def read_vdata(FILENAME):
"""Reads all vdata fields in an HDF-EOS file and places those fields
in a dictionary.
PARAMETERS:
-----------
FILENAME: Name of HDF-EOS file.
OUTPUTS:
--------
vdata_dict: Dictionary of vdata fields.
"""
# Prepare to read the data.
f = HDF(FILENAME, SDC.READ) # Open the file
vs = f.vstart() # Start the vdata interface
data_info_list = vs.vdatainfo() # List the vdata fields
vdata_fieldnames = [a[0] for a in data_info_list] # Get the names
# Load the data, place in dictionary
vdata_dict = {}
for field in vdata_fieldnames:
vdata_dict[field] = np.squeeze(np.asarray(vs.attach(field)[:]))
# terminate the vdata interface, close the file.
vs.end()
f.close()
return vdata_dict
def get_solor_angle(myd1km_filename_str):
try: # open hdf
myd021km_data = SD(myd1km_filename_str)
# 1.SDS Reading
zenith_sds = myd021km_data.select('SensorZenith') # 经纬度
zenith = zenith_sds.get() # 2
azimuth_sds = myd021km_data.select('SensorAzimuth') # 经纬度
azimuth = azimuth_sds.get()
return zenith * 0.01, azimuth * 0.01
f = HDF(myd1km_filename_str, SDC.READ)
vs = f.vstart()
data_info_list = vs.vdatainfo()
# Vset table
# L1B swam matedata
L1B_Swath_Matedata_VD = vs.attach('Level 1B Swath Metadata')
# Read [Swath/scan type]
sd_info = L1B_Swath_Matedata_VD.inquire()
all_metadata = L1B_Swath_Matedata_VD.read(sd_info[0])
L1B_Swath_Matedata_VD.detach() # __del__ with handle this.
vs.end() # Use with, so __del__ can do this.
f.close()
return all_metadata
except HDF4Error:
print("Unexpected error:(get_solor_angle)", sys.exc_info()[0])
print('READ ERROR......:' + myd1km_filename_str)
return None
def hdf4lookup(self, path, swath):
hdf = HDF(path)
sd = SD(path)
vs = hdf.vstart()
v = hdf.vgstart()
vg = v.attach(swath)
vg_members = vg.tagrefs()
vg0_members = {}
for tag, ref in vg_members:
vg0 = v.attach(ref)
if tag == HC.DFTAG_VG:
vg0_members[vg0._name] = vg0.tagrefs()
vg0.detach
vg.detach
lookup_dict = {}
for key in vg0_members.keys():
for tag, ref in vg0_members[key]:
if tag == HC.DFTAG_NDG:
# f = open(swath + '.txt', 'a'); f.writelines('#' + key + '#' + '\n'); f.close()
sds = sd.select(sd.reftoindex(ref))
name = sds.info()[0]
lookup_dict[name] = [tag, ref]
sds.endaccess()
elif tag == HC.DFTAG_VH:
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
lookup_dict[name] = [tag, ref]
v.end()
vs.end()
sd.end()
return lookup_dict
def getStartEndTime(fileAbsPath):
hdf = HDF(fileAbsPath)
vs = hdf.vstart()
ref = vs.find('start_time')
vd = vs.attach(ref)
startTime = vd.read(1)[0][0]
startDateTime = datetime.datetime(int(startTime[0:4]), int(startTime[4:6]),
int(startTime[6:8]),
int(startTime[8:10]),
int(startTime[10:12]),
int(startTime[12:14]))
# startDateTime = startTime[0:4] + '-' + startTime[4:6] + '-' + startTime[6:8] + 'T' + startTime[8:10] + ':' + startTime[10:12] + ':' + startTime[12:14] + 'Z'
ref = vs.find('end_time')
vd = vs.attach(ref)
endTime = vd.read(1)[0][0]
endDateTime = datetime.datetime(int(endTime[0:4]), int(endTime[4:6]),
int(endTime[6:8]), int(endTime[8:10]),
int(endTime[10:12]), int(endTime[12:14]))
# endDateTime = endTime[0:4] + '-' + endTime[4:6] + '-' + endTime[6:8] + 'T' + endTime[8:10] + ':' + endTime[10:12] + ':' + endTime[12:14] + 'Z'
vs.end()
return startDateTime, endDateTime
def require_VD_info_hdf(file_in):
'''Print and Return VD variable names and dimentions in a HDF-EOS file'''
#--information from input file--
f=HDF(file_in)
vs=f.vstart()
var_info=vs.vdatainfo()
print("--Variables in ",file_in,"-->")
for item in var_info:
print(item)
return var_info
def proc(indir, outdir, inname, outname):
path = indir + "/" + inname
hdf = HDF(path)
sd = SD(path)
vs = hdf.vstart()
v = hdf.vgstart()
mod_vg = v.attach("MOD_Grid_monthly_CMG_VI")
vg_members = mod_vg.tagrefs()
# print vg_members
mod_vg = v.attach("MOD_Grid_monthly_CMG_VI")
tag, ref = mod_vg.tagrefs()[0]
# print tag, ref
vg0 = v.attach(ref)
# print vg0._name
tagrefs = vg0.tagrefs()
# print tagrefs
for tag, ref in tagrefs:
if tag == HC.DFTAG_NDG:
sds = sd.select(sd.reftoindex(ref))
name = sds.info()[0]
# print name
if name == "CMG 0.05 Deg Monthly NDVI":
sd = SD(path)
sds = sd.select(sd.reftoindex(ref))
ndvi = np.float64(sds.get())
sds.endaccess()
elif name == "CMG 0.05 Deg Monthly EVI":
sd = SD(path)
sds = sd.select(sd.reftoindex(ref))
evi = np.float64(sds.get())
sds.endaccess()
sd.end()
v.end()
data = ndvi
name = outdir + "/" + outname + ".tif"
cols = 7200
rows = 3600
originX = -180.0
originY = 90.0
pixelWidth = 0.05
pixelHeight = -0.05
driver = gdal.GetDriverByName('GTiff')
newRasterfn = name
outRaster = driver.Create(newRasterfn, cols, rows, 1, gdal.GDT_Float32)
outRaster.SetGeoTransform(
(originX, pixelWidth, 0, originY, 0, pixelHeight))
outband = outRaster.GetRasterBand(1)
outband.WriteArray(data)
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromEPSG(4326)
outRaster.SetProjection(outRasterSRS.ExportToWkt())
outband.FlushCache()
def getAdjustmentParams(fileAbsPath):
hdf = HDF(fileAbsPath)
vs = hdf.vstart()
ref = vs.find('Radar_Reflectivity.valid_range')
vd = vs.attach(ref)
validRange = vd.read(1)[0][0]
ref = vs.find('Radar_Reflectivity.factor')
vd = vs.attach(ref)
reflectivityFactor = vd.read(1)[0][0]
vs.end()
return validRange, reflectivityFactor
def read_vd_hdf(file_in,var_in,dimsz):
'''Read data stored as a VD variable in input HDF-EOS file.'''
'''Inputs are the file path and the required variable name.'''
'''Outputs are the data (numpy array). '''
#--information from input file--
hdf = HDF(file_in)
vs = hdf.vstart()
vd = vs.attach(var_in)
var_data = np.array(vd.read(int(dimsz)))
vd.detach()
return var_data #,var_dimn
def load_vd(fname, varnames):
'''return list containing vdata structures specified by list varnames,
contained in hdf4 file with filename fname'''
f = HDF(fname)
data_list = []
vs = f.vstart()
for name in varnames:
vd = vs.attach(name)
data_list.append(vd[:])
vd.detach()
vs.end()
f.close()
return data_list
def read_hdf_VD(file_in,var_in):
'''Read Vdata sets (table, 1D) in the input HDF-EOS file. '''
'''Currently coded for surface variables of A-Train granule. '''
'''Inputs are the file path and the required variable name. '''
'''Outputs are the data (numpy array) and dimentions(numpy). '''
#--information from input file--
f=HDF(file_in)
vs=f.vstart()
vd=vs.attach(var_in) #return var data from vs group
var_data=np.array(vd[:]).ravel() #convert data into flatted ndarray
#print(var_data.dtype)
var_dimn=np.array(var_data.shape)
return var_data,var_dimn
def read_data_from_hdf(inputfile):
wavelengths = []
depth = []
downwelling_downcast = []
downwelling_upcast = []
upwelling_downcast = []
upwelling_upcast = []
# open the hdf file read-only
# Initialize the SD, V and VS interfaces on the file.
hdf = HDF(inputfile)
vs = hdf.vstart()
v = hdf.vgstart()
# Attach and read the contents of the Profiler vgroup
vg = v.attach(v.find('Profiler'))
for tag, ref in vg.tagrefs():
assert(tag == HC.DFTAG_VH)
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
if name == "ED_hyperspectral_downcast":
x = vd.read(nrecs)
downwelling_downcast = np.asarray([i[3:] for i in x])
wavelengths = np.asarray([float(x) for x in fields[3:]])
depth = np.asarray([i[2] for i in x])
elif name == "ED_hyperspectral_upcast":
downwelling_upcast = np.asarray([i[3:] for i in vd.read(nrecs)])
elif name == "LU_hyperspectral_downcast":
upwelling_downcast = np.asarray([i[3:] for i in vd.read(nrecs)])
elif name == "LU_hyperspectral_upcast":
upwelling_upcast = np.asarray([i[3:] for i in vd.read(nrecs)])
vd.detach()
# Close vgroup
vg.detach()
#clean up
v.end()
vs.end()
hdf.close()
return wavelengths, depth, downwelling_downcast, downwelling_upcast, \
upwelling_downcast, upwelling_upcast
def HDFread1D(filename, variable):
"""
Read HDF file in vs in simple mode
"""
# Read the file
f = HDF(filename)
# Initialize v mode
vs = f.vstart()
# extrect data
var = vs.attach(variable)
Var = np.array(var[:]).ravel()
# Close the file
var.detach()
vs.end()
f.close()
return Var
def HDFread(filename, variable, Class=None):
"""
Extract the data for non scientific data in V mode of hdf file
"""
hdf = HDF(filename, HC.READ)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename)
vs = hdf.vstart()
v = hdf.vgstart()
# Encontrar el puto id de las Geolocation Fields
if Class == None:
ref = v.findclass('SWATH Vgroup')
else:
ref = v.findclass(Class)
# Open all data of the class
vg = v.attach(ref)
# All fields in the class
members = vg.tagrefs()
nrecs = []
names = []
for tag, ref in members:
# Vdata tag
vd = vs.attach(ref)
# nrecs, intmode, fields, size, name = vd.inquire()
nrecs.append(vd.inquire()[0]) # number of records of the Vdata
names.append(vd.inquire()[-1]) # name of the Vdata
vd.detach()
idx = names.index(variable)
var = vs.attach(members[idx][1])
V = var.read(nrecs[idx])
var.detach()
# Terminate V, VS and SD interfaces.
v.end()
vs.end()
sd.end()
# Close HDF file.
hdf.close()
return np.array(V).ravel()
def get_train_data(self,
index_tuple,
band_select=[
0, 1, 2, 3, 4, 6, 7, 18, 19, 20, 21, 22, 23, 24, 25,
26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37
]):
'''
[Varification & Night eliminite]
:param index_tuple:
:return:
'''
# [band_num, rowsize]
raw_data = self.get_by_index(index_tuple, verify=False)
# Verify
raw_data = raw_data[band_select, ...]
valid_flag = np.ones([raw_data.shape[1]], dtype=bool)
for idx in range(raw_data.shape[0]):
valid_flag = valid_flag & (raw_data[idx, ...] > 32767)
# Daytime mode
try: # open hdf
f = HDF(self.filename, SDC.READ)
vs = f.vstart()
data_info_list = vs.vdatainfo()
# Vset table
# L1B swam matedata
L1B_Swath_Matedata_VD = vs.attach('Level 1B Swath Metadata')
# Read [Swath/scan type]
svath_matedata = L1B_Swath_Matedata_VD[:]
for idx in range(valid_flag.shape[0]):
if svath_matedata[int(
(index_tuple[0][idx]) / 10)][2] == 'D ':
valid_flag[idx] = True
else:
valid_flag[idx] = False
L1B_Swath_Matedata_VD.detach() # __del__ with handle this.
vs.end() # Use with, so __del__ can do this.
f.close()
except ValueError:
print("Unexpected error:", sys.exc_info()[0])
print('READ ERROR......(%d):' % self.filename)
return 0, 0, False
raw_data = self.get_by_index(index_tuple, verify=False, scaled=True)
raw_data = raw_data[band_select, ...]
return raw_data, valid_flag
def get_swath_metadata(myd1km_filename_str):
try: # open hdf
f = HDF(myd1km_filename_str, SDC.READ)
vs = f.vstart()
data_info_list = vs.vdatainfo()
# Vset table
# L1B swam matedata
L1B_Swath_Matedata_VD = vs.attach('Level 1B Swath Metadata')
# Read [Swath/scan type]
sd_info = L1B_Swath_Matedata_VD.inquire()
all_metadata = L1B_Swath_Matedata_VD.read(sd_info[0])
L1B_Swath_Matedata_VD.detach() # __del__ with handle this.
vs.end() # Use with, so __del__ can do this.
f.close()
return all_metadata
except HDF4Error:
print("Unexpected error:", sys.exc_info()[0])
print('READ ERROR......:' + myd1km_filename_str)
return None
def showHDFinfo(self):
if len(self.dirLst) > 1: drs = self.dirLst[0]
else: drs = self.dirLst
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)
describevg(ref, v, vs)
except HDF4Error, msg: # no more vgroup
break
def look(self, path, mem_list, lp_list):
data = {}
for name in mem_list: # subdata sets type data
tag = lp_list[name][0]
ref = lp_list[name][1]
if tag == HC.DFTAG_NDG:
sd = SD(path)
sds = sd.select(sd.reftoindex(ref))
data[name] = np.float64(sds.get())
sds.endaccess()
sd.end()
elif tag == HC.DFTAG_VH: #vd type data
hdf = HDF(path)
vs = hdf.vstart()
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
data[name] = np.full(nrecs, np.float64(vd.read()[0]))
vs.end()
hdf.close()
return data
def read_vd_hdf2(file_in,var_in):
'''Read data stored as a VD variable in input HDF-EOS file.'''
'''Inputs are the file path and the required variable name.'''
'''Outputs are the data (numpy array). '''
#--information from input file--
hdf = HDF(file_in) # the HDF file
vs = hdf.vstart() # initialize VS interface on HDF file
### vdinfo = vs.vdatainfo() # return info about all vdatas
vd = vs.attach(var_in) # open a vdata given its name
dimsz = vd.inquire()[0] # return 5 elements:
# 1. # of records
# 2. interlace mode
# 3. list of vdata field names
# 4. size in bytes of the vdata record
# 5. name of the vdata
var_data = np.array(vd.read(dimsz)) #read a number of records
vd.detach() # close the vdata
vs.end() # terminate the vdata interface
hdf.close() # close the HDF file
return var_data,dimsz
def get_myd1km_sci_time(myd1km_filename_str):
try: # open hdf
f = HDF(myd1km_filename_str, SDC.READ)
vs = f.vstart()
data_info_list = vs.vdatainfo()
# Vset table
# L1B swam matedata
L1B_Swath_Matedata_VD = vs.attach('Level 1B Swath Metadata')
# Read [Swath/scan type]
begin = L1B_Swath_Matedata_VD[0]
begin = begin[4]
end = L1B_Swath_Matedata_VD[-1]
end = end[4]
L1B_Swath_Matedata_VD.detach() # __del__ with handle this.
vs.end() # Use with, so __del__ can do this.
f.close()
return begin, end, True
except HDF4Error:
print("Unexpected error:", sys.exc_info()[0])
print('READ ERROR......:' + myd1km_filename_str)
return 0, 0, False
def getCoordsULLR(fileAbsPath, start, stop):
# coordinates every 10 pixels
nodes = range(start, stop, 10)
hdf = HDF(fileAbsPath)
vs = hdf.vstart()
ref = vs.find('Latitude')
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
latitude = np.array(vd.read(nrecs))
ref = vs.find('Longitude')
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
longitude = np.array(vd.read(nrecs))
lat = []
lon = []
for n in nodes:
lat.append(latitude[n][0])
lon.append(longitude[n][0])
# Append the last pixel coordinates: it is 9 pixel distant from the
# previous one, and not 10 px like the others!!!
lat.append(latitude[stop - 1][0])
lon.append(longitude[stop - 1][0])
vs.end()
coords = []
for x, y in zip(lat, lon):
coords.append(x)
coords.append(y)
# Nodes are 101: 100 points with distance 10 px
# and the last one with distance 9 px from the 990th
return coords, len(nodes) + 1
from pyhdf.HDF import *
from pyhdf.VS import *
f = HDF('inventory.hdf', # Open file 'inventory.hdf' in write mode
HC.WRITE|HC.CREATE) # creating it if it does not exist
vs = f.vstart() # init vdata interface
vd = vs.attach('INVENTORY', 1) # attach vdata 'INVENTORY' in write mode
# Update the `status' vdata attribute. The attribute length must not
# change. We call the attribute info() method, which returns a list where
# number of values (eg string length) is stored at index 2.
# We then assign a left justified string of exactly that length.
len = vd.attr('status').info()[2]
vd.status = '%-*s' % (len, 'phase 3 done')
# Update record at index 1 (second record)
vd[1] = ('Z4367', 'surprise', 10, 3.1, 44.5)
# Update record at index 4, and those after
vd[4:] = (
('QR231', 'toy', 12, 2.5, 45),
('R3389', 'robot', 3, 45, 2000),
('R3390', 'robot2', 8, 55, 2050)
)
vd.detach() # "close" the vdata
vs.end() # terminate the vdata interface
f.close() # close the HDF file
# Create a simple 3x3 float array.
sds = sd.create(name, SDC.FLOAT32, (3, 3))
# Initialize array
sds[:] = ((0, 1, 2), (3, 4, 5), (6, 7, 8))
# "close" dataset.
sds.endaccess()
# Create HDF file
filename = 'inventory.hdf'
hdf = HDF(filename, HC.WRITE | HC.CREATE)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename, SDC.WRITE) # SD interface
vs = hdf.vstart() # vdata interface
v = hdf.vgstart() # vgroup interface
# Create vdata named 'INVENTORY'.
vdatacreate(vs, 'INVENTORY')
# Create dataset named "ARR_3x3"
sdscreate(sd, 'ARR_3x3')
# Attach the vdata and the dataset.
vd = vs.attach('INVENTORY')
sds = sd.select('ARR_3x3')
# Create vgroup named 'TOTAL'.
vg = v.create('TOTAL')
# Add vdata to the vgroup
def get_train_data_map(self,
index_tuple,
kernel_radius=5,
band_select=[
0, 1, 2, 3, 4, 6, 7, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37
]):
'''
[Varification & Night eliminite]
:param index_tuple:
:return:
'''
# [band_num, rowsize]
selected_band_num = len(band_select)
kernel_size = 2 * kernel_radius + 1
# Verify
valid_flag = np.ones([index_tuple[0].shape[0]], dtype=bool)
# Daytime mode
try: # open hdf
f = HDF(self.filename, SDC.READ)
vs = f.vstart()
data_info_list = vs.vdatainfo()
# Vset table
# L1B swam matedata
L1B_Swath_Matedata_VD = vs.attach('Level 1B Swath Metadata')
# Read [Swath/scan type]
svath_matedata = L1B_Swath_Matedata_VD[:]
for idx in range(valid_flag.shape[0]):
if svath_matedata[int(
(index_tuple[0][idx]) / 10)][2] == 'D ':
valid_flag[idx] = True
else:
valid_flag[idx] = False
L1B_Swath_Matedata_VD.detach() # __del__ with handle this.
vs.end() # Use with, so __del__ can do this.
f.close()
except ValueError:
print("Unexpected error:", sys.exc_info()[0])
print('READ ERROR......(%d):' % self.filename)
return 0, 0, False
if np.sum(valid_flag) == 0:
return None, valid_flag
raw_data = np.empty([
index_tuple[0].shape[0], selected_band_num, kernel_size,
kernel_size
])
# Create a Mat of kernel
kernel_i = np.empty([kernel_size, kernel_size], dtype=np.int16)
for i in range(kernel_size):
for j in range(kernel_size):
kernel_i[i, j] = i
kernel_i -= kernel_radius
kernel_i = np.reshape(kernel_i, kernel_i.size)
kernel_j = np.transpose(kernel_i)
kernel_j = np.reshape(kernel_j, kernel_j.size)
for idx in range(index_tuple[0].shape[0]):
if not valid_flag[idx]:
continue
temp_kernel_i = kernel_i + index_tuple[0][idx]
if np.sum((temp_kernel_i < 0) | (temp_kernel_i >= self.width)) > 0:
valid_flag[idx] = False
continue
temp_kernel_j = kernel_j + index_tuple[1][idx]
if np.sum((temp_kernel_j < 0)
| (temp_kernel_j >= self.height)) > 0:
valid_flag[idx] = False
continue
temp = (self.all_band[..., temp_kernel_i,
temp_kernel_j])[band_select, ...]
if np.sum(temp[..., int(kernel_size * kernel_size * 0.5)] > 32767):
valid_flag[idx] = False
else:
indeces = temp > 32767
temp[indeces] = 0
raw_data[idx, ...] = temp.reshape(selected_band_num,
kernel_size, kernel_size)
for idx in range(selected_band_num):
raw_data[:, idx, :, :] = (
raw_data[:, idx, :, :] -
self.offset[band_select[idx]]) * self.scale[band_select[idx]]
return raw_data, valid_flag
def PFlux(year1, month1, day1):
filename = external_dir + "swepam_data_1day.hdf"
hdf = HDF(filename)
#Initialize the V interface on the HDF file.
v = hdf.vgstart()
vs = hdf.vstart()
#Scan all vgroups in the file
ref = -1
refnum = v.getid(ref)
vg = v.attach(refnum)
#print "----------------"
#print "name:", vg._name, "class:",vg._class, "tag,ref:",
#print vg._tag, vg._refnum
# Show the number of members of each main object type.
#print "members: ", vg._nmembers,
#print "datasets:", vg.nrefs(HC.DFTAG_NDG),
#print "vdataset: ", vg.nrefs(HC.DFTAG_VH),
#print "vgroups: ", vg.nrefs(HC.DFTAG_VG)
# Read the contents of the vgroup.
members = vg.tagrefs()
# Display info about each member.
index = -1
for tag, ref in members:
index += 1
# print "member index", index
# Vdata tag
if tag == HC.DFTAG_VH:
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
# print " vdata:",name, "tag,ref:",tag, ref
# print " fields:",fields
# print " nrecs:",nrecs
vd.detach()
# SDS tag
elif tag == HC.DFTAG_NDG:
sds = sd.select(sd.reftoindex(ref))
name, rank, dims, type, nattrs = sds.info()
# print " dataset:",name, "tag,ref:", tag, ref
# print " dims:",dims
# print " type:",type
sds.endaccess()
# VS tag
elif tag == HC.DFTAG_VG:
vg0 = v.attach(ref)
# print " vgroup:", vg0._name, "tag,ref:", tag, ref
vg0.detach()
# Unhandled tag
else:
print("unhandled tag,ref", tag, ref)
# Close vgroup
members = vg.tagrefs()
(tag, ref) in members
vd = vs.attach(ref)
nrecs, intmode, fields, size, name = vd.inquire()
alldata = vd.read(nrecs)
vd.detach()
vg.detach()
v.end()
hdf.close()
data = np.array(alldata)
# input
# (y,m,d) = (1998,3,1)
year = data[:, 0]
day = data[:, 1]
hr = data[:, 2]
min = data[:, 3]
sec = data[:, 4]
fp_year = data[:, 5]
fp_doy = data[:, 6]
pdensity = data[:, 8]
speed = data[:, 11]
start = datetime.date(int(year[0]), 1,
1) + datetime.timedelta(int(day[0]) - 1)
end = datetime.date(int(year[-1]), 1,
1) + datetime.timedelta(int(day[-1]) - 1)
# print "star Date and End Date:", start, end
delta1 = datetime.date(year1, month1, day1) - start
index = delta1.days
# print "index ....", index
if index >= (nrecs - 1):
print("ERROR: the input time is too new")
# break
elif index < 0:
print("ERROR: the input time is too old")
# break
else:
avePF = 0
num = 0
for i in range(0, 90):
j = index - i
if j < 0:
print("ERROR: the index is out of the array")
break
else:
if pdensity[j] > 0 and speed[j] > 0:
avePF = avePF + pdensity[j] * speed[j]
num = num + 1
avePF = avePF / num
# print "the 90 days average proton flux is:",avePF, num
return avePF
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
def genCloudSatFigure(filepath, data_field, title, label, plot_type, cmap):
"""Ingest CloudSat overpasses (HDF) and plot
parameters of interest like snowfall rates as
observed by the CPR.
"""
# Load and unpack HDF
f = HDF(filepath)
vs = f.vstart()
Latitude = vs.attach('Latitude')
Longitude = vs.attach('Longitude')
Time = vs.attach('Profile_time')
UTC = vs.attach('UTC_start')
if plot_type == PlotType.SFCS:
snowfall_rate_sfc = vs.attach('snowfall_rate_sfc')
c = snowfall_rate_sfc[:]
c = flatten(c)
snowfall_rate_sfc.detach()
if plot_type == PlotType.TEMP:
EC_Height = vs.attach('EC_height')
b = EC_Height[:]
b = flatten(b)
EC_Height.detach()
a = Time[:]
a = flatten(a)
d = Longitude[:]
d = flatten(d)
utc_start = UTC[0][0]
Latitude.detach() # "close" the vdata
Longitude.detach() # "close" the vdata
Time.detach() # "close" the vdata
UTC.detach() # "close" the vdata
vs.end() # terminate the vdata interface
f.close()
#---------- Read HDF Files ----------#
cpr_2b_geoprof = SD(filepath, SDC.READ)
offset = 28000 # Position along granule
span = 1000 # Plot length
if plot_type != PlotType.SFCS:
if plot_type == PlotType.REFL or plot_type == PlotType.MASK or plot_type == PlotType.SNOW:
cpr_2b_geoprof_height = cpr_2b_geoprof.select('Height')
cpr_2b_geoprof_height_data = cpr_2b_geoprof_height.get()
cpr_2b_geoprof_radar_reflectivity = cpr_2b_geoprof.select(data_field)
cpr_2b_geoprof_radar_reflectivity_data = cpr_2b_geoprof_radar_reflectivity.get()
if plot_type == PlotType.TEMP or plot_type == PlotType.SNOW:
cpr_2b_geoprof_radar_reflectivity_data[cpr_2b_geoprof_radar_reflectivity_data < 0] = math.nan
elif plot_type == PlotType.POWR:
cpr_2b_geoprof_radar_reflectivity_data[cpr_2b_geoprof_radar_reflectivity_data < 0] = math.nan
fillvalue = 15360
missing = -8888
img = np.zeros((span,125))
if plot_type == PlotType.REFL:
img.fill(-30)
factor = 1
if plot_type == PlotType.REFL:
factor = 0.01
for i in np.arange(span):
for j in np.arange(125):
if plot_type == PlotType.TEMP:
k = int( 125 * (b[j] + 5000) / 35000 )
else:
k = int( 125 * (cpr_2b_geoprof_height_data[i+offset,j] + 5000) / 35000 )
if cpr_2b_geoprof_radar_reflectivity_data[i+offset,j] > -3000 and \
cpr_2b_geoprof_radar_reflectivity_data[i+offset,j] < 2100:
img[i,k] = cpr_2b_geoprof_radar_reflectivity_data[i+offset,j] * factor
# Begin plotting granule
fig = plt.figure(figsize=(18, 6))
ax = plt.subplot(111)
im = ax.imshow(img.T, interpolation='bilinear', cmap=cmap, origin='lower', extent=[0,200,-10,60])
plt.title(title)
plt.ylabel('Height (km)')
plt.xlabel('Time')
plt.ylim(0,20)
pylab.yticks([0,5,10,15,20],[0,5,10,15,20])
position_tick_labels = [str(round(a[offset]+utc_start, 3)), str(round(a[offset+200]+utc_start, 3)), str(round(a[offset+400]+utc_start, 3)), str(round(a[offset+600]+utc_start, 3)), str(round(a[offset+800]+utc_start, 3)), str(round(a[offset+1000]+utc_start, 3))]
pylab.xticks([0,40,80,120,160, 200], position_tick_labels)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="3%", pad=0.10)
if plot_type == PlotType.MASK:
plt.colorbar(im, cax=cax, boundaries=[-10,0,10,20,30,40], ticks=[-10,0,10,20,30,40], label=label)
elif plot_type == PlotType.TEMP:
plt.colorbar(im, cax=cax, label=label, boundaries=[200, 220, 240, 260, 280], ticks=[200, 220, 240, 260, 280])
else:
plt.colorbar(im, cax=cax, label=label)
plt.savefig("cloudsat_radar_reflectivity.png")
plt.show()
else:
fig = plt.figure(figsize=(15.5, 2.4))
c = c[offset:offset+span]
index = np.arange(len(c))
plt.plot(index, c, color="#6699ff")
plt.fill(index, c, color="#6699ff")
plt.grid()
plt.title("CloudSat Surface Snowfall")
plt.xlabel("Time")
plt.ylabel("Rate (mm / hr)")
plt.ylim(0,3)
plt.xlim(0,span)
position_tick_labels = [str(round(a[offset]+utc_start, 3)), str(round(a[offset+200]+utc_start, 3)), str(round(a[offset+400]+utc_start, 3)), str(round(a[offset+600]+utc_start, 3)), str(round(a[offset+800]+utc_start, 3)), str(round(a[offset+1000]+utc_start, 3))]
pylab.xticks([0,200,400,600,800,1000], position_tick_labels)
plt.show()
vg0.detach()
# Unhandled tag
else:
print "unhandled tag,ref",tag,ref
# Close vgroup
vg.detach()
# Open HDF file in readonly mode.
filename = sys.argv[1]
hdf = HDF(filename)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename)
vs = hdf.vstart()
v = hdf.vgstart()
# Scan all vgroups in the file.
ref = -1
while 1:
try:
ref = v.getid(ref)
except HDF4Error,msg: # no more vgroup
break
describevg(ref)
# Terminate V, VS and SD interfaces.
v.end()
vs.end()
sd.end()
# Unhandled tag
else:
print "unhandled tag,ref", tag, ref
# Close vgroup
vg.detach()
# Open HDF file in readonly mode.
filename = sys.argv[1]
hdf = HDF(filename)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename)
vs = hdf.vstart()
v = hdf.vgstart()
# Scan all vgroups in the file.
ref = -1
while 1:
try:
ref = v.getid(ref)
except HDF4Error, msg: # no more vgroup
break
describevg(ref)
# Terminate V, VS and SD interfaces.
v.end()
vs.end()
sd.end()
def sdscreate(sd, name):
# Create a simple 3x3 float array.
sds = sd.create(name, SDC.FLOAT32, (3,3))
# Initialize array
sds[:] = ((0,1,2),(3,4,5),(6,7,8))
# "close" dataset.
sds.endaccess()
# Create HDF file
filename = 'inventory.hdf'
hdf = HDF(filename, HC.WRITE|HC.CREATE)
# Initialize the SD, V and VS interfaces on the file.
sd = SD(filename, SDC.WRITE) # SD interface
vs = hdf.vstart() # vdata interface
v = hdf.vgstart() # vgroup interface
# Create vdata named 'INVENTORY'.
vdatacreate(vs, 'INVENTORY')
# Create dataset named "ARR_3x3"
sdscreate(sd, 'ARR_3x3')
# Attach the vdata and the dataset.
vd = vs.attach('INVENTORY')
sds = sd.select('ARR_3x3')
# Create vgroup named 'TOTAL'.
vg = v.create('TOTAL')
# Add vdata to the vgroup
import pprint
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
import matplotlib.cm as cm
#----------------------------------------------------------------------------------------#
path_LIDR = '/mnt/Sanduchera/ftp1.cloudsat.cira.colostate.edu/2B-CLDCLASS-LIDAR.P_R04/'
file_path = ''
file_name = '2008183012329_11573_CS_2B-CLDCLASS-LIDAR_GRANULE_P_R04_E02.hdf'
#----------------------------------------------------------------------------------------#
# Read HDF Files (VD data) Latitude & Longitude
f = HDF(file_path + file_name, SDC.READ)
vs = f.vstart()
Latitude = vs.attach('Latitude')
Longitude = vs.attach('Longitude')
a = Latitude[:]
Latitude.detach()
Longitude.detach()
vs.end()
f.close()
#----------------------------------------------------------------------------------------#
# SDS Data
file = SD(file_path + file_name, SDC.READ)
from __future__ import print_function
from pyhdf.HDF import *
from pyhdf.VS import *
f = HDF('inventory.hdf') # open 'inventory.hdf' in read mode
vs = f.vstart() # init vdata interface
vd = vs.attach('INVENTORY') # attach vdata 'INVENTORY' in read mode
# Display some vdata attributes
print("status:", vd.status)
print("vdata: ", vd._name) # predefined attribute: vdata name
print("nrecs: ", vd._nrecs) # predefined attribute: num records
# Display value of attribute 'unit' for all fields on which
# this attribute is set
print("units: ", end=' ')
for fieldName in vd._fields: # loop over all field names
try:
# instantiate field and obtain value of attribute 'unit'
v = vd.field(fieldName).unit
print("%s: %s" % (fieldName, v), end=' ')
except: # no 'unit' attribute: ignore
pass
print("")
print("")
# Display table header.
header = "%-7s %-12s %3s %4s %8s" % tuple(vd._fields)
print("-" * len(header))
print(header)
