Advanced Drawing with Pixi.js

Stars, bursts, gears, wedges, polygons, arcs, and dashes all drawn using Pixi.js, the HTML5 creation engine with the fastest, most flexible 2D WebGL renderer.

It was many years ago when adding a burst to a graphic I stumbled upon the Funky Monkey drawing script by Ric Ewing. Comments embedded in the function immediately resonated with me:

Burst is a method for drawing star bursts. If you’ve ever worked with an advertising department, you know what they are ;-)

Clients tend to want them, Developers tend to hate them…

Thought I’d bring these back to life leveraging modern web.

Here are seven of those drawing shapes ported to Pixi.js.

Drawing Dashes

Draws a dashed line from point x1, y1 to point x2, y2.


  • target — Graphics instance on which dashed line will be drawn
  • x1 — Starting position along x-axis
  • y1 — Starting position along y-axis
  • x2 — Final position along x-axis
  • y2 — Final position along y-axis
  • dashLength — Length of each dash, in pixels
  • spaceLength — Space between dashes, in pixels
function drawDash(target,
                  dashLength = 5,
                  spaceLength = 5) {
  let x = x2 - x1;
  let y = y2 - y1;
  let hyp = Math.sqrt((x) * (x) + (y) * (y));
  let units = hyp / (dashLength + spaceLength);
  let dashSpaceRatio = dashLength / (dashLength + spaceLength);
  let dashX = (x / units) * dashSpaceRatio;
  let spaceX = (x / units) - dashX;
  let dashY = (y / units) * dashSpaceRatio;
  let spaceY = (y / units) - dashY;
  target.moveTo(x1, y1);
  while (hyp > 0) {
    x1 += dashX;
    y1 += dashY;
    hyp -= dashLength;
    if (hyp < 0) {
      x1 = x2;
      y1 = y2;
    target.lineTo(x1, y1);
    x1 += spaceX;
    y1 += spaceY;
    target.moveTo(x1, y1);
    hyp -= spaceLength;
  target.moveTo(x2, y2);

#web-development #graphics #javascript #programming #pixi.js

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Buddha Community

Advanced Drawing with Pixi.js

NBB: Ad-hoc CLJS Scripting on Node.js


Not babashka. Node.js babashka!?

Ad-hoc CLJS scripting on Node.js.


Experimental. Please report issues here.

Goals and features

Nbb's main goal is to make it easy to get started with ad hoc CLJS scripting on Node.js.

Additional goals and features are:

  • Fast startup without relying on a custom version of Node.js.
  • Small artifact (current size is around 1.2MB).
  • First class macros.
  • Support building small TUI apps using Reagent.
  • Complement babashka with libraries from the Node.js ecosystem.


Nbb requires Node.js v12 or newer.

How does this tool work?

CLJS code is evaluated through SCI, the same interpreter that powers babashka. Because SCI works with advanced compilation, the bundle size, especially when combined with other dependencies, is smaller than what you get with self-hosted CLJS. That makes startup faster. The trade-off is that execution is less performant and that only a subset of CLJS is available (e.g. no deftype, yet).


Install nbb from NPM:

$ npm install nbb -g

Omit -g for a local install.

Try out an expression:

$ nbb -e '(+ 1 2 3)'

And then install some other NPM libraries to use in the script. E.g.:

$ npm install csv-parse shelljs zx

Create a script which uses the NPM libraries:

(ns script
  (:require ["csv-parse/lib/sync$default" :as csv-parse]
            ["fs" :as fs]
            ["path" :as path]
            ["shelljs$default" :as sh]
            ["term-size$default" :as term-size]
            ["zx$default" :as zx]
            ["zx$fs" :as zxfs]
            [nbb.core :refer [*file*]]))

(prn (path/resolve "."))

(prn (term-size))

(println (count (str (fs/readFileSync *file*))))

(prn (sh/ls "."))

(prn (csv-parse "foo,bar"))

(prn (zxfs/existsSync *file*))

(zx/$ #js ["ls"])

Call the script:

$ nbb script.cljs
#js {:columns 216, :rows 47}
#js ["node_modules" "package-lock.json" "package.json" "script.cljs"]
#js [#js ["foo" "bar"]]
$ ls


Nbb has first class support for macros: you can define them right inside your .cljs file, like you are used to from JVM Clojure. Consider the plet macro to make working with promises more palatable:

(defmacro plet
  [bindings & body]
  (let [binding-pairs (reverse (partition 2 bindings))
        body (cons 'do body)]
    (reduce (fn [body [sym expr]]
              (let [expr (list '.resolve 'js/Promise expr)]
                (list '.then expr (list 'clojure.core/fn (vector sym)

Using this macro we can look async code more like sync code. Consider this puppeteer example:

(-> (.launch puppeteer)
      (.then (fn [browser]
               (-> (.newPage browser)
                   (.then (fn [page]
                            (-> (.goto page "")
                                (.then #(.screenshot page #js{:path "screenshot.png"}))
                                (.catch #(js/console.log %))
                                (.then #(.close browser)))))))))

Using plet this becomes:

(plet [browser (.launch puppeteer)
       page (.newPage browser)
       _ (.goto page "")
       _ (-> (.screenshot page #js{:path "screenshot.png"})
             (.catch #(js/console.log %)))]
      (.close browser))

See the puppeteer example for the full code.

Since v0.0.36, nbb includes promesa which is a library to deal with promises. The above plet macro is similar to promesa.core/let.

Startup time

$ time nbb -e '(+ 1 2 3)'
nbb -e '(+ 1 2 3)'   0.17s  user 0.02s system 109% cpu 0.168 total

The baseline startup time for a script is about 170ms seconds on my laptop. When invoked via npx this adds another 300ms or so, so for faster startup, either use a globally installed nbb or use $(npm bin)/nbb script.cljs to bypass npx.


NPM dependencies

Nbb does not depend on any NPM dependencies. All NPM libraries loaded by a script are resolved relative to that script. When using the Reagent module, React is resolved in the same way as any other NPM library.


To load .cljs files from local paths or dependencies, you can use the --classpath argument. The current dir is added to the classpath automatically. So if there is a file foo/bar.cljs relative to your current dir, then you can load it via (:require [ :as fb]). Note that nbb uses the same naming conventions for namespaces and directories as other Clojure tools: foo-bar in the namespace name becomes foo_bar in the directory name.

To load dependencies from the Clojure ecosystem, you can use the Clojure CLI or babashka to download them and produce a classpath:

$ classpath="$(clojure -A:nbb -Spath -Sdeps '{:aliases {:nbb {:replace-deps {com.github.seancorfield/honeysql {:git/tag "v2.0.0-rc5" :git/sha "01c3a55"}}}}}')"

and then feed it to the --classpath argument:

$ nbb --classpath "$classpath" -e "(require '[honey.sql :as sql]) (sql/format {:select :foo :from :bar :where [:= :baz 2]})"
["SELECT foo FROM bar WHERE baz = ?" 2]

Currently nbb only reads from directories, not jar files, so you are encouraged to use git libs. Support for .jar files will be added later.

Current file

The name of the file that is currently being executed is available via nbb.core/*file* or on the metadata of vars:

(ns foo
  (:require [nbb.core :refer [*file*]]))

(prn *file*) ;; "/private/tmp/foo.cljs"

(defn f [])
(prn (:file (meta #'f))) ;; "/private/tmp/foo.cljs"


Nbb includes reagent.core which will be lazily loaded when required. You can use this together with ink to create a TUI application:

$ npm install ink


(ns ink-demo
  (:require ["ink" :refer [render Text]]
            [reagent.core :as r]))

(defonce state (r/atom 0))

(doseq [n (range 1 11)]
  (js/setTimeout #(swap! state inc) (* n 500)))

(defn hello []
  [:> Text {:color "green"} "Hello, world! " @state])

(render (r/as-element [hello]))


Working with callbacks and promises can become tedious. Since nbb v0.0.36 the promesa.core namespace is included with the let and do! macros. An example:

(ns prom
  (:require [promesa.core :as p]))

(defn sleep [ms]
   (fn [resolve _]
     (js/setTimeout resolve ms))))

(defn do-stuff
   (println "Doing stuff which takes a while")
   (sleep 1000)

(p/let [a (do-stuff)
        b (inc a)
        c (do-stuff)
        d (+ b c)]
  (prn d))
$ nbb prom.cljs
Doing stuff which takes a while
Doing stuff which takes a while

Also see API docs.


Since nbb v0.0.75 applied-science/js-interop is available:

(ns example
  (:require [applied-science.js-interop :as j]))

(def o (j/lit {:a 1 :b 2 :c {:d 1}}))

(prn (j/select-keys o [:a :b])) ;; #js {:a 1, :b 2}
(prn (j/get-in o [:c :d])) ;; 1

Most of this library is supported in nbb, except the following:

  • destructuring using :syms
  • property access using .-x notation. In nbb, you must use keywords.

See the example of what is currently supported.


See the examples directory for small examples.

Also check out these projects built with nbb:


See API documentation.

Migrating to shadow-cljs

See this gist on how to convert an nbb script or project to shadow-cljs.



  • babashka >= 0.4.0
  • Clojure CLI >=
  • Node.js 16.5.0 (lower version may work, but this is the one I used to build)

To build:

  • Clone and cd into this repo
  • bb release

Run bb tasks for more project-related tasks.

Download Details:
Author: borkdude
Download Link: Download The Source Code
Official Website: 
License: EPL-1.0

#node #javascript

Trevor  Russel

Trevor Russel


Realtime Face Emotion Recognition using Transfer Learning in TensorFlow

The dataset which we’ll use is fer2013 which was published in International Conference on Machine Learning in 2013.
Let’s dive into the project, first open a new project using Jupyter Notebook or any other environment you like. First and foremost, importing the libraries

import tensorflow as tf 
import cv2 
import os
import matplotlib.pyplot as plt 
import pandas as pd
import numpy as np

OpenCV-Python is a library of Python bindings designed to solve computer vision problems.cv2.imread() method loads an image from the specified file. If the image cannot be read (because of the missing file, improper permissions, unsupported or invalid format) then this method returns an empty matrix. We can read the image through the following line of code:

img_array = cv2.imread(‘train/0/Training_3908.jpg’)

To check the size of the image, we use:


The matplotlib function imshow() creates an image from a 2-dimensional numpy array. The image will have one square for each element of the array. The color of each square is determined by the value of the corresponding array element and the color map used by imshow().


Now we’ll create a variable containing the directory name and a list which will contain the names of the folders inside that directory. In my case, I have renamed the folder according to the emotion labels.

Datadirectory = "Training/"
Classes = ["0","1","2","3","4","5","6"]

The ImageNet dataset contains images of fixed size of 224*224 and have RGB channels but as fer2013 has images of size 48*48 so we’ll have to resize the images. To resize an image, OpenCV provides cv2.resize() function. cv2.cvtColor() method is used to convert an image from one color space to another.

img_size = 224 
new_array = cv2.resize(img_array, (img_size, img_size))
plt.imshow(cv2.cvtColor(new_array, cv2.COLOR_BGR2RGB))

The reason behind executing the above code is that we’re using transfer learning so for transfer learning if we want to use any deep learning classifier then these dimensions must be same. Now we’ll read all the images and will convert them to array.

training_Data = []
def create_training_Data():
  for category in Classes:
    path = os.path.join(Datadirectory, category)
    class_num = Classes.index(category)
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path, img))
        new_array = cv2.resize(img_array, (img_size, img_size))
        training_Data.append([new_array, class_num])
      except Exception as e:

Let’s call the function:


To make our deep learning architecture dynamic and robust, let’s shuffle the sequence:

import random

Let’s separate the features and labels. We’ll use deep learning architecture MobileNet which takes 4 dimensions, so we’ll reshape the features list.

X = []
y = []
for features, label in training_Data:
X = np.array(X).reshape(-1, img_size, img_size, 3) 
# 3 is the channel for RGB

Among the best practices for training a Neural Network is to normalize your data to obtain a mean close to 0. Normalizing the data generally speeds up learning and leads to faster convergence. Let’s normalize the data before training

X =X/255.0

Now we’ll train our deep learning model using transfer learning

from tensorflow import keras
from tensorflow.keras import layers

Keras Applications are deep learning models that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning. Here is a chart of some available models.

Now we’ll use MobileNetV2

model = tf.keras.applications.MobileNetV2()

Let’s change the base input

base_input = model.layers[0].input

As we want seven classes, so let’s cut down output

base_output = model.layers[-2].output

The dense layer is a neural network layer that is connected deeply, which means each neuron in the dense layer receives input from all neurons of its previous layer. The activation function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer. It can be as simple as a step function that turns the neuron output on and off, depending on a rule or threshold. Here we’re using relu as activation function.

final_output = layers.Dense(128)(base_output) 
final_output = layers.Activation(‘relu’)(final_output) 
final_output = layers.Dense(64)(final_output)
final_output = layers.Activation(‘relu’)(final_output)
final_output = layers.Dense(7, activation=’softmax’)(final_output) 

Let’s create our new model.

new_model = keras.Model(inputs = base_input, outputs = final_output)

Compile defines the loss function, the optimizer, and the metrics. That’s all. It has nothing to do with the weights and you can compile a model as many times as you want without causing any problem to pre-trained weights. You need a compiled model to train (because training uses the loss function and the optimizer).

new_model.compile(loss=”sparse_categorical_crossentropy”, optimizer = “adam”, metrics = [“accuracy”])

Trains the model for 25 number of epochs., Y, epochs = 25)

Here is the code to save the model.‘Final_model_95p07.h5’)

The below code is to test it using a live webcam.

import cv2 # pip install opencv-python
#pip install opencv-contrib-python full package
#from deepface import DeepFace #pip install deepface
path = "haarcascade_frontalface_default.xml"
font_scale = 1.5
#set the rectangle background to white
rectangle_bgr = (255, 255, 255)
#make a black image
img = np.zeros((500, 500))
#set some text
text = "Some text in a box!"
# get the width and height of the text box
(text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=1)[0]
# set the text start position
text_offset_x = 10
text_offset_y = img.shape[0] - 25
#make the coords of the box with a small padding of two pixels
box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height - 2))
cv2.rectangle(img, box_coords[0], box_coords[1], rectangle_bgr, cv2.FILLED)
cv2.putText(img, text, (text_offset_x, text_offset_y), font, fontScale=font_scale, color=(0, 0, 0), thickness=1)
cap = cv2.VideoCapture(1)
# Check if the webcam is opened correctly
if not cap.isOpened():
  cap = cv2.VideoCapture(0)
if not cap.isOpened():
  raise IOError("Cannot open webcam")
while True:
  ret, frame =
  #eye_cascade = cv2.CascadeClassifier( + 'haarcascade_eye.xml')
  faceCascade = cv2.CascadeClassifier( + 'haarcascade_frontalface_default.xml')
  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  faces = faceCascade.detectMultiScale(gray,1.1,4)
  for x,y,w,h in faces:
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = frame[y:y+h, x:x+w]
    cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
    facess = faceCascade.detectMultiScale(roi_gray)
    if len(facess) == 0:
      print("Face not detected")
      for (ex,ey,ew,eh) in facess:
        face_roi = roi_color[ey: ey+eh, ex:ex + ew] ## cropping the face
    final_image = cv2.resize(face_roi, (224,224))
    final_image = np.expand_dims(final_image,axis=0) ## need fourth dimension
    final_image = final_image/255.0
Predictions = new_model.predict(final_image)
font_scale = 1.5
    font = cv2.FONT_HERSHEY_PLAIN
      status = "Angry"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
elif (np.argmax(Predictions)==1):
      status = "Disgust"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
elif (np.argmax(Predictions)==2):
      status = "Fear"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
    elif (np.argmax(Predictions)==3):
      status = "Happy"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
elif (np.argmax(Predictions)==4):
      status = "Sad"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
elif (np.argmax(Predictions)==5):
      status = "Surprise"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
      status = "Neutral"
x1,y1,w1,h1 = 0,0,175,75
      #Draw black background rectangle
      cv2.rectangle(frame, (x1, x1), (x1 + w1, y1 + h1), (0,0,0), -1)
      #Addd text
      cv2.putText(frame, status, (x1 + int(w1/10), y1 + int(h1/2)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
cv2.putText(frame, status,(100,150),font, 3,(0, 0, 255),2,cv2.LINE_4)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 0, 255))
cv2.imshow('Face Emotion Recognition', frame)
if cv2.waitKey(2) & 0xFF == ord('q'):

Originally published by MUHAMMAD AZEEM RAO at Medium

#deep-learning #tensorflow #neural-networks #artificial-intelligence #facial-expressions

Dylan  Iqbal

Dylan Iqbal


Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. 

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.   

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. 

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. 

Check out the infographic by clicking on the button below:

Python Matplotlib cheat sheet

With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.


Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data 

1D Data 

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)

2D Data or Images 

>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

>>> import matplotlib.pyplot as plt


>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))


>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)

Save Plot 

>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot


Plotting Routines 

1D Data 

>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields 

>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 




The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30] 
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting Customize Plot 

Colors, Color Bars & Color Maps 

>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
            cmap= 'seismic' )


>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")


>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid') 
>>> plt.plot(x,y,ls= '--') 
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
>>> plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations 

>>> ax.text(1,
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 


>>> plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts 

Limits & Autoscaling 

>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis


>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements


>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines 

>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward

Have this Cheat Sheet at your fingertips

Original article source at

#matplotlib #cheatsheet #python

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