def modsel_dotplot(mods, onlyIC=False, only_scz=False):
AIC = [mods[f].aic for f in mods.keys()]
BIC = [mods[f].bic_llf for f in mods.keys()]
def get_pvalues(m, param='Dx[T.SCZ]'):
try:
return(m.pvalues[param])
except KeyError:
return(np.nan)
pvalue_SCZ = [get_pvalues(mods[f], param='Dx[T.SCZ]') for f in mods.keys()]
pvalue_ASD = [get_pvalues(mods[f], param='Dx[T.ASD]') for f in mods.keys()]
naxes_max = 3 if only_scz else 4
naxes = 2 if onlyIC else naxes_max
fig, ax = plt.subplots(1, naxes, figsize=(naxes * 3, 4))
g = dotplots.dot_plot(AIC, lines=list(mods.keys()), ax=ax[0])
g = dotplots.dot_plot(BIC, lines=list(mods.keys()), ax=ax[1], show_names='right')
ax[0].set_title('Model fit: AIC')
ax[1].set_title('Model fit: BIC')
if not onlyIC:
g = dotplots.dot_plot(pvalue_SCZ, lines=list(mods.keys()), ax=ax[2], show_names='right')
ax[2].set_xscale('log')
ax[2].set_title('Dx[T.SCZ]: p-value')
if not only_scz:
g = dotplots.dot_plot(pvalue_ASD, lines=list(mods.keys()), ax=ax[3], show_names='right')
ax[3].set_xscale('log')
ax[3].set_title('Dx[T.ASD]: p-value')
return((fig, ax))
def my_dotplot(feature, mods):
tvalues = pd.Series({endog: mods[endog].tvalues[feature] for endog in mods.keys()})
pvalues = pd.Series({endog: mods[endog].pvalues[feature] for endog in mods.keys()})
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
fig.suptitle(feature + ' significance')
ax[0].plot([0, 0], [0, 12])
g = dotplots.dot_plot(tvalues, lines=tvalues.index, ax=ax[0])
ax[0].set_xlim(np.array([-1, 1]) * 1.1 * tvalues.abs().max())
ax[0].set_xlabel('t-value')
g = dotplots.dot_plot(pvalues, lines=pvalues.index, ax=ax[1], show_names='right')
ax[1].set_xscale('log')
ax[1].set_xlabel('p-value')
return((fig, ax))
def plot_forest(self, ax=None, **kwds):
"""Forest plot with means and confidence intervals
Parameters
----------
ax : None or matplotlib axis instance
If ax is provided, then the plot will be added to it.
kwds : optional keyword arguments
Keywords are forwarded to the dot_plot function that creates the
plot.
Returns
-------
fig : Matplotlib figure instance
See Also
--------
dot_plot
"""
from statsmodels.graphics.dotplots import dot_plot
res_df = self.summary_frame()
hw = np.abs(res_df[["ci_low", "ci_upp"]] - res_df[["eff"]].values)
fig = dot_plot(points=res_df["eff"],
intervals=hw,
lines=res_df.index,
line_order=res_df.index,
**kwds)
return fig
def plot_forest(self, ax=None, **kwds):
from statsmodels.graphics.dotplots import dot_plot
res_df = self.summary_frame()
hw = np.abs(res_df[["ci_low", "ci_upp"]] - res_df[["eff"]].values)
fig = dot_plot(points=res_df["eff"], intervals=hw,
lines=res_df.index, line_order=res_df.index, **kwds)
return fig
def plot_forest(self,
alpha=0.05,
use_t=None,
use_exp=False,
ax=None,
**kwds):
"""Forest plot with means and confidence intervals
Parameters
----------
ax : None or matplotlib axis instance
If ax is provided, then the plot will be added to it.
alpha : float in (0, 1)
Significance level for confidence interval. Nominal coverage is
``1 - alpha``.
use_t : None or bool
If use_t is None, then the attribute `use_t` determines whether
normal or t-distribution is used for confidence intervals.
Specifying use_t overrides the attribute.
If use_t is false, then confidence intervals are based on the
normal distribution. If it is true, then the t-distribution is
used.
use_exp : bool
If `use_exp` is True, then the effect size and confidence limits
will be exponentiated. This transform log-odds-ration into
odds-ratio, and similarly for risk-ratio.
ax : AxesSubplot, optional
If given, this axes is used to plot in instead of a new figure
being created.
kwds : optional keyword arguments
Keywords are forwarded to the dot_plot function that creates the
plot.
Returns
-------
fig : Matplotlib figure instance
See Also
--------
dot_plot
"""
from statsmodels.graphics.dotplots import dot_plot
res_df = self.summary_frame(alpha=alpha, use_t=use_t)
if use_exp:
res_df = np.exp(res_df[["eff", "ci_low", "ci_upp"]])
hw = np.abs(res_df[["ci_low", "ci_upp"]] - res_df[["eff"]].values)
fig = dot_plot(points=res_df["eff"],
intervals=hw,
lines=res_df.index,
line_order=res_df.index,
**kwds)
return fig
def test_all(close_figures):
if pdf_output:
from matplotlib.backends.backend_pdf import PdfPages
pdf = PdfPages("test_dotplot.pdf")
else:
pdf = None
# Basic dotplot with points only
plt.clf()
points = range(20)
ax = plt.axes()
fig = dot_plot(points, ax=ax)
ax.set_title("Basic horizontal dotplot")
close_or_save(pdf, fig)
# Basic vertical dotplot
plt.clf()
points = range(20)
ax = plt.axes()
fig = dot_plot(points, ax=ax, horizontal=False)
ax.set_title("Basic vertical dotplot")
close_or_save(pdf, fig)
# Tall and skinny
plt.figure(figsize=(4,12))
ax = plt.axes()
vals = np.arange(40)
fig = dot_plot(points, ax=ax)
ax.set_title("Tall and skinny dotplot")
ax.set_xlabel("x axis label")
close_or_save(pdf, fig)
# Short and wide
plt.figure(figsize=(12,4))
ax = plt.axes()
vals = np.arange(40)
fig = dot_plot(points, ax=ax, horizontal=False)
ax.set_title("Short and wide dotplot")
ax.set_ylabel("y axis label")
close_or_save(pdf, fig)
# Tall and skinny striped dotplot
plt.figure(figsize=(4,12))
ax = plt.axes()
points = np.arange(40)
fig = dot_plot(points, ax=ax, striped=True)
ax.set_title("Tall and skinny striped dotplot")
ax.set_xlim(-10, 50)
close_or_save(pdf, fig)
# Short and wide striped
plt.figure(figsize=(12,4))
ax = plt.axes()
points = np.arange(40)
fig = dot_plot(points, ax=ax, striped=True, horizontal=False)
ax.set_title("Short and wide striped dotplot")
ax.set_ylim(-10, 50)
close_or_save(pdf, fig)
# Basic dotplot with few points
plt.figure()
ax = plt.axes()
points = np.arange(4)
fig = dot_plot(points, ax=ax)
ax.set_title("Basic horizontal dotplot with few lines")
close_or_save(pdf, fig)
# Basic dotplot with few points
plt.figure()
ax = plt.axes()
points = np.arange(4)
fig = dot_plot(points, ax=ax, horizontal=False)
ax.set_title("Basic vertical dotplot with few lines")
close_or_save(pdf, fig)
# Manually set the x axis limits
plt.figure()
ax = plt.axes()
points = np.arange(20)
fig = dot_plot(points, ax=ax)
ax.set_xlim(-10, 30)
ax.set_title("Dotplot with adjusted horizontal range")
close_or_save(pdf, fig)
# Left row labels
plt.clf()
ax = plt.axes()
lines = ["ABCDEFGH"[np.random.randint(0, 8)] for k in range(20)]
points = np.random.normal(size=20)
fig = dot_plot(points, lines=lines, ax=ax)
ax.set_title("Dotplot with user-supplied labels in the left margin")
close_or_save(pdf, fig)
# Left and right row labels
plt.clf()
ax = plt.axes()
points = np.random.normal(size=20)
lines = ["ABCDEFGH"[np.random.randint(0, 8)] + "::" + str(k+1)
for k in range(20)]
fig = dot_plot(points, lines=lines, ax=ax, split_names="::")
ax.set_title("Dotplot with user-supplied labels in both margins")
close_or_save(pdf, fig)
# Both sides row labels
plt.clf()
ax = plt.axes([0.1, 0.1, 0.88, 0.8])
points = np.random.normal(size=20)
lines = ["ABCDEFGH"[np.random.randint(0, 8)] + "::" + str(k+1)
for k in range(20)]
fig = dot_plot(points, lines=lines, ax=ax, split_names="::",
horizontal=False)
txt = ax.set_title("Vertical dotplot with user-supplied labels in both margins")
txt.set_position((0.5, 1.06))
close_or_save(pdf, fig)
# Custom colors and symbols
plt.clf()
ax = plt.axes([0.1, 0.07, 0.78, 0.85])
points = np.random.normal(size=20)
lines = np.kron(range(5), np.ones(4)).astype(np.int32)
styles = np.kron(np.ones(5), range(4)).astype(np.int32)
marker_props = {k: {"color": "rgbc"[k], "marker": "osvp"[k],
"ms": 7, "alpha": 0.6} for k in range(4)}
fig = dot_plot(points, lines=lines, styles=styles, ax=ax,
marker_props=marker_props)
ax.set_title("Dotplot with custom colors and symbols")
close_or_save(pdf, fig)
# Basic dotplot with symmetric intervals
plt.clf()
ax = plt.axes()
points = range(20)
fig = dot_plot(points, intervals=np.ones(20), ax=ax)
ax.set_title("Dotplot with symmetric intervals")
close_or_save(pdf, fig)
# Basic dotplot with symmetric intervals, pandas inputs.
plt.clf()
ax = plt.axes()
points = pd.Series(range(20))
intervals = pd.Series(np.ones(20))
fig = dot_plot(points, intervals=intervals, ax=ax)
ax.set_title("Dotplot with symmetric intervals (Pandas inputs)")
close_or_save(pdf, fig)
# Basic dotplot with nonsymmetric intervals
plt.clf()
ax = plt.axes()
points = np.arange(20)
intervals = [(1, 3) for i in range(20)]
fig = dot_plot(points, intervals=intervals, ax=ax)
ax.set_title("Dotplot with nonsymmetric intervals")
close_or_save(pdf, fig)
# Vertical dotplot with nonsymmetric intervals
plt.clf()
ax = plt.axes()
points = np.arange(20)
intervals = [(1, 3) for i in range(20)]
fig = dot_plot(points, intervals=intervals, ax=ax, horizontal=False)
ax.set_title("Vertical dotplot with nonsymmetric intervals")
close_or_save(pdf, fig)
# Dotplot with nonsymmetric intervals, adjust line properties
plt.clf()
ax = plt.axes()
points = np.arange(20)
intervals = [(1, 3) for x in range(20)]
line_props = {0: {"color": "lightgrey",
"solid_capstyle": "round"}}
fig = dot_plot(points, intervals=intervals, line_props=line_props, ax=ax)
ax.set_title("Dotplot with custom line properties")
close_or_save(pdf, fig)
# Dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5*np.random.normal(size=40)
lines = np.kron(range(20), (1,1))
intervals = [(1,3) for k in range(40)]
styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
ax=ax, stacked=True)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with two points per line")
close_or_save(pdf, fig)
# Dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
fig = dot_plot(points, intervals=intervals, lines=lines,
styles=styles, ax=ax, stacked=True,
styles_order=["Dog", "Cat"])
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with two points per line (reverse order)")
close_or_save(pdf, fig)
# Vertical dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5*np.random.normal(size=40)
lines = np.kron(range(20), (1,1))
intervals = [(1,3) for k in range(40)]
styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
ax=ax, stacked=True, horizontal=False)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Vertical dotplot with two points per line")
close_or_save(pdf, fig)
# Vertical dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
styles_order = ["Dog", "Cat"]
fig = dot_plot(points, intervals=intervals, lines=lines,
styles=styles, ax=ax, stacked=True,
horizontal=False, styles_order=styles_order)
handles, labels = ax.get_legend_handles_labels()
lh = dict(zip(labels, handles))
handles = [lh[l] for l in styles_order]
leg = plt.figlegend(handles, styles_order, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Vertical dotplot with two points per line (reverse order)")
close_or_save(pdf, fig)
# Vertical dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5*np.random.normal(size=40)
lines = np.kron(range(20), (1,1))
intervals = [(1,3) for k in range(40)]
styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
ax=ax, stacked=True, striped=True, horizontal=False)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
plt.ylim(-20, 20)
ax.set_title("Vertical dotplot with two points per line")
close_or_save(pdf, fig)
# Dotplot with color-matched points and intervals
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5*np.random.normal(size=40)
lines = np.kron(range(20), (1,1))
intervals = [(1,3) for k in range(40)]
styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
marker_props = {"Cat": {"color": "orange"},
"Dog": {"color": "purple"}}
line_props = {"Cat": {"color": "orange"},
"Dog": {"color": "purple"}}
fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
ax=ax, stacked=True, marker_props=marker_props,
line_props=line_props)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with color-matched points and intervals")
close_or_save(pdf, fig)
# Dotplot with color-matched points and intervals
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5*np.random.normal(size=40)
lines = np.kron(range(20), (1,1))
intervals = [(1,3) for k in range(40)]
styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
marker_props = {"Cat": {"color": "orange"},
"Dog": {"color": "purple"}}
line_props = {"Cat": {"color": "orange"},
"Dog": {"color": "purple"}}
fig = dot_plot(points, intervals=intervals, lines=lines, styles=styles,
ax=ax, stacked=True, marker_props=marker_props,
line_props=line_props, horizontal=False)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with color-matched points and intervals")
close_or_save(pdf, fig)
# Dotplot with sections
plt.clf()
ax = plt.axes()
points = range(30)
lines = np.kron(range(15), (1,1)).astype(np.int32)
styles = np.kron(np.ones(15), (0,1)).astype(np.int32)
sections = np.kron((0,1,2), np.ones(10)).astype(np.int32)
sections = [["Axx", "Byy", "Czz"][k] for k in sections]
fig = dot_plot(points, lines=lines, styles=styles, sections=sections, ax=ax)
ax.set_title("Dotplot with sections")
close_or_save(pdf, fig)
# Vertical dotplot with sections
plt.clf()
ax = plt.axes([0.1,0.1,0.9,0.75])
points = range(30)
lines = np.kron(range(15), (1,1)).astype(np.int32)
styles = np.kron(np.ones(15), (0,1)).astype(np.int32)
sections = np.kron((0,1,2), np.ones(10)).astype(np.int32)
sections = [["Axx", "Byy", "Czz"][k] for k in sections]
fig = dot_plot(points, lines=lines, styles=styles,
sections=sections, ax=ax, horizontal=False)
txt = ax.set_title("Vertical dotplot with sections")
txt.set_position((0.5, 1.08))
close_or_save(pdf, fig)
# Reorder sections
plt.clf()
ax = plt.axes()
points = range(30)
lines = np.kron(range(15), (1,1)).astype(np.int32)
styles = np.kron(np.ones(15), (0,1)).astype(np.int32)
sections = np.kron((0,1,2), np.ones(10)).astype(np.int32)
sections = [["Axx", "Byy", "Czz"][k] for k in sections]
fig = dot_plot(points, lines=lines, styles=styles, sections=sections, ax=ax,
section_order=["Byy", "Axx", "Czz"])
ax.set_title("Dotplot with sections in specified order")
close_or_save(pdf, fig)
# Reorder the lines.
plt.figure()
ax = plt.axes()
points = np.arange(4)
lines = ["A", "B", "C", "D"]
line_order = ["B", "C", "A", "D"]
fig = dot_plot(points, lines=lines, line_order=line_order, ax=ax)
ax.set_title("Dotplot with reordered lines")
close_or_save(pdf, fig)
# Format labels
plt.clf()
points = range(20)
lines = ["%d::%d" % (i, 100+i) for i in range(20)]
fmt_left = lambda x : "lft_" + x
fmt_right = lambda x : "rgt_" + x
ax = plt.axes()
fig = dot_plot(points, lines=lines, ax=ax, split_names="::",
fmt_left_name=fmt_left, fmt_right_name=fmt_right)
ax.set_title("Horizontal dotplot with name formatting")
close_or_save(pdf, fig)
# Right names only
plt.clf()
points = range(20)
lines = ["%d::%d" % (i, 100+i) for i in range(20)]
ax = plt.axes()
fig = dot_plot(points, lines=lines, ax=ax, split_names="::",
show_names="right")
ax.set_title("Show right names only")
close_or_save(pdf, fig)
# Dotplot with different numbers of points per line
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5*np.random.normal(size=40)
lines = []
ii = 0
while len(lines) < 40:
for k in range(np.random.randint(1, 4)):
lines.append(ii)
ii += 1
styles = np.kron(np.ones(20), (0,1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points, lines=lines, styles=styles,
ax=ax, stacked=True)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles, labels, "center right", numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with different numbers of points per line")
close_or_save(pdf, fig)
if pdf_output:
pdf.close()
def test_all():
if pdf_output:
from matplotlib.backends.backend_pdf import PdfPages
pdf = PdfPages("test_dotplot.pdf")
else:
pdf = None
# Basic dotplot with points only
plt.clf()
points = range(20)
ax = plt.axes()
fig = dot_plot(points, ax=ax)
ax.set_title("Basic horizontal dotplot")
close_or_save(pdf, fig)
# Basic vertical dotplot
plt.clf()
points = range(20)
ax = plt.axes()
fig = dot_plot(points, ax=ax, horizontal=False)
ax.set_title("Basic vertical dotplot")
close_or_save(pdf, fig)
# Tall and skinny
plt.figure(figsize=(4, 12))
ax = plt.axes()
vals = np.arange(40)
fig = dot_plot(points, ax=ax)
ax.set_title("Tall and skinny dotplot")
ax.set_xlabel("x axis label")
close_or_save(pdf, fig)
# Short and wide
plt.figure(figsize=(12, 4))
ax = plt.axes()
vals = np.arange(40)
fig = dot_plot(points, ax=ax, horizontal=False)
ax.set_title("Short and wide dotplot")
ax.set_ylabel("y axis label")
close_or_save(pdf, fig)
# Tall and skinny striped dotplot
plt.figure(figsize=(4, 12))
ax = plt.axes()
points = np.arange(40)
fig = dot_plot(points, ax=ax, striped=True)
ax.set_title("Tall and skinny striped dotplot")
ax.set_xlim(-10, 50)
close_or_save(pdf, fig)
# Short and wide striped
plt.figure(figsize=(12, 4))
ax = plt.axes()
points = np.arange(40)
fig = dot_plot(points, ax=ax, striped=True, horizontal=False)
ax.set_title("Short and wide striped dotplot")
ax.set_ylim(-10, 50)
close_or_save(pdf, fig)
# Basic dotplot with few points
plt.figure()
ax = plt.axes()
points = np.arange(4)
fig = dot_plot(points, ax=ax)
ax.set_title("Basic horizontal dotplot with few lines")
close_or_save(pdf, fig)
# Basic dotplot with few points
plt.figure()
ax = plt.axes()
points = np.arange(4)
fig = dot_plot(points, ax=ax, horizontal=False)
ax.set_title("Basic vertical dotplot with few lines")
close_or_save(pdf, fig)
# Manually set the x axis limits
plt.figure()
ax = plt.axes()
points = np.arange(20)
fig = dot_plot(points, ax=ax)
ax.set_xlim(-10, 30)
ax.set_title("Dotplot with adjusted horizontal range")
close_or_save(pdf, fig)
# Left row labels
plt.clf()
ax = plt.axes()
lines = ["ABCDEFGH"[np.random.randint(0, 8)] for k in range(20)]
points = np.random.normal(size=20)
fig = dot_plot(points, lines=lines, ax=ax)
ax.set_title("Dotplot with user-supplied labels in the left margin")
close_or_save(pdf, fig)
# Left and right row labels
plt.clf()
ax = plt.axes()
points = np.random.normal(size=20)
lines = [
"ABCDEFGH"[np.random.randint(0, 8)] + "::" + str(k + 1)
for k in range(20)
]
fig = dot_plot(points, lines=lines, ax=ax, split_names="::")
ax.set_title("Dotplot with user-supplied labels in both margins")
close_or_save(pdf, fig)
# Both sides row labels
plt.clf()
ax = plt.axes([0.1, 0.1, 0.88, 0.8])
points = np.random.normal(size=20)
lines = [
"ABCDEFGH"[np.random.randint(0, 8)] + "::" + str(k + 1)
for k in range(20)
]
fig = dot_plot(points,
lines=lines,
ax=ax,
split_names="::",
horizontal=False)
txt = ax.set_title(
"Vertical dotplot with user-supplied labels in both margins")
txt.set_position((0.5, 1.06))
close_or_save(pdf, fig)
# Custom colors and symbols
plt.clf()
ax = plt.axes([0.1, 0.07, 0.78, 0.85])
points = np.random.normal(size=20)
lines = np.kron(range(5), np.ones(4)).astype(np.int32)
styles = np.kron(np.ones(5), range(4)).astype(np.int32)
#marker_props = {k: {"color": "rgbc"[k], "marker": "osvp"[k],
# "ms": 7, "alpha": 0.6} for k in range(4)}
# python 2.6 compat, can be removed later
marker_props = dict((k, {
"color": "rgbc"[k],
"marker": "osvp"[k],
"ms": 7,
"alpha": 0.6
}) for k in range(4))
fig = dot_plot(points,
lines=lines,
styles=styles,
ax=ax,
marker_props=marker_props)
ax.set_title("Dotplot with custom colors and symbols")
close_or_save(pdf, fig)
# Basic dotplot with symmetric intervals
plt.clf()
ax = plt.axes()
points = range(20)
fig = dot_plot(points, intervals=np.ones(20), ax=ax)
ax.set_title("Dotplot with symmetric intervals")
close_or_save(pdf, fig)
# Basic dotplot with symmetric intervals, pandas inputs.
plt.clf()
ax = plt.axes()
points = pd.Series(range(20))
intervals = pd.Series(np.ones(20))
fig = dot_plot(points, intervals=intervals, ax=ax)
ax.set_title("Dotplot with symmetric intervals (Pandas inputs)")
close_or_save(pdf, fig)
# Basic dotplot with nonsymmetric intervals
plt.clf()
ax = plt.axes()
points = np.arange(20)
intervals = [(1, 3) for i in range(20)]
fig = dot_plot(points, intervals=intervals, ax=ax)
ax.set_title("Dotplot with nonsymmetric intervals")
close_or_save(pdf, fig)
# Vertical dotplot with nonsymmetric intervals
plt.clf()
ax = plt.axes()
points = np.arange(20)
intervals = [(1, 3) for i in range(20)]
fig = dot_plot(points, intervals=intervals, ax=ax, horizontal=False)
ax.set_title("Vertical dotplot with nonsymmetric intervals")
close_or_save(pdf, fig)
# Dotplot with nonsymmetric intervals, adjust line properties
plt.clf()
ax = plt.axes()
points = np.arange(20)
intervals = [(1, 3) for x in range(20)]
line_props = {0: {"color": "lightgrey", "solid_capstyle": "round"}}
fig = dot_plot(points, intervals=intervals, line_props=line_props, ax=ax)
ax.set_title("Dotplot with custom line properties")
close_or_save(pdf, fig)
# Dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5 * np.random.normal(size=40)
lines = np.kron(range(20), (1, 1))
intervals = [(1, 3) for k in range(40)]
styles = np.kron(np.ones(20), (0, 1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with two points per line")
close_or_save(pdf, fig)
# Dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True,
styles_order=["Dog", "Cat"])
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with two points per line (reverse order)")
close_or_save(pdf, fig)
# Vertical dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5 * np.random.normal(size=40)
lines = np.kron(range(20), (1, 1))
intervals = [(1, 3) for k in range(40)]
styles = np.kron(np.ones(20), (0, 1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True,
horizontal=False)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Vertical dotplot with two points per line")
close_or_save(pdf, fig)
# Vertical dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
styles_order = ["Dog", "Cat"]
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True,
horizontal=False,
styles_order=styles_order)
handles, labels = ax.get_legend_handles_labels()
lh = dict(zip(labels, handles))
handles = [lh[l] for l in styles_order]
leg = plt.figlegend(handles,
styles_order,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Vertical dotplot with two points per line (reverse order)")
close_or_save(pdf, fig)
# Vertical dotplot with two points per line and a legend
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5 * np.random.normal(size=40)
lines = np.kron(range(20), (1, 1))
intervals = [(1, 3) for k in range(40)]
styles = np.kron(np.ones(20), (0, 1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True,
striped=True,
horizontal=False)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
plt.ylim(-20, 20)
ax.set_title("Vertical dotplot with two points per line")
close_or_save(pdf, fig)
# Dotplot with color-matched points and intervals
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5 * np.random.normal(size=40)
lines = np.kron(range(20), (1, 1))
intervals = [(1, 3) for k in range(40)]
styles = np.kron(np.ones(20), (0, 1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
marker_props = {"Cat": {"color": "orange"}, "Dog": {"color": "purple"}}
line_props = {"Cat": {"color": "orange"}, "Dog": {"color": "purple"}}
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True,
marker_props=marker_props,
line_props=line_props)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with color-matched points and intervals")
close_or_save(pdf, fig)
# Dotplot with color-matched points and intervals
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5 * np.random.normal(size=40)
lines = np.kron(range(20), (1, 1))
intervals = [(1, 3) for k in range(40)]
styles = np.kron(np.ones(20), (0, 1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
marker_props = {"Cat": {"color": "orange"}, "Dog": {"color": "purple"}}
line_props = {"Cat": {"color": "orange"}, "Dog": {"color": "purple"}}
fig = dot_plot(points,
intervals=intervals,
lines=lines,
styles=styles,
ax=ax,
stacked=True,
marker_props=marker_props,
line_props=line_props,
horizontal=False)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with color-matched points and intervals")
close_or_save(pdf, fig)
# Dotplot with sections
plt.clf()
ax = plt.axes()
points = range(30)
lines = np.kron(range(15), (1, 1)).astype(np.int32)
styles = np.kron(np.ones(15), (0, 1)).astype(np.int32)
sections = np.kron((0, 1, 2), np.ones(10)).astype(np.int32)
sections = [["Axx", "Byy", "Czz"][k] for k in sections]
fig = dot_plot(points,
lines=lines,
styles=styles,
sections=sections,
ax=ax)
ax.set_title("Dotplot with sections")
close_or_save(pdf, fig)
# Vertical dotplot with sections
plt.clf()
ax = plt.axes([0.1, 0.1, 0.9, 0.75])
points = range(30)
lines = np.kron(range(15), (1, 1)).astype(np.int32)
styles = np.kron(np.ones(15), (0, 1)).astype(np.int32)
sections = np.kron((0, 1, 2), np.ones(10)).astype(np.int32)
sections = [["Axx", "Byy", "Czz"][k] for k in sections]
fig = dot_plot(points,
lines=lines,
styles=styles,
sections=sections,
ax=ax,
horizontal=False)
txt = ax.set_title("Vertical dotplot with sections")
txt.set_position((0.5, 1.08))
close_or_save(pdf, fig)
# Reorder sections
plt.clf()
ax = plt.axes()
points = range(30)
lines = np.kron(range(15), (1, 1)).astype(np.int32)
styles = np.kron(np.ones(15), (0, 1)).astype(np.int32)
sections = np.kron((0, 1, 2), np.ones(10)).astype(np.int32)
sections = [["Axx", "Byy", "Czz"][k] for k in sections]
fig = dot_plot(points,
lines=lines,
styles=styles,
sections=sections,
ax=ax,
section_order=["Byy", "Axx", "Czz"])
ax.set_title("Dotplot with sections in specified order")
close_or_save(pdf, fig)
# Reorder the lines.
plt.figure()
ax = plt.axes()
points = np.arange(4)
lines = ["A", "B", "C", "D"]
line_order = ["B", "C", "A", "D"]
fig = dot_plot(points, lines=lines, line_order=line_order, ax=ax)
ax.set_title("Dotplot with reordered lines")
close_or_save(pdf, fig)
# Dotplot with different numbers of points per line
plt.clf()
ax = plt.axes([0.1, 0.1, 0.75, 0.8])
points = 5 * np.random.normal(size=40)
lines = []
ii = 0
while len(lines) < 40:
for k in range(np.random.randint(1, 4)):
lines.append(ii)
ii += 1
styles = np.kron(np.ones(20), (0, 1)).astype(np.int32)
styles = [["Cat", "Dog"][i] for i in styles]
fig = dot_plot(points, lines=lines, styles=styles, ax=ax, stacked=True)
handles, labels = ax.get_legend_handles_labels()
leg = plt.figlegend(handles,
labels,
"center right",
numpoints=1,
handletextpad=0.0001)
leg.draw_frame(False)
ax.set_title("Dotplot with different numbers of points per line")
close_or_save(pdf, fig)
if pdf_output:
pdf.close()