Electron microscopy
Python Automation and Machine Learning for ICs: Chapter L
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -
Python Automation and Machine Learning for ICs                                                               http://www.globalsino.com/ICs/        

Table of Contents/Index 
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix



LASSO (Least Absolute Shrinkage and Selection Operator) Introduction
Skills Network Labs (SN Labs, IBM) Introduction
Leveraging precision, speed, and automation: Integrating Mask R-CNN and YOLOv8 Introduction
"Extract, Transform, Load" (ETL) and " Extract, Load, Transform" (ELT) processes Introduction
YOLOv8 (You Only Look Once, version 8) Introduction
Comparison btween data lake and data warehouse  Introduction
Using proxy labels, building a labeling system, and utilizing a labeling service when historical labeled data is unavailable for ML projects Introduction
OLS (Ordinary Least Squares) regression model Introduction
Big data lifecycle Introduction
Impact of corpus narrowness on language model training Introduction
Guess and check algorithm with a combination of a for loop and an if statement Introduction
Labor cost of data analysis with and without automation and ML techniques Introduction
Trade-off between minimizing loss and minimizing complexity Introduction
Mean squared error (MSE) (L2 loss function, Euclidean loss) Introduction
Python libraries for Bayesian ML techniques Introduction
L1 Loss (Absolute Loss or Mean Absolute Error (MAE)) Introduction
Language Model Introduction
Replaces a symbol/character/letter in a string Introduction
Plot with letters/words/character as x-/y-axis Introduction
Linear Programming (LP) algorithm Introduction
Constraint ("limit"/"range") satisfaction Introduction
Local Search in ML Introduction
Likelihood weighting Introduction
First-order logic (FOL) Introduction
Logical statements Introduction
Modus ponens (a logical inference rule) Introduction
Logic Puzzle in ML Introduction
Propositional Logic Algorithms in ML Introduction
Cost/loss function versus reward function Introduction
Linearization Introduction
Linear Quadratic Regulation (LQR) Introduction
Q-Learning with Function Approximation (Deep Q-Network - DQN) Introduction
Nonlinear extensions of Independent Component Analysis (ICA) Introduction
Maximum Likelihood Estimation (MLE) of single Gaussian (normal) distribution Introduction
Learning algorithm (ensemble learning) and pipeline Introduction
Logistic regression as a one-neuron/single-layer neural network (connection between linear & activation parts) Introduction
Number of neurons and layers in neural network Introduction
Comparison among sigmoid, hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions Introduction
Neuron (= linear + activation) introduction Introduction
Gini Loss Introduction
Misclassification loss in decision trees Introduction
Linear model versus polynomial model Introduction
No Free Lunch Theorems Introduction
Learning theory Introduction
Leave-One-Out Cross-Validation (LOOCV) Introduction
"Norm" of parameters, and L1 Norm (Manhattan Norm) and L2 Norm (Euclidean Norm) Introduction
Logistic regression and Naive Bayes Introduction
Open datasets, and open-source tools and libraries for ML practice Introduction
Learning theory Introduction
  Generalization Introduction
  Bias and variance, and bias-variance trade-off in ML Introduction
  Model Complexity Introduction
  Convergence and Optimization Introduction
  Sample Complexity Introduction
  Probably Approximately Correct (PAC) learning Introduction
  Margin Theory Introduction
  No Free Lunch Theorems Introduction
Layers Introduction
Categorical features preprocessing layers Introduction
Keras preprocessing layers Introduction
tf.keras.layers.Hashing Introduction
tf.keras.layers.CategoryEncoding Introduction
tf.keras.layers.Discretization Introduction
keras.layers.normalization Introduction
tf.keras.layers.StringLookup Introduction
tf.keras.layers.IntegerLookup Introduction
tf.keras.layers.TextVectorization Introduction
Output the web links obtained by Google Search Introduction
Mirror/reflect image from left to right/from top to bottom Introduction
Non-linearity in machine learning Introduction
(Find) file size/find the largest/smallest file in a directory/folder Introduction
.pack(side=LEFT)/.pack(side=RIGHT)/.place(x=, y=) --- position of the buttons (Code)
Locate/find the center of a bright (maximum/highest intensity) spot in an image & find nearest white pixel to a given/specifical pixel location on an binary image Introduction
Linear algebra Introduction
.LabelEncoder() Introduction
Rectified Linear Units (ReLUs) Introduction
Least squares fit Introduction
Long short-term memory (LSTM) Introduction
Script execution limited by retry time Introduction
Line detection on image Introduction
Automatically review, scroll, click webpage and its link Introduction
Mouse left single click Introduction
Left click (Introduction)
Left click a specific position Introduction
Lock desktop Introduction
Launch the existing opened application if there is or start a new one if there is not Introduction
Loops (e.g. for loop) for 2D (two-dimensional) plot Introduction
Convert capital alphabet letters/characters to number Introduction
Type capital letters Introduction
Modify/replace the line in a text file if a line contains specific string Introduction
Move the mouse/cursor to the left or right Introduction
Bind/link multiple commands to buttons Introduction
Copy and then store it into memory and it can be pasted for use later Introduction
Get the latest/newest/most recent file in a folder within certain time/days Introduction
Select/input a folder/directory/path for later to be called to use Introduction
Close file after reading a file: avoid file locking Introduction
watchdog to look for filesystem changes Introduction
Check the letters and symbols starting and ending with code.
Calibrate and put a scale bar, and draw a line segment on an image Introduction
Calculator of length accuracy in 3D structure Introduction
Merge/combine two text files into a new text file, add a new line to the beginning of a text file Introduction
Count the numbers of uppercase letters, lowercase letters and spaces in a string and then swap the cases of the letters. code
Draw a line in a image code
Draw lines manually and then label them with arrows code
Remove letters or characters on either side (both left and right sides) and stops when neither such letters no characters on either side code
Calculate the coordinates of a point in a given rectangle and the distance of a given point to a line code
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values CSV: Introduction. code.
Bail out/terminate of a loop code
Loop through a string from left to right code1, code2
Loop through numbers in a range code
Load/launch images and ColorMixing in DigitalMicrograph Introduction
Draw lines and arrows in a ppt Introduction
Linear correlation between two variables with Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Kendall's Tau, Linear Regression, Coefficient of Determination and Correlation Ratio
.LINE (code)
Stability/reliability of locateCenterOnScreen() Introduction
Find latitude and longitude of a place in a map Introduction
Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase) Introduction
Check if a directory is empty; find the position/index of a particular file/folder in a file directory/path; remove folder or file level by level (or layer by laer) from its directory/path Introduction
matplotlib.pyplot axis/text color (title, xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) Introduction
top and left for pptx Introduction
break and functions to exit a loop: Stop the loop, e.g. while loop immediately. code1, code2 Introduction.
check if a letter is in a string Introduction
Plot pixel intensities (histogram) along a line of an image Introduction
Global access to a local variable inside a function from outside of the function externally Introduction
Top (ranking, best, must know) Python libraries/modules Introduction
Measure length/distance on an image w/o calibrated bar Introduction
Rotate (alignment) an image by line along the x- or y-axis Introduction
Get pixel location/coordinates on an image using mouse click/events Introduction
Crop/snip part of a image with definition by a pixel line Introduction
Quit/exit/stop a process (including by pressing a letter) Introduction
Supervised learning Introduction
Extract the index of a string element in a list Introduction
Unsupervised learning Introduction
Reinforcement learning Introduction
Break/exit/skip a function/code line after a certain time Introduction
Libraries used to convert incident documents into numerical vectors Introduction
Linear Support Vector Classifier (Linear SVC) Introduction
Change/capitalize the case of the first letter of a string (first letter: uppercase; other letters: lowercase) Introduction
Ranking/most popular programming languages for data analysts Introduction
Remove \n in string or new line in txt/text file Introduction
Remove duplicate/same lines in a text file Introduction
Data labeling and annotation in supervised machine learning Introduction
Global defects and local defects identified by defect denoising Introduction
Laplacian filtering Introduction
Clustering of Laplacian Introduction
Draw circles/lines on images Introduction
Table of applications of Python and its libraries Introduction
L2 regularization/Ridge/ridge regularization/Tikhonov regularization Introduction
Support Vector Machines (SVM) and Logistic Regression Introduction
Hidden layer in deep learning neural network Introduction
Convolutional Layers (CONV) in Deep Learning Introduction
Fully Connected Layers (FC) in Deep Learning Introduction
Laplace smoothing/Laplace correction/add-one smoothing Introduction
Comparison between Poisson distribution, Gaussian (normal) distribution and logistic regression Introduction
Logistic regression versus Gaussian discriminant analysis Introduction
Joint likelihood Introduction
Cross entropy (log loss/logistic loss) Introduction
Softmax regression (multinomial logistic regression)/softmax multi-class network/softmax classifier Introduction
Canonical response function/canonical link function Introduction
Learning rule in ML Introduction
GLM (Generalized Linear Model) Introduction
Negative log likelihood (NLL) Introduction
Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution) Introduction
Perceptron algorithm and logistic regression Introduction
Update parameters θj using gradient of the loss function Introduction
Logistic function/sigmoid function Introduction
Logistic regression Introduction
Logistic regression versus linear regression Introduction
Linear regression versus classification Introduction
Comparison between mean squared error (MSE), absolute error (L1 Loss) and fourth-power loss
Comparison between L1 Regularization and L1 Loss (absolute loss or mean absolute error (MAE)) Introduction
Convolution and convolutional layers  Introduction
Latent features and latent variables Introduction
Bandwidth parameter (τ) in LWR and KDE Introduction
Parametric learning algorithm Introduction
Non-parametric learning algorithm Introduction
Locally Weighted Regression (LWR) Introduction
Learning Algorithm (estimator) Introduction
Learning rate Introduction
Linear regression and its algorithm Introduction
Generalization Error/Generalization Loss/Test Error/Expected Error of Hypothesis/Risk Introduction
Lipschitzness/Lipschitz continuity Introduction
Check existence of phrase on text file line-by-line Introduction
Expected risk (population risk, expected value of loss or error) Introduction
Central Limit Theorem (CLT) Introduction
Empirical loss/training loss Introduction
Empericial loss versus population loss Introduction
Linear Discriminant Analysis Introduction
IDLE (integrated development and learning environment) and integrated development environment (IDE) Introduction
Natural Language Processing (NLP) approaches in addressing the Failure Analysis (FA) search problem Introduction
Natural Language Processing (NLP) versus Text Introduction
Bayes' theorem (Bayes rule or Bayes law) in machine learning Introduction
Analysis of papers/publications/literature in machine learning and Python applications Introduction
Multiple linear regression Introduction
Likelihood and maximum Likelihood estimation (MLE) Introduction
"Label space" in machine learning Introduction
Predicted label Introduction
"True label" ("observed label") in machine learning Introduction
Predicted label versus predictor (feature) Introduction
Loss (risk, cost, objective) function Introduction
Supervised, unsupervised and reinforcement learning Introduction
Read line-by-line from a text file Introduction
Extract the first or last N letters from a string Introduction
Nearest/most similar lyrics of a sentence/text to a CSV file Introduction
Natural language inference Introduction
NLTK (Natural Language Toolkit) Introduction
Large and small datasets in ML Introduction
(Text and image) contrastive learning Introduction
Remove/reload/unload an imported module/function/script Introduction
Access and use SQL Database on SSMS (Microsoft SQL Server Management Studio Express) with pyodbc: localhost, insert rows, update, count updated, delete rows, comparision between extract data by Python and SQL itself Introduction
Count number of lines in a text file Introduction
Pyvis: An interactive geometric graph network/link/landscape Introduction
Networkit: a network/link/landscape tool Introduction
Loop through a Python dictionary Introduction
Delete the column/row in a CSV file if they are empty or less than a number (or header/index only) Introduction
scipy.optimize.linprog function Introduction
Plot multiple images on the same figure by hiding x- and y-(tick) labels on axis Introduction
Fairness Analysis and Python Libraries Introduction
Convert set into a list and vice versa Introduction
Lock a file to prevent deleting, and then release the file once job is done Introduction
Lock a file to prevent deleting, and then release the file once job is done Introduction
Exception LookupError (string index) Introduction
OverflowError (too large to store) Introduction
Calculation with combinations of variables from lists
Remove empty strings from list of strings Introduction
Count how many empty strings in a list Introduction
Check if two lists are same/identical Introduction
Remove the substring after the first or last character "::" in a given string, or extract the substring between the first and last "::" Introduction
Penalized regression (Lasso and Ridge) Introduction
Compare (pattern/ratio of) two different columns, check whether column values match in DataFrame Introduction
Check whether one column contains number only and another column contains letters only or mixture of numbers and letters in DataFrame Introduction
Check the difference between two columns in DataFrame Introduction
Get the last line in a text file Introduction
Check if Windows/PC screen is locked Introduction
Check all the imported/current modules/libraries Introduction
Check if all the (and how many, length of a string) characters in the text are digits/numbers Introduction
Different behavior of automation execution (e.g. pyautogui) locally or remotely through internet
Data plot with labels Introduction
Leetcode for Google/Amazon Introduction
Create a log (log.log) file to monitor script execution Introduction
Last n days/weeks/months (.to_datetime(x), .set_index(y), .last(z), .reset_index(), and .max() in pandas) Introduction
Lists/list(). A list is a collection of objects. List is denoted with square brackets []. Declaration: myList = list(). Unlike strings, lists are mutable, a list can also contain list(s). Lists in memory are considered to be an object in memory. code1, code2: loop, code3: loop, code4: loop. mixed list and replace an item in the list. copy method. print all iterms in a list in a seperate line. data structures. General. code. Introduction
Check if one list is subset of another Introduction
Check if a list is empty or not Introduction
Check if an item/element is in a list or not Introduction
Check if a file/folder exists or not (Cannot find a specific file/folder? a specific folder in the path? select specific folders to form a string, split a dos path into its components, and then print the list, or check files with extension) Introduction
Check if an element is in a sublist of a list Introduction
Get/list immediate subdirectories/subfolders; get only the last part of a path/folder/drive; split a dos path into its components, and then print the list Introduction
Access elements in a list and sublist Introduction
Replace/substitute a item in a list Introduction
List all files and directories which has specific files or files with specific extensions Introduction
Get/list immediate subdirectories/subfolders; get only the last part of a path Introduction
Find minimum and maximum values in a list Introduction
Change/swap values in a list Introduction
Modify a list (e.g. add/insert an item between items) Introduction
Plot a list of x, y coordinates to an image Introduction
Sort a list Introduction
Split a sentence/string into list of words, remove all special characters from a sentence Introduction
Modify file path/directory by changing folder names by merging a list Introduction
Limit event/action numbers in the event List, then stop Introduction
Loop through a list (e.g. for loop) Introduction
Remove an item/element/duplicates from a list Introduction
Get the list of the methods for a function Introduction
index("") In list. (code)
Extract elements from a list (different way from removing elements to get part of the list) Introduction
Convert csv/dataframe column to a list or vice versa Introduction
Create dictionary from nested (sublist) list and get the values with keys Introduction
Convert between numpy array, string or list of string Introduction
Check if two lists have the same elements Introduction
Extract the least/most frequency/duplicate/occurrence element in a list Introduction
Find common/different elements/items between two lists/sets Introduction
Collect the file list in a folder into a csv file Introduction
os.listdir(). Output the file list of images in a folder, but only returns the names. code. code. code. code. (code). (code). Introduction
Data structures (Data science, and comparison between list, tuple, set, dictionary) Introduction
Split a list into columns Introduction
Comparison between =, ==, .copy(), copy.copy() for "list": changes of "list" Introduction
count() in csv/list Introduction
Sum a list of numbers in any length Introduction
Convert a list to a matrix Introduction
Reference list items by position code1, code2
Convert/change the case of all letters/word into uppercase (capital) or lowercase in a list of strings Introduction
Find duplicate items in a list Introduction
Form a list of strings from an old string with all the 6 digits by removing all special characters or spaces Introduction
Plot a figure with a colored arrow between text lines/steps Introduction
__add__, __call__, __contains__, __delitem__, __delattr__, __eq__, __enter__, __ge__, __getattribute__, __getnewargs__, __getattr__, __getitem__, __gt__, __hash__, __reduce__, __iadd__, __imul__, __init_subclass__, __index__, __int__, __invert__, __new__, __neg__, __reduce_ex__, __reversed__, __rmul__, __radd__, __rand__, __rdivmod__, __rfloordiv__, __rlshift__, __rmod__, __ror__, __round__,__rpow__, __rrshift__, __rshift__, __rsub__, __rtruediv__, __rxor__, __dir__, __doc__, __divmod__, __iter__, __le__, __lt__, __len__, __ne__, __repr__, __setattr__, __setitem__, __sizeof__, __lshift__, __sub__, __subclasshook__, __str__ Introduction
Reverse a list Introduction
locals() Introduction
Convert a sentence/text to a list Introduction
Add letter/commas/numbers/characters to the end/beginning of strings in a list Introduction
Replace the lines between two lines “xx” and “yy” in a text file with new lines Introduction
Remove string 0s from the back/end of a list until non-zero values Introduction
Duplicate/repeat the same words/elements in a string/list Introduction
Extract the last column as subdataframe Introduction
Optimizing failure analysis processes in semiconductor labs using machine learning Introduction
Hide x-axis tick labels (only show some labels) where x values are under certain conditions Introduction
Font size of tick labels in plot Introduction
Cheatsheet of list Introduction
Set logarithmic scale (exponential) for y-axis in plots Introduction
Populate the table with logarithmic format in pptx Introduction
PermissionError (E.g. file cannot be written when it is open/locked) Introduction
Output data if any or same element in a string are in two lists  Introduction
HTTP(Hypertext Transfer Protocol)/URL (Uniform Resource Locator) Introduction

lineterminator CSV: string (length 1), default None, Character to break file into lines. Only valid with C parser
lineterminator CSV: code.
left_index=, right_index= CSV: (code)
.loc[] and .iloc[] Introduction
line CSV: Print line by line from a CSV file: code.
driver.findElement(By.name("q")).sendKeys ("")
dom =document.getElementById("")
driver.findElement(By.partialLinkText(" NextP")).click()
.locateOnScreen() .locateOnScreen('looksLikeThis.png') returns (left, top, width, height) on the image which the screenshot is taken from. Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. This function mostly is useless. Introduction. (code)
top and left for locateOnScreen() Introduction
.locateCenterOnScreen() Uses pyscreeze. x, y = MySearch_img to get the x- and y-coordinates of centers of the feature. Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. Introduction. (code)
.locateAllOnScreen() Difference between pyautogui.locateOnScreen("anImage") and pyautogui.locateOnScreen("anImage", minSearchTime=): minSearchTime = amount of time in seconds to repeat taking screenshots and trying to locate a match. Introduction. (code)
.Label() Introduction
lambda Introduction. Lambda functions can be used in places where we expect variables. code.
class watchdog.events.LoggingEventHandler Bases: watchdog.events.FileSystemEventHandler
Logs all the events captured.
class watchdog.events.LoggingEventHandler

Bases: watchdog.events.FileSystemEventHandler
Logs all the events captured.

Insert/add text or line Insert text or new text lines to a specific position, at the end of a line, in a file. Introduction
math.log(x,y) Returns the natural logarithm of x to base y.
math.log2(x) Returns the base-2 logarithm of x.
lower() Returns the string by converting all the characters of the string to lower case. Introduction. code.
LoggingEventHandler (code)
len() Is a function to get the length of a collection. It returns the length of the string. Plus: The last character of a given string can also be printed. Example code. copy method. all kinds of len().
LightGBM Is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
Faster training speed and higher efficiency.
Lower memory usage.
Better accuracy.
Support of parallel, distributed, and GPU learning.
Capable of handling large-scale data.
scipy.linalg Linear algebra routines and matrix decompositions extending beyond those provided in numpy.linalg. scipy.linalg contains all the functions that are in numpy.linalg. Additionally, scipy.linalg also has some other advanced functions that are not in numpy.linalg. scipy.linalg operations can be applied equally to numpy.matrix or to 2D numpy.ndarray objects. code.
scipy.linalg.norm This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms, depending on the value of the ord parameter. For tensors with rank different from 1 or 2, only ord=None is supported. It is for an old release of SciPy (version 0.14.0).
from scipy.linalg import norm code
scipy.linalg.block_diag Create a block diagonal matrix from the provided arrays.
scipy.linalg.circulant Create a circulant matrix.
scipy.linalg.companion Create a companion matrix.
scipy.linalg.convolution_matrix Create a convolution matrix.
scipy.linalg.dft Create a discrete Fourier transform matrix.
scipy.linalg.fiedler Create a symmetric Fiedler matrix.
scipy.linalg.fiedler_companion Create a Fiedler companion matrix.
scipy.linalg.hadamard Create an Hadamard matrix.
scipy.linalg.hankel Create a Hankel matrix.
scipy.linalg.helmert Create a Helmert matrix.
scipy.linalg.hilbert Create a Hilbert matrix.
scipy.linalg.invhilbert Create the inverse of a Hilbert matrix.
scipy.linalg.leslie Create a Leslie matrix.
scipy.linalg.pascal Create a Pascal matrix.
scipy.linalg.invpascal Create the inverse of a Pascal matrix.
scipy.linalg.toeplitz Create a Toeplitz matrix.
linalg.multi_dot(arrays, *[, out]) Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order.
linalg.matrix_power(a, n) Raise a square matrix to the (integer) power n.
linalg.cholesky(a) Cholesky decomposition.
linalg.qr(a[, mode]) Compute the qr factorization of a matrix.
linalg.svd(a[, full_matrices, compute_uv, …]) Singular Value Decomposition.
linalg.norm(x[, ord, axis, keepdims]) Matrix or vector norm.
linalg.cond(x[, p]) Compute the condition number of a matrix.
linalg.det(a) Compute the determinant of an array.
linalg.matrix_rank(M[, tol, hermitian]) Return matrix rank of array using SVD method
linalg.slogdet(a) Compute the sign and (natural) logarithm of the determinant of an array.
linalg.solve(a, b) Solve a linear matrix equation, or system of linear scalar equations.
linalg.tensorsolve(a, b[, axes]) Solve the tensor equation a x = b for x.
linalg.lstsq(a, b[, rcond]) Return the least-squares solution to a linear matrix equation.
linalg.inv(a) Compute the (multiplicative) inverse of a matrix. code.
linalg.pinv(a[, rcond, hermitian]) Compute the (Moore-Penrose) pseudo-inverse of a matrix.
linalg.tensorinv(a[, ind]) Compute the ‘inverse’ of an N-dimensional array.
from scipy.linalg import eigh Print "selected eigenvalues" and "complex ndarray": code.
linalg.LinAlgError Generic Python-exception-derived object raised by linalg functions.
skimage.measure.label(input[, neighbors, ...]) Label connected regions of an integer array.
skimage.measure.LineModel() Total least squares estimator for 2D lines.
skimage.measure.LineModelND() Total least squares estimator for N-dimensional lines.
smtp.login() code.
(min_val, max_val, min_loc, max_loc) = cv2.minMaxLoc() Returns the max and min intensity values as an array
that includes the location of these intensities. Takes the correlation result and returns a 4-tuple which includes the minimum correlation value, the maximum correlation value, the (x, y)-coordinate of the minimum value, and the (x, y)-coordinate of the maximum value, respectively. Max_Val is the location with the highest intensity in the image, corresponding to the best matching input image with regard to the defined template.. code. code. code.
cv2.line() Draw a line in a image. (code). code
label Label for plot legend
logy Use logarithmic scaling on the y-axis
legend Add a subplot legend (True by default)
'ls' code.
np.linspace() Introduction. General, code. code.
numpy.linalg This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms, depending on the value of the ord parameter. (code).
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values CSV: Introduction. code.
.press(Button.left) (code)
.release(Button.left) (code)
.click(Button.left, x) x clicks of mouse. (code)
hotkey('l') Introduction
hotkey('left') Introduction
.topleft (code)
.top/.bottom/.left/.right Introduction
'xyz'.isalpha() Check if string is alphabet (letter, or one type of character)
import logging (code).
.format.line (code)
os.path.lexists() (code)
.set_xticklabels() (code)