# Path: cmepy/model.py
# PROPENSITIES = 'propensities'
# TRANSITIONS = 'transitions'
# NAME = 'name'
# INITIAL_STATE = 'initial_state'
# SHAPE = 'shape'
# SPECIES_NAMES = 'species'
# SPECIES_COUNTS = 'species_counts'
# REACTION_NAMES = 'reactions'
# ENTRIES = frozenset([
#     PROPENSITIES,
#     TRANSITIONS,
#     NAME,
#     INITIAL_STATE,
#     SHAPE,
#     SPECIES_NAMES,
#     SPECIES_COUNTS,
#     REACTION_NAMES,
# ])
# class Model(dict):
#     def __init__(self, **entries):
#     def _ensure_shape_invariance(self, x):
#     def __getattribute__(self, attrname):
#     def validate(self):
# def create(**entries):
# def raise_error(error_type, descr, value, message):
# def must_be_within_range(value, descr=None, min_value=None, max_value=None):
# def must_have_length(value, descr=None, min_len=None, max_len=None):
# def must_be_callable(value, descr=None):
# def must_be_string_like(value, descr=None):
# def must_be_int_like(value, descr=None, min_value=None, max_value=None):
# def must_be_mapping(value, descr=None):
# def must_contain_item(value, descr, item):
# def validate_name(value):
# def validate_propensities(value):
# def validate_species_names(value):
# def validate_species_counts(value):
# def validate_transitions(value):
# def validate_initial_state(value):
# def validate_shape(value):
# def validate_model(m):


import math
import numpy
    import pylab
from cmepy import model
    from cmepy import solver, recorder
        species = (
            'A',
            'B',
        ),
        species_counts = (
            species_count_a,
            species_count_b,
        ),
        shape = shape,
        initial_state = (10, 10)
    )

def create_time_dependencies():
    """
    create time dependencies for the competing clonotypes model
    """
    # 0-th and 2-nd reactions are scaled by the following
    # time dependent factor
    return {frozenset([0, 2]) : lambda t : math.exp(-0.1*t)}

def main():
    """
    Solves the competing clonotypes model and plots results
    """
    
    
    
    m = create_model()
    
    s = solver.create(

# Path: cmepy/model.py
# PROPENSITIES = 'propensities'
# TRANSITIONS = 'transitions'
# NAME = 'name'
# INITIAL_STATE = 'initial_state'
# SHAPE = 'shape'
# SPECIES_NAMES = 'species'
# SPECIES_COUNTS = 'species_counts'
# REACTION_NAMES = 'reactions'
# ENTRIES = frozenset([
#     PROPENSITIES,
#     TRANSITIONS,
#     NAME,
#     INITIAL_STATE,
#     SHAPE,
#     SPECIES_NAMES,
#     SPECIES_COUNTS,
#     REACTION_NAMES,
# ])
# class Model(dict):
#     def __init__(self, **entries):
#     def _ensure_shape_invariance(self, x):
#     def __getattribute__(self, attrname):
#     def validate(self):
# def create(**entries):
# def raise_error(error_type, descr, value, message):
# def must_be_within_range(value, descr=None, min_value=None, max_value=None):
# def must_have_length(value, descr=None, min_len=None, max_len=None):
# def must_be_callable(value, descr=None):
# def must_be_string_like(value, descr=None):
# def must_be_int_like(value, descr=None, min_value=None, max_value=None):
# def must_be_mapping(value, descr=None):
# def must_contain_item(value, descr, item):
# def validate_name(value):
# def validate_propensities(value):
# def validate_species_names(value):
# def validate_species_counts(value):
# def validate_transitions(value):
# def validate_initial_state(value):
# def validate_shape(value):
# def validate_model(m):


# Path: cmepy/models/burr08.py
import math
import numpy
    import pylab
from cmepy import model
    from cmepy import solver, recorder

        species = (
            'A',
            'B',
        ),
        species_counts = (
            species_count_a,
            species_count_b,
        ),
        shape = shape,
        initial_state = (10, 10)
    )

def create_time_dependencies():
    """
    create time dependencies for the competing clonotypes model
    """
    # 0-th and 2-nd reactions are scaled by the following
    # time dependent factor
    return {frozenset([0, 2]) : lambda t : math.exp(-0.1*t)}

def main():
    """
    Solves the competing clonotypes model and plots results
    """
    
    
    
    m = create_model()
    
    s = solver.create(

        model = m,