May 22, 2020 · May 1, 2020 · May 1, 2020 · May 6, 2020 · May 6, 2020 · May 6, 2020
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
                        stats['med'] = med

        if conf_intervals is not None:
            ifnp.shape(conf_intervals)[0] != len(bxpstats):
            iflen(conf_intervals) != len(bxpstats):
                raise ValueError(
                    "'conf_intervals' and 'x' have different lengths")
            else:
        if histtype == 'barstacked' and not stacked:
            stacked = True

        # basic input validation
        input_empty = np.size(x) == 0
        # Massage 'x' for processing.
        x = cbook._reshape_2D(x, 'x')
        nx = len(x)  # number of datasets
        if len(w) != nx:
            raise ValueError('weights should have the same shape as x')

        input_empty = True
        for xi, wi in zip(x, w):
            if wi is not None and len(wi) != len(xi):
            len_xi = len(xi)
            if wi is not None and len(wi) != len_xi:
                raise ValueError('weights should have the same shape as x')
            if len_xi:
                input_empty = False

        if color is None:
            color = [self._get_lines.get_next_color() for i in range(nx)]
diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
        else:
            if isinstance(x, np.ma.MaskedArray) and x.ndim > 1:
                raise ValueError("Masked arrays must be 1-D")
            x = np.asanyarray(x)
            try:
                x = np.asanyarray(x)
            except (np.VisibleDeprecationWarning, ValueError):
                # NumPy 1.19 raises a warning about ragged arrays, but we want
                # to accept basically anything here.
                x = np.asanyarray(x, dtype=object)
            if x.ndim == 1:
                x = safe_masked_invalid(x)
                seqlist[i] = True
    Use Fortran ordering to convert ndarrays and lists of iterables to lists of
    1D arrays.

    Lists of iterables are converted by applying `np.asarray` to each of their
    elements.  1D ndarrays are returned in a singleton list containing them.
    2D ndarrays are converted to the list of their *columns*.
    Lists of iterables are converted by applying `np.asanyarray` to each of
 theirelements.  1D ndarrays are returned in a singleton list containing
 them.2D ndarrays are converted to the list of their *columns*.

    *name* is used to generate the error message for invalid inputs.
    """
    # Iterate over columns for ndarrays, over rows otherwise.
    X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))
    # Iterate over columns for ndarrays.
    if isinstance(X, np.ndarray):
        X = X.T

        if len(X) == 0:
            return [[]]
        elif X.ndim == 1 and np.ndim(X[0]) == 0:
            # 1D array of scalars: directly return it.
            return [X]
        elif X.ndim in [1, 2]:
            # 2D array, or 1D array of iterables: flatten them first.
            return [np.reshape(x, -1) for x in X]
        else:
            raise ValueError(f'{name} must have 2 or fewer dimensions')

    # Iterate over list of iterables.
    if len(X) == 0:
        return [[]]
    elif X.ndim == 1 and np.ndim(X[0]) == 0:

    result = []
    is_1d = True
    for xi in X:
        xi = np.asanyarray(xi)
        nd = np.ndim(xi)
        if nd > 1:
            raise ValueError(f'{name} must have 2 or fewer dimensions')
        elif nd == 1 and len(xi) != 1:
            is_1d = False
        result.append(xi.reshape(-1))

    if is_1d:
        # 1D array of scalars: directly return it.
        return [X]
    elif X.ndim in [1, 2]:
        # 2D array, or 1D array of iterables: flatten them first.
        return [np.reshape(x, -1) for x in X]
        return [np.reshape(result, -1)]
    else:
        raise ValueError("{} must have 2 or fewer dimensions".format(name))
        # 2D array, or 1D array of iterables: use flattened version.
        return result


 def violin_stats(X, method, points=100, quantiles=None):
        quantiles = _reshape_2D(quantiles, "quantiles")
    # Else, mock quantiles if is none or empty
    else:
        quantiles = [[]] *np.shape(X)[0]
        quantiles = [[]] *len(X)

    # quantiles should has the same size as dataset
    ifnp.shape(X)[:1] !=np.shape(quantiles)[:1]:
    iflen(X) !=len(quantiles):
        raise ValueError("List of violinplot statistics and quantiles values"
                         " must have the same length")

    try:
        return y.index.values, y.values
    except AttributeError:
        pass
    try:
        y = _check_1d(y)
    except (np.VisibleDeprecationWarning, ValueError):
        # NumPy 1.19 will warn on ragged input, and we can't actually use it.
        pass
    else:
        return np.arange(y.shape[0], dtype=float), y
    raise ValueError('Input could not be cast to an at-least-1D NumPy array')


 def safe_first_element(obj):
diff --git a/lib/matplotlib/tests/test_backend_svg.py b/lib/matplotlib/tests/test_backend_svg.py
            elif obj.axes is None:
                return False
        if isinstance(obj, plt.Line2D):
            if np.array(obj.get_data()).shape == (2, 1):
            xdata, ydata = obj.get_data()
            if len(xdata) == len(ydata) == 1:
                return False
            elif not hasattr(obj, "axes") or obj.axes is None:
                return False
diff --git a/lib/mpl_toolkits/mplot3d/art3d.py b/lib/mpl_toolkits/mplot3d/art3d.py
        """
        Set 3D segments.
        """
        self._segments3d =np.asanyarray(segments)
        self._segments3d = segments
        LineCollection.set_segments(self, [])

    def do_3d_projection(self, renderer):
Original file line number	Diff line number	Diff line change
Expand Up		@@ -3778,7 +3778,7 @@ def boxplot(self, x, notch=None, sym=None, vert=None, whis=None,
		stats['med'] = med

		if conf_intervals is not None:
		ifnp.shape(conf_intervals)[0] != len(bxpstats):
		iflen(conf_intervals) != len(bxpstats):
		raise ValueError(
		"'conf_intervals' and 'x' have different lengths")
		else:
Expand DownExpand Up		@@ -6555,8 +6555,6 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
		if histtype == 'barstacked' and not stacked:
		stacked = True

		# basic input validation
		input_empty = np.size(x) == 0
		# Massage 'x' for processing.
		x = cbook._reshape_2D(x, 'x')
		nx = len(x) # number of datasets
Expand All		@@ -6581,9 +6579,13 @@ def hist(self, x, bins=None, range=None, density=False, weights=None,
		if len(w) != nx:
		raise ValueError('weights should have the same shape as x')

		input_empty = True
		for xi, wi in zip(x, w):
		if wi is not None and len(wi) != len(xi):
		len_xi = len(xi)
		if wi is not None and len(wi) != len_xi:
		raise ValueError('weights should have the same shape as x')
		if len_xi:
		input_empty = False

		if color is None:
		color = [self._get_lines.get_next_color() for i in range(nx)]
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -989,7 +989,12 @@ def _combine_masks(*args):
		else:
		if isinstance(x, np.ma.MaskedArray) and x.ndim > 1:
		raise ValueError("Masked arrays must be 1-D")
		x = np.asanyarray(x)
		try:
		x = np.asanyarray(x)
		except (np.VisibleDeprecationWarning, ValueError):
		# NumPy 1.19 raises a warning about ragged arrays, but we want
		# to accept basically anything here.
		x = np.asanyarray(x, dtype=object)
		if x.ndim == 1:
		x = safe_masked_invalid(x)
		seqlist[i] = True
Expand DownExpand Up		@@ -1316,24 +1321,48 @@ def _reshape_2D(X, name):
		Use Fortran ordering to convert ndarrays and lists of iterables to lists of
		1D arrays.

		Lists of iterables are converted by applying `np.asarray` to each of their
		elements. 1D ndarrays are returned in a singleton list containing them.
		2D ndarrays are converted to the list of their columns.
		Lists of iterables are converted by applying `np.asanyarray` to each of
		theirelements. 1D ndarrays are returned in a singleton list containing
		them.2D ndarrays are converted to the list of their columns.

		name is used to generate the error message for invalid inputs.
		"""
		# Iterate over columns for ndarrays, over rows otherwise.
		X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))
		# Iterate over columns for ndarrays.
		if isinstance(X, np.ndarray):
		X = X.T

		if len(X) == 0:
		return [[]]
		elif X.ndim == 1 and np.ndim(X[0]) == 0:
		# 1D array of scalars: directly return it.
		return [X]
		elif X.ndim in [1, 2]:
		# 2D array, or 1D array of iterables: flatten them first.
		return [np.reshape(x, -1) for x in X]
		else:
		raise ValueError(f'{name} must have 2 or fewer dimensions')

		# Iterate over list of iterables.
		if len(X) == 0:
		return [[]]
		elif X.ndim == 1 and np.ndim(X[0]) == 0:

		result = []
		is_1d = True
		for xi in X:
		xi = np.asanyarray(xi)
		nd = np.ndim(xi)
		if nd > 1:
		raise ValueError(f'{name} must have 2 or fewer dimensions')
		elif nd == 1 and len(xi) != 1:
		is_1d = False
		result.append(xi.reshape(-1))

		if is_1d:
		# 1D array of scalars: directly return it.
		return [X]
		elif X.ndim in [1, 2]:
		# 2D array, or 1D array of iterables: flatten them first.
		return [np.reshape(x, -1) for x in X]
		return [np.reshape(result, -1)]
		else:
		raise ValueError("{} must have 2 or fewer dimensions".format(name))
		# 2D array, or 1D array of iterables: use flattened version.
		return result


		def violin_stats(X, method, points=100, quantiles=None):
Expand DownExpand Up		@@ -1399,10 +1428,10 @@ def violin_stats(X, method, points=100, quantiles=None):
		quantiles = _reshape_2D(quantiles, "quantiles")
		# Else, mock quantiles if is none or empty
		else:
		quantiles = [[]] *np.shape(X)[0]
		quantiles = [[]] *len(X)

		# quantiles should has the same size as dataset
		ifnp.shape(X)[:1] !=np.shape(quantiles)[:1]:
		iflen(X) !=len(quantiles):
		raise ValueError("List of violinplot statistics and quantiles values"
		" must have the same length")

Expand DownExpand Up		@@ -1577,8 +1606,15 @@ def index_of(y):
		try:
		return y.index.values, y.values
		except AttributeError:
		pass
		try:
		y = _check_1d(y)
		except (np.VisibleDeprecationWarning, ValueError):
		# NumPy 1.19 will warn on ragged input, and we can't actually use it.
		pass
		else:
		return np.arange(y.shape[0], dtype=float), y
		raise ValueError('Input could not be cast to an at-least-1D NumPy array')


		def safe_first_element(obj):
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -191,7 +191,8 @@ def include(gid, obj):
		elif obj.axes is None:
		return False
		if isinstance(obj, plt.Line2D):
		if np.array(obj.get_data()).shape == (2, 1):
		xdata, ydata = obj.get_data()
		if len(xdata) == len(ydata) == 1:
		return False
		elif not hasattr(obj, "axes") or obj.axes is None:
		return False
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -257,7 +257,7 @@ def set_segments(self, segments):
		"""
		Set 3D segments.
		"""
		self._segments3d =np.asanyarray(segments)
		self._segments3d = segments
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		LineCollection.set_segments(self, [])

		def do_3d_projection(self, renderer):
Expand Down