tstoolbox.tstoolbox.filter

tstoolbox.tstoolbox.filter(filter_types, filter_pass, butterworth_order=10, lowpass_cutoff=None, highpass_cutoff=None, window_len=3, pad_mode='reflect', input_ts='-', start_date=None, end_date=None, columns=None, dropna='no', skiprows=None, index_type='datetime', names=None, clean=False, print_input=False, source_units=None, target_units=None, round_index=None, float_format='g', tablefmt='csv')

Apply different filters to the time-series.

Parameters:

• filter_types (Union[Literal['bartlett', 'blackman', 'butterworth', 'fft', 'flat', 'hamming', 'hanning', 'kalman', 'lecolazet1', 'lecolazet2', 'tide_doodson', 'tide_fft', 'tide_usgs'], List[Literal['bartlett', 'blackman', 'butterworth', 'fft', 'flat', 'hamming', 'hanning', 'kalman', 'lecolazet1', 'lecolazet2', 'tide_doodson', 'tide_fft', 'tide_usgs']]]) –

  One or more of bartlett, blackman, butterworth, fft, flat, hamming, hanning, kalman, lecolazet1, lecolazet2, tide_doodson, tide_fft, tide_usgs

  The “fft” and “butterworth” types are configured by cutoff frequencies lowpass_cutoff, and highpass_cutoff, by process defined in filter_pass. The “fft” is the Fast Fourier Transform filter in the frequency domain.

  Doodson filter

  The Doodson X0 filter is a simple filter designed to damp out the main tidal frequencies. It takes hourly values, 19 values either side of the central one. A weighted average is taken with the following weights

  (1010010110201102112 0 2112011020110100101)/30.

  In “Data Analysis and Methods in Oceanography”:

  ”The cosine-Lanczos filter, the transform filter, and the Butterworth filter are often preferred to the Godin filter, to earlier Doodson filter, because of their superior ability to remove tidal period variability from oceanic signals.”

• filter_pass (Literal['lowpass', 'highpass', 'bandpass', 'bandstop']) – OneOf(“lowpass”, “highpass”, “bandpass”, “bandstop”) Indicates what frequencies to block for the “fft” and “butterworth” filters.

• butterworth_order (int) –

  [optional, default is 10]

  The order of the butterworth filter.

• lowpass_cutoff (Optional[float]) –

  [optional, default is None, used only if filter is “fft” or “butterworth” and required if filter_pass equals “lowpass”, “bandpass” or “bandstop”]

  The low frequency cutoff when filter_pass equals “lowpass”, “bandpass”, or “bandstop”.

• highpass_cutoff (Optional[float]) –

  [optional, default is None, used only if filter is “fft” or “butterworth” and required if filter_pass equals “highpass”, “bandpass” or “bandstop”]

  The high frequency cutoff when filter_pass equals “highpass”, “bandpass”, or “bandstop”.

• window_len (int) –

  [optional, default is 3]

  ”flat”, “hanning”, “hamming”, “bartlett”, “blackman” Time-series is padded by one half the window length on each end. The window_len is then used for the length of the convolution kernel.

  ”fft” Will soften the edges of the “fft” filter in the frequency domain. The larger the number the softer the filter edges. A value of 1 will have a brick wall step function which may introduce frequencies into the filtered output.

  ”tide_usgs”, “tide_doodson” The window_len is set to 33 for “tide_usgs” and 39 for “tide_doodson”.

• pad_mode (Optional[Literal['edge', 'maximum', 'mean', 'median', 'minimum', 'reflect', 'symmetric', 'wrap']]) –

  [optional, default is “reflect”]

  The method used to pad the time-series. Uses some of the methods in numpy.pad.

  The pad methods “edge”, “maximum”, “mean”, “median”, “minimum”, “reflect”, “symmetric”, “wrap” are available because they require no extra arguments.

• input_ts (str) –

  [optional though required if using within Python, default is ‘-’ (stdin)]

  Whether from a file or standard input, data requires a single line header of column names. The default header is the first line of the input, but this can be changed for CSV files using the ‘skiprows’ option.

  Most common date formats can be used, but the closer to ISO 8601 date/time standard the better.

  Comma-separated values (CSV) files or tab-separated values (TSV):

  File separators will be automatically detected.
  
  Columns can be selected by name or index, where the index for
  data columns starts at 1.
  

  Command line examples:

  Keyword Example	Description
  –input_ts=fn.csv	read all columns from ‘fn.csv’
  –input_ts=fn.csv,2,1	read data columns 2 and 1 from ‘fn.csv’
  –input_ts=fn.csv,2,skiprows=2	read data column 2 from ‘fn.csv’, skipping first 2 rows so header is read from third row
  –input_ts=fn.xlsx,2,Sheet21	read all data from 2nd sheet all data from “Sheet21” of ‘fn.xlsx’
  –input_ts=fn.hdf5,Table12,T2	read all data from table “Table12” then all data from table “T2” of ‘fn.hdf5’
  –input_ts=fn.wdm,210,110	read DSNs 210, then 110 from ‘fn.wdm’
  –input_ts=’-’	read all columns from standard input (stdin)
  –input_ts=’-’ –columns=4,1	read column 4 and 1 from standard input (stdin)

  If working with CSV or TSV files you can use redirection rather than use –input_ts=fname.csv. The following are identical:

  From a file:

  command subcmd –input_ts=fname.csv

  From standard input (since ‘–input_ts=-’ is the default:

  command subcmd < fname.csv

  Can also combine commands by piping:

  command subcmd < filein.csv | command subcmd1 > fileout.csv

  Python library examples:

  You must use the `input_ts=...` option where `input_ts` can be
  one of a [pandas DataFrame, pandas Series, dict, tuple, list,
  StringIO, or file name].
  

• start_date (str) –

  [optional, defaults to first date in time-series, input filter]

  The start_date of the series in ISOdatetime format, or ‘None’ for beginning.

• end_date (str) –

  [optional, defaults to last date in time-series, input filter]

  The end_date of the series in ISOdatetime format, or ‘None’ for end.

• columns –

  [optional, defaults to all columns, input filter]

  Columns to select out of input. Can use column names from the first line header or column numbers. If using numbers, column number 1 is the first data column. To pick multiple columns; separate by commas with no spaces. As used in toolbox_utils pick command.

  This solves a big problem so that you don’t have to create a data set with a certain column order, you can rearrange columns when data is read in.

• dropna (str) –

  [optional, defauls it ‘no’, input filter]

  Set dropna to ‘any’ to have records dropped that have NA value in any column, or ‘all’ to have records dropped that have NA in all columns. Set to ‘no’ to not drop any records. The default is ‘no’.

• skiprows (list-like or integer or callable) –

  [optional, default is None which will infer header from first line, input filter]

  Line numbers to skip (0-indexed) if a list or number of lines to skip at the start of the file if an integer.

  If used in Python can be a callable, the callable function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be

  lambda x: x in [0, 2].

• index_type (str) –

  [optional, default is ‘datetime’, output format]

  Can be either ‘number’ or ‘datetime’. Use ‘number’ with index values that are Julian dates, or other epoch reference.

• names (str) –

  [optional, default is None, transformation]

  If None, the column names are taken from the first row after ‘skiprows’ from the input dataset.

  MUST include a name for all columns in the input dataset, including the index column.

• clean –

  [optional, default is False, input filter]

  The ‘clean’ command will repair a input index, removing duplicate index values and sorting.

• print_input –

  [optional, default is False, output format]

  If set to ‘True’ will include the input columns in the output table.

• source_units (str) –

  [optional, default is None, transformation]

  If unit is specified for the column as the second field of a ‘:’ delimited column name, then the specified units and the ‘source_units’ must match exactly.

  Any unit string compatible with the ‘pint’ library can be used.

• target_units (str) –

  [optional, default is None, transformation]

  The purpose of this option is to specify target units for unit conversion. The source units are specified in the header line of the input or using the ‘source_units’ keyword.

  The units of the input time-series or values are specified as the second field of a ‘:’ delimited name in the header line of the input or in the ‘source_units’ keyword.

  Any unit string compatible with the ‘pint’ library can be used.

  This option will also add the ‘target_units’ string to the column names.

• round_index –

  [optional, default is None which will do nothing to the index, output format]

  Round the index to the nearest time point. Can significantly improve the performance since can cut down on memory and processing requirements, however be cautious about rounding to a very course interval from a small one. This could lead to duplicate values in the index.

• float_format –

  [optional, output format]

  Format for float numbers.

• tablefmt (str) –

  [optional, default is ‘csv’, output format]

  The table format. Can be one of ‘csv’, ‘tsv’, ‘plain’, ‘simple’, ‘grid’, ‘pipe’, ‘orgtbl’, ‘rst’, ‘mediawiki’, ‘latex’, ‘latex_raw’ and ‘latex_booktabs’.