[FEAT] Add output check to the test

tests/test_tools.py

@@ -1,42 +1,66 @@
 import pytest
 from glidertest import fetchers, tools
 import matplotlib.pyplot as plt
+import math
+import numpy as np
 
 
-def test_plots():
+def test_plots(start_prof=0, end_prof=100):
     ds = fetchers.load_sample_dataset()
-    tools.plot_basic_vars(ds, end_prof=int(ds.PROFILE_NUMBER.max()))
+    fig, ax = tools.plot_basic_vars(ds,start_prof=start_prof, end_prof=end_prof)
+    assert ax[0].get_ylabel() == 'Depth (m)'
+    assert ax[0].get_xlabel() == f'Average Temperature [C] \nbetween profile {start_prof} and {end_prof}'
+    return fig
 
 
-def test_up_down_bias():
+def test_up_down_bias(v_res=1, xlabel='Salinity'):
     ds = fetchers.load_sample_dataset()
     fig, ax = plt.subplots()
-    df = tools.updown_bias(ds, var='PSAL', v_res=1)
-    tools.plot_updown_bias(df, ax,  xlabel='Salinity')
+    df = tools.updown_bias(ds, var='PSAL', v_res=v_res)
+    bins = np.unique(np.round(ds.DEPTH,0))
+    ncell = math.ceil(len(bins)/v_res)
+    assert len(df) == ncell
+    tools.plot_updown_bias(df, ax,  xlabel=xlabel)
+    lims = np.abs(df.dc)
+    assert ax.get_xlim() == (-np.nanpercentile(lims, 99.5), np.nanpercentile(lims, 99.5))
+    assert ax.get_ylim() == (df.depth.max() + 1, -df.depth.max() / 30)
+    assert ax.get_xlabel() == xlabel
+
 
-
-def test_chl():
+def test_chl(var1='CHLA', var2='BBP700'):
     ds = fetchers.load_sample_dataset()
-    tools.optics_first_check(ds, var='CHLA')
-    tools.optics_first_check(ds, var='BBP700')
+    ax = tools.optics_first_check(ds, var=var1)
+    assert ax.get_ylabel() == var1
+    ax = tools.optics_first_check(ds, var=var2)
+    assert ax.get_ylabel() == var2
     with pytest.raises(KeyError) as e:
         tools.optics_first_check(ds, var='nonexistent_variable')
 
 
-def test_quench_sequence():
+def test_quench_sequence(xlabel='Temperature [C]',ylim=45):
     ds = fetchers.load_sample_dataset()
     if not "TIME" in ds.indexes.keys():
         ds = ds.set_xindex('TIME')
     fig, ax = plt.subplots()
-    tools.plot_section_with_srss(ds, 'CHLA')
+    tools.plot_section_with_srss(ds, 'CHLA', ax,ylim=ylim)
+    assert ax.get_ylabel() == 'Depth [m]'
+    assert ax.get_ylim() == (ylim, -ylim / 30)
+
     dayT, nightT = tools.day_night_avg(ds, sel_var='TEMP')
-    fig, ax = plt.subplots()
-    tools.plot_daynight_avg(dayT, nightT, ax, xlabel='Temperature [C]') 
+    assert len(nightT.dat.dropna()) > 0
+    assert len(dayT.dat.dropna()) > 0
+
+    fig, ax = tools.plot_daynight_avg(dayT, nightT,xlabel=xlabel) 
+    assert ax.get_ylabel() == 'Depth [m]'
+    assert ax.get_xlabel() == xlabel
 
-def test_temporal_drift():
+def test_temporal_drift(var='DOXY'):
     ds = fetchers.load_sample_dataset()
     fig, ax = plt.subplots(1, 2)
-    tools.check_temporal_drift(ds,'DOXY', ax)
+    tools.check_temporal_drift(ds,var, ax)
+    assert ax[1].get_ylabel() == 'Depth (m)'
+    assert ax[0].get_ylabel() == var
+    assert ax[1].get_xlim() == (np.nanpercentile(ds[var], 0.01), np.nanpercentile(ds[var], 99.99))
     tools.check_temporal_drift(ds,'CHLA')
 
 def test_profile_check():
@@ -50,4 +74,4 @@ def test_check_monotony():
     temperature_monotony = tools.check_monotony(ds.TEMP)
     assert profile_number_monotony
     assert not temperature_monotony
-
+