Interpolation Example¶
Interpolation allows you to create data points inside of a defined range. It interpolates the data points, the interpolate_table function reassigns the independent variable to a user defined list and interpolates all the other values in data, returning a data table with the same format. It should be noted that this does not extrapolate, so if the new range is outside the old range it will fail.
In [1]:
from pyMez import *
In [2]:
from pyMez.Code.Analysis.Interpolation import *
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# first we define a table where the independent x-axis is the first column
x_data=np.linspace(-10,10,10).tolist()
data_table=[[x,2.1*x,1.25*x**2] for x in x_data]
column_names=["x","line","second_order"]
data_table=AsciiDataTable(data=data_table,
column_names=column_names,
header=["A test of the data table"],
column_types=["float","float","float"])
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print(data_table)
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# now we want a lot of data points in between the defined range
interpolated_table=interpolate_table(table=data_table,independent_variable_list=np.linspace(-5,5,50))
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print(interpolated_table)
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%matplotlib inline
# for demonstation we can now plot to compare them
plt.plot(data_table["x"],data_table["line"],label="original")
plt.plot(interpolated_table["x"],interpolated_table["line"],label="interpolated")
plt.legend()
Out[7]:
In [8]:
# The second order
plt.plot(data_table["x"],data_table["second_order"],label="original")
plt.plot(interpolated_table["x"],interpolated_table["second_order"],label="interpolated")
plt.legend()
Out[8]:
