researchers, practitioners and educators to present and discuss the most recent innovations, trends, experiences and concerns in the field of software engineering. Note that ICSE: International Conference on Software Engineering(R) is a registered trademark. Learn more...
To read answers more, enter your username and password to log in.


stk.molecular.topologies.cof module¶
Defines COF topologies.
How the assembly process works for 2D COF topologies.¶
Linker topologies.¶
Linker topologies are those which use a building block with two
functional groups and a building block with three or more functional
groups. These are implemented as subclasses to
LinkerCOFLattice
.
As for any assembly process, there are 2 main stages: placement of building blocks and yielding of functional groups which react.
A 2D COF topology can be defined through vertices and the edges which
connect them. The building block with 3 or more functional groups
sits on the vertices while the building block with 2 functional groups
sits on the edges. To assemble a COF topology, first the vertices
and edges are defined. They are represented by the classes
Vertex
and Edge
. Each of these objects defines their
positions in terms of the fractional coordinates along the
a
, b
and c
vectors of a unit cell. In addition, each
of these objects defines a set of “fg positiions”.
For edges, there are two fg positions, 0 and 1. 0 is the position of the fg which gets connected to an fg on the first vertex connected by the edge, while 1 is the position of the fg which gets connected to the second vertex connected by the edge. Identification of which fg of a di-functionalized building block sits on which fg position is done in the following way. Check the distance of each fg to the first vertex. The closer one is on position 0, the further one is on position 1.
For vertices there are multiple fg positions. The number of
fg positions on a vertex is equal to the number of functional
groups the building block placed on the vertex has. The fg
positions are labelled from 0
to n
where n
is one less than
the number of functional groups in the building block. Going clockwise
around the vertex, starting at the 12 o clock position, the positions
are labelled starting at 0. After placing and aligning a building block
on a vertex, it is necessary to identify which fg sits on
which fg position. Alignment is done by rotating the building block
around the z axis so that the distance of one of the fgs
and one of the edges is minimized. Because each edge keeps a record
of which position it connects, the aligned fg can be assigned to
a position. Then the fgs are assigned to the next positions
in clockwise order.
To join molecules simply go through all the edges in a topology. Each
edge holds the vertices it is connected to and the positions on those
vertices it connects. Each edge and vertex defines
a dictionary which maps the position to the fg which sits on
it. For each edge take the fg at position 0
and the fg
position on the vertex which is connceted to it. Use the fg
position on the connected vertex to get the id of the fg which
is to be connected. The same can be done for the fg at position
1
on the edge.
Periodic bonds are not added to the rdkit molecule, instead they
are registered in Periodic.periodic_bonds
.
-
class
stk.molecular.topologies.cof.
COFLattice
(scale_func=<function bb_size>)¶ Bases:
stk.molecular.topologies.base.Topology
A base class for periodic topologies.
This class behaves almost exactly like
Topology
with only minor additions to suit the representation of COF periodic lattices. Thedel_atoms()
method is extended to collect the coordinates of any deleted atoms. This is necessary for positioning terminating atoms on islands generated from the periodic structure byPeriodic.island()
.-
scale_func
¶ A function which takes a
MacroMolecule
as its only argument returns a number. The number is used to scale the size of the unit cell. Seebb_size()
for an example.Type: function
-
bonded_fgs
(macro_mol)¶ An iterator which yields functional groups to be bonded.
This iterator must yield
tuple`s of :class:
.FunctionalGroup` molecules. These are the functional groups in the macromolecule which need to be bonded.This
tuple
gets passed toReactor.react()
.Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Raises: NotImplementedError
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
place_mols
(macro_mol)¶ Places building blocks.
The
rdkit
molecules of the building blocks are combined into a singlerdkit
molecule and placed into macro_mol.mol.The function is also reponsible for updating
bb_counter
.This function must also add the tags
'bb_index'
and'mol_index'
to every atom in the molecule. The'bb_index'
tag identifies which building block the atom belongs to. The building block is identified by its index withinMacroMolecule.building_blocks
. The'mol_index'
identifies which molecule of a specific building the atom belongs to. For example, ifbb_index = 1
andmol_index = 3
the atom belongs to the 4th molecule ofmacro_mol.building_blocks[1]
to be added to the macromolecule. The utility functionadd_fragment_props()
is provided to help with this.Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Raises: NotImplementedError
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
-
class
stk.molecular.topologies.cof.
Edge
(v1, v2, joint_positions, bond=[0, 0, 0])¶ Bases:
object
Represents a bond in a COF unit cell.
This class is used for positioning and aligning ditopic building blocks in 2D COF topologies.
-
v2
¶ The second vertex connected by the edge.
Type: :class:Vertex`
-
joint_positions
¶ Holds 2 numbers. The first is the id of the position on
v1
which gets connected by the edge. The second is the id of the position onv2
which gets connected by the edge.Type: tuple
ofint
-
bond
¶ Indicates if the bond is periodic.
Type: list
ofint
:
-
frac_coord
¶ The position of the edge in terms of the fractional positions along the
a
,b
andc
vectors of a unit cell.Type: list
of :class`float`
-
fg_map
¶ Maps the id of a position to the
FunctionalGroup
which sits on it. The positions are0
and1
where0
is the position which is closer tov1
while1
is the position closer tov2
.Type: dict
-
calc_coord
(cell_params)¶ Calculates the position of the edge in a cell.
Parameters: cell_params ( list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell.Returns: The position. Return type: numpy.ndarray
-
create_fg_map
(macro_mol, fgs, cell_params)¶ Creates the attribute
fg_map
.Parameters: - macro_mol (
MacroMolecule
) – The macromolecule being built. - fgs (
list
ofFunctionalGroup
) – The functional groups which need to be mapped to edge positions. - cell_params (
list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell.
Returns: None
Return type: NoneType
- macro_mol (
-
direction
(cell_params)¶ The direction vector of the edge.
Parameters: cell_params ( list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell.Returns: The direction vector. Return type: numpy.ndarray
-
fg_centroid
(macro_mol, cell_params)¶ The centroid of the fgs connected to the edge.
So the fgs NOT sitting on the edge itself.
Parameters: - macro_mol (
MacroMolecule
) – The macromolecule being assembled. - cell_params (
list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell.
Returns: The centroid of the fgs connected to those on the edge.
Return type: numpy.ndarray
- macro_mol (
-
fg_direction
(macro_mol, cell_params)¶ Calculates the direction vector between the fgs.
Parameters: - macro_mol (
MacroMolecule
) – The macromolecule being assembled. - cell_params (
list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell.
Returns: The direction vector.
Return type: numpy.ndarray
- macro_mol (
-
place_mol
(macro_mol, cell_params, mol, alignment)¶ Places and aligned a building block along the edge.
Parameters: - macro_mol (
MacroMolecule
) – The macromolecule being built. - cell_params (
list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell. - mol (
StructUnit3
) – The building block to be placed. - alignment (
int
) – Can be1
or-1
to align mol either parallel or anti-parallel with the edge.
Returns: The rdkit instance of the placed mol.
Return type: rdkit.Mol
- macro_mol (
-
-
class
stk.molecular.topologies.cof.
Hexagonal
(ditopic_directions=None, multitopic_aligners=None, scale_func=<function linker_cof_scale_func>)¶ Bases:
stk.molecular.topologies.cof.LinkerCOFLattice
-
a
= array([1, 0, 0])¶
-
b
= array([0.5 , 0.866, 0. ])¶
-
bonded_fgs
(macro_mol)¶ Yield functional groups to be bonded.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Yields: tuple
ofFunctionalGroup
– The functional groups to bond.
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
c
= array([0. , 0. , 2.88666936])¶
-
cell_dimensions
= [array([1, 0, 0]), array([0.5 , 0.866, 0. ]), array([0. , 0. , 2.88666936])]¶
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
edges
= [<stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>]¶
-
place_mols
(macro_mol)¶ Places the building blocks on the topology.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: None Return type: NoneType
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
v1
= <stk.molecular.topologies.cof.Vertex object>¶
-
v2
= <stk.molecular.topologies.cof.Vertex object>¶
-
v3
= <stk.molecular.topologies.cof.Vertex object>¶
-
v4
= <stk.molecular.topologies.cof.Vertex object>¶
-
vertices
= [<stk.molecular.topologies.cof.Vertex object>, <stk.molecular.topologies.cof.Vertex object>, <stk.molecular.topologies.cof.Vertex object>, <stk.molecular.topologies.cof.Vertex object>]¶
-
-
class
stk.molecular.topologies.cof.
Honeycomb
(ditopic_directions=None, multitopic_aligners=None, scale_func=<function linker_cof_scale_func>)¶ Bases:
stk.molecular.topologies.cof.LinkerCOFLattice
-
a
= array([1, 0, 0])¶
-
b
= array([0.5 , 0.866, 0. ])¶
-
bonded_fgs
(macro_mol)¶ Yield functional groups to be bonded.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Yields: tuple
ofFunctionalGroup
– The functional groups to bond.
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
c
= array([0. , 0. , 2.88666936])¶
-
cell_dimensions
= [array([1, 0, 0]), array([0.5 , 0.866, 0. ]), array([0. , 0. , 2.88666936])]¶
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
edges
= [<stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>]¶
-
place_mols
(macro_mol)¶ Places the building blocks on the topology.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: None Return type: NoneType
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
v1
= <stk.molecular.topologies.cof.Vertex object>¶
-
v2
= <stk.molecular.topologies.cof.Vertex object>¶
-
vertices
= [<stk.molecular.topologies.cof.Vertex object>, <stk.molecular.topologies.cof.Vertex object>]¶
-
-
class
stk.molecular.topologies.cof.
Kagome
(ditopic_directions=None, multitopic_aligners=None, scale_func=<function linker_cof_scale_func>)¶ Bases:
stk.molecular.topologies.cof.LinkerCOFLattice
-
a
= array([1, 0, 0])¶
-
b
= array([0.5 , 0.866, 0. ])¶
-
bonded_fgs
(macro_mol)¶ Yield functional groups to be bonded.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Yields: tuple
ofFunctionalGroup
– The functional groups to bond.
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
c
= array([0. , 0. , 2.88666936])¶
-
cell_dimensions
= [array([1, 0, 0]), array([0.5 , 0.866, 0. ]), array([0. , 0. , 2.88666936])]¶
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
edges
= [<stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>]¶
-
place_mols
(macro_mol)¶ Places the building blocks on the topology.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: None Return type: NoneType
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
v1
= <stk.molecular.topologies.cof.Vertex object>¶
-
v2
= <stk.molecular.topologies.cof.Vertex object>¶
-
v3
= <stk.molecular.topologies.cof.Vertex object>¶
-
vertices
= [<stk.molecular.topologies.cof.Vertex object>, <stk.molecular.topologies.cof.Vertex object>, <stk.molecular.topologies.cof.Vertex object>]¶
-
-
class
stk.molecular.topologies.cof.
LinkerCOFLattice
(ditopic_directions=None, multitopic_aligners=None, scale_func=<function linker_cof_scale_func>)¶ Bases:
stk.molecular.topologies.cof.COFLattice
For 2D COF topologies which use a linker.
These are topologies which consist of 2 building blocks. One which has two functional groups and one which has 3 or more functional groups.
This class will have to be extended by subclasses which define the vertices and edges which a particular topology consists of.
-
vertices
¶ Class attribute added when defining a subclass. Holds the vertices which make up the topology.
Type: list
ofVertex
-
edges
¶ Class attribute added when defining a subclass. Holds the edges which make up the topology.
Type: list
ofEdge
-
ditopic_directions
¶ For each edge in the topology, holds
1
or-1
to indicate if the building block is placed parallel or anti-parallel to the edge.Type: list
ofint
-
multitopic_aligners
¶ For each vertex in the topology, holds the value of a functional group id of the multitopic bulding block. This is the functional group which gets aligned at a given edge.
Type: list
ofint
-
bonded_fgs
(macro_mol)¶ Yield functional groups to be bonded.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Yields: tuple
ofFunctionalGroup
– The functional groups to bond.
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
place_mols
(macro_mol)¶ Places the building blocks on the topology.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: None Return type: NoneType
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
-
class
stk.molecular.topologies.cof.
NoLinkerCOFLattice
(scale_func=<function bb_size>)¶ Bases:
stk.molecular.topologies.cof.COFLattice
-
bonded_fgs
(macro_mol)¶ An iterator which yields functional groups to be bonded.
This iterator must yield
tuple`s of :class:
.FunctionalGroup` molecules. These are the functional groups in the macromolecule which need to be bonded.This
tuple
gets passed toReactor.react()
.Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Raises: NotImplementedError
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
place_mols
(macro_mol)¶ Places building blocks.
The
rdkit
molecules of the building blocks are combined into a singlerdkit
molecule and placed into macro_mol.mol.The function is also reponsible for updating
bb_counter
.This function must also add the tags
'bb_index'
and'mol_index'
to every atom in the molecule. The'bb_index'
tag identifies which building block the atom belongs to. The building block is identified by its index withinMacroMolecule.building_blocks
. The'mol_index'
identifies which molecule of a specific building the atom belongs to. For example, ifbb_index = 1
andmol_index = 3
the atom belongs to the 4th molecule ofmacro_mol.building_blocks[1]
to be added to the macromolecule. The utility functionadd_fragment_props()
is provided to help with this.Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Raises: NotImplementedError
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
-
class
stk.molecular.topologies.cof.
NoLinkerHoneycomb
(scale_func=<function bb_size>)¶ Bases:
stk.molecular.topologies.cof.NoLinkerCOFLattice
Represents a hexagonal lattice with 2 tritopic building blocks.
-
a
= array([1, 0, 0])¶
-
b
= array([0.5 , 0.866, 0. ])¶
-
bonded_fgs
(macro_mol)¶ Yields functional groups to be bonded.
Notes
The
periodic_bonds
attribute is filled withPeriodicBond
instances.Parameters: macro_mol ( MacroMolecule
) – The unit cell being constructed.Yields: tuple
ofint
– The ids of the functional groups to be bonded.
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
c
= array([0. , 0. , 2.88666936])¶
-
cell_dimensions
= [array([1, 0, 0]), array([0.5 , 0.866, 0. ]), array([0. , 0. , 2.88666936])]¶
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
place_mols
(macro_mol)¶ Places and aligns building blocks in the unit cell.
Notes
This method modifies macro_mol. An
rdkit
molecule of the unit cell with the building blocks not joined up is placed in themol
attribute.Parameters: macro_mol ( MacroMolecule
) – The unit cell being constructed.Returns: None Return type: NoneType
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
vertices
= [array([0.5 , 0.28866667, 1.44333468]), array([1. , 0.57733333, 1.44333468])]¶
-
-
class
stk.molecular.topologies.cof.
Square
(ditopic_directions=None, multitopic_aligners=None, scale_func=<function linker_cof_scale_func>)¶ Bases:
stk.molecular.topologies.cof.LinkerCOFLattice
-
a
= array([1, 0, 0])¶
-
b
= array([0, 1, 0])¶
-
bonded_fgs
(macro_mol)¶ Yield functional groups to be bonded.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Yields: tuple
ofFunctionalGroup
– The functional groups to bond.
-
build
(macro_mol, bb_conformers=None)¶ Assembles
rdkit
instances of macromolecules.This method places an
rdkit
molecule of the assembled macromolecule into themol
attribute ofMacroMolecule
. It also creates theMacroMolecule.bb_counter
andMacroMolecule.bonds_made
attributes.Parameters: - macro_mol (
MacroMolecule
) – TheMacroMolecule
instance which needs to be built. - bb_conformers (
list
ofint
, optional) – The ids of the building block conformers to be used. Must be equal in length to building_blocks and orders must correspond. IfNone
, then-1
is used for all building blocks.
Returns: None
Return type: NoneType
- macro_mol (
-
c
= array([0, 0, 1])¶
-
cell_dimensions
= [array([1, 0, 0]), array([0, 1, 0]), array([0, 0, 1])]¶
-
cleanup
(macro_mol)¶ Performs final clean up actions on an assembled molecule.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
edges
= [<stk.molecular.topologies.cof.Edge object>, <stk.molecular.topologies.cof.Edge object>]¶
-
place_mols
(macro_mol)¶ Places the building blocks on the topology.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: None Return type: NoneType
-
prepare
(macro_mol)¶ Performs ops between placing and reacting building blocks.
Parameters: macro_mol ( MacroMolecule
) – The molecule being assembled.Returns: None Return type: NoneType
-
v1
= <stk.molecular.topologies.cof.Vertex object>¶
-
vertices
= [<stk.molecular.topologies.cof.Vertex object>]¶
-
-
class
stk.molecular.topologies.cof.
Vertex
(frac_coord)¶ Bases:
object
Represents a vertex of a 2D COF topology.
-
frac_coord
¶ The fractional positions along the
a
,b
andc
vectors of the vector.Type: tuple
offloat
-
fg_map
¶ Maps the id of a fg position on the vertex to the
FunctionalGroup
sitting on it.Type: dict
-
aligned_position
()¶ Returns the position of the fg aligned by
place_mol()
.Returns: The id of a position of on the vertex. The fg which gets aligned during place_mol()
sits on this position.Return type: int
-
calc_coord
(cell_params, cell_position=None)¶ Calculate the position of the vertex within a unit cell.
Parameters: - cell_params (
list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell. - cell_position (
list
ofint
, optional) – Indicates if the vertex is in a periodic cell or not. Analogous to the defintion of a periodic bond.
Returns: The position.
Return type: numpy.ndarray
- cell_params (
-
create_fg_map
(macro_mol, cell_params, fgs, aligned_fg)¶ Creates the attribute
fg_map
.Parameters: - macro_mol (
MacroMolecule
) – The macromolecule being built. - cell_params (
list
ofnumpy.ndarray
) – Thea
,b
andc
vectors of the unit cell. - fgs (
list
ofFunctionalGroup
) – The functional groups being placed on the vertex. - aligned_fg (
int
) – The id of the functional group to be aligned with an edge. The id corresponods to the value in the building block, not the macromolecule.
Returns: None
Return type: NoneType
- macro_mol (
-
place_mol
(cell_params, mol, aligner)¶ Places and aligns a molecule on the vertex.
Parameters: - cell_params (
list
ofnumpy.array
) – Thea
,b
andc
vectors of the unit cell. - mol (
StructUnit3
) – The building block to be placed. - aligner (
int
) – The id of the functional group to be aligned with an edge.
Returns: The rdkit instance of the placed mol.
Return type: rdkit.Mol
- cell_params (
-
-
stk.molecular.topologies.cof.
bb_size
(macro_mol)¶ Sums the diameters of the building blocks.
Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: The sum of the diameters of the building blocks. Used to scale the size of unit cells. Return type: float
-
stk.molecular.topologies.cof.
linker_cof_scale_func
(macro_mol)¶ Calculates the scale factor for
LinkerCOFLattice
.Parameters: macro_mol ( MacroMolecule
) – The macromolecule being assembled.Returns: A product of the sum of the diameters of the building blocks and the number of vertices in the unit cell. Return type: float


{% extends "layout.html" %} {% block title %}Home{% endblock %} {% block head %} {{ super() }} {% endblock %} {% block content %}
Have you ever wanted a Special:LinkSearch report, but for every wiki? Then this tool's for you.
Linkspam searches for usages of a domain across all open public wikis. Reports include http and https links to the top-level domain and subdomains.


This connector provides sinks that can request document actions to an
Elasticsearch Index. To use this connector, add one
of the following dependencies to your project, depending on the version
of the Elasticsearch installation:
Maven Dependency
Supported since
Elasticsearch version
flink-connector-elasticsearch_2.11
1.0.0
1.x
flink-connector-elasticsearch2_2.11
1.0.0
2.x
flink-connector-elasticsearch5_2.11
1.3.0
5.x
flink-connector-elasticsearch6_2.11
1.6.0
6 and later versions
Note that the streaming connectors are currently not part of the binary
distribution. See here for information
about how to package the program with the libraries for cluster execution.
Installing Elasticsearch
Instructions for setting up an Elasticsearch cluster can be found
here.
Make sure to set and remember a cluster name. This must be set when
creating an ElasticsearchSink for requesting document actions against your cluster.
Elasticsearch Sink
The ElasticsearchSink uses a TransportClient (before 6.x) or RestHighLevelClient (starting with 6.x) to communicate with an
Elasticsearch cluster.
The example below shows how to configure and create a sink:
For Elasticsearch versions that still uses the now deprecated TransportClient to communicate
with the Elasticsearch cluster (i.e., versions equal or below 5.x), note how a Map of Strings
is used to configure the ElasticsearchSink. This config map will be directly
forwarded when creating the internally used TransportClient.
The configuration keys are documented in the Elasticsearch documentation
here.
Especially important is the cluster.name parameter that must correspond to
the name of your cluster.
For Elasticsearch 6.x and above, internally, the RestHighLevelClient is used for cluster communication.
By default, the connector uses the default configurations for the REST client. To have custom
configuration for the REST client, users can provide a RestClientFactory implementation when
setting up the ElasticsearchClient.Builder that builds the sink.
Also note that the example only demonstrates performing a single index
request for each incoming element. Generally, the ElasticsearchSinkFunction
can be used to perform multiple requests of different types (ex.,
DeleteRequest, UpdateRequest, etc.).
Internally, each parallel instance of the Flink Elasticsearch Sink uses
a BulkProcessor to send action requests to the cluster.
This will buffer elements before sending them in bulk to the cluster. The BulkProcessor
executes bulk requests one at a time, i.e. there will be no two concurrent
flushes of the buffered actions in progress.
Elasticsearch Sinks and Fault Tolerance
With Flink’s checkpointing enabled, the Flink Elasticsearch Sink guarantees
at-least-once delivery of action requests to Elasticsearch clusters. It does
so by waiting for all pending action requests in the BulkProcessor at the
time of checkpoints. This effectively assures that all requests before the
checkpoint was triggered have been successfully acknowledged by Elasticsearch, before
proceeding to process more records sent to the sink.

To use fault tolerant Elasticsearch Sinks, checkpointing of the topology needs to be enabled at the execution environment:
NOTE: Users can disable flushing if they wish to do so, by calling
disableFlushOnCheckpoint() on the created ElasticsearchSink. Be aware
that this essentially means the sink will not provide any strong
delivery guarantees anymore, even with checkpoint for the topology enabled.
Communication using Embedded Node (only for Elasticsearch 1.x)
For Elasticsearch versions 1.x, communication using an embedded node is
also supported. See here
for information about the differences between communicating with Elasticsearch
with an embedded node and a TransportClient.
Below is an example of how to create an ElasticsearchSink use an
embedded node instead of a TransportClient:
The difference is that now we do not need to provide a list of addresses
of Elasticsearch nodes.
Handling Failing Elasticsearch Requests
Elasticsearch action requests may fail due to a variety of reasons, including
temporarily saturated node queue capacity or malformed documents to be indexed.
The Flink Elasticsearch Sink allows the user to specify how request
failures are handled, by simply implementing an ActionRequestFailureHandler and
providing it to the constructor.
Below is an example:
The above example will let the sink re-add requests that failed due to
queue capacity saturation and drop requests with malformed documents, without
failing the sink. For all other failures, the sink will fail. If a ActionRequestFailureHandler
is not provided to the constructor, the sink will fail for any kind of error.
Note that onFailure is called for failures that still occur only after the
BulkProcessor internally finishes all backoff retry attempts.
By default, the BulkProcessor retries to a maximum of 8 attempts with
an exponential backoff. For more information on the behaviour of the
internal BulkProcessor and how to configure it, please see the following section.
By default, if a failure handler is not provided, the sink uses a
NoOpFailureHandler that simply fails for all kinds of exceptions. The
connector also provides a RetryRejectedExecutionFailureHandler implementation
that always re-add requests that have failed due to queue capacity saturation.
IMPORTANT: Re-adding requests back to the internal BulkProcessor
on failures will lead to longer checkpoints, as the sink will also
need to wait for the re-added requests to be flushed when checkpointing.
For example, when using RetryRejectedExecutionFailureHandler, checkpoints
will need to wait until Elasticsearch node queues have enough capacity for
all the pending requests. This also means that if re-added requests never
succeed, the checkpoint will never finish.
Failure handling for Elasticsearch 1.x: For Elasticsearch 1.x, it
is not feasible to match the type of the failure because the exact type
could not be retrieved through the older version Java client APIs (thus,
the types will be general Exceptions and only differ in the
failure message). In this case, it is recommended to match on the
provided REST status code.
Configuring the Internal Bulk Processor
The internal BulkProcessor can be further configured for its behaviour
on how buffered action requests are flushed, by setting the following values in
the provided Map<String, String>:
bulk.flush.max.actions: Maximum amount of actions to buffer before flushing.
bulk.flush.max.size.mb: Maximum size of data (in megabytes) to buffer before flushing.
bulk.flush.interval.ms: Interval at which to flush regardless of the amount or size of buffered actions.
For versions 2.x and above, configuring how temporary request errors are
retried is also supported:
bulk.flush.backoff.enable: Whether or not to perform retries with backoff delay for a flush
if one or more of its actions failed due to a temporary EsRejectedExecutionException.
bulk.flush.backoff.type: The type of backoff delay, either CONSTANT or EXPONENTIAL
bulk.flush.backoff.delay: The amount of delay for backoff. For constant backoff, this
is simply the delay between each retry. For exponential backoff, this is the initial base delay.
bulk.flush.backoff.retries: The amount of backoff retries to attempt.
More information about Elasticsearch can be found here.
Packaging the Elasticsearch Connector into an Uber-Jar
For the execution of your Flink program, it is recommended to build a
so-called uber-jar (executable jar) containing all your dependencies
(see here for further information).
Alternatively, you can put the connector’s jar file into Flink’s lib/ folder to make it available
system-wide, i.e. for all job being run.


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Test of applying different dimension restrictions to the cropper
Min Width
Max Width
Min Height
Max Height
x1:
y1:
x2:
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Concur is a brand new Web UI Framework for Haskell that seeks to take the best parts from Elm, Reflex, and React.
Check out the github repo for more information.
This repo hosts the various benchmarks for Concur


Module: Mongoid::Config::Environment
Overview
Encapsulates logic for getting environment information.
Instance Method Summary collapse
-
#env_name ⇒ String
Get the name of the environment that Mongoid is running under.
-
#load_yaml(path, environment = nil) ⇒ Hash
private
Load the yaml from the provided path and return the settings for the specified environment, or for the current Mongoid environment.
Instance Method Details
#env_name ⇒ String
Get the name of the environment that Mongoid is running under.
Uses the following sources in order:
-
If
::Rails
is defined,Rails.env
. -
If
::Sinatra
is defined,Sinatra::Base.environment
. -
RACK_ENV
-
+MONGOID_ENV*
29 30 31 32 33 34 35 36 37 |
# File 'lib/mongoid/config/environment.rb', line 29 def env_name if defined?(::Rails) return ::Rails.env end if defined?(::Sinatra) return ::Sinatra::Base.environment.to_s end ENV["RACK_ENV"] || ENV["MONGOID_ENV"] or raise Errors::NoEnvironment end |
#load_yaml(path, environment = nil) ⇒ Hash
This method is part of a private API. You should avoid using this method if possible, as it may be removed or be changed in the future.
Load the yaml from the provided path and return the settings for the specified environment, or for the current Mongoid environment.
53 54 55 56 |
# File 'lib/mongoid/config/environment.rb', line 53 def load_yaml(path, environment = nil) env = environment ? environment.to_s : env_name YAML.load(ERB.new(File.new(path).read).result)[env] end |


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Last minute saved:
November 22, 2009
Flower dreams fall curtains every night
the bands and the people dance the feeling seems right
-but the next day's- coming round-
Wake up! Its dawn. Light and fall asleep the hours seem long
yet fly by- you wait
too long it’s dark before its five now
did find
and no smile flashed before your eyes
the house is cold by midnight
the people and memories gone away
It’s time to dream the wars they fall before your eyes
the sounds they haunt your sleep at night
and wake without a rustling sound of someone
run out and meet the friends who wait in the drive
you'll drive away before you dress right, your shoes are left behind
that road that goes nowhere and follows the lake shore
and end up with tears in your-eyes in that car
you found no happy thought that you were looking for-
That night you thought there was nothing you live for
no fire nor hierophant to find
in your eyes there was something you were hiding
I closed mine
the movement they didn't - seem right
was swinging out of this world
too fast, too slow
everything just turned-round so fast
at last this time
there was somewhere to- could go
and find that those people were always there
in a dream-like-world that slips beneath the forms here
and breath your take you there or in daylight just think about
this last night.
I would never let tears leave your eyes
until- (you and I, or) until the world ends I will find each drop


A new data type
Before talking about the Either data type, we need to know what a data type is.
A data type is an abstraction that encapsulates one reusable coding pattern.
In our case, the Either data type encapsulates two other objects: Left and Right. These two objects represent Error and Success respectively.
This is a common pattern for Functional Programming.
Left and Right
Left and Right, are classes wich represent the error and success data. Therefore one of the two attributes will always be valued. This is an example done in the Kotlin language:
sealed class Either<out L, out R> {
data class Left<out L, out R>(val a: L) : Either<L, R>()
data class Right<out L, out R>(val b: R) : Either<L, R>()
}
fun <E> E.left() = Either.Left<E, Nothing>(this)
fun <T> T.right() = Either.Right<Nothing, T>(this)
This way we can return our object to directly encapsulate a valid result or an exception without stopping execution in case of an error.
Apply Either type
Suppose we have written a Verifier class, which checks the veracity of a JWT token. At this point, instead of returning a result or throwing an exception in case of error, we return an Either type.
interface Verifier {
/ **
* @param jwt a jwt token
* @return authentication credentials, or an error if the validation fails
* /
fun verify(jwt: String): Either<JWTVerificationException, TokenAuthentication>
}
Inside our implementation of Verifier, we’re wrapping the problematic code with an extension method called unsafeVerify. We use the extension methods that we defined above to create both sides of an Either:
private fun JWTVerifier.unsafeVerify(jwt: String): Either<JWTVerificationException, TokenAuthentication> = try {
verifyByCallingExternalApi(jwt).right()
} catch (e: JWTVerificationException) {
e.left()
}
We can use a when expression thanks to having defined our Either as a sealed class.
val result = verifier.verify(jwt)
when (result) {
is Either.Left -> ResponseEntity.badRequest().build()
is Either.Right -> ResponseEntity.ok("Worked!")
}
Py…Either
Python doesn’t have a type like Either, but we can implement it so that with every exception, we can create our own Either type.
class Either:
"""
Either is an entity whose value can be of two different types,
called left and right. By convention, Right is for the success case and
Left for the error one. It’s a common pattern in the functional community.
It allows us to express the fact that a call might return a correct value
or an error, and differentiate between the two of them. The Left/Right
naming pattern is just a convention, though. It can help people who have
used the nomenclature in existing libraries. You can use a different
convention that makes more sense for your team, such as Error/Success,
for instance.
"""
class Left:
"""
Default exception in either data type
"""
def __init__(self, error):
self.error = error
def __str__(self):
return f'{self.error}'
def __bool__(self):
return False
class Right:
"""
Default return value in either data type
"""
def __init__(self, result):
self.data = result
def __str__(self):
return f'{self.data}'
def __bool__(self):
if self.data:
return True
def __init__(self, left: Either.Left = None, right: Either.Right = None):
self.left = left
self.right = right
def __str__(self):
if self:
return str(self.right.data)
else:
return str(self.left.error)
def __repr__(self):
return f"<Either object, right={self.right.data}, left={self.left.error}>"
def __bool__(self):
if self.right:
return True
At this point, we have an object capable of saving itself from the state of exception and success. Let’s test it!
try:
final = Either(None, some_func(some_args))
except Exception as err:
final = Either(err, None)
print(final)
At this point, we can build this type of control in the statement of our functions (or with specific exceptions).
def my_func(number):
try:
either = Either(None, (number // 0))
except ZeroDivisionError as err:
either = Either(err, None)
return either
In this way our function will always return an Either data type.
Conclusion
This new type of object is changing the way programs are written, in order to handle or not handle certain errors. For example, server or web applications must somehow handle the exceptions that arrive and continue their work. With an Either type available, these types of applications can never be interrupted by user exceptions and allow the developer to intercept what needs to be changed. This new type of data is introduced in new programming languages such as Go, Rust and Kotlin, but we have seen that it is possible to implement it even where it is not native as in python, adapting our objects and our functions to return a very efficient Either data type.


Free Code Camp Progress
Front End Develoment Certification
Projects
Basic
Build Tribute Page
Build a Personal Portfolio Webpage
Intermediate
Build a Ramdon Quote Machine
Show the Local Weather
Build a Wikipedia Viewer
Use the Twitchtv JSON API
Avanced
Build a JavaScript Calculator
Build a Pomodoro Clock
Build a Tic Tac Toe Game
Build a Simon Game
Algorithm Challenges
Basic
Reverse a String
Factorialize a Number
Check for Palindromes
Find the Longest Word in a String
Title Case a Sentence
Return Largest Numbers in Arrays
Confirm the Ending
Repeat a string repeat a string
Truncate a string
Chunky Monkey
Slasher Flick
Mutations
Falsy Bouncer
Seek and Destroy
Where do I belong
Caesars Cipher
Intermediate
Sum All Numbers in a Range
Diff Two Arrays
Roman Numeral Converter
Wherefore art thou
Search and Replace
Pig Latin
DNA Pairing
Missing letters
Boo who
Sorted Union
Convert HTML Entities
Spinal Tap Case
Sum All Odd Fibonacci Numbers
Sum All Primes
Smallest Common Multiple
Finders Keepers
Drop it
Steamroller
Binary Agents
Everything Be True
Arguments Optional
Avanced
Validate US Telephone Numbers
Symmetric Difference
Exact Change
Inventory Update
No repeats please
Friendly Date Ranges
Make a Person
Map the Debris
Pairwise
Data Visualization Certidication
React Projects
Markdown Previewer
Camper LeaderBoard
Recipe Box
Back End Develoment Certification
API Projects
Timestamp Microservice
Request Header Parser Microservice
Url Shortener Microservice
Image Search Absctraction Layer
File Metadata Microservice


Class SplFileInfo
Direct known subclasses
Symfony\Component\HttpFoundation\File\FileIndirect known subclasses
Symfony\Component\HttpFoundation\File\UploadedFile
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Opinion Sorting Machine
What's the thing you love the most? We don't know, and neither do you. But we can help.
Add Item
Run Machine
Sorting time!
Return


Note: The talking images in the demo use mp3 audio, therefore, will work only on Chrome and Firefox. To make it work in Safari, create your talking images with mp3 payload instead of ogg.


Ford responds to Tesla's new Cybertruck
by Graham Rapier- Tesla CEO Elon Musk unveiled Cybertruck, the company’s first passenger truck, on Thursday night.
- During the presentation, Musk took shots at traditional makers in his usual fashion.
- “We’ve always focused on serving our truck customers regardless of what others say or do,” a Ford spokesperson told Business Insider.
- Sign up for Business Insider’s transportation newsletter, Shifting Gears, to get more stories like this in your inbox.
- Visit Business Insider’s homepage for more stories.
Elon Musk didn’t hold back on his usual criticism of traditional automakers as heunveiled Tesla’s long-awaited Crybertruck on Thursday. But Ford doesn’t seem to scared.
“Trucks have been the same for a very long time, like 100 years,” Musk told the crowd at Tesla’s design centre in Hawthorne, California. “We wanted to try something different.”
After showing the Tesla truck could withstand more damage from a sledgehammer than a regular truck’s door, Musk said “You want a truck that’s really tough, not fake tough.”
That might be the case, but for America’s consumers, the truck they have wanted for decades has been the Ford F-150.
In response to Musk’s shade at it and other automakers, Ford sent the following statement:
“With America’s best-selling truck for 42 years, we’ve always focused on serving our truck customers regardless of what others say or do. We look forward to our all-new F-150 hybrid coming next year and all-electric F-150 in a few years.
Musk has long had disdain for traditional automakers. And for the most part, he’s successfully beat them at their own game, at least when it comes to electric vehicles. The Model 3 was the best -selling electric vehicle in the US for the first half of 2019, according to data from InsideEVs.
Tesla could likely produce up to 175,000 in its first full year of production in 2020, analysts estimate. That’s a far cry from Ford’s roughly 1 million every year.
“In a nutshell, we believe this next generation Cybertruck model could help Tesla expand its market opportunity outside its core customer base over time,” Daniel Ives, an analyst at Wedbush, said in a note to clients.
“Although gaining market share with stalwarts such as Ford and GM entrenched in this landscape will be a difficult task for Fremont.”


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