Daniel  Hughes

Daniel Hughes


FederatedScope: An Easy-to-use Federated Learning Platform in Python

FederatedScope is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry. Based on an event-driven architecture, FederatedScope integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively.

A detailed tutorial is provided on our website: federatedscope.io

You can try FederatedScope via FederatedScope Playground or Google Colab.


  • new [10-05-2022] Our benchmark paper for personalized FL, pFL-Bench has been accepted by NeurIPS'22, Dataset and Benchmark Track!
  • new [08-18-2022] Our KDD 2022 paper on federated graph learning receives the KDD Best Paper Award for ADS track!
  • new [07-30-2022] We release FederatedScope v0.2.0!
  • [06-17-2022] We release pFL-Bench, a comprehensive benchmark for personalized Federated Learning (pFL), containing 10+ datasets and 20+ baselines. [code, pdf]
  • [06-17-2022] We release FedHPO-B, a benchmark suite for studying federated hyperparameter optimization. [code, pdf]
  • [06-17-2022] We release B-FHTL, a benchmark suit for studying federated hetero-task learning. [code, pdf]
  • [06-13-2022] Our project was receiving an attack, which has been resolved. More details.
  • [05-25-2022] Our paper FederatedScope-GNN has been accepted by KDD'2022!
  • [05-06-2022] We release FederatedScope v0.1.0!

Code Structure

├── federatedscope
│   ├── core           
│   |   ├── workers              # Behaviors of participants (i.e., server and clients)
│   |   ├── trainers             # Details of local training
│   |   ├── aggregators          # Details of federated aggregation
│   |   ├── configs              # Customizable configurations
│   |   ├── monitors             # The monitor module for logging and demonstrating  
│   |   ├── communication.py     # Implementation of communication among participants   
│   |   ├── fed_runner.py        # The runner for building and running an FL course
│   |   ├── ... ..
│   ├── cv                       # Federated learning in CV        
│   ├── nlp                      # Federated learning in NLP          
│   ├── gfl                      # Graph federated learning          
│   ├── autotune                 # Auto-tunning for federated learning         
│   ├── contrib                          
│   ├── main.py           
│   ├── ... ...          
├── scripts                      # Scripts for reproducing existing algorithms
├── benchmark                    # We release several benchmarks for convenient and fair comparisons
├── doc                          # For automatic documentation
├── enviornment                  # Installation requirements and provided docker files
├── materials                    # Materials of related topics (e.g., paper lists)
│   ├── notebook                        
│   ├── paper_list                                        
│   ├── tutorial                                       
│   ├── ... ...                                      
├── tests                        # Unittest modules for continuous integration
└── setup.py

Quick Start

We provide an end-to-end example for users to start running a standard FL course with FederatedScope.

Step 1. Installation

First of all, users need to clone the source code and install the required packages (we suggest python version >= 3.9). You can choose between the following two installation methods (via docker or conda) to install FederatedScope.

git clone https://github.com/alibaba/FederatedScope.git
cd FederatedScope

Use Docker

You can build docker image and run with docker env (cuda 11 and torch 1.10):

docker build -f environment/docker_files/federatedscope-torch1.10.Dockerfile -t alibaba/federatedscope:base-env-torch1.10 .
docker run --gpus device=all --rm -it --name "fedscope" -w $(pwd) alibaba/federatedscope:base-env-torch1.10 /bin/bash

If you need to run with down-stream tasks such as graph FL, change the requirement/docker file name into another one when executing the above commands:

# environment/requirements-torch1.10.txt -> 

# environment/docker_files/federatedscope-torch1.10.Dockerfile ->

Note: You can choose to use cuda 10 and torch 1.8 via changing torch1.10 to torch1.8. The docker images are based on the nvidia-docker. Please pre-install the NVIDIA drivers and nvidia-docker2 in the host machine. See more details here.

Use Conda

We recommend using a new virtual environment to install FederatedScope:

conda create -n fs python=3.9
conda activate fs

If your backend is torch, please install torch in advance (torch-get-started). For example, if your cuda version is 11.3 please execute the following command:

conda install -y pytorch=1.10.1 torchvision=0.11.2 torchaudio=0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge

For users with Apple M1 chips:

conda install pytorch torchvision torchaudio -c pytorch
# Downgrade torchvision to avoid segmentation fault
python -m pip install torchvision==0.11.3

Finally, after the backend is installed, you can install FederatedScope from source:

From source

python setup.py install

# Or (for dev mode)
pip install -e .[dev]
pre-commit install

Now, you have successfully installed the minimal version of FederatedScope. (Optinal) For application version including graph, nlp and speech, run:

bash environment/extra_dependencies_torch1.10-application.sh

Step 2. Prepare datasets

To run an FL task, users should prepare a dataset. The DataZoo provided in FederatedScope can help to automatically download and preprocess widely-used public datasets for various FL applications, including CV, NLP, graph learning, recommendation, etc. Users can directly specify cfg.data.type = DATASET_NAMEin the configuration. For example,

cfg.data.type = 'femnist'

To use customized datasets, you need to prepare the datasets following a certain format and register it. Please refer to Customized Datasets for more details.

Step 3. Prepare models

Then, users should specify the model architecture that will be trained in the FL course. FederatedScope provides a ModelZoo that contains the implementation of widely adopted model architectures for various FL applications. Users can set up cfg.model.type = MODEL_NAME to apply a specific model architecture in FL tasks. For example,

cfg.model.type = 'convnet2'

FederatedScope allows users to use customized models via registering. Please refer to Customized Models for more details about how to customize a model architecture.

Step 4. Start running an FL task

Note that FederatedScope provides a unified interface for both standalone mode and distributed mode, and allows users to change via configuring.

Standalone mode

The standalone mode in FederatedScope means to simulate multiple participants (servers and clients) in a single device, while participants' data are isolated from each other and their models might be shared via message passing.

Here we demonstrate how to run a standard FL task with FederatedScope, with setting cfg.data.type = 'FEMNIST'and cfg.model.type = 'ConvNet2' to run vanilla FedAvg for an image classification task. Users can customize training configurations, such as cfg.federated.total_round_num, cfg.dataloader.batch_size, and cfg.train.optimizer.lr, in the configuration (a .yaml file), and run a standard FL task as:

# Run with default configurations
python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml
# Or with custom configurations
python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml federate.total_round_num 50 dataloader.batch_size 128

Then you can observe some monitored metrics during the training process as:

INFO: Server has been set up ...
INFO: Model meta-info: <class 'federatedscope.cv.model.cnn.ConvNet2'>.
... ...
INFO: Client has been set up ...
INFO: Model meta-info: <class 'federatedscope.cv.model.cnn.ConvNet2'>.
... ...
INFO: {'Role': 'Client #5', 'Round': 0, 'Results_raw': {'train_loss': 207.6341676712036, 'train_acc': 0.02, 'train_total': 50, 'train_loss_regular': 0.0, 'train_avg_loss': 4.152683353424072}}
INFO: {'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_loss': 209.0940284729004, 'train_acc': 0.02, 'train_total': 50, 'train_loss_regular': 0.0, 'train_avg_loss': 4.1818805694580075}}
INFO: {'Role': 'Client #8', 'Round': 0, 'Results_raw': {'train_loss': 202.24929332733154, 'train_acc': 0.04, 'train_total': 50, 'train_loss_regular': 0.0, 'train_avg_loss': 4.0449858665466305}}
INFO: {'Role': 'Client #6', 'Round': 0, 'Results_raw': {'train_loss': 209.43883895874023, 'train_acc': 0.06, 'train_total': 50, 'train_loss_regular': 0.0, 'train_avg_loss': 4.1887767791748045}}
INFO: {'Role': 'Client #9', 'Round': 0, 'Results_raw': {'train_loss': 208.83140087127686, 'train_acc': 0.0, 'train_total': 50, 'train_loss_regular': 0.0, 'train_avg_loss': 4.1766280174255375}}
INFO: ----------- Starting a new training round (Round #1) -------------
... ...
INFO: Server: Training is finished! Starting evaluation.
INFO: Client #1: (Evaluation (test set) at Round #20) test_loss is 163.029045
... ...
INFO: Server: Final evaluation is finished! Starting merging results.
... ...

Distributed mode

The distributed mode in FederatedScope denotes running multiple procedures to build up an FL course, where each procedure plays as a participant (server or client) that instantiates its model and loads its data. The communication between participants is already provided by the communication module of FederatedScope.

To run with distributed mode, you only need to:

  • Prepare isolated data file and set up cfg.distribute.data_file = PATH/TO/DATA for each participant;
  • Change cfg.federate.model = 'distributed', and specify the role of each participant by cfg.distributed.role = 'server'/'client'.
  • Set up a valid address by cfg.distribute.server_host/client_host = x.x.x.x and cfg.distribute.server_port/client_port = xxxx. (Note that for a server, you need to set up server_host and server_port for listening messages, while for a client, you need to set up client_host and client_port for listening as well as server_host and server_port for joining in an FL course)

We prepare a synthetic example for running with distributed mode:

# For server
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_server.yaml distribute.data_file 'PATH/TO/DATA' distribute.server_host x.x.x.x distribute.server_port xxxx

# For clients
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_client_1.yaml distribute.data_file 'PATH/TO/DATA' distribute.server_host x.x.x.x distribute.server_port xxxx distribute.client_host x.x.x.x distribute.client_port xxxx
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_client_2.yaml distribute.data_file 'PATH/TO/DATA' distribute.server_host x.x.x.x distribute.server_port xxxx distribute.client_host x.x.x.x distribute.client_port xxxx
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_client_3.yaml distribute.data_file 'PATH/TO/DATA' distribute.server_host x.x.x.x distribute.server_port xxxx distribute.client_host x.x.x.x distribute.client_port xxxx

An executable example with generated toy data can be run with (a script can be found in scripts/run_distributed_lr.sh):

# Generate the toy data
python scripts/distributed_scripts/gen_data.py

# Firstly start the server that is waiting for clients to join in
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_server.yaml distribute.data_file toy_data/server_data distribute.server_host distribute.server_port 50051

# Start the client #1 (with another process)
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_client_1.yaml distribute.data_file toy_data/client_1_data distribute.server_host distribute.server_port 50051 distribute.client_host distribute.client_port 50052
# Start the client #2 (with another process)
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_client_2.yaml distribute.data_file toy_data/client_2_data distribute.server_host distribute.server_port 50051 distribute.client_host distribute.client_port 50053
# Start the client #3 (with another process)
python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_client_3.yaml distribute.data_file toy_data/client_3_data distribute.server_host distribute.server_port 50051 distribute.client_host distribute.client_port 50054

And you can observe the results as (the IP addresses are anonymized with 'x.x.x.x'):

INFO: Server: Listen to x.x.x.x:xxxx...
INFO: Server has been set up ...
Model meta-info: <class 'federatedscope.core.lr.LogisticRegression'>.
... ...
INFO: Client: Listen to x.x.x.x:xxxx...
INFO: Client (address x.x.x.x:xxxx) has been set up ...
Client (address x.x.x.x:xxxx) is assigned with #1.
INFO: Model meta-info: <class 'federatedscope.core.lr.LogisticRegression'>.
... ...
{'Role': 'Client #2', 'Round': 0, 'Results_raw': {'train_avg_loss': 5.215108394622803, 'train_loss': 333.7669372558594, 'train_total': 64}}
{'Role': 'Client #1', 'Round': 0, 'Results_raw': {'train_total': 64, 'train_loss': 290.9668884277344, 'train_avg_loss': 4.54635763168335}}
----------- Starting a new training round (Round #1) -------------
... ...
INFO: Server: Training is finished! Starting evaluation.
INFO: Client #1: (Evaluation (test set) at Round #20) test_loss is 30.387419
... ...
INFO: Server: Final evaluation is finished! Starting merging results.
... ...


As a comprehensive FL platform, FederatedScope provides the fundamental implementation to support requirements of various FL applications and frontier studies, towards both convenient usage and flexible extension, including:

  • Personalized Federated Learning: Client-specific model architectures and training configurations are applied to handle the non-IID issues caused by the diverse data distributions and heterogeneous system resources.
  • Federated Hyperparameter Optimization: When hyperparameter optimization (HPO) comes to Federated Learning, each attempt is extremely costly due to multiple rounds of communication across participants. It is worth noting that HPO under the FL is unique and more techniques should be promoted such as low-fidelity HPO.
  • Privacy Attacker: The privacy attack algorithms are important and convenient to verify the privacy protection strength of the design FL systems and algorithms, which is growing along with Federated Learning.
  • Graph Federated Learning: Working on the ubiquitous graph data, Graph Federated Learning aims to exploit isolated sub-graph data to learn a global model, and has attracted increasing popularity.
  • Recommendation: As a number of laws and regulations go into effect all over the world, more and more people are aware of the importance of privacy protection, which urges the recommender system to learn from user data in a privacy-preserving manner.
  • Differential Privacy: Different from the encryption algorithms that require a large amount of computation resources, differential privacy is an economical yet flexible technique to protect privacy, which has achieved great success in database and is ever-growing in federated learning.
  • ...

More supports are coming soon! We have prepared a tutorial to provide more details about how to utilize FederatedScope to enjoy your journey of Federated Learning!

Materials of related topics are constantly being updated, please refer to FL-Recommendation, Federated-HPO, Personalized FL, Federated Graph Learning, FL-NLP, FL-Attacker, FL-Incentive-Mechanism and so on.


The classes and methods of FederatedScope have been well documented so that users can generate the API references by:

pip install -r requirements-doc.txt
make html

We put the API references on our website.

Besides, we provide documents for executable scripts and customizable configurations.


FederatedScope is released under Apache License 2.0.


If you find FederatedScope useful for your research or development, please cite the following paper:

  title = {FederatedScope: A Flexible Federated Learning Platform for Heterogeneity},
  author = {Xie, Yuexiang and Wang, Zhen and Gao, Dawei and Chen, Daoyuan and Yao, Liuyi and Kuang, Weirui and Li, Yaliang and Ding, Bolin and Zhou, Jingren},
  journal={arXiv preprint arXiv:2204.05011},
  year = {2022},

More publications can be found in the Publications.


We greatly appreciate any contribution to FederatedScope! We provide a developer version of FederatedScope with additional pre-commit hooks to perform commit checks compared to the official version:

# Install the developer version
pip install -e .[dev]
pre-commit install

# Or switch to the developer version from the official version
pip install pre-commit
pre-commit install
pre-commit run --all-files

You can refer to Contributing to FederatedScope for more details.

Welcome to join in our Slack channel, or DingDing group (please scan the following QR code) for discussion.


| Code Structure | Quick Start | Advanced | Documentation | Publications | Contributing |

Download Details:

Author: alibaba
Source Code: https://github.com/alibaba/FederatedScope

License: Apache-2.0 license


What is GEEK

Buddha Community

FederatedScope: An Easy-to-use Federated Learning Platform in Python
Chloe  Butler

Chloe Butler


Pdf2gerb: Perl Script Converts PDF Files to Gerber format


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:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. 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 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
    .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
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .030,  #heavy-current traces; be careful with these ones!
#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_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
    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 =>
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,

#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


#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


Ray  Patel

Ray Patel


Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services


When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Shardul Bhatt

Shardul Bhatt


Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.


Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

#python development services #python development company #python app development #python development #python in web development #python software development

Sival Alethea

Sival Alethea


Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=rfscVS0vtbw&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=3

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#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python