SciencePlots: Matplotlib styles for scientific plotting
SciencePlots
#python
#matplotlib
@scientific_programming
SciencePlots
#python
#matplotlib
@scientific_programming
Scientific Programming
The Elements of Statistical Learning https://web.stanford.edu/~hastie/Papers/ESLII.pdf #learning #book @scientific_programming
GitHub
GitHub - empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks: A series of Python Jupyter notebooks that help you better…
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book - empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory. IDTxl provides functionality to estimate the following measures:
For network inference:
🌱multivariate transfer entropy (TE)/Granger causality (GC)
🌱 multivariate mutual information (MI)
🌱 bivariate TE/GC
🌱 bivariate MI
For analysis of node dynamics:
🌱 active information storage (AIS)
🌱partial information decomposition (PID)
https://github.com/pwollstadt/IDTxl
#information_theory
#network_inference
#transfer_entropy
#python
For network inference:
🌱multivariate transfer entropy (TE)/Granger causality (GC)
🌱 multivariate mutual information (MI)
🌱 bivariate TE/GC
🌱 bivariate MI
For analysis of node dynamics:
🌱 active information storage (AIS)
🌱partial information decomposition (PID)
https://github.com/pwollstadt/IDTxl
#information_theory
#network_inference
#transfer_entropy
#python
GitHub
GitHub - pwollstadt/IDTxl: The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference…
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theo...
🔆 STAN
☘️ Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation.
☘️ Stan interfaces with the most popular data analysis languages (R, #python, shell, MATLAB, Julia, Stata) and runs on all major platforms (Linux, Mac, Windows).
🌱 Stan User’s Guide
🌱 PyStan Guide
To install simply use:
$ pip3 install pystan
☘️ Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation.
☘️ Stan interfaces with the most popular data analysis languages (R, #python, shell, MATLAB, Julia, Stata) and runs on all major platforms (Linux, Mac, Windows).
🌱 Stan User’s Guide
🌱 PyStan Guide
To install simply use:
$ pip3 install pystan
mc-stan.org
Stan User’s Guide
Stan user’s guide with examples and programming techniques.
Here is where I start to learn #Machine_Learning:
The course is available here:
Machine Learning, Andrew Ng
The whole course can be downloaded from here at once.
GitHub for Exercises in #Python.
You can check the solution in "solution" branch in case.
The course is available here:
Machine Learning, Andrew Ng
The whole course can be downloaded from here at once.
GitHub for Exercises in #Python.
You can check the solution in "solution" branch in case.
Coursera
Supervised Machine Learning: Regression and Classification
In the first course of the Machine Learning ... Enroll for free.
NetPyNE
#NetPyNE (Networks using #Python and #NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.
Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.
#simulator
#NetPyNE (Networks using #Python and #NEURON) is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.
Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.
#simulator
Linge-Langtangen2016_Book_ProgrammingForComputations-Pyt.pdf
4.4 MB
Programming for Computations – Python
Hans Petter Langtangen
A Gentle Introduction to Numerical
Simulations with Python
Open access book
#book
#python
#basic
Hans Petter Langtangen
A Gentle Introduction to Numerical
Simulations with Python
Open access book
#book
#python
#basic
We have this awesome function called sublots_mosaic where you can pass us a layout id'ed on name
axd = plt.subplot_mosaic(
"""
ABD
CCD
""")
Link
#matplotlib
#python
axd = plt.subplot_mosaic(
"""
ABD
CCD
""")
Link
#matplotlib
#python
I read this paper a couple of days age,
https://nature.com/articles/ncomms13928
Unfortunately the attached code was in #R, So today I learned R! and provided an interface to #Python for the code.
GitHub
https://nature.com/articles/ncomms13928
Unfortunately the attached code was in #R, So today I learned R! and provided an interface to #Python for the code.
GitHub
Always use close!
I spent hours on a large code to find out I had not closed the pool in #multiprocessing in #Python
It gives you "too many files open", misleading nasty error 😤.
I spent hours on a large code to find out I had not closed the pool in #multiprocessing in #Python
It gives you "too many files open", misleading nasty error 😤.