def draw(self):
""" Plot a vertical line for each token in the current data table.
The line is drawn in a subplot matching the factor level
combination in that row. The horizontal position corresponds to the
token id so that tokens that occur in the same part of the corpus
will also have lines that are placed close to each other. """
def plot_facet(data, color):
lineplot(
x=data["coquery_invisible_corpus_id"],
y=data[self._groupby[-1]],
order=self._levels[-1],
palette=self.options["color_palette_values"],
data=data)
#sns.despine(self.g.fig,
#left=False, right=False, top=False, bottom=False)
self.map_data(plot_facet)
self.g.set_axis_labels(utf8(self.options["label_x_axis"]), utf8(self.options["label_y_axis"]))
self.g.set(xlim=(0, options.cfg.main_window.Session.Corpus.get_corpus_size(filters=[])))
python类despine()的实例源码
def cross_section_cndl(data, factor_name):
'''???????????????
??????????????
??
------------------------------
data:DataFrame(index:[Date,IDs],factor1,factor2,...)
factor_name:str
'''
data = data.reset_index()
sns.set(style='ticks')
ax = sns.boxplot(x='Date', y=factor_name, data=data, palette='PRGn')
sns.despine(offset=10, trim=True)
return ax
# ??2
# ?????, ?????????????
def swarm(data,x,y,xscale='linear',yscale='linear'):
# set default pretty settings from Seaborn
sns.set(style="white", palette="muted")
sns.set_context("notebook", font_scale=1, rc={"lines.linewidth": 0.2})
# createthe plot
g = sns.swarmplot(x=x, y=y, data=data, palette='RdYlGn')
plt.tick_params(axis='both', which='major', pad=10)
g.set(xscale=xscale)
g.set(yscale=yscale)
# Setting plot limits
start = data[y].min().min()
plt.ylim(start,);
sns.despine()
def histogram(data,variables):
sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 0})
sns.set_style('white')
var_length = len(variables)
fig, axes = plt.subplots(1, var_length, figsize=(19, 5))
for i in range(var_length):
axes[i].hist(data[variables[i]],lw=0,color="indianred",bins=8);
axes[i].tick_params(axis='both', which='major', pad=15)
axes[i].set_xlabel(variables[i])
axes[i].set_yticklabels("");
sns.despine(left=True)
def plot_time_corrs(subjects, axes):
x = np.arange(1, 5)
palette = [".2", ".5"]
for subj, ax in zip(subjects, axes):
res_fname = "correlation_analysis/{}_rest_ifs.pkz".format(subj)
res = moss.load_pkl(res_fname)
for line, color in zip(res.corr_times.T, palette):
ax.plot(x, line, "o-", color=color, ms=3, clip_on=False)
sig = res.corr_times_pctiles > 95
ax.plot(x[sig], np.ones(sig.sum()) * .0025,
marker=(6, 2, 0), ls="", mew=.35, mec=".2", ms=3)
ax.set(xticks=x, xlim=(.6, 4.4), ylim=(0, .07))
sns.despine(ax=ax, trim=True)
plt.setp(axes[1:], yticklabels=[])
axes[0].set_ylabel("Correlation (r)")
def image(path, costs):
ys = ['0', '1', '2', '3', '4', '5', '6', '7+', 'X']
xs = [costs.get(k, 0) for k in ys]
sns.set_style('white')
sns.set(font='Concourse C3', font_scale=3)
g = sns.barplot(ys, xs, palette=['grey'] * len(ys))
g.axes.yaxis.set_ticklabels([])
rects = g.patches
sns.set(font='Concourse C3', font_scale=2)
for rect, label in zip(rects, xs):
if label == 0:
continue
height = rect.get_height()
g.text(rect.get_x() + rect.get_width()/2, height + 0.5, label, ha='center', va='bottom')
g.margins(y=0, x=0)
sns.despine(left=True, bottom=True)
g.get_figure().savefig(path, transparent=True, pad_inches=0, bbox_inches='tight')
plt.clf() # Clear all data from matplotlib so it does not persist across requests.
return path
def plot_decision_function(score_df, partition, output_file):
"""
Plots the decision function for a given partition (either 'train' or
'test') and saves a figure to file.
Arguments:
:param score_df: a specific folds decision scores and status
:param partition: either 'train' or 'test' will plot performance
:param output_file: file to output the figure
"""
ax = sns.kdeplot(score_df.ix[(score_df.status == 1) &
(score_df.partition == partition), :]
.decision, color='red', label='Deficient',
shade=True)
ax = sns.kdeplot(score_df.ix[(score_df.status == 0) &
(score_df.partition == partition), :]
.decision, color='blue', label='Wild-Type',
shade=True)
ax.set(xlabel='Decision Function', ylabel='Density')
ax.set_title('Classifier Decision Function')
sns.despine()
plt.tight_layout()
plt.savefig(output_file)
plt.close()
def makeDishwasherFig(ax=None, zNorm=True, save=True):
# ts = getGoodDishwasherTs()
# ts.data = ar.zNormalizeCols(ts.data)
ts = getFig1Ts(zNorm=True, whichTs=WHICH_DISHWASHER_TS)
# ax = ts.plot(useWhichLabels=['ZC'], showLabels=False, capYLim=900)
colors = DISHWASHER_COLOR_PALETTE * 3 # cycles thru almost three times
colors[DISHWASHER_DIM_TO_HIGHLIGHT] = DISHWASHER_HIGHLIGHT_COLOR
colors = colors[:ts.data.shape[1]]
ts.data[:, 2] /= 2 # scale the ugliest dim to make pic prettier
ax = ts.plot(showLabels=False, showBounds=False, capYLim=900, ax=ax,
colors=colors) # resets palette...
# ax = ts.plot(showLabels=False, showBounds=False, capYLim=900, ax=None) # works
# ax.plot(ts.data[:, DISHWASHER_DIM_TO_HIGHLIGHT], color=DISHWASHER_HIGHLIGHT_COLOR)
# sb.set_palette(DEFAULT_SB_PALETTE)
sb.despine(left=True)
ax.set_title("Dishwasher", y=TITLE_Y_POS)
# ax.set_xlabel("Minute")
plt.tight_layout()
if save:
saveFigWithName('dishwasher')
# ------------------------------------------------ MSRC
def makeGarbageDimTs():
np.random.seed(123)
seqLen = 750
squareLen = seqLen / 17.
seq = synth.notSoRandomWalk(seqLen, std=.05,
trendFilterLength=(seqLen // 2), lpfLength=2)
sb.set_style('white')
_, ax = plt.subplots()
# color = sb.color_palette()[1]
# ax.plot(seq, lw=4, color="#660000") # red I'm using in keynote
ax.plot(seq, lw=4, color="#CC0000") # red I'm using in keynote
ax.set_xlim([-squareLen, seqLen + squareLen])
ax.set_ylim([np.min(seq) * 2, np.max(seq) * 2])
sb.despine(left=True)
plt.show()
# def makeMethodsWarpedTs():
# ================================================================ Better Fig1
plots.py 文件源码
项目：Comparative-Annotation-Toolkit
作者: ComparativeGenomicsToolkit
项目源码
文件源码
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def _generic_histogram(bars, legend_labels, title_string, pdf, ax, fig, ylabel, names, box_label, bbox_to_anchor):
fig.legend([x[0] for x in bars[::-1]], legend_labels[::-1], bbox_to_anchor=bbox_to_anchor, frameon=True,
title=box_label)
ax.set_title(title_string)
ax.set_ylabel(ylabel)
set_ticks(names, ax)
ax.xaxis.set_ticks(np.arange(0, len(names)) + bar_width / 2.0)
sns.despine(top=True, right=True)
multipage_close(pdf)
def plot_sequence_count(flu, fname=None, fs=12):
# make figure with region counts
import seaborn as sns
date_bins = pivots_to_dates(flu.pivots)
sns.set_style('ticks')
region_label = {'global': 'Global', 'NA': 'N America', 'AS': 'Asia', 'EU': 'Europe', 'OC': 'Oceania'}
regions_abbr = ['global', 'NA', 'AS', 'EU', 'OC']
region_colors = {r:col for r, col in zip(regions_abbr,
sns.color_palette(n_colors=len(regions_abbr)))}
fig, ax = plt.subplots(figsize=(8, 3))
count_by_region = flu.mutation_frequency_counts
drop = 3
tmpcounts = np.zeros(len(flu.pivots[drop:]))
plt.bar(date_bins[drop:], count_by_region['global'][drop:], width=18, \
linewidth=0, label="Other", color="#bbbbbb", clip_on=False)
for region in region_groups:
if region!='global':
plt.bar(date_bins[drop:], count_by_region[region][drop:],
bottom=tmpcounts, width=18, linewidth=0,
label=region_label[region], color=region_colors[region], clip_on=False)
tmpcounts += count_by_region[region][drop:]
make_date_ticks(ax, fs=fs)
ax.set_ylabel('Sample count')
ax.legend(loc=3, ncol=1, bbox_to_anchor=(1.02, 0.53))
plt.subplots_adjust(left=0.1, right=0.82, top=0.94, bottom=0.22)
sns.despine()
if fname is not None:
plt.savefig(fname)
def explore_feature_variation(self, col=None, use_target=False, **kwargs):
'''
Produces univariate plots of a given set of columns. Barplots are used
for categorical columns while histograms (with fitted density functinos)
are used for numerical columns.
If use_target is true, then the variation of the given set of columns
with respect to the response variable are used (e.g., 2d scatter
plots, boxplots, etc).
Parameters
----------
col : a string of a column name, or a list of many columns names or
None (default). If col is None, all columns will be used.
use_target : bool, default False
Whether to use the target column in the plots.
**kwargs: additional arguments to be passed to seaborn's distplot or
to pandas's plotting utilities..
'''
self._validate_params(params_list = {'col':col},
expected_types= {'col':[str,list,type(None)]})
if type(col) is str: col = [col]
if col is None: col = self._get_all_features()
if use_target == False:
for column in col:
if self.is_numeric(self.df[column]) == True:
plt.figure(column)
#sns.despine(left=True)
sns.distplot(self.df[column], color="m", **kwargs)
plt.title(column)
plt.tight_layout()
#plt.figure('boxplot')
#sns.boxplot(x=self.df[col], palette="PRGn")
#sns.despine(offset=10, trim=True)
elif self.is_categorical(self.df[column]) == True:
#print self.df[column].describe()
plt.figure(column)
#sns.despine(left=True)
if len(self.df[column].unique()) > 30:
self.df[column].value_counts()[:20][::-1].plot.barh(**kwargs)
#top = pd.DataFrame(data=top)
#sns.barplot(y=top.index, x=top)
else:
self.df[column].value_counts()[::-1].plot.barh(**kwargs)
#sns.countplot(y=self.df[column])
plt.title(column)
plt.tight_layout()
else:
raise TypeError('TYPE IS NOT SUPPORTED')
else: # use target variable
for column in col:
self.explore_features_covariation(col1=column, col2=self.y, **kwargs)
def kde(x,y,title='',color='YlGnBu',xscale='linear',yscale='linear'):
sns.set_style('white')
sns.set_context('notebook', font_scale=1, rc={"lines.linewidth": 0.5})
g = sns.kdeplot(x,y,shade=True, cut=2, cmap=color, shade_lowest=False, legend=True, set_title="test")
plt.tick_params(axis='both', which='major', pad=10)
sns.plt.title(title)
g.set(xscale=xscale)
g.set(yscale=yscale)
sns.despine()
def regression(data,x,y,xscale='linear',yscale='linear'):
sns.set_context("notebook", font_scale=.8, rc={"lines.linewidth": 0})
sns.set_style('white')
g = sns.regplot(x=x, y=y, data=data)
plt.tick_params(axis='both', which='major', pad=10)
g.set(xscale=xscale)
g.set(yscale=yscale)
sns.despine()
def plot_points(df, axes):
for exp, ax in zip(["dots", "sticks", "rest"], axes):
exp_df = pd.melt(df.query("exp == @exp"),
"subj", ["within", "between"], "test", "corr")
sns.pointplot(x="test", y="corr", hue="test", data=exp_df,
dodge=.5, join=False, ci=95,
palette=[".15", ".5"], ax=ax)
plt.setp(ax.lines, linewidth=2)
sns.pointplot(x="test", y="corr", hue="subj", data=exp_df,
palette=[".75"], scale=.75, ax=ax)
plt.setp(ax.collections[:], facecolor="w", zorder=20)
ax.legend_ = None
ax.set(ylabel="",
xlabel="",
xticks=[-.1, 1.1],
xticklabels=["Same\ncontext", "Different\ncontext"])
axes[0].set(ylim=(0, .105), ylabel="Timeseries correlation (r)")
axes[1].set(ylim=(0, .0525))
axes[2].set(ylim=(0, .0525))
for ax in axes:
sns.despine(ax=ax, trim=True)
def plot_prediction_curves(subjects, axes, exp):
res_ftemp = "spatial_analysis/{}_{}_ifs.pkz"
for subj, ax in zip(subjects, axes):
res = moss.load_pkl(res_ftemp.format(subj, exp))
x = res.steps
norm = res.null.mean()
real = res.real / norm
pint = res.pint / norm
ax.plot(x, real, "o-", color=".15",
ms=2.5, clip_on=False)
ax.fill_between(x, *pint, color=".4", alpha=.3)
cross_x, cross_y = res.intersect
cross_y /= norm
ax.plot([cross_x, cross_x], [0, cross_y],
lw=.8, dashes=[3, 1], color=".5", zorder=0)
ax.set(xlim=(0, 40), ylim=(0, 2),
xticks=np.linspace(0, 40, 5),
yticks=[0, 1, 2],
yticklabels=[0, 1, 2])
sns.despine(ax=ax)
ylabel = "Normalized error"
plt.setp(axes[1:7], yticklabels=[])
axes[0].set(ylabel=ylabel)
if exp == "dots":
plt.setp(axes[8:], yticklabels=[])
plt.setp(axes[:7], xticklabels=[])
axes[7].set_ylabel(ylabel)
def plot_distance_corrs(subjects, axes, exp):
for subj, ax in zip(subjects, axes):
res_fname = "correlation_analysis/{}_{}_ifs.pkz".format(subj, exp)
res = moss.load_pkl(res_fname)
x = res.distance_thresh
for dim, color, marker in zip(["3D", "2D"], [".5", ".2"], ["x", "+"]):
same, diff = res.corr_distance[dim].T
ax.plot(x, same - diff, "o-", color=color, ms=3, clip_on=False)
sig = res.corr_distance_pctiles[dim] > 95
stary = -.005 if exp == "dots" else -.0025
ax.plot(x[sig], np.ones(sig.sum()) * stary,
marker=marker, ls="", mew=.35, mec=".2", ms=3)
ylim = (-.01, .08) if exp == "dots" else (-.005, .04)
yticks = np.array([0, .01, .02, .03, .04])
yticks = yticks * 2 if exp == "dots" else yticks
ax.set(xlim=(-2, 42), ylim=ylim, yticks=yticks)
sns.despine(ax=ax, trim=True)
ylabel = "Subnetwork strength\n($r_{\mathrm{same}} - r_{\mathrm{diff}}$)"
plt.setp(axes[1:7], yticklabels=[])
axes[0].set_ylabel(ylabel)
if exp == "dots":
plt.setp(axes[8:], yticklabels=[])
plt.setp(axes[:7], xticklabels=[])
axes[7].set_ylabel(ylabel)
def plot_swarms(df, axes, palette):
for exp, ax in zip(["dots", "sticks"], axes):
exp_df = df.query("experiment == @exp")
ax.axhline(.5, .1, .9, dashes=(5, 2), color=".6")
ax.set(ylim=(.4, .9), yticks=[.4, .5, .6, .7, .8, .9])
sns.pointplot(x="roi", y="acc", data=exp_df,
palette=palette, join=False, ci=None, ax=ax)
points_to_lines(ax, lw=3)
sns.swarmplot(x="roi", y="acc", data=exp_df, size=4,
color=".85", # facecolor="none",
linewidth=1, edgecolor=".4", ax=ax)
ax.set(xlabel="", ylabel="", xticklabels=["IFS", "MFC"])
ax_l, ax_r = axes
ax_l.set(ylabel="Decoding accuracy")
ax_r.set(yticks=[])
ax_l.text(.5, .91, "Experiment 1", ha="center", va="center", size=7.5)
ax_r.text(.5, .91, "Experiment 2", ha="center", va="center", size=7.5)
sns.despine(ax=ax_l, trim=True)
sns.despine(ax=ax_r, left=True, trim=True)
def plot_cluster_error(ax):
res_ftemp = "spatial_analysis/{}_{}_ifs.pkz"
for exp in ["dots", "sticks"]:
subjects = get_subject_order(exp)
color = get_colormap(exp, as_cmap=False)[20]
errs = []
for subj in subjects:
res = moss.load_pkl(res_ftemp.format(subj, exp))
x = res.steps
norm = res.null.mean()
errs.append(res.real / norm)
errs = np.vstack(errs)
mean = errs.mean(axis=0)
ax.plot(x, mean, color=color, lw=2)
sem = stats.sem(errs, axis=0)
ax.fill_between(x, mean - sem, mean + sem, alpha=.2, color=color)
ax.axhline(y=1, lw=1, dashes=[5, 2],
color=".5", zorder=0,
xmin=.02, xmax=.98)
ax.set(xlim=(0, 42),
ylim=(.55, 1.45),
yticks=[.6, .8, 1, 1.2, 1.4],
xticks=[0, 10, 20, 30, 40],
xlabel="Neighborhood radius (mm)",
ylabel="Normalized error")
sns.despine(ax=ax, trim=True)
def plot_corrmats(subjects, axes, exp):
for subj, ax in zip(subjects, axes):
fname = "correlation_analysis/{}_{}_ifs.pkz".format(subj, exp)
corrmat = moss.load_pkl(fname).corrmat
ax.imshow(corrmat - np.eye(len(corrmat)),
cmap="RdBu_r", vmin=-.15, vmax=.15,
rasterized=True)
ax.set(xticks=[], yticks=[])
sns.despine(ax=ax, left=True, bottom=True)
def plot_scatters(subjects, axes):
ftemp = "correlation_analysis/{}_{}_ifs.pkz"
for subj, ax in zip(subjects, axes):
sticks = moss.load_pkl(ftemp.format(subj, "sticks")).corrmat
rest = moss.load_pkl(ftemp.format(subj, "rest")).corrmat
triu = np.triu_indices_from(rest, 1)
ax.scatter(sticks[triu], rest[triu], s=3, linewidth=.2,
color=".6", edgecolor="w",
rasterized=True)
ax.plot([-.2, .8], [-.2, .8], lw=1, dashes=(5, 2), color=".3")
plt.setp(axes,
xlim=(-.25, .8), ylim=(-.25, .8),
xticks=np.linspace(-.2, .8, 6),
yticks=np.linspace(-.2, .8, 6),
aspect="equal")
plt.setp(axes[1:], yticklabels=[])
for ax in axes:
sns.despine(ax=ax, trim=True)
plt.setp(ax.get_xticklabels(), size=6)
plt.setp(ax.get_yticklabels(), size=6)
def plot_kdes(subjects, axes):
ftemp = "correlation_analysis/{}_{}_ifs.pkz"
for subj, ax in zip(subjects, axes):
sticks = moss.load_pkl(ftemp.format(subj, "sticks")).corrmat
rest = moss.load_pkl(ftemp.format(subj, "rest")).corrmat
triu = np.triu_indices_from(rest, 1)
sns.kdeplot(sticks[triu], color=".15",
label="residual", ax=ax)
sns.kdeplot(rest[triu], color=".45", dashes=[4, 1],
label="resting", ax=ax)
plt.setp(axes,
xlim=(-.25, .8), ylim=(0, 17),
xticks=np.linspace(-.2, .8, 6),
yticks=[])
for ax in axes:
sns.despine(ax=ax, left=True, trim=True)
plt.setp(ax.get_xticklabels(), size=6)
plt.setp(ax.get_yticklabels(), size=6)
axes[0].legend(bbox_to_anchor=(1.2, .8))
for ax in axes[1:]:
ax.legend_ = None
def start_plotting(fig_size, fig_pos, style="white", rc=None, despine=False):
with sns.axes_style(style, rc):
fig = plt.figure(figsize=fig_size)
if not fig_pos:
ax = fig.add_subplot(111)
else:
ax = fig.add_axes(fig_pos)
if despine:
sns.despine(left=True)
return fig, ax
def plot_box( self, fname_out = None):
sns.boxplot(x="Method", y="r2", data=self.df_best_expand, palette="PRGn")
sns.despine(offset=10, trim=True)
plt.ylabel( r"$r^2$")
plt.xlabel( "Methods")
if fname_out is not None:
plt.savefig( fname_out) # index should be stored.
elif self.fname is not None:
fname_out = self.fname[:-4] + '_box.eps'
print( 'Default: the figure of self.df_best_expand is saved to', fname_out)
plt.savefig( fname_out)
def plot_box( self, fname_out = None):
sns.boxplot(x="Method", y="r2", data=self.df_best_expand, palette="PRGn")
sns.despine(offset=10, trim=True)
plt.ylabel( r"$r^2$")
plt.xlabel( "Methods")
if fname_out is not None:
plt.savefig( fname_out) # index should be stored.
elif self.fname is not None:
fname_out = self.fname[:-4] + '_box.eps'
print( 'Default: the figure of self.df_best_expand is saved to', fname_out)
plt.savefig( fname_out)
def boxplot_expension( pdr, method_l, x="Group", y="RP", hue="Method"):
# method_l = ['No_Regression', 'Mean_Compensation', 'Linear', 'Exp']
val_s = y
pdw = expension_4_boxplot( pdr, method_l, x=x, y=y, hue=hue)
sns.boxplot(x="Group", y=val_s, hue="Method", data=pdw, palette="PRGn")
sns.despine(offset=10, trim=True)
def print_2D( points,label,id_map ):
'''
points: N_samples * 2
label: (int) N_samples
id_map: map label id to its name
'''
fig = plt.figure()
#current_palette = sns.color_palette("RdBu_r", max(label)+1)
n_cell,_ = points.shape
if n_cell > 500:
s = 10
else:
s = 20
ax = plt.subplot(111)
print( np.unique(label) )
for i in np.unique(label):
ax.scatter( points[label==i,0], points[label==i,1], c=current_palette[i], label=id_map[i], s=s,marker=markers_keys[i] )
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1,
box.width, box.height * 0.9])
ax.legend(scatterpoints=1,loc='upper center',
bbox_to_anchor=(0.5,-0.08),ncol=6,
fancybox=True,
prop={'size':8}
)
sns.despine()
return fig
def makeMsrcFig(ax=None, save=True):
ts = getGoodMsrcTs()
ax = ts.plot(showLabels=False, showBounds=False, ax=ax)
sb.despine(left=True)
ax.set_title("MSRC-12", y=TITLE_Y_POS)
# ax.set_xlabel("Time (sample)")
plt.tight_layout()
if save:
saveFigWithName('msrc')
# ------------------------------------------------ UCR
def makeUcrFig(ax=None, save=True):
ts = getGoodUcrTs()
ax = ts.plot(showLabels=False, showBounds=False, ax=ax, linewidths=3.)
sb.despine(left=True)
ax.set_title("UCR", y=TITLE_Y_POS)
# ax.set_xlabel("Time (sample)")
plt.tight_layout()
if save:
saveFigWithName('ucr')
# ------------------------------------------------ Tidigits
# @memory.cache
def makeTidigitsFig(ax=None, save=True, whichTs=-1):
ts = getGoodTidigitsTs(whichTs=whichTs)
# ts.data = ar.meanNormalizeCols(ts.data)
ax = ts.plot(showLabels=False, showBounds=False, ax=ax, linewidths=3.)
sb.despine(left=True)
ax.set_title("TIDIGITS", y=TITLE_Y_POS)
# ax.set_xlabel("Time (sample)")
plt.tight_layout()
if save:
saveFigWithName('tidigits')
# ------------------------------------------------ Combined
