Python 如何绘制未连接所有点的样条曲线
我试图用样条线绘制大量的数据通过这些点,应该是这样的 但是当我试着用plotly做这件事时,样条曲线坚持要穿过所有像这样的点 当第一个图像仅为数据点,第二个图像为样条曲线时 我尝试的代码是Python 如何绘制未连接所有点的样条曲线,python,plotly,spline,Python,Plotly,Spline,我试图用样条线绘制大量的数据通过这些点,应该是这样的 但是当我试着用plotly做这件事时,样条曲线坚持要穿过所有像这样的点 当第一个图像仅为数据点,第二个图像为样条曲线时 我尝试的代码是 dates = [dates_arr] x = dates.strftime("%Y-%m-%d") y = [data_points] xy_data = go.Scatter(x=x, y=y, mode='markers', marker=dict(size=4), name='AAPL') mov
dates = [dates_arr]
x = dates.strftime("%Y-%m-%d")
y = [data_points]
xy_data = go.Scatter(x=x, y=y, mode='markers', marker=dict(size=4),
name='AAPL')
mov_avg = go.Scatter(x=x, y=y, name="spline",text=
["spline"],hoverinfo='text+name',line_shape='spline', line_smoothing = 1.3)
data = [xy_data, mov_avg]
py.iplot(data, filename='Spline fit')
#################################
first_plot_url = py.plot(data, filename='apple stock moving average',
auto_open=True, )
有人知道吗?在第一幅图像中,样条曲线是所有数据点的近似值。在代码段中,
spline
是一个属性,设置为数据点的图形表示之间的行。这些是非常不同的事情。要实现您所期望的目标,您应该仔细查看用户对和的贡献np8
和Matthew Drury
。您还应该仔细了解不同样条曲线是如何计算的。下面的图是由名为Snippet 2的代码示例生成的,其中a是估计的:下面的整个内容。它相当大,但这主要是因为函数get\u natural\u cubic\u spline\u model
from。plotly部分仅遵循以下逻辑:
片段1:仅关注绘图部分
# data points
points = go.Scatter(
x = x,
y = y,
mode = 'markers',
name = 'iris')
# spline
line = go.Scatter(
x = df_spline['x'],
y = df_spline['y_est'],
mode = 'lines',
name = 'spline')
# gather data
data=[points, line]
# build figure
fig=go.Figure(data)
# plot
fig.show()
绘图:
片段2:整件事
# imports
import plotly.express as px
import plotly.graph_objs as go
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
# sample data set
iris = px.data.iris() # iris is a pandas DataFrame
x=iris['sepal_length']
y=iris['sepal_width']
# spline using function from https://stackoverflow.com/questions/51321100/python-natural-smoothing-splines
def get_natural_cubic_spline_model(x, y, minval=None, maxval=None, n_knots=None, knots=None):
"""
Get a natural cubic spline model for the data.
For the knots, give (a) `knots` (as an array) or (b) minval, maxval and n_knots.
If the knots are not directly specified, the resulting knots are equally
space within the *interior* of (max, min). That is, the endpoints are
*not* included as knots.
Parameters
----------
x: np.array of float
The input data
y: np.array of float
The outpur data
minval: float
Minimum of interval containing the knots.
maxval: float
Maximum of the interval containing the knots.
n_knots: positive integer
The number of knots to create.
knots: array or list of floats
The knots.
Returns
--------
model: a model object
The returned model will have following method:
- predict(x):
x is a numpy array. This will return the predicted y-values.
"""
if knots:
spline = NaturalCubicSpline(knots=knots)
else:
spline = NaturalCubicSpline(max=maxval, min=minval, n_knots=n_knots)
p = Pipeline([
('nat_cubic', spline),
('regression', LinearRegression(fit_intercept=True))
])
p.fit(x, y)
return p
class AbstractSpline(BaseEstimator, TransformerMixin):
"""Base class for all spline basis expansions."""
def __init__(self, max=None, min=None, n_knots=None, n_params=None, knots=None):
if knots is None:
if not n_knots:
n_knots = self._compute_n_knots(n_params)
knots = np.linspace(min, max, num=(n_knots + 2))[1:-1]
max, min = np.max(knots), np.min(knots)
self.knots = np.asarray(knots)
@property
def n_knots(self):
return len(self.knots)
def fit(self, *args, **kwargs):
return self
class NaturalCubicSpline(AbstractSpline):
"""Apply a natural cubic basis expansion to an array.
The features created with this basis expansion can be used to fit a
piecewise cubic function under the constraint that the fitted curve is
linear *outside* the range of the knots.. The fitted curve is continuously
differentiable to the second order at all of the knots.
This transformer can be created in two ways:
- By specifying the maximum, minimum, and number of knots.
- By specifying the cutpoints directly.
If the knots are not directly specified, the resulting knots are equally
space within the *interior* of (max, min). That is, the endpoints are
*not* included as knots.
Parameters
----------
min: float
Minimum of interval containing the knots.
max: float
Maximum of the interval containing the knots.
n_knots: positive integer
The number of knots to create.
knots: array or list of floats
The knots.
"""
def _compute_n_knots(self, n_params):
return n_params
@property
def n_params(self):
return self.n_knots - 1
def transform(self, X, **transform_params):
X_spl = self._transform_array(X)
if isinstance(X, pd.Series):
col_names = self._make_names(X)
X_spl = pd.DataFrame(X_spl, columns=col_names, index=X.index)
return X_spl
def _make_names(self, X):
first_name = "{}_spline_linear".format(X.name)
rest_names = ["{}_spline_{}".format(X.name, idx)
for idx in range(self.n_knots - 2)]
return [first_name] + rest_names
def _transform_array(self, X, **transform_params):
X = X.squeeze()
try:
X_spl = np.zeros((X.shape[0], self.n_knots - 1))
except IndexError: # For arrays with only one element
X_spl = np.zeros((1, self.n_knots - 1))
X_spl[:, 0] = X.squeeze()
def d(knot_idx, x):
def ppart(t): return np.maximum(0, t)
def cube(t): return t*t*t
numerator = (cube(ppart(x - self.knots[knot_idx]))
- cube(ppart(x - self.knots[self.n_knots - 1])))
denominator = self.knots[self.n_knots - 1] - self.knots[knot_idx]
return numerator / denominator
for i in range(0, self.n_knots - 2):
X_spl[:, i+1] = (d(i, X) - d(self.n_knots - 2, X)).squeeze()
return X_spl
# spline calculations
m1=get_natural_cubic_spline_model(x, y, minval=min(x), maxval=max(x), n_knots=6)
y_est_m1=m1.predict(x)
# gather results and sort them so that the line is not messed up
df_spline=pd.DataFrame({'x':x,
'y':y,
'y_est':m1.predict(x)})
df_spline=df_spline.sort_values(by=['x'])
### PLOTLY ###
# data source
points = go.Scatter(
x = x,
y = y,
mode = 'markers',
name = 'iris')
# spline
line = go.Scatter(
x = df_spline['x'],
y = df_spline['y_est'],
mode = 'lines',
name = 'spline')
# gather data
data=[points, line]
# build figure
fig=go.Figure(data)
# plot
fig.show()