Source code for cameo.util

# Copyright 2013 Novo Nordisk Foundation Center for Biosustainability, DTU.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import, print_function

import colorsys
import inspect
import itertools
import logging
import math
import platform
import re
from collections import OrderedDict
from datetime import datetime
from functools import partial
from itertools import islice
from time import time
from uuid import uuid1

import numpy
import pandas
import pkg_resources
from cobra.util.context import HistoryManager
from numpy.random import RandomState

logger = logging.getLogger(__name__)

_BIOMASS_RE_ = re.compile("biomass", re.IGNORECASE)

[docs]class frozendict(dict): def __init__(self, iterable, **kwargs): super(frozendict, self).__init__(iterable, **kwargs)
[docs] def popitem(self): raise AttributeError("'frozendict' object has no attribute 'popitem")
[docs] def pop(self, k, d=None): raise AttributeError("'frozendict' object has no attribute 'pop")
def __setitem__(self, key, value): raise AttributeError("'frozendict' object has no attribute '__setitem__")
[docs] def setdefault(self, k, d=None): raise AttributeError("'frozendict' object has no attribute 'setdefault")
def __delitem__(self, key): raise AttributeError("'frozendict' object has no attribute '__delitem__") def __hash__(self): return hash(tuple(sorted(self.items())))
[docs] def update(self, E=None, **F): raise AttributeError("'frozendict' object has no attribute 'update")
[docs]def float_ceil(val, decimals=0): """ Like ceil but the number of decimals can be set. Equivalent of $$round(val + 1e^{-decimals}/2, decimals)$$ val: float, numpy.array The initial value. decimals: int The number of decimal places. Returns ------- float, numpy.array """ aux = math.pow(10, -decimals) / 2 return numpy.round(val + aux, decimals)
[docs]def float_floor(val, decimals=0): """ Like floor but the number of decimals can be set. Equivalent of $$round(val - 1e^{-decimals}/2, decimals)$$ val: float, numpy.array The initial value. decimals: int The number of decimal places. Returns ------- float, numpy.array """ aux = math.pow(10, -decimals) / 2 return numpy.round(val - aux, decimals)
[docs]class ProblemCache(object): """ Variable and constraint cache for models. To be used in complex methods that require many extra variables and constraints when one must run simulations with the same method many times. It allows rollback to the previous state in case one iteration fails to build the problem or generates an invalid state. """ def __init__(self, model): self.history_manager = None self._model = model self.variables = {} self.constraints = {} self.objective = None self.original_objective = model.solver.objective self._contexts = [HistoryManager()] self.transaction_id = None
[docs] def begin_transaction(self): """ Creates a time point. If rollback is called, the variables and constrains will be reverted to this point. """ self._contexts.append(HistoryManager())
@property def model(self): return self._model def _append_constraint(self, constraint_id, create, *args, **kwargs): constraint = self.constraints[constraint_id] = create(self._model, constraint_id, *args, **kwargs) assert constraint_id in self.constraints self._model.solver.add(constraint) def _remove_constraint(self, constraint_id): constraint = self.constraints.pop(constraint_id) self._model.solver.remove(constraint) def _append_variable(self, variable_id, create, *args, **kwargs): variable = self.variables[variable_id] = create(self._model, variable_id, *args, **kwargs) assert variable_id in self.variables self._model.solver.add(variable) def _remove_variable(self, variable_id): variable = self.variables.pop(variable_id) self._model.solver.remove(variable) def _rebuild_variable(self, variable): (type, lb, ub, name) = variable.type,, variable.ub, def rebuild(): self._model.solver.remove(variable) new_variable = self.model.solver.interface.Variable(name, lb=lb, ub=ub, type=type) self.variables[name] = variable self._model.solver.add(new_variable, sloppy=True) return rebuild
[docs] def add_constraint(self, constraint_id, create, update, *args, **kwargs): """ Adds a new cached constraint. The create and update functions must have the following signatures: >>> create(model, constraint_id, *args) >>> update(model, constraint, *args) "args" in the first example must match args on the second example. Parameters ---------- constraint_id : str The identifier of the constraint create : function A function that creates an optlang.interface.Constraint update : function a function that updates an optlang.interface.Constraint """ context = self._contexts[-1] if constraint_id not in self.constraints: self._append_constraint(constraint_id, create, *args, **kwargs) context(partial(self._remove_constraint, constraint_id)) elif update is not None: update(self._model, self.constraints[constraint_id], *args, **kwargs) assert constraint_id in self.constraints
[docs] def add_variable(self, variable_id, create, update, *args, **kwargs): """ Adds a new cached variable. The create and update functions must have the following signatures: >>> create(model, variable_id, *args) >>> update(model, variable, *args) "args" in the first example must match args on the second example. Parameters ---------- variable_id : str The identifier of the constraint create : function A function that creates an optlang.interface.Variable update : function a function that updates an optlang.interface.Variable """ context = self._contexts[-1] if variable_id not in self.variables: self._append_variable(variable_id, create, *args, **kwargs) context(partial(self._remove_variable, variable_id)) elif update is not None: # rebuild_function = self._rebuild_variable(self.variables[variable_id]) update(self._model, self.variables[variable_id], *args, **kwargs) # context(rebuild_function) assert variable_id in self.variables
[docs] def add_objective(self, create, update, *args): context = self._contexts[-1] if self.objective is None: previous_objective = self._model.solver.objective self.model.solver.objective = self.objective = create(self._model, *args) context(partial(setattr, self._model.solver, 'objective', previous_objective)) elif update: previous_objective = self._model.solver.objective self.model.solver.objective = self.objective = update(self._model, *args) context(partial(setattr, self._model.solver, 'objective', previous_objective))
[docs] def reset(self): """ Removes all constraints and variables from the cache. """ variables = list(self.variables.keys()) constraints = list(self.constraints.keys()) while len(self._contexts) > 0: manager = self._contexts.pop() manager.reset() self._contexts.append(HistoryManager()) assert all(var_id not in self._model.solver.variables for var_id in variables) assert all(const_id not in self._model.solver.constraints for const_id in constraints) self.variables = {} self.constraints = {} self._model.objective = self.original_objective self.objective = None
[docs] def rollback(self): """ Returns to the previous transaction start point. """ if len(self._contexts) < 2: raise RuntimeError("Start transaction must be called before rollback") self._contexts.pop().reset()
def __enter__(self): """ Allows problem cache to be used with a _with_ statement. Examples -------- You want to run room/lmoma for every single knockout. >>> with ProblemCache(model) as cache: >>> for reaction in reactions: >>> reaction.knock_out() >>> result = lmoma(model, reference=reference, cache=cache) Returns ------- ProblemCache returns itself """ return self def __exit__(self, exc_type, exc_val, exc_tb): self.reset()
[docs]class RandomGenerator(object): def __init__(self, seed=None): self._random = RandomState(seed=seed)
[docs] def seed(self, seed): self._random = RandomState(seed=seed)
[docs] def random(self): return self._random.rand()
[docs] def randint(self, a, b=None): if b is None: b = a a = 0 r = self._random.randint(a, high=b, size=1) return r[0]
[docs] def sample(self, population, k): if k == 0: return [] return list(self._random.choice(population, size=k, replace=False))
def __getattr__(self, attr): return getattr(self._random, attr) def __getstate__(self): return {'_random': self._random} def __setstate__(self, d): self._random = d['_random']
[docs] def uniform(self, low=0.0, high=1.0, size=None): return self._random.uniform(low, high, size)
[docs]class Singleton(object): """ Singleton class to be extended """ _instance = None def __new__(cls, *args, **kwargs): if not cls._instance: cls._instance = super(Singleton, cls).__new__(cls) return cls._instance
[docs]class AutoVivification(dict): """Implementation of perl's autovivification feature. Checkout""" def __getitem__(self, item): try: return dict.__getitem__(self, item) except KeyError: value = self[item] = type(self)() return value
[docs]class TimeMachine(object): """Travel back and forth in time.""" def __init__(self): super(TimeMachine, self).__init__() self.history = OrderedDict() def __call__(self, do=None, undo=None, bookmark=None): output = do() current_time = time() if bookmark is None: entry_id = uuid1() else: entry_id = bookmark # make sure that entry is added to the end of history self.history.pop(entry_id, None) self.history[entry_id] = {'unix_epoch': current_time, 'undo': undo, 'redo': do} return entry_id, output def __str__(self): info = '\n' for item in self.history.items(): info += self._history_item_to_str(item) return info def __enter__(self): return self def __exit__(self, type, value, traceback): self.reset() @staticmethod def _history_item_to_str(item): info = '' uuid, entry = item info += datetime.fromtimestamp(entry['unix_epoch']).strftime('%Y-%m-%d %H:%M:%S') + '\n' undo_entry = entry['undo'] try: # partial (if .keywords is None print {} instead) elements = undo_entry.func, undo_entry.args, undo_entry.keywords or {} info += 'undo: ' + ' '.join([str(elem) for elem in elements]) + '\n' except AttributeError: # normal python function info += 'undo: ' + undo_entry.__name__ + '\n' redo_entry = entry['redo'] try: elements = redo_entry.func, redo_entry.args, redo_entry.keywords or {} # partial info += 'redo: ' + ' '.join([str(elem) for elem in elements]) + '\n' except AttributeError: info += 'redo: ' + redo_entry.__name__ + '\n' return info
[docs] def undo(self, bookmark=None): if bookmark is None: try: (uuid, entry) = self.history.popitem() entry['undo']() except KeyError: # history is empty pass elif bookmark in list(self.history.keys()): uuid = False while uuid is not bookmark: (uuid, entry) = self.history.popitem() entry['undo']() else: raise Exception( 'Provided bookmark %s cannot be found in the time machine.')
[docs] def redo(self): raise NotImplementedError
[docs] def reset(self): if self.history: # history is not empty self.undo(bookmark=list(self.history.keys())[0])
[docs]class Timer(object): """Taken from""" def __init__(self, name=None): = name def __enter__(self): self.tstart = time() def __exit__(self, type, value, traceback): if print('[%s]' %, end=' ') print('Elapsed: %s' % (time() - self.tstart))
[docs]class IntelliContainer(object): def __init__(self, **kwargs): self._dict = dict(**kwargs) def __getattr__(self, value): return self._dict.get(value) def __setitem__(self, key, value): self._dict[key] = value def __iter__(self): return iter(self._dict.values()) def __dir__(self): return list(self._dict.keys())
[docs]def inheritdocstring(name, bases, attrs): """Use as metaclass to inherit class and method docstrings from parent. Adapted from""" temp = type('temporaryclass', bases, {}) if '__doc__' not in attrs or not attrs["__doc__"]: # create a temporary 'parent' to (greatly) simplify the MRO search for cls in inspect.getmro(temp): if cls.__doc__ is not None: attrs['__doc__'] = cls.__doc__ break for attr_name, attr in attrs.items(): if not attr.__doc__: for cls in inspect.getmro(temp): try: if getattr(cls, attr_name).__doc__ is not None: attr.__doc__ = getattr(cls, attr_name).__doc__ break except (AttributeError, TypeError): continue return type(name, bases, attrs)
[docs]def partition_(lst, n): """Partition a list into n bite size chunks.""" division = len(lst) / float(n) return [lst[int(round(division * i)): int(round(division * (i + 1)))] for i in range(n)]
[docs]def partition(ite, n): """Partition an iterable into n bite size chunks.""" try: length = len(ite) except TypeError: ite = list(ite) length = len(ite) division = length / float(n) iterator = iter(ite) return [list(islice(iterator, 0, round(division * (i + 1)) - round(division * i))) for i in range(n)]
[docs]def flatten(input_list): return [item for sublist in input_list for item in sublist]
[docs]def generate_colors(n): hsv_tuples = [(v * 1.0 / n, 0.5, 0.5) for v in range(n)] color_map = {} for i in range(n): rgb = colorsys.hsv_to_rgb(*hsv_tuples[i]) color = tuple(int(channel * 256) for channel in rgb) color_map[i] = '#%02x%02x%02x' % color return color_map
[docs]def memoize(function, memo={}): def wrapper(*args): if args in memo: return memo[args] else: rv = function(*args) memo[args] = rv return rv return wrapper
[docs]def get_system_info(): package_info = list() for dist in pkg_resources.working_set: req = str(dist.as_requirement()) package_info.append(req) return dict(package_info=package_info, platform=platform.platform(), machine=platform.machine(), system=platform.system())
[docs]def in_ipnb(): """ Check if it is running inside an IPython Notebook (updated for new notebooks) """ return
[docs]def str_to_valid_variable_name(s): """Adapted from""" # Remove invalid characters s = re.sub('[^0-9a-zA-Z_]', '_', s) # Remove leading characters until we find a letter or underscore s = re.sub('^[^a-zA-Z_]+', '', s) return s
[docs]def zip_repeat(long_iter, short_iter): """ Zips two iterable objects but repeats the second one if it is shorter than the first one. Parameters ---------- long_iter: iterable short_iter: iterable Returns ------- generator """ for i, j in zip(long_iter, itertools.cycle(short_iter)): yield i, j
[docs]def pick_one(iterable): """ Helper function that returns an element of an iterable (it the iterable is ordered this will be the first element). """ it = iter(iterable) return next(it)
[docs]def reduce_reaction_set(reaction_set, groups): """ Reduces a set of reactions according to a number of groups of reactions. The reduction will be performed so that the resulting set will contain no more than 1 reaction from each group. Reactions that are not in any of the groups will remain in the set. Parameters ---------- reaction_set: Set groups: Iterable of sets Returns ------- Set """ reaction_set = set(reaction_set) # Make a shallow copy result = [] for group in groups: intersection = group & reaction_set if intersection: # If any elements of group are in reaction_set, add one of these to result result.append(pick_one(intersection)) reaction_set = reaction_set - intersection result = set(result) | reaction_set # Add the remaining reactions to result return result
[docs]def decompose_reaction_groups(reaction_groups, reactions): """ reaction_groups : list A list with dictionaries (element: relative_coefficient) reactions : list, set, tuple A collection of reactions. Returns ------- list A list of all possible group substitutions. """ to_keep = [] to_replace = {} for element in reactions: for g in reaction_groups: if element in g: to_replace[element] = g.keys() break if element not in to_replace: to_keep.append(element) return (list(combo) + to_keep for combo in itertools.product(*to_replace.values()))
[docs]def current_solver_name(model): """Give a string representation for an optlang interface. Parameters ---------- model : cobra.Model A model Returns ------- string The name of the interface as a string """ interface = model.solver.interface.__name__ return re.sub(r"optlang.|.interface", "", interface)