Merge pull request #415 from biosustain/move_example_inputs_into_package

Move example inputs into package

.github/workflows/release.yml

@@ -14,7 +14,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v1
       with:
-        python-version: 3.9
+        python-version: '3.9'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.github/workflows/run_integration_tests.yml

@@ -11,10 +11,10 @@ jobs:
 
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.7
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: 3.7
+        python-version: '3.9'
     - name: Install Maud
       run: |
         python -m pip install --upgrade pip

.github/workflows/run_tox.yml

@@ -11,14 +11,14 @@ jobs:
 
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: 3.8
-    - name: Set up Python 3.9
+        python-version: '3.9'
+    - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
-        python-version: 3.9
+        python-version: '3.10'
     - name: Install Maud
       run: |
         python -m pip install --upgrade pip

MANIFEST.in

@@ -1,2 +1,2 @@
-recursive-include tests/data *
 recursive-include src/maud/stan *.stan
+recursive-include src/maud/data *

README.rst

@@ -70,9 +70,9 @@ To run the simple linear model, use the following command:
 
 This will compile the Stan program at `src/maud/inference_model.stan
 <https://github.com/biosustain/Maud/blob/master/src/maud/inference_model.stan>`_, 
-then run the resulting binary file using the data at `tests/data/linar.toml
-<https://github.com/biosustain/Maud/blob/master/tests/data/linear.toml>`_, storing
-the results in csv files starting with
+then run the resulting binary file using the data at `src/maud/data/example_inputs/linear
+<https://github.com/biosustain/Maud/blob/master/src/maud/data/example_inputs/linear>`_, storing
+the results in a folder whose name starts with
 :code:`model_output_linear`.
 
 The `sample` command can be configured in a few ways - to check out all the

setup.cfg

@@ -4,7 +4,7 @@ url = https://github.com/biosustain/Maud
 download_url = https://pypi.org/project/maud-metabolic-models/
 author = Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark
 author_email = [email protected]
-version = 0.4.0.0
+version = 0.4.0.1
 # Please consult https://pypi.org/classifiers/ for a full list.
 classifiers =
     Development Status :: 2 - Pre-Alpha
@@ -30,6 +30,7 @@ include_package_data=True
 install_requires =
     pip >= 20
     arviz >= 0.12.1
+    importlib_resources >= 3.2
     numpy
     scipy
     sympy

src/maud/cli.py

@@ -22,20 +22,15 @@
 
 import arviz as az
 import click
+import importlib_resources
 
+from maud.data.example_inputs import linear
 from maud.getting_idatas import get_idata
 from maud.loading_maud_inputs import load_maud_input
 from maud.running_stan import predict, sample, simulate, variational
 
 
-RELATIVE_PATH_EXAMPLE = "../../tests/data/linear"
-
-
-def get_example_path(relative_path_example):
-    """Get absolute path to file containing example input."""
-
-    here = os.path.dirname(os.path.abspath(__file__))
-    return os.path.join(here, relative_path_example)
+EXAMPLE_INPUT_PATH = importlib_resources.files(linear)._paths[0]
 
 
 @click.group()
@@ -50,7 +45,7 @@ def cli():
 @click.argument(
     "data_path",
     type=click.Path(exists=True, dir_okay=True, file_okay=False),
-    default=get_example_path(RELATIVE_PATH_EXAMPLE),
+    default=EXAMPLE_INPUT_PATH,
 )
 def sample_command(data_path, output_dir):
     """Generate MCMC samples given a user input directory.
@@ -149,7 +144,7 @@ def do_predict(data_path: str):
 @click.argument(
     "data_path",
     type=click.Path(exists=True, dir_okay=True, file_okay=False),
-    default=get_example_path(RELATIVE_PATH_EXAMPLE),
+    default=EXAMPLE_INPUT_PATH,
 )
 def simulate_command(data_path, output_dir, n):
     """Generate draws from the initial values."""
@@ -266,7 +261,7 @@ def do_simulate(data_path, output_dir, n):
 @click.argument(
     "data_path",
     type=click.Path(exists=True, dir_okay=True, file_okay=False),
-    default=get_example_path(RELATIVE_PATH_EXAMPLE),
+    default=EXAMPLE_INPUT_PATH,
 )
 def variational_command(data_path, output_dir):
     """Generate MCMC samples given a user input directory.

src/maud/data/example_inputs/linear/priors.toml

@@ -0,0 +1,37 @@
+kcat = [
+  {exploc = 1, scale = 0.6, reaction = "r1", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, reaction = "r2", enzyme = "r2"},
+  {exploc = 1, scale = 0.6, reaction = "r3", enzyme = "r3"},
+]
+
+km = [
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "e", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "c", enzyme = "r1"},
+  {exploc = 1, scale = 0.6, metabolite = "M1", compartment = "c", enzyme = "r2"},
+  {exploc = 1, scale = 0.6, metabolite = "M2", compartment = "c", enzyme = "r3"},
+  {exploc = 1, scale = 0.6, metabolite = "M2", compartment = "e", enzyme = "r3"},
+]
+
+transfer_constant = [
+  {exploc = 1, scale = 0.6, enzyme = "r1"},
+  {exploc = 1, scale = 0.6, enzyme = "r2"},
+]
+
+dissociation_constant = [
+  {exploc = 1, scale = 0.6, enzyme = "r1", metabolite = "M2", compartment = "c", modification_type = "activation"},
+  {exploc = 1, scale = 0.6, enzyme = "r2", metabolite = "M1", compartment = "c", modification_type = "inhibition"},
+]
+
+dgf = [
+  {location = -1, metabolite = "M1", scale = 0.05},
+  {location = -2, metabolite = "M2", scale = 0.05},
+]
+
+psi = [
+  {location = -0.95, scale = 0.2, experiment = "condition1"},
+  {location = -0.95, scale = 0.2, experiment = "condition2"},
+]
+
+ki = [
+  {exploc = 1, scale = 0.6, enzyme = "r2", reaction = "r2", metabolite = "M1", compartment = "c"},
+]