Statistics¶
Methods Summary
Evaluate the integral of the dependent variables over all dimensions. |
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Evaluate the mean coordinate of a dependent variable along each dimension. |
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Evaluate the variance of the dependent variables along each dimension. |
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Evaluate the standard deviation of the dependent variables along each dimension. |
Method Documentation
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csdmpy.statistics.
integral
(csdm)[source]¶ Evaluate the integral of the dependent variables over all dimensions.
- Parameters
csdm – A csdm object.
- Returns
A list of integrals corresponding to the list of the dependent variables. If only one dependent variable is present, return a quantity instead.
Example
>>> import csdmpy.statistics as stat >>> x = np.arange(100) * 2 - 100.0 >>> gauss = np.exp(-((x - 5.) ** 2) / (2 * 4. ** 2)) >>> csdm = cp.as_csdm(gauss, unit='T') >>> csdm.dimensions[0] = cp.as_dimension(x, unit="m") >>> stat.integral(csdm) <Quantity 10.0265131 m T>
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csdmpy.statistics.
mean
(csdm)[source]¶ Evaluate the mean coordinate of a dependent variable along each dimension.
- Parameters
csdm – A csdm object.
- Returns
A list of tuples, where each tuple represents the mean coordinates of the dependent variables. If only one dependent variable is present, return a tuple of coordinates instead.
Example
>>> stat.mean(csdm) (<Quantity 5. m>,)
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csdmpy.statistics.
var
(csdm)[source]¶ Evaluate the variance of the dependent variables along each dimension.
- Parameters
csdm – A csdm object.
- Returns
A list of tuples, where each tuple is the variance along the dimensions of the dependent variables. If only one dependent variable is present, return a tuple instead.
Example
>>> stat.var(csdm) (<Quantity 16. m2>,)
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csdmpy.statistics.
std
(csdm)[source]¶ Evaluate the standard deviation of the dependent variables along each dimension.
- Parameters
csdm – A csdm object.
- Returns
A list of tuples, where each tuple is the standard deviation along the dimensions of the dependent variables. If only one dependent variable is present, return a tuple instead.
Example
>>> stat.std(csdm) (<Quantity 4. m>,)