Modules#

hatyan.cli module#

Console script for hatyan.

hatyan --help

hatyan.hatyan_core module#

hatyan_core.py contains core components that wrap around schureman/foreman definitions

hatyan.hatyan_core.get_const_list_hatyan(listtype)[source]#

Definition of several hatyan components lists, taken from the tidegui initializetide.m code, often originating from corresponcence with Koos Doekes

Parameters#

listtypestr

The type of the components list to be retrieved, options:

‘all’: all available components in hatyan_python
‘all_originalorder’: all 195 hatyan components in original hatyan-FORTRAN order
‘year’: default list of 94 hatyan components
‘halfyear’: list of 88 components to be used when analyzing approximately half a year of data
‘month’: list of 21 components to be used when analyzing one month of data. If desired, the K1 component can be splitted in P1/K1, N2 in N2/Nu2, S2 in T2/S2/K2 and 2MN2 in Labda2/2MN2.
‘month_deepwater’: list of 21 components to be used when analyzing one month of data for deep water (from tidegui).
‘springneap’: list of 14 components to be used when analyzing one spring neap period (approximately 15 days) of data
‘day’: list of 10 components to be used when analyzing one day
‘day_tidegui’: list of 5 components to be used when analyzing one day ir two tidal cycles (from tidegui)
‘tidalcycle’: list of 6 components to be used when analyzing one tidal cycle (approximately 12 hours and 25 minutes)

Raises#

Exception: DESCRIPTION.

Returns#

const_list_hatyanlist of str: A list of component names.

hatyan.analysis_prediction module#

analysis_prediction.py contains hatyan definitions related to tidal analysis and prediction.

hatyan.analysis_prediction.analysis(ts, const_list, nodalfactors=True, fu_alltimes=True, xfac=False, source='schureman', cs_comps=None, analysis_perperiod=False, return_allperiods=False, max_matrix_condition=12)[source]#

Analysis of timeseries. Optionally processes a timeseries per year and vector averages the results afterwards. The timezone of the timeseries, will also be reflected in the phases of the resulting component set, so the resulting component set can be used to make a prediction in the original timezone.

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries to be analysed, as obtained from e.g. readts_*.
const_listlist, pandas.Series or str: list or pandas.Series: contains the tidal constituent names for which to analyse the provided timeseries ts. str: a predefined name of a component set for hatyan_core.get_const_list_hatyan()
nodalfactorsbool: Whether or not to apply nodal factors. The default is True.
fu_alltimesbool: Whether to calculate nodal factors in middle of the analysis/prediction period (default) or on every timestep. The default is True.
xfacbool: Whether or not to apply x-factors. The default is False.
sourceTYPE: DESCRIPTION. The default is ‘schureman’.
cs_compspandas.DataFrame, optional: contains the from/derive component lists for components splitting, as well as the amplitude factor and the increase in degrees. Only relevant for analysis. The default is None.
max_matrix_condition: float or int: the maximum condition of the xTx matrix. The default is 12.
analysis_perperiodFalse or Y/Q/W, optional: caution, it tries to analyse each year/quarter/month, but skips if it fails. The default is False.
return_allperiodsbool, optional: Only relevant if analysis_perperiod is not None. The default is False.

Returns#

COMP_mean_pdpandas.DataFrame: The DataFrame contains the component data with component names as index, and colums ‘A’ and ‘phi_deg’.
COMP_all_pdpandas.DataFrame, optional: The same as COMP_mean_pd, but with all years added with MultiIndex

hatyan.analysis_prediction.prediction(comp, times=None, timestep=None)[source]#

generates a tidal prediction from a set of components A and phi values. The component set has the same timezone as the timeseries used to create it. If times is timezone-naive the resulting prediction will be in component timezone. If times is timezone-aware the resulting prediction will be converted to that timezone. If a components dataframe contains multiple column levels (multiple periods), The prediction is a concatenation of predictions of all periods (based on the respective A/phi values).

Parameters#

comppd.DataFrame: The DataFrame contains the component data with component names as index, and colums ‘A’ and ‘phi_deg’.
times(pd.DatetimeIndex,slice), optional: pd.DatetimeIndex with prediction timeseries or slice(tstart,stop,timestep) to construct it from. If None, pd.DatetimeIndex is constructed from the tstart/tstop/timestep metadata attrs of the comp object. Only allowed/relevant for component dataframes with single-level columns (single period). The default is None.
timestepstr: Only allowed/relevant for component dataframes with multi-level columns (different periods). The string is parsed with pandas.tseries.frequencies.to_offset(). The default is None.

Returns#

ts_predictionTYPE: The DataFrame contains a ‘values’ column and a pd.DatetimeIndex as index, it contains the prediction times and values.

hatyan.components module#

components.py contains all the definitions related to hatyan components.

hatyan.components.merge_componentgroups(comp_main, comp_sec)[source]#

Merges the provided component groups into one

Parameters#

comp_mainpd.DataFrame: The reference component dataframe (with A/phi columns).
comp_secpd.DataFrame: The dataframe with the components that will be used to overwrite the components in comp_main.

Returns#

comp_mergedpd.DataFrame: The merged dataframe, a copy of comp_main with all components from comp_sec overwritten.

hatyan.components.plot_components(comp, comp_allperiods=None, comp_validation=None, sort_freqs=True)[source]#

Create a plot with the provided analysis results

Parameters#

compTYPE: DESCRIPTION.
comp_allyearsTYPE, optional: DESCRIPTION. The default is None.
comp_validationTYPE, optional: DESCRIPTION. The default is None.
sort_freqsBOOL, optional: Whether to sort the component list on frequency or not, without sorting it is possible to plot components that are not available in hatyan. The default is True.

Returns#

figmatplotlib.figure.Figure: The generated figure handle, with which the figure can be adapted and saved.
axs(tuple of) matplotlib.axes._subplots.AxesSubplot: The generated axis handle, whith which the figure can be adapted.

hatyan.components.read_components(filename)[source]#

Reads analysis results from a file.

Parameters#

filenameTYPE: DESCRIPTION.

Raises#

Exception: DESCRIPTION.

Returns#

TYPE: DESCRIPTION.

hatyan.components.write_components(comp, filename)[source]#

Writes the provided analysis results to a file

Parameters#

compTYPE: DESCRIPTION.
filenameTYPE: DESCRIPTION.
metadataTYPE, optional: DESCRIPTION. The default is None.

Returns#

None.

hatyan.timeseries module#

timeseries.py contains all definitions related to hatyan timeseries.

class hatyan.timeseries.Timeseries_Statistics(ts)[source]#

Bases: object

returns several statistics of the provided timeseries as a Timeseries_Statistics class, which is a like a dict that pretty prints automatically.

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries to be checked.

Returns#

stats: class Timeseries_Statistics: Timeseries_Statistics is a like a dict that pretty prints automatically.

hatyan.timeseries.calc_HWLW(ts, calc_HWLW345=False, buffer_hr=6)[source]#

Calculates extremes (high and low waters) for the provided timeseries. This definition uses scipy.signal.find_peaks() with arguments ‘distance’ and ‘prominence’. The minimal ‘distance’ between two high or low water peaks is based on the M2 period: 12.42/1.5=8.28 hours for HW and 12.42/1.7=7.30 hours for LW (larger because of aggers). The prominence for local extremes is set to 0.01m, to filter out very minor dips in the timeseries. If there are two equal high or low water values, the first one is taken. There are no main high/low waters calculated within 6 hours of the start/end of the timeseries (keyword buffer_hr), since these can be invalid. Since scipy.signal.find_peaks() warns about nan values, those are removed first. Nans/gaps can influence the results since find_peaks does not know about time registration. This is also tricky for input timeseries with varying time interval.

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries with a tidal prediction or water level measurements.
calc_HWLW345boolean, optional: Whether to also calculate local extremes, first/second low waters and ‘aggers’. The default is False, in which case only extremes per tidal period are calculated. When first/second low waters and aggers are calculated, the local extremes around highwater (eg double highwaters and dips) are filtered out first.

Returns#

data_pd_HWLWpandas.DataFrame: The DataFrame contains colums ‘times’, ‘values’ and ‘HWLWcode’, it contains the times, values and codes of the timeseries that are extremes. 1 (high water) and 2 (low water). And if calc_HWLW345=True also 3 (first low water), 4 (agger) and 5 (second low water).

hatyan.timeseries.calc_HWLW12345to12(data_HWLW_12345)[source]#

Parameters#

data_HWLW12345TYPE: DESCRIPTION.

Returns#

None.

hatyan.timeseries.calc_HWLWnumbering(ts_ext, station=None, doHWLWcheck=True)[source]#

For calculation of the extremes numbering, w.r.t. the first high water at Cadzand in 2000 (occurred on 1-1-2000 at approximately 9:45). The number of every high and low water is calculated by taking the time difference between itself and the first high water at Cadzand, correcting it with the station phase difference (M2phasediff). Low waters are searched for half an M2 period from the high waters. By adding a search window of half the period of M2 (searchwindow_hr), even strong time variance between consecutive high or low waters should be caputered.

Parameters#

ts_extpandas.DataFrame: The DataFrame should contain a ‘values’ and ‘HWLWcode’ column and a pd.DatetimeIndex as index, it contains the times, values and codes of the timeseries that are extremes.
station: string, optional: The station for which the M2 phase difference should be retrieved from data_M2phasediff_perstation.txt. This value is the phase difference in degrees of the occurrence of the high water generated by the same tidal wave as the first high water in 2000 at Cadzand (actually difference between M2 phases of stations). This value is used to correct the search window of high/low water numbering. The default is None. Providing a value will result in a proper HWLWno, corresponing to CADZD. Providing None will result in a HWLWno that is a multiple of 360degrees/M2_period_hr off (positive or negative). This is only an issue when comparing different stations, not comparing e.g. measured and predicted HW values of one station.

Raises#

Exception: DESCRIPTION.

Returns#

ts_extpandas.DataFrame: The input DataFrame with the column ‘HWLWno’ added, which contains the numbers of the extremes.

hatyan.timeseries.crop_timeseries(ts, times, onlyfull=True)[source]#

Crops the provided timeseries

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries.
timesslice: slice(tstart,tstop).

Raises#

Exception: DESCRIPTION.

Returns#

ts_pd_outTYPE: DESCRIPTION.

hatyan.timeseries.get_diaxycoords(filename, crs)[source]#

hatyan.timeseries.plot_HWLW_validatestats(ts_ext, ts_ext_validation)[source]#

This definition calculates (and plots and prints) some statistics when comparing extreme values. This is done by calculating the extreme number (sort of relative to Cadzand 1jan2000, but see ‘warning’) and subtracting the ts_ext and ts_ext_validation dataframes based on these numbers (and HWLWcode). It will only result in values for the overlapping extremes, other values will be NaN and are not considered for the statistics. Warning: the calculated extreme numbers in this definition are not corrected for the real phase difference with the M2phasediff argument, the calculated extreme are fine for internal use (to match corresponding extremes) but the absolute number might be incorrect.

Parameters#

ts_extpandas.DataFrame: The DataFrame should contain a ‘values’ and ‘HWLW_code’ column and a pd.DatetimeIndex as index, it contains the times, values and codes of the timeseries that are extremes.
ts_ext_validationpandas.DataFrame: The DataFrame should contain a ‘values’ and ‘HWLW_code’ column and a pd.DatetimeIndex as index, values and codes of the timeseries that are extremes.

Returns#

figmatplotlib.figure.Figure: The generated figure handle, with which the figure can be adapted and saved.
axs(tuple of) matplotlib.axes._subplots.AxesSubplot: The generated axis handle, whith which the figure can be adapted.

hatyan.timeseries.plot_timeseries(ts, ts_validation=None, ts_ext=None, ts_ext_validation=None)[source]#

Creates a plot with the provided timeseries

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries.
ts_validationpandas.DataFrame, optional: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries. The default is None.
ts_extpandas.DataFrame, optional: The DataFrame should contain a ‘values’ and ‘HWLW_code’ column and a pd.DatetimeIndex as index, it contains the times, values and codes of the timeseries that are extremes. The default is None.
ts_ext_validationpandas.DataFrame, optional: The DataFrame should contain a ‘values’ and ‘HWLW_code’ column and a pd.DatetimeIndex as index, it contains the times, values and codes of the timeseries that are extremes. The default is None.

Returns#

figmatplotlib.figure.Figure: The generated figure handle, with which the figure can be adapted and saved.
axs(tuple of) matplotlib.axes._subplots.AxesSubplot: The generated axis handle, whith which the figure can be adapted.

hatyan.timeseries.read_dia(filename, station=None, block_ids=None, allow_duplicates=False)[source]#

Reads an equidistant or non-equidistant dia file, or a list of dia files. Also works for diafiles containing multiple blocks for one station.

Parameters#

filenameTYPE: DESCRIPTION.
stationTYPE: DESCRIPTION. The default is None.
block_idsint, list of int or ‘allstation’, optional: DESCRIPTION. The default is None.

Returns#

data_pdpandas.core.frame.DataFrame: DataFrame with a ‘values’ column and a pd.DatetimeIndex as index in case of an equidistant file, or more columns in case of a non-equidistant file.

hatyan.timeseries.read_noos(filename, datetime_format='%Y%m%d%H%M', na_values=None)[source]#

Reads a noos file

Parameters#

filenameTYPE: DESCRIPTION.
datetime_formatTYPE, optional: DESCRIPTION. The default is ‘%Y%m%d%H%M’.
na_valuesTYPE, optional: DESCRIPTION. The default is None.

Returns#

data_pdTYPE: DESCRIPTION.

hatyan.timeseries.resample_timeseries(ts, timestep_min, tstart=None, tstop=None)[source]#

resamples the provided timeseries, only overlapping timesteps are selected, so no interpolation. with tstart/tstop it is possible to extend the timeseries with NaN values.

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ and ‘HWLW_code’ column and a pd.DatetimeIndex as index, it contains the timeseries to be resampled.
timestep_minint: the amount of minutes with which to resample the timeseries.
tstartdt.datetime, optional: the start date for the resampled timeseries, the default is None which results in using the start date of the input ts.
tstopdt.datetime, optional: the stop date for the resampled timeseries, the default is None which results in using the stop date of the input ts.

Returns#

data_pd_resamplepandas.DataFrame with a ‘values’ column and a pd.DatetimeIndex as index: the resampled timeseries.

hatyan.timeseries.timeseries_fft(ts_residue, min_prominence=1000, max_freqdiff=None, plot_fft=True, source='schureman')[source]#

hatyan.timeseries.write_dia(ts, filename, headerformat='dia')[source]#

Writes the timeseries to an equidistant dia file or the extremes to a non-equidistant dia file. This is only supported for timeseries with a UTC+1 timestamp, since DONAR (and therefore dia) data is always in UTC+1 (MET/CET), also during summertime periods.

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries. In case of extremes, the DataFrame should also contain a ‘HWLW_code’ column.
stationTYPE: DESCRIPTION.
vertrefTYPE: DESCRIPTION.
filenameTYPE: DESCRIPTION.

Raises#

Exception: DESCRIPTION.

Returns#

None.

hatyan.timeseries.write_netcdf(ts, filename, ts_ext=None, nosidx=False, mode='w')[source]#

Writes the timeseries to a netCDF file

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries.
stationstr: DESCRIPTION.
vertrefstr: DESCRIPTION.
filenamestr: The filename of the netCDF file that will be written.
ts_extpandas.DataFrame, optional: The DataFrame should contain a ‘values’ and ‘HWLW_code’ column and a pd.DatetimeIndex as index, it contains the times, values and codes of the timeseries that are extremes. The default is None.
tzone_hrint, optional: The timezone (GMT+tzone_hr) that applies to the data. The default is 1 (MET).

Returns#

None.

hatyan.timeseries.write_noos(ts, filename)[source]#

Writes the timeseries to a noos file

Parameters#

tspandas.DataFrame: The DataFrame should contain a ‘values’ column and a pd.DatetimeIndex as index, it contains the timeseries.
filenameTYPE: DESCRIPTION.

Returns#

None.

hatyan.ddlpy_helpers module#

ddlpy_helpers.py contains functions to convert ddlpy timeseries dataframes to hatyan timeseries dataframes. The package ddlpy is available at Deltares/ddlpy

hatyan.ddlpy_helpers.ddlpy_to_hatyan(ddlpy_meas, ddlpy_meas_exttyp=None)[source]#

Convert ddlpy measurements to hatyan timeseries dataframe.

Parameters#

ddlpy_measpd.DataFrame: ddlpy measurements dataframe. It is assumed that it contains numeric values that represent waterlevel timeseries or waterlevel extremes (measured or astro).
ddlpy_meas_exttyppd.DataFrame, optional: ddlpy measurements dataframe. If it is supplied it is assumed that it contains alfanumeric values and these represent tidal extreme types (high- and low waters). The default is None.

Returns#

pd.DataFrame: hatyan timeseries DataFrame with values/qualitycode/status columns. If ddlpy_meas_typ is supplied, this DataFrame will also include a HWLWcode column.

hatyan.astrog module#

astrog.py contains all astro-related definitions, previously embedded in a separate program but now part of hatyan.

hatyan.astrog.astrog_anomalies(tFirst, tLast, dT_fortran=False)[source]#

Makes use of the definitions dT, astrab and astrac. Calculates lunar anomalies. The lunar anomalies are independent of coordinates.

Parameters#

tFirstdatetime.datetime or string (“yyyymmdd”): Start of timeframe for output.
tLastdatetime.datetime or string (“yyyymmdd”): End of timeframe for output.
dT_fortranboolean, optional: Reproduce fortran difference between universal time and terrestrial time (dT). Can be True (for latest fortran reproduction) or False (international definition). The default is False.

Raises#

Exception: Checks input times tFirst and tLast.

Returns#

astrog_dfpandas DataFrame: datetime: lunar anomaly in UTC (datetime) type: type of anomaly (1=perigeum, 2=apogeum)

hatyan.astrog.astrog_culminations(tFirst, tLast, dT_fortran=False)[source]#

Makes use of the definitions dT, astrab and astrac. Calculates lunar culminations, parallax and declination. By default the lunar culmination is calculated at coordinates lon=0 (Greenwich), since EHMOON is used to calculate it. Possible to add lon-correction at end of definition.

Parameters#

tFirstpd.Timestamp, datetime.datetime or string (“yyyymmdd”): Start of timeframe for output.
tLastpd.Timestamp, datetime.datetime or string (“yyyymmdd”): End of timeframe for output.
dT_fortranboolean, optional: Reproduce fortran difference between universal time and terrestrial time (dT). Can be True (for latest fortran reproduction) or False (international definition). The default is False.

Raises#

Exception: Checks input times tFirst and tLast.

Returns#

astrog_dfpandas DataFrame: datetime: lunar culmination at Greenwich in UTC (datetime) type: type of culmination (1=lower, 2=upper) parallax: lunar parallax (degrees) declination: lunar declination (degrees)

hatyan.astrog.astrog_moonriseset(tFirst, tLast, dT_fortran=False, lon=5.3876, lat=52.1562)[source]#

Makes use of the definitions dT, astrab and astrac. Calculates moonrise and -set at requested location.

Parameters#

tFirstdatetime.datetime or string (“yyyymmdd”): Start of timeframe for output.
tLastdatetime.datetime or string (“yyyymmdd”): End of timeframe for output.
dT_fortranboolean, optional: Reproduce fortran difference between universal time and terrestrial time (dT). Can be True (for latest fortran reproduction) or False (international definition). The default is False.
lonfloat, optional: Longitude, defined positive eastward. The default is -5.3876 (Amersfoort).
latfloat, optional: Latitude, defined positive northward, cannot exceed 59 (too close to poles). The default is 52.1562 (Amersfoort).

Raises#

Exception: Checks input times tFirst and tLast.

Returns#

astrog_dfpandas DataFrame: datetime: time of rise or set in UTC (datetime) type: type (1=moonrise, 2=moonset)

hatyan.astrog.astrog_phases(tFirst, tLast, dT_fortran=False)[source]#

Makes use of the definitions dT, astrab and astrac. Calculates lunar phases. The lunar phases are independent of coordinates.

Parameters#

tFirstdatetime.datetime or string (“yyyymmdd”): Start of timeframe for output.
tLastdatetime.datetime or string (“yyyymmdd”): End of timeframe for output.
dT_fortranboolean, optional: Reproduce fortran difference between universal time and terrestrial time (dT). Can be True (for latest fortran reproduction) or False (international definition). The default is False.

Raises#

Exception: Checks input times tFirst and tLast.

Returns#

astrog_dfpandas DataFrame: datetime: lunar phase in UTC (datetime) type: type of phase (1=FQ, 2=FM, 3=LQ, 4=NM)

hatyan.astrog.astrog_seasons(tFirst, tLast, dT_fortran=False)[source]#

Makes use of the definitions dT, astrab and astrac. Calculates astronomical seasons. The seasons are independent of coordinates.

Parameters#

tFirstdatetime.datetime or string (“yyyymmdd”): Start of timeframe for output.
tLastdatetime.datetime or string (“yyyymmdd”): End of timeframe for output.
dT_fortranboolean, optional: Reproduce fortran difference between universal time and terrestrial time (dT). Can be True (for latest fortran reproduction) or False (international definition). The default is False.

Raises#

Exception: Checks input times tFirst and tLast.

Returns#

astrog_dfpandas DataFrame: datetime: start of astronomical season in UTC (datetime) type: type of astronomical season (1=spring, 2=summer, 3=autumn, 4=winter)

hatyan.astrog.astrog_sunriseset(tFirst, tLast, dT_fortran=False, lon=5.3876, lat=52.1562)[source]#

Makes use of the definitions dT, astrab and astrac. Calculates sunrise and -set at requested location.

Parameters#

tFirstdatetime.datetime or string (“yyyymmdd”): Start of timeframe for output.
tLastdatetime.datetime or string (“yyyymmdd”): End of timeframe for output.
dT_fortranboolean, optional: Reproduce fortran difference between universal time and terrestrial time (dT). Can be True (for latest fortran reproduction) or False (international definition). The default is False.
lonfloat, optional: Longitude, defined positive eastward. The default is -5.3876 (Amersfoort).
latfloat, optional: Latitude, defined positive northward, cannot exceed 59 (too close to poles). The default is 52.1562 (Amersfoort).

Raises#

Exception: Checks input times tFirst and tLast.

Returns#

astrog_dfpandas DataFrame: datetime: time of rise or set in UTC (datetime) type: type (1=sunrise, 2=sunset)

hatyan.astrog.convert2perday(dataframeIn, timeformat='%H:%M %Z')[source]#

converts normal astrog pd.DataFrame to one with the same information restructured per day

Parameters#

dataframeInpd.DataFrame: with columns ‘datetime’ and ‘type_str’.
timeformatstr, optional: format of the timestrings in dataframeOut. The default is ‘%H:%M %Z’.

Returns#

dataframeOutpd.DataFrame: The ‘datetime’ column contains dates, with columns containing all unique ‘type_str’ values.

hatyan.astrog.plot_astrog_diff(pd_python, pd_fortran, typeCol='type', typeUnit='-', typeLab=None, typeBand=None, timeBand=None)[source]#

Plots results of FORTRAN and python verison of astrog for visual inspection. Top plot shows values or type, middle plot shows time difference, bottom plot shows value/type difference.

Parameters#

pd_pythonpandas DataFrame: DataFrame from astrog (python) with times (UTC).
pd_fortranpandas DataFrame: DataFrame from astrog (FORTRAN) with times (UTC).
typeUnitstring, optional: Unit of provided values/types. The default is ‘-‘.
typeLabTYPE, optional: Labels of provided types. The default is [‘rise’,’set’].
typeBandlist of floats, optional: Expected bandwith of accuracy of values/types. The default is [-.5,.5].
timeBandlist of floats, optional: Expected bandwith of accuracy of times (seconds). The default is [0,60].
timeLimlist of floats, optional: Time limits of x-axis. The default is None (takes limits from pd_python).

Returns#

figfigure handle: Output figure.
axsaxis handles: Axes in figure.