Source code for holoviews.util.transform

from __future__ import division

import operator
import sys

from types import BuiltinFunctionType, BuiltinMethodType, FunctionType, MethodType

import numpy as np
import param

from ..core.data import PandasInterface
from ..core.dimension import Dimension
from ..core.util import basestring, pd, resolve_dependent_value, unique_iterator


def _maybe_map(numpy_fn):
    def fn(values, *args, **kwargs):
        series_like = hasattr(values, 'index') and not isinstance(values, list)
        map_fn = (getattr(values, 'map_partitions', None) or
                  getattr(values, 'map_blocks', None))
        if map_fn:
            if series_like:
                return map_fn(
                    lambda s: type(s)(numpy_fn(s, *args, **kwargs),
                                      index=s.index))
            else:
                return map_fn(lambda s: numpy_fn(s, *args, **kwargs))
        else:
            if series_like:
                return type(values)(
                    numpy_fn(values, *args, **kwargs),
                    index=values.index,
                )
            else:
                return numpy_fn(values, *args, **kwargs)
    return fn


[docs]def norm(values, min=None, max=None): """Unity-based normalization to scale data into 0-1 range. (values - min) / (max - min) Args: values: Array of values to be normalized min (float, optional): Lower bound of normalization range max (float, optional): Upper bound of normalization range Returns: Array of normalized values """ min = np.min(values) if min is None else min max = np.max(values) if max is None else max return (values - min) / (max-min)
[docs]def lognorm(values, min=None, max=None): """Unity-based normalization on log scale. Apply the same transformation as matplotlib.colors.LogNorm Args: values: Array of values to be normalized min (float, optional): Lower bound of normalization range max (float, optional): Upper bound of normalization range Returns: Array of normalized values """ min = np.log(np.min(values)) if min is None else np.log(min) max = np.log(np.max(values)) if max is None else np.log(max) return (np.log(values) - min) / (max-min)
[docs]class iloc(object): """Implements integer array indexing for dim expressions. """ __name__ = 'iloc' def __init__(self, dim_expr): self.expr = dim_expr self.index = slice(None) def __getitem__(self, index): self.index = index return dim(self.expr, self) def __call__(self, values): return values[self.index]
@_maybe_map def bin(values, bins, labels=None): """Bins data into declared bins Bins data into declared bins. By default each bin is labelled with bin center values but an explicit list of bin labels may be defined. Args: values: Array of values to be binned bins: List or array containing the bin boundaries labels: List of labels to assign to each bin If the bins are length N the labels should be length N-1 Returns: Array of binned values """ bins = np.asarray(bins) if labels is None: labels = (bins[:-1] + np.diff(bins)/2.) else: labels = np.asarray(labels) dtype = 'float' if labels.dtype.kind == 'f' else 'O' binned = np.full_like(values, (np.nan if dtype == 'f' else None), dtype=dtype) for lower, upper, label in zip(bins[:-1], bins[1:], labels): condition = (values > lower) & (values <= upper) binned[np.where(condition)[0]] = label return binned @_maybe_map def categorize(values, categories, default=None): """Maps discrete values to supplied categories. Replaces discrete values in input array with a fixed set of categories defined either as a list or dictionary. Args: values: Array of values to be categorized categories: List or dict of categories to map inputs to default: Default value to assign if value not in categories Returns: Array of categorized values """ uniq_cats = list(unique_iterator(values)) cats = [] for c in values: if isinstance(categories, list): cat_ind = uniq_cats.index(c) if cat_ind < len(categories): cat = categories[cat_ind] else: cat = default else: cat = categories.get(c, default) cats.append(cat) result = np.asarray(cats) # Convert unicode to object type like pandas does if result.dtype.kind in ['U', 'S']: result = result.astype('object') return result digitize = _maybe_map(np.digitize) isin = _maybe_map(np.isin) astype = _maybe_map(np.asarray) round_ = _maybe_map(np.round) def _python_isin(array, values): return [v in values for v in array] python_isin = _maybe_map(_python_isin) function_types = ( BuiltinFunctionType, BuiltinMethodType, FunctionType, MethodType, np.ufunc, iloc )
[docs]class dim(object): """ dim transform objects are a way to express deferred transforms on Datasets. dim transforms support all mathematical and bitwise operators, NumPy ufuncs and methods, and provide a number of useful methods for normalizing, binning and categorizing data. """ _binary_funcs = { operator.add: '+', operator.and_: '&', operator.eq: '==', operator.floordiv: '//', operator.ge: '>=', operator.gt: '>', operator.le: '<=', operator.lshift: '<<', operator.lt: '<', operator.mod: '%', operator.mul: '*', operator.ne: '!=', operator.or_: '|', operator.pow: '**', operator.rshift: '>>', operator.sub: '-', operator.truediv: '/'} _builtin_funcs = {abs: 'abs', round_: 'round'} _custom_funcs = { norm: 'norm', lognorm: 'lognorm', bin: 'bin', categorize: 'categorize', digitize: 'digitize', isin: 'isin', python_isin: 'isin', astype: 'astype', round_: 'round', iloc: 'iloc', } _numpy_funcs = { np.any: 'any', np.all: 'all', np.cumprod: 'cumprod', np.cumsum: 'cumsum', np.max: 'max', np.mean: 'mean', np.min: 'min', np.sum: 'sum', np.std: 'std', np.var: 'var', np.log: 'log', np.log10: 'log10'} _unary_funcs = {operator.pos: '+', operator.neg: '-', operator.not_: '~'} _all_funcs = [_binary_funcs, _builtin_funcs, _custom_funcs, _numpy_funcs, _unary_funcs] _namespaces = {'numpy': 'np'} namespace = 'numpy' _accessor = None def __init__(self, obj, *args, **kwargs): ops = [] self._ns = np.ndarray self.coerce = kwargs.get('coerce', True) if isinstance(obj, basestring): self.dimension = Dimension(obj) elif isinstance(obj, Dimension): self.dimension = obj else: self.dimension = obj.dimension ops = obj.ops if args: fn = args[0] else: fn = None if fn is not None: if not (isinstance(fn, function_types+(basestring,)) or any(fn in funcs for funcs in self._all_funcs)): raise ValueError('Second argument must be a function, ' 'found %s type' % type(fn)) ops = ops + [{'args': args[1:], 'fn': fn, 'kwargs': kwargs, 'reverse': kwargs.pop('reverse', False)}] self.ops = ops def __getstate__(self): return self.__dict__ def __setstate__(self, state): self.__dict__.update(state) @property def _current_accessor(self): if self.ops and self.ops[-1]['kwargs'].get('accessor'): return self.ops[-1]['fn'] def __call__(self, *args, **kwargs): if (not self.ops or not isinstance(self.ops[-1]['fn'], basestring) or 'accessor' not in self.ops[-1]['kwargs']): raise ValueError("Cannot call method on %r expression. " "Only methods accessed via namspaces, " "e.g. dim(...).df or dim(...).xr), " "can be called. " % self) op = self.ops[-1] if op['fn'] == 'str': new_op = dict(op, fn=astype, args=(str,), kwargs={}) else: new_op = dict(op, args=args, kwargs=kwargs) return self.clone(self.dimension, self.ops[:-1]+[new_op]) def __getattr__(self, attr): if attr in dir(self): return type(self)(self, attr, accessor=True) raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attr)) def __dir__(self): ns = self._ns if self._current_accessor: ns = getattr(ns, self._current_accessor) extras = {attr for attr in dir(ns) if not attr.startswith('_')} try: return sorted(set(super(dim, self).__dir__()) | extras) except Exception: return sorted(set(dir(type(self))) | set(self.__dict__) | extras) def __hash__(self): return hash(repr(self))
[docs] def clone(self, dimension=None, ops=None, dim_type=None): """ Creates a clone of the dim expression optionally overriding the dim and ops. """ dim_type = dim_type or type(self) if dimension is None: dimension = self.dimension new_dim = dim_type(dimension) if ops is None: ops = list(self.ops) new_dim.ops = ops return new_dim
[docs] @classmethod def register(cls, key, function): """ Register a custom dim transform function which can from then on be referenced by the key. """ cls._custom_funcs[key] = function
@property def params(self): params = {} for op in self.ops: op_args = list(op['args'])+list(op['kwargs'].values()) for op_arg in op_args: if 'panel' in sys.modules: from panel.widgets.base import Widget if isinstance(op_arg, Widget): op_arg = op_arg.param.value if (isinstance(op_arg, param.Parameter) and isinstance(op_arg.owner, param.Parameterized)): params[op_arg.name+str(id(op))] = op_arg return params # Namespace properties @property def df(self): return self.clone(dim_type=df_dim) @property def np(self): return self.clone(dim_type=dim) @property def xr(self): return self.clone(dim_type=xr_dim) # Builtin functions def __abs__(self): return type(self)(self, abs) def __round__(self, ndigits=None): args = () if ndigits is None else (ndigits,) return type(self)(self, round_, *args) # Unary operators def __neg__(self): return type(self)(self, operator.neg) def __not__(self): return type(self)(self, operator.not_) def __invert__(self): return type(self)(self, operator.inv) def __pos__(self): return type(self)(self, operator.pos) # Binary operators def __add__(self, other): return type(self)(self, operator.add, other) def __and__(self, other): return type(self)(self, operator.and_, other) def __div__(self, other): return type(self)(self, operator.div, other) def __eq__(self, other): return type(self)(self, operator.eq, other) def __floordiv__(self, other): return type(self)(self, operator.floordiv, other) def __ge__(self, other): return type(self)(self, operator.ge, other) def __gt__(self, other): return type(self)(self, operator.gt, other) def __le__(self, other): return type(self)(self, operator.le, other) def __lt__(self, other): return type(self)(self, operator.lt, other) def __lshift__(self, other): return type(self)(self, operator.lshift, other) def __mod__(self, other): return type(self)(self, operator.mod, other) def __mul__(self, other): return type(self)(self, operator.mul, other) def __ne__(self, other): return type(self)(self, operator.ne, other) def __or__(self, other): return type(self)(self, operator.or_, other) def __rshift__(self, other): return type(self)(self, operator.rshift, other) def __pow__(self, other): return type(self)(self, operator.pow, other) def __sub__(self, other): return type(self)(self, operator.sub, other) def __truediv__(self, other): return type(self)(self, operator.truediv, other) # Reverse binary operators def __radd__(self, other): return type(self)(self, operator.add, other, reverse=True) def __rand__(self, other): return type(self)(self, operator.and_, other) def __rdiv__(self, other): return type(self)(self, operator.div, other, reverse=True) def __rfloordiv__(self, other): return type(self)(self, operator.floordiv, other, reverse=True) def __rlshift__(self, other): return type(self)(self, operator.rlshift, other) def __rmod__(self, other): return type(self)(self, operator.mod, other, reverse=True) def __rmul__(self, other): return type(self)(self, operator.mul, other, reverse=True) def __ror__(self, other): return type(self)(self, operator.or_, other, reverse=True) def __rpow__(self, other): return type(self)(self, operator.pow, other, reverse=True) def __rrshift__(self, other): return type(self)(self, operator.rrshift, other) def __rsub__(self, other): return type(self)(self, operator.sub, other, reverse=True) def __rtruediv__(self, other): return type(self)(self, operator.truediv, other, reverse=True) ## NumPy operations def __array_ufunc__(self, *args, **kwargs): ufunc = args[0] kwargs = {k: v for k, v in kwargs.items() if v is not None} return type(self)(self, ufunc, **kwargs) def clip(self, min=None, max=None): if min is None and max is None: raise ValueError('One of max or min must be given.') return type(self)(self, np.clip, a_min=min, a_max=max) def any(self, *args, **kwargs): return type(self)(self, np.any, *args, **kwargs) def all(self, *args, **kwargs): return type(self)(self, np.all, *args, **kwargs) def cumprod(self, *args, **kwargs): return type(self)(self, np.cumprod, *args, **kwargs) def cumsum(self, *args, **kwargs): return type(self)(self, np.cumsum, *args, **kwargs) def max(self, *args, **kwargs): return type(self)(self, np.max, *args, **kwargs) def mean(self, *args, **kwargs): return type(self)(self, np.mean, *args, **kwargs) def min(self, *args, **kwargs): return type(self)(self, np.min, *args, **kwargs) def sum(self, *args, **kwargs): return type(self)(self, np.sum, *args, **kwargs) def std(self, *args, **kwargs): return type(self)(self, np.std, *args, **kwargs) def var(self, *args, **kwargs): return type(self)(self, np.var, *args, **kwargs) def log(self, *args, **kwargs): return type(self)(self, np.log, *args, **kwargs) def log10(self, *args, **kwargs): return type(self)(self, np.log10, *args, **kwargs) ## Custom functions def astype(self, dtype): return type(self)(self, astype, dtype=dtype) def round(self, decimals=0): return type(self)(self, round_, decimals=decimals) def digitize(self, *args, **kwargs): return type(self)(self, digitize, *args, **kwargs) def isin(self, *args, **kwargs): if kwargs.pop('object', None): return type(self)(self, python_isin, *args, **kwargs) return type(self)(self, isin, *args, **kwargs) @property def iloc(self): return iloc(self)
[docs] def bin(self, bins, labels=None): """Bins continuous values. Bins continuous using the provided bins and assigns labels either computed from each bins center point or from the supplied labels. Args: bins: List or array containing the bin boundaries labels: List of labels to assign to each bin If the bins are length N the labels should be length N-1 """ return type(self)(self, bin, bins, labels=labels)
[docs] def categorize(self, categories, default=None): """Replaces discrete values with supplied categories Replaces discrete values in input array into a fixed set of categories defined either as a list or dictionary. Args: categories: List or dict of categories to map inputs to default: Default value to assign if value not in categories """ return type(self)(self, categorize, categories=categories, default=default)
[docs] def lognorm(self, limits=None): """Unity-based normalization log scale. Apply the same transformation as matplotlib.colors.LogNorm Args: limits: tuple of (min, max) defining the normalization range """ kwargs = {} if limits is not None: kwargs = {'min': limits[0], 'max': limits[1]} return type(self)(self, lognorm, **kwargs)
[docs] def norm(self, limits=None): """Unity-based normalization to scale data into 0-1 range. (values - min) / (max - min) Args: limits: tuple of (min, max) defining the normalization range """ kwargs = {} if limits is not None: kwargs = {'min': limits[0], 'max': limits[1]} return type(self)(self, norm, **kwargs)
[docs] @classmethod def pipe(cls, func, *args, **kwargs): """ Wrapper to give multidimensional transforms a more intuitive syntax. For a custom function 'func' with signature (*args, **kwargs), call as dim.pipe(func, *args, **kwargs). """ args = list(args) # make mutable for k, arg in enumerate(args): if isinstance(arg, basestring): args[k] = cls(arg) return cls(args[0], func, *args[1:], **kwargs)
@property def str(self): "Casts values to strings or provides str accessor." return type(self)(self, 'str', accessor=True) # Other methods
[docs] def applies(self, dataset, strict=False): """ Determines whether the dim transform can be applied to the Dataset, i.e. whether all referenced dimensions can be resolved. """ from ..element import Graph if isinstance(self.dimension, dim): applies = self.dimension.applies(dataset) elif self.dimension.name == '*': applies = True else: lookup = self.dimension if strict else self.dimension.name applies = dataset.get_dimension(lookup) is not None if isinstance(dataset, Graph) and not applies: applies = dataset.nodes.get_dimension(lookup) is not None for op in self.ops: args = op.get('args') if not args: continue for arg in args: if isinstance(arg, dim): applies &= arg.applies(dataset) kwargs = op.get('kwargs') for kwarg in kwargs.values(): if isinstance(kwarg, dim): applies &= kwarg.applies(dataset) return applies
def interface_applies(self, dataset, coerce): return True def _resolve_op(self, op, dataset, data, flat, expanded, ranges, all_values, keep_index, compute, strict): args = op['args'] fn = op['fn'] kwargs = dict(op['kwargs']) fn_name = self._numpy_funcs.get(fn) if fn_name and hasattr(data, fn_name): if 'axis' not in kwargs and not isinstance(fn, np.ufunc): kwargs['axis'] = None fn = fn_name if isinstance(fn, basestring): accessor = kwargs.pop('accessor', None) fn_args = [] else: accessor = False fn_args = [data] for arg in args: if isinstance(arg, dim): arg = arg.apply( dataset, flat, expanded, ranges, all_values, keep_index, compute, strict ) arg = resolve_dependent_value(arg) fn_args.append(arg) fn_kwargs = {} for k, v in kwargs.items(): if isinstance(v, dim): v = v.apply( dataset, flat, expanded, ranges, all_values, keep_index, compute, strict ) fn_kwargs[k] = resolve_dependent_value(v) args = tuple(fn_args[::-1] if op['reverse'] else fn_args) kwargs = dict(fn_kwargs) return fn, fn_name, args, kwargs, accessor def _apply_fn(self, dataset, data, fn, fn_name, args, kwargs, accessor, drange): if (((fn is norm) or (fn is lognorm)) and drange != {} and not ('min' in kwargs and 'max' in kwargs)): data = fn(data, *drange) elif isinstance(fn, basestring): method = getattr(data, fn, None) if method is None: mtype = 'attribute' if accessor else 'method' raise AttributeError( "%r could not be applied to '%r', '%s' %s " "does not exist on %s type." % (self, dataset, fn, mtype, type(data).__name__) ) if accessor: data = method else: try: data = method(*args, **kwargs) except Exception as e: if 'axis' in kwargs: kwargs.pop('axis') data = method(*args, **kwargs) else: raise e else: data = fn(*args, **kwargs) return data def _compute_data(self, data, drop_index, compute): """ Implements conversion of data from namespace specific object, e.g. pandas Series to NumPy array. """ if hasattr(data, 'compute') and compute: data = data.compute() return data def _coerce(self, data): """ Implements coercion of data from current data format to the namespace specific datatype. """ return data
[docs] def apply(self, dataset, flat=False, expanded=None, ranges={}, all_values=False, keep_index=False, compute=True, strict=False): """Evaluates the transform on the supplied dataset. Args: dataset: Dataset object to evaluate the expression on flat: Whether to flatten the returned array expanded: Whether to use the expanded expand values ranges: Dictionary for ranges for normalization all_values: Whether to evaluate on all values Whether to evaluate on all available values, for some element types, such as Graphs, this may include values not included in the referenced column keep_index: For data types that support indexes, whether the index should be preserved in the result. compute: For data types that support lazy evaluation, whether the result should be computed before it is returned. strict: Whether to strictly check for dimension matches (if False, counts any dimensions with matching names as the same) Returns: values: NumPy array computed by evaluating the expression """ from ..element import Graph dimension = self.dimension if expanded is None: expanded = not ((dataset.interface.gridded and dimension in dataset.kdims) or (dataset.interface.multi and dataset.interface.isunique(dataset, dimension, True))) if not self.applies(dataset) and (not isinstance(dataset, Graph) or not self.applies(dataset.nodes)): raise KeyError("One or more dimensions in the expression %r " "could not resolve on '%s'. Ensure all " "dimensions referenced by the expression are " "present on the supplied object." % (self, dataset)) if not self.interface_applies(dataset, coerce=self.coerce): if self.coerce: raise ValueError("The expression %r assumes a %s-like " "API but the dataset contains %s data " "and cannot be coerced." % (self, self.namespace, dataset.interface.datatype)) else: raise ValueError("The expression %r assumes a %s-like " "API but the dataset contains %s data " "and coercion is disabled." % (self, self.namespace, dataset.interface.datatype)) if isinstance(dataset, Graph): if dimension in dataset.kdims and all_values: dimension = dataset.nodes.kdims[2] dataset = dataset if dimension in dataset else dataset.nodes dataset = self._coerce(dataset) if self.namespace != 'numpy': compute_for_compute = False keep_index_for_compute = True else: compute_for_compute = compute keep_index_for_compute = keep_index if dimension.name == '*': data = dataset.data eldim = None else: lookup = dimension if strict else dimension.name eldim = dataset.get_dimension(lookup).name data = dataset.interface.values( dataset, lookup, expanded=expanded, flat=flat, compute=compute_for_compute, keep_index=keep_index_for_compute ) for op in self.ops: fn, fn_name, args, kwargs, accessor = self._resolve_op( op, dataset, data, flat, expanded, ranges, all_values, keep_index_for_compute, compute_for_compute, strict ) drange = ranges.get(eldim, {}) drange = drange.get('combined', drange) data = self._apply_fn(dataset, data, fn, fn_name, args, kwargs, accessor, drange) drop_index = keep_index_for_compute and not keep_index compute = not compute_for_compute and compute if (drop_index or compute): data = self._compute_data(data, drop_index, compute) return data
def __repr__(self): op_repr = "'%s'" % self.dimension accessor = False for i, o in enumerate(self.ops): if i == 0: prev = 'dim({repr}' elif accessor: prev = '{repr}' else: prev = '({repr}' fn = o['fn'] ufunc = isinstance(fn, np.ufunc) args = ', '.join([repr(r) for r in o['args']]) if o['args'] else '' kwargs = o['kwargs'] prev_accessor = accessor accessor = kwargs.pop('accessor', None) kwargs = sorted(kwargs.items(), key=operator.itemgetter(0)) kwargs = '%s' % ', '.join(['%s=%r' % item for item in kwargs]) if kwargs else '' if fn in self._binary_funcs: fn_name = self._binary_funcs[o['fn']] if o['reverse']: format_string = '{args}{fn}'+prev else: format_string = prev+'){fn}{args}' if any(isinstance(a, dim) for a in o['args']): format_string = format_string.replace('{args}', '({args})') elif fn in self._unary_funcs: fn_name = self._unary_funcs[fn] format_string = '{fn}' + prev else: if isinstance(fn, basestring): fn_name = fn else: fn_name = fn.__name__ if fn in self._builtin_funcs: fn_name = self._builtin_funcs[fn] format_string = '{fn}'+prev elif isinstance(fn, basestring): if accessor: sep = '' if op_repr.endswith(')') or prev_accessor else ')' format_string = prev+sep+'.{fn}' else: format_string = prev+').{fn}(' elif fn in self._numpy_funcs: fn_name = self._numpy_funcs[fn] format_string = prev+').{fn}(' elif isinstance(fn, iloc): format_string = prev+').iloc[{0}]'.format(repr(fn.index)) elif fn in self._custom_funcs: fn_name = self._custom_funcs[fn] format_string = prev+').{fn}(' elif ufunc: fn_name = str(fn)[8:-2] if not (prev.startswith('dim') or prev.endswith(')')): format_string = '{fn}' + prev else: format_string = '{fn}(' + prev if fn_name in dir(np): format_string = '.'.join([self._namespaces['numpy'], format_string]) else: format_string = prev+', {fn}' if accessor: pass elif args: if not format_string.endswith('('): format_string += ', ' format_string += '{args}' if kwargs: format_string += ', {kwargs}' elif kwargs: if not format_string.endswith('('): format_string += ', ' format_string += '{kwargs}' # Insert accessor if i == 0 and self._accessor: idx = format_string.index(')') format_string = ''.join([ format_string[:idx], ').', self._accessor, format_string[idx+1:] ]) op_repr = format_string.format(fn=fn_name, repr=op_repr, args=args, kwargs=kwargs) if op_repr.count('(') - op_repr.count(')') > 0: op_repr += ')' if not self.ops: op_repr = 'dim({repr})'.format(repr=op_repr) if op_repr.count('(') - op_repr.count(')') > 0: op_repr += ')' return op_repr
[docs]class df_dim(dim): """ A subclass of dim which provides access to the DataFrame namespace along with tab-completion and type coercion allowing the expression to be applied on any columnar dataset. """ namespace = 'dataframe' _accessor = 'pd' def __init__(self, obj, *args, **kwargs): super(df_dim, self).__init__(obj, *args, **kwargs) self._ns = pd.Series def interface_applies(self, dataset, coerce): return (not dataset.interface.gridded and (coerce or isinstance(dataset.interface, PandasInterface))) def _compute_data(self, data, drop_index, compute): if hasattr(data, 'compute') and compute: data = data.compute() if not drop_index: return data if compute and hasattr(data, 'to_numpy'): return data.to_numpy() return data.values def _coerce(self, dataset): if self.interface_applies(dataset, coerce=False): return dataset pandas_interfaces = param.concrete_descendents(PandasInterface) datatypes = [intfc.datatype for intfc in pandas_interfaces.values() if dataset.interface.multi == intfc.multi] return dataset.clone(datatype=datatypes)
[docs]class xr_dim(dim): """ A subclass of dim which provides access to the xarray DataArray namespace along with tab-completion and type coercion allowing the expression to be applied on any gridded dataset. """ namespace = 'xarray' _accessor = 'xr' def __init__(self, obj, *args, **kwargs): try: import xarray as xr except ImportError: raise ImportError("XArray could not be imported, dim().xr " "requires the xarray to be available.") super(xr_dim, self).__init__(obj, *args, **kwargs) self._ns = xr.DataArray def interface_applies(self, dataset, coerce): return (dataset.interface.gridded and (coerce or dataset.interface.datatype == 'xarray')) def _compute_data(self, data, drop_index, compute): if drop_index: data = data.data if hasattr(data, 'compute') and compute: data = data.compute() return data def _coerce(self, dataset): if self.interface_applies(dataset, coerce=False): return dataset return dataset.clone(datatype=['xarray'])
[docs]def lon_lat_to_easting_northing(longitude, latitude): """ Projects the given longitude, latitude values into Web Mercator (aka Pseudo-Mercator or EPSG:3857) coordinates. Longitude and latitude can be provided as scalars, Pandas columns, or Numpy arrays, and will be returned in the same form. Lists or tuples will be converted to Numpy arrays. Args: longitude latitude Returns: (easting, northing) Examples: easting, northing = lon_lat_to_easting_northing(-74,40.71) easting, northing = lon_lat_to_easting_northing( np.array([-74]),np.array([40.71]) ) df=pandas.DataFrame(dict(longitude=np.array([-74]),latitude=np.array([40.71]))) df.loc[:, 'longitude'], df.loc[:, 'latitude'] = lon_lat_to_easting_northing( df.longitude,df.latitude ) """ if isinstance(longitude, (list, tuple)): longitude = np.array(longitude) if isinstance(latitude, (list, tuple)): latitude = np.array(latitude) origin_shift = np.pi * 6378137 easting = longitude * origin_shift / 180.0 with np.errstate(divide='ignore', invalid='ignore'): northing = np.log( np.tan((90 + latitude) * np.pi / 360.0) ) * origin_shift / np.pi return easting, northing
[docs]def easting_northing_to_lon_lat(easting, northing): """ Projects the given easting, northing values into longitude, latitude coordinates. easting and northing values are assumed to be in Web Mercator (aka Pseudo-Mercator or EPSG:3857) coordinates. Args: easting northing Returns: (longitude, latitude) """ if isinstance(easting, (list, tuple)): easting = np.array(easting) if isinstance(northing, (list, tuple)): northing = np.array(northing) origin_shift = np.pi * 6378137 longitude = easting * 180.0 / origin_shift with np.errstate(divide='ignore'): latitude = np.arctan( np.exp(northing * np.pi / origin_shift) ) * 360.0 / np.pi - 90 return longitude, latitude