###############################################################################
# Obtaining a datapoint from a dataset
# ++++++++++++++++++++++++++++++++++++
# More often than not, our observed data is stored in a file on disk.
# We can read in a dataset and pull datapoints from it.
#
# For more information about the time domain data set, see :ref:`Time domain dataset`
# The data file name
dataFile = [dataFolder + 'Skytem_High.txt', dataFolder + 'Skytem_Low.txt']
# The EM system file name
systemFile = [dataFolder + 'SkytemHM-SLV.stm', dataFolder + 'SkytemLM-SLV.stm']
################################################################################
# Initialize and read an EM data set
D = TdemData()
D.read(dataFile, systemFile)
################################################################################
# Get a datapoint from the dataset
tdp = D.datapoint(0)
plt.figure()
tdp.plot()
################################################################################
# Using a time domain datapoint
# +++++++++++++++++++++++++++++
################################################################################
# We can define a 1D layered earth model, and use it to predict some data
par = StatArray(np.r_[500.0, 20.0], "Conductivity", "$\frac{S}{m}$")
# Obtaining a datapoint from a dataset
# ++++++++++++++++++++++++++++++++++++
# More often than not, our observed data is stored in a file on disk.
# We can read in a dataset and pull datapoints from it.
#
# For more information about the time domain data set, see :ref:`Time domain dataset`
# The data file name
dataFile = dataFolder + 'Skytem.csv'
# The EM system file name
systemFile = [dataFolder + 'SkytemHM-SLV.stm', dataFolder + 'SkytemLM-SLV.stm']
################################################################################
# Initialize and read an EM data set
# Prepare the dataset so that we can read a point at a time.
Dataset = TdemData._initialize_sequential_reading(dataFile, systemFile)
# Get a datapoint from the file.
tdp = Dataset._read_record()
Dataset._file.close()
# plt.figure()
# tdp.plot()
################################################################################
# Using a time domain datapoint
# +++++++++++++++++++++++++++++
################################################################################
# We can define a 1D layered earth model, and use it to predict some data
par = StatArray(np.r_[500.0, 20.0], "Conductivity", "$\frac{S}{m}$")
mod = Model1D(edges=np.r_[0, 75.0, np.inf], parameters=par)
# The data file name
dataFiles = dataFolder + 'Skytem_small.csv'
# dataFiles = dataFolder + 'Skytem.csv'
# The EM system file name
systemFiles = [
dataFolder + 'SkytemHM-SLV.stm', dataFolder + 'SkytemLM-SLV.stm'
]
from pathlib import Path
for f in systemFiles[:1]:
txt = Path(f).read_text()
print(txt)
################################################################################
# Read in the data from file
TD = TdemData.read_csv(dataFiles, systemFiles)
################################################################################
# Plot the locations of the data points
plt.figure(1, figsize=(8, 6))
_ = TD.scatter2D()
################################################################################
# Plot all the data along the specified line
plt.figure(2, figsize=(8, 6))
_ = TD.plotLine(100101.0, log=10)
################################################################################
# Or, plot specific channels in the data
plt.figure(3, figsize=(8, 6))
_ = TD.plot(system=0, channels=[1, 3, 5], log=10)
# Send and recieve a single datapoint from the file.
if master:
fd = FdemData()
fd._initLineByLineRead(dataPath+"Resolve2.txt", dataPath+"FdemSystem2.stm")
fdp = fd._readSingleDatapoint()
for i in range(1, size):
fdp1 = fd._readSingleDatapoint()
fdp1.Isend(dest=i, world=world, systems=systems)
else:
fdp = FdemDataPoint().Irecv(source=0, world=world, systems=systems)
assert np.allclose(fdp.data, fdSave.data[rank, :], equal_nan=True), Exception("Could not use FdemData.Isend/Irecv, with pre-existing system class. Rank {}".format(rank))
tdSave = TdemData()
tdSave.read([dataPath+"Skytem_High.txt", dataPath+"Skytem_Low.txt"], [dataPath+"SkytemHM-SLV.stm", dataPath+"SkytemLM-SLV.stm"])
### Test Time Domain Data
if master:
td = tdSave
else:
td = TdemData()
#Bcast
td1 = td.Bcast(world)
assert np.allclose(td1.data, tdSave.data, equal_nan=True), Exception("Could not use TdemData.Bcast. Rank {}".format(rank))
# # Scatterv
starts, chunks = myMPI.loadBalance1D_shrinkingArrays(tdSave.nPoints, size)
i0 = starts[rank]
# myMPI.orderedPrint(world, td1._std[:, td1.iActive])
# myMPI.orderedPrint(world, td1._predictedData[:, td1.iActive])
# # Scatterv
# starts, chunks = myMPI.loadBalance_shrinkingArrays(td1.nPoints, size)
# td1 = td.Scatterv(starts, chunks, world)
# myMPI.orderedPrint(world, np.asarray([td1.x, td1.y, td1.z]), "TdemData.Scatterv")
# myMPI.orderedPrint(world, td1._data[:, td1.iActive])
# myMPI.orderedPrint(world, td1._std[:, td1.iActive])
# myMPI.orderedPrint(world, td1._predictedData[:, td1.iActive])
# Point by Point read in and send
if master:
td = TdemData()
td._initLineByLineRead([dataPath+"Skytem_High.txt", dataPath+"Skytem_Low.txt"], [dataPath+"SkytemHM-SLV.stm", dataPath+"SkytemLM-SLV.stm"])
# # Send and recieve a single datapoint from the file.
# if master:
# for i in range(1, size):
# tdp = td._readSingleDatapoint()
# tdp.Isend(dest=i, world=world)
# else:
# tdp = TdemDataPoint().Irecv(source=0, world=world)
# myMPI.orderedPrint(world, tdp.summary(True), "FdemData.Isend/Irecv")
# Testing pre-read in system classes
sysPath = [dataPath+"SkytemHM-SLV.stm", dataPath+"SkytemLM-SLV.stm"]
systems = []
# The following list of parameters can be given either a single value or a list of values # of length equal to the number of systems in the data. If one value is specified and there # are multiple systems, that value is used for all of them. # self.initialRelativeError # self.minimumRelativeError # self.maximumRelativeError # self.initialAdditiveError # self.minimumAdditiveError # self.maximumAdditiveError # self.relativeErrorProposalVariance # self.additiveErrorProposalVariance # ------------------------------------------------------- # Define whether this parameter file uses time domain or frequency domain data! from geobipy import TdemData data_type = TdemData() # ------------------------------------------------------- # ------------------------------------------------------- # General file structure information. # ------------------------------------------------------- # Specify the folder to the data dataDirectory = "..//Data" # Data File Name. If there are multiple, encompass them with [ , ]. This is a standard python list of str. dataFilename = dataDirectory + "//Aeroquest.txt" # dataFilename = [dataDirectory + "//DataFile1.txt", dataDirectory + "//DataFile2.txt"] # System File Name. If there are multiple, encompass them with [ , ]. This is a standard python list of str. systemFilename = dataDirectory + "//Aeroquest.stm" # systemFilename = [dataDirectory + "//SystemFile1.stm", dataDirectory + "//SystemFile2.stm"]
###############################################################################
# Obtaining a datapoint from a dataset
# ++++++++++++++++++++++++++++++++++++
# More often than not, our observed data is stored in a file on disk.
# We can read in a dataset and pull datapoints from it.
#
# For more information about the time domain data set, see :ref:`Time domain dataset`
# The data file name
dataFile = [dataFolder + 'Skytem_High.txt', dataFolder + 'Skytem_Low.txt']
# The EM system file name
systemFile = [dataFolder + 'SkytemHM-SLV.stm', dataFolder + 'SkytemLM-SLV.stm']
################################################################################
# Initialize and read an EM data set
D = TdemData.read_csv(dataFile, systemFile)
################################################################################
# Get a datapoint from the dataset
tdp = D.datapoint(0)
plt.figure()
tdp.plot()
################################################################################
# Using a time domain datapoint
# +++++++++++++++++++++++++++++
################################################################################
# We can define a 1D layered earth model, and use it to predict some data
par = StatArray(np.r_[500.0, 20.0], "Conductivity", "$\frac{S}{m}$")