def encode(output_bufr_file, RadarData, WMOID):
# Get the data for the wind profilers
WindComponentData = RadarData.getWindComponents()
GateSpaceWidths = RadarData.getGateSpace()
if len(GateSpaceWidths) > 1:
MaxWidth = 0
for Width in GateSpaceWidths:
if Width > MaxWidth:
MaxWidth = Width
else:
MaxWidth = GateSpaces[0]
print('Using Pulse Mode ' + str(MaxWidth))
for DateTime in WindComponentData:
print('Processing ' + str(DateTime))
print(MaxWidth)
print(WindComponentData[DateTime].keys())
if MaxWidth in WindComponentData[DateTime].keys():
print('Processing high mode only. Pulse ' + str(MaxWidth)
+ 'ns')
WindComponents = WindComponentData[DateTime][MaxWidth]
# Create tge buffer for the hour block of data.
bufr = BUFRInterfaceECMWF(verbose=True)
# fill sections 0, 1, 2 and 3 in the BUFR table
num_subsets = 1
bufr.fill_sections_0123( # ECMWF
# wind profiler . Also know as Message Type (Table A)
# Message sub-type
# L2B processing facility
bufr_code_centre=59,
bufr_obstype=2,
bufr_subtype=7,
bufr_table_local_version=1,
bufr_table_master=0,
bufr_table_master_version=3,
bufr_code_subcentre=0,
num_subsets=num_subsets,
bufr_compression_flag=0,
)
bufr.setup_tables()
# define a descriptor list
template = BufrTemplate()
print('adding {0} descriptors'.format(10))
template.add_descriptors(
WMO_BLOCK_NUM,
WMO_STATION_NUM,
DD_LATITUDE_COARSE_ACCURACY,
DD_LONGITUDE_COARSE_ACCURACY,
STATION_HEIGHT,
YEAR,
MONTH,
MDAY,
HOUR,
MIN,
TIME_SIGNIFICANCE,
TIME_PERIOD,
DD_WIND_SPEED,
DD_WIND_DIR,
DD_PRESSURE,
DD_TEMPERATURE,
RAINFALL_SWE,
DD_RELATIVE_HUMD,
HEIGHT_INCREMENT,
WIND_PROFILER_SUB_MODE_INFO,
HEIGHT_INCREMENT,
HEIGHT_INCREMENT,
HEIGHT_INCREMENT,
)
# delay replication for the next 10 descriptors
template.add_replicated_descriptors(
len(WindComponents),
WIND_PROFILER_MODE_INFO,
WIND_PROFILER_QC_RESULTS,
TOTAL_NUMBER,
WIND_U_COMPONENT,
WIND_V_COMPONENT,
STD_DEV_HORIZONTAL_WIND_SPEED,
TOTAL_NUMBER,
RADAR_BACK_SCATTER,
WIND_W_COMPONENT,
STD_DEV_VERTICAL_SPEED,
)
bufr.register_and_expand_descriptors(template)
# activate this one if the encoding crashes without clear cause:
# bufr.estimated_num_bytes_for_encoding = 25000
# retrieve the length of the expanded descriptor list
exp_descr_list_length = bufr.ktdexl
# fill the values array with some dummy varying data
num_values = exp_descr_list_length
values = np.zeros(num_values, dtype=np.float64) # this is the default
# note: these two must be identical for now, otherwise the
# python to fortran interface breaks down. This also ofcourse is the
# cause of the huge memory use of cvals in case num_values is large.
num_cvalues = num_values
cvals = np.zeros((num_cvalues, 80), dtype=np.character)
# note that python starts counting with 0, unlike fortran,
# so there is no need to take (subset-1)
# i = subset*exp_descr_list_length
values[0] = WMOID[0:2] # WMO Block Number
values[1] = WMOID[2:5] # WMO Station #
values[2] = RadarData.getLatitude() # Latitude
values[3] = RadarData.getLongitude()
values[4] = RadarData.getElev() # Elevation of Station (meters)
values[5] = DateTime.timetuple().tm_year # year
values[6] = DateTime.timetuple().tm_mon # month
values[7] = DateTime.timetuple().tm_mday # day
values[8] = DateTime.timetuple().tm_hour # hour
values[9] = 0 # minute
values[10] = 2 # Time Significance
values[11] = -60 # Time Period
values[12] = 1 # Wind Speed
values[13] = 1 # Wind Dir
values[14] = 1 # Pressure
values[15] = 1 # Temperature
values[16] = .2 # Rainfall
values[17] = 1 # Realative Humidty
GateSpace = int(MaxWidth) * 1.e-9 * 3.e+8 / 2.
values[18] = GateSpace # Height Increment
values[19] = 0 # Wind Profiler Sub Mode
values[20] = GateSpace # Height Increment
values[21] = GateSpace # Height Increment
values[22] = GateSpace # Height Increment
print('Number of gates ' + str(len(WindComponents)))
for i in range(0, len(WindComponents)):
for t in range(0, 10):
# Calulcate the correct index in the BUFR
rec = i * 10 + 23 + t
if WindComponents[i][0] <= 1000 \
and WindComponents[i][1] <= 1000:
WindRadians = math.radians(WindComponents[i][1])
VelocityU = -WindComponents[i][0] \
* math.sin(WindRadians)
VelocityV = -WindComponents[i][0] \
* math.cos(WindRadians)
TotalNumber = WindComponents[i][2]
SnrDB = WindComponents[i][3]
else:
VelocityU = VelocityV = TotalNumbe = SnrDB = -99
values[rec] = float('NaN') # level mode
continue
if rec % 10 == 3:
values[rec] = 1 # level mode
if rec % 10 == 4:
values[rec] = 0 # Quality Control test
if rec % 10 == 5:
values[rec] = TotalNumber # Total Number (with respect to accumlation or average)
if rec % 10 == 6:
values[rec] = VelocityU # U-Component
if rec % 10 == 7:
values[rec] = VelocityV # V-Component
if rec % 10 == 8:
values[rec] = 0.000 # Std Deviation of horizontal wind speed
if rec % 10 == 9:
values[rec] = TotalNumber # Total Number (with respect to accumlation or average)
if rec % 10 == 0:
values[rec] = SnrDB / 100 # Radar Back Scatter (Peak Power) x100
if rec % 10 == 1:
values[rec] = 0.000 # W-Component x100
if rec % 10 == 2:
values[rec] = 0.000 # Std Deviation of vertical wind
# do the encoding to binary format
bufr.encode_data(values, cvals)
HeaderString = '''
287
IUAK01 PANC %02d%02d00
''' \
% (DateTime.timetuple().tm_mday,
DateTime.timetuple().tm_hour)
if not os.path.exists(OutputPath):
os.makedirs(OutputPath)
OutputFile = \
'%s/IUPTO2_%s_%02d%02d00_216234297.bufr.%04d%02d%02d%02d' \
% (
OutputPath,
RadarData.getSiteID().upper(),
DateTime.timetuple().tm_mon,
DateTime.timetuple().tm_mday,
DateTime.timetuple().tm_year,
DateTime.timetuple().tm_mon,
DateTime.timetuple().tm_mday,
DateTime.timetuple().tm_hour,
)
# Remove file if exsists
if os.path.exists(OutputFile):
os.remove(OutputFile)
bf1 = open(OutputFile, 'ab')
bf1.write(HeaderString)
bf1.close()
# get an instance of the RawBUFRFile class
bf1 = RawBUFRFile()
# open the file for writing
bf1.open(OutputFile, 'ab')
# write the encoded BUFR message
bf1.write_raw_bufr_msg(bufr.encoded_message)
# close the file
bf1.close()
# #]
print('succesfully written BUFR encoded data to file: ',
OutputFile)
def encoding_example(output_bufr_file):
# #[
"""
wrap the example in a function to circumvent the pylint
convention of requiring capitals for constants in the global
scope (since most of these variables are not constants at all))
"""
bufr = BUFRInterfaceECMWF(verbose=True)
# fill sections 0, 1, 2 and 3
num_subsets = 4
bufr.fill_sections_0123(
bufr_code_centre=98, # ECMWF
bufr_obstype=3, # sounding
bufr_subtype=251, # L1B
bufr_table_local_version=1,
bufr_table_master=0,
bufr_table_master_version=15,
bufr_code_subcentre=0, # L2B processing facility
num_subsets=num_subsets,
bufr_compression_flag=0)
# 64=compression/0=no compression
# determine information from sections 0123 to construct the BUFR table
# names expected by the ECMWF BUFR library and create symlinks to the
# default tables if needed
bufr.setup_tables()
# define a descriptor list
template = BufrTemplate(verbose=True)
template.add_descriptors(
DD_D_DATE_YYYYMMDD, # 0
DD_D_TIME_HHMM) # 1
# delay replication for the next 2 descriptors
# allow at most 2 delayed replications
template.add_delayed_replic_descriptors(2, DD_PRESSURE, DD_TEMPERATURE)
# replicate the next 2 descriptors 3 times
template.add_replicated_descriptors(3, DD_LATITUDE_HIGH_ACCURACY,
DD_LONGITUDE_HIGH_ACCURACY)
bufr.register_and_expand_descriptors(template)
# activate this one if the encoding crashes without clear cause:
# bufr.estimated_num_bytes_for_encoding = 25000
# retrieve the length of the expanded descriptor list
exp_descr_list_length = bufr.ktdexl
print("exp_descr_list_length = ", exp_descr_list_length)
# fill the values array with some dummy varying data
num_values = exp_descr_list_length * num_subsets
values = np.zeros(num_values, dtype=np.float64) # this is the default
# note: these two must be identical for now, otherwise the
# python to fortran interface breaks down. This also ofcourse is the
# cause of the huge memory use of cvals in case num_values is large.
num_cvalues = num_values
cvals = np.zeros((num_cvalues, 80), dtype=np.character)
for subset in range(num_subsets):
# note that python starts counting with 0, unlike fortran,
# so there is no need to take (subset-1)
i = subset * exp_descr_list_length
values[i] = 1999 # year
i = i + 1
values[i] = 12 # month
i = i + 1
values[i] = 31 # day
i = i + 1
values[i] = 23 # hour
i = i + 1
values[i] = 59 - subset # minute
i = i + 1
values[i] = 2 # delayed replication factor
# this delayed replication factor determines the actual number
# of values to be stored for this particular subset
# even if it is less then the number given in kdata above !
for repl in range(2):
i = i + 1
values[i] = 1013.e2 - 100.e2 * subset + i + repl # pressure [pa]
i = i + 1
values[i] = 273.15 - 10. * subset + i + repl # temperature [K]
for repl in range(3):
i = i + 1
values[i] = 51.82 + 0.05 * subset + i + repl # latitude
i = i + 1
values[i] = 5.25 + 0.1 * subset + i + repl # longitude
# do the encoding to binary format
bufr.encode_data(values, cvals)
# get an instance of the RawBUFRFile class
bf1 = RawBUFRFile()
# open the file for writing
bf1.open(output_bufr_file, 'wb')
# write the encoded BUFR message
bf1.write_raw_bufr_msg(bufr.encoded_message)
# close the file
bf1.close()
def encoding_example(output_bufr_file):
# #[
"""
wrap the example in a function to circumvent the pylint
convention of requiring capitals for constants in the global
scope (since most of these variables are not constants at all))
"""
bufr = BUFRInterfaceECMWF(verbose=True)
# fill sections 0, 1, 2 and 3
num_subsets = 4
bufr.fill_sections_0123(bufr_code_centre=98, # ECMWF
bufr_obstype=3, # sounding
bufr_subtype=251, # L1B
bufr_table_local_version=1,
bufr_table_master=0,
bufr_table_master_version=15,
bufr_code_subcentre=0, # L2B processing facility
num_subsets=num_subsets,
bufr_compression_flag=0)
# 64=compression/0=no compression
# determine information from sections 0123 to construct the BUFR table
# names expected by the ECMWF BUFR library and create symlinks to the
# default tables if needed
bufr.setup_tables()
# define a descriptor list
template = BufrTemplate()
template.add_descriptors(DD_D_DATE_YYYYMMDD, # 0
DD_D_TIME_HHMM) # 1
# delay replication for the next 2 descriptors
# allow at most 2 delayed replications
template.add_delayed_replic_descriptors(2,
DD_PRESSURE,
DD_TEMPERATURE)
# replicate the next 2 descriptors 3 times
template.add_replicated_descriptors(3,
DD_LATITUDE_HIGH_ACCURACY,
DD_LONGITUDE_HIGH_ACCURACY)
bufr.register_and_expand_descriptors(template)
# activate this one if the encoding crashes without clear cause:
# bufr.estimated_num_bytes_for_encoding = 25000
# retrieve the length of the expanded descriptor list
exp_descr_list_length = bufr.ktdexl
print "exp_descr_list_length = ", exp_descr_list_length
# fill the values array with some dummy varying data
num_values = exp_descr_list_length*num_subsets
values = np.zeros(num_values, dtype=np.float64) # this is the default
# note: these two must be identical for now, otherwise the
# python to fortran interface breaks down. This also ofcourse is the
# cause of the huge memory use of cvals in case num_values is large.
num_cvalues = num_values
cvals = np.zeros((num_cvalues, 80), dtype=np.character)
for subset in range(num_subsets):
# note that python starts counting with 0, unlike fortran,
# so there is no need to take (subset-1)
i = subset*exp_descr_list_length
values[i] = 1999 # year
i = i+1
values[i] = 12 # month
i = i+1
values[i] = 31 # day
i = i+1
values[i] = 23 # hour
i = i+1
values[i] = 59 - subset # minute
i = i+1
values[i] = 2 # delayed replication factor
# this delayed replication factor determines the actual number
# of values to be stored for this particular subset
# even if it is less then the number given in kdata above !
for repl in range(2):
i = i+1
values[i] = 1013.e2 - 100.e2*subset+i+repl # pressure [pa]
i = i+1
values[i] = 273.15 - 10.*subset+i+repl # temperature [K]
for repl in range(3):
i = i+1
values[i] = 51.82 + 0.05*subset+i+repl # latitude
i = i+1
values[i] = 5.25 + 0.1*subset+i+repl # longitude
# do the encoding to binary format
bufr.encode_data(values, cvals)
# get an instance of the RawBUFRFile class
bf1 = RawBUFRFile()
# open the file for writing
bf1.open(output_bufr_file, 'wb')
# write the encoded BUFR message
bf1.write_raw_bufr_msg(bufr.encoded_message)
# close the file
bf1.close()
def encode(output_bufr_file, RadarData, WMOID):
# Get the data for the wind profilers
WindComponentData = RadarData.getWindComponents()
GateSpaceWidths = RadarData.getGateSpace()
if len(GateSpaceWidths) > 1:
MaxWidth = 0
for Width in GateSpaceWidths:
if Width > MaxWidth:
MaxWidth = Width
else:
MaxWidth = GateSpaces[0]
print('Using Pulse Mode ' + str(MaxWidth))
for DateTime in WindComponentData:
print('Processing ' + str(DateTime))
print(MaxWidth)
print(WindComponentData[DateTime].keys())
if MaxWidth in WindComponentData[DateTime].keys():
print('Processing high mode only. Pulse ' + str(MaxWidth) + 'ns')
WindComponents = WindComponentData[DateTime][MaxWidth]
# Create tge buffer for the hour block of data.
bufr = BUFRInterfaceECMWF(verbose=True)
# fill sections 0, 1, 2 and 3 in the BUFR table
num_subsets = 1
bufr.fill_sections_0123( # ECMWF
# wind profiler . Also know as Message Type (Table A)
# Message sub-type
# L2B processing facility
bufr_code_centre=59,
bufr_obstype=2,
bufr_subtype=7,
bufr_table_local_version=1,
bufr_table_master=0,
bufr_table_master_version=3,
bufr_code_subcentre=0,
num_subsets=num_subsets,
bufr_compression_flag=0,
)
bufr.setup_tables()
# define a descriptor list
template = BufrTemplate()
print('adding {0} descriptors'.format(10))
template.add_descriptors(
WMO_BLOCK_NUM,
WMO_STATION_NUM,
DD_LATITUDE_COARSE_ACCURACY,
DD_LONGITUDE_COARSE_ACCURACY,
STATION_HEIGHT,
YEAR,
MONTH,
MDAY,
HOUR,
MIN,
TIME_SIGNIFICANCE,
TIME_PERIOD,
DD_WIND_SPEED,
DD_WIND_DIR,
DD_PRESSURE,
DD_TEMPERATURE,
RAINFALL_SWE,
DD_RELATIVE_HUMD,
HEIGHT_INCREMENT,
WIND_PROFILER_SUB_MODE_INFO,
HEIGHT_INCREMENT,
HEIGHT_INCREMENT,
HEIGHT_INCREMENT,
)
# delay replication for the next 10 descriptors
template.add_replicated_descriptors(
len(WindComponents),
WIND_PROFILER_MODE_INFO,
WIND_PROFILER_QC_RESULTS,
TOTAL_NUMBER,
WIND_U_COMPONENT,
WIND_V_COMPONENT,
STD_DEV_HORIZONTAL_WIND_SPEED,
TOTAL_NUMBER,
RADAR_BACK_SCATTER,
WIND_W_COMPONENT,
STD_DEV_VERTICAL_SPEED,
)
bufr.register_and_expand_descriptors(template)
# activate this one if the encoding crashes without clear cause:
# bufr.estimated_num_bytes_for_encoding = 25000
# retrieve the length of the expanded descriptor list
exp_descr_list_length = bufr.ktdexl
# fill the values array with some dummy varying data
num_values = exp_descr_list_length
values = np.zeros(num_values,
dtype=np.float64) # this is the default
# note: these two must be identical for now, otherwise the
# python to fortran interface breaks down. This also ofcourse is the
# cause of the huge memory use of cvals in case num_values is large.
num_cvalues = num_values
cvals = np.zeros((num_cvalues, 80), dtype=np.character)
# note that python starts counting with 0, unlike fortran,
# so there is no need to take (subset-1)
# i = subset*exp_descr_list_length
values[0] = WMOID[0:2] # WMO Block Number
values[1] = WMOID[2:5] # WMO Station #
values[2] = RadarData.getLatitude() # Latitude
values[3] = RadarData.getLongitude()
values[4] = RadarData.getElev() # Elevation of Station (meters)
values[5] = DateTime.timetuple().tm_year # year
values[6] = DateTime.timetuple().tm_mon # month
values[7] = DateTime.timetuple().tm_mday # day
values[8] = DateTime.timetuple().tm_hour # hour
values[9] = 0 # minute
values[10] = 2 # Time Significance
values[11] = -60 # Time Period
values[12] = 1 # Wind Speed
values[13] = 1 # Wind Dir
values[14] = 1 # Pressure
values[15] = 1 # Temperature
values[16] = .2 # Rainfall
values[17] = 1 # Realative Humidty
GateSpace = int(MaxWidth) * 1.e-9 * 3.e+8 / 2.
values[18] = GateSpace # Height Increment
values[19] = 0 # Wind Profiler Sub Mode
values[20] = GateSpace # Height Increment
values[21] = GateSpace # Height Increment
values[22] = GateSpace # Height Increment
print('Number of gates ' + str(len(WindComponents)))
for i in range(0, len(WindComponents)):
for t in range(0, 10):
# Calulcate the correct index in the BUFR
rec = i * 10 + 23 + t
if WindComponents[i][0] <= 1000 \
and WindComponents[i][1] <= 1000:
WindRadians = math.radians(WindComponents[i][1])
VelocityU = -WindComponents[i][0] \
* math.sin(WindRadians)
VelocityV = -WindComponents[i][0] \
* math.cos(WindRadians)
TotalNumber = WindComponents[i][2]
SnrDB = WindComponents[i][3]
else:
VelocityU = VelocityV = TotalNumbe = SnrDB = -99
values[rec] = float('NaN') # level mode
continue
if rec % 10 == 3:
values[rec] = 1 # level mode
if rec % 10 == 4:
values[rec] = 0 # Quality Control test
if rec % 10 == 5:
values[
rec] = TotalNumber # Total Number (with respect to accumlation or average)
if rec % 10 == 6:
values[rec] = VelocityU # U-Component
if rec % 10 == 7:
values[rec] = VelocityV # V-Component
if rec % 10 == 8:
values[
rec] = 0.000 # Std Deviation of horizontal wind speed
if rec % 10 == 9:
values[
rec] = TotalNumber # Total Number (with respect to accumlation or average)
if rec % 10 == 0:
values[
rec] = SnrDB / 100 # Radar Back Scatter (Peak Power) x100
if rec % 10 == 1:
values[rec] = 0.000 # W-Component x100
if rec % 10 == 2:
values[rec] = 0.000 # Std Deviation of vertical wind
# do the encoding to binary format
bufr.encode_data(values, cvals)
HeaderString = '''
287
IUAK01 PANC %02d%02d00
''' \
% (DateTime.timetuple().tm_mday,
DateTime.timetuple().tm_hour)
if not os.path.exists(OutputPath):
os.makedirs(OutputPath)
OutputFile = \
'%s/IUPTO2_%s_%02d%02d00_216234297.bufr.%04d%02d%02d%02d' \
% (
OutputPath,
RadarData.getSiteID().upper(),
DateTime.timetuple().tm_mon,
DateTime.timetuple().tm_mday,
DateTime.timetuple().tm_year,
DateTime.timetuple().tm_mon,
DateTime.timetuple().tm_mday,
DateTime.timetuple().tm_hour,
)
# Remove file if exsists
if os.path.exists(OutputFile):
os.remove(OutputFile)
bf1 = open(OutputFile, 'ab')
bf1.write(HeaderString)
bf1.close()
# get an instance of the RawBUFRFile class
bf1 = RawBUFRFile()
# open the file for writing
bf1.open(OutputFile, 'ab')
# write the encoded BUFR message
bf1.write_raw_bufr_msg(bufr.encoded_message)
# close the file
bf1.close()
# #]
print('succesfully written BUFR encoded data to file: ',
OutputFile)
