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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?










0












$begingroup$


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?



enter image description here










share|improve this question









$endgroup$
















    0












    $begingroup$


    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?



    enter image description here










    share|improve this question









    $endgroup$














      0












      0








      0





      $begingroup$


      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?



      enter image description here










      share|improve this question









      $endgroup$




      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?



      enter image description here







      scipy interpolation






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 37 mins ago









      monmon

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      1073




















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