michaelosthege

# 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:

michaelosthege

"""
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
"""

michaelosthege

michaelosthege

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

michaelosthege

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))

michaelosthege

"""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

michaelosthege

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

michaelosthege

michaelosthege

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)

michaelosthege

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)