本文整理汇总了Python中matplotlib.mlab.normpdf方法的典型用法代码示例。如果您正苦于以下问题：Python mlab.normpdf方法的具体用法？Python mlab.normpdf怎么用？Python mlab.normpdf使用的例子？那么恭喜您, 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模块matplotlib.mlab的用法示例。

在下文中一共展示了mlab.normpdf方法的11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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: histogram_demo

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def histogram_demo(ax):

# example data

mu = 100 # mean of distribution

sigma = 15 # standard deviation of distribution

x = mu + sigma * np.random.randn(10000)

num_bins = 50

# The histogram of the data.

_, bins, _ = ax.hist(x, num_bins, normed=1, label='data')

# Add a 'best fit' line.

y = mlab.normpdf(bins, mu, sigma)

ax.plot(bins, y, '-s', label='best fit')

ax.legend()

ax.set_xlabel('Smarts')

ax.set_ylabel('Probability')

ax.set_title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')

开发者ID:tonysyu，项目名称:matplotlib-style-gallery，代码行数:21，

示例2: plot_t_value_hist

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def plot_t_value_hist(

img_path='~/ni_data/ofM.dr/l1/as_composite/sub-5703/ses-ofM/sub-5703_ses-ofM_task-EPI_CBV_chr_longSOA_tstat.nii.gz',

roi_path='~/ni_data/templates/roi/DSURQEc_ctx.nii.gz',

mask_path='/usr/share/mouse-brain-atlases/dsurqec_200micron_mask.nii',

save_as='~/qc_tvalues.pdf',

):

"""Make t-value histogram plot"""

f, axarr = plt.subplots(1, sharex=True)

roi = nib.load(path.expanduser(roi_path))

roi_data = roi.get_data()

mask = nib.load(path.expanduser(mask_path))

mask_data = mask.get_data()

idx = np.nonzero(np.multiply(roi_data,mask_data))

img = nib.load(path.expanduser(img_path))

data = img.get_data()[idx]

(mu, sigma) = norm.fit(data)

n, bins, patches = axarr.hist(data,'auto',normed=1, facecolor='green', alpha=0.75)

y = mlab.normpdf(bins, mu, sigma)

axarr.plot(bins, y, 'r--', linewidth=2)

axarr.set_title('Histogram of t-values $\mathrm{(\mu=%.3f,\ \sigma=%.3f}$)' %(mu, sigma))

axarr.set_xlabel('t-values')

plt.savefig(path.expanduser(save_as))

开发者ID:IBT-FMI，项目名称:SAMRI，代码行数:27，代码来源:qc.py

示例3: plot_z

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def plot_z(self,figsize=(15,5)):

import matplotlib.pyplot as plt

import seaborn as sns

if hasattr(self, 'sample'):

sns.distplot(self.prior.transform(self.sample), rug=False, hist=False,label=self.method + ' estimate of ' + self.name)

elif hasattr(self, 'value') and hasattr(self, 'std'):

x = np.linspace(self.value-self.std*3.5,self.value+self.std*3.5,100)

plt.figure(figsize=figsize)

if self.prior.transform_name is None:

plt.plot(x,mlab.normpdf(x,self.value,self.std),label=self.method + ' estimate of ' + self.name)

else:

sims = self.prior.transform(np.random.normal(self.value,self.std,100000))

sns.distplot(sims, rug=False, hist=False,label=self.method + ' estimate of ' + self.name)

plt.xlabel('Value')

plt.legend()

plt.show()

else:

raise ValueError("No information on latent variable to plot!")

开发者ID:RJT1990，项目名称:pyflux，代码行数:23，

示例4: _fitted_E_plot

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True,

verbose=False, two_gauss_model=False, lw=2.5, color='k',

alpha=0.5, fillcolor=None):

"""Plot a fitted model overlay on a FRET histogram."""

if ax is None:

ax2 = gca()

else:

ax2 = plt.twinx(ax=ax)

ax2.grid(False)

if d.fit_E_curve and show_model:

x = r_[-0.2:1.21:0.002]

y = d.fit_E_model(x, d.fit_E_res[i, :])

scale = F*d.fit_E_model_F[i]

if two_gauss_model:

assert d.fit_E_res.shape[1] > 2

if d.fit_E_res.shape[1] == 5:

m1, s1, m2, s2, a1 = d.fit_E_res[i, :]

a2 = (1-a1)

elif d.fit_E_res.shape[1] == 6:

m1, s1, a1, m2, s2, a2 = d.fit_E_res[i, :]

y1 = a1*normpdf(x, m1, s1)

y2 = a2*normpdf(x, m2, s2)

ax2.plot(x, scale*y1, ls='--', lw=lw, alpha=alpha, color=color)

ax2.plot(x, scale*y2, ls='--', lw=lw, alpha=alpha, color=color)

if fillcolor is None:

ax2.plot(x, scale*y, lw=lw, alpha=alpha, color=color)

else:

ax2.fill_between(x, scale*y, lw=lw, alpha=alpha, edgecolor=color,

facecolor=fillcolor, zorder=10)

if verbose:

print('Fit Integral:', np.trapz(scale*y, x))

ax2.axvline(d.E_fit[i], lw=3, color=red, ls='--', alpha=0.6)

xtext = 0.6 if d.E_fit[i] < 0.6 else 0.2

if d.nch > 1 and not no_E:

ax2.text(xtext, 0.81, "CH%d: $E_{fit} = %.3f$" % (i+1, d.E_fit[i]),

transform=gca().transAxes, fontsize=16,

bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5))

开发者ID:tritemio，项目名称:FRETBursts，代码行数:41，

示例5: _rerender

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def _rerender(self):

nmr_maps = len(self.maps_to_show)

if self._show_trace:

nmr_maps *= 2

grid = GridSpec(nmr_maps, 1, left=0.04, right=0.96, top=0.94, bottom=0.06, hspace=0.2)

i = 0

for map_name in self.maps_to_show:

samples = self._voxels[map_name]

if self._sample_indices is not None:

samples = samples[:, self._sample_indices]

title = map_name

if map_name in self.names:

title = self.names[map_name]

if isinstance(self._nmr_bins, dict) and map_name in self._nmr_bins:

nmr_bins = self._nmr_bins[map_name]

else:

nmr_bins = self._nmr_bins

hist_plot = plt.subplot(grid[i])

try:

n, bins, patches = hist_plot.hist(np.nan_to_num(samples[self.voxel_ind, :]), nmr_bins, normed=True)

plt.title(title)

i += 1

if self._fit_gaussian:

mu, sigma = norm.fit(samples[self.voxel_ind, :])

bincenters = 0.5*(bins[1:] + bins[:-1])

y = mlab.normpdf(bincenters, mu, sigma)

hist_plot.plot(bincenters, y, 'r', linewidth=1)

if self._show_trace:

trace_plot = plt.subplot(grid[i])

trace_plot.plot(samples[self.voxel_ind, :])

i += 1

except IndexError:

pass

开发者ID:robbert-harms，项目名称:MDT，代码行数:43，

示例6: render

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def render(d, x, primary, secondary, parameter, norm_and_curve=False):

fig, ax = plt.subplots()

fig.suptitle(string.upper("%s vs. %s" % (primary, secondary)), fontsize=14, fontweight='bold')

n, bins, patches = plt.hist(x, 50, normed=norm_and_curve, facecolor='green', alpha=0.75)

if norm_and_curve:

mean = np.mean(x)

variance = np.var(x)

sigma = np.sqrt(variance)

y = mlab.normpdf(bins, mean, sigma)

l = plt.plot(bins, y, 'r--', linewidth=1)

ax.set_title('n = %d' % len(x))

units = PARAMETER_TO_UNITS[parameter] if parameter in PARAMETER_TO_UNITS else PARAMETER_TO_UNITS["sst"]

ax.set_xlabel("%s - %s %s" % (primary, secondary, units))

if norm_and_curve:

ax.set_ylabel("Probability per unit difference")

else:

ax.set_ylabel("Frequency")

plt.grid(True)

sio = StringIO()

plt.savefig(sio, format='png')

d['plot'] = sio.getvalue()

开发者ID:apache，项目名称:incubator-sdap-nexus，代码行数:30，

示例7: plot_distribution

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def plot_distribution(self, mean, sigma, array):

vlines = [mean-(1*sigma), mean, mean+(1*sigma)]

for val in vlines:

plt.axvline(val, color='k', linestyle='--')

bins = np.linspace(mean-(4*sigma), mean+(4*sigma), 200)

plt.hist(array, bins, alpha=0.5)

y = mlab.normpdf(bins, mean, sigma)

plt.plot(bins, y, 'r--')

plt.subplots_adjust(left=0.15)

plt.show()

print mean, sigma

开发者ID:UKPLab，项目名称:acl2017-interactive_summarizer，代码行数:15，

示例8: plot_distribution

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def plot_distribution(self, mean, sigma, array):

vlines = [mean-(1*sigma), mean, mean+(1*sigma)]

for val in vlines:

plt.axvline(val, color='k', linestyle='--')

bins = np.linspace(mean-(4*sigma), mean+(4*sigma), 200)

plt.hist(array, bins, alpha=0.5)

y = mlab.normpdf(bins, mean, sigma)

plt.plot(bins, y, 'r--')

plt.subplots_adjust(left=0.15)

plt.show()

print(mean, sigma)

开发者ID:UKPLab，项目名称:acl2017-interactive_summarizer，代码行数:15，

示例9: makeSpectre

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def makeSpectre(transitions, sigma, step):

""" Build a spectrum from transitions energies. For each transitions a gaussian

function of width sigma is added in order to mimick natural broadening.

:param transitions: list of transitions for readTransitions()

:type transititions: list

:param sigma: gaussian width in eV

:type sigma: float

:param step: number of absissa value

:type step: int

:return: absissa and spectrum value in this order

:rtype: list, list

"""

# max and min transition energies

minval = min([val[0] for val in transitions]) - 5.0 * sigma

maxval = max([val[0] for val in transitions]) + 5.0 * sigma

# points

npts = int((maxval - minval) / step) + 1

# absice

eneval = sp.linspace(minval, maxval, npts)

spectre = sp.zeros(npts)

for trans in transitions:

spectre += trans[2] * normpdf(eneval, trans[0], sigma)

return eneval, spectre

开发者ID:gVallverdu，项目名称:myScripts，代码行数:32，

示例10: plot_normal

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def plot_normal(ax, arr):

"""

:param ax:

:param mu:

:param variance:

:return:

"""

mu = arr.mean()

variance = arr.var()

sigma = math.sqrt(variance)

x = np.linspace(mu - 6 * sigma, mu + 6 * sigma, 100)

if mu != 0 and sigma != 0:

ax.plot(x, mlab.normpdf(x, mu, sigma))

开发者ID:SanPen，项目名称:GridCal，代码行数:17，

示例11: plot_hist

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# 需要导入模块: from matplotlib import mlab [as 别名]

# 或者: from matplotlib.mlab import normpdf [as 别名]

def plot_hist(

image,

threshold=0.0,

fit_line=False,

normfreq=True,

## plot label arguments

title=None,

grid=True,

xlabel=None,

ylabel=None,

## other plot arguments

facecolor="green",

alpha=0.75,

):

"""

Plot a histogram from an ANTsImage

Arguments

---------

image : ANTsImage

image from which histogram will be created

"""

img_arr = image.numpy().flatten()

img_arr = img_arr[np.abs(img_arr) > threshold]

if normfreq != False:

normfreq = 1.0 if normfreq == True else normfreq

n, bins, patches = plt.hist(

img_arr, 50, normed=normfreq, facecolor=facecolor, alpha=alpha

)

if fit_line:

# add a 'best fit' line

y = mlab.normpdf(bins, img_arr.mean(), img_arr.std())

l = plt.plot(bins, y, "r--", linewidth=1)

if xlabel is not None:

plt.xlabel(xlabel)

if ylabel is not None:

plt.ylabel(ylabel)

if title is not None:

plt.title(title)

plt.grid(grid)

plt.show()

开发者ID:ANTsX，项目名称:ANTsPy，代码行数:47，

注：本文中的matplotlib.mlab.normpdf方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。