reda.containers package

Different data containers available in REDA

Submodules

reda.containers.BaseContainer module

.

class reda.containers.BaseContainer.BaseContainer(data=None, electrode_positions=None, topography=None, metadata=None, **kwargs)[source]

Bases: LoggingClass, ImportersBase, ExportersBase

.

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

apply_k(k)

Apply geometric factors to the data

check_dataframe(dataframe)

Check the given dataframe for the required type and columns

compute_K_analytical(spacing, **kwargs)

Compute geometrical factors over the homogeneous half-space with a constant electrode spacing

create_copy()

Create a copy if the object.

delete_measurements(row_or_rows)

Delete one or more measurements by index of the DataFrame.

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

filter(query[, inplace, reassess_norrec])

Use a query statement to filter data.

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

has_multiple_timesteps()

Return True if container has multiple timesteps.

histogram([column, filename, log10])

Plot a histogram of one data column

merge_container(container)

Merge the data and electrode positions from another container into this one.

plot_electrode_positions_2d([ax, use_y_axis])

Create a 2D scatter plot for the electrode positions.

plot_histogram([column, filename, log10])

Wrapper for self.histogram

plot_topography_2d([ax, use_y_axis])

Plot topography points for x/z coordinates.

pseudosection_type1([column, filename, log10])

Plot a pseudosection of type 1 for the given column.

pseudosection_type2([column, filename, log10])

Plot a pseudosection of the given column.

pseudosection_type3([column, filename, ...])

Plot a pseudosection of a given column.

replace_electrode_positions(coordinates)

Replace the imported electrode coordinates by new ones.

setup_logger([name])

Setup a logger

sub_filter(subset, filter[, inplace])

Apply a filter to subset of the data

to_configs()

Return a config object that contains the measurement configurations (a,b,m,n) from the data

compute_K_numerical

load_metadata

print_data_journal

print_log

pseudosection

save_metadata
apply_k(k)[source]

Apply geometric factors to the data

check_dataframe(dataframe)[source]

Check the given dataframe for the required type and columns

compute_K_analytical(spacing, **kwargs)[source]

Compute geometrical factors over the homogeneous half-space with a constant electrode spacing

compute_K_numerical(settings=None, keep_dir=None, **kwargs)[source]
create_copy()[source]

Create a copy if the object. This is useful to investigate different filtering strategies.

delete_measurements(row_or_rows)[source]

Delete one or more measurements by index of the DataFrame.

Resets the DataFrame index.

Parameters:
row_or_rowsint or list of ints

Row numbers (starting with zero) of the data DataFrame (ert.data) to delete

Returns:
None
filter(query, inplace=True, reassess_norrec=True)[source]

Use a query statement to filter data. Note that you specify the data to be removed!

Parameters:
querystring

The query string to be evaluated. Is directly provided to pandas.DataFrame.query

inplacebool

if True, change the container dataframe in place (defaults to True). Otherwise, return a new ERT container which contains the filtered data.

reassess_norrecbool (True)

if True, then recompute normal-reciprocal differences after applying the filter.

Returns:
resultreda.ERT

ERT container with filtered data

has_multiple_timesteps()[source]

Return True if container has multiple timesteps.

histogram(column='r', filename=None, log10=False, **kwargs)[source]

Plot a histogram of one data column

load_metadata(filename)[source]
plot_electrode_positions_2d(ax=None, use_y_axis=False)[source]

Create a 2D scatter plot for the electrode positions. By default use the x and z coordinates.

Parameters:
axmatplotlib.axes, optional

Axes object to plot to. If None, create a new figure

use_y_axisbool, optional

If true then use the y coordinates instead of z coordinates for plotting

Returns:
figmatplotlib.Figure|None

The Figure object related to ax. None if no electrode positions were registered yet.

axmatplotlib.Axes|None

The axes object plotted to. None if no electrode positions were registered yet.

plot_histogram(column='r', filename=None, log10=False, **kwargs)[source]

Wrapper for self.histogram

plot_topography_2d(ax=None, use_y_axis=False)[source]

Plot topography points for x/z coordinates. If present, also plot electrode positions.

Parameters:
axmatplotlib.Axes|None, optional

If provided, plot into this axes object

use_y_axisbool, optional

If true then use the y coordinates instead of z coordinates for plotting

Returns:
figmatplotlib.Figure|None

figure object or None if not topography data is present

axmatplotlib.Axes|None

axes object or None if not topography data is present

pseudosection(column='r', filename=None, log10=False, **kwargs)[source]
pseudosection_type1(column='r', filename=None, log10=False, **kwargs)[source]

Plot a pseudosection of type 1 for the given column. Note that this function only works with dipole-dipole data at the moment.

Parameters:
columnstring, optional

Column to plot into the pseudosection, default: r

filenamestring, optional

if not None, save the resulting figure directory to disc

log10bool, optional

if True, then plot values in log10, default: False

**kwargsdict

all additional parameters are directly provided to reda.plotters.pseudoplots.PS.plot_pseudosection_type2()

Returns:
figmatplotlib.Figure

matplotlib figure object ax : matplotlib.axes matplotlib axes object

cbcolorbar object

matplotlib colorbar object

pseudosection_type2(column='r', filename=None, log10=False, **kwargs)[source]

Plot a pseudosection of the given column. Note that this function only works with dipole-dipole data at the moment.

Parameters:
columnstring, optional

Column to plot into the pseudosection, default: r

filenamestring, optional

if not None, save the resulting figure directory to disc

log10bool, optional

if True, then plot values in log10, default: False

**kwargsdict

all additional parameters are directly provided to reda.plotters.pseudoplots.PS.plot_pseudosection_type2()

Returns:
figmatplotlib.Figure

matplotlib figure object ax : matplotlib.axes matplotlib axes object

cbcolorbar object

matplotlib colorbar object

pseudosection_type3(column='r', filename=None, log10=False, crmod_settings=None, **kwargs)[source]

Plot a pseudosection of a given column. Use sensitivity-based center of gravities for locations.

replace_electrode_positions(coordinates)[source]

Replace the imported electrode coordinates by new ones. This function assumes and expects that the number of new coordinates is the same as the old ones (i.e., a simple replacement).

If the input is a pandas DataFrame, assume that the columns x, y, z are present.

Parameters:
coordinatesstr|numpy.ndarray|pandas.DataFrame
save_metadata(filename)[source]
sub_filter(subset, filter, inplace=True)[source]

Apply a filter to subset of the data

Examples

.subquery(
    'timestep == 2',
    'R > 4',
)
to_configs()[source]

Return a config object that contains the measurement configurations (a,b,m,n) from the data

Returns:
config_objreda.ConfigManager
class reda.containers.BaseContainer.ExportersBase[source]

Bases: object

This class provides wrappers for most of the exporter functions and is meant to be inherited by the ERT data container.

See also

Importers

Methods

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.
export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

Multiple timesteps are exported to multiple filenames.

Parameters:
datapandas.DataFrame

DataFrame with at least a, b, m, n and r.

electrodespandas.DataFrame

DataFrame with electrode positions.

filenamestr

String of the output filename.

export_crtomo(group, norrec='all', store_errors=False)

Save a dataset to a CRTomo-compatible .crt file

Parameters:
filenamestring

Output filename

grouppandas.group

Data group

norrecstring

Which data to export Possible values: all|norrec|nor|rec

store_errorsbool

If true, store errors of the data in a separate column

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

Multiple timesteps are exported to multiple filenames.

Parameters:
datapandas.DataFrame

DataFrame with at least a, b, m, n and r.

electrodespandas.DataFrame

DataFrame with electrode positions.

filenamestr

String of the output filename.

class reda.containers.BaseContainer.ImportersBase[source]

Bases: object

Base class for all importer classes

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

merge_container(container)

Merge the data and electrode positions from another container into this one.
add_dataframe(data, timestep=None, **kwargs)[source]

Add data to the container using another DataFrame

Parameters:
datapandas.DataFrame

Measurement data in the form of a DataFrame, must adhere to the container constraints (i.e., must have all required columns) and electrode positions already registered must match.

get_norrec_pairs(test_column='rdiff')[source]

Return a dataframe that contains only valid normal-reciprocal pairs

Parameters:
test_column: str

This column is used to check for valid normal-reciprocal pairs. All data points for which this column is not NaN will be returned.

merge_container(container)[source]

Merge the data and electrode positions from another container into this one.

reda.containers.CR module

Complex-resistivity container

class reda.containers.CR.CR(data=None, electrode_positions=None, topography=None)[source]

Bases: BaseContainer, CRImporters

.

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

apply_k(k)

Apply geometric factors to the data

check_dataframe(dataframe)

Check the given dataframe for the required type and columns

compute_K_analytical(spacing, **kwargs)

Compute geometrical factors over the homogeneous half-space with a constant electrode spacing

create_copy()

Create a copy if the object.

delete_measurements(row_or_rows)

Delete one or more measurements by index of the DataFrame.

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_to_crtomo_td_manager(grid[, norrec])

Return a ready-initialized tdman object from the CRTomo tools.

filter(query[, inplace, reassess_norrec])

Use a query statement to filter data.

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

has_multiple_timesteps()

Return True if container has multiple timesteps.

histogram([column, filename, log10])

Plot a histogram of one data column

import_bert(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

import_crtomo_data(filename)

Import a CRTomo-style measurement file (usually: volt.dat).

import_mpt(filename, **kwargs)

MPT DAS 1 FD importer

import_pygimli(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

merge_container(container)

Merge the data and electrode positions from another container into this one.

plot_electrode_positions_2d([ax, use_y_axis])

Create a 2D scatter plot for the electrode positions.

plot_histogram([column, filename, log10])

Wrapper for self.histogram

plot_topography_2d([ax, use_y_axis])

Plot topography points for x/z coordinates.

pseudosection_type1([column, filename, log10])

Plot a pseudosection of type 1 for the given column.

pseudosection_type2([column, filename, log10])

Plot a pseudosection of the given column.

pseudosection_type3([column, filename, ...])

Plot a pseudosection of a given column.

replace_electrode_positions(coordinates)

Replace the imported electrode coordinates by new ones.

setup_logger([name])

Setup a logger

sub_filter(subset, filter[, inplace])

Apply a filter to subset of the data

to_configs()

Return a config object that contains the measurement configurations (a,b,m,n) from the data

compute_K_numerical

load_metadata

print_data_journal

print_log

pseudosection

save_metadata
check_dataframe(dataframe)[source]

Check the given dataframe for the required type and columns

export_to_crtomo_td_manager(grid, norrec='norrec')[source]

Return a ready-initialized tdman object from the CRTomo tools.

WARNING: Not timestep aware!

Parameters:
gridcrtomo.crt_grid

A CRTomo grid instance

norrecstr (nor|rec|norrec)

Which data to export. Default: norrec (all)

class reda.containers.CR.CRImporters[source]

Bases: ImportersBase

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

import_bert(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

import_crtomo_data(filename)

Import a CRTomo-style measurement file (usually: volt.dat).

import_mpt(filename, **kwargs)

MPT DAS 1 FD importer

import_pygimli(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

merge_container(container)

Merge the data and electrode positions from another container into this one.
import_bert(filename, **kwargs)[source]

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

Parameters:
filenamestring

File path to .ohm file

verbosebool, optional

Enables extended debug output

reciprocalsint, optional

if provided, then assume that this is a reciprocal measurement where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

Returns:
datapandas.DataFrame

The measurement data

elecspandas.DataFrame

Electrode positions (columns: X, Y, Z)

topographyNone

No topography information is provided at the moment

import_crtomo_data(filename)[source]

Import a CRTomo-style measurement file (usually: volt.dat).

Parameters:
filenamestr

path to data file

import_mpt(filename, **kwargs)[source]

MPT DAS 1 FD importer

timestep: int or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Reads a time domain MPT DAS-1 data file (.Data) and prepares information in pandas DataFrame for further processing.

Parameters:
filenamestr

path to input file

Returns:
datapandas.DataFrame

Contains the measurement data

electrodespandas.DataFrame

Contains electrode positions (None at the moment)

topographyNone

No topography information is contained in the text files, so we always return None

import_pygimli(filename, **kwargs)[source]

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

Parameters:
filenamestring

File path to .ohm file

verbosebool, optional

Enables extended debug output

reciprocalsint, optional

if provided, then assume that this is a reciprocal measurement where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

Returns:
datapandas.DataFrame

The measurement data

elecspandas.DataFrame

Electrode positions (columns: X, Y, Z)

topographyNone

No topography information is provided at the moment

reda.containers.ERT module

class reda.containers.ERT.ERT(data=None, electrode_positions=None, topography=None, metadata=None, **kwargs)[source]

Bases: BaseContainer, ERTImporters, ERTExporters

.

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

apply_k(k)

Apply geometric factors to the data

check_dataframe(dataframe)

Check the given dataframe for the required type and columns

compute_K_analytical(spacing, **kwargs)

Compute geometrical factors over the homogeneous half-space with a constant electrode spacing

compute_reciprocal_errors([key])

Compute reciprocal erros following LaBrecque et al. (1996) according to:.

create_copy()

Create a copy if the object.

delete_measurements(row_or_rows)

Delete one or more measurements by index of the DataFrame.

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_to_pygimli_scheme([norrec, timestep])

Export the data into a pygimili.DataContainerERT object.

export_tsert(filename, version, **kwargs)

Export data to TSERT

filter(query[, inplace, reassess_norrec])

Use a query statement to filter data.

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

has_multiple_timesteps()

Return True if container has multiple timesteps.

histogram([column, filename, log10])

Plot a histogram of one data column

import_bert(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

import_crtomo_data(filename, **kwargs)

Import a CRTomo-style measurement file (usually: volt.dat).

import_mpt(filename, **kwargs)

MPT DAS 1 importer

import_pygimli(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

import_syscal_bin(filename, **kwargs)

Syscal import

import_syscal_txt(filename, **kwargs)

Syscal import

import_tsert(filename[, timesteps, version])

TSERT import

merge_container(container)

Merge the data and electrode positions from another container into this one.

plot_electrode_positions_2d([ax, use_y_axis])

Create a 2D scatter plot for the electrode positions.

plot_histogram([column, filename, log10])

Wrapper for self.histogram

plot_topography_2d([ax, use_y_axis])

Plot topography points for x/z coordinates.

pseudosection_type1([column, filename, log10])

Plot a pseudosection of type 1 for the given column.

pseudosection_type2([column, filename, log10])

Plot a pseudosection of the given column.

pseudosection_type3([column, filename, ...])

Plot a pseudosection of a given column.

replace_electrode_positions(coordinates)

Replace the imported electrode coordinates by new ones.

setup_logger([name])

Setup a logger

sub_filter(subset, filter[, inplace])

Apply a filter to subset of the data

to_configs()

Return a config object that contains the measurement configurations (a,b,m,n) from the data

tsert_summary(filename, **kwargs)

Try to open a given filename (usually a .h5 file) as a TSERT file and print out a summary of contained data.

compute_K_numerical

load_metadata

print_data_journal

print_log

pseudosection

save_metadata
check_dataframe(dataframe)[source]

Check the given dataframe for the required type and columns

compute_reciprocal_errors(key='r')[source]

Compute reciprocal erros following LaBrecque et al. (1996) according to:

\[\epsilon = \left|\frac{2(|R_n| - |R_r|)}{|R_n| + |R_r|}\right|\]
Parameters:
keystr

Parameter to calculate the reciprocal error for (default is “r”).

Examples

>>> import reda
>>> ert = reda.ERT()
>>> ert.data = reda.utils.norrec.get_test_df()
>>> ert.data = pd.DataFrame([
...     [1,2,3,4,95],
...     [3,4,2,1,-105]], columns=list("abmnr")
... )
>>> ert.compute_reciprocal_errors()
>>> ert.data["error"].mean() == 0.1
True
class reda.containers.ERT.ERTExporters[source]

Bases: object

Methods

export_to_pygimli_scheme([norrec, timestep])

Export the data into a pygimili.DataContainerERT object.

export_tsert(filename, version, **kwargs)

Export data to TSERT
export_to_pygimli_scheme(norrec='nor', timestep=None)[source]

Export the data into a pygimili.DataContainerERT object.

For now, do NOT set any sensor positions

Parameters:
Returns:
export_tsert(filename, version, **kwargs)[source]

Export data to TSERT

class reda.containers.ERT.ERTImporters[source]

Bases: ImportersBase

This class provides wrappers for most of the importer functions and is meant to be inherited by the ERT data container.

See also

Exporters

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

import_bert(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

import_crtomo_data(filename, **kwargs)

Import a CRTomo-style measurement file (usually: volt.dat).

import_mpt(filename, **kwargs)

MPT DAS 1 importer

import_pygimli(filename, **kwargs)

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

import_syscal_bin(filename, **kwargs)

Syscal import

import_syscal_txt(filename, **kwargs)

Syscal import

import_tsert(filename[, timesteps, version])

TSERT import

merge_container(container)

Merge the data and electrode positions from another container into this one.

tsert_summary(filename, **kwargs)

Try to open a given filename (usually a .h5 file) as a TSERT file and print out a summary of contained data.
import_bert(filename, **kwargs)[source]

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

Parameters:
filenamestring

File path to .ohm file

verbosebool, optional

Enables extended debug output

reciprocalsint, optional

if provided, then assume that this is a reciprocal measurement where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

Returns:
datapandas.DataFrame

The measurement data

elecspandas.DataFrame

Electrode positions (columns: X, Y, Z)

topographyNone

No topography information is provided at the moment

import_crtomo_data(filename, **kwargs)[source]

Import a CRTomo-style measurement file (usually: volt.dat).

Parameters:
filenamestr

path to data file

import_mpt(filename, **kwargs)[source]

MPT DAS 1 importer

timestepint or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Reads a any MPT DAS-1 data file (.Data), e.g. TD/FD/SIP, and prepares information in pandas DataFrame for further processing.

Parameters:
filenamestring

path to input file

Returns:
datapandas.DataFrame

Contains the measurement data

electrodespandas.DataFrame

Contains electrode positions (None at the moment)

topographyNone

No topography information is contained in the text files, so we always return None

import_pygimli(filename, **kwargs)[source]

BERT .ohm file importConstruct pandas data frame from BERT`s unified data format (.ohm).

Parameters:
filenamestring

File path to .ohm file

verbosebool, optional

Enables extended debug output

reciprocalsint, optional

if provided, then assume that this is a reciprocal measurement where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

Returns:
datapandas.DataFrame

The measurement data

elecspandas.DataFrame

Electrode positions (columns: X, Y, Z)

topographyNone

No topography information is provided at the moment

import_syscal_bin(filename, **kwargs)[source]

Syscal import

timestep: int or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Read a .bin file generated by the IRIS Instruments Syscal Systems

and return a curated Pandas dataframe for further processing. This dataframe contains only information currently deemed important. Use the function reda.importers.iris_syscal_pro_binary._import_bin to extract ALL information from a given .bin file.

Note that the .bin files contain electrodes positions for the electrodes. Electrode numbers are then inferred from those.

Parameters:
filenamestring|io.BytesIO

path to input filename or io.BytesIO object containing the binary data

assume_regular_electrodes_xNone|tuple(nr_electrodes, spacing)

If not None, then assume measurements were taken using a profile of regular electrodes in x directions. Fill in any electrodes not used in the data to align the logical electrode numbers wit the physical electrode numbers. Use the electrode spacing set in the system (usually a default of 1 m is used), and then change this to the real spacing using the ‘elecs_transform_reg_spacing_x’ parameter.

elecs_transform_reg_spacing_xtuple(old, new)|None, optional

If not None, then assume a regular electrode spacing in x direction with spacing ‘old’. This is often 1 m, a default of the systems. However, in reality often other spacings are used, and this parameter will lead to a transformation of the old (device-specific) spacing to the true spacing.

shift_by_xyztuple|list|numpy.ndarray of size 1 or 2 or 3, optional

If set, shift electrode positions by adding this vector Length of 1 assumes that only the x coordinate of the vector differs from zero. Length of 2 assume a shift in (x,z) direction.

This parameter is evaluated after the ‘elecs_transform_reg_spacing_x’ parameter - as such you can and must use real spacings in this case.

reciprocalsint, optional

if provided, then assume that this is a reciprocal measurement where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

check_meas_numsbool, optional

if True, then check that the measurement numbers are consecutive. Don’t return data after a jump to smaller measurement numbers (this usually indicates that more data points were downloaded than are part of a specific measurement. Default: True

skip_rowsint, optional

Ignore this number of rows at the beginning, e.g., because they were inadvertently imported from an earlier measurement. Default: 0

Returns:
datapandas.DataFrame

Contains the measurement data

elec_mgrreda.utils.electrode_manager.electrode_manager

Electrode manager that manages the electrode positions

topographyNone

No topography information is contained in the text files, so we always return None

import_syscal_txt(filename, **kwargs)[source]

Syscal import

timestep: int or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Import Syscal measurements from a text file, exported as ‘Spreadsheet’.

At this point we expect only x-coordinates to be exported. As such the columns Spa.1 to Spa.4 are assumed to be x coordinates for a,b,m,n.

Parameters:
filename: str

Input filename

assume_regular_electrodes_xNone|tuple(nr_electrodes, spacing)

If not None, then assume measurements were taken using a profile of regular electrodes in x directions. Fill in any electrodes not used in the data to align the logical electrode numbers with the physical electrode numbers. Use the electrode spacing set in the system (usually a default of 1 m is used), and then change this to the real spacing using the ‘elecs_transform_reg_spacing_x’ parameter.

elecs_transform_reg_spacing_xtuple(old, new)|None, optional

If not None, then assume a regular electrode spacing in x direction with spacing ‘old’. This is often 1 m, a default of the systems. However, in reality often other spacings are used, and this parameter will lead to a transformation of the old (device-specific) spacing to the true spacing.

shift_by_xyztuple|list|numpy.ndarray of size 1 or 2 or 3, optional

If set, shift electrode positions by adding this vector Length of 1 assumes that only the x coordinate of the vector differs from zero. Length of 2 assume a shift in (x,z) direction.

This parameter is evaluated after the ‘elecs_transform_reg_spacing_x’ parameter - as such you can and must use real spacings in this case.

reciprocals: int, optional

if provided, then assume that this is a reciprocal measurements where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

Returns:
data: pandas.DataFrame

Contains the measurement data

elec_mgrreda.utils.electrode_manager.electrode_manager

Electrode manager that manages the electrode positions

topography: None

No topography information is contained in the text files, so we always return None

import_tsert(filename, timesteps='all', version='base', **kwargs)[source]

TSERT import

Parameters:
filenamestr

Path to hdf file to import data from

timestepsstr|list|datetime.datetime

Timesteps that should be imported

versionstr

Which version of the data to load. Time steps that do not have this specific version are ignored. Default: base version

tsert_summary(filename, **kwargs)[source]

Try to open a given filename (usually a .h5 file) as a TSERT file and print out a summary of contained data.

Parameters:
filenamestr

Filename of data file

reda.containers.SIP module

Container for Spectral Induced Polarization (SIP) measurements

class reda.containers.SIP.SIP(data=None, electrode_positions=None, topography=None)[source]

Bases: BaseContainer, SIPImporters

.

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

apply_k(k)

Apply geometric factors to the data

check_dataframe(dataframe)

Check the given dataframe for the required type and columns

compute_K_analytical(spacing, **kwargs)

Compute geometrical factors over the homogeneous half-space with a constant electrode spacing

create_copy()

Create a copy if the object.

delete_measurements(row_or_rows)

Delete one or more measurements by index of the DataFrame.

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

filter(query[, inplace, reassess_norrec])

Use a query statement to filter data.

frequencies()

Return all frequencies

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

has_multiple_timesteps()

Return True if container has multiple timesteps.

histogram([column, filename, log10])

Plot a histogram of one data column

import_mpt(filename, **kwargs)

MPT DAS 1 FD importer

import_sip04(filename[, timestep])

SIP04 data import

merge_container(container)

Merge the data and electrode positions from another container into this one.

plot_electrode_positions_2d([ax, use_y_axis])

Create a 2D scatter plot for the electrode positions.

plot_histogram([column, filename, log10])

Wrapper for self.histogram

plot_topography_2d([ax, use_y_axis])

Plot topography points for x/z coordinates.

pseudosection_type1([column, filename, log10])

Plot a pseudosection of type 1 for the given column.

pseudosection_type2([column, filename, log10])

Plot a pseudosection of the given column.

pseudosection_type3([column, filename, ...])

Plot a pseudosection of a given column.

reduce_duplicate_frequencies()

In case multiple frequencies were measured, average them and compute std, min, max values for zt.

replace_electrode_positions(coordinates)

Replace the imported electrode coordinates by new ones.

setup_logger([name])

Setup a logger

sub_filter(subset, filter[, inplace])

Apply a filter to subset of the data

to_configs()

Return a config object that contains the measurement configurations (a,b,m,n) from the data

compute_K_numerical

export_specs_to_ascii

load_metadata

print_data_journal

print_log

pseudosection

save_metadata
check_dataframe(dataframe)[source]

Check the given dataframe for the required type and columns

export_specs_to_ascii(frequency_file, data_file)[source]
frequencies()[source]

Return all frequencies

reduce_duplicate_frequencies()[source]

In case multiple frequencies were measured, average them and compute std, min, max values for zt.

In case timesteps were added (i.e., multiple separate measurements), group over those and average for each timestep.

Examples

import tempfile
import reda
with tempfile.TemporaryDirectory() as fid:
    reda.data.download_data('sip04_fs_06', fid)
    sip = reda.SIP()
    sip.import_sip04(fid + '/sip_dataA.mat', timestep=0)
    # well, add the spectrum again as another timestep
    sip.import_sip04(fid + '/sip_dataA.mat', timestep=1)
df = sip.reduce_duplicate_frequencies()
class reda.containers.SIP.SIPImporters[source]

Bases: ImportersBase

This class provides wrappers for most of the importer functions, and is meant to be inherited by the data containers

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

import_mpt(filename, **kwargs)

MPT DAS 1 FD importer

import_sip04(filename[, timestep])

SIP04 data import

merge_container(container)

Merge the data and electrode positions from another container into this one.
import_mpt(filename, **kwargs)[source]

MPT DAS 1 FD importer

timestep: int or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Reads a time domain MPT DAS-1 data file (.Data) and prepares information in pandas DataFrame for further processing.

Parameters:
filenamestr

path to input file

Returns:
datapandas.DataFrame

Contains the measurement data

electrodespandas.DataFrame

Contains electrode positions (None at the moment)

topographyNone

No topography information is contained in the text files, so we always return None

import_sip04(filename, timestep=None)[source]

SIP04 data import

Parameters:
filename: string

Path to .mat or .csv file containing SIP-04 measurement results

Examples

import tempfile
import reda
with tempfile.TemporaryDirectory() as fid:
    reda.data.download_data('sip04_fs_01', fid)
    sip = reda.SIP()
    sip.import_sip04(fid + '/sip_dataA.mat')

reda.containers.TDIP module

Time-domain IP (induced polarization) container

class reda.containers.TDIP.TDIP(data=None, electrode_positions=None, topography=None)[source]

Bases: BaseContainer, TDIPImporters

.

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

apply_k(k)

Apply geometric factors to the data

check_dataframe(dataframe)

Check the given dataframe for the required type and columns

compute_K_analytical(spacing, **kwargs)

Compute geometrical factors over the homogeneous half-space with a constant electrode spacing

create_copy()

Create a copy if the object.

delete_measurements(row_or_rows)

Delete one or more measurements by index of the DataFrame.

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

filter(query[, inplace, reassess_norrec])

Use a query statement to filter data.

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

has_multiple_timesteps()

Return True if container has multiple timesteps.

histogram([column, filename, log10])

Plot a histogram of one data column

import_mpt(filename, **kwargs)

MPT DAS 1 TD importer

import_syscal_bin(filename, **kwargs)

Syscal import

merge_container(container)

Merge the data and electrode positions from another container into this one.

plot_decay_curve([filename, index_nor, ...])

Plot decay curve

plot_electrode_positions_2d([ax, use_y_axis])

Create a 2D scatter plot for the electrode positions.

plot_histogram([column, filename, log10])

Wrapper for self.histogram

plot_topography_2d([ax, use_y_axis])

Plot topography points for x/z coordinates.

pseudosection_type1([column, filename, log10])

Plot a pseudosection of type 1 for the given column.

pseudosection_type2([column, filename, log10])

Plot a pseudosection of the given column.

pseudosection_type3([column, filename, ...])

Plot a pseudosection of a given column.

replace_electrode_positions(coordinates)

Replace the imported electrode coordinates by new ones.

setup_logger([name])

Setup a logger

sub_filter(subset, filter[, inplace])

Apply a filter to subset of the data

to_configs()

Return a config object that contains the measurement configurations (a,b,m,n) from the data

to_cr([b, estimate_b, conversion_parameters])

Convert container to a complex resistivity container, using the CPA-conversion.

compute_K_numerical

load_metadata

print_data_journal

print_log

pseudosection

save_metadata
check_dataframe(dataframe)[source]

Check the given dataframe for the required type and columns

plot_decay_curve(filename=None, index_nor=None, index_rec=None, nr_id=None, abmn=None, return_fig=False)[source]

Plot decay curve

Input scheme: We recognize three ways to specify the quadrupoles to plot (in descending priority):

  1. indices for normal/reciprocal

  2. by specifying the id

  3. by specifying abmn (note that here the exact quadrupole must be present. For example, when (1,2,4,3) is requested, (2,1,4,3) will not be used).

Parameters:
filenamestring, optional

If given, filename to plot to.

Returns:
figmatplotlib.Figure

Figure object, only returned if return_fig=True

to_cr(b=-1.5, estimate_b=False, conversion_parameters={'n': 100, 'nhc': 4, 'phi_max': -1, 'phi_min': -100, 'tmax': 1.7, 'tmin': 0.12, 'tpuls': 2})[source]

Convert container to a complex resistivity container, using the CPA-conversion.

Kemna, 2000

COMPLEX RESISTIVITY COPPER MlNERALlZATlONt SPECTRA OF PORPHYRY Van Voorhis, G. D.; Nelson, P. H.; Drake, T. L. Geophysics (1973 Jan 1) 38 (1): 49-60.

Application of complex resistivity tomography to field data from a kerosene-contaminated siteGold Open Access Authors: A. Kemna, E. Räkers and A. Binley DOI: 10.3997/2214-4609.201407300

Gianluca Fiandaca, Esben Auken, Anders Vest Christiansen, and Aurélie Gazoty (2012). ”Time-domain-induced polarization: Full-decay forward modeling and 1D laterally constrained inversion of Cole-Cole parameters.” GEOPHYSICS, 77(3), E213-E225. https://doi.org/10.1190/geo2011-0217.1

Parameters:
bfloat

Factor for the conversion of chargeability into phase: phase = b * chargeability Default value equals -1.5, which is a good approximation for many cases

estimate_bbool

If “True” the factor b is estimated according to the CPA model, for a given set of conversion parameters (provided via “conversion_paramaters”)

conversion_parametersdict

Holds the conversion parameters which are the basis for the estimation of the factor b. Parameters in the dictionary:

phi_minfloat

Lower phase boundary

phi_maxfloat

Upper phase boundary

nint

Number of values in the phase field

nhcint

Number of half cycles

tpulsfloat

Duration of one puls

tminfloat

Lower time boundary

tmaxfloat

Upper time boundary

class reda.containers.TDIP.TDIPImporters[source]

Bases: ImportersBase

This class provides wrappers for most of the importer functions and is meant to be inherited by the TDIP data container.

See also

Exporters

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

import_mpt(filename, **kwargs)

MPT DAS 1 TD importer

import_syscal_bin(filename, **kwargs)

Syscal import

merge_container(container)

Merge the data and electrode positions from another container into this one.
import_mpt(filename, **kwargs)[source]

MPT DAS 1 TD importer

timestep: int or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Reads a time domain MPT DAS-1 data file (.Data) and prepares information in pandas DataFrame for further processing.

Parameters:
filenamestr

path to input file

Returns:
datapandas.DataFrame

Contains the measurement data

electrodespandas.DataFrame

Contains electrode positions (None at the moment)

topographyNone

No topography information is contained in the text files, so we always return None

import_syscal_bin(filename, **kwargs)[source]

Syscal import

timestep: int or datetime.datetime

if provided use this value to set the ‘timestep’ column of the produced dataframe. Default: 0

Read a .bin file generated by the IRIS Instruments Syscal Systems

and return a curated Pandas dataframe for further processing. This dataframe contains only information currently deemed important. Use the function reda.importers.iris_syscal_pro_binary._import_bin to extract ALL information from a given .bin file.

Note that the .bin files contain electrodes positions for the electrodes. Electrode numbers are then inferred from those.

Parameters:
filenamestring|io.BytesIO

path to input filename or io.BytesIO object containing the binary data

assume_regular_electrodes_xNone|tuple(nr_electrodes, spacing)

If not None, then assume measurements were taken using a profile of regular electrodes in x directions. Fill in any electrodes not used in the data to align the logical electrode numbers wit the physical electrode numbers. Use the electrode spacing set in the system (usually a default of 1 m is used), and then change this to the real spacing using the ‘elecs_transform_reg_spacing_x’ parameter.

elecs_transform_reg_spacing_xtuple(old, new)|None, optional

If not None, then assume a regular electrode spacing in x direction with spacing ‘old’. This is often 1 m, a default of the systems. However, in reality often other spacings are used, and this parameter will lead to a transformation of the old (device-specific) spacing to the true spacing.

shift_by_xyztuple|list|numpy.ndarray of size 1 or 2 or 3, optional

If set, shift electrode positions by adding this vector Length of 1 assumes that only the x coordinate of the vector differs from zero. Length of 2 assume a shift in (x,z) direction.

This parameter is evaluated after the ‘elecs_transform_reg_spacing_x’ parameter - as such you can and must use real spacings in this case.

reciprocalsint, optional

if provided, then assume that this is a reciprocal measurement where only the electrode cables were switched. The provided number N is treated as the maximum electrode number, and denotations are renamed according to the equation \(X_n = N - (X_a - 1)\)

check_meas_numsbool, optional

if True, then check that the measurement numbers are consecutive. Don’t return data after a jump to smaller measurement numbers (this usually indicates that more data points were downloaded than are part of a specific measurement. Default: True

skip_rowsint, optional

Ignore this number of rows at the beginning, e.g., because they were inadvertently imported from an earlier measurement. Default: 0

Returns:
datapandas.DataFrame

Contains the measurement data

elec_mgrreda.utils.electrode_manager.electrode_manager

Electrode manager that manages the electrode positions

topographyNone

No topography information is contained in the text files, so we always return None

reda.containers.sEIT module

Spectral Electrical Impedance Tomography (sEIT) container

This container holds multi-frequency (spectral) imaging data, that is multiple SIP/EIS spectra for different four-point spreads, usually used for subsequent tomographic analysis.

class reda.containers.sEIT.sEIT(dataframe=None)[source]

Bases: BaseContainer, sEITImporters

Attributes:
Nf

Shortcut for self.nr_frequencies

abmn
frequencies

Return the frequencies contained in the data set

nr_frequencies

Return the number of frequencies in the data set

nr_timesteps

Return the number of timesteps registered with this container

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

apply_k(k)

Apply geometric factors to the data

check_dataframe(dataframe)

Check the given dataframe for the required columns

compute_K_analytical(spacing)

Assuming an equal electrode spacing, compute the K-factor over a homogeneous half-space.

create_copy()

Create a copy if the object.

debye_decomposition_one_spectrum(abmn, spectrum)

Conduct a Debye Decomposition on each spectrum.

delete_measurements(row_or_rows)

Delete one or more measurements by index of the DataFrame.

export_bert(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_crtomo(group[, norrec, store_errors])

Save a dataset to a CRTomo-compatible .crt file

export_pygimli(electrodes, filename)

Export to unified data format used in pyGIMLi & BERT.

export_to_crtomo_multi_frequency(directory)

Export the sEIT data into data files that can be read by CRTomo.

export_to_crtomo_one_frequency(volt_file, ...)

Export one frequency into a CRTomo volt.dat file

export_to_crtomo_seit_manager(grid[, norrec])

Return a ready-initialized seit-manager object from the CRTomo tools.

export_to_crtomo_td_manager(grid, frequency)

Return a ready-initialized tdman object from the CRTomo tools.

filter(query[, inplace])

Use a query statement to filter data.

filter_incomplete_spectra([flimit, percAccept])

Remove all data points that belong to spectra that did not retain at least percAccept percent of the number of data points.

fix_sign_with_K()

Swap electrode denotations so that geometrical (K) factors become positive.

get_configs()

Return all unique (a,b,m,n) configurations in this container.

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

get_spectrum([nr_id, abmn, withK, plot_filename])

Return a spectrum and its reciprocal counter part, if present in the dataset.

has_multiple_timesteps()

Return True if container has multiple timesteps.

histogram([column, filename, log10])

Plot a histogram of one data column

import_crtomo(directory[, frequency_file, ...])

CRTomo importerLoad sEIT data from data directory.

import_eit_fzj(filename, configfile[, ...])

EIT data import for FZJ Medusa systems

import_mpt_das1(filename[, timestep])

Import MPT DAS-1 SIP data

import_sip256c(filename[, settings, reciprocal])

Radic SIP256c data import

keep_frequencies(frequencies)

Keep only the requested frequencies, remove everything else.

keep_only_configs(configs_to_keep[, frequency])

Keep only the supplied configs.

merge_container(container)

Merge the data and electrode positions from another container into this one.

plot_all_spectra(outdir)

This is a convenience function to plot ALL spectra currently stored in the container.

plot_electrode_positions_2d([ax, use_y_axis])

Create a 2D scatter plot for the electrode positions.

plot_histogram([column, filename, log10])

Wrapper for self.histogram

plot_histograms([column, primary_dim, filename])

Plot a histograms for all frequencies of one data column

plot_pseudosections(column[, filename, ...])

Create a multi-plot with one pseudosection of type 1 for each frequency.

plot_topography_2d([ax, use_y_axis])

Plot topography points for x/z coordinates.

pseudosection_type1([column, filename, log10])

Plot a pseudosection of type 1 for the given column.

pseudosection_type2([column, filename, log10])

Plot a pseudosection of the given column.

pseudosection_type3([column, filename, ...])

Plot a pseudosection of a given column.

query(query[, inplace])

State what you want to keep

remove_frequencies(fmin, fmax)

Remove frequencies outside the provided range from the dataset.

replace_electrode_positions(coordinates)

Replace the imported electrode coordinates by new ones.

scatter_norrec([filename, individual])

Create a scatter plot for all diff pairs

setup_logger([name])

Setup a logger

sub_filter(subset, filter[, inplace])

Apply a filter to subset of the data

subquery(subset, filter[, inplace])

Examples

to_configs()

Return a config object that contains the measurement configurations (a,b,m,n) from the data

compute_K_numerical

correct_for_cable_inductances

debye_decomposition_all_spectra

export_to_ascii

load_metadata

print_data_journal

print_log

pseudosection

save_metadata
property Nf

Shortcut for self.nr_frequencies

property abmn
check_dataframe(dataframe)[source]

Check the given dataframe for the required columns

compute_K_analytical(spacing)[source]

Assuming an equal electrode spacing, compute the K-factor over a homogeneous half-space.

For more complex grids, please refer to the module: reda.utils.geometric_factors

Parameters:
spacingfloat

Electrode spacing

correct_for_cable_inductances(inductance_matrix)[source]
debye_decomposition_all_spectra(gen_plots_in_dir=None)[source]
debye_decomposition_one_spectrum(abmn, spectrum, gen_plots_in_dir=None)[source]

Conduct a Debye Decomposition on each spectrum. Save certain parameters in sEIT.data columns and/or return fit data

export_to_ascii(file_data='data.dat')[source]
export_to_crtomo_multi_frequency(directory, norrec='norrec')[source]

Export the sEIT data into data files that can be read by CRTomo.

Parameters:
directorystring

output directory. will be created if required

norrecstring (nor|rec|norrec)

Which data to export. Default: norrec

export_to_crtomo_one_frequency(volt_file, frequency, norrec='norrec')[source]

Export one frequency into a CRTomo volt.dat file

Parameters:
volt_filestring

output file. Will be overwritten if it exists

frequencyfloat

frequency to export

norrecstr (nor|rec|norrec)

Which data to export. Default: norrec

export_to_crtomo_seit_manager(grid, norrec='norrec')[source]

Return a ready-initialized seit-manager object from the CRTomo tools. This function only works if the crtomo_tools are installed.

WARNING: Not timestep aware!

Parameters:
gridcrtomo.crt_grid

A CRTomo grid instance

norrecstr (nor|rec|norrec)

Which data to export. Default: norrec (all)

export_to_crtomo_td_manager(grid, frequency, norrec='norrec')[source]

Return a ready-initialized tdman object from the CRTomo tools. Use the given frequency data to initialize it.

WARNING: Not timestep aware!

Parameters:
gridcrtomo.crt_grid

A CRTomo grid instance

frequencyfloat

The frequency to export data for

norrecstr (nor|rec|norrec)

Which data to export. Default: norrec (all)

filter(query, inplace=True)[source]

Use a query statement to filter data. Note that you specify the data to be removed!

Parameters:
querystring

The query string to be evaluated. Is directly provided to pandas.DataFrame.query

inplacebool, optional (default: True)

if True, change the container dataframe in place (defaults to True)

Returns:
resultpandas.DataFrame

DataFrame that contains the result of the filter application

filter_incomplete_spectra(flimit=1000, percAccept=85)[source]

Remove all data points that belong to spectra that did not retain at least percAccept percent of the number of data points.

Parameters:
flimitfloat, optional (default: 1000)

The frequency limit up to which the criterium will be applied. The value is included into the range (<= flimit)

percAcceptfloat, optional (default: 85)

The percentage of retained data points below which a spectrum is removed

..warning::

This function does not honor additional dimensions (e.g., timesteps) yet!

fix_sign_with_K()[source]

Swap electrode denotations so that geometrical (K) factors become positive. Also, swap signs of all parameters affected by this process.

Affected parameters, at the moment, are:

  • k

  • r

  • Vmn

  • Zt

  • rho_a

  • rpha

Parameters:
dataframepandas.DateFrame

dataframe holding the data

Returns:
dataframepandas.DateFrame

the fixed dataframe

property frequencies

Return the frequencies contained in the data set

get_configs()[source]

Return all unique (a,b,m,n) configurations in this container. Does not differentiate between frequencies.

Returns:
unique_configsNx4 numpy.ndarray

The configurations, 1. indexed (starting with 1)

get_spectrum(nr_id=None, abmn=None, withK=False, plot_filename=None)[source]

Return a spectrum and its reciprocal counter part, if present in the dataset. Optimally, refer to the spectrum by its normal-reciprocal id.

If the timestep column is present, then return dictionaries for normal and reciprocal data, with one sip_response object associated with each timestep.

If the parameter plot_filename is specified, then plots will be created using the SIP objects. If multiple timesteps are present, then the parameter plot_filename will be used as a template, and the timesteps will be appended for each plot.

Parameters:
withKbool

If True, and the column “k” exists, then return an apparent spectrum with geometric factors included

Returns:
spectrum_norreda.eis.plots.sip_response or dict or None

Normal spectrum. None if no normal spectrum is available

spectrum_recreda.eis.plots.sip_response or dict or None

Reciprocal spectrum. None if no reciprocal spectrum is available

figmatplotlib.Figure.Figure, optional

Figure object (only if plot_filename is set)

keep_frequencies(frequencies)[source]

Keep only the requested frequencies, remove everything else.

Parameters:
frequenciestuple

Tuple of frequencies to keep.

keep_only_configs(configs_to_keep, frequency=None)[source]

Keep only the supplied configs. If frequency is provided, only this frequency is filtered and all other frequencies are left as is.

Parameters:
property nr_frequencies

Return the number of frequencies in the data set

property nr_timesteps

Return the number of timesteps registered with this container

plot_all_spectra(outdir)[source]

This is a convenience function to plot ALL spectra currently stored in the container. It is useful to asses whether data filters do perform correctly.

Note that the function just iterates over all ids and plots the corresponding spectra, thus it is slow.

Spectra a named using the format: %.2i_spectrum_id_{}.png.

Parameters:
outdirstring

Output directory to store spectra in. Created if it does not exist.

plot_histograms(column='r', primary_dim=None, filename=None, **kwargs)[source]

Plot a histograms for all frequencies of one data column

Parameters:
columnstr, optional

data column to plot. defaults to “r” for resistance

primary_dimNone|str

???

filenameNone|str

Prefix for filename. Do not add a file ending here, as additional string will be appended here.

**kwargsdict

???

TODO: Check saving to file for more than one secondary dimension
Parameters
———-
plot_pseudosections(column, filename=None, return_fig=False)[source]

Create a multi-plot with one pseudosection of type 1 for each frequency.

Parameters:
columnstring

which column to plot

filenameNone|string

output filename. If set to None, do not write to file. Default: None

return_figbool

if True, return the generated figure object, also if filename is set. Default: False

Returns:
figNone|matplotlib.Figure

if return_fig is set to True or filename is None, return the generated Figure object

query(query, inplace=True)[source]

State what you want to keep

remove_frequencies(fmin, fmax)[source]

Remove frequencies outside the provided range from the dataset.

fmin and max will also be excluded.

Parameters:
fminfloat

Minimal frequency to be excluded

fmaxfloat

Maximal frequency to be excluded

scatter_norrec(filename=None, individual=False)[source]

Create a scatter plot for all diff pairs

Parameters:
filenamestring, optional

if given, save plot to file

individualbool, optional

if set to True, return one figure for each row

Returns:
figmatplotlib.Figure or list of matplotlib.Figure.Figure

objects the figure object

axeslist of matplotlib.axes

the individual axes

subquery(subset, filter, inplace=True)[source]

Examples

subquery(
    'timestep == 2',
    'R > 4',
)
class reda.containers.sEIT.sEITImporters[source]

Bases: ImportersBase

This class provides wrappers for most of the importer functions, and is meant to be inherited by the data containers

Methods

add_dataframe(data[, timestep])

Add data to the container using another DataFrame

get_norrec_pairs([test_column])

Return a dataframe that contains only valid normal-reciprocal pairs

import_crtomo(directory[, frequency_file, ...])

CRTomo importerLoad sEIT data from data directory.

import_eit_fzj(filename, configfile[, ...])

EIT data import for FZJ Medusa systems

import_mpt_das1(filename[, timestep])

Import MPT DAS-1 SIP data

import_sip256c(filename[, settings, reciprocal])

Radic SIP256c data import

merge_container(container)

Merge the data and electrode positions from another container into this one.
import_crtomo(directory, frequency_file='frequencies.dat', data_prefix='volt_', no_norrec=False, **kwargs)[source]

CRTomo importerLoad sEIT data from data directory. This function loads data previously exported from reda using reda.exporters.crtomo.write_files_to_directory

Parameters:
directorystring

input directory

frequency_filestring, optional

file (located in directory) that contains the frequencies

data_prefix: string, optional

for each frequency a corresponding data file must be present in the input directory. Frequencies and files are matched by sorting the frequencies AND the filenames, retrieved using glob and the data_prefix

Returns:
dfpandas.DataFrame

A DataFrame suitable for the sEIT container

electrodesNone

No electrode data is imported

topographyNone

No topography data is imported

import_eit_fzj(filename, configfile, correction_file=None, timestep=None, **kwargs)[source]

EIT data import for FZJ Medusa systems

import_mpt_das1(filename, timestep=None, **kwargs)[source]

Import MPT DAS-1 SIP data

Parameters:
filenamestr

Data file

timestepobject, optional

Timestep of the measurement, default: None

import_sip256c(filename, settings=None, reciprocal=None, **kwargs)[source]

Radic SIP256c data import