本文整理汇总了Python中seaborn.heatmap方法的典型用法代码示例。如果您正苦于以下问题:Python seaborn.heatmap方法的具体用法?Python seaborn.heatmap怎么用?Python seaborn.heatmap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seaborn
的用法示例。
在下文中一共展示了seaborn.heatmap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: plotSigHeats
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plotSigHeats(signals,markets,start=0,step=2,size=1,iters=6):
"""
打印信号回测盈损热度图,寻找参数稳定岛
"""
sigMat = pd.DataFrame(index=range(iters),columns=range(iters))
for i in range(iters):
for j in range(iters):
climit = start + i*step
wlimit = start + j*step
caps,poss = plotSigCaps(signals,markets,climit=climit,wlimit=wlimit,size=size,op=False)
sigMat[i][j] = caps[-1]
sns.heatmap(sigMat.values.astype(np.float64),annot=True,fmt='.2f',annot_kws={"weight": "bold"})
xTicks = [i+0.5 for i in range(iters)]
yTicks = [iters-i-0.5 for i in range(iters)]
xyLabels = [str(start+i*step) for i in range(iters)]
_, labels = plt.yticks(yTicks,xyLabels)
plt.setp(labels, rotation=0)
_, labels = plt.xticks(xTicks,xyLabels)
plt.setp(labels, rotation=90)
plt.xlabel('Loss Stop @')
plt.ylabel('Profit Stop @')
return sigMat
示例2: time_align_visualize
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def time_align_visualize(alignments, time, y, namespace='time_align'):
plt.figure()
heat = np.flip(alignments + alignments.T +
np.eye(alignments.shape[0]), axis=0)
sns.heatmap(heat, cmap="YlGnBu", vmin=0, vmax=1)
plt.savefig(namespace + '_heatmap.svg')
G = nx.from_numpy_matrix(alignments)
G = nx.maximum_spanning_tree(G)
pos = {}
for i in range(len(G.nodes)):
pos[i] = np.array([time[i], y[i]])
mst_edges = set(nx.maximum_spanning_tree(G).edges())
weights = [ G[u][v]['weight'] if (not (u, v) in mst_edges) else 8
for u, v in G.edges() ]
plt.figure()
nx.draw(G, pos, edges=G.edges(), width=10)
plt.ylim([-1, 1])
plt.savefig(namespace + '.svg')
示例3: spatial_heatmap
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def spatial_heatmap(array, path, title=None, color="Greens", figformat="png"):
"""Taking channel information and creating post run channel activity plots."""
logging.info("Nanoplotter: Creating heatmap of reads per channel using {} reads."
.format(array.size))
activity_map = Plot(
path=path + "." + figformat,
title="Number of reads generated per channel")
layout = make_layout(maxval=np.amax(array))
valueCounts = pd.value_counts(pd.Series(array))
for entry in valueCounts.keys():
layout.template[np.where(layout.structure == entry)] = valueCounts[entry]
plt.figure()
ax = sns.heatmap(
data=pd.DataFrame(layout.template, index=layout.yticks, columns=layout.xticks),
xticklabels="auto",
yticklabels="auto",
square=True,
cbar_kws={"orientation": "horizontal"},
cmap=color,
linewidths=0.20)
ax.set_title(title or activity_map.title)
activity_map.fig = ax.get_figure()
activity_map.save(format=figformat)
plt.close("all")
return [activity_map]
示例4: test_plot_SignaturePhMag
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def test_plot_SignaturePhMag(fig_test, fig_ref):
# fig test
ext = extractors.Signature()
kwargs = ext.get_default_params()
kwargs.update(
feature="SignaturePhMag",
value=[[1, 2, 3, 4]],
ax=fig_test.subplots(),
plot_kws={},
time=[1, 2, 3, 4],
magnitude=[1, 2, 3, 4],
error=[1, 2, 3, 4],
features={"PeriodLS": 1, "Amplitude": 10},
)
ext.plot(**kwargs)
# expected
eax = fig_ref.subplots()
eax.set_title(
f"SignaturePhMag - {kwargs['phase_bins']}x{kwargs['mag_bins']}"
)
eax.set_xlabel("Phase")
eax.set_ylabel("Magnitude")
sns.heatmap(kwargs["value"], ax=eax, **kwargs["plot_kws"])
示例5: plot_heatmap
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_heatmap(
D, xordering=None, yordering=None, xticklabels=None,
yticklabels=None, vmin=None, vmax=None, ax=None):
import seaborn as sns
D = np.copy(D)
if ax is None:
_, ax = plt.subplots()
if xticklabels is None:
xticklabels = np.arange(D.shape[0])
if yticklabels is None:
yticklabels = np.arange(D.shape[1])
if xordering is not None:
xticklabels = xticklabels[xordering]
D = D[:,xordering]
if yordering is not None:
yticklabels = yticklabels[yordering]
D = D[yordering,:]
sns.heatmap(
D, yticklabels=yticklabels, xticklabels=xticklabels,
linewidths=0.2, cmap='BuGn', ax=ax, vmin=vmin, vmax=vmax)
ax.set_xticklabels(xticklabels, rotation=90)
ax.set_yticklabels(yticklabels, rotation=0)
return ax
示例6: _plot_weights
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def _plot_weights(self, title, file, layer_index=0, vmin=-5, vmax=5):
import seaborn as sns
sns.set_context("paper")
layers = self.iwp.estimator.steps[-1][1].coefs_
layer = layers[layer_index]
f, ax = plt.subplots(figsize=(18, 12))
weights = pd.DataFrame(layer)
weights.index = self.iwp.inputs
sns.set(font_scale=1.1)
# Draw a heatmap with the numeric values in each cell
sns.heatmap(
weights, annot=True, fmt=".1f", linewidths=.5, ax=ax,
cmap="difference", center=0, vmin=vmin, vmax=vmax,
# annot_kws={"size":14},
)
ax.tick_params(labelsize=18)
f.tight_layout()
f.savefig(file)
示例7: plot_heat
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_heat(ax, sat_delta_ti, min_limit):
""" Plot satmut deltas.
Args:
ax (Axis): matplotlib axis to plot to.
sat_delta_ti (4 x L_sm array): Single target delta matrix for saturated mutagenesis region,
min_limit (float): Minimum heatmap limit.
"""
vlim = max(min_limit, abs(sat_delta_ti).max())
sns.heatmap(
sat_delta_ti,
linewidths=0,
cmap='RdBu_r',
vmin=-vlim,
vmax=vlim,
xticklabels=False,
ax=ax)
ax.yaxis.set_ticklabels('ACGT', rotation='horizontal') # , size=10)
示例8: plot_kernel
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_kernel(kernel_weights, out_pdf):
depth, width = kernel_weights.shape
fig_width = 2 + 1.5*np.log2(width)
# normalize
kernel_weights -= kernel_weights.mean(axis=0)
# plot
sns.set(font_scale=1.5)
plt.figure(figsize=(fig_width, depth))
sns.heatmap(kernel_weights, cmap='PRGn', linewidths=0.2, center=0)
ax = plt.gca()
ax.set_xticklabels(range(1,width+1))
if depth == 4:
ax.set_yticklabels('ACGT', rotation='horizontal')
else:
ax.set_yticklabels(range(1,depth+1), rotation='horizontal')
plt.savefig(out_pdf)
plt.close()
示例9: plot_heat
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_heat(ax, sat_delta_ti, min_limit):
""" Plot satmut deltas.
Args:
ax (Axis): matplotlib axis to plot to.
sat_delta_ti (4 x L_sm array): Single target delta matrix for saturated mutagenesis region,
min_limit (float): Minimum heatmap limit.
"""
vlim = max(min_limit, np.nanmax(np.abs(sat_delta_ti)))
sns.heatmap(
sat_delta_ti,
linewidths=0,
cmap='RdBu_r',
vmin=-vlim,
vmax=vlim,
xticklabels=False,
ax=ax)
ax.yaxis.set_ticklabels('ACGT', rotation='horizontal') # , size=10)
示例10: plot_heat
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_heat(ax, sat_score_ti, min_limit=None):
""" Plot satmut deltas.
Args:
ax (Axis): matplotlib axis to plot to.
sat_delta_ti (L_sm x 4 array): Single target delta matrix for saturated mutagenesis region,
"""
if np.max(sat_score_ti) < min_limit:
vmax = min_limit
else:
vmax = None
sns.heatmap(
sat_score_ti.T,
linewidths=0,
xticklabels=False,
yticklabels=False,
cmap='Blues',
vmax=vmax,
ax=ax)
# yticklabels break the plot for some reason
# ax.yaxis.set_ticklabels('ACGT', rotation='horizontal')
示例11: heatmap
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def heatmap(self, pct_change=False, **kwargs):
"""
Generate a seaborn heatmap for correlations between assets.
Parameters:
- pct_change: Whether or not to show the correlations of the
daily percent change in price or just use
the closing price.
- kwargs: Keyword arguments to pass down to `sns.heatmap()`
Returns:
A seaborn heatmap
"""
pivot = self.data.pivot_table(
values='close', index=self.data.index, columns='name'
)
if pct_change:
pivot = pivot.pct_change()
return sns.heatmap(pivot.corr(), annot=True, center=0, **kwargs)
示例12: plot_dailyhold
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_dailyhold(self, start=None, end=None):
"""
使用热力图画出每日持仓
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 8))
sns.heatmap(self.account.daily_hold.reset_index().set_index('date').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax)
ax.set_title('HOLD TABLE --ACCOUNT: {}'.format(self.account.account_cookie))
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt
示例13: plot_signal
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_signal(self, start=None, end=None):
"""
使用热力图画出买卖信号
"""
start = self.account.start_date if start is None else start
end = self.account.end_date if end is None else end
_, ax = plt.subplots(figsize=(20, 18))
sns.heatmap(self.account.trade.reset_index().drop('account_cookie',
axis=1).set_index('datetime').loc[start:end],
cmap="YlGnBu",
linewidths=0.05,
ax=ax)
ax.set_title('SIGNAL TABLE --ACCOUNT: {}'.format(self.account.account_cookie))
ax.set_xlabel('Code')
ax.set_ylabel('DATETIME')
return plt
示例14: plot_unsmoothed
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_unsmoothed():
corpus, T = generate_corpus()
L = LDA(T)
L.train(corpus, verbose=False)
fig, axes = plt.subplots(1, 2)
ax1 = sns.heatmap(L.beta, xticklabels=[], yticklabels=[], ax=axes[0])
ax1.set_xlabel("Topics")
ax1.set_ylabel("Words")
ax1.set_title("Recovered topic-word distribution")
ax2 = sns.heatmap(L.gamma, xticklabels=[], yticklabels=[], ax=axes[1])
ax2.set_xlabel("Topics")
ax2.set_ylabel("Documents")
ax2.set_title("Recovered document-topic distribution")
plt.savefig("img/plot_unsmoothed.png", dpi=300)
plt.close("all")
示例15: plot_embedding
# 需要导入模块: import seaborn [as 别名]
# 或者: from seaborn import heatmap [as 别名]
def plot_embedding(embed:OrderedDict, feat:str, savename:Optional[str]=None, settings:PlotSettings=PlotSettings()) -> None:
r'''
Visualise weights in provided categorical entity-embedding matrix
Arguments:
embed: state_dict of trained nn.Embedding
feat: name of feature embedded
savename: Optional name of file to which to save the plot of feature importances
settings: :class:`~lumin.plotting.plot_settings.PlotSettings` class to control figure appearance
'''
with sns.axes_style(**settings.style):
plt.figure(figsize=(settings.w_small, settings.h_small))
sns.heatmap(to_np(embed['weight']), annot=True, fmt='.1f', linewidths=.5, cmap=settings.div_palette, annot_kws={'fontsize':settings.leg_sz})
plt.xlabel("Embedding", fontsize=settings.lbl_sz, color=settings.lbl_col)
plt.ylabel(feat, fontsize=settings.lbl_sz, color=settings.lbl_col)
plt.xticks(fontsize=settings.tk_sz, color=settings.tk_col)
plt.yticks(fontsize=settings.tk_sz, color=settings.tk_col)
plt.title(settings.title, fontsize=settings.title_sz, color=settings.title_col, loc=settings.title_loc)
if savename is not None: plt.savefig(settings.savepath/f'{savename}{settings.format}', bbox_inches='tight')
plt.show()