How to understand the return values of scipy.interpolate.splrep The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow to mask or crop IDW resultsHow to calculate VIF in python 3.6 with scipy 1.0.0?Are Hadoop and Python SciPy used for the same?How to combine scipy sparse csr matrix to pandas dataframe. | Combining text feature with numerical featuresError in Calculating the z Score for Normality Detection using numpy and scipyRegression of complex functions over the sphere using neural networksHow can I transform (pre-process) pure count data for PCA analysis?
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How to understand the return values of scipy.interpolate.splrep
The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to mask or crop IDW resultsHow to calculate VIF in python 3.6 with scipy 1.0.0?Are Hadoop and Python SciPy used for the same?How to combine scipy sparse csr matrix to pandas dataframe. | Combining text feature with numerical featuresError in Calculating the z Score for Normality Detection using numpy and scipyRegression of complex functions over the sphere using neural networksHow can I transform (pre-process) pure count data for PCA analysis?
$begingroup$
Background
Continuation of Spline interpolation - why cube with 2nd derivative
as following Cubic Spline Interpolation in youtube. The example in the youtube is below.
Implemented using scipy.interpolate.splrep and try to understand what the returns of the splrep function are.
Given the set of data points (x[i], y[i]) determine a smooth spline approximation of degree k on the interval xb <= x <= xe.
Returns
tck : tuple
A tuple
(t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
import numpy as np
from pylab import plt, mpl
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline
def create_plot(x, y, styles, labels, axlabels):
plt.figure(figsize=(10, 6))
for i in range(len(x)):
plt.plot(x[i], y[i], styles[i], label=labels[i])
plt.xlabel(axlabels[0])
plt.ylabel(axlabels[1])
plt.legend(loc=0)
x = np.array([3.0, 4.5, 7.0, 9.0])
y = np.array([2.5, 1.0, 2.5, 0.5])
create_plot([x], [y], ['b'], ['y'], ['x', 'y'])
import scipy.interpolate as spi
interpolation = spi.splrep(x, y, k=3)
IX = np.linspace(3, 9, 100)
IY = spi.splev(IX, interpolation)
create_plot(
[x, IX],
[y, IY],
['b', 'ro'],
['x', 'IY:interpolation'],
['x', 'y']
)
Questions
How to interpret and understand the return values and which resources to look into to understand?
A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
The return value on Knots
interpolation[0]
array([3., 3., 3., 3., 9., 9., 9., 9.])
I thought the first tuple element would be the knots which would be the x, but not. What are these 3., 3. ... values?
The return values on B-spline co-efficient
interpolation[1]
array([ 2.5 , -2.21111111, 6.18888889, 0.5 , 0. , 0. , 0. , 0. ])
Please help or suggest where I should look into and what to understand about "B-spline coefficient" to be able to interpret these values?
The solution of the first interval is (0.186566, 1.6667, 0.24689), hence I thought these values would be in the 2nd element, but not. How the solution values would relate to the return values?
scipy interpolation
asked 37 mins ago
monmon
1073
$endgroup$
add a comment |
$begingroup$
Background
Continuation of Spline interpolation - why cube with 2nd derivative
as following Cubic Spline Interpolation in youtube. The example in the youtube is below.
Implemented using scipy.interpolate.splrep and try to understand what the returns of the splrep function are.
Given the set of data points (x[i], y[i]) determine a smooth spline approximation of degree k on the interval xb <= x <= xe.
Returns
tck : tuple
A tuple
(t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
import numpy as np
from pylab import plt, mpl
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline
def create_plot(x, y, styles, labels, axlabels):
plt.figure(figsize=(10, 6))
for i in range(len(x)):
plt.plot(x[i], y[i], styles[i], label=labels[i])
plt.xlabel(axlabels[0])
plt.ylabel(axlabels[1])
plt.legend(loc=0)
x = np.array([3.0, 4.5, 7.0, 9.0])
y = np.array([2.5, 1.0, 2.5, 0.5])
create_plot([x], [y], ['b'], ['y'], ['x', 'y'])
import scipy.interpolate as spi
interpolation = spi.splrep(x, y, k=3)
IX = np.linspace(3, 9, 100)
IY = spi.splev(IX, interpolation)
create_plot(
[x, IX],
[y, IY],
['b', 'ro'],
['x', 'IY:interpolation'],
['x', 'y']
)
Questions
How to interpret and understand the return values and which resources to look into to understand?
A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
The return value on Knots
interpolation[0]
array([3., 3., 3., 3., 9., 9., 9., 9.])
I thought the first tuple element would be the knots which would be the x, but not. What are these 3., 3. ... values?
The return values on B-spline co-efficient
interpolation[1]
array([ 2.5 , -2.21111111, 6.18888889, 0.5 , 0. , 0. , 0. , 0. ])
Please help or suggest where I should look into and what to understand about "B-spline coefficient" to be able to interpret these values?
The solution of the first interval is (0.186566, 1.6667, 0.24689), hence I thought these values would be in the 2nd element, but not. How the solution values would relate to the return values?
scipy interpolation
asked 37 mins ago
monmon
1073
$endgroup$
add a comment |
$begingroup$
Background
Continuation of Spline interpolation - why cube with 2nd derivative
as following Cubic Spline Interpolation in youtube. The example in the youtube is below.
Implemented using scipy.interpolate.splrep and try to understand what the returns of the splrep function are.
Given the set of data points (x[i], y[i]) determine a smooth spline approximation of degree k on the interval xb <= x <= xe.
Returns
tck : tuple
A tuple
(t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
import numpy as np
from pylab import plt, mpl
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline
def create_plot(x, y, styles, labels, axlabels):
plt.figure(figsize=(10, 6))
for i in range(len(x)):
plt.plot(x[i], y[i], styles[i], label=labels[i])
plt.xlabel(axlabels[0])
plt.ylabel(axlabels[1])
plt.legend(loc=0)
x = np.array([3.0, 4.5, 7.0, 9.0])
y = np.array([2.5, 1.0, 2.5, 0.5])
create_plot([x], [y], ['b'], ['y'], ['x', 'y'])
import scipy.interpolate as spi
interpolation = spi.splrep(x, y, k=3)
IX = np.linspace(3, 9, 100)
IY = spi.splev(IX, interpolation)
create_plot(
[x, IX],
[y, IY],
['b', 'ro'],
['x', 'IY:interpolation'],
['x', 'y']
)
Questions
How to interpret and understand the return values and which resources to look into to understand?
A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
The return value on Knots
interpolation[0]
array([3., 3., 3., 3., 9., 9., 9., 9.])
I thought the first tuple element would be the knots which would be the x, but not. What are these 3., 3. ... values?
The return values on B-spline co-efficient
interpolation[1]
array([ 2.5 , -2.21111111, 6.18888889, 0.5 , 0. , 0. , 0. , 0. ])
Please help or suggest where I should look into and what to understand about "B-spline coefficient" to be able to interpret these values?
The solution of the first interval is (0.186566, 1.6667, 0.24689), hence I thought these values would be in the 2nd element, but not. How the solution values would relate to the return values?
scipy interpolation
asked 37 mins ago
monmon
1073
$endgroup$
Background
Continuation of Spline interpolation - why cube with 2nd derivative
as following Cubic Spline Interpolation in youtube. The example in the youtube is below.
Implemented using scipy.interpolate.splrep and try to understand what the returns of the splrep function are.
Given the set of data points (x[i], y[i]) determine a smooth spline approximation of degree k on the interval xb <= x <= xe.
Returns
tck : tuple
A tuple
(t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
import numpy as np
from pylab import plt, mpl
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
%matplotlib inline
def create_plot(x, y, styles, labels, axlabels):
plt.figure(figsize=(10, 6))
for i in range(len(x)):
plt.plot(x[i], y[i], styles[i], label=labels[i])
plt.xlabel(axlabels[0])
plt.ylabel(axlabels[1])
plt.legend(loc=0)
x = np.array([3.0, 4.5, 7.0, 9.0])
y = np.array([2.5, 1.0, 2.5, 0.5])
create_plot([x], [y], ['b'], ['y'], ['x', 'y'])
import scipy.interpolate as spi
interpolation = spi.splrep(x, y, k=3)
IX = np.linspace(3, 9, 100)
IY = spi.splev(IX, interpolation)
create_plot(
[x, IX],
[y, IY],
['b', 'ro'],
['x', 'IY:interpolation'],
['x', 'y']
)
Questions
How to interpret and understand the return values and which resources to look into to understand?
A tuple (t,c,k) containing the vector of knots, the B-spline coefficients, and the degree of the spline.
The return value on Knots
interpolation[0]
array([3., 3., 3., 3., 9., 9., 9., 9.])
I thought the first tuple element would be the knots which would be the x, but not. What are these 3., 3. ... values?
The return values on B-spline co-efficient
interpolation[1]
array([ 2.5 , -2.21111111, 6.18888889, 0.5 , 0. , 0. , 0. , 0. ])
Please help or suggest where I should look into and what to understand about "B-spline coefficient" to be able to interpret these values?
The solution of the first interval is (0.186566, 1.6667, 0.24689), hence I thought these values would be in the 2nd element, but not. How the solution values would relate to the return values?
scipy interpolation
scipy interpolation
asked 37 mins ago
monmon
1073
asked 37 mins ago
monmon
1073
asked 37 mins ago
monmon
1073
asked 37 mins ago
monmon
1073
asked 37 mins ago
monmon
1073
1073
add a comment |
add a comment |
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