Michael Osthege michaelosthege

## compress_colors.py
# MIT License

# Copyright (c) 2022 Michael Osthege

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

## mypy_groupby.py
"""
Reformats piped mypy output to group by the error code.

Author:  Michael Osthege
License: MIT

Usage
-----
mypy -p mypackage --show-error-codes | python mypy_groupby.py
"""

## _R0_regions.ipynb

      
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                michaelosthege
                / _R0_regions.ipynb
            
            
              Last active
              August 12, 2020 19:21
            
              
                Analysis of Rt.live model estimates of R0 - a comparison between US regions (alternative link: https://nbviewer.jupyter.org/gist/michaelosthege/6cd14970dd789247176c4d4a1dd28051/_R0_regions.ipynb) 
              
          
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## install-pymc3.bat
conda create -n pm3 python=3.7 pandas jupyter matplotlib mkl-service libpython m2w64-toolchain coverage xlrd openpyxl scipy -y
activate pm3

# install last release from PyPI
pip install pymc3
# or latest version directly from master:
pip install git+https://github.com/pymc-devs/pymc3
# or from master in editable mode:
git clone https//github.com/pymc-devs/pymc3 pymc3
cd pymc3

## integration_op.py
import base64
import hashlib
import theano
import theano.tensor as tt


def make_hashable(obj):
    """Makes tuples, lists, dicts, sets and frozensets hashable."""
    if isinstance(obj, (tuple, list)):
        return tuple((make_hashable(e) for e in obj))

## lorenz.py
"""Sampling parameters of a lorenz attractor.

The forward pass integrates the lorenz attractor ODE system using
tt.scan with a Runge-Kutta integrator.  The predicted high-resolution
timecourse is interpolated down so it can be compared to low-density
observations.
"""
import abc
import numpy
import pymc3

## pandas_snippets.py

# saving a DataFrame to an Excel spreadsheet
writer = pandas.ExcelWriter(fp_xlsx)
df.to_excel(writer, 'Sheetname')
writer.save()

## Leigh1968_harelynx.csv

          
            year
            hare
            lynx

            
              1847
              21000
              49000

            
              1848
              12000
              21000

            
              1849
              24000
              9000

            
              1850
              50000
              7000

            
              1851
              80000
              5000

            
              1852
              80000
              5000

            
              1853
              90000
              11000

            
              1854
              69000
              22000

            
              1855
              80000
              33000

## linear_interpolation.py
import numpy
from matplotlib import pyplot


def interpolate(x_fix, y_fix, x_var):
    x_repeat = numpy.tile(x_var[:,None], (len(x_fix),))
    distances = numpy.abs(x_repeat - x_fix)

    x_indices = numpy.searchsorted(x_fix, x_var)

## theano_numpy_ODE.py
import theano
import theano.tensor as T
import numpy
import h5py
import os
import time


def rungekuttastep(h, y, fprime, *theta):
    k1 = h*fprime(y,*theta)
	# MIT License

	# Copyright (c) 2022 Michael Osthege

	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is
	# furnished to do so, subject to the following conditions:
	"""
	Reformats piped mypy output to group by the error code.

	Author: Michael Osthege
	License: MIT

	Usage
	-----
	mypy -p mypackage --show-error-codes \| python mypy_groupby.py
	"""
	conda create -n pm3 python=3.7 pandas jupyter matplotlib mkl-service libpython m2w64-toolchain coverage xlrd openpyxl scipy -y
	activate pm3

	# install last release from PyPI
	pip install pymc3
	# or latest version directly from master:
	pip install git+https://github.com/pymc-devs/pymc3
	# or from master in editable mode:
	git clone https//github.com/pymc-devs/pymc3 pymc3
	cd pymc3
	import base64
	import hashlib
	import theano
	import theano.tensor as tt


	def make_hashable(obj):
	"""Makes tuples, lists, dicts, sets and frozensets hashable."""
	if isinstance(obj, (tuple, list)):
	return tuple((make_hashable(e) for e in obj))
	"""Sampling parameters of a lorenz attractor.

	The forward pass integrates the lorenz attractor ODE system using
	tt.scan with a Runge-Kutta integrator. The predicted high-resolution
	timecourse is interpolated down so it can be compared to low-density
	observations.
	"""
	import abc
	import numpy
	import pymc3

	# saving a DataFrame to an Excel spreadsheet
	writer = pandas.ExcelWriter(fp_xlsx)
	df.to_excel(writer, 'Sheetname')
	writer.save()
year	hare	lynx
1847	21000	49000
1848	12000	21000
1849	24000	9000
1850	50000	7000
1851	80000	5000
1852	80000	5000
1853	90000	11000
1854	69000	22000
1855	80000	33000
	import numpy
	from matplotlib import pyplot


	def interpolate(x_fix, y_fix, x_var):
	x_repeat = numpy.tile(x_var[:,None], (len(x_fix),))
	distances = numpy.abs(x_repeat - x_fix)

	x_indices = numpy.searchsorted(x_fix, x_var)
	import theano
	import theano.tensor as T
	import numpy
	import h5py
	import os
	import time


	def rungekuttastep(h, y, fprime, *theta):
	k1 = hfprime(y,theta)