def kde_plot(self):
"""Create a kde plot in seaborn using the style sheet and chosen variable values.
:param x_var: The variable you want on the x axis
:type x_var: str
:param y_var: The variable you want on the y axis
:type y_var: str
"""
warnings.warn("Deprecated: Will be moved into Seaborn.py soon.tm",
DeprecationWarning)
# Validate the arguments provided
self._validate_variable_args(locals(),
locals().values(), ["custom_ranking"])
# Set defaults before plotting
figure, axis = plt.subplots(figsize=(self.figure_x, self.figure_y))
sns.despine(figure, left=True, bottom=True)
# Generate the plot
# NB Some arguments are left at default that are given sensible defaults by seaborn
plot = sns.kdeplot(data=self._data)
# Write out the plot to chosen write directory as a png
self.write_plot(plot)
return plot
def bar_plot(self, x_var, y_var, gradient_variable):
"""Create a bar plot in seaborn using the style sheet and chosen variable values.
:param x_var: The variable you want on the x axis
:type x_var: str
:param y_var: The variable you want on the y axis
:type y_var: str
:param gradient_variable: A variable to apply a gradient of colour to the points
:type gradient_variable: str
:param size_variable: A variable that will be used to decide the line width
:type size_variable: str
"""
warnings.warn("Deprecated: Will be moved into Seaborn.py soon.tm",
DeprecationWarning)
# Validate the arguments provided
self._validate_variable_args(locals(),
locals().values(), ["custom_ranking"])
# Set defaults before plotting
figure, axis = plt.subplots(figsize=(self.figure_x, self.figure_y))
sns.despine(figure, left=True, bottom=True)
# Generate the plot
# NB Some arguments are left at default that are given sensible defaults by seaborn
plot = sns.barplot(x=x_var,
y=y_var,
data=self._data,
hue=gradient_variable)
# Write out the plot to chosen write directory as a png
self.write_plot(plot)
return plot
def _forest_plot_plot(self, weight_area):
# Instatiate the figure (we will recreate the axes, so don't need them)
fig, ax2 = StyleSheet(dpi=300).seaborn_figure(return_figure=True)
# TODO: +5 and -5 work with this number of rows/cols but this should be tested
# with other data sets to see if it needs to be proportionate to number of rows/cols
# Set the figure size so it's proportionate to the amount of data in the figure
# todo Agreed, this needs to be generalised but cannot think of how to do that right now.
fig.set_size_inches(5, 5)
# Make sure the two subplots use available space using tight_layout()
plt.tight_layout()
# # Remove distance between the table and the graph
# plt.subplots_adjust(wspace=0.1, hspace=0)
# # Create the subplots to our data requirements (makes sure that the rows of the
# # table will line up with the graph we draw).
# ax2 = plt.subplot2grid(
# (no_of_rows, 2), (1, 1), rowspan=no_of_rows - 1, colspan=1
# )
# Plotting the forest plot
# print(self._store_data)
# print(self._store_data.iloc[:,3])
ax2.set(xlim=(min(self._store_data.iloc[:, 3]),
max(self._store_data.iloc[:, 4])),
ylim=(0, 10))
# Make sure the plots are ordered correctly against the table
plt.gca().invert_yaxis()
# Altering the appearance of the axis ticks and labels
ax2.tick_params(
axis="y", # changes apply to the x-axis
which="both", # both major and minor ticks are affected
labelleft=False, # ticks along the bottom edge are off
top=False,
) # ticks along the top edge are off
sns.despine(left=True)
# Ensure that there are as many colours as there are values
self.number_of_colours = 10
# Drawing the actual lines and squares on the plot
# print(self._store_data.values)
# print(self.palette())
for index, ((exposure, eff_size, se, lower, upper),
colour) in enumerate(
zip(self._store_data.values, self.palette())):
# print(eff_size)
ax2.plot([lower, upper], [index + 0.5, index + 0.5], color=colour)
ax2.plot([eff_size], [index + 0.5],
marker="s",
markersize=0.1 * weight_area,
color=colour)
# Draw the dotted line
ax2.plot([0, 0], [0, 10], "--", color="White")
self.write_plot(plt)
def box_plot(
self,
x_var,
y_var,
gradient_variable=None,
custom_ranking=None,
orientation=None,
):
"""Create a box plot in seaborn using the style sheet and chosen variable values.
:param x_var: The variable you want on the x axis
:type x_var: str
:param y_var: The variable you want on the y axis
:type y_var: str
:param gradient_variable: A variable to apply a gradient of colour to the points
:type gradient_variable: str
:param custom_ranking: A list of rankings to use instead of the default set
:type custom_ranking: list
:param orientation: Whether plot should be vertical ("v") or horizontal ("h")
:type orientation: str
:return: The seaborn plot is returned, and .png image saved to the write directory
:rtype: matplotlib.axes._subplots.AxesSubplot
"""
warnings.warn("Deprecated: Will be moved into Seaborn.py soon.tm",
DeprecationWarning)
# Validate the arguments provided
self._validate_variable_args(locals(),
locals().values(),
["custom_ranking", "orientation"])
# Set defaults before plotting
figure, axis = plt.subplots(figsize=(self.figure_x, self.figure_y))
sns.despine(figure, left=True, bottom=True)
# Generate the plot
# NB Some arguments are left at default that are given sensible defaults by seaborn
plot = sns.boxplot(
x=x_var,
y=y_var,
data=self._data,
palette=self.palette(),
linewidth=self.outline_width,
hue=gradient_variable,
orient=orientation,
hue_order=custom_ranking,
order=custom_ranking,
ax=axis,
)
# # Write out the plot
self.write_plot(plot)
return plot
def residual_plot(self,
x_var,
y_var,
ignore_na=True,
colour=None,
legend_label=None):
"""Regress y_var on x_var, and then draw a scatterplot of the residuals.
:param x_var: The variable you want on the x axis
:type x_var: str
:param y_var: The variable you want on the y axis
:type y_var: str
:param ignore_na: If True, ignore observations with missing data when fitting & plotting
:type ignore_na: bool
:param colour: Colour to use for all elements of the plot
:type colour: matplotlib color
:param legend_label: Label that will be used in plot legend
:type legend_lable: str
:return: The seaborn plot is returned, and .png image saved to the write directory
:rtype: matplotlib.axes._subplots.AxesSubplot
"""
warnings.warn("Deprecated: Will be moved into Seaborn.py soon.tm",
DeprecationWarning)
# Validate the arguments provided
self._validate_variable_args(locals(),
locals().values(),
["ignore_na", "colour", "legend_label"])
# Set defaults before plotting
figure, axis = plt.subplots(figsize=(self.figure_x, self.figure_y))
sns.despine(figure, left=True, bottom=True)
# Generate the plot
# NB Some arguments are left at default that are given sensible defaults by seaborn
plot = sns.residplot(
x=x_var,
y=y_var,
data=self._data,
dropna=ignore_na,
label=legend_label,
color=colour,
ax=axis,
)
# Write out the plot to chosen write directory as a png
self.write_plot(plot)
return plot
def seaborn_figure(self, return_figure=False):
"""
Creates a default figures
"""
self.set_seaborn_style()
figure, axis = plt.subplots(figsize=(self.figure_x, self.figure_y))
sns.despine(figure,
left=self.d_spline_left,
bottom=self.d_spline_bottom,
right=self.d_spline_right,
top=self.d_spline_top)
axis.spines["bottom"].set_color("White")
axis.xaxis.label.set_color("White")
axis.tick_params(axis='x', colors='White')
if return_figure:
return figure, axis
else:
return axis