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    "info": {
        "author": "Leander D. Goldbach",
        "author_email": "l.d.goldbach@students.uu.nl",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "\n# gpvolve\n\n*A python package for extracting tevolutionary trajectories from genotype-phenotype-maps*\n\nA Python API for the simulation and analysis of evolution in genotype-phenotype space.\nYou can use this library to:\n\n   1. Build a markov state model from a genotype-phenotype-map.\n   2. Find clusters of genotypes that represent metastable states of the system, using PCCA+.\n   3. Compute fluxes and pathways between pairs of genotypes and/or clusters of interest, using Transition Path Theory.\n   4. Visualize the outputs of all of the above.\n\n## Basic Example\n\nBuild a Markov model from a genotype-phenotype map.\n```python\n\n# Import base class, Transition Path Theory class and functions for building Markov Model.\nfrom gpvolve import GenotypePhenotypeMSM, TransitionPathTheory, linear_skew, mccandlish, find_max\n\n# Import visualization tool.\nfrom gpvolve.visualization import plot_network\n\n# Import GenotypePhenotypeMap class for handling genotype-phenotype data.\nfrom gpmap import GenotypePhenotypeMap\n\n# Helper functions.\nfrom scipy.sparse import dok_matrix\n\n# Genotype-phenotype map data.\nwildtype = \"AAA\"\ngenotypes = [\"AAA\", \"AAT\", \"ATA\", \"TAA\", \"ATT\", \"TAT\", \"TTA\", \"TTT\"]\nphenotypes = [0.8, 0.81, 0.88, 0.89, 0.82, 0.82, 0.95, 1.0]\n\n# Instantiate Markov model class.\ngpm = GenotypePhenotypeMap(wildtype=wildtype,\n                            genotypes=genotypes,\n                            phenotypes=phenotypes)\n\n\n# Instantiate a evolutionary Markov State Model from the genotype-phenotype map.\ngpmsm = GenotypePhenotypeMSM(gpm)\n```\nApply an evolutionary model to describe transitions between genotypes.\n```python\n# Map fitnesses to phenotypes.\ngpmsm.apply_selection(fitness_function=linear_skew, selection_gradient=1)\n\n# Build Markov State Model based on 'mccandlish' fixation probability function.\ngpmsm.build_transition_matrix(fixation_model=mccandlish, population_size=100)\n\n# Find global fitness peak.\nfitness_peak = find_max(gpmsm=gpmsm, attribute='fitness')\n```\n\nCalculate and plot the fluxes between wildtype and triple mutant.\n```python\n\n# Compute fluxes from wildtype to fitness peak.\nfluxes = TransitionPathTheory(gpmsm, source=[0], target=[fitness_peak])\n\n# Normalize flux.\nnorm_fluxes = fluxes.net_flux/fluxes.total_flux\n\n# Plot the network and the fluxes\nfig, ax = plot_network(gpmsm,\n                        flux=dok_matrix(norm_fluxes),\n                        edge_labels=True,\n                        colorbar=True)\n\n```\n\n![](img/basic_example.png)\n\n\n## Install\n\nTo install from PyPI:\n```\npip install gpvolve\n```\n\nTo install a development version:\n```\ngit clone https://github.com/harmslab/gpvolve\ncd gpvolve\npip install  -e .\n```\n\n\n\n\n",
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