Module epispot.plots.native
The native module contains various plotting mechanisms utilizing matplotlib.
Additionally, in order to display scientifically polished graphs, this module uses the
Science-Plots package.We strongly encourage you to cite the authors of this package as demonstrated below:
John Garrett. (2021, June 2). SciencePlots (v1.0.8) (Version 1.0.8). Zenodo. http://doi.org/10.5281/zenodo.4893230
Unfortunately, the SciencePlots package is only available on pip at this time.
If you use an Anaconda system, you should still have no problem installing it since pip comes pre-installed with most versions.
Install with:
pip install SciencePlots
Additionally, all functions in this module have a latex flag which determines whether to use LaTeX or not.
In order to use LaTeX, please follow the installation instructions for your operating system listed on the
SciencePlots project README.
Structure:
- model()
- stacked()
Expand source code
"""
The `native` module contains various plotting mechanisms utilizing `matplotlib`.
Additionally, in order to display scientifically polished graphs, this module uses the
[Science-Plots](https://pypi.org/project/SciencePlots/1.0.8/) package.\
We strongly encourage you to cite the authors of this package as demonstrated below:
> John Garrett. (2021, June 2). SciencePlots (v1.0.8) (Version 1.0.8). Zenodo. http://doi.org/10.5281/zenodo.4893230
Unfortunately, the `SciencePlots` package is only available on `pip` at this time.
If you use an Anaconda system, you should still have no problem installing it since `pip` comes pre-installed with most versions.
Install with:
```shell
pip install SciencePlots
```
Additionally, all functions in this module have a `latex` flag which determines whether to use LaTeX or not.
In order to use LaTeX, please follow the installation instructions for your operating system listed on the
[`SciencePlots` project README](https://github.com/garrettj403/SciencePlots#faq).
## Structure:
- model()
- stacked()
"""
from . import plt
plt.style.use(['science', 'no-latex']) # default style
def model(Model, time_frame, title='Compartment Populations over Time',
starting_state=None, names=None, show_susceptible=False,
log=False, latex=True):
"""
Plots the results of one model using `matplotlib`.
The results are displayed natively via a `matplotlib` window.
There are various ways to customize the generated plots by modifying
the time frame or compartments displayed.
Additionally, `matplotlib` allows editing plots even after
they have been created to change things like colors, margins, etc.
- Model: An `epispot.models.Model` object
- time_frame: A `range()` describing the time period to plot
- title: (`='Compartment Populations over Time`) The title of the plot
- starting_state: (default:inherited) Initial model state (see `epispot.models.Model.integrate` parameter `starting_state`)
- names: (default:`Model.layer_names`) A list of names for each of the compartments
- show_susceptible: (`=False`) Boolean value describing whether or not to plot the Susceptible compartment.\
**This assumes that the Susceptible compartment is the first in `Model`**\
Note:\
> This can potentially result in less visibility for other compartments
> since usually the Susceptible compartment comprises of many, many
> more individuals than the other compartments combined.
- log: (`=False`) Boolean value indicating whether or not to use a logarithmic scale when plotting `Model`
- latex: (`=True`) Turn off if you do not have LaTeX installed or want quicker loading times
- return: `pyplot` figure (display with `.show()`)
"""
if latex:
plt.style.use('science')
DataFrame = {}
System = Model.integrate(time_frame, starting_state=starting_state)
# parameter substitutions
if names is None:
names = Model.layer_names
# setup
for name in Model.layer_names:
DataFrame[name] = []
for day in System:
for i, compartment in enumerate(day):
DataFrame[Model.layer_names[i]].append(compartment)
# plotting
plt.figure(figsize=(9, 5))
for compartment, _ in enumerate(Model.layers):
if (not show_susceptible and compartment != 0) or show_susceptible:
plt.plot(time_frame, DataFrame[Model.layer_names[compartment]], label=names[compartment])
if log:
plt.yscale('log')
plt.title(title)
plt.legend()
return plt
def stacked(Model, time_frame, title='Compartment Populations over Time',
starting_state=None, compartments=None, names=None, show_susceptible=False,
log=False, latex=True):
"""
Plots the results of one model using `matplotlib`.
The results are displayed natively via a `matplotlib` window as a stacked area chart.
There are various ways to customize the generated plots by modifying
the time frame or compartments displayed.
Additionally, `matplotlib` allows editing plots even after
they have been created to change things like colors, margins, etc.
- Model: An `epispot.models.Model` object
- time_frame: A `range()` describing the time period to plot
- title: (`='Compartment Populations over Time`) The title of the plot
- starting_state: (default:inherited) Initial model state (see `epispot.models.Model.integrate` parameter `starting_state`)
- compartments: (default:all) The indices of the compartments in the model to plot;
all other compartments will be hidden
- names: (default:`Model.layer_names`) A list of names for each of the compartments
- show_susceptible: (`=False`) Boolean value describing whether or not to plot the Susceptible compartment.\
**This assumes that the Susceptible compartment is the first in `Model`**\
Note:\
> This can potentially result in less visibility for other compartments
> since usually the Susceptible compartment comprises of many, many
> more individuals than the other compartments combined.
- log: (`=False`) Boolean value indicating whether or not to use a logarithmic scale when plotting `Model`
- latex: (`=True`) Turn off if you do not have LaTeX installed or want quicker loading times
- return: `pyplot` figure (display with `.show()`)
"""
if latex:
plt.style.use('science')
DataFrame = {}
System = Model.integrate(time_frame, starting_state=starting_state)
# parameter substitutions
if compartments is None:
compartments = list(range(len(Model.layers)))
if names is None:
names = Model.layer_names
# setup
for name in Model.layer_names:
DataFrame[name] = []
for day in System:
for i, compartment in enumerate(day):
DataFrame[Model.layer_names[i]].append(compartment)
if not show_susceptible:
for i, compartment in enumerate(compartments):
if compartment == 0:
del compartments[i]
break
# plotting
plt.figure(figsize=(9, 5))
plt.stackplot(time_frame, *[DataFrame[Model.layer_names[compartment]] for compartment in compartments],
labels=[names[compartment] for compartment in compartments])
if log:
plt.yscale('log')
plt.title(title)
plt.legend(loc="lower center", bbox_to_anchor=(0.5, -0.2), borderaxespad=0, ncol=2)
plt.tight_layout()
return pltFunctions
def model(Model, time_frame, title='Compartment Populations over Time', starting_state=None, names=None, show_susceptible=False, log=False, latex=True)-
Plots the results of one model using
matplotlib. The results are displayed natively via amatplotlibwindow. There are various ways to customize the generated plots by modifying the time frame or compartments displayed. Additionally,matplotliballows editing plots even after they have been created to change things like colors, margins, etc.- Model: An
Modelobject - time_frame: A
range()describing the time period to plot - title: (
='Compartment Populations over Time) The title of the plot - starting_state: (default:inherited) Initial model state (see
Model.integrate()parameterstarting_state) - names: (default:
Model.layer_names) A list of names for each of the compartments - show_susceptible: (
=False) Boolean value describing whether or not to plot the Susceptible compartment. This assumes that the Susceptible compartment is the first inModelNote: > This can potentially result in less visibility for other compartments > since usually the Susceptible compartment comprises of many, many > more individuals than the other compartments combined. - log: (
=False) Boolean value indicating whether or not to use a logarithmic scale when plottingModel - latex: (
=True) Turn off if you do not have LaTeX installed or want quicker loading times - return:
pyplotfigure (display with.show())
Expand source code
def model(Model, time_frame, title='Compartment Populations over Time', starting_state=None, names=None, show_susceptible=False, log=False, latex=True): """ Plots the results of one model using `matplotlib`. The results are displayed natively via a `matplotlib` window. There are various ways to customize the generated plots by modifying the time frame or compartments displayed. Additionally, `matplotlib` allows editing plots even after they have been created to change things like colors, margins, etc. - Model: An `epispot.models.Model` object - time_frame: A `range()` describing the time period to plot - title: (`='Compartment Populations over Time`) The title of the plot - starting_state: (default:inherited) Initial model state (see `epispot.models.Model.integrate` parameter `starting_state`) - names: (default:`Model.layer_names`) A list of names for each of the compartments - show_susceptible: (`=False`) Boolean value describing whether or not to plot the Susceptible compartment.\ **This assumes that the Susceptible compartment is the first in `Model`**\ Note:\ > This can potentially result in less visibility for other compartments > since usually the Susceptible compartment comprises of many, many > more individuals than the other compartments combined. - log: (`=False`) Boolean value indicating whether or not to use a logarithmic scale when plotting `Model` - latex: (`=True`) Turn off if you do not have LaTeX installed or want quicker loading times - return: `pyplot` figure (display with `.show()`) """ if latex: plt.style.use('science') DataFrame = {} System = Model.integrate(time_frame, starting_state=starting_state) # parameter substitutions if names is None: names = Model.layer_names # setup for name in Model.layer_names: DataFrame[name] = [] for day in System: for i, compartment in enumerate(day): DataFrame[Model.layer_names[i]].append(compartment) # plotting plt.figure(figsize=(9, 5)) for compartment, _ in enumerate(Model.layers): if (not show_susceptible and compartment != 0) or show_susceptible: plt.plot(time_frame, DataFrame[Model.layer_names[compartment]], label=names[compartment]) if log: plt.yscale('log') plt.title(title) plt.legend() return plt - Model: An
def stacked(Model, time_frame, title='Compartment Populations over Time', starting_state=None, compartments=None, names=None, show_susceptible=False, log=False, latex=True)-
Plots the results of one model using
matplotlib. The results are displayed natively via amatplotlibwindow as a stacked area chart. There are various ways to customize the generated plots by modifying the time frame or compartments displayed. Additionally,matplotliballows editing plots even after they have been created to change things like colors, margins, etc.- Model: An
Modelobject - time_frame: A
range()describing the time period to plot - title: (
='Compartment Populations over Time) The title of the plot - starting_state: (default:inherited) Initial model state (see
Model.integrate()parameterstarting_state) - compartments: (default:all) The indices of the compartments in the model to plot; all other compartments will be hidden
- names: (default:
Model.layer_names) A list of names for each of the compartments - show_susceptible: (
=False) Boolean value describing whether or not to plot the Susceptible compartment. This assumes that the Susceptible compartment is the first inModelNote: > This can potentially result in less visibility for other compartments > since usually the Susceptible compartment comprises of many, many > more individuals than the other compartments combined. - log: (
=False) Boolean value indicating whether or not to use a logarithmic scale when plottingModel - latex: (
=True) Turn off if you do not have LaTeX installed or want quicker loading times - return:
pyplotfigure (display with.show())
Expand source code
def stacked(Model, time_frame, title='Compartment Populations over Time', starting_state=None, compartments=None, names=None, show_susceptible=False, log=False, latex=True): """ Plots the results of one model using `matplotlib`. The results are displayed natively via a `matplotlib` window as a stacked area chart. There are various ways to customize the generated plots by modifying the time frame or compartments displayed. Additionally, `matplotlib` allows editing plots even after they have been created to change things like colors, margins, etc. - Model: An `epispot.models.Model` object - time_frame: A `range()` describing the time period to plot - title: (`='Compartment Populations over Time`) The title of the plot - starting_state: (default:inherited) Initial model state (see `epispot.models.Model.integrate` parameter `starting_state`) - compartments: (default:all) The indices of the compartments in the model to plot; all other compartments will be hidden - names: (default:`Model.layer_names`) A list of names for each of the compartments - show_susceptible: (`=False`) Boolean value describing whether or not to plot the Susceptible compartment.\ **This assumes that the Susceptible compartment is the first in `Model`**\ Note:\ > This can potentially result in less visibility for other compartments > since usually the Susceptible compartment comprises of many, many > more individuals than the other compartments combined. - log: (`=False`) Boolean value indicating whether or not to use a logarithmic scale when plotting `Model` - latex: (`=True`) Turn off if you do not have LaTeX installed or want quicker loading times - return: `pyplot` figure (display with `.show()`) """ if latex: plt.style.use('science') DataFrame = {} System = Model.integrate(time_frame, starting_state=starting_state) # parameter substitutions if compartments is None: compartments = list(range(len(Model.layers))) if names is None: names = Model.layer_names # setup for name in Model.layer_names: DataFrame[name] = [] for day in System: for i, compartment in enumerate(day): DataFrame[Model.layer_names[i]].append(compartment) if not show_susceptible: for i, compartment in enumerate(compartments): if compartment == 0: del compartments[i] break # plotting plt.figure(figsize=(9, 5)) plt.stackplot(time_frame, *[DataFrame[Model.layer_names[compartment]] for compartment in compartments], labels=[names[compartment] for compartment in compartments]) if log: plt.yscale('log') plt.title(title) plt.legend(loc="lower center", bbox_to_anchor=(0.5, -0.2), borderaxespad=0, ncol=2) plt.tight_layout() return plt - Model: An