def scatter_2d(self, x, y, df=None, x_units="f", x_ticks=None, y_units="f", y_ticks=None, plot_buffer=True,
size=5, axis_limits=True, color=style.style_grey, facecolor="w", alpha=0.8,
x_rotate=None, ax=None):
"""
Documentation:
---
Description:
Create 2-dimensional scatter plot.
---
Parameters:
x : array or string
Either 1-dimensional array of values or a column name in a Pandas DataFrame.
y : array or string
Either 1-dimensional array of values or a column name in a Pandas DataFrame.
df : Pandas DataFrame, default=None
Pandas DataFrame containing data to plot. Can be any size - plotted columns will be
chosen by columns names specified in x and y parameters.
x_units : str, default='f'
Determines unit of measurement for x-axis tick labels. 'f' displays float. 'p' displays
percentages, d' displays dollars. Repeat character (e.g 'ff' or 'ddd') for additional
decimal places.
x_ticks : array, default=None
Custom x-tick labels.
y_units : str, default='f'
Determines unit of measurement for x-axis tick labels. 'f' displays float. 'p' displays
percentages, d' displays dollars. Repeat character (e.g 'ff' or 'ddd') for additional
decimal places.
y_ticks : array, default=None
Custom y-tick labels.
plot_buffer : bool, default=True
Controls whether dynamic plot buffer function is executed.
size : int or float, default=5
Size of scattered dots.
axis_limits : bool, default=True
Controls whether dynamic axis limit setting function is executed.
color : str (color code of some sort), default=style.style_grey
Color of scattered dots
facecolor : str (color code of some sort), default='w'
Face color of scattered dots.
alpha : float, default=0.8
Controls transparency of objects. Accepts value between 0.0 and 1.0.
x_rotate : int, default=None
Rotates x-axis tick mark labels x degrees.
ax : axes object, default=None
Axis object for the visualization.
"""
if ax is None:
ax = self.ax
# if a Pandas DataFrame is passed to function, create x and y arrays using columns names passed into function
if df is not None:
x = df[x].values.reshape(-1, 1)
y = df[y].values.reshape(-1, 1)
# else reshape arrays
else:
x = x.reshape(-1, 1)
y = y.reshape(-1, 1)
# plot 2-dimensional scatter
plt.scatter(
x=x,
y=y * 100 if "p" in y_units else y,
color=color,
s=size * self.chart_scale,
alpha=alpha,
facecolor=facecolor,
linewidth=0.167 * self.chart_scale,
)
# optionally set axis lower / upper limits
if axis_limits:
x_min, x_max, y_min, y_max = util.util_set_axes(x=x, y=y)
plt.axis([x_min, x_max, y_min, y_max])
# optionally create smaller buffer around plot area to prevent cutting off elements
if plot_buffer:
util.util_plot_buffer(ax=ax, x=0.02, y=0.02)
# optionally creates custom x-tick labels
if x_ticks is not None:
ax.set_xticks(x_ticks)
# optionally creates custom y-tick labels
if y_ticks is not None:
ax.set_yticks(y_ticks)
# format x and y ticklabels
ax.set_yticklabels(
ax.get_yticklabels() * 100 if "p" in y_units else ax.get_yticklabels(),
rotation=0,
fontsize=1.0 * self.chart_scale,
color=style.style_grey,
)
ax.set_xticklabels(
ax.get_xticklabels() * 100 if "p" in y_units else ax.get_xticklabels(),
rotation=0,
fontsize=1.0 * self.chart_scale,
color=style.style_grey,
)
# use label formatter utility function to customize chart labels
util.util_label_formatter(ax=ax, x_units=x_units, y_units=y_units, x_rotate=x_rotate)
def line(self,
x,
y,
label=None,
df=None,
linecolor=style.style_grey,
linestyle=None,
bbox=(1.2, 0.9),
x_units="f",
x_ticks=None,
y_units="f",
y_ticks=None,
marker_on=False,
plot_buffer=False,
axis_limits=False,
ax=None):
"""
Documentation:
---
Description:
Create single line plot.
---
Parameters:
x : list, array or string
1-dimensional array of values to plot along x-axis
y : list, array or string
1-dimensional array of values to plot along y-axis
label : str : default=None
Legend label for line.
df : Pandas DataFrame, default=None
Pandas DataFrame containing data to plot. can be any size, as plotted columns will be chosen
by columns names specified in x and y parameters.
linecolor : str, default=style.style_grey
Line color.
linestyle : str, default=None
Line style.
bbox : tuple of floats, default=(1.2, 0.9)
Coordinates for determining legend position.
x_units : str, default='f'
Determines unit of measurement for x-axis tick labels. 's' displays string. 'f' displays float.
'p' displays percentages, 'd' displays dollars. Repeat character (e.g 'ff' or 'ddd') for
additional decimal places.
x_ticks : array, default=None
Custom x-tick labels.
y_units : str, default='f'
Determines unit of measurement for y-axis tick labels. 's' displays string. 'f' displays float.
'p' displays percentages, 'd' displays dollars. Repeat character (e.g 'ff' or 'ddd') for
additional decimal places.
y_ticks : array, default=None
Custom y_tick labels.
marker_on : bool, default=False
Controls whether to show line with markers for each data element.
plot_buffer : bool, default=False
Controls whether dynamic plot buffer function is executed to ensure visual elements are
not cut-off at the figure borders.
axis_limits : bool, default=False
Controls whether dynamic axis limit setting function is executed.
ax : axes object, default=None
Axis object for the visualization.
"""
if ax is None:
ax = self.ax
# if a Pandas DataFrame is passed to function, create x and y arrays using columns names passed into function
if df is not None:
if isinstance(df.index, pd.core.indexes.base.Index):
x = df.index.values
else:
x = df[x].values
y = df[y].values
else:
# convert input list to array
x = np.array(x) if isinstance(x, list) else x
y = np.array(y) if isinstance(y, list) else y
# reshape arrays if necessar
x = x.reshape(-1, 1) if len(x.shape) == 1 else x
y = y.reshape(-1, 1) if len(y.shape) == 1 else y
# add line to plot
plt.plot(
x,
y * 100 if "p" in y_units else y,
color=linecolor,
linestyle=linestyle,
linewidth=0.247 * self.chart_scale,
label=label,
marker="." if marker_on else None,
markersize=17 if marker_on else None,
markerfacecolor="w" if marker_on else None,
markeredgewidth=2.2 if marker_on else None,
)
# add legend to figure
if label is not None:
plt.legend(
loc="upper right",
bbox_to_anchor=bbox,
ncol=1,
frameon=True,
fontsize=1.1 * self.chart_scale,
)
# optionally set axis lower / upper limits
if axis_limits:
x_min, x_max, y_min, y_max = util.util_set_axes(x=x, y=y)
plt.axis([x_min, x_max, y_min, y_max])
# optionally create smaller buffer around plot area to prevent cutting off elements
if plot_buffer:
util.util_plot_buffer(ax=ax, x=0.02, y=0.02)
# optionally creates custom x-tick labels
if x_ticks is not None:
ax.set_xticks(x_ticks)
# optionally creates custom y-tick labels
if y_ticks is not None:
ax.set_yticks(y_ticks)
# format x and y ticklabels
ax.set_yticklabels(
ax.get_yticklabels() * 100 if "p" in y_units else ax.get_yticklabels(),
rotation=0,
fontsize=1.0 * self.chart_scale,
color=style.style_grey,
)
ax.set_xticklabels(
ax.get_xticklabels() * 100 if "p" in y_units else ax.get_xticklabels(),
rotation=0,
fontsize=1.0 * self.chart_scale,
color=style.style_grey,
)
# axis tick label formatting
util.util_label_formatter(ax=ax, x_units=x_units, y_units=y_units)
def scatter_2d_hue(self, x, y, target, label, df=None, x_units="f", x_ticks=None, y_units="f", y_ticks=None,
plot_buffer=True, size=10, axis_limits=True, color=style.style_grey, facecolor="w",
bbox=(1.2, 0.9), color_map="viridis", alpha=0.8, x_rotate=None, ax=None):
"""
Documentation:
---
Description:
Create 2-dimensional scatter plot with a third dimension represented as a color hue in the
scatter dots.
---
Parameters:
x : array or string
Either 1-dimensional array of values or a column name in a Pandas DataFrame.
y : array or string
Either 1-dimensional array of values or a column name in a Pandas DataFrame.
target : array or string
Either 1-dimensional array of values or a column name in a Pandas DataFrame.
label : list
Labels corresponding to color hue.
df : Pandas DataFrame, default=None
Pandas DataFrame containing data to plot. Can be any size - plotted columns will be
chosen by columns names specified in x and y parameters.
x_units : str, default='d'
Determines unit of measurement for x-axis tick labels. 'f' displays float. 'p' displays
percentages, d' displays dollars. Repeat character (e.g 'ff' or 'ddd') for additional
decimal places.
x_ticks : array, default=None
Custom x-tick labels.
y_units : str, default='d'
Determines unit of measurement for x-axis tick labels. 'f' displays float. 'p' displays
percentages, d' displays dollars. Repeat character (e.g 'ff' or 'ddd') for additional
decimal places.
y_ticks : array, default=None
Custom y-tick labels.
plot_buffer : bool, default=True
Controls whether dynamic plot buffer function is executed.
size : int or float, default=10
Size of scattered dots.
axis_limits : bool, default=True
Controls whether dynamic axis limit setting function is executed.
color : str (color code of some sort), default=style.style_grey
Color of scattered dots
facecolor : str (color code of some sort), default='w'
Face color of scattered dots
bbox : tuple of floats, default=(1.2, 0.9)
Coordinates for determining legend position.
color_map : str specifying built-in matplotlib colormap, default="viridis"
Color map applied to plots.
alpha : float, default=0.8
Controls transparency of objects. Accepts value between 0.0 and 1.0.
x_rotate : int, default=None
Rotates x-axis tick mark labels x degrees.
ax : axes object, default=None
Axis object for the visualization.
"""
if ax is None:
ax = self.ax
# if a Pandas DataFrame is passed to function, create x and y and target arrays using columns names
# passed into function. Also concatenates columns into single object
if df is not None:
x = df[[x, y, target]].values
x = df[x].values
y = df[y].values
target = df[target].values
# concatenate the x, y and target arrays
else:
x = np.c_[x, y, target]
# unique target values
target_ids = np.unique(x[:, 2])
# generate color list
color_list = style.color_gen(name=color_map, num=len(target_ids))
# loop through sets of target values, labels and colors to create 2_d scatter with hue
for target_id, target_name, color in zip(target_ids, label, color_list):
plt.scatter(
x=x[x[:, 2] == target_id][:, 0],
y=x[x[:, 2] == target_id][:, 1],
color=color,
label=target_name,
s=size * self.chart_scale,
alpha=alpha,
facecolor="w",
linewidth=0.234 * self.chart_scale,
)
# add legend to figure
if label is not None:
plt.legend(
loc="upper right",
bbox_to_anchor=bbox,
ncol=1,
frameon=True,
fontsize=1.1 * self.chart_scale,
)
# optionally set axis lower / upper limits
if axis_limits:
x_min, x_max, y_min, y_max = util.util_set_axes(x=x, y=y)
plt.axis([x_min, x_max, y_min, y_max])
# optionally create smaller buffer around plot area to prevent cutting off elements
if plot_buffer:
util.util_plot_buffer(ax=ax, x=0.02, y=0.02)
# optionally creates custom x-tick labels
if x_ticks is not None:
ax.set_xticks(x_ticks)
# optionally creates custom y-tick labels
if y_ticks is not None:
ax.set_yticks(y_ticks)
# format x and y ticklabels
ax.set_yticklabels(
ax.get_yticklabels() * 100 if "p" in y_units else ax.get_yticklabels(),
rotation=0,
fontsize=1.0 * self.chart_scale,
color=style.style_grey,
)
ax.set_xticklabels(
ax.get_xticklabels() * 100 if "p" in y_units else ax.get_xticklabels(),
rotation=0,
fontsize=1.0 * self.chart_scale,
color=style.style_grey,
)
# use label formatter utility function to customize chart labels
util.util_label_formatter(ax=ax, x_units=x_units, y_units=y_units, x_rotate=x_rotate)
def multi_line(self,
x,
y,
label=None,
df=None,
linecolor=None,
linestyle=None,
bbox=(1.2, 0.9),
x_units="f",
x_ticks=None,
y_units="f",
y_ticks=None,
marker_on=False,
plot_buffer=False,
axis_limits=False,
color_map="viridis",
ax=None):
"""
Documentation:
Description:
Create single plot with multiple lines. Capable of adjusting which axis will have the same
data for each line and which will have different data for each line.
---
Parameters:
x : array or string
Either 1-dimensional array of values, a multidimensional array of values, a list of columns
in a Pandas DataFrame, or a column name in a Pandas DataFrame.
y : array or string
Either 1-dimensional array of values, a multidimensional array of values, a list of columns
in a Pandas DataFrame, or a column name in a Pandas DataFrame.
label : list of strings : default=None
Custom legend label for each line.
df : Pandas DataFrame, default=None
Pandas DataFrame containing data to plot. Can be any size, as plotted columns will be chosen
by columns names specified in x and y parameters.
linecolor : str, default=None
Line colors. If None, utilizes color_map
linestyle : str, default=None
Line style.
bbox : tuple, default=(1.2, 0.9)
Coordinates for determining legend position.
x_units : str, default='d'
Determines unit of measurement for x-axis tick labels. 's' displays string. 'f' displays float.
'p' displays percentages, 'd' displays dollars. Repeat character (e.g 'ff' or 'ddd') for
additional decimal places.
x_ticks : array, default=None
Custom x-tick labels.
y_units : str, default='d'
Determines unit of measurement for x-axis tick labels. 's' displays string. 'f' displays float.
'p' displays percentages, 'd' displays dollars. Repeat character (e.g 'ff' or 'ddd') for
additional decimal places.
y_ticks : array, default=None
Custom y-tick labels.
marker_on : bool, default=False
Controls whether to show line with markers for each data element.
plot_buffer : bool, default=False
Controls whether dynamic plot buffer function is executed to ensure visual elements are
not cut-off at the figure borders.
axis_limits : bool, default=False
Controls whether dynamic axis limit setting function is executed.
color_map : str specifying built-in matplotlib colormap, default="viridis"
Color map applied to plots.
ax : axes object, default=None
Axis object for the visualization.
"""
if ax is None:
ax = self.ax
# if a Pandas DataFrame is passed to function, create x and y arrays using columns names passed into function
if df is not None:
if isinstance(df.index, pd.core.indexes.base.Index):
x = df.index.values
else:
x = df[x].values
y = df[y].values
else:
# convert input list to array
x = np.array(x) if isinstance(x, list) else x
y = np.array(y) if isinstance(y, list) else y
x = x.reshape(-1, 1) if len(x.shape) == 1 else x
y = y.reshape(-1, 1) if len(y.shape) == 1 else y
# generate color list
color_list = style.color_gen(name=color_map, num=y.shape[1])
# add multiple lines to plot
for ix in np.arange(y.shape[1]):
y_col = y[:, ix]
plt.plot(
x,
y_col * 100 if "p" in y_units else y_col,
color=linecolor if linecolor is not None else color_list[ix],
linestyle=linestyle
if linestyle is not None else style.style_line_style[0],
linewidth=0.247 * self.chart_scale,
label=label[ix] if label is not None else None,
marker="." if marker_on else None,
markersize=17 if marker_on else None,
markerfacecolor="w" if marker_on else None,
markeredgewidth=2.2 if marker_on else None,
)
# add legend to figure
if label is not None:
plt.legend(
loc="upper right",
bbox_to_anchor=bbox,
ncol=1,
frameon=True,
fontsize=1.1 * self.chart_scale,
)
# optionally set axis lower / upper limits
if axis_limits:
x_min, x_max, y_min, y_max = util.util_set_axes(x=x, y=y)
plt.axis([x_min, x_max, y_min, y_max])
# optionally create smaller buffer around plot area to prevent cutting off elements
if plot_buffer:
util.util_plot_buffer(ax=ax, x=0.02, y=0.02)
# optionally creates custom x-tick labels
if x_ticks is not None:
ax.set_xticks(x_ticks)
# optionally creates custom y-tick labels
if y_ticks is not None:
ax.set_yticks(y_ticks)
# format x and y ticklabels
ax.set_yticklabels(
ax.get_yticklabels() * 100 if "p" in y_units else ax.get_yticklabels(),
rotation=0,
fontsize=1.1 * self.chart_scale,
color=style.style_grey,
)
ax.set_xticklabels(
ax.get_xticklabels() * 100 if "p" in y_units else ax.get_xticklabels(),
rotation=0,
fontsize=1.1 * self.chart_scale,
color=style.style_grey,
)
# axis tick label formatting
util.util_label_formatter(ax=ax, x_units=x_units, y_units=y_units)