def lon_lt_ticks(self, x, pos):
'''
Define ticks to include local time in addition to longitude. Assumes
that x is longitude in degrees.
'''
import math
import gitm_plot_rout as gpr
# Calculate the local time in hours
lth = gpr.glon_to_localtime(self['time'], x)
ltm = int((lth - math.floor(lth)) * 60.0)
# Build the format string
fmtstring = "{:g}$^\circ$\n {:02d}:{:02d}".format(x, int(lth), ltm)
return (fmtstring)
def lon_lt_ticks(self, x, pos):
'''
Define ticks to include local time in addition to longitude. Assumes
that x is longitude in degrees.
'''
import math
import gitm_plot_rout as gpr
# Calculate the local time in hours
lth = gpr.glon_to_localtime(self['time'], x)
ltm = int((lth - math.floor(lth)) * 60.0)
# Build the format string
fmtstring = "{:g}$^\circ$\n {:02d}:{:02d}".format(x, int(lth), ltm)
return(fmtstring)
def gitm_single_alt_image(plot_type,
zkey,
gData,
lat_index=-1,
lon_index=-1,
title=None,
figname=None,
draw=True,
xkey="dLat",
lon_lt=False,
hold_key=None,
geo_key=None,
hold_value=None,
color="b",
marker="o",
line=":",
zmax=None,
zmin=None,
amax=None,
amin=None,
xmax=None,
xmin=None,
zcenter=False,
add_hmf2=False,
hcolor="k",
hline="--",
add_fieldlines=False,
apex_alts=list(),
acolor="k",
aline="-",
*args,
**kwargs):
'''
Creates a rectangular or polar map projection plot for a specified latitude
range.
Input: plot_type = key to determine plot type (linear, contour, scatter)
zkey = key for z variable (ie 'Vertical TEC')
gData = gitm bin structure
lat_index = index of constant latitude (default -1, none)
lon_index = index of constant longitude (default -1, none)
title = plot title
figname = file name to save figure as (default is none)
xkey = for contour plots specify an x key (default dLat)
(options dLat/dLon/Latitude/Longitude)
lon_lt = Add local time to the x axis (default=False). Will
cause problems unless xkey is dLon.
hold_key = Key to coordinate in which desired location is specified
geo_key = Geographic coordinate corresponding to hold_key coord.
hold_value = value of desired, specific coordinate location
color = linear color (default blue) for b&w contour, enter 'k'
marker = linear marker type (default circles)
line = linear line type (default dotted)
zmax = z key maximum (None to compute automatically, default)
zmin = z key minimum (None to compute automatically, default)
amax = a key maximum (None to compute automatically, default)
amin = a key minimum (None to compute automatically, default)
xmax = x key maximum for countour plots (or None to compute
automatically, default)
xmin = x key minimum for contour plots (or None to compute
automatically, default)
zcenter = Center z axis about zero? (default is False)
add_hmf2 = Add line showing hmF2? (default=False)
hcolor = Line color for hmF2 line (default=black)
hline = Line style for hmF2 line (default=dashes)
add_fieldlines = Add dipole field lines (default=False)
apex_alts = List of apex altitudes to plot field lines at
(default=list(), will pick a range of apex alts)
acolor = Line color for field lines (default="k")
aline = Line style for field lines (default="-")
'''
# Initialize the altitude and x variable limits
if (amin == None or amax == None):
tmin, tmax = gpr.find_data_limits([gData], "Altitude", lat_index,
lon_index, -2, 30)
if amin == None:
amin = tmin
amin = math.ceil(amin / 10000.0) * 10.0
if amax == None:
amax = tmax
amax = math.floor(amax / 10000.0) * 10.0
if (plot_type.find("linear") < 0 and (xmin == None or xmax == None)):
if gData[xkey].attrs['scale'].find("exp") >= 0:
raw = True
else:
raw = False
tmin, tmax = gpr.find_data_limits([gData],
xkey,
lat_index,
lon_index,
-2,
6,
raw=raw)
if xmin == None:
xmin = tmin
if xmax == None:
xmax = tmax
# Initialize the input data indices
datadim = [gData.attrs['nAlt']]
if lat_index >= 0:
latmin = lat_index
latmax = latmin + 1
else:
latmin = 0
latmax = gData.attrs['nLat']
datadim.insert(0, gData.attrs['nLat'])
if lon_index >= 0:
lonmin = lon_index
lonmax = lonmin + 1
else:
lonmin = 0
lonmax = gData.attrs['nLon']
datadim.insert(0, gData.attrs['nLon'])
# Initialize the data
alt_data = np.array(gData['Altitude'][lonmin:lonmax, latmin:latmax, :])
alt_data = alt_data.reshape(datadim)
if plot_type.find("linear") >= 0:
# Perform interpolation to plot data at a specific location instead of a
# grid point
if (gData.has_key(hold_key) and hold_value is not None):
if geo_key.find("Lon") >= 0:
lonmin = 0
lonmax = gData.attrs['nLon']
elif geo_key.find("Lat") >= 0:
latmin = 0
latmax = gData.attrs['nLat']
i_data = gpr.create_linear_input_array(hold_key,
hold_value,
"Altitude", [zkey],
gData,
alt_data,
lonmin=lonmin,
lonmax=lonmax,
latmin=latmin,
latmax=latmax,
altmax=gData.attrs['nAlt'])
x_data = np.array(i_data[zkey])
if zmin == None:
xmin = np.nanmin(x_data)
if zmax == None:
xmax = np.nanmax(x_data)
else:
if (zmin == None or zmax == None):
if gData[zkey].attrs['scale'].find("exp") >= 0:
raw = True
else:
raw = False
tmin, tmax = gpr.find_data_limits([gData],
zkey,
lat_index,
lon_index,
-2,
6,
raw=raw)
if zmin == None:
xmin = tmin
if zmax == None:
xmax = tmax
x_data = np.array(gData[zkey][lonmin:lonmax, latmin:latmax, :])
x_data = x_data.reshape(datadim)
x_name = gData[zkey].attrs['name']
x_scale = gData[zkey].attrs['scale']
x_units = gData[zkey].attrs['units']
z_data = []
z_name = ""
z_scale = ""
z_units = ""
else:
if color.find('k') == 0:
color = False
else:
color = True
marker = True
line = zcenter
# Perform interpolation to plot data at a specific location instead of a
# grid point
if (gData.has_key(hold_key) and hold_value is not None):
ilon = 0
ilat = 0
x_data = np.array(gData[xkey][lonmin:lonmax, latmin:latmax,
0]).flatten()
if geo_key.find("Lon") >= 0:
ilon = -1
elif geo_key.find("Lat") >= 0:
ilat = -1
i_data = gpr.create_contour_input_array(hold_key,
hold_value,
xkey,
"Altitude", [zkey],
gData,
x_data,
alt_data[0, :],
lonmin=ilon,
latmin=ilat)
x_data = np.array(i_data[xkey])
z_data = np.array(i_data[zkey])
alt_data = np.array(i_data['Altitude'])
if zmin == None:
zmin = np.nanmin(z_data)
if zmax == None:
zmax = np.nanmax(z_data)
else:
if (zmin == None or zmax == None):
if gData[zkey].attrs['scale'].find("exp") >= 0:
raw = True
else:
raw = False
tmin, tmax = gpr.find_data_limits([gData],
zkey,
lat_index,
lon_index,
-2,
6,
raw=raw)
if zmin == None:
zmin = tmin
if zmax == None:
zmax = tmax
x_data = np.array(gData[xkey][lonmin:lonmax, latmin:latmax, :])
x_data = x_data.reshape(datadim)
z_data = np.array(gData[zkey][lonmin:lonmax, latmin:latmax, :])
z_data = z_data.reshape(datadim)
x_name = gData[xkey].attrs['name']
x_scale = gData[xkey].attrs['scale']
x_units = gData[xkey].attrs['units']
z_name = gData[zkey].attrs['name']
z_scale = gData[zkey].attrs['scale']
z_units = gData[zkey].attrs['units']
# Initialize the new figure
f, ax = pap.plot_single_alt_image(plot_type,
x_data,
alt_data / 1000.0,
z_data,
x_name,
x_scale,
x_units,
"km",
z_name=z_name,
z_scale=z_scale,
z_units=z_units,
xmin=xmin,
xmax=xmax,
amin=amin,
amax=amax,
zmin=zmin,
zmax=zmax,
title=title,
draw=False,
color1=color,
color2=marker,
color3=line)
# Add local time to longitude axis, if desired
if lon_lt:
xfmt = FuncFormatter(gData.lon_lt_ticks)
ax.xaxis.set_major_formatter(xfmt)
ax.set_xlabel("Longitude \ Local Time")
plt.subplots_adjust(bottom=.13)
# Add hmF2 if desired
if add_hmf2 and not gData.has_key("hmF2"):
gData.calc_2dion()
if not gData.has_key("hmF2"):
print module_name, "WARNING: hmF2 data is not available"
add_hmF2 = False
if add_hmf2:
if plot_type.find("linear") >= 0:
x = np.array([xmin, xmax])
if (gData.has_key(hold_key) and hold_value is not None):
if geo_key.find("Lon") >= 0:
x_data = gData[hold_key][:, latmin:latmax,
int(gData.attrs['nAlt'] /
2)].flatten()
y_data = gData['hmF2'][:, latmin:latmax, 0].flatten()
elif geo_key.find("Lat") >= 0:
x_data = gData[hold_key][lonmin:lonmax, :,
int(gData.attrs['nAlt'] /
2)].flatten()
y_data = gData['hmF2'][20:21, :, 0].flatten()
hold = interpolate.interp1d(x_data, y_data)
hmf2 = hold(hold_value)
else:
hmf2 = gData['hmF2'][lonmin:lonmax, latmin:latmax, 0]
y = np.array([hmf2, hmf2])
else:
if (gData.has_key(hold_key) and hold_value is not None):
if geo_key.find("Lon") >= 0:
lonmin = 0
lonmax = gData.attrs['nLon']
elif geo_key.find("Lat") >= 0:
latmin = 0
latmax = gData.attrs['nLat']
x = x_data[0, :]
i_data = gpr.create_linear_input_array(hold_key,
hold_value,
xkey, ['hmF2'],
gData,
x,
lonmin=lonmin,
lonmax=lonmax,
latmin=latmin,
latmax=latmax)
y = np.array(i_data['hmF2'])
else:
x = x_data
y = np.array(gData['hmF2'][lonmin:lonmax, latmin:latmax, 0])
y = y.reshape(datadim[0:-1])
ax.plot(x, y, color=hcolor, linestyle=hline, linewidth=2)
# Add field lines, if desired
if add_fieldlines and plot_type.find("contour") >= 0:
lon = None
dec = None
lon_units = "radians"
lat_units = "degrees"
lat_type = "geographic"
if hold_key == None:
hold_key = "Longitude"
hold_value = float(gData[hold_key][lonmin:lonmax, 0, 0])
if xkey == "Latitude" or xkey == "dLat":
from scipy import interpolate
x = x_data[0]
if xkey == "Latitude":
lat_units = "radians"
# If the geographic equator is on the x-axis, find the longitude
# and declination at the geomagnetic equator
ikeys = ["Declination"]
if hold_key != "Longitude" and hold_key != "dLon":
ikeys.append("Longitude")
i_data = gpr.create_linear_input_array(
hold_key,
hold_value,
"Inclination",
ikeys,
gData,
x_data[0],
lonmax=lonmax,
lonmin=lonmin,
latmax=latmax,
latmin=latmin,
altmax=gData.attrs['nAlt'])
else:
x = x_data[:, 0]
i_data = dict()
i_data['Inclination'] = gData['Inclination'][lonmin:lonmax,
latmin:latmax, 0]
i_data['Inclination'] = i_data['Inclination'].reshape(latmax)
i_data['Declination'] = gData['Declination'][lonmin:lonmax,
latmin:latmax, 0]
i_data['Declination'] = i_data['Declination'].reshape(latmax)
# Remove any NaN
ikeys.append("Inclination")
test = range(len(i_data[ikeys[0]]))
for i in ikeys:
test *= i_data[i]
good = [i for i, t in enumerate(test) if not np.isnan(t)]
if len(good) > 2:
for i in ikeys:
i_data[i] = i_data[i].take(good)
# Interpolate good data
tckdec = interpolate.splrep(i_data['Inclination'],
i_data['Declination'],
s=0)
dec = interpolate.splev(0.0, tckdec, der=0)
if i_data.has_key("Longitude"):
tcklon = interpolate.splrep(i_data['Inclination'],
i_data['Longitude'],
s=0)
lon = interpolate.splev(0.0, tcklon, der=0)
else:
lon = hold_value
if np.isnan(dec) or np.isnan(lon):
print "WARNING: unable to interpolate lon and dec at meq"
lat_type = None
else:
# Add local time to the title
lt = gpr.glon_to_localtime(gData['time'], lon, lon_units)
h = int(lt)
m = int((lt - h) * 60.0)
ax.set_title("{:}, {:02d}:{:02d} SLT".format(title, h, m),
size="medium")
else:
print "WARNING: unable to find longitude and declination at meq"
lat_type = None
elif xkey == "Inclination":
lat_type = "inclination"
elif xkey == "Magnetic Latitude":
lat_type = "magnetic"
else:
print "WARNING: can't output field lines when xaxis is", xkey
lat_type = None
if lat_type is not None:
if len(apex_alts) < 1:
# Set default apex altitudes
apex_alts = [
amin + float(i + 1) * 200.0
for i in range(int(math.ceil((amax - amin) / 200.0)))
]
for alt in apex_alts:
gpr.add_dipole_fieldline(ax,
alt,
x,
lat_units=lat_units,
lat_type=lat_type,
longitude=lon,
lon_units=lon_units,
declination=dec,
color=acolor,
linestyle=aline)
if draw:
# Draw to screen.
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, ax
def gitm_single_alt_image(plot_type, zkey, gData, lat_index=-1, lon_index=-1,
title=None, figname=None, draw=True, xkey="dLat",
lon_lt=False, hold_key=None, geo_key=None,
hold_value=None, color="b", marker="o", line=":",
zmax=None, zmin=None, amax=None, amin=None, xmax=None,
xmin=None, zcenter=False, add_hmf2=False, hcolor="k",
hline="--", add_fieldlines=False, apex_alts=list(),
acolor="k", aline="-", *args, **kwargs):
'''
Creates a rectangular or polar map projection plot for a specified latitude
range.
Input: plot_type = key to determine plot type (linear, contour, scatter)
zkey = key for z variable (ie 'Vertical TEC')
gData = gitm bin structure
lat_index = index of constant latitude (default -1, none)
lon_index = index of constant longitude (default -1, none)
title = plot title
figname = file name to save figure as (default is none)
xkey = for contour plots specify an x key (default dLat)
(options dLat/dLon/Latitude/Longitude)
lon_lt = Add local time to the x axis (default=False). Will
cause problems unless xkey is dLon.
hold_key = Key to coordinate in which desired location is specified
geo_key = Geographic coordinate corresponding to hold_key coord.
hold_value = value of desired, specific coordinate location
color = linear color (default blue) for b&w contour, enter 'k'
marker = linear marker type (default circles)
line = linear line type (default dotted)
zmax = z key maximum (None to compute automatically, default)
zmin = z key minimum (None to compute automatically, default)
amax = a key maximum (None to compute automatically, default)
amin = a key minimum (None to compute automatically, default)
xmax = x key maximum for countour plots (or None to compute
automatically, default)
xmin = x key minimum for contour plots (or None to compute
automatically, default)
zcenter = Center z axis about zero? (default is False)
add_hmf2 = Add line showing hmF2? (default=False)
hcolor = Line color for hmF2 line (default=black)
hline = Line style for hmF2 line (default=dashes)
add_fieldlines = Add dipole field lines (default=False)
apex_alts = List of apex altitudes to plot field lines at
(default=list(), will pick a range of apex alts)
acolor = Line color for field lines (default="k")
aline = Line style for field lines (default="-")
'''
# Initialize the altitude and x variable limits
if(amin == None or amax == None):
tmin, tmax = gpr.find_data_limits([gData], "Altitude", lat_index,
lon_index, -2, 30)
if amin == None:
amin = tmin
amin = math.ceil(amin / 10000.0) * 10.0
if amax == None:
amax = tmax
amax = math.floor(amax / 10000.0) * 10.0
if(plot_type.find("linear") < 0 and (xmin == None or xmax == None)):
if gData[xkey].attrs['scale'].find("exp") >= 0:
raw = True
else:
raw = False
tmin, tmax = gpr.find_data_limits([gData], xkey, lat_index, lon_index,
-2, 6, raw=raw)
if xmin == None:
xmin = tmin
if xmax == None:
xmax = tmax
# Initialize the input data indices
datadim = [gData.attrs['nAlt']]
if lat_index >= 0:
latmin = lat_index
latmax = latmin + 1
else:
latmin = 0
latmax = gData.attrs['nLat']
datadim.insert(0, gData.attrs['nLat'])
if lon_index >= 0:
lonmin = lon_index
lonmax = lonmin + 1
else:
lonmin = 0
lonmax = gData.attrs['nLon']
datadim.insert(0, gData.attrs['nLon'])
# Initialize the data
alt_data = np.array(gData['Altitude'][lonmin:lonmax,latmin:latmax,:])
alt_data = alt_data.reshape(datadim)
if plot_type.find("linear") >= 0:
# Perform interpolation to plot data at a specific location instead of a
# grid point
if(gData.has_key(hold_key) and hold_value is not None):
if geo_key.find("Lon") >= 0:
lonmin = 0
lonmax = gData.attrs['nLon']
elif geo_key.find("Lat") >= 0:
latmin = 0
latmax = gData.attrs['nLat']
i_data = gpr.create_linear_input_array(hold_key, hold_value,
"Altitude", [zkey], gData,
alt_data, lonmin=lonmin,
lonmax=lonmax, latmin=latmin,
latmax=latmax,
altmax=gData.attrs['nAlt'])
x_data = np.array(i_data[zkey])
if zmin == None:
xmin = np.nanmin(x_data)
if zmax == None:
xmax = np.nanmax(x_data)
else:
if(zmin == None or zmax == None):
if gData[zkey].attrs['scale'].find("exp") >= 0:
raw = True
else:
raw = False
tmin, tmax = gpr.find_data_limits([gData],zkey,lat_index,
lon_index,-2,6,raw=raw)
if zmin == None:
xmin = tmin
if zmax == None:
xmax = tmax
x_data = np.array(gData[zkey][lonmin:lonmax,latmin:latmax,:])
x_data = x_data.reshape(datadim)
x_name = gData[zkey].attrs['name']
x_scale = gData[zkey].attrs['scale']
x_units = gData[zkey].attrs['units']
z_data = []
z_name = ""
z_scale = ""
z_units = ""
else:
if color.find('k') == 0:
color = False
else:
color = True
marker=True
line=zcenter
# Perform interpolation to plot data at a specific location instead of a
# grid point
if(gData.has_key(hold_key) and hold_value is not None):
ilon = 0
ilat = 0
x_data = np.array(gData[xkey][lonmin:lonmax,latmin:latmax,
0]).flatten()
if geo_key.find("Lon") >= 0:
ilon = -1
elif geo_key.find("Lat") >= 0:
ilat = -1
i_data = gpr.create_contour_input_array(hold_key, hold_value, xkey,
"Altitude", [zkey], gData,
x_data, alt_data[0,:],
lonmin=ilon, latmin=ilat)
x_data = np.array(i_data[xkey])
z_data = np.array(i_data[zkey])
alt_data = np.array(i_data['Altitude'])
if zmin == None:
zmin = np.nanmin(z_data)
if zmax == None:
zmax = np.nanmax(z_data)
else:
if(zmin == None or zmax == None):
if gData[zkey].attrs['scale'].find("exp") >= 0:
raw = True
else:
raw = False
tmin, tmax = gpr.find_data_limits([gData],zkey,lat_index,
lon_index, -2, 6, raw=raw)
if zmin == None:
zmin = tmin
if zmax == None:
zmax = tmax
x_data = np.array(gData[xkey][lonmin:lonmax,latmin:latmax,:])
x_data = x_data.reshape(datadim)
z_data = np.array(gData[zkey][lonmin:lonmax,latmin:latmax,:])
z_data = z_data.reshape(datadim)
x_name = gData[xkey].attrs['name']
x_scale = gData[xkey].attrs['scale']
x_units = gData[xkey].attrs['units']
z_name = gData[zkey].attrs['name']
z_scale = gData[zkey].attrs['scale']
z_units = gData[zkey].attrs['units']
# Initialize the new figure
f, ax = pap.plot_single_alt_image(plot_type, x_data, alt_data/1000.0,
z_data, x_name, x_scale, x_units, "km",
z_name=z_name, z_scale=z_scale,
z_units=z_units, xmin=xmin, xmax=xmax,
amin=amin, amax=amax, zmin=zmin,
zmax=zmax, title=title, draw=False,
color1=color, color2=marker, color3=line)
# Add local time to longitude axis, if desired
if lon_lt:
xfmt = FuncFormatter(gData.lon_lt_ticks)
ax.xaxis.set_major_formatter(xfmt)
ax.set_xlabel("Longitude \ Local Time")
plt.subplots_adjust(bottom=.13)
# Add hmF2 if desired
if add_hmf2 and not gData.has_key("hmF2"):
gData.calc_2dion()
if not gData.has_key("hmF2"):
print module_name, "WARNING: hmF2 data is not available"
add_hmF2 = False
if add_hmf2:
if plot_type.find("linear") >= 0:
x = np.array([xmin, xmax])
if(gData.has_key(hold_key) and hold_value is not None):
if geo_key.find("Lon") >= 0:
x_data = gData[hold_key][:,latmin:latmax, int(gData.attrs['nAlt']/2)].flatten()
y_data = gData['hmF2'][:,latmin:latmax,0].flatten()
elif geo_key.find("Lat") >= 0:
x_data = gData[hold_key][lonmin:lonmax,:,int(gData.attrs['nAlt']/2)].flatten()
y_data = gData['hmF2'][20:21,:,0].flatten()
hold = interpolate.interp1d(x_data, y_data)
hmf2 = hold(hold_value)
else:
hmf2 = gData['hmF2'][lonmin:lonmax,latmin:latmax,0]
y = np.array([hmf2, hmf2])
else:
if(gData.has_key(hold_key) and hold_value is not None):
if geo_key.find("Lon") >= 0:
lonmin = 0
lonmax = gData.attrs['nLon']
elif geo_key.find("Lat") >= 0:
latmin = 0
latmax = gData.attrs['nLat']
x = x_data[0,:]
i_data = gpr.create_linear_input_array(hold_key, hold_value,
xkey, ['hmF2'], gData,
x, lonmin=lonmin,
lonmax=lonmax,
latmin=latmin,
latmax=latmax)
y = np.array(i_data['hmF2'])
else:
x = x_data
y = np.array(gData['hmF2'][lonmin:lonmax,latmin:latmax,0])
y = y.reshape(datadim[0:-1])
ax.plot(x, y, color=hcolor, linestyle=hline, linewidth=2)
# Add field lines, if desired
if add_fieldlines and plot_type.find("contour")>=0:
lon = None
dec = None
lon_units = "radians"
lat_units = "degrees"
lat_type = "geographic"
if hold_key == None:
hold_key = "Longitude"
hold_value = float(gData[hold_key][lonmin:lonmax,0,0])
if xkey == "Latitude" or xkey == "dLat":
from scipy import interpolate
x = x_data[0]
if xkey == "Latitude":
lat_units = "radians"
# If the geographic equator is on the x-axis, find the longitude
# and declination at the geomagnetic equator
ikeys = ["Declination"]
if hold_key != "Longitude" and hold_key != "dLon":
ikeys.append("Longitude")
i_data = gpr.create_linear_input_array(hold_key, hold_value,
"Inclination", ikeys,
gData, x_data[0],
lonmax=lonmax,
lonmin=lonmin,
latmax=latmax,
latmin=latmin, altmax=gData.attrs['nAlt'])
else:
x = x_data[:,0]
i_data = dict()
i_data['Inclination'] = gData['Inclination'][lonmin:lonmax,
latmin:latmax,0]
i_data['Inclination'] = i_data['Inclination'].reshape(latmax)
i_data['Declination'] = gData['Declination'][lonmin:lonmax,
latmin:latmax,0]
i_data['Declination'] = i_data['Declination'].reshape(latmax)
# Remove any NaN
ikeys.append("Inclination")
test = range(len(i_data[ikeys[0]]))
for i in ikeys:
test *= i_data[i]
good = [i for i,t in enumerate(test) if not np.isnan(t)]
if len(good) > 2:
for i in ikeys:
i_data[i] = i_data[i].take(good)
# Interpolate good data
tckdec = interpolate.splrep(i_data['Inclination'],
i_data['Declination'], s=0)
dec = interpolate.splev(0.0, tckdec, der=0)
if i_data.has_key("Longitude"):
tcklon = interpolate.splrep(i_data['Inclination'],
i_data['Longitude'], s=0)
lon = interpolate.splev(0.0, tcklon, der=0)
else:
lon = hold_value
if np.isnan(dec) or np.isnan(lon):
print "WARNING: unable to interpolate lon and dec at meq"
lat_type = None
else:
# Add local time to the title
lt = gpr.glon_to_localtime(gData['time'], lon, lon_units)
h = int(lt)
m = int((lt - h) * 60.0)
ax.set_title("{:}, {:02d}:{:02d} SLT".format(title, h, m),
size="medium")
else:
print "WARNING: unable to find longitude and declination at meq"
lat_type = None
elif xkey == "Inclination":
lat_type = "inclination"
elif xkey == "Magnetic Latitude":
lat_type = "magnetic"
else:
print "WARNING: can't output field lines when xaxis is", xkey
lat_type = None
if lat_type is not None:
if len(apex_alts) < 1:
# Set default apex altitudes
apex_alts = [amin+float(i+1)*200.0 for i in
range(int(math.ceil((amax-amin)/200.0)))]
for alt in apex_alts:
gpr.add_dipole_fieldline(ax, alt, x, lat_units=lat_units,
lat_type=lat_type, longitude=lon,
lon_units=lon_units, declination=dec,
color=acolor, linestyle=aline)
if draw:
# Draw to screen.
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, ax
