"""
binstats - Bin spatial data and determine statistics per bin
"""
from pygmt.clib import Session
from pygmt.helpers import (
GMTTempFile,
build_arg_string,
fmt_docstring,
kwargs_to_strings,
use_alias,
)
from pygmt.io import load_dataarray
[docs]
@fmt_docstring
@use_alias(
C="statistic",
E="empty",
G="outgrid",
I="spacing",
N="normalize",
R="region",
S="search_radius",
V="verbose",
W="weight",
a="aspatial",
b="binary",
h="header",
i="incols",
r="registration",
)
@kwargs_to_strings(I="sequence", R="sequence", i="sequence_comma")
def binstats(data, **kwargs):
r"""
Bin spatial data and determine statistics per bin.
Reads arbitrarily located (x,y[,z][,w]) points
(2-4 columns) from ``data`` and for each
node in the specified grid layout determines which points are
within the given radius. These points are then used in the
calculation of the specified statistic. The results may be
presented as is or may be normalized by the circle area to
perhaps give density estimates.
Full option list at :gmt-docs:`gmtbinstats.html`
{aliases}
Parameters
----------
data : str, {table-like}
A file name of an ASCII data table or a 2-D
{table-classes}.
{outgrid}
statistic : str
**a**\|\ **d**\|\ **g**\|\ **i**\|\ **l**\|\ **L**\|\ **m**\|\ **n**\
\|\ **o**\|\ **p**\|\ **q**\ [*quant*]\|\ **r**\|\ **s**\|\ **u**\
\|\ **U**\|\ **z**.
Choose the statistic that will be computed per node based on the
points that are within *radius* distance of the node. Select one of:
- **a** for mean (average)
- **d** for median absolute deviation (MAD)
- **g** for full (max-min) range
- **i** for 25-75% interquartile range
- **l** for minimum (low)
- **L** for minimum of positive values only
- **m** for median
- **n** the number of values
- **o** for LMS scale
- **p** for mode (maximum likelihood)
- **q** for selected quantile (append desired quantile in
0-100% range [50])
- **r** for the r.m.s.
- **s** for standard deviation
- **u** for maximum (upper)
- **U** for maximum of negative values only
- **z** for the sum
empty : float
Set the value assigned to empty nodes [Default is NaN].
normalize : bool
Normalize the resulting grid values by the area represented by the
search *radius* [no normalization].
search_radius : float or str
Set the *search_radius* that determines which data points are
considered close to a node. Append the distance unit.
Not compatible with ``tiling``.
weight : str
Input data have an extra column containing observation point weight.
If weights are given then weighted statistical quantities will be
computed while the count will be the sum of the weights instead of
number of points. If the weights are actually uncertainties
(one sigma) then append **+s** and weight = 1/sigma.
{spacing}
{region}
{verbose}
{aspatial}
{binary}
{header}
{incols}
{registration}
Returns
-------
ret: xarray.DataArray or None
Return type depends on whether the ``outgrid`` parameter is set:
- :class:`xarray.DataArray` if ``outgrid`` is not set
- None if ``outgrid`` is set (grid output will be stored in file set by
``outgrid``)
"""
with GMTTempFile(suffix=".nc") as tmpfile:
with Session() as lib:
file_context = lib.virtualfile_from_data(check_kind="vector", data=data)
with file_context as infile:
if (outgrid := kwargs.get("G")) is None:
kwargs["G"] = outgrid = tmpfile.name # output to tmpfile
lib.call_module(
module="binstats", args=build_arg_string(kwargs, infile=infile)
)
return load_dataarray(outgrid) if outgrid == tmpfile.name else None