In [75]:
fig = plt.figure(figsize=(15,10),facecolor='w')
G = gridspec.GridSpec(1,2,wspace=0.16,hspace=0.0)
ax1=plt.subplot(G[0,0],facecolor='none')
# plotting the horizontal bars (number of samples per ant genera per location)
plt.ylim(-0.5,20.5);plt.xlim(0,80)
for x in range(0,82,10):
ax1.axvspan(xmin=x,xmax=x+5,ymin=ax1.get_ylim()[0],ymax=ax1.get_ylim()[1],facecolor="#bababa",alpha=0.1,zorder=1)
y=0 ; yticks=[]
height=1
for loc in locations[::-1]: # iterate through locations ordered geographically
temp=datatococa[datatococa['Locality']==loc].groupby(by='Ant')['Collection'].count().reset_index() # columns: Ant Collection
yticks.append(y)
left=0
for spp in temp['Ant'].unique(): # iterate through ant species
width=temp[temp['Ant']==spp]['Collection'].values
ax1.barh(y=y,width=width,height=height,left=left,color=ant_dict[spp],alpha=1)
left+=width
y+=2 if loc in change_areas else 1
legend(ant_dict,marker='s',fontsize=13,ax=ax1,loc='lower left',bbox_to_anchor=(0.5, 0.2)) # (x, y, width, height)
[ax1.spines[loc].set_visible(False) for loc in ['top','right','left']]
plt.yticks(ticks=yticks,labels=['%s. %s'%(i[0],i[1]) for i in loc_tuples[::-1]],fontsize=16,fontname='Arial',color='#4f4f4f')
plt.xticks(color='#4f4f4f',fontsize=15)
# plotting the map
ax2=plt.subplot(G[0,1])
plt.xlim(0,ncols); plt.ylim(0,nrows)
# add grid behind
for y in range(0,6500,350):
ax2.hlines(y=y,xmin=0,xmax=ncols,lw=1,ls='--',color='#bababa',zorder=1,alpha=0.4)
for x in range(int(ax2.get_xlim()[0]),int(ax2.get_xlim()[1]),422):
ax2.vlines(x=x,ymin=0,ymax=int(ax2.get_xlim()[1]),lw=1,ls='--',color='#bababa',zorder=1,alpha=0.4)
vmin=1; vmax=5300
# lazy plotting the tiff
sns.heatmap(rasterplot,cmap=cmap,cbar=False,vmin=vmin,vmax=vmax,alpha=1,zorder=2,ax=ax2,xticklabels=False,yticklabels=False)
# plotting polygons to shade countries other than Colombia
for loc in polygons.keys():
ax2.add_collection(PatchCollection(polygons[loc],facecolor='w',edgecolor='w',alpha=0.5,linewidth=0.1,zorder=2))
ax2.plot()
[ax2.spines[loc].set_visible(True) for loc in ['top','right','left','bottom']]
for loc in ax2.spines:
sp=ax2.spines[loc]
sp.set_zorder(6)
# adding the degrees on each axis
plt.xticks(labels=['%2.f$^\circ$'%(x) for x in range(-79,-67,1)],ticks=[x for x in range(int(ax2.get_xlim()[0]),int(ax2.get_xlim()[1]),422)],fontname='Arial',fontsize=14)
ax2.set_yticks(ticks=[y for y in range(0,6500,350)]) #,labels=['%2.f'%(y) for y in range(-5,13,1)],fontname='Arial',fontsize=15)
ax2.set_yticklabels(labels=['%2.f$^\circ$'%(y) for y in range(-5,14,1)][::-1],fontname='Arial',fontsize=14)
plt.tick_params(axis='both', which='major', pad=5,labelcolor='#4f4f4f',color='#4f4f4f')
ax2.yaxis.tick_right()
# plotting the collecting locations and their numbers
ax2.scatter(datatococa['longs'],datatococa['lats'],s=42,zorder=4,facecolor=datatococa['Locality'].map(area_dict).map(area_dict_col),edgecolor='none')
ax2.scatter(datatococa['longs'],datatococa['lats'],s=100,zorder=3,facecolor='#e6e6e6',alpha=1,edgecolor='none') # bacground
for row in datatococa.groupby(by=['Locality']).first().reset_index().itertuples(): # plot the population numbers using the first coordinate in the table
ax2.text(x=row.longs-180,y=row.lats-80,s='%s'%(loc_num_dict[row.Locality]),fontname='Arial',fontsize=12,zorder=5)
# legend
legend(area_dict_col,marker='o',fontsize=13,ax=ax2,loc='upper right',style='normal',bbox_to_anchor=(0.99,0.99),markersize=9,labelspacing=0.5) # (x, y, width, height)
# adding distance scale
ax2.hlines(y=5700,xmin=345,xmax=345+(350*4.5),lw=1,ls='-',zorder=3)
ax2.text(x=(345+345+(350*4.5))/2,y=5600,s='500 Km',fontsize=12,fontname='Arial',ha='center',zorder=3)
# adding colorbar
axcb=fig.add_axes([0.57,0.16, 0.15, 0.015], frame_on=False) # left(leftx) | bottom | width | height
cb=mpl.colorbar.ColorbarBase(axcb,cmap=cmap,norm=mpl.colors.Normalize(vmin=vmin,vmax=vmax),orientation='horizontal',alpha=1.0,drawedges=False)#
axcb.xaxis.set_major_locator(mpl.ticker.MultipleLocator(2500))
axcb.set_xlim(0,5300)
axcb.xaxis.set_label_position('top')
axcb.set_xlabel('Relief',size=12)
# plt.savefig('./Fig1_map_antcollections.pdf',bpi=300)
plt.show()
