def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Ice Particle Diameter'
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
#
# The file has many 'Ice Particle Diameter' dataset under
# different Vgroup.
#
# Use HDFView to look up ref number.
index = hdf.reftoindex(38)
data4D = hdf.select(index)
data = data4D[0, :, :, 0].astype(np.float64)
# Read attributes.
attrs = data4D.attributes(full=1)
fva = attrs["_FillValue"]
fillvalue = fva[0]
ua = attrs["units"]
units = ua[0]
# Apply the fill value.
data[data == fillvalue] = np.nan
datam = np.ma.masked_array(data, mask=np.isnan(data))
# The lat and lon should be calculated following [1].
lat = np.linspace(89.5, -89.5, 180)
# Generate Monthly Hourly Avgs which is 3 hour interval (3*8hr = 24hrs) [2].
MHA = np.linspace(1, 8, 8)
plt.contourf(MHA, lat, datam.T)
plt.ylabel('Latitude (degrees_north)')
plt.xlabel('Monthly 3-hourly')
plt.xticks(MHA, [
'00-03 GMT', '03-06 GMT', '06-09 GMT', '09-12 GMT', '12-15 GMT',
'15-18 GMT', '18-21 GMT', '21-24 GMT'
],
fontsize='8')
cb = plt.colorbar()
cb.set_label(units)
basename = os.path.basename(FILE_NAME)
# I guess Stats=0 means "mean" and Stats=1 means "standard
# deviation." See [3] and page 154 of [4].
plt.title('{0}\n{1}'.format(
basename,
'/1.0 Degree Zonal/Monthly Hourly Averages/Cloud Layer High/\n Ice Particle Diameter (Mean)'
),
fontsize=11)
fig = plt.gcf()
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Ice Particle Diameter'
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
#
# The file has many 'Ice Particle Diameter' dataset under
# different Vgroup.
#
# Use HDFView to look up ref number.
index = hdf.reftoindex(38)
data4D = hdf.select(index)
data = data4D[0,:,:,0].astype(np.float64)
# Read attributes.
attrs = data4D.attributes(full=1)
fva=attrs["_FillValue"]
fillvalue = fva[0]
ua=attrs["units"]
units = ua[0]
# Apply the fill value.
data[data == fillvalue] = np.nan
datam = np.ma.masked_array(data, mask=np.isnan(data))
# The lat and lon should be calculated following [1].
lat = np.linspace(89.5, -89.5, 180)
# Generate Monthly Hourly Avgs which is 3 hour interval (3*8hr = 24hrs) [2].
MHA = np.linspace(1, 8, 8)
plt.contourf(MHA, lat, datam.T)
plt.ylabel('Latitude (degrees_north)')
plt.xlabel('Monthly 3-hourly')
plt.xticks(MHA, ['00-03 GMT','03-06 GMT','06-09 GMT','09-12 GMT','12-15 GMT','15-18 GMT','18-21 GMT','21-24 GMT'], fontsize='8')
cb = plt.colorbar()
cb.set_label(units)
basename = os.path.basename(FILE_NAME)
# I guess Stats=0 means "mean" and Stats=1 means "standard
# deviation." See [3] and page 154 of [4].
plt.title('{0}\n{1}'.format(basename, '/1.0 Degree Zonal/Monthly Hourly Averages/Cloud Layer High/\n Ice Particle Diameter (Mean)'), fontsize=11)
fig = plt.gcf()
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Ice Particle Diameter'
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
#
# The file has many 'Ice Particle Diameter' dataset under
# different Vgroup.
#
# Use HDFView to look up ref number.
index = hdf.reftoindex(38)
data4D = hdf.select(index)
data = data4D[0, 3, :, 0].astype(np.float64)
# Read attributes.
attrs = data4D.attributes(full=1)
fva = attrs["_FillValue"]
fillvalue = fva[0]
ua = attrs["units"]
units = ua[0]
# Apply the fill value.
data[data == fillvalue] = np.nan
datam = np.ma.masked_array(data, mask=np.isnan(data))
# The lat and lon should be calculated following [1].
lat = np.linspace(89.5, -89.5, 180)
plt.plot(lat, data)
plt.xlabel('Latitude (degrees_north)')
plt.ylabel('{0} ({1})'.format(DATAFIELD_NAME, units))
basename = os.path.basename(FILE_NAME)
plt.title('{0}\n{1}'.format(
basename,
'/1.0 Degree Zonal/Monthly Hourly Averages/Cloud Layer High/\n Ice Particle Diameter (Mean; 09-12 GMT)'
),
fontsize=11)
fig = plt.gcf()
# plt.show()
pngfile = "{0}.py.line.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Ice Particle Diameter'
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
#
# The file has many 'Ice Particle Diameter' dataset under
# different Vgroup.
#
# Use HDFView to look up ref number.
index = hdf.reftoindex(38)
data4D = hdf.select(index)
data = data4D[0,3,:,0].astype(np.float64)
# Read attributes.
attrs = data4D.attributes(full=1)
fva=attrs["_FillValue"]
fillvalue = fva[0]
ua=attrs["units"]
units = ua[0]
# Apply the fill value.
data[data == fillvalue] = np.nan
datam = np.ma.masked_array(data, mask=np.isnan(data))
# The lat and lon should be calculated following [1].
lat = np.linspace(89.5, -89.5, 180)
plt.plot(lat, data)
plt.xlabel('Latitude (degrees_north)')
plt.ylabel('{0} ({1})'.format(DATAFIELD_NAME, units))
basename = os.path.basename(FILE_NAME)
plt.title('{0}\n{1}'.format(basename, '/1.0 Degree Zonal/Monthly Hourly Averages/Cloud Layer High/\n Ice Particle Diameter (Mean; 09-12 GMT)'), fontsize=11)
fig = plt.gcf()
# plt.show()
pngfile = "{0}.py.line.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Extent'
if USE_GDAL:
import gdal
GRID_NAME = 'Southern Hemisphere'
gname = 'HDF4_EOS:EOS_GRID:"{0}":{1}:{2}'.format(
FILE_NAME, GRID_NAME, DATAFIELD_NAME)
gdset = gdal.Open(gname)
data = gdset.ReadAsArray()
meta = gdset.GetMetadata()
x0, xinc, _, y0, _, yinc = gdset.GetGeoTransform()
nx, ny = (gdset.RasterXSize, gdset.RasterYSize)
del gdset
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset. Dataset name 'Extent' exists under different groups.
# Use reference number to resolve ambiguity.
data2D = hdf.select(hdf.reftoindex(12))
data = data2D[:, :].astype(np.float64)
# Read global attribute.
fattrs = hdf.attributes(full=1)
ga = fattrs["StructMetadata.0"]
gridmeta = ga[0]
# Construct the grid. The needed information is in a global attribute
# called 'StructMetadata.0'. Use regular expressions to tease out the
# extents of the grid.
ul_regex = re.compile(
r'''UpperLeftPointMtrs=\(
(?P<upper_left_x>[+-]?\d+\.\d+)
,
(?P<upper_left_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = ul_regex.search(gridmeta)
x0 = np.float(match.group('upper_left_x'))
y0 = np.float(match.group('upper_left_y'))
lr_regex = re.compile(
r'''LowerRightMtrs=\(
(?P<lower_right_x>[+-]?\d+\.\d+)
,
(?P<lower_right_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = lr_regex.search(gridmeta)
x1 = np.float(match.group('lower_right_x'))
y1 = np.float(match.group('lower_right_y'))
ny, nx = data.shape
xinc = (x1 - x0) / nx
yinc = (y1 - y0) / ny
x = np.linspace(x0, x0 + xinc * nx, nx)
y = np.linspace(y0, y0 + yinc * ny, ny)
xv, yv = np.meshgrid(x, y)
# Reproject into WGS84
lamaz = pyproj.Proj("+proj=laea +a=6371228 +lat_0=-90 +lon_0=0 +units=m")
wgs84 = pyproj.Proj("+init=EPSG:4326")
lon, lat = pyproj.transform(lamaz, wgs84, xv, yv)
# Use a south polar azimuthal equal area projection.
m = Basemap(projection='splaea', resolution='l', boundinglat=-60, lon_0=0)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90, 0, 15), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180, 180, 30), labels=[0, 0, 0, 1])
# Bin the data as follows:
# 0 -- snow-free land
# 1-20% sea ice -- blue
# 21-40% sea ice -- blue-cyan
# 41-60% sea ice -- blue
# 61-80% sea ice -- cyan-blue
# 81-100% sea ice -- cyan
# 101 -- permanent ice
# 103 -- dry snow
# 252 mixed pixels at coastlines
# 255 ocean
lst = [
'#004400', '#0000ff', '#0044ff', '#0088ff', '#00ccff', '#00ffff',
'#ffffff', '#440044', '#191919', '#000000', '#8888cc'
]
cmap = mpl.colors.ListedColormap(lst)
bounds = [0, 1, 21, 41, 61, 81, 101, 103, 104, 252, 255]
tickpts = [0.5, 11, 31, 51, 71, 91, 102, 103.5, 178, 253.5]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# The corners cause trouble, so chop them out.
idx = slice(5, 721)
m.pcolormesh(lon[idx, idx],
lat[idx, idx],
data[idx, idx],
latlon=True,
cmap=cmap,
norm=norm)
color_bar = plt.colorbar()
color_bar.set_ticks(tickpts)
color_bar.set_ticklabels([
'snow-free\nland', '1-20% sea ice', '21-40% sea ice', '41-60% sea ice',
'61-80% sea ice', '81-100% sea ice', 'permanent\nice', 'dry\nsnow',
'mixed pixels\nat coastlines', 'ocean'
])
color_bar.draw_all()
basename = os.path.basename(FILE_NAME)
long_name = DATAFIELD_NAME
plt.title('{0}\n{1}'.format(basename, long_name))
fig = plt.gcf()
# plt.show()
pngfile = "{0}.1.py.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Net radiant flux'
if USE_NETCDF4:
from netCDF4 import Dataset
nc = Dataset(FILE_NAME)
var = nc.variables[DATAFIELD_NAME]
data = var[:].astype(np.float64)
latitude = nc.variables['Colatitude'][:]
longitude = nc.variables['Longitude'][:]
# Read attributes.
units = var.units
fillvalue = var._FillValue
long_name = var.long_name
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
# The file has many 'Net radiation flux' dataset under
# different Vgroup.
#
# Use HDFView to look up ref number.
index = hdf.reftoindex(141)
data1D = hdf.select(index)
data = data1D[:].astype(np.double)
# Read geolocation datasets.
lat = hdf.select(hdf.reftoindex(185))
latitude = lat[:]
lon = hdf.select(hdf.reftoindex(184))
longitude = lon[:]
# Read attributes.
attrs = data1D.attributes(full=1)
ua=attrs["units"]
units = ua[0]
fva=attrs["_FillValue"]
fillvalue = fva[0]
lna=attrs["long_name"]
long_name = lna[0]
# Apply the fill value attribute.
data[data == fillvalue] = np.nan
data = np.ma.masked_array(data, np.isnan(data))
# Adjust lat values.
latitude = 90 - latitude
plt.plot(latitude, data)
plt.xlabel('Latitude (degrees_north)')
plt.ylabel('{0} ({1})'.format(DATAFIELD_NAME, units))
basename = os.path.basename(FILE_NAME)
plt.title('{0}\n{1}\n{2} at Longitude={3} (degrees_east)'.format(basename, long_name, DATAFIELD_NAME, longitude[0]), fontsize=11)
fig = plt.gcf()
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
# Identify the data field.
DATAFIELD_NAME = 'Extent'
if USE_GDAL:
import gdal
GRID_NAME = 'Southern Hemisphere'
gname = 'HDF4_EOS:EOS_GRID:"{0}":{1}:{2}'.format(FILE_NAME,
GRID_NAME,
DATAFIELD_NAME)
gdset = gdal.Open(gname)
data = gdset.ReadAsArray()
meta = gdset.GetMetadata()
x0, xinc, _, y0, _, yinc = gdset.GetGeoTransform()
nx, ny = (gdset.RasterXSize, gdset.RasterYSize)
del gdset
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset. Dataset name 'Extent' exists under different groups.
# Use reference number to resolve ambiguity.
data2D = hdf.select(hdf.reftoindex(12))
data = data2D[:,:].astype(np.float64)
# Read global attribute.
fattrs = hdf.attributes(full=1)
ga = fattrs["StructMetadata.0"]
gridmeta = ga[0]
# Construct the grid. The needed information is in a global attribute
# called 'StructMetadata.0'. Use regular expressions to tease out the
# extents of the grid.
ul_regex = re.compile(r'''UpperLeftPointMtrs=\(
(?P<upper_left_x>[+-]?\d+\.\d+)
,
(?P<upper_left_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = ul_regex.search(gridmeta)
x0 = np.float(match.group('upper_left_x'))
y0 = np.float(match.group('upper_left_y'))
lr_regex = re.compile(r'''LowerRightMtrs=\(
(?P<lower_right_x>[+-]?\d+\.\d+)
,
(?P<lower_right_y>[+-]?\d+\.\d+)
\)''', re.VERBOSE)
match = lr_regex.search(gridmeta)
x1 = np.float(match.group('lower_right_x'))
y1 = np.float(match.group('lower_right_y'))
ny, nx = data.shape
xinc = (x1 - x0) / nx
yinc = (y1 - y0) / ny
x = np.linspace(x0, x0 + xinc*nx, nx)
y = np.linspace(y0, y0 + yinc*ny, ny)
xv, yv = np.meshgrid(x, y)
# Reproject into WGS84
lamaz = pyproj.Proj("+proj=laea +a=6371228 +lat_0=-90 +lon_0=0 +units=m")
wgs84 = pyproj.Proj("+init=EPSG:4326")
lon, lat= pyproj.transform(lamaz, wgs84, xv, yv)
# Use a south polar azimuthal equal area projection.
m = Basemap(projection='splaea', resolution='l',
boundinglat=-60, lon_0=0)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90, 0, 15), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180, 180, 30), labels=[0, 0, 0, 1])
# Bin the data as follows:
# 0 -- snow-free land
# 1-20% sea ice -- blue
# 21-40% sea ice -- blue-cyan
# 41-60% sea ice -- blue
# 61-80% sea ice -- cyan-blue
# 81-100% sea ice -- cyan
# 101 -- permanent ice
# 103 -- dry snow
# 252 mixed pixels at coastlines
# 255 ocean
lst = ['#004400',
'#0000ff',
'#0044ff',
'#0088ff',
'#00ccff',
'#00ffff',
'#ffffff',
'#440044',
'#191919',
'#000000',
'#8888cc']
cmap = mpl.colors.ListedColormap(lst)
bounds = [0, 1, 21, 41, 61, 81, 101, 103, 104, 252, 255]
tickpts = [0.5, 11, 31, 51, 71, 91, 102, 103.5, 178, 253.5]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# The corners cause trouble, so chop them out.
idx = slice(5, 721)
m.pcolormesh(lon[idx, idx], lat[idx, idx], data[idx, idx],
latlon=True, cmap=cmap, norm=norm)
color_bar = plt.colorbar()
color_bar.set_ticks(tickpts)
color_bar.set_ticklabels(['snow-free\nland',
'1-20% sea ice',
'21-40% sea ice',
'41-60% sea ice',
'61-80% sea ice',
'81-100% sea ice',
'permanent\nice',
'dry\nsnow',
'mixed pixels\nat coastlines',
'ocean'])
color_bar.draw_all()
basename = os.path.basename(FILE_NAME)
long_name = DATAFIELD_NAME
plt.title('{0}\n{1}'.format(basename, long_name))
fig = plt.gcf()
# plt.show()
pngfile = "{0}.1.py.png".format(basename)
fig.savefig(pngfile)
def run(FILE_NAME):
DATAFIELD_NAME = '89.0V_Res.5B_TB_(not-resampled)'
if USE_NETCDF4:
from netCDF4 import Dataset
nc = Dataset(FILE_NAME)
data = nc.variables[DATAFIELD_NAME][:].astype(np.float64)
latitude = nc.variables['Latitude'][:]
longitude = nc.variables['Longitude'][:]
# Replace the filled value with NaN, replace with a masked array.
# Apply the scaling equation. These attributes are named in a VERY
# non-standard manner.
scale_factor = getattr(nc.variables[DATAFIELD_NAME], 'SCALE FACTOR')
add_offset = nc.variables[DATAFIELD_NAME].OFFSET
else:
from pyhdf.SD import SD, SDC
hdf = SD(FILE_NAME, SDC.READ)
# Read dataset.
data2D = hdf.select(DATAFIELD_NAME)
data = data2D[:,:].astype(np.float64)
# Read geolocation dataset.
# This product has multiple 'Latitude' and 'Longitude' pair under different groups.
lat = hdf.select(hdf.reftoindex(192)) # Use HDFView to get ref number 192.
latitude = lat[:,:]
lon = hdf.select(hdf.reftoindex(194)) # Use HDFView to get ref number 194.
longitude = lon[:,:]
# Retrieve attributes.
attrs = data2D.attributes(full=1)
sfa=attrs["SCALE FACTOR"]
scale_factor = sfa[0]
aoa=attrs["OFFSET"]
add_offset = aoa[0]
data[data == -32768] = np.nan
data = data * scale_factor + add_offset
datam = np.ma.masked_array(data, np.isnan(data))
units = "degrees K"
long_name = DATAFIELD_NAME
# Since the swath starts near the south pole, but also extends over the
# north pole, the equidistant cylindrical becomes a possibly poor choice
# for a projection. We show the full global map plus a limited polar map.
fig = plt.figure(figsize=(15, 6))
ax1 = plt.subplot(1, 2, 1)
m = Basemap(projection='cyl', resolution='l',
llcrnrlat=-90, urcrnrlat=90,
llcrnrlon=-180, urcrnrlon=180)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(-90, 91, 30), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180, 181., 45), labels=[0, 0, 0, 1])
m.pcolormesh(longitude, latitude, datam, latlon=True)
ax2 = plt.subplot(1, 2, 2)
m = Basemap(projection='npstere', resolution='l',
boundinglat=65, lon_0=0)
m.drawcoastlines(linewidth=0.5)
m.drawparallels(np.arange(60, 81, 10), labels=[1, 0, 0, 0])
m.drawmeridians(np.arange(-180., 181., 30.), labels=[1, 0, 0, 1])
m.pcolormesh(longitude, latitude, datam, latlon=True)
cax = plt.axes([0.92, 0.1, 0.03, 0.8])
cb = plt.colorbar(cax=cax)
cb.set_label(units)
basename = os.path.basename(FILE_NAME)
fig = plt.gcf()
fig.suptitle('{0}\n{1}'.format(basename, long_name))
# plt.show()
pngfile = "{0}.py.png".format(basename)
fig.savefig(pngfile)
def hdfget(self, id=False, section=False, group=False):
#for i, drs in enumerate(self.dirLst[0:500]):
for i, drs in enumerate(self.dirLst[:]):
#print drs
hdf = HDF(drs, HC.READ)
sd = SD(drs)
vs = hdf.vstart()
v = hdf.vgstart()
# Scan all vgroups in the file.
ref = -1
while 1:
try:
ref = v.getid(ref)
vg = v.attach(ref)
if vg._name == section:
# Read the contents of the vgroup.
members = vg.tagrefs()
# Display info about each member.
index = -1
for tag, ref2 in members:
index += 1
#print "member index", index
# Vdata tag
if tag == HC.DFTAG_VH:
vd = vs.attach(ref2)
nrecs, intmode, fields, size, name = vd.inquire(
)
if vd._name == group:
self.HDF.setdefault(id + '_fields',
[]).append(fields)
self.HDF.setdefault(id + '_nrecs',
[]).append(nrecs)
self.HDF.setdefault(id + '_data',
[]).append(
np.asarray(vd[:]))
vd.detach()
# SDS tag
elif tag == HC.DFTAG_NDG:
sds = sd.select(sd.reftoindex(ref2))
name, rank, dims, type, nattrs = sds.info()
sds.endaccess()
elif tag == HC.DFTAG_VG:
vg0 = v.attach(ref2)
vg0.detach()
else:
print "unhandled tag,ref", tag, ref2
except HDF4Error, msg: # no more vgroup
break