sicor.Tools.NM package

Submodules

sicor.Tools.NM.c_digitize module

sicor.Tools.NM.interp_spectral_n_1 module

Fast interpolation in 1-dim arrays.

sicor.Tools.NM.interp_spectral_n_1.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_1.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 1 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension,

not including values for the spectral dimension

maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_1.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_10 module

Fast interpolation in 10-dim arrays.

sicor.Tools.NM.interp_spectral_n_10.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_10.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 10 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension,

not including values for the spectral dimension

maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_10.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_11 module

Fast interpolation in 11-dim arrays.

sicor.Tools.NM.interp_spectral_n_11.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_11.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 11 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_11.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_12 module

Fast interpolation in 12-dim arrays.

sicor.Tools.NM.interp_spectral_n_12.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_12.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 12 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_12.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_13 module

Fast interpolation in 13-dim arrays.

sicor.Tools.NM.interp_spectral_n_13.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_13.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 13 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_13.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_14 module

Fast interpolation in 14-dim arrays.

sicor.Tools.NM.interp_spectral_n_14.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_14.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 14 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean cac hes hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_14.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_2 module

Fast interpolation in 2-dim arrays.

sicor.Tools.NM.interp_spectral_n_2.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_2.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 2 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each

dimension, not including values for the spectral dimension

maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_2.sign(x)

Jited signum function.

sicor.Tools.NM.interp_spectral_n_3 module

Fast interpolation in 3-dim arrays.

sicor.Tools.NM.interp_spectral_n_3.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_3.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 3 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_3.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_4 module

Fast interpolation in 4-dim arrays.

sicor.Tools.NM.interp_spectral_n_4.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_4.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 4 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_4.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_5 module

sicor.Tools.NM.interp_spectral_n_5.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_5.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 5 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_5.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_6 module

Fast interpolation in 6-dim arrays.

sicor.Tools.NM.interp_spectral_n_6.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_6.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 6 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_6.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_7 module

Fast interpolation in 7-dim arrays.

sicor.Tools.NM.interp_spectral_n_7.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_7.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 7 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_7.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_8 module

Fast interpolation in 8-dim arrays.

sicor.Tools.NM.interp_spectral_n_8.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_8.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 8 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_8.sign(x)

Jit version of signum function.

sicor.Tools.NM.interp_spectral_n_9 module

Fast interpolation in 9-dim arrays.

sicor.Tools.NM.interp_spectral_n_9.int1d(nx, xx, yy, x)

nx: length of xx and yy vector xx: indepent data yy: dependent data return: linearly interpolated point

class sicor.Tools.NM.interp_spectral_n_9.intp(data, axes=None, jacobean=True, caching=False, maxsize=2000, hash_pattern='%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,%.2f,')

Bases: object

Wrapper class for 3D interpolation with intp 9 dimensions

data : data: numpy array, last dimension is spectral one, over which interpolation is beeing carried over axes : list of 1D numpy arrays of shape data.shape(i), contaoning the scales for each dimension, not including values for the spectral dimension maxsize: max number of cached results, both for Jacobean and non Jacobean caches hash_pattern: string to convert py array/list to hash, e.g. n_dim”%.4f” % tuple(pt)

interpolate(pt)

point at which should be interpolated pt: nump float array, interpolation point return: interpolation point,gradient

settings(jacobean, caching)

Consistently set values for jacobean and caching (separate for Jacobean)

sicor.Tools.NM.interp_spectral_n_9.sign(x)

Jit version of signum function.

sicor.Tools.NM.interpolate_n module

class sicor.Tools.NM.interpolate_n.interpolate_n(lut, axes)

Bases: object

wrapper arround ‘map_coordinates’ n-dimensional linear interpolation

Example:

1. initialisation:

   from sicor.Tools.NM.interpolate_n import interpolate_n as intpn

   pn=intpn.interpolate_n(LUT,axes)

   LUT is a Nd numpy array axes is a tupel of 1d numpy arrays

   The number of axes must correspond to the number of dimensions an the size of the axes must correspond to the size of the dimensions

2. recall:

   >>> result=pn.recall(pos)
     pos   is a (ndim,nsample) array with the positions
           if is_index=True pos is already a float index
   

recall(positions, is_index=False)

Module contents