tstoolbox.tstoolbox.gof

tstoolbox.tstoolbox.gof(obs_col=1, sim_col=2, stats='default', replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False, start_date=None, end_date=None, round_index=None, clean=False, index_type='datetime', source_units=None, target_units=None, kge_sr=1.0, kge09_salpha=1.0, kge12_sgamma=1.0, kge_sbeta=1.0)

Will calculate goodness of fit statistics between two time-series.

The first time series must be the observed, the second the simulated series. You can only give two time-series.

Parameters:

• obs_col – If integer represents the column number of standard input. Can be If integer represents the column number of standard input. Can be a csv, wdm, hdf or xlsx file following format specified in ‘tstoolbox read …’.

• sim_col – If integer represents the column number of standard input. Can be a csv, wdm, hdf or xlsx file following format specified in ‘tstoolbox read …’.

• stats (str) –

  [optional, Python: list, Command line: comma separated string, default is ‘default’]

  Comma separated list of statistical measures.

  You can select two groups of statistical measures.

  stats	Description
  default	A subset of common statistic measures
  all	All available statistic measures

  The ‘default’ set of statistics are:

  stats	Description
  me	Mean error or bias -inf < ME < inf, close to 0 is better
  pc_bias	Percent Bias -inf < PC_BIAS < inf, close to 0 is better
  apc_bias	Absolute Percent Bias 0 <= APC_BIAS < inf, close to 0 is better
  rmsd	Root Mean Square Deviation/Error 0 <= RMSD < inf, smaller is better
  crmsd	Centered Root Mean Square Deviation/Error
  corrcoef	Pearson Correlation coefficient (r) -1 <= r <= 1 1 perfect positive correlation 0 complete randomness -1 perfect negative correlation
  coefdet	Coefficient of determination (r^2) 0 <= r^2 <= 1 1 perfect correlation 0 complete randomness
  murphyss	Murphy Skill Score
  nse	Nash-Sutcliffe Efficiency -inf < NSE < 1, larger is better
  kge09	Kling-Gupta Efficiency, 2009 -inf < KGE09 < 1, larger is better
  kge12	Kling-Gupta Efficiency, 2012 -inf < KGE12 < 1, larger is better
  index_agreement	Index of agreement (d) 0 <= d < 1, larger is better
  brierss	Brier Skill Score
  mae	Mean Absolute Error 0 <= MAE < 1, larger is better
  mean	observed mean, simulated mean
  stdev	observed stdev, simulated stdev

  Additional statistics:

  stats	Description
  acc	Anomaly correlation coefficient (ACC) -1 <= r <= 1 1 positive correlation of variation in anomalies 0 complete randomness of variation in anomalies -1 negative correlation of variation in anomalies
  d1	Index of agreement (d1) 0 <= d1 < 1, larger is better
  d1_p	Legate-McCabe Index of Agreement 0 <= d1_p < 1, larger is better
  d	Index of agreement (d) 0 <= d < 1, larger is better
  dmod	Modified index of agreement (dmod) 0 <= dmod < 1, larger is better
  drel	Relative index of agreement (drel) 0 <= drel < 1, larger is better
  dr	Refined index of agreement (dr) -1 <= dr < 1, larger is better
  ed	Euclidean distance in vector space 0 <= ed < inf, smaller is better
  g_mean_diff	Geometric mean difference
  h1_mahe	H1 absolute error
  h1_mhe	H1 mean error
  h1_rmshe	H1 root mean square error
  h2_mahe	H2 mean absolute error
  h2_mhe	H2 mean error
  h2_rmshe	H2 root mean square error
  h3_mahe	H3 mean absolute error
  h3_mhe	H3 mean error
  h3_rmshe	H3 root mean square error
  h4_mahe	H4 mean absolute error
  h4_mhe	H4 mean error
  h4_rmshe	H4 root mean square error
  h5_mahe	H5 mean absolute error
  h5_mhe	H5 mean error
  h5_rmshe	H5 root mean square error
  h6_mahe	H6 mean absolute error
  h6_mhe	H6 mean error
  h6_rmshe	H6 root mean square error
  h7_mahe	H7 mean absolute error
  h7_mhe	H7 mean error
  h7_rmshe	H7 root mean square error
  h8_mahe	H8 mean absolute error
  h8_mhe	H8 mean error
  h8_rmshe	H8 root mean square error
  h10_mahe	H10 mean absolute error
  h10_mhe	H10 mean error
  h10_rmshe	H10 root mean square error
  irmse	Inertial root mean square error (IRMSE) 0 <= irmse < inf, smaller is better
  lm_index	Legate-McCabe Efficiency Index 0 <= lm_index < 1, larger is better
  maape	Mean Arctangent Absolute Percentage Error (MAAPE) 0 <= maape < pi/2, smaller is better
  male	Mean absolute log error 0 <= male < inf, smaller is better
  mapd	Mean absolute percentage deviation (MAPD)
  mape	Mean absolute percentage error (MAPE) 0 <= mape < inf, 0 indicates perfect correlation
  mase	Mean absolute scaled error
  mb_r	Mielke-Berry R value (MB R) 0 <= mb_r < 1, larger is better
  mdae	Median absolute error (MdAE) 0 <= mdae < inf, smaller is better
  mde	Median error (MdE) -inf < mde < inf, closer to zero is better
  mdse	Median squared error (MdSE) 0 < mde < inf, closer to zero is better
  mean_var	Mean variance
  me	Mean error -inf < me < inf, closer to zero is better
  mle	Mean log error -inf < mle < inf, closer to zero is better
  mse	Mean squared error 0 <= mse < inf, smaller is better
  msle	Mean squared log error 0 <= msle < inf, smaller is better
  ned	Normalized Euclidian distance in vector space 0 <= ned < inf, smaller is better
  nrmse_iqr	IQR normalized root mean square error 0 <= nrmse_iqr < inf, smaller is better
  nrmse_mean	Mean normalized root mean square error 0 <= nrmse_mean < inf, smaller is better
  nrmse_range	Range normalized root mean square error 0 <= nrmse_range < inf, smaller is better
  nse_mod	Modified Nash-Sutcliffe efficiency (NSE mod) -inf < nse_mod < 1, larger is better
  nse_rel	Relative Nash-Sutcliffe efficiency (NSE rel) -inf < nse_mod < 1, larger is better
  rmse	Root mean square error 0 <= rmse < inf, smaller is better
  rmsle	Root mean square log error 0 <= rmsle < inf, smaller is better
  sa	Spectral Angle (SA) -pi/2 <= sa < pi/2, closer to 0 is better
  sc	Spectral Correlation (SC) -pi/2 <= sc < pi/2, closer to 0 is better
  sga	Spectral Gradient Angle (SGA) -pi/2 <= sga < pi/2, closer to 0 is better
  sid	Spectral Information Divergence (SID) -pi/2 <= sid < pi/2, closer to 0 is better
  smape1	Symmetric Mean Absolute Percentage Error (1) (SMAPE1) 0 <= smape1 < 100, smaller is better
  smape2	Symmetric Mean Absolute Percentage Error (2) (SMAPE2) 0 <= smape2 < 100, smaller is better
  spearman_r	Spearman rank correlation coefficient -1 <= spearman_r <= 1 1 perfect positive correlation 0 complete randomness -1 perfect negative correlation
  ve	Volumetric Efficiency (VE) 0 <= ve < 1, smaller is better
  watt_m	Watterson’s M (M) -1 <= watt_m < 1, larger is better

• replace_nan (float) – If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.

• replace_inf (float) – If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.

• remove_neg (boolean) – If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.

• remove_zero (boolean) – If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.

• 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.

• 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.

• clean –

  [optional, default is False, input filter]

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

• 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.

• 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.

• 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’.

• float_format –

  [optional, output format]

  Format for float numbers.

• kge_sr (float) –

  [optional, defaults to 1.0]

  Scaling factor for kge09 and kge12 correlation.

• kge09_salpha (float) –

  [optional, defaults to 1.0]

  Scaling factor for kge09 alpha.

• kge12_sgamma (float) –

  [optional, defaults to 1.0]

  Scaling factor for kge12 beta.

• kge_sbeta (float) –

  [optional, defaults to 1.0]

  Scaling factor for kge09 and kge12 beta.