def plot_parker(coords,data,varname='',title=None,region='',filename='/Users/lederer/tmp/rompy.mickett.png',n=1,x_axis_style='kilometers',resolution='i',x_axis_offset=0,clim=None,cmap=None,labeled_contour_gap=None, caxis_label=None):
fig = Figure(facecolor='white',figsize=(12.0,9.0))
fontsize = 8
cmap,sm,norm = make_cmap_sm_norm(d=data,clim=clim,cmap=cmap)
ax1 = fig.add_axes([0.1, 0.55, 0.65, 0.4]) # top 20 meters
ax2 = fig.add_axes([0.1, 0.1, 0.65, 0.4]) # full column
ax3 = fig.add_axes([0.7, 0.55, 0.3, 0.4],axis_bgcolor='white')#'#298FAF') # map of domain containing curtain
cax = fig.add_axes([0.84, 0.1, 0.02, 0.4],frameon=False) # subplot for the color axis
x_axis_as_km = utils.coords_to_km(coords)
station_locations = x_axis_as_km[0:-1:n]
my_plot11 = ax1.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot12 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
if int(labeled_contour_gap) == labeled_contour_gap:
contour_label_fmt = '%d'
else:
contour_label_fmt = '%1.2f'
solid_contours = np.arange(clim[0],clim[-1],labeled_contour_gap)
my_plot13 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax1.clabel(my_plot13,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
my_plot14 = ax1.plot(station_locations, 1.5*np.ones(len(station_locations)),'v',color='grey')
ax1.fill_between(x_axis_as_km,coords['zm'][0,:],ax1.get_ylim()[0],color='grey')
ax1.set_ylim((-20,2))
ax1.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax1.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_plot21 = ax2.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot22 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
my_plot23 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax2.clabel(my_plot23,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
ax2.fill_between(x_axis_as_km,coords['zm'][0,:],-1000.0,color='grey')
ax2.set_ylim((np.min(coords['zm'][:])-20.0),2)
ax2.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax2.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_colorbar = fig.colorbar(sm,cax=cax)
if not caxis_label == None:
my_colorbar.set_label(caxis_label)
if labeled_contour_gap is not None:
my_colorbar.add_lines(my_plot23)
if title==None:
ax1.set_title('%s %s from a ROMS run' % (region,varname))
else:
ax1.set_title(title)
ax1.set_ylabel('depth in meters',position=(0.05,0))
ax1.set_xticks(station_locations)
ax1.set_xticklabels('')
if x_axis_style == 'kilometers' or x_axis_style == 'kilometer':
td = 10 #tick_distance
left_most_tick_label = -x_axis_offset + (x_axis_offset % td)
left_most_tick = left_most_tick_label + x_axis_offset
ax2.set_xticks(np.arange(left_most_tick,x_axis_as_km[-1],td))
ax2.set_xticklabels([int(num) for num in np.arange(left_most_tick_label, x_axis_as_km[-1],td)])
# ax2.set_xticks(td*np.arange(x_axis_as_km[-1]/td) + (x_axis_offset % td))
# ax2.set_xticklabels([int(num) for num in np.arange(-int(x_axis_offset - x_axis_offset % td),x_axis_as_km[-1],td)])
for xticklabel in ax2.get_xticklabels():
xticklabel.set_fontsize(fontsize)
ax2.set_xlabel('Kilometers')
elif x_axis_style == 'stations' or x_axis_style == 'station':
if region == 'Hood Canal':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.hood_canal_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
elif region == 'Main Basin':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.main_basin_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
else:
ax2.set_xticks(x_axis_as_km)
ax2.set_xticklabels('')
ax2.set_xlabel('Kilometers')
# make map in the top right corner
# these lat lon values are derived from the curtain defined for the plot
# lllat = np.min(coords['ym'])
# urlat = np.max(coords['ym'])
# lllon = np.min(coords['xm'])
# urlon = np.max(coords['xm'])
# lat lon values for the inset map show a close-up of the Puget Sound
lllat = 47.0
urlat = 48.5
lllon = -123.3
urlon = -122.2
m = Basemap(projection='merc',llcrnrlat=lllat,urcrnrlat=urlat,llcrnrlon=lllon,urcrnrlon=urlon,resolution=resolution,ax=ax3)
x,y = m(*(coords['xm'],coords['ym']))
# pcm = m.plot(x,y,'r')
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color='#ECECEC')
pcm1 = m.plot(x,y,'r',linewidth=0.5)
pcm2 = m.plot(x[0:-1:n],y[0:-1:n],'.k')
FigureCanvas(fig).print_png(filename)
def plot_mickett(coords,data,varname='',region='',filename='/Users/lederer/tmp/rompy.mickett.png',n=1,x_axis_style='kilometers',x_axis_offset=0,clim=None,cmap=None,labeled_contour_gap=None):
fig = Figure(facecolor='white')
fontsize = 8
cmap,sm,norm = make_cmap_sm_norm(d=data,clim=clim,cmap=cmap)
ax1 = fig.add_axes([0.1, 0.55, 0.75, 0.4])
ax2 = fig.add_axes([0.1, 0.1, 0.75, 0.4])
cax = fig.add_axes([0.9, 0.1, 0.02, 0.8],frameon=False)
x_axis_as_km = utils.coords_to_km(coords)
station_locations = x_axis_as_km[0:-1:n]
#
# if not clim == None:
# norm = Normalize(vmin=clim[0],vmax=clim[-1],clip=False)
# sm = ScalarMappable(norm=norm,cmap=cmap)
# sm.set_clim(vmin=clim[0],vmax=clim[-1])
# sm.set_array(np.array([0]))
# else:
# norm = None
#
my_plot11 = ax1.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot12 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
if int(labeled_contour_gap) == labeled_contour_gap:
contour_label_fmt = '%d'
else:
contour_label_fmt = '%1.2f'
solid_contours = np.arange(clim[0],clim[-1],labeled_contour_gap)
# ax1_xlim = ax1.get_xlim()
my_plot13 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax1.clabel(my_plot13,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
# ax1.set_xlim(ax1_xlim)
my_plot14 = ax1.plot(station_locations, 1.5*np.ones(len(station_locations)),'v',color='grey')
ax1.fill_between(x_axis_as_km,coords['zm'][0,:],ax1.get_ylim()[0],color='grey')
# ax1.set_ylim((-20,ax1.get_ylim()[1]))
ax1.set_ylim((-20,2))
ax1.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax1.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_plot21 = ax2.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot22 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
# ax2_xlim = ax2.get_xlim()
my_plot23 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax2.clabel(my_plot23,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
# ax2.set_xlim = ax2_xlim
# print(ax2.get_ylim())
# ax2.fill_between(x_axis_as_km,coords['zm'][0,:],ax2.get_ylim()[0],color='grey')
ax2.fill_between(x_axis_as_km,coords['zm'][0,:],-1000.0,color='grey')
# ax2.set_ylim(ax2.get_ylim()[0],2)
# print(ax2.get_ylim())
ax2.set_ylim((np.min(coords['zm'][:])-20.0),2)
# print(ax2.get_ylim())
ax2.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax2.get_yticklabels():
yticklabel.set_fontsize(fontsize)
# if clim == None:
# sm = my_plot11
my_colorbar = fig.colorbar(sm,cax=cax)
if labeled_contour_gap is not None:
my_colorbar.add_lines(my_plot23)
ax1.set_title('%s %s from a ROMS run' % (region,varname))
ax1.set_ylabel('depth in meters',position=(0.05,0))
# ax1.set_xticks(10*np.arange(x_axis_as_km[-1]/10))
ax1.set_xticks(station_locations)
ax1.set_xticklabels('')
if x_axis_style == 'kilometers' or x_axis_style == 'kilometer':
#tick_list = x_axis_as_km[::n]
#ax2.set_xticks(tick_list)
#ax2.set_xticklabels([int(tick) for tick in tick_list],size=fontsize)
td = 10 #tick_distance
ax2.set_xticks(td*np.arange(x_axis_as_km[-1]/td) + (x_axis_offset % td))
ax2.set_xticklabels([int(num) for num in np.arange(-int(x_axis_offset - x_axis_offset % td),x_axis_as_km[-1],td)])
for xticklabel in ax2.get_xticklabels():
xticklabel.set_fontsize(fontsize)
ax2.set_xlabel('Kilometers')
elif x_axis_style == 'stations' or x_axis_style == 'station':
if region == 'Hood Canal':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.hood_canal_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
elif region == 'Main Basin':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.main_basin_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
else:
ax2.set_xticks(x_axis_as_km)
ax2.set_xticklabels('')
ax2.set_xlabel('Kilometers')
FigureCanvas(fig).print_png(filename)
def plot_parker(coords,data,filename='rompy.parker.png',title=None,varname='',region='',n=None,x_axis_style='kilometers',x_axis_offset=0,clim=None,cmap=None,labeled_contour_gap=None, caxis_label=None,inset='Puget Sound',label=None,label_ind=None,ctd_ind=None,**kwargs):
'''
plot_parker is a plotting routine that simplifies the plotting of ROMS curtain plots. Here's a list of what all the arguments do and what format they should be in:
coords: coords is a dictionary, specifically one of the return variables from one of the rompy extraction functions
data: data is numpy array, as returned by a rompy extraction function
filename: save plot as filename.
title: [OPTIONAL] a string that will be used as the title in the plot, ignoring any definition for varname or region
varname: [OPTIONAL] a string of for the variable being plotted. It's only use is in making the title for the plot if the title is not specified
region: [OPTIONAL] a string of for the region being plotted. It's only use is in making the title for the plot if the title is not specified
n: [OPTIONAL] used to to define where station carrots are placed on the plot when using the original sections
x_axis_style: [OPTIONAL]
'''
fig = Figure(facecolor='white',figsize=(12.0,9.0))
fontsize = 8
cmap,sm,norm = make_cmap_sm_norm(d=data,clim=clim,cmap=cmap)
ax1 = fig.add_axes([0.1, 0.55, 0.65, 0.4]) # top 20 meters
ax2 = fig.add_axes([0.1, 0.1, 0.65, 0.4]) # full column
ax3 = fig.add_axes([0.7, 0.55, 0.3, 0.4],axis_bgcolor='white')#'#298FAF') # map of domain containing curtain
cax = fig.add_axes([0.84, 0.1, 0.02, 0.4],frameon=False) # subplot for the color axis
x_axis_as_km = utils.coords_to_km(coords)
my_plot11 = ax1.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot12 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
if int(labeled_contour_gap) == labeled_contour_gap:
contour_label_fmt = '%d'
else:
contour_label_fmt = '%1.2f'
solid_contours = np.arange(clim[0],clim[-1],labeled_contour_gap)
my_plot13 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax1.clabel(my_plot13,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
if n > 0:
station_locations = x_axis_as_km[0:-1:n]
elif ctd_ind is not None:
station_locations = []
for ctd in ctd_ind:
station_locations.append(x_axis_as_km[ctd])
else:
station_locatons = None
my_plot14 = ax1.plot(station_locations, 1.5*np.ones(len(station_locations)),'v',color='grey')
ax1.fill_between(x_axis_as_km,coords['zm'][0,:],ax1.get_ylim()[0],color='grey')
ax1.set_ylim((-20,2))
ax1.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax1.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_plot21 = ax2.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot22 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
my_plot23 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax2.clabel(my_plot23,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
ax2.fill_between(x_axis_as_km,coords['zm'][0,:],-1000.0,color='grey')
ax2.set_ylim((np.min(coords['zm'][:])-20.0),2)
ax2.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax2.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_colorbar = fig.colorbar(sm,cax=cax)
if not caxis_label == None:
my_colorbar.set_label(caxis_label)
if labeled_contour_gap is not None:
my_colorbar.add_lines(my_plot23)
if title==None:
ax1.set_title('%s %s from a ROMS run' % (region,varname))
else:
ax1.set_title(title)
ax1.set_ylabel('depth in meters',position=(0.05,0))
if label is not None and label_ind is not None:
if len(label) == len(label_ind):
label_ticks = []
for i in range(len(label)):
# ax1.text(x_axis_as_km[label_ind[i]], ax1.get_ylim()[0], label[i], horizontalalignment='center', verticalalignment='center')
label_ticks.append(x_axis_as_km[label_ind[i]])
ax1.set_xticks(label_ticks)
ax1.set_xticklabels(label,size=fontsize)
else:
ax1.set_xticks(station_locations)
ax1.set_xticklabels('')
if x_axis_style == 'kilometers' or x_axis_style == 'kilometer':
td = 10 #tick_distance
left_most_tick_label = -x_axis_offset + (x_axis_offset % td)
left_most_tick = left_most_tick_label + x_axis_offset
ax2.set_xticks(np.arange(left_most_tick,x_axis_as_km[-1],td))
ax2.set_xticklabels([int(num) for num in np.arange(left_most_tick_label, x_axis_as_km[-1],td)])
# ax2.set_xticks(td*np.arange(x_axis_as_km[-1]/td) + (x_axis_offset % td))
# ax2.set_xticklabels([int(num) for num in np.arange(-int(x_axis_offset - x_axis_offset % td),x_axis_as_km[-1],td)])
for xticklabel in ax2.get_xticklabels():
xticklabel.set_fontsize(fontsize)
ax2.set_xlabel('Kilometers')
elif x_axis_style == 'stations' or x_axis_style == 'station':
if region == 'Hood Canal':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.hood_canal_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
elif region == 'Main Basin':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.main_basin_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
else:
ax2.set_xticks(x_axis_as_km)
ax2.set_xticklabels('')
ax2.set_xlabel('Kilometers')
# make map in the top right corner
# these lat lon values are derived from the curtain defined for the plot
# lllat = np.min(coords['ym'])
# urlat = np.max(coords['ym'])
# lllon = np.min(coords['xm'])
# urlon = np.max(coords['xm'])
# lat lon values for the inset map show a close-up of the Puget Sound
if inset == 'Puget Sound':
lllat = 47.0
urlat = 48.5
lllon = -123.3
urlon = -122.2
elif inset == 'Strait of Georgia':
lllat = 48.0450
urlat = 49.4859
lllon = -123.6237
urlon = -122.5305
elif len(inset) == 4:
lllat = inset[0]
lllon = inset[1]
urlat = inset[2]
urlon = inset[3]
m = Basemap(projection='merc',llcrnrlat=lllat,urcrnrlat=urlat,llcrnrlon=lllon,urcrnrlon=urlon,resolution='c',ax=ax3)
x,y = m(*(coords['xm'],coords['ym']))
# pcm = m.plot(x,y,'r')
if inset == 'Puget Sound':
coast_lon, coast_lat = utils.get_coastline('detailed')
elif inset == 'Strait of Georgia':
coast_lon, coast_lat = utils.get_coastline('regional')
# This is a kludge to deal with a bug in matplotlib when plotting a subset of the regional coastline
# coast_lat[coast_lat<lllat] = np.nan
coast_lat[coast_lat>urlat] = np.nan
# coast_lon[coast_lon<lllon] = np.nan
# coast_lon[coast_lon>urlon] = np.nan
coast_lon_proj,coast_lat_proj = m(*(coast_lon,coast_lat))
# m.drawcoastlines(linewidth=0.5)
# m.fillcontinents(color='#ECECEC')
m.plot(coast_lon_proj,coast_lat_proj,'k',linewidth=0.5)
pcm1 = m.plot(x,y,'r',linewidth=0.5)
if n > 0:
pcm2 = m.plot(x[0:-1:n],y[0:-1:n],'.k')
if ctd_ind is not None:
for ctd in ctd_ind:
pcm2 = m.plot(x[ctd],y[ctd],'.k')
FigureCanvas(fig).print_png(filename)
def plot_parker(coords,
data,
filename='rompy.parker.png',
title=None,
varname='',
region='',
n=None,
x_axis_style='kilometers',
x_axis_offset=0,
clim=None,
cmap=None,
labeled_contour_gap=None,
caxis_label=None,
inset='Puget Sound',
label=None,
label_ind=None,
ctd_ind=None,
**kwargs):
'''
plot_parker is a plotting routine that simplifies the plotting of ROMS curtain plots. Here's a list of what all the arguments do and what format they should be in:
coords: coords is a dictionary, specifically one of the return variables from one of the rompy extraction functions
data: data is numpy array, as returned by a rompy extraction function
filename: save plot as filename.
title: [OPTIONAL] a string that will be used as the title in the plot, ignoring any definition for varname or region
varname: [OPTIONAL] a string of for the variable being plotted. It's only use is in making the title for the plot if the title is not specified
region: [OPTIONAL] a string of for the region being plotted. It's only use is in making the title for the plot if the title is not specified
n: [OPTIONAL] used to to define where station carrots are placed on the plot when using the original sections
x_axis_style: [OPTIONAL]
'''
fig = Figure(facecolor='white', figsize=(12.0, 9.0))
fontsize = 8
cmap, sm, norm = make_cmap_sm_norm(d=data, clim=clim, cmap=cmap)
ax1 = fig.add_axes([0.1, 0.55, 0.65, 0.4]) # top 20 meters
ax2 = fig.add_axes([0.1, 0.1, 0.65, 0.4]) # full column
ax3 = fig.add_axes(
[0.7, 0.55, 0.3, 0.4],
axis_bgcolor='white') #'#298FAF') # map of domain containing curtain
cax = fig.add_axes([0.84, 0.1, 0.02, 0.4],
frameon=False) # subplot for the color axis
x_axis_as_km = utils.coords_to_km(coords)
my_plot11 = ax1.contourf(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
norm=norm,
cmap=cmap)
my_plot12 = ax1.contour(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
linewidths=1,
linestyle=None,
norm=norm,
cmap=cmap)
if labeled_contour_gap is not None:
if int(labeled_contour_gap) == labeled_contour_gap:
contour_label_fmt = '%d'
else:
contour_label_fmt = '%1.2f'
solid_contours = np.arange(clim[0], clim[-1], labeled_contour_gap)
my_plot13 = ax1.contour(np.tile(x_axis_as_km,
(coords['zm'].shape[0], 1)),
coords['zm'],
data,
solid_contours,
colors='k',
linewidths=0.5)
ax1.clabel(my_plot13,
inline=True,
fmt=contour_label_fmt,
fontsize=fontsize)
if n > 0:
station_locations = x_axis_as_km[0:-1:n]
elif ctd_ind is not None:
station_locations = []
for ctd in ctd_ind:
station_locations.append(x_axis_as_km[ctd])
else:
station_locatons = None
my_plot14 = ax1.plot(station_locations,
1.5 * np.ones(len(station_locations)),
'v',
color='grey')
ax1.fill_between(x_axis_as_km,
coords['zm'][0, :],
ax1.get_ylim()[0],
color='grey')
ax1.set_ylim((-20, 2))
ax1.set_xlim((0, x_axis_as_km[-1]))
for yticklabel in ax1.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_plot21 = ax2.contourf(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
norm=norm,
cmap=cmap)
my_plot22 = ax2.contour(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
linewidths=1,
linestyle=None,
norm=norm,
cmap=cmap)
if labeled_contour_gap is not None:
my_plot23 = ax2.contour(np.tile(x_axis_as_km,
(coords['zm'].shape[0], 1)),
coords['zm'],
data,
solid_contours,
colors='k',
linewidths=0.5)
ax2.clabel(my_plot23,
inline=True,
fmt=contour_label_fmt,
fontsize=fontsize)
ax2.fill_between(x_axis_as_km, coords['zm'][0, :], -1000.0, color='grey')
ax2.set_ylim((np.min(coords['zm'][:]) - 20.0), 2)
ax2.set_xlim((0, x_axis_as_km[-1]))
for yticklabel in ax2.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_colorbar = fig.colorbar(sm, cax=cax)
if not caxis_label == None:
my_colorbar.set_label(caxis_label)
if labeled_contour_gap is not None:
my_colorbar.add_lines(my_plot23)
if title == None:
ax1.set_title('%s %s from a ROMS run' % (region, varname))
else:
ax1.set_title(title)
ax1.set_ylabel('depth in meters', position=(0.05, 0))
if label is not None and label_ind is not None:
if len(label) == len(label_ind):
label_ticks = []
for i in range(len(label)):
# ax1.text(x_axis_as_km[label_ind[i]], ax1.get_ylim()[0], label[i], horizontalalignment='center', verticalalignment='center')
label_ticks.append(x_axis_as_km[label_ind[i]])
ax1.set_xticks(label_ticks)
ax1.set_xticklabels(label, size=fontsize)
else:
ax1.set_xticks(station_locations)
ax1.set_xticklabels('')
if x_axis_style == 'kilometers' or x_axis_style == 'kilometer':
td = 10 #tick_distance
left_most_tick_label = -x_axis_offset + (x_axis_offset % td)
left_most_tick = left_most_tick_label + x_axis_offset
ax2.set_xticks(np.arange(left_most_tick, x_axis_as_km[-1], td))
ax2.set_xticklabels([
int(num)
for num in np.arange(left_most_tick_label, x_axis_as_km[-1], td)
])
# ax2.set_xticks(td*np.arange(x_axis_as_km[-1]/td) + (x_axis_offset % td))
# ax2.set_xticklabels([int(num) for num in np.arange(-int(x_axis_offset - x_axis_offset % td),x_axis_as_km[-1],td)])
for xticklabel in ax2.get_xticklabels():
xticklabel.set_fontsize(fontsize)
ax2.set_xlabel('Kilometers')
elif x_axis_style == 'stations' or x_axis_style == 'station':
if region == 'Hood Canal':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.hood_canal_station_list(), size=fontsize)
ax2.set_xlabel('Station ID')
elif region == 'Main Basin':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.main_basin_station_list(), size=fontsize)
ax2.set_xlabel('Station ID')
else:
ax2.set_xticks(x_axis_as_km)
ax2.set_xticklabels('')
ax2.set_xlabel('Kilometers')
# make map in the top right corner
# these lat lon values are derived from the curtain defined for the plot
# lllat = np.min(coords['ym'])
# urlat = np.max(coords['ym'])
# lllon = np.min(coords['xm'])
# urlon = np.max(coords['xm'])
# lat lon values for the inset map show a close-up of the Puget Sound
if inset == 'Puget Sound':
lllat = 47.0
urlat = 48.5
lllon = -123.3
urlon = -122.2
elif inset == 'Strait of Georgia':
lllat = 48.0450
urlat = 49.4859
lllon = -123.6237
urlon = -122.5305
elif inset == 'JdF_PS':
lllat = 46.0
urlat = 49.0
lllon = -124.9
urlon = -122.2
elif len(inset) == 4:
lllat = inset[0]
lllon = inset[1]
urlat = inset[2]
urlon = inset[3]
m = Basemap(projection='merc',
llcrnrlat=lllat,
urcrnrlat=urlat,
llcrnrlon=lllon,
urcrnrlon=urlon,
resolution='c',
ax=ax3)
x, y = m(*(coords['xm'], coords['ym']))
# pcm = m.plot(x,y,'r')
if inset == 'Puget Sound':
coast_lon, coast_lat = utils.get_coastline('detailed')
elif inset == 'Strait of Georgia' or inset == 'JdF_PS':
coast_lon, coast_lat = utils.get_coastline('regional')
# This is a kludge to deal with a bug in matplotlib when plotting a subset of the regional coastline
# coast_lat[coast_lat<lllat] = np.nan
coast_lat[coast_lat > urlat] = np.nan
# coast_lon[coast_lon<lllon] = np.nan
# coast_lon[coast_lon>urlon] = np.nan
coast_lon_proj, coast_lat_proj = m(*(coast_lon, coast_lat))
# m.drawcoastlines(linewidth=0.5)
# m.fillcontinents(color='#ECECEC')
m.plot(coast_lon_proj, coast_lat_proj, 'k', linewidth=0.5)
pcm1 = m.plot(x, y, 'r', linewidth=0.5)
if n > 0:
pcm2 = m.plot(x[0:-1:n], y[0:-1:n], '.k')
if ctd_ind is not None:
for ctd in ctd_ind:
pcm2 = m.plot(x[ctd], y[ctd], '.k')
FigureCanvas(fig).print_png(filename)
def plot_mickett(coords,
data,
varname='',
region='',
filename='/Users/lederer/tmp/rompy.mickett.png',
n=1,
x_axis_style='kilometers',
x_axis_offset=0,
clim=None,
cmap=None,
labeled_contour_gap=None):
fig = Figure(facecolor='white')
fontsize = 8
cmap, sm, norm = make_cmap_sm_norm(d=data, clim=clim, cmap=cmap)
ax1 = fig.add_axes([0.1, 0.55, 0.75, 0.4])
ax2 = fig.add_axes([0.1, 0.1, 0.75, 0.4])
cax = fig.add_axes([0.9, 0.1, 0.02, 0.8], frameon=False)
x_axis_as_km = utils.coords_to_km(coords)
station_locations = x_axis_as_km[0:-1:n]
#
# if not clim == None:
# norm = Normalize(vmin=clim[0],vmax=clim[-1],clip=False)
# sm = ScalarMappable(norm=norm,cmap=cmap)
# sm.set_clim(vmin=clim[0],vmax=clim[-1])
# sm.set_array(np.array([0]))
# else:
# norm = None
#
my_plot11 = ax1.contourf(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
norm=norm,
cmap=cmap)
my_plot12 = ax1.contour(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
linewidths=1,
linestyle=None,
norm=norm,
cmap=cmap)
if labeled_contour_gap is not None:
if int(labeled_contour_gap) == labeled_contour_gap:
contour_label_fmt = '%d'
else:
contour_label_fmt = '%1.2f'
solid_contours = np.arange(clim[0], clim[-1], labeled_contour_gap)
# ax1_xlim = ax1.get_xlim()
my_plot13 = ax1.contour(np.tile(x_axis_as_km,
(coords['zm'].shape[0], 1)),
coords['zm'],
data,
solid_contours,
colors='k',
linewidths=0.5)
ax1.clabel(my_plot13,
inline=True,
fmt=contour_label_fmt,
fontsize=fontsize)
# ax1.set_xlim(ax1_xlim)
my_plot14 = ax1.plot(station_locations,
1.5 * np.ones(len(station_locations)),
'v',
color='grey')
ax1.fill_between(x_axis_as_km,
coords['zm'][0, :],
ax1.get_ylim()[0],
color='grey')
# ax1.set_ylim((-20,ax1.get_ylim()[1]))
ax1.set_ylim((-20, 2))
ax1.set_xlim((0, x_axis_as_km[-1]))
for yticklabel in ax1.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_plot21 = ax2.contourf(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
norm=norm,
cmap=cmap)
my_plot22 = ax2.contour(np.tile(x_axis_as_km, (coords['zm'].shape[0], 1)),
coords['zm'],
data,
100,
linewidths=1,
linestyle=None,
norm=norm,
cmap=cmap)
if labeled_contour_gap is not None:
# ax2_xlim = ax2.get_xlim()
my_plot23 = ax2.contour(np.tile(x_axis_as_km,
(coords['zm'].shape[0], 1)),
coords['zm'],
data,
solid_contours,
colors='k',
linewidths=0.5)
ax2.clabel(my_plot23,
inline=True,
fmt=contour_label_fmt,
fontsize=fontsize)
# ax2.set_xlim = ax2_xlim
# print(ax2.get_ylim())
# ax2.fill_between(x_axis_as_km,coords['zm'][0,:],ax2.get_ylim()[0],color='grey')
ax2.fill_between(x_axis_as_km, coords['zm'][0, :], -1000.0, color='grey')
# ax2.set_ylim(ax2.get_ylim()[0],2)
# print(ax2.get_ylim())
ax2.set_ylim((np.min(coords['zm'][:]) - 20.0), 2)
# print(ax2.get_ylim())
ax2.set_xlim((0, x_axis_as_km[-1]))
for yticklabel in ax2.get_yticklabels():
yticklabel.set_fontsize(fontsize)
# if clim == None:
# sm = my_plot11
my_colorbar = fig.colorbar(sm, cax=cax)
if labeled_contour_gap is not None:
my_colorbar.add_lines(my_plot23)
ax1.set_title('%s %s from a ROMS run' % (region, varname))
ax1.set_ylabel('depth in meters', position=(0.05, 0))
# ax1.set_xticks(10*np.arange(x_axis_as_km[-1]/10))
ax1.set_xticks(station_locations)
ax1.set_xticklabels('')
if x_axis_style == 'kilometers' or x_axis_style == 'kilometer':
#tick_list = x_axis_as_km[::n]
#ax2.set_xticks(tick_list)
#ax2.set_xticklabels([int(tick) for tick in tick_list],size=fontsize)
td = 10 #tick_distance
ax2.set_xticks(td * np.arange(x_axis_as_km[-1] / td) +
(x_axis_offset % td))
ax2.set_xticklabels([
int(num) for num in np.arange(
-int(x_axis_offset - x_axis_offset % td), x_axis_as_km[-1], td)
])
for xticklabel in ax2.get_xticklabels():
xticklabel.set_fontsize(fontsize)
ax2.set_xlabel('Kilometers')
elif x_axis_style == 'stations' or x_axis_style == 'station':
if region == 'Hood Canal':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.hood_canal_station_list(), size=fontsize)
ax2.set_xlabel('Station ID')
elif region == 'Main Basin':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.main_basin_station_list(), size=fontsize)
ax2.set_xlabel('Station ID')
else:
ax2.set_xticks(x_axis_as_km)
ax2.set_xticklabels('')
ax2.set_xlabel('Kilometers')
FigureCanvas(fig).print_png(filename)
def plot_parker(coords,data,varname='',title=None,region='',filename='/Users/lederer/tmp/rompy.mickett.png',n=1,x_axis_style='kilometers',resolution='i',x_axis_offset=0,clim=None,cmap=None,labeled_contour_gap=None, caxis_label=None):
fig = Figure(facecolor='white',figsize=(12.0,9.0))
fontsize = 8
cmap,sm,norm = make_cmap_sm_norm(d=data,clim=clim,cmap=cmap)
ax1 = fig.add_axes([0.1, 0.55, 0.65, 0.4]) # top 20 meters
ax2 = fig.add_axes([0.1, 0.1, 0.65, 0.4]) # full column
ax3 = fig.add_axes([0.7, 0.55, 0.3, 0.4],axis_bgcolor='white')#'#298FAF') # map of domain containing curtain
cax = fig.add_axes([0.84, 0.1, 0.02, 0.4],frameon=False) # subplot for the color axis
x_axis_as_km = utils.coords_to_km(coords)
station_locations = x_axis_as_km[0:-1:n]
my_plot11 = ax1.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot12 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
if int(labeled_contour_gap) == labeled_contour_gap:
contour_label_fmt = '%d'
else:
contour_label_fmt = '%1.2f'
solid_contours = np.arange(clim[0],clim[-1],labeled_contour_gap)
my_plot13 = ax1.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax1.clabel(my_plot13,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
my_plot14 = ax1.plot(station_locations, 1.5*np.ones(len(station_locations)),'v',color='grey')
ax1.fill_between(x_axis_as_km,coords['zm'][0,:],ax1.get_ylim()[0],color='grey')
ax1.set_ylim((-20,2))
ax1.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax1.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_plot21 = ax2.contourf(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,norm=norm,cmap=cmap)
my_plot22 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,100,linewidths=1,linestyle=None,norm=norm,cmap=cmap)
if labeled_contour_gap is not None:
my_plot23 = ax2.contour(np.tile(x_axis_as_km,(coords['zm'].shape[0],1)),coords['zm'],data,solid_contours,colors='k',linewidths=0.5)
ax2.clabel(my_plot23,inline=True,fmt=contour_label_fmt,fontsize=fontsize)
ax2.fill_between(x_axis_as_km,coords['zm'][0,:],-1000.0,color='grey')
ax2.set_ylim((np.min(coords['zm'][:])-20.0),2)
ax2.set_xlim((0,x_axis_as_km[-1]))
for yticklabel in ax2.get_yticklabels():
yticklabel.set_fontsize(fontsize)
my_colorbar = fig.colorbar(sm,cax=cax)
if not caxis_label == None:
my_colorbar.set_label(caxis_label)
if labeled_contour_gap is not None:
my_colorbar.add_lines(my_plot23)
if title==None:
ax1.set_title('%s %s from a ROMS run' % (region,varname))
else:
ax1.set_title(title)
ax1.set_ylabel('depth in meters',position=(0.05,0))
ax1.set_xticks(station_locations)
ax1.set_xticklabels('')
if x_axis_style == 'kilometers' or x_axis_style == 'kilometer':
td = 10 #tick_distance
left_most_tick_label = -x_axis_offset + (x_axis_offset % td)
left_most_tick = left_most_tick_label + x_axis_offset
ax2.set_xticks(np.arange(left_most_tick,x_axis_as_km[-1],td))
ax2.set_xticklabels([int(num) for num in np.arange(left_most_tick_label, x_axis_as_km[-1],td)])
# ax2.set_xticks(td*np.arange(x_axis_as_km[-1]/td) + (x_axis_offset % td))
# ax2.set_xticklabels([int(num) for num in np.arange(-int(x_axis_offset - x_axis_offset % td),x_axis_as_km[-1],td)])
for xticklabel in ax2.get_xticklabels():
xticklabel.set_fontsize(fontsize)
ax2.set_xlabel('Kilometers')
elif x_axis_style == 'stations' or x_axis_style == 'station':
if region == 'Hood Canal':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.hood_canal_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
elif region == 'Main Basin':
tick_list = x_axis_as_km[::n]
ax2.set_xticks(tick_list)
ax2.set_xticklabels(utils.main_basin_station_list(),size=fontsize)
ax2.set_xlabel('Station ID')
else:
ax2.set_xticks(x_axis_as_km)
ax2.set_xticklabels('')
ax2.set_xlabel('Kilometers')
# make map in the top right corner
# these lat lon values are derived from the curtain defined for the plot
# lllat = np.min(coords['ym'])
# urlat = np.max(coords['ym'])
# lllon = np.min(coords['xm'])
# urlon = np.max(coords['xm'])
# lat lon values for the inset map show a close-up of the Puget Sound
lllat = 47.0
urlat = 48.5
lllon = -123.3
urlon = -122.2
# m = Basemap(projection='merc',llcrnrlat=lllat,urcrnrlat=urlat,llcrnrlon=lllon,urcrnrlon=urlon,resolution=resolution,ax=ax3)
m = Basemap(projection='merc',llcrnrlat=lllat,urcrnrlat=urlat,llcrnrlon=lllon,urcrnrlon=urlon,resolution='c',ax=ax3)
x,y = m(*(coords['xm'],coords['ym']))
# pcm = m.plot(x,y,'r')
coast_lon, coast_lat = m(*(utils.get_coastline('detailed')))
# print(coast_lat)
# print(coast_lon)
# m.drawcoastlines(linewidth=0.5)
# m.fillcontinents(color='#ECECEC')
m.plot(coast_lon,coast_lat,'k',linewidth=0.5)
pcm1 = m.plot(x,y,'r',linewidth=0.5)
pcm2 = m.plot(x[0:-1:n],y[0:-1:n],'.k')
FigureCanvas(fig).print_png(filename)
