using TimeSeries_TimedeltaFormatter_improved also in plot_aardappelgr…

…afiek (#78)
Deltares-research · Jun 14, 2024 · 7129e54 · 7129e54
1 parent f36f710
commit 7129e54
Showing 1 changed file with 26 additions and 22 deletions.
diff --git a/kenmerkendewaarden/havengetallen.py b/kenmerkendewaarden/havengetallen.py
@@ -14,7 +14,10 @@
                                                 compute_actual_counts,
                                                 compute_expected_counts,
                                                 )
-from kenmerkendewaarden.utils import raise_extremes_with_aggers
+from kenmerkendewaarden.utils import (raise_extremes_with_aggers,
+                                      TimeSeries_TimedeltaFormatter_improved,
+                                      )
+from matplotlib.ticker import MultipleLocator
 
 __all__ = ["calc_havengetallen",
            "plot_HWLW_pertimeclass",
@@ -200,49 +203,50 @@ def plot_HWLW_pertimeclass(df_ext, df_havengetallen):
 
 
 def plot_aardappelgrafiek(df_havengetallen):
-
-    HWLW_culmhr_summary = df_havengetallen.loc[:11].copy() #remove mean column
-
-    def timeTicks(x, pos):
-        d = dt.timedelta(hours=np.abs(x))
-        if np.sign(x)>0:
-            d_str = str(d)
-        else:
-            d_str = '-'+str(d)
-        return d_str
+    # remove mean column
+    HWLW_culmhr_summary = df_havengetallen.loc[:11].copy()
 
     fig, (ax1,ax2) = plt.subplots(1,2,figsize=(7.5,4), sharex=False)
     ax1.set_title('HW')
     ax1.set_xlabel('maansverloop in uu:mm:ss' )
     ax1.set_ylabel('waterstand in m t.o.v. NAP')
-    ax1.plot(HWLW_culmhr_summary['HW_delay_median'].dt.total_seconds()/3600,HWLW_culmhr_summary['HW_values_median'],'.-')
-    ax1.xaxis.set_major_formatter(timeTicks)
+    ax1.plot(HWLW_culmhr_summary['HW_delay_median'], HWLW_culmhr_summary['HW_values_median'],'.-')
+    ax1.xaxis.set_major_formatter(TimeSeries_TimedeltaFormatter_improved())
     ax1.grid()
     ax2.set_title('LW')
     ax2.set_xlabel('maansverloop in uu:mm:ss' )
     ax2.set_ylabel('waterstand in m t.o.v. NAP')
-    ax2.plot(HWLW_culmhr_summary['LW_delay_median'].dt.total_seconds()/3600,HWLW_culmhr_summary['LW_values_median'],'.-')
-    ax2.xaxis.set_major_formatter(timeTicks)
+    ax2.plot(HWLW_culmhr_summary['LW_delay_median'], HWLW_culmhr_summary['LW_values_median'],'.-')
+    ax2.xaxis.set_major_formatter(TimeSeries_TimedeltaFormatter_improved())
     ax2.grid()
     for iH,row in HWLW_culmhr_summary.iterrows():
-        ax1.text(row['HW_delay_median'].total_seconds()/3600,row['HW_values_median'], str(int(iH)))
-        ax2.text(row['LW_delay_median'].total_seconds()/3600,row['LW_values_median'], str(int(iH)))
+        # plt.text only supports values as nanoseconds
+        ax1.text(row['HW_delay_median'].total_seconds()*1e9, row['HW_values_median'], str(int(iH)))
+        ax2.text(row['LW_delay_median'].total_seconds()*1e9, row['LW_values_median'], str(int(iH)))
     #set equal ylims
     ax1_xlimmean = np.mean(ax1.get_xlim())
     ax2_xlimmean = np.mean(ax2.get_xlim())
     ax1_ylimmean = np.mean(ax1.get_ylim())
     ax2_ylimmean = np.mean(ax2.get_ylim())
-    xlimrange = 2
+    xlimrange = 2*3600e9 # in nanoseconds
     ylimrange = 1
     ax1.set_xlim([ax1_xlimmean-xlimrange/2,ax1_xlimmean+xlimrange/2])
     ax2.set_xlim([ax2_xlimmean-xlimrange/2,ax2_xlimmean+xlimrange/2])
     ax1.set_ylim([ax1_ylimmean-ylimrange/2,ax1_ylimmean+ylimrange/2])
     ax2.set_ylim([ax2_ylimmean-ylimrange/2,ax2_ylimmean+ylimrange/2])
+    # set nice xtick interval
+    ax1.xaxis.set_major_locator(MultipleLocator(base=0.5*3600e9))
+    ax2.xaxis.set_major_locator(MultipleLocator(base=0.5*3600e9))
+
     #plot gemtij dotted lines
-    ax1.plot(ax1.get_xlim(),[HWLW_culmhr_summary['HW_values_median'].mean(),HWLW_culmhr_summary['HW_values_median'].mean()],'k--')
-    ax1.plot([HWLW_culmhr_summary['HW_delay_median'].mean().total_seconds()/3600,HWLW_culmhr_summary['HW_delay_median'].mean().total_seconds()/3600],ax1.get_ylim(),'k--')
-    ax2.plot(ax2.get_xlim(),[HWLW_culmhr_summary['LW_values_median'].mean(),HWLW_culmhr_summary['LW_values_median'].mean()],'k--')
-    ax2.plot([HWLW_culmhr_summary['LW_delay_median'].mean().total_seconds()/3600,HWLW_culmhr_summary['LW_delay_median'].mean().total_seconds()/3600],ax2.get_ylim(),'k--')
+    ax1.hlines(HWLW_culmhr_summary['HW_values_median'].mean(), 
+               ax1.get_xlim()[0], ax1.get_xlim()[1], color='k', linestyle='--')
+    ax1.vlines(HWLW_culmhr_summary['HW_delay_median'].mean().total_seconds()*1e9, 
+               ax1.get_ylim()[0], ax1.get_ylim()[1], color='k', linestyle='--')
+    ax2.hlines(HWLW_culmhr_summary['LW_values_median'].mean(), 
+               ax2.get_xlim()[0], ax2.get_xlim()[1], color='k', linestyle='--')
+    ax2.vlines(HWLW_culmhr_summary['LW_delay_median'].mean().total_seconds()*1e9, 
+               ax2.get_ylim()[0], ax2.get_ylim()[1], color='k', linestyle='--')
     fig.tight_layout()
 
     axs = (ax1,ax2)