diff --git a/.github/workflows/sphinx.yaml b/.github/workflows/sphinx.yaml
index 2431d16..6eaf20b 100644
--- a/.github/workflows/sphinx.yaml
+++ b/.github/workflows/sphinx.yaml
@@ -49,8 +49,8 @@ jobs:
# Building
- name: Render Quarto File
run: |
- quarto render README.qmd --to rst
- mv README.rst docs/index.rst
+ quarto render README.qmd --to md
+ mv README.md docs/index.md
- name: Build HTML using Sphinx
run: sphinx-build -M html docs docs/build/
diff --git a/README.md b/README.md
index 5cbfb5c..71431fe 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
# epiworldpy: Python bindings for epiworld
-[](https://github.com/UofUEpiBio/epiworldpy/actions/workflows/pip.yaml)
+[](https://github.com/UofUEpiBio/epiworldpy/actions/workflows/pip.yaml)
+[](https://pypi.org/project/epiworldpy)
This is a python wrapper of the [`epiworld c++`
library](https://github.com/UofUEpiBio/epiworld/), an ABM simulation
@@ -16,8 +16,12 @@ target="_blank">implemented in R.
# Installation
-- clone this repository
-- `pip install ./epiworldpy`
+- clone this repository
+- `pip install ./epiworldpy`
+
+# API
+
+You can find API documentation on the API page.
# Examples
@@ -31,7 +35,7 @@ use the following data:
import epiworldpy as epiworld
# Create a SEIR model (susceptible, exposed, infectious, recovered), representing COVID-19.
-covid19 = epiworld.ModelSEIR(
+covid19 = epiworld.ModelSEIRCONN(
name = 'covid-19',
n = 10000,
prevalence = .01,
@@ -62,7 +66,7 @@ covid19.print(False)
- Update infected individuals (runs daily)
Virus(es):
- - covid-19 (baseline prevalence: 1.00%)
+ - covid-19
Tool(s):
(none)
@@ -73,7 +77,7 @@ covid19.print(False)
- Prob. Recovery : 0.1400
- Prob. Transmission : 0.1000
-
+
Let’s run it and to see what we get:
@@ -99,15 +103,15 @@ covid19.print(False)
Number of entities : 0
Days (duration) : 100 (of 100)
Number of viruses : 1
- Last run elapsed t : 14.00ms
- Last run speed : 70.84 million agents x day / second
+ Last run elapsed t : 22.00ms
+ Last run speed : 43.64 million agents x day / second
Rewiring : off
Global events:
- Update infected individuals (runs daily)
Virus(es):
- - covid-19 (baseline prevalence: 1.00%)
+ - covid-19
Tool(s):
(none)
@@ -130,7 +134,7 @@ covid19.print(False)
- Infected 0.00 0.00 0.86 0.14
- Recovered 0.00 0.00 0.00 1.00
-
+
We can now visualize the model’s compartments:
@@ -173,8 +177,9 @@ plt.grid(True)
plt.show()
```
-
+![]()
We can get the effective reproductive number, over time, too:
@@ -186,7 +191,7 @@ plt.figure(figsize=(10, 6))
for virus_id, virus_data in enumerate(reproductive_data):
average_rts = list()
-
+
for date_data in virus_data:
if not date_data:
continue
@@ -204,8 +209,9 @@ plt.grid(True)
plt.show()
```
-
+![]()
Let’s do the same for generation time:
@@ -246,8 +252,9 @@ plt.grid(True)
plt.show()
```
-
+![]()
Epiworld records agent-agent interactions, and we can graph those too.
In the below example, we only track all cases stemming from a specific
@@ -271,10 +278,10 @@ fig, ax = plt.subplots(figsize=(6,4))
to_track = { source[0] }
def update(frame):
ax.clear()
-
+
agents_involved_today = set()
agents_relationships_we_care_about = []
-
+
# Get only the agents involved in the current frame.
for i in range(len(start)):
if start[i] <= frame <= end[i]:
@@ -312,5 +319,4 @@ plt.show()
-
+
diff --git a/README.qmd b/README.qmd
index 0f21509..2428a12 100644
--- a/README.qmd
+++ b/README.qmd
@@ -1,10 +1,10 @@
---
format: gfm
-jupyter: python3
title: "epiworldpy: Python bindings for epiworld"
---
-[](https://github.com/UofUEpiBio/epiworldpy/actions/workflows/pip.yaml)
+[](https://github.com/UofUEpiBio/epiworldpy/actions/workflows/pip.yaml)
+[](https://pypi.org/project/epiworldpy)
This is a python wrapper of the [`epiworld c++` library][epiworld-git], an ABM
simulation engine. This is possible using the
@@ -20,9 +20,9 @@ The `epiworld` module is already
# API
-You can find API documentation on the [API page](/api.html).
+You can find API documentation on the API page.
-# Examples
+# Examples
## Basic
@@ -61,7 +61,7 @@ We can now visualize the model's compartments:
```{python}
#| label: series-visualization
-#| fig-cap: "COVID-19 SEIR model data"
+#| fig-cap: ""
import numpy as np
import matplotlib.pyplot as plt
@@ -105,7 +105,7 @@ We can get the effective reproductive number, over time, too:
```{python}
#| label: rt-visualization
-#| fig-cap: "COVID-19 SEIR model effective reproductive number"
+#| fig-cap: ""
reproductive_data = covid19.get_db().get_reproductive_number()
# Start the plotting!
@@ -113,7 +113,7 @@ plt.figure(figsize=(10, 6))
for virus_id, virus_data in enumerate(reproductive_data):
average_rts = list()
-
+
for date_data in virus_data:
if not date_data:
continue
@@ -135,7 +135,7 @@ Let's do the same for generation time:
```{python}
#| label: gentime-visualization
-#| fig-cap: "COVID-19 SEIR model generation time"
+#| fig-cap: ""
from collections import defaultdict
generation_time = covid19.get_db().get_generation_time()
@@ -176,7 +176,7 @@ Epiworld records agent-agent interactions, and we can graph those too. In the be
```{python}
#| label: contact-visualization
-#| fig-cap: "COVID-19 SEIR model agent contact"
+#| fig-cap: ""
#| eval: false
import networkx as nx
@@ -196,10 +196,10 @@ fig, ax = plt.subplots(figsize=(6,4))
to_track = { source[0] }
def update(frame):
ax.clear()
-
+
agents_involved_today = set()
agents_relationships_we_care_about = []
-
+
# Get only the agents involved in the current frame.
for i in range(len(start)):
if start[i] <= frame <= end[i]:
@@ -236,7 +236,7 @@ plt.show()
-
+
[epiworld-git]: https://github.com/UofUEpiBio/epiworld/
-[`pybind11`]: https://pybind11.readthedocs.io/en/stable/
\ No newline at end of file
+[`pybind11`]: https://pybind11.readthedocs.io/en/stable/
diff --git a/README_files/figure-commonmark/contact-visualization-output-1.gif b/README_files/figure-markdown_strict/contact-visualization-output-1.gif
similarity index 100%
rename from README_files/figure-commonmark/contact-visualization-output-1.gif
rename to README_files/figure-markdown_strict/contact-visualization-output-1.gif
diff --git a/README_files/figure-commonmark/gentime-visualization-output-1.png b/README_files/figure-markdown_strict/gentime-visualization-output-1.png
similarity index 99%
rename from README_files/figure-commonmark/gentime-visualization-output-1.png
rename to README_files/figure-markdown_strict/gentime-visualization-output-1.png
index ff0ceba..08c6946 100644
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diff --git a/README_files/figure-commonmark/rt-visualization-output-1.png b/README_files/figure-markdown_strict/rt-visualization-output-1.png
similarity index 99%
rename from README_files/figure-commonmark/rt-visualization-output-1.png
rename to README_files/figure-markdown_strict/rt-visualization-output-1.png
index 657cc59..4158c79 100644
Binary files a/README_files/figure-commonmark/rt-visualization-output-1.png and b/README_files/figure-markdown_strict/rt-visualization-output-1.png differ
diff --git a/README_files/figure-commonmark/series-visualization-output-1.png b/README_files/figure-markdown_strict/series-visualization-output-1.png
similarity index 99%
rename from README_files/figure-commonmark/series-visualization-output-1.png
rename to README_files/figure-markdown_strict/series-visualization-output-1.png
index 9f657c5..ec3d5b6 100644
Binary files a/README_files/figure-commonmark/series-visualization-output-1.png and b/README_files/figure-markdown_strict/series-visualization-output-1.png differ
diff --git a/docs/conf.py b/docs/conf.py
index e44706e..a540a6c 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -31,6 +31,7 @@
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.napoleon',
+ 'myst_parser',
]
autosummary_generate = True
@@ -41,7 +42,7 @@
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = ['.rst', '.md']
# The encoding of source files.
#source_encoding = 'utf-8-sig'
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..71431fe
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,322 @@
+# epiworldpy: Python bindings for epiworld
+
+
+[](https://github.com/UofUEpiBio/epiworldpy/actions/workflows/pip.yaml)
+[](https://pypi.org/project/epiworldpy)
+
+This is a python wrapper of the [`epiworld c++`
+library](https://github.com/UofUEpiBio/epiworld/), an ABM simulation
+engine. This is possible using the
+[`pybind11`](https://pybind11.readthedocs.io/en/stable/) library (which
+rocks!).
+
+The `epiworld` module is already
+implemented in R.
+
+# Installation
+
+- clone this repository
+- `pip install ./epiworldpy`
+
+# API
+
+You can find API documentation on the API page.
+
+# Examples
+
+## Basic
+
+Here we show how to create a `SEIR` object and add terms to it. We will
+use the following data:
+
+``` python
+# Loading the module
+import epiworldpy as epiworld
+
+# Create a SEIR model (susceptible, exposed, infectious, recovered), representing COVID-19.
+covid19 = epiworld.ModelSEIRCONN(
+ name = 'covid-19',
+ n = 10000,
+ prevalence = .01,
+ contact_rate = 2.0,
+ transmission_rate = .1,
+ incubation_days = 7.0,
+ recovery_rate = 0.14
+)
+
+# Taking a look
+covid19.print(False)
+```
+
+ ________________________________________________________________________________
+ ________________________________________________________________________________
+ SIMULATION STUDY
+
+ Name of the model : Susceptible-Exposed-Infected-Removed (SEIR) (connected)
+ Population size : 10000
+ Agents' data : (none)
+ Number of entities : 0
+ Days (duration) : 0 (of 0)
+ Number of viruses : 1
+ Last run elapsed t : -
+ Rewiring : off
+
+ Global events:
+ - Update infected individuals (runs daily)
+
+ Virus(es):
+ - covid-19
+
+ Tool(s):
+ (none)
+
+ Model parameters:
+ - Avg. Incubation days : 7.0000
+ - Contact rate : 2.0000
+ - Prob. Recovery : 0.1400
+ - Prob. Transmission : 0.1000
+
+
+
+Let’s run it and to see what we get:
+
+``` python
+# Run for 100 days with a seed of 223.
+covid19.run(100, 223)
+
+# Print an overview.
+covid19.print(False)
+```
+
+ _________________________________________________________________________
+ Running the model...
+ ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| done.
+ done.
+ ________________________________________________________________________________
+ ________________________________________________________________________________
+ SIMULATION STUDY
+
+ Name of the model : Susceptible-Exposed-Infected-Removed (SEIR) (connected)
+ Population size : 10000
+ Agents' data : (none)
+ Number of entities : 0
+ Days (duration) : 100 (of 100)
+ Number of viruses : 1
+ Last run elapsed t : 22.00ms
+ Last run speed : 43.64 million agents x day / second
+ Rewiring : off
+
+ Global events:
+ - Update infected individuals (runs daily)
+
+ Virus(es):
+ - covid-19
+
+ Tool(s):
+ (none)
+
+ Model parameters:
+ - Avg. Incubation days : 7.0000
+ - Contact rate : 2.0000
+ - Prob. Recovery : 0.1400
+ - Prob. Transmission : 0.1000
+
+ Distribution of the population at time 100:
+ - (0) Susceptible : 9900 -> 7275
+ - (1) Exposed : 100 -> 269
+ - (2) Infected : 0 -> 292
+ - (3) Recovered : 0 -> 2164
+
+ Transition Probabilities:
+ - Susceptible 1.00 0.00 0.00 0.00
+ - Exposed 0.00 0.85 0.15 0.00
+ - Infected 0.00 0.00 0.86 0.14
+ - Recovered 0.00 0.00 0.00 1.00
+
+
+
+We can now visualize the model’s compartments:
+
+``` python
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Get the data from the database
+history = covid19.get_db().get_hist_total()
+
+# Extract unique states and dates
+unique_states = np.unique(history['states'])
+unique_dates = np.unique(history['dates'])
+
+# Remove some data that will mess with scaling
+unique_states = np.delete(unique_states, np.where(unique_states == 'Susceptible'))
+
+# Initialize a dictionary to store time series data for each state
+time_series_data = {state: [] for state in unique_states}
+
+# Populate the time series data for each state
+for state in unique_states:
+ for date in unique_dates:
+ # Get the count for the current state and date
+ mask = (history['states'] == state) & (history['dates'] == date)
+ count = history['counts'][mask][0]
+ time_series_data[state].append(count)
+
+# Start the plotting!
+plt.figure(figsize=(10, 6))
+
+for state in unique_states:
+ plt.plot(unique_dates, time_series_data[state], label=state)
+
+plt.xlabel('Day')
+plt.ylabel('Count')
+plt.title('COVID-19 SEIR Model Data')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+![]()
+
+We can get the effective reproductive number, over time, too:
+
+``` python
+reproductive_data = covid19.get_db().get_reproductive_number()
+
+# Start the plotting!
+plt.figure(figsize=(10, 6))
+
+for virus_id, virus_data in enumerate(reproductive_data):
+ average_rts = list()
+
+ for date_data in virus_data:
+ if not date_data:
+ continue
+
+ keys_array = np.array(list(date_data.values()), dtype=np.float64)
+ average_rts.append(np.mean(keys_array))
+
+ plt.plot(range(0, len(virus_data)-1), average_rts, label=f"Virus {virus_id}")
+
+plt.xlabel('Date')
+plt.ylabel('Effective Reproductive Rate')
+plt.title('COVID-19 SEIR Model Effective Reproductive Rate')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+![]()
+
+Let’s do the same for generation time:
+
+``` python
+from collections import defaultdict
+
+generation_time = covid19.get_db().get_generation_time()
+agents = generation_time['agents']
+viruses = generation_time['viruses']
+times = generation_time['times']
+gentimes = generation_time['gentimes']
+
+# Data formatting
+unique_viruses = np.unique(viruses)
+data = defaultdict(lambda: defaultdict(list))
+
+for agent, virus, time, gentime in zip(agents, viruses, times, gentimes):
+ data[virus][time].append(gentime)
+
+average_data = {virus: {} for virus in unique_viruses}
+
+for virus, time_dict in data.items():
+ for time, gentime_list in time_dict.items():
+ average_data[virus][time] = np.mean(gentime_list)
+
+# Plotting
+plt.figure(figsize=(10, 6))
+for virus, time_dict in average_data.items():
+ times = sorted(time_dict.keys())
+ gentimes = [time_dict[time] for time in times]
+ plt.plot(times, gentimes, label=f'Virus {virus}')
+
+plt.xlabel('Date')
+plt.ylabel('Generation Time')
+plt.title('COVID-19 SEIR Model Generation Time')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+![]()
+
+Epiworld records agent-agent interactions, and we can graph those too.
+In the below example, we only track all cases stemming from a specific
+index case, despite the model having a prevalence of 0.01.
+
+``` python
+import networkx as nx
+from matplotlib.animation import FuncAnimation
+
+transmissions = covid19.get_db().get_transmissions()
+start = transmissions['source_exposure_dates']
+end = transmissions['dates']
+source = transmissions['sources']
+target = transmissions['targets']
+days = max(end)
+
+graph = nx.Graph()
+fig, ax = plt.subplots(figsize=(6,4))
+
+# Animation function
+to_track = { source[0] }
+def update(frame):
+ ax.clear()
+
+ agents_involved_today = set()
+ agents_relationships_we_care_about = []
+
+ # Get only the agents involved in the current frame.
+ for i in range(len(start)):
+ if start[i] <= frame <= end[i]:
+ agents_involved_today.add((source[i], target[i]))
+
+ # Get only today's agents who have some connection to agents
+ # we've seen before.
+ for agent in agents_involved_today:
+ if agent[0] in to_track or agent[1] in to_track:
+ to_track.add(agent[0])
+ to_track.add(agent[1])
+ graph.add_edge(agent[0], agent[1])
+
+ # Lay and space them out.
+ pos = nx.kamada_kawai_layout(graph)
+
+ options = {
+ "with_labels": True,
+ "node_size": 300,
+ "font_size": 6,
+ "node_color": "white",
+ "edgecolors": "white",
+ "linewidths": 1,
+ "width": 1,
+ }
+
+ # Graph!
+ nx.draw_networkx(graph, pos, **options)
+ ax.set_title(f"COVID-19 SEIR Model Agent Contact (Day {frame})")
+
+ani = FuncAnimation(fig, update, frames=int(days/3), interval=200, repeat=False)
+plt.show()
+```
+
+
+
+
diff --git a/pyproject.toml b/pyproject.toml
index ebacf0b..7230841 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,7 +28,7 @@ classifiers = [
[project.optional-dependencies]
test = ["pytest"]
-doc = ["nbformat", "nbclient", "jupyter", "epiworldpy[viz]"]
+doc = ["nbformat", "nbclient", "jupyter", "epiworldpy[viz]", "myst_parser"]
viz = ["ipympl>=0.8", "matplotlib>=3.5.0", "networkx>=3.0", "scipy>=1.0"]
[tool.scikit-build.wheel]