本文整理汇总了Python中matplotlib.pylab.savefig方法的典型用法代码示例。如果您正苦于以下问题:Python pylab.savefig方法的具体用法?Python pylab.savefig怎么用?Python pylab.savefig使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类matplotlib.pylab
的用法示例。
在下文中一共展示了pylab.savefig方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: generate_png_chess_dp_vertex
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def generate_png_chess_dp_vertex(self):
"""Produces pictures of the dominant product vertex a chessboard convention"""
import matplotlib.pylab as plt
plt.ioff()
dab2v = self.get_dp_vertex_doubly_sparse()
for i, ab in enumerate(dab2v):
fname = "chess-v-{:06d}.png".format(i)
print('Matrix No.#{}, Size: {}, Type: {}'.format(i+1, ab.shape, type(ab)), fname)
if type(ab) != 'numpy.ndarray': ab = ab.toarray()
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.set_aspect('equal')
plt.imshow(ab, interpolation='nearest', cmap=plt.cm.ocean)
plt.colorbar()
plt.savefig(fname)
plt.close(fig)
示例2: plot_metrics
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_metrics(metric_list, save_path=None):
# runs through each test case and adds a set of bars to a plot. Saves
f, (ax1) = plt.subplots(1, 1)
plt.grid(True)
print_metrics(metric_list)
bar_metrics(metric_list[0], ax1, index=0)
bar_metrics(metric_list[1], ax1, index=1)
bar_metrics(metric_list[2], ax1, index=2)
if save_path is None:
save_path = "img/bar_" + key + ".png"
plt.savefig(save_path, dpi=400)
示例3: plotallfuncs
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plotallfuncs(allfuncs=allfuncs):
from matplotlib import pylab as pl
pl.ioff()
nnt = NNTester(npoints=1000)
lpt = LinearTester(npoints=1000)
for func in allfuncs:
print(func.title)
nnt.plot(func, interp=False, plotter='imshow')
pl.savefig('%s-ref-img.png' % func.func_name)
nnt.plot(func, interp=True, plotter='imshow')
pl.savefig('%s-nn-img.png' % func.func_name)
lpt.plot(func, interp=True, plotter='imshow')
pl.savefig('%s-lin-img.png' % func.func_name)
nnt.plot(func, interp=False, plotter='contour')
pl.savefig('%s-ref-con.png' % func.func_name)
nnt.plot(func, interp=True, plotter='contour')
pl.savefig('%s-nn-con.png' % func.func_name)
lpt.plot(func, interp=True, plotter='contour')
pl.savefig('%s-lin-con.png' % func.func_name)
pl.ion()
示例4: plot_feat_importance
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_feat_importance(feature_names, clf, name):
pylab.clf()
coef_ = clf.coef_
important = np.argsort(np.absolute(coef_.ravel()))
f_imp = feature_names[important]
coef = coef_.ravel()[important]
inds = np.argsort(coef)
f_imp = f_imp[inds]
coef = coef[inds]
xpos = np.array(range(len(coef)))
pylab.bar(xpos, coef, width=1)
pylab.title('Feature importance for %s' % (name))
ax = pylab.gca()
ax.set_xticks(np.arange(len(coef)))
labels = ax.set_xticklabels(f_imp)
for label in labels:
label.set_rotation(90)
filename = name.replace(" ", "_")
pylab.savefig(os.path.join(
CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:23,代码来源:utils.py
示例5: plot_entropy
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_entropy():
pylab.clf()
pylab.figure(num=None, figsize=(5, 4))
title = "Entropy $H(X)$"
pylab.title(title)
pylab.xlabel("$P(X=$coin will show heads up$)$")
pylab.ylabel("$H(X)$")
pylab.xlim(xmin=0, xmax=1.1)
x = np.arange(0.001, 1, 0.001)
y = -x * np.log2(x) - (1 - x) * np.log2(1 - x)
pylab.plot(x, y)
# pylab.xticks([w*7*24 for w in [0,1,2,3,4]], ['week %i'%(w+1) for w in
# [0,1,2,3,4]])
pylab.autoscale(tight=True)
pylab.grid(True)
filename = "entropy_demo.png"
pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:23,代码来源:demo_mi.py
示例6: plot_confusion_matrix
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_confusion_matrix(cm, genre_list, name, title):
pylab.clf()
pylab.matshow(cm, fignum=False, cmap='Blues', vmin=0, vmax=1.0)
ax = pylab.axes()
ax.set_xticks(range(len(genre_list)))
ax.set_xticklabels(genre_list)
ax.xaxis.set_ticks_position("bottom")
ax.set_yticks(range(len(genre_list)))
ax.set_yticklabels(genre_list)
pylab.title(title)
pylab.colorbar()
pylab.grid(False)
pylab.show()
pylab.xlabel('Predicted class')
pylab.ylabel('True class')
pylab.grid(False)
pylab.savefig(
os.path.join(CHART_DIR, "confusion_matrix_%s.png" % name), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:20,代码来源:utils.py
示例7: plot_roc
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_roc(auc_score, name, tpr, fpr, label=None):
pylab.clf()
pylab.figure(num=None, figsize=(5, 4))
pylab.grid(True)
pylab.plot([0, 1], [0, 1], 'k--')
pylab.plot(fpr, tpr)
pylab.fill_between(fpr, tpr, alpha=0.5)
pylab.xlim([0.0, 1.0])
pylab.ylim([0.0, 1.0])
pylab.xlabel('False Positive Rate')
pylab.ylabel('True Positive Rate')
pylab.title('ROC curve (AUC = %0.2f) / %s' %
(auc_score, label), verticalalignment="bottom")
pylab.legend(loc="lower right")
filename = name.replace(" ", "_")
pylab.savefig(
os.path.join(CHART_DIR, "roc_" + filename + ".png"), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:19,代码来源:utils.py
示例8: plot_feat_importance
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_feat_importance(feature_names, clf, name):
pylab.figure(num=None, figsize=(6, 5))
coef_ = clf.coef_
important = np.argsort(np.absolute(coef_.ravel()))
f_imp = feature_names[important]
coef = coef_.ravel()[important]
inds = np.argsort(coef)
f_imp = f_imp[inds]
coef = coef[inds]
xpos = np.array(list(range(len(coef))))
pylab.bar(xpos, coef, width=1)
pylab.title('Feature importance for %s' % (name))
ax = pylab.gca()
ax.set_xticks(np.arange(len(coef)))
labels = ax.set_xticklabels(f_imp)
for label in labels:
label.set_rotation(90)
filename = name.replace(" ", "_")
pylab.savefig(os.path.join(
CHART_DIR, "feat_imp_%s.png" % filename), bbox_inches="tight")
开发者ID:PacktPublishing,项目名称:Building-Machine-Learning-Systems-With-Python-Second-Edition,代码行数:23,代码来源:utils.py
示例9: plotallfuncs
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plotallfuncs(allfuncs=allfuncs):
from matplotlib import pylab as pl
pl.ioff()
nnt = NNTester(npoints=1000)
lpt = LinearTester(npoints=1000)
for func in allfuncs:
print(func.title)
nnt.plot(func, interp=False, plotter='imshow')
pl.savefig('%s-ref-img.png' % func.__name__)
nnt.plot(func, interp=True, plotter='imshow')
pl.savefig('%s-nn-img.png' % func.__name__)
lpt.plot(func, interp=True, plotter='imshow')
pl.savefig('%s-lin-img.png' % func.__name__)
nnt.plot(func, interp=False, plotter='contour')
pl.savefig('%s-ref-con.png' % func.__name__)
nnt.plot(func, interp=True, plotter='contour')
pl.savefig('%s-nn-con.png' % func.__name__)
lpt.plot(func, interp=True, plotter='contour')
pl.savefig('%s-lin-con.png' % func.__name__)
pl.ion()
示例10: plot_pr
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_pr(auc_score, precision, recall, label=None, figure_path=None):
"""绘制R/P曲线"""
try:
from matplotlib import pylab
pylab.figure(num=None, figsize=(6, 5))
pylab.xlim([0.0, 1.0])
pylab.ylim([0.0, 1.0])
pylab.xlabel('Recall')
pylab.ylabel('Precision')
pylab.title('P/R (AUC=%0.2f) / %s' % (auc_score, label))
pylab.fill_between(recall, precision, alpha=0.5)
pylab.grid(True, linestyle='-', color='0.75')
pylab.plot(recall, precision, lw=1)
pylab.savefig(figure_path)
except Exception as e:
print("save image error with matplotlib")
pass
示例11: plotKChart
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plotKChart(self, misClassDict, saveFigPath):
kList = []
misRateList = []
for k, misClassNum in misClassDict.iteritems():
kList.append(k)
misRateList.append(1.0 - 1.0/k*misClassNum)
fig = plt.figure(saveFigPath)
plt.plot(kList, misRateList, 'r--')
plt.title(saveFigPath)
plt.xlabel('k Num.')
plt.ylabel('Misclassified Rate')
plt.legend(saveFigPath)
plt.grid(True)
plt.savefig(saveFigPath)
plt.show()
################################### PART3 TEST ########################################
# 例子
示例12: drawMapqHistogram
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def drawMapqHistogram(self):
""" From matplot lib plots a Mappping qualty histogram
"""
#1. PLOT BUILDING
readsMapq = self.mapping_stats.mapping_quality_reads
mapqList = list(range(len(readsMapq)))
matplotlib.pyplot.ioff()
figure = plt.figure()
plt.bar(mapqList,readsMapq,width=1,align='center',facecolor='blue', alpha=0.75)
plt.xlabel('MapQ')
plt.ylabel('Fragments')
plt.title('MapQ Histogram')
plt.axis([0, 60,min(readsMapq), max(readsMapq)])
plt.grid(True)
pylab.savefig(self.png_mapq_histogram)
plt.close(figure)
示例13: main
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def main():
args = parse_args()
jobpath, taskids = parse_jobpath_and_taskids(args)
for taskid in taskids:
taskpath = os.path.join(jobpath, taskid)
if args.lap is not None:
prefix, bLap = ModelReader.getPrefixForLapQuery(taskpath, args.lap)
if bLap != args.lap:
print 'Using saved lap: ', bLap
else:
prefix = 'Best' # default
hmodel = ModelReader.load_model(taskpath, prefix)
plotModelInNewFigure(jobpath, hmodel, args)
if args.savefilename is not None:
pylab.show(block=False)
pylab.savefig(args.savefilename % (taskid))
if args.savefilename is None:
pylab.show(block=True)
示例14: matrix
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def matrix(msg, mobj):
"""
Interpret a user string, convert it to a list and graph it as a matrix
Uses ast.literal_eval to parse input into a list
"""
fname = bot_data("{}.png".format(mobj.author.id))
try:
list_input = literal_eval(msg)
if not isinstance(list_input, list):
raise ValueError("Not a list")
m = np_matrix(list_input)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.set_aspect('equal')
plt.imshow(m, interpolation='nearest', cmap=plt.cm.ocean)
plt.colorbar()
plt.savefig(fname)
await client.send_file(mobj.channel, fname)
f_remove(fname)
return
except Exception as ex:
logger("!matrix: {}".format(ex))
return await client.send_message(mobj.channel, "Failed to render graph")
示例15: plot_performance_profiles
# 需要导入模块: from matplotlib import pylab [as 别名]
# 或者: from matplotlib.pylab import savefig [as 别名]
def plot_performance_profiles(problems, solvers):
"""
Plot performance profiles in matplotlib for specified problems and solvers
"""
# Remove OSQP polish solver
solvers = solvers.copy()
for s in solvers:
if "polish" in s:
solvers.remove(s)
df = pd.read_csv('./results/%s/performance_profiles.csv' % problems)
plt.figure(0)
for solver in solvers:
plt.plot(df["tau"], df[solver], label=solver)
plt.xlim(1., 10000.)
plt.ylim(0., 1.)
plt.xlabel(r'Performance ratio $\tau$')
plt.ylabel('Ratio of problems solved')
plt.xscale('log')
plt.legend()
plt.grid()
plt.show(block=False)
results_file = './results/%s/%s.png' % (problems, problems)
print("Saving plots to %s" % results_file)
plt.savefig(results_file)