zflag = bool(raw_input('Load another z axis key? (empty for False): '))
#------------------------------------------
# Load the GITM binaries
gitmbins = gt.load_multiple_gitm_bin(gitmlist)
#---------------------------------------------
# Determine the limits of each z parameter. To do this we need to open
# one of the gitm files
for z,zkey in enumerate(zkeys):
# Find the index for the specified altitude. This will be the same for
# all files, longitudes, and latititudes.
aindex = gpr.find_alt_index(gitmbins[0], 1, 1, zalts[z], zunits[z])
zindexes.append(aindex)
# Find the indexes for the specified latitude limits. The latitude will
# be the same for all files and longitudes. The northern limit will be
# the same for all plot types.
(nlon,nlat) = gpr.find_lon_lat_index(gitmbins[0], -1.0 , nlimits[z],
"degrees")
if zplot == "nspolar" or zplot == "snapshot":
slimit = -1.0 * nlimits[z]
else:
slimit = slimits[z]
(slon,slat) = gpr.find_lon_lat_index(gitmbins[0], 361.0, slimit, "degrees")
def plot_net_gitm_comp(plot_type, lon_data, lat_data, obs_data, obs_name,
obs_scale, obs_units, diff_data, diff_name, diff_scale,
diff_units, gitm_key, gitm_alt, gdata, gitm_name,
diff_max=None, zmax=None, zmin=None, title=None,
color=True, bcolor='#747679', data_coff=False,
diff_coff=True, figname=None, draw=True, latlim1=90,
latlim2=-90, linc=6, tlon=90, meq=False, earth=False,
map_list=[], faspect=True, term_datetime=None,
extra_lines=False, *args, **kwargs):
'''
Creates three plots of a specified type, one showing the observations, one
showing the GITM data, and one showing the difference between the two.
Input: plot_type = key to determine plot type (rectangular, polar,
nsglobal, or snapshot)
lon_data = Numpy array with longitude data for matching model-obs
points
lat_data = Numpy array with latitude data for matching model-obs
points
obs_data = Numpy array with observational data for matching
model-obs points
obs_name = Name portion of the observational data label
obs_scale = Scale (linear/exponential) for plotting obs. data
obs_units = Unit portion of the observational data label
diff_data = Numpy array with differences for matching model-obs
points
gitm_key = Key for the GITM data
gitm_alt = Altitude in km to plot the GITM data at. For a 2D
variable like hmF2 or TEC, use 0.0 km.
gdata = GitmBin structure with model observations.
gitm_name = Name portion of the GITM data label
diff_max = Maximum value for the difference (absolute value),
if None, will be determined in script (default=None)
zmin = minimum value for z variable (default=None)
zmax = maximum value for z variable (default=None)
title = Plot title (default=None)
color = Color (True, default) or black and white (False)?
bcolor = Background color (default=)
data_coff = Center the data color scale about zero (False, default)?
diff_coff = Center the diff color scale about zero (True, default)?
figname = Output figure name with a .png suffix (default=None)
draw = Draw to screen? (default=True)
latlim1 = First latitude limit (degrees North, default=90).
Purpose varies depending on plot type. For rectangular,
this is the northern latitude limit. For polar, this
is the latitude at the center of the dial. For
snapshot, this is the lower boundary of polar dials.
It is not used for nsglobal.
latlim2 = Second latitude limit (degrees North, default=-90).
Purpose varies depending on plot type. For rectangular,
this is the southern latitude limit. For polar, this
is the latitude at the edge of the dial. This option is
not used with the snapshot or nsglobal option.
linc = Number of latitude tick incriments (default=6)
tlon = Longitude on top of the polar dial (degrees East,
default=90)
meq = Add a line for the geomagnetic equator?
(default=False)
earth = Include continent outlines for Earth (default=False)
map_list = List of map handles for the specified plot_type
(default=empty list)
faspect = Keep a true aspect ratio for maps? (default=True)
term_datetime = Include the solar terminator by shading the night
time regions? If so, include a datetime object
with the UT for this map. Only used if earth=True.
extra_lines = Plot a specified lines (good for showing regional
boundaries) (default=False). Provide a list of lists
which have the format shown:
[x np.array, y np.array, style string (eg 'k-')]
where x is in degrees longitude and y is in
degrees latitude
Output: f = handle to figure
'''
rout_name = "plot_net_gitm_comp"
# Get the desired color bars
data_color = gpr.choose_contour_map(color, data_coff)
diff_color = gpr.choose_contour_map(color, diff_coff)
# Get the altitude index
ialt = 0
if gitm_alt > 0.0:
ialt = gpr.find_alt_index(gdata, 0, 0, alt, units="km")
# Initialize the z variables, if desired. GITM and Observational data
# should share the same scale.
if(zmin is None):
obsmin = np.nanmin(obs_data)
gitmin = np.nanmin(gdata[gitm_key][:,:,ialt])
zmin = min(obsmin,gitmin)
if(zmax is None):
obsmax = np.nanmax(obs_data)
gitmax = np.nanmax(gdata[gitm_key][:,:,ialt])
zmax = max(obsmax, gitmax)
zran = round((zmax-zmin)/6.0)
if(zran != 0.0):
zmin = math.floor(float("{:.14f}".format(zmin / zran))) * zran
zmax = math.ceil(float("{:.14f}".format(zmax / zran))) * zran
# Set the difference max/min limits, if desired
if diff_max is None:
diff_max = max(np.nanmax(diff_data), abs(np.nanmin(diff_data)))
diff_min = -1.0 * diff_max
# Initialize the figure, setting the height for a 3 subfigure stack
fwidth = 6
fheight = 12
if(plot_type.find("global") > 0):
fwidth *= 1.5
if(plot_type.find("shot") > 0):
fwidth *= 1.5
fheight *= 1.5
f = plt.figure(figsize=(fwidth,fheight))
# Plot the three datasets using the desired format
if plot_type.find("shot") > 0:
if len(map_list) == 3:
ml = map_list[0]
mn = map_list[1]
ms = map_list[2]
else:
ml = None
mn = None
ms = None
# Output the observations as a scatter plot
axl,ml,axn,mn,axs,ms = p3g.plot_snapshot_subfigure(f, 3, 0, lat_data,
lon_data, obs_data,
obs_name, obs_scale,
obs_units, zmax,
zmin, data_color,
tlon=tlon,
blat=latlim1,
xl=False, yl=False,
xt=False,
bcolor=bcolor,
meq=meq, earth=earth,
ml=ml, mn=mn, ms=ms,
faspect=faspect,
term_datetime=term_datetime)
# Output the gitm data as a contour after ensuring that the GITM array
# isn't padded to include unrealistic latitudes
(i, imin) = gpr.find_lon_lat_index(gdata, 0.0, -90.0, "degrees")
(i, imax) = gpr.find_lon_lat_index(gdata, 0.0, 90.0, "degrees")
imax += 1
p3g.plot_snapshot_subfigure(f, 3, 1,
np.array(gdata['dLat'][:,imin:imax,ialt]),
np.array(gdata['dLon'][:,imin:imax,ialt]),
np.array(gdata[gitm_key][:,imin:imax,ialt]),
gitm_name, gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"], zmax, zmin,
data_color, cb=True, cloc="r", tlon=tlon,
blat=latlim1, title=False, xl=False,
xt=False, bcolor=bcolor, meq=meq,
earth=earth, ml=ml, mn=mn, ms=ms,
faspect=faspect, data_type="contour",
term_datetime=term_datetime)
# Output the differences as a scatter plot
p3g.plot_snapshot_subfigure(f, 3, 2, lat_data, lon_data, diff_data,
diff_name, diff_scale, diff_units, diff_max,
diff_min, diff_color, tlon=tlon,
blat=latlim1, title=False, yl=False,
bcolor=bcolor, meq=meq, earth=earth, ml=ml,
mn=mn, ms=ms, faspect=faspect,
term_datetime=term_datetime)
map_list = list([ml, mn, ms])
elif plot_type.find("nsglobal") >= 0:
if len(map_list) == 2:
mn = map_list[0]
ms = map_list[1]
else:
mn = None
ms = None
# Check for boundary lines to plot
eline_north = False
eline_south = False
if type(extra_lines) is list:
if len(extra_lines) >= 1:
eline_north = extra_lines[0]
if len(extra_lines) >= 2:
eline_south = extra_lines[1]
else:
print "Only one boundary provided, plotting in north"
else:
print "No boundaries provided, better to declare as False"
# Output the observations as a scatter plot
axn1,mn,axs1,ms = p3g.plot_nsglobal_subfigure(f, 3, 0, lat_data,
lon_data, obs_data,
obs_name, obs_scale,
obs_units, zmax, zmin,
data_color, title=True, cb=True,
elat=latlim1, tlon=tlon, rl=False,
tl=False, bcolor=bcolor,
earth=earth, mn=mn, ms=ms,
faspect=faspect, term_datetime=term_datetime,
extra_line_n=eline_north,
extra_line_s=eline_south)
# Output the gitm data as a contour after ensuring that the GITM array
# isn't padded to include unrealistic latitudes
(i, imin) = gpr.find_lon_lat_index(gdata, 0.0, -90.0, "degrees")
(i, imax) = gpr.find_lon_lat_index(gdata, 0.0, 90.0, "degrees")
imax += 1
axn2,mn,axs2,ms = p3g.plot_nsglobal_subfigure(f, 3, 1, np.array(gdata['dLat'][:,imin:imax,ialt]), np.array(gdata['dLon'][:,imin:imax,ialt]), np.array(gdata[gitm_key][:,imin:imax,ialt]), gitm_name, gdata[gitm_key].attrs["scale"], gdata[gitm_key].attrs["units"], zmax, zmin, data_color, title=False, cb=True, elat=latlim1, tlon=tlon, tl=False, bcolor=bcolor, earth=earth, mn=mn, ms=ms, data_type="contour", term_datetime=term_datetime, extra_line_n=eline_north, extra_line_s=eline_south)
# Output the differences as a scatter plot
p3g.plot_nsglobal_subfigure(f, 3, 2, lat_data, lon_data, diff_data,
diff_name, diff_scale, diff_units, diff_max,
diff_min, diff_color, title=False, cb=True,
elat=latlim1, tlon=tlon, rl=False, bcolor=bcolor,
earth=earth, mn=mn, ms=ms, faspect=faspect,
term_datetime=term_datetime,
extra_line_n=eline_north,
extra_line_s=eline_south)
map_list = list([mn, ms])
elif plot_type.find("rect") >= 0:
if len(map_list) == 1:
m = map_list[0]
else:
m = None
# Output the observations as a scatter plot
ax = f.add_subplot(3,1,1)
con1, m = p3g.plot_rectangular_3D_global(ax, lat_data, lon_data,
obs_data, obs_name, obs_scale,
obs_units, zmin, zmax,
data_color, nlat=latlim1,
slat=latlim2, linc=linc,
cloc="r", xl=False, xt=False,
yl=False, meq=meq,
bcolor=bcolor, earth=earth,
m=m, faspect=faspect,
term_datetime=term_datetime)
# Output the gitm data as a contour
ax = f.add_subplot(3,1,2)
con2, m = p3g.plot_rectangular_3D_global(ax, np.array(gdata['dLat'][:,:,ialt]), np.array(gdata['dLon'][:,:,ialt]), np.array(gdata[gitm_key][:,:,ialt]),
gitm_name,
gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"],
zmin, zmax, data_color,
nlat=latlim1, slat=latlim2,
linc=linc, cb=True, cloc="r",
xl=False, xt=False,
bcolor=bcolor, meq=meq,
earth=earth, m=m,
faspect=faspect,
data_type="contour",
term_datetime=term_datetime)
# Adjust plot dimensions if necessary
if not earth:
con1_dim = list(con1.axes.get_position().bounds)
con2_dim = list(con2.ax.get_position().bounds)
con2_dim[2] = con1_dim[2]
con2.ax.set_position(con2_dim)
# Output the differences as a scatter plot
ax = f.add_subplot(3,1,3)
p3g.plot_rectangular_3D_global(ax, lat_data, lon_data, diff_data,
diff_name, diff_scale, diff_units,
diff_min, diff_max, diff_color,
nlat=latlim1, slat=latlim2, linc=linc,
cloc="r", yl=False, bcolor=bcolor,
meq=meq, earth=earth, m=m,
faspect=faspect,
term_datetime=term_datetime)
map_list = list([m])
elif plot_type.find("polar") >= 0:
if len(map_list) == 1:
m = map_list[0]
else:
m = None
pf = True
if earth:
pf = False
# Output the observations as a scatter plot
ax = f.add_subplot(3,1,1, polar=pf)
con1,m = p3g.plot_polar_3D_global(ax, 3, lat_data, lon_data, obs_data,
obs_name, obs_scale, obs_units, zmin,
zmax, data_color, center_lat=latlim1,
edge_lat=latlim2, linc=linc,
top_lon=tlon, cloc="r", tl=False,
rl=False, bcolor=bcolor, earth=earth,
m=m, faspect=faspect,
term_datetime=term_datetime)
# Output the gitm data as a contour after ensuring that the GITM
# array isn't padded to include unrealistic latitudes
ax = f.add_subplot(3,1,2, polar=pf)
(i, imin) = gpr.find_lon_lat_index(gdata, 0.0, -90.0, "degrees")
(i, imax) = gpr.find_lon_lat_index(gdata, 0.0, 90.0, "degrees")
imax += 1
con2,m = p3g.plot_polar_3D_global(ax, 3, np.array(gdata['dLat'][:,imin:imax,ialt]), np.array(gdata['dLon'][:,imin:imax,ialt]), np.array(gdata[gitm_key][:,imin:imax,ialt]),
gitm_name,
gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"], zmin,
zmax, data_color, center_lat=latlim1,
edge_lat=latlim2, linc=linc,
top_lon=tlon, cb=True, cloc="r",
tl=False, bcolor=bcolor, earth=earth,
m=m, faspect=faspect,
data_type="contour",
term_datetime=term_datetime)
con1_dim = list(con1.axes.get_position().bounds)
con2_dim = list(con2.ax.get_position().bounds)
con2_dim[0] = con2_dim[0] - 0.05
con2_dim[2] = con1_dim[2]
con2.ax.set_position(con2_dim)
# Output the differences as a scatter plot
ax = f.add_subplot(3,1,3, polar=pf)
p3g.plot_polar_3D_global(ax, 3, lat_data, lon_data, diff_data,
diff_name, diff_scale, diff_units, diff_min,
diff_max, diff_color, center_lat=latlim1,
edge_lat=latlim2, linc=linc, top_lon=tlon,
cloc="r", rl=False, bcolor=bcolor, earth=earth,
m=m, faspect=faspect,
term_datetime=term_datetime)
map_list = list([m])
else:
print rout_name, "ERROR: uknown plot type [", plot_type, "]"
return
if title:
f.suptitle(title, size="medium")
# Adjust subplot locations
if plot_type.find("rect") >= 0 or plot_type.find("polar") >= 0:
plt.subplots_adjust(left=.15)
# Draw to screen if desired
if draw:
if plt.isinteractive():
plt.draw() #In interactive mode, you just "draw".
else:
# W/o interactive mode, "show" stops the user from typing more
# at the terminal until plots are drawn.
plt.show()
# Save output file
if figname is not None:
plt.savefig(figname)
return(f, map_list)
def plot_net_gitm_comp(plot_type,
lon_data,
lat_data,
obs_data,
obs_name,
obs_scale,
obs_units,
diff_data,
diff_name,
diff_scale,
diff_units,
gitm_key,
gitm_alt,
gdata,
gitm_name,
diff_max=None,
zmax=None,
zmin=None,
title=None,
color=True,
bcolor='#747679',
data_coff=False,
diff_coff=True,
figname=None,
draw=True,
latlim1=90,
latlim2=-90,
linc=6,
tlon=90,
meq=False,
earth=False,
map_list=[],
faspect=True,
term_datetime=None,
extra_lines=False,
*args,
**kwargs):
'''
Creates three plots of a specified type, one showing the observations, one
showing the GITM data, and one showing the difference between the two.
Input: plot_type = key to determine plot type (rectangular, polar,
nsglobal, or snapshot)
lon_data = Numpy array with longitude data for matching model-obs
points
lat_data = Numpy array with latitude data for matching model-obs
points
obs_data = Numpy array with observational data for matching
model-obs points
obs_name = Name portion of the observational data label
obs_scale = Scale (linear/exponential) for plotting obs. data
obs_units = Unit portion of the observational data label
diff_data = Numpy array with differences for matching model-obs
points
gitm_key = Key for the GITM data
gitm_alt = Altitude in km to plot the GITM data at. For a 2D
variable like hmF2 or TEC, use 0.0 km.
gdata = GitmBin structure with model observations.
gitm_name = Name portion of the GITM data label
diff_max = Maximum value for the difference (absolute value),
if None, will be determined in script (default=None)
zmin = minimum value for z variable (default=None)
zmax = maximum value for z variable (default=None)
title = Plot title (default=None)
color = Color (True, default) or black and white (False)?
bcolor = Background color (default=)
data_coff = Center the data color scale about zero (False, default)?
diff_coff = Center the diff color scale about zero (True, default)?
figname = Output figure name with a .png suffix (default=None)
draw = Draw to screen? (default=True)
latlim1 = First latitude limit (degrees North, default=90).
Purpose varies depending on plot type. For rectangular,
this is the northern latitude limit. For polar, this
is the latitude at the center of the dial. For
snapshot, this is the lower boundary of polar dials.
It is not used for nsglobal.
latlim2 = Second latitude limit (degrees North, default=-90).
Purpose varies depending on plot type. For rectangular,
this is the southern latitude limit. For polar, this
is the latitude at the edge of the dial. This option is
not used with the snapshot or nsglobal option.
linc = Number of latitude tick incriments (default=6)
tlon = Longitude on top of the polar dial (degrees East,
default=90)
meq = Add a line for the geomagnetic equator?
(default=False)
earth = Include continent outlines for Earth (default=False)
map_list = List of map handles for the specified plot_type
(default=empty list)
faspect = Keep a true aspect ratio for maps? (default=True)
term_datetime = Include the solar terminator by shading the night
time regions? If so, include a datetime object
with the UT for this map. Only used if earth=True.
extra_lines = Plot a specified lines (good for showing regional
boundaries) (default=False). Provide a list of lists
which have the format shown:
[x np.array, y np.array, style string (eg 'k-')]
where x is in degrees longitude and y is in
degrees latitude
Output: f = handle to figure
'''
rout_name = "plot_net_gitm_comp"
# Get the desired color bars
data_color = gpr.choose_contour_map(color, data_coff)
diff_color = gpr.choose_contour_map(color, diff_coff)
# Get the altitude index
ialt = 0
if gitm_alt > 0.0:
ialt = gpr.find_alt_index(gdata, 0, 0, alt, units="km")
# Initialize the z variables, if desired. GITM and Observational data
# should share the same scale.
if (zmin is None):
obsmin = np.nanmin(obs_data)
gitmin = np.nanmin(gdata[gitm_key][:, :, ialt])
zmin = min(obsmin, gitmin)
if (zmax is None):
obsmax = np.nanmax(obs_data)
gitmax = np.nanmax(gdata[gitm_key][:, :, ialt])
zmax = max(obsmax, gitmax)
zran = round((zmax - zmin) / 6.0)
if (zran != 0.0):
zmin = math.floor(float("{:.14f}".format(zmin / zran))) * zran
zmax = math.ceil(float("{:.14f}".format(zmax / zran))) * zran
# Set the difference max/min limits, if desired
if diff_max is None:
diff_max = max(np.nanmax(diff_data), abs(np.nanmin(diff_data)))
diff_min = -1.0 * diff_max
# Initialize the figure, setting the height for a 3 subfigure stack
fwidth = 6
fheight = 12
if (plot_type.find("global") > 0):
fwidth *= 1.5
if (plot_type.find("shot") > 0):
fwidth *= 1.5
fheight *= 1.5
f = plt.figure(figsize=(fwidth, fheight))
# Plot the three datasets using the desired format
if plot_type.find("shot") > 0:
if len(map_list) == 3:
ml = map_list[0]
mn = map_list[1]
ms = map_list[2]
else:
ml = None
mn = None
ms = None
# Output the observations as a scatter plot
axl, ml, axn, mn, axs, ms = p3g.plot_snapshot_subfigure(
f,
3,
0,
lat_data,
lon_data,
obs_data,
obs_name,
obs_scale,
obs_units,
zmax,
zmin,
data_color,
tlon=tlon,
blat=latlim1,
xl=False,
yl=False,
xt=False,
bcolor=bcolor,
meq=meq,
earth=earth,
ml=ml,
mn=mn,
ms=ms,
faspect=faspect,
term_datetime=term_datetime)
# Output the gitm data as a contour after ensuring that the GITM array
# isn't padded to include unrealistic latitudes
(i, imin) = gpr.find_lon_lat_index(gdata, 0.0, -90.0, "degrees")
(i, imax) = gpr.find_lon_lat_index(gdata, 0.0, 90.0, "degrees")
imax += 1
p3g.plot_snapshot_subfigure(f,
3,
1,
np.array(gdata['dLat'][:, imin:imax,
ialt]),
np.array(gdata['dLon'][:, imin:imax,
ialt]),
np.array(gdata[gitm_key][:, imin:imax,
ialt]),
gitm_name,
gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"],
zmax,
zmin,
data_color,
cb=True,
cloc="r",
tlon=tlon,
blat=latlim1,
title=False,
xl=False,
xt=False,
bcolor=bcolor,
meq=meq,
earth=earth,
ml=ml,
mn=mn,
ms=ms,
faspect=faspect,
data_type="contour",
term_datetime=term_datetime)
# Output the differences as a scatter plot
p3g.plot_snapshot_subfigure(f,
3,
2,
lat_data,
lon_data,
diff_data,
diff_name,
diff_scale,
diff_units,
diff_max,
diff_min,
diff_color,
tlon=tlon,
blat=latlim1,
title=False,
yl=False,
bcolor=bcolor,
meq=meq,
earth=earth,
ml=ml,
mn=mn,
ms=ms,
faspect=faspect,
term_datetime=term_datetime)
map_list = list([ml, mn, ms])
elif plot_type.find("nsglobal") >= 0:
if len(map_list) == 2:
mn = map_list[0]
ms = map_list[1]
else:
mn = None
ms = None
# Check for boundary lines to plot
eline_north = False
eline_south = False
if type(extra_lines) is list:
if len(extra_lines) >= 1:
eline_north = extra_lines[0]
if len(extra_lines) >= 2:
eline_south = extra_lines[1]
else:
print "Only one boundary provided, plotting in north"
else:
print "No boundaries provided, better to declare as False"
# Output the observations as a scatter plot
axn1, mn, axs1, ms = p3g.plot_nsglobal_subfigure(
f,
3,
0,
lat_data,
lon_data,
obs_data,
obs_name,
obs_scale,
obs_units,
zmax,
zmin,
data_color,
title=True,
cb=True,
elat=latlim1,
tlon=tlon,
rl=False,
tl=False,
bcolor=bcolor,
earth=earth,
mn=mn,
ms=ms,
faspect=faspect,
term_datetime=term_datetime,
extra_line_n=eline_north,
extra_line_s=eline_south)
# Output the gitm data as a contour after ensuring that the GITM array
# isn't padded to include unrealistic latitudes
(i, imin) = gpr.find_lon_lat_index(gdata, 0.0, -90.0, "degrees")
(i, imax) = gpr.find_lon_lat_index(gdata, 0.0, 90.0, "degrees")
imax += 1
axn2, mn, axs2, ms = p3g.plot_nsglobal_subfigure(
f,
3,
1,
np.array(gdata['dLat'][:, imin:imax, ialt]),
np.array(gdata['dLon'][:, imin:imax, ialt]),
np.array(gdata[gitm_key][:, imin:imax, ialt]),
gitm_name,
gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"],
zmax,
zmin,
data_color,
title=False,
cb=True,
elat=latlim1,
tlon=tlon,
tl=False,
bcolor=bcolor,
earth=earth,
mn=mn,
ms=ms,
data_type="contour",
term_datetime=term_datetime,
extra_line_n=eline_north,
extra_line_s=eline_south)
# Output the differences as a scatter plot
p3g.plot_nsglobal_subfigure(f,
3,
2,
lat_data,
lon_data,
diff_data,
diff_name,
diff_scale,
diff_units,
diff_max,
diff_min,
diff_color,
title=False,
cb=True,
elat=latlim1,
tlon=tlon,
rl=False,
bcolor=bcolor,
earth=earth,
mn=mn,
ms=ms,
faspect=faspect,
term_datetime=term_datetime,
extra_line_n=eline_north,
extra_line_s=eline_south)
map_list = list([mn, ms])
elif plot_type.find("rect") >= 0:
if len(map_list) == 1:
m = map_list[0]
else:
m = None
# Output the observations as a scatter plot
ax = f.add_subplot(3, 1, 1)
con1, m = p3g.plot_rectangular_3D_global(ax,
lat_data,
lon_data,
obs_data,
obs_name,
obs_scale,
obs_units,
zmin,
zmax,
data_color,
nlat=latlim1,
slat=latlim2,
linc=linc,
cloc="r",
xl=False,
xt=False,
yl=False,
meq=meq,
bcolor=bcolor,
earth=earth,
m=m,
faspect=faspect,
term_datetime=term_datetime)
# Output the gitm data as a contour
ax = f.add_subplot(3, 1, 2)
con2, m = p3g.plot_rectangular_3D_global(
ax,
np.array(gdata['dLat'][:, :, ialt]),
np.array(gdata['dLon'][:, :, ialt]),
np.array(gdata[gitm_key][:, :, ialt]),
gitm_name,
gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"],
zmin,
zmax,
data_color,
nlat=latlim1,
slat=latlim2,
linc=linc,
cb=True,
cloc="r",
xl=False,
xt=False,
bcolor=bcolor,
meq=meq,
earth=earth,
m=m,
faspect=faspect,
data_type="contour",
term_datetime=term_datetime)
# Adjust plot dimensions if necessary
if not earth:
con1_dim = list(con1.axes.get_position().bounds)
con2_dim = list(con2.ax.get_position().bounds)
con2_dim[2] = con1_dim[2]
con2.ax.set_position(con2_dim)
# Output the differences as a scatter plot
ax = f.add_subplot(3, 1, 3)
p3g.plot_rectangular_3D_global(ax,
lat_data,
lon_data,
diff_data,
diff_name,
diff_scale,
diff_units,
diff_min,
diff_max,
diff_color,
nlat=latlim1,
slat=latlim2,
linc=linc,
cloc="r",
yl=False,
bcolor=bcolor,
meq=meq,
earth=earth,
m=m,
faspect=faspect,
term_datetime=term_datetime)
map_list = list([m])
elif plot_type.find("polar") >= 0:
if len(map_list) == 1:
m = map_list[0]
else:
m = None
pf = True
if earth:
pf = False
# Output the observations as a scatter plot
ax = f.add_subplot(3, 1, 1, polar=pf)
con1, m = p3g.plot_polar_3D_global(ax,
3,
lat_data,
lon_data,
obs_data,
obs_name,
obs_scale,
obs_units,
zmin,
zmax,
data_color,
center_lat=latlim1,
edge_lat=latlim2,
linc=linc,
top_lon=tlon,
cloc="r",
tl=False,
rl=False,
bcolor=bcolor,
earth=earth,
m=m,
faspect=faspect,
term_datetime=term_datetime)
# Output the gitm data as a contour after ensuring that the GITM
# array isn't padded to include unrealistic latitudes
ax = f.add_subplot(3, 1, 2, polar=pf)
(i, imin) = gpr.find_lon_lat_index(gdata, 0.0, -90.0, "degrees")
(i, imax) = gpr.find_lon_lat_index(gdata, 0.0, 90.0, "degrees")
imax += 1
con2, m = p3g.plot_polar_3D_global(ax,
3,
np.array(gdata['dLat'][:, imin:imax,
ialt]),
np.array(gdata['dLon'][:, imin:imax,
ialt]),
np.array(gdata[gitm_key][:,
imin:imax,
ialt]),
gitm_name,
gdata[gitm_key].attrs["scale"],
gdata[gitm_key].attrs["units"],
zmin,
zmax,
data_color,
center_lat=latlim1,
edge_lat=latlim2,
linc=linc,
top_lon=tlon,
cb=True,
cloc="r",
tl=False,
bcolor=bcolor,
earth=earth,
m=m,
faspect=faspect,
data_type="contour",
term_datetime=term_datetime)
con1_dim = list(con1.axes.get_position().bounds)
con2_dim = list(con2.ax.get_position().bounds)
con2_dim[0] = con2_dim[0] - 0.05
con2_dim[2] = con1_dim[2]
con2.ax.set_position(con2_dim)
# Output the differences as a scatter plot
ax = f.add_subplot(3, 1, 3, polar=pf)
p3g.plot_polar_3D_global(ax,
3,
lat_data,
lon_data,
diff_data,
diff_name,
diff_scale,
diff_units,
diff_min,
diff_max,
diff_color,
center_lat=latlim1,
edge_lat=latlim2,
linc=linc,
top_lon=tlon,
cloc="r",
rl=False,
bcolor=bcolor,
earth=earth,
m=m,
faspect=faspect,
term_datetime=term_datetime)
map_list = list([m])
else:
print rout_name, "ERROR: uknown plot type [", plot_type, "]"
return
if title:
f.suptitle(title, size="medium")
# Adjust subplot locations
if plot_type.find("rect") >= 0 or plot_type.find("polar") >= 0:
plt.subplots_adjust(left=.15)
# Draw to screen if desired
if draw:
if plt.isinteractive():
plt.draw() #In interactive mode, you just "draw".
else:
# W/o interactive mode, "show" stops the user from typing more
# at the terminal until plots are drawn.
plt.show()
# Save output file
if figname is not None:
plt.savefig(figname)
return (f, map_list)
