def plot_3D_alt(ax,
x_data,
alt_data,
z_data,
x_name,
x_scale,
x_units,
alt_units,
z_name,
z_scale,
z_units,
xmin=None,
xmax=None,
amin=None,
amax=None,
zmin=None,
zmax=None,
xinc=6,
ainc=6,
zinc=6,
cb=True,
cloc="r",
color=True,
zcenter=False,
title=None,
tloc="t",
xl=True,
xt=True,
yl=True,
yt=True,
plot_type="contour",
*args,
**kwargs):
'''
Creates a single polar projection, with the latitude center and range
determined by the input.
Input: ax = axis handle
x_data = 2D numpy array containing x-axis data
alt_data = 2D numpy array containing y-axis altitude data
z_data = 1D or 2D numpy array containing data to plot using a
color scale
x_name = Name of x-axis data
x_scale = Plot x-axis data using a linear or exponetial scale?
x_units = x-axis data units
alt_units = y-axis altitude units (m or km)
z_name = Name of z-axis data
z_scale = Plot z-axis data using a linear or exponetial scale?
z_units = z-axis data units
xmin = minimum value for x variable (default=None)
xmax = maximum value for x variable (default=None)
amin = minimum value for altitude (default=None)
amax = maximum value for altitude (default=None)
zmin = minimum value for z variable (default=None)
zmax = maximum value for z variable (default=None)
xinc = number of tick incriments for x variable (default 6)
ainc = number of tick incriments for altitude (default 6)
zinc = number of tick incriments for z variable (default 6)
cb = Add a colorbar (default is True)
cloc = Colorbar location (t=top, r=right, l=left, b=bottom,
default is right)
color = Color plot or B&W (default is True for color)
zcenter = Should the z range be centered about zero (default is
False, for uncentered)
title = plot title (default is none)
tloc = title location (t=top, r=right, l=left, b=bottom,
default is top)
xl = Include x label (default is True)
xt = Include x ticks (default is True)
yl = Include y label. This defaults to placing an altitude
label on the left axis. If a non-Boolian value is
provided, it is assumed to be a string that will be
used as a right axis label. (default is True)
yt = Include y ticks (default is True)
plot_type = Make a scatter or contour plot? (default=contour)
'''
# Set the x, a, and z ranges
if (xmin is None):
xmin = np.nanmin(x_data)
if (xmax is None):
xmax = np.nanmax(x_data)
arange = xmax - xmin
xwidth = arange / xinc
if (zmin is None):
zmin = np.nanmin(z_data)
if (zmax is None):
zmax = np.nanmax(z_data)
if zcenter and abs(zmin) != zmax:
arange = max(abs(zmin), zmax)
zmax = arange
zmin = -1.0 * arange
arange = zmax - zmin
zwidth = arange / zinc
if (amin is None):
amin = np.nanmin(alt_data)
if (amax is None):
amax = np.nanmax(alt_data)
arange = amax - amin
awidth = arange / ainc
# Determine the z scale
if z_scale.find("exp") >= 0:
v = np.logspace(math.log10(zmin),
math.log10(zmax),
zinc * 10,
endpoint=True)
norm = LogNorm(vmin=zmin, vmax=zmax)
else:
norm = None
v = np.linspace(zmin, zmax, zinc * 10, endpoint=True)
# Plot the data
col = gpr.choose_contour_map(color, zcenter)
if plot_type.find("scatter") >= 0:
con = ax.scatter(x_data,
alt_data,
c=z_data,
cmap=get_cmap(col),
norm=norm,
vmin=zmin,
vmax=zmax,
edgecolors="none",
s=10)
cax = con.axes
else:
con = ax.contourf(x_data,
alt_data,
z_data,
v,
cmap=get_cmap(col),
norm=norm,
vmin=zmin,
vmax=zmax)
cax = con.ax
# Configure axis
if yt:
ytics = MultipleLocator(awidth)
ax.yaxis.set_major_locator(ytics)
else:
ax.yaxis.set_major_formatter(FormatStrFormatter(""))
if yl is True:
ax.set_ylabel('Altitude ($km$)')
elif yl is not False:
ax.set_ylabel(yl)
ax.yaxis.set_label_position("right")
plt.ylim(amin, amax)
if x_scale.find("exponential") >= 0:
ax.set_xscale('log')
elif xt:
xtics = MultipleLocator(xwidth)
ax.xaxis.set_major_locator(xtics)
else:
ax.xaxis.set_major_formatter(FormatStrFormatter(""))
if xl:
ax.set_xlabel(r'%s ($%s$)' % (x_name, x_units))
plt.xlim(xmin, xmax)
# Set the title
if title:
rot = 'horizontal'
yloc = 1.05
xloc = 0.5
if tloc == "b":
yloc = -.1
elif tloc != "t":
rot = 'vertical'
yloc = 0.5
xloc = -.2
if tloc == "r":
xloc = 1.1
title = ax.set_title(title,
y=yloc,
size='medium',
x=xloc,
rotation=rot)
# Change the background color
ax.patch.set_facecolor('#747679')
# Add a colorbar
if cb:
orient = 'vertical'
if (cloc == 't' or cloc == 'b'):
orient = 'horizontal'
cbar = gpr.add_colorbar(con, zmin, zmax, zinc, orient, z_scale, z_name,
z_units)
if (cloc == 'l' or cloc == 't'):
bp = list(cbar.ax.get_position().bounds)
cp = list(cax.get_position().bounds)
if (cloc == 't'):
cp[1] = bp[1]
bp[1] = cp[1] + cp[3] + 0.085
else:
bp[0] = 0.125
cp[0] = bp[0] + 0.1 + bp[2]
cax.set_position(cp)
cbar.ax.set_position(bp)
return con
def plot_3D_alt(ax, x_data, alt_data, z_data, x_name, x_scale, x_units,
alt_units, z_name, z_scale, z_units, xmin=None, xmax=None,
amin=None, amax=None, zmin=None, zmax=None, xinc=6, ainc=6,
zinc=6, cb=True, cloc="r", color=True, zcenter=False,
title=None, tloc="t", xl=True, xt=True, yl=True, yt=True,
plot_type="contour", *args, **kwargs):
'''
Creates a single polar projection, with the latitude center and range
determined by the input.
Input: ax = axis handle
x_data = 2D numpy array containing x-axis data
alt_data = 2D numpy array containing y-axis altitude data
z_data = 1D or 2D numpy array containing data to plot using a
color scale
x_name = Name of x-axis data
x_scale = Plot x-axis data using a linear or exponetial scale?
x_units = x-axis data units
alt_units = y-axis altitude units (m or km)
z_name = Name of z-axis data
z_scale = Plot z-axis data using a linear or exponetial scale?
z_units = z-axis data units
xmin = minimum value for x variable (default=None)
xmax = maximum value for x variable (default=None)
amin = minimum value for altitude (default=None)
amax = maximum value for altitude (default=None)
zmin = minimum value for z variable (default=None)
zmax = maximum value for z variable (default=None)
xinc = number of tick incriments for x variable (default 6)
ainc = number of tick incriments for altitude (default 6)
zinc = number of tick incriments for z variable (default 6)
cb = Add a colorbar (default is True)
cloc = Colorbar location (t=top, r=right, l=left, b=bottom,
default is right)
color = Color plot or B&W (default is True for color)
zcenter = Should the z range be centered about zero (default is
False, for uncentered)
title = plot title (default is none)
tloc = title location (t=top, r=right, l=left, b=bottom,
default is top)
xl = Include x label (default is True)
xt = Include x ticks (default is True)
yl = Include y label. This defaults to placing an altitude
label on the left axis. If a non-Boolian value is
provided, it is assumed to be a string that will be
used as a right axis label. (default is True)
yt = Include y ticks (default is True)
plot_type = Make a scatter or contour plot? (default=contour)
'''
# Set the x, a, and z ranges
if(xmin is None):
xmin = np.nanmin(x_data)
if(xmax is None):
xmax = np.nanmax(x_data)
arange = xmax - xmin
xwidth = arange / xinc
if(zmin is None):
zmin = np.nanmin(z_data)
if(zmax is None):
zmax = np.nanmax(z_data)
if zcenter and abs(zmin) != zmax:
arange = max(abs(zmin), zmax)
zmax = arange
zmin = -1.0 * arange
arange = zmax - zmin
zwidth = arange / zinc
if(amin is None):
amin = np.nanmin(alt_data)
if(amax is None):
amax = np.nanmax(alt_data)
arange = amax - amin
awidth = arange / ainc
# Determine the z scale
if z_scale.find("exp") >= 0:
v = np.logspace(math.log10(zmin), math.log10(zmax), zinc*10,
endpoint=True)
norm = LogNorm(vmin=zmin, vmax=zmax)
else:
norm = None
v = np.linspace(zmin, zmax, zinc*10, endpoint=True)
# Plot the data
col = gpr.choose_contour_map(color, zcenter)
if plot_type.find("scatter") >= 0:
con = ax.scatter(x_data, alt_data, c=z_data, cmap=get_cmap(col),
norm=norm, vmin=zmin, vmax=zmax, edgecolors="none",
s=10)
cax = con.axes
else:
con = ax.contourf(x_data, alt_data, z_data, v, cmap=get_cmap(col),
norm=norm, vmin=zmin, vmax=zmax)
cax = con.ax
# Configure axis
if yt:
ytics = MultipleLocator(awidth)
ax.yaxis.set_major_locator(ytics)
else:
ax.yaxis.set_major_formatter(FormatStrFormatter(""))
if yl is True:
ax.set_ylabel('Altitude ($km$)')
elif yl is not False:
ax.set_ylabel(yl)
ax.yaxis.set_label_position("right")
plt.ylim(amin, amax)
if x_scale.find("exponential") >= 0:
ax.set_xscale('log')
elif xt:
xtics = MultipleLocator(xwidth)
ax.xaxis.set_major_locator(xtics)
else:
ax.xaxis.set_major_formatter(FormatStrFormatter(""))
if xl:
ax.set_xlabel(r'%s ($%s$)' % (x_name, x_units))
plt.xlim(xmin, xmax)
# Set the title
if title:
rot = 'horizontal'
yloc = 1.05
xloc = 0.5
if tloc == "b":
yloc = -.1
elif tloc != "t":
rot = 'vertical'
yloc = 0.5
xloc = -.2
if tloc == "r":
xloc = 1.1
title = ax.set_title(title,y=yloc,size='medium',x=xloc,rotation=rot)
# Change the background color
ax.patch.set_facecolor('#747679')
# Add a colorbar
if cb:
orient = 'vertical'
if(cloc == 't' or cloc == 'b'):
orient = 'horizontal'
cbar = gpr.add_colorbar(con, zmin, zmax, zinc, orient, z_scale, z_name,
z_units)
if(cloc == 'l' or cloc == 't'):
bp = list(cbar.ax.get_position().bounds)
cp = list(cax.get_position().bounds)
if(cloc == 't'):
cp[1] = bp[1]
bp[1] = cp[1] + cp[3] + 0.085
else:
bp[0] = 0.125
cp[0] = bp[0] + 0.1 + bp[2]
cax.set_position(cp)
cbar.ax.set_position(bp)
return con
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)
