Let’s pair program gravity simulation (using JavaScript and p5.js, with Dan Shiffman)

In this episode me and Dan Shiffman from the Coding Train pair program, code, develop a gravity simulation in JavaScript using p5.js (processing.js derivative).

#javascript #programming

A Practical Guide of Unsupervised Learning Algorithms

In this article, learn about Machine Learning Tutorial: A Practical Guide of Unsupervised Learning Algorithms. Machine learning is a fast-growing technology that allows computers to learn from the past and predict the future. It uses numerous algorithms for building mathematical models and predicting future trends. Machine learning (ML) has widespread applications in the industry, including speech recognition, image recognition, churn prediction, email filtering, chatbot development, recommender systems, and much more.

Machine learning (ML) can be classified into three main categories; supervised, unsupervised, and reinforcement learning. In supervised learning, the model is trained on labeled data. While in unsupervised learning, unlabeled data is provided to the model to predict the outcomes. Reinforcement learning is feedback learning in which the agent collects a reward for each correct action and gets a penalty for a wrong decision. The goal of the learning agent is to get maximum reward points and deduce the error.

What is Unsupervised Learning?

In unsupervised learning, the model learns from unlabeled data without proper supervision.

Unsupervised learning uses machine learning techniques to cluster unlabeled data based on similarities and differences. The unsupervised algorithms discover hidden patterns in data without human supervision. Unsupervised learning aims to arrange the raw data into new features or groups together with similar patterns of data.

For instance, to predict the churn rate, we provide unlabeled data to our model for prediction. There is no information given that customers have churned or not. The model will analyze the data and find hidden patterns to categorize into two clusters: churned and non-churned customers.

Unsupervised Learning Approaches

Unsupervised algorithms can be used for three tasks—clustering, dimensionality reduction, and association. Below, we will highlight some commonly used clustering and association algorithms.

Clustering Techniques

Clustering, or cluster analysis, is a popular data mining technique for unsupervised learning. The clustering approach works to group non-labeled data based on similarities and differences. Unlike supervised learning, clustering algorithms discover natural groupings in data.

A good clustering method produces high-quality clusters having high intra-class similarity (similar data within a cluster) and less intra-class similarity (cluster data is dissimilar to other clusters).

It can be defined as the grouping of data points into various clusters containing similar data points. The same objects remain in the group that has fewer similarities with other groups. Here, we will discuss two popular clustering techniques: K-Means clustering and DBScan Clustering.

K-Means Clustering

K-Means is the simplest unsupervised technique used to solve clustering problems. It groups the unlabeled data into various clusters. The K value defines the number of clusters you need to tell the system how many to create.

K-Means is a centroid-based algorithm in which each cluster is associated with the centroid. The goal is to minimize the sum of the distances between the data points and their corresponding clusters.

It is an iterative approach that breaks down the unlabeled data into different clusters so that each data point belongs to a group with similar characteristics.

K-means clustering performs two tasks:

Using an iterative process to create the best value of K.
Each data point is assigned to its closest k-center. The data point that is closer to the particular k-center makes a cluster.

An illustration of K-means clustering. Image source

DBScan Clustering

“DBScan” stands for “Density-based spatial clustering of applications with noise.” There are three main words in DBscan: density, clustering, and noise. Therefore, this algorithm uses the notion of density-based clustering to form clusters and detect the noise.

Clusters are usually dense regions that are separated by lower density regions. Unlike the k-means algorithm, which works only on well-separated clusters, DBscan has a wider scope and can create clusters within the cluster. It discovers clusters of various shapes and sizes from a large set of data, which consists of noise and outliers.

There are two parameters in the DBScan algorithm:

minPts: The threshold, or the minimum number of points grouped together for a region considered as a dense region.

eps(ε): The distance measure used to locate the points in the neighborhood.

An illustration of density-based clustering. Image Source

Association Rule Mining

An association rule mining is a popular data mining technique. It finds interesting correlations in large numbers of data items. This rule shows how frequently items occur in a transaction.

Market Basket Analysis is a typical example of an association rule mining that finds relationships between items in the grocery store. It enables retailers to identify and analyze the associations between items that people frequently buy.

Important terminology used in association rules:

Support: It tells us about the combination of items bought frequently or frequently bought items.

Confidence: It tells us how often the items A and B occur together, given the number of times A occurs.

Lift: The lift indicates the strength of a rule over the random occurrence of A and B. For instance, A->B, the life value is 5. It means that if you buy A, the occurrence of buying B is five times.

The Apriori algorithm is a well-known association rule based technique.

Apriori algorithm

The Apriori algorithm was proposed by R. Agarwal and R. Srikant in 1994 to find the frequent items in the dataset. The algorithm’s name is based on the fact that it uses prior knowledge of frequently occurring things.

The Apriori algorithm finds frequently occurring items with minimum support.

It consists of two steps:

Generation of candidate itemsets.
Removing items that are infrequent and don’t fulfill the criteria of minimum support.

Practical Implementation of Unsupervised Algorithms

In this tutorial, you will learn about the implementation of various unsupervised algorithms in Python. Scikit-learn is a powerful Python library widely used for various unsupervised learning tasks. It is an open-source library that provides numerous robust algorithms, which include classification, dimensionality reduction, clustering techniques, and association rules.

Let’s begin!

1. K-Means algorithm

Now let’s dive deep into the implementation of the K-Means algorithm in Python. We’ll break down each code snippet so that you can understand it easily.

Import libraries

First of all, we will import the required libraries and get access to the functions.

#Let's import the required libraries
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

Loading the dataset

The dataset is taken from the kaggle website. You can easily download it from the given link. To load the dataset, we use the pd.read_csv() function. head() returns the first five rows of the dataset.

my_data = pd.read_csv('Customers_Mall.csv.') my_data.head()

The dataset contains five columns: customer ID, gender, age, annual income in (K$), and spending score from 1-100.

Data Preprocessing

The info() function is used to get quick information about the dataset. It shows the number of entries, columns, total non-null values, memory usage, and datatypes.

my_data.info()

To check the missing values in the dataset, we use isnull().sum(), which returns the total number of null values.

#Check missing values 
my_data.isnull().sum()

The box plot or whisker plot is used to detect outliers in the dataset. It also shows a statistical five number summary, which includes the minimum, first quartile, median (2nd quartile), third quartile, and maximum.

my_data.boxplot(figsize=(8,4))

Using Box Plot, we’ve detected an outlier in the annual income column. Now we will try to remove it before training our model.

#let's remove outlier from data
med =61
my_data["Annual Income (k$)"] = np.where(my_data["Annual Income (k$)"] >
 120,med,my_data['Annual Income (k$)'])

The outlier in the annual income column has been removed now to confirm we used the box plot again.

my_data.boxplot(figsize=(8,5))

Data Visualization

A histogram is used to illustrate the important features of the distribution of data. The hist() function is used to show the distribution of data in each numerical column.

my_data.hist(figsize=(6,6))

The correlation heatmap is used to find the potential relationships between variables in the data and to display the strength of those relationships. To display the heatmap, we have used the seaborn plotting library.

plt.figure(figsize=(10,6)) sns.heatmap(my_data.corr(), annot=True, cmap='icefire').set_title('seaborn') plt.show()

Choosing the Best K Value

The iloc() function is used to select a particular cell of the data. It enables us to select a value that belongs to a specific row or column. Here, we’ve chosen the annual income and spending score columns.

X_val = my_data.iloc[:, 3:].values X_val

# Loading Kmeans Library

from sklearn.cluster import KMeans

Now we will select the best value for K using the Elbow’s method. It is used to determine the optimal number of clusters in K-means clustering.

my_val = []

for i in range(1,11):

    kmeans = KMeans(n_clusters = i, init='k-means++', random_state = 123)

    kmeans.fit(X_val)

    my_val.append(kmeans.inertia_)

The sklearn.cluster.KMeans() is used to choose the number of clusters along with the initialization of other parameters. To display the result, just call the variable.

my_val #Visualization of clusters using elbow’s method plt.plot(range(1,11),my_val) plt.xlabel('The No of clusters') plt.ylabel('Outcome') plt.title('The Elbow Method') plt.show()

Through Elbow’s Method, when the graph looks like an arm, then the elbow on the arm is the best value of K. In this case, we’ve taken K=3, which is the optimal value for K.

kmeans = KMeans(n_clusters = 3, init='k-means++') kmeans.fit(X_val) #To show centroids of clusters kmeans.cluster_centers_ #Prediction of K-Means clustering y_kmeans = kmeans.fit_predict(X_val) y_kmeans

Splitting the dataset into three clusters

The scatter graph is used to plot the classification results of our dataset into three clusters.

plt.scatter(X_val[y_kmeans == 0,0], X_val[y_kmeans == 0,1], c='red',s=100)

plt.scatter(X_val[y_kmeans == 1,0], X_val[y_kmeans == 1,1], c='green',s=100)

plt.scatter(X_val[y_kmeans == 2,0], X_val[y_kmeans == 2,1], c='orange',s=100)

plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='brown')

plt.title('K-Means Unsupervised Learning')

plt.show()

2. Apriori Algorithm

To implement the apriori algorithm, we will utilize “The Bread Basket” dataset. The dataset is available on Kaggle and you can download it from the link. This algorithm suggests products based on the user’s purchase history. Walmart has greatly utilized the algorithm to recommend relevant items to its users.

Let’s implement the Apriori algorithm in Python.

Import libraries

To implement the algorithm, we need to import some important libraries.

import pandas as pd

import matplotlib.pyplot as plt

import numpy as np

import seaborn as sns

Loading the dataset

The dataset contains five columns and 20507 entries. The data_time is a prominent column and we can extract many vital insights from it.

my_data= pd.read_csv("bread basket.csv") my_data.head()

Data Preprocessing

Convert the data_time into an appropriate format.

my_data['date_time'] = pd.to_datetime(my_data['date_time'])

#Total No of unique customers

my_data['Transaction'].nunique()

Now we want to extract new columns from the data_time to extract meaningful information from the data.

#Let's extract date

my_data['date'] = my_data['date_time'].dt.date

#Let's extract time

my_data['time'] = my_data['date_time'].dt.time

#Extract month and replacing it with String

my_data['month'] = my_data['date_time'].dt.month

my_data['month'] = my_data['month'].replace((1,2,3,4,5,6,7,8,9,10,11,12), 

                                          ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug',

                                          'Sep','Oct','Nov','Dec'))

#Extract hour

my_data[‘hour’] = my_data[‘date_time’].dt.hour

# Replacing hours with text

hr_num = (1,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23)

hr_obj = (‘1-2′,’7-8′,’8-9′,’9-10′,’10-11′,’11-12′,’12-13′,’13-14′,’14-15’,

’15-16′,’16-17′,’17-18′,’18-19′,’19-20′,’20-21′,’21-22′,’22-23′,’23-24′)

my_data[‘hour’] = my_data[‘hour’].replace(hr_num, hr_obj)

# Extracting weekday and replacing it with String

my_data[‘weekday’] = my_data[‘date_time’].dt.weekday

my_data[‘weekday’] = my_data[‘weekday’].replace((0,1,2,3,4,5,6),

(‘Mon’,’Tues’,’Wed’,’Thur’,’Fri’,’Sat’,’Sun’))

#Now drop date_time column

my_data.drop(‘date_time’, axis = 1, inplace = True)

After extracting the date, time, month, and hour columns, we dropped the data_time column.

Now to display, we simply use the head() function to see the changes in the dataset.

my_data.head()

# cleaning the item column

my_data[‘Item’] = my_data[‘Item’].str.strip()

my_data[‘Item’] = my_data[‘Item’].str.lower()

my_data.head()

Data Visualization

To display the top 10 items purchased by customers, we used a barplot() of the seaborn library.

plt.figure(figsize=(10,5))

sns.barplot(x=my_data.Item.value_counts().head(10).index, y=my_data.Item.value_counts().head(10).values,palette='RdYlGn')

plt.xlabel('No of Items', size = 17)

plt.xticks(rotation=45)

plt.ylabel('Total Items', size = 18)

plt.title('Top 10 Items purchased', color = 'blue', size = 23)

plt.show()

From the graph, coffee is the top item purchased by the customers, followed by bread.

Now, to display the number of orders received each month, the groupby() function is used along with barplot() to visually show the results.

mon_Tran =my_data.groupby('month')['Transaction'].count().reset_index() mon_Tran.loc[:,"mon_order"] =[4,8,12,2,1,7,6,3,5,11,10,9] mon_Tran.sort_values("mon_order",inplace=True) plt.figure(figsize=(12,5)) sns.barplot(data = mon_Tran, x = "month", y = "Transaction") plt.xlabel('Months', size = 14) plt.ylabel('Monthly Orders', size = 14) plt.title('No of orders received each month', color = 'blue', size = 18) plt.show()

To show the number of orders received each day, we applied groupby() to the weekday column.

wk_Tran = my_data.groupby('weekday')['Transaction'].count().reset_index()

wk_Tran.loc[:,"wk_ord"] = [4,0,5,6,3,1,2]

wk_Tran.sort_values("wk_ord",inplace=True)

plt.figure(figsize=(11,4))

sns.barplot(data = wk_Tran, x = "weekday", y = "Transaction",palette='RdYlGn')

plt.xlabel('Week Day', size = 14)

plt.ylabel('Per day orders', size = 14)

plt.title('Orders received per day', color = 'blue', size = 18)

plt.show()

Implementation of the Apriori Algorithm

We import the mlxtend library to implement the association rules and count the number of items.

from mlxtend.frequent_patterns import association_rules, apriori

tran_str= my_data.groupby(['Transaction', 'Item'])['Item'].count().reset_index(name ='Count')

tran_str.head(8)

Now we’ll make a mxn matrix where m=transaction and n=items, and each row represents whether the item was in the transaction or not.

Mar_baskt = tran_str.pivot_table(index='Transaction', columns='Item', values='Count', aggfunc='sum').fillna(0)

Mar_baskt.head()

We want to make a function that returns 0 and 1. 0 means that the item wasn’t present in the transaction, while 1 means the item exists.

def encode(val):

    if val<=0:

        return 0

    if val>=1:

        return 1

#Let's apply the function to the dataset

Basket=Mar_baskt.applymap(encode)

Basket.head()

#using apriori algorithm to set min_support 0.01 means 1% freq_items = apriori(Basket, min_support = 0.01,use_colnames = True) freq_items.head()

Using the association_rules() function to generate the most frequent items from the dataset.

App_rule= association_rules(freq_items, metric = "lift", min_threshold = 1) App_rule.sort_values('confidence', ascending = False, inplace = True) App_rule.head()

From the above implementation, the most frequent items are coffee and toast, both having a lift value of 1.47 and a confidence value of 0.70.

3. Principal Component Analysis

Principal component analysis (PCA) is one of the most widely used unsupervised learning techniques. It can be used for various tasks, including dimensionality reduction, information compression, exploratory data analysis and Data de-noising.

Let’s use the PCA algorithm!

First we import the required libraries to implement this algorithm.

import numpy as np 

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

from sklearn.decomposition import PCA

from sklearn.datasets import load_digits

Loading the Dataset

To implement the PCA algorithm the load_digits dataset of Scikit-learn is used which can easily be loaded using the below command. The dataset contains images data which include 1797 entries and 64 columns.

#Load the dataset

my_data= load_digits()

#Creating features

X_value = my_data.data

#Creating target

#Let's check the shape of X_value

X_value.shape

#Each image is 8x8 pixels therefore 64px my_data.images[10] #Let's display the image plt.gray() plt.matshow(my_data.images[34]) plt.show()

Now let’s project data from 64 columns to 16 to show how 16 dimensions classify the data.

X_val = my_data.data 

y_val = my_data.target

my_pca = PCA(16)  

X_projection = my_pca.fit_transform(X_val)

print(X_val.shape)

print(X_projection.shape)

Using colormap we visualize that with only ten dimensions we can classify the data points. Now we’ll select the optimal number of dimensions (principal components) by which data can be reduced into lower dimensions.

plt.scatter(X_projection[:, 0], X_projection[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("gist_heat",12))

plt.colorbar();

pca=PCA().fit(X_val)

plt.plot(np.cumsum(my_pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')

Based on the below graph, only 12 components are required to explain more than 80% of the variance which is still better than computing all the 64 features. Thus, we’ve reduced the large number of dimensions into 12 dimensions to avoid the dimensionality curse. pca=PCA().fit(X_val)

plt.plot(np.cumsum(pca.explained_variance_ratio_))

plt.xlabel('Principal components')

plt.ylabel('Explained variance')



#Let's visualize how it looks like

Unsupervised_pca = PCA(12)  

X_pro = Unsupervised_pca.fit_transform(X_val)

print("New Data Shape is =>",X_pro.shape)

#Let's Create a scatter plot

plt.scatter(X_pro[:, 0], X_pro[:, 1], c=y_val, edgecolor='white',

            cmap=plt.cm.get_cmap("nipy_spectral",10))

plt.colorbar();

Wrapping Up

In this machine learning tutorial, we’ve implemented the Kmeans, Apriori, and PCA algorithms. These are some of the most widely used algorithms, having numerous industrial applications and solve many real world problems. For instance, K-means clustering is used in astronomy to study stellar and galaxy spectra, solar polarization spectra, and X-ray spectra. And, Apriori is used by retail stores to optimize their product inventory.

Dreaming of becoming a data scientist or data analyst even without a university and a college degree? Do you need the knowledge of data science and analysis for promotions in your current role?

Are you interested in securing your dream job in data science and analysis and looking for a way to get started, we can help you? With over 10 years of experience in data science and data analysis, we will teach you the rubrics, guiding you with one-on-one lessons from the fundamentals until you become a pro.

Our courses are affordable and easy to understand with numerous exercises and assignments you can learn from. At the completion of our courses, you’ll be readily equipped with technical and practical skills to take on any data science and data analysis role in companies, collaborate effectively among teams and help businesses meet and exceed their objectives by extracting actionable insights from data.

Original article sourced at: https://thedatascientist.com

#machine-learning

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

Design the PCB using your favorite CAD or drawing software.
Print the top and bottom copper and top silk screen layers to a PDF file.
Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
Use a Gerber viewer to double-check the output against the original PCB design.
Make adjustments as needed.
Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl

JavaScript Daily

1632537859

NBB: Ad-hoc CLJS Scripting on Node.js

Nbb

Not babashka. Node.js babashka!?

Ad-hoc CLJS scripting on Node.js.

Status

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.

Requirements

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).

Usage

Install nbb from NPM:

$ npm install nbb -g

Omit -g for a local install.

Try out an expression:

$ nbb -e '(+ 1 2 3)'
6

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
"/private/tmp/test-script"
#js {:columns 216, :rows 47}
510
#js ["node_modules" "package-lock.json" "package.json" "script.cljs"]
#js [#js ["foo" "bar"]]
true
$ ls
node_modules
package-lock.json
package.json
script.cljs

Macros

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)
                                        body))))
            body
            binding-pairs)))

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 "https://clojure.org")
                                (.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 "https://clojure.org")
       _ (-> (.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)'
6
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.

Dependencies

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.

Classpath

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 [foo.bar :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"

Reagent

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

ink-demo.cljs:

(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]))

Promesa

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]
  (js/Promise.
   (fn [resolve _]
     (js/setTimeout resolve ms))))

(defn do-stuff
  []
  (p/do!
   (println "Doing stuff which takes a while")
   (sleep 1000)
   1))

(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
3

Also see API docs.

Js-interop

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.

Examples

See the examples directory for small examples.

Also check out these projects built with nbb:

c64core: retrocompute aesthetics twitter bot
gallery.cljs: script to download walpapers from windowsonearth.org.
nbb-action-example: example of a Github action built with nbb.

API

See API documentation.

Migrating to shadow-cljs

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

Build

Prequisites:

babashka >= 0.4.0
Clojure CLI >= 1.10.3.933
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: https://github.com/borkdude/nbb
License: EPL-1.0

#node #javascript

Armando Bruen

1591421880