Python Automation and Machine Learning for EM and ICs

CSV: A delimiter / separator to split fields on. The delimiter can be tab, comma, space, semicolon, etc. With sep=None, read_csv will try to infer the delimiter automatically in some cases by “sniffing”. The separator may be specified as a regular expression, e.g. ‘|\s*’ can be used to indicate a pipe plus arbitrary whitespace.

skip_blank_lines

CSV: whether to skip over blank lines rather than interpreting them as NaN values

skiprows

CSV: A collection of numbers for rows in the file to skip. Can also be an integer to skip the first n rows

squeeze

CSV: if True then output with only one column is turned into Series

sep

CSV: Field delimiter for the output file (default ”,”)

Series.to_csv()

CSV: Is a 1-D ndarray with axis labels and writes the given series object to a comma-separated values (csv) file/format. code.

.set_option()

CSV: Set the value of a single option. (code)

skip_footer

CSV: number of lines to skip at bottom of file (default 0) (Unsupported with engine=’c’)

csv.Sniffer()

CSV: The Sniffer class is used to deduce the format of a CSV file. It expects a sample string, not a file. code.

skiprows

Code. code. code.

skipfooter

CSV: skip rows from bottom. code.

skipinitialspace

CSV: boolean, default False, Skip spaces after delimiter

skiprows = lambda x:

Code. code. code.

Search in csv file

Instruction. code. code.

.sum()

Sum and percentage for csv. Introduction.

StandardScaler

(code)

.size()

The size (height and width) of the PC screen, or called resolution. Introduction.

Selenium

Locators

driver.get("")

Codes

Chrome Inspect

Getting path

Application

driver.find_element(By.xpath(""))

//label/span

More complicated: need to scroll the mouse and trace down the code to find the correct path (code). Introduction.

driver.find_element(By.xpath("")).click()

<button class = ""> </button> By.XPATH

Copy XPath (code). Introduction.

click()

driver.find_element(By.xpath("").sendKeys("")

Copy XPath (code). Introduction.

sendKeys("")

Copy full XPath (code). Introduction.

More complicated: need to scroll the mouse and trace down the code to find the correct path (code) (code). Introduction.

find_element_by_xpath()

XPath is the language used for locating nodes in an XML document. One of the main reasons of using XPath is when you don’t have a suitable id or name attribute for the element.
      <html>
      <body>
      <form id="loginForm">
      <input name="username" type="text" />
      <input name="password" type="password" />
      <input name="continue" type="submit" value="Login" />
      <input name="continue" type="button" value="Clear" />
      </form>
      </body>
      </html>
The first form elements can like this:
      login_form = driver.find_element_by_xpath("/html/body/form[1]")
      login_form = driver.find_element_by_xpath("//form[1]")
      login_form = driver.find_element_by_xpath("//form[@id='loginForm']")
The second element (username) can be like this:
      username = driver.find_element_by_xpath("//form[input/@name='username']")
      username = driver.find_element_by_xpath("//form[@id='loginForm']/input[1]")
      username = driver.find_element_by_xpath("//input[@name='username']")
The third element (Clear Button) can be like this:
      clear_button = driver.find_element_by_xpath("//input[@name='continue'][@type='button']")
      clear_button = driver.find_element_by_xpath("//form[@id='loginForm']/input[4]")
(code). Introduction.

driver.findElement(By.id(""))

<input id=> (code). Introduction.

Find ID

By.ID click()

driver.findElement(By.id("")).sendKeys("")

<input type=> (code). Introduction.

Find ID

sendKeys("")

find_element_by_id()

Use this when the id attribute of the element known, e.g.:
      <html>
      <body>
      <form id="loginForm">
      <input name="username" type="text" />
      <input name="password" type="password" />
      <input name="Submit" type="Submit" value="Login" />
      </form>
      </body>
      </html>
Then, the form element can be:
      MyLogin = driver.find_element_by_id('loginForm')
(code). Introduction.

dom =document.getElementById("")

driver.findElement(By.linkText("NextPage")).click()

find_element_by_link_text()
find_element_by_partial_link_text()

Use those when the link text used within an anchor tag is known: the first element with the link text matching the provided value will be returned, e.g.:
      <html>
      <body>
      <p>Are you sure you want to do this?</p>
      <a href="continue.html">Continue</a>
      <a href="cancel.html">Cancel</a>
      </body>
      </html>
The continue.html link can be like this:
      continue_link = driver.find_element_by_link_text('Continue')
      continue_link = driver.find_element_by_partial_link_text('Conti')
(code). Introduction.

driver.findElement(By.partialLinkText(" NextP")).click()

find_element_by_css_selector()

Use this, when you want to locate an element using CSS selector syntax, e.g.:
      <html>
      <body>
      <p class="content">Site content goes here.</p>
      </body>
      </html>
The “p” element can be like this:
      content = driver.find_element_by_css_selector('p.content')
(code). Introduction.

driver.FindElement(By.CssSelector(""))

chooseFile.sendKeys("")

driver.findElement(By.NAME("q")).sendKeys ("")

find_element_by_name()

Use this when the name attribute of the element is known, e.g.:
      <html>
      <body>
      <form id="loginForm">
      <input name="username" type="text" />
      <input name="password" type="password" />
      <input name="continue" type="submit" value="Login" />
      <input name="continue" type="button" value="Clear" />
      </form>
      </body>
      </html>
Then, the username & password elements can be:
      username = driver.find_element_by_name('username')
      password = driver.find_element_by_name('password')
      continue = driver.find_element_by_name('continue')
(code). Introduction.

driver.findElement(By.tagName("select")).Click()

find_element_by_tag_name()

Use this, when you want to locate an element by tag name, e.g.:
      <html>
      <body>
      <h1>Welcome</h1>
      <p>Site content goes here.</p>
      </body>
      </html>
The heading (h1) element can be like this:
      heading1 = driver.find_element_by_tag_name('h1')
(code). Introduction.

driver.findElement(By.className(""))

find_element_by_class_name()

Use this, when you want to locate an element by class name, e.g.:
      <html>
      <body>
      <p class="content">Site content goes here.</p>
      </body>
      </html>
The “p” element can be like this:
      content = driver.find_element_by_class_name('content')
(code). Introduction.

Navigators

driver.get(“http://globalsino.com”)

Navigate to URL

driver.navigate().to(“http://globalsino.com”)

Navigate to URL

driver.navigate().refresh()

Refresh page

driver.navigate().forward()

Navigate forwards in browser history

driver.navigate().back()

Navigate backward in browser history

Upload files to webpages

Introduction

Webdriver

Methods to locate elements in a page

click()

(code), (code).

scroll()

Scroll the mouse wheel function and pass an integer number of “clicks” to scroll. .scroll(amount_to_scroll, x=moveToX, y=moveToY). (code)

sort

Similar to sorted().

.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. 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)

.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)

from selenium.webdriver.common.keys import Keys

(code)

split()

Introduction. Split a string by dots, split a file name by dots, split a file name from its extension. code. (code).

splitlines()

Split a string into a list where each line is a list item. code.

sys.modules

Is a dictionary mapping the names of imported modules to the module object holding the code. code.

sys.platform

(code)

sys.version

(code)

sys.version_info

(code)

sys.argv

Introduction

sys.stderr.write()

Prints the message as the given parameter to the stderr.(code)

sys.stderr.flush()

Forces it to "flush" the buffer, meaning that it will write everything in the buffer to the terminal, even if normally it would wait before doing so.(code)

sys.path

Is a list of strings that specifies the search path for modules. Basically this tells Python what locations to look in when it tries to import a module. code.

sys.platform

Is a platform identifier. You can use this to append platform specific modules to sys.path, import different modules depending on platform or run different pieces of code. code.

sys.exit()

Allows the developer to exit from Python. The exit function takes an optional argument, typically an integer, that gives an exit status. Zero is considered a “successful termination”.

watchdog.utils.bricks.SkipRepeatsQueue

Thread-safe event queue based on a special queue that skips adding the same event (FileSystemEvent) multiple times consecutively. Thus avoiding dispatching multiple event handling calls when multiple identical events are produced quicker than an observer can consume them.

schedule(event_handler, path, recursive=False)

Schedules watching a path and calls appropriate methods specified in the given event handler in response to file system events.
Parameters: event_handler (watchdog.events.FileSystemEventHandler or a subclass) – An event handler instance that has appropriate event handling methods which will be called by the observer in response to file system events.
path (str) – Directory path that will be monitored.
recursive (bool) – True if events will be emitted for sub-directories traversed recursively; False otherwise.
Returns: An ObservedWatch object instance representing a watch.

should_keep_running()

Determines whether the thread should continue running.

stop()

Signals the thread to stop.

stat_info(path)

Returns a stat information object for the specified path from the snapshot.
Attached information is subject to change. Do not use unless you specify stat in constructor. Use inode(), mtime(), isdir() instead.
Parameters: path – The path for which stat information should be obtained from a snapshot.

math.sqrt()

Calculate the square root of a number by importing math and using math.sqrt()

math.sin(x)

Returns the arc sine of x, in radians.

Syntax of a function

def <name of the function> (list of parameters):
<body>
code

os.startfile()

(code)

with mss.mss() as sct

(code)

seek()

Sets the file's current position at the offset, namely to go back the file (otherwise the file cannot be read again). The argument is optional and defaults to 0, which means absolute file positioning, other values are 1 which means seek relative to the current position and 2 means seek relative to the file's end. Introduction. txt. code. (code)

startswith() and endswith()

Introduction. String.startswith(value-required, start-optional, end-optional). returns True if the string starts with the specified value, otherwise False. Start: an Integer specifying at which position to start the search; end: an Integer specifying at which position to end the search. code. (code)

"skip"

Skip characters/letters in a string: Introduction.

sct.grab()

(code)

strip()

Returns a copy of the string by removing both the leading and the trailing characters. string.strip([chars]): chars (optional) - a string specifying the set of characters to be removed from the left and right part of the string. Introduction. code. txt.

strip([chars])

Returns copy with leading/trailing characters removed.

year, month, date, hour, minute, and second

"datefmt='%Y-%m-%d %H:%M:%S')": year, month, date, hour, minute, and second. Instruction.

space used in scripts

Example code

showinfo

code. code.

set_axis_off()

image.

style

code.

side=

code. (code).

Sets

Introduction, and data structures. Abstraction of a mathematical set

str()

Introduction. Returns a string which is fairly human readable. code.

"str()" and "," difference

(code)

datetime.strftime(format)

Return a string representing the date and time, controlled by an explicit format string. (code)

string / multilinestring

A string is a predefined object which contains characters. Loop through a string from left to right. Introduction. code1, code2.

String

A string is a predefined object which contains characters. Example code

os.path.splitext() method

Is used to split the path name into a pair root and ext. Here, ext stands for extension and has the extension portion of the specified path while root is everything except ext part. ext is empty if specified path does not have any extension. If the specified path has leading period (‘.’), it then will be ignored. Introduction. Examples are:

shutil.copyfileobj

code.

shutil.copy()

(code) (code)

SQLAlchemy

It was designed for efficient and high-performing database-access. It is a library with well-known enterprise-level patterns.

SciPy

Has a number of user-friendly and efficient numerical routines. These include routines for optimization and numerical integration.

scipy.signal

Contains a few functions that can perform the brute force correlations. For a 1D signal, the function is "correlate", and for a 2D signal, the correct function is correlate2d. This function will receive two arrays and the second array should be small in size.

scipy.integrate

Numerical integration

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.

from scipy.linalg import eigh

Print "selected eigenvalues" and "complex ndarray": code.

skimage.metrics

from skimage.metrics import structural_similarity as compare_ssim
import argparse
import imutils
import cv2
code

skimage.measure.approximate_polygon(coords, ...)

Approximate a polygonal chain with the specified tolerance.

skimage.measure.block_reduce(image, block_size)

Down-sample image by applying function to local blocks.

skimage.measure.compare_mse(im1, im2)

Compute the mean-squared error between two images.

skimage.measure.compare_nrmse(im_true, im_test)

Compute the normalized root mean-squared error (NRMSE) between two images.

skimage.measure.compare_psnr(im_true, im_test)

Compute the peak signal to noise ratio (PSNR) for an image.

skimage.measure.correct_mesh_orientation(...)

Correct orientations of mesh faces.

skimage.measure.find_contours(array, level)

Find iso-valued contours in a 2D array for a given level value.

skimage.measure.grid_points_in_poly

Test whether points on a specified grid are inside a polygon.

skimage.measure.label(input[, neighbors, ...])

Label connected regions of an integer array.

skimage.measure.marching_cubes(volume, level)

Marching cubes algorithm to find iso-valued surfaces in 3d volumetric data

skimage.measure.mesh_surface_area(verts, faces)

Compute surface area, given vertices & triangular faces

skimage.measure.moments(image[, order])

Calculate all raw image moments up to a certain order.

skimage.measure.moments_central(image, cr, cc)

Calculate all central image moments up to a certain order.

skimage.measure.moments_hu(nu)

Calculate Hu’s set of image moments.

skimage.measure.moments_normalized(mu[, order])

Calculate all normalized central image moments up to a certain order.

skimage.measure.perimeter(image[, neighbourhood])

Calculate total perimeter of all objects in binary image.

skimage.measure.points_in_poly

Test whether points lie inside a polygon.

skimage.measure.profile_line(img, src, dst)

Return the intensity profile of an image measured along a scan line.

skimage.measure.ransac(data, model_class, ...)

Fit a model to data with the RANSAC (random sample consensus) algorithm.

skimage.measure.regionprops(label_image[, ...])

Measure properties of labeled image regions.

skimage.measure.structural_similarity(*args, ...)

Deprecated function. Use compare_ssim instead.

skimage.measure.subdivide_polygon(coords[, ...])

Subdivision of polygonal curves using B-Splines.

skimage.measure.CircleModel()

Total least squares estimator for 2D circles.

skimage.measure.EllipseModel()

Total least squares estimator for 2D ellipses.

skimage.measure.LineModel()

Total least squares estimator for 2D lines.

skimage.measure.LineModelND()

Total least squares estimator for N-dimensional lines.

linalg.svd(a[, full_matrices, compute_uv, …])

Singular Value Decomposition.

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.

__name__/str

name/type: Introduction. the function name. Example code

__qualname__/str

name/type: the qualified function name. Example code

Image.save()

Saves this image under the given filename. If no format is specified, the format to use is determined from the filename extension, if possible. code1, code2.

Workbook.save

(code)

.sheetnames

Access sheet names from an excel sheet. (code)

Scikit-image

Is a collection of algorithms for image processing, which is a Python image processing toolbox for SciPy.
Installation:
# Update pip
python -m pip install -U pip
# Install scikit-image
or, pip install --upgrade scikit-image
python -m pip install -U scikit-image

SymPy

Is an open-source library for symbolic math. With very simple and comprehensible code that is easily extensible, SymPy is a full-fledged Computer Algebra System (CAS). It is written in Python, and hence does not need external libraries.

Scrapy

Is a good library if your motive is fast, high-level screen scraping and web crawling. It can be used for purposes from data mining to monitoring and automated testing.

SSIM-PIL

Compare two images using the structural similarity algorithm (SSIM), or compute image difference. The images need to have the same resolution. Installation: pip install SSIM-PIL. code. code.

.show()

code. code.

cv2.imshow() and "xyz"..imshow()

A method is used to display an image in a window. The window automatically fits to the image size. First argument is a window name which is a string. Second argument is our image. Introduction. code. code. Code.

skimage.measure.structural_similarity(*args, ...)

Deprecated function. Use compare_ssim instead.

skimage.measure.subdivide_polygon(coords[, ...])

Subdivision of polygonal curves using B-Splines.

Statsmodel

Is for finance and statistical analysis

Seaborn

(code). When it comes to visualisation of statistical models like heat maps, Seaborn is among the reliable sources. This library is derived from Matplotlib and closely integrated with Pandas data structures.

SimplelTK

Povides a simplified interface to ITK in a variety of languages. A user can either download pre-built binaries, if they are available for the desired platform and language, or SimpleITK can be built from the source code.

SKiDL

v2.rectangle(image, start_point, end_point, color of border line, border thickness)

border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.

v2.rectangle(image, start_point, end_point, color of border line, border thickness)

border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.

Get dimensions (sizes) of image:
dimensions = img.shape
Get height, width, number of channels in image
height = img.shape[0]
width = img.shape[1]
channels = img.shape[2]

Introduction. General, code. code. code. code. code. code.

cv2.setMouseCallback

Mouse clicks: introduction. code. code.

Split a list into columns

Introduction

ScaleBar()

Introduction. scalebar = ScaleBar(dx, units="m", dimension="si-length", label=None, length_fraction=None, height_fraction=None, width_fraction=None, location=None, pad=None, border_pad=None, sep=None, frameon=None, color=None, box_color=None, box_alpha=None, scale_loc=None, label_loc=None, font_properties=None, label_formatter=None, scale_formatter=None, fixed_value=None, fixed_units=None, animated=False, rotation=None)
dx (required):
        Size of one pixel in units specified by the next argument.
units:
        Units of dx. The units needs to be valid for the specified dimension. Default: m.
label:
        Optional label associated with the scale bar. Default: None, no label is shown. The position of the label with respect to the scale bar can be adjusted using label_loc argument.
length_fraction:
        Desired length of the scale bar as a fraction of the subplot's width. Default: None, value from matplotlibrc or 0.2.
height_fraction:
        Deprecated, use width_fraction.
width_fraction:
        Width of the scale bar as a fraction of the subplot's height. Default: None, value from matplotlibrc or 0.01.
loc:
        Alias for location.
pad:
        Padding inside the box, as a fraction of the font size. Default: None, value from matplotlibrc or 0.2.
border_pad:
        Padding outside the box, fraction of the font size. Default: None, value from matplotlibrc or 0.1.
sep:
        Separation in points between the scale bar and scale, and between the scale bar and label. Default: None, value from matplotlibrc or 5.
frameon:
        Whether to draw a box behind the scale bar, scale and label. Default: None, value from matplotlibrc or True.
color:
        Color for the scale bar, scale and label. Default: None, value from matplotlibrc or k (black).
box_color:
        Background color of the box. Default: None, value from matplotlibrc or w (white).
box_alpha:
        Transparency of box. Default: None, value from matplotlibrc or 1.0 (opaque).
scale_loc:
        Location of the scale with respect to the scale bar. Either bottom, top, left, right. Default: None, value from matplotlibrc or bottom.
label_loc:
        Location of the label with respect to the scale bar. Either bottom, top, left, right. Default: None, value from matplotlibrc or top.
font_properties:
        Font properties of the scale and label text, specified either as dict or str. See FontProperties for the arguments. Default: None, default font properties of matplotlib.
label_formatter:
        Deprecated, use scale_formatter.
scale_formatter:
        Custom function called to format the scale. Needs to take 2 arguments - the scale value and the unit. Default: None which results in.
fixed_value:
        Value for the scale. The length of the scale bar is calculated based on the specified pixel size dx. Default: None, the value is automatically determined based on length_fraction.
fixed_units:
        Units of the fixed_value. Default: None, if fixed value is not None, the units of dx are used.
animated:
        Animation state. Default: False
rotation:
        Whether to create a scale bar based on the x-axis (default) or y-axis. rotation can either be horizontal or vertical. Note you might have to adjust scale_loc and label_loc to achieve desired layout. Default: None, value from matplotlibrc or horizontal.
Dimension of dx and units. It can either be equal:
        si-length (default): scale bar showing km, m, cm, etc.
         imperial-length: scale bar showing in, ft, yd, mi, etc.
         si-length-reciprocal: scale bar showing 1/m, 1/cm, etc.
         pixel-length: scale bar showing px, kpx, Mpx, etc.
         angle: scale bar showing °, ʹ (minute of arc) or ʹʹ (second of arc)
         a matplotlib_scalebar.dimension._Dimension object.
Dimension of dx and units. It can either be equal:
        si-length (default): scale bar showing km, m, cm, etc.

style

Style string, like 'ko--', to be passed to matplotlib

sharex

If subplots=True, share the same x-axis, linking ticks and limits

sharey

If subplots=True, share the same y-axis

sort_columns

Plot columns in alphabetical order; by default uses existing column order

.subplots/.subplot

Plot each DataFrame column in a separate subplot. E.g. .subplots(nrows=5, ncols=10): 5 rows and 13 columns. The third argument specifies which rectangle will contain the plot specified by the following function calls. As a convenience, the commas separating the three arguments in the subplot routine can be omitted, provided they are all single-digit arguments. E.g. plt.subplot(2, 1, 1) = plt.subplot(211). Can be used to compare different views of data side by side in an array. code. code. image. code. (code).

sharex

All subplots should use the same x-axis ticks (adjusting the xlim will affect all subplots)

sharey

All subplots should use the same y-axis ticks (adjusting the ylim will affect all subplots)

subplot_kw

Dict of keywords passed to add_subplot call used to create each subplot

Speed comparison with and without numpy

Introduction

shape/shape[]

(code)

size

(code)

Numpy splicing

draw a grey or color point or shape in an image. code.

.sort(axis=0)

code.

Split columns and merge in csv

CSV: Split columns and then merge the splits in a csv file. Introduction

Search/print/output the rows

CSV: Print the rows if their cell values are greater than a specific value, in the csv file with numbers only; output the rows if the cell value is in a specific range. Introduction. code.

Sort a csv file with column

CSV: Instruction: used "key = operator.itemgetter()".

Skip rows and/or columns in csv

CSV: Introduction.

t.shape()

“classic”, “arrow”, “turtle”, “circle”, “square” and “triangle”. (code)

t.speed()

Is used to change the speed of the turtle by the value of the argument that it takes. E.g. .speed(speed=None). The turtle’s speed lies in the range 0-10. If input is a number greater than 10 or smaller than 0.5, speed is set to 0. ‘fastest’ : 0; ‘fast’ : 10; ‘normal’ : 6; ‘slow’ : 3; ‘slowest’ : 1. (code)

t.Screen()

Is used to set the size and position of the main window. (code)

t.screensize()

turtle.screensize(canvwidth=None, canvheight=None, bg=None). If there is no parameter, then it is getting the actual screen size. (code)

Collections of geometric shape

(code)

.set_window_position()

(code)

execute_script("window.scrollBy(0, 250)")

(code)

win32api.SetCursorPos()

(code)