lint, use mode None instead of -1

docs/examples/snow-detection/snow-mode.py

@@ -600,9 +600,9 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
 modeled_power_cols = [c for c in data.columns if "Modeled Power" in c]
 
 col = 1
-l = loss_cols[col]
-m = mode_cols[col]
-p = modeled_power_cols[col]
+los = loss_cols[col]
+mod = mode_cols[col]
+pwr = modeled_power_cols[col]
 
 # Color intervals by mode
 cmap = {0: 'r',
@@ -614,7 +614,7 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
 fig, ax = plt.subplots(figsize=(10, 10))
 date_form = DateFormatter("%m/%d")
 ax.xaxis.set_major_formatter(date_form)
-temp = data[~data[m].isna()]
+temp = data[~data[mod].isna()]
 
 # Plot each day individually so we are not exaggerating losses
 days_mapped = temp.index.map(lambda x: x.date())
@@ -623,17 +623,17 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
 
 for d in days:
     temp_grouped = grouped.get_group(d)
-    ax.plot(temp_grouped[p], c='k', ls='--')
-    ax.plot(temp_grouped[p]- temp_grouped[l], c='k')
-    ax.fill_between(temp_grouped.index, temp_grouped[p] - temp_grouped[l],
-                    temp_grouped[p], color='k', alpha=0.2)
+    ax.plot(temp_grouped[pwr], c='k', ls='--')
+    ax.plot(temp_grouped[pwr] - temp_grouped[los], c='k')
+    ax.fill_between(temp_grouped.index, temp_grouped[pwr] - temp_grouped[los],
+                    temp_grouped[pwr], color='k', alpha=0.2)
 
-    chng_pts = np.ravel(np.where(temp_grouped[m].values[:-1]
-                                 - temp_grouped[m].values[1:] != 0))
+    chng_pts = np.ravel(np.where(temp_grouped[mod].values[:-1]
+                                 - temp_grouped[mod].values[1:] != 0))
 
     if len(chng_pts) == 0:
         ax.axvspan(temp_grouped.index[0], temp_grouped.index[-1],
-                   color=cmap[temp_grouped.at[temp_grouped.index[-1], m]],
+                   color=cmap[temp_grouped.at[temp_grouped.index[-1], mod]],
                    alpha=0.05)
     else:
         set1 = np.append([0], chng_pts)
@@ -643,7 +643,7 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
             my_index = temp_grouped.index[start:end]
             ax.axvspan(
                 temp_grouped.index[start], temp_grouped.index[end],
-                color=cmap[temp_grouped.at[temp_grouped.index[end], m]],
+                color=cmap[temp_grouped.at[temp_grouped.index[end], mod]],
                 alpha=0.05)
 
 # Add different colored intervals to legend

pvanalytics/features/snow.py

@@ -143,7 +143,7 @@ def get_transmission(measured_e_e, modeled_e_e, i_mp):
 
 
 def categorize_old(vmp_ratio, transmission, voltage, min_dcv,
-               threshold_vratio, threshold_transmission):
+                   threshold_vratio, threshold_transmission):
 
     """
     Categorizes electrical behavior into a snow-related mode.
@@ -262,7 +262,7 @@ def categorize(vmp_ratio, transmission, voltage, min_dcv,
        50th Photovoltaic Specialists Conference (PVSC), San Juan, PR, USA,
        2023, pp. 1-5, :doi:`10.1109/PVSC48320.2023.10360065`.
     """
-    mode = np.zeros_like(voltage)
+    mode = np.zeros_like(voltage, dtype=int)
 
     umin = voltage > min_dcv  # necessary for all modes except 0
     uvr = np.where(vmp_ratio > threshold_vratio, 3, 1)
@@ -271,6 +271,6 @@ def categorize(vmp_ratio, transmission, voltage, min_dcv,
     mode = umin * (uvr + utrans)
 
     # preserve nan
-    mode[np.isnan(vmp_ratio) | np.isnan(transmission)] = -1
-    
-    return mode
+    mode[np.isnan(vmp_ratio) | np.isnan(transmission)] = None
+
+    return mode

pvanalytics/tests/features/test_snow.py

@@ -27,8 +27,8 @@ def test_categorize():
     threshold_vratio = 0.7
     threshold_transmission = 0.6
     # np.nan, vr<thres, vr<thres, vr=thres, vr>thres, vr>thres, vr<thres
-    # vo>thres, vo>thres, vo>thres, vo>thres, vo>thres, vo>thres, vo<thres 
-    # tr<thres, np.nan, tr>thres, tr<thres, tr<thres, tr<thres, tr>thres 
+    # vo>thres, vo>thres, vo>thres, vo>thres, vo>thres, vo>thres, vo<thres
+    # tr<thres, np.nan, tr>thres, tr<thres, tr<thres, tr<thres, tr>thres
     expected = np.array([np.nan, np.nan, 2, 2, 2, 4, 0])
     result = snow.categorize(vmp_ratio, transmission, voltage, min_dcv,
                              threshold_vratio, threshold_transmission)