plottoolbox.plottoolbox.hexbin

plottoolbox.plottoolbox.hexbin(input_ts='-', reduce_C_function=<function mean>, gridsize=100, columns=None, start_date=None, end_date=None, clean=False, skiprows=None, index_type='datetime', names=None, ofilename='plot.png', xtitle='', ytitle='', title='', figsize='10, 6.0', legend=None, legend_names=None, xaxis='arithmetic', yaxis='arithmetic', xlim=None, ylim=None, xlabel_rotation=0, ylabel_rotation=0, por=False, invert_xaxis=False, invert_yaxis=False, dropna='all', source_units=None, target_units=None, plot_styles='bright')

[x, y, optional third data column] Hexbin plot.

Only available for a single x,y pair with an additional, optional data column.

If the data column is not provided, the number of points in each bin is shown.

If the data column is provided, the reduce_C_function is applied to all values within each hexagon cell.

Parameters:

• 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].
  

• reduce_C_function (callable, default np.mean) – Function of one argument that reduces all the values in a bin to a single number. The available options at the command line are “np.mean”, “np.max”, “np.sum”, “np.std”. Using the Python API can use any callable.

• gridsize (int or tuple of (int, int), default 100) – The number of hexagons in the x-direction. The corresponding number of hexagons in the y-direction is chosen in a way that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the x-direction and the y-direction.

• 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.

• xtitle (str) –

  [optional, default depends on type]

  Title of x-axis.

• ytitle (str) –

  [optional, default depends on type]

  Title of y-axis.

• 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.

• xlim –

  [optional, default is based on range of x values]

  Comma separated lower and upper limits for the x-axis of the plot. For example, ‘–xlim 1,1000’ would limit the plot from 1 to 1000, where ‘–xlim ,1000’ would base the lower limit on the data and set the upper limit to 1000.

• ylim –

  [optional, default is based on range of y values]

  Comma separated lower and upper limits for the y-axis of the plot. See xlim for examples.

• grid –

  [optional, default is False]

  Whether to plot grid lines on the major ticks.

• xlabel_rotation (int) –

  [optional]

  Rotation for major x-axis labels for plots.

• ylabel_rotation (int) –

  [optional]

  Rotation for major y-axis labels for plots.

• por –

  [optional]

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

• invert_xaxis –

  [optional, default is False]

  Invert the x-axis.

• invert_yaxis –

  [optional, default is False]

  Invert the y-axis.

• 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’.

• 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