Add functions and update of calc_ref_state.py to plot lat-lon fields

calc_ref_state.py

@@ -9,6 +9,8 @@
 import numpy as np      # 
 import time             # to measure elapsed time
 from get_data import get_data_woa2009 as read_data
+from myplot import myplot_2dmap # plot of lat-lon fields
+from myplot_inputs import mycalc_create_pn # create plotname
 # ############################################################################################
 
 # Input 0 ####################################################################################
@@ -23,7 +25,7 @@ print 'MANUAL DEFINED OUTPUTS MISSING'
 dim       = ('lon','lat','z','time',)
 dir_plot  = '/glusterfs/inspect/users/xg911182/Code/Python/plots_test/'
 variables = {'s':{},'theta':{},'grd':[],'valid':[]}
-
+lonlat_reg = [25.0, 25.0+360.0, -90.0, 90.0] # plotting range for 2dmap
 # Input 1 ####################################################################################
 # Physical constants
 # input         : None 
@@ -142,3 +144,36 @@ end_time = time.time()
 print 'Elapsed time to plot and save reference state: '+ '{:.2f}'.format(end_time-beg_time)+'s'
 print ' ----------------------------------------------'
 
+# step g #####################################################################################
+# plot of full 2d fields
+# input         : None
+# output        : None
+# saved/created : plots saved in dir_plot
+#     dir_plot  : output of Input 0 
+# ############################################################################################
+beg_time = time.time()
+print ''
+print ' ----------------------------------------------'
+print 'Insert code to plot full 2d fields'
+# define plotname
+list_allkeys = data.keys()
+i_dep = 0
+i_t   = 0
+date_str = '2009'
+for i_key in range(0,len(list_allkeys)):
+    print 'put this plotname stuff into a function'
+    pname_str = mycalc_create_pn({'lonlat':lonlat_reg})
+    plotname_dum = dir_plot + 'Map2d_' + list_allkeys[i_key] + pname_str['lonlat'] + \
+        '_z' + str(grd.z[i_dep]) + grd.Uz + '_' + date_str + '.png'
+    # define plot input
+    title_in = 'Depth='+'{:.1f}'.format(grd.z[i_dep])+grd.Uz
+    data_in = data[list_allkeys[i_key]]['val'][:,:,i_dep,i_t]
+    data_in[data_in==data[list_allkeys[i_key]]['fill_value']] = np.nan
+    myplot_2dmap(data_in,grd,lonlat_range=lonlat_reg,saveplot=1,\
+        title_c=title_in,plotname=plotname_dum)
+    del data_in, title_in, pname_str,plotname_dum
+print ' ----------------------------------------------'
+end_time = time.time()
+print 'Elapsed time to plot 2d fields (lat-lon): '+ '{:.2f}'.format(end_time-beg_time)+'s'
+print ' ----------------------------------------------'
+

mycalc_matrix.py

@@ -0,0 +1,108 @@
+def mymatrix_smooth(data_in,smooth_dim=(0,),data_per=(0,),window_len=3,window='step'):
+    # from mymatrix_operations import mymatrix_smooth
+    import numpy as np
+    from mymath import mymath_multiplylist
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # general input info %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    shape_data_in = data_in.shape
+    n_dim = len(shape_data_in)
+    # reshape matrix to get smoothing dimension into 0 and 1 dimension
+    rs_beg = np.array(range(0,n_dim))
+    rs_end = np.array(range(0,n_dim))
+    if n_dim > 1:
+        rs_beg[np.array([0,smooth_dim[0]])] = (smooth_dim[0],0)
+        if len(smooth_dim) > 1:
+            rs_beg[np.array([1,smooth_dim[1]])] = (rs_beg[smooth_dim[1]],rs_beg[1])
+            rs_end[np.array([1,smooth_dim[1]])] = (smooth_dim[1],1)
+        rs_end[np.array([0,smooth_dim[0]])] = (rs_end[smooth_dim[0]],rs_end[0])
+    # transpose data, so that smooth_dim are in the first two dimensions
+    data_in = data_in.transpose(rs_beg)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # error checking %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if len(smooth_dim) > 2:
+        exit("smoothing only possible in two dimensions")
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # define window function %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    window_len = int(window_len)
+    if window=='step':
+        window_num = np.ones(window_len/2+1);
+    elif window=='hanning':
+        window_num = (np.cos(np.array(range(0,window_len/2+1),dtype=float)/(window_len/2)*np.pi)+1)/2;
+    else:
+        print 'Defined window function not available for smoothing. No smoothing.'
+        window_num = 0;
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # create smoothing matrices for matrix multiplications %%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # Allocate smoothing matrix
+    if n_dim == 1:
+        smooth_ML = 1.
+        smooth_MR = np.zeros((shape_data_in[0],shape_data_in[0]),dtype=float)
+        np.fill_diagonal(smooth_MR[:], window_num[0])
+    else:
+        smooth_ML = np.zeros((shape_data_in[smooth_dim[0]],shape_data_in[smooth_dim[0]]),dtype=float)
+        if len(smooth_dim) > 1:
+            smooth_MR = np.zeros((shape_data_in[smooth_dim[1]],shape_data_in[smooth_dim[1]]),dtype=float)
+            np.fill_diagonal(smooth_MR[:], window_num[0])
+        else:
+            smooth_MR = 1.
+        np.fill_diagonal(smooth_ML[:], window_num[0])
+
+    # Fill smoothing matrix with window_num values
+    if n_dim == 1:
+        for i_d in range(1,window_len/2+1):
+            np.fill_diagonal(smooth_MR[i_d:], window_num[i_d])
+            np.fill_diagonal(smooth_MR[:,i_d:], window_num[i_d])
+            # modify smooth_matrix in case of periodic data
+            if data_per[0] == 1:
+                np.fill_diagonal(smooth_MR[(shape_data_in[0]-i_d):], window_num[i_d])
+                np.fill_diagonal(smooth_MR[:,(shape_data_in[0]-i_d):], window_num[i_d])
+    else:
+        for i_d in range(1,window_len/2+1):
+            np.fill_diagonal(smooth_ML[i_d:], window_num[i_d])
+            np.fill_diagonal(smooth_ML[:,i_d:], window_num[i_d])
+            # modify smooth_matrix in case of periodic data
+            if data_per[0] == 1:
+                np.fill_diagonal(smooth_ML[(shape_data_in[0]-i_d):], window_num[i_d])
+                np.fill_diagonal(smooth_ML[:,(shape_data_in[0]-i_d):], window_num[i_d])
+
+        if len(smooth_dim) > 1:
+            for i_d in range(1,window_len/2+1):
+                np.fill_diagonal(smooth_MR[i_d:], window_num[i_d])
+                np.fill_diagonal(smooth_MR[:,i_d:], window_num[i_d])
+                # modify smooth_matrix in case of periodic data
+                if data_per[1] == 1:
+                    np.fill_diagonal(smooth_MR[(shape_data_in[1]-i_d):], window_num[i_d])
+                    np.fill_diagonal(smooth_MR[:,(shape_data_in[1]-i_d):], window_num[i_d])
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # apply smoothing matrices to input data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if n_dim == 1:
+        data_in = data_in.dot(smooth_MR)
+        data_in = data_in/(np.ones(shape_data_in).dot(smooth_MR))
+    elif n_dim == 2:
+        data_in = smooth_ML.dot(data_in).dot(smooth_MR)
+        data_in = data_in/(smooth_ML.dot(np.ones(shape_data_in)).dot(smooth_MR))
+    else:
+        shape_data_in_new = (shape_data_in[0],shape_data_in[1],mymath_multiplylist(shape_data_in[2:]))
+        data_in = np.reshape(data_in,shape_data_in_new)
+        # loop over all other dimensions
+        for i_d in range(0,mymath_multiplylist(shape_data_in[2:])):
+            # smoothing in first dimension
+            if len(smooth_dim):
+                data_in[:,:,i_d] = smooth_ML.dot(data_in[:,:,i_d]).dot(smooth_MR)
+                data_in[:,:,i_d] = data_in[:,:,i_d]/(smooth_ML.dot(np.ones(shape_data_in[:2])).dot(smooth_MR))
+        data_in = np.reshape(data_in,shape_data_in)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # define putput data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # transpose data back into original shape
+    data_in = data_in.transpose(rs_end)
+
+    return data_in

myplot.py

@@ -0,0 +1,285 @@
+def myplot_1d(xxx,yyy,FS=22,trend=0,rm_yyy=0,yyy_per=0,col_c=('b','r','k'),label_c=('None'),xlabel_c='no xlabel info',ylabel_c='no ylabel info',saveplot=0,plotname='dummy.png',window='step'):
+    # from myplot import myplot_1d
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # input choices for myplot_1d %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # col_c=('b','r','k') : Default plotting colors, if more timeseries than num-
+    #             ber of colors, a longer list of colors is necessary
+    # FS=[22]   : Font Size for text, labels, etc
+    # label_c=('None') : Specification of lines in legend. To use no legend, set
+    #             label_c=('None')
+    # plotname='dummy.png' : in case of saveplot=1, the plot will be saved under
+    #             plotname (don't forget to include the path in plotname)
+    # rm_yyy=0  : window size (in data points) for smoothing, see further 'window'
+    #             and yyy_per
+    # saveplot=0 : save the plotting? 1 for yes, 0 for no, see further plotname
+    # trend=0   : Include information about the trend of the timeseries (as text)
+    # window='step' : In case of smoothing (rm_yyy), this defines the window 
+    #             function used for smoothing. Available windows can be found in
+    #             in my_matrix_operations.py under the function mymatrix_smooth
+    # xlabel_c='no xlabel info' : Text for xlabel
+    # ylabel_c='no ylabel info' : Text for ylabel
+    # yyy_per=0 : data periodic? (relevant for smoothing)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # load modules and functions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    from mymath import mymath_multiplylist
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import math
+    from mycalc_matrix import mymatrix_smooth
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # data handling and specification
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # general data info
+    shape_yyy = yyy.shape
+    n_dim = len(shape_yyy)
+    if n_dim == 1:
+        yyy = np.tile(yyy,(2,1))
+        shape_yyy = yyy.shape
+    xxxmin = np.min(xxx); xxxmax = np.max(xxx); xxxd = xxxmax-xxxmin;
+    yyymin = np.min(yyy); yyymax = np.max(yyy); yyyd = yyymax-yyymin;
+    # smoothing of yyy
+    if rm_yyy > 0:
+        yyy_sm = [None] * mymath_multiplylist(shape_yyy)
+        yyy_sm = np.reshape(yyy_sm,shape_yyy)
+        for i_d in range(0,n_dim):
+            yyy_sm[i_d,:] = mymatrix_smooth(yyy[i_d,:],data_per=(yyy_per,),window_len=rm_yyy,window=window);
+    # calculate trend of input data
+    if trend == 1:
+        yyy_trend = [None] * n_dim
+        for i_d in range(0,n_dim):
+            yyy_trend_d = np.polyfit(xxx,yyy[i_d,:],1)
+            yyy_trend[i_d] = yyy_trend_d[0]
+            del yyy_trend_d
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # general plotting setup
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    mpl_fig = plt.figure(figsize=(24,8))
+    mpl_fig.patch.set_facecolor('w')
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # plotting (yyy)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    line_data    = [None] * n_dim
+    line_data_sm = [None] * n_dim
+    for i_d in range(0,n_dim):
+        if rm_yyy > 0:
+            line_data[i_d], = plt.plot(xxx,yyy[i_d,:],color=col_c[i_d],linestyle='-.',lw=2,label=label_c[i_d])
+            line_data_sm[i_d], = plt.plot(xxx,yyy_sm[i_d,:],color=col_c[i_d],linestyle='-',lw=2,label=label_c[i_d]+' (av='+str(rm_yyy)+'#,'+window+')')
+        else:
+            line_data[i_d], = plt.plot(xxx,yyy[i_d,:],color=col_c[i_d],linestyle='-',lw=2,label=label_c[i_d])
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # plotting (additional horizontal lines)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    for i_d in range(0,n_dim):
+        plt.plot(xxx[[0,-1]],np.mean(yyy[i_d,:])*np.ones(2,'d'),color=col_c[i_d],lw=2)
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # include text
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # include trend information
+    if trend == 1:
+        exponent = int(math.floor(math.log10(abs(yyy_trend[0]))))
+        coeff    = round(yyy_trend[0]/float(10**exponent),1)
+        if exponent==0:
+            trend_str = str(coeff)
+        else:
+            trend_str = str(coeff)+'x1e'+str(exponent)
+        if yyymax-np.mean(yyy)>np.mean(yyy[0,:])-yyymin:
+            plt.text(xxxmax-xxxd/5,yyymax-yyyd/20,'Trend: '+trend_str, fontsize=FS-2)
+        else:
+            plt.text(xxxmax-xxxd/5,yyymin+yyyd/80,'Trend: '+trend_str, fontsize=FS-2)
+        del coeff, exponent, trend_str
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # labeling, axis, etc
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    plt.ylabel(ylabel_c, fontsize=FS)
+    plt.xlabel(xlabel_c, fontsize=FS)
+    plt.xticks(fontsize=FS) 
+    plt.yticks(fontsize=FS) 
+    plt.xlim(xxxmin,xxxmax)
+    if yyymax-np.mean(yyy)>np.mean(yyy)-yyymin:
+        loc_c = 2
+        if rm_yyy > 0:
+            line_dummy = [None] * (n_dim*2)
+            line_dummy[0::2] = line_data
+            line_dummy[1::2] = line_data_sm
+        else:
+            line_dummy = line_data
+    else:
+        loc_c = 3
+        if rm_yyy > 0:
+            line_dummy = [None] * (n_dim*2)
+            line_dummy[0::2] = line_data
+            line_dummy[1::2] = line_data_sm
+        else:
+            line_dummy = line_data
+    plt.legend(handles=line_dummy,loc=loc_c, prop={'size': FS-4})
+
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # save plot
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if saveplot:
+        print 'save plot under: ' + plotname
+        plt.savefig(plotname,dpi=300,bbox_inches='tight')
+    else:
+        plt.show()
+    plt.close()
+
+#def myplot_2dmap(xxx,yyy,FS=22,trend=0,rm_yyy=0,yyy_per=0,col_c=('b','r','k'),label_c=('None'),xlabel_c='no xlabel info',ylabel_c='no ylabel info',saveplot=0,plotname='dummy.png',window='step',title_c=[]):
+def myplot_2dmap(data_in,grd,FS=22,plotname='dummy.png',saveplot=0,lonlat_range=[0.0,360.0,-90.0,90.0],\
+    xlabel_c=[],ylabel_c=[],title_c=[]):
+    # from myplot import myplot_2dmap
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # input choices for myplot_1d %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # data_in is the 2d field for plotting
+    # lonlat_range=[0.0,360.0,-90.0,90.0] : longitude/latitude range used for
+    #     plotting
+    # plotname='dummy.png' : in case of saveplot=1, the plot will be saved under
+    #     plotname (don't forget to include the path in plotname)
+    # saveplot=0 : save the plotting? 1 for yes, 0 for no, see further plotname
+    # FS=[22] : Font Size for text, labels, etc
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # load modules and functions
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    import numpy as np
+    import matplotlib.pyplot as plt
+    import math
+    from mycalc_matrix import mymatrix_smooth
+    from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
+    # => cartopy instead of basemap
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # general plotting setup
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    mpl_fig = plt.figure(edgecolor='w',figsize=(18./360.*np.diff(lonlat_range[0:2]),12./180.*np.diff(lonlat_range[2:4])))
+    mpl_fig.patch.set_facecolor('w')
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # define map projection, include grid and map 
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    mapproj = Basemap(projection='cyl',llcrnrlat=np.min(lonlat_range[2:4]), llcrnrlon=np.min(lonlat_range[0:2]),\
+        urcrnrlat=np.max(lonlat_range[2:4]), urcrnrlon=np.max(lonlat_range[0:2]))
+    mapproj.drawcoastlines()
+    mapproj.drawparallels(np.array([-90, -45, 0, 45, 90]),labels=[1,0,0,0])
+    mapproj.drawmeridians(np.array([0, 90, 180, 270, 360]),labels=[0,0,0,1])
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # redefine longitudes
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # Create 2D lat/lon arrays for Basemap
+    for i_d in range(0,grd.Nlat):
+        data_in[:,i_d],lon_shift = shiftgrid(360.0*(np.min(lonlat_range[0:2])<0)+np.min(lonlat_range[0:2]), \
+            data_in[:,i_d], grd.lon, start=True, cyclic=360.0)
+    yyy, xxx  = np.meshgrid(grd.lat,lon_shift)
+    #yyy, xxx  = np.meshgrid(grd.lat,grd.lon)
+    #data_in,lon_shift = shiftgrid(360.0*(np.min(lonlat_range[0:2])<0)+np.min(lonlat_range[0:2]), \
+    #    data_in, xxx, start=True, cyclic=360.0-np.mean(np.diff(grd.lon)))
+    lonall, latall = mapproj(xxx, yyy)
+    del yyy, xxx
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # plotting of data_in
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    mymapf = plt.contourf(lonall, latall, data_in, 10, cmap=plt.cm.Reds)
+    mymap  = plt.contour(lonall, latall, data_in, 10, colors='k')
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # include contour labels, colorbar, etc.
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    fmt_d = int(math.floor(np.max((0,-(np.log10(np.nanmean(data_in[:]))-2)))))
+    plt.clabel(mymap, fontsize=FS, colors='w', fmt='%.'+str(fmt_d)+'f')
+    del fmt_d
+    if title_c:
+        plt.title(title_c, fontsize=FS)
+    plt.colorbar(mymapf, orientation='horizontal', shrink=0.95)
+    #plt.colorbar(mymapf, orientation='horizontal', shrink=0.64)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # labeling, axis, etc
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    plt.xticks(fontsize=FS) 
+    plt.yticks(fontsize=FS) 
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # save plot
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if saveplot:
+        print 'save plot under: ' + plotname
+        plt.savefig(plotname,dpi=300,bbox_inches='tight')
+    else:
+        plt.show()
+    plt.close()
+    del data_in, FS, plotname, lonlat_range
+
+
+#def myplot_2d(xxx,yyy,FS=22,trend=0,rm_yyy=0,yyy_per=0,col_c=('b','r','k'),label_c=('None'),xlabel_c='no xlabel info',ylabel_c='no ylabel info',saveplot=0,plotname='dummy.png',window='step'):
+def myplot_2d(xxx,yyy,zzz,FS=22,plotname='dummy.png',saveplot=0,xlabel_c=[],ylabel_c=[],\
+    d_xtick=[],cbarlabel_c=[],title_c=[],con_lines=[]):
+    # from myplot import myplot_2d
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # input choices for myplot_2d %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
+    # zzz is the 2d field for plotting
+    # plotname='dummy.png' : in case of saveplot=1, the plot will be saved under
+    #     plotname (don't forget to include the path in plotname)
+    # saveplot=0 : save the plotting? 1 for yes, 0 for no, see further plotname
+    # FS=[22] : Font Size for text, labels, etc
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # load modules and functions
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    import numpy as np
+    import matplotlib.pyplot as plt
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # general plotting setup
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    mpl_fig = plt.figure(edgecolor='w')
+    mpl_fig.patch.set_facecolor('w')
+    ax = plt.gca()
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # plotting of zzz
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    #plt.pcolor(xxx,yyy,zzz,vmin=np.nanmin(zzz),vmax=np.nanmax(zzz))
+    mmm = np.ma.masked_where(np.isnan(zzz),zzz)
+    plt.pcolor(xxx,yyy,mmm,vmin=np.nanmin(zzz),vmax=np.nanmax(zzz))
+    ax.set_facecolor('gray')
+    #plt.imshow(zzz)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # plotting additional contour lines
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if con_lines:
+        con_lines_val = con_lines['value']
+        for i_d in range(0,len(con_lines_val)):
+            plt.plot(con_lines_val[i_d][0],con_lines_val[i_d][1],con_lines['col'][i_d],label=con_lines['col'][i_d])
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # include contour labels, colorbar, etc.
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    cbar = plt.colorbar()
+    if cbarlabel_c:
+        cbar.set_label(cbarlabel_c, rotation=270, labelpad=10)
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # labeling, axis, etc
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if d_xtick:
+        xtick_c = np.arange(xxx.min()+d_xtick*(xxx.min()%d_xtick>0)-\
+            xxx.min()%d_xtick,xxx.max()-xxx.max()%d_xtick+d_xtick,d_xtick)
+        plt.xticks(xtick_c,fontsize=FS) 
+        del xtick_c
+    else:
+        plt.xticks(fontsize=FS) 
+    plt.yticks(fontsize=FS) 
+    if ylabel_c:
+        plt.ylabel(ylabel_c, fontsize=FS)
+    if xlabel_c:
+        plt.xlabel(xlabel_c, fontsize=FS)
+    if title_c:
+        plt.title(title_c, fontsize=FS)
+    # set axis range
+    plt.xlim((xxx.min(),xxx.max()))
+    plt.ylim((yyy.min(),yyy.max()))
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    # save plot
+    # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+    if saveplot:
+        print 'save plot under: ' + plotname
+        plt.savefig(plotname,dpi=300,bbox_inches='tight')
+    else:
+        plt.show()
+    plt.close()
+    del FS, plotname

myplot_inputs.py

@@ -0,0 +1,33 @@
+def mycalc_create_pn(dict_in):
+
+    # Allocation
+    pname_str = {}
+    # check if restriction to region exist
+    list_allkeys = dict_in.keys()
+    if 'lonlat' in list_allkeys:
+        dummy = dict_in['lonlat']
+        if dummy[0] < 0:
+            pn1 = '{:03d}'.format(int(abs(round(dummy[0])))) + 'W'
+        elif dummy[0] > 360:
+            pn1 = '{:03d}'.format(int(round(dummy[0])-360)) + 'E'
+        else:
+            pn1 = '{:03d}'.format(int(round(dummy[0]))) + 'E'
+        if dummy[1] < 0:
+            pn2 = '{:03d}'.format(int(abs(round(dummy[1])))) + 'W'
+        elif dummy[1] > 360:
+            pn2 = '{:03d}'.format(int(round(dummy[1])-360)) + 'E'
+        else:
+            pn2 = '{:03d}'.format(int(round(dummy[1]))) + 'E'
+        if dummy[2] < 0:
+            pn3 = '{:02d}'.format(int(abs(round(dummy[2])))) + 'S'
+        else:
+            pn3 = '{:02d}'.format(int(round(dummy[2]))) + 'N'
+        if dummy[3] < 0:
+            pn4 = '{:02d}'.format(int(abs(round(dummy[3])))) + 'S'
+        else:
+            pn4 = '{:02d}'.format(int(round(dummy[3]))) + 'N'
+        pname_str['lonlat'] = '_reg' + pn1 + 'to' + pn2 + pn3 + 'to' + pn4
+        del dummy
+
+    del dict_in, list_allkeys
+    return pname_str