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 datashader.glyphs.glyph import isnull from numba import cuda as nb_cuda try: from datashader.transfer_functions._cuda_utils import (cuda_atomic_nanmin, cuda_atomic_nanmax) except ImportError: cuda_atomic_nanmin, cuda_atomic_nanmmax = None, None try: import cudf except Exception: cudf = None from .utils import Expr, ngjit, nansum_missing 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): if cudf and isinstance(df, cudf.DataFrame): import cupy if df[self.column].dtype.kind == 'f': nullval = np.nan else: nullval = 0 return cupy.array(df[self.column].to_gpu_array(fillna=nullval)) elif isinstance(df, xr.Dataset): # DataArray could be backed by numpy or cupy array return df[self.column].data else: return df[self.column].values class CategoryPreprocess(Preprocess): """Base class for categorizing preprocessors.""" @property def cat_column(self): """Returns name of categorized column""" return self.column def categories(self, input_dshape): """Returns list of categories corresponding to input shape""" raise NotImplementedError("categories not implemented") def validate(self, in_dshape): """Validates input shape""" raise NotImplementedError("validate not implemented") def apply(self, df): """Applies preprocessor to DataFrame and returns array""" raise NotImplementedError("apply not implemented") class category_codes(CategoryPreprocess): """Extract just the category codes from a categorical column.""" def categories(self, input_dshape): return input_dshape.measure[self.column].categories def validate(self, in_dshape): if not self.column in in_dshape.dict: raise ValueError("specified column not found") if not isinstance(in_dshape.measure[self.column], ct.Categorical): raise ValueError("input must be categorical") def apply(self, df): if cudf and isinstance(df, cudf.DataFrame): return df[self.column].cat.codes.to_gpu_array() else: return df[self.column].cat.codes.values class category_modulo(category_codes): """ A variation on category_codes that assigns categories using an integer column, modulo a base. Category is computed as (column_value - offset)%modulo. """ # couldn't find anything in the datashape docs about how to check if a CType is an integer, so just define a big set IntegerTypes = {ct.bool_, ct.uint8, ct.uint16, ct.uint32, ct.uint64, ct.int8, ct.int16, ct.int32, ct.int64} def __init__(self, column, modulo, offset=0): super().__init__(column) self.offset = offset self.modulo = modulo def _hashable_inputs(self): return super()._hashable_inputs() + (self.offset, self.modulo) def categories(self, in_dshape): return list(range(self.modulo)) def validate(self, in_dshape): if not self.column in in_dshape.dict: raise ValueError("specified column not found") if in_dshape.measure[self.column] not in self.IntegerTypes: raise ValueError("input must be an integer column") def apply(self, df): result = (df[self.column] - self.offset) % self.modulo if cudf and isinstance(df, cudf.DataFrame): return result.to_gpu_array() else: return result.values class category_binning(category_modulo): """ A variation on category_codes that assigns categories by binning a continuous-valued column. The number of categories returned is always nbins+1. The last category (nbin) is for NaNs in the data column, as well as for values under/over the binned interval (when include_under or include_over is False). Parameters ---------- column: column to use lower: lower bound of first bin upper: upper bound of last bin nbins: number of bins include_under: if True, values below bin 0 are assigned to category 0 include_over: if True, values above the last bin (nbins-1) are assigned to category nbin-1 """ def __init__(self, column, lower, upper, nbins, include_under=True, include_over=True): super().__init__(column, nbins + 1) # +1 category for NaNs and clipped values self.bin0 = lower self.binsize = (upper - lower) / float(nbins) self.nbins = nbins self.bin_under = 0 if include_under else nbins self.bin_over = nbins-1 if include_over else nbins def _hashable_inputs(self): return super()._hashable_inputs() + (self.bin0, self.binsize, self.bin_under, self.bin_over) def validate(self, in_dshape): if not self.column in in_dshape.dict: raise ValueError("specified column not found") def apply(self, df): if cudf and isinstance(df, cudf.DataFrame): ## dunno how to do this in CUDA raise NotImplementedError("this feature is not implemented in cuda") else: value = df[self.column].values index = ((value - self.bin0) / self.binsize).astype(int) index[index < 0] = self.bin_under index[index >= self.nbins] = self.bin_over index[np.isnan(value)] = self.nbins return index class category_values(CategoryPreprocess): """Extract a category and a value column from a dataframe as (2,N) numpy array of values.""" def __init__(self, categorizer, value_column): super().__init__(value_column) self.categorizer = categorizer @property def inputs(self): return (self.categorizer.column, self.column) @property def cat_column(self): """Returns name of categorized column""" return self.categorizer.column def categories(self, input_dshape): return self.categorizer.categories def validate(self, in_dshape): return self.categorizer.validate(in_dshape) def apply(self, df): a = self.categorizer.apply(df) if cudf and isinstance(df, cudf.DataFrame): import cupy if df[self.column].dtype.kind == 'f': nullval = np.nan else: nullval = 0 a = cupy.asarray(a) b = cupy.asarray(df[self.column].to_gpu_array(fillna=nullval)) return cupy.stack((a, b), axis=-1) else: b = df[self.column].values return np.stack((a, b), axis=-1) 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),) def _build_bases(self, cuda=False): return (self,) def _build_temps(self, cuda=False): return () def _build_create(self, dshape): return self._create def _build_append(self, dshape, schema, cuda=False): if cuda: if self.column is None: return self._append_no_field_cuda else: return self._append_cuda else: if self.column is None: return self._append_no_field else: 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`` for which the field is optional""" 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 @staticmethod def _finalize(bases, cuda=False, **kwargs): return xr.DataArray(bases[0], **kwargs) class count(OptionalFieldReduction): """Count elements in each bin, returning the result as a uint32. Parameters ---------- column : str, optional If provided, only counts elements in ``column`` that are not ``NaN``. Otherwise, counts every element. """ _dshape = dshape(ct.uint32) # CPU append functions @staticmethod @ngjit def _append_no_field(x, y, agg): agg[y, x] += 1 @staticmethod @ngjit def _append(x, y, agg, field): if not isnull(field): agg[y, x] += 1 # GPU append functions @staticmethod @nb_cuda.jit(device=True) def _append_no_field_cuda(x, y, agg): nb_cuda.atomic.add(agg, (y, x), 1) @staticmethod @nb_cuda.jit(device=True) def _append_cuda(x, y, agg, field): if not isnull(field): nb_cuda.atomic.add(agg, (y, x), 1) @staticmethod def _create(shape, array_module): return array_module.zeros(shape, dtype='u4') @staticmethod def _combine(aggs): return aggs.sum(axis=0, dtype='u4') class by(Reduction): """Apply the provided reduction separately per category. Parameters ---------- cats: str or CategoryPreprocess instance Name of column to aggregate over, or a categorizer object that returns categories. Resulting aggregate has an outer dimension axis along the categories present. reduction : Reduction Per-category reduction function. """ def __init__(self, cat_column, reduction=count()): # set basic categorizer if isinstance(cat_column, CategoryPreprocess): self.categorizer = cat_column elif isinstance(cat_column, str): self.categorizer = category_codes(cat_column) else: raise TypeError("first argument must be a column name or a CategoryPreprocess instance") self.column = self.categorizer.column # for backwards compatibility with count_cat self.columns = (self.categorizer.column, getattr(reduction, 'column', None)) self.reduction = reduction # if a value column is supplied, set category_values preprocessor if self.val_column is not None: self.preprocess = category_values(self.categorizer, self.val_column) else: self.preprocess = self.categorizer def __hash__(self): return hash((type(self), self._hashable_inputs(), self.categorizer._hashable_inputs(), self.reduction)) def _build_temps(self, cuda=False): return tuple(by(self.categorizer, tmp) for tmp in self.reduction._build_temps(cuda)) @property def cat_column(self): return self.columns[0] @property def val_column(self): return self.columns[1] def validate(self, in_dshape): self.preprocess.validate(in_dshape) self.reduction.validate(in_dshape) def out_dshape(self, input_dshape): cats = self.categorizer.categories(input_dshape) red_shape = self.reduction.out_dshape(input_dshape) return dshape(Record([(c, red_shape) for c in cats])) @property def inputs(self): return (self.preprocess, ) def _build_create(self, out_dshape): n_cats = len(out_dshape.measure.fields) return lambda shape, array_module: self.reduction._build_create( out_dshape)(shape + (n_cats,), array_module) def _build_bases(self, cuda=False): bases = self.reduction._build_bases(cuda) if len(bases) == 1 and bases[0] is self: return bases return tuple(by(self.categorizer, base) for base in bases) def _build_append(self, dshape, schema, cuda=False): return self.reduction._build_append(dshape, schema, cuda) def _build_combine(self, dshape): return self.reduction._combine def _build_finalize(self, dshape): cats = list(self.categorizer.categories(dshape)) def finalize(bases, cuda=False, **kwargs): kwargs['dims'] += [self.cat_column] kwargs['coords'][self.cat_column] = cats return self.reduction._finalize(bases, cuda=cuda, **kwargs) return finalize 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_no_field(x, y, agg): agg[y, x] = True _append_no_field_cuda = _append_no_field @staticmethod @ngjit def _append(x, y, agg, field): if not isnull(field): agg[y, x] = True _append_cuda =_append @staticmethod def _create(shape, array_module): return array_module.zeros(shape, dtype='bool') @staticmethod def _combine(aggs): return aggs.sum(axis=0, dtype='bool') class _upsample(Reduction): """"Special internal class used for upsampling""" _dshape = dshape(Option(ct.float64)) @staticmethod def _finalize(bases, cuda=False, **kwargs): return xr.DataArray(bases[0], **kwargs) @property def inputs(self): return (extract(self.column),) @staticmethod def _create(shape, array_module): # Use uninitialized memory, the upsample function must explicitly set unused # values to nan return array_module.empty(shape, dtype='f8') @staticmethod @ngjit def _append(x, y, agg, field): # not called, the upsample function must set agg directly pass @staticmethod @ngjit def _append_cuda(x, y, agg, field): # not called, the upsample function must set agg directly pass @staticmethod def _combine(aggs): return np.nanmax(aggs, axis=0) class FloatingReduction(Reduction): """Base classes for reductions that always have floating-point dtype.""" _dshape = dshape(Option(ct.float64)) @staticmethod def _create(shape, array_module): return array_module.full(shape, np.nan, dtype='f8') @staticmethod def _finalize(bases, cuda=False, **kwargs): return xr.DataArray(bases[0], **kwargs) class _sum_zero(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 def _create(shape, array_module): return array_module.zeros(shape, dtype='f8') @staticmethod @ngjit def _append(x, y, agg, field): if not isnull(field): agg[y, x] += field @staticmethod @ngjit def _append_cuda(x, y, agg, field): if not isnull(field): nb_cuda.atomic.add(agg, (y, x), field) @staticmethod def _combine(aggs): return aggs.sum(axis=0, dtype='f8') class sum(FloatingReduction): """Sum of all elements in ``column``. Elements of resulting aggregate are nan if they are not updated. 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)) # Cuda implementation def _build_bases(self, cuda=False): if cuda: return (_sum_zero(self.column), any(self.column)) else: return (self,) @staticmethod def _finalize(bases, cuda=False, **kwargs): if cuda: sums, anys = bases x = np.where(anys, sums, np.nan) return xr.DataArray(x, **kwargs) else: return xr.DataArray(bases[0], **kwargs) # Single pass CPU implementation # These methods will only be called if _build_bases returned (self,) @staticmethod @ngjit def _append(x, y, agg, field): if not isnull(field): if isnull(agg[y, x]): agg[y, x] = field else: agg[y, x] += field @staticmethod def _combine(aggs): return nansum_missing(aggs, axis=0) 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. """ @staticmethod def _create(shape, array_module): return array_module.full(shape, 0.0, dtype='f8') def _build_temps(self, cuda=False): return (_sum_zero(self.column), count(self.column)) def _build_append(self, dshape, schema, cuda=False): if cuda: raise ValueError("""\ The 'std' and 'var' reduction operations are not yet supported on the GPU""") return super(m2, self)._build_append(dshape, schema, cuda) @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 isnull(field): if count > 0: 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): with np.errstate(divide='ignore', invalid='ignore'): mu = np.nansum(sums, axis=0) / ns.sum(axis=0) return np.nansum(Ms + ns*(sums/ns - mu)**2, axis=0) 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 isnull(agg[y, x]): agg[y, x] = field elif agg[y, x] > field: agg[y, x] = field @staticmethod @ngjit def _append_cuda(x, y, agg, field): cuda_atomic_nanmin(agg, (y, x), field) @staticmethod def _combine(aggs): return np.nanmin(aggs, axis=0) 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 isnull(agg[y, x]): agg[y, x] = field elif agg[y, x] < field: agg[y, x] = field @staticmethod @ngjit def _append_cuda(x, y, agg, field): cuda_atomic_nanmax(agg, (y, x), field) @staticmethod def _combine(aggs): return np.nanmax(aggs, axis=0) class count_cat(by): """Count of all elements in ``column``, grouped by category. Alias for `by(...,count())`, for backwards compatibility. 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 __init__(self, column): super(count_cat, self).__init__(column, count()) 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)) def _build_bases(self, cuda=False): return (_sum_zero(self.column), count(self.column)) @staticmethod def _finalize(bases, cuda=False, **kwargs): sums, counts = bases with np.errstate(divide='ignore', invalid='ignore'): x = np.where(counts > 0, sums/counts, np.nan) return xr.DataArray(x, **kwargs) 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)) def _build_bases(self, cuda=False): return (_sum_zero(self.column), count(self.column), m2(self.column)) @staticmethod def _finalize(bases, cuda=False, **kwargs): sums, counts, m2s = bases with np.errstate(divide='ignore', invalid='ignore'): x = np.where(counts > 0, m2s / counts, np.nan) return xr.DataArray(x, **kwargs) 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)) def _build_bases(self, cuda=False): return (_sum_zero(self.column), count(self.column), m2(self.column)) @staticmethod def _finalize(bases, cuda=False, **kwargs): sums, counts, m2s = bases with np.errstate(divide='ignore', invalid='ignore'): x = np.where(counts > 0, np.sqrt(m2s / counts), np.nan) return xr.DataArray(x, **kwargs) 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, array_module): 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") 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, array_module): 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") 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, array_module): 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") 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))) __all__ = list(set([_k for _k,_v in locals().items() if isinstance(_v,type) and (issubclass(_v,Reduction) or _v is summary) and _v not in [Reduction, OptionalFieldReduction, FloatingReduction, m2]])) + \ ['category_modulo', 'category_binning']