import warnings import itertools from copy import copy from functools import partial from collections import UserString from collections.abc import Iterable, Sequence, Mapping from numbers import Number from datetime import datetime import numpy as np import pandas as pd import matplotlib as mpl from ._decorators import ( share_init_params_with_map, ) from .external.version import Version from .palettes import ( QUAL_PALETTES, color_palette, ) from .utils import ( _check_argument, get_color_cycle, remove_na, ) class SemanticMapping: """Base class for mapping data values to plot attributes.""" # -- Default attributes that all SemanticMapping subclasses must set # Whether the mapping is numeric, categorical, or datetime map_type = None # Ordered list of unique values in the input data levels = None # A mapping from the data values to corresponding plot attributes lookup_table = None def __init__(self, plotter): # TODO Putting this here so we can continue to use a lot of the # logic that's built into the library, but the idea of this class # is to move towards semantic mappings that are agnostic about the # kind of plot they're going to be used to draw. # Fully achieving that is going to take some thinking. self.plotter = plotter def map(cls, plotter, *args, **kwargs): # This method is assigned the __init__ docstring method_name = f"_{cls.__name__[:-7].lower()}_map" setattr(plotter, method_name, cls(plotter, *args, **kwargs)) return plotter def _check_list_length(self, levels, values, variable): """Input check when values are provided as a list.""" # Copied from _core/properties; eventually will be replaced for that. message = "" if len(levels) > len(values): message = " ".join([ f"\nThe {variable} list has fewer values ({len(values)})", f"than needed ({len(levels)}) and will cycle, which may", "produce an uninterpretable plot." ]) values = [x for _, x in zip(levels, itertools.cycle(values))] elif len(values) > len(levels): message = " ".join([ f"The {variable} list has more values ({len(values)})", f"than needed ({len(levels)}), which may not be intended.", ]) values = values[:len(levels)] if message: warnings.warn(message, UserWarning, stacklevel=6) return values def _lookup_single(self, key): """Apply the mapping to a single data value.""" return self.lookup_table[key] def __call__(self, key, *args, **kwargs): """Get the attribute(s) values for the data key.""" if isinstance(key, (list, np.ndarray, pd.Series)): return [self._lookup_single(k, *args, **kwargs) for k in key] else: return self._lookup_single(key, *args, **kwargs) @share_init_params_with_map class HueMapping(SemanticMapping): """Mapping that sets artist colors according to data values.""" # A specification of the colors that should appear in the plot palette = None # An object that normalizes data values to [0, 1] range for color mapping norm = None # A continuous colormap object for interpolating in a numeric context cmap = None def __init__( self, plotter, palette=None, order=None, norm=None, ): """Map the levels of the `hue` variable to distinct colors. Parameters ---------- # TODO add generic parameters """ super().__init__(plotter) data = plotter.plot_data.get("hue", pd.Series(dtype=float)) if data.isna().all(): if palette is not None: msg = "Ignoring `palette` because no `hue` variable has been assigned." warnings.warn(msg, stacklevel=4) else: map_type = self.infer_map_type( palette, norm, plotter.input_format, plotter.var_types["hue"] ) # Our goal is to end up with a dictionary mapping every unique # value in `data` to a color. We will also keep track of the # metadata about this mapping we will need for, e.g., a legend # --- Option 1: numeric mapping with a matplotlib colormap if map_type == "numeric": data = pd.to_numeric(data) levels, lookup_table, norm, cmap = self.numeric_mapping( data, palette, norm, ) # --- Option 2: categorical mapping using seaborn palette elif map_type == "categorical": cmap = norm = None levels, lookup_table = self.categorical_mapping( data, palette, order, ) # --- Option 3: datetime mapping else: # TODO this needs actual implementation cmap = norm = None levels, lookup_table = self.categorical_mapping( # Casting data to list to handle differences in the way # pandas and numpy represent datetime64 data list(data), palette, order, ) self.map_type = map_type self.lookup_table = lookup_table self.palette = palette self.levels = levels self.norm = norm self.cmap = cmap def _lookup_single(self, key): """Get the color for a single value, using colormap to interpolate.""" try: # Use a value that's in the original data vector value = self.lookup_table[key] except KeyError: if self.norm is None: # Currently we only get here in scatterplot with hue_order, # because scatterplot does not consider hue a grouping variable # So unused hue levels are in the data, but not the lookup table return (0, 0, 0, 0) # Use the colormap to interpolate between existing datapoints # (e.g. in the context of making a continuous legend) try: normed = self.norm(key) except TypeError as err: if np.isnan(key): value = (0, 0, 0, 0) else: raise err else: if np.ma.is_masked(normed): normed = np.nan value = self.cmap(normed) return value def infer_map_type(self, palette, norm, input_format, var_type): """Determine how to implement the mapping.""" if palette in QUAL_PALETTES: map_type = "categorical" elif norm is not None: map_type = "numeric" elif isinstance(palette, (dict, list)): map_type = "categorical" elif input_format == "wide": map_type = "categorical" else: map_type = var_type return map_type def categorical_mapping(self, data, palette, order): """Determine colors when the hue mapping is categorical.""" # -- Identify the order and name of the levels levels = categorical_order(data, order) n_colors = len(levels) # -- Identify the set of colors to use if isinstance(palette, dict): missing = set(levels) - set(palette) if any(missing): err = "The palette dictionary is missing keys: {}" raise ValueError(err.format(missing)) lookup_table = palette else: if palette is None: if n_colors <= len(get_color_cycle()): colors = color_palette(None, n_colors) else: colors = color_palette("husl", n_colors) elif isinstance(palette, list): colors = self._check_list_length(levels, palette, "palette") else: colors = color_palette(palette, n_colors) lookup_table = dict(zip(levels, colors)) return levels, lookup_table def numeric_mapping(self, data, palette, norm): """Determine colors when the hue variable is quantitative.""" if isinstance(palette, dict): # The presence of a norm object overrides a dictionary of hues # in specifying a numeric mapping, so we need to process it here. levels = list(sorted(palette)) colors = [palette[k] for k in sorted(palette)] cmap = mpl.colors.ListedColormap(colors) lookup_table = palette.copy() else: # The levels are the sorted unique values in the data levels = list(np.sort(remove_na(data.unique()))) # --- Sort out the colormap to use from the palette argument # Default numeric palette is our default cubehelix palette # TODO do we want to do something complicated to ensure contrast? palette = "ch:" if palette is None else palette if isinstance(palette, mpl.colors.Colormap): cmap = palette else: cmap = color_palette(palette, as_cmap=True) # Now sort out the data normalization if norm is None: norm = mpl.colors.Normalize() elif isinstance(norm, tuple): norm = mpl.colors.Normalize(*norm) elif not isinstance(norm, mpl.colors.Normalize): err = "``hue_norm`` must be None, tuple, or Normalize object." raise ValueError(err) if not norm.scaled(): norm(np.asarray(data.dropna())) lookup_table = dict(zip(levels, cmap(norm(levels)))) return levels, lookup_table, norm, cmap @share_init_params_with_map class SizeMapping(SemanticMapping): """Mapping that sets artist sizes according to data values.""" # An object that normalizes data values to [0, 1] range norm = None def __init__( self, plotter, sizes=None, order=None, norm=None, ): """Map the levels of the `size` variable to distinct values. Parameters ---------- # TODO add generic parameters """ super().__init__(plotter) data = plotter.plot_data.get("size", pd.Series(dtype=float)) if data.notna().any(): map_type = self.infer_map_type( norm, sizes, plotter.var_types["size"] ) # --- Option 1: numeric mapping if map_type == "numeric": levels, lookup_table, norm, size_range = self.numeric_mapping( data, sizes, norm, ) # --- Option 2: categorical mapping elif map_type == "categorical": levels, lookup_table = self.categorical_mapping( data, sizes, order, ) size_range = None # --- Option 3: datetime mapping # TODO this needs an actual implementation else: levels, lookup_table = self.categorical_mapping( # Casting data to list to handle differences in the way # pandas and numpy represent datetime64 data list(data), sizes, order, ) size_range = None self.map_type = map_type self.levels = levels self.norm = norm self.sizes = sizes self.size_range = size_range self.lookup_table = lookup_table def infer_map_type(self, norm, sizes, var_type): if norm is not None: map_type = "numeric" elif isinstance(sizes, (dict, list)): map_type = "categorical" else: map_type = var_type return map_type def _lookup_single(self, key): try: value = self.lookup_table[key] except KeyError: normed = self.norm(key) if np.ma.is_masked(normed): normed = np.nan value = self.size_range[0] + normed * np.ptp(self.size_range) return value def categorical_mapping(self, data, sizes, order): levels = categorical_order(data, order) if isinstance(sizes, dict): # Dict inputs map existing data values to the size attribute missing = set(levels) - set(sizes) if any(missing): err = f"Missing sizes for the following levels: {missing}" raise ValueError(err) lookup_table = sizes.copy() elif isinstance(sizes, list): # List inputs give size values in the same order as the levels sizes = self._check_list_length(levels, sizes, "sizes") lookup_table = dict(zip(levels, sizes)) else: if isinstance(sizes, tuple): # Tuple input sets the min, max size values if len(sizes) != 2: err = "A `sizes` tuple must have only 2 values" raise ValueError(err) elif sizes is not None: err = f"Value for `sizes` not understood: {sizes}" raise ValueError(err) else: # Otherwise, we need to get the min, max size values from # the plotter object we are attached to. # TODO this is going to cause us trouble later, because we # want to restructure things so that the plotter is generic # across the visual representation of the data. But at this # point, we don't know the visual representation. Likely we # want to change the logic of this Mapping so that it gives # points on a normalized range that then gets un-normalized # when we know what we're drawing. But given the way the # package works now, this way is cleanest. sizes = self.plotter._default_size_range # For categorical sizes, use regularly-spaced linear steps # between the minimum and maximum sizes. Then reverse the # ramp so that the largest value is used for the first entry # in size_order, etc. This is because "ordered" categories # are often though to go in decreasing priority. sizes = np.linspace(*sizes, len(levels))[::-1] lookup_table = dict(zip(levels, sizes)) return levels, lookup_table def numeric_mapping(self, data, sizes, norm): if isinstance(sizes, dict): # The presence of a norm object overrides a dictionary of sizes # in specifying a numeric mapping, so we need to process it # dictionary here levels = list(np.sort(list(sizes))) size_values = sizes.values() size_range = min(size_values), max(size_values) else: # The levels here will be the unique values in the data levels = list(np.sort(remove_na(data.unique()))) if isinstance(sizes, tuple): # For numeric inputs, the size can be parametrized by # the minimum and maximum artist values to map to. The # norm object that gets set up next specifies how to # do the mapping. if len(sizes) != 2: err = "A `sizes` tuple must have only 2 values" raise ValueError(err) size_range = sizes elif sizes is not None: err = f"Value for `sizes` not understood: {sizes}" raise ValueError(err) else: # When not provided, we get the size range from the plotter # object we are attached to. See the note in the categorical # method about how this is suboptimal for future development. size_range = self.plotter._default_size_range # Now that we know the minimum and maximum sizes that will get drawn, # we need to map the data values that we have into that range. We will # use a matplotlib Normalize class, which is typically used for numeric # color mapping but works fine here too. It takes data values and maps # them into a [0, 1] interval, potentially nonlinear-ly. if norm is None: # Default is a linear function between the min and max data values norm = mpl.colors.Normalize() elif isinstance(norm, tuple): # It is also possible to give different limits in data space norm = mpl.colors.Normalize(*norm) elif not isinstance(norm, mpl.colors.Normalize): err = f"Value for size `norm` parameter not understood: {norm}" raise ValueError(err) else: # If provided with Normalize object, copy it so we can modify norm = copy(norm) # Set the mapping so all output values are in [0, 1] norm.clip = True # If the input range is not set, use the full range of the data if not norm.scaled(): norm(levels) # Map from data values to [0, 1] range sizes_scaled = norm(levels) # Now map from the scaled range into the artist units if isinstance(sizes, dict): lookup_table = sizes else: lo, hi = size_range sizes = lo + sizes_scaled * (hi - lo) lookup_table = dict(zip(levels, sizes)) return levels, lookup_table, norm, size_range @share_init_params_with_map class StyleMapping(SemanticMapping): """Mapping that sets artist style according to data values.""" # Style mapping is always treated as categorical map_type = "categorical" def __init__( self, plotter, markers=None, dashes=None, order=None, ): """Map the levels of the `style` variable to distinct values. Parameters ---------- # TODO add generic parameters """ super().__init__(plotter) data = plotter.plot_data.get("style", pd.Series(dtype=float)) if data.notna().any(): # Cast to list to handle numpy/pandas datetime quirks if variable_type(data) == "datetime": data = list(data) # Find ordered unique values levels = categorical_order(data, order) markers = self._map_attributes( markers, levels, unique_markers(len(levels)), "markers", ) dashes = self._map_attributes( dashes, levels, unique_dashes(len(levels)), "dashes", ) # Build the paths matplotlib will use to draw the markers paths = {} filled_markers = [] for k, m in markers.items(): if not isinstance(m, mpl.markers.MarkerStyle): m = mpl.markers.MarkerStyle(m) paths[k] = m.get_path().transformed(m.get_transform()) filled_markers.append(m.is_filled()) # Mixture of filled and unfilled markers will show line art markers # in the edge color, which defaults to white. This can be handled, # but there would be additional complexity with specifying the # weight of the line art markers without overwhelming the filled # ones with the edges. So for now, we will disallow mixtures. if any(filled_markers) and not all(filled_markers): err = "Filled and line art markers cannot be mixed" raise ValueError(err) lookup_table = {} for key in levels: lookup_table[key] = {} if markers: lookup_table[key]["marker"] = markers[key] lookup_table[key]["path"] = paths[key] if dashes: lookup_table[key]["dashes"] = dashes[key] self.levels = levels self.lookup_table = lookup_table def _lookup_single(self, key, attr=None): """Get attribute(s) for a given data point.""" if attr is None: value = self.lookup_table[key] else: value = self.lookup_table[key][attr] return value def _map_attributes(self, arg, levels, defaults, attr): """Handle the specification for a given style attribute.""" if arg is True: lookup_table = dict(zip(levels, defaults)) elif isinstance(arg, dict): missing = set(levels) - set(arg) if missing: err = f"These `{attr}` levels are missing values: {missing}" raise ValueError(err) lookup_table = arg elif isinstance(arg, Sequence): arg = self._check_list_length(levels, arg, attr) lookup_table = dict(zip(levels, arg)) elif arg: err = f"This `{attr}` argument was not understood: {arg}" raise ValueError(err) else: lookup_table = {} return lookup_table # =========================================================================== # class VectorPlotter: """Base class for objects underlying *plot functions.""" _semantic_mappings = { "hue": HueMapping, "size": SizeMapping, "style": StyleMapping, } # TODO units is another example of a non-mapping "semantic" # we need a general name for this and separate handling semantics = "x", "y", "hue", "size", "style", "units" wide_structure = { "x": "@index", "y": "@values", "hue": "@columns", "style": "@columns", } flat_structure = {"x": "@index", "y": "@values"} _default_size_range = 1, 2 # Unused but needed in tests, ugh def __init__(self, data=None, variables={}): self._var_levels = {} # var_ordered is relevant only for categorical axis variables, and may # be better handled by an internal axis information object that tracks # such information and is set up by the scale_* methods. The analogous # information for numeric axes would be information about log scales. self._var_ordered = {"x": False, "y": False} # alt., used DefaultDict self.assign_variables(data, variables) for var, cls in self._semantic_mappings.items(): # Create the mapping function map_func = partial(cls.map, plotter=self) setattr(self, f"map_{var}", map_func) # Call the mapping function to initialize with default values getattr(self, f"map_{var}")() @classmethod def get_semantics(cls, kwargs, semantics=None): """Subset a dictionary arguments with known semantic variables.""" # TODO this should be get_variables since we have included x and y if semantics is None: semantics = cls.semantics variables = {} for key, val in kwargs.items(): if key in semantics and val is not None: variables[key] = val return variables @property def has_xy_data(self): """Return True at least one of x or y is defined.""" return bool({"x", "y"} & set(self.variables)) @property def var_levels(self): """Property interface to ordered list of variables levels. Each time it's accessed, it updates the var_levels dictionary with the list of levels in the current semantic mappers. But it also allows the dictionary to persist, so it can be used to set levels by a key. This is used to track the list of col/row levels using an attached FacetGrid object, but it's kind of messy and ideally fixed by improving the faceting logic so it interfaces better with the modern approach to tracking plot variables. """ for var in self.variables: try: map_obj = getattr(self, f"_{var}_map") self._var_levels[var] = map_obj.levels except AttributeError: pass return self._var_levels def assign_variables(self, data=None, variables={}): """Define plot variables, optionally using lookup from `data`.""" x = variables.get("x", None) y = variables.get("y", None) if x is None and y is None: self.input_format = "wide" plot_data, variables = self._assign_variables_wideform( data, **variables, ) else: self.input_format = "long" plot_data, variables = self._assign_variables_longform( data, **variables, ) self.plot_data = plot_data self.variables = variables self.var_types = { v: variable_type( plot_data[v], boolean_type="numeric" if v in "xy" else "categorical" ) for v in variables } return self def _assign_variables_wideform(self, data=None, **kwargs): """Define plot variables given wide-form data. Parameters ---------- data : flat vector or collection of vectors Data can be a vector or mapping that is coerceable to a Series or a sequence- or mapping-based collection of such vectors, or a rectangular numpy array, or a Pandas DataFrame. kwargs : variable -> data mappings Behavior with keyword arguments is currently undefined. Returns ------- plot_data : :class:`pandas.DataFrame` Long-form data object mapping seaborn variables (x, y, hue, ...) to data vectors. variables : dict Keys are defined seaborn variables; values are names inferred from the inputs (or None when no name can be determined). """ # Raise if semantic or other variables are assigned in wide-form mode assigned = [k for k, v in kwargs.items() if v is not None] if any(assigned): s = "s" if len(assigned) > 1 else "" err = f"The following variable{s} cannot be assigned with wide-form data: " err += ", ".join(f"`{v}`" for v in assigned) raise ValueError(err) # Determine if the data object actually has any data in it empty = data is None or not len(data) # Then, determine if we have "flat" data (a single vector) if isinstance(data, dict): values = data.values() else: values = np.atleast_1d(np.asarray(data, dtype=object)) flat = not any( isinstance(v, Iterable) and not isinstance(v, (str, bytes)) for v in values ) if empty: # Make an object with the structure of plot_data, but empty plot_data = pd.DataFrame() variables = {} elif flat: # Handle flat data by converting to pandas Series and using the # index and/or values to define x and/or y # (Could be accomplished with a more general to_series() interface) flat_data = pd.Series(data).copy() names = { "@values": flat_data.name, "@index": flat_data.index.name } plot_data = {} variables = {} for var in ["x", "y"]: if var in self.flat_structure: attr = self.flat_structure[var] plot_data[var] = getattr(flat_data, attr[1:]) variables[var] = names[self.flat_structure[var]] plot_data = pd.DataFrame(plot_data) else: # Otherwise assume we have some collection of vectors. # Handle Python sequences such that entries end up in the columns, # not in the rows, of the intermediate wide DataFrame. # One way to accomplish this is to convert to a dict of Series. if isinstance(data, Sequence): data_dict = {} for i, var in enumerate(data): key = getattr(var, "name", i) # TODO is there a safer/more generic way to ensure Series? # sort of like np.asarray, but for pandas? data_dict[key] = pd.Series(var) data = data_dict # Pandas requires that dict values either be Series objects # or all have the same length, but we want to allow "ragged" inputs if isinstance(data, Mapping): data = {key: pd.Series(val) for key, val in data.items()} # Otherwise, delegate to the pandas DataFrame constructor # This is where we'd prefer to use a general interface that says # "give me this data as a pandas DataFrame", so we can accept # DataFrame objects from other libraries wide_data = pd.DataFrame(data, copy=True) # At this point we should reduce the dataframe to numeric cols numeric_cols = [ k for k, v in wide_data.items() if variable_type(v) == "numeric" ] wide_data = wide_data[numeric_cols] # Now melt the data to long form melt_kws = {"var_name": "@columns", "value_name": "@values"} use_index = "@index" in self.wide_structure.values() if use_index: melt_kws["id_vars"] = "@index" try: orig_categories = wide_data.columns.categories orig_ordered = wide_data.columns.ordered wide_data.columns = wide_data.columns.add_categories("@index") except AttributeError: category_columns = False else: category_columns = True wide_data["@index"] = wide_data.index.to_series() plot_data = wide_data.melt(**melt_kws) if use_index and category_columns: plot_data["@columns"] = pd.Categorical(plot_data["@columns"], orig_categories, orig_ordered) # Assign names corresponding to plot semantics for var, attr in self.wide_structure.items(): plot_data[var] = plot_data[attr] # Define the variable names variables = {} for var, attr in self.wide_structure.items(): obj = getattr(wide_data, attr[1:]) variables[var] = getattr(obj, "name", None) # Remove redundant columns from plot_data plot_data = plot_data[list(variables)] return plot_data, variables def _assign_variables_longform(self, data=None, **kwargs): """Define plot variables given long-form data and/or vector inputs. Parameters ---------- data : dict-like collection of vectors Input data where variable names map to vector values. kwargs : variable -> data mappings Keys are seaborn variables (x, y, hue, ...) and values are vectors in any format that can construct a :class:`pandas.DataFrame` or names of columns or index levels in ``data``. Returns ------- plot_data : :class:`pandas.DataFrame` Long-form data object mapping seaborn variables (x, y, hue, ...) to data vectors. variables : dict Keys are defined seaborn variables; values are names inferred from the inputs (or None when no name can be determined). Raises ------ ValueError When variables are strings that don't appear in ``data``. """ plot_data = {} variables = {} # Data is optional; all variables can be defined as vectors if data is None: data = {} # TODO should we try a data.to_dict() or similar here to more # generally accept objects with that interface? # Note that dict(df) also works for pandas, and gives us what we # want, whereas DataFrame.to_dict() gives a nested dict instead of # a dict of series. # Variables can also be extracted from the index attribute # TODO is this the most general way to enable it? # There is no index.to_dict on multiindex, unfortunately try: index = data.index.to_frame() except AttributeError: index = {} # The caller will determine the order of variables in plot_data for key, val in kwargs.items(): # First try to treat the argument as a key for the data collection. # But be flexible about what can be used as a key. # Usually it will be a string, but allow numbers or tuples too when # taking from the main data object. Only allow strings to reference # fields in the index, because otherwise there is too much ambiguity. try: val_as_data_key = ( val in data or (isinstance(val, (str, bytes)) and val in index) ) except (KeyError, TypeError): val_as_data_key = False if val_as_data_key: # We know that __getitem__ will work if val in data: plot_data[key] = data[val] elif val in index: plot_data[key] = index[val] variables[key] = val elif isinstance(val, (str, bytes)): # This looks like a column name but we don't know what it means! err = f"Could not interpret value `{val}` for parameter `{key}`" raise ValueError(err) else: # Otherwise, assume the value is itself data # Raise when data object is present and a vector can't matched if isinstance(data, pd.DataFrame) and not isinstance(val, pd.Series): if np.ndim(val) and len(data) != len(val): val_cls = val.__class__.__name__ err = ( f"Length of {val_cls} vectors must match length of `data`" f" when both are used, but `data` has length {len(data)}" f" and the vector passed to `{key}` has length {len(val)}." ) raise ValueError(err) plot_data[key] = val # Try to infer the name of the variable variables[key] = getattr(val, "name", None) # Construct a tidy plot DataFrame. This will convert a number of # types automatically, aligning on index in case of pandas objects plot_data = pd.DataFrame(plot_data) # Reduce the variables dictionary to fields with valid data variables = { var: name for var, name in variables.items() if plot_data[var].notnull().any() } return plot_data, variables def iter_data( self, grouping_vars=None, *, reverse=False, from_comp_data=False, by_facet=True, allow_empty=False, dropna=True, ): """Generator for getting subsets of data defined by semantic variables. Also injects "col" and "row" into grouping semantics. Parameters ---------- grouping_vars : string or list of strings Semantic variables that define the subsets of data. reverse : bool If True, reverse the order of iteration. from_comp_data : bool If True, use self.comp_data rather than self.plot_data by_facet : bool If True, add faceting variables to the set of grouping variables. allow_empty : bool If True, yield an empty dataframe when no observations exist for combinations of grouping variables. dropna : bool If True, remove rows with missing data. Yields ------ sub_vars : dict Keys are semantic names, values are the level of that semantic. sub_data : :class:`pandas.DataFrame` Subset of ``plot_data`` for this combination of semantic values. """ # TODO should this default to using all (non x/y?) semantics? # or define grouping vars somewhere? if grouping_vars is None: grouping_vars = [] elif isinstance(grouping_vars, str): grouping_vars = [grouping_vars] elif isinstance(grouping_vars, tuple): grouping_vars = list(grouping_vars) # Always insert faceting variables if by_facet: facet_vars = {"col", "row"} grouping_vars.extend( facet_vars & set(self.variables) - set(grouping_vars) ) # Reduce to the semantics used in this plot grouping_vars = [ var for var in grouping_vars if var in self.variables ] if from_comp_data: data = self.comp_data else: data = self.plot_data if dropna: data = data.dropna() levels = self.var_levels.copy() if from_comp_data: for axis in {"x", "y"} & set(grouping_vars): if self.var_types[axis] == "categorical": if self._var_ordered[axis]: # If the axis is ordered, then the axes in a possible # facet grid are by definition "shared", or there is a # single axis with a unique cat -> idx mapping. # So we can just take the first converter object. converter = self.converters[axis].iloc[0] levels[axis] = converter.convert_units(levels[axis]) else: # Otherwise, the mappings may not be unique, but we can # use the unique set of index values in comp_data. levels[axis] = np.sort(data[axis].unique()) elif self.var_types[axis] == "datetime": levels[axis] = mpl.dates.date2num(levels[axis]) elif self.var_types[axis] == "numeric" and self._log_scaled(axis): levels[axis] = np.log10(levels[axis]) if grouping_vars: grouped_data = data.groupby( grouping_vars, sort=False, as_index=False ) grouping_keys = [] for var in grouping_vars: grouping_keys.append(levels.get(var, [])) iter_keys = itertools.product(*grouping_keys) if reverse: iter_keys = reversed(list(iter_keys)) for key in iter_keys: # Pandas fails with singleton tuple inputs pd_key = key[0] if len(key) == 1 else key try: data_subset = grouped_data.get_group(pd_key) except KeyError: # XXX we are adding this to allow backwards compatibility # with the empty artists that old categorical plots would # add (before 0.12), which we may decide to break, in which # case this option could be removed data_subset = data.loc[[]] if data_subset.empty and not allow_empty: continue sub_vars = dict(zip(grouping_vars, key)) yield sub_vars, data_subset.copy() else: yield {}, data.copy() @property def comp_data(self): """Dataframe with numeric x and y, after unit conversion and log scaling.""" if not hasattr(self, "ax"): # Probably a good idea, but will need a bunch of tests updated # Most of these tests should just use the external interface # Then this can be re-enabled. # raise AttributeError("No Axes attached to plotter") return self.plot_data if not hasattr(self, "_comp_data"): comp_data = ( self.plot_data .copy(deep=False) .drop(["x", "y"], axis=1, errors="ignore") ) for var in "yx": if var not in self.variables: continue parts = [] grouped = self.plot_data[var].groupby(self.converters[var], sort=False) for converter, orig in grouped: with pd.option_context('mode.use_inf_as_null', True): orig = orig.dropna() if var in self.var_levels: # TODO this should happen in some centralized location # it is similar to GH2419, but more complicated because # supporting `order` in categorical plots is tricky orig = orig[orig.isin(self.var_levels[var])] comp = pd.to_numeric(converter.convert_units(orig)) if converter.get_scale() == "log": comp = np.log10(comp) parts.append(pd.Series(comp, orig.index, name=orig.name)) if parts: comp_col = pd.concat(parts) else: comp_col = pd.Series(dtype=float, name=var) comp_data.insert(0, var, comp_col) self._comp_data = comp_data return self._comp_data def _get_axes(self, sub_vars): """Return an Axes object based on existence of row/col variables.""" row = sub_vars.get("row", None) col = sub_vars.get("col", None) if row is not None and col is not None: return self.facets.axes_dict[(row, col)] elif row is not None: return self.facets.axes_dict[row] elif col is not None: return self.facets.axes_dict[col] elif self.ax is None: return self.facets.ax else: return self.ax def _attach( self, obj, allowed_types=None, log_scale=None, ): """Associate the plotter with an Axes manager and initialize its units. Parameters ---------- obj : :class:`matplotlib.axes.Axes` or :class:'FacetGrid` Structural object that we will eventually plot onto. allowed_types : str or list of str If provided, raise when either the x or y variable does not have one of the declared seaborn types. log_scale : bool, number, or pair of bools or numbers If not False, set the axes to use log scaling, with the given base or defaulting to 10. If a tuple, interpreted as separate arguments for the x and y axes. """ from .axisgrid import FacetGrid if isinstance(obj, FacetGrid): self.ax = None self.facets = obj ax_list = obj.axes.flatten() if obj.col_names is not None: self.var_levels["col"] = obj.col_names if obj.row_names is not None: self.var_levels["row"] = obj.row_names else: self.ax = obj self.facets = None ax_list = [obj] # Identify which "axis" variables we have defined axis_variables = set("xy").intersection(self.variables) # -- Verify the types of our x and y variables here. # This doesn't really make complete sense being here here, but it's a fine # place for it, given the current system. # (Note that for some plots, there might be more complicated restrictions) # e.g. the categorical plots have their own check that as specific to the # non-categorical axis. if allowed_types is None: allowed_types = ["numeric", "datetime", "categorical"] elif isinstance(allowed_types, str): allowed_types = [allowed_types] for var in axis_variables: var_type = self.var_types[var] if var_type not in allowed_types: err = ( f"The {var} variable is {var_type}, but one of " f"{allowed_types} is required" ) raise TypeError(err) # -- Get axis objects for each row in plot_data for type conversions and scaling facet_dim = {"x": "col", "y": "row"} self.converters = {} for var in axis_variables: other_var = {"x": "y", "y": "x"}[var] converter = pd.Series(index=self.plot_data.index, name=var, dtype=object) share_state = getattr(self.facets, f"_share{var}", True) # Simplest cases are that we have a single axes, all axes are shared, # or sharing is only on the orthogonal facet dimension. In these cases, # all datapoints get converted the same way, so use the first axis if share_state is True or share_state == facet_dim[other_var]: converter.loc[:] = getattr(ax_list[0], f"{var}axis") else: # Next simplest case is when no axes are shared, and we can # use the axis objects within each facet if share_state is False: for axes_vars, axes_data in self.iter_data(): ax = self._get_axes(axes_vars) converter.loc[axes_data.index] = getattr(ax, f"{var}axis") # In the more complicated case, the axes are shared within each # "file" of the facetgrid. In that case, we need to subset the data # for that file and assign it the first axis in the slice of the grid else: names = getattr(self.facets, f"{share_state}_names") for i, level in enumerate(names): idx = (i, 0) if share_state == "row" else (0, i) axis = getattr(self.facets.axes[idx], f"{var}axis") converter.loc[self.plot_data[share_state] == level] = axis # Store the converter vector, which we use elsewhere (e.g comp_data) self.converters[var] = converter # Now actually update the matplotlib objects to do the conversion we want grouped = self.plot_data[var].groupby(self.converters[var], sort=False) for converter, seed_data in grouped: if self.var_types[var] == "categorical": if self._var_ordered[var]: order = self.var_levels[var] else: order = None seed_data = categorical_order(seed_data, order) converter.update_units(seed_data) # -- Set numerical axis scales # First unpack the log_scale argument if log_scale is None: scalex = scaley = False else: # Allow single value or x, y tuple try: scalex, scaley = log_scale except TypeError: scalex = log_scale if "x" in self.variables else False scaley = log_scale if "y" in self.variables else False # Now use it for axis, scale in zip("xy", (scalex, scaley)): if scale: for ax in ax_list: set_scale = getattr(ax, f"set_{axis}scale") if scale is True: set_scale("log") else: if Version(mpl.__version__) >= Version("3.3"): set_scale("log", base=scale) else: set_scale("log", **{f"base{axis}": scale}) # For categorical y, we want the "first" level to be at the top of the axis if self.var_types.get("y", None) == "categorical": for ax in ax_list: try: ax.yaxis.set_inverted(True) except AttributeError: # mpl < 3.1 if not ax.yaxis_inverted(): ax.invert_yaxis() # TODO -- Add axes labels def _log_scaled(self, axis): """Return True if specified axis is log scaled on all attached axes.""" if not hasattr(self, "ax"): return False if self.ax is None: axes_list = self.facets.axes.flatten() else: axes_list = [self.ax] log_scaled = [] for ax in axes_list: data_axis = getattr(ax, f"{axis}axis") log_scaled.append(data_axis.get_scale() == "log") if any(log_scaled) and not all(log_scaled): raise RuntimeError("Axis scaling is not consistent") return any(log_scaled) def _add_axis_labels(self, ax, default_x="", default_y=""): """Add axis labels if not present, set visibility to match ticklabels.""" # TODO ax could default to None and use attached axes if present # but what to do about the case of facets? Currently using FacetGrid's # set_axis_labels method, which doesn't add labels to the interior even # when the axes are not shared. Maybe that makes sense? if not ax.get_xlabel(): x_visible = any(t.get_visible() for t in ax.get_xticklabels()) ax.set_xlabel(self.variables.get("x", default_x), visible=x_visible) if not ax.get_ylabel(): y_visible = any(t.get_visible() for t in ax.get_yticklabels()) ax.set_ylabel(self.variables.get("y", default_y), visible=y_visible) # XXX If the scale_* methods are going to modify the plot_data structure, they # can't be called twice. That means that if they are called twice, they should # raise. Alternatively, we could store an original version of plot_data and each # time they are called they operate on the store, not the current state. def scale_native(self, axis, *args, **kwargs): # Default, defer to matplotlib raise NotImplementedError def scale_numeric(self, axis, *args, **kwargs): # Feels needed to completeness, what should it do? # Perhaps handle log scaling? Set the ticker/formatter/limits? raise NotImplementedError def scale_datetime(self, axis, *args, **kwargs): # Use pd.to_datetime to convert strings or numbers to datetime objects # Note, use day-resolution for numeric->datetime to match matplotlib raise NotImplementedError def scale_categorical(self, axis, order=None, formatter=None): """ Enforce categorical (fixed-scale) rules for the data on given axis. Parameters ---------- axis : "x" or "y" Axis of the plot to operate on. order : list Order that unique values should appear in. formatter : callable Function mapping values to a string representation. Returns ------- self """ # This method both modifies the internal representation of the data # (converting it to string) and sets some attributes on self. It might be # a good idea to have a separate object attached to self that contains the # information in those attributes (i.e. whether to enforce variable order # across facets, the order to use) similar to the SemanticMapping objects # we have for semantic variables. That object could also hold the converter # objects that get used, if we can decouple those from an existing axis # (cf. https://github.com/matplotlib/matplotlib/issues/19229). # There are some interactions with faceting information that would need # to be thought through, since the converts to use depend on facets. # If we go that route, these methods could become "borrowed" methods similar # to what happens with the alternate semantic mapper constructors, although # that approach is kind of fussy and confusing. # TODO this method could also set the grid state? Since we like to have no # grid on the categorical axis by default. Again, a case where we'll need to # store information until we use it, so best to have a way to collect the # attributes that this method sets. # TODO if we are going to set visual properties of the axes with these methods, # then we could do the steps currently in CategoricalPlotter._adjust_cat_axis # TODO another, and distinct idea, is to expose a cut= param here _check_argument("axis", ["x", "y"], axis) # Categorical plots can be "univariate" in which case they get an anonymous # category label on the opposite axis. if axis not in self.variables: self.variables[axis] = None self.var_types[axis] = "categorical" self.plot_data[axis] = "" # If the "categorical" variable has a numeric type, sort the rows so that # the default result from categorical_order has those values sorted after # they have been coerced to strings. The reason for this is so that later # we can get facet-wise orders that are correct. # XXX Should this also sort datetimes? # It feels more consistent, but technically will be a default change # If so, should also change categorical_order to behave that way if self.var_types[axis] == "numeric": self.plot_data = self.plot_data.sort_values(axis, kind="mergesort") # Now get a reference to the categorical data vector cat_data = self.plot_data[axis] # Get the initial categorical order, which we do before string # conversion to respect the original types of the order list. # Track whether the order is given explicitly so that we can know # whether or not to use the order constructed here downstream self._var_ordered[axis] = order is not None or cat_data.dtype.name == "category" order = pd.Index(categorical_order(cat_data, order)) # Then convert data to strings. This is because in matplotlib, # "categorical" data really mean "string" data, so doing this artists # will be drawn on the categorical axis with a fixed scale. # TODO implement formatter here; check that it returns strings? if formatter is not None: cat_data = cat_data.map(formatter) order = order.map(formatter) else: cat_data = cat_data.astype(str) order = order.astype(str) # Update the levels list with the type-converted order variable self.var_levels[axis] = order # Now ensure that seaborn will use categorical rules internally self.var_types[axis] = "categorical" # Put the string-typed categorical vector back into the plot_data structure self.plot_data[axis] = cat_data return self class VariableType(UserString): """ Prevent comparisons elsewhere in the library from using the wrong name. Errors are simple assertions because users should not be able to trigger them. If that changes, they should be more verbose. """ # TODO we can replace this with typing.Literal on Python 3.8+ allowed = "numeric", "datetime", "categorical" def __init__(self, data): assert data in self.allowed, data super().__init__(data) def __eq__(self, other): assert other in self.allowed, other return self.data == other def variable_type(vector, boolean_type="numeric"): """ Determine whether a vector contains numeric, categorical, or datetime data. This function differs from the pandas typing API in two ways: - Python sequences or object-typed PyData objects are considered numeric if all of their entries are numeric. - String or mixed-type data are considered categorical even if not explicitly represented as a :class:`pandas.api.types.CategoricalDtype`. Parameters ---------- vector : :func:`pandas.Series`, :func:`numpy.ndarray`, or Python sequence Input data to test. boolean_type : 'numeric' or 'categorical' Type to use for vectors containing only 0s and 1s (and NAs). Returns ------- var_type : 'numeric', 'categorical', or 'datetime' Name identifying the type of data in the vector. """ # If a categorical dtype is set, infer categorical if pd.api.types.is_categorical_dtype(vector): return VariableType("categorical") # Special-case all-na data, which is always "numeric" if pd.isna(vector).all(): return VariableType("numeric") # Special-case binary/boolean data, allow caller to determine # This triggers a numpy warning when vector has strings/objects # https://github.com/numpy/numpy/issues/6784 # Because we reduce with .all(), we are agnostic about whether the # comparison returns a scalar or vector, so we will ignore the warning. # It triggers a separate DeprecationWarning when the vector has datetimes: # https://github.com/numpy/numpy/issues/13548 # This is considered a bug by numpy and will likely go away. with warnings.catch_warnings(): warnings.simplefilter( action='ignore', category=(FutureWarning, DeprecationWarning) ) if np.isin(vector, [0, 1, np.nan]).all(): return VariableType(boolean_type) # Defer to positive pandas tests if pd.api.types.is_numeric_dtype(vector): return VariableType("numeric") if pd.api.types.is_datetime64_dtype(vector): return VariableType("datetime") # --- If we get to here, we need to check the entries # Check for a collection where everything is a number def all_numeric(x): for x_i in x: if not isinstance(x_i, Number): return False return True if all_numeric(vector): return VariableType("numeric") # Check for a collection where everything is a datetime def all_datetime(x): for x_i in x: if not isinstance(x_i, (datetime, np.datetime64)): return False return True if all_datetime(vector): return VariableType("datetime") # Otherwise, our final fallback is to consider things categorical return VariableType("categorical") def infer_orient(x=None, y=None, orient=None, require_numeric=True): """Determine how the plot should be oriented based on the data. For historical reasons, the convention is to call a plot "horizontally" or "vertically" oriented based on the axis representing its dependent variable. Practically, this is used when determining the axis for numerical aggregation. Parameters ---------- x, y : Vector data or None Positional data vectors for the plot. orient : string or None Specified orientation, which must start with "v" or "h" if not None. require_numeric : bool If set, raise when the implied dependent variable is not numeric. Returns ------- orient : "v" or "h" Raises ------ ValueError: When `orient` is not None and does not start with "h" or "v" TypeError: When dependent variable is not numeric, with `require_numeric` """ x_type = None if x is None else variable_type(x) y_type = None if y is None else variable_type(y) nonnumeric_dv_error = "{} orientation requires numeric `{}` variable." single_var_warning = "{} orientation ignored with only `{}` specified." if x is None: if str(orient).startswith("h"): warnings.warn(single_var_warning.format("Horizontal", "y")) if require_numeric and y_type != "numeric": raise TypeError(nonnumeric_dv_error.format("Vertical", "y")) return "v" elif y is None: if str(orient).startswith("v"): warnings.warn(single_var_warning.format("Vertical", "x")) if require_numeric and x_type != "numeric": raise TypeError(nonnumeric_dv_error.format("Horizontal", "x")) return "h" elif str(orient).startswith("v"): if require_numeric and y_type != "numeric": raise TypeError(nonnumeric_dv_error.format("Vertical", "y")) return "v" elif str(orient).startswith("h"): if require_numeric and x_type != "numeric": raise TypeError(nonnumeric_dv_error.format("Horizontal", "x")) return "h" elif orient is not None: err = ( "`orient` must start with 'v' or 'h' or be None, " f"but `{repr(orient)}` was passed." ) raise ValueError(err) elif x_type != "categorical" and y_type == "categorical": return "h" elif x_type != "numeric" and y_type == "numeric": return "v" elif x_type == "numeric" and y_type != "numeric": return "h" elif require_numeric and "numeric" not in (x_type, y_type): err = "Neither the `x` nor `y` variable appears to be numeric." raise TypeError(err) else: return "v" def unique_dashes(n): """Build an arbitrarily long list of unique dash styles for lines. Parameters ---------- n : int Number of unique dash specs to generate. Returns ------- dashes : list of strings or tuples Valid arguments for the ``dashes`` parameter on :class:`matplotlib.lines.Line2D`. The first spec is a solid line (``""``), the remainder are sequences of long and short dashes. """ # Start with dash specs that are well distinguishable dashes = [ "", (4, 1.5), (1, 1), (3, 1.25, 1.5, 1.25), (5, 1, 1, 1), ] # Now programmatically build as many as we need p = 3 while len(dashes) < n: # Take combinations of long and short dashes a = itertools.combinations_with_replacement([3, 1.25], p) b = itertools.combinations_with_replacement([4, 1], p) # Interleave the combinations, reversing one of the streams segment_list = itertools.chain(*zip( list(a)[1:-1][::-1], list(b)[1:-1] )) # Now insert the gaps for segments in segment_list: gap = min(segments) spec = tuple(itertools.chain(*((seg, gap) for seg in segments))) dashes.append(spec) p += 1 return dashes[:n] def unique_markers(n): """Build an arbitrarily long list of unique marker styles for points. Parameters ---------- n : int Number of unique marker specs to generate. Returns ------- markers : list of string or tuples Values for defining :class:`matplotlib.markers.MarkerStyle` objects. All markers will be filled. """ # Start with marker specs that are well distinguishable markers = [ "o", "X", (4, 0, 45), "P", (4, 0, 0), (4, 1, 0), "^", (4, 1, 45), "v", ] # Now generate more from regular polygons of increasing order s = 5 while len(markers) < n: a = 360 / (s + 1) / 2 markers.extend([ (s + 1, 1, a), (s + 1, 0, a), (s, 1, 0), (s, 0, 0), ]) s += 1 # Convert to MarkerStyle object, using only exactly what we need # markers = [mpl.markers.MarkerStyle(m) for m in markers[:n]] return markers[:n] def categorical_order(vector, order=None): """Return a list of unique data values. Determine an ordered list of levels in ``values``. Parameters ---------- vector : list, array, Categorical, or Series Vector of "categorical" values order : list-like, optional Desired order of category levels to override the order determined from the ``values`` object. Returns ------- order : list Ordered list of category levels not including null values. """ if order is None: if hasattr(vector, "categories"): order = vector.categories else: try: order = vector.cat.categories except (TypeError, AttributeError): try: order = vector.unique() except AttributeError: order = pd.unique(vector) if variable_type(vector) == "numeric": order = np.sort(order) order = filter(pd.notnull, order) return list(order)