Updating docs to handle_nan

CHANGES.txt

@@ -142,4 +142,4 @@
           approaches are now intended to be applied as penalties to GPP after P Model
           fitting, allowing the two to be compared from the same P Model outputs.
 0.10.1  - Addition of draft extention of subdaily memory_effect function that allows for
-          missing data arising from non-estimable Jmax and Vcmax.
+          missing data arising from non-estimable Jmax and Vcmax.

docs/source/users/pmodel/subdaily_details/subdaily_calculations.md

@@ -20,7 +20,7 @@ steps used in the estimation process in order to show intermediates results but
 practice, as shown in the [worked example](worked_example), most of these calculations
 are handled internally by the model fitting in `pyrealm`.
 
-```{code-cell} ipython3
+```{code-cell}
 :tags: [hide-input]
 
 from importlib import resources
@@ -46,16 +46,15 @@ The code below uses half hourly data from 2014 for the [BE-Vie FluxNET
 site](https://fluxnet.org/doi/FLUXNET2015/BE-Vie), which was also used as a
 demonstration in {cite:t}`mengoli:2022a`.
 
-```{code-cell} ipython3
-
+```{code-cell}
 data_path = resources.files("pyrealm_build_data") / "subdaily_BE_Vie_2014.csv"
 data = np.genfromtxt(
     data_path,
     names=True,
     delimiter=",",
     dtype=None,
     encoding="UTF8",
-    missing_values = "NA",
+    missing_values="NA",
 )
 
 # Extract the key half hourly timestep variables
@@ -74,7 +73,7 @@ This dataset can then be used to calculate the photosynthetic environment at the
 subdaily timescale. The code below also estimates GPP under the standard P Model with no
 slow responses for comparison.
 
-```{code-cell} ipython3
+```{code-cell}
 # Calculate the photosynthetic environment
 subdaily_env = PModelEnvironment(
     tc=temp_subdaily,
@@ -100,7 +99,7 @@ best to sample those conditions. Typically those might be the observed environme
 conditions at the observation closest to noon, or the mean environmental conditions in a
 window around noon.
 
-```{code-cell} ipython3
+```{code-cell}
 # Create the fast slow scaler
 fsscaler = FastSlowScaler(datetime_subdaily)
 
@@ -114,19 +113,18 @@ fsscaler.set_window(
 pmodel_fastslow = FastSlowPModel(
     env=subdaily_env,
     fs_scaler=fsscaler,
+    handle_nan=True,
     ppfd=ppfd_subdaily,
     fapar=fapar_subdaily,
 )
 ```
 
-```{code-cell} ipython3
+```{code-cell}
 :tags: [hide-input]
 
 idx = np.arange(48 * 120, 48 * 130)
 plt.figure(figsize=(10, 4))
-plt.plot(
-    datetime_subdaily[idx], pmodel_subdaily.gpp[idx], label="Instantaneous model"
-)
+plt.plot(datetime_subdaily[idx], pmodel_subdaily.gpp[idx], label="Instantaneous model")
 plt.plot(datetime_subdaily[idx], pmodel_fastslow.gpp[idx], "r-", label="Slow responses")
 plt.ylabel = "GPP"
 plt.legend(frameon=False)
@@ -145,7 +143,7 @@ The daily average conditions during the acclimation window can be sampled and us
 inputs to the standard P Model to calculate the optimal behaviour of plants under those
 conditions.
 
-```{code-cell} ipython3
+```{code-cell}
 # Get the daily acclimation conditions for the forcing variables
 temp_acclim = fsscaler.get_daily_means(temp_subdaily)
 co2_acclim = fsscaler.get_daily_means(co2_subdaily)
@@ -176,7 +174,7 @@ temperatures so $J_{max}$ and $V_{cmax}$ must first be standardised to expected
 at 25°C. This is acheived by multiplying by the reciprocal of the exponential part of
 the Arrhenius equation ($h^{-1}$ in {cite}`mengoli:2022a`).
 
-```{code-cell} ipython3
+```{code-cell}
 # Are these any of the existing values in the constants?
 ha_vcmax25 = 65330
 ha_jmax25 = 43900
@@ -191,18 +189,18 @@ jmax25_acclim = pmodel_acclim.jmax * (1 / calc_ftemp_arrh(tk_acclim, ha_jmax25))
 The memory effect can now be applied to the three parameters with slow
 responses to calculate realised values, here using the default 15 day window.
 
-```{code-cell} ipython3
+```{code-cell}
 # Calculation of memory effect in xi, vcmax25 and jmax25
 xi_real = memory_effect(pmodel_acclim.optchi.xi, alpha=1 / 15)
-vcmax25_real = memory_effect(vcmax25_acclim, alpha=1 / 15)
-jmax25_real = memory_effect(jmax25_acclim, alpha=1 / 15)
+vcmax25_real = memory_effect(vcmax25_acclim, alpha=1 / 15, handle_nan=True)
+jmax25_real = memory_effect(jmax25_acclim, alpha=1 / 15, handle_nan=True)
 ```
 
 The plots below show the instantaneously acclimated values for  $J_{max25}$,
 $V_{cmax25}$ and $\xi$ in grey along with the realised slow reponses.
 applied.
 
-```{code-cell} ipython3
+```{code-cell}
 :tags: [hide-input]
 
 fig, axes = plt.subplots(1, 3, figsize=(16, 5))
@@ -236,7 +234,7 @@ temperature at fast scales:
 * These values are adjusted to the actual half hourly temperatures to give the fast
   responses of $J_{max}$ and $V_{cmax}$.
 
-```{code-cell} ipython3
+```{code-cell}
 tk_subdaily = subdaily_env.tc + pmodel_subdaily.const.k_CtoK
 
 # Fill the realised jmax and vcmax from subdaily to daily
@@ -256,7 +254,7 @@ $\Gamma^\ast$ with the realised slow responses of $\xi$. The original implementa
 $\Gamma^{\ast}$ and $c_a$, interpolated to the subdaily timescale and the actual
 subdaily variation in VPD.
 
-```{code-cell} ipython3
+```{code-cell}
 # Interpolate xi to subdaily scale
 xi_subdaily = fsscaler.fill_daily_to_subdaily(xi_real)
 
@@ -273,7 +271,7 @@ Model, where $c_i$ includes the slow responses of $\xi$ and $V_{cmax}$ and $J_{m
 include the slow responses of $V_{cmax25}$ and $J_{max25}$ and fast responses to
 temperature.
 
-```{code-cell} ipython3
+```{code-cell}
 # Calculate Ac
 Ac_subdaily = (
     vcmax_subdaily
@@ -300,3 +298,7 @@ GPP_subdaily = np.minimum(Ac_subdaily, Aj_subdaily) * PModelConst.k_c_molmass
 diff = GPP_subdaily - pmodel_fastslow.gpp
 print(np.nanmin(diff), np.nanmax(diff))
 ```
+
+```{code-cell}
+
+```

docs/source/users/pmodel/subdaily_details/worked_example.md

@@ -63,7 +63,7 @@ focal_datetime = np.where(datetimes == np.datetime64("2018-06-12 12:00:00"))[0]
 # Plot the temperature data for an example timepoint and show the sites
 focal_temp = ds["Tair"][focal_datetime] - 273.15
 focal_temp.plot()
-plt.plot(sites["lon"], sites["lat"], "xr");
+plt.plot(sites["lon"], sites["lat"], "xr")
 ```
 
 The WFDE data need some conversion for use in the PModel, along with the definition of
@@ -119,7 +119,12 @@ fsscaler.set_window(
     half_width=np.timedelta64(1, "h"),
 )
 fs_pmod = FastSlowPModel(
-    env=pm_env, fs_scaler=fsscaler, fapar=fapar, ppfd=ppfd, alpha=1 / 15
+    env=pm_env,
+    fs_scaler=fsscaler,
+    handle_nan=True,
+    fapar=fapar,
+    ppfd=ppfd,
+    alpha=1 / 15,
 )
 ```
 
@@ -161,7 +166,7 @@ ax2.set_title("Fast Slow")
 # Add a colour bar
 subfig2.colorbar(
     im, ax=[ax1, ax2], shrink=0.55, label=r"GPP ($\mu g C\,m^{-2}\,s^{-1}$)"
-);
+)
 ```
 
 ## Time series predictions
@@ -200,7 +205,3 @@ for ax, st in zip(axes, sites["stid"].values):
 axes[0].legend(loc="lower center", bbox_to_anchor=[0.5, 1], ncols=2, frameon=False)
 plt.tight_layout()
 ```
-
-```{code-cell}
-
-```