Source code for datashader.reductions

from __future__ import absolute_import, division, print_function

import numpy as np
from datashape import dshape, isnumeric, Record, Option
from datashape import coretypes as ct
from toolz import concat, unique
import xarray as xr

from .utils import Expr, ngjit


class Preprocess(Expr):
    """Base clase for preprocessing steps."""
    def __init__(self, column):
        self.column = column

    @property
    def inputs(self):
        return (self.column,)


class extract(Preprocess):
    """Extract a column from a dataframe as a numpy array of values."""
    def apply(self, df):
        return df[self.column].values


class category_codes(Preprocess):
    """Extract just the category codes from a categorical column."""
    def apply(self, df):
        return df[self.column].cat.codes.values


class Reduction(Expr):
    """Base class for per-bin reductions."""
    def __init__(self, column=None):
        self.column = column

    def validate(self, in_dshape):
        if not self.column in in_dshape.dict:
            raise ValueError("specified column not found")
        if not isnumeric(in_dshape.measure[self.column]):
            raise ValueError("input must be numeric")

    def out_dshape(self, in_dshape):
        return self._dshape

    @property
    def inputs(self):
        return (extract(self.column),)

    @property
    def _bases(self):
        return (self,)

    @property
    def _temps(self):
        return ()

    def _build_create(self, dshape):
        return self._create

    def _build_append(self, dshape):
        return self._append

    def _build_combine(self, dshape):
        return self._combine

    def _build_finalize(self, dshape):
        return self._finalize


class OptionalFieldReduction(Reduction):
    """Base class for things like ``count`` or ``any``"""
    def __init__(self, column=None):
        self.column = column

    @property
    def inputs(self):
        return (extract(self.column),) if self.column is not None else ()

    def validate(self, in_dshape):
        pass

    def _build_append(self, dshape):
        return self._append if self.column is None else self._append_non_na

    @staticmethod
    def _finalize(bases, **kwargs):
        return xr.DataArray(bases[0], **kwargs)


[docs]class count(OptionalFieldReduction): """Count elements in each bin. Parameters ---------- column : str, optional If provided, only counts elements in ``column`` that are not ``NaN``. Otherwise, counts every element. """ _dshape = dshape(ct.int32) @staticmethod @ngjit def _append(x, y, agg): agg[y, x] += 1 @staticmethod @ngjit def _append_non_na(x, y, agg, field): if not np.isnan(field): agg[y, x] += 1 @staticmethod def _create(shape): return np.zeros(shape, dtype='i4') @staticmethod def _combine(aggs): return aggs.sum(axis=0, dtype='i4')
[docs]class any(OptionalFieldReduction): """Whether any elements in ``column`` map to each bin. Parameters ---------- column : str, optional If provided, only elements in ``column`` that are ``NaN`` are skipped. """ _dshape = dshape(ct.bool_) @staticmethod @ngjit def _append(x, y, agg): agg[y, x] = True @staticmethod @ngjit def _append_non_na(x, y, agg, field): if not np.isnan(field): agg[y, x] = True @staticmethod def _create(shape): return np.zeros(shape, dtype='bool') @staticmethod def _combine(aggs): return aggs.sum(axis=0, dtype='bool')
class FloatingReduction(Reduction): """Base classes for reductions that always have floating-point dtype.""" _dshape = dshape(Option(ct.float64)) @staticmethod def _create(shape): return np.full(shape, np.nan, dtype='f8') @staticmethod def _finalize(bases, **kwargs): return xr.DataArray(bases[0], **kwargs)
[docs]class sum(FloatingReduction): """Sum of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ @staticmethod @ngjit def _append(x, y, agg, field): if not np.isnan(field): if np.isnan(agg[y, x]): agg[y, x] = field else: agg[y, x] += field @staticmethod def _combine(aggs): missing_vals = np.isnan(aggs) all_empty = np.bitwise_and.reduce(missing_vals, axis=0) set_to_zero = missing_vals & ~all_empty return np.where(set_to_zero, 0, aggs).sum(axis=0)
[docs]class m2(FloatingReduction): """Sum of square differences from the mean of all elements in ``column``. Intermediate value for computing ``var`` and ``std``, not intended to be used on its own. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ @property def _temps(self): return (sum(self.column), count(self.column)) @staticmethod @ngjit def _append(x, y, m2, field, sum, count): # sum & count are the results of sum[y, x], count[y, x] before being # updated by field if not np.isnan(field): if count == 0: m2[y, x] = 0 else: u1 = np.float64(sum) / count u = np.float64(sum + field) / (count + 1) m2[y, x] += (field - u1) * (field - u) @staticmethod def _combine(Ms, sums, ns): mu = np.nansum(sums, axis=0) / ns.sum(axis=0) return np.nansum(Ms + ns*(sums/ns - mu)**2, axis=0)
[docs]class min(FloatingReduction): """Minimum value of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ @staticmethod @ngjit def _append(x, y, agg, field): if np.isnan(agg[y, x]): agg[y, x] = field elif agg[y, x] > field: agg[y, x] = field @staticmethod def _combine(aggs): return np.nanmin(aggs, axis=0)
[docs]class max(FloatingReduction): """Maximum value of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ @staticmethod @ngjit def _append(x, y, agg, field): if np.isnan(agg[y, x]): agg[y, x] = field elif agg[y, x] < field: agg[y, x] = field @staticmethod def _combine(aggs): return np.nanmax(aggs, axis=0)
[docs]class count_cat(Reduction): """Count of all elements in ``column``, grouped by category. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be categorical. Resulting aggregate has a outer dimension axis along the categories present. """ def validate(self, in_dshape): if not isinstance(in_dshape.measure[self.column], ct.Categorical): raise ValueError("input must be categorical") def out_dshape(self, input_dshape): cats = input_dshape.measure[self.column].categories return dshape(Record([(c, ct.int32) for c in cats])) @property def inputs(self): return (category_codes(self.column),) def _build_create(self, out_dshape): n_cats = len(out_dshape.measure.fields) return lambda shape: np.zeros(shape + (n_cats,), dtype='i4') @staticmethod @ngjit def _append(x, y, agg, field): agg[y, x, field] += 1 @staticmethod def _combine(aggs): return aggs.sum(axis=0, dtype='i4') def _build_finalize(self, dshape): cats = list(dshape[self.column].categories) def finalize(bases, **kwargs): dims = kwargs['dims'] + [self.column] coords = kwargs['coords'] + [cats] return xr.DataArray(bases[0], dims=dims, coords=coords) return finalize
[docs]class mean(Reduction): """Mean of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ _dshape = dshape(Option(ct.float64)) @property def _bases(self): return (sum(self.column), count(self.column)) @staticmethod def _finalize(bases, **kwargs): sums, counts = bases with np.errstate(divide='ignore', invalid='ignore'): x = sums/counts return xr.DataArray(x, **kwargs)
[docs]class var(Reduction): """Variance of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ _dshape = dshape(Option(ct.float64)) @property def _bases(self): return (sum(self.column), count(self.column), m2(self.column)) @staticmethod def _finalize(bases, **kwargs): sums, counts, m2s = bases with np.errstate(divide='ignore', invalid='ignore'): x = m2s/counts return xr.DataArray(x, **kwargs)
[docs]class std(Reduction): """Standard Deviation of all elements in ``column``. Parameters ---------- column : str Name of the column to aggregate over. Column data type must be numeric. ``NaN`` values in the column are skipped. """ _dshape = dshape(Option(ct.float64)) @property def _bases(self): return (sum(self.column), count(self.column), m2(self.column)) @staticmethod def _finalize(bases, **kwargs): sums, counts, m2s = bases with np.errstate(divide='ignore', invalid='ignore'): x = np.sqrt(m2s/counts) return xr.DataArray(x, **kwargs)
[docs]class first(Reduction): """First value encountered in ``column``. Useful for categorical data where an actual value must always be returned, not an average or other numerical calculation. Currently only supported for rasters, externally to this class. Parameters ---------- column : str Name of the column to aggregate over. If the data type is floating point, ``NaN`` values in the column are skipped. """ _dshape = dshape(Option(ct.float64)) @staticmethod def _append(x, y, agg): raise NotImplementedError("first is currently implemented only for rasters") @staticmethod def _create(shape): raise NotImplementedError("first is currently implemented only for rasters") @staticmethod def _combine(aggs): raise NotImplementedError("first is currently implemented only for rasters") @staticmethod def _finalize(bases, **kwargs): raise NotImplementedError("first is currently implemented only for rasters")
[docs]class last(Reduction): """Last value encountered in ``column``. Useful for categorical data where an actual value must always be returned, not an average or other numerical calculation. Currently only supported for rasters, externally to this class. Parameters ---------- column : str Name of the column to aggregate over. If the data type is floating point, ``NaN`` values in the column are skipped. """ _dshape = dshape(Option(ct.float64)) @staticmethod def _append(x, y, agg): raise NotImplementedError("last is currently implemented only for rasters") @staticmethod def _create(shape): raise NotImplementedError("last is currently implemented only for rasters") @staticmethod def _combine(aggs): raise NotImplementedError("last is currently implemented only for rasters") @staticmethod def _finalize(bases, **kwargs): raise NotImplementedError("last is currently implemented only for rasters")
[docs]class mode(Reduction): """Mode (most common value) of all the values encountered in ``column``. Useful for categorical data where an actual value must always be returned, not an average or other numerical calculation. Currently only supported for rasters, externally to this class. Implementing it for other glyph types would be difficult due to potentially unbounded data storage requirements to store indefinite point or line data per pixel. Parameters ---------- column : str Name of the column to aggregate over. If the data type is floating point, ``NaN`` values in the column are skipped. """ _dshape = dshape(Option(ct.float64)) @staticmethod def _append(x, y, agg): raise NotImplementedError("mode is currently implemented only for rasters") @staticmethod def _create(shape): raise NotImplementedError("mode is currently implemented only for rasters") @staticmethod def _combine(aggs): raise NotImplementedError("mode is currently implemented only for rasters") @staticmethod def _finalize(bases, **kwargs): raise NotImplementedError("mode is currently implemented only for rasters")
[docs]class summary(Expr): """A collection of named reductions. Computes all aggregates simultaneously, output is stored as a ``xarray.Dataset``. Examples -------- A reduction for computing the mean of column "a", and the sum of column "b" for each bin, all in a single pass. >>> import datashader as ds >>> red = ds.summary(mean_a=ds.mean('a'), sum_b=ds.sum('b')) """ def __init__(self, **kwargs): ks, vs = zip(*sorted(kwargs.items())) self.keys = ks self.values = vs def __hash__(self): return hash((type(self), tuple(self.keys), tuple(self.values))) def validate(self, input_dshape): for v in self.values: v.validate(input_dshape) def out_dshape(self, in_dshape): return dshape(Record([(k, v.out_dshape(in_dshape)) for (k, v) in zip(self.keys, self.values)])) @property def inputs(self): return tuple(unique(concat(v.inputs for v in self.values)))