9: ['2019-12-20T16:58:30', '2019-12-20T16:59:15'],
10: ['2019-12-23T17:32:35', '2019-12-23T17:33:27'],
11: ['2019-07-11T17:41:44', '2019-07-11T18:42:48'],
12: ['2019-07-11T18:04:46', '2019-07-11T18:05:36'],
14: ['2019-09-02T14:01:41', '2019-09-02T14:02:15'], # todo
16: ['2019-09-03T19:22:20', '2019-09-03T19:22:54'],
}
if time_ranges_nord.get(probe):
# Recalc zeroing_azimuth with zeroed scaling coefs
cfg_in = {
'tables': [tbl],
'db_path': db_path_tank,
'time_range_nord': time_ranges_nord[probe]
}
with pd.HDFStore(db_path_calibr_scalling, mode='r') as store:
for tbl, coefs in h5_names_gen(cfg_in):
del coefs[
'azimuth_shift_deg'] # to calculate shift of uncorrected data
# Calculation:
dict_matrices = {
'//coef//H//azimuth_shift_deg':
zeroing_azimuth(store, tbl, time_ranges_nord[probe],
coefs, cfg_in)
}
h5copy_coef(None, db_path_tank, tbl, dict_matrices=dict_matrices)
h5copy_coef(db_path_tank,
db_path_calibr_scalling,
tbl,
ok_to_replace_group=True)
else:
l.warning('Inlab time_ranges_nord not defined')
def main(config: ConfigType) -> None:
"""
----------------------------
Save data to Pandas HDF5 store*.h5
----------------------------
The store contains tables for each device and each device table contains log with metadata of recording sessions
:param config: with fields:
- in - mapping with fields:
- tables_log: - log table name or pattern str for it: in pattern '{}' will be replaced by data table name
- cols_good_data: -
['dt_from_utc', 'db', 'db_path', 'table_nav']
- out - mapping with fields:
- cols: can use i - data row number and i_log_row - log row number that is used to load data range
- cols_log: can use i - log row number
- text_date_format
- file_name_fun, file_name_fun_log - {fun} part of "lambda rec_num, t_st, t_en: {fun}" string to compile function
for name of data and log text files
- sep
"""
global cfg
cfg = to_vaex_hdf5.cfg_dataclasses.main_init(config, cs_store_name)
cfg_in = cfg.pop('input')
cfg_in['cfgFile'] = cs_store_name
cfg['in'] = cfg_in
# try:
# cfg = to_vaex_hdf5.cfg_dataclasses.main_init_input_file(cfg, cs_store_name, )
# except Ex_nothing_done:
# pass # existed db is not mandatory
device_path, cfg['out']['db_path'] = device_in_out_paths(
db_path=cfg['out'].get('db_path'),
path_cruise=cfg['in']['path_cruise'],
device_short_name=cfg['in']['probes_prefix'],
device_dir_pattern='*inclinometer*')
out = cfg['out']
# h5init(cfg['in'], out)
probes = cfg['in']['probes'] or range(
1, 41) # sets default range, specify your values before line ---
raw_root, probe_is_incl = re.subn('INCL_?', 'INKL_',
cfg['in']['probes_prefix'].upper())
# some parameters that depends of probe type (indicated by probes_prefix)
p_type = defaultdict(
# baranov's format
constant_factory({
'correct_fun':
partial(correct_txt,
mod_file_name=mod_incl_name,
sub_str_list=[
b'^\r?(?P<use>20\d{2}(\t\d{1,2}){5}(\t\d{5}){8}).*',
b'^.+'
]),
'fs':
10,
'format':
'Baranov',
}),
{
'incl': {
'correct_fun':
partial(
correct_txt,
mod_file_name=mod_incl_name,
sub_str_list=[
b'^(?P<use>20\d{2}(,\d{1,2}){5}(,\-?\d{1,6}){6}(,\d{1,2}\.\d{2})(,\-?\d{1,3}\.\d{2})).*',
b'^.+'
]),
'fs':
5,
'format':
'Kondrashov',
},
'voln': {
'correct_fun':
partial(
correct_txt,
mod_file_name=mod_incl_name,
sub_str_list=[
b'^(?P<use>20\d{2}(,\d{1,2}){5}(,\-?\d{1,8})(,\-?\d{1,2}\.\d{2}){2}).*',
b'^.+'
]),
'fs':
5,
#'tbl_prefix': 'w',
'format':
'Kondrashov',
}
})
if st(1, 'Save inclinometer or wavegage data from ASCII to HDF5'):
# Note: Can not find additional not corrected files for same probe if already have any corrected in search path (move them out if need)
i_proc_probe = 0 # counter of processed probes
i_proc_file = 0 # counter of processed files
# patten to identify only _probe_'s raw data files that need to correct '*INKL*{:0>2}*.[tT][xX][tT]':
raw_parent = dir_incl / '_raw' # raw_parent /=
if cfg['in']['raw_subdir'] is None:
cfg['in']['raw_subdir'] = ''
dir_out = raw_parent / re.sub(r'[.\\/ *?]', '_',
cfg['in']['raw_subdir'])
# sub replaces multilevel subdirs to 1 level that correct_fun() can only make
def dt_from_utc_2000(probe):
""" Correct time of probes started without time setting. Raw date must start from 2000-01-01T00:00"""
return (
datetime(year=2000, month=1, day=1) -
cfg['in']['time_start_utc'][probe]
) if cfg['in']['time_start_utc'].get(probe) else timedelta(0)
# convert cfg['in']['dt_from_utc'] keys to int
cfg['in']['dt_from_utc'] = {
int(p): v
for p, v in cfg['in']['dt_from_utc'].items()
}
# convert cfg['in']['t_start_utc'] to cfg['in']['dt_from_utc'] and keys to int
cfg['in']['dt_from_utc'].update( # overwriting the 'time_start_utc' where already exist
{int(p): dt_from_utc_2000(p) for p, v in cfg['in']['time_start_utc'].items()}
)
# make cfg['in']['dt_from_utc'][0] be default value
cfg['in']['dt_from_utc'] = defaultdict(
constant_factory(cfg['in']['dt_from_utc'].pop(0, timedelta(0))),
cfg['in']['dt_from_utc'])
for probe in probes:
raw_found = []
raw_pattern_file = str(
Path(glob.escape(cfg['in']['raw_subdir'])) /
cfg['in']['raw_pattern'].format(prefix=raw_root, number=probe))
correct_fun = p_type[cfg['in']['probes_prefix']]['correct_fun']
# if not archive:
if (not re.match(r'.*(\.zip|\.rar)$', cfg['in']['raw_subdir'],
re.IGNORECASE)) and raw_parent.is_dir():
raw_found = list(raw_parent.glob(raw_pattern_file))
if not raw_found:
# Check if already have corrected files for probe generated by correct_txt(). If so then just use them
raw_found = list(
dir_out.glob(
f"{cfg['in']['probes_prefix']}{probe:0>2}.txt"))
if raw_found:
print('corrected csv file', [r.name for r in raw_found],
'found')
correct_fun = lambda x, dir_out: x
elif not cfg['in']['raw_subdir']:
continue
for file_in in (raw_found or open_csv_or_archive_of_them(
raw_parent, binary_mode=False, pattern=raw_pattern_file)):
file_in = correct_fun(file_in, dir_out=dir_out)
if not file_in:
continue
tbl = file_in.stem # f"{cfg['in']['probes_prefix']}{probe:0>2}"
# tbl = re.sub('^((?P<i>inkl)|w)_0', lambda m: 'incl' if m.group('i') else 'w', # correct name
# re.sub('^[\d_]*|\*', '', file_in.stem).lower()), # remove date-prefix if in name
csv2h5(
[
str(
Path(__file__).parent / 'ini' /
f"csv_{'inclin' if probe_is_incl else 'wavegage'}_{p_type[cfg['in']['probes_prefix']]['format']}.ini"
),
'--path',
str(file_in),
'--blocksize_int',
'50_000_000', # 50Mbt
'--table',
tbl,
'--db_path',
str(db_path),
# '--log', str(scripts_path / 'log/csv2h5_inclin_Kondrashov.log'),
# '--b_raise_on_err', '0', # ?
'--b_interact',
'0',
'--fs_float',
str(p_type[cfg['in']['probes_prefix']]
['fs']), #f'{fs(probe, file_in.stem)}',
'--dt_from_utc_seconds',
str(cfg['in']['dt_from_utc'][probe].total_seconds()),
'--b_del_temp_db',
'1',
] +
(['--csv_specific_param_dict', 'invert_magnitometr: True']
if probe_is_incl else []),
**{
'filter': {
'min_date':
cfg['filter']['min_date'].get(
probe, np.datetime64(0, 'ns')),
'max_date':
cfg['filter']['max_date'].get(
probe, np.datetime64('now', 'ns')
), # simple 'now' works in sinchronious mode
}
})
# Get coefs:
l.info(
f"Adding coefficients to {db_path}/{tbl} from {cfg['in']['db_coefs']}"
)
try:
h5copy_coef(cfg['in']['db_coefs'], db_path, tbl)
except KeyError as e: # Unable to open object (component not found)
l.warning(
'No coefs to copy?'
) # write some dummy coefficients to can load Veusz patterns:
h5copy_coef(None,
db_path,
tbl,
dict_matrices=dict_matrices_for_h5(tbl=tbl))
except OSError as e:
l.warning(
'Not found DB with coefs?'
) # write some dummy coefficients to can load Veusz patterns:
h5copy_coef(None,
db_path,
tbl,
dict_matrices=dict_matrices_for_h5(tbl=tbl))
i_proc_file += 1
else:
print('no', raw_pattern_file, end=', ')
i_proc_probe += 1
print('Ok:', i_proc_probe, 'probes,', i_proc_file, 'files processed.')
cfg_in['tables'] = ['incl30']
from inclinometer.incl_h5clc import h5_names_gen
from inclinometer.h5inclinometer_coef import rot_matrix_x, rot_matrix_y #rotate_x, rotate_y
# R*[xyz]. As we next will need apply coefs Ag = Rz*Ry*Rx we can incorporate this
# operation by precalculate it adding known angles on each axes to Rz,Ry,Rx.
# If rotation is 180 deg, then we can add it only to Rx. Modified coef: Ag_new = Rz*Ry*R(x+180)
# R(x+180) = Rx*Rx180 equivalent to rotate Ag.T in opposite direction:
# Ag_new = rotate_x()
# inclinometer changed so that applying coefs returns rotated data fiels vectors:
# Out_rotated = Ag * In
# We rotate it back:
# Out = rotate(Out_rotated) =
# after angle after calibration to some angle P so determine angle relative to vertical
# by rotate data vector in opposite dir: Out = Ag * R_back * In. This equivalent to have new coef by apply rotation to Ag:
# Ag_new = Ag * R_back = (R_back.T * Ag.T).T = rotate_forward(Ag.T).T =
# Applying calibration coef will get data in inverted basis so we need rotate it after:
#
# coefs['Ag'] = rotate_x(coefs['Ag'], angle_degrees=180)
# coefs['Ah'] = rotate_x(coefs['Ah'], angle_degrees=180)
# dfLogOld, cfg_out['db'], cfg_out['b_skip_if_up_to_date'] = h5temp_open(**cfg_out)
for i1, (tbl, coefs) in enumerate(h5_names_gen(cfg_in), start=1):
# using property of rotation around same axis: R(x, θ1)@R(x, θ2) = R(x, θ1 + θ2)
coefs['Ag'] = coefs['Ag'] @ rot_matrix_x(np.cos(np.pi), np.sin(np.pi))
coefs['Ah'] = coefs['Ah'] @ rot_matrix_x(np.cos(np.pi), np.sin(np.pi))
coefs['azimuth_shift_deg'] = 180
h5copy_coef(None,
cfg['out']['db_path'],
tbl,
dict_matrices=dict_matrices_for_h5(coefs,
tbl,
to_nested_keys=True))
# Calculate velocity and average
if st(2):
# if aggregate_period_s is None then not average and write to *_proc_noAvg.h5 else loading from that h5 and writing to _proc.h5
if not cfg['out']['aggregate_period_s']:
cfg['out']['aggregate_period_s'] = [
None, 2, 600,
3600 if 'w' in cfg['in']['probes_prefix'] else 7200
]
if cfg['in']['azimuth_add']:
if 'Lat' in cfg['in']['azimuth_add']:
from datetime import datetime
# add magnetic declination,° for used coordinates
# todo: get time
azimuth_add = mag_dec(cfg['in']['azimuth_add']['Lat'],
cfg['in']['azimuth_add']['Lon'],
datetime(2020, 9, 10),
depth=-1)
else:
azimuth_add = 0
if 'constant' in cfg['in']['azimuth_add']:
# and add constant. For example, subtruct declination at the calibration place if it was applied
azimuth_add += cfg['in']['azimuth_add'][
'constant'] # add -6.65644183° to account for calibration in Kaliningrad
for aggregate_period_s in cfg['out']['aggregate_period_s']:
if aggregate_period_s is None:
db_path_in = db_path
db_path_out = db_path.with_name(
f'{db_path.stem}_proc_noAvg.h5')
else:
db_path_in = db_path.with_name(f'{db_path.stem}_proc_noAvg.h5')
db_path_out = f'{db_path.stem}_proc.h5' # or separately: '_proc{aggregate_period_s}.h5'
args = [
Path(incl_h5clc.__file__).with_name(
f'incl_h5clc_{db_path.stem}.yaml'),
# if no such file all settings are here
'--db_path',
str(db_path_in),
# ! 'incl.*|w\d*' inclinometers or wavegauges w\d\d # 'incl09':
'--tables_list',
'incl.*' if not cfg['in']['probes'] else
f"incl.*(?:{'|'.join('{:0>2}'.format(p) for p in cfg['in']['probes'])})",
'--aggregate_period',
f'{aggregate_period_s}S' if aggregate_period_s else '',
'--out.db_path',
str(db_path_out),
'--table',
f'V_incl_bin{aggregate_period_s}'
if aggregate_period_s else 'V_incl',
'--verbose',
'INFO', #'DEBUG' get many numba messages
'--b_del_temp_db',
'1',
# '--calc_version', 'polynom(force)', # depreshiated
# '--chunksize', '20000',
# '--not_joined_h5_path', f'{db_path.stem}_proc.h5',
]
# if aggregate_period_s <= 5: # [s], do not need split csv for big average interval
# args += (['--split_period', '1D'])
if aggregate_period_s is None: # proc. parameters (if we have saved proc. data then when aggregating we are not processing)
args += ([
'--max_dict',
'M[xyz]:4096',
# Note: for Baranov's prog 4096 is not suited
# '--time_range_zeroing_dict', "incl19: '2019-11-10T13:00:00', '2019-11-10T14:00:00'\n," # not works - use kwarg
# '--time_range_zeroing_list', '2019-08-26T04:00:00, 2019-08-26T05:00:00'
'--split_period',
'1D'
] if subs_made else [
'--bad_p_at_bursts_starts_peroiod',
'1H',
])
# csv splitted by 1day (default for no avg) and monolith csv if aggregate_period_s==600
if aggregate_period_s not in cfg['out'][
'aggregate_period_s_not_to_text']: # , 300, 600]:
args += ['--text_path', str(db_path.parent / 'text_output')]
kwarg = {
'in': {
'min_date': cfg['filter']['min_date'][0],
'max_date': cfg['filter']['max_date'][0],
'time_range_zeroing': cfg['in']['time_range_zeroing'],
'azimuth_add': azimuth_add
}
}
# If need all data to be combined one after one:
# set_field_if_no(kwarg, 'in', {})
# kwarg['in'].update({
#
# 'tables': [f'incl{i:0>2}' for i in min_date.keys() if i!=0],
# 'dates_min': min_date.values(), # in table list order
# 'dates_max': max_date.values(), #
# })
# set_field_if_no(kwarg, 'out', {})
# kwarg['out'].update({'b_all_to_one_col': 'True'})
incl_h5clc.main(args, **kwarg)
# Calculate spectrograms.
if st(3): # Can be done at any time after step 1
def raise_ni():
raise NotImplementedError(
'Can not proc probes having different fs in one run: you need to do it separately'
)
args = [
Path(incl_h5clc.__file__).with_name(
f'incl_h5spectrum{db_path.stem}.yaml'),
# if no such file all settings are here
'--db_path',
str(db_path.with_name(f'{db_path.stem}_proc_noAvg.h5')),
'--tables_list',
f"{cfg['in']['probes_prefix']}.*", # inclinometers or wavegauges w\d\d ## 'w02', 'incl.*',
# '--aggregate_period', f'{aggregate_period_s}S' if aggregate_period_s else '',
'--min_date',
datetime64_str(cfg['filter']['min_date'][0]),
'--max_date',
datetime64_str(cfg['filter']['max_date']
[0]), # '2019-09-09T16:31:00', #17:00:00
# '--max_dict', 'M[xyz]:4096', # use if db_path is not ends with _proc_noAvg.h5 i.e. need calc velocity
'--out.db_path',
f"{db_path.stem.replace('incl', cfg['in']['probes_prefix'])}_proc_psd.h5",
# '--table', f'psd{aggregate_period_s}' if aggregate_period_s else 'psd',
'--fs_float',
f"{fs(probes[0], cfg['in']['probes_prefix'])}",
# (lambda x: x == x[0])(np.vectorize(fs)(probes, prefix))).all() else raise_ni()
#
# '--time_range_zeroing_list', '2019-08-26T04:00:00, 2019-08-26T05:00:00'
# '--verbose', 'DEBUG',
# '--chunksize', '20000',
'--b_interact',
'0',
]
if 'w' in cfg['in']['probes_prefix']:
args += [
'--split_period',
'1H',
'--dt_interval_minutes',
'10', # burst mode
'--fmin',
'0.0001',
'--fmax',
'4'
]
else:
args += [
'--split_period',
'2H',
'--fmin',
'0.0004', #0.0004
'--fmax',
'1.05'
]
incl_h5spectrum.main(args)
# Draw in Veusz
if st(4):
b_images_only = True # False
pattern_path = db_path.parent / r'vsz_5min\191119_0000_5m_incl19.vsz' # r'vsz_5min\191126_0000_5m_w02.vsz'
if not b_images_only:
pattern_bytes_slice_old = re.escape(b'((5828756, 5830223, None),)')
# Length of not adjacent intervals, s (set None to not allow)
period = '1D'
length = '5m' # period # '1D'
dt_custom_s = pd_period_to_timedelta(
length) if length != period else None # None # 60 * 5
if True:
# Load starts and assign ends
t_intervals_start = pd.read_csv(
cfg['in']['path_cruise'] /
r'vsz+h5_proc\intervals_selected.txt',
converters={
'time_start': lambda x: np.datetime64(x, 'ns')
},
index_col=0).index
edges = (pd.DatetimeIndex(t_intervals_start),
pd.DatetimeIndex(t_intervals_start + dt_custom_s)
) # np.zeros_like()
else:
# Generate periodic intervals
t_interval_start, t_intervals_end = intervals_from_period(
datetime_range=np.array(
[
cfg['filter']['min_date']['0'],
cfg['filter']['max_date']['0']
],
# ['2018-08-11T18:00:00', '2018-09-06T00:00:00'],
# ['2019-02-11T13:05:00', '2019-03-07T11:30:00'],
# ['2018-11-16T15:19', '2018-12-14T14:35'],
# ['2018-10-22T12:30', '2018-10-27T06:30:00'],
'datetime64[s]'),
period=period)
edges = (pd.DatetimeIndex([t_interval_start
]).append(t_intervals_end[:-1]),
pd.DatetimeIndex(t_intervals_end))
for i, probe in enumerate(probes):
probe_name = f"{cfg['in']['probes_prefix']}{probe:02}" # table name in db
l.info('Draw %s in Veusz: %d intervals...', probe_name,
edges[0].size)
# for i_interval, (t_interval_start, t_interval_end) in enumerate(zip(pd.DatetimeIndex([t_interval_start]).append(t_intervals_end[:-1]), t_intervals_end), start=1):
cfg_vp = {'veusze': None}
for i_interval, (t_interval_start,
t_interval_end) in enumerate(zip(*edges),
start=1):
# if i_interval < 23: #<= 0: # TEMPORARY Skip this number of intervals
# continue
if period != length:
t_interval_start = t_interval_end - pd.Timedelta(
dt_custom_s, 's')
try: # skipping absent probes
start_end = h5q_interval2coord(
db_path=str(db_path),
table=f'/{probe_name}',
t_interval=(t_interval_start, t_interval_end))
if not len(start_end):
break # no data
except KeyError:
break # device name not in specified range, go to next name
pattern_path_new = pattern_path.with_name(
f"{t_interval_start:%y%m%d_%H%M}_{length}_{probe_name}.vsz"
)
# Modify pattern file
if not b_images_only:
probe_name_old = re.match('.*((?:incl|w)\d*).*',
pattern_path.name).groups()[0]
bytes_slice = bytes(
'(({:d}, {:d}, None),)'.format(*(start_end +
np.int32([-1, 1]))),
'ascii')
def f_replace(line):
"""
Replace in file
1. probe name
2. slice
"""
# if i_interval == 1:
line, ok = re.subn(bytes(probe_name_old, 'ascii'),
bytes(probe_name, 'ascii'), line)
if ok: # can be only in same line
line = re.sub(pattern_bytes_slice_old, bytes_slice,
line)
return line
if not rep_in_file(pattern_path,
pattern_path_new,
f_replace=f_replace):
l.warning('Veusz pattern not changed!')
# break
elif cfg_vp['veusze']:
cfg_vp['veusze'].Load(str(pattern_path_new))
elif cfg_vp['veusze']:
cfg_vp['veusze'].Load(str(pattern_path_new))
txt_time_range = \
"""
"[['{:%Y-%m-%dT%H:%M}', '{:%Y-%m-%dT%H:%M}']]" \
""".format(t_interval_start, t_interval_end)
print(f'{i_interval}. {txt_time_range}', end=' ')
cfg_vp = veuszPropagate.main(
[
Path(veuszPropagate.__file__).parent.with_name(
'veuszPropagate.ini'),
# '--data_yield_prefix', '-',
'--path',
str(
db_path
), # use for custom loading from db and some source is required
'--tables_list',
f'/{probe_name}', # 181022inclinometers/ \d*
'--pattern_path',
str(pattern_path_new),
# fr'd:\workData\BalticSea\190801inclinometer_Schuka\{probe_name}_190807_1D.vsz',
# str(db_path.parent / dir_incl / f'{probe_name}_190211.vsz'), #warning: create file with small name
# '--before_next', 'restore_config',
# '--add_to_filename', f"_{t_interval_start:%y%m%d_%H%M}_{length}",
'--filename_fun',
f'lambda tbl: "{pattern_path_new.name}"',
'--add_custom_list',
'USEtime', # nAveragePrefer',
'--add_custom_expressions_list',
txt_time_range,
# + """
# ", 5"
# """,
'--b_update_existed',
'True',
'--export_pages_int_list',
'1, 2', # 0 for all '6, 7, 8', #'1, 2, 3'
# '--export_dpi_int', '200',
'--export_format',
'emf',
'--b_interact',
'0',
'--b_images_only',
f'{b_images_only}',
'--return',
'<embedded_object>', # reuse to not bloat memory
],
veusze=cfg_vp['veusze'])
def h5_velocity_by_intervals_gen(
cfg: Mapping[str, Any],
cfg_out: Mapping[str, Any]) -> Iterator[Tuple[str, Tuple[Any, ...]]]:
"""
Loads data and calculates velocity: many intervals from many of hdf5 tables sequentially.
:param cfg: dict with fields:
['proc']['dt_interval'] - numpy.timedelta64 time interval of loading data
one group of fields:
1. 'split_period', pandas interval str, as required by intervals_from_period() to cover all data by it
'overlap'
2. 'time_intervals_start' - manually specified starts of intercals
:param cfg_out: fields must be provided:
- see h5_names_gen(cfg_in, cfg_out) requirements
:return:
"""
# Prepare cycle
if cfg_out.get('split_period'):
def gen_loaded(tbl):
"""
Variant 1. Generate regular intervals (may be with overlap)
:param tbl:
:return:
"""
cfg['in']['table'] = tbl
# To obtain ``t_intervals_start`` used in query inside gen_data_on_intervals(cfg_out, cfg)
# we copy its content here:
t_prev_interval_start, t_intervals_start = intervals_from_period(
**cfg['in'], period=cfg_out['split_period'])
if cfg['proc']['overlap']:
dt_shifts = np.arange(
0, 1,
(1 - cfg['proc']['overlap'])) * pd_period_to_timedelta(
cfg_out['split_period'])
t_intervals_start = (t_intervals_start.to_numpy(
dtype="datetime64[ns]")[np.newaxis].T +
dt_shifts).flatten()
if cfg['in']['max_date']:
idel = t_intervals_start.searchsorted(
np.datetime64(
cfg['in']['max_date'] -
pd_period_to_timedelta(cfg_out['split_period'])))
t_intervals_start = t_intervals_start[:idel]
cfg['in'][
'time_intervals_start'] = t_intervals_start # to save queried time - see main()
cfg_filter = None
cfg_in_columns_saved = cfg['in']['columns']
for start_end in h5q_starts2coord(
cfg['in']['db_path'],
cfg['in']['table'],
t_intervals_start,
dt_interval=cfg['proc']['dt_interval']):
a = h5_load_range_by_coord(**cfg['in'],
range_coordinates=start_end)
if cfg_filter is None: # only 1 time
# corrects columns if they are not exact mutch to faster h5_load_range_by_coord() next time
cfg['in']['columns'] = a.columns # temporary
# and exclude absent fields to not filter warning of no such column in filt_data_dd()
detect_filt = f"m(ax|in)_({'|'.join(cfg['in']['columns'])})"
cfg_filter = {
k: v
for k, v in cfg['filter'].items()
if re.match(detect_filt, k)
}
d, i_burst = filt_data_dd(a, cfg['in']['dt_between_bursts'],
cfg['in']['dt_hole_warning'],
cfg_filter)
n_bursts = len(i_burst)
if n_bursts > 1: # 1st is always 0
l.info('gaps found: (%s)! at %s', n_bursts - 1,
i_burst[1:] - 1)
df0 = d.compute()
if not len(df0):
continue
start_end = df0.index[[0, -1]].values
yield df0, start_end
cfg['in'][
'columns'] = cfg_in_columns_saved # recover to not affect next file
else:
query_range_pattern = "index>=Timestamp('{}') & index<=Timestamp('{}')"
def gen_loaded(tbl):
"""
Variant 2. Generate intervals at specified start values with same width cfg['proc']['dt_interval']
:param tbl:
:return:
"""
for start_end in zip(
cfg['in']['time_intervals_start'],
cfg['in']['time_intervals_start'] +
cfg['proc']['dt_interval']):
query_range_lims = pd.to_datetime(start_end)
qstr = query_range_pattern.format(*query_range_lims)
l.info(f'query:\n%s... ', qstr)
df0 = store.select(tbl, where=qstr, columns=None)
yield df0, start_end
dt_interval_in_its_units = cfg['proc']['dt_interval'].astype(int)
dt_interval_units = np.datetime_data(cfg['proc']['dt_interval'])[0]
data_name_suffix = f'{dt_interval_in_its_units}{dt_interval_units}'
# Cycle
with pd.HDFStore(cfg['in']['db_path'], mode='r') as store:
for (tbl, coefs) in h5_names_gen(cfg['in'], cfg_out):
# Get data in ranges
for df0, start_end in gen_loaded(tbl):
if cfg['in']['db_path'].stem.endswith('proc_noAvg'):
df = df0
else: # loading source data needed to be processed to calc velocity
df0 = filter_local(df0, cfg['filter'])
df = incl_calc_velocity_nodask(df0,
**coefs,
cfg_filter=cfg['in'],
cfg_proc=cfg['proc'])
data_name = f'{tbl}/PSD_{start_end[0]}{data_name_suffix}'
yield (df, tbl, data_name)