Module epispot.models
The epispot.models classes store different types of epidemiological
models in a compact form useful for integration. Models can be
differentiated, integrated, and examined by calling class methods.
Additionally, epispot models are portable—they can be used throughout
the package to generate plots, run predictions, etc.
Expand source code
"""
The `epispot.models` classes store different types of epidemiological
models in a compact form useful for integration. Models can be
differentiated, integrated, and examined by calling class methods.
Additionally, epispot models are portable—they can be used throughout
the package to generate plots, run predictions, etc.
"""
from copy import deepcopy
from . import warnings
from . import np
class Model:
"""
The base model class for
[compartmental models](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology).
Compartmental models are models composed of various sub-models,
known as "compartments." For example, the common SIR model is an
example of a compartmental model with the Susceptible, Infected,
and Removed compartments.
.. versionadded:: v3.0.0-alpha-2
"""
def __init__(self, initial_population, comps=None, comp_map=None,
matrix=None):
"""
Initialize the `Model` class; all optional parameters can be
added through the `epispot.models.Model.add` method.
## **Parameters**
`initial_population`: Population at time zero
`comps=None`: List of compartment classes to create the model
`comp_map=None`: Map of how all the compartments connect.
The map should consist of a list of lists.
Each sublist represents the connections of the
corresponding compartment in the `comps` list.
This sublist should contain the indices of each
of the compartments in `comps` that it connects to.
If the compartment does not connect to any other
compartments, leave the sublist blank.
`matrix=None`: Rate and probability matrix describing the
exchange rates between compartments. Like `map`,
this is a list of lists. Unlike `map`, however,
this matrix must not skip entries (i.e. no blank
sublists). Each sublist should contain rate and
probability information in a tuple for every
compartment. If the information is not
necessary, use the tuple `(1, 1)` or `None` as a
placeholder.
## **Example**
Let's say we have three compartments `A`, `B`, and `C`.
These three compartments connect as shown below:
```text
┌───────────────────┐
│ ▼
┌───┐ ┌───┐ ┌───┐
│ A │ ──▶ │ C │ ──▶ │ B │
└───┘ └───┘ └───┘
```
To create a compartmental model with these three classes, use:
```python
comps = [A, B, C],
map = [
[1, 2], # A
[], # B
[1] # C
],
matrix = [
[None, (1/2, 1/3), (1/2, 1/3)], # A[A, B, C]
[None, None, None], # B[A, B, C]
[None, (1/2, 1/3), None] # C[A, B, C]
]
```
This creates a compartmental model where all the connections
have a probability of `1/2` and rate of `1/3`.
## **Additional Notes**
This feature is currently only released to alpha versions of
epispot. This will likely be used (with minor changes) in the
full release of epispot v3. For more information about this
feature, or if you're interested in giving feedback, see the
discussion
[here on GitHub](https://github.com/epispot/epispot/issues/73).
.. warning::
As this is currently an alpha feature, the new compartmental
models in epispot are subject to change.
"""
self.initial_population = initial_population
self.compartments = comps
if self.compartments:
self.names = [comp.name for comp in self.compartments]
self.map = comp_map
self.matrix = matrix
self.aggregated = None
self.compiled = False
def compile(self, custom=False):
"""
Run a series of checks of the model and initialize some
class-wide variables.
## **Parameters**
`custom=False`: Flag indicating if the model is using custom
compartments. If this is `False` (the default),
all compartment compatibility checks will have
to pass or an error will be raised. If this is
`True`, those checks are bypassed since the
model cannot check for custom compartments.
## **Additional Notes**
Adding, removing, or modifying compartments after this step
will automatically de-compile the model, requiring it to be
compiled again after changes have been made.
.. important::
Only run after all the compartments have been
added to the model.
"""
if self.compiled: # pragma: no cover
warnings.warn("It looks like you're compiling a model more "
"than once. For clarity, it is recommended "
"that you only compile models once, and then "
"again if (and only if) changes have been "
"made.")
# run model checks to ensure that the model is valid
if not custom:
for i, compartment in enumerate(self.compartments):
compartment._check(self.map[i], self.compartments)
# aggregate all compartments by type
self.aggregated = {}
for i, compartment in enumerate(self.compartments):
if compartment.config['type'] not in self.aggregated:
self.aggregated[compartment.config['type']] = []
self.aggregated[compartment.config['type']].append(i)
self.compiled = True
def diff(self, time, system):
"""
Differentiate `epispot.models.Model`. Used by
`epispot.models.Model.integrate` for evaluating model
predictions.
## **Parameters**
`time`: Time to take the derivative at. This is important for
some time-dependent variables like compartment
parameters.
`system`: System of state values (e.g `[973, 12, 15]`). This is
propagated to each of the individual compartments in
the model.
## **Return**
List of corresponding compartment derivatives.
"""
if not self.compiled: # pragma: no cover
warnings.warn('An epispot model has not been compiled yet. '
'Triggering integration will automatically '
'compile the model.')
self.compile()
derivative = np.zeros((len(self.compartments), ))
for num, compartment in enumerate(self.compartments):
if num in self.aggregated['Susceptible']:
delta = compartment.diff(time,
system,
num,
self.map[num],
self.matrix[num],
infecteds=
self.aggregated['Infected'])
else:
delta = compartment.diff(time,
system,
num,
self.map[num],
self.matrix[num])
derivative += delta
return derivative
def integrate(self, timesteps, starting_state=None):
"""
Integrate the model using `epispot.models.Model.diff` to
arrive at future predictions using
[Euler's Method](https://en.wikipedia.org/wiki/Euler_method).
By default, the step size (Δ) is set to exactly 1 day, as this
is usually the period for which epidemiological parameters are
estimated for. However, in future versions, we plan to update
this to add support for variable values of Δ.
## **Parameters**
`timesteps`: range of evenly-spaced times starting at the
epidemic start time and ending at the time of
prediction.
`starting_state=None`: List of initial values for each
compartment. This is used as the initial
vector for the integration process.
If no `starting_state` is provided, it
will default to the having only 1 person
in the next non-Susceptible compartment.
## **Return**
A list of lists. Each sublist is a vector representing the
value of each compartment at that specific time. The sublists
range according to the `timesteps` parameter.
## **Example**
For example, the following would be an expected return type for
an SIR model with a population of `100`.
```python
[
[99, 1, 0], # S, I, R on day 1
[98, 2, 0], # S, I, R on day 2
[95, 3, 2], # S, I, R on day 3
...,
[23, 24, 53] # final prediction
]
```
## **Additional Notes**
`delta` is expected to be added as an optional parameter in
future releases of epispot v3. For now, however, it is set
to 1 day and cannot be changed.
"""
# checks to make sure the model has been compiled
if not self.compiled: # pragma: no cover
warnings.warn('An epispot model has not been compiled yet. '
'Triggering integration will automatically '
'compile the model.')
self.compile()
# initial parameter setup
results = []
delta = 1
if starting_state is not None:
system = starting_state
else:
system = np.zeros(len(self.compartments))
system[0] = self.initial_population - 1
system[1] = 1
for timestep in timesteps:
# calculate the derivative for each compartment at this
# timestep and update the system accordingly
derivatives = self.diff(timestep, system)
system += delta * derivatives
results.append(deepcopy(system))
return results
def add(self, comp, comp_map, matrix):
"""
Add a compartment to the model. This can also be done by
initializing the `epispot.models.Model` class beforehand.
## **Parameters**
`comp`: Compartment class (e.g. `Susceptible()` or `Infected()`)
`comp_map`: Slice of the larger `map` specified in
`epispot.models.Model`. This should simply include the
compartment connections for this specific compartment.
`matrix`: Slice of the larger `matrix` specified in
`epispot.models.Model`. As with `map`, this should
only include the rates and probabilities for this
compartment's connections.
## **Error Handling**
Initializing some parameters in `epispot.models.Model` without
initializing all of them will raise a `ValueError`.
## **Additional Notes**
See the documentation for `epispot.models.Model` for more help
and examples.
"""
if self.compiled:
self.compiled = False
if (self.compartments, self.map, self.matrix) == (None, None, None):
self.compartments = [comp]
self.names = [comp.name]
self.map = [comp_map]
self.matrix = [matrix]
elif self.compartments is not None and self.map is not None and \
self.matrix is not None:
self.compartments.append(comp)
self.names.append(comp.name)
self.map.append(comp_map)
self.matrix.append(matrix)
else: # pragma: no cover
raise ValueError('Parameters for `epispot.models.Model` '
'have not been specified correctly.\n'
'If either `comps`, `map`, or `matrix` '
'have been initialized, then *all* '
'parameters must be initialized.')
def rename(self, names):
"""
Assign names to each compartment in the model.
## Parameters
`names`: A list of names corresponding to `comps`
"""
self.names = names
for i, comp in enumerate(self.compartments):
comp.name = names[i]Classes
class Model (initial_population, comps=None, comp_map=None, matrix=None)-
The base model class for compartmental models. Compartmental models are models composed of various sub-models, known as "compartments." For example, the common SIR model is an example of a compartmental model with the Susceptible, Infected, and Removed compartments.
Added in version: v3.0.0-alpha-2
Initialize the
Modelclass; all optional parameters can be added through theModel.add()method.Parameters
initial_population: Population at time zerocomps=None: List of compartment classes to create the modelcomp_map=None: Map of how all the compartments connect. The map should consist of a list of lists. Each sublist represents the connections of the corresponding compartment in thecompslist. This sublist should contain the indices of each of the compartments incompsthat it connects to. If the compartment does not connect to any other compartments, leave the sublist blank.matrix=None: Rate and probability matrix describing the exchange rates between compartments. Likemap, this is a list of lists. Unlikemap, however, this matrix must not skip entries (i.e. no blank sublists). Each sublist should contain rate and probability information in a tuple for every compartment. If the information is not necessary, use the tuple(1, 1)orNoneas a placeholder.Example
Let's say we have three compartments
A,B, andC. These three compartments connect as shown below:┌───────────────────┐ │ ▼ ┌───┐ ┌───┐ ┌───┐ │ A │ ──▶ │ C │ ──▶ │ B │ └───┘ └───┘ └───┘To create a compartmental model with these three classes, use:
comps = [A, B, C], map = [ [1, 2], # A [], # B [1] # C ], matrix = [ [None, (1/2, 1/3), (1/2, 1/3)], # A[A, B, C] [None, None, None], # B[A, B, C] [None, (1/2, 1/3), None] # C[A, B, C] ]This creates a compartmental model where all the connections have a probability of
1/2and rate of1/3.Additional Notes
This feature is currently only released to alpha versions of epispot. This will likely be used (with minor changes) in the full release of epispot v3. For more information about this feature, or if you're interested in giving feedback, see the discussion here on GitHub.
Warning
As this is currently an alpha feature, the new compartmental models in epispot are subject to change.
Expand source code
class Model: """ The base model class for [compartmental models](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology). Compartmental models are models composed of various sub-models, known as "compartments." For example, the common SIR model is an example of a compartmental model with the Susceptible, Infected, and Removed compartments. .. versionadded:: v3.0.0-alpha-2 """ def __init__(self, initial_population, comps=None, comp_map=None, matrix=None): """ Initialize the `Model` class; all optional parameters can be added through the `epispot.models.Model.add` method. ## **Parameters** `initial_population`: Population at time zero `comps=None`: List of compartment classes to create the model `comp_map=None`: Map of how all the compartments connect. The map should consist of a list of lists. Each sublist represents the connections of the corresponding compartment in the `comps` list. This sublist should contain the indices of each of the compartments in `comps` that it connects to. If the compartment does not connect to any other compartments, leave the sublist blank. `matrix=None`: Rate and probability matrix describing the exchange rates between compartments. Like `map`, this is a list of lists. Unlike `map`, however, this matrix must not skip entries (i.e. no blank sublists). Each sublist should contain rate and probability information in a tuple for every compartment. If the information is not necessary, use the tuple `(1, 1)` or `None` as a placeholder. ## **Example** Let's say we have three compartments `A`, `B`, and `C`. These three compartments connect as shown below: ```text ┌───────────────────┐ │ ▼ ┌───┐ ┌───┐ ┌───┐ │ A │ ──▶ │ C │ ──▶ │ B │ └───┘ └───┘ └───┘ ``` To create a compartmental model with these three classes, use: ```python comps = [A, B, C], map = [ [1, 2], # A [], # B [1] # C ], matrix = [ [None, (1/2, 1/3), (1/2, 1/3)], # A[A, B, C] [None, None, None], # B[A, B, C] [None, (1/2, 1/3), None] # C[A, B, C] ] ``` This creates a compartmental model where all the connections have a probability of `1/2` and rate of `1/3`. ## **Additional Notes** This feature is currently only released to alpha versions of epispot. This will likely be used (with minor changes) in the full release of epispot v3. For more information about this feature, or if you're interested in giving feedback, see the discussion [here on GitHub](https://github.com/epispot/epispot/issues/73). .. warning:: As this is currently an alpha feature, the new compartmental models in epispot are subject to change. """ self.initial_population = initial_population self.compartments = comps if self.compartments: self.names = [comp.name for comp in self.compartments] self.map = comp_map self.matrix = matrix self.aggregated = None self.compiled = False def compile(self, custom=False): """ Run a series of checks of the model and initialize some class-wide variables. ## **Parameters** `custom=False`: Flag indicating if the model is using custom compartments. If this is `False` (the default), all compartment compatibility checks will have to pass or an error will be raised. If this is `True`, those checks are bypassed since the model cannot check for custom compartments. ## **Additional Notes** Adding, removing, or modifying compartments after this step will automatically de-compile the model, requiring it to be compiled again after changes have been made. .. important:: Only run after all the compartments have been added to the model. """ if self.compiled: # pragma: no cover warnings.warn("It looks like you're compiling a model more " "than once. For clarity, it is recommended " "that you only compile models once, and then " "again if (and only if) changes have been " "made.") # run model checks to ensure that the model is valid if not custom: for i, compartment in enumerate(self.compartments): compartment._check(self.map[i], self.compartments) # aggregate all compartments by type self.aggregated = {} for i, compartment in enumerate(self.compartments): if compartment.config['type'] not in self.aggregated: self.aggregated[compartment.config['type']] = [] self.aggregated[compartment.config['type']].append(i) self.compiled = True def diff(self, time, system): """ Differentiate `epispot.models.Model`. Used by `epispot.models.Model.integrate` for evaluating model predictions. ## **Parameters** `time`: Time to take the derivative at. This is important for some time-dependent variables like compartment parameters. `system`: System of state values (e.g `[973, 12, 15]`). This is propagated to each of the individual compartments in the model. ## **Return** List of corresponding compartment derivatives. """ if not self.compiled: # pragma: no cover warnings.warn('An epispot model has not been compiled yet. ' 'Triggering integration will automatically ' 'compile the model.') self.compile() derivative = np.zeros((len(self.compartments), )) for num, compartment in enumerate(self.compartments): if num in self.aggregated['Susceptible']: delta = compartment.diff(time, system, num, self.map[num], self.matrix[num], infecteds= self.aggregated['Infected']) else: delta = compartment.diff(time, system, num, self.map[num], self.matrix[num]) derivative += delta return derivative def integrate(self, timesteps, starting_state=None): """ Integrate the model using `epispot.models.Model.diff` to arrive at future predictions using [Euler's Method](https://en.wikipedia.org/wiki/Euler_method). By default, the step size (Δ) is set to exactly 1 day, as this is usually the period for which epidemiological parameters are estimated for. However, in future versions, we plan to update this to add support for variable values of Δ. ## **Parameters** `timesteps`: range of evenly-spaced times starting at the epidemic start time and ending at the time of prediction. `starting_state=None`: List of initial values for each compartment. This is used as the initial vector for the integration process. If no `starting_state` is provided, it will default to the having only 1 person in the next non-Susceptible compartment. ## **Return** A list of lists. Each sublist is a vector representing the value of each compartment at that specific time. The sublists range according to the `timesteps` parameter. ## **Example** For example, the following would be an expected return type for an SIR model with a population of `100`. ```python [ [99, 1, 0], # S, I, R on day 1 [98, 2, 0], # S, I, R on day 2 [95, 3, 2], # S, I, R on day 3 ..., [23, 24, 53] # final prediction ] ``` ## **Additional Notes** `delta` is expected to be added as an optional parameter in future releases of epispot v3. For now, however, it is set to 1 day and cannot be changed. """ # checks to make sure the model has been compiled if not self.compiled: # pragma: no cover warnings.warn('An epispot model has not been compiled yet. ' 'Triggering integration will automatically ' 'compile the model.') self.compile() # initial parameter setup results = [] delta = 1 if starting_state is not None: system = starting_state else: system = np.zeros(len(self.compartments)) system[0] = self.initial_population - 1 system[1] = 1 for timestep in timesteps: # calculate the derivative for each compartment at this # timestep and update the system accordingly derivatives = self.diff(timestep, system) system += delta * derivatives results.append(deepcopy(system)) return results def add(self, comp, comp_map, matrix): """ Add a compartment to the model. This can also be done by initializing the `epispot.models.Model` class beforehand. ## **Parameters** `comp`: Compartment class (e.g. `Susceptible()` or `Infected()`) `comp_map`: Slice of the larger `map` specified in `epispot.models.Model`. This should simply include the compartment connections for this specific compartment. `matrix`: Slice of the larger `matrix` specified in `epispot.models.Model`. As with `map`, this should only include the rates and probabilities for this compartment's connections. ## **Error Handling** Initializing some parameters in `epispot.models.Model` without initializing all of them will raise a `ValueError`. ## **Additional Notes** See the documentation for `epispot.models.Model` for more help and examples. """ if self.compiled: self.compiled = False if (self.compartments, self.map, self.matrix) == (None, None, None): self.compartments = [comp] self.names = [comp.name] self.map = [comp_map] self.matrix = [matrix] elif self.compartments is not None and self.map is not None and \ self.matrix is not None: self.compartments.append(comp) self.names.append(comp.name) self.map.append(comp_map) self.matrix.append(matrix) else: # pragma: no cover raise ValueError('Parameters for `epispot.models.Model` ' 'have not been specified correctly.\n' 'If either `comps`, `map`, or `matrix` ' 'have been initialized, then *all* ' 'parameters must be initialized.') def rename(self, names): """ Assign names to each compartment in the model. ## Parameters `names`: A list of names corresponding to `comps` """ self.names = names for i, comp in enumerate(self.compartments): comp.name = names[i]Methods
def add(self, comp, comp_map, matrix)-
Add a compartment to the model. This can also be done by initializing the
Modelclass beforehand.Parameters
comp: Compartment class (e.g.Susceptible()orInfected())comp_map: Slice of the largermapspecified inModel. This should simply include the compartment connections for this specific compartment.matrix: Slice of the largermatrixspecified inModel. As withmap, this should only include the rates and probabilities for this compartment's connections.Error Handling
Initializing some parameters in
Modelwithout initializing all of them will raise aValueError.Additional Notes
See the documentation for
Modelfor more help and examples.Expand source code
def add(self, comp, comp_map, matrix): """ Add a compartment to the model. This can also be done by initializing the `epispot.models.Model` class beforehand. ## **Parameters** `comp`: Compartment class (e.g. `Susceptible()` or `Infected()`) `comp_map`: Slice of the larger `map` specified in `epispot.models.Model`. This should simply include the compartment connections for this specific compartment. `matrix`: Slice of the larger `matrix` specified in `epispot.models.Model`. As with `map`, this should only include the rates and probabilities for this compartment's connections. ## **Error Handling** Initializing some parameters in `epispot.models.Model` without initializing all of them will raise a `ValueError`. ## **Additional Notes** See the documentation for `epispot.models.Model` for more help and examples. """ if self.compiled: self.compiled = False if (self.compartments, self.map, self.matrix) == (None, None, None): self.compartments = [comp] self.names = [comp.name] self.map = [comp_map] self.matrix = [matrix] elif self.compartments is not None and self.map is not None and \ self.matrix is not None: self.compartments.append(comp) self.names.append(comp.name) self.map.append(comp_map) self.matrix.append(matrix) else: # pragma: no cover raise ValueError('Parameters for `epispot.models.Model` ' 'have not been specified correctly.\n' 'If either `comps`, `map`, or `matrix` ' 'have been initialized, then *all* ' 'parameters must be initialized.') def compile(self, custom=False)-
Run a series of checks of the model and initialize some class-wide variables.
Parameters
custom=False: Flag indicating if the model is using custom compartments. If this isFalse(the default), all compartment compatibility checks will have to pass or an error will be raised. If this isTrue, those checks are bypassed since the model cannot check for custom compartments.Additional Notes
Adding, removing, or modifying compartments after this step will automatically de-compile the model, requiring it to be compiled again after changes have been made.
Important
Only run after all the compartments have been added to the model.
Expand source code
def compile(self, custom=False): """ Run a series of checks of the model and initialize some class-wide variables. ## **Parameters** `custom=False`: Flag indicating if the model is using custom compartments. If this is `False` (the default), all compartment compatibility checks will have to pass or an error will be raised. If this is `True`, those checks are bypassed since the model cannot check for custom compartments. ## **Additional Notes** Adding, removing, or modifying compartments after this step will automatically de-compile the model, requiring it to be compiled again after changes have been made. .. important:: Only run after all the compartments have been added to the model. """ if self.compiled: # pragma: no cover warnings.warn("It looks like you're compiling a model more " "than once. For clarity, it is recommended " "that you only compile models once, and then " "again if (and only if) changes have been " "made.") # run model checks to ensure that the model is valid if not custom: for i, compartment in enumerate(self.compartments): compartment._check(self.map[i], self.compartments) # aggregate all compartments by type self.aggregated = {} for i, compartment in enumerate(self.compartments): if compartment.config['type'] not in self.aggregated: self.aggregated[compartment.config['type']] = [] self.aggregated[compartment.config['type']].append(i) self.compiled = True def diff(self, time, system)-
Differentiate
Model. Used byModel.integrate()for evaluating model predictions.Parameters
time: Time to take the derivative at. This is important for some time-dependent variables like compartment parameters.system: System of state values (e.g[973, 12, 15]). This is propagated to each of the individual compartments in the model.Return
List of corresponding compartment derivatives.
Expand source code
def diff(self, time, system): """ Differentiate `epispot.models.Model`. Used by `epispot.models.Model.integrate` for evaluating model predictions. ## **Parameters** `time`: Time to take the derivative at. This is important for some time-dependent variables like compartment parameters. `system`: System of state values (e.g `[973, 12, 15]`). This is propagated to each of the individual compartments in the model. ## **Return** List of corresponding compartment derivatives. """ if not self.compiled: # pragma: no cover warnings.warn('An epispot model has not been compiled yet. ' 'Triggering integration will automatically ' 'compile the model.') self.compile() derivative = np.zeros((len(self.compartments), )) for num, compartment in enumerate(self.compartments): if num in self.aggregated['Susceptible']: delta = compartment.diff(time, system, num, self.map[num], self.matrix[num], infecteds= self.aggregated['Infected']) else: delta = compartment.diff(time, system, num, self.map[num], self.matrix[num]) derivative += delta return derivative def integrate(self, timesteps, starting_state=None)-
Integrate the model using
Model.diff()to arrive at future predictions using Euler's Method. By default, the step size (Δ) is set to exactly 1 day, as this is usually the period for which epidemiological parameters are estimated for. However, in future versions, we plan to update this to add support for variable values of Δ.Parameters
timesteps: range of evenly-spaced times starting at the epidemic start time and ending at the time of prediction.starting_state=None: List of initial values for each compartment. This is used as the initial vector for the integration process. If nostarting_stateis provided, it will default to the having only 1 person in the next non-Susceptible compartment.Return
A list of lists. Each sublist is a vector representing the value of each compartment at that specific time. The sublists range according to the
timestepsparameter.Example
For example, the following would be an expected return type for an SIR model with a population of
100.[ [99, 1, 0], # S, I, R on day 1 [98, 2, 0], # S, I, R on day 2 [95, 3, 2], # S, I, R on day 3 ..., [23, 24, 53] # final prediction ]Additional Notes
deltais expected to be added as an optional parameter in future releases of epispot v3. For now, however, it is set to 1 day and cannot be changed.Expand source code
def integrate(self, timesteps, starting_state=None): """ Integrate the model using `epispot.models.Model.diff` to arrive at future predictions using [Euler's Method](https://en.wikipedia.org/wiki/Euler_method). By default, the step size (Δ) is set to exactly 1 day, as this is usually the period for which epidemiological parameters are estimated for. However, in future versions, we plan to update this to add support for variable values of Δ. ## **Parameters** `timesteps`: range of evenly-spaced times starting at the epidemic start time and ending at the time of prediction. `starting_state=None`: List of initial values for each compartment. This is used as the initial vector for the integration process. If no `starting_state` is provided, it will default to the having only 1 person in the next non-Susceptible compartment. ## **Return** A list of lists. Each sublist is a vector representing the value of each compartment at that specific time. The sublists range according to the `timesteps` parameter. ## **Example** For example, the following would be an expected return type for an SIR model with a population of `100`. ```python [ [99, 1, 0], # S, I, R on day 1 [98, 2, 0], # S, I, R on day 2 [95, 3, 2], # S, I, R on day 3 ..., [23, 24, 53] # final prediction ] ``` ## **Additional Notes** `delta` is expected to be added as an optional parameter in future releases of epispot v3. For now, however, it is set to 1 day and cannot be changed. """ # checks to make sure the model has been compiled if not self.compiled: # pragma: no cover warnings.warn('An epispot model has not been compiled yet. ' 'Triggering integration will automatically ' 'compile the model.') self.compile() # initial parameter setup results = [] delta = 1 if starting_state is not None: system = starting_state else: system = np.zeros(len(self.compartments)) system[0] = self.initial_population - 1 system[1] = 1 for timestep in timesteps: # calculate the derivative for each compartment at this # timestep and update the system accordingly derivatives = self.diff(timestep, system) system += delta * derivatives results.append(deepcopy(system)) return results def rename(self, names)-
Assign names to each compartment in the model.
Parameters
names: A list of names corresponding tocompsExpand source code
def rename(self, names): """ Assign names to each compartment in the model. ## Parameters `names`: A list of names corresponding to `comps` """ self.names = names for i, comp in enumerate(self.compartments): comp.name = names[i]