FdemData

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@FdemData_Class Module describing an EMData Set where channels are associated with an xyz co-ordinate

class geobipy.src.classes.data.dataset.FdemData.FdemData(system=None, **kwargs)

Class extension to geobipy.Data defining a Fourier domain electro magnetic data set

FdemData(nPoints, nFrequencies, system)

Parameters:

nPoints (int, optional) – Number of observations in the data set
nFrequencies (int, optional) – Number of measurement frequencies
system (str or geobipy.FdemSystem, optional) –
- If str: Must be a file name from which to read FD system information.
- If FdemSystem: A deepcopy is made.

Returns:

out – Contains x, y, z, elevation, and data values for a frequency domain dataset.

Return type:

FdemData

Notes

FdemData.read() requires a data filename and a system class or system filename to be specified. The data file is structured using columns with the first line containing header information. The header should contain the following entries Line [ID or FID] [X or N or northing] [Y or E or easting] [Z or DTM or dem_elev] [Alt or Laser or bheight] [I Q] … [I Q] Do not include brackets [] [I Q] are the in-phase and quadrature values for each measurement frequency.

If a system filename is given, it too is structured using columns with the first line containing header information Each subsequent row contains the information for each measurement frequency

freq tor tmom tx ty tz ror rmom rx ry rz 378 z 1 0 0 0 z 1 7.93 0 0 1776 z 1 0 0 0 z 1 7.91 0 0 …

where tor and ror are the orientations of the transmitter/reciever loops [x or z]. tmom and rmom are the moments of the loops. t/rx,y,z are the loop offsets from the observation locations in the data file.

Bcast(world, root=0)

Broadcast the FdemData using MPI

Parameters:: world (mpi4py.MPI.COMM_WORLD) – MPI communicator
Returns:: out – A copy of the data on each core
Return type:: geobipy.FdemData

Examples

>>> from mpi4py import MPI
>>> from geobipy import FdemData

>>> world = MPI.COMM_WORLD

>>> rank = world.rank

>>> if (rank == 0): # Only the master reads in the data
>>>     D = FdemData()
>>>     D.read(dataFile, systemFile)
>>> else:
>>>     D = FdemData() # Must instantiate an empty object to Bcast

>>> D2 = D.Bcast(world)

Scatterv(starts, chunks, world, root=0)

Distributes the FdemData between all cores using MPI

Parameters:

starts (array of ints) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the starting index for a chunk to be sent to that core. e.g. starts[0] is the starting index for rank = 0.
chunks (array of ints) – 1D array of ints with size equal to the number of MPI ranks. Each element gives the size of a chunk to be sent to that core. e.g. chunks[0] is the chunk size for rank = 0.
world (mpi4py.MPI.COMM_WORLD) – The MPI communicator

Returns:

out – The data distributed amongst cores

Return type:

geobipy.FdemData

Examples

>>> from mpi4py import MPI
>>> from geobipy import FdemData
>>> import numpy as np

>>> world = MPI.COMM_WORLD

>>> rank = world.rank

>>> if (rank == 0): # Only the master reads in the data
>>>     D = FdemData()
>>>     D.read(dataFile, systemFile)
>>> else:
>>>     D = FdemData() # Must instantiate an empty object to Bcast

>>> # In this example, assume there are 10 data and 4 cores
>>> start = asarray([0, 2, 4, 6])
>>> chunks = asarray([2, 2, 2, 4])

>>> D2 = D.Scatterv(start, chunks, world)

append(other)

Append pointclouds together

Parameters:: other (geobipy.PointCloud3D) – 3D pointcloud

createHdf(parent, myName, withPosterior=True, fillvalue=None): Create the hdf group metadata in file parent: HDF object to create a group inside myName: Name of the group

datapoint(index=None, fiducial=None)

Get the ith data point from the data set

Parameters:

index (int, optional) – Index of the data point to get.
fiducial (float, optional) – Fiducial of the data point to get.

Returns:

out – The data point.

Return type:

geobipy.FdemDataPoint

Raises:

Exception – If neither an index or fiducial are given.

fileInformation(): Description of the data file.

classmethod fromHdf(grp, **kwargs): Reads the object from a HDF group

getFrequency(channel, system=0)

Return the measurement frequency of the channel

Parameters:

channel (int) – Channel number
system (int, optional) – System number

Returns:

out – The measurement frequency of the channel

Return type:

float

getMeasurementType(channel, system=0)

Returns the measurement type of the channel

Parameters:

channel (int) – Channel number
system (int, optional) – System number

Returns:

out – Either “In-Phase “ or “Quadrature “

Return type:

str

property nActiveData

Number of active data per data point.

For each data point, counts the number of channels that are NOT nan.

Returns:: out – Number of active data
Return type:: int

plotLine(line, system=0, x='index', **kwargs): Plot the specified line

plot_data(x='index', channels=None, **kwargs)

Plots the specifed channels as a line plot.

Plots the channels along a specified co-ordinate e.g. ‘x’. A legend is auto generated.

Parameters:

xAxis (str) – If xAxis is ‘index’, returns numpy.arange(self.nPoints) If xAxis is ‘x’, returns self.x If xAxis is ‘y’, returns self.y If xAxis is ‘z’, returns self.z If xAxis is ‘r2d’, returns cumulative distance along the line in 2D using x and y. If xAxis is ‘r3d’, returns cumulative distance along the line in 3D using x, y, and z.
channels (ints, optional) – The indices of the channels to plot. All are plotted if channels is None.
legend (bool) – Attach a legend to the plot. Default is True.

Returns:

ax (matplotlib.axes) – Plot axes handle
legend (matplotlib.legend.Legend) – The attached legend.