Adding profiling (poetry and new test)

I've not included the data file that the test uses. Will confer and consider best way of handling large file in the context of a cloud-based CI test

pyproject.toml

@@ -27,6 +27,7 @@ scipy = "^1.7.3"
 Bottleneck = "^1.3.5"
 tabulate = "^0.8.10"
 dacite = "^1.6.0"
+pytest-profiling = "^1.7.0"
 
 [tool.poetry.group.types.dependencies]
 types-tabulate = "^0.9.0.0"

tests/pmodel/test_profiling.py

@@ -0,0 +1,36 @@
+import pytest
+
+def test_profiling_example():
+
+    from importlib import resources
+    import xarray
+    import numpy as np
+    from pyrealm.pmodel import PModel, PModelEnvironment
+
+    # Loading the dataset:
+    dpath = resources.files("pyrealm_build_data") / "inputs_data_24.25.nc"   
+
+    ds = xarray.load_dataset(dpath)
+
+    # Variable set up
+    # Air temperature in Kelvin
+    tc = (ds["temp"]).to_numpy()
+    # Atmospheric pressure in Pascals
+    patm = ds["patm"].to_numpy()
+    # Obtain VPD and remove negative values
+    vpd = ds["vpd"].to_numpy()
+    vpd = np.clip(vpd, 0, np.inf)
+    # Extract fAPAR (unitless)
+    fapar = ds["fapar"].to_numpy()
+    # Gather PPFD µmole/m2/s1
+    ppfd = ds["ppfd"].to_numpy()
+    # Define atmospheric CO2 concentration (ppm)
+    co2 = np.ones_like(tc) * 400
+
+    # Generate and check the PModelEnvironment
+    pm_env = PModelEnvironment(tc=tc, patm=patm, vpd=vpd, co2=co2)
+
+    # Standard PModel
+    pmod = PModel(env=pm_env, kphio=1 / 8)
+    pmod.estimate_productivity(fapar=fapar, ppfd=ppfd)
+    pmod.summarize()