def symmatplot(mat,
p_mat=None,
names=None,
cmap="Spectral_r",
cmap_range=None,
cbar=True,
ax=None,
**kwargs):
"""Plot a symettric matrix with colormap and statistic values."""
if ax is None:
ax = plt.subplot(111)
nvars = len(mat)
plotmat = mat.copy()
plotmat[np.triu_indices(nvars)] = np.nan
if cmap_range is None:
vmax = np.nanmax(plotmat) * 1.15
vmin = np.nanmin(plotmat) * 1.15
elif len(cmap_range) == 2:
vmin, vmax = cmap_range
else:
raise ValueError("cmap_range argument not understood")
mat_img = ax.matshow(plotmat, cmap=cmap, vmin=vmin, vmax=vmax, **kwargs)
if cbar:
plt.colorbar(mat_img)
if p_mat is None:
p_mat = np.ones((nvars, nvars))
for i, j in zip(*np.triu_indices(nvars, 1)):
val = mat[i, j]
stars = moss.sig_stars(p_mat[i, j])
ax.text(j,
i,
"\n%.3g\n%s" % (val, stars),
fontdict=dict(ha="center", va="center"))
if names is None:
names = ["var%d" % i for i in range(nvars)]
for i, name in enumerate(names):
ax.text(i,
i,
name,
fontdict=dict(ha="center", va="center", weight="bold"))
ticks = np.linspace(.5, nvars - .5, nvars)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(())
ax.set_yticklabels(())
ax.grid(True, linestyle="-")
return ax
def symmatplot(mat, p_mat=None, names=None, cmap="coolwarm", cmap_range=None,
cbar=True, ax=None, **kwargs):
"""Plot a symettric matrix with colormap and statistic values."""
if ax is None:
ax = plt.gca()
nvars = len(mat)
if isinstance(mat, pd.DataFrame):
plotmat = mat.values.copy()
mat = mat.values
else:
plotmat = mat.copy()
plotmat[np.triu_indices(nvars)] = np.nan
if cmap_range is None:
vmax = np.nanmax(plotmat) * 1.15
vmin = np.nanmin(plotmat) * 1.15
elif len(cmap_range) == 2:
vmin, vmax = cmap_range
else:
raise ValueError("cmap_range argument not understood")
mat_img = ax.matshow(plotmat, cmap=cmap, vmin=vmin, vmax=vmax, **kwargs)
if cbar:
plt.colorbar(mat_img, shrink=.75)
if p_mat is None:
p_mat = np.ones((nvars, nvars))
for i, j in zip(*np.triu_indices(nvars, 1)):
val = mat[i, j]
stars = moss.sig_stars(p_mat[i, j])
ax.text(j, i, "\n%.3g\n%s" % (val, stars),
fontdict=dict(ha="center", va="center"))
if names is None:
names = ["var%d" % i for i in range(nvars)]
for i, name in enumerate(names):
ax.text(i, i, name, fontdict=dict(ha="center", va="center",
weight="bold", rotation=45))
ticks = np.linspace(.5, nvars - .5, nvars)
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(())
ax.set_yticklabels(())
ax.grid(True, linestyle="-")
return ax
def regplot(x,
y,
data=None,
corr_func=stats.pearsonr,
xlabel="",
ylabel="",
ci=95,
size=None,
annotloc=None,
color=None,
reg_kws=None,
scatter_kws=None,
dist_kws=None,
text_kws=None):
"""Scatterplot with regreesion line, marginals, and correlation value.
Parameters
----------
x : sequence
independent variables
y : sequence
dependent variables
data : dataframe, optional
if dataframe is given, x, and y are interpreted as
string keys mapping to dataframe column names
corr_func : callable, optional
correlation function; expected to take two arrays
and return a (statistic, pval) tuple
xlabel, ylabel : string, optional
label names
ci : int or None
confidence interval for the regression line
size: int
figure size (will be a square; only need one int)
annotloc : two or three tuple
(xpos, ypos [, horizontalalignment])
color : matplotlib color scheme
color of everything but the regression line
overridden by passing `color` to subfunc kwargs
{reg, scatter, dist, text}_kws: dicts
further keyword arguments for the constituent plots
"""
# Interperet inputs
if data is not None:
xlabel, ylabel = x, y
x = np.array(data[x])
y = np.array(data[y])
# Set up the figure and axes
size = 6 if size is None else size
fig = plt.figure(figsize=(size, size))
ax_scatter = fig.add_axes([0.05, 0.05, 0.75, 0.75])
ax_x_marg = fig.add_axes([0.05, 0.82, 0.75, 0.13])
ax_y_marg = fig.add_axes([0.82, 0.05, 0.13, 0.75])
# Plot the scatter
if scatter_kws is None:
scatter_kws = {}
if color is not None and "color" not in scatter_kws:
scatter_kws.update(color=color)
marker = scatter_kws.pop("markerstyle", "o")
alpha_maker = stats.norm(0, 100)
alpha = alpha_maker.pdf(len(x)) / alpha_maker.pdf(0)
alpha = max(alpha, .1)
alpha = scatter_kws.pop("alpha", alpha)
ax_scatter.plot(x, y, marker, alpha=alpha, mew=0, **scatter_kws)
ax_scatter.set_xlabel(xlabel)
ax_scatter.set_ylabel(ylabel)
# Marginal plots using our distplot function
if dist_kws is None:
dist_kws = {}
if color is not None and "color" not in dist_kws:
dist_kws.update(color=color)
if "legend" not in dist_kws:
dist_kws["legend"] = False
distplot(x, ax=ax_x_marg, **dist_kws)
distplot(y, ax=ax_y_marg, vertical=True, **dist_kws)
for ax in [ax_x_marg, ax_y_marg]:
ax.set_xticklabels([])
ax.set_yticklabels([])
# Regression line plot
xlim = ax_scatter.get_xlim()
a, b = np.polyfit(x, y, 1)
if reg_kws is None:
reg_kws = {}
reg_color = reg_kws.pop("color", "#222222")
ax_scatter.plot(xlim, np.polyval([a, b], xlim), color=reg_color, **reg_kws)
# Bootstrapped regression standard error
if ci is not None:
xx = np.linspace(xlim[0], xlim[1], 100)
def _bootstrap_reg(x, y):
fit = np.polyfit(x, y, 1)
return np.polyval(fit, xx)
boots = moss.bootstrap(x, y, func=_bootstrap_reg)
ci_lims = [50 - ci / 2., 50 + ci / 2.]
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax_scatter.fill_between(xx, *ci_band, color=reg_color, alpha=.15)
ax_scatter.set_xlim(xlim)
# Calcluate a correlation statistic and p value
r, p = corr_func(x, y)
msg = "%s: %.3f (p=%.3g%s)" % (corr_func.__name__, r, p, moss.sig_stars(p))
if annotloc is None:
xmin, xmax = xlim
x_range = xmax - xmin
if r < 0:
xloc, align = xmax - x_range * .02, "right"
else:
xloc, align = xmin + x_range * .02, "left"
ymin, ymax = ax_scatter.get_ylim()
y_range = ymax - ymin
yloc = ymax - y_range * .02
else:
if len(annotloc) == 3:
xloc, yloc, align = annotloc
else:
xloc, yloc = annotloc
align = "left"
if text_kws is None:
text_kws = {}
ax_scatter.text(xloc, yloc, msg, ha=align, va="top", **text_kws)
# Set the axes on the marginal plots
ax_x_marg.set_xlim(ax_scatter.get_xlim())
ax_x_marg.set_yticks([])
ax_y_marg.set_ylim(ax_scatter.get_ylim())
ax_y_marg.set_xticks([])
def symmatplot(mat, p_mat=None, names=None, cmap="Greys", cmap_range=None,
cbar=True, annot=True, diag_names=True, ax=None, **kwargs):
"""Plot a symettric matrix with colormap and statistic values."""
if ax is None:
ax = plt.gca()
nvars = len(mat)
if isinstance(mat, pd.DataFrame):
plotmat = mat.values.copy()
mat = mat.values
else:
plotmat = mat.copy()
plotmat[np.triu_indices(nvars)] = np.nan
if cmap_range is None:
vmax = np.nanmax(plotmat) * 1.15
vmin = np.nanmin(plotmat) * 1.15
elif len(cmap_range) == 2:
vmin, vmax = cmap_range
else:
raise ValueError("cmap_range argument not understood")
mat_img = ax.matshow(plotmat, cmap=cmap, vmin=vmin, vmax=vmax, **kwargs)
if cbar:
plt.colorbar(mat_img, shrink=.75)
if p_mat is None:
p_mat = np.ones((nvars, nvars))
if annot:
for i, j in zip(*np.triu_indices(nvars, 1)):
val = mat[i, j]
stars = moss.sig_stars(p_mat[i, j])
ax.text(j, i, "\n%.3g\n%s" % (val, stars),
fontdict=dict(ha="center", va="center"))
else:
fill = np.ones_like(plotmat)
fill[np.tril_indices_from(fill, -1)] = np.nan
ax.matshow(fill, cmap="Greys", vmin=0, vmax=0, zorder=2)
if names is None:
names = ["var%d" % i for i in range(nvars)]
if diag_names:
for i, name in enumerate(names):
ax.text(i, i, name, fontdict=dict(ha="center", va="center",
weight="bold", rotation=45))
ax.set_xticklabels(())
ax.set_yticklabels(())
else:
ax.xaxis.set_ticks_position("bottom")
xnames = names if annot else names[:-1]
ax.set_xticklabels(xnames, rotation=90)
ynames = names if annot else names[1:]
ax.set_yticklabels(ynames)
minor_ticks = np.linspace(-.5, nvars - 1.5, nvars)
ax.set_xticks(minor_ticks, True)
ax.set_yticks(minor_ticks, True)
major_ticks = np.linspace(0, nvars - 1, nvars)
xticks = major_ticks if annot else major_ticks[:-1]
ax.set_xticks(xticks)
yticks = major_ticks if annot else major_ticks[1:]
ax.set_yticks(yticks)
ax.grid(False, which="major")
ax.grid(True, which="minor", linestyle="-")
return ax
def regplot(x, y, data=None, corr_func=stats.pearsonr, func_name=None,
xlabel="", ylabel="", ci=95, size=None, annotloc=None, color=None,
reg_kws=None, scatter_kws=None, dist_kws=None, text_kws=None):
"""Scatterplot with regresion line, marginals, and correlation value.
Parameters
----------
x : sequence or string
Independent variable.
y : sequence or string
Dependent variable.
data : dataframe, optional
If dataframe is given, x, and y are interpreted as string keys
for selecting to dataframe column names.
corr_func : callable, optional
Correlation function; expected to take two arrays and return a
numeric or (statistic, pval) tuple.
func_name : string, optional
Use in lieu of function name for fit statistic annotation.
xlabel, ylabel : string, optional
Axis label names if inputs are not Pandas objects or to override.
ci : int or None
Confidence interval for the regression estimate.
size: int
Figure size (will be a square; only need one int).
annotloc : two or three tuple
Specified with (xpos, ypos [, horizontalalignment]).
color : matplotlib color scheme
Color of everything but the regression line; can be overridden by
passing `color` to subfunc kwargs.
{reg, scatter, dist, text}_kws: dicts
Further keyword arguments for the constituent plots.
"""
# Interperet inputs
if data is not None:
if not xlabel:
xlabel = x
if not ylabel:
ylabel = y
x = data[x].values
y = data[y].values
else:
if hasattr(x, "name") and not xlabel:
if x.name is not None:
xlabel = x.name
if hasattr(y, "name") and not ylabel:
if y.name is not None:
ylabel = y.name
x = np.asarray(x)
y = np.asarray(y)
# Set up the figure and axes
size = mpl.rcParams["figure.figsize"][0] if size is None else size
fig = plt.figure(figsize=(size, size))
ax_scatter = fig.add_axes([0.05, 0.05, 0.75, 0.75])
ax_x_marg = fig.add_axes([0.05, 0.82, 0.75, 0.13])
ax_y_marg = fig.add_axes([0.82, 0.05, 0.13, 0.75])
# Plot the scatter
if scatter_kws is None:
scatter_kws = {}
if color is not None and "color" not in scatter_kws:
scatter_kws.update(color=color)
marker = scatter_kws.pop("markerstyle", "o")
alpha_maker = stats.norm(0, 100)
alpha = alpha_maker.pdf(len(x)) / alpha_maker.pdf(0)
alpha = max(alpha, .1)
alpha = scatter_kws.pop("alpha", alpha)
ax_scatter.plot(x, y, marker, alpha=alpha, mew=0, **scatter_kws)
ax_scatter.set_xlabel(xlabel)
ax_scatter.set_ylabel(ylabel)
# Marginal plots using our distplot function
if dist_kws is None:
dist_kws = {}
if color is not None and "color" not in dist_kws:
dist_kws.update(color=color)
dist_kws["xlabel"] = False
distplot(x, ax=ax_x_marg, **dist_kws)
distplot(y, ax=ax_y_marg, vertical=True, **dist_kws)
for ax in [ax_x_marg, ax_y_marg]:
ax.set_xticklabels([])
ax.set_yticklabels([])
# Regression line plot
xlim = ax_scatter.get_xlim()
a, b = np.polyfit(x, y, 1)
if reg_kws is None:
reg_kws = {}
reg_color = reg_kws.pop("color", "#222222")
ax_scatter.plot(xlim, np.polyval([a, b], xlim),
color=reg_color, **reg_kws)
# Bootstrapped regression standard error
if ci is not None:
xx = np.linspace(xlim[0], xlim[1], 100)
def _bootstrap_reg(x, y):
fit = np.polyfit(x, y, 1)
return np.polyval(fit, xx)
boots = moss.bootstrap(x, y, func=_bootstrap_reg)
ci_lims = [50 - ci / 2., 50 + ci / 2.]
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax_scatter.fill_between(xx, *ci_band, color=reg_color, alpha=.15)
ax_scatter.set_xlim(xlim)
# Calcluate a fit statistic and p value
if func_name is None:
func_name = corr_func.__name__
out = corr_func(x, y)
try:
s, p = out
msg = "%s: %.3f (p=%.3g%s)" % (func_name, s, p, moss.sig_stars(p))
except TypeError:
s = corr_func(x, y)
msg = "%s: %.3f" % (func_name, s)
if annotloc is None:
xmin, xmax = xlim
x_range = xmax - xmin
# Assume the fit statistic is correlation-esque for some
# intuition on where the fit annotation should go
if s < 0:
xloc, align = xmax - x_range * .02, "right"
else:
xloc, align = xmin + x_range * .02, "left"
ymin, ymax = ax_scatter.get_ylim()
y_range = ymax - ymin
yloc = ymax - y_range * .02
else:
if len(annotloc) == 3:
xloc, yloc, align = annotloc
else:
xloc, yloc = annotloc
align = "left"
if text_kws is None:
text_kws = {}
ax_scatter.text(xloc, yloc, msg, ha=align, va="top", **text_kws)
# Set the axes on the marginal plots
ax_x_marg.set_xlim(ax_scatter.get_xlim())
ax_x_marg.set_yticks([])
ax_y_marg.set_ylim(ax_scatter.get_ylim())
ax_y_marg.set_xticks([])
def symmatplot(mat, p_mat=None, names=None, cmap="Greys", cmap_range=None,
cbar=True, annot=True, diag_names=True, ax=None, **kwargs):
"""Plot a symettric matrix with colormap and statistic values."""
if ax is None:
ax = plt.gca()
nvars = len(mat)
if isinstance(mat, pd.DataFrame):
plotmat = mat.values.copy()
mat = mat.values
else:
plotmat = mat.copy()
plotmat[np.triu_indices(nvars)] = np.nan
if cmap_range is None:
vmax = np.nanmax(plotmat) * 1.15
vmin = np.nanmin(plotmat) * 1.15
elif len(cmap_range) == 2:
vmin, vmax = cmap_range
else:
raise ValueError("cmap_range argument not understood")
mat_img = ax.matshow(plotmat, cmap=cmap, vmin=vmin, vmax=vmax, **kwargs)
if cbar:
plt.colorbar(mat_img, shrink=.75)
if p_mat is None:
p_mat = np.ones((nvars, nvars))
if annot:
for i, j in zip(*np.triu_indices(nvars, 1)):
val = mat[i, j]
stars = moss.sig_stars(p_mat[i, j])
ax.text(j, i, "\n%.2g\n%s" % (val, stars),
fontdict=dict(ha="center", va="center"))
else:
fill = np.ones_like(plotmat)
fill[np.tril_indices_from(fill, -1)] = np.nan
ax.matshow(fill, cmap="Greys", vmin=0, vmax=0, zorder=2)
if names is None:
names = ["var%d" % i for i in range(nvars)]
if diag_names:
for i, name in enumerate(names):
ax.text(i, i, name, fontdict=dict(ha="center", va="center",
weight="bold", rotation=45))
ax.set_xticklabels(())
ax.set_yticklabels(())
else:
ax.xaxis.set_ticks_position("bottom")
xnames = names if annot else names[:-1]
ax.set_xticklabels(xnames, rotation=90)
ynames = names if annot else names[1:]
ax.set_yticklabels(ynames)
minor_ticks = np.linspace(-.5, nvars - 1.5, nvars)
ax.set_xticks(minor_ticks, True)
ax.set_yticks(minor_ticks, True)
major_ticks = np.linspace(0, nvars - 1, nvars)
xticks = major_ticks if annot else major_ticks[:-1]
ax.set_xticks(xticks)
yticks = major_ticks if annot else major_ticks[1:]
ax.set_yticks(yticks)
ax.grid(False, which="major")
ax.grid(True, which="minor", linestyle="-")
return ax
def regplot(x, y, data=None, corr_func=stats.pearsonr, func_name=None,
xlabel="", ylabel="", ci=95, size=None, annotloc=None, color=None,
reg_kws=None, scatter_kws=None, dist_kws=None, text_kws=None):
"""Scatterplot with regresion line, marginals, and correlation value.
Parameters
----------
x : sequence or string
Independent variable.
y : sequence or string
Dependent variable.
data : dataframe, optional
If dataframe is given, x, and y are interpreted as string keys
for selecting to dataframe column names.
corr_func : callable, optional
Correlation function; expected to take two arrays and return a
numeric or (statistic, pval) tuple.
func_name : string, optional
Use in lieu of function name for fit statistic annotation.
xlabel, ylabel : string, optional
Axis label names if inputs are not Pandas objects or to override.
ci : int or None
Confidence interval for the regression estimate.
size: int
Figure size (will be a square; only need one int).
annotloc : two or three tuple
Specified with (xpos, ypos [, horizontalalignment]).
color : matplotlib color scheme
Color of everything but the regression line; can be overridden by
passing `color` to subfunc kwargs.
{reg, scatter, dist, text}_kws: dicts
Further keyword arguments for the constituent plots.
"""
# Interperet inputs
if data is not None:
if not xlabel:
xlabel = x
if not ylabel:
ylabel = y
x = data[x].values
y = data[y].values
else:
if hasattr(x, "name") and not xlabel:
if x.name is not None:
xlabel = x.name
if hasattr(y, "name") and not ylabel:
if y.name is not None:
ylabel = y.name
x = np.asarray(x)
y = np.asarray(y)
# Set up the figure and axes
size = mpl.rcParams["figure.figsize"][1] if size is None else size
fig = plt.figure(figsize=(size, size))
ax_scatter = fig.add_axes([0.05, 0.05, 0.75, 0.75])
ax_x_marg = fig.add_axes([0.05, 0.82, 0.75, 0.13])
ax_y_marg = fig.add_axes([0.82, 0.05, 0.13, 0.75])
# Plot the scatter
if scatter_kws is None:
scatter_kws = {}
if color is not None and "color" not in scatter_kws:
scatter_kws.update(color=color)
marker = scatter_kws.pop("markerstyle", "o")
mew = scatter_kws.pop("mew", 0)
alpha_maker = stats.norm(0, 100)
alpha = alpha_maker.pdf(len(x)) / alpha_maker.pdf(0)
alpha = max(alpha, .1)
alpha = scatter_kws.pop("alpha", alpha)
ax_scatter.plot(x, y, marker, alpha=alpha, mew=mew, **scatter_kws)
ax_scatter.set_xlabel(xlabel)
ax_scatter.set_ylabel(ylabel)
# Marginal plots using our distplot function
if dist_kws is None:
dist_kws = {}
if color is not None and "color" not in dist_kws:
dist_kws.update(color=color)
dist_kws["axlabel"] = False
distplot(x, ax=ax_x_marg, **dist_kws)
distplot(y, ax=ax_y_marg, vertical=True, **dist_kws)
for ax in [ax_x_marg, ax_y_marg]:
ax.set_xticklabels([])
ax.set_yticklabels([])
# Regression line plot
xlim = ax_scatter.get_xlim()
a, b = np.polyfit(x, y, 1)
if reg_kws is None:
reg_kws = {}
reg_color = reg_kws.pop("color", "#222222")
yhat = np.polyval([a, b], xlim)
ax_scatter.plot(xlim, yhat, color=reg_color, **reg_kws)
# This is a hack to get the annotation to work
reg = ax_scatter.plot(xlim, yhat, lw=0)
# Bootstrapped regression standard error
if ci is not None:
xx = np.linspace(xlim[0], xlim[1], 100)
def _bootstrap_reg(x, y):
fit = np.polyfit(x, y, 1)
return np.polyval(fit, xx)
boots = moss.bootstrap(x, y, func=_bootstrap_reg)
ci_lims = [50 - ci / 2., 50 + ci / 2.]
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax_scatter.fill_between(xx, *ci_band, color=reg_color, alpha=.15)
ax_scatter.set_xlim(xlim)
# Calcluate a fit statistic and p value
if func_name is None:
func_name = corr_func.__name__
out = corr_func(x, y)
try:
s, p = out
msg = "%s: %.2g (p=%.2g%s)" % (func_name, s, p, moss.sig_stars(p))
except TypeError:
s = corr_func(x, y)
msg = "%s: %.3f" % (func_name, s)
if text_kws is None:
text_kws = {}
ax_scatter.legend(reg, [msg], loc="best", prop=text_kws)
# Set the axes on the marginal plots
ax_x_marg.set_xlim(ax_scatter.get_xlim())
ax_x_marg.set_yticks([])
ax_y_marg.set_ylim(ax_scatter.get_ylim())
ax_y_marg.set_xticks([])
def regplot(x, y, data=None, corr_func=stats.pearsonr, xlabel="", ylabel="",
ci=95, size=None, annotloc=None, color=None, reg_kws=None,
scatter_kws=None, dist_kws=None, text_kws=None):
"""Scatterplot with regreesion line, marginals, and correlation value.
Parameters
----------
x : sequence
independent variables
y : sequence
dependent variables
data : dataframe, optional
if dataframe is given, x, and y are interpreted as
string keys mapping to dataframe column names
corr_func : callable, optional
correlation function; expected to take two arrays
and return a (statistic, pval) tuple
xlabel, ylabel : string, optional
label names
ci : int or None
confidence interval for the regression line
size: int
figure size (will be a square; only need one int)
annotloc : two or three tuple
(xpos, ypos [, horizontalalignment])
color : matplotlib color scheme
color of everything but the regression line
overridden by passing `color` to subfunc kwargs
{reg, scatter, dist, text}_kws: dicts
further keyword arguments for the constituent plots
"""
# Interperet inputs
if data is not None:
xlabel, ylabel = x, y
x = np.array(data[x])
y = np.array(data[y])
# Set up the figure and axes
size = 6 if size is None else size
fig = plt.figure(figsize=(size, size))
ax_scatter = fig.add_axes([0.05, 0.05, 0.75, 0.75])
ax_x_marg = fig.add_axes([0.05, 0.82, 0.75, 0.13])
ax_y_marg = fig.add_axes([0.82, 0.05, 0.13, 0.75])
# Plot the scatter
if scatter_kws is None:
scatter_kws = {}
if color is not None and "color" not in scatter_kws:
scatter_kws.update(color=color)
marker = scatter_kws.pop("markerstyle", "o")
alpha_maker = stats.norm(0, 100)
alpha = alpha_maker.pdf(len(x)) / alpha_maker.pdf(0)
alpha = max(alpha, .1)
alpha = scatter_kws.pop("alpha", alpha)
ax_scatter.plot(x, y, marker, alpha=alpha, mew=0, **scatter_kws)
ax_scatter.set_xlabel(xlabel)
ax_scatter.set_ylabel(ylabel)
# Marginal plots using our distplot function
if dist_kws is None:
dist_kws = {}
if color is not None and "color" not in dist_kws:
dist_kws.update(color=color)
if "legend" not in dist_kws:
dist_kws["legend"] = False
distplot(x, ax=ax_x_marg, **dist_kws)
distplot(y, ax=ax_y_marg, vertical=True, **dist_kws)
for ax in [ax_x_marg, ax_y_marg]:
ax.set_xticklabels([])
ax.set_yticklabels([])
# Regression line plot
xlim = ax_scatter.get_xlim()
a, b = np.polyfit(x, y, 1)
if reg_kws is None:
reg_kws = {}
reg_color = reg_kws.pop("color", "#222222")
ax_scatter.plot(xlim, np.polyval([a, b], xlim),
color=reg_color, **reg_kws)
# Bootstrapped regression standard error
if ci is not None:
xx = np.linspace(xlim[0], xlim[1], 100)
def _bootstrap_reg(x, y):
fit = np.polyfit(x, y, 1)
return np.polyval(fit, xx)
boots = moss.bootstrap(x, y, func=_bootstrap_reg)
ci_lims = [50 - ci / 2., 50 + ci / 2.]
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax_scatter.fill_between(xx, *ci_band, color=reg_color, alpha=.15)
ax_scatter.set_xlim(xlim)
# Calcluate a correlation statistic and p value
r, p = corr_func(x, y)
msg = "%s: %.3f (p=%.3g%s)" % (corr_func.__name__, r, p, moss.sig_stars(p))
if annotloc is None:
xmin, xmax = xlim
x_range = xmax - xmin
if r < 0:
xloc, align = xmax - x_range * .02, "right"
else:
xloc, align = xmin + x_range * .02, "left"
ymin, ymax = ax_scatter.get_ylim()
y_range = ymax - ymin
yloc = ymax - y_range * .02
else:
if len(annotloc) == 3:
xloc, yloc, align = annotloc
else:
xloc, yloc = annotloc
align = "left"
if text_kws is None:
text_kws = {}
ax_scatter.text(xloc, yloc, msg, ha=align, va="top", **text_kws)
# Set the axes on the marginal plots
ax_x_marg.set_xlim(ax_scatter.get_xlim())
ax_x_marg.set_yticks([])
ax_y_marg.set_ylim(ax_scatter.get_ylim())
ax_y_marg.set_xticks([])