def lmplot(ax):
n = 80
c = "#222222"
rs = np.random.RandomState(5)
x = rs.normal(4, 1, n)
y = 2 + 1.5 * x + rs.normal(0, 3, n)
ax.plot(x, y, "o", c=c, alpha=.8)
xx = np.linspace(1 + 1e-9, 7 - 1e-9, 100)
lmpred = lambda x, y: np.polyval(np.polyfit(x, y, 1), xx)
yy = lmpred(x, y)
ax.plot(xx, yy, c=c)
boots = moss.bootstrap(x, y, func=lmpred, n_boot=100)
ci = moss.percentiles(boots, [2.5, 97.5], 0)
ax.fill_between(xx, *ci, alpha=.15, color=c)
ax.set_title("lmplot()")
def lmplot(ax):
n = 80
c = "#222222"
rs = np.random.RandomState(5)
x = rs.normal(4, 1, n)
y = 2 + 1.5 * x + rs.normal(0, 3, n)
ax.plot(x, y, "o", c=c, alpha=.8)
xx = np.linspace(1 + 1e-9, 7 - 1e-9, 100)
lmpred = lambda x, y: np.polyval(np.polyfit(x, y, 1), xx)
yy = lmpred(x, y)
ax.plot(xx, yy, c=c)
boots = moss.bootstrap(x, y, func=lmpred, n_boot=100)
ci = moss.percentiles(boots, [2.5, 97.5], 0)
ax.fill_between(xx, *ci, alpha=.15, color=c)
ax.set_title("lmplot()")
ax = plt.subplot(gs[:2, 0])
plt.title("lmplot()")
n = 80
c = "#222222"
rs = np.random.RandomState(5)
x = rs.normal(4, 1, n)
y = 2 + 1.5 * x + rs.normal(0, 3, n)
ax.plot(x, y, "o", c=c, alpha=.8)
xx = np.linspace(1, 7, 100)
lmpred = lambda x, y: np.polyval(np.polyfit(x, y, 1), xx)
yy = lmpred(x, y)
ax.plot(xx, yy, c=c)
boots = moss.bootstrap(x, y, func=lmpred, n_boot=100)
ci = moss.percentiles(boots, [2.5, 97.5], 0)
ax.fill_between(xx, *ci, alpha=.15, color=c)
# Timeseries plot
# ---------------
ax = plt.subplot(gs[:2, 1])
plt.title("tsplot()")
n = 20
t = 10
ax.set_xlim(0, t)
x = np.linspace(0, t, 100)
s = np.array([stats.gamma.pdf(x, a) for a in [3, 5, 7]])
d = s[:, np.newaxis, :]
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 lmplot(x,
y,
data,
color=None,
row=None,
col=None,
x_estimator=None,
x_ci=95,
fit_line=True,
ci=95,
truncate=False,
sharex=True,
sharey=True,
palette="hls",
size=None,
scatter_kws=None,
line_kws=None,
palette_kws=None):
"""Plot a linear model from a DataFrame.
Parameters
----------
x, y : strings
column names in `data` DataFrame for x and y variables
data : DataFrame
source of data for the model
color : string, optional
DataFrame column name to group the model by color
row, col : strings, optional
DataFrame column names to make separate plot facets
x_estimator : callable, optional
Interpret X values as factor labels and use this function
to plot the point estimate and bootstrapped CI
x_ci : int optional
size of confidence interval for x_estimator error bars
fit_line : bool, optional
if True fit a regression line by color/row/col and plot
ci : int, optional
confidence interval for the regression line
truncate : bool, optional
if True, only fit line from data min to data max
sharex, sharey : bools, optional
only relevant if faceting; passed to plt.subplots
palette : seaborn color palette argument
if using separate plots by color, draw with this color palette
size : float, optional
size (plots are square) for each plot facet
{scatter, line}_kws : dictionary
keyword arguments to pass to the underlying plot functions
palette_kws : dictionary
keyword arguments for seaborn.color_palette
"""
# TODO
# - position_{dodge, jitter}
# - legend when fit_line is False
# - truncate fit
# - wrap title when wide
# - wrap columns
# First sort out the general figure layout
if size is None:
size = mpl.rcParams["figure.figsize"][1]
nrow = 1 if row is None else len(data[row].unique())
ncol = 1 if col is None else len(data[col].unique())
f, axes = plt.subplots(nrow,
ncol,
sharex=sharex,
sharey=sharey,
figsize=(size * ncol, size * nrow))
axes = np.atleast_2d(axes).reshape(nrow, ncol)
if nrow == 1:
row_masks = [np.repeat(True, len(data))]
else:
row_vals = np.sort(data[row].unique())
row_masks = [data[row] == val for val in row_vals]
if ncol == 1:
col_masks = [np.repeat(True, len(data))]
else:
col_vals = np.sort(data[col].unique())
col_masks = [data[col] == val for val in col_vals]
if palette_kws is None:
palette_kws = {}
# Sort out the plot colors
color_factor = color
if color is None:
hue_masks = [np.repeat(True, len(data))]
colors = ["#222222"]
else:
hue_vals = np.sort(data[color].unique())
hue_masks = [data[color] == val for val in hue_vals]
colors = color_palette(palette, len(hue_masks), **palette_kws)
# Default keyword arguments for plot components
if scatter_kws is None:
scatter_kws = {}
if line_kws is None:
line_kws = {}
# First walk through the facets and plot the scatters
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
ax = axes[row_i, col_j]
if not sharex or (row_i + 1 == len(row_masks)):
ax.set_xlabel(x)
if not sharey or col_j == 0:
ax.set_ylabel(y)
# Title the plot if we are faceting
title = ""
if row is not None:
title += "%s = %s" % (row, row_vals[row_i])
if row is not None and col is not None:
title += " | "
if col is not None:
title += "%s = %s" % (col, col_vals[col_j])
ax.set_title(title)
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
if x_estimator is not None:
ms = scatter_kws.pop("ms", 7)
mew = scatter_kws.pop("mew", 0)
x_vals = data_ijk[x].unique()
y_grouped = [
np.array(data_ijk[y][data_ijk[x] == v]) for v in x_vals
]
y_est = [x_estimator(y_i) for y_i in y_grouped]
y_boots = [
moss.bootstrap(np.array(y_i), func=x_estimator)
for y_i in y_grouped
]
ci_lims = [50 - x_ci / 2., 50 + x_ci / 2.]
y_ci = [moss.percentiles(y_i, ci_lims) for y_i in y_boots]
y_error = ci_to_errsize(np.transpose(y_ci), y_est)
ax.plot(x_vals,
y_est,
"o",
mew=mew,
ms=ms,
color=color,
**scatter_kws)
ax.errorbar(x_vals, y_est, y_error, fmt=None, ecolor=color)
else:
ms = scatter_kws.pop("ms", 4)
mew = scatter_kws.pop("mew", 0)
ax.plot(data_ijk[x],
data_ijk[y],
"o",
color=color,
mew=mew,
ms=ms,
**scatter_kws)
for ax_i in np.ravel(axes):
ax_i.set_xmargin(.05)
ax_i.autoscale_view()
# Now walk through again and plot the regression estimate
# and a confidence interval for the regression line
if fit_line:
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
ax = axes[row_i, col_j]
xlim = ax.get_xlim()
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
x_vals = np.array(data_ijk[x])
y_vals = np.array(data_ijk[y])
# Sort out the limit of the fit
if truncate:
xx = np.linspace(x_vals.min(), x_vals.max(), 100)
else:
xx = np.linspace(xlim[0], xlim[1], 100)
# Inner function to bootstrap the regression
def _bootstrap_reg(x, y):
fit = np.polyfit(x, y, 1)
return np.polyval(fit, xx)
# Regression line confidence interval
if ci is not None:
ci_lims = [50 - ci / 2., 50 + ci / 2.]
boots = moss.bootstrap(x_vals,
y_vals,
func=_bootstrap_reg)
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax.fill_between(xx, *ci_band, color=color, alpha=.15)
fit = np.polyfit(x_vals, y_vals, 1)
reg = np.polyval(fit, xx)
if color_factor is None:
label = ""
else:
label = hue_vals[hue_k]
ax.plot(xx, reg, color=color, label=str(label), **line_kws)
ax.set_xlim(xlim)
# Plot the legend on the upper left facet and adjust the layout
if color_factor is not None:
axes[0, 0].legend(loc="best", title=color_factor)
plt.tight_layout()
def tsplot(x,
data,
err_style=["ci_band"],
ci=68,
interpolate=True,
estimator=np.mean,
n_boot=10000,
smooth=False,
err_palette=None,
ax=None,
**kwargs):
"""Plot timeseries from a set of observations.
Parameters
----------
x : n_tp array
x values
data : n_obs x n_tp array
array of timeseries data where first axis is e.g. subjects
err_style : list of strings
names of ways to plot uncertainty across observations from set of
{ci_band, ci_bars, boot_traces, book_kde, obs_traces, obs_points}
ci : int or list of ints
confidence interaval size(s). if a list, it will stack the error
plots for each confidence interval
estimator : callable
function to determine centralt tendency and to pass to bootstrap
must take an ``axis`` argument
n_boot : int
number of bootstrap iterations
smooth : boolean
whether to perform a smooth bootstrap (resample from KDE)
ax : axis object, optional
plot in given axis; if None creates a new figure
kwargs : further keyword arguments for main call to plot()
Returns
-------
ax : matplotlib axis
axis with plot data
"""
if ax is None:
ax = plt.subplot(111)
# Bootstrap the data for confidence intervals
boot_data = moss.bootstrap(data,
n_boot=n_boot,
smooth=smooth,
axis=0,
func=estimator)
ci_list = hasattr(ci, "__iter__")
if not ci_list:
ci = [ci]
ci_vals = [(50 - w / 2, 50 + w / 2) for w in ci]
cis = [moss.percentiles(boot_data, ci, axis=0) for ci in ci_vals]
central_data = estimator(data, axis=0)
# Plot the timeseries line to get its color
line, = ax.plot(x, central_data, **kwargs)
color = line.get_color()
line.remove()
kwargs.pop("color", None)
# Use subroutines to plot the uncertainty
for style in err_style:
# Grab the function from the global environment
try:
plot_func = globals()["_plot_%s" % style]
except KeyError:
raise ValueError("%s is not a valid err_style" % style)
# Possibly set up to plot each observation in a different color
if err_palette is not None and "obs" in style:
orig_color = color
color = color_palette(err_palette, len(data), desat=.99)
plot_kwargs = dict(ax=ax,
x=x,
data=data,
boot_data=boot_data,
central_data=central_data,
color=color)
for ci_i in cis:
plot_kwargs["ci"] = ci_i
plot_func(**plot_kwargs)
if err_palette is not None and "obs" in style:
color = orig_color
# Replot the central trace so it is prominent
marker = kwargs.pop("marker", "" if interpolate else "o")
linestyle = kwargs.pop("linestyle", "-" if interpolate else "")
ax.plot(x,
central_data,
color=color,
marker=marker,
linestyle=linestyle,
**kwargs)
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 lmplot(x, y, data, color=None, row=None, col=None, col_wrap=None,
x_estimator=None, x_ci=95, n_boot=5000, fit_reg=True,
order=1, ci=95, logistic=False, truncate=False,
x_partial=None, y_partial=None, x_jitter=None, y_jitter=None,
sharex=True, sharey=True, palette="husl", size=None,
scatter_kws=None, line_kws=None, palette_kws=None):
"""Plot a linear model from a DataFrame.
Parameters
----------
x, y : strings
column names in `data` DataFrame for x and y variables
data : DataFrame
source of data for the model
color : string, optional
DataFrame column name to group the model by color
row, col : strings, optional
DataFrame column names to make separate plot facets
col_wrap : int, optional
wrap col variable at this width - cannot be used with row facet
x_estimator : callable, optional
Interpret X values as factor labels and use this function
to plot the point estimate and bootstrapped CI
x_ci : int optional
size of confidence interval for x_estimator error bars
n_boot : int, optional
number of bootstrap iterations to perform
fit_reg : bool, optional
if True fit a regression model by color/row/col and plot
order : int, optional
order of the regression polynomial to fit (default = 1)
ci : int, optional
confidence interval for the regression line
logistic : bool, optional
fit the regression line with logistic regression
truncate : bool, optional
if True, only fit line from data min to data max
{x, y}_partial : string or list of strings, optional
regress these variables out of the factors before plotting
{x, y}_jitter : float, optional
parameters for uniformly distributed random noise added to positions
sharex, sharey : bools, optional
only relevant if faceting; passed to plt.subplots
palette : seaborn color palette argument
if using separate plots by color, draw with this color palette
size : float, optional
size (plots are square) for each plot facet
{scatter, line}_kws : dictionary
keyword arguments to pass to the underlying plot functions
palette_kws : dictionary
keyword arguments for seaborn.color_palette
"""
# TODO
# - legend when fit_line is False
# - wrap title when wide
# First sort out the general figure layout
if size is None:
size = mpl.rcParams["figure.figsize"][1]
if col is None and col_wrap is not None:
raise ValueError("Need column facet variable for `col_wrap`")
if row is not None and col_wrap is not None:
raise ValueError("Cannot facet rows when using `col_wrap`")
nrow = 1 if row is None else len(data[row].unique())
ncol = 1 if col is None else len(data[col].unique())
if col_wrap is not None:
ncol = col_wrap
nrow = int(np.ceil(len(data[col].unique()) / col_wrap))
f, axes = plt.subplots(nrow, ncol, sharex=sharex, sharey=sharey,
figsize=(size * ncol, size * nrow))
axes = np.atleast_2d(axes).reshape(nrow, ncol)
if nrow == 1 or col_wrap is not None:
row_masks = [np.repeat(True, len(data))]
else:
row_vals = np.sort(data[row].unique())
row_masks = [data[row] == val for val in row_vals]
if ncol == 1:
col_masks = [np.repeat(True, len(data))]
else:
col_vals = np.sort(data[col].unique())
col_masks = [data[col] == val for val in col_vals]
if x_partial is not None:
if not isinstance(x_partial, list):
x_partial = [x_partial]
if y_partial is not None:
if not isinstance(y_partial, list):
y_partial = [y_partial]
if palette_kws is None:
palette_kws = {}
# Sort out the plot colors
color_factor = color
if color is None:
hue_masks = [np.repeat(True, len(data))]
colors = ["#222222"]
else:
hue_vals = np.sort(data[color].unique())
hue_masks = [data[color] == val for val in hue_vals]
colors = color_palette(palette, len(hue_masks), **palette_kws)
# Default keyword arguments for plot components
if scatter_kws is None:
scatter_kws = {}
if line_kws is None:
line_kws = {}
# First walk through the facets and plot the scatters
scatter_ms = scatter_kws.pop("ms", 4)
scatter_mew = mew = scatter_kws.pop("mew", 0)
scatter_alpha = mew = scatter_kws.pop("alpha", .77)
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
if col_wrap is not None:
f_row = col_j // ncol
f_col = col_j % ncol
else:
f_row, f_col = row_i, col_j
ax = axes[f_row, f_col]
if f_row + 1 == nrow:
ax.set_xlabel(x)
if f_col == 0:
ax.set_ylabel(y)
# Title the plot if we are faceting
title = ""
if row is not None:
title += "%s = %s" % (row, row_vals[row_i])
if row is not None and col is not None:
title += " | "
if col is not None:
title += "%s = %s" % (col, col_vals[col_j])
ax.set_title(title)
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
if x_estimator is not None:
ms = scatter_kws.pop("ms", 7)
mew = scatter_kws.pop("mew", 0)
x_vals = data_ijk[x].unique()
y_vals = data_ijk[y]
if y_partial is not None:
for var in y_partial:
conf = data_ijk[var]
conf -= conf.mean()
y_mean = y_vals.mean()
y_vals = moss.vector_reject(y_vals - y_mean, conf)
y_vals += y_mean
y_grouped = [np.array(y_vals[data_ijk[x] == v])
for v in x_vals]
y_est = [x_estimator(y_i) for y_i in y_grouped]
y_boots = [moss.bootstrap(np.array(y_i),
func=x_estimator,
n_boot=n_boot)
for y_i in y_grouped]
ci_lims = [50 - x_ci / 2., 50 + x_ci / 2.]
y_ci = [moss.percentiles(y_i, ci_lims) for y_i in y_boots]
y_error = ci_to_errsize(np.transpose(y_ci), y_est)
ax.plot(x_vals, y_est, "o", mew=mew, ms=ms,
color=color, **scatter_kws)
ax.errorbar(x_vals, y_est, y_error,
fmt=None, ecolor=color)
else:
x_ = data_ijk[x]
y_ = data_ijk[y]
if x_partial is not None:
for var in x_partial:
conf = data_ijk[var]
conf -= conf.mean()
x_mean = x_.mean()
x_ = moss.vector_reject(x_ - x_mean, conf)
x_ += x_mean
if y_partial is not None:
for var in y_partial:
conf = data_ijk[var]
conf -= conf.mean()
y_mean = y_.mean()
y_ = moss.vector_reject(y_ - y_mean, conf)
y_ += y_mean
if x_jitter is not None:
x_ += np.random.uniform(-x_jitter, x_jitter, x_.shape)
if y_jitter is not None:
y_ += np.random.uniform(-y_jitter, y_jitter, y_.shape)
ax.plot(x_, y_, "o", color=color, alpha=scatter_alpha,
mew=scatter_mew, ms=scatter_ms, **scatter_kws)
for ax_i in np.ravel(axes):
ax_i.set_xmargin(.05)
ax_i.autoscale_view()
# Now walk through again and plot the regression estimate
# and a confidence interval for the regression line
if fit_reg:
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
if col_wrap is not None:
f_row = col_j // ncol
f_col = col_j % ncol
else:
f_row, f_col = row_i, col_j
ax = axes[f_row, f_col]
xlim = ax.get_xlim()
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
x_vals = np.array(data_ijk[x])
y_vals = np.array(data_ijk[y])
if not len(x_vals):
continue
# Sort out the limit of the fit
if truncate:
xx = np.linspace(x_vals.min(),
x_vals.max(), 100)
else:
xx = np.linspace(xlim[0], xlim[1], 100)
xx_ = sm.add_constant(xx, prepend=True)
# Inner function to bootstrap the regression
def _regress(x, y):
if logistic:
x_ = sm.add_constant(x, prepend=True)
fit = sm.GLM(y, x_,
family=sm.families.Binomial()).fit()
reg = fit.predict(xx_)
else:
fit = np.polyfit(x, y, order)
reg = np.polyval(fit, xx)
return reg
# Remove nuisance variables with vector rejection
if x_partial is not None:
for var in x_partial:
conf = data_ijk[var]
conf -= conf.mean()
x_mean = x_vals.mean()
x_vals = moss.vector_reject(x_vals - x_mean, conf)
x_vals += x_mean
if y_partial is not None:
for var in y_partial:
conf = data_ijk[var]
conf -= conf.mean()
y_mean = y_vals.mean()
y_vals = moss.vector_reject(y_vals - y_mean, conf)
y_vals += y_mean
# Regression line confidence interval
if ci is not None:
ci_lims = [50 - ci / 2., 50 + ci / 2.]
boots = moss.bootstrap(x_vals, y_vals,
func=_regress,
n_boot=n_boot)
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax.fill_between(xx, *ci_band, color=color, alpha=.15)
# Regression line
reg = _regress(x_vals, y_vals)
if color_factor is None:
label = ""
else:
label = hue_vals[hue_k]
ax.plot(xx, reg, color=color,
label=str(label), **line_kws)
ax.set_xlim(xlim)
# Plot the legend on the upper left facet and adjust the layout
if color_factor is not None and color_factor not in [row, col]:
axes[0, 0].legend(loc="best", title=color_factor)
plt.tight_layout()
def tsplot(data, time=None, unit=None, condition=None, value=None,
err_style="ci_band", ci=68, interpolate=True, color=None,
estimator=np.mean, n_boot=5000, err_palette=None, err_kws=None,
legend=True, ax=None, **kwargs):
"""Plot one or more timeseries with flexible representation of uncertainty.
This function can take data specified either as a long-form (tidy)
DataFrame or as an ndarray with dimensions for sampling unit, time, and
(optionally) condition. The interpretation of some of the other parameters
changes depending on the type of object passed as data.
Parameters
----------
data : DataFrame or ndarray
Data for the plot. Should either be a "long form" dataframe or an
array with dimensions (unit, time, condition). In both cases, the
condition field/dimension is optional. The type of this argument
determines the interpretation of the next few parameters.
time : string or series-like
Either the name of the field corresponding to time in the data
DataFrame or x values for a plot when data is an array. If a Series,
the name will be used to label the x axis.
value : string
Either the name of the field corresponding to the data values in
the data DataFrame (i.e. the y coordinate) or a string that forms
the y axis label when data is an array.
unit : string
Field in the data DataFrame identifying the sampling unit (e.g.
subject, neuron, etc.). The error representation will collapse over
units at each time/condition observation. This has no role when data
is an array.
condition : string or Series-like
Either the name of the field identifying the condition an observation
falls under in the data DataFrame, or a sequence of names with a length
equal to the size of the third dimension of data. There will be a
separate trace plotted for each condition. If condition is a Series
with a name attribute, the name will form the title for the plot
legend (unless legend is set to False).
err_style : string or list of strings or None
Names of ways to plot uncertainty across units from set of
{ci_band, ci_bars, boot_traces, book_kde, unit_traces, unit_points}.
Can use one or more than one method.
ci : float or list of floats in [0, 100]
Confidence interaval size(s). If a list, it will stack the error
plots for each confidence interval. Only relevant for error styles
with "ci" in the name.
interpolate : boolean
Whether to do a linear interpolation between each timepoint when
plotting. The value of this parameter also determines the marker
used for the main plot traces, unless marker is specified as a keyword
argument.
color : seaborn palette or matplotlib color name or dictionary
Palette or color for the main plots and error representation (unless
plotting by unit, which can be separately controlled with err_palette).
If a dictionary, should map condition name to color spec.
estimator : callable
Function to determine central tendency and to pass to bootstrap
must take an ``axis`` argument.
n_boot : int
Number of bootstrap iterations.
err_palette: seaborn palette
Palette name or list of colors used when plotting data for each unit.
err_kws : dict, optional
Keyword argument dictionary passed through to matplotlib function
generating the error plot,
ax : axis object, optional
Plot in given axis; if None creates a new figure
kwargs :
Other keyword arguments are passed to main plot() call
Returns
-------
ax : matplotlib axis
axis with plot data
"""
# Sort out default values for the parameters
if ax is None:
ax = plt.gca()
if err_kws is None:
err_kws = {}
# Handle different types of input data
if isinstance(data, pd.DataFrame):
xlabel = time
ylabel = value
# Condition is optional
if condition is None:
condition = pd.Series(np.ones(len(data)))
legend = False
legend_name = None
n_cond = 1
else:
legend = True and legend
legend_name = condition
n_cond = len(data[condition].unique())
else:
data = np.asarray(data)
# Data can be a timecourse from a single unit or
# several observations in one condition
if data.ndim == 1:
data = data[np.newaxis, :, np.newaxis]
elif data.ndim == 2:
data = data[:, :, np.newaxis]
n_unit, n_time, n_cond = data.shape
# Units are experimental observations. Maybe subjects, or neurons
if unit is None:
units = np.arange(n_unit)
unit = "unit"
units = np.repeat(units, n_time * n_cond)
ylabel = None
# Time forms the xaxis of the plot
if time is None:
times = np.arange(n_time)
else:
times = np.asarray(time)
xlabel = None
if hasattr(time, "name"):
xlabel = time.name
time = "time"
times = np.tile(np.repeat(times, n_cond), n_unit)
# Conditions split the timeseries plots
if condition is None:
conds = range(n_cond)
legend = False
if isinstance(color, dict):
err = "Must have condition names if using color dict."
raise ValueError(err)
else:
conds = np.asarray(condition)
legend = True and legend
if hasattr(condition, "name"):
legend_name = condition.name
else:
legend_name = None
condition = "cond"
conds = np.tile(conds, n_unit * n_time)
# Value forms the y value in the plot
if value is None:
ylabel = None
else:
ylabel = value
value = "value"
# Convert to long-form DataFrame
data = pd.DataFrame(dict(value=data.ravel(),
time=times,
unit=units,
cond=conds))
# Set up the err_style and ci arguments for teh loop below
if isinstance(err_style, string_types):
err_style = [err_style]
elif err_style is None:
err_style = []
if not hasattr(ci, "__iter__"):
ci = [ci]
# Set up the color palette
if color is None:
colors = color_palette()
elif isinstance(color, dict):
colors = [color[c] for c in data[condition].unique()]
else:
try:
colors = color_palette(color, n_cond)
except ValueError:
color = mpl.colors.colorConverter.to_rgb(color)
colors = [color] * n_cond
# Do a groupby with condition and plot each trace
for c, (cond, df_c) in enumerate(data.groupby(condition, sort=False)):
df_c = df_c.pivot(unit, time, value)
x = df_c.columns.values.astype(np.float)
# Bootstrap the data for confidence intervals
boot_data = moss.bootstrap(df_c.values, n_boot=n_boot,
axis=0, func=estimator)
cis = [moss.ci(boot_data, v, axis=0) for v in ci]
central_data = estimator(df_c.values, axis=0)
# Get the color for this condition
color = colors[c]
# Use subroutines to plot the uncertainty
for style in err_style:
# Allow for null style (only plot central tendency)
if style is None:
continue
# Grab the function from the global environment
try:
plot_func = globals()["_plot_%s" % style]
except KeyError:
raise ValueError("%s is not a valid err_style" % style)
# Possibly set up to plot each observation in a different color
if err_palette is not None and "unit" in style:
orig_color = color
color = color_palette(err_palette, len(df_c.values))
# Pass all parameters to the error plotter as keyword args
plot_kwargs = dict(ax=ax, x=x, data=df_c.values,
boot_data=boot_data,
central_data=central_data,
color=color, err_kws=err_kws)
# Plot the error representation, possibly for multiple cis
for ci_i in cis:
plot_kwargs["ci"] = ci_i
plot_func(**plot_kwargs)
if err_palette is not None and "unit" in style:
color = orig_color
# Plot the central trace
marker = kwargs.pop("marker", "" if interpolate else "o")
linestyle = kwargs.pop("linestyle", "-" if interpolate else "")
label = kwargs.pop("label", cond if legend else "_nolegend_")
ax.plot(x, central_data, color=color, label=label,
marker=marker, linestyle=linestyle, **kwargs)
# Pad the sides of the plot only when not interpolating
ax.set_xlim(x.min(), x.max())
x_diff = x[1] - x[0]
if not interpolate:
ax.set_xlim(x.min() - x_diff, x.max() + x_diff)
# Add the plot labels
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if legend:
ax.legend(loc=0, title=legend_name)
return ax
def tsplot(data,
time=None,
unit=None,
condition=None,
value=None,
err_style="ci_band",
ci=68,
interpolate=True,
color=None,
estimator=np.mean,
n_boot=5000,
err_palette=None,
err_kws=None,
legend=True,
ax=None,
**kwargs):
"""Plot one or more timeseries with flexible representation of uncertainty.
This function can take data specified either as a long-form (tidy)
DataFrame or as an ndarray with dimensions for sampling unit, time, and
(optionally) condition. The interpretation of some of the other parameters
changes depending on the type of object passed as data.
Parameters
----------
data : DataFrame or ndarray
Data for the plot. Should either be a "long form" dataframe or an
array with dimensions (unit, time, condition). In both cases, the
condition field/dimension is optional. The type of this argument
determines the interpretation of the next few parameters.
time : string or series-like
Either the name of the field corresponding to time in the data
DataFrame or x values for a plot when data is an array. If a Series,
the name will be used to label the x axis.
value : string
Either the name of the field corresponding to the data values in
the data DataFrame (i.e. the y coordinate) or a string that forms
the y axis label when data is an array.
unit : string
Field in the data DataFrame identifying the sampling unit (e.g.
subject, neuron, etc.). The error representation will collapse over
units at each time/condition observation. This has no role when data
is an array.
condition : string or Series-like
Either the name of the field identifying the condition an observation
falls under in the data DataFrame, or a sequence of names with a length
equal to the size of the third dimension of data. There will be a
separate trace plotted for each condition. If condition is a Series
with a name attribute, the name will form the title for the plot
legend (unless legend is set to False).
err_style : string or list of strings or None
Names of ways to plot uncertainty across units from set of
{ci_band, ci_bars, boot_traces, book_kde, unit_traces, unit_points}.
Can use one or more than one method.
ci : float or list of floats in [0, 100]
Confidence interaval size(s). If a list, it will stack the error
plots for each confidence interval. Only relevant for error styles
with "ci" in the name.
interpolate : boolean
Whether to do a linear interpolation between each timepoint when
plotting. The value of this parameter also determines the marker
used for the main plot traces, unless marker is specified as a keyword
argument.
color : seaborn palette or matplotlib color name or dictionary
Palette or color for the main plots and error representation (unless
plotting by unit, which can be separately controlled with err_palette).
If a dictionary, should map condition name to color spec.
estimator : callable
Function to determine central tendency and to pass to bootstrap
must take an ``axis`` argument.
n_boot : int
Number of bootstrap iterations.
err_palette: seaborn palette
Palette name or list of colors used when plotting data for each unit.
err_kws : dict, optional
Keyword argument dictionary passed through to matplotlib function
generating the error plot,
ax : axis object, optional
Plot in given axis; if None creates a new figure
kwargs :
Other keyword arguments are passed to main plot() call
Returns
-------
ax : matplotlib axis
axis with plot data
"""
# Sort out default values for the parameters
if ax is None:
ax = plt.gca()
if err_kws is None:
err_kws = {}
# Handle case where data is an array
if isinstance(data, pd.DataFrame):
xlabel = time
ylabel = value
# Condition is optional
if condition is None:
condition = pd.Series(np.ones(len(data)))
legend = False
legend_name = None
n_cond = 1
else:
legend = True and legend
legend_name = condition
n_cond = len(data[condition].unique())
else:
data = np.asarray(data)
# Data can be a timecourse from a single unit or
# several observations in one condition
if data.ndim == 1:
data = data[np.newaxis, :, np.newaxis]
elif data.ndim == 2:
data = data[:, :, np.newaxis]
n_unit, n_time, n_cond = data.shape
# Units are experimental observations. Maybe subjects, or neurons
if unit is None:
units = np.arange(n_unit)
unit = "unit"
units = np.repeat(units, n_time * n_cond)
ylabel = None
# Time forms the xaxis of the plot
if time is None:
times = np.arange(n_time)
else:
times = np.asarray(time)
xlabel = None
if hasattr(time, "name"):
xlabel = time.name
time = "time"
times = np.tile(np.repeat(times, n_cond), n_unit)
# Conditions split the timeseries plots
if condition is None:
conds = range(n_cond)
legend = False
if isinstance(color, dict):
err = "Must have condition names if using color dict."
raise ValueError(err)
else:
conds = np.asarray(condition)
legend = True and legend
if hasattr(condition, "name"):
legend_name = condition.name
else:
legend_name = None
condition = "cond"
conds = np.tile(conds, n_unit * n_time)
# Value forms the y value in the plot
if value is None:
ylabel = None
else:
ylabel = value
value = "value"
# Convert to long-form DataFrame
data = pd.DataFrame(
dict(value=data.ravel(), time=times, unit=units, cond=conds))
# Set up the err_style and ci arguments for teh loop below
if not hasattr(err_style, "__iter__"):
err_style = [err_style]
elif err_style is None:
err_style = []
if not hasattr(ci, "__iter__"):
ci = [ci]
# Set up the color palette
if color is None:
colors = color_palette()
elif isinstance(color, dict):
colors = [color[c] for c in data[condition].unique()]
else:
try:
colors = color_palette(color, n_cond)
except ValueError:
color = mpl.colors.colorConverter.to_rgb(color)
colors = [color] * n_cond
# Do a groupby with condition and plot each trace
for c, (cond, df_c) in enumerate(data.groupby(condition, sort=False)):
df_c = df_c.pivot(unit, time, value)
x = df_c.columns.values.astype(np.float)
# Bootstrap the data for confidence intervals
boot_data = moss.bootstrap(df_c.values,
n_boot=n_boot,
axis=0,
func=estimator)
cis = [moss.ci(boot_data, v, axis=0) for v in ci]
central_data = estimator(df_c.values, axis=0)
# Get the color for this condition
color = colors[c]
# Use subroutines to plot the uncertainty
for style in err_style:
# Allow for null style (only plot central tendency)
if style is None:
continue
# Grab the function from the global environment
try:
plot_func = globals()["_plot_%s" % style]
except KeyError:
raise ValueError("%s is not a valid err_style" % style)
# Possibly set up to plot each observation in a different color
if err_palette is not None and "unit" in style:
orig_color = color
color = color_palette(err_palette, len(df_c.values))
# Pass all parameters to the error plotter as keyword args
plot_kwargs = dict(ax=ax,
x=x,
data=df_c.values,
boot_data=boot_data,
central_data=central_data,
color=color,
err_kws=err_kws)
# Plot the error representation, possibly for multiple cis
for ci_i in cis:
plot_kwargs["ci"] = ci_i
plot_func(**plot_kwargs)
if err_palette is not None and "unit" in style:
color = orig_color
# Plot the central trace
marker = kwargs.pop("marker", "" if interpolate else "o")
linestyle = kwargs.pop("linestyle", "-" if interpolate else "")
label = kwargs.pop("label", cond if legend else "_nolegend_")
ax.plot(x,
central_data,
color=color,
label=label,
marker=marker,
linestyle=linestyle,
**kwargs)
# Pad the sides of the plot only when not interpolating
ax.set_xlim(x.min(), x.max())
x_diff = x[1] - x[0]
if not interpolate:
ax.set_xlim(x.min() - x_diff, x.max() + x_diff)
# Add the plot labels
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if legend:
ax.legend(loc=0, title=legend_name)
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 lmplot(x, y, data, color=None, row=None, col=None, col_wrap=None,
x_estimator=None, x_ci=95, x_bins=None, n_boot=5000, fit_reg=True,
order=1, ci=95, logistic=False, truncate=False,
x_partial=None, y_partial=None, x_jitter=None, y_jitter=None,
sharex=True, sharey=True, palette="husl", size=None,
scatter_kws=None, line_kws=None, palette_kws=None):
"""Plot a linear model with faceting, color binning, and other options.
Parameters
----------
x, y : strings
Column names in `data` DataFrame for x and y variables.
data : DataFrame
Dource of data for the model.
color : string, optional
DataFrame column name to group the model by color.
row, col : strings, optional
DataFrame column names to make separate plot facets.
col_wrap : int, optional
Wrap col variable at this width - cannot be used with row facet.
x_estimator : callable, optional
Interpret X values as factor labels and use this function
to plot the point estimate and bootstrapped CI.
x_ci : int optional
Size of confidence interval for x_estimator error bars.
x_bins : sequence of floats, optional
Bin the x variable with these values. Implies that x_estimator is
mean, unless otherwise provided.
n_boot : int, optional
Number of bootstrap iterations to perform.
fit_reg : bool, optional
If True fit a regression model by color/row/col and plot.
order : int, optional
Order of the regression polynomial to fit.
ci : int, optional
Confidence interval for the regression line.
logistic : bool, optional
Fit the regression line with logistic regression.
truncate : bool, optional
If True, only fit line from data min to data max.
{x, y}_partial : string or list of strings, optional
Regress these variables out of the factors before plotting.
{x, y}_jitter : float, optional
Parameters for uniformly distributed random noise added to positions.
sharex, sharey : bools, optional
Only relevant if faceting; passed to plt.subplots.
palette : seaborn color palette argument
If using separate plots by color, draw with this color palette.
size : float, optional
Size (plots are square) for each plot facet.
{scatter, line}_kws : dictionary
Keyword arguments to pass to the underlying plot functions.
palette_kws : dictionary
Keyword arguments for seaborn.color_palette.
"""
# TODO
# - legend when fit_line is False
# First sort out the general figure layout
if size is None:
size = mpl.rcParams["figure.figsize"][1]
if col is None and col_wrap is not None:
raise ValueError("Need column facet variable for `col_wrap`")
if row is not None and col_wrap is not None:
raise ValueError("Cannot facet rows when using `col_wrap`")
nrow = 1 if row is None else len(data[row].unique())
ncol = 1 if col is None else len(data[col].unique())
if col_wrap is not None:
ncol = col_wrap
nrow = int(np.ceil(len(data[col].unique()) / col_wrap))
f, axes = plt.subplots(nrow, ncol, sharex=sharex, sharey=sharey,
figsize=(size * ncol, size * nrow))
axes = np.atleast_2d(axes).reshape(nrow, ncol)
if nrow == 1 or col_wrap is not None:
row_masks = [np.repeat(True, len(data))]
else:
row_vals = np.sort(data[row].unique())
row_masks = [data[row] == val for val in row_vals]
if ncol == 1:
col_masks = [np.repeat(True, len(data))]
else:
col_vals = np.sort(data[col].unique())
col_masks = [data[col] == val for val in col_vals]
if x_bins is not None:
x_estimator = np.mean if x_estimator is None else x_estimator
x_bins = np.c_[x_bins]
if x_partial is not None:
if not isinstance(x_partial, list):
x_partial = [x_partial]
if y_partial is not None:
if not isinstance(y_partial, list):
y_partial = [y_partial]
if palette_kws is None:
palette_kws = {}
# Sort out the plot colors
color_factor = color
if color is None:
hue_masks = [np.repeat(True, len(data))]
colors = ["#222222"]
else:
hue_vals = np.sort(data[color].unique())
hue_masks = [data[color] == val for val in hue_vals]
colors = color_palette(palette, len(hue_masks), **palette_kws)
# Default keyword arguments for plot components
if scatter_kws is None:
scatter_kws = {}
if line_kws is None:
line_kws = {}
# First walk through the facets and plot the scatters
scatter_ms = scatter_kws.pop("ms", 4)
scatter_mew = mew = scatter_kws.pop("mew", 0)
scatter_alpha = mew = scatter_kws.pop("alpha", .77)
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
if col_wrap is not None:
f_row = col_j // ncol
f_col = col_j % ncol
else:
f_row, f_col = row_i, col_j
ax = axes[f_row, f_col]
if f_row + 1 == nrow:
ax.set_xlabel(x)
if f_col == 0:
ax.set_ylabel(y)
# Title the plot if we are faceting
title = ""
if row is not None:
title += "%s = %s" % (row, row_vals[row_i])
if row is not None and col is not None:
title += " | "
if col is not None:
title += "%s = %s" % (col, col_vals[col_j])
if size < 3:
title = title.replace(" | ", "\n")
ax.set_title(title)
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
if x_estimator is not None:
ms = scatter_kws.pop("ms", 7)
mew = scatter_kws.pop("mew", 0)
if x_bins is None:
x_vals = data_ijk[x].unique()
x_data = data_ijk[x]
else:
dist = distance.cdist(np.c_[data_ijk[x]], x_bins)
x_vals = x_bins.ravel()
x_data = x_bins[np.argmin(dist, axis=1)].ravel()
y_vals = data_ijk[y]
if y_partial is not None:
for var in y_partial:
conf = data_ijk[var]
conf -= conf.mean()
y_mean = y_vals.mean()
y_vals = moss.vector_reject(y_vals - y_mean, conf)
y_vals += y_mean
y_grouped = [np.array(y_vals[x_data == v])
for v in x_vals]
y_est = [x_estimator(y_i) for y_i in y_grouped]
y_boots = [moss.bootstrap(np.array(y_i),
func=x_estimator,
n_boot=n_boot)
for y_i in y_grouped]
ci_lims = [50 - x_ci / 2., 50 + x_ci / 2.]
y_ci = [moss.percentiles(y_i, ci_lims) for y_i in y_boots]
y_error = ci_to_errsize(np.transpose(y_ci), y_est)
ax.plot(x_vals, y_est, "o", mew=mew, ms=ms,
color=color, **scatter_kws)
ax.errorbar(x_vals, y_est, y_error,
fmt=None, ecolor=color)
else:
x_ = data_ijk[x]
y_ = data_ijk[y]
if x_partial is not None:
for var in x_partial:
conf = data_ijk[var]
conf -= conf.mean()
x_mean = x_.mean()
x_ = moss.vector_reject(x_ - x_mean, conf)
x_ += x_mean
if y_partial is not None:
for var in y_partial:
conf = data_ijk[var]
conf -= conf.mean()
y_mean = y_.mean()
y_ = moss.vector_reject(y_ - y_mean, conf)
y_ += y_mean
if x_jitter is not None:
x_ += np.random.uniform(-x_jitter, x_jitter, x_.shape)
if y_jitter is not None:
y_ += np.random.uniform(-y_jitter, y_jitter, y_.shape)
ax.plot(x_, y_, "o", color=color, alpha=scatter_alpha,
mew=scatter_mew, ms=scatter_ms, **scatter_kws)
for ax_i in np.ravel(axes):
ax_i.set_xmargin(.05)
ax_i.autoscale_view()
# Now walk through again and plot the regression estimate
# and a confidence interval for the regression line
if fit_reg:
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
if col_wrap is not None:
f_row = col_j // ncol
f_col = col_j % ncol
else:
f_row, f_col = row_i, col_j
ax = axes[f_row, f_col]
xlim = ax.get_xlim()
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
x_vals = np.array(data_ijk[x])
y_vals = np.array(data_ijk[y])
if not len(x_vals):
continue
# Sort out the limit of the fit
if truncate:
xx = np.linspace(x_vals.min(),
x_vals.max(), 100)
else:
xx = np.linspace(xlim[0], xlim[1], 100)
xx_ = sm.add_constant(xx, prepend=True)
# Inner function to bootstrap the regression
def _regress(x, y):
if logistic:
x_ = sm.add_constant(x, prepend=True)
fit = sm.GLM(y, x_,
family=sm.families.Binomial()).fit()
reg = fit.predict(xx_)
else:
fit = np.polyfit(x, y, order)
reg = np.polyval(fit, xx)
return reg
# Remove nuisance variables with vector rejection
if x_partial is not None:
for var in x_partial:
conf = data_ijk[var]
conf -= conf.mean()
x_mean = x_vals.mean()
x_vals = moss.vector_reject(x_vals - x_mean, conf)
x_vals += x_mean
if y_partial is not None:
for var in y_partial:
conf = data_ijk[var]
conf -= conf.mean()
y_mean = y_vals.mean()
y_vals = moss.vector_reject(y_vals - y_mean, conf)
y_vals += y_mean
# Regression line confidence interval
if ci is not None:
ci_lims = [50 - ci / 2., 50 + ci / 2.]
boots = moss.bootstrap(x_vals, y_vals,
func=_regress,
n_boot=n_boot)
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax.fill_between(xx, *ci_band, color=color, alpha=.15)
# Regression line
reg = _regress(x_vals, y_vals)
if color_factor is None:
label = ""
else:
label = hue_vals[hue_k]
ax.plot(xx, reg, color=color,
label=str(label), **line_kws)
ax.set_xlim(xlim)
# Plot the legend on the upper left facet and adjust the layout
if color_factor is not None and color_factor not in [row, col]:
axes[0, 0].legend(loc="best", title=color_factor)
plt.tight_layout()
def tsplot(x, data, err_style="ci_band", ci=68, interpolate=True,
estimator=np.mean, n_boot=10000, smooth=False,
err_palette=None, ax=None, err_kws=None, **kwargs):
"""Plot timeseries from a set of observations.
Parameters
----------
x : n_tp array
x values
data : n_obs x n_tp array
array of timeseries data where first axis is observations. other
objects (e.g. DataFrames) are converted to an array if possible
err_style : string or list of strings
names of ways to plot uncertainty across observations from set of
{ci_band, ci_bars, boot_traces, book_kde, obs_traces, obs_points}
ci : int or list of ints
confidence interaval size(s). if a list, it will stack the error
plots for each confidence interval
estimator : callable
function to determine centralt tendency and to pass to bootstrap
must take an ``axis`` argument
n_boot : int
number of bootstrap iterations
smooth : boolean
whether to perform a smooth bootstrap (resample from KDE)
ax : axis object, optional
plot in given axis; if None creates a new figure
err_kws : dict, optional
keyword argument dictionary passed through to matplotlib
function generating the error plot
kwargs : further keyword arguments for main call to plot()
Returns
-------
ax : matplotlib axis
axis with plot data
"""
if ax is None:
ax = plt.gca()
if err_kws is None:
err_kws = {}
# Bootstrap the data for confidence intervals
data = np.asarray(data)
boot_data = moss.bootstrap(data, n_boot=n_boot, smooth=smooth,
axis=0, func=estimator)
ci_list = hasattr(ci, "__iter__")
if not ci_list:
ci = [ci]
ci_vals = [(50 - w / 2, 50 + w / 2) for w in ci]
cis = [moss.percentiles(boot_data, v, axis=0) for v in ci_vals]
central_data = estimator(data, axis=0)
# Plot the timeseries line to get its color
line, = ax.plot(x, central_data, **kwargs)
color = line.get_color()
line.remove()
kwargs.pop("color", None)
# Use subroutines to plot the uncertainty
if not hasattr(err_style, "__iter__"):
err_style = [err_style]
for style in err_style:
# Grab the function from the global environment
try:
plot_func = globals()["_plot_%s" % style]
except KeyError:
raise ValueError("%s is not a valid err_style" % style)
# Possibly set up to plot each observation in a different color
if err_palette is not None and "obs" in style:
orig_color = color
color = color_palette(err_palette, len(data), desat=.99)
plot_kwargs = dict(ax=ax, x=x, data=data,
boot_data=boot_data,
central_data=central_data,
color=color, err_kws=err_kws)
for ci_i in cis:
plot_kwargs["ci"] = ci_i
plot_func(**plot_kwargs)
if err_palette is not None and "obs" in style:
color = orig_color
# Replot the central trace so it is prominent
marker = kwargs.pop("marker", "" if interpolate else "o")
linestyle = kwargs.pop("linestyle", "-" if interpolate else "")
ax.plot(x, central_data, color=color,
marker=marker, linestyle=linestyle, **kwargs)
return ax
X = column_stack((randn(1000, 4), ones(1000))) w = rand(5) # Large sample high noise; N = 1000, noise std = 5 y_large_n = dot(X, w) + randn(1000) * 5 # Small sample low noise; high signal to noise ratio y_lownoise = dot(X[:100], w) + randn(100) # Small sample, high noise y_noisy = dot(X[:100], w) + randn(100) * 5 # Bootstrap all three models n_boot = 1000 w_boot1 = moss.bootstrap(X, y_large_n, n_boot=n_boot, func=ols_fit) w_boot2 = moss.bootstrap(X[:100], y_lownoise, n_boot=n_boot, func=ols_fit) w_boot3 = moss.bootstrap(X[:100], y_noisy, n_boot=n_boot, func=ols_fit) # The median of these bootstrapped estimates are the weights for each model. # You could also use the mean if you prefer. w_model1 = median(w_boot1, axis=0) w_model2 = median(w_boot2, axis=0) w_model3 = median(w_boot3, axis=0) # Compute the 95% confidence intervals on parameter estimates pcts = [2.5, 97.5] ci1 = moss.percentiles(w_boot1, pcts, axis=0) ci2 = moss.percentiles(w_boot2, pcts, axis=0) ci3 = moss.percentiles(w_boot3, pcts, axis=0)
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 lmplot(x, y, data, color=None, row=None, col=None,
x_estimator=None, x_ci=95,
fit_line=True, ci=95, truncate=False,
sharex=True, sharey=True, palette="hls", size=None,
scatter_kws=None, line_kws=None, palette_kws=None):
"""Plot a linear model from a DataFrame.
Parameters
----------
x, y : strings
column names in `data` DataFrame for x and y variables
data : DataFrame
source of data for the model
color : string, optional
DataFrame column name to group the model by color
row, col : strings, optional
DataFrame column names to make separate plot facets
x_estimator : callable, optional
Interpret X values as factor labels and use this function
to plot the point estimate and bootstrapped CI
x_ci : int optional
size of confidence interval for x_estimator error bars
fit_line : bool, optional
if True fit a regression line by color/row/col and plot
ci : int, optional
confidence interval for the regression line
truncate : bool, optional
if True, only fit line from data min to data max
sharex, sharey : bools, optional
only relevant if faceting; passed to plt.subplots
palette : seaborn color palette argument
if using separate plots by color, draw with this color palette
size : float, optional
size (plots are square) for each plot facet
{scatter, line}_kws : dictionary
keyword arguments to pass to the underlying plot functions
palette_kws : dictionary
keyword arguments for seaborn.color_palette
"""
# TODO
# - position_{dodge, jitter}
# - legend when fit_line is False
# - truncate fit
# - wrap title when wide
# - wrap columns
# First sort out the general figure layout
if size is None:
size = mpl.rcParams["figure.figsize"][1]
nrow = 1 if row is None else len(data[row].unique())
ncol = 1 if col is None else len(data[col].unique())
f, axes = plt.subplots(nrow, ncol, sharex=sharex, sharey=sharey,
figsize=(size * ncol, size * nrow))
axes = np.atleast_2d(axes).reshape(nrow, ncol)
if nrow == 1:
row_masks = [np.repeat(True, len(data))]
else:
row_vals = np.sort(data[row].unique())
row_masks = [data[row] == val for val in row_vals]
if ncol == 1:
col_masks = [np.repeat(True, len(data))]
else:
col_vals = np.sort(data[col].unique())
col_masks = [data[col] == val for val in col_vals]
if palette_kws is None:
palette_kws = {}
# Sort out the plot colors
color_factor = color
if color is None:
hue_masks = [np.repeat(True, len(data))]
colors = ["#222222"]
else:
hue_vals = np.sort(data[color].unique())
hue_masks = [data[color] == val for val in hue_vals]
colors = color_palette(palette, len(hue_masks), **palette_kws)
# Default keyword arguments for plot components
if scatter_kws is None:
scatter_kws = {}
if line_kws is None:
line_kws = {}
# First walk through the facets and plot the scatters
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
ax = axes[row_i, col_j]
if not sharex or (row_i + 1 == len(row_masks)):
ax.set_xlabel(x)
if not sharey or col_j == 0:
ax.set_ylabel(y)
# Title the plot if we are faceting
title = ""
if row is not None:
title += "%s = %s" % (row, row_vals[row_i])
if row is not None and col is not None:
title += " | "
if col is not None:
title += "%s = %s" % (col, col_vals[col_j])
ax.set_title(title)
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
if x_estimator is not None:
ms = scatter_kws.pop("ms", 7)
mew = scatter_kws.pop("mew", 0)
x_vals = data_ijk[x].unique()
y_grouped = [np.array(data_ijk[y][data_ijk[x] == v])
for v in x_vals]
y_est = [x_estimator(y_i) for y_i in y_grouped]
y_boots = [moss.bootstrap(np.array(y_i), func=x_estimator)
for y_i in y_grouped]
ci_lims = [50 - x_ci / 2., 50 + x_ci / 2.]
y_ci = [moss.percentiles(y_i, ci_lims) for y_i in y_boots]
y_error = ci_to_errsize(np.transpose(y_ci), y_est)
ax.plot(x_vals, y_est, "o", mew=mew, ms=ms,
color=color, **scatter_kws)
ax.errorbar(x_vals, y_est, y_error,
fmt=None, ecolor=color)
else:
ms = scatter_kws.pop("ms", 4)
mew = scatter_kws.pop("mew", 0)
ax.plot(data_ijk[x], data_ijk[y], "o",
color=color, mew=mew, ms=ms, **scatter_kws)
for ax_i in np.ravel(axes):
ax_i.set_xmargin(.05)
ax_i.autoscale_view()
# Now walk through again and plot the regression estimate
# and a confidence interval for the regression line
if fit_line:
for row_i, row_mask in enumerate(row_masks):
for col_j, col_mask in enumerate(col_masks):
ax = axes[row_i, col_j]
xlim = ax.get_xlim()
for hue_k, hue_mask in enumerate(hue_masks):
color = colors[hue_k]
data_ijk = data[row_mask & col_mask & hue_mask]
x_vals = np.array(data_ijk[x])
y_vals = np.array(data_ijk[y])
# Sort out the limit of the fit
if truncate:
xx = np.linspace(x_vals.min(),
x_vals.max(), 100)
else:
xx = np.linspace(xlim[0], xlim[1], 100)
# Inner function to bootstrap the regression
def _bootstrap_reg(x, y):
fit = np.polyfit(x, y, 1)
return np.polyval(fit, xx)
# Regression line confidence interval
if ci is not None:
ci_lims = [50 - ci / 2., 50 + ci / 2.]
boots = moss.bootstrap(x_vals, y_vals,
func=_bootstrap_reg)
ci_band = moss.percentiles(boots, ci_lims, axis=0)
ax.fill_between(xx, *ci_band, color=color, alpha=.15)
fit = np.polyfit(x_vals, y_vals, 1)
reg = np.polyval(fit, xx)
if color_factor is None:
label = ""
else:
label = hue_vals[hue_k]
ax.plot(xx, reg, color=color,
label=str(label), **line_kws)
ax.set_xlim(xlim)
# Plot the legend on the upper left facet and adjust the layout
if color_factor is not None:
axes[0, 0].legend(loc="best", title=color_factor)
plt.tight_layout()
