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sparse.py

Source:sparse.py

...300        return self.subtype301# ----------------------------------------------------------------------------302# Array303_sparray_doc_kwargs = dict(klass='SparseArray')304def _get_fill(arr):305    # type: (SparseArray) -> ndarray306    """307    Create a 0-dim ndarray containing the fill value308    Parameters309    ----------310    arr : SparseArray311    Returns312    -------313    fill_value : ndarray314        0-dim ndarray with just the fill value.315    Notes316    -----317    coerce fill_value to arr dtype if possible318    int64 SparseArray can have NaN as fill_value if there is no missing319    """320    try:321        return np.asarray(arr.fill_value, dtype=arr.dtype.subtype)322    except ValueError:323        return np.asarray(arr.fill_value)324def _sparse_array_op(left, right, op, name):325    """326    Perform a binary operation between two arrays.327    Parameters328    ----------329    left : Union[SparseArray, ndarray]330    right : Union[SparseArray, ndarray]331    op : Callable332        The binary operation to perform333    name str334        Name of the callable.335    Returns336    -------337    SparseArray338    """339    # type: (SparseArray, SparseArray, Callable, str) -> Any340    if name.startswith('__'):341        # For lookups in _libs.sparse we need non-dunder op name342        name = name[2:-2]343    # dtype used to find corresponding sparse method344    ltype = left.dtype.subtype345    rtype = right.dtype.subtype346    if not is_dtype_equal(ltype, rtype):347        subtype = find_common_type([ltype, rtype])348        ltype = SparseDtype(subtype, left.fill_value)349        rtype = SparseDtype(subtype, right.fill_value)350        # TODO(GH-23092): pass copy=False. Need to fix astype_nansafe351        left = left.astype(ltype)352        right = right.astype(rtype)353        dtype = ltype.subtype354    else:355        dtype = ltype356    # dtype the result must have357    result_dtype = None358    if left.sp_index.ngaps == 0 or right.sp_index.ngaps == 0:359        with np.errstate(all='ignore'):360            result = op(left.get_values(), right.get_values())361            fill = op(_get_fill(left), _get_fill(right))362        if left.sp_index.ngaps == 0:363            index = left.sp_index364        else:365            index = right.sp_index366    elif left.sp_index.equals(right.sp_index):367        with np.errstate(all='ignore'):368            result = op(left.sp_values, right.sp_values)369            fill = op(_get_fill(left), _get_fill(right))370        index = left.sp_index371    else:372        if name[0] == 'r':373            left, right = right, left374            name = name[1:]375        if name in ('and', 'or') and dtype == 'bool':376            opname = 'sparse_{name}_uint8'.format(name=name)377            # to make template simple, cast here378            left_sp_values = left.sp_values.view(np.uint8)379            right_sp_values = right.sp_values.view(np.uint8)380            result_dtype = np.bool381        else:382            opname = 'sparse_{name}_{dtype}'.format(name=name, dtype=dtype)383            left_sp_values = left.sp_values384            right_sp_values = right.sp_values385        sparse_op = getattr(splib, opname)386        with np.errstate(all='ignore'):387            result, index, fill = sparse_op(388                left_sp_values, left.sp_index, left.fill_value,389                right_sp_values, right.sp_index, right.fill_value)390    if result_dtype is None:391        result_dtype = result.dtype392    return _wrap_result(name, result, index, fill, dtype=result_dtype)393def _wrap_result(name, data, sparse_index, fill_value, dtype=None):394    """395    wrap op result to have correct dtype396    """397    if name.startswith('__'):398        # e.g. __eq__ --> eq399        name = name[2:-2]400    if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):401        dtype = np.bool402    fill_value = lib.item_from_zerodim(fill_value)403    if is_bool_dtype(dtype):404        # fill_value may be np.bool_405        fill_value = bool(fill_value)406    return SparseArray(data,407                       sparse_index=sparse_index,408                       fill_value=fill_value,409                       dtype=dtype)410class SparseArray(PandasObject, ExtensionArray, ExtensionOpsMixin):411    """412    An ExtensionArray for storing sparse data.413    .. versionchanged:: 0.24.0414       Implements the ExtensionArray interface.415    Parameters416    ----------417    data : array-like418        A dense array of values to store in the SparseArray. This may contain419        `fill_value`.420    sparse_index : SparseIndex, optional421    index : Index422    fill_value : scalar, optional423        Elements in `data` that are `fill_value` are not stored in the424        SparseArray. For memory savings, this should be the most common value425        in `data`. By default, `fill_value` depends on the dtype of `data`:426        =========== ==========427        data.dtype  na_value428        =========== ==========429        float       ``np.nan``430        int         ``0``431        bool        False432        datetime64  ``pd.NaT``433        timedelta64 ``pd.NaT``434        =========== ==========435        The fill value is potentiall specified in three ways. In order of436        precedence, these are437        1. The `fill_value` argument438        2. ``dtype.fill_value`` if `fill_value` is None and `dtype` is439           a ``SparseDtype``440        3. ``data.dtype.fill_value`` if `fill_value` is None and `dtype`441           is not a ``SparseDtype`` and `data` is a ``SparseArray``.442    kind : {'integer', 'block'}, default 'integer'443        The type of storage for sparse locations.444        * 'block': Stores a `block` and `block_length` for each445          contiguous *span* of sparse values. This is best when446          sparse data tends to be clumped together, with large447          regsions of ``fill-value`` values between sparse values.448        * 'integer': uses an integer to store the location of449          each sparse value.450    dtype : np.dtype or SparseDtype, optional451        The dtype to use for the SparseArray. For numpy dtypes, this452        determines the dtype of ``self.sp_values``. For SparseDtype,453        this determines ``self.sp_values`` and ``self.fill_value``.454    copy : bool, default False455        Whether to explicitly copy the incoming `data` array.456    """457    __array_priority__ = 15458    _pandas_ftype = 'sparse'459    _subtyp = 'sparse_array'  # register ABCSparseArray460    def __init__(self, data, sparse_index=None, index=None, fill_value=None,461                 kind='integer', dtype=None, copy=False):462        from pandas.core.internals import SingleBlockManager463        if isinstance(data, SingleBlockManager):464            data = data.internal_values()465        if fill_value is None and isinstance(dtype, SparseDtype):466            fill_value = dtype.fill_value467        if isinstance(data, (type(self), ABCSparseSeries)):468            # disable normal inference on dtype, sparse_index, & fill_value469            if sparse_index is None:470                sparse_index = data.sp_index471            if fill_value is None:472                fill_value = data.fill_value473            if dtype is None:474                dtype = data.dtype475            # TODO: make kind=None, and use data.kind?476            data = data.sp_values477        # Handle use-provided dtype478        if isinstance(dtype, compat.string_types):479            # Two options: dtype='int', regular numpy dtype480            # or dtype='Sparse[int]', a sparse dtype481            try:482                dtype = SparseDtype.construct_from_string(dtype)483            except TypeError:484                dtype = pandas_dtype(dtype)485        if isinstance(dtype, SparseDtype):486            if fill_value is None:487                fill_value = dtype.fill_value488            dtype = dtype.subtype489        if index is not None and not is_scalar(data):490            raise Exception("must only pass scalars with an index ")491        if is_scalar(data):492            if index is not None:493                if data is None:494                    data = np.nan495            if index is not None:496                npoints = len(index)497            elif sparse_index is None:498                npoints = 1499            else:500                npoints = sparse_index.length501            dtype = infer_dtype_from_scalar(data)[0]502            data = construct_1d_arraylike_from_scalar(503                data, npoints, dtype504            )505        if dtype is not None:506            dtype = pandas_dtype(dtype)507        # TODO: disentangle the fill_value dtype inference from508        # dtype inference509        if data is None:510            # XXX: What should the empty dtype be? Object or float?511            data = np.array([], dtype=dtype)512        if not is_array_like(data):513            try:514                # probably shared code in sanitize_series515                from pandas.core.internals.construction import sanitize_array516                data = sanitize_array(data, index=None)517            except ValueError:518                # NumPy may raise a ValueError on data like [1, []]519                # we retry with object dtype here.520                if dtype is None:521                    dtype = object522                    data = np.atleast_1d(np.asarray(data, dtype=dtype))523                else:524                    raise525        if copy:526            # TODO: avoid double copy when dtype forces cast.527            data = data.copy()528        if fill_value is None:529            fill_value_dtype = data.dtype if dtype is None else dtype530            if fill_value_dtype is None:531                fill_value = np.nan532            else:533                fill_value = na_value_for_dtype(fill_value_dtype)534        if isinstance(data, type(self)) and sparse_index is None:535            sparse_index = data._sparse_index536            sparse_values = np.asarray(data.sp_values, dtype=dtype)537        elif sparse_index is None:538            sparse_values, sparse_index, fill_value = make_sparse(539                data, kind=kind, fill_value=fill_value, dtype=dtype540            )541        else:542            sparse_values = np.asarray(data, dtype=dtype)543            if len(sparse_values) != sparse_index.npoints:544                raise AssertionError("Non array-like type {type} must "545                                     "have the same length as the index"546                                     .format(type=type(sparse_values)))547        self._sparse_index = sparse_index548        self._sparse_values = sparse_values549        self._dtype = SparseDtype(sparse_values.dtype, fill_value)550    @classmethod551    def _simple_new(cls, sparse_array, sparse_index, dtype):552        # type: (np.ndarray, SparseIndex, SparseDtype) -> 'SparseArray'553        new = cls([])554        new._sparse_index = sparse_index555        new._sparse_values = sparse_array556        new._dtype = dtype557        return new558    def __array__(self, dtype=None, copy=True):559        fill_value = self.fill_value560        if self.sp_index.ngaps == 0:561            # Compat for na dtype and int values.562            return self.sp_values563        if dtype is None:564            # Can NumPy represent this type?565            # If not, `np.result_type` will raise. We catch that566            # and return object.567            if is_datetime64_any_dtype(self.sp_values.dtype):568                # However, we *do* special-case the common case of569                # a datetime64 with pandas NaT.570                if fill_value is NaT:571                    # Can't put pd.NaT in a datetime64[ns]572                    fill_value = np.datetime64('NaT')573            try:574                dtype = np.result_type(self.sp_values.dtype, type(fill_value))575            except TypeError:576                dtype = object577        out = np.full(self.shape, fill_value, dtype=dtype)578        out[self.sp_index.to_int_index().indices] = self.sp_values579        return out580    def __setitem__(self, key, value):581        # I suppose we could allow setting of non-fill_value elements.582        # TODO(SparseArray.__setitem__): remove special cases in583        # ExtensionBlock.where584        msg = "SparseArray does not support item assignment via setitem"585        raise TypeError(msg)586    @classmethod587    def _from_sequence(cls, scalars, dtype=None, copy=False):588        return cls(scalars, dtype=dtype)589    @classmethod590    def _from_factorized(cls, values, original):591        return cls(values, dtype=original.dtype)592    # ------------------------------------------------------------------------593    # Data594    # ------------------------------------------------------------------------595    @property596    def sp_index(self):597        """598        The SparseIndex containing the location of non- ``fill_value`` points.599        """600        return self._sparse_index601    @property602    def sp_values(self):603        """604        An ndarray containing the non- ``fill_value`` values.605        Examples606        --------607        >>> s = SparseArray([0, 0, 1, 0, 2], fill_value=0)608        >>> s.sp_values609        array([1, 2])610        """611        return self._sparse_values612    @property613    def dtype(self):614        return self._dtype615    @property616    def fill_value(self):617        """618        Elements in `data` that are `fill_value` are not stored.619        For memory savings, this should be the most common value in the array.620        """621        return self.dtype.fill_value622    @fill_value.setter623    def fill_value(self, value):624        self._dtype = SparseDtype(self.dtype.subtype, value)625    @property626    def kind(self):627        """628        The kind of sparse index for this array. One of {'integer', 'block'}.629        """630        if isinstance(self.sp_index, IntIndex):631            return 'integer'632        else:633            return 'block'634    @property635    def _valid_sp_values(self):636        sp_vals = self.sp_values637        mask = notna(sp_vals)638        return sp_vals[mask]639    def __len__(self):640        return self.sp_index.length641    @property642    def _null_fill_value(self):643        return self._dtype._is_na_fill_value644    def _fill_value_matches(self, fill_value):645        if self._null_fill_value:646            return isna(fill_value)647        else:648            return self.fill_value == fill_value649    @property650    def nbytes(self):651        return self.sp_values.nbytes + self.sp_index.nbytes652    @property653    def density(self):654        """655        The percent of non- ``fill_value`` points, as decimal.656        Examples657        --------658        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)659        >>> s.density660        0.6661        """662        r = float(self.sp_index.npoints) / float(self.sp_index.length)663        return r664    @property665    def npoints(self):666        """667        The number of non- ``fill_value`` points.668        Examples669        --------670        >>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)671        >>> s.npoints672        3673        """674        return self.sp_index.npoints675    @property676    def values(self):677        """678        Dense values679        """680        return self.to_dense()681    def isna(self):682        from pandas import isna683        # If null fill value, we want SparseDtype[bool, true]684        # to preserve the same memory usage.685        dtype = SparseDtype(bool, self._null_fill_value)686        return type(self)._simple_new(isna(self.sp_values),687                                      self.sp_index, dtype)688    def fillna(self, value=None, method=None, limit=None):689        """690        Fill missing values with `value`.691        Parameters692        ----------693        value : scalar, optional694        method : str, optional695            .. warning::696               Using 'method' will result in high memory use,697               as all `fill_value` methods will be converted to698               an in-memory ndarray699        limit : int, optional700        Returns701        -------702        SparseArray703        Notes704        -----705        When `value` is specified, the result's ``fill_value`` depends on706        ``self.fill_value``. The goal is to maintain low-memory use.707        If ``self.fill_value`` is NA, the result dtype will be708        ``SparseDtype(self.dtype, fill_value=value)``. This will preserve709        amount of memory used before and after filling.710        When ``self.fill_value`` is not NA, the result dtype will be711        ``self.dtype``. Again, this preserves the amount of memory used.712        """713        if ((method is None and value is None) or714                (method is not None and value is not None)):715            raise ValueError("Must specify one of 'method' or 'value'.")716        elif method is not None:717            msg = "fillna with 'method' requires high memory usage."718            warnings.warn(msg, PerformanceWarning)719            filled = interpolate_2d(np.asarray(self), method=method,720                                    limit=limit)721            return type(self)(filled, fill_value=self.fill_value)722        else:723            new_values = np.where(isna(self.sp_values), value, self.sp_values)724            if self._null_fill_value:725                # This is essentially just updating the dtype.726                new_dtype = SparseDtype(self.dtype.subtype, fill_value=value)727            else:728                new_dtype = self.dtype729        return self._simple_new(new_values, self._sparse_index, new_dtype)730    def shift(self, periods=1, fill_value=None):731        if not len(self) or periods == 0:732            return self.copy()733        if isna(fill_value):734            fill_value = self.dtype.na_value735        subtype = np.result_type(fill_value, self.dtype.subtype)736        if subtype != self.dtype.subtype:737            # just coerce up front738            arr = self.astype(SparseDtype(subtype, self.fill_value))739        else:740            arr = self741        empty = self._from_sequence(742            [fill_value] * min(abs(periods), len(self)),743            dtype=arr.dtype744        )745        if periods > 0:746            a = empty747            b = arr[:-periods]748        else:749            a = arr[abs(periods):]750            b = empty751        return arr._concat_same_type([a, b])752    def _first_fill_value_loc(self):753        """754        Get the location of the first missing value.755        Returns756        -------757        int758        """759        if len(self) == 0 or self.sp_index.npoints == len(self):760            return -1761        indices = self.sp_index.to_int_index().indices762        if not len(indices) or indices[0] > 0:763            return 0764        diff = indices[1:] - indices[:-1]765        return np.searchsorted(diff, 2) + 1766    def unique(self):767        uniques = list(algos.unique(self.sp_values))768        fill_loc = self._first_fill_value_loc()769        if fill_loc >= 0:770            uniques.insert(fill_loc, self.fill_value)771        return type(self)._from_sequence(uniques, dtype=self.dtype)772    def _values_for_factorize(self):773        # Still override this for hash_pandas_object774        return np.asarray(self), self.fill_value775    def factorize(self, na_sentinel=-1):776        # Currently, ExtensionArray.factorize -> Tuple[ndarray, EA]777        # The sparsity on this is backwards from what Sparse would want. Want778        # ExtensionArray.factorize -> Tuple[EA, EA]779        # Given that we have to return a dense array of labels, why bother780        # implementing an efficient factorize?781        labels, uniques = algos.factorize(np.asarray(self),782                                          na_sentinel=na_sentinel)783        uniques = SparseArray(uniques, dtype=self.dtype)784        return labels, uniques785    def value_counts(self, dropna=True):786        """787        Returns a Series containing counts of unique values.788        Parameters789        ----------790        dropna : boolean, default True791            Don't include counts of NaN, even if NaN is in sp_values.792        Returns793        -------794        counts : Series795        """796        from pandas import Index, Series797        keys, counts = algos._value_counts_arraylike(self.sp_values,798                                                     dropna=dropna)799        fcounts = self.sp_index.ngaps800        if fcounts > 0:801            if self._null_fill_value and dropna:802                pass803            else:804                if self._null_fill_value:805                    mask = isna(keys)806                else:807                    mask = keys == self.fill_value808                if mask.any():809                    counts[mask] += fcounts810                else:811                    keys = np.insert(keys, 0, self.fill_value)812                    counts = np.insert(counts, 0, fcounts)813        if not isinstance(keys, ABCIndexClass):814            keys = Index(keys)815        result = Series(counts, index=keys)816        return result817    # --------818    # Indexing819    # --------820    def __getitem__(self, key):821        if isinstance(key, tuple):822            if len(key) > 1:823                raise IndexError("too many indices for array.")824            key = key[0]825        if is_integer(key):826            return self._get_val_at(key)827        elif isinstance(key, tuple):828            data_slice = self.values[key]829        elif isinstance(key, slice):830            # special case to preserve dtypes831            if key == slice(None):832                return self.copy()833            # TODO: this logic is surely elsewhere834            # TODO: this could be more efficient835            indices = np.arange(len(self), dtype=np.int32)[key]836            return self.take(indices)837        else:838            # TODO: I think we can avoid densifying when masking a839            # boolean SparseArray with another. Need to look at the840            # key's fill_value for True / False, and then do an intersection841            # on the indicies of the sp_values.842            if isinstance(key, SparseArray):843                if is_bool_dtype(key):844                    key = key.to_dense()845                else:846                    key = np.asarray(key)847            if com.is_bool_indexer(key) and len(self) == len(key):848                return self.take(np.arange(len(key), dtype=np.int32)[key])849            elif hasattr(key, '__len__'):850                return self.take(key)851            else:852                raise ValueError("Cannot slice with '{}'".format(key))853        return type(self)(data_slice, kind=self.kind)854    def _get_val_at(self, loc):855        n = len(self)856        if loc < 0:857            loc += n858        if loc >= n or loc < 0:859            raise IndexError('Out of bounds access')860        sp_loc = self.sp_index.lookup(loc)861        if sp_loc == -1:862            return self.fill_value863        else:864            return libindex.get_value_at(self.sp_values, sp_loc)865    def take(self, indices, allow_fill=False, fill_value=None):866        if is_scalar(indices):867            raise ValueError("'indices' must be an array, not a "868                             "scalar '{}'.".format(indices))869        indices = np.asarray(indices, dtype=np.int32)870        if indices.size == 0:871            result = []872            kwargs = {'dtype': self.dtype}873        elif allow_fill:874            result = self._take_with_fill(indices, fill_value=fill_value)875            kwargs = {}876        else:877            result = self._take_without_fill(indices)878            kwargs = {'dtype': self.dtype}879        return type(self)(result, fill_value=self.fill_value, kind=self.kind,880                          **kwargs)881    def _take_with_fill(self, indices, fill_value=None):882        if fill_value is None:883            fill_value = self.dtype.na_value884        if indices.min() < -1:885            raise ValueError("Invalid value in 'indices'. Must be between -1 "886                             "and the length of the array.")887        if indices.max() >= len(self):888            raise IndexError("out of bounds value in 'indices'.")889        if len(self) == 0:890            # Empty... Allow taking only if all empty891            if (indices == -1).all():892                dtype = np.result_type(self.sp_values, type(fill_value))893                taken = np.empty_like(indices, dtype=dtype)894                taken.fill(fill_value)895                return taken896            else:897                raise IndexError('cannot do a non-empty take from an empty '898                                 'axes.')899        sp_indexer = self.sp_index.lookup_array(indices)900        if self.sp_index.npoints == 0:901            # Avoid taking from the empty self.sp_values902            taken = np.full(sp_indexer.shape, fill_value=fill_value,903                            dtype=np.result_type(type(fill_value)))904        else:905            taken = self.sp_values.take(sp_indexer)906            # sp_indexer may be -1 for two reasons907            # 1.) we took for an index of -1 (new)908            # 2.) we took a value that was self.fill_value (old)909            new_fill_indices = indices == -1910            old_fill_indices = (sp_indexer == -1) & ~new_fill_indices911            # Fill in two steps.912            # Old fill values913            # New fill values914            # potentially coercing to a new dtype at each stage.915            m0 = sp_indexer[old_fill_indices] < 0916            m1 = sp_indexer[new_fill_indices] < 0917            result_type = taken.dtype918            if m0.any():919                result_type = np.result_type(result_type,920                                             type(self.fill_value))921                taken = taken.astype(result_type)922                taken[old_fill_indices] = self.fill_value923            if m1.any():924                result_type = np.result_type(result_type, type(fill_value))925                taken = taken.astype(result_type)926                taken[new_fill_indices] = fill_value927        return taken928    def _take_without_fill(self, indices):929        to_shift = indices < 0930        indices = indices.copy()931        n = len(self)932        if (indices.max() >= n) or (indices.min() < -n):933            if n == 0:934                raise IndexError("cannot do a non-empty take from an "935                                 "empty axes.")936            else:937                raise IndexError("out of bounds value in 'indices'.")938        if to_shift.any():939            indices[to_shift] += n940        if self.sp_index.npoints == 0:941            # edge case in take...942            # I think just return943            out = np.full(indices.shape, self.fill_value,944                          dtype=np.result_type(type(self.fill_value)))945            arr, sp_index, fill_value = make_sparse(out,946                                                    fill_value=self.fill_value)947            return type(self)(arr, sparse_index=sp_index,948                              fill_value=fill_value)949        sp_indexer = self.sp_index.lookup_array(indices)950        taken = self.sp_values.take(sp_indexer)951        fillable = (sp_indexer < 0)952        if fillable.any():953            # TODO: may need to coerce array to fill value954            result_type = np.result_type(taken, type(self.fill_value))955            taken = taken.astype(result_type)956            taken[fillable] = self.fill_value957        return taken958    def searchsorted(self, v, side="left", sorter=None):959        msg = "searchsorted requires high memory usage."960        warnings.warn(msg, PerformanceWarning, stacklevel=2)961        if not is_scalar(v):962            v = np.asarray(v)963        v = np.asarray(v)964        return np.asarray(self, dtype=self.dtype.subtype).searchsorted(965            v, side, sorter966        )967    def copy(self, deep=False):968        if deep:969            values = self.sp_values.copy()970        else:971            values = self.sp_values972        return self._simple_new(values, self.sp_index, self.dtype)973    @classmethod974    def _concat_same_type(cls, to_concat):975        fill_values = [x.fill_value for x in to_concat]976        fill_value = fill_values[0]977        # np.nan isn't a singleton, so we may end up with multiple978        # NaNs here, so we ignore tha all NA case too.979        if not (len(set(fill_values)) == 1 or isna(fill_values).all()):980            warnings.warn("Concatenating sparse arrays with multiple fill "981                          "values: '{}'. Picking the first and "982                          "converting the rest.".format(fill_values),983                          PerformanceWarning,984                          stacklevel=6)985            keep = to_concat[0]986            to_concat2 = [keep]987            for arr in to_concat[1:]:988                to_concat2.append(cls(np.asarray(arr), fill_value=fill_value))989            to_concat = to_concat2990        values = []991        length = 0992        if to_concat:993            sp_kind = to_concat[0].kind994        else:995            sp_kind = 'integer'996        if sp_kind == 'integer':997            indices = []998            for arr in to_concat:999                idx = arr.sp_index.to_int_index().indices.copy()1000                idx += length  # TODO: wraparound1001                length += arr.sp_index.length1002                values.append(arr.sp_values)1003                indices.append(idx)1004            data = np.concatenate(values)1005            indices = np.concatenate(indices)1006            sp_index = IntIndex(length, indices)1007        else:1008            # when concatentating block indices, we don't claim that you'll1009            # get an identical index as concating the values and then1010            # creating a new index. We don't want to spend the time trying1011            # to merge blocks across arrays in `to_concat`, so the resulting1012            # BlockIndex may have more blocs.1013            blengths = []1014            blocs = []1015            for arr in to_concat:1016                idx = arr.sp_index.to_block_index()1017                values.append(arr.sp_values)1018                blocs.append(idx.blocs.copy() + length)1019                blengths.append(idx.blengths)1020                length += arr.sp_index.length1021            data = np.concatenate(values)1022            blocs = np.concatenate(blocs)1023            blengths = np.concatenate(blengths)1024            sp_index = BlockIndex(length, blocs, blengths)1025        return cls(data, sparse_index=sp_index, fill_value=fill_value)1026    def astype(self, dtype=None, copy=True):1027        """1028        Change the dtype of a SparseArray.1029        The output will always be a SparseArray. To convert to a dense1030        ndarray with a certain dtype, use :meth:`numpy.asarray`.1031        Parameters1032        ----------1033        dtype : np.dtype or ExtensionDtype1034            For SparseDtype, this changes the dtype of1035            ``self.sp_values`` and the ``self.fill_value``.1036            For other dtypes, this only changes the dtype of1037            ``self.sp_values``.1038        copy : bool, default True1039            Whether to ensure a copy is made, even if not necessary.1040        Returns1041        -------1042        SparseArray1043        Examples1044        --------1045        >>> arr = SparseArray([0, 0, 1, 2])1046        >>> arr1047        [0, 0, 1, 2]1048        Fill: 01049        IntIndex1050        Indices: array([2, 3], dtype=int32)1051        >>> arr.astype(np.dtype('int32'))1052        [0, 0, 1, 2]1053        Fill: 01054        IntIndex1055        Indices: array([2, 3], dtype=int32)1056        Using a NumPy dtype with a different kind (e.g. float) will coerce1057        just ``self.sp_values``.1058        >>> arr.astype(np.dtype('float64'))1059        ... # doctest: +NORMALIZE_WHITESPACE1060        [0, 0, 1.0, 2.0]1061        Fill: 01062        IntIndex1063        Indices: array([2, 3], dtype=int32)1064        Use a SparseDtype if you wish to be change the fill value as well.1065        >>> arr.astype(SparseDtype("float64", fill_value=np.nan))1066        ... # doctest: +NORMALIZE_WHITESPACE1067        [nan, nan, 1.0, 2.0]1068        Fill: nan1069        IntIndex1070        Indices: array([2, 3], dtype=int32)1071        """1072        dtype = self.dtype.update_dtype(dtype)1073        subtype = dtype._subtype_with_str1074        sp_values = astype_nansafe(self.sp_values,1075                                   subtype,1076                                   copy=copy)1077        if sp_values is self.sp_values and copy:1078            sp_values = sp_values.copy()1079        return self._simple_new(sp_values,1080                                self.sp_index,1081                                dtype)1082    def map(self, mapper):1083        """1084        Map categories using input correspondence (dict, Series, or function).1085        Parameters1086        ----------1087        mapper : dict, Series, callable1088            The correspondence from old values to new.1089        Returns1090        -------1091        SparseArray1092            The output array will have the same density as the input.1093            The output fill value will be the result of applying the1094            mapping to ``self.fill_value``1095        Examples1096        --------1097        >>> arr = pd.SparseArray([0, 1, 2])1098        >>> arr.apply(lambda x: x + 10)1099        [10, 11, 12]1100        Fill: 101101        IntIndex1102        Indices: array([1, 2], dtype=int32)1103        >>> arr.apply({0: 10, 1: 11, 2: 12})1104        [10, 11, 12]1105        Fill: 101106        IntIndex1107        Indices: array([1, 2], dtype=int32)1108        >>> arr.apply(pd.Series([10, 11, 12], index=[0, 1, 2]))1109        [10, 11, 12]1110        Fill: 101111        IntIndex1112        Indices: array([1, 2], dtype=int32)1113        """1114        # this is used in apply.1115        # We get hit since we're an "is_extension_type" but regular extension1116        # types are not hit. This may be worth adding to the interface.1117        if isinstance(mapper, ABCSeries):1118            mapper = mapper.to_dict()1119        if isinstance(mapper, compat.Mapping):1120            fill_value = mapper.get(self.fill_value, self.fill_value)1121            sp_values = [mapper.get(x, None) for x in self.sp_values]1122        else:1123            fill_value = mapper(self.fill_value)1124            sp_values = [mapper(x) for x in self.sp_values]1125        return type(self)(sp_values, sparse_index=self.sp_index,1126                          fill_value=fill_value)1127    def to_dense(self):1128        """1129        Convert SparseArray to a NumPy array.1130        Returns1131        -------1132        arr : NumPy array1133        """1134        return np.asarray(self, dtype=self.sp_values.dtype)1135    # TODO: Look into deprecating this in favor of `to_dense`.1136    get_values = to_dense1137    # ------------------------------------------------------------------------1138    # IO1139    # ------------------------------------------------------------------------1140    def __setstate__(self, state):1141        """Necessary for making this object picklable"""1142        if isinstance(state, tuple):1143            # Compat for pandas < 0.24.01144            nd_state, (fill_value, sp_index) = state1145            sparse_values = np.array([])1146            sparse_values.__setstate__(nd_state)1147            self._sparse_values = sparse_values1148            self._sparse_index = sp_index1149            self._dtype = SparseDtype(sparse_values.dtype, fill_value)1150        else:1151            self.__dict__.update(state)1152    def nonzero(self):1153        if self.fill_value == 0:1154            return self.sp_index.to_int_index().indices,1155        else:1156            return self.sp_index.to_int_index().indices[self.sp_values != 0],1157    # ------------------------------------------------------------------------1158    # Reductions1159    # ------------------------------------------------------------------------1160    def _reduce(self, name, skipna=True, **kwargs):1161        method = getattr(self, name, None)1162        if method is None:1163            raise TypeError("cannot perform {name} with type {dtype}".format(1164                name=name, dtype=self.dtype))1165        if skipna:1166            arr = self1167        else:1168            arr = self.dropna()1169        # we don't support these kwargs.1170        # They should only be present when called via pandas, so do it here.1171        # instead of in `any` / `all` (which will raise if they're present,1172        # thanks to nv.validate1173        kwargs.pop('filter_type', None)1174        kwargs.pop('numeric_only', None)1175        kwargs.pop('op', None)1176        return getattr(arr, name)(**kwargs)1177    def all(self, axis=None, *args, **kwargs):1178        """1179        Tests whether all elements evaluate True1180        Returns1181        -------1182        all : bool1183        See Also1184        --------1185        numpy.all1186        """1187        nv.validate_all(args, kwargs)1188        values = self.sp_values1189        if len(values) != len(self) and not np.all(self.fill_value):1190            return False1191        return values.all()1192    def any(self, axis=0, *args, **kwargs):1193        """1194        Tests whether at least one of elements evaluate True1195        Returns1196        -------1197        any : bool1198        See Also1199        --------1200        numpy.any1201        """1202        nv.validate_any(args, kwargs)1203        values = self.sp_values1204        if len(values) != len(self) and np.any(self.fill_value):1205            return True1206        return values.any().item()1207    def sum(self, axis=0, *args, **kwargs):1208        """1209        Sum of non-NA/null values1210        Returns1211        -------1212        sum : float1213        """1214        nv.validate_sum(args, kwargs)1215        valid_vals = self._valid_sp_values1216        sp_sum = valid_vals.sum()1217        if self._null_fill_value:1218            return sp_sum1219        else:1220            nsparse = self.sp_index.ngaps1221            return sp_sum + self.fill_value * nsparse1222    def cumsum(self, axis=0, *args, **kwargs):1223        """1224        Cumulative sum of non-NA/null values.1225        When performing the cumulative summation, any non-NA/null values will1226        be skipped. The resulting SparseArray will preserve the locations of1227        NaN values, but the fill value will be `np.nan` regardless.1228        Parameters1229        ----------1230        axis : int or None1231            Axis over which to perform the cumulative summation. If None,1232            perform cumulative summation over flattened array.1233        Returns1234        -------1235        cumsum : SparseArray1236        """1237        nv.validate_cumsum(args, kwargs)1238        if axis is not None and axis >= self.ndim:  # Mimic ndarray behaviour.1239            raise ValueError("axis(={axis}) out of bounds".format(axis=axis))1240        if not self._null_fill_value:1241            return SparseArray(self.to_dense()).cumsum()1242        return SparseArray(self.sp_values.cumsum(), sparse_index=self.sp_index,1243                           fill_value=self.fill_value)1244    def mean(self, axis=0, *args, **kwargs):1245        """1246        Mean of non-NA/null values1247        Returns1248        -------1249        mean : float1250        """1251        nv.validate_mean(args, kwargs)1252        valid_vals = self._valid_sp_values1253        sp_sum = valid_vals.sum()1254        ct = len(valid_vals)1255        if self._null_fill_value:1256            return sp_sum / ct1257        else:1258            nsparse = self.sp_index.ngaps1259            return (sp_sum + self.fill_value * nsparse) / (ct + nsparse)1260    def transpose(self, *axes):1261        """1262        Returns the SparseArray.1263        """1264        return self1265    @property1266    def T(self):1267        """1268        Returns the SparseArray.1269        """1270        return self1271    # ------------------------------------------------------------------------1272    # Ufuncs1273    # ------------------------------------------------------------------------1274    def __array_wrap__(self, array, context=None):1275        from pandas.core.dtypes.generic import ABCSparseSeries1276        ufunc, inputs, _ = context1277        inputs = tuple(x.values if isinstance(x, ABCSparseSeries) else x1278                       for x in inputs)1279        return self.__array_ufunc__(ufunc, '__call__', *inputs)1280    _HANDLED_TYPES = (np.ndarray, numbers.Number)1281    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):1282        out = kwargs.get('out', ())1283        for x in inputs + out:1284            if not isinstance(x, self._HANDLED_TYPES + (SparseArray,)):1285                return NotImplemented1286        special = {'add', 'sub', 'mul', 'pow', 'mod', 'floordiv', 'truediv',1287                   'divmod', 'eq', 'ne', 'lt', 'gt', 'le', 'ge', 'remainder'}1288        if compat.PY2:1289            special.add('div')1290        aliases = {1291            'subtract': 'sub',1292            'multiply': 'mul',1293            'floor_divide': 'floordiv',1294            'true_divide': 'truediv',1295            'power': 'pow',1296            'remainder': 'mod',1297            'divide': 'div',1298            'equal': 'eq',1299            'not_equal': 'ne',1300            'less': 'lt',1301            'less_equal': 'le',1302            'greater': 'gt',1303            'greater_equal': 'ge',1304        }1305        flipped = {1306            'lt': '__gt__',1307            'le': '__ge__',1308            'gt': '__lt__',1309            'ge': '__le__',1310            'eq': '__eq__',1311            'ne': '__ne__',1312        }1313        op_name = ufunc.__name__1314        op_name = aliases.get(op_name, op_name)1315        if op_name in special and kwargs.get('out') is None:1316            if isinstance(inputs[0], type(self)):1317                return getattr(self, '__{}__'.format(op_name))(inputs[1])1318            else:1319                name = flipped.get(op_name, '__r{}__'.format(op_name))1320                return getattr(self, name)(inputs[0])1321        if len(inputs) == 1:1322            # No alignment necessary.1323            sp_values = getattr(ufunc, method)(self.sp_values, **kwargs)1324            fill_value = getattr(ufunc, method)(self.fill_value, **kwargs)1325            return self._simple_new(sp_values,1326                                    self.sp_index,1327                                    SparseDtype(sp_values.dtype, fill_value))1328        result = getattr(ufunc, method)(*[np.asarray(x) for x in inputs],1329                                        **kwargs)1330        if out:1331            if len(out) == 1:1332                out = out[0]1333            return out1334        if type(result) is tuple:1335            return tuple(type(self)(x) for x in result)1336        elif method == 'at':1337            # no return value1338            return None1339        else:1340            return type(self)(result)1341    def __abs__(self):1342        return np.abs(self)1343    # ------------------------------------------------------------------------1344    # Ops1345    # ------------------------------------------------------------------------1346    @classmethod1347    def _create_unary_method(cls, op):1348        def sparse_unary_method(self):1349            fill_value = op(np.array(self.fill_value)).item()1350            values = op(self.sp_values)1351            dtype = SparseDtype(values.dtype, fill_value)1352            return cls._simple_new(values, self.sp_index, dtype)1353        name = '__{name}__'.format(name=op.__name__)1354        return compat.set_function_name(sparse_unary_method, name, cls)1355    @classmethod1356    def _create_arithmetic_method(cls, op):1357        def sparse_arithmetic_method(self, other):1358            op_name = op.__name__1359            if isinstance(other, (ABCSeries, ABCIndexClass)):1360                # Rely on pandas to dispatch to us.1361                return NotImplemented1362            if isinstance(other, SparseArray):1363                return _sparse_array_op(self, other, op, op_name)1364            elif is_scalar(other):1365                with np.errstate(all='ignore'):1366                    fill = op(_get_fill(self), np.asarray(other))1367                    result = op(self.sp_values, other)1368                if op_name == 'divmod':1369                    left, right = result1370                    lfill, rfill = fill1371                    return (_wrap_result(op_name, left, self.sp_index, lfill),1372                            _wrap_result(op_name, right, self.sp_index, rfill))1373                return _wrap_result(op_name, result, self.sp_index, fill)1374            else:1375                other = np.asarray(other)1376                with np.errstate(all='ignore'):1377                    # TODO: delete sparse stuff in core/ops.py1378                    # TODO: look into _wrap_result1379                    if len(self) != len(other):1380                        raise AssertionError(...

0002_auto_20210108_1807.py

Source:0002_auto_20210108_1807.py

1# Generated by Django 3.1.4 on 2021-01-08 18:072from django.db import migrations, models3class Migration(migrations.Migration):4    dependencies = [5        ('module', '0001_initial'),6    ]7    operations = [8        migrations.RemoveField(9            model_name='module',10            name='icon',11        ),12        migrations.AddField(13            model_name='module',14            name='icon_name',15            field=models.CharField(blank=True, choices=[('house-door.svg', 'house-door.svg'), ('graph-up.svg', 'graph-up.svg'), ('sort-alpha-up-alt.svg', 'sort-alpha-up-alt.svg'), ('eye-slash-fill.svg', 'eye-slash-fill.svg'), ('arrows-angle-expand.svg', 'arrows-angle-expand.svg'), ('layout-text-window.svg', 'layout-text-window.svg'), ('textarea.svg', 'textarea.svg'), ('slack.svg', 'slack.svg'), ('tv-fill.svg', 'tv-fill.svg'), ('hash.svg', 'hash.svg'), ('file-medical-fill.svg', 'file-medical-fill.svg'), ('calculator-fill.svg', 'calculator-fill.svg'), ('twitter.svg', 'twitter.svg'), ('bookmark-heart.svg', 'bookmark-heart.svg'), ('star.svg', 'star.svg'), ('at.svg', 'at.svg'), ('type-italic.svg', 'type-italic.svg'), ('hand-index.svg', 'hand-index.svg'), ('volume-off.svg', 'volume-off.svg'), ('telephone-plus-fill.svg', 'telephone-plus-fill.svg'), ('arrow-down-left.svg', 'arrow-down-left.svg'), ('hand-index-thumb.svg', 'hand-index-thumb.svg'), ('calendar2-range.svg', 'calendar2-range.svg'), ('dice-4.svg', 'dice-4.svg'), ('inboxes-fill.svg', 'inboxes-fill.svg'), ('bag-x-fill.svg', 'bag-x-fill.svg'), ('caret-down-square-fill.svg', 'caret-down-square-fill.svg'), ('thermometer-half.svg', 'thermometer-half.svg'), ('calendar2-day-fill.svg', 'calendar2-day-fill.svg'), ('alt.svg', 'alt.svg'), ('heart-half.svg', 'heart-half.svg'), ('pause-btn.svg', 'pause-btn.svg'), ('plus-circle-fill.svg', 'plus-circle-fill.svg'), ('mic.svg', 'mic.svg'), ('person-lines-fill.svg', 'person-lines-fill.svg'), ('markdown.svg', 'markdown.svg'), ('shield-minus.svg', 'shield-minus.svg'), ('file-earmark-richtext-fill.svg', 'file-earmark-richtext-fill.svg'), ('trophy-fill.svg', 'trophy-fill.svg'), ('hdd.svg', 'hdd.svg'), ('shop.svg', 'shop.svg'), ('skip-backward-circle.svg', 'skip-backward-circle.svg'), ('bar-chart.svg', 'bar-chart.svg'), ('phone-landscape.svg', 'phone-landscape.svg'), ('suit-spade.svg', 'suit-spade.svg'), ('wrench.svg', 'wrench.svg'), ('badge-vo.svg', 'badge-vo.svg'), ('house-door-fill.svg', 'house-door-fill.svg'), ('sort-down.svg', 'sort-down.svg'), ('plus-circle.svg', 'plus-circle.svg'), ('skip-end-circle.svg', 'skip-end-circle.svg'), ('inboxes.svg', 'inboxes.svg'), ('envelope-open-fill.svg', 'envelope-open-fill.svg'), ('file-arrow-down.svg', 'file-arrow-down.svg'), ('file-x.svg', 'file-x.svg'), ('record-btn.svg', 'record-btn.svg'), ('basket.svg', 'basket.svg'), ('award-fill.svg', 'award-fill.svg'), ('badge-8k.svg', 'badge-8k.svg'), ('flower3.svg', 'flower3.svg'), ('skip-start-circle-fill.svg', 'skip-start-circle-fill.svg'), ('voicemail.svg', 'voicemail.svg'), ('arrow-up-down.svg', 'arrow-up-down.svg'), ('ui-checks-grid.svg', 'ui-checks-grid.svg'), ('upc.svg', 'upc.svg'), ('tablet.svg', 'tablet.svg'), ('bag-plus.svg', 'bag-plus.svg'), ('calendar3.svg', 'calendar3.svg'), ('calendar2-x-fill.svg', 'calendar2-x-fill.svg'), ('arrow-up-left-circle.svg', 'arrow-up-left-circle.svg'), ('chat-square-text.svg', 'chat-square-text.svg'), ('toggle-on.svg', 'toggle-on.svg'), ('patch-plus.svg', 'patch-plus.svg'), ('peace.svg', 'peace.svg'), ('file-word.svg', 'file-word.svg'), ('chat-right-quote.svg', 'chat-right-quote.svg'), ('file-earmark-person.svg', 'file-earmark-person.svg'), ('bell.svg', 'bell.svg'), ('share.svg', 'share.svg'), ('easel.svg', 'easel.svg'), ('layout-text-window-reverse.svg', 'layout-text-window-reverse.svg'), ('file-bar-graph-fill.svg', 'file-bar-graph-fill.svg'), ('pentagon.svg', 'pentagon.svg'), ('table.svg', 'table.svg'), ('patch-question.svg', 'patch-question.svg'), ('gem.svg', 'gem.svg'), ('person-fill.svg', 'person-fill.svg'), ('file-earmark-excel-fill.svg', 'file-earmark-excel-fill.svg'), ('bookmark-star-fill.svg', 'bookmark-star-fill.svg'), ('option.svg', 'option.svg'), ('stop-circle.svg', 'stop-circle.svg'), ('layout-sidebar-inset-reverse.svg', 'layout-sidebar-inset-reverse.svg'), ('bag-check.svg', 'bag-check.svg'), ('badge-cc.svg', 'badge-cc.svg'), ('octagon-fill.svg', 'octagon-fill.svg'), ('pause-circle.svg', 'pause-circle.svg'), ('arrow-down-square.svg', 'arrow-down-square.svg'), ('hdd-network-fill.svg', 'hdd-network-fill.svg'), ('chevron-right.svg', 'chevron-right.svg'), ('peace-fill.svg', 'peace-fill.svg'), ('journal-bookmark-fill.svg', 'journal-bookmark-fill.svg'), ('collection.svg', 'collection.svg'), ('headphones.svg', 'headphones.svg'), ('chat-square.svg', 'chat-square.svg'), ('filter-square.svg', 'filter-square.svg'), ('blockquote-left.svg', 'blockquote-left.svg'), ('folder-fill.svg', 'folder-fill.svg'), ('crop.svg', 'crop.svg'), ('play-circle.svg', 'play-circle.svg'), 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0003_auto_20210110_1957.py

Source:0003_auto_20210110_1957.py

1# Generated by Django 3.1.4 on 2021-01-10 19:572from django.db import migrations, models3class Migration(migrations.Migration):4    dependencies = [5        ('module', '0002_auto_20210108_1807'),6    ]7    operations = [8        migrations.AlterField(9            model_name='module',10            name='icon_name',11            field=models.CharField(blank=True, choices=[('alarm-fill.svg', 'alarm-fill.svg'), ('alarm.svg', 'alarm.svg'), ('align-bottom.svg', 'align-bottom.svg'), ('align-center.svg', 'align-center.svg'), ('align-end.svg', 'align-end.svg'), ('align-middle.svg', 'align-middle.svg'), ('align-start.svg', 'align-start.svg'), ('align-top.svg', 'align-top.svg'), ('alt.svg', 'alt.svg'), ('app-indicator.svg', 'app-indicator.svg'), ('app.svg', 'app.svg'), ('archive-fill.svg', 'archive-fill.svg'), ('archive.svg', 'archive.svg'), ('arrow-90deg-down.svg', 'arrow-90deg-down.svg'), ('arrow-90deg-left.svg', 'arrow-90deg-left.svg'), ('arrow-90deg-right.svg', 'arrow-90deg-right.svg'), 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'text-left.svg'), ('text-paragraph.svg', 'text-paragraph.svg'), ('text-right.svg', 'text-right.svg'), ('textarea-resize.svg', 'textarea-resize.svg'), ('textarea-t.svg', 'textarea-t.svg'), ('textarea.svg', 'textarea.svg'), ('thermometer-half.svg', 'thermometer-half.svg'), ('thermometer.svg', 'thermometer.svg'), ('three-dots-vertical.svg', 'three-dots-vertical.svg'), ('three-dots.svg', 'three-dots.svg'), ('toggle-off.svg', 'toggle-off.svg'), ('toggle-on.svg', 'toggle-on.svg'), ('toggle2-off.svg', 'toggle2-off.svg'), ('toggle2-on.svg', 'toggle2-on.svg'), ('toggles.svg', 'toggles.svg'), ('toggles2.svg', 'toggles2.svg'), ('tools.svg', 'tools.svg'), ('trash-fill.svg', 'trash-fill.svg'), ('trash.svg', 'trash.svg'), ('trash2-fill.svg', 'trash2-fill.svg'), ('trash2.svg', 'trash2.svg'), ('tree-fill.svg', 'tree-fill.svg'), ('tree.svg', 'tree.svg'), ('triangle-fill.svg', 'triangle-fill.svg'), ('triangle-half.svg', 'triangle-half.svg'), ('triangle.svg', 'triangle.svg'), ('trophy-fill.svg', 'trophy-fill.svg'), ('trophy.svg', 'trophy.svg'), ('truck-flatbed.svg', 'truck-flatbed.svg'), ('truck.svg', 'truck.svg'), ('tv-fill.svg', 'tv-fill.svg'), ('tv.svg', 'tv.svg'), ('twitch.svg', 'twitch.svg'), ('twitter.svg', 'twitter.svg'), ('type-bold.svg', 'type-bold.svg'), ('type-h1.svg', 'type-h1.svg'), ('type-h2.svg', 'type-h2.svg'), ('type-h3.svg', 'type-h3.svg'), ('type-italic.svg', 'type-italic.svg'), ('type-strikethrough.svg', 'type-strikethrough.svg'), ('type-underline.svg', 'type-underline.svg'), ('type.svg', 'type.svg'), ('ui-checks-grid.svg', 'ui-checks-grid.svg'), ('ui-checks.svg', 'ui-checks.svg'), ('ui-radios-grid.svg', 'ui-radios-grid.svg'), ('ui-radios.svg', 'ui-radios.svg'), ('union.svg', 'union.svg'), ('unlock-fill.svg', 'unlock-fill.svg'), ('unlock.svg', 'unlock.svg'), ('upc-scan.svg', 'upc-scan.svg'), ('upc.svg', 'upc.svg'), ('upload.svg', 'upload.svg'), ('vector-pen.svg', 'vector-pen.svg'), ('view-list.svg', 'view-list.svg'), ('view-stacked.svg', 'view-stacked.svg'), ('vinyl-fill.svg', 'vinyl-fill.svg'), ('vinyl.svg', 'vinyl.svg'), ('voicemail.svg', 'voicemail.svg'), ('volume-down-fill.svg', 'volume-down-fill.svg'), ('volume-down.svg', 'volume-down.svg'), ('volume-mute-fill.svg', 'volume-mute-fill.svg'), ('volume-mute.svg', 'volume-mute.svg'), ('volume-off-fill.svg', 'volume-off-fill.svg'), ('volume-off.svg', 'volume-off.svg'), ('volume-up-fill.svg', 'volume-up-fill.svg'), ('volume-up.svg', 'volume-up.svg'), ('vr.svg', 'vr.svg'), ('wallet-fill.svg', 'wallet-fill.svg'), ('wallet.svg', 'wallet.svg'), ('wallet2.svg', 'wallet2.svg'), ('watch.svg', 'watch.svg'), ('wifi-1.svg', 'wifi-1.svg'), ('wifi-2.svg', 'wifi-2.svg'), ('wifi-off.svg', 'wifi-off.svg'), ('wifi.svg', 'wifi.svg'), ('window.svg', 'window.svg'), ('wrench.svg', 'wrench.svg'), ('x-circle-fill.svg', 'x-circle-fill.svg'), ('x-circle.svg', 'x-circle.svg'), ('x-diamond-fill.svg', 'x-diamond-fill.svg'), ('x-diamond.svg', 'x-diamond.svg'), ('x-octagon-fill.svg', 'x-octagon-fill.svg'), ('x-octagon.svg', 'x-octagon.svg'), ('x-square-fill.svg', 'x-square-fill.svg'), ('x-square.svg', 'x-square.svg'), ('x.svg', 'x.svg'), ('youtube.svg', 'youtube.svg'), ('zoom-in.svg', 'zoom-in.svg'), ('zoom-out.svg', 'zoom-out.svg')], max_length=100, verbose_name='Ãcono'),12        ),...

test_array.py

Source:test_array.py

...937])938@pytest.mark.parametrize("fill_value", [939    np.nan, 0, 1940])941def test_unique_na_fill(arr, fill_value):942    a = pd.SparseArray(arr, fill_value=fill_value).unique()943    b = pd.Series(arr).unique()944    assert isinstance(a, SparseArray)945    a = np.asarray(a)946    tm.assert_numpy_array_equal(a, b)947def test_unique_all_sparse():948    # https://github.com/pandas-dev/pandas/issues/23168949    arr = SparseArray([0, 0])950    result = arr.unique()951    expected = SparseArray([0])952    tm.assert_sp_array_equal(result, expected)953def test_map():954    arr = SparseArray([0, 1, 2])955    expected = SparseArray([10, 11, 12], fill_value=10)...

test_arithmetics.py

Source:test_arithmetics.py

1import operator2import numpy as np3import pytest4import pandas as pd5from pandas.core.sparse.api import SparseDtype6import pandas.util.testing as tm7class TestSparseArrayArithmetics(object):8    _base = np.array9    _klass = pd.SparseArray10    def _assert(self, a, b):11        tm.assert_numpy_array_equal(a, b)12    def _check_numeric_ops(self, a, b, a_dense, b_dense):13        with np.errstate(invalid='ignore', divide='ignore'):14            # Unfortunately, trying to wrap the computation of each expected15            # value is with np.errstate() is too tedious.16            # sparse & sparse17            self._assert((a + b).to_dense(), a_dense + b_dense)18            self._assert((b + a).to_dense(), b_dense + a_dense)19            self._assert((a - b).to_dense(), a_dense - b_dense)20            self._assert((b - a).to_dense(), b_dense - a_dense)21            self._assert((a * b).to_dense(), a_dense * b_dense)22            self._assert((b * a).to_dense(), b_dense * a_dense)23            # pandas uses future division24            self._assert((a / b).to_dense(), a_dense * 1.0 / b_dense)25            self._assert((b / a).to_dense(), b_dense * 1.0 / a_dense)26            # ToDo: FIXME in GH 1384327            if not (self._base == pd.Series and28                    a.dtype.subtype == np.dtype('int64')):29                self._assert((a // b).to_dense(), a_dense // b_dense)30                self._assert((b // a).to_dense(), b_dense // a_dense)31            self._assert((a % b).to_dense(), a_dense % b_dense)32            self._assert((b % a).to_dense(), b_dense % a_dense)33            self._assert((a ** b).to_dense(), a_dense ** b_dense)34            self._assert((b ** a).to_dense(), b_dense ** a_dense)35            # sparse & dense36            self._assert((a + b_dense).to_dense(), a_dense + b_dense)37            self._assert((b_dense + a).to_dense(), b_dense + a_dense)38            self._assert((a - b_dense).to_dense(), a_dense - b_dense)39            self._assert((b_dense - a).to_dense(), b_dense - a_dense)40            self._assert((a * b_dense).to_dense(), a_dense * b_dense)41            self._assert((b_dense * a).to_dense(), b_dense * a_dense)42            # pandas uses future division43            self._assert((a / b_dense).to_dense(), a_dense * 1.0 / b_dense)44            self._assert((b_dense / a).to_dense(), b_dense * 1.0 / a_dense)45            # ToDo: FIXME in GH 1384346            if not (self._base == pd.Series and47                    a.dtype.subtype == np.dtype('int64')):48                self._assert((a // b_dense).to_dense(), a_dense // b_dense)49                self._assert((b_dense // a).to_dense(), b_dense // a_dense)50            self._assert((a % b_dense).to_dense(), a_dense % b_dense)51            self._assert((b_dense % a).to_dense(), b_dense % a_dense)52            self._assert((a ** b_dense).to_dense(), a_dense ** b_dense)53            self._assert((b_dense ** a).to_dense(), b_dense ** a_dense)54    def _check_bool_result(self, res):55        assert isinstance(res, self._klass)56        assert isinstance(res.dtype, SparseDtype)57        assert res.dtype.subtype == np.bool58        assert isinstance(res.fill_value, bool)59    def _check_comparison_ops(self, a, b, a_dense, b_dense):60        with np.errstate(invalid='ignore'):61            # Unfortunately, trying to wrap the computation of each expected62            # value is with np.errstate() is too tedious.63            #64            # sparse & sparse65            self._check_bool_result(a == b)66            self._assert((a == b).to_dense(), a_dense == b_dense)67            self._check_bool_result(a != b)68            self._assert((a != b).to_dense(), a_dense != b_dense)69            self._check_bool_result(a >= b)70            self._assert((a >= b).to_dense(), a_dense >= b_dense)71            self._check_bool_result(a <= b)72            self._assert((a <= b).to_dense(), a_dense <= b_dense)73            self._check_bool_result(a > b)74            self._assert((a > b).to_dense(), a_dense > b_dense)75            self._check_bool_result(a < b)76            self._assert((a < b).to_dense(), a_dense < b_dense)77            # sparse & dense78            self._check_bool_result(a == b_dense)79            self._assert((a == b_dense).to_dense(), a_dense == b_dense)80            self._check_bool_result(a != b_dense)81            self._assert((a != b_dense).to_dense(), a_dense != b_dense)82            self._check_bool_result(a >= b_dense)83            self._assert((a >= b_dense).to_dense(), a_dense >= b_dense)84            self._check_bool_result(a <= b_dense)85            self._assert((a <= b_dense).to_dense(), a_dense <= b_dense)86            self._check_bool_result(a > b_dense)87            self._assert((a > b_dense).to_dense(), a_dense > b_dense)88            self._check_bool_result(a < b_dense)89            self._assert((a < b_dense).to_dense(), a_dense < b_dense)90    def _check_logical_ops(self, a, b, a_dense, b_dense):91        # sparse & sparse92        self._check_bool_result(a & b)93        self._assert((a & b).to_dense(), a_dense & b_dense)94        self._check_bool_result(a | b)95        self._assert((a | b).to_dense(), a_dense | b_dense)96        # sparse & dense97        self._check_bool_result(a & b_dense)98        self._assert((a & b_dense).to_dense(), a_dense & b_dense)99        self._check_bool_result(a | b_dense)100        self._assert((a | b_dense).to_dense(), a_dense | b_dense)101    def test_float_scalar(self):102        values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])103        for kind in ['integer', 'block']:104            a = self._klass(values, kind=kind)105            self._check_numeric_ops(a, 1, values, 1)106            self._check_numeric_ops(a, 0, values, 0)107            self._check_numeric_ops(a, 3, values, 3)108            a = self._klass(values, kind=kind, fill_value=0)109            self._check_numeric_ops(a, 1, values, 1)110            self._check_numeric_ops(a, 0, values, 0)111            self._check_numeric_ops(a, 3, values, 3)112            a = self._klass(values, kind=kind, fill_value=2)113            self._check_numeric_ops(a, 1, values, 1)114            self._check_numeric_ops(a, 0, values, 0)115            self._check_numeric_ops(a, 3, values, 3)116    def test_float_scalar_comparison(self):117        values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])118        for kind in ['integer', 'block']:119            a = self._klass(values, kind=kind)120            self._check_comparison_ops(a, 1, values, 1)121            self._check_comparison_ops(a, 0, values, 0)122            self._check_comparison_ops(a, 3, values, 3)123            a = self._klass(values, kind=kind, fill_value=0)124            self._check_comparison_ops(a, 1, values, 1)125            self._check_comparison_ops(a, 0, values, 0)126            self._check_comparison_ops(a, 3, values, 3)127            a = self._klass(values, kind=kind, fill_value=2)128            self._check_comparison_ops(a, 1, values, 1)129            self._check_comparison_ops(a, 0, values, 0)130            self._check_comparison_ops(a, 3, values, 3)131    def test_float_same_index(self):132        # when sp_index are the same133        for kind in ['integer', 'block']:134            values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])135            rvalues = self._base([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan])136            a = self._klass(values, kind=kind)137            b = self._klass(rvalues, kind=kind)138            self._check_numeric_ops(a, b, values, rvalues)139            values = self._base([0., 1., 2., 6., 0., 0., 1., 2., 1., 0.])140            rvalues = self._base([0., 2., 3., 4., 0., 0., 1., 3., 2., 0.])141            a = self._klass(values, kind=kind, fill_value=0)142            b = self._klass(rvalues, kind=kind, fill_value=0)143            self._check_numeric_ops(a, b, values, rvalues)144    def test_float_same_index_comparison(self):145        # when sp_index are the same146        for kind in ['integer', 'block']:147            values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])148            rvalues = self._base([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan])149            a = self._klass(values, kind=kind)150            b = self._klass(rvalues, kind=kind)151            self._check_comparison_ops(a, b, values, rvalues)152            values = self._base([0., 1., 2., 6., 0., 0., 1., 2., 1., 0.])153            rvalues = self._base([0., 2., 3., 4., 0., 0., 1., 3., 2., 0.])154            a = self._klass(values, kind=kind, fill_value=0)155            b = self._klass(rvalues, kind=kind, fill_value=0)156            self._check_comparison_ops(a, b, values, rvalues)157    def test_float_array(self):158        values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])159        rvalues = self._base([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])160        for kind in ['integer', 'block']:161            a = self._klass(values, kind=kind)162            b = self._klass(rvalues, kind=kind)163            self._check_numeric_ops(a, b, values, rvalues)164            self._check_numeric_ops(a, b * 0, values, rvalues * 0)165            a = self._klass(values, kind=kind, fill_value=0)166            b = self._klass(rvalues, kind=kind)167            self._check_numeric_ops(a, b, values, rvalues)168            a = self._klass(values, kind=kind, fill_value=0)169            b = self._klass(rvalues, kind=kind, fill_value=0)170            self._check_numeric_ops(a, b, values, rvalues)171            a = self._klass(values, kind=kind, fill_value=1)172            b = self._klass(rvalues, kind=kind, fill_value=2)173            self._check_numeric_ops(a, b, values, rvalues)174    def test_float_array_different_kind(self):175        values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])176        rvalues = self._base([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])177        a = self._klass(values, kind='integer')178        b = self._klass(rvalues, kind='block')179        self._check_numeric_ops(a, b, values, rvalues)180        self._check_numeric_ops(a, b * 0, values, rvalues * 0)181        a = self._klass(values, kind='integer', fill_value=0)182        b = self._klass(rvalues, kind='block')183        self._check_numeric_ops(a, b, values, rvalues)184        a = self._klass(values, kind='integer', fill_value=0)185        b = self._klass(rvalues, kind='block', fill_value=0)186        self._check_numeric_ops(a, b, values, rvalues)187        a = self._klass(values, kind='integer', fill_value=1)188        b = self._klass(rvalues, kind='block', fill_value=2)189        self._check_numeric_ops(a, b, values, rvalues)190    def test_float_array_comparison(self):191        values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])192        rvalues = self._base([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan])193        for kind in ['integer', 'block']:194            a = self._klass(values, kind=kind)195            b = self._klass(rvalues, kind=kind)196            self._check_comparison_ops(a, b, values, rvalues)197            self._check_comparison_ops(a, b * 0, values, rvalues * 0)198            a = self._klass(values, kind=kind, fill_value=0)199            b = self._klass(rvalues, kind=kind)200            self._check_comparison_ops(a, b, values, rvalues)201            a = self._klass(values, kind=kind, fill_value=0)202            b = self._klass(rvalues, kind=kind, fill_value=0)203            self._check_comparison_ops(a, b, values, rvalues)204            a = self._klass(values, kind=kind, fill_value=1)205            b = self._klass(rvalues, kind=kind, fill_value=2)206            self._check_comparison_ops(a, b, values, rvalues)207    def test_int_array(self):208        # have to specify dtype explicitly until fixing GH 667209        dtype = np.int64210        values = self._base([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype)211        rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype)212        for kind in ['integer', 'block']:213            a = self._klass(values, dtype=dtype, kind=kind)214            assert a.dtype == SparseDtype(dtype)215            b = self._klass(rvalues, dtype=dtype, kind=kind)216            assert b.dtype == SparseDtype(dtype)217            self._check_numeric_ops(a, b, values, rvalues)218            self._check_numeric_ops(a, b * 0, values, rvalues * 0)219            a = self._klass(values, fill_value=0, dtype=dtype, kind=kind)220            assert a.dtype == SparseDtype(dtype)221            b = self._klass(rvalues, dtype=dtype, kind=kind)222            assert b.dtype == SparseDtype(dtype)223            self._check_numeric_ops(a, b, values, rvalues)224            a = self._klass(values, fill_value=0, dtype=dtype, kind=kind)225            assert a.dtype == SparseDtype(dtype)226            b = self._klass(rvalues, fill_value=0, dtype=dtype, kind=kind)227            assert b.dtype == SparseDtype(dtype)228            self._check_numeric_ops(a, b, values, rvalues)229            a = self._klass(values, fill_value=1, dtype=dtype, kind=kind)230            assert a.dtype == SparseDtype(dtype, fill_value=1)231            b = self._klass(rvalues, fill_value=2, dtype=dtype, kind=kind)232            assert b.dtype == SparseDtype(dtype, fill_value=2)233            self._check_numeric_ops(a, b, values, rvalues)234    def test_int_array_comparison(self):235        # int32 NI ATM236        for dtype in ['int64']:237            values = self._base([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype)238            rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype)239            for kind in ['integer', 'block']:240                a = self._klass(values, dtype=dtype, kind=kind)241                b = self._klass(rvalues, dtype=dtype, kind=kind)242                self._check_comparison_ops(a, b, values, rvalues)243                self._check_comparison_ops(a, b * 0, values, rvalues * 0)244                a = self._klass(values, dtype=dtype, kind=kind, fill_value=0)245                b = self._klass(rvalues, dtype=dtype, kind=kind)246                self._check_comparison_ops(a, b, values, rvalues)247                a = self._klass(values, dtype=dtype, kind=kind, fill_value=0)248                b = self._klass(rvalues, dtype=dtype, kind=kind, fill_value=0)249                self._check_comparison_ops(a, b, values, rvalues)250                a = self._klass(values, dtype=dtype, kind=kind, fill_value=1)251                b = self._klass(rvalues, dtype=dtype, kind=kind, fill_value=2)252                self._check_comparison_ops(a, b, values, rvalues)253    def test_bool_same_index(self):254        # GH 14000255        # when sp_index are the same256        for kind in ['integer', 'block']:257            values = self._base([True, False, True, True], dtype=np.bool)258            rvalues = self._base([True, False, True, True], dtype=np.bool)259            for fill_value in [True, False, np.nan]:260                a = self._klass(values, kind=kind, dtype=np.bool,261                                fill_value=fill_value)262                b = self._klass(rvalues, kind=kind, dtype=np.bool,263                                fill_value=fill_value)264                self._check_logical_ops(a, b, values, rvalues)265    def test_bool_array_logical(self):266        # GH 14000267        # when sp_index are the same268        for kind in ['integer', 'block']:269            values = self._base([True, False, True, False, True, True],270                                dtype=np.bool)271            rvalues = self._base([True, False, False, True, False, True],272                                 dtype=np.bool)273            for fill_value in [True, False, np.nan]:274                a = self._klass(values, kind=kind, dtype=np.bool,275                                fill_value=fill_value)276                b = self._klass(rvalues, kind=kind, dtype=np.bool,277                                fill_value=fill_value)278                self._check_logical_ops(a, b, values, rvalues)279    def test_mixed_array_float_int(self):280        for rdtype in ['int64']:281            values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])282            rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype)283            for kind in ['integer', 'block']:284                a = self._klass(values, kind=kind)285                b = self._klass(rvalues, kind=kind)286                assert b.dtype == SparseDtype(rdtype)287                self._check_numeric_ops(a, b, values, rvalues)288                self._check_numeric_ops(a, b * 0, values, rvalues * 0)289                a = self._klass(values, kind=kind, fill_value=0)290                b = self._klass(rvalues, kind=kind)291                assert b.dtype == SparseDtype(rdtype)292                self._check_numeric_ops(a, b, values, rvalues)293                a = self._klass(values, kind=kind, fill_value=0)294                b = self._klass(rvalues, kind=kind, fill_value=0)295                assert b.dtype == SparseDtype(rdtype)296                self._check_numeric_ops(a, b, values, rvalues)297                a = self._klass(values, kind=kind, fill_value=1)298                b = self._klass(rvalues, kind=kind, fill_value=2)299                assert b.dtype == SparseDtype(rdtype, fill_value=2)300                self._check_numeric_ops(a, b, values, rvalues)301    def test_mixed_array_comparison(self):302        # int32 NI ATM303        for rdtype in ['int64']:304            values = self._base([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan])305            rvalues = self._base([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype)306            for kind in ['integer', 'block']:307                a = self._klass(values, kind=kind)308                b = self._klass(rvalues, kind=kind)309                assert b.dtype == SparseDtype(rdtype)310                self._check_comparison_ops(a, b, values, rvalues)311                self._check_comparison_ops(a, b * 0, values, rvalues * 0)312                a = self._klass(values, kind=kind, fill_value=0)313                b = self._klass(rvalues, kind=kind)314                assert b.dtype == SparseDtype(rdtype)315                self._check_comparison_ops(a, b, values, rvalues)316                a = self._klass(values, kind=kind, fill_value=0)317                b = self._klass(rvalues, kind=kind, fill_value=0)318                assert b.dtype == SparseDtype(rdtype)319                self._check_comparison_ops(a, b, values, rvalues)320                a = self._klass(values, kind=kind, fill_value=1)321                b = self._klass(rvalues, kind=kind, fill_value=2)322                assert b.dtype == SparseDtype(rdtype, fill_value=2)323                self._check_comparison_ops(a, b, values, rvalues)324class TestSparseSeriesArithmetic(TestSparseArrayArithmetics):325    _base = pd.Series326    _klass = pd.SparseSeries327    def _assert(self, a, b):328        tm.assert_series_equal(a, b)329    def test_alignment(self):330        da = pd.Series(np.arange(4))331        db = pd.Series(np.arange(4), index=[1, 2, 3, 4])332        sa = pd.SparseSeries(np.arange(4), dtype=np.int64, fill_value=0)333        sb = pd.SparseSeries(np.arange(4), index=[1, 2, 3, 4],334                             dtype=np.int64, fill_value=0)335        self._check_numeric_ops(sa, sb, da, db)336        sa = pd.SparseSeries(np.arange(4), dtype=np.int64, fill_value=np.nan)337        sb = pd.SparseSeries(np.arange(4), index=[1, 2, 3, 4],338                             dtype=np.int64, fill_value=np.nan)339        self._check_numeric_ops(sa, sb, da, db)340        da = pd.Series(np.arange(4))341        db = pd.Series(np.arange(4), index=[10, 11, 12, 13])342        sa = pd.SparseSeries(np.arange(4), dtype=np.int64, fill_value=0)343        sb = pd.SparseSeries(np.arange(4), index=[10, 11, 12, 13],344                             dtype=np.int64, fill_value=0)345        self._check_numeric_ops(sa, sb, da, db)346        sa = pd.SparseSeries(np.arange(4), dtype=np.int64, fill_value=np.nan)347        sb = pd.SparseSeries(np.arange(4), index=[10, 11, 12, 13],348                             dtype=np.int64, fill_value=np.nan)349        self._check_numeric_ops(sa, sb, da, db)350@pytest.mark.parametrize("op", [351    operator.eq,352    operator.add,353])354def test_with_list(op):355    arr = pd.SparseArray([0, 1], fill_value=0)356    result = op(arr, [0, 1])357    expected = op(arr, pd.SparseArray([0, 1]))358    tm.assert_sp_array_equal(result, expected)359@pytest.mark.parametrize('ufunc', [360    np.abs, np.exp,361])362@pytest.mark.parametrize('arr', [363    pd.SparseArray([0, 0, -1, 1]),364    pd.SparseArray([None, None, -1, 1]),365])366def test_ufuncs(ufunc, arr):367    result = ufunc(arr)368    fill_value = ufunc(arr.fill_value)369    expected = pd.SparseArray(ufunc(np.asarray(arr)), fill_value=fill_value)370    tm.assert_sp_array_equal(result, expected)371@pytest.mark.parametrize("a, b", [372    (pd.SparseArray([0, 0, 0]), np.array([0, 1, 2])),373    (pd.SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),374    (pd.SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),375    (pd.SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),376    (pd.SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])),377])378@pytest.mark.parametrize("ufunc", [379    np.add,380    np.greater,381])382def test_binary_ufuncs(ufunc, a, b):383    # can't say anything about fill value here.384    result = ufunc(a, b)385    expected = ufunc(np.asarray(a), np.asarray(b))386    assert isinstance(result, pd.SparseArray)387    tm.assert_numpy_array_equal(np.asarray(result), expected)388def test_ndarray_inplace():389    sparray = pd.SparseArray([0, 2, 0, 0])390    ndarray = np.array([0, 1, 2, 3])391    ndarray += sparray392    expected = np.array([0, 3, 2, 3])393    tm.assert_numpy_array_equal(ndarray, expected)394def test_sparray_inplace():395    sparray = pd.SparseArray([0, 2, 0, 0])396    ndarray = np.array([0, 1, 2, 3])397    sparray += ndarray398    expected = pd.SparseArray([0, 3, 2, 3], fill_value=0)399    tm.assert_sp_array_equal(sparray, expected)400@pytest.mark.parametrize("fill_value", [True, False])401def test_invert(fill_value):402    arr = np.array([True, False, False, True])403    sparray = pd.SparseArray(arr, fill_value=fill_value)404    result = ~sparray405    expected = pd.SparseArray(~arr, fill_value=not fill_value)406    tm.assert_sp_array_equal(result, expected)407@pytest.mark.parametrize("fill_value", [0, np.nan])408@pytest.mark.parametrize("op", [operator.pos, operator.neg])409def test_unary_op(op, fill_value):410    arr = np.array([0, 1, np.nan, 2])411    sparray = pd.SparseArray(arr, fill_value=fill_value)412    result = op(sparray)413    expected = pd.SparseArray(op(arr), fill_value=op(fill_value))...

ImageDraw.py

Source:ImageDraw.py

...340        from . import ImageDraw2 as handler341    if im:342        im = handler.Draw(im)343    return im, handler344def floodfill(image, xy, value, border=None, thresh=0):345    """346    (experimental) Fills a bounded region with a given color.347    :param image: Target image.348    :param xy: Seed position (a 2-item coordinate tuple). See349        :ref:`coordinate-system`.350    :param value: Fill color.351    :param border: Optional border value.  If given, the region consists of352        pixels with a color different from the border color.  If not given,353        the region consists of pixels having the same color as the seed354        pixel.355    :param thresh: Optional threshold value which specifies a maximum356        tolerable difference of a pixel value from the 'background' in357        order for it to be replaced. Useful for filling regions of358        non-homogeneous, but similar, colors....

test_take.py

Source:test_take.py

1# -*- coding: utf-8 -*-2from datetime import datetime3import re4import numpy as np5import pytest6from pandas._libs.tslib import iNaT7from pandas.compat import long8import pandas.core.algorithms as algos9import pandas.util.testing as tm10@pytest.fixture(params=[True, False])11def writeable(request):12    return request.param13# Check that take_nd works both with writeable arrays14# (in which case fast typed memory-views implementation)15# and read-only arrays alike.16@pytest.fixture(params=[17    (np.float64, True),18    (np.float32, True),19    (np.uint64, False),20    (np.uint32, False),21    (np.uint16, False),22    (np.uint8, False),23    (np.int64, False),24    (np.int32, False),25    (np.int16, False),26    (np.int8, False),27    (np.object_, True),28    (np.bool, False),29])30def dtype_can_hold_na(request):31    return request.param32@pytest.fixture(params=[33    (np.int8, np.int16(127), np.int8),34    (np.int8, np.int16(128), np.int16),35    (np.int32, 1, np.int32),36    (np.int32, 2.0, np.float64),37    (np.int32, 3.0 + 4.0j, np.complex128),38    (np.int32, True, np.object_),39    (np.int32, "", np.object_),40    (np.float64, 1, np.float64),41    (np.float64, 2.0, np.float64),42    (np.float64, 3.0 + 4.0j, np.complex128),43    (np.float64, True, np.object_),44    (np.float64, "", np.object_),45    (np.complex128, 1, np.complex128),46    (np.complex128, 2.0, np.complex128),47    (np.complex128, 3.0 + 4.0j, np.complex128),48    (np.complex128, True, np.object_),49    (np.complex128, "", np.object_),50    (np.bool_, 1, np.object_),51    (np.bool_, 2.0, np.object_),52    (np.bool_, 3.0 + 4.0j, np.object_),53    (np.bool_, True, np.bool_),54    (np.bool_, '', np.object_),55])56def dtype_fill_out_dtype(request):57    return request.param58class TestTake(object):59    # Standard incompatible fill error.60    fill_error = re.compile("Incompatible type for fill_value")61    def test_1d_with_out(self, dtype_can_hold_na, writeable):62        dtype, can_hold_na = dtype_can_hold_na63        data = np.random.randint(0, 2, 4).astype(dtype)64        data.flags.writeable = writeable65        indexer = [2, 1, 0, 1]66        out = np.empty(4, dtype=dtype)67        algos.take_1d(data, indexer, out=out)68        expected = data.take(indexer)69        tm.assert_almost_equal(out, expected)70        indexer = [2, 1, 0, -1]71        out = np.empty(4, dtype=dtype)72        if can_hold_na:73            algos.take_1d(data, indexer, out=out)74            expected = data.take(indexer)75            expected[3] = np.nan76            tm.assert_almost_equal(out, expected)77        else:78            with pytest.raises(TypeError, match=self.fill_error):79                algos.take_1d(data, indexer, out=out)80            # No Exception otherwise.81            data.take(indexer, out=out)82    def test_1d_fill_nonna(self, dtype_fill_out_dtype):83        dtype, fill_value, out_dtype = dtype_fill_out_dtype84        data = np.random.randint(0, 2, 4).astype(dtype)85        indexer = [2, 1, 0, -1]86        result = algos.take_1d(data, indexer, fill_value=fill_value)87        assert ((result[[0, 1, 2]] == data[[2, 1, 0]]).all())88        assert (result[3] == fill_value)89        assert (result.dtype == out_dtype)90        indexer = [2, 1, 0, 1]91        result = algos.take_1d(data, indexer, fill_value=fill_value)92        assert ((result[[0, 1, 2, 3]] == data[indexer]).all())93        assert (result.dtype == dtype)94    def test_2d_with_out(self, dtype_can_hold_na, writeable):95        dtype, can_hold_na = dtype_can_hold_na96        data = np.random.randint(0, 2, (5, 3)).astype(dtype)97        data.flags.writeable = writeable98        indexer = [2, 1, 0, 1]99        out0 = np.empty((4, 3), dtype=dtype)100        out1 = np.empty((5, 4), dtype=dtype)101        algos.take_nd(data, indexer, out=out0, axis=0)102        algos.take_nd(data, indexer, out=out1, axis=1)103        expected0 = data.take(indexer, axis=0)104        expected1 = data.take(indexer, axis=1)105        tm.assert_almost_equal(out0, expected0)106        tm.assert_almost_equal(out1, expected1)107        indexer = [2, 1, 0, -1]108        out0 = np.empty((4, 3), dtype=dtype)109        out1 = np.empty((5, 4), dtype=dtype)110        if can_hold_na:111            algos.take_nd(data, indexer, out=out0, axis=0)112            algos.take_nd(data, indexer, out=out1, axis=1)113            expected0 = data.take(indexer, axis=0)114            expected1 = data.take(indexer, axis=1)115            expected0[3, :] = np.nan116            expected1[:, 3] = np.nan117            tm.assert_almost_equal(out0, expected0)118            tm.assert_almost_equal(out1, expected1)119        else:120            for i, out in enumerate([out0, out1]):121                with pytest.raises(TypeError, match=self.fill_error):122                    algos.take_nd(data, indexer, out=out, axis=i)123                # No Exception otherwise.124                data.take(indexer, out=out, axis=i)125    def test_2d_fill_nonna(self, dtype_fill_out_dtype):126        dtype, fill_value, out_dtype = dtype_fill_out_dtype127        data = np.random.randint(0, 2, (5, 3)).astype(dtype)128        indexer = [2, 1, 0, -1]129        result = algos.take_nd(data, indexer, axis=0,130                               fill_value=fill_value)131        assert ((result[[0, 1, 2], :] == data[[2, 1, 0], :]).all())132        assert ((result[3, :] == fill_value).all())133        assert (result.dtype == out_dtype)134        result = algos.take_nd(data, indexer, axis=1,135                               fill_value=fill_value)136        assert ((result[:, [0, 1, 2]] == data[:, [2, 1, 0]]).all())137        assert ((result[:, 3] == fill_value).all())138        assert (result.dtype == out_dtype)139        indexer = [2, 1, 0, 1]140        result = algos.take_nd(data, indexer, axis=0,141                               fill_value=fill_value)142        assert ((result[[0, 1, 2, 3], :] == data[indexer, :]).all())143        assert (result.dtype == dtype)144        result = algos.take_nd(data, indexer, axis=1,145                               fill_value=fill_value)146        assert ((result[:, [0, 1, 2, 3]] == data[:, indexer]).all())147        assert (result.dtype == dtype)148    def test_3d_with_out(self, dtype_can_hold_na):149        dtype, can_hold_na = dtype_can_hold_na150        data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)151        indexer = [2, 1, 0, 1]152        out0 = np.empty((4, 4, 3), dtype=dtype)153        out1 = np.empty((5, 4, 3), dtype=dtype)154        out2 = np.empty((5, 4, 4), dtype=dtype)155        algos.take_nd(data, indexer, out=out0, axis=0)156        algos.take_nd(data, indexer, out=out1, axis=1)157        algos.take_nd(data, indexer, out=out2, axis=2)158        expected0 = data.take(indexer, axis=0)159        expected1 = data.take(indexer, axis=1)160        expected2 = data.take(indexer, axis=2)161        tm.assert_almost_equal(out0, expected0)162        tm.assert_almost_equal(out1, expected1)163        tm.assert_almost_equal(out2, expected2)164        indexer = [2, 1, 0, -1]165        out0 = np.empty((4, 4, 3), dtype=dtype)166        out1 = np.empty((5, 4, 3), dtype=dtype)167        out2 = np.empty((5, 4, 4), dtype=dtype)168        if can_hold_na:169            algos.take_nd(data, indexer, out=out0, axis=0)170            algos.take_nd(data, indexer, out=out1, axis=1)171            algos.take_nd(data, indexer, out=out2, axis=2)172            expected0 = data.take(indexer, axis=0)173            expected1 = data.take(indexer, axis=1)174            expected2 = data.take(indexer, axis=2)175            expected0[3, :, :] = np.nan176            expected1[:, 3, :] = np.nan177            expected2[:, :, 3] = np.nan178            tm.assert_almost_equal(out0, expected0)179            tm.assert_almost_equal(out1, expected1)180            tm.assert_almost_equal(out2, expected2)181        else:182            for i, out in enumerate([out0, out1, out2]):183                with pytest.raises(TypeError, match=self.fill_error):184                    algos.take_nd(data, indexer, out=out, axis=i)185                # No Exception otherwise.186                data.take(indexer, out=out, axis=i)187    def test_3d_fill_nonna(self, dtype_fill_out_dtype):188        dtype, fill_value, out_dtype = dtype_fill_out_dtype189        data = np.random.randint(0, 2, (5, 4, 3)).astype(dtype)190        indexer = [2, 1, 0, -1]191        result = algos.take_nd(data, indexer, axis=0,192                               fill_value=fill_value)193        assert ((result[[0, 1, 2], :, :] == data[[2, 1, 0], :, :]).all())194        assert ((result[3, :, :] == fill_value).all())195        assert (result.dtype == out_dtype)196        result = algos.take_nd(data, indexer, axis=1,197                               fill_value=fill_value)198        assert ((result[:, [0, 1, 2], :] == data[:, [2, 1, 0], :]).all())199        assert ((result[:, 3, :] == fill_value).all())200        assert (result.dtype == out_dtype)201        result = algos.take_nd(data, indexer, axis=2,202                               fill_value=fill_value)203        assert ((result[:, :, [0, 1, 2]] == data[:, :, [2, 1, 0]]).all())204        assert ((result[:, :, 3] == fill_value).all())205        assert (result.dtype == out_dtype)206        indexer = [2, 1, 0, 1]207        result = algos.take_nd(data, indexer, axis=0,208                               fill_value=fill_value)209        assert ((result[[0, 1, 2, 3], :, :] == data[indexer, :, :]).all())210        assert (result.dtype == dtype)211        result = algos.take_nd(data, indexer, axis=1,212                               fill_value=fill_value)213        assert ((result[:, [0, 1, 2, 3], :] == data[:, indexer, :]).all())214        assert (result.dtype == dtype)215        result = algos.take_nd(data, indexer, axis=2,216                               fill_value=fill_value)217        assert ((result[:, :, [0, 1, 2, 3]] == data[:, :, indexer]).all())218        assert (result.dtype == dtype)219    def test_1d_other_dtypes(self):220        arr = np.random.randn(10).astype(np.float32)221        indexer = [1, 2, 3, -1]222        result = algos.take_1d(arr, indexer)223        expected = arr.take(indexer)224        expected[-1] = np.nan225        tm.assert_almost_equal(result, expected)226    def test_2d_other_dtypes(self):227        arr = np.random.randn(10, 5).astype(np.float32)228        indexer = [1, 2, 3, -1]229        # axis=0230        result = algos.take_nd(arr, indexer, axis=0)231        expected = arr.take(indexer, axis=0)232        expected[-1] = np.nan233        tm.assert_almost_equal(result, expected)234        # axis=1235        result = algos.take_nd(arr, indexer, axis=1)236        expected = arr.take(indexer, axis=1)237        expected[:, -1] = np.nan238        tm.assert_almost_equal(result, expected)239    def test_1d_bool(self):240        arr = np.array([0, 1, 0], dtype=bool)241        result = algos.take_1d(arr, [0, 2, 2, 1])242        expected = arr.take([0, 2, 2, 1])243        tm.assert_numpy_array_equal(result, expected)244        result = algos.take_1d(arr, [0, 2, -1])245        assert result.dtype == np.object_246    def test_2d_bool(self):247        arr = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 1]], dtype=bool)248        result = algos.take_nd(arr, [0, 2, 2, 1])249        expected = arr.take([0, 2, 2, 1], axis=0)250        tm.assert_numpy_array_equal(result, expected)251        result = algos.take_nd(arr, [0, 2, 2, 1], axis=1)252        expected = arr.take([0, 2, 2, 1], axis=1)253        tm.assert_numpy_array_equal(result, expected)254        result = algos.take_nd(arr, [0, 2, -1])255        assert result.dtype == np.object_256    def test_2d_float32(self):257        arr = np.random.randn(4, 3).astype(np.float32)258        indexer = [0, 2, -1, 1, -1]259        # axis=0260        result = algos.take_nd(arr, indexer, axis=0)261        result2 = np.empty_like(result)262        algos.take_nd(arr, indexer, axis=0, out=result2)263        tm.assert_almost_equal(result, result2)264        expected = arr.take(indexer, axis=0)265        expected[[2, 4], :] = np.nan266        tm.assert_almost_equal(result, expected)267        # this now accepts a float32! # test with float64 out buffer268        out = np.empty((len(indexer), arr.shape[1]), dtype='float32')269        algos.take_nd(arr, indexer, out=out)  # it works!270        # axis=1271        result = algos.take_nd(arr, indexer, axis=1)272        result2 = np.empty_like(result)273        algos.take_nd(arr, indexer, axis=1, out=result2)274        tm.assert_almost_equal(result, result2)275        expected = arr.take(indexer, axis=1)276        expected[:, [2, 4]] = np.nan277        tm.assert_almost_equal(result, expected)278    def test_2d_datetime64(self):279        # 2005/01/01 - 2006/01/01280        arr = np.random.randint(281            long(11045376), long(11360736), (5, 3)) * 100000000000282        arr = arr.view(dtype='datetime64[ns]')283        indexer = [0, 2, -1, 1, -1]284        # axis=0285        result = algos.take_nd(arr, indexer, axis=0)286        result2 = np.empty_like(result)287        algos.take_nd(arr, indexer, axis=0, out=result2)288        tm.assert_almost_equal(result, result2)289        expected = arr.take(indexer, axis=0)290        expected.view(np.int64)[[2, 4], :] = iNaT291        tm.assert_almost_equal(result, expected)292        result = algos.take_nd(arr, indexer, axis=0,293                               fill_value=datetime(2007, 1, 1))294        result2 = np.empty_like(result)295        algos.take_nd(arr, indexer, out=result2, axis=0,296                      fill_value=datetime(2007, 1, 1))297        tm.assert_almost_equal(result, result2)298        expected = arr.take(indexer, axis=0)299        expected[[2, 4], :] = datetime(2007, 1, 1)300        tm.assert_almost_equal(result, expected)301        # axis=1302        result = algos.take_nd(arr, indexer, axis=1)303        result2 = np.empty_like(result)304        algos.take_nd(arr, indexer, axis=1, out=result2)305        tm.assert_almost_equal(result, result2)306        expected = arr.take(indexer, axis=1)307        expected.view(np.int64)[:, [2, 4]] = iNaT308        tm.assert_almost_equal(result, expected)309        result = algos.take_nd(arr, indexer, axis=1,310                               fill_value=datetime(2007, 1, 1))311        result2 = np.empty_like(result)312        algos.take_nd(arr, indexer, out=result2, axis=1,313                      fill_value=datetime(2007, 1, 1))314        tm.assert_almost_equal(result, result2)315        expected = arr.take(indexer, axis=1)316        expected[:, [2, 4]] = datetime(2007, 1, 1)317        tm.assert_almost_equal(result, expected)318    def test_take_axis_0(self):319        arr = np.arange(12).reshape(4, 3)320        result = algos.take(arr, [0, -1])321        expected = np.array([[0, 1, 2], [9, 10, 11]])322        tm.assert_numpy_array_equal(result, expected)323        # allow_fill=True324        result = algos.take(arr, [0, -1], allow_fill=True, fill_value=0)325        expected = np.array([[0, 1, 2], [0, 0, 0]])326        tm.assert_numpy_array_equal(result, expected)327    def test_take_axis_1(self):328        arr = np.arange(12).reshape(4, 3)329        result = algos.take(arr, [0, -1], axis=1)330        expected = np.array([[0, 2], [3, 5], [6, 8], [9, 11]])331        tm.assert_numpy_array_equal(result, expected)332        # allow_fill=True333        result = algos.take(arr, [0, -1], axis=1, allow_fill=True,334                            fill_value=0)335        expected = np.array([[0, 0], [3, 0], [6, 0], [9, 0]])336        tm.assert_numpy_array_equal(result, expected)337class TestExtensionTake(object):338    # The take method found in pd.api.extensions339    def test_bounds_check_large(self):340        arr = np.array([1, 2])341        with pytest.raises(IndexError):342            algos.take(arr, [2, 3], allow_fill=True)343        with pytest.raises(IndexError):344            algos.take(arr, [2, 3], allow_fill=False)345    def test_bounds_check_small(self):346        arr = np.array([1, 2, 3], dtype=np.int64)347        indexer = [0, -1, -2]348        with pytest.raises(ValueError):349            algos.take(arr, indexer, allow_fill=True)350        result = algos.take(arr, indexer)351        expected = np.array([1, 3, 2], dtype=np.int64)352        tm.assert_numpy_array_equal(result, expected)353    @pytest.mark.parametrize('allow_fill', [True, False])354    def test_take_empty(self, allow_fill):355        arr = np.array([], dtype=np.int64)356        # empty take is ok357        result = algos.take(arr, [], allow_fill=allow_fill)358        tm.assert_numpy_array_equal(arr, result)359        with pytest.raises(IndexError):360            algos.take(arr, [0], allow_fill=allow_fill)361    def test_take_na_empty(self):362        result = algos.take(np.array([]), [-1, -1], allow_fill=True,363                            fill_value=0.0)364        expected = np.array([0., 0.])365        tm.assert_numpy_array_equal(result, expected)366    def test_take_coerces_list(self):367        arr = [1, 2, 3]368        result = algos.take(arr, [0, 0])369        expected = np.array([1, 1])...

lazy.py

Source:lazy.py

...13        if self.data is None:14            _fill_lock.acquire()15            try:16                if self.data is None:17                    self._fill()18            finally:19                _fill_lock.release()20        return self.data[key.upper()]21    def __contains__(self, key):22        if self.data is None:23            _fill_lock.acquire()24            try:25                if self.data is None:26                    self._fill()27            finally:28                _fill_lock.release()29        return key in self.data30    def __iter__(self):31        if self.data is None:32            _fill_lock.acquire()33            try:34                if self.data is None:35                    self._fill()36            finally:37                _fill_lock.release()38        return iter(self.data)39    def __len__(self):40        if self.data is None:41            _fill_lock.acquire()42            try:43                if self.data is None:44                    self._fill()45            finally:46                _fill_lock.release()47        return len(self.data)48    def keys(self):49        if self.data is None:50            _fill_lock.acquire()51            try:52                if self.data is None:53                    self._fill()54            finally:55                _fill_lock.release()56        return self.data.keys()57class LazyList(list):58    """List populated on first use."""59    _props = [60        '__str__', '__repr__', '__unicode__',61        '__hash__', '__sizeof__', '__cmp__',62        '__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__',63        'append', 'count', 'index', 'extend', 'insert', 'pop', 'remove',64        'reverse', 'sort', '__add__', '__radd__', '__iadd__', '__mul__',65        '__rmul__', '__imul__', '__contains__', '__len__', '__nonzero__',66        '__getitem__', '__setitem__', '__delitem__', '__iter__',67        '__reversed__', '__getslice__', '__setslice__', '__delslice__']...

Using AI Code Generation

1var ghost = require('ghostjs');2  .then(function() {3    return ghost.set('viewportSize', {width: 1024, height: 768});4  })5  .then(function() {6    return ghost.fill('form[action*="/search"] [name=q]', 'ghostjs');7  })8  .then(function() {9    return ghost.click('form[action*="/search"] [type=submit]');10  })11  .then(function() {12    return ghost.wait(5000);13  })14  .then(function() {15    return ghost.screenshot('google.png');16  })17  .then(function() {18    return ghost.close();19  })20  .catch(function(e) {21    console.log(e);22  });23### ghost.open(url, options)24### ghost.set(key, value)

Using AI Code Generation

1var ghost = require('ghostjs');2  ghost.fill('input[name="q"]', 'Hello World', function() {3    ghost.press('enter', function() {4      ghost.wait(1000, function() {5        ghost.screenshot('google.png', function() {6          ghost.exit();7        });8      });9    });10  });11});12### ghost.open(url, callback)13### ghost.fill(selector, value, callback)14### ghost.press(keys, callback)15* `keys` - A string of keys to press. Can be any of the following: `enter`, `tab`, `escape`, `space`, `backspace`, `delete`, `up`, `down`, `left`, `right`, `home`, `end`, `pageup`, `pagedown`, `insert`, `f1`, `f2`, `f3`, `f4`, `f5`, `f6`, `f7`, `f8`, `f9`, `f10`, `f11`, `f12`, `0`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `a`, `b`, `c`, `d`, `e`, `f`, `g`, `h`, `i`, `j`, `k`, `l`, `m`, `n`, `o`, `p`, `q`, `r`, `s`, `t`, `u`, `v`, `w`, `x`, `y`, `z`, `+`, `-`, `*`, `/`, `=`, `!`, `@`, `#`, `$`, `%`, `^`, `&`, `(`, `)`, `~`, `|`, `[`, `]`, `{`, `}`, `;`, `:`, `,`, `.`,

Using AI Code Generation

1var ghost = require('ghostjs');2  .then(function() {3    return ghost.fill('input[name=q]', 'hello world');4  })5  .then(function() {6    return ghost.click('input[name=btnK]');7  })8  .then(function() {9    return ghost.wait(5000);10  })11  .then(function() {12    return ghost.screenshot('google.png');13  })14  .then(function() {15    return ghost.exit();16  })17  .catch(function(err) {18    console.log(err);19  });20### ghost.open(url)21### ghost.back()22### ghost.forward()23### ghost.reload()24### ghost.wait(ms)25### ghost.fill(selector, text)26### ghost.click(selector)27### ghost.screenshot(filename)28### ghost.exit()

Using AI Code Generation

1var ghost = require('ghostjs');2    ghost.fill('#gbqfq', 'ghostjs', function(err, ghost) {3        ghost.click('#gbqfb', function(err, ghost) {4            ghost.wait(5000, function(err, ghost) {5                ghost.screenshot('google.png', function(err, ghost) {6                    ghost.exit();7                });8            });9        });10    });11});12### ghost.open(url, callback)13});14### ghost.exit()15ghost.exit();16### ghost.screenshot(filepath, callback)17ghost.screenshot('google.png', function(err, ghost) {18});19### ghost.wait(milliseconds, callback)20ghost.wait(5000, function(err, ghost) {21});22### ghost.click(selector, callback)23ghost.click('#gbqfb', function(err, ghost) {24});25### ghost.fill(selector, value, callback)26ghost.fill('#gbqfq', 'ghostjs', function(err, ghost) {27});28### ghost.html(callback)29ghost.html(function(err, html, ghost) {30});31### ghost.title(callback)32ghost.title(function(err, title, ghost) {33});34### ghost.exists(selector, callback)35ghost.exists('#gbqfb', function(err, exists, ghost) {36});37### ghost.visible(selector, callback)38ghost.visible('#gbqfb', function(err, visible, ghost) {39});

Using AI Code Generation

1var ghost = require('ghostjs');2  ghost.fill('#gbqfq', 'ghostjs', function(){3    ghost.screenshot('google.png', function(){4      ghost.exit();5    });6  });7});8var ghost = require('ghostjs');9  ghost.capturePage('google.png', function(){10    ghost.exit();11  });12});13var ghost = require('ghostjs');14  ghost.screenshot('google.png', function(){15    ghost.exit();16  });17});18var ghost = require('ghostjs');19ghost.set('viewportSize', {width: 1024, height: 768}, function(){20    ghost.screenshot('google.png', function(){21      ghost.exit();22    });23  });24});25var ghost = require('ghostjs');

Using AI Code Generation

1var ghost = require('ghostjs');2    .type('#lst-ib', 'GhostJS')3    .then(function() {4        return ghost.fill('#tsf', {5        });6    })7    .then(function() {8        return ghost.screenshot();9    })10    .then(function(image) {11        image.saveAs('google.png');12    })13    .run();14### ghost.open(url)15### ghost.close()16### ghost.screenshot()17### ghost.fill(selector, data)18### ghost.type(selector, text)19### ghost.click(selector)20### ghost.wait(ms)21### ghost.waitForSelector(selector)22### ghost.waitForVisible(selector)23### ghost.waitForText(selector, text)24### ghost.waitForUrl(url)25### ghost.waitForPageLoad()26### ghost.waitForAjax()27### ghost.waitForFunction(fn, arg1, arg2, ...)28### ghost.run()29### ghost.then(fn)30### ghost.then(function() { ... })31### ghost.then([function() { ... }, function() { ... }, ...])32### ghost.then(function() { ... }).then(function() { ... })33### ghost.then(function() { ... }).then(function() { ... }).run()34### ghost.get(selector)35### ghost.getValue(selector

Using AI Code Generation

1const ghostjs = require('ghostjs');2const fill = ghostjs.fill;3const click = ghostjs.click;4const open = ghostjs.open;5const screenshot = ghostjs.screenshot;6const wait = ghostjs.wait;7const type = ghostjs.type;8const key = ghostjs.key;9const scroll = ghostjs.scroll;10const hover = ghostjs.hover;11const select = ghostjs.select;12const close = ghostjs.close;13const press = ghostjs.press;14const mouse = ghostjs.mouse;15const drag = ghostjs.drag;16const drop = ghostjs.drop;17const upload = ghostjs.upload;18const assert = ghostjs.assert;19const assertText = ghostjs.assertText;20const assertTitle = ghostjs.assertTitle;21const assertURL = ghostjs.assertURL;22const assertExists = ghostjs.assertExists;23const assertVisible = ghostjs.assertVisible;24const assertValue = ghostjs.assertValue;25const assertAttribute = ghostjs.assertAttribute;26const assertStyle = ghostjs.assertStyle;27const assertCookie = ghostjs.assertCookie;28const assertAlert = ghostjs.assertAlert;29const assertPrompt = ghostjs.assertPrompt;30const assertConfirm = ghostjs.assertConfirm;31const saveScreenshot = ghostjs.saveScreenshot;32const saveHTML = ghostjs.saveHTML;33const saveCookies = ghostjs.saveCookies;34const savePDF = ghostjs.savePDF;35const clearCookies = ghostjs.clearCookies;36const clearLocalStorage = ghostjs.clearLocalStorage;37const clearSessionStorage = ghostjs.clearSessionStorage;38const clearCache = ghostjs.clearCache;39const clearAll = ghostjs.clearAll;40const setCookie = ghostjs.setCookie;41const getCookie = ghostjs.getCookie;42const deleteCookie = ghostjs.deleteCookie;43const setViewport = ghostjs.setViewport;44const setProxy = ghostjs.setProxy;45const setBasicAuth = ghostjs.setBasicAuth;46const setHeaders = ghostjs.setHeaders;47const setUserAgent = ghostjs.setUserAgent;48const setExtraHTTPHeaders = ghostjs.setExtraHTTPHeaders;49const setOfflineMode = ghostjs.setOfflineMode;50const setRequestInterception = ghostjs.setRequestInterception;51const setGeolocation = ghostjs.setGeolocation;52const setJavaScriptEnabled = ghostjs.setJavaScriptEnabled;53const setIgnoreHTTPSErrors = ghostjs.setIgnoreHTTPSErrors;54const setBypassCSP = ghostjs.setBypassCSP;55const setDefaultNavigationTimeout = ghostjs.setDefaultNavigationTimeout;

Using AI Code Generation

1const ghost = require('ghostjs');2const assert = require('assert');3describe('GhostJS', function() {4  it('should fill input field', function() {5      .fill('input[title="Search"]', 'GhostJS')6      .click('input[name="btnK"]')7      .waitForUrl(/q=GhostJS/)8      .then(function() {9        return ghost.evaluate(function() {10          return document.title;11        });12      })13      .then(function(title) {14        assert.equal(title, 'GhostJS - Google Search');15      });16  });17});18### ghost.open(url, options)19* `options` - The options to pass to [puppeteer's `goto`](

Using AI Code Generation

1ghost.fill('#myField', 'some value');2ghost.capture('myScreenshot.png', { format: 'png' });3ghost.click('#myButton');4ghost.evaluate(function() {5  return document.title;6});7ghost.evaluateAsync(function(done) {8  setTimeout(function() {9    done(document.title);10  }, 1000);11});12ghost.waitFor(function() {13  return document.title === 'Google';14});15ghost.waitForSelector('#myButton');16ghost.waitForSelector('some text');17ghost.waitForPageLoad();18ghost.waitForPopup();

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.