Sayyed's blog (Posts about python)

Introduction to matplot

Abdul Sayyed — Thu, 30 Jul 2020 05:22:01 GMT

Matplotlib ¶

UserGuide ¶

It is plotting library for 2D plotting used in academic publising on hardcopy and in interactive environment.
It can be used in Python script, in Python and IPython shells, Jupyter notebook, web application servers. These four...

What it can generate ?¶

It can draw plot, histograms, power spectra, bar charts, errorcharts, scatterplots,etc
Sample Plots
Thumbnail gallery

How it is installed ?¶

It can be installed as its own package
With third party distribution
From source, it can be built
Clone the latest repo

Its Dependencies ? on [30 July 2020] Following is taken from documentation¶

txt

Python (>= 3.6)
FreeType (>= 2.3)
libpng (>= 1.2)
NumPy (>= 1.11)
setuptools
cycler (>= 0.10.0)
dateutil (>= 2.1)
kiwisolver (>= 1.0.0)
pyparsing

- To get the better user interface toolkit, optional can be insalled.

tk (>= 8.3, != 8.6.0 or 8.6.1): for the Tk-based backends;
PyQt4 (>= 4.6) or PySide (>= 1.0.3): for the Qt4-based backends;
PyQt5: for the Qt5-based backends;
PyGObject: for the GTK3-based backends;
wxpython (>= 4): for the WX-based backends;
cairocffi (>= 0.8) or pycairo: for the cairo-based backends;
Tornado: for the WebAgg backend;
For better support of animation output format and image file formats, LaTeX, etc., you can install the following:

ffmpeg/avconv: for saving movies;
ImageMagick: for saving animated gifs;
Pillow (>= 3.4): for a larger selection of image file formats: JPEG, BMP, and TIFF image files;
LaTeX and GhostScript (>=9.0) : for rendering text with LaTeX.

Concepts behind the matplotlib¶

The work is done on many levels from general to specific.
Once can visiulize data easily as well as control necessary high and low level detail.
It is all done through object library so the more specific can be accused its less specific object.
matplotlib is said to be the state-machine environment provide by matplotlib.pyplot module.

Pyplot is like a matlab environment, so should not be difficult. The first level in object hirararcy is the pyplot library. The user uses this object to draw figures and controls its attributes.

In [ ]:

import matplotlib.pyplot as pyplot
import numpy as np

fig = pyplot.figure()
fig.suptitle('No axes on this figure')

# Draw two by two figures ( that is four boxex)
fig.ax_lst = pyplot.subplots(2,2)
# Draw only one
fig.ax_lst = pyplot.subplots(1,1)

The above figure is just a description how easy it is to plat a figure, it is very simple and other software such as r, matplot provides the smae high level abstraction.

Note: Matplotlib figures and plots works with numpy arrays as input. Other libraries arrly-like object such as pandas np.matrix may or may not work. It is better that they can be converted to np.array object.

Using `numpy` to draw a sin funciton¶

In [56]:

# run above cells so that librararie are imported, if not, import them again

import matplotlib.pyplot as pyplot
import numpy as np 

x = np.arange(0,10,0.2)
y = np.sin(x)

# creating a figure and set of subplots. pyplot.subplots() creates two object at one time, it implicitly 
# creates a fig object and show a subplot created in ax
fig, ax = pyplot.subplots()

# Though ax points to a subplots objects, it is still empty so fill it
ax.plot(x,y)
pyplot.show()

In [59]:

### What happened above ?

# `np.arange`, numpy has number of function that creates an array as shown below.
# The function above creates an arry startgin from 0 and ending to 10 with a difference of 0.2
import numpy;a = numpy.arange(0,10,0.2);a

Out[59]:

array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4,
       2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. ,
       5.2, 5.4, 5.6, 5.8, 6. , 6.2, 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6,
       7.8, 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8])

In [60]:

# We need to draw something, and we decided to draw the output of `sin` funciton so we saved the outpu in y variable
# The return value of `sin(x)` function that is a tuple and need to be printed using print(y)
b = np.sin(a) #print(y)

In [68]:

# Now we have x that goes from 0 to 10 with a difference of 0.2 that is we are going to plot 
# values of y against x. to plot a figure we need to use matplot.pyplot object. This method provides a subplot object that
# is very convinent to plotting any values using object within fig object 
import matplotlib.pyplot as plt;
fig, ax = plt.subplots()

In [75]:

# The above draws only an empty figure, even though `plt.show()` is not even used explicitly. 
# pyplot to plot an object we have used the following
ax.plot(a,b)
pyplot.show()

In [77]:

### The above does not work it has to be done in one go
fig2,fx=plt.subplots()
fx.plot(a,b)
plt.show()

In [ ]:

Neuro Imagin with Machine Learning

Abdul Sayyed — Thu, 23 Jul 2020 19:14:41 GMT

What is nilearn 1.0 ?¶

nilearn is a python way of doing work (statistical analysis) with Neuro Imaging in Python using machine learning. It uses scikit-learn Python toolbox.
It is a subset of a family of work done in python language related with Neuro Imaging.
It is not a new technique or new science but provides modern pythonic way of dealing with old analysis done on neuro imaging data such as MVPA, decoding, predictive modelling, functional connectivity, brain parcellations, connectomes.
It can also be used on different modalaties of fMRI such as task fMRI, resting fMRI, or VBM data
nilearn makes the work of neuro imaging with machine learning a specific domain, or feature engineering construction.

Installation 1.1¶

Use pip install -U nilearn or use existing conda environment to install nilearn

Check installation 1.2¶

Following line will check its installaiton.

In [1]:

import nilearn as ni

In [3]:

ni.version

Out[3]:

<module 'nilearn.version' from '/home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nilearn/version.py'>

In [4]:

from nilearn import plotting

In [5]:

# Plotting glass brain
plotting.plot_glass_brain("data/sample-nifiti-file.nii")

Out[5]:

<nilearn.plotting.displays.OrthoProjector at 0x7fde728a2100>

In [6]:

# plotting anatomical brain.
plotting.plot_anat("data/sample-nifiti-file.nii")

Out[6]:

<nilearn.plotting.displays.OrthoSlicer at 0x7fde6f4886d0>

In [7]:

# plotting
plotting.plot_epi("data/sample-nifiti-file.nii")

Out[7]:

<nilearn.plotting.displays.OrthoSlicer at 0x7fde6d874100>

In [9]:

# Using plot_img
plotting.plot_img("data/sample-nifiti-file.nii")

Out[9]:

<nilearn.plotting.displays.OrthoSlicer at 0x7fde6d62aeb0>

In [26]:

# To read dicom daga
import pydicom as pd 
import pydicom.data 
# To plot it
import matplotlib.pyplot as plot

base = "data/"
pass_dicom = "IM-0001-0001.dcm"
fn = pd.data.data_manager.get_files(base,pass_dicom)[0]
ds = pd.dcmread(fn)

# To view read image
plot.imshow(ds.pixel_array, cmap=plot.cm.bone)

Out[26]:

<matplotlib.image.AxesImage at 0x7fde850be490>

In [ ]:

001_intro

Abdul Sayyed — Fri, 10 Jul 2020 14:43:54 GMT

Jupyter note book basics¶

In [1]:

import os
print(os.name)
print(os.getcwd())
print(os.listdir( '.'))

posix
/home/sayyed/neuro-science/projects/nikola/mysite/notebooks
['002_intro.ipynb', '001_intro.ipynb', '002_basics.ipynb', '.ipynb_checkpoints', '005_intro.ipynb', '003_intro.ipynb', '004_intro.ipynb']

In [1]:

npm.install()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-c09f0e0ad088> in <module>
----> 1 npm.install()

NameError: name 'npm' is not defined

Running code from different kernel¶

In [5]:

%%bash
which bash

/usr/bin/bash

In [6]:

%%HTML
<h4>This is html 4 heading </h4>

This is html 4 heading

In [ ]:

Checking-nipype-Installation

Abdul Sayyed — Fri, 10 Jul 2020 14:43:54 GMT

Checking `nipype` installation¶

There are number of ways to check its installation as shown below.

In [2]:

import nipype; print(nipype.__version__)

1.5.0

In [1]:

import nipype; nipype.test()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
~/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/__init__.py in __call__(self, doctests, parallel)
     36         try:
---> 37             import pytest
     38         except ImportError:

ModuleNotFoundError: No module named 'pytest'

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-1-bac8365228b1> in <module>
----> 1 import nipype; nipype.test()

~/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/__init__.py in __call__(self, doctests, parallel)
     37             import pytest
     38         except ImportError:
---> 39             raise RuntimeError("py.test not installed, run: pip install pytest")
     40         args = []
     41         if not doctests:

RuntimeError: py.test not installed, run: pip install pytest

The above simply says that pytest is not installed and using the pip install it.
Installed pytest using pip install pytest and run the text again. When test is run it gives the following error.
Docs: https://docs.pytest.org/en/latest/warnings.html ======================================================== short test summary info ========================================================= ERROR ../../../../anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/documenter.py - ModuleNotFoundError: No module nam... ERROR ../../../../anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/apidoc/init.py - ModuleNotFoundError: No modul... ERROR ../../../../anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/apidoc/docstring.py - ModuleNotFoundError: No modu... !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Interrupted: 3 errors during collection !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ===================================================== 9 warnings, 3 errors in 57.98s =====================================================
The error above is related to the Sphinix package which comes with nipype. We wil see it later.

In [3]:

import nipype as ny
ny.get_info()

Out[3]:

{'pkg_path': '/home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype',
 'commit_source': 'archive substitution',
 'commit_hash': '%h',
 'nipype_version': '1.5.0',
 'sys_version': '3.8.2 | packaged by conda-forge | (default, Apr 24 2020, 08:20:52) \n[GCC 7.3.0]',
 'sys_executable': '/home/sayyed/anaconda3/envs/nipype/bin/python',
 'sys_platform': 'linux',
 'numpy_version': '1.18.5',
 'scipy_version': '1.5.0',
 'networkx_version': '2.4',
 'nibabel_version': '3.1.1',
 'traits_version': '6.1.0'}

Testing the installation¶

In [3]:

import nipype; nipype.test()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
~/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/__init__.py in __call__(self, doctests, parallel)
     36         try:
---> 37             import pytest
     38         except ImportError:

ModuleNotFoundError: No module named 'pytest'

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
<ipython-input-3-bac8365228b1> in <module>
----> 1 import nipype; nipype.test()

~/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/__init__.py in __call__(self, doctests, parallel)
     37             import pytest
     38         except ImportError:
---> 39             raise RuntimeError("py.test not installed, run: pip install pytest")
     40         args = []
     41         if not doctests:

RuntimeError: py.test not installed, run: pip install pytest

In [ ]:

1. I installed `pytest` using `pip install pytest` it rand the test and filed on `Sphinix extension`.
2. When tried to import `nilearn` it also threw an error of not finding this package.

In [4]:

import nipype; nipype.test()

============================= test session starts ==============================
platform linux -- Python 3.8.2, pytest-5.4.3, py-1.9.0, pluggy-0.13.1
rootdir: /home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype, inifile: pytest.ini
collected 2885 items / 2 errors / 2883 selected

==================================== ERRORS ====================================
________________ ERROR collecting sphinxext/apidoc/__init__.py _________________
/home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/apidoc/__init__.py:5: in <module>
    from sphinxcontrib.napoleon import (
E   ModuleNotFoundError: No module named 'sphinxcontrib.napoleon'
________________ ERROR collecting sphinxext/apidoc/docstring.py ________________
/home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/py/_path/local.py:704: in pyimport
    __import__(modname)
/home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/apidoc/__init__.py:5: in <module>
    from sphinxcontrib.napoleon import (
E   ModuleNotFoundError: No module named 'sphinxcontrib.napoleon'
=========================== short test summary info ============================
ERROR ../../../../../anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/apidoc/__init__.py
ERROR ../../../../../anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/sphinxext/apidoc/docstring.py
!!!!!!!!!!!!!!!!!!! Interrupted: 2 errors during collection !!!!!!!!!!!!!!!!!!!!
============================== 2 errors in 17.92s ==============================

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-4-bac8365228b1> in <module>
----> 1 import nipype; nipype.test()

~/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/__init__.py in __call__(self, doctests, parallel)
     48             args.append("-n auto")
     49         args.append(os.path.dirname(__file__))
---> 50         pytest.main(args=args)
     51 
     52 

~/anaconda3/envs/nipype/lib/python3.8/site-packages/_pytest/config/__init__.py in main(args, plugins)
    122         else:
    123             try:
--> 124                 ret = config.hook.pytest_cmdline_main(
    125                     config=config
    126                 )  # type: Union[ExitCode, int]

~/anaconda3/envs/nipype/lib/python3.8/site-packages/pluggy/hooks.py in __call__(self, *args, **kwargs)
    284                     stacklevel=2,
    285                 )
--> 286         return self._hookexec(self, self.get_hookimpls(), kwargs)
    287 
    288     def call_historic(self, result_callback=None, kwargs=None, proc=None):

~/anaconda3/envs/nipype/lib/python3.8/site-packages/pluggy/manager.py in _hookexec(self, hook, methods, kwargs)
     91         # called from all hookcaller instances.
     92         # enable_tracing will set its own wrapping function at self._inner_hookexec
---> 93         return self._inner_hookexec(hook, methods, kwargs)
     94 
     95     def register(self, plugin, name=None):

~/anaconda3/envs/nipype/lib/python3.8/site-packages/pluggy/manager.py in <lambda>(hook, methods, kwargs)
     82             )
     83         self._implprefix = implprefix
---> 84         self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
     85             methods,
     86             kwargs,

    [... skipping hidden 3 frame]

~/anaconda3/envs/nipype/lib/python3.8/site-packages/_pytest/main.py in pytest_cmdline_main(config)
    238 
    239 def pytest_cmdline_main(config):
--> 240     return wrap_session(config, _main)
    241 
    242 

~/anaconda3/envs/nipype/lib/python3.8/site-packages/_pytest/main.py in wrap_session(config, doit)
    233                     session.exitstatus = exc.returncode
    234                 sys.stderr.write("{}: {}\n".format(type(exc).__name__, exc))
--> 235         config._ensure_unconfigure()
    236     return session.exitstatus
    237 

~/anaconda3/envs/nipype/lib/python3.8/site-packages/_pytest/config/__init__.py in _ensure_unconfigure(self)
    823         if self._configured:
    824             self._configured = False
--> 825             self.hook.pytest_unconfigure(config=self)
    826             self.hook.pytest_configure._call_history = []
    827         while self._cleanup:

~/anaconda3/envs/nipype/lib/python3.8/site-packages/pluggy/hooks.py in __call__(self, *args, **kwargs)
    284                     stacklevel=2,
    285                 )
--> 286         return self._hookexec(self, self.get_hookimpls(), kwargs)
    287 
    288     def call_historic(self, result_callback=None, kwargs=None, proc=None):

~/anaconda3/envs/nipype/lib/python3.8/site-packages/pluggy/manager.py in _hookexec(self, hook, methods, kwargs)
     91         # called from all hookcaller instances.
     92         # enable_tracing will set its own wrapping function at self._inner_hookexec
---> 93         return self._inner_hookexec(hook, methods, kwargs)
     94 
     95     def register(self, plugin, name=None):

~/anaconda3/envs/nipype/lib/python3.8/site-packages/pluggy/manager.py in <lambda>(hook, methods, kwargs)
     82             )
     83         self._implprefix = implprefix
---> 84         self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
     85             methods,
     86             kwargs,

    [... skipping hidden 3 frame]

~/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/conftest.py in pytest_unconfigure(config)
     42 def pytest_unconfigure(config):
     43     # Delete temp folder after session is finished
---> 44     shutil.rmtree(temp_folder)

~/anaconda3/envs/nipype/lib/python3.8/shutil.py in rmtree(path, ignore_errors, onerror)
    704             orig_st = os.lstat(path)
    705         except Exception:
--> 706             onerror(os.lstat, path, sys.exc_info())
    707             return
    708         try:

~/anaconda3/envs/nipype/lib/python3.8/shutil.py in rmtree(path, ignore_errors, onerror)
    702         # lstat()/open()/fstat() trick.
    703         try:
--> 704             orig_st = os.lstat(path)
    705         except Exception:
    706             onerror(os.lstat, path, sys.exc_info())

FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpqoloi4y8'

In [2]:

# Import the nipype module
import nipype
# Optional: Use the following lines to increase verbosity of output
nipype.config.set('logging', 'workflow_level', 'CRITICAL')
nipype.config.set('logging', 'interface_level', 'CRITICAL')
nipype.logging.update_logging(nipype.config)
# Run the test: Increase verbosity parameter for more info
nipype.test()

ERROR: usage: ipykernel_launcher.py [options] [file_or_dir] [file_or_dir] [...]
ipykernel_launcher.py: error: unrecognized arguments: --doctest-modules
  inifile: /home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype/pytest.ini
  rootdir: /home/sayyed/anaconda3/envs/nipype/lib/python3.8/site-packages/nipype

In [ ]:

Python Virtual Enviorenment

Abudl Sayyed — Sun, 05 Jul 2020 22:51:59 GMT

Virtual environments

In python there are many ways to create virtual environment, they are mentiond below.
1. Using python3 built in command.
2. Package manager like conda or pip

Learning-pelican

Abdul Sayyed — Sun, 05 Jul 2020 11:40:00 GMT

What are we using to build a site.

Pelican library installed with
Markdown parsing library
Jinja2, a template engine
pip or conda virtual environment that a user create where the above are installed and kept serprately.
Other buildig tools
1. Flask tutorial. A web framework.
2. Green unicorn or gunicorn is web server Gateway implementation (WSGI) tha tis used to run Python web aaplication.
3. Jinja2. An implementation of a template engine.This link contains many useful sites.

What guides are available to use.

Full Stack Python

Building a Basic site.

Select your working director, or cd into it.
Create a virtual env, if not created. Activate your environment
First conda install pip. It will install the following

he following NEW packages will be INSTALLED:

  _libgcc_mutex      conda-forge/linux-64::_libgcc_mutex-0.1-conda_forge
  _openmp_mutex      conda-forge/linux-64::_openmp_mutex-4.5-0_gnu
  ca-certificates    conda-forge/linux-64::ca-certificates-2020.6.20-hecda079_0
  certifi            conda-forge/linux-64::certifi-2020.6.20-py38h32f6830_0
  ld_impl_linux-64   conda-forge/linux-64::ld_impl_linux-64-2.34-h53a641e_5
  libffi             conda-forge/linux-64::libffi-3.2.1-he1b5a44_1007
  libgcc-ng          conda-forge/linux-64::libgcc-ng-9.2.0-h24d8f2e_2
  libgomp            conda-forge/linux-64::libgomp-9.2.0-h24d8f2e_2
  libstdcxx-ng       conda-forge/linux-64::libstdcxx-ng-9.2.0-hdf63c60_2
  ncurses            conda-forge/linux-64::ncurses-6.1-hf484d3e_1002
  openssl            conda-forge/linux-64::openssl-1.1.1g-h516909a_0
  pip                conda-forge/noarch::pip-20.1.1-py_1
  python             conda-forge/linux-64::python-3.8.3-cpython_he5300dc_0
  python_abi         conda-forge/linux-64::python_abi-3.8-1_cp38
  readline           conda-forge/linux-64::readline-8.0-hf8c457e_0
  setuptools         conda-forge/linux-64::setuptools-49.1.0-py38h32f6830_0
  sqlite             conda-forge/linux-64::sqlite-3.32.3-hcee41ef_0
  tk                 conda-forge/linux-64::tk-8.6.10-hed695b0_0
  wheel              conda-forge/noarch::wheel-0.34.2-py_1
  xz                 conda-forge/linux-64::xz-5.2.5-h516909a_0
  zlib               conda-forge/linux-64::zlib-1.2.11-h516909a_1006

Note : pip and python both are installed. keep a record of pip -V and python --version

Install pelican, the latest guide is here. It uses this command pip install pelican[Markdown]. Though it gives this error

ERROR: nbconvert 5.6.1 requires entrypoints>=0.2.2, which is not installed.
ERROR: bleach 3.1.5 requires packaging, which is not installed.

It can be installed seprataley. Install pelican and markdown parsing libraries with pip ,e.g., pip install pelican==3.7.1 markdown==2.6.8
- Installing pelican would install feedgenerator, jinja2, pygments, docutils, pytx, blinker and unidecode
- Optionally you can install using pip other librarirs lke piloow, beautifulsoup4, cssmin, cssprefixer, cssutil, pretty, six, smartypans and typogrify webassets

Time to time it can be upgraded pip install --upgrade pelican

Start pellican pelican-quickstart

   Where do you want to create your new web site? [.] 
> What will be the title of this web site? nipype
> Who will be the author of this web site? Abdul Sayyed
> What will be the default language of this web site? [en] 
> Do you want to specify a URL prefix? e.g., https://example.com   (Y/n) Y
> What is your URL prefix? (see above example; no trailing slash) http://AbdulSayyed.github.io/jupyter-pelican
> Do you want to enable article pagination? (Y/n) y
> How many articles per page do you want? [10] 5
> What is your time zone? [Europe/Paris] Europe/London
> Do you want to generate a tasks.py/Makefile to automate generation and publishing? (Y/n) y
> Do you want to upload your website using FTP? (y/N) n
> Do you want to upload your website using SSH? (y/N) n
> Do you want to upload your website using Dropbox? (y/N) n
> Do you want to upload your website using S3? (y/N) n
> Do you want to upload your website using Rackspace Cloud Files? (y/N) n
> Do you want to upload your website using GitHub Pages? (y/N) y
> Is this your personal page (username.github.io)? (y/N) y
Done. Your new project is available at /home/sayyed/neuro-science/projects/nipype

Following files are created.

Makefile          pelicanconf.py
content/           publishconf.py output/

make command is used to genereat/start many task such as , making html, cleaning, regenerating serving etc. It is generated according to the input which we have used while creating a basic infrasturcture.
content/ directory is used to write markdown contents wile other two files are for configuation.
Run make html and the contents of our markdown file will be converted into html in a newly created directory called output. You can also run pelican content to generate HTML.
Run make serve and server will start on a local host post on port 8000. You can cd into output directory and run python -m pelican.server
You can also use pelican -s pelicanconf.py -o output content to make html instead of make html
You can cd to ouput then use python -m http.server
Write your post in content/ directory using post or blog or any other directory like code/ etc and keep your post or articles seprate.
If using sublime use this [package] https://packagecontrol.io/packages/Pelican. Once installed use Ctr+shift+p and write pelican help wil come. You can cofigure the metadata.

The contents of pelican conf file

#!/usr/bin/env python
# -*- coding: utf-8 -*- #
from __future__ import unicode_literals

AUTHOR = 'Abdul Sayyed'
SITENAME = 'Neuroimaging in Python'
SITEURL = ''

PATH = 'content'

TIMEZONE = 'Europe/London'

DEFAULT_LANG = 'en'

# Feed generation is usually not desired when developing
FEED_ALL_ATOM = None
CATEGORY_FEED_ATOM = None
TRANSLATION_FEED_ATOM = None
AUTHOR_FEED_ATOM = None
AUTHOR_FEED_RSS = None

# Blogroll
LINKS = (('Pelican', 'http://getpelican.com/'),
         ('Python.org', 'http://python.org/'),
         ('Jinja2', 'http://jinja.pocoo.org/'),
         ('You can modify those links in your config file', '#'),)

# Social widget
SOCIAL = (('You can add links in your config file', '#'),
          ('Another social link', '#'),)

DEFAULT_PAGINATION = 5

# Uncomment following line if you want document-relative URLs when developing
#RELATIVE_URLS = True

Running your site

make html and make serve are two main commands.
First time is run the out put is below

Adding first post

Created first.md in content folder echo "# First markdown post" >>./content/first.md
Add follwoing meta dat at the top.

Title: My First Post
Date: 2020-07-05 12:40
Status: published
Category: nipype
Tags: python, neuroscience
Slug: First-Post
Authors: Abdul Sayyed
Summary: Alcohol apophenia nodal point dead plastic long-chain hydrocarbons lights neon. Dome sub-orbital DIY render-farm youtube systema katana tiger-team shrine tank-traps paranoid pre--ware soul-delay boy voodoo god gang. Beef noodles market papier-mache faded skyscraper-ware numinous disposable sub-orbital sunglasses Kowloon math. Render-farm dome digital media tube girl DIY drugs 3D-printed network refrigerator wristwatch construct papier-mache sign.

The output is bleow.
Added and committed then push to remote.

Adding functionality for `jupyter notebook`

First create a repo and committ then start working with dev branch

git init .
git add --all
git commit -m "@master: initial commit"
git checkout -b dev

Install a plug in from danielfrg/pelican-jupyter
First create a plug in folder mkdir -p plugins
Down load or clone somewhere temporarily and only copy pelican_jupyter folder into your plugin folder.

Inform your config file about the plugins

open pelicanconf.py and add the following after the last line.

MARKUP = ("md", "ipynb")

from pelican_jupyter import markup as nb_markup
PLUGINS = [nb_markup]
IPYNB_MARKUP_USE_FIRST_CELL = True

IGNORE_FILES = [".ipynb_checkpoints"]

Now the structure is ready to pubish a notbook.
Install jupyter lab
conda install jupyterlab, it wil install all other requirement for the job.
Start jupyter lab
Write your first notebook with an extension of .ipynb, in order for this book to be published directly it has to have markdown in first cell. Add the follwoing in first cell and in other cell carry on doing some pyton work.

- title: My notebook
- author: John Doe
- date: 2018-05-11
- category: pyhton
- tags: pip

Save and close the juptyer lab
Run make html and you will get some kind of error.

CRITICAL: ModuleNotFoundError: No module named 'pelican_jupyter'

Since our plugins is a part of this module that we did not install as it is in developing stages.
Now install pip install pelican_jupyter
Run and start the server
You should see your .ipynb running directly as a blog post.
The output is as follows
In order to get live reload install invoke using pip install invoke along with livereload
Then start your server with invoke livereload.

Adding themes

Using pelican-bootstrap3. Clone it in main directory
Add the following in your pelicanconf.py.

THEME = 'pelican-themes/pelican-bootstrap3'
JINJA_ENVIRONMENT = {'extensions': ['jinja2.ext.i18n']}

Adding new plugins to the pelicanconf.py

To implement i18n, you need to include the plugins. The best choice is i18n_subsites plugin.
Since we already have the directory plugins where our plugins are kept. We do not need to have all the plugins which are present. But to first get them locally. You need to add the whole repo locallely
clone it recusively git clone --recursive https://github.com/getpelican/pelican-plugins
Add follwoing line to your pelicanconf.py file

# plugins
PLUGIN_PATHS = ['./plugins']
PLUGINS = ['i18n_subsites']

Copy paste i18n_subsites folder into you plugins/ folder. Once done rebuilt the site and run the server.
Notice the change, now new theme is working.

Customising the views

New custom .css and .js files are added in a new folder under content\extra.
We need to tell pelicanconf.py where are they stroed.
Add the following contents in the config file

pelicanconf.py

CUSTOM_CSS = 'static/css/custom.css'
CUSTOM_JS = 'static/js/custom.js'

STATIC_PATHS = [ 'extra' ]

EXTRA_PATH_METADATA = {
    'extra/custom.css': {'path': 'static/css/custom.css'},
    'extra/custom.js': {'path': 'static/js/custom.js'}
}

Adding pages to the site

Pages are not post but about you or the sites.
Create a new folder under content/pages and add a new file called about.md.
Tell config file about the pages, add the following to your config file

# Paths
PATH = 'content'
PAGE_PATHS = ['pages']
ARTICLE_PATHS = ['posts']

# Top menus
DISPLAY_CATEGORIES_ON_MENU = False
DISPLAY_PAGES_ON_MENU = True

if every thing is working then use this below.

Creating a sample notebook

Start jupyter notebook or jupyter lab from nipype env, but it will give error a they are not installed in this envioronment.

Note: To avoid this problem, you need to install ipykernel i your environment pip insall --user ipykernel

python-basics

Abdul Sayyed — Thu, 08 Aug 2019 19:53:11 GMT

An introductory book on Python by Allen Think Python

Languages primitive data

Variables are primitive data type in Python and are declared without any key word num1 = 100 hence Python is said to be dynamically implicitly typed language because the type of the variable is defined at run time implicitly.
There are Built in data type: such as numbers { integers, floats, complex numbers}, strings, lists, tuples and dictionaries.

Rules for Variable names

The only characters that are allowed are letters, numbers, and underscores. Also, they can't start with numbers.
When declaring variable you don't need a var or Var like other languages, just type the name of the variable and assigned some value to it.
x = 10 her x is a variable which is assigned 10, it can be deleted by using del operator.
Many other languages have special operators such as '++' as a short cut for 'x += 1'. Python does not have these.
If a variable is not defined ( assigned a value) trying to use it will cause an error. ( name is not defined)
a = b = c = 2 , her 2 is assigned to all a, b and c variable.
To see the internal type use print(type(a)) you should get //
int(...), float(...), str(...) is used to cast one type to another type.
Multiple assignment is possible as well

 a,b = 10,20   # a is 10 while b is 20
 a,b = b, a+b  # a is given b's value while b is given a+b value.
 a = b = c = 2  # here 2 is assigned to all a, b and c variable.

    >>> a = 4
    >>> b = 6
    >>>
    >>> a + b
    10
    >>>
    >>> a += 2
    >>> a
    6
    >>>
    >>> a = "Python"
    >>> c = "MATLAB"
    >>> z = " > "
    >>> a + z + c
    'Python > MATLAB'

Blocks in other language but indentation in Python

Python uses indentation (white space at the beginning of a line) to delimit blocks of code. Other languages, such as C, use curly braces to accomplish this, but in Python indentation is mandatory; programs won't work without it. As you can see, the statements in the if should be indented.
An else statement follows an if statement, and contains code that is called when the if statement evaluates to False.
As with if statements, the code inside the block should be indented.

x = 4
if x == 5:
   print("Yes")
else:
   print("No")

The short cut to else if is elif, colon is must and second line must be indented as opposed to curly brackets {
An if .. elfi ..elif sequence is replacement for the switch or case in other languages. Python does not have swithc, case.

Boolean Logic

and, or, != , > and < and not operators are used

1 == 1 and 2 == 2 would yield True. In other language instead of and an ampersand sign & is used to represent and.
not 1 == 1, first 1==1 is executed then inverted with not.
if 2!=3: is comparing 2 is not equal to 3

True and False

True and False are special objects in Python. They are of type bool (for Boolean).

>>>
>>> strange_election = True
>>> uncomfortable_choices = True
>>> satisfying_experience = False
>>> strange_election and uncomfortable_choices
True
>>> strange_election and satisfying_experience
False
>>> strange_election and not satisfying_experience
True
We often use these in if statements:

>>>
>>> if strange_election and not satisfying_experience:
...     print('Watching a lot of news')
Watching a lot of news

operators

// floor division, is used for remainder while % modules is used for Quotient.

Operator Precedence or order of operations

== has a higher precedence than or, unless specified with parenthesis.
False == False or True would become True == False and would yield True.
parentheses first, then exponentiation, then multiplication/division, and then addition/subtraction.
1)P-Parentheses 2)E-Exponents 3)M-Multiplication || D-Division 4)A-Addition || S-Subtraction
If same precedence it is from ....
What is the output of -10**2, the answer is -100 because has a higher precedence than -. To avoid this problem do code safely. (-10) 2 will give the desired result

()
Parentheses (grouping)

f(args...)
Function call

x[index:index]
Slicing

x[index]
Subscription

x.attribute
Attribute reference

**
Exponentiation

~x
Bitwise not, it flips the binary number so if (~2) means (~0010) would become (1101) becomes 13.

+x, -x
Positive, negative

*, /, %
Multiplication, division, remainder

+, -
Addition, subtraction

<<, >>
Bitwise shifts

&
Bitwise AND

^
Bitwise XOR

|
Bitwise OR

in, not in, is, is not, <, <=,  >,  >=,
<>, !=, ==  
Comparisons, membership, identity

not x
Boolean NOT

and
Boolean AND

or
Boolean OR

lambda
Lambda expression

Would you write this if not 1 + 1 == y or x == 4 and 7 == 8: in your code ? The answer is no, not at all because it is not clear. Though if given we can take help from precedence order but the wise thing to do is to write it with clear parentheses so when you see your own code after a month you know exactly what you want.
All comparison operations in Python have the same priority.
So the better way is to write it like not (1+1) == y or (x ==4 and 7==8) or ((1+1) == y) or (....)` depending what is your intention.
Coming back t precedence operator, say we need to solve the following
not 1 + 1 == y or x == 4 and 7 == 8
Step 1: question to ask which operator has precedence +, == or , and
- First + Second ==, third not fourth and fifth or. according to data-flair

x =4, y=2
First step: 1 +1 --> not 2 ==y or x ==4 and 7 ==8
Second step: 2 == y == --> not True or True and False
Third step: not --> False or True and False
Fourth step: and ---> False or False --> False

While Loops

While loop runs forever if condition is true, it is like if statement but if only run once.
in while loop break breaks the loop while continue stop execution and go back to the beginning of while loop.
while statements are another example with an initial test followed by an indented block. Here’s an example where we find the largest Fibonacci number less than 1000:

Note: in any loop when you use print() command it automatically print a new line, to avoid this behaviour an keyword end can be used as shown below:

>>> a, b = 0, 1
>>> while a < 1000:
...     print(a, end=',')
...     a, b = b, a+b
...
0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,
---

- Notice the initial while test: while fibonacci < 1000:, followed by the indented while block. Unlike the if statement, Python will continue to run the statements in the while block until the conditional in the while test evaluates to False.

```py
>>>
>>> last_but_1 = 0
>>> fibonacci = 1
>>> while fibonacci < 1000:
...     last_but_2 = last_but_1
...     last_but_1 = fibonacci
...     fibonacci = last_but_2 + last_but_1
>>> print("Largest Fibonacci < 1000 is", last_but_1)
Largest Fibonacci < 1000 is 987

A pass statement can be used if programe requries no action but it is needed for some purpose for example you want to include some logic in future but can not come up with something, so you are saying let it go as it is now we will see what to put here.

def initlog(*args):
    pass   # Remember to implement this!

class MyClass:
    pass

Types

To see the internal type use print(type(a)) you should get <class int>
int(...), float(...), str(...) is used to cast one type to another type.

Augmented Assignment

Augmented assignment is a single statement combining a binary operation and an assignment statement such as +=, -=, etc.

Strings

Concatinaiton : +
Multiplication : * how many times you want that string to be printed
indexing : string indexing starts with [0], so if name = "Sayyed" then name[2] would yield y
negative indexing : name[-1] would give the last letter in a string
Slicing: To get the multiple charachter from a string called slicing: name[1:4] would yield Sayyed because a being 1 while e being at index 4,

//str[start:end] # items start through end-1
//str[start:]    # items start through the rest of the array
//str[:end]      # items from the beginning through end-1
//str[:]

in Operator
String length
Character escaping: \' or \" or \
Basic string method: upper and lower
String formatting: it is taken from c language, it is used to combine a string with another variable without using + as in call

x = "tem's 10 dollara!"

print("Hello it is me! %s" %x) // in place of %s the value of x will be printed, for decimal and numerical %d is used

subject = "English"
name = "John"

print("This is my %s and I like %s , %(subject,name)) // will replace the variable

Strings can have quotes or double quotes, there’s no difference in Python:

    >>>
    >>> first = 'a string'
    >>> second = "b string"
    >>> first + second
    'a stringb string'
    Length of a string:
    >>>
    >>> len(first)
    8

Strings and numbers are different:

    >>>
    >>> number = "9"
    >>> number + 6
    Traceback (most recent call last):
       ...
    TypeError: cannot concatenate 'str' and 'int' objects

We can convert between numbers and strings:

    >>>
    >>> int(number) + 6
    15
    >>> str(9)
    '9'
    However...
>>>
>>> number = "nine"
>>> int(number)
Traceback (most recent call last):
...
ValueError: invalid literal for int() with base 10: 'nine'

Strings are sequences

>>>
>>> # Raises a TypeError
>>> # my_string[1] = 'N'
Adding strings
>>>
>>> my_string + ' with added insight'
'interesting text with added insight'

Like lists, strings are sequences (have length, can be iterated, can index, can slice).

>>>
>>> # Length
>>> len(my_string)
16
>>>
>>> # Iterable
>>> for c in my_string:
...     print(c)
i
n
t
e
r
e
s
t
i
n
g

t
e
x
t

String methods

Strings have lots of interesting methods. In IPython, try tab-complete on a string variable name, followed by a period – e.g. type my_string., followed by the tab key. See also the list of string methods in the Python docs.
One interesting method is replace. It returns a new string that is a copy of the input, but replacing instances of one string with another:

>>>
>>> another_string = my_string.replace('interesting', 'extraordinary')
>>> another_string
'extraordinary text'

Notice that the original string has not changed (it’s immutable):

>>>
>>> my_string
'interesting text'

Use the split method to break a string into a list of strings. By default, split will split the string at any white space (spaces, tab characters or line breaks):

>>>
>>> my_string.split()
['interesting', 'text']
Pass a character to split to split the string at that character:
>>>
>>> another_example = 'one:two:three'
>>> another_example.split(":")
['one', 'two', 'three']

The strip method returns a new string with spaces, tabs and end of line characters removed from the beginning and end of the string:

>>>
>>> # A string with a newline character at the end
>>> my_string = ' a string\n'
>>> my_string
' a string\n'
>>> my_string.strip()
'a string'

Inserting values into strings
See: Inserting values into strings.

String formatting

To combine strins and non strings you have converted the non strings to strings
String formatting provides a more powerful way to embed non-strings within strings. String formatting uses a string's format method to substitute a number of arguments in the string.

# string formatting
nums = [4, 5, 6]
msg = "Numbers: {0} {1} {2}". format(nums[0], nums[1], nums[2])
print(msg)
print("{0}{1}{0}".format("abra", "cad")) # abracadabra

String useful function

print(min(1, 2, 3, 4, 0, 2, 1))
print(max([1, 4, 9, 2, 5, 6, 8]))
print(abs(-99))
print(abs(42))
print(sum([1, 2, 3, 4, 5]))

More string

>>>
>>> example = "mary had a little lamb"
>>> print(example)
mary had a little lamb
String slicing:
>>>
>>> example[0]
'm'
>>>
>>> example[0:4]
'mary'
You can split strings with any character. This breaks up the string, returning a list of strings broken at the separator character:
>>>
>>> example.split(" ")
['mary', 'had', 'a', 'little', 'lamb']
>>>
>>> example.split(" ")[4]
'lamb'
You can split with any character:

>>>
>>> another_example = 'one:two:three'
>>> another_example.split(":")
['one', 'two', 'three']
You can also strip a string. That returns a new string with spaces, tabs and end of line characters removed from the beginning and end:

>>>
>>> my_string = ' a string\n'
>>> my_string
' a string\n'
>>> my_string.strip()
'a string'
Adding strings:

>>>
>>> example + " or two"
'mary had a little lamb or two'
Puting strings into other strings:

>>>
>>> subject_id = "sub1"
>>> print("Subject {} is excellent".format(subject_id))
Subject sub1 is excellent
>>>
>>> age = 29
>>> print("Subject {} is {} years old".format(subject_id, age))
Subject sub1 is 29 years old
You can do more complex formatting of numbers and strings using formatting options after a : in the placeholder for the string. See: https://docs.python.org/3.5/library/string.html#format-examples.

>>>
>>> print("Subject {:02d} is here".format(4))
Subject 04 is here

String formatting when printing taken from C

x = "tem's 10 dollara!

print("Hello it is me! %s" % x) // in place of %s the value of x will be printed, for decimal and numerical %d is used

subject = "English"
name = "John"

print("This is my %s and I like %s , %(subject,name)) // will replace the variable

Built in Data Structure

List are enclosesd in square brackets l=[1,2,"Three"] and ordered sequences of objects
Tuple are enclosed in parenthesis t=(1,2,"Three"), they are also ordered objects but are faster than list and can not be changed thus called immutables
Dicts are enclosed in curly brackets `d={"a":1, "b":2}
Strings are enclosed in single or double quotation marks,can contain only chrarcters and are built using the set() built in function.
Sets are constuected from unique objects as they can not be duplictated s1=set_a([1,2,3,4]) and s2=set_b([2,3,4,5,6]) then s1 | s2 would yield {1,2,3,4,5,6}. They are mutable.
Union s1 |s2
Intersection s1 & s2
Subset s1 < s2 # False or True
Difference s1 - s2 # {1}
Symmetric Difference s1 ^ s2 # {1,5,6}
Frozensets

Lists are mutable in python that is they can be changed using index or can be appended

Array are called list in Python which is not an intelligent thing to do. They are also object and used to store an indexed items in indexed list using square brackets with commas.. Same as in array its index starts with zero.
Unlike other languages Python list can be nested and can store heterogeneous data. `things = ["Hello",90,[2.3, 56], 3.89]. To get to the inside list need to use two brackets. things[2][0] should give 2.3
Lists can be added ( concatinated / inserted) and multiplied in the same way as strings.
To check if an item is in a list, the in operator can be used. It returns True if the item occurs one or more times in the list, and False if it doesn't.
Note that the append method does not return the list, it just changes the list in-place. Python returns None from the append method:

words = ["spam", "egg", "spam", "sausage"]
print("spam" in words)
print("egg" in words)
print("tomato" in words)

A Python list is like a cell array in MATLAB, or a list in R.
To check if an item is not in a list, you can use the not operator in one of the following ways:

nums = [1, 2, 3]
print(not 4 in nums)
print(4 not in nums)
// The above both are same
print(not 3 in nums)
print(3 not in nums)

Appending list item using append method, while len gives the length of an array but it is not a method but a built in function

numbers = [1,2,3]
numbers.append(4)
print(numbers) // [1,2,3,4]
print(len(numbers)) // 4
animals = ['elephant', 'lion', 'tiger', "giraffe"]  # create new list
print(animals)

animals += ["monkey", 'dog']    # add two items to the list
print(animals)

The insert method is similar to append, except that it allows you to insert a new item at any position in the list, as opposed to just at the end.

words = ["Python", "is", "fun", 1]
index = 3
words.insert(index,"!")
print(words) // []

animals = ['elephant', 'lion', 'tiger', "giraffe", "monkey", 'dog']   # create new list
print(animals)

animals[1:3] = ['cat']    # replace 2 items -- 'lion' and 'tiger' with one item -- 'cat'
print(animals)

animals[1:3] = []     # remove 2 items -- 'cat' and 'giraffe' from the list
print(animals)

animals= []  # List is empty now
print(animals)

The index method finds the first occurrence of a list item and returns its index.
If the item isn't in the list, it raises a ValueError.
List slices can also have a third number.

squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
print(squares[::2]) # It simply means that from the start to tthe end with 2 steps
print(squares[2:8:3])

numbers = [12,13,14]
numbers.index(12) // should return 0

A list may be reversed

squares = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
print(squares[1:-1])
# to print in reverse order
print(squares[::-1])

List Comprehensions
List comprehensions are a useful way of quickly creating lists whose contents obey a simple rule. For example, we can do the following:

# to print squrare from 100 numbers
squares= [i**2 for i in range(11)]
print(squares)

# To impose the condition u can use if, here only even numbers are generated.

squares = [i**2 for i in range(11) if i**2/2 == 0]
print(squares)

- This will produce a memory error if not careful `even = [2*i for i in range(10**100)]`

- The `index` method finds the first occurrence of a list item and returns its index.
-  

If the item isn't in the list, it raises a ValueError.

```py
numbers = [12,13,14]
numbers.index(12) // should return 0

iterating over the list
Python indices are 0-based
Indices for Python sequences start at 0. For Python, the first element is at - index 0, the second element is at index 1, and so on:
- Negative indices
Negative numbers as indices count back from the end of the list. For example, use index -1 to return the last element in the list:

>>>
>>> my_list
[9, 4, 7, 0, 8]
>>> my_list[-1]
8

In Python, variable names point to an object.
When you do another_variable = a_variable, you are telling the name another_variable to point to the same object as the name a_variable. When objects are mutable, this can be confusing:

>>>
>>> another_list = my_list
>>> another_list
[9, 99, 7, 0, 8]

my_list points to a list object in memory. When you do another_list
my_list, it tells Python that another_list points to the same object. So, if we modify the list, pointed to by my_list, we also modify the value of another_list, because my_list and another_list point at the same list.

>>>
>>> my_list[1] = 101
>>> another_list
[9, 101, 7, 0, 8]

This is the third from last element:

>>>
>>> my_list[-3]
7

>>> # Can be indexed
>>> my_list[1]
4
>>> # Can be sliced
>>> my_list[0:2]
[9, 4]
>>>
>>> for e in my_list:
...     print(e)
...
9
4
7
0
8
. Our Reference: 115758.002

There are a few more useful functions and methods for lists.

max(list): Returns the list item with the maximum value
min(list): Returns the list item with minimum value
list.count(obj): Returns a count of how many times an item occurs in a list
list.remove(obj): Removes an object from a list
list.reverse(): Reverses objects in a list
The range function creates a sequential list of numbers.The code below generates a list containing all of the integers, up to 10.

list(range(10)) would result in [1,2.......10] 
print(list(range(5))) would result in [0,1,2,3,4]
square = [1,4,9,16,25]
print(squares[1:4]) // 4,9,16

The reason it is converted into list because otherwise it is an object of not type an array but range, thus casting is necessary.
If range is called with one argument, it produces an object with values from 0 to that argument.If it is called with two arguments, it produces values from the first to the second.

print(list(range(1,4))) // [1,2,3]
similarly  range(4) and range(0,4) are equal

range can have a third argument, which determines the interval of the sequence produced. This third argument must be an integer.

print(numbers(list(range(10,22,2)))) would print [10,12,14......20]

Tuples

Tuples are almost identical to lists. The only significant difference between tuples and lists is that tuples cannot be changed: you cannot add, change, or delete elements from the tuple. Tuples are constructed by a comma operator enclosed in parentheses, for example (a, b, c). A single item tuple must have a trailing comma, such as (d,).
Trying to reassign a value in a tuple causes a TypeError.
Tuples can be created without the parentheses, by just separating the values with commas.`my_one = "1","2","3"
An empty tuple is created using an empty parenthesis pair: tpl = ()
Tuples are faster than lists, but they cannot be changed.

- A tuple with two elements:

```python
>>>
>>> two_tuple = (1, 5)
>>> two_tuple
(1, 5)

There is a little complication when making a tuple with one element:

>>>
>>> not_a_tuple = (1)
>>> not_a_tuple
1

This is because Python can’t tell that you meant this to be a tuple, rather than an expression with parentheses round it:

>>>
>>> not_a_tuple = (1 + 5 + 3)
>>> not_a_tuple
9

To tell Python that you mean this to be a length-one tuple, add a comma after the element, and before the closing parenthesis:

>>>
>>> one_tuple = (1,)
>>> one_tuple
(1,)

Dictionary

It is similar to a list, except that you access its values by looking up a key instead of an index. A key can be any string or a number. Dictionaries are enclosed in curly braces e.g. dct = {'key1' : "value1", 'key2' : "value2"}

phone_book = {"John": 123, "Jane": 234, "Jerard": 345} # "John", "Jane" and "Jerard" are keys and numbers are values
print(phone_book)

# Add new item to the dictionary
phone_book["Jill"] = 345
print(phone_book)

# Remove key-value pair from phone_book
del phone_book['John']

print(phone_book['Jane'])

There are a lot of useful methods in dictionaries such as keys() and values(). You can explore the rest using Ctrl + Space after a dict_name followed by a dot.

phone_book = {"John": 123, "Jane": 234, "Jerard": 345}  # create new dictionary
print(phone_book)

# Add new item to the dictionary
phone_book["Jill"] = 456
print(phone_book)

print(phone_book.keys())

print(phone_book.values())

// in 
grocery_list = ["fish", "tomato", 'apples']   # create new list

print("tomato" in grocery_list)    # check that grocery_list contains "tomato" item

grocery_dict = {"fish": 1, "tomato": 6, 'apples': 3}   # create new dictionary

print("fish" in grocery_list)

More adding in dictionary

squares = {1: 1, 2: 4, 3: "error", 4: 16,}
squares[8] = 64
squares[3] = 9
print(squares)

use of in and notin

To determine whether a key is in a dictionary, you can use in and not in, just as you can for a list.

mums = { 1: one,
         2: two, }

print ( 1 in mums) # true
print (4 not in nums) # True

A useful dictionary method is get. It does the same thing as indexing, but if the key is not found in the dictionary it returns another specified value instead ('None', by default).

pairs = { 1: apple,
           "orange": [1,2,3],
        True: False,
        None: "True"
}

print(pairs.get("orange")) # [1,2,3]
print(pairs.get(7)) # None
print(pairs.get(123,"not in this dictionary!")) # not in this dictionary!
# Remove key-value pair from phone_book
del phone_book['John']
print(phone_book['Jane'])

There are a lot of useful methods in dictionaries such as keys() and values(). You can explore the rest using Ctrl + Space after a dict_name followed by a dot.

phone_book = {"John": 123, "Jane": 234, "Jerard": 345}  # create new dictionary
print(phone_book)

# Add new item to the dictionary
phone_book["Jill"] = 456
print(phone_book)

print(phone_book.keys())

print(phone_book.values())

// in 
grocery_list = ["fish", "tomato", 'apples']   # create new list

print("tomato" in grocery_list)    # check that grocery_list contains "tomato" item

grocery_dict = {"fish": 1, "tomato": 6, 'apples': 3}   # create new dictionary

print("fish" in grocery_list)

Iteration

for loop is used to iterate each item in a list or any other collection, while can also be used for this purpose.

numbers = list[1,2,3,4,5,6,7,8,9,10]
counter = len(numbers)
index = 0
while index <= counter - 1:
    do some operations

The easy alternative for the above is for loop which exist in most of the languages, in some language it is used as foreach.

for (... in list):
    do some operation

words = ["hello2","me", "day2"]
for w in words:
    print(w)

# similarly slice can be used

>>>
>>> for i in range(10):
...     print(i)
0
1
2
3
4
5
6
7
8
9
Identation is crucial!

>>>
>>> # for i in range(10):
>>> # print(i)
Watch out for mistakes:

>>>
>>> for i in range(10):
...     j = i + 1
>>> print(j)
10
Ifs and breaks
>>>
>>> a = 2
>>> b = 5
>>> c = a + b
>>> if c < 6:
...     print("yes")
>>>
>>> if c < 6:
...     print("yes")
... else:
...     print("no")
no
>>>
>>> if c < 6:
...     print("yes")
... elif c > 6:
...     print("no")
... else:
...     print("kind of")
no
>>>
>>> if True:
...     print("true, true!")
true, true!
>>>
>>> if False:
...     print("never!")
>>>
>>> for i in range(10):
...     if i == 6:
...         break
...     print(i)
0
1
2
3
4
5
>>>
>>> for i in range(10):
...     if i == 6:
...         continue
...     print(i)
0
1
2
3
4
5
7
8
9

The easy alternative for the above is for loop which exist in most of the languages, in some language it is used as foreach.

for (... in list):
    do some operation

Function

In python a variabl or a funciton must be defined first before it can be used, it is unlike js, but like other strongly typed languages.
Same as in any other language except def is used instead of function or any language specific identifier. similarly return keyword is used like js.
Scoping of variables applies with the block, variable defined in function blocks are local to only that function and are only visible inside the function.
def key word is used to declare function
In python, funciton are objects and can be assinged to any variable and passed like an objects as js.
If an argument passed is a function, then recieving function must know and treat it as a fucniton.
You can import a module or object under a different name using the as keyword. This is mainly used when a module or object has a long or confusing name.

#For example:
#from math import sqrt as square_root

print(square_root(100))

def hello_world(): # function definition
    print("It is a string!")

// invoking a function
hello_world()

// passing a parameter
def foo(x):
    print("x is =" + x)

foo(10)

// To return a variable just use return value

// passing a function as a parameter

def multiply(x,y):
    return x*y

# define another function
def repeat(func,x,y)
    return func(func(x,y),func(x,y))

a =2
b =8

// call multiply
print (repeat(multiply,a,b))

# return a variable just use return value

Classes and Objects in Python

Declaring a class

class MyClass:
    x = 10;
    def grow(num):
        print(num + num)

// Declaring an object of class
maths_class = MyClass()
mathis_class.grow(10) // calling an object

self in Python is equal to thisin js and pointer * in c to give a reference to its method to access the variable inside the function.The self parameter is a Python convention. self is the first parameter passed to any class method. Python will use the self parameter to refer to the object being created.

A constructor method in Python is __init__(self)

Modules and packages

Modules in Python are simply Python files with the .py extension containing Python definitions and statements. Modules can be handy when you want to use your function in a number of programs without copying its definition into each program. Modules are imported from other modules using the import keyword and the file name without an extension. The first time a module is loaded into a running Python script, it is initialized by executing the code in the module once.
A module is written in a following way and saved in a file say my_module.py then this file is imported in another file,when it is imported it is run once.

""" documentation string for module my_module
This module contains hello_world function
"""
def hello_world(name):
    print("Hello, World! My name is %s" % name)

The idea is same, you define some function in one file, and import the whole module in an other file and use its functionality. If only one method is needed to be imported it can be done by using from ..... import ..... where first ... are filled with the module name and the second one is filled with method name.

Built in Module

Python comes with a library of standard modules. Remember that you can use Ctrl + Space after a dot (.) to explore available methods of a module.

Read and Write file

To read file use file = open("filename","r") in read mode. Then read its text using for lines in f.readlines(): and then get it printed using print(lines) and then close th stream file.close()

f = open("input.txt", "r")   # here we open file "input.txt". Second argument used to identify that we want to read file
                             # Note: if you want to write to the file use "w" as second argument

for line in f.readlines():   # read lines
    print(line)

f.close()                   # It's important to close the file to free up any system resources.

f1 = open("input1.txt", "r")

for i in f1.readlines():
    print(i)
    break  // To break after first line

f1.close()

If you open a file using "w" (write) as the second argument, a new empty file will be created. Note that if another file with the same name exists, it will be deleted. If you want to add some content to an existing file, you should use the "a" (append) modifier.

zoo = ['lion', "elephant", 'monkey']

if __name__ == "__main__":
    f = open("output.txt", "a")

    for i in zoo:
        f.write(i)
        f.write(" ")

    f.close()

Files:Write lines to a text file:

>>>
>>> fobj = open("/tmp/important_notes.txt", "wt")
>>> type(fobj)
<...>
>>>
>>> fobj.write("captains log 672828: I had a banana for breakfast.\n")
51
>>> fobj.write("captains log 672829: I should watch less TV.\n")
45
>>> fobj.close()
Read lines from a text file:

>>>
>>> fobj = open("/tmp/important_notes.txt", "rt")
>>> print(fobj.readline())
captains log 672828: I had a banana for breakfast.

>>> print(fobj.readline())
captains log 672829: I should watch less TV.
>>>
>>> print(fobj.readline())
Close a file when you’ve finished with it:

fobj.close()
One way to read all the lines from a text file:

>>>
>>> fobj = open("/tmp/important_notes.txt", "rt")
>>> lines = fobj.readlines()
>>> fobj.close()
>>> len(lines)
2
>>> print(lines[0])
captains log 672828: I had a banana for breakfast.

Working with files

A new way to open files is to use:

with open(filename) as f:
    print(f.read())

# or 
try:
    print(1)
    assert 2+2 ==5
except AssertionError:
    print(3)
except:
    print(4)

Range in Python 3 returns a “range object”. It’s like a list, but isn’t quite a list.

range in Python 3 returns a range object. It is a sequence, and so it is rather like a list [4]. When you use range with one argument, the argument value is the stop index. For example, to make a range object generating the numbers from 0 up to but not including 5:

>>>
>>> my_range = range(5)
>>> my_range
range(0, 5)
You can make a range object into a list by using list:

>>>
>>> list(range(5))
[0, 1, 2, 3, 4]
A range object is a sequence:

>>>
>>> # Has a length
>>> len(my_range)
5
>>> # Is iterable
>>> for e in my_range:
...    print(e)
0
1
2
3
4
>>> # Can be indexed
>>> my_range[1]
1
>>> # Can be sliced
>>> my_range[0:2]
range(0, 2)
>>>
>>> range(10)
range(0, 10)

You can make it into a list by using the list constructor. A constructor is like a function, but it creates a new object, in this case a new object of type list.

>>>
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
You can also set the start element for range:
>>>
>>> list(range(2, 7))
[2, 3, 4, 5, 6]
Use in to ask if a element is a collection of things, such as a range, or a list:
>>>
>>> 5 in range(2, 7)
True
>>> 5 in [2, 5, 7]
True
>>>
>>> 9 in range(2, 7)
    False
    Sets
    Sets are unordered, and unique.

“Unordered” means the order is arbitrary, and Python reserves the right to return the elements in any order it likes:

>>>
>>> our_work = set(["metacognition", "mindwandering", "perception"])
>>> print(our_work)  
{'mindwandering', 'perception', 'metacognition'}
If you want to get a version of the set that is ordered, use sorted, which returns a sorted list:
>>>
>>> sorted(our_work)
['metacognition', 'mindwandering', 'perception']
You can’t index a set, because the indices 0, or 1, or 2 don’t correspond to any particular element (because the set is unordered):
>>>
>>> our_work[0]
Traceback (most recent call last):
...
TypeError: 'set' object does not support indexing
Add to a set with the add method:
>>>
>>> our_work.add("consciousness")
>>> print(our_work)  
{'mindwandering', 'perception', 'metacognition', 'consciousness'}
>>>
>>> our_work.add("consciousness")
>>> print(our_work)  
{'mindwandering', 'perception', 'metacognition', 'consciousness'}
>>> our_work.add("consciousness")
>>> print(our_work)  
{'mindwandering', 'perception', 'metacognition', 'consciousness'}
You can subtract sets:
>>>
>>> competing_labs_work = set(["motor control", "decision making", "memory", "consciousness"])
>>> what_we_should_focus_on = our_work - competing_labs_work
>>> print(what_we_should_focus_on)  
{'mindwandering', 'perception', 'metacognition'}
>>>
>>> what_we_should_avoid = our_work.intersection(competing_labs_work)
>>> print(what_we_should_avoid)
{'consciousness'}
Sets have lengths as well:
>>>
>>> len(what_we_should_focus_on)
3

Set the start element for range by passing two arguments:

>>>
>>> my_range = range(1, 7)
>>> my_range
range(1, 7)
>>> list(my_range)
[1, 2, 3, 4, 5, 6]
Set the step size with a third argument:
>>>
>>> my_range = range(1, 7, 2)
>>> my_range
range(1, 7, 2)
>>> list(my_range)
[1, 3, 5]
One common use of range is to iterate over a sequence of numbers in a for loop:
>>>
>>> for i in range(5):
...    print(i)
...
0
1
2
3
4
Sets
Sets are collections of unique elements, with no defined order. Python reserves the right to order set elements in any way it chooses:
>>>
>>> # Only unique elements collected in the set
>>> my_set = set((5, 3, 1, 3))
>>> my_set  
{1, 5, 3}
Because there is no defined order, you cannot index into a set:

Reusing code

DRY principle : Dont repeat yourself
WET principle : We enjoy typing

Function Calls

print("Hello world!")
range(2, 20)
str(12)
range(10, 20, 3)

Docstrings

(documentation strings) serve a similar purpose to comments, as they are designed to explain code. However, they are more specific and have a different syntax. They are created by putting a multiline string containing an explanation of the function below the function's first line.

 """
  Print a word with an
  exclamation mark following it.
  """

Standard library or built-in module

Python standard library like other languages is installed when Python is installed which contains many useful functions such as string, re, datetime, math, random os, multiprocessing, subproces,socket , email, json, doctest, unittest, pdf, argpares and sys.
It is a very extensive standard library.
Some of the modules in the standard library are written in Python, and some are written in C. Most are available on all platforms, but some are Windows or Unix specific.ly too many. The complete documentation for the standard library is available online at www.python.org
Many third-party Python modules are stored on the Python Package Index (PyPI).
The best way to install these is using a program called pip.
This comes installed by default with modern distributions of Python. If you don't have it, it is easy to install online. Once you have it, installing libraries from PyPI is easy.
Look up the name of the library you want to install, go to the command line (for Windows it will be the Command Prompt), and enter pip install library_name. Once you've done this, import the library and use it in your code.
Using pip is the standard way of installing libraries on most operating systems, but some libraries have prebuilt binaries for Windows. These are normal executable files that let you install libraries with a GUI the same way you would install other programs.

`None` object is used to represent the absence of a value.

It is similar to null in other programming languages. Like other "empty" values, such as 0, [] and the empty string, It is False when converted to a Boolean variable. When entered at the Python console, it is displayed as the empty string.
The None object is returned by any function that doesn't explicitly return anything else.
None is also a special object in Python. By convention, Python often uses None to mean that no valid value resulted from an operation, or to signal that we don’t have a value for a parameter.

>>>
>>> type(None)
<class 'NoneType'>
Unlike most other values in Python, the default display output from None, is nothing:

>>>
>>> None
Equals
As for MATLAB and R, = is for assignment, == is for testing equality.

>>>
>>> a = 1
>>> a
1
>>> a == 1

Common Python Exceptions
- importError: an import fails;
- IndexError: a list is indexed with an out-of-range number;
- NameError: an unknown variable is used;
- SyntaxError: the code can't be parsed properly;
- TypeError: a function is called on a value of an inappropriate type;
- ValueError: a function is called on a value of the correct type, but with an inappropriate value.
- To handle exceptions, and to call code when an exception occurs, you can use a try/except statement.
- The try block contains code that might throw an exception. If that exception occurs, the code in the try block stops being executed, and the code in the except block is run. If no error occurs, the code in the except block doesn't run.
A try statement can have multiple different except blocks to handle different exceptions. Multiple exceptions can also be put into a single except block using parentheses, to have the except block handle all of them.
Try catch finlly is also availale in python, in fact another version is present

Raising Exception

An exception can be raised forcefully by using raise command as raise(ValueError)

Assertion

Programmers often place assertions at the start of a function to check for valid input, and after a function call to check for valid output.

Microsoft Curriculum (image from coursera)

Numbers

There are two types of numbers in Python: integer and floating point. In Python, an integer is an object of type int, and a float is an object of type float.

    >>>
    >>> a = 99
    >>> type(a)
    <class 'int'>
    >>> b = 99.0
    >>> type(b)
    <class 'float'>

You can create ints and floats by using int and float like this:

>>>
>>> float('1')
1.0
>>> float(1)
1.0
>>> int('1')
1
>>> int(1)
1
+, -, * or / on a mix of floats and ints, give floats:
>>>
>>> a + b
198.0
>>> a * b
9801.0

Dividing an int by an int also gives a float – but this is only true by default for Python >= 3 (see [1]):

```>>>

1 / 2 0.5 If you only want the integer part of the division, use //

1 // 2 0 1.0 // 2.0 0.0

- Python has built-in function called round:

round(5.0 / 2.0) 2 The % operator on numbers gives you the remainder of integer division (also known as the modulus):

5 % 2 1

5.0 % 2.0 1.0

---

[//]: #( check the contents below and correct it)

```py
>>> print ('C:This is my \name') # C:This is my 
name # a new line is printed because of `\n` new line escape character, but if you put `r` a raw string it is avoided
>>>print(r'C:This is my \name')

String literal ( a literal means something that is enclosed in quotes) can be span multiple lines. One way is tuse triple quotes '''......''' or `"....."

print("""\
This is a new line:
so is This

the abo ve is a blank line and now some tabs            after tabs some :::no-loc text="":::
""") # end

Note: String literals next to each other are automatically concatenated. "hell" "o" will become hello. It is useful when a long string can be broken into two lines.
But a variable and literal can not be concatenated without a + sign.

x = `hello`
y = x `python` # error you need to use `+`
y = x + `python` # is right.

With string indexing and slicing is allowed like done with lists or arrays.

>>> word[:2]   # character from the beginning to position 2 (excluded)
'Py'
>>> word[4:]   # characters from position 4 (included) to the end
'on'
>>> word[-2:]  # characters from the second-last (included) to the end
'on'



+---+---+---+---+---+---+
 | P | y | t | h | o | n |
 +---+---+---+---+---+---+
 0   1   2   3   4   5   6
-6  -5  -4  -3  -2  -1

Sayyed's blog (Posts about python)

Introduction to matplot

Matplotlib¶

UserGuide¶

What it can generate ?¶

How it is installed ?¶

Its Dependencies ? on [30 July 2020] Following is taken from documentation¶

Concepts behind the matplotlib¶

Using numpy to draw a sin funciton¶

Neuro Imagin with Machine Learning

What is nilearn 1.0 ?¶

Installation 1.1¶

Check installation 1.2¶

001_intro

Jupyter note book basics¶

Running code from different kernel¶

This is html 4 heading

Checking-nipype-Installation

Checking nipype installation¶

Testing the installation¶

Python Virtual Enviorenment

Virtual environments

Learning-pelican

What are we using to build a site.

What guides are available to use.

Building a Basic site.

The contents of pelican conf file

Running your site

Adding first post

Adding functionality for jupyter notebook

Inform your config file about the plugins

Then start your server with invoke livereload.

Adding themes

Adding new plugins to the pelicanconf.py

Customising the views

Adding pages to the site

Creating a sample notebook

python-basics

An introductory book on Python by Allen Think Python

Languages primitive data

Rules for Variable names

Blocks in other language but indentation in Python

Boolean Logic

True and False

operators

Operator Precedence or order of operations

While Loops

Types

Augmented Assignment

Strings

Strings can have quotes or double quotes, there’s no difference in Python:

Strings and numbers are different:

We can convert between numbers and strings:

Strings are sequences

String methods

String formatting

String useful function

String formatting when printing taken from C

Built in Data Structure

Lists are mutable in python that is they can be changed using index or can be appended

There are a few more useful functions and methods for lists.

Tuples

Dictionary

Iteration

Function

Classes and Objects in Python

Modules and packages

Built in Module

Read and Write file

Files:Write lines to a text file:

Working with files

Range in Python 3 returns a “range object”. It’s like a list, but isn’t quite a list.

Set the start element for range by passing two arguments:

Reusing code

Function Calls

Docstrings

Standard library or built-in module

None object is used to represent the absence of a value.

Common Python Exceptions

Raising Exception

Matplotlib ¶

UserGuide ¶

Using `numpy` to draw a sin funciton¶

Checking `nipype` installation¶

Adding functionality for `jupyter notebook`

Then start your server with `invoke livereload`.

`None` object is used to represent the absence of a value.