Пример #1
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 def coordinateGrid_sanity(self):
     x = np.arange(5)
     y = np.arange(10) + 10.
     z = np.arange(2) + 100.
     g = fuf.coordinateGrid(x, y, z)
     self.assertEqual(g[0, 0, 0, 0], 0.0)
     self.assertEqual(g[0, 0, 0, 1], 10.0)
     self.assertEqual(g[0, 0, 0, 2], 100.0)
     self.assertEqual(g[1, 0, 1, 0], 1.0)
     self.assertEqual(g[1, 2, 1, 1], 12.0)
     self.assertEqual(g[1, 2, 1, 2], 101.0)
Пример #2
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 def coordinateGrid_sanity(self):
   x = np.arange(5)
   y = np.arange(10) + 10.
   z = np.arange(2) + 100.
   g = fuf.coordinateGrid(x, y, z)
   self.assertEqual(g[0,0,0,0], 0.0)
   self.assertEqual(g[0,0,0,1], 10.0)
   self.assertEqual(g[0,0,0,2], 100.0)
   self.assertEqual(g[1,0,1,0], 1.0)
   self.assertEqual(g[1,2,1,1], 12.0)
   self.assertEqual(g[1,2,1,2], 101.0)
Пример #3
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 def sanity_coordinateGridExample(self):
   from PyAstronomy import funcFit as fuf
   import numpy as np
   
   # Constructing the two individual coordinate axes
   x = np.linspace(-2.,2.,50)
   y = np.linspace(-2.,2.,50)
   
   # Applying funcFit's "coordinateGrid" helper function
   # to built appropriate array-index -> coordinate mapping
   # needed for nD fitting.
   g = fuf.coordinateGrid(x, y)
   
   print "(x, y) coordinates at index (11, 28): ", g[11,28]
Пример #4
0
  def sanity_2dGaussFit(self):
    from PyAstronomy import funcFit as fuf
    import numpy as np
    import matplotlib.pylab as plt
    
    # Constructing the individual coordinate axes
    x = np.linspace(-2.,2.,50)
    y = np.linspace(-2.,2.,50)
    # Applying funcFit's "coordinateGrid" helper function
    # to built appropriate array-index -> coordinate mapping
    # needed for nD fitting.
    g = fuf.coordinateGrid(x, y)
    
    # Create the 2d-Gaussian model and assign
    # some model parameters.
    gf = fuf.GaussFit2d()
    gf["sigx"] = 0.75
    gf["sigy"] = 0.4
    gf["A"] = 1.0
    gf["rho"] = 0.4
    
    # Get the "data" by evaluating the model
    # and adding some noise. Note that the coordinate
    # mapping (array g) is passed to evaluate here.
    im = gf.evaluate(g)
    im += np.reshape(np.random.normal(0.0, 0.1, 2500), (50,50))
    err = np.ones((50,50))*0.1
    
    # Thaw parameters and fit
    gf.thaw(["A", "rho"])
    gf.fit(g, im, yerr=err)
    
    # Show the resulting parameter values ...
    gf.parameterSummary()
    
    # ... and plot the result.
    plt.title("Image data")
    plt.imshow(np.transpose(im), origin="lower")
#    plt.show()
    plt.title("Residuals")
    plt.imshow(np.transpose(im - gf.evaluate(g)), origin="lower")