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Python Automation and Machine Learning for ICs: Chapter U
- 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



Apache Spark User Interface (UI)  Introduction
User-Defined Schema (UDS) for Domain-Specific Languages (DSL) and Structured Query Language (SQL) Introduction
IoU (Intersection over Union) Introduction
Feature selection: removing unnecessary (constant & quasi constant) features Introduction
Understanding and extracting insights from unstructured data with ML Introduction
Semantic Segmentation Using U-Net with EfficientNet and Pixelshuffle Introduction
Using proxy labels, building a labeling system, and utilizing a labeling service when historical labeled data is unavailable for ML projects Introduction
Principal Component Analysis (PCA) versus Uniform Manifold Approximation and Projection (UMAP) Introduction
Uniform Manifold Approximation and Projection (UMAP) Introduction
Feature selection: Univariate feature selection
Unary constraint Introduction
Unconditional probability Introduction
Universal quantification Introduction
Uninformed search/blind search algorithms Introduction
Comparison among sigmoid, hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions Introduction
Updating Hypothesis (ĥ) and/or Parameter θ^ in ML Introduction
Probability density function (PDF): comparisons between (normal (gaussian) distribution, uniform distribution, exponential distribution and poisson distribution) Introduction
Underfitting in ML Introduction
Rectified Linear Units (ReLUs) Introduction
Unsupervised learning Introduction
Clustering Introduction
Check updated new files in a folder Introduction
Convolutional Autoencoder (CAE) Introduction
Examples of UiPath applications Introduction
Installation of UiPath and its packages and creation of new projects Introduction
Comparison between Python, Blue Prism, UiPath, Automation Anywhere Introduction
Union Bound/Boole's Inequality Introduction
Change/capitalize the case of the first letter of a string (first letter: uppercase; other letters: lowercase) Introduction
Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method code
Work (read, write, and merge and unmerge cells) in Excel sheets Introduction
Select/input a folder/directory/path for later to be called to use Introduction
Build databases with different/uncertain number of members Introduction
Count the numbers of uppercase letters, lowercase letters and spaces in a string and then swap the cases of the letters. code
Compute the difference between two images by using Structural Similarity Index with "pip install --upgrade imutils" code. code
Pickling and unpickling of pandas DataFrame (.pkl) Introduction
Upload files to webpages Introduction
Open an image to file from URL (webpage), then it can be saved in PC code, code. open with color changed.
send_keys() and its uploading images to webpage Introduction
Avoid two or multiple plots being wrongly/incorrectly/unnecessarily mixed/overlap Introduction
Merge dictionaries (update(), **, chain(), ChainMap(), |, |=) Introduction
Lock a file to prevent deleting, and then release/unlock the file once job is done Introduction
Codes: Automation of Mouse Movements and Clicks, and keyboard control (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, ctypes, uiautomation and Sikuli) Introduction
Principle and troubleshooting: Automation of Mouse Movements and Clicks (comparison among pyautogui, pygetwindow, pydirectinput, autoit, Quartz, platform, ctypes, uiautomation and Sikuli) Introduction
Strong machine learning and NLP departments in universities Introduction
Uniform convergence Introduction
Update parameters θj using gradient of the loss function Introduction
What are the students in university Mainly focusing on? Introduction
Get username and password with getpass Introduction
Supervised, unsupervised and reinforcement learning Introduction
Overfitting and underfitting Introduction
Read a frequently updated file periodically (similar to watchdog) 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 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
Convert/change the case of all letters/word into uppercase (capital) or lowercase in a list of strings Introduction
Remove unwanted/unnecessary parts from strings in a column of dataframe Introduction
Safely use credentials (username and password) in Python project Introduction
Remove string 0s from the back/end of a list until non-zero values Introduction
HTTP(Hypertext Transfer Protocol)/URL (Uniform Resource Locator) Introduction


usecols CSV: Only store the columns which are need so that a subset of columns is returned, resulting in much faster parsing time and lower memory usage. code.
usecols CSV: skip column. code. code. code. code.
from pptx.util import Inches (code)
from pptx.util import Pt (code)
unique() CSV: (code) (code)
.text() (code)
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.
watchdog.utils.BaseThread Producer thread base class subclassed by event emitters that generate events and populate a queue with them.
Parameters: event_queue (watchdog.events.EventQueue) – The event queue to populate with generated events.
watch (ObservedWatch) – The watch to observe and produce events for.
timeout (float) – Timeout (in seconds) between successive attempts at reading events.
watchdog.utils.BaseThread Consumer thread base class subclassed by event observer threads that dispatch events from an event queue to appropriate event handlers.
Parameters: timeout (float) – Event queue blocking timeout (in seconds).


Unschedules a watch.
Parameters: watch (An instance of ObservedWatch or a subclass of ObservedWatch) – The watch to unschedule.
unschedule_all() Unschedules all watches and detaches all associated event handlers.
class watchdog.utils.BaseThread
class watchdog.utils.dirsnapshot.DirectorySnapshot(path, recursive=True, walker_callback=<function <lambda> at 0x7f78fbeed500>, stat=<built-in function stat>, listdir=<built-in function listdir>) A snapshot of stat information of files in a directory.
path (str) – The directory path for which a snapshot should be taken.
recursive (bool) – True if the entire directory tree should be included in the snapshot; False otherwise.
walker_callback –
Deprecated since version 0.7.2.
stat –
Use custom stat function that returns a stat structure for path. Currently only st_dev, st_ino, st_mode and st_mtime are needed.
A function with the signature walker_callback(path, stat_info) which will be called for every entry in the directory tree.
listdir – Use custom listdir function. See os.listdir for details.

class watchdog.utils.dirsnapshot.DirectorySnapshotDiff(ref, snapshot)

Compares two directory snapshots and creates an object that represents the difference between the two snapshots.
ref (DirectorySnapshot) – The reference directory snapshot.
snapshot (DirectorySnapshot) – The directory snapshot which will be compared with the reference snapshot.

upper() Returns the string by converting all the characters of the string to upper case. code. code.
urllib urllib is a package that collects several modules for working with URLs:
         urllib.error containing the exceptions raised by urllib.request
         urllib.parse for parsing URLs
         urllib.robotparser for parsing robots.txt files
urllib.request Defines functions and classes that help to open the URL (for opening and reading URLs/www webpages). Introduction. code.
urlretrieve Is used for retrieving the files, and the URL is assigned.
use_index Use the object index for tick labels
Count duplicates/occurrence and show unique values in csv files CSV: Introduction
hotkey('up') Introduction
hotkey('u') Introduction
.mouseUp() Move the mouse and then release it. .mouseUp(x=moveToX, y=moveToY, button='left'). .click() function is just a convenient wrapper around these two .mouseDown() and .mouseUp() function calls. (code)
random.uniform() (code).