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plt.title("Big Title (+4 bigger than rest)", size=aa.plot_gcfs()+4) + >>> plt.title("Two points bigger title", size=aa.plot_gcfs()+2) >>> plt.tight_layout() >>> plt.show() diff --git a/aaanalysis/plotting/plot_set_legend_.py b/aaanalysis/plotting/plot_set_legend_.py index 0f93cdc7..3a1f2c7b 100644 --- a/aaanalysis/plotting/plot_set_legend_.py +++ b/aaanalysis/plotting/plot_set_legend_.py @@ -9,134 +9,114 @@ # I Helper functions -def _items_grouped(lst): - for i in range(1, len(lst)): - if lst[i] == lst[i - 1]: # Same item as before, continue - continue - if lst[i] in lst[:i]: # Different item but already appeared earlier in the list - return False - return True - - -def _check_validity(item, valid_items, item_name, list_cat): - """General check validity function.""" - if item is None: - return [item] * len(list_cat) - item_is_list = isinstance(item, list) - if not item_is_list: - if item not in valid_items: - raise ValueError(f"'{item_name}' ('{item}') must be one of following: {valid_items}") - else: - list_items = [item] * len(list_cat) - else: - wrong_items = [x for x in item if x not in valid_items] - if len(wrong_items) != 0: - raise ValueError( - f"'{item_name}' contains wrong items ('{wrong_items}')! Must be one of following: {valid_items}") - if len(item) != len(list_cat): - raise ValueError(f"Length must match of '{item_name}' ({item}) and categories ({list_cat}).") - else: - list_items = item - return list_items - -# Checking functios +# Checking functions for list inputs def check_list_cat(dict_color=None, list_cat=None): - """""" - if list_cat is None: + """Ensure items in list_cat are keys in dict_color and match in length.""" + if not list_cat: return list(dict_color.keys()) - # Check if all elements of list_cat are keys in dict_colors if not all(elem in dict_color for elem in list_cat): missing_keys = [elem for elem in list_cat if elem not in dict_color] raise ValueError(f"The following keys in 'list_cat' are not in 'dict_colors': {', '.join(missing_keys)}") - # Check if lengths match if len(dict_color) != len(list_cat): - raise ValueError(f"Length must match between 'list_cat' ({len(list_cat)}) and 'dict_colors' ({len(dict_color)}).") - if list_cat is None: - raise ValueError("'list_cat' was not set properly.") + raise ValueError( + f"Length must match between 'list_cat' ({len(list_cat)}) and 'dict_colors' ({len(dict_color)}).") return list_cat def check_labels(list_cat=None, labels=None): - """""" + """Validate labels and match their length to list_cat.""" if labels is None: labels = list_cat - # If list_cat is provided, check its length against labels if len(list_cat) != len(labels): raise ValueError(f"Length must match of 'labels' ({len(labels)}) and categories ({len(list_cat)}).") - if labels is None: - raise ValueError("'labels' was not set properly.") return labels +# Checking functions for inputs that can be list or single values (redundancy accepted for better user communication) +def check_hatches(marker=None, hatch=None, list_cat=None): + """Check validity of list_hatche.""" + valid_hatches = ['/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'] + # Check if hatch can be chosen + if marker and hatch: + raise ValueError(f"'hatch' can only be applied to the default marker, not to ('{marker}'). Set it to None.") + + # Check if hatch is valid + if isinstance(hatch, str): + if hatch not in valid_hatches: + raise ValueError(f"'hatch' ('{hatch}') must be one of following: {valid_hatches}") + if isinstance(hatch, list): + wrong_hatch = [x for x in hatch if x not in valid_hatches] + if len(wrong_hatch) != 0: + raise ValueError( + f"'hatch' contains wrong values ('{wrong_hatch}')! Should be one of following: {valid_hatches}") + if len(hatch) != len(list_cat): + raise ValueError(f"Length must match of 'hatch' ({hatch}) and categories ({list_cat}).") + + # Create hatch list + list_hatch = [hatch] * len(list_cat) if not isinstance(hatch, list) else hatch + return list_hatch + + def check_marker(marker=None, list_cat=None, lw=0): - """Check validity of marker.""" - all_markers = list(mlines.Line2D.markers.keys()) - all_markers.append("-") # Allow '-' as a valid marker for a line + """Check validity of markers""" + # Add '-' for line and None for default marker + valid_markers = [None, "-"] + list(mlines.Line2D.markers.keys()) + + # Check if marker is valid if marker == "-" and lw <= 0: raise ValueError(f"If marker is '{marker}', 'lw' ({lw}) must be > 0.") - return _check_validity(marker, all_markers, 'marker', list_cat) + if not isinstance(marker, list) and marker not in valid_markers: + raise ValueError(f"'marker' ('{marker}') must be one of following: {valid_markers}") + if isinstance(marker, list): + wrong_markers = [x for x in marker if x not in valid_markers] + if len(wrong_markers) != 0: + raise ValueError(f"'marker' contains wrong values ('{wrong_markers}'). Should be one of following: {valid_markers}") + if len(marker) != len(list_cat): + raise ValueError(f"Length must match of 'marker' ({marker}) and categories ({list_cat}).") + + # Create marker list + list_marker = [marker] * len(list_cat) if not isinstance(marker, list) else marker + return list_marker + def check_marker_size(marker_size=None, list_cat=None): """""" + # Check if marker_size is valid if isinstance(marker_size, (int, float)): ut.check_number_range(name='marker_size', val=marker_size, min_val=0, accept_none=True, just_int=False) - marker_size = [marker_size] * len(list_cat) - return marker_size - elif marker_size is None: - marker_size = [marker_size] * len(list_cat) - return marker_size elif isinstance(marker_size, list): for i in marker_size: ut.check_number_range(name='marker_size', val=i, min_val=0, accept_none=True, just_int=False) - return marker_size elif isinstance(marker_size, list) and len(marker_size) != len(list_cat): raise ValueError(f"Length must match of 'marker_size' (marker_size) and categories ({list_cat}).") else: raise ValueError(f"'marker_size' has wrong data type: {type(marker_size)}") + # Create marker_size list + list_marker_size = [marker_size] * len(list_cat) if not isinstance(marker_size, list) else marker_size + return list_marker_size -def check_linestyle(marker_linestyle=None, list_cat=None): +def check_linestyle(linestyle=None, list_cat=None): """Check validity of linestyle.""" _lines = ['-', '--', '-.', ':', ] _names = ["solid", "dashed", "dashed-doted", "dotted"] - dict_names_lines = dict(zip(_names, _lines)) - # Names to line styles - if isinstance(marker_linestyle, str) and marker_linestyle in _names: - marker_linestyle = dict_names_lines[marker_linestyle] - elif isinstance(marker_linestyle, list): - marker_linestyle = [dict_names_lines.get(x, x) for x in marker_linestyle] - valid_linestyles = dict(zip(_lines, _names)) - return _check_validity(marker_linestyle, valid_linestyles, 'marker_linestyle', list_cat) - - -def check_hatches(marker=None, hatch=None, list_cat=None): - """""" - valid_hatches = ['/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'] - # Check if hatch can be chosen - if hatch is None: - hatches = [None] * len(list_cat) - if marker is not None: - raise ValueError(f"'marker' ('{marker}') must be None if 'hatch' ('{hatch}') is given.") - # Check if hatch is valid - if isinstance(hatch, str): - if hatch not in valid_hatches: - raise ValueError(f"'hatch' ('{hatch}') must be one of following: {valid_hatches}") - hatches = [hatch] * len(list_cat) - # Check if hatch match with categories - if isinstance(hatch, list): - wrong_hatch = [x for x in hatch if x not in valid_hatches] - if len(wrong_hatch) != 0: + valid_mls = _lines + _names + # Check if marker_linestyle is valid + if isinstance(linestyle, str): + if linestyle not in valid_mls: + raise ValueError(f"'marker_linestyle' ('{linestyle}') must be one of following: {_lines}," + f"or their names: {_names} ") + if isinstance(linestyle, list): + wrong_mls = [x for x in linestyle if x not in valid_mls] + if len(wrong_mls) != 0: raise ValueError( - f"'hatch' contains wrong values ('{wrong_hatch}')! Must be one of following: {valid_hatches}") - if len(hatch) != len(list_cat): - raise ValueError(f"Length must match of 'hatch' ({hatch}) and categories ({list_cat}).") - else: - hatches = hatch - else: - hatches = [None] * len(list_cat) - if not isinstance(hatches, list): - raise ValueError("'hutch' not set properly") - return hatches + f"'marker_linestyle' contains wrong values ('{wrong_mls}')! Should be one of following: {valid_mls}") + if len(linestyle) != len(list_cat): + raise ValueError(f"Length must match of 'marker_linestyle' ({linestyle}) and categories ({list_cat}).") + # Create list_marker_linestyle list + list_marker_linestyle = [linestyle] * len(list_cat) if not isinstance(linestyle, list) else linestyle + return list_marker_linestyle + # Helper function def _create_marker(color, label, marker, marker_size, lw, edgecolor, linestyle, hatch): @@ -187,11 +167,11 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, weight: str = "normal", fontsize_weight: str = "normal", # Line, Marker, and Area - lw: Union[int, float] = 0, - edgecolor: Optional[str] = None, marker: Optional[Union[str, int, list]] = None, marker_size: Union[int, float, List[Union[int, float]]] = 10, - marker_linestyle: Union[str, list] = "-", + lw: Union[int, float] = 0, + linestyle: Union[str, list] = "-", + edgecolor: str = "white", hatch: Optional[Union[str, List[str]]] = None, # Title title: str = None, @@ -199,7 +179,7 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, **kwargs ) -> Union[plt.Axes, Tuple[List, List[str]]]: """ - Sets a customizable legend for a plot. + Sets a customizable legend for a plot. Legends can be flexbily adjusted based on ``handles`` or categories and colors provided in ``dict_color``. This functions comprises the most convinient settings for ``func:`matplotlib.pyplot.legend``. @@ -231,7 +211,7 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, handletextpad Horizontal spacing bewtween legend handle (marker) and label. handlelength - Length of legend handle (marker). + Length of legend handle. fontsize Font size for the legend text. fontsize_title @@ -240,16 +220,16 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, Weight of the font. fontsize_weight Font weight for the legend title. - lw - Line width for legend items. If negative, corners are rounded. - edgecolor - Edge color for legend items. marker - Marker for legend items. '-' is added to the + Marker for legend items. '-' is added for lines. marker_size Marker size for legend items. - marker_linestyle - Marker linestyle of legend items. + lw + Line width for legend items. If negative, corners are rounded. + linestyle + Style of line. Only applied for if marker is '-' (line). + edgecolor + Edge color of legend items. hatch Filling pattern for default marker. title @@ -257,12 +237,12 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, title_align_left Whether to align the title to the left. **kwargs - Furhter key word arguments for :func:`matplotlib.axes.Axes.legend`. + Furhter key word arguments for :attr:`matplotlib.axes.Axes.legend`. Returns ------- ax - The axes with the legend applied. If ``return_handles=True``, it returns ``handles`` and ``labels`` instead. + Axes on which legend is applied to. Examples -------- @@ -277,7 +257,7 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, >>> aa.plot_settings() >>> sns.barplot(x='Classes', y='Values', data=data, palette=colors) >>> sns.despine() - >>> dict_color = dict(zip(["Class A", "Class B", "Class C"], colors)) + >>> dict_color = {"Group 1": "black", "Group 2": "black"} >>> aa.plot_set_legend(dict_color=dict_color, ncol=3, x=0, y=1.1, handletextpad=0.4) >>> plt.tight_layout() >>> plt.show() @@ -285,11 +265,11 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, See Also -------- * More examples in `Plotting Prelude `_. - * `Matplotlib markers `. - * `Linestyles of markers ` - * `Hatches `_ filling patterns. - * :func:`matplotlib.lines.Line2D` for available marker shapes and line properties. - * :func:`matplotlib.axes.Axes`, which is the core object in matplotlib. + * `Matplotlib markers `_. + * `Linestyles of markers `_. + * `Hatches `_, which are filling patterns. + * :class:`matplotlib.lines.Line2D` for available marker shapes and line properties. + * :class:`matplotlib.axes.Axes`, which is the core object in matplotlib. * :func:`matplotlib.pyplot.gca` to get the current Axes instance. """ # Check input @@ -302,10 +282,9 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, labels = check_labels(list_cat=list_cat, labels=labels) hatch = check_hatches(marker=marker, hatch=hatch, list_cat=list_cat) # Must be before check_marker ! - marker = check_marker(marker=marker, list_cat=list_cat, lw=lw) marker_size = check_marker_size(marker_size, list_cat=list_cat) - marker_linestyle = check_linestyle(marker_linestyle=marker_linestyle, list_cat=list_cat) + linestyle = check_linestyle(linestyle=linestyle, list_cat=list_cat) ut.check_bool(name="title_align_left", val=title_align_left) ut.check_bool(name="loc_out", val=loc_out) @@ -326,41 +305,25 @@ def plot_set_legend(ax: Optional[plt.Axes] = None, ax.legend_.remove() # Update legend arguments - edgecolor = "white" if edgecolor is None else edgecolor - args = dict(loc=loc, - ncol=ncol, - fontsize=fontsize, - labelspacing=labelspacing, - columnspacing=columnspacing, - handletextpad=handletextpad, - handlelength=handlelength, - borderpad=0, # Fractional whitespace inside the legend border. - title=title, - edgecolor=edgecolor, - prop={"weight": weight, "size": fontsize}) + args = dict(loc=loc, ncol=ncol, fontsize=fontsize, labelspacing=labelspacing, columnspacing=columnspacing, + handletextpad=handletextpad, handlelength=handlelength, borderpad=0, title=title, + edgecolor=edgecolor, prop={"weight": weight, "size": fontsize}) args.update(kwargs) if fontsize_title: args["title_fontproperties"] = {"weight": fontsize_weight, "size": fontsize_title} - # Adjust x and y if loc_out: - # Set values under legend - x = 0 if x is None else x - y = -0.25 if y is None else y - - if x is not None or y is not None: - # Set default bbox values - x = 1 if x is None else x - y = 1 if y is None else y - args["bbox_to_anchor"] = (x, y) - - # Generate legend items - handles = [_create_marker(dict_color[cat], labels[i], marker[i], marker_size[i], lw, edgecolor, marker_linestyle[i], hatch[i]) + x, y = x or 0, y or -0.25 + if x or y: + args["bbox_to_anchor"] = (x or 1, y or 1) + + # Create handles and legend + handles = [_create_marker(dict_color[cat], labels[i], marker[i], marker_size[i], lw, edgecolor, linestyle[i], hatch[i]) for i, cat in enumerate(list_cat)] - # Create the legend + legend = ax.legend(handles=handles, labels=labels, **args) - # Align title if needed if title_align_left: legend._legend_box.align = "left" return ax + diff --git a/aaanalysis/plotting/plot_set_legend_copy.py b/aaanalysis/plotting/plot_set_legend_copy.py new file mode 100644 index 00000000..c281914c --- /dev/null +++ b/aaanalysis/plotting/plot_set_legend_copy.py @@ -0,0 +1,354 @@ +""" +This is a script for setting plot legend. +""" +from typing import Optional, List, Dict, Union, Tuple +import matplotlib as mpl +from matplotlib import pyplot as plt +from aaanalysis import utils as ut +import matplotlib.lines as mlines + +def _check_validity(item, valid_items, item_name, list_cat): + """General check validity function.""" + if item is None: + return [item] * len(list_cat) + item_is_list = isinstance(item, list) + if not item_is_list: + if item not in valid_items: + raise ValueError(f"'{item_name}' ('{item}') must be one of following: {valid_items}") + else: + list_items = [item] * len(list_cat) + else: + wrong_items = [x for x in item if x not in valid_items] + if len(wrong_items) != 0: + raise ValueError( + f"'{item_name}' contains wrong items ('{wrong_items}')! Must be one of following: {valid_items}") + if len(item) != len(list_cat): + raise ValueError(f"Length must match of '{item_name}' ({item}) and categories ({list_cat}).") + else: + list_items = item + return list_items + +def check_marker(marker=None, list_cat=None, lw=0): + """Check validity of marker.""" + all_markers = list(mlines.Line2D.markers.keys()) + all_markers.append("-") # Allow '-' as a valid marker for a line + if marker == "-" and lw <= 0: + raise ValueError(f"If marker is '{marker}', 'lw' ({lw}) must be > 0.") + return _check_validity(marker, all_markers, 'marker', list_cat) + + +# I Helper functions +# Helper functions +def _is_valid_item(item, valid_items): + """Check if the item is valid.""" + return item in valid_items + + +def _is_valid_list(item_list, valid_items): + """Check if all items in the list are valid.""" + return all(x in valid_items for x in item_list) + + +def _match_length(item_list, reference_list): + """Check if the length of item_list matches reference_list.""" + return len(item_list) == len(reference_list) + + +# Checking functions +def check_list_cat(dict_color=None, list_cat=None): + """Ensure items in list_cat are keys in dict_color and match in length.""" + if not list_cat: + return list(dict_color.keys()) + if not all(elem in dict_color for elem in list_cat): + missing_keys = [elem for elem in list_cat if elem not in dict_color] + raise ValueError(f"The following keys in 'list_cat' are not in 'dict_colors': {', '.join(missing_keys)}") + if len(dict_color) != len(list_cat): + raise ValueError( + f"Length must match between 'list_cat' ({len(list_cat)}) and 'dict_colors' ({len(dict_color)}).") + return list_cat + + +def check_labels(list_cat=None, labels=None): + """Validate labels and match their length to list_cat.""" + if not labels: + labels = list_cat + if len(list_cat) != len(labels): + raise ValueError(f"Length must match of 'labels' ({len(labels)}) and categories ({len(list_cat)}).") + return labels + +def check_marker(marker=None, list_cat=None, lw=0): + all_markers = list(mlines.Line2D.markers.keys()) + all_markers.append("-") + if marker == "-" and lw <= 0: + raise ValueError(f"If marker is '{marker}', 'lw' ({lw}) must be > 0.") + if isinstance(marker, list): + if not _is_valid_list(marker, all_markers): + wrong_items = [x for x in marker if x not in all_markers] + raise ValueError( + f"'marker' contains wrong items ('{wrong_items}')! Must be one of following: {all_markers}") + if not _match_length(marker, list_cat): + raise ValueError(f"Length must match of 'marker' ({marker}) and categories ({list_cat}).") + elif not _is_valid_item(marker, all_markers): + raise ValueError(f"'marker' ('{marker}') must be one of following: {all_markers}") + return [marker] * len(list_cat) if not isinstance(marker, list) else marker + +def check_marker_size(marker_size=None, list_cat=None): + """Validate marker sizes.""" + if isinstance(marker_size, (int, float)): + ut.check_number_range(name='marker_size', val=marker_size, min_val=0, accept_none=True, just_int=False) + return [marker_size] * len(list_cat) + elif isinstance(marker_size, list): + for i in marker_size: + ut.check_number_range(name='marker_size', val=i, min_val=0, accept_none=True, just_int=False) + if len(marker_size) != len(list_cat): + raise ValueError(f"Length must match of 'marker_size' and categories ({list_cat}).") + return marker_size + elif marker_size is None: + return [None] * len(list_cat) + else: + raise ValueError(f"'marker_size' has wrong data type: {type(marker_size)}") + + +def check_linestyle(marker_linestyle=None, list_cat=None): + """Check validity of linestyle.""" + _lines = ['-', '--', '-.', ':'] + _names = ["solid", "dashed", "dashed-doted", "dotted"] + dict_names_lines = dict(zip(_names, _lines)) + valid_linestyles = set(_lines + _names) + + if isinstance(marker_linestyle, str) and marker_linestyle in _names: + marker_linestyle = dict_names_lines[marker_linestyle] + elif isinstance(marker_linestyle, list): + marker_linestyle = [dict_names_lines.get(x, x) for x in marker_linestyle] + + if isinstance(marker_linestyle, list): + if not _is_valid_list(marker_linestyle, valid_linestyles): + wrong_items = [x for x in marker_linestyle if x not in valid_linestyles] + raise ValueError( + f"'marker_linestyle' contains wrong items ('{wrong_items}')! Must be one of following: {_lines + _names}") + if not _match_length(marker_linestyle, list_cat): + raise ValueError( + f"Length must match of 'marker_linestyle' ({marker_linestyle}) and categories ({list_cat}).") + elif not _is_valid_item(marker_linestyle, valid_linestyles): + raise ValueError(f"'marker_linestyle' ('{marker_linestyle}') must be one of following: {_lines + _names}") + return [marker_linestyle] * len(list_cat) if not isinstance(marker_linestyle, list) else marker_linestyle + + +def check_hatches(marker=None, hatch=None, list_cat=None): + """Check validity of hatches.""" + valid_hatches = [None, '/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'] + + if marker is None: + raise ValueError("'marker' must be set if using 'hatch'.") + + if isinstance(hatch, list): + if not _is_valid_list(hatch, valid_hatches): + wrong_items = [x for x in hatch if x not in valid_hatches] + raise ValueError( + f"'hatch' contains wrong items ('{wrong_items}')! Must be one of following: {valid_hatches}") + if not _match_length(hatch, list_cat): + raise ValueError(f"Length must match of 'hatch' ({hatch}) and categories ({list_cat}).") + elif not _is_valid_item(hatch, valid_hatches): + raise ValueError(f"'hatch' ('{hatch}') must be one of following: {valid_hatches}") + return [hatch] * len(list_cat) if not isinstance(hatch, list) else hatch + + +# Helper function +def _create_marker(color, label, marker, marker_size, lw, edgecolor, linestyle, hatch): + """Create custom marker based on input.""" + args = {'facecolor': color, 'label': label, 'lw': lw, 'hatch': hatch, "edgecolor": edgecolor} + + if marker is None: + return mpl.patches.Patch(**args) + + if marker == "-": + if lw <= 0: + raise ValueError("'lw' should be greater than 0 if 'marker' is a line ('-').") + return plt.Line2D([0, 1], [0, 1], color=color, linestyle=linestyle, lw=lw, label=label) + + return plt.Line2D(xdata=[0], ydata=[0], marker=marker, color=edgecolor, markerfacecolor=color, + markersize=marker_size, label=label, lw=0, markeredgewidth=lw) + + +# II Main function +def plot_set_legend(ax: Optional[plt.Axes] = None, + # Categories and colors + dict_color: Optional[Dict[str, str]] = None, + list_cat: Optional[List[str]] = None, + labels: Optional[List[str]] = None, + # Position and Layout + loc: str = "upper left", + loc_out: bool = False, + y: Optional[Union[int, float]] = None, + x: Optional[Union[int, float]] = None, + ncol: int = 3, + labelspacing: Union[int, float] = 0.2, + columnspacing: Union[int, float] = 1.0, + handletextpad: Union[int, float] = 0.8, + handlelength: Union[int, float] = 2, + # Font and Style + fontsize: Optional[Union[int, float]] = None, + fontsize_title: Optional[Union[int, float]] = None, + weight: str = "normal", + fontsize_weight: str = "normal", + # Line, Marker, and Area + lw: Union[int, float] = 0, + edgecolor: Optional[str] = "white", + marker: Optional[Union[str, int, list]] = None, + marker_size: Union[int, float, List[Union[int, float]]] = 10, + marker_linestyle: Union[str, list] = "-", + hatch: Optional[Union[str, List[str]]] = None, + # Title + title: str = None, + title_align_left: bool = True, + **kwargs + ) -> Union[plt.Axes, Tuple[List, List[str]]]: + """ + Sets a customizable legend for a plot. + + Legends can be flexbily adjusted based on ``handles`` or categories and colors provided in ``dict_color``. + This functions comprises the most convinient settings for ``func:`matplotlib.pyplot.legend``. + + Parameters + ---------- + ax + The axes to attach the legend to. If not provided, the current axes will be used. + dict_color + A dictionary mapping categories to colors. + list_cat + List of categories to include in the legend (keys of ``dict_color``). + labels + Labels for legend items corresponding to given categories. + loc + Location for the legend. + loc_out + If ``True``, sets automatically ``x=0`` and ``y=-0.25`` if they are ``None``. + y + The y-coordinate for the legend's anchor point. + x + The x-coordinate for the legend's anchor point. + ncol + Number of columns in the legend, at least 1. + labelspacing + Vertical spacing between legend items. + columnspacing + Horizontal spacing between legend columns. + handletextpad + Horizontal spacing bewtween legend handle (marker) and label. + handlelength + Length of legend handle (marker). + fontsize + Font size for the legend text. + fontsize_title + Font size for the legend title. + weight + Weight of the font. + fontsize_weight + Font weight for the legend title. + lw + Line width for legend items. If negative, corners are rounded. + edgecolor + Edge color for legend items. + marker + Marker for legend items. '-' is added to the + marker_size + Marker size for legend items. + marker_linestyle + Marker linestyle of legend items. + hatch + Filling pattern for default marker. + title + Title for the legend. + title_align_left + Whether to align the title to the left. + **kwargs + Furhter key word arguments for :func:`matplotlib.axes.Axes.legend`. + + Returns + ------- + ax + The axes with the legend applied. If ``return_handles=True``, it returns ``handles`` and ``labels`` instead. + + Examples + -------- + .. plot:: + :include-source: + + >>> import matplotlib.pyplot as plt + >>> import seaborn as sns + >>> import aaanalysis as aa + >>> data = {'Classes': ['A', 'B', 'C'], 'Values': [23, 27, 43]} + >>> colors = aa.plot_get_clist() + >>> aa.plot_settings() + >>> sns.barplot(x='Classes', y='Values', data=data, palette=colors) + >>> sns.despine() + >>> dict_color = {"Group 1": "black", "Group 2": "black"} + >>> aa.plot_set_legend(dict_color=dict_color, ncol=3, x=0, y=1.1, handletextpad=0.4) + >>> plt.tight_layout() + >>> plt.show() + + See Also + -------- + * More examples in `Plotting Prelude `_. + * `Matplotlib markers `_. + * `Linestyles of markers `_. + * `Hatches `_, which are filling patterns. + * :class:`matplotlib.lines.Line2D` for available marker shapes and line properties. + * :class:`matplotlib.axes.Axes`, which is the core object in matplotlib. + * :func:`matplotlib.pyplot.gca` to get the current Axes instance. + """ + # Check input + ut.check_ax(ax=ax, accept_none=True) + if ax is None: + ax = plt.gca() + + ut.check_dict(name="dict_color", val=dict_color, accept_none=False) + list_cat = check_list_cat(dict_color=dict_color, list_cat=list_cat) + labels = check_labels(list_cat=list_cat, labels=labels) + + hatch = check_hatches(marker=marker, hatch=hatch, list_cat=list_cat) # Must be before check_marker ! + + marker = check_marker(marker=marker, list_cat=list_cat, lw=lw) + marker_size = check_marker_size(marker_size, list_cat=list_cat) + marker_linestyle = check_linestyle(marker_linestyle=marker_linestyle, list_cat=list_cat) + + ut.check_bool(name="title_align_left", val=title_align_left) + ut.check_bool(name="loc_out", val=loc_out) + + ut.check_number_range(name="ncol", val=ncol, min_val=1, accept_none=True, just_int=True) + ut.check_number_val(name="x", val=x, accept_none=True, just_int=False) + ut.check_number_val(name="y", val=y, accept_none=True, just_int=False) + ut.check_number_val(name="lw", val=lw, accept_none=True, just_int=False) + + args_non_neg = {"labelspacing": labelspacing, "columnspacing": columnspacing, + "handletextpad": handletextpad, "handlelength": handlelength, + "fontsize": fontsize, "fontsize_legend": fontsize_title} + for key in args_non_neg: + ut.check_number_range(name=key, val=args_non_neg[key], min_val=0, accept_none=True, just_int=False) + + # Remove existing legend + if ax.get_legend() is not None and len(ax.get_legend().get_lines()) > 0: + ax.legend_.remove() + + # Update legend arguments + args = dict(loc=loc, ncol=ncol, fontsize=fontsize, labelspacing=labelspacing, columnspacing=columnspacing, + handletextpad=handletextpad, handlelength=handlelength, borderpad=0, title=title, + edgecolor=edgecolor, prop={"weight": weight, "size": fontsize}) + args.update(kwargs) + + if fontsize_title: + args["title_fontproperties"] = {"weight": fontsize_weight, "size": fontsize_title} + + if loc_out: + x, y = x or 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aaanalysis.plot_set_legend

-aaanalysis.plot_set_legend(ax=None, dict_color=None, list_cat=None, labels=None, loc='upper left', loc_out=False, y=None, x=None, ncol=3, labelspacing=0.2, columnspacing=1.0, handletextpad=0.8, handlelength=2, fontsize=None, fontsize_title=None, weight='normal', fontsize_weight='normal', lw=0, edgecolor=None, marker=None, marker_size=10, marker_linestyle='-', hatch=None, title=None, title_align_left=True, **kwargs)[source]
-

Sets a customizable legend for a plot.

+aaanalysis.plot_set_legend(ax=None, dict_color=None, list_cat=None, labels=None, loc='upper left', loc_out=False, y=None, x=None, ncol=3, labelspacing=0.2, columnspacing=1.0, handletextpad=0.8, handlelength=2, fontsize=None, fontsize_title=None, weight='normal', fontsize_weight='normal', marker=None, marker_size=10, lw=0, linestyle='-', edgecolor='white', hatch=None, title=None, title_align_left=True, **kwargs)[source] +
+

Sets a customizable legend for a plot.

+

Legends can be flexbily adjusted based on handles or categories and colors provided in dict_color. This functions comprises the most convinient settings for func:`matplotlib.pyplot.legend.

@@ -149,24 +151,24 @@

aaanalysis.plot_set_legendUnion[int, float]) – Vertical spacing between legend items.

  • columnspacing (Union[int, float]) – Horizontal spacing between legend columns.

  • handletextpad (Union[int, float]) – Horizontal spacing bewtween legend handle (marker) and label.

  • -
  • handlelength (Union[int, float]) – Length of legend handle (marker).

  • +
  • handlelength (Union[int, float]) – Length of legend handle.

  • fontsize (Union[int, float, None]) – Font size for the legend text.

  • fontsize_title (Union[int, float, None]) – Font size for the legend title.

  • weight (str) – Weight of the font.

  • fontsize_weight (str) – Font weight for the legend title.

  • -
  • lw (Union[int, float]) – Line width for legend items. If negative, corners are rounded.

  • -
  • edgecolor (Optional[str]) – Edge color for legend items.

  • -
  • marker (Union[str, int, list, None]) – Marker for legend items. ‘-’ is added to the

  • +
  • marker (Union[str, int, list, None]) – Marker for legend items. ‘-’ is added for lines.

  • marker_size (Union[int, float, List[Union[int, float]]]) – Marker size for legend items.

  • -
  • marker_linestyle (Union[str, list]) – Marker linestyle of legend items.

  • +
  • lw (Union[int, float]) – Line width for legend items. If negative, corners are rounded.

  • +
  • linestyle (Union[str, list]) – Style of line. Only applied for if marker is ‘-’ (line).

  • +
  • edgecolor (str) – Edge color of legend items.

  • hatch (Union[str, List[str], None]) – Filling pattern for default marker.

  • title (Optional[str]) – Title for the legend.

  • title_align_left (bool) – Whether to align the title to the left.

  • -
  • **kwargs – Furhter key word arguments for matplotlib.axes.Axes.legend().

  • +
  • **kwargs – Furhter key word arguments for matplotlib.axes.Axes.legend.

  • Returns:
    -

    The axes with the legend applied. If return_handles=True, it returns handles and labels instead.

    +

    Axes on which legend is applied to.

    Return type:

    ax

    @@ -182,7 +184,7 @@

    aaanalysis.plot_set_legend>>> aa.plot_settings() >>> sns.barplot(x='Classes', y='Values', data=data, palette=colors) >>> sns.despine() ->>> dict_color = dict(zip(["Class A", "Class B", "Class C"], colors)) +>>> dict_color = {"Group 1": "black", "Group 2": "black"} >>> aa.plot_set_legend(dict_color=dict_color, ncol=3, x=0, y=1.1, handletextpad=0.4) >>> plt.tight_layout() >>> plt.show() @@ -197,11 +199,11 @@

    aaanalysis.plot_set_legendSee also

    diff --git a/docs/build/html/generated/plotting_prelude.html b/docs/build/html/generated/plotting_prelude.html index ad1118dd..2a277100 100644 --- a/docs/build/html/generated/plotting_prelude.html +++ b/docs/build/html/generated/plotting_prelude.html @@ -146,37 +146,44 @@

    Plotting Preludeaa.plot_setting function with our optimized color set to get this:

    import aaanalysis as aa
    +
     colors = aa.plot_get_clist()
     aa.plot_settings()
     sns.barplot(x='Classes', y='Values', data=data, palette=colors)
     sns.despine()
    -plt.title("Adjusted by AAanalysis 1")
    +plt.title("Adjusted by AAanalysis")
     plt.tight_layout()
     plt.show()
     
    ../_images/output_5_0.png -

    The settings can be easily adjusted and colors are provided for up to 9 -classes:

    +

    The settings can be easily adjusted. Use our customized sets of 2 to 9 +colors for an appealing comparison of categorical data via +aa.plot_get_clist:

    data = {'Classes': ['Class A', 'Class B', 'Class C', "Class D", "Class E"], 'Values': [23, 27, 43, 9, 14]}
    +
     colors = aa.plot_get_clist(n_colors=5)
     aa.plot_settings(no_ticks_x=True, short_ticks_y=True, grid=True, grid_axis="y")
    +
     sns.barplot(x='Classes', y='Values', data=data, palette=colors)
     sns.despine()
    -plt.title("Adjusted by AAanalysis 2")
    +plt.title("Adjusted by AAanalysis (grid, no x ticks)")
     plt.tight_layout()
     plt.show()
     
    ../_images/output_7_0.png -

    Retrieve the set font size and create an independent legend like this:

    +

    You can easily increase the fontsize of the labels in matching to the +other elements and create an independent legend like this:

    data = {'Classes': ['Class A', 'Class B', 'Class C', "Class D", "Class E"], 'Values': [23, 27, 43, 9, 14]}
     colors = aa.plot_get_clist(n_colors=5)
     aa.plot_settings(no_ticks_x=True, short_ticks_y=True)
     sns.barplot(x='Classes', y='Values', data=data, palette=colors, hatch=["/", "/", "/", ".", "."])
    -fontsize = aa.plot_gcfs()
     sns.despine()
    -plt.title("Adjusted by AAanalysis 3")
    +
    +fontsize = aa.plot_gcfs()
    +plt.title("Adjusted by AAanalysis (hatches, smaller title)", size=fontsize-2)
    +
     dict_color = {"Group 1": "black", "Group 2": "black"}
     aa.plot_set_legend(dict_color=dict_color, ncol=1, x=0.7, y=0.9, hatch=["/", "."])
     plt.tight_layout()
    diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
    index 607f3a1a..89213029 100644
    --- a/docs/build/html/searchindex.js
    +++ b/docs/build/html/searchindex.js
    @@ -1 +1 @@
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mpl.rcParams["font.sans-serif"] - def test_fig_format_pdf(self): - aa.plot_settings(fig_format="pdf") - assert mpl.rcParams["pdf.fonttype"] == 42 - def test_weight_bold(self): aa.plot_settings(weight_bold=True) assert mpl.rcParams["axes.labelweight"] == "bold" @@ -57,10 +53,6 @@ def test_invalid_font(self): with pytest.raises(ValueError): aa.plot_settings(font="InvalidFont") - def test_invalid_fig_format(self): - with pytest.raises(ValueError): - aa.plot_settings(fig_format="exe") - def test_invalid_grid_axis(self): with pytest.raises(ValueError): aa.plot_settings(grid_axis="z") @@ -81,21 +73,19 @@ class TestPlotSettingsComplexCases: @given(st.floats(min_value=0, allow_nan=False, allow_infinity=False), st.sampled_from(["Arial", "Verdana", "Helvetica", "DejaVu Sans"]), - st.sampled_from(["pdf", "png", "ps", "eps", "svg"]), st.booleans(), st.booleans(), st.booleans(), st.booleans(), st.booleans(), st.sampled_from(["x", "y", "both"])) - @example(1.5, "Arial", "pdf", True, False, False, True, False, "y") - @example(1.0, "Verdana", "png", False, True, True, False, True, "x") + @example(1.5, "Arial", True, False, False, True, False, "y") + @example(1.0, "Verdana", False, True, True, False, True, "x") @settings(max_examples=5) - def test_complex_positive_cases(self, font_scale, font, fig_format, weight_bold, adjust_only_font, adjust_further_elements, grid, no_ticks, grid_axis): - aa.plot_settings(font_scale=font_scale, font=font, fig_format=fig_format, weight_bold=weight_bold, adjust_only_font=adjust_only_font, adjust_further_elements=adjust_further_elements, grid=grid, no_ticks=no_ticks, grid_axis=grid_axis) + def test_complex_positive_cases(self, font_scale, font, weight_bold, adjust_only_font, adjust_further_elements, grid, no_ticks, grid_axis): + aa.plot_settings(font_scale=font_scale, font=font, weight_bold=weight_bold, adjust_only_font=adjust_only_font, adjust_further_elements=adjust_further_elements, grid=grid, no_ticks=no_ticks, grid_axis=grid_axis) @given(st.floats(max_value=-0.01, allow_nan=False, allow_infinity=False), - st.text(), st.text(), st.booleans(), st.booleans(), @@ -103,9 +93,9 @@ def test_complex_positive_cases(self, font_scale, font, fig_format, weight_bold, st.booleans(), st.booleans(), st.text()) - @example(-1.0, "InvalidFont", "exe", True, False, False, True, False, "z") + @example(-1.0, "InvalidFont", True, False, False, True, False, "z") @settings(max_examples=5) - def test_complex_negative_cases(self, font_scale, font, fig_format, weight_bold, adjust_only_font, adjust_further_elements, grid, no_ticks, grid_axis): + def test_complex_negative_cases(self, font_scale, font, weight_bold, adjust_only_font, adjust_further_elements, grid, no_ticks, grid_axis): with pytest.raises(Exception): - aa.plot_settings(font_scale=font_scale, font=font, fig_format=fig_format, weight_bold=weight_bold, adjust_only_font=adjust_only_font, adjust_further_elements=adjust_further_elements, grid=grid, no_ticks=no_ticks, grid_axis=grid_axis) + aa.plot_settings(font_scale=font_scale, font=font, weight_bold=weight_bold, adjust_only_font=adjust_only_font, adjust_further_elements=adjust_further_elements, grid=grid, no_ticks=no_ticks, grid_axis=grid_axis) diff --git a/tutorials/plotting_prelude.ipynb b/tutorials/plotting_prelude.ipynb index ecea13da..80dcd73e 100644 --- a/tutorials/plotting_prelude.ipynb +++ b/tutorials/plotting_prelude.ipynb @@ -85,12 +85,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "outputs": [ { "data": { "text/plain": "

    ", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -98,19 +98,20 @@ ], "source": [ "import aaanalysis as aa\n", + "\n", "colors = aa.plot_get_clist()\n", "aa.plot_settings()\n", "sns.barplot(x='Classes', y='Values', data=data, palette=colors)\n", "sns.despine()\n", - "plt.title(\"Adjusted by AAanalysis 1\")\n", + "plt.title(\"Adjusted by AAanalysis\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-26T14:17:38.634028946Z", - "start_time": "2023-09-26T14:17:38.140638333Z" + "end_time": "2023-09-26T18:32:44.777456654Z", + "start_time": "2023-09-26T18:32:44.553829981Z" } }, "id": "f2353d6d788f0c36" @@ -118,7 +119,7 @@ { "cell_type": "markdown", "source": [ - "The settings can be easily adjusted and colors are provided for up to 9 classes:" + "The settings can be easily adjusted. Use our customized sets of 2 to 9 colors for an appealing comparison of categorical data via `aa.plot_get_clist`:" ], "metadata": { "collapsed": false @@ -127,12 +128,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -140,19 +141,21 @@ ], "source": [ "data = {'Classes': ['Class A', 'Class B', 'Class C', \"Class D\", \"Class E\"], 'Values': [23, 27, 43, 9, 14]}\n", + "\n", "colors = aa.plot_get_clist(n_colors=5)\n", "aa.plot_settings(no_ticks_x=True, short_ticks_y=True, grid=True, grid_axis=\"y\")\n", + "\n", "sns.barplot(x='Classes', y='Values', data=data, palette=colors)\n", "sns.despine()\n", - "plt.title(\"Adjusted by AAanalysis 2\")\n", + "plt.title(\"Adjusted by AAanalysis (grid, no x ticks)\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-26T14:17:40.419637155Z", - "start_time": "2023-09-26T14:17:40.074591522Z" + "end_time": "2023-09-26T18:32:09.686953052Z", + "start_time": "2023-09-26T18:32:09.440374953Z" } }, "id": "e4f4597893bf7638" @@ -160,7 +163,7 @@ { "cell_type": "markdown", "source": [ - "Retrieve the set font size and create an independent legend like this:" + "You can easily increase the fontsize of the labels in matching to the other elements and create an independent legend like this:" ], "metadata": { "collapsed": false @@ -169,12 +172,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "outputs": [ { "data": { "text/plain": "
    ", - "image/png": 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" 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}, "metadata": {}, "output_type": "display_data" @@ -185,9 +188,11 @@ "colors = aa.plot_get_clist(n_colors=5)\n", "aa.plot_settings(no_ticks_x=True, short_ticks_y=True)\n", "sns.barplot(x='Classes', y='Values', data=data, palette=colors, hatch=[\"/\", \"/\", \"/\", \".\", \".\"])\n", - "fontsize = aa.plot_gcfs()\n", "sns.despine()\n", - "plt.title(\"Adjusted by AAanalysis 3\")\n", + "\n", + "fontsize = aa.plot_gcfs()\n", + "plt.title(\"Adjusted by AAanalysis (hatches, smaller title)\", size=fontsize-2)\n", + "\n", "dict_color = {\"Group 1\": \"black\", \"Group 2\": \"black\"}\n", "aa.plot_set_legend(dict_color=dict_color, ncol=1, x=0.7, y=0.9, hatch=[\"/\", \".\"])\n", "plt.tight_layout()\n", @@ -196,21 +201,11 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-09-26T14:17:42.831449468Z", - "start_time": "2023-09-26T14:17:42.134541486Z" + "end_time": "2023-09-26T18:32:24.256965791Z", + "start_time": "2023-09-26T18:32:23.868476115Z" } }, "id": "69ce1a8d9552157c" - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - }, - "id": "75a8f6e2d267c8c0" } ], "metadata": {

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(Not applicable as Docstrings are inline comments; no bash commands.) +### Sphinx directives + + :mod: - Reference a Python module. + Example: :mod:`os` would link to the documentation for the os module. + + :func: - Reference a Python function. + Example: :func:`print` would link to the documentation for the print function. + + :data: - Reference a module-level variable. + Example: :data:`sys.path` would link to the documentation for the sys.path variable. + + :const: - Reference a "constant". This can be any immutable primitive data type (like a string or number). + Example: :const:`True` would link to the documentation where the Python constant True is described. + + :class: - Reference a Python class. + Example: :class:`matplotlib.axes.Axes` would link to the documentation for the Axes class in the matplotlib.axes module. + + :meth: - Reference a method of a Python class. + Example: :meth:`str.split` would link to the documentation for the split method of the str class. + + :attr: - Reference a class attribute. + Example: :attr:`Exception.args` would link to the documentation for the args attribute of the Exception class. + + :exc: - Reference a Python exception. + Example: :exc:`ValueError` would link to the documentation for the ValueError exception. + --- ## Continuous Integration/Continuous Deployment (CI/CD) diff --git a/docs/source/generated/output_5_0.png b/docs/source/generated/output_5_0.png index 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