plottoolbox.plottoolbox.target

plottoolbox.plottoolbox.target(input_ts='-', columns=None, start_date=None, end_date=None, clean=False, skiprows=None, index_type='datetime', names=None, ofilename='plot.png', title='', figsize='10,6.0', legend=None, colors='auto', linestyles='auto', markerstyles=' ', style='auto', por=False, round_index=None, source_units=None, target_units=None, plot_styles='bright', **kwds)

[obs column, sim N columns] Creates a “target” diagram to plot goodness of fit.

“target” creates a target diagram that compares three goodness of fit statistics on one plot. The three goodness of fit statistics calculated and displayed are bias, root mean square deviation, and centered root mean square deviation. The data columns have to be organized as ‘observed,simulated1,simulated2,simulated3,…etc.’

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 …’.

• 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].
  

• 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.

• 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.

• clean –

  [optional, default is False, input filter]

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

• 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.

• ofilename (str) –

  [optional, defaults to ‘plot.png’]

  Output filename for the plot. Extension defines the type, for example ‘filename.png’ will create a PNG file.

  If used within Python, and ofilename is None will return the Matplotlib figure that can then be changed or added to as needed.

• title (str) –

  [optional, defaults to ‘’]

  Title of chart.

• figsize (str) –

  [optional, defaults to ‘10,6.5’]

  The ‘width,height’ of plot in inches.

• legend (bool) –

  [optional, default is True]

  Whether to create a legend or not.

• legend_names –

  [optional, default is None]

  If the default of None will take legend names from columns tiles in the input dataset. Otherwise will take names from the legend_names list.

• colors –

  [optional, default is ‘auto’]

  The default ‘auto’ will cycle through matplotlib colors in the chosen style.

  At the command line supply a comma separated matplotlib color codes, or within Python a list of color code strings.

  Can identify colors in four different ways.

  1. Use ‘CN’ where N is a number from 0 to 9 that gets the Nth color from the current style.

  2. Single character code from the table below.

  Code	Color
  b	blue
  g	green
  r	red
  c	cyan
  m	magenta
  y	yellow
  k	black

  3. Number between 0 and 1 that represents the level of gray, where 0 is white an 1 is black.

  4. Any of the HTML color names.

  HTML Color Names
  red
  burlywood
  chartreuse
  …etc.

  Color reference: http://matplotlib.org/api/colors_api.html

• linestyles –

  [optional, default to ‘auto’]

  If ‘auto’ will iterate through the available matplotlib line types. Otherwise on the command line a comma separated list, or a list of strings if using the Python API.

  To not display lines use a space (’ ‘) as the linestyle code.

  Separated ‘colors’, ‘linestyles’, and ‘markerstyles’ instead of using the ‘style’ keyword.

  Code	Lines
  -	solid
  –	dashed
  -.	dash_dot
  :	dotted
  None	draw nothing
  ’ ‘	draw nothing
  ’’	draw nothing

  Line reference: http://matplotlib.org/api/artist_api.html

• markerstyles –

  [optional, default to ‘ ‘]

  The default ‘ ‘ will not plot a marker. If ‘auto’ will iterate through the available matplotlib marker types. Otherwise on the command line a comma separated list, or a list of strings if using the Python API.

  Separated ‘colors’, ‘linestyles’, and ‘markerstyles’ instead of using the ‘style’ keyword.

  Code	Markers
  .	point
  o	circle
  v	triangle down
  ^	triangle up
  <	triangle left
  >	triangle right
  1	tri_down
  2	tri_up
  3	tri_left
  4	tri_right
  8	octagon
  s	square
  p	pentagon
  *	star
  h	hexagon1
  H	hexagon2
  +	plus
  x	x
  D	diamond
  d	thin diamond
  _	hline
  None	nothing
  ’ ‘	nothing
  ’’	nothing

  Marker reference: http://matplotlib.org/api/markers_api.html

• style –

  [optional, default is None]

  Still available, but if None is replaced by ‘colors’, ‘linestyles’, and ‘markerstyles’ options. Currently the ‘style’ option will override the others.

  Comma separated matplotlib style strings per time-series. Just combine codes in ‘ColorMarkerLine’ order, for example ‘r*–’ is a red dashed line with star marker.

• por –

  [optional]

  Plot from first good value to last good value. Strips NANs from beginning and 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.

• 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.

• plot_styles (str) –

  [optional, default is “default”]

  Set the style of the plot. One or more of Matplotlib styles “classic”, “Solarize_Light2”, “bmh”, “dark_background”, “fast”, “fivethirtyeight”, “ggplot”, “grayscale”, “seaborn”, “seaborn-bright”, “seaborn-colorblind”, “seaborn-dark”, “seaborn-dark-palette”, “seaborn-darkgrid”, “seaborn-deep”, “seaborn-muted”, “seaborn-notebook”, “seaborn-paper”, “seaborn-pastel”, “seaborn-poster”, “seaborn-talk”, “seaborn-ticks”, “seaborn-white”, “seaborn-whitegrid”, “tableau-colorblind10”, and

  The main SciencePlots styles are “science”, “grid”, “ieee”, “scatter”, “notebook”, “high-vis”, “bright”, “vibrant”, “muted”, and “retro”.

  Other SciencPlots styles that are less common or intended to modify other styles are, “cjk-jp-font.mplstyle”, “cjk-kr-font.mplstyle”, “cjk-sc-font.mplstyle”, “cjk-tc-font.mplstyle”, “high-contrast.mplstyle”, “latex-sans.mplstyle”, “light.mplstyle”, “nature.mplstyle”, “no-latex.mplstyle”, “pgf.mplstyle”, “russian-font.mplstyle”, and “std-colors.mplstyle”.

  If multiple styles then each over rides some or all of the characteristics of the previous.

  Color Blind Appropriate Styles

  The styles “seaborn-colorblind”, “tableau-colorblind10”, “bright”, “vibrant”, and “muted” are all styles that are setup to be able to be distinguished by someone with color blindness.

  Black, White, and Gray Styles

  The “ieee” style is appropriate for black, white, and gray, however the “ieee” also will change the chart size to fit in a column of the “IEEE” journal.

  The “grayscale” is another style useful for photo-copyable black, white, nd gray.

  Matplotlib styles:

  https://matplotlib.org/3.3.1/gallery/style_sheets/style_sheets_reference.html

  SciencePlots styles:

  https://github.com/garrettj403/SciencePlots