tropy.io_tools package¶

Submodules¶

tropy.io_tools.HRIT module¶

tropy.io_tools.HRIT.SEVIRI_channel_list()¶

tropy.io_tools.HRIT.bit_conversion(input_set, inbit=8, outbit=10)¶

An integer array which is based on a n-bit representation is converted to an integer array based on a m-bit representation.

output_set = bit_conversion(input_set, inbit=8, outbit=10)

input_set : numpy integer array in a n-bit representation inbit : number of bits for input_set (n) outbit : number of bits for output_set (m)

output_set : numpy integer array in a m-bit representation

The size of input_set should be a multiple of inbit / outbit!

tropy.io_tools.HRIT.channel_segment_sets(set_name)¶

Outputs a predefined set of channels and segments’

chan_seg = channel_segment_sets(set_name)

set_name: name of a predefined set

chan_seg: Dictionary of channels with a list of segments.

tropy.io_tools.HRIT.combine_segments(seg_dict, missing=-1)¶

Combines individual segments of SEVIRI scans. Segments will be sorted by key.

combined = combine_two_segments(seg_list)

seg_list: dictionary of segment, sorted by key from bottom to top

combined: combination of both segments

tropy.io_tools.HRIT.combine_two_segments(seg1, seg2)¶

Combines two segments where seg1 is the upper and seg2 the lower segment.

seg = combine_two_segments(seg1,seg2)

seg1: upper segment seg2: lower segment

seg: combination of both segments

tropy.io_tools.HRIT.divide_two_segments(seg)¶

Divides one segment into two with half the line size.

seg1, seg2 = divide_two_segments(seg)

seg: combination of two segments

seg1: upper segment seg2: lower segment

tropy.io_tools.HRIT.get_HRIT_from_arch(day, chan_seg={'IR_016': [1, 2, 3, 4, 5, 6, 7, 8], 'VIS006': [1, 2, 3, 4, 5, 6, 7, 8], 'VIS008': [1, 2, 3, 4, 5, 6, 7, 8]}, scan_type='pzs', tarname=None, arch_dir=None, out_path=None)¶

Gets and decompresses the original HRIT files from archive. A typical set of channels and segments can be chosen.

file_list = get_HRIT_from_arch(day, chan_set = ‘nc-full’, scan_type = ‘pzs’ , arch_dir = None, out_path = None)

day: datetime object which include day and time of MSG time slot chan_set = name for a channel-segment set scan_type: which type of msg is used,

) allowed keys are: ‘pzs’ and ‘rss’

arch_dir = archive directory out_path = directory where the HRIT files are saved.

file_list : list of extracted HRIT files

tropy.io_tools.HRIT.read_HRIT_data(day, chan_seg, calibrate=True, scan_type='pzs', arch_dir=None, standard_seg=(464, 3712), add_meta=False, **kwargs)¶

Read HRIT data given a time slot, satellite type and channel-segment set

combined = read_HRIT_data(day, chan_seg, calibrate=True, scan_type = ‘pzs’, arch_dir = None, add_meta = False, **kwargs)

day = datetime object which include day and time of MSG time slot chan_seg = a channel-segment set, dictionary of channel names with lists of segments calibrate = flag, if slope / offset calibration should be done scan_type = which type of msg is used,

) allowed keys are: ‘pzs’ and ‘rss’

arch_dir = archive directory add_meta = flag, if meta data should be added out_path = directory where the HRIT files are saved.

combined = dictionary of retrieved channels with combined segments

tropy.io_tools.HRIT.read_HRIT_seg_data(hrit_file)¶

Reads directly the counts data for an HRIT segment.

seg_data = read_HRIT_seg_data(hrit_file)

hrit_file : HRIT file of the considered segment

seg_data: integer segment data field of radiance counts

tropy.io_tools.HRIT.read_slope_offset_from_prolog(pro_file, NJUMP=387065)¶

Reads directly slope and offset from HRIT PRO file.

slope, offset = read_slope_offset_from_prolog(pro_file)

pro_file : PROLOG file of the considered time slot

slope: slope of the data offset: offset of the data

Following transformation is applied:

DATA = SLOPE * COUNTS + OFFSET

if COUNTS are valid data points!

!! ATTENTION !! Jump in binary file is hard-coded!

tropy.io_tools.HRIT.segments_for_region(region, Nrow=3712, Nseg=8, seg_size=464)¶

tropy.io_tools.HRIT.write_HRIT_seg_data(seg_data, hrit_file)¶

Reads directly the counts data for an HRIT segment.

write_HRIT_seg_data(seg_data, hrit_file)

hrit_file : HRIT file of the considered segment seg_data: integer segment data field of radiance counts

tropy.io_tools.bit_conversion module¶

tropy.io_tools.data_collection module¶

class tropy.io_tools.data_collection.DataCollection¶

Bases: dict

add(vname, array, setting='bt', subpath=None)¶

list()¶

save(fname)¶

class tropy.io_tools.data_collection.Dataset(name, data=None, setting={'_FillValue': 0, 'add_offset': 0, 'dtype': '|u2', 'longname': 'Brightness Temperature', 'scale_factor': 0.01, 'unit': 'K'})¶

Bases: object

The Dataset class is a building block of the DataCollection class which will be used to easy input and output dataset sets.

add(array)¶

array()¶

attrs()¶

load(fname, subpath=None)¶

save(fname, subpath=None, mode='overwrite', time_out=300.0)¶

tropy.io_tools.data_settings module¶

tropy.io_tools.data_settings.Dge_settings()¶

tropy.io_tools.data_settings.bt_settings()¶

tropy.io_tools.data_settings.histogram_settings()¶

tropy.io_tools.data_settings.refl_settings()¶

tropy.io_tools.data_settings.settings(vartype='bt')¶

tropy.io_tools.data_settings.standard_int_setting(longname='Unknown', unit='Unknown')¶

tropy.io_tools.data_settings.standard_real_setting(longname='Unknown', unit='Unknown')¶

tropy.io_tools.file_status module¶

tropy.io_tools.file_status.test(fname)¶: Returns status of a file:

True: if open False: if not open

tropy.io_tools.find_latest_slot module¶

tropy.io_tools.hdf module¶

tropy.io_tools.hdf.dict_merge(a, b)¶

Recursively merges dict’s. not just simple a[‘key’] = b[‘key’], if both a and bhave a key who’s value is a dict then dict_merge is called on both values and the result stored in the returned dictionary.

from https://www.xormedia.com/recursively-merge-dictionaries-in-python/

tropy.io_tools.hdf.get_seviri_chan(chan_list, day, scan_type='rss', fname=None, calibrate=True, add_meta=False, add_geo=False, arch_dir=None)¶

Reads MSG - SEVIRI radiance of a given channel for a given date.

var = get_seviri_chan(chan_list, day, scan_type = ‘rss’, calibrate=True, add_meta = False)

chan_list: name of channel, e.g. ir_108 or IR_108 (case does not matter): or list of channel names

day: datetime object which include day and time of MSG time slot

scan_type: sets of scanning modus, i.e. ‘rss’ or ‘pzs’ (DEFAULT: ‘rss’)

calibrate : optional, decides if output is radiance (True) or counts (False)

add_meta: meta data of the MSG-SEVIRI hdf file

var: dictionary including radiance/counts of channel <ch_name> at date <day>: and if chosen meta data

tropy.io_tools.hdf.list_hdf_groups(fname)¶

Makes a list of hdf groups.

Only the 1st level is implemented, TDB: recursive listing.

glist = list_hdf_groups(fname)

fname: filename of hdf file

glist: list of group names

tropy.io_tools.hdf.read_dict_cont(f, d)¶: Recursively read nested dictionaries.

tropy.io_tools.hdf.read_dict_from_hdf(fname)¶

The content of an hdf file with arbitrary depth of subgroups is saved in nested dictionaries.

d = read_dict_from_hdf(fname)

fname: filename of output hdf file

d: nested dictionary which contains the data

tropy.io_tools.hdf.read_var_from_hdf(fname, vname, subpath=None)¶

A specific variable is read from an hdf file. Group and subgroups can be specified.

v = read_var_from_hdf(fname, vname, subpath = None)

fname: filename of input hdf file vname: variable name as given in hdf file

subpath: group or subgroup path with /

v: variable as numpy array

tropy.io_tools.hdf.save_dict2hdf(fname, d, mode='w')¶

The content of nested dictionaries of arbritrary depth is saved into an hdf file.

The key, subkeys, etc. are mapped into the hdf-groups directory structure.

save_dict2hdf(fname, d)

fname: filename of output hdf file d: dictionary which contains the data

None

tropy.io_tools.hdf.save_dict_cont(f, d)¶: Recursively saves nested dictionaries.

tropy.io_tools.hdf.update_dict_in_hdf(fname, din)¶

The content of nested dictionaries of arbritrary depth is updated in an hdf file.

The key, subkeys, etc. are mapped into the hdf-groups directory structure.

update_dict_in_hdf(fname, d)

fname: filename of output hdf file d: dictionary which contains the data

None

tropy.io_tools.netcdf module¶

tropy.io_tools.netcdf.read_icon_2d_data(fname, var_list, itime=0)¶

tropy.io_tools.netcdf.read_icon_4d_data(fname, var_list, itime=0, itime2=None)¶

Read netcdf data.

dset = read_icon_4d_data(fname, var_list, itime = 0)

fname: netcdf filename var_list: variable name or list of variables itime (OPTIONAL): index of 1st dimension (assumed to be time) to be read only

if itime = None, the full field is read

dset: dictionary containing the fields

tropy.io_tools.netcdf.read_icon_dimension(fname, dim_name)¶

tropy.io_tools.netcdf.read_icon_georef(fname)¶

tropy.io_tools.netcdf.read_icon_time(fname, itime=0)¶

tropy.io_tools.netcdf.roundTime(dt=None, roundTo=60)¶

Round a datetime object to any time laps in seconds: dt : datetime.datetime object, default now.

roundTo : Closest number of seconds to round to, default 1 minute. Author: Thierry Husson 2012 - Use it as you want but don’t blame me.

tropy.io_tools.netcdf.save_icon_georef(fname, geopath=None)¶

tropy.io_tools.netcdf.save_icon_time_reference(fname, outfile=None)¶