Adjust pl.Musical and pl.MusicalClassified for defense talk

Plots.py

@@ -793,8 +793,8 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
     ## Dummy for legend
     #axs1.scatter(0,-50,s=20,marker='s',color='black',alpha=0.7,label=r'3 $\leq$ Kp $\leq$ 4+$^+$')
     axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,label=r'SN')
-    axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,linestyle='--',label=r'HMC$_{11y}$')
-    axs1.scatter(0,-50,s=20,marker='s',color='dimgray',label=r'Event')
+    #axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,linestyle='--',label=r'HMC$_{11y}$')
+    axs1.scatter(0,-50,s=20,marker='o',color='dimgray',label=r'Storm peak')
 
     # Plot storm events from catalogs (papers)
     cm = plt.cm.get_cmap('seismic')
@@ -807,7 +807,11 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
         x_t[i] = np.mod(diff_t[i].total_seconds()/(60*60*24),SolarRot)
 
     z = Reference[KnownE,0]
-    sc = axs1.scatter(x_t, y_t, c=z, vmin=0.0, vmax=1.0, s=5, cmap=cm, zorder = 3)
+    znew = np.zeros(len(z),dtype=float)+0.25
+    z1 = np.where(z==1)[0]
+    znew[z1] = 0.75
+    
+    sc = axs1.scatter(x_t, y_t, c=znew, vmin=0.0, vmax=1.0, s=10, cmap=cm, zorder = 3)
     cbar_axis = fig.add_axes([0.45, 0.032, 0.3, 0.01]) 
     cbar = plt.colorbar(sc,cax=cbar_axis,use_gridspec=False, orientation ='horizontal',ticks=[0,0.5,1])
     #cbar.set_label('CME probability',fontsize=8)
@@ -820,10 +824,12 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
 
     # Plots class occurrences
     nip = 0
+    c = [0.75,0.25]
     for i in [1,0]:
         zi = np.where(z==i)[0]
         ni, bins = np.histogram(HTime[Reference[KnownE[zi],1],4],np.arange(1900,2017,1))
-        axs2.barh(np.arange(1900.5,2016.5,1), ni, left = nip, height=0.9, color=cm(float(i)),zorder=5)
+        #axs2.barh(np.arange(1900.5,2016.5,1), ni, left = nip, height=0.9, color=cm(float(i)),zorder=5)
+        axs2.barh(np.arange(1900.5,2016.5,1), ni, left = nip, height=0.9, color=cm(c[i]),zorder=5)
         nip += ni
             
     #Turner_CIR = np.where(np.logical_and(Reference[:,2] == b'Turner',Target['type'] ==0))[0]
@@ -864,7 +870,7 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
 
     handles, labels = axs1.get_legend_handles_labels()
     order = [0,1]
-    axs1.legend([handles[idx] for idx in order],[labels[idx] for idx in order], ncol=5,frameon=True,fontsize=11,loc='center left',bbox_to_anchor=[-0.1,-0.037])
+    axs1.legend([handles[idx] for idx in order],[labels[idx] for idx in order], ncol=5,frameon=True,fontsize=11,loc='center left',bbox_to_anchor=[-0.15,-0.037])
     axs1.set_ylim([np.nanmin(y),np.nanmax(y)])
     
     #handles, labels = axs1.get_legend_handles_labels()
@@ -886,7 +892,7 @@ def Musical(Time,HTime,Storms,Kp_all,KpHours_all,SN,SNYears,HMC,HMC_filt,Referen
     axs3.plot((SN[SN_start:SN_end]/max(SN[SN_start:SN_end]))**1,SNYears[SN_start:SN_end],color='dimgray',linewidth=1.5, label='SN')
 
     # Plot HMC threshold scaling
-    axs3.plot((HMC_filt/min(HMC_filt))**1,HTime[:,4],color='dimgray',linestyle='--',linewidth=1.5, label='HMC')
+    # axs3.plot((HMC_filt/min(HMC_filt))**1,HTime[:,4],color='dimgray',linestyle='--',linewidth=1.5, label='HMC')
     
     # Plot histogram
     n, bins, patches = axs2.hist(x=HTime[Storms,4],bins=np.arange(1900,2017,1),orientation='horizontal',histtype='stepfilled',color='silver',alpha=1)
@@ -981,8 +987,8 @@ def MusicalClassified(Time,HTime,Reference,Source,Storms,Class_Proba,Class,Kp_al
     ## Dummy for legend
     #axs1.scatter(0,-50,s=20,marker='s',color='black',alpha=0.7,label=r'Kp $\geq$ 4$^+$')
     axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,label=r'SN', zorder=5)
-    axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,linestyle='--',label=r'HMC$_{11y}$', zorder=5)
-    axs1.scatter(0,-50,s=20,marker='s',color='black',label=r'Event')
+    #axs1.plot([0,1],[-50,-25],color='dimgray',linewidth=1.5,linestyle='--',label=r'HMC$_{11y}$', zorder=5)
+    axs1.scatter(0,-50,s=20,marker='o',color='black',label=r'Storm peak')
 
     # Plot storm events (my picks)
     CIRs_pred = np.where(Class_Proba[:,0] > 0.5)[0]
@@ -1011,7 +1017,7 @@ def MusicalClassified(Time,HTime,Reference,Source,Storms,Class_Proba,Class,Kp_al
     
     handles, labels = axs1.get_legend_handles_labels()
     order = [0,1]
-    axs1.legend([handles[idx] for idx in order],[labels[idx] for idx in order], ncol=5,frameon=True,fontsize=11,loc='center left',bbox_to_anchor=[-0.1,-0.037])
+    axs1.legend([handles[idx] for idx in order],[labels[idx] for idx in order], ncol=5,frameon=True,fontsize=11,loc='center left',bbox_to_anchor=[-0.15,-0.037])
     axs1.set_ylim([np.nanmin(y),np.nanmax(y)])
 
     ################################################
@@ -1048,7 +1054,7 @@ def MusicalClassified(Time,HTime,Reference,Source,Storms,Class_Proba,Class,Kp_al
     SN_end = np.where(SNYears == 2016.5)[0][0]
     #axs3.plot((SN[SN_start:SN_end,1]-np.min(SN[SN_start:SN_end,1]))/np.ptp(SN[SN_start:SN_end,1]),SN[SN_start:SN_end,0],color='black',linewidth=1.5, label='SN')
     axs3.plot((SN[SN_start:SN_end]/max(SN[SN_start:SN_end]))**1,SNYears[SN_start:SN_end],color='white',linewidth=1.5, label='SN')
-    axs3.plot((HMC_filt/min(HMC_filt))**1,HTime[:,4],color='white',linestyle='--',linewidth=1.5, label='HMC')
+    #axs3.plot((HMC_filt/min(HMC_filt))**1,HTime[:,4],color='white',linestyle='--',linewidth=1.5, label='HMC')
 
     ####################################################################
     #Class occurrence in separate plot