Fix/resolve warnings

CHANGELOG.md

@@ -22,12 +22,26 @@ All notable changes to this project will be documented in this file.
 ### Added
 
 
+### Changed
+
+### Fixed
+
+## [0.8.1] - 2024-05-31
+
+### Added
+
+- Added `PyQt6` as dev dependency to allow developers to view plots in the development environment
+
 ### Changed
 
 - Removed the redundant function `evaluate_sample` from `sampling.py`
 
 ### Fixed
 
+- Switched from `importlib.resources.path` to `importlib.resources.files` due to deprecation
+- Removed argument name in call to `files(...)` that is changing from `package` to `anchor`
+- Removed unused kwargs passed to the plotting function `contour` that generated a warning in `test_heat_model()`
+- Bug that changed the model attribute `central_param` during `inference`
 
 ## [0.8.0] - 2024-03-21
 

eulerpi/core/sampling.py

@@ -147,15 +147,14 @@ def run_emcee_sampling(
     # Calculate the standard deviation of the data for each dimension
     data_stdevs = calc_kernel_width(data)
     sampling_dim = slice.shape[0]
-    central_param = model.central_param
-    param_limits = model.param_limits
 
     # Initialize each walker at a Gaussian-drawn random, slightly different parameter close to the central parameter.
     # compute element-wise min of the difference between the central parameter and the lower sampling limit and the difference between the central parameter and the upper sampling limit
     d_min = np.minimum(
-        central_param - param_limits[:, 0], param_limits[:, 1] - central_param
+        model.central_param - model.param_limits[:, 0],
+        model.param_limits[:, 1] - model.central_param,
     )
-    initial_walker_positions = central_param[slice] + d_min[slice] * (
+    initial_walker_positions = model.central_param[slice] + d_min[slice] * (
         np.random.rand(num_walkers, sampling_dim) - 0.5
     )
 

eulerpi/core/transformations.py

@@ -80,10 +80,8 @@ def evaluate_density(
 
     """
 
-    limits = model.param_limits
-
     # Build the full parameter vector for evaluation based on the passed param slice and the constant central points
-    fullParam = model.central_param
+    fullParam = model.central_param.copy()
     fullParam[slice] = param
 
     # Check if the tried parameter is within the just-defined bounds and return the lowest possible density if not.

eulerpi/examples/cpp/CMakeLists.txt

@@ -1,10 +1,11 @@
-cmake_minimum_required(VERSION 3.4...3.18)
+cmake_minimum_required(VERSION 3.4...3.29)
 project(cpp_model LANGUAGES CXX)
 
 find_package(Eigen3 REQUIRED)
 # Maybe you have to do something linke this: sudo ln -s /usr/include/eigen3/Eigen /usr/include/Eigen
 message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}")
 
+set(PYBIND11_FINDPYTHON ON)
 find_package(pybind11 REQUIRED)
 pybind11_add_module(cpp_model wrapper.cpp cpp_model.hpp) # Add wrapper code and all library source files
 

eulerpi/examples/sbml/sbml_caffeine_model.py

@@ -18,7 +18,7 @@ def __init__(
     ) -> None:
         param_ids = ["A", "B"]
         timepoints = np.array([0.5, 1.0])
-        sbml_files = files(package="eulerpi.examples.sbml")
+        sbml_files = files("eulerpi.examples.sbml")
         sbml_file_name = "Caffeine_2Wks_Exponential_decay.xml"
         with as_file(sbml_files.joinpath(sbml_file_name)) as sbml_file:
             super().__init__(

eulerpi/examples/temperature/temperature.py

@@ -69,10 +69,11 @@ def jacobian(self, param):
 
 class TemperatureArtificial(Temperature, ArtificialModelInterface):
     def generate_artificial_params(self, num_data_points: int = -1):
-        paramPath = importlib.resources.path(
-            "eulerpi.examples.temperature", "TemperatureArtificialParams.csv"
-        )
-        true_param_sample = np.loadtxt(paramPath, delimiter=",", ndmin=2)
+        param_resource = importlib.resources.files(
+            "eulerpi.examples.temperature"
+        ).joinpath("TemperatureArtificialParams.csv")
+        with importlib.resources.as_file(param_resource) as param_file:
+            true_param_sample = np.loadtxt(param_file, delimiter=",", ndmin=2)
         return true_param_sample
 
 

pyproject.toml

@@ -49,6 +49,7 @@ sphinx-rtd-theme = "^2.0.0"
 myst-parser = "^3.0.1"
 jupyter = "^1.0.0"
 nbmake = "^1.5.3"
+pyqt6 = "^6.7.0"
 
 [build-system]
 requires = ["poetry-core"]

tests/test_examples.py

@@ -117,15 +117,6 @@ def test_examples(example, inference_type):
     ModelClass = getattr(module, className)
     model: Model = ModelClass()
 
-    # generate artificial data if necessary
-    if model.is_artificial():
-        num_data_points = 100
-        params = model.generate_artificial_params(num_data_points)
-        data = model.generate_artificial_data(params)
-    else:
-        assert dataFileName is not None
-        data = importlib.resources.path(module_location, dataFileName)
-
     # Define kwargs for inference
     kwargs = {}
     kwargs["inference_type"] = inference_type
@@ -137,11 +128,27 @@ def test_examples(example, inference_type):
     elif inference_type == InferenceType.SPARSE_GRID:
         kwargs["num_levels"] = 3
 
-    params, sim_res, densities, result_manager = inference(
-        model,
-        data,
-        **kwargs,
-    )
+    # generate artificial data if necessary
+    if model.is_artificial():
+        num_data_points = 100
+        params = model.generate_artificial_params(num_data_points)
+        data = model.generate_artificial_data(params)
+        params, sim_res, densities, result_manager = inference(
+            model,
+            data,
+            **kwargs,
+        )
+    else:
+        assert dataFileName is not None
+        data_resource = importlib.resources.files(module_location).joinpath(
+            dataFileName
+        )
+        with importlib.resources.as_file(data_resource) as data_file:
+            params, sim_res, densities, result_manager = inference(
+                model,
+                data_file,
+                **kwargs,
+            )
 
     # get all keys of the params dict
     full_slice_str = list(params.keys())[0]

tests/test_fixed_params.py

@@ -36,15 +36,14 @@ def temp_name(low_T, high_T):
 def test_fixed_params(temperatures):
     """ """
     model: Model = TemperatureWithFixedParams(*temperatures)
-    data = importlib.resources.path(
-        "eulerpi.examples.temperature", "TemperatureData.csv"
-    )
-
     run_name = temp_name(*temperatures)
-
-    inference(
-        model,
-        data,
-        num_steps=100,
-        run_name=run_name,
-    )
+    data_resource = importlib.resources.files(
+        "eulerpi.examples.temperature"
+    ).joinpath("TemperatureData.csv")
+    with importlib.resources.as_file(data_resource) as data_file:
+        inference(
+            model,
+            data_file,
+            num_steps=100,
+            run_name=run_name,
+        )

tests/test_heat.py

@@ -43,9 +43,7 @@ def test_heat_model():
         np.arange(0, 1, 0.1),
         # np.arange(np.min(u), np.max(u), (np.max(u) - np.min(u)) / 8),
         linewidths=2,
-        color="k",
         extent=extent,
-        aspect=1,
     )
     ax.clabel(cset, inline=True, fmt="%1.2f", fontsize=10)