Source code for memote.experimental.growth

# -*- coding: utf-8 -*-

# Copyright 2018 Novo Nordisk Foundation Center for Biosustainability,
# Technical University of Denmark.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Provide an interface for growth experiments."""

from __future__ import absolute_import

import logging

from pandas import DataFrame

from memote.experimental.experiment import Experiment


__all__ = ("GrowthExperiment",)

LOGGER = logging.getLogger(__name__)


class GrowthExperiment(Experiment):
    """Represent a growth experiment."""

    SCHEMA = "growth.json"

    def __init__(self, **kwargs):
        """
        Initialize a growth experiment.

        Parameters
        ----------
        kwargs

        """
        super(GrowthExperiment, self).__init__(**kwargs)

    def load(self, dtype_conversion=None):
        """
        Load the data table and corresponding validation schema.

        Parameters
        ----------
        dtype_conversion : dict
            Column names as keys and corresponding type for loading the data.
            Please take a look at the `pandas documentation
            <https://pandas.pydata.org/pandas-docs/stable/io.html#specifying-column-data-types>`__
            for detailed explanations.

        """
        if dtype_conversion is None:
            dtype_conversion = {"growth": str}
        super(GrowthExperiment, self).load(dtype_conversion=dtype_conversion)
        self.data["growth"] = self.data["growth"].isin(self.TRUTHY)

    def evaluate(self, model):
        """Evaluate in silico growth rates."""
        with model:
            if self.medium is not None:
                self.medium.apply(model)
            if self.objective is not None:
                model.objective = self.objective
            model.add_cons_vars(self.constraints)
            growth = list()
            for row in self.data.itertuples(index=False):
                with model:
                    exchange = model.reactions.get_by_id(row.exchange)
                    if bool(exchange.reactants):
                        exchange.lower_bound = -row.uptake
                    else:
                        exchange.upper_bound = row.uptake
                    growth.append(model.slim_optimize() >= self.minimal_growth_rate)
        return DataFrame({"exchange": self.data["exchange"], "growth": growth})