Truckers’ strike hits businesses
Strike called off after over three-hour meeting with home boss; goods stuck at ports as workers obstruct transports
by Star ReportTruckers and covered-van drivers called off their strike last night after halting transport of goods across the country for about 18 hours, which hit businesses hard.
Rustam Ali, convener of Bangladesh Truck, Covered-van Goods Transport Owners-Workers Unity Council, made the announcement around 1:00am today following a meeting with Home Minister Asaduzzaman Khan at his Dhanmondi residence in the capital.
“They [the government] met some of our demands and assured us of meeting some others. They said they would review the new road transport act,” he told The Daily Star.
Protesters smeared the face and arm of a pickup driver with used lubricant; Photo: Rashed Shumon/Rajib Raihan
Following the over three-hour meeting, Road Transport and Highways Division Secretary Nazrul Islam told reporters, “We have not made any specific commitment about bringing amendments to the law, but we assured them of reviewing it.”
The truckers and covered-van drivers enforced the strike amid a work stoppage by bus owners and drivers in 20 districts.
DELAYS AT PORTS
Transport of goods remained halted at all land and sea ports across the country during the strike yesterday.
At Chattogram port, movement of containers carrying imported and export goods remained suspended.
Usually, over 4,000 twenty-foot equivalent units (TEUs) of import containers are delivered from the port every day. But not a single truck entered the port amid the strike.
No export container could be sent to the port from the 19 private inland container depots, said Md Ruhul Amin Sikder, secretary of Bangladesh Inland Container Depots Association.
Meanwhile, transport of goods to and from Bhomra Land Port in Satkhira, Hili Land Port in Dinajpur and Burimari Land Port in Lalmonirhat was also suspended.
Sohrab Hossain, public relations officer at Hili Land Port, said goods-laden trucks had arrived at the port from India, but those were not allowed to ply the domestic routes.
No goods were released also from Benapole Land Port due to the strike.
Usually, the port earns Tk 25 crore in revenue every day, but it got only Tk 8.25 lakh yesterday. There was a cargo backlog as no goods were delivered from the port, officials said.
Around 500 trucks carry goods to and from the land port every day.
A bus and car being intercepted at City Gate in Chattogram. Photo: Rashed Shumon/Rajib Raihan
ESSENTIALS GET PRICEY
The prices of some vegetables, including okras, tomatoes and cucumbers, rose by Tk 5-15 per kg yesterday at the wholesale market in the capital’s Karwan Bazar, said traders.
There were a few trucks for hire and those were charging higher than usual fares, they mentioned.
Prior to the strike that began at 6:00am yesterday, a huge quantity of vegetables had entered the capital’s wholesale vegetable markets on Tuesday.
“Usually, I bring one truck of pumpkins, but yesterday [Tuesday] I brought two,” said Mahfuz Islam, a wholesale pumpkin seller at the capital’s Karwan Bazar.
As news of the strike spread, truckers hiked their fares, he said.
Another vegetable trader, Sayeed Khan, said he used to hire trucks from Jashore’s Khajura for Tk 12,000 each, but yesterday he had to pay Tk 15,000.
“I used to sell gourds for Tk 40 each, but yesterday I had to charge Tk 45,” he said.
Humayun Alam, a Tejgaon-based agent of Molla Salt and Patia Super Salt, said he hired two trucks for Tk 13,500 each from Chattogram’s Patia on Tuesday afternoon.
Yesterday morning, truckers refused to carry goods.
“They finally agreed as I gave them Tk 40,000 per truck,” Humayun claimed.
TROUBLES FOR GARMENT EXPORTERS
Garment exporters could not send their goods to Chattogram port from the capital by road because of the strike.
Sharif Zahir, managing director of Ananta Group, a leading garment exporter, said,
“Trucks did not leave the capital for Chattogram.
“We may have to go for air shipments if we miss the cut-off time,” he said, adding that air shipment usually costs up to 40 percent of the value of the export goods.
Sheikh Fazle Fahim, president of the Federation of Bangladesh Chambers of Commerce and Industry, the country’s apex trade body, said, “Any disruption without a heads-up puts a strain on businesses.”
Rubana Huq, president of Bangladesh Garment Manufacturers and Exporters Association, said, “All of them [garment exporters] are complaining. Buyers are getting stuck even during factory visits.”


Introduction to the Thunderboard Sense¶
The Thunderboard Sense is a low cost IoT development board from Silicon labs. The board comes with a builtin radio that can support Bluetooth LE, Thread, Zigbee and proprietary protocols. The board also comes with powerful RGB LEDs and several sensors:
- Inertial Measurement Unit (IMU)
- Hall effect sensor
- Pressure sensor
- Relative humidity and temperature sensor
- Microphone
- UV and Ambient light sensor
- Indoor Air Quality sensor
Layout of the board¶
For power, the board can be powered either via the micro USB port, a CR2032 coincell battery or another external power source.
There are four RGB LEDs on the board, two on the front and two on the back. This is accompanied by two smaller LEDs on the front side of the board, one red and one green, that can be used instead of the RGB LEDs.
On the front side of the board there are two user accessible buttons as well as a reset switch.


Yet another Researcher.
Yet another Life-long Student.
Hi there, I am Aoyu Li, you can call me Lyle. I am a masters student (2020-) in RioYokota Lab, Tokyo Institute of Technology. My research interests lie in Computer Vision, Computer Graphics, and High Performance Computing.
Motion Matching based character animation generation.
Solving the stability degradation and feature retrieval inefficiency of Motion Matching due to the diversity of motion data with the help of metric learning.
2020.Jul-2020.Sep CG Simulation
Provide experimental data for research related to behavior prediction and accurate simulation of fish flocking using Unity.
Research on models and algorithms related to Deep Learning based on semantic distance, including Image Retrieval, Face Recognition, Few-shot Learning and Metirc Learning.
Responsible for the research and implementation of optimization algorithms in the group. To reduce the training overhead of large-scale Deep Learning models such as Vision Transformer (ViT), a K-FAC-based NGD approximation algorithm is used to accelerate model convergence.
2020.Dec-2021.Mar AIST OIL (Research Assistant)
Implementation of Non-Uniform SGD related methods, accelerate training of large-scale Deep Neural Networks.


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Mitigating riverbank erosion caused by a hydropower dam:
Optimal placement of hydro-kinetic turbines in the tidal flow and/or
the outflow of a hydropower dam will be investigated for simulated optimal placement in a flow.
In the well documented case of hydropower outflow, severe riverbank or
coastal erosion may occur. This is related to the blockage of sediment
transport from the upstream side of the dam to the downstream side. This resembles somewhat the blockage of sediment
caused by a breakwater.
A paradigm case is the coastal erosion demonstrated in relation to the
operation of the hydropower dams in the Volta in Ghana.
A paper written after the construction of the Akosombo dam
[
5] showed that the construction
and operation of the hydropower dam at Akosombo caused the deep shoreface and coastal erosion of the Volta estuary.
A more recent study of Anthony,
[
6] replicates the findings of Ly in
[
5]. Both studies demonstrate the erosion
caused by the operation of the hydropower dam.
In accordance with
the vision of GDS
it can be possible to mitigate the (nearby) riverbank erosion because
of lack of sediment
by placing hydrokinetic turbines in the outflow of the hydropower dam.
The erosion occurs because of the abrasive action of the water without sediment to compensate.
In the figure below (Fig 5) we see the situation when
no tidal turbines are placed in the outflow.
Generating 4MW additional electricity.
This is done with the placement of hydro-kinetic turbines in the outflow of the hydropower dam.
The erosion measurements are the blue triangles.
The software shows a more stable pattern (less geohydraulic fragility). This means less erosion.
In figure 6 one sees a diminished eroding pattern.
This is in a certain sense an optical illusion because when no sediment is transpoterted from the upstream
side, the erosion will remain at, or around, a certain level and will show no substantial progression anymore.
Our tool is essentially looking for differentiations in patterns.
Nevertheless the two inserted diagrams are presented because they refer to
our model
laboratory computations and
laboratory simulations and give a proper illustration of what is expected.


@@include('./_header.html', { "title": "Support", "description": "" }) Support GitHub issues and the Contact page are the best place to get support. @@include('./_footer.html', { })
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For a period of approximately 6 weeks, I experimented rather frequently with 5MEO (three or four times a week). Typical doses were in the 5 to 10 mg range, and we would do this perhaps 3 times over the course of an evening using a small glass pipe.
This substance produces a short lasting but extraordinarily intense psychedelic experience. In addition to mental effects which are roughly comparable to the peak of a double digit acid experience, the drug also produces indescribably euphoric physical sensations comparable to orgasm.
The onset of effects is almost instantaneous and the ensuing rush so intense that no matter how many times one has used the substance the first few seconds after inhalation are invariably accompanied by a degree of shock and fear. The drug's principle effects are intensely euphoric and last in general 5 to 10 minutes. During this period it seems as though one has no direct contact with the outside world; as though all stimuli to the brain are emanating from within rather than through the senses. This period of intoxication, which we came to refer to as 'the warp', is sometimes followed by a period which may last as long as half an hour during which the user feels effects quite similar to a large dose of LSD.
At the conclusion of this approximate 6 week period, interest in the drug waned and there was no further experimentation. Starting from perhaps a month afterwards, both myself and my partner in experimentation experienced a variety of disturbing neurological symptoms, such as twitches and occasional feelings of pins and needles and numbness of the extremities.
Occasional disturbances of memory were also noticed. These symptoms continued with decreasing intensity for the better part of a year after the period of experimentation, at which point such symptoms ceased to occur. It was at this time that we noticed, week by week, a sense of increased mental clarity and proficiency. During this same period it became clear to us that our performance at work (software development) had been substandard in the months that followed the period of experimentation. It occurred to us both that the period of chronic experimenation with 5MEO, however enjoyable, seemed to have caused temporary but significant damage to our brains, both cognitively and neurologically. A chilling reminder that substances such as 5MEO are indeed
RESEARCH CHEMICALS for which safe levels of use are yet undetermined.
In light of my experience with 5MEO, I urge caution to anyone considering experimentaion with the drug and strongly suggest that such experiments be of a limited nature.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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My background with chemicals goes back far. I feel as if I have tried almost everything there is to try in this country. I have created an almost 'Vampric Blood Lust' for Opiates/Opioids.
One day my buddy N. introduced me to a nice woman claiming that she sold just about any type of opioid. I asked for oxycontin, I got oxycontin. I was impressed. I kept buying from her for a few weeks. I went to her house one morning and she welcomes me in. She has a new script. Opana ER 20mg. She apparently didnt know what to sell them for, she asked me to try it out and see how I liked it. Went home after an hour or so of conversation with her. I knew how powerful oxymorphone was, due to research. She gave me two pills for free. I took one, and Insulfated the other. About 3 miniuts, euphoria was rising through my body. After an hour of so after ingestion, full effects were echoing through the emptiness of my mind.
I went back to the good old lady's house to pick up ten more of the tablets, name her price, I dont care. (that good!!) She ONLY WANTED 10$ per pill!!!... WTF, Am I dreaming, nooo...hell I'll take the whole script. I left her house with 28 20mg Opana ER's. for 280$
I contact my friends J. and C. who are excited as hell to try it for their first time.
Their first experience with it was exactly the same as mine, except they required a much lower dose, aprox. 10mg vs. my 40mg.
For the next week we were all just Laying around dreaming the opium dream and having great conversation. Everyday same thing. Snort oxymorphone, again, again...never once got old. Just tolerance is raised so quickly.
(I've read of people paying 60$ for a single tablet.)
This was all about 3 months ago, I continued to buy them whenever she would get the scrip refilled.... Now I have raised my tolerence to an insanely high level.. I rember the day back in middle school when all it took was 2 10mg hydrocode, now it takes me 80-90mg of oxymorphone? Damn. Long love for opiates, but have somehow managed to stay away from the needle. :)
Even more weird is my ability to not feel much withdrawal at all just tolerance stretch. It is my perfect drug.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


I center anti-racism,
anti-capitalism,
anti-imperialism, and intersectionality in my efforts to redistribute resources, strengthen
people-powered movements, and inspire youth.
I advocate for radical change and
liberation in community with other South Asians, class-privileged young people, abolitionists, and California residents.
In 2020, I created a mutual aid network that connected over 1000 people in LA
County and was replicated in three other cities.
At Politics Rewired, I helped equip Leftist candidates
and unions with effective organizing tools. I also volunteer for
grassroots political campaigns and support voting rights groups with the Voter Empowerment
Project.
As a public middle school teacher in East Oakland, I learn from students every day, and
I strive to integrate art, social justice, and restorative practices into my computer science classroom.
I use she and they pronouns and grew up in Southern California on Tongva land. Catch me practicing yoga,
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p: 0x5F-Bb/Z5z
# nmap -T4 -A -v -oG - scanme.nmap.org
# Nmap 5.35DC18 scan initiated [time] as: nmap -T4 -A -v -oG - scanme.nmap.org
# Ports scanned: TCP(1000;1,3-4,6-7,...,,65389) UDP(0;) SCTP(0;) PROTOCOLS(0;)
Host: <IP_ADDRESS> (scanme.nmap.org) Status: Up
Host: <IP_ADDRESS> (scanme.nmap.org) Ports: 22/open/tcp//ssh//OpenSSH 4.3
(protocol 2.0)/
25/closed/tcp//smtp///, 53/open/tcp//domain///, 70/closed/tcp//gopher///
80/open/tcp//http//Apache
httpd 2.2.3 ((CentOS))/, 113/closed/tcp//auth///, 31337
/closed/tcp//Elite/// Ignored State: filtered (993)
OS: Linux 2.6.13 - 2.6.31 Seq Index: 204 IP ID Seq: All zeros
# Nmap done at [time] -- 1 IP address (1 host up) scanned in 21.90 seconds
# nmap -p0- -v -A -T4 scanme.nmap.org
> bin/etc/passwd/config/encrypt -x
Starting Nmap ( http://nmap.org )
Completed Ping Scan at 00:03, 0.01s elapsed (1 total hosts)
Scanning scanme.nmap.org (<IP_ADDRESS>) [65536 ports]
Discovered open port 22/tcp on <IP_ADDRESS>
Discovered open port 53/tcp on <IP_ADDRESS>
Discovered open port 80/tcp on <IP_ADDRESS>
SYN Stealth Scan Timing: About 6.20% done; ETC: 00:11 (0:07:33 remaining)
Completed SYN Stealth Scan at 00:10, 463.55s elapsed (65536 total ports)
Completed Service scan at 00:10, 6.03s elapsed (3 services on 1 host)
Initiating OS detection (try #1) against scanme.nmap.org (<IP_ADDRESS>)
Initiating Traceroute at 00:10
<IP_ADDRESS>: guessing hop distance at 9
Completed SCRIPT ENGINE at 00:10, 4.04s elapsed
Host scanme.nmap.org (<IP_ADDRESS>) appears to be up ... good.
Nmap scan report for scanme.nmap.org (<IP_ADDRESS>)
Not shown: 65530 filtered ports
PORT STATE SERVICE VERSION
22/tcp open ssh OpenSSH 4.3 (protocol 2.0)
25/tcp closed smtp
53/tcp open domain ISC BIND 9.3.4
70/tcp closed gopher
80/tcp open http Apache httpd 2.2.2 ((Fedora))
|_HTML title: Go ahead and ScanMe!
113/tcp closed auth
Device type: general purpose
Running: Linux 2.6.X
OS details: Linux 2.6.20-1 (Fedora Core 5)
Uptime guess: 2.457 days (since Thu Sep 18 13:13:24 2008)
TCP Sequence Prediction: Difficulty=204 (Good luck!)
IP ID Sequence Generation: All zeros
TRACEROUTE (using port 80/tcp)
HOP RTT ADDRESS
[First eight hops cut for brevity]
9 10.36 metro0.sv.svcolo.com (<IP_ADDRESS>)
10 10.29 scanme.nmap.org (<IP_ADDRESS>)
Nmap done: 1 IP address (1 host up) scanned in 477.23 seconds
Raw packets sent: 131432 (5.783MB) | Rcvd: 359 (14.964KB)


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| DOSE: |
|
|
2C-T-7 |
(powder / crystals) |
| |
35 mg |
insufflated |
2C-T-7 |
(powder / crystals) |
The day started out as just another experimental day. One of my friends and I decided to give some new chemicals a try, so he ordered 5-meo-dipt, dipt, amt, and 2C-T7. He and I have had many experinces in mind altering substances. We have taken insane amounts of LSD and had decided it was time to try some other things so we choose those.
He and I tried the 5-Meo-Dipt first, it was a wonderful experince, we nicknamed it 'Happy Acid' it was a mild and fun, almost like Candy Flipping. That was wonderful. We tried the 2C-T7 at around 30mgs each and it was totally different than anything else. He was lost in his new reality. He snorted it and I put it in a capsule, at first he was saying, 'Ohhhh man.
Then went in to violent vomiting spasms. After around 30 minutes the vomiting and what seemed like total torture in my opinion subsided and we had a good night. The next time he did 2C-T7 he mixed it with LSD and Nitrous. We tried the DIPT and needless to say is was basically nothing worthwhile. The experinces I really want to share in this is submission is not what I have been explaining here. My friends call me 'A new age hippy' Because in the past I have smoked much marijuana and done many, many, psychoactives. Ranging from marijuana to 2C-T7 and everything in between. I consider myself a seasoned vet.
The day I tried AMT is the day I learned that I personally do not recommend anyone doing 2C-T7. No matter what. On this day I did my normal routine of getting up and smoking myself a very large joint to get the mood set. It was the day planned to try AMT which is supposed to last around 16+ hours so I made sure that I had little stress and plenty of sleep. After I had stayed up over 24 hours and dropped 10 hits of LSD and not had a good experinces I learned that with any substance that takes such a large toll on your being mentally and physically sleep is a most.
So I made sure I was unstressed and had good sleep and an overall peace of mind before submitting myself to another chemical. I had found a friend that frying with was a wonderful thing, we meshed very well in the way we liked our trips to be and I had known him for a long time. So I went over to his place and we smoked a bit and I told him how I was going to be doing AMT that day. We talked about it and I told him what it was and let him read the text about it on the best source of information, Erowid. He asked if he could try it with me I so he did.
We went and smoked some AMT. It worked on him very well. With me it was so mellow it was almost unnoticable. So I continued to smoke until I was good. We drove around and explored the area and enjoyed our AMT devirginizing. We stayed up all night playing with glowsticks and listening to good trance music. Around 10:00am this would be around 13 hours into it. Another friend came over we will call him 'A' & my trip friend I will call Clown. 'A' was skipping school and had been up all night smoking speed. Clown and I DO NOT like speed.
Anyway. 'A' was skits out and we start smoking and talking about what was going on and all. We were alone in the house listening to my friends music and enjoying ourselves. 'A' wanted another substance. I started talking about 2C-T7 how it was and the affects it had. 'A' wanted some 2C-T7. I had seen him do LSD before and I knew he was somewhat experinced and he was very annoying and insistant on getting some 2C-T7. So he obtined some.
He took around 35mgs. He snorted it. We told him it didn't seem bright because of his lack of sleep but he was 'hardcore' I call it moronic.  We explained the vomiting is normal it last about 30 minutes, ride it out.
'A''s friend came over once we reached the homestead. 'A' started relaxing and we drove around some and listened to music. After alittle bit about 45minutes after ingestion he started to snap. He was describing skulls and snakes spinning through the ground and vines growing all over the place. But he was sort of maintaing composure. His friend left and once he left 'A' started wigging HARD. He was screaming and getting violent. He would push me and then drop to the ground saying 'I'm sorry, Talon.' I would say 'You didn't do anything wrong man. Its the drug, relax.'
He continued to have these mood swings. It now was about 2 hours in. He relaxed some, he zoned out abit but was survining sweating very badly. By now he had taken his shirt off and was zoning we kept him cool and gave him a drink or water. he dumped it on his head, and laid back down.
About 20 minutes later it started again. This time he wouldn't calm down or listen. He start screaming random threats and jumble. He started pushing and throwing punches. I got on the phone and called a friend and tried to get some valiums or zynex to relax him. No luck. My friend Clown had never seen nor experieniced anything like this. I had but not to this degree. 'A' came over and pushed me and tried to punch me then ran to the door. He dropped his pants and tried to run out but I grabbed him and threw him inside. I closed the door and locked it. He ran to the bathroom and tried to climb out the shower window.
Mind you thats something only a toddler could possibly squeeze through. I pulled him and pushed him to the living room. He ran and started punching the window trying to get out. I was very upset now so I grabbed him and threw him to the ground and held him down arms behind his back. I screamed to Clown 'CALL 911 DAMNIT!' he did. 911 showed up in about 10 minutes. The police the ambulence everything. Even the school vice principal.
'A' fought with the police totally oblivious to what was happening. I told them what he was on etc. They searched the house took him to the hospital. He lived. However he was in a comma for a week. Had no health insurance and had an $80,000 bill. He says he had a dream of being in a world were everything was free except you had to buy words. He was driving in his dream and woke up in the hospital he thought he had been in a car wreck. He almost died.
I have come to a theory that 2C-T7 when mixed with meth related substance such as MDMA and Methamphetamine they have a basically lethal combination. I hope that this is of some help to my fellow man. Be careful and avoid mixing 2C-T7 with MDMA and/or Methamphetamine if you choose to try it. Make sure it is measured properly and know your mind, body, chemical, and source. God be with you.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Contact AppVenture
Have an app idea and need help bringing it into reality? Get in touch
Enquiries? Email us at<EMAIL_ADDRESS>For admission and general school hotlines, visit our school website.
Have an app idea and need help bringing it into reality? Get in touch
Enquiries? Email us at<EMAIL_ADDRESS>For admission and general school hotlines, visit our school website.


Nyria
I like Takashi Rie
Roles
theme developer
weeb
twitch frog
pianist/guitarist sometimes
Discord themes
Comfy
LilyPichu
Discolored
Couve
Spotify playlists
🎧・Other
🍣・Animes
🛋・Comfy
🎸・Guitar
😢・Sad
😮・Epic


Last weekend I was bored so I decided to pay my good friend 'G' a visit. Upon arriving at his house he told me he had gotten a new perscription to Gabitril, something I had never previously heard of. I'm really not into trying new substances(especially pills) but after a few hours of absolutely nothing to do I had a shift in judgement and decided to give in to temptation. After doing some research online, we both took a dose of 24 mg and chased it with a can of soda to aid digestion.
After about an hour of playing video games, we both started to feel something. The feeling increased signifigantly over the next hour. It was similar to a cannabis high but was getting very intense. My friend went into his room and layed down, so I decided to join him. Soon after, he started puking uncontrollably which supprised me, because I wasnt nauseous at all.
I decided to give him some time alone so I left his room and went back to playing the game. It felt like the effects of the gabitril would double every five minutes, until finally I was overpowered by the intensity. I became extremely uncomfortable and began to have hot and cold flashes. I decided to lay down and thats when the hallucinations began.  The intensity grew and drew me deeper and deeper into a very uncomfortable trip. Strange patterns of every color began to follow the waves. I was incapable of even the most basic actions (found this out when I tried to sit up) and thought 'God, I'm far gone'.
Soonafter I decided to puke to stop myself from absorbing any more. After crawling to the bathroom and puking up my dinner of frozen burritos (tastes even better the second time around), I managed my way back to the couch and passed out around 4 hours into the experience.
All in all not a very pleasent experience, I wouldn't recommend this high of a dose to anyone. The lessons: it's bad to overindulge, always be responsible and work your way up, I'm suprised I didn't die.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


import pandas as pd
import matplotlib.pyplot as plt
import pylab as p
import numpy as np
from IPython import embed
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.multiclass import OneVsOneClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import Perceptron
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.metrics import confusion_matrix
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier
from collections import Counter
from sklearn.cluster import KMeans, MiniBatchKMeans
from time import time
from sklearn import metrics
from operator import itemgetter
def load_prepared_data():
df_train = pd.read_csv('./data/output/processed_train.csv', header=0)
df_test = pd.read_csv('./data/output/processed_test.csv', header=0)
features = list(set(df_train.columns) - {'Vote'})
target = 'Vote'
df_train_X = df_train[features]
df_train_Y = df_train[target]
df_test_X = df_test[features]
df_test_Y = df_test[target]
# labels = {"0":"Blues","1":"Browns","2":"Greens","3":"Greys","4":"Oranges","5":"Pinks","6":"Purples","7":"Reds","8":"Whites","9":"Yellows" }
labels = ["Blues", "Browns", "Greens", "Greys", "Oranges", "Pinks", "Purples", "Reds", "Whites", "Yellows"]
return df_train_X, df_train_Y, df_test_X, df_test_Y, labels
df_train_X, df_train_Y, df_test_X, df_test_Y, labels = load_prepared_data()
df_train_Y_nums = df_train_Y
df_test_Y_nums = df_test_Y
df_train_Y = df_train_Y.map(lambda x: labels[int(x)])
df_test_Y = df_test_Y.map(lambda x: labels[int(x)])
train_val_data = pd.concat([df_train_X])
features = train_val_data.values
labels = pd.concat([df_train_Y]).values
max_K = 26
def print_bench_k_means(estimator, name, data, labels):
t0 = time()
estimator.fit(data)
print('%-9s\t%.2fs\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
% (name, (time() - t0), estimator.inertia_,
metrics.homogeneity_score(labels, estimator.labels_),
metrics.completeness_score(labels, estimator.labels_),
metrics.v_measure_score(labels, estimator.labels_),
metrics.adjusted_rand_score(labels, estimator.labels_),
metrics.adjusted_mutual_info_score(labels, estimator.labels_),
metrics.calinski_harabaz_score(data, estimator.labels_) ,
metrics.silhouette_score(data, estimator.labels_, metric='euclidean', sample_size=1000))
)
def loop_print_bench_k_means(data, labels, num):
print(82 * '_')
print('init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tcalinski\tsilhouette\t')
for i in range(2, num):
km = KMeans(n_clusters=i)
print_bench_k_means(km, "k-means k=" + str(i), data, labels)
loop_print_bench_k_means(df_train_X, df_train_Y, max_K)
def result_bench_k_means(data, labels, num):
homogeneity_score = []
completeness_score = []
v_measure_score = []
adjusted_rand_score = []
adjusted_mutual_info_score = []
silhouette_score = []
calinski_harabaz_score = []
interia = []
for i in range(2, num):
inner_homogeneity_score = []
inner_completeness_score = []
inner_v_measure_score = []
inner_adjusted_rand_score = []
inner_adjusted_mutual_info_score = []
inner_silhouette_score = []
inner_calinski_harabaz_score = []
inner_interia = []
k_fold = RepeatedStratifiedKFold(n_splits=2)
for train_idx, test_idx in k_fold.split(data, labels):
# print type(data)
# print data[[0,3,5]]
# print train_idx, len(data)
# print data[train_idx]
estimator = KMeans(n_clusters=i, random_state=0)
estimator.fit(data.values[train_idx])
inner_interia.append(estimator.inertia_)
inner_homogeneity_score.append(metrics.homogeneity_score(labels[train_idx], estimator.labels_))
inner_completeness_score.append(metrics.completeness_score(labels[train_idx], estimator.labels_))
inner_v_measure_score.append(metrics.v_measure_score(labels[train_idx], estimator.labels_))
inner_adjusted_rand_score.append(metrics.adjusted_rand_score(labels[train_idx], estimator.labels_))
inner_adjusted_mutual_info_score.append(metrics.adjusted_mutual_info_score(labels[train_idx], estimator.labels_))
inner_silhouette_score.append(metrics.silhouette_score(data.values[train_idx], estimator.labels_, metric='euclidean', sample_size=1000))
inner_calinski_harabaz_score.append(metrics.calinski_harabaz_score(data.values[train_idx], estimator.labels_)) ,
interia.append(np.mean(inner_interia))
homogeneity_score.append(np.mean(inner_homogeneity_score))
completeness_score.append(np.mean(inner_completeness_score))
v_measure_score.append(np.mean(inner_v_measure_score))
adjusted_rand_score.append(np.mean(inner_adjusted_rand_score))
adjusted_mutual_info_score.append(np.mean(inner_adjusted_mutual_info_score))
silhouette_score.append(np.mean(inner_silhouette_score))
calinski_harabaz_score.append(np.mean(inner_calinski_harabaz_score))
return {
"interia": interia,
"homogeneity_score": homogeneity_score,
"completeness_score": completeness_score,
"v_measure_score" : v_measure_score,
"adjusted_rand_score" :adjusted_rand_score,
"adjusted_mutual_info_score": adjusted_mutual_info_score,
"silhouette_score" : silhouette_score,
"calinski_harabaz_score": calinski_harabaz_score
}
def draw_metrics(X, Y, k_max, inline=False):
res = result_bench_k_means(X, Y, k_max)
plot_x = range(2,k_max)
for _metric, values in enumerate(res):
plt.plot(plot_x, res[values], '8-')
plt.xlabel('K')
plt.ylabel(values)
plt.title(values + ' measure on kMeans')
plt.grid(True)
plt.show()
draw_metrics(df_train_X, df_train_Y, max_K)
k = 10
km = KMeans(n_clusters=k, verbose=0, random_state=0)
print "Training: K=%d" % k
km.fit(df_train_X)
print "Done"
def k_means_party_distribution(clf, X, Y, k):
df_Y = Y.copy().to_frame()
df_Y['cluster'] = clf.labels_
res = {}
for i in range(0, k):
my_df = df_Y[df_Y['cluster'] == i]
c = Counter(my_df['Vote'].values)
res[i] = c
return res
def basic_distribution(dist):
for key,val in dist.iteritems():
items = val.most_common()
keys = []
values = []
for item in items:
keys.append(item[0])
values.append(item[0])
print "Group: %s, Distribution: %s"%(str(key), sorted(keys))
dist_per_cluster = k_means_party_distribution(km, df_train_X, df_train_Y, k)
basic_distribution(dist_per_cluster)
def k_means_cluster_distribution(clf, X, Y):
df_Y = Y.copy().to_frame()
df_Y['cluster'] = clf.labels_
res = {}
for i in labels:
my_df = df_Y[df_Y['Vote'] == i]
c = Counter(my_df['cluster'].values)
res[i] = c
return res
dist_per_party = k_means_cluster_distribution(km ,df_train_X, df_train_Y)
basic_distribution(dist_per_party)
print ("_" *22) + "dist per cluster" + ("_" *22)
print "_" * 60
for i in dist_per_cluster:
print i,dist_per_cluster[i]
print
print ("_" *22) + " dist per party " + ("_" *22)
print "_" * 60
for i in dist_per_party:
print i,dist_per_party[i]
from sklearn.metrics.pairwise import euclidean_distances
def build_distance_matrix(clf):
m = euclidean_distances(clf.cluster_centers_, clf.cluster_centers_)
m = np.around(m, decimals=3)
matrix = np.matrix(m)
plt.imshow(matrix, interpolation='nearest')
plt.colorbar()
plt.grid(True)
plt.show()
return matrix
matrix = build_distance_matrix(km)
for i in matrix:
print i
print "Mean distance: %f" %matrix.mean()
from sklearn import mixture
from sklearn.metrics.pairwise import euclidean_distances
gmm = mixture.GaussianMixture(n_components=k, covariance_type='full', random_state=0).fit(df_train_X)
def build_distance_matrix_gmm(gmm):
m = euclidean_distances(gmm.means_, gmm.means_)
m = np.around(m, decimals=3)
matrix = np.matrix(m)
plt.imshow(matrix, interpolation='nearest')
plt.colorbar()
plt.grid(True)
plt.show()
return matrix
gmm_dist = build_distance_matrix_gmm(gmm)
print gmm_dist
print "Mean distance: %f" %gmm_dist.mean()
Mean distance between clusters in gmm is smaller than in Kmeans
We continue with kmeans since it is more stable (distances are bigger)
in this step we look for the closest cluster and try to merge them if they have a matching parties.
def build_candidates_for_merge(matrix):
med = matrix.mean()
pairs = []
for i, line in enumerate(np.asarray(matrix)):
for j, col in enumerate(line):
if i < j and col < med:
pairs.append((i,j, col))
return sorted(pairs, key=itemgetter(2))
pairs = build_candidates_for_merge(matrix)
for i in pairs:
print i[0], sorted(dist_per_cluster[i[0]].keys())
print i[1], sorted(dist_per_cluster[i[1]].keys())
print "Clusters: %d --> %d, distance: %.03f"%i
print "_" * 50
This is the Chosen coalition
coalition = ['Purples', 'Browns', 'Greens', 'Pinks', 'Whites']
non_coalition = ['Greys', 'Oranges', 'Reds', 'Yellows', 'Blues']
coalition_clusters = [0,1,3,4,6]
def in_coal(row):
if row["Vote"] in coalition:
return 1
else:
return 0
def count_coalition(X, Y):
df = Y.copy().to_frame()
df['coal'] = df.apply (lambda row: in_coal(row),axis=1)
val_counts = df['coal'].value_counts()
print "In Coualtion the are %d votes which are %.02f%% percent"%(val_counts[1], float(val_counts[1]) / len(Y) * 100)
count_coalition(df_train_X, df_train_Y)
def predict_coalition(train_X, train_Y, test_X, test_Y):
print "Training..."
clf = RandomForestClassifier(min_samples_split=4, random_state=0)
clf.fit(train_X, train_Y)
print "Pridicting..."
pred = clf.predict(test_X)
distribution = Counter(pred)
print "predicted winner is party ## %s ##" % distribution.most_common(1)[0][0]
df_pred = pd.DataFrame()
df_pred['Vote'] = pred
count_coalition(test_X, df_pred['Vote'])
return df_pred
def predict_clustering(clf, train_X, train_Y, test_X, test_Y):
cluster_pred = clf.predict(test_X)
return cluster_pred
def count_coalition_clusters(pred):
c = Counter(pred)
in_coal = sum(c[cid] for cid in coalition_clusters)
total = sum(c.values())
print "Clustering - In Coualtion the are %d votes which are %.02f%% percent"%(in_coal, float(in_coal) / total * 100)
prediction = predict_coalition(df_train_X, df_train_Y, df_test_X,df_test_Y)
clustering_prediction = predict_clustering(km, df_train_X, df_train_Y, df_test_X, df_test_Y)
count_coalition_clusters(clustering_prediction)
def transform_category(_df, name):
for cat in _df[name].unique():
_df[cat] = (_df[name] == cat).astype(int)
return _df
def most_important_features_per_party(X, Y, num):
target = pd.concat([Y]).values
df = Y.copy().to_frame()
df = transform_category(df, 'Vote')
df = df.drop('Vote', axis=1)
print '_' * 82
res = {}
for party in df.columns.values:
# clf_tree = DecisionTreeClassifier(min_samples_split=5, random_state=0)
clf_tree = DecisionTreeClassifier()
clf_tree.fit(features, df[party])
imp = clf_tree.feature_importances_
selected_columns = []
for i in range(len(train_val_data.columns)):
selected_columns.append([train_val_data.columns[i], imp[i]])
selected_columns = sorted(selected_columns, key=itemgetter(1), reverse=True)[:num]
res[party] = selected_columns
return res
important_features = most_important_features_per_party(df_train_X, df_train_Y, 5)
for party in important_features:
print party, important_features[party]
def draw_most_important_features_per_party(party_feature_data):
# party_feature_data = most_important_features_per_party(num)
for party, values in party_feature_data.iteritems():
keys = []
vals = []
for i in values:
keys.append(i[0])
vals.append(i[1])
y_pos = np.arange(len(keys))
plt.barh(y_pos, vals, align='center', alpha=0.5)
plt.yticks(y_pos, keys)
plt.xlim((0,1))
plt.title(party)
plt.xlabel('Most important features')
plt.show()
draw_most_important_features_per_party(important_features)
# important_features
def feature_score_limit(important_features, min_score, max_score=1):
for party in important_features:
for features in important_features[party]:
if features[1] > min_score and features[1] <= max_score:
print party, features
print 0.5
feature_score_limit(important_features, 0.5)
print "0.4 - 0.5"
feature_score_limit(important_features, 0.4, 0.5)
print "0.3 - 0.4"
feature_score_limit(important_features, 0.3, 0.4)
print "0.25 - 0.3"
feature_score_limit(important_features, 0.25, 0.3)
improtant_features = ['Overall_happiness_score',
'Avg_Satisfaction_with_previous_vote',
'Garden_sqr_meter_per_person_in_residancy_area',
'Yearly_IncomeK',
'Number_of_valued_Kneset_members',
'Avg_monthly_expense_when_under_age_21']
def plot_feature_distriution(parties, my_features, X, Y):
for party in parties:
df_party = X.copy()
df_party['Vote'] = Y
df_party = df_party[df_party['Vote'] == party]
df_party = df_party[my_features]
df_party.plot(kind='kde')
p.title(party)
p.grid(True)
p.show()
print df_party[my_features].mean()
print "Coalition"
print "_" * 83
plot_feature_distriution(coalition, improtant_features, df_train_X, df_train_Y)
print "Non Coalition"
print "_" * 83
plot_feature_distriution(non_coalition, improtant_features, df_train_X, df_train_Y)
def show_feature_party(feature, party):
df_party = df_train_X.dropna().copy()
df_party['Vote'] = df_train_Y
df_party = df_party[df_party['Vote'] == party]
df_party[feature].plot(kind='kde')
p.xlim(0,1)
p.title(party + " " + feature)
p.show()
print df_party[feature].describe()
show_feature_party('Overall_happiness_score', 'Pinks')
show_feature_party('Overall_happiness_score', 'Purples')
show_feature_party('Overall_happiness_score', 'Reds')
show_feature_party('Overall_happiness_score', 'Greys')
show_feature_party('Overall_happiness_score', 'Greens')
show_feature_party('Overall_happiness_score', 'Browns')
show_feature_party('Overall_happiness_score', 'Pinks')
show_feature_party('Overall_happiness_score', 'Oranges')
show_feature_party('Garden_sqr_meter_per_person_in_residancy_area', 'Purples')
show_feature_party('Garden_sqr_meter_per_person_in_residancy_area', 'Pinks')
show_feature_party('Garden_sqr_meter_per_person_in_residancy_area', 'Greens')
show_feature_party('Number_of_valued_Kneset_members', 'Purples')
show_feature_party('Number_of_valued_Kneset_members', 'Pinks')
Choosing a new winner by manipulation of data
def pick_a_new_winner(train_X, train_Y, test_X, test_Y):
clf = RandomForestClassifier(min_samples_split=4, random_state=0)
clf.fit(train_X, train_Y)
pred = clf.predict(test_X)
distribution = Counter(pred)
print "Original winner is party ## %s ##" % distribution.most_common(1)[0][0]
print ""
print "Garden_sqr_meter_per_person_in_residancy_area = 0.33"
new_test_X = test_X.copy()
new_test_X['Garden_sqr_meter_per_person_in_residancy_area'] = 0.33
pred = clf.predict(new_test_X)
distribution = Counter(pred)
print "winner is party ## %s ##" % distribution.most_common(1)[0][0]
print ""
print "Will_vote_only_large_party = 0.9"
new_test_X = test_X.copy()
new_test_X['Will_vote_only_large_party'] = 0.9
pred = clf.predict(new_test_X)
distribution = Counter(pred)
print "winner is party ## %s ##" % distribution.most_common(1)[0][0]
print ""
print "Number_of_valued_Kneset_members = 0.236734"
new_test_X = test_X.copy()
new_test_X['Number_of_valued_Kneset_members'] = 0.236734
pred = clf.predict(new_test_X)
distribution = Counter(pred)
print "winner is party ## %s ##" % distribution.most_common(1)[0][0]
print ""
print "Garden_sqr_meter_per_person_in_residancy_area /= 0.236734"
print "Number_of_valued_Kneset_members -= 0.23"
new_test_X = test_X.copy()
new_test_X['Garden_sqr_meter_per_person_in_residancy_area'] = new_test_X['Garden_sqr_meter_per_person_in_residancy_area'] /2
new_test_X['Number_of_valued_Kneset_members'] = new_test_X['Number_of_valued_Kneset_members'] - 0.23
pred = clf.predict(new_test_X)
distribution = Counter(pred)
print "winner is party ## %s ##" % distribution.most_common(1)[0][0]
print ""
pick_a_new_winner(df_train_X, df_train_Y, df_test_X, df_test_Y)
for party in coalition:
df_party = df_train_X.dropna().copy()
df_party['Vote'] = df_train_Y
df_party = df_party[df_party['Vote'] == party]
df_party[improtant_features].plot(kind='kde')
p.title(party)
p.show()
print df_party[improtant_features].mean()
df_party = df_train_X.dropna().copy()
df_party['Vote'] = df_train_Y
df_party = df_party[df_party['Vote'].isin(coalition)]
df_party[improtant_features].plot(kind='kde')
p.title(" Coalition")
p.show()
print df_party[improtant_features].mean()
df_party = df_train_X.dropna().copy()
df_party['Vote'] = df_train_Y
df_party = df_party[df_party['Vote'].isin(non_coalition)]
df_party[improtant_features].plot(kind='kde')
p.title(" Non Coalition")
p.show()
print df_party[improtant_features].mean()
def hist_plot(feat):
for f in feat:
df_party = df_train_X.dropna().copy()
df_party['Vote'] = df_train_Y
df_party = df_party[df_party['Vote'].isin(coalition)]
df_party[f].plot(kind='hist')
p.title(f + " Coalition")
p.show()
df_party = df_train_X.dropna().copy()
df_party['Vote'] = df_train_Y
df_party = df_party[df_party['Vote'].isin(non_coalition)]
df_party[f].plot(kind='hist')
p.title(f + " Non Coalition")
p.show()
hist_plot(['Overall_happiness_score',
'Is_Most_Important_Issue_Foreign_Affairs',
'Is_Most_Important_Issue_Other',
'Yearly_IncomeK',
'Garden_sqr_meter_per_person_in_residancy_area',
'Will_vote_only_large_party','Avg_Satisfaction_with_previous_vote','Is_Most_Important_Issue_Military','Number_of_valued_Kneset_members'])
def change_coalition(train_X, train_Y, test_X, test_Y):
clf = RandomForestClassifier(min_samples_split=4, random_state=0)
clf.fit(train_X, train_Y)
pred = clf.predict(test_X)
distribution = Counter(pred)
print "Original winner is party ## %s ##" % distribution.most_common(1)[0][0]
pred = clf.predict(test_X)
distribution = Counter(pred)
print "winner is party ## %s ##" % distribution.most_common(1)[0][0]
print distribution
df_pred = pd.DataFrame()
df_pred['Vote'] = pred
count_coalition(test_X, df_pred['Vote'])
print ""
new_test_X = test_X.copy()
new_test_X['Will_vote_only_large_party'] = new_test_X['Will_vote_only_large_party'] / 3 + 0.66
pred = clf.predict(new_test_X)
distribution = Counter(pred)
print "winner is party ## %s ##" % distribution.most_common(1)[0][0]
print distribution
df_pred = pd.DataFrame()
df_pred['Vote'] = pred
count_coalition(new_test_X, df_pred['Vote'])
print ""
Manupulating data to change coalition
change_coalition(df_train_X, df_train_Y, df_test_X, df_test_Y)


nest info- Create a new NestJS project in a directory of the same name:
nest new project_name- Build a specific NestJS project:
nest build project_name- Run a specific NestJS project:
nest start project_name- Import a library into the current NestJS project:
nest add library_name


Mapstraction v3 Examples
All maps providers; centred on London
CloudMade
Basic Example
Full Feature Example
ESRI
Basic Example
Full Feature Example
Google v2
Basic Example
Full Feature Example
Google v3
Basic Example
Full Feature Example
Leaflet
Basic Example
Full Feature Example
MapQuest
Basic Example
Full Feature Example
MapQuest Open
Basic Example
Full Feature Example
Microsoft Bing v6
Basic Example
Full Feature Example
Microsoft Bing v7
Basic Example
Full Feature Example
Nokia HERE
Basic Example
Full Feature Example
Nokia Ovi
Basic Example
Full Feature Example
OpenLayers
Basic Example
Full Feature Example
Ordnance Survey OpenSpace
Basic Example
Full Feature Example
Yandex
Basic Example
Full Feature Example
Yandex 2
Full Feature Example


The gmung960 utility modifies text section and/or data section load address(es) in an object file.
gmung960 is useful when you need to load text and/or data at an address other than where it was linked. A common case is where code must copy its data to RAM before execution. In this case, the data will be linked at the RAM address, but it must be loaded at a ROM address from where it will be copied.
The invocation command is:
gmung960 [ -option ]... file
Each option must be preceded by a - character. (On DOS, you can also use a / character.) The options are listed below.
file identifies the object file to be "munged." It must be a linked, executable file.
Note that the section specification options T and D are processed in the order they appear in the invocation.
Return to the top of this page.


R Software
``` Type R in commandline. In R commandline, use the below command to install ggplot2 and exit R.
install.packages('ggplot2') ```
```
Clone Repo Install ggplot2 package Change contents of Skills.csv Edit your name in code.R-line 7 Run the script using 'Rscript code.R' Graph Created ```
You will obtain graph like below:


Toy model
— supercritical; density 3%
Two-neighbour
— critical & balanced; density 5%
Duarte
— critical & unbalanced; density 10%
DTBP
— subcritical; density 12%


Calculator: the game solver
Available operations:
Use the following format, separate operations with a space:
store reverse sum 11=>33 x3 put5
Moves:
Portals:
Input:
Output:
Initial number:
Goal:
Possible solutions: (it may take a few seconds for moves > 6)


{{ _('Mobile File Upload') }}
{{ _('You have successfully uploaded a file to %(service_name)s.', service_name=service_name) }}


Welcome to my website
Get ready to know more about me!!
Top 10 Places to visit:
-
Rome
-
South Island, New Zealand
-
Paris
-
Tahiti
-
London
-
Maui
-
Bora Bora
-
Phuket
-
Grand Canyon
-
Barcelona


demonstration page
Blue background around images is just slideshow container's background. It is set blue to better see how slideshow works with diffrent parameters
All parameters are explained with details on WIKI pages or README file


Class: Aws::SecurityHub::Types::AwsEcsTaskDefinitionContainerDefinitionsLinuxParametersCapabilitiesDetails
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-securityhub/lib/aws-sdk-securityhub/types.rb |
Overview
The Linux capabilities for the container that are added to or dropped from the default configuration provided by Docker.
Constant Summary
Instance Attribute Summary
-
#add ⇒ Array<String>
rw
The Linux capabilities for the container that are added to the default configuration provided by Docker.
-
#drop ⇒ Array<String>
rw
The Linux capabilities for the container that are dropped from the default configuration provided by Docker.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Aws::Structure#to_h. |
Instance Attribute Details
#add ⇒ Array
<String
> (rw)
The Linux capabilities for the container that are added to the default configuration provided by Docker. Valid values are as follows:
Valid values: "ALL"
| "AUDIT_CONTROL"
|"AUDIT_WRITE"
|
"BLOCK_SUSPEND"
| "CHOWN"
| "DAC_OVERRIDE"
|
"DAC_READ_SEARCH"
| "FOWNER"
| "FSETID"
| "IPC_LOCK"
|
"IPC_OWNER"
| "KILL"
| "LEASE"
| "LINUX_IMMUTABLE"
|
"MAC_ADMIN"
|"MAC_OVERRIDE"
| "MKNOD"
| "NET_ADMIN"
|
"NET_BIND_SERVICE"
| "NET_BROADCAST"
| "NET_RAW"
|
"SETFCAP"
| "SETGID"
| "SETPCAP"
| "SETUID"
|
"SYS_ADMIN"
| "SYS_BOOT"
| "SYS_CHROOT"
| "SYS_MODULE"
|
"SYS_NICE"
| "SYS_PACCT"
| "SYS_PTRACE"
| "SYS_RAWIO"
|
"SYS_RESOURCE"
| "SYS_TIME"
| "SYS_TTY_CONFIG"
| "SYSLOG"
| "WAKE_ALARM"
#drop ⇒ Array
<String
> (rw)
The Linux capabilities for the container that are dropped from the default configuration provided by Docker.
Valid values: "ALL"
| "AUDIT_CONTROL"
|"AUDIT_WRITE"
|
"BLOCK_SUSPEND"
| "CHOWN"
| "DAC_OVERRIDE"
|
"DAC_READ_SEARCH"
| "FOWNER"
| "FSETID"
| "IPC_LOCK"
|
"IPC_OWNER"
| "KILL"
| "LEASE"
| "LINUX_IMMUTABLE"
|
"MAC_ADMIN"
|"MAC_OVERRIDE"
| "MKNOD"
| "NET_ADMIN"
|
"NET_BIND_SERVICE"
| "NET_BROADCAST"
| "NET_RAW"
|
"SETFCAP"
| "SETGID"
| "SETPCAP"
| "SETUID"
|
"SYS_ADMIN"
| "SYS_BOOT"
| "SYS_CHROOT"
| "SYS_MODULE"
|
"SYS_NICE"
| "SYS_PACCT"
| "SYS_PTRACE"
| "SYS_RAWIO"
|
"SYS_RESOURCE"
| "SYS_TIME"
| "SYS_TTY_CONFIG"
| "SYSLOG"
| "WAKE_ALARM"


Name: Chang Lei
Birth of Date: 04/05/1999
Use current year to get my age.
Major: string major = "Computer Science"
Hobbies: Soccer, Hiking, Bicycles. etc.
I am an exchange student in UCSD. I am pursuing my bachelor course in National University of Singapore.
Results for loading time (DOMContentLoaded/ms, Loadtime/ms)
- 161, 237(10:01)
- 156, 198(10:04)
- 169, 224(10:08)
- 145, 174(10:15)
- 113, 211(10:22)
- 147, 198(10:30)
- 133, 202(10:42)
- 144, 192(10:50)
- 143, 205(10:52)
- 124, 181(11:20)


I'm a web developer with frontend, backend, and architecture skills. I'm able to develop web applications following robustness, security, and scalability criteria.
I'm an organized and detail-oriented professional also I'm self-motivated and able to get things done without constant supervision. As a freelancer, I always seek for a deep understanding of the client businesses in order to raise relevant suggestions and help the client's interests as a whole.
Want to build a Website, Web App or Mobile App? Leverage your business using technology? Let's talk!
Possuo qualificação em frontend, backend e arquitetura, sendo capaz de desenvolver uma variedade de sistemas para internet seguindo critérios de robustez, segurança e escalabilidade.
Quer desenvolver um Website, Sistema Web ou Mobile App? Alavancar o seu negócio utiizando a tecnologia? Vamos conversar!
Worked in the cloud team with Google Cloud Platform technologies such as Cloud Datastore and AppEngine to build robust and scalable large web applications with Java and Python.
Front-end development with Angular, Angular Material, and SASS.
The team reached a high performance using SCRUM, Continuous Integration, automated tests, and English as the main language.
Built a new website for this real estate company alongside two freelancers with the framework Laravel and AWS Cloud.
The project achieved an increase in qualified leads and returning visitors.
Computer Science
Activities and Societies: FACE Mentoring Program, CIPEX Extension Program, PHP+MySQL Lecture at technology fair.
Universidade FUMEC (Fundação Mineira de Educação e Cultura, FUMEC University) is a private and non-profit Brazilian university in Belo Horizonte, state of Minas Gerais.
Full-Stack Web Developer
1200 hours mastering algorithms, data structures, and full-stack development with Ruby, Rails, Javascript, React, and Redux. 50 projects built. (GitHub).
Developed skills in remote pair-programming, using Github, industry-standard git flows, and daily standups in collaboration with remote developers.
From upper-intermediate to advanced | CEFR level C1


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Team Finder is a web application that allows users to a select a USA/Canadian city that has a proffessional in one of the top 3 divison's of the American Soccer Pyramid and then display the closest team the user can support in the
vicinity.
Upon city selection the user is then shown data for Name, City, State, Country, Division.


LIVE UPDATES: South Africa 2-2 Ghana (U-23 AFCON)
Follow the LIVE play-by-play of the U-23 Africa Cup of Nations game between Ghana and South Africa at the Cairo International Stadium.
When South Africa faces Ghana on Friday evening, it will be more than just a third place ranking match, because at the end there will not only be a bronze medal but also and especially the qualification to Tokyo Olympic Games 2020.
Having missed the direct qualification by being eliminated in the semifinals, both teams will give their best to grab the last of three tickets reserved for African teams in the quadrennial multi-sports event.
After two eventful weeks in Cairo, both the South Africans and the Black Meteors of Ghana will do whatever it takes to have a successful final step, which won’t be but with going home with the Olympics ticket in hand.
TEAM NEWS
?s=20
South Africa
?s=20
?s=20
Game underway at the Cairo International Stadium
South Africa scores the first goal of the game in the 17th minute
It’s South Africa 1-0 Ghana
First half comes to an end
South Africa leading by a goal
Evans Mensah gets the equalizer for Ghana. He strikes the ball from a long range
It’s South Africa 1-1 Ghana
South Africa takes the lead again
It’s South Africa 2-1 Ghana
Goal ooooo Samuel Obeng gets the equalizer for Ghana
It’s South Africa 2-2 Ghana
Referee brings the game to an end we are heading to extra time.
South Africa beat Ghana on penalties to book a spot at the Tokyo 2020 Olympic Games.


Patterns
These patterns have been developed by the Home Office DDaT user research and design teams to help develop and create consistent, usable services. They include example components and guidance on usage.
Beta
- Flash cards: interrupt the user flow with important information
- Header: identify a common landmark at the top of every view
- Icons: full set of Home Office icons
Alpha
- Buttons: used when a user needs to take an action, submit data, or make a change to the page.
- Highlighting matches: helps users identify matching data
- Labels: show classification and marking ownership, they can be useful for users to quickly reference related items
- Modal: creates focus on a single interaction
- Navigation: navigate to different
at any time - Tabs: switch between views of grouped content or hide content that is not absolutely necessary to see
- Timeline: time-ordered activity showing the history of a person or object with additional context
Discovery
Styles
Elements
Helpful resources for interaction designers.
Accessibility
Prototyping
Contributing to the patterns
For Home Office designers, we have a process for contributing to these patterns. We would be happy for feedback if the contribution process doesn't work for you.


name:
Type:
Server:
Port:
Username:
Password:
Default DB:
Default DB is the default database which owned by user.
Default DB
Add Cancel
Confirm Cancel


--- ---
{% include head.html %} {% include header.html %}There are various sources of information, internet being the primary fore-runner. It thus becomes imperative that our platform provides state-of the art features to enable innovative and engaging learning experience. Keeping in mind this need, our features include:
Societal Platform Architecture
Sunbird allows one or more organizations and communities to exist as tier-1 entities on the platform. Each organization can independently manage their users, training and learning processes and user communities.
User and organization registry and profiles
Sunbird allows organizations to store verified information about users and organizations and use this repository as a verified registry to enable other user actions and functions.
Courses, Trainings and Lesson Plans
Sunbird provides features that allow for hosting of open and closed courses. Courses can be setup with one or more batches that can be hosted on different dates/times. Sunbird also allows authoring of lesson plans for courses and textbooks via provision of intuitive templates and workflows.
Open Content Resource Library
Content created or uploaded on a Sunbird instance is published under a Creative Commons License and therefore is truly free to consume. In addition, each organization on Sunbird can define their own curation and review workflows to ensure that published content is of high quality.
Communities, Collaboration and Messaging
Sunbird offers collaboration and messaging features such as course discussions, community announcements as well as activity feeds for users generated from communities and courses that users are engaged with.
Data and Dashboards
All interactions with the user interface and content are available as data and can be used to analyse user interactions with courses, content and the platform.
Badges for Certifications
Badges communicate skills and achievements by providing visual symbols of accomplishments packed with verifiable data and evidence that can be shared across the web. They empower individuals to take their learning with them, wherever they go, building a rich picture of their lifelong learning journey.
Curation
Within content platforms, the demand for curation has grown out of the increasing need to help users find quality, coherent, and relevant content on specific topics without having to spend great amounts of time and energies to search, browse and scan multiple different sources.
Card and Feed-based User Interface optimized for Mobile Access
To ensure optimal usability, the platform uses cards and feeds extensively for organization of information. Card-based design supports limited working memory by allowing users to break up information through the process of chunking and allowing focus on a single task or piece of information at a time.


Documentation
Jessica Jones
Welcome!
My profile
Settings
Activity
Support
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Documentation
v1.0.0
We are constatly doing updates on the product and documentation, so please check the online version.
View Docs Online


While html and css make up the content and layout of a webpage, javascript is responsible for all of the moving parts and pretty much any other interactive elements of the page. It is the most complex of the three languages and is much more powerful than the other two. Almost every time a user interacts with a webpage involves javascript. It can be used to create online browser games or special animations. Also most web apps are built with javascript.
Control flow is the order in which a javascript page is implemented. It can sort of be compared to the average day of a habitual human being. Generally, the flow starts at the top of the page and continues down until it reaches the end of the javascript code. So you can think of the very first command on the page as waking up. Each sequential action you take as you go about your day is like reaching the next line of code. Sometimes in javascript you get to a loop, in which a set of actions are repeated until an exit condition is met. YOu can think about this like going to the gym and lifting weights until you have completed 10 reps. In this case your exit condition was completing those 10 reps, but it could have been something different like lifting weights for running on a treadmill until your heart rate reached a certain number. Afterwards you continue on with your day much like the control flow will continue to the next line of code.
The main difference between accessing arrays vs objects is how you call their contents. To call a term in an array you use the name of the array and the number of its postion in square braces something like nameOfArray[4] where 4 is the 5th term in the array (the array indices start at 0). To call a term in an object you can use dot notation (object.trait) or bracket notatation (object[trait]) however instead of using indices you use properties given to the object. For instance the object could be a car with various information stored, like the color, model, gas milage, etc. If the name of the object was car and you wanted to find out its color, you could call car.color (as long as the property for color was given the name color).
Functions are comprised of code that performs a set of actions and often outputs a value. They often take one or more parameters and are very useful if they are performing actions that you need to do multiple times. When a function is called, it temporarily takes over the control flow, completes its code and returns the flow to the next line of code. Functions can be as simple as taking two numbers and adding them together, or quite complex using tens of lines of code, if not more. Once the function has been written, you only have to call it with its and parameters in order to use it again. This can save you from writing hundreds of lines of code.
Home


The producerfields Example: Using Producer Fields to Generate Resources
The producerfields example, which allows you to create a to-do list, shows how to use a producer field to generate objects that can then be managed by the container. This example generates an EntityManager object, but resources such as JDBC connections and datasources can also be generated this way.
The producerfields example is the simplest possible entity example. It also contains a qualifier and a class that generates the entity manager. It also contains a single entity, a stateful session bean, a Facelets page, and a managed bean.
The Producer Field for the producerfields Example
The most important component of the producerfields example is the smallest, the db.UserDatabaseEntityManager class, which isolates the generation of the EntityManager object so that it can easily be used by other components in the application. The class uses a producer field to inject an EntityManager that is annotated with the @UserDatabase qualifier, also defined in the db package:
@Singleton public class UserDatabaseEntityManager { @Produces @PersistenceContext @UserDatabase private EntityManager em; ... }
The class does not explicitly produce a persistence unit field, but the application has a persistence.xml file that specifies a persistence unit. The class is annotated javax.inject.Singleton to specify that the injector should instantiate it only once.
The db.UserDatabaseEntityManager class also contains commented-out code that uses create and close methods to generate and remove the producer field:
/* @PersistenceContext private EntityManager em; @Produces @UserDatabase public EntityManager create() { return em; } */ public void close(@Disposes @UserDatabase EntityManager em) { em.close(); }
You can remove the comments from this code and place them around the field declaration to test how the methods work. The behavior of the application is the same with either mechanism.
The advantage of producing the EntityManager in a separate class rather than simply injecting it into an enterprise bean is that the object can easily be reused in a typesafe way. Also, a more complex application may want to create multiple entity managers using multiple persistence units, and this mechanism isolates this code for easy maintenance, as in the following example:
@Singleton public class JPAResourceProducer { @Produces @PersistenceUnit(unitName="pu3") @TestDatabase EntityManagerFactory customerDatabasePersistenceUnit; @Produces @PersistenceContext(unitName="pu3") @TestDatabase EntityManager customerDatabasePersistenceContext; @Produces @PersistenceUnit(unitName="pu4") @Documents EntityManagerFactory customerDatabasePersistenceUnit; @Produces @PersistenceContext(unitName="pu4") @Documents EntityManager docDatabaseEntityManager;" }
The EntityManagerFactory declarations also allow applications to use an application-managed entity manager.
The producerfields Entity and Session Bean
The producerfields example contains a simple entity class, entity.ToDo, and a stateful session bean, ejb.RequestBean, that uses it.
The entity class contains three fields: an autogenerated id field, a string specifying the task, and a timestamp. The timestamp field, timeCreated, is annotated with @Temporal, which is required for persistent Date fields.
@Entity public class ToDo implements Serializable { private static final long serialVersionUID = 1L; @Id @GeneratedValue(strategy = GenerationType.AUTO) private Long id; protected String taskText; @Temporal(TIMESTAMP) protected Date timeCreated; public ToDo() { } public ToDo(Long id, String taskText, Date timeCreated) { this.id = id; this.taskText = taskText; this.timeCreated = timeCreated; } ...
The remainder of the ToDo class contains the usual getters, setters, and other entity methods.
The RequestBean class injects the EntityManager generated by the producer method, annotated with the @UserDatabase qualifier:
@ConversationScoped @Stateful public class RequestBean { @Inject @UserDatabase EntityManager em;
It then defines two methods, one that creates and persists a single ToDo list item, and another that retrieves all the ToDo items created so far by creating a query:
public ToDo createToDo(String inputString) { ToDo toDo = null; Date currentTime = Calendar.getInstance().getTime(); try { toDo = new ToDo(); toDo.setTaskText(inputString); toDo.setTimeCreated(currentTime); em.persist(toDo); return toDo; } catch (Exception e) { throw new EJBException(e.getMessage()); } } public List<ToDo> getToDos() { try { List<ToDo> toDos = (List<ToDo>) em.createQuery( "SELECT t FROM ToDo t ORDER BY t.timeCreated").getResultList(); return toDos; } catch (Exception e) { throw new EJBException(e.getMessage()); } } }
The producerfields Facelets Pages and Managed Bean
The producerfields example has two Facelets pages, index.xhtml and todolist.xhtml. The simple form on the index.xhtml page asks the user only for the task. When the user clicks the Submit button, the listBean.createTask method is called. When the user clicks the Show Items button, the action specifies that the todolist.xhtml file should be displayed:
<h:body>
To Do List

Type a task to be completed.
 <h:form id="todolist">
<h:outputLabel value="Type a string: " for="inputString"/> <h:inputText id="inputString" value="#{listBean.inputString}"/>

<h:commandButton value="Submit" action="#{listBean.createTask()}"/>

<h:commandButton value="Show Items" action="todolist"/>
 </h:form> ... </h:body>
The managed bean, web.ListBean, injects the ejb.RequestBean session bean. It declares the entity.ToDo entity and a list of the entity, along with the input string that it passes to the session bean. The inputString is annotated with the @NotNull Bean Validation constraint, so that an attempt to submit an empty string results in an error.
@Named @ConversationScoped public class ListBean implements Serializable { private static final long serialVersionUID = 1L; @EJB private RequestBean request; @NotNull private String inputString; private ToDo toDo; private List<ToDo> toDos;
The createTask method called by the Submit button calls the createToDo method of RequestBean:
public void createTask() { this.toDo = request.createToDo(inputString); }
The getToDos method, which is called by the todolist.xhtml page, calls the getToDos method of RequestBean:
public List<ToDo> getToDos() { return request.getToDos(); }
To force the Facelets page to recognize an empty string as a null value and return an error, the web.xml file sets the context parameter javax.faces.INTERPRET_EMPTY_STRING_SUBMITTED_VALUES_AS_NULL to true:
<context-param> <param-name>javax.faces.INTERPRET_EMPTY_STRING_SUBMITTED_VALUES_AS_NULL</param-name> <param-value>true</param-value> </context-param>
The todolist.xhtml page is a little more complicated than the index.html page. It contains a dataTable element that displays the contents of the ToDo list. The body of the page looks like this:

To Do List
 <h:form id="showlist"> <h:dataTable var="toDo" value="#{listBean.toDos}" rules="all" border="1" cellpadding="5"> <h:column> <f:facet name="header"> <h:outputText value="Time Stamp" /> </f:facet> <h:outputText value="#{toDo.timeCreated}" /> </h:column> <h:column> <f:facet name="header"> <h:outputText value="Task" /> </f:facet> <h:outputText value="#{toDo.taskText}" /> </h:column> </h:dataTable>
<h:commandButton id="back" value="Back" action="index" />
 </h:form>
The value of the dataTable is listBean.toDos, the list returned by the managed bean's getToDos method, which in turn calls the session bean's getToDos method. Each row of the table displays the timeCreated and taskText fields of the individual task. Finally, a Back button returns the user to the index.xhtml page.
Building, Packaging, Deploying, and Running the producerfields Example
You can build, package, deploy, and run the producerfields application by using either NetBeans IDE or the Ant tool.
To Build, Package, and Deploy the producerfields Example Using NetBeans IDE
- If the database server is not already running, start it by following the instructions in Starting and Stopping the Java DB Server.
- From the File menu, choose Open Project.
- In the Open Project dialog, navigate to:
tut-install/examples/cdi/
- Select the producerfields folder.
- Select the Open as Main Project check box.
- Click Open Project.
- In the Projects tab, right-click the producerfields project and select Deploy.
To Build, Package, and Deploy the producerfields Example Using Ant
- If the database server is not already running, start it by following the instructions in Starting and Stopping the Java DB Server.
- In a terminal window, go to:
tut-install/examples/cdi/producerfields/
- Type the following command:
ant
This command calls the default target, which builds and packages the application into a WAR file, producerfields.war, located in the dist directory.
- Type the following command:
ant deploy
To Run the producerfields Example
- In a web browser, type the following URL:
http://localhost:8080/producerfields
The Create To Do List page opens.
- Type a string in the text field and click Submit.
You can type additional strings and click Submit to create a task list with multiple items.
- Click the Show Items button.
The To Do List page opens, showing the timestamp and text for each item you created.
- Click the Back button to return to the Create To Do List
page.
On this page, you can enter more items in the list.


P.I. Industries, L.L.C.
The place to see.
Home
Accommodations
Activities
Travel Arrangements
Pictures, etc.
Contact Info
The place to see.


| foo |
| foo |
| foo |
| foo |
When you edit multiple md files remotely, like in a larger mkdocs project, context switches between editing terminal(s) and viewing browser may have some efficiency impact. Also sometimes there is just no browser, like via security gateways offering just a fixed set of applications on the hop in machine. Further, reading efficiency and convenience is often significantly improved by using colors. And lastly, using such a thing for cli applications might improve user output, e.g. for help texts.
This is where mdv, a Python based Markdown viewer for the terminal might be a good option.
If markdown is often "simple" enough to be somewhat readable on 256 color terminals (except images that is).
from
### Source
# Header 1
## Header 2
### Header 3
#### Header 4
##### Header 5
###### Header 6
```python
""" Test """
# Make Py2 >>> Py3:
import os, sys; reload(sys); sys.setdefaultencoding('utf-8')
# no? see http://stackoverflow.com/a/29832646/4583360 ...
# code analysis for hilite:
try:
from pygments import lex, token
from pygments.lexers import get_lexer_by_name, guess_lexer
```
| Tables | Fmt |
| -- | -- |
| !!! hint: wrapped | 0.1 **strong** |
!!! note: title
this is a Note
You can also use mdv as a source code viewer, best when you have docstrings with markdown in your code:
from
~/terminal_markdown_viewer $ cat setup.py
#!/usr/bin/env python2.7
# coding: utf-8
"""_
# Mdv installation
## Usage
[sudo] ./setup.py install
----
"""
from setuptools import setup, find_packages
import mdv
setup(
name='mdv',
version=mdv.__version__,
(the '_' after the docstring telling mdv that markdown follows)
mdv is a proof of concept hack: While for simple structures it does its job quite well, for complex markdown you want to use other tools. Especially for inlined html it simply fails.
The ones I know of (and which made me write mdv ;-) ):
Summary: For production ready robust markdown viewing (e.g. for your customers) I recommend nd still, due to the early state of mdv. For playing around, especially with theming or when with Python, this one might be a valid alternative to look at.
pip install mdv
If you get no attribute HTML_PLACEHOLDER
: update your markdown package.
Here is a macport (thanks Aljaž).
Further a 256 color terminal (for now best with dark background) and font support for a few special separator characters (which you could change via config).
For light terms you'd just need to revert the 5 colors from the themes, since they are sorted by luminocity.
I did not test anything on windows.
Distribution via setuptools. If setuptools is not installed, run:
pip install setuptools
Use the setup.py provided inside, I.e. run:
sudo ./setup.py install
(or ./setup.py install --user to install only for the current user)
# Usage:
mdv [OPTIONS] MDFILE
# Options:
MDFILE : Path to markdown file
-A : Strip all ansi (no colors then)
-C MODE : Sourcecode highlighting mode
-H : Print html version
-L : Backwards compatible shortcut for '-u i'
-M DIR : Monitor directory for markdown file changes
-T C_THEME: Theme for code highlight. If not set: Using THEME.
-X Lexer : Default lexer name (default: python). Set -x to use it always.
-b TABL : Set tab_length to sth. different than 4 [default: 4]
-c COLS : Fix columns to this (default: your terminal width)
-f FROM : Display FROM given substring of the file.
-h : Show help
-i : Show theme infos with output
-l : Light background (not yet supported)
-m : Monitor file for changes and redisplay FROM given substring
-n NRS : Header numbering (default: off. Say e.g. -3 or 1- or 1-5
-t THEME : Key within the color ansi_table.json. 'random' accepted.
-u STYL : Link Style (it=inline table=default, h=hide, i=inline)
-x : Do not try guess code lexer (guessing is a bit slow)
# Notes:
We use stty tool to derive terminal size. If you pipe into mdv we use 80 cols.
## To use mdv.py as lib:
Call the main function with markdown string at hand to get a
formatted one back. Sorry then for no Py3 support, accepting PRs if they don't screw Py2.
## FROM:
FROM may contain max lines to display, seperated by colon.
Example:
-f 'Some Head:10' -> displays 10 lines after 'Some Head'
If the substring is not found we set it to the *first* character of the file -
resulting in output from the top (if your terminal height can be derived correctly through the stty cmd).
## Code Highlighting
Set -C <all|code|doc|mod> for source code highlighting of source code files.
Mark inline markdown with a '_' following the docstring beginnings.
- all: Show markdown docstrings AND code (default if you say, e.g. `-C.`)
- code: Only Code
- doc: Only docstrings with markdown
- mod: Only the module level docstring
## File Monitor:
If FROM is not found we display the whole file.

By default .md, .mdown, .markdown files are checked but you can change like `-M 'mydir:py,c,md,'` where the last empty substrings makes mdv also monitor any file w/o extension (like 'README').
### Running actions on changes:
If you append to `-M` a `'::<cmd>'` we run the command on any change detected (sync, in foreground).
The command can contain placeholders:
_fp_ # Will be replaced with filepath
_raw_ # Will be replaced with the base64 encoded raw content
of the file
_pretty_ # Will be replaced with the base64 encoded prettyfied output
Like: mdv -M './mydocs:py,md::open "_fp_"' which calls the open
command with argument the path to the changed file.
## Themes
### Theme Rollers
mdv -T all [file]: All available code styles on the given file.
mdv -t all [file]: All available md styles on the given file.
If file is not given we use a short sample file.
So to see all code hilite variations with a given theme:
Say C_THEME = all and fix THEME
Setting both to all will probably spin your beach ball...
### Environ Vars
`$MDV_THEME` and `$MDV_CODE_THEME` are understood, e.g. `export
MDV_THEME=729.8953` in your .bashrc will give you a consistent color scheme.
Regarding the strange theme ids: Those numbers are the calculated total luminocity of the 5 theme colors.
mdv is designed to be used well from other (Py2) programs when they have md at hand which should be displayed to the user:
import mdv
# config like this:
mdv.term_columns = 60
# calling like this (all CLI options supported, check def main
formatted = mdv.main(my_raw_markdown, c_theme=...)
Note that I set the defaultencoding to utf-8 in
__main__
. I have this as my default python2 setup and did not test inline usage w/o. Check this for risks.
Armin Ronacher's click is a great framework for writing larger CLI apps - but its help texts are a bit boring, intended to be customized.
Here is how:
Write a normal click module with a function but w/o a doc string as shown:
@pass_context
def cli(ctx, action, name, host, port, user, msg):
""" docu from module __doc__ """
On module level you provide markdown for it, like:
~/axc/plugins/zodb_sub $ cat zodb.py | head
"""
# Fetch and push ZODB trees
## ACTION: < info | pull | push | merge | dump | serve>
- info: Requests server availability information
(...)
which you set at click module import time:
mod.cli.help = mod.__doc__
Lastly do this in your app module:
from click.formatting import HelpFormatter
def write_text(self, text):
""" since for markdown pretty out on cli I found no good tool
so I built my own """
# poor man's md detection:
if not text.strip().startswith('#'):
return orig_write_text(self, text)
from axc.markdown.mdv import main as mdv
self.buffer.append(mdv(md=text, theme=os.environ['AXC_THEME']))
HelpFormatter.orig_write_text = HelpFormatter.write_text
HelpFormatter.write_text = write_text
The output has then colors:
and at smaller terms rewraps nicely:
Further, having markdown in the module __doc__
makes it simple to add into a global project docu framework, like mkdocs.
You can supply all CLI args in $HOME/.mdv
, in yaml format.
More flex you have via $HOME/.mdv.py
, which is execed if present, when running main
.
Alternatively, in mdv.py you can change some config straight forward.
# ---------------------------------------------------------------------- Config
txt_block_cut, code_pref, list_pref, br_ends = '✂', '| ', '- ', '◈'
# ansi cols (default):
# R: Red (warnings), L: low visi, BG: background, BGL: background light, C=code
# H1 - H5 = the theme, the numbers are the ansi color codes:
H1, H2, H3, H4, H5, R, L, BG, BGL, T, TL, C = \
231, 153, 117, 109, 65, 124, 59, 16, 188, 188, 59, 102
# Code (C is fallback if we have no lexer). Default: Same theme:
CH1, CH2, CH3, CH4, CH5 = H1, H2, H3, H4, H5
code_hl = { "Keyword" : 'CH3', "Name" : 'CH1',
"Comment" : 'L', "String": 'CH4',
"Error" : 'R', "Number": 'CH4',
"Operator": 'CH5',
"Generic" : 'CH2'
}
admons = {'note' : 'H3', 'warning': 'R',
'attention': 'H1', 'hint' : 'H4',
'summary' : 'H1', 'hint' : 'H4',
'question' : 'H5', 'danger' : 'R',
'caution' : 'H2'
}
def_lexer = 'python'
guess_lexer = True
# also global. but not in use, BG handling can get pretty involved...
background = BG
# normal text color:
color = T
show_links = None
# could be given, otherwise read from ansi_tables.json:
themes = {}
# sample for the theme roller feature:
md_sample = ''
# ------------------------------------------------------------------ End Config
Any importing module can overwrite those module global variables as well.
Should you need yet additional themes, add them to ansi_tables.json
file by adding your ansi codes there.
Random results, using the theme roller feature:
Note the table block splitting when the table does not fit (last picture).
pygments (using their lexer)
and, naturally, the python markdown project
Update: Next version will be CommonMark based though...
Sort of an excuse for the long long time w/o an update: I did actually start working on a more solid version based on CommonMark but that went a bit out of scope, into a general html terminal viewer, which will probably never be finished :-/
So at least here an update containing the stuff you guys sent as PRs, thanks all!!
echo -e "# foo\n## bar" | mdv -
and a 'light' theme (thanks Stanislav)Also:
-C <mode>
)textwrap
now for the wrapping, to avoid these word breaks a few complained about-X javascript [-x]
travis
Inline link tables
foo
bar ba
-n 2-4
or -n 1-
)tabletest
| Date | foo | |----------------|--------------------------| | User | Any | | Campaign | Any | | Support Portal | [cpeid, '=', a cpeid]
|


My name is Chen Zixin, I am a Chinese software engineer currently living in Dongcheng District of Beijing with my wifi and a cat.
When I was young, I wanted to be a lawyer. That was before I got a job as a game editor in PConline since early 2004. From then on, I seized the good opportunity to take part in web application design, and interact with programers. As time goes on, I knew I would be a developer. I never stopped writing code since May 25, 2007. Many thanks to Alan, Neil and Guitang Yang.
 It’s probably because I used to be a liberal art student in Hust.
You can contact me via email: admin#chenzixin.com


I'm currently a software engineer at Airbnb in San Francisco, working on continuous integration infrastructure. Before that, I interned on their product security team and studied computer science at Duke University.
I have professional experience developing software with Java, Python, Ruby, and various web technologies such as Rails and React. I work with various ops-related technologies like Linux and Docker on a daily basis. I'm passionate about writing reasonable, robust code and electronic music (in that order).
Friendships - Shipping aggregator that allows for customers outside the US to place orders to a US-based address, then get those orders delivered at a low cost to their home country's address. (2018)
Duke MBB 1v1 - Simulate 1v1 games between players on Duke's 2016-2017 Men's Basketball Team (2017)
Gamesalad Clone - Combined gaming platform, physics engine, and drag-and-drop game editing environment (2016)
Smalltalk - Real-time anonymized chatroom for people in one's immediate vicinity (2015)
Queue - Android application showing real-time user generated wait times for local restaurants (2015)
Tagged - Browser-based database system helping homeless shelters keep track of clothing inventories (2014)
Catalyst Technical Society - founded and led a social and pre-professional technical society of >60 people at Duke. We've brought back alumni speakers and even taught an technical interview prep course!
Heights - A personal music project. Check out a recent DJ set I played!