pycsamt.zonge.avg#

AVG - Main user-facing data container for Zonge datasets.

This module provides the AVG class, which serves as the primary entry point and high-level facade for interacting with a complete Zonge AVG dataset. It composes all other components (Header, Z, Resistivity, Phase, and QC metrics) into a single, convenient container.

Classes

`AMTAVG`([verbose])	Extends AVG with tensor components and analytical methods.
`AVG`([verbose])	High-level façade for a Zonge AVG/AMTAVG dataset.
`BaseAVG`([verbose])	Base class for AVG data handling and file writing.

class pycsamt.zonge.avg.BaseAVG(verbose=False)[source]#

Bases: Zonge

Base class for AVG data handling and file writing.

This class serves as the core engine for reading, parsing, and writing Zonge AVG data files. It handles the logic for identifying file types (legacy vs. modern), transforming legacy data, and populating a structured data model.

Parameters:

verbose (bool, default False) – If True, log messages will be printed to the console during file reading and writing operations, providing insight into the process.

Variables:

info (DataInfo) – An aggregator object that holds all the individual data components (e.g., Header, Station, Z, Resistivity).
verbose (bool) – Controls the verbosity of logging output.
_kind ({1, 2} or None) – Indicates the detected file format: 1 for legacy, 2 for modern. None if no file has been read.
_source_path (pathlib.Path or None) – The path to the source file that was read.

Notes

This class is typically not instantiated directly by users. Instead, the AVG class, which inherits from BaseAVG, is the primary user-facing entry point.

The read method is the main entry point for data ingestion and is designed to be flexible, accepting file paths or in-memory data structures.

Examples

While direct use is uncommon, one could use BaseAVG like so:

>>> from pycsamt.zonge.avg import BaseAVG
>>> avg_processor = BaseAVG(verbose=True)
>>> avg_processor.read('path/to/your/data.avg')
>>> avg_processor.write('path/to/output.avg', fmt='modern')

See also

AVG: The primary user-facing class for AVG data.
DataInfo: The main component aggregator used by this class.

read(source, meta=None)[source]#

Read, parse, and populate components from a data source.

This is the primary data ingestion method for the class. It is designed to be flexible, accepting various data sources and orchestrating the entire process of parsing, transforming, and populating the internal data components.

Parameters:

source (str, Path, AVGFrame, or pd.DataFrame) –
The data source to load. This can be:
- A string or pathlib.Path pointing to a Zonge AVG file (both legacy and modern formats are supported).
- A pandas.DataFrame containing AVG data. Note that if the DataFrame is not already standardized, it will be automatically standardized by AVGFrame.
- An existing AVGFrame object.
meta (mapping, optional) – An optional dictionary of metadata. This is primarily used when source is a pd.DataFrame to provide header-like information.

Returns:

self – The method returns the instance of the class, allowing for convenient method chaining.

Return type:

BaseAVG

Raises:

TypeError – If the source is of an unsupported type.
FileNotFoundError – If source is a path that does not exist.
AvgFileError – If a file source cannot be reliably classified as either legacy (kind-1) or modern (kind-2).
AvgDataError – If parsing fails due to malformed or missing data.

Notes

The read method encapsulates a complete data processing pipeline:

File Handling: If a path is provided, it reads the file and uses classify_avg_format() to determine the file kind.
Transformation: If a legacy (kind-1) file is detected, it automatically uses the LegacyAVGBase transformer to convert the data to a modern, structured format.
Standardization: If a raw pd.DataFrame is provided, it is wrapped in an AVGFrame, which triggers the automatic standardization of column names to the internal canonical schema.
Component Population: The final, clean DataFrame and metadata are passed to the DataInfo component, which in turn populates all the individual data components (e.g., Station, Z, Resistivity).

Examples

>>> from pycsamt.zonge import AVG
>>> # Read from a modern file
>>> avg = AVG().read('data/avg/K2.avg')
>>>
>>> # Read from a legacy file (will be auto-transformed)
>>> avg_legacy = AVG().read('data/avg/K1.avg')
>>>
>>> # Read from an in-memory DataFrame
>>> import pandas as pd
>>> df = pd.DataFrame({'Freq': [1024], 'Resistivity': [100]})
>>> avg_from_df = AVG().read(df)

add_topography(stn_file, utm_zone=None, epsg=None)[source]#

Read and attach station topography data.

This method reads a Zonge .stn file (or a DataFrame with the same structure) and attaches a populated Topography object to the instance.

Parameters:

stn_file (str, Path, or DataFrame) – The path to the .stn file or a pre-loaded DataFrame containing the station location data.
utm_zone (str)
epsg (int)

Returns:

self – The method returns the instance, allowing for method chaining.

Return type:

BaseAVG

to_modern(path=None, *, stamp=True, float_fmt='%.6g', na_rep='*', header_spaces=False)[source]#

Write data to a modern (kind-2) AVG file.

This method serializes the object’s data into a modern, CSAVGW-style .avg file. It delegates the core writing and formatting logic to the write_avg() function.

Parameters:

path (str or pathlib.Path, optional) – The output file path. If None, a default filename (e.g., K2_modern.avg) is created in the current working directory.
stamp (bool, default True) – If True, a $Written=<timestamp> line is added to the header for provenance.
float_fmt (str, default "%.6g") – The format specifier for floating-point numbers.
na_rep (str, default "*") – The string representation for missing (NaN) values.
header_spaces (bool, default False) – If True, adds spaces around the equals sign in header keywords (e.g., $Key = Value).

Notes

This method intelligently prepares the data for writing by: 1. Assembling a complete header by calling the

to_keywords() method on the header component.

Generating a file banner from the hardware component.
Filtering out any artificial data rows (e.g., those with NaN in the ‘rho’ column) that may have been created during a legacy-to-modern transformation.

See also

to_legacy: Write data to a legacy (kind-1) file.
pycsamt.zonge.utils.write_avg: The underlying file writing function.

to_legacy(path=None, *, precision=4, na_rep='*')[source]#

Write data to a legacy (kind-1) AVG file.

This method reconstructs the classic, fixed-width format of older Zonge AVG files. It uses the populated data components to generate the header and formatted data rows.

Parameters:

path (str or pathlib.Path, optional) – The output file path. If None, a default filename (e.g., K2_legacy.avg) is created in the current working directory.
precision (int, default 4) – The number of decimal places for scientific notation fields (e.g., ‘Emag’, ‘Resistivity’).
na_rep (str, default "*") – The string representation for missing (NaN) values.

Notes

The conversion from a modern, comprehensive data structure to the legacy format involves several key steps:

Header Synthesis: It creates a minimal legacy header using $ASPACE and $XMTR keywords derived from the header component.
Column Translation: It translates the internal canonical column names (e.g., ‘pc_emag’) back to their legacy equivalents (e.g., ‘%Emag’) for correct formatting.
Data Filtering: It first filters out any artificial data rows (e.g., those with NaN in the ‘rho’ column) to ensure the output is clean.

Be aware that some information from a modern dataset may be lost in this conversion, as the legacy format does not support all the fields of the modern format.

See also

to_modern: Write data to a modern (kind-2) file.

write(path=None, *, fmt='auto', **kwargs)[source]#

Write AVG data to a file in the specified format.

This method acts as a high-level dispatcher, calling either the to_legacy() or to_modern() method based on the fmt parameter. It provides a single, convenient entry point for file serialization.

Parameters:

path (str or pathlib.Path, optional) – The output file path. If None, a default filename will be generated based on the source file’s name.
fmt ({'legacy', 'kind1', 'modern', 'kind2', 'auto'}, default 'auto') – The desired output format. - ‘legacy’ or ‘kind1’: Writes a fixed-width legacy file. - ‘modern’ or ‘kind2’: Writes a CSV-based modern file. - ‘auto’: Defaults to the modern format.
**kwargs – Additional keyword arguments to be passed to the specific writing method (to_legacy or to_modern). For example, precision for to_legacy or float_fmt for to_modern.

Raises:

ValueError – If an unsupported fmt is specified.
NotReadError – If this method is called before data has been loaded.

See also

to_legacy: Writes data to a legacy (kind-1) file.
to_modern: Writes data to a modern (kind-2) file.

property summary[source]#

Provide a Bunch object with a summary of key attributes.

This property aggregates the most important descriptive metadata and statistics from the loaded dataset into a single, easy-to-read object. It is useful for quickly inspecting the contents and overall scope of the survey data.

Returns:: A Bunch object (which behaves like a dictionary with attribute-style access) containing key information such as the source filename, data format kind, project name, number of stations and frequencies, and their respective ranges.
Return type:: pycsamt.api.bunch.Bunch

Notes

The summary property is read-only and is generated on-demand by introspecting the various data components (e.g., header, station, frequency). If no data has been loaded, it will return a Bunch with a ‘status’ key indicating this.

The Bunch object has a user-friendly string representation, making it ideal for printing in interactive sessions.

Examples

>>> from pycsamt.zonge import AVG
>>> avg = AVG.from_file('data/avg/K2.avg')
>>> print(avg.summary)
<Bunch object with keys: source_file, data_kind, project, ...>
>>> avg.summary.num_stations
28

asdict()[source]#

Return a shallow, JSON-serializable representation.

This method aggregates the keyword dictionaries from all header components into a single dictionary, providing a complete, serializable view of the survey’s metadata.

Returns:: A dictionary containing all header information.
Return type:: dict

class pycsamt.zonge.avg.AVG(verbose=False)[source]#

Bases: BaseAVG

High-level façade for a Zonge AVG/AMTAVG dataset.

An AVG instance loads a raw text file, normalizes its content, and hydrates a full suite of data components, including stations, frequencies, impedance, quality metrics, and header blocks. This makes the entire survey accessible through strongly-typed attributes [1]_.

Parameters:

verbose (bool, default False) – If True, log messages will be printed to the console during file reading and writing operations.

Variables:

header (pycsamt.zonge.heads.Header) – Aggregates Hardware, SurveyAnnotation, SurveyConfiguration, and Rx/Tx property blocks.
station (pycsamt.zonge.survey.Station) – Manages survey line geometry and station coordinates.
frequency (pycsamt.zonge.meas.Frequency) – Manages the frequency axis for the measurements.
phase (resistivity,) – Containers for apparent resistivity and phase data.
z (pycsamt.zonge.z.Z) – Component for computing the complex impedance tensor.
df (pd.DataFrame or None) – The core tidy DataFrame containing all available data columns after parsing and normalization.

from_file(path, verbose=False)[source]#

Classmethod to load and parse an AVG file. This is the primary entry point for creating an AVG instance.

Parameters:

path (str | Path)
verbose (bool)

Return type:

AVG

read(source, meta=None)#: Populates the object from a source (file, DataFrame, etc.).

write(path, fmt='auto', \*\*kwargs)#: Writes the data to a file, dispatching to to_modern or to_legacy based on the fmt argument.

to_xarray()[source]#

Exports the entire dataset into a single, comprehensive xarray.Dataset.

Parameters:

include_qc (bool)
include_z (bool)

to_tensor(var='z', \*\*kwargs)[source]#

Exports a specific variable as a 2x2 NumPy tensor.

Parameters:

var (str)
station (int | float | None)
agg (str | None)
fill_value (float)
sort_freq (bool)
align (str)

Return type:

tuple[ndarray, ndarray, ndarray]

asdict()#: Returns a JSON-serializable dictionary of all header metadata.

Notes

Zonge Engineering produced two AVG file formats:

Kind-1 (Legacy): Fixed-width text tables with minimal
metadata.
Kind-2 (Modern): CSV-based format with rich
$key=value headers, used by modern AMTAVG and ASTATIC.

The loader automatically detects the format and transforms it into a canonical, tidy DataFrame. All subsequent processing is format-agnostic. Zonge placeholders (*) are converted to NaN.

Examples

Load a file, access a component, and write back:

>>> from pycsamt.zonge import AVG
>>> avg = AVG.from_file('LCS01.avg', verbose=True)
>>> # Access resistivity for a specific component
>>> rho_xy = avg.resistivity.frame[
...     avg.resistivity.frame.comp == 'ExHy'
... ]
>>> avg.write('LCS01_clean.avg')

Build from an in-memory DataFrame:

>>> from pycsamt.zonge.utils import load_avg
>>> df, meta = load_avg('raw.avg')
>>> avg = AVG()
>>> avg.read(df, meta=meta)
>>> print(avg.station.span)

References

classmethod from_file(path, *, verbose=False)[source]#

Load and parse an AVG file from a path.

This classmethod is the primary factory for creating a fully populated AVG instance directly from a file on disk. It handles the entire data loading pipeline.

Parameters:

path (str or pathlib.Path) – The filesystem path to the .avg file. Both legacy (kind-1) and modern (kind-2) formats are supported.
verbose (bool, default False) – If True, log messages will be printed to the console during the file loading and parsing process.

Returns:

A new, fully initialized instance of the AVG class containing all the data and metadata from the file.

Return type:

AVG

Notes

This method is a convenient wrapper around the more general read() method. It creates an instance of the class and then immediately calls obj.read(path) to perform the actual data loading and component population.

Examples

>>> from pycsamt.zonge import AVG
>>> # Load a modern AVG file
>>> avg = AVG.from_file('data/avg/K2.avg', verbose=True)
>>> print(avg.summary)

See also

read: The underlying method that performs the data ingestion.

to_xarray(*, include_qc=True, include_z=True)[source]#

Export the complete dataset to a single xarray.Dataset.

This is the primary export method for creating a comprehensive, multi-dimensional, and self-describing dataset. It aggregates the primary data variables (resistivity, phase), the computed impedance tensor (Z), and all available quality control (QC) metrics into a single object.

Parameters:

include_qc (bool, default True) – If True, all available QC components (e.g., pc_emag, s_phz) will be included as data variables in the dataset.
include_z (bool, default True) – If True, the real and imaginary parts of the complex impedance tensor (Z) and its propagated error will be included as data variables.

Returns:

A dataset with coordinates (station, freq, comp) and data variables for all included measurements and QC metrics. The dataset’s attributes are populated from the AVG file’s header.

Return type:

xarray.Dataset

Notes

This method provides a high-level, convenient way to get all the processed data into a standard format for analysis and plotting. It gracefully handles cases where optional components (like certain QC metrics) are not available in the source data by skipping them.

Examples

>>> from pycsamt.zonge import AVG
>>> avg = AVG.from_file('data/avg/K2.avg')
>>> # Export all available data
>>> ds = avg.to_xarray()
>>> print(ds.data_vars)
Data variables:
    rho        (station, freq, comp) float64 ...
    phase      (station, freq, comp) float64 ...
    z_real     (station, freq, comp) float64 ...
    ...

See also

to_tensor: Export a single variable to a NumPy tensor.

to_tensor(var='z', *, station=None, agg='mean', fill_value=nan, sort_freq=True, align='union')[source]#

Export a specific variable as a 2x2 tensor.

This is a convenience method that provides direct access to the underlying tensor-shaping capabilities of the data components. It delegates the call to the appropriate component (e.g., Z, Resistivity) to reshape the selected variable into a NumPy array.

Parameters:

var ({'z', 'z_real', 'z_imag', 'z_err', 'rho', 'phase'}, default 'z') – The specific data variable to export as a tensor.
station (int or float, optional) – If provided, the method returns a 3D tensor for only this station. If None, it returns a 4D tensor containing data for all stations.
agg (str or None, default 'mean') – The aggregation method to use if duplicate measurements exist for the same station, frequency, and component.
fill_value (float, default np.nan) – The value to use for missing elements in the tensor grid.
sort_freq (bool, default True) – If True, the frequency axis of the tensor will be sorted.
align ({'union', 'intersection'}, default 'union') – For multi-station tensors, determines how to handle different frequency sets between stations.

Returns:

tensor (numpy.ndarray) – The data reshaped into a tensor. Shape is (n_freq, 2, 2) for a single station or (n_station, n_freq, 2, 2) for multiple stations.
freqs (numpy.ndarray) – The frequency axis for the tensor.
stations (numpy.ndarray) – The station axis for the tensor (empty for a single- station request).

Return type:

tuple[ndarray, ndarray, ndarray]

See also

to_xarray: Export the entire dataset to a labeled xarray object.
pycsamt.zonge.tensor.TensorBase.to_tensor: The underlying implementation.

property header[source]#: Access the Header component.

property station[source]#: Access the Station component.

property z[source]#: Access the impedance (Z) component.

property resistivity[source]#: Access the Resistivity component.

property phase[source]#: Access the Phase component.

property frequency[source]#: Access the Frequency component.

property df: DataFrame | None[source]#: Access the core tidy DataFrame containing all available data columns after parsing and normalization.

fit(source, meta=None)#

Read, parse, and populate components from a data source.

This is the primary data ingestion method for the class. It is designed to be flexible, accepting various data sources and orchestrating the entire process of parsing, transforming, and populating the internal data components.

Parameters:

source (str, Path, AVGFrame, or pd.DataFrame) –
The data source to load. This can be:
- A string or pathlib.Path pointing to a Zonge AVG file (both legacy and modern formats are supported).
- A pandas.DataFrame containing AVG data. Note that if the DataFrame is not already standardized, it will be automatically standardized by AVGFrame.
- An existing AVGFrame object.
meta (mapping, optional) – An optional dictionary of metadata. This is primarily used when source is a pd.DataFrame to provide header-like information.

Returns:

self – The method returns the instance of the class, allowing for convenient method chaining.

Return type:

BaseAVG

Raises:

TypeError – If the source is of an unsupported type.
FileNotFoundError – If source is a path that does not exist.
AvgFileError – If a file source cannot be reliably classified as either legacy (kind-1) or modern (kind-2).
AvgDataError – If parsing fails due to malformed or missing data.

Notes

The read method encapsulates a complete data processing pipeline:

File Handling: If a path is provided, it reads the file and uses classify_avg_format() to determine the file kind.
Transformation: If a legacy (kind-1) file is detected, it automatically uses the LegacyAVGBase transformer to convert the data to a modern, structured format.
Standardization: If a raw pd.DataFrame is provided, it is wrapped in an AVGFrame, which triggers the automatic standardization of column names to the internal canonical schema.
Component Population: The final, clean DataFrame and metadata are passed to the DataInfo component, which in turn populates all the individual data components (e.g., Station, Z, Resistivity).

Examples

>>> from pycsamt.zonge import AVG
>>> # Read from a modern file
>>> avg = AVG().read('data/avg/K2.avg')
>>>
>>> # Read from a legacy file (will be auto-transformed)
>>> avg_legacy = AVG().read('data/avg/K1.avg')
>>>
>>> # Read from an in-memory DataFrame
>>> import pandas as pd
>>> df = pd.DataFrame({'Freq': [1024], 'Resistivity': [100]})
>>> avg_from_df = AVG().read(df)

class pycsamt.zonge.avg.AMTAVG(verbose=False)[source]#

Bases: AVG

Extends AVG with tensor components and analytical methods.

This class inherits from AVG and provides a more specialized, analysis-focused interface. It is designed for users who need to perform advanced geophysical data processing and manipulation. It is designed for working directly with specific tensor elements (e.g., z_xy, rho_yx).

The primary features of this class include:

Direct, property-based access to individual components of the impedance, resistivity, and phase tensors as pandas Series (e.g., z_xy, res_yx_err).
A suite of methods for advanced data processing, such as tensor rotation, phase unwrapping, and statistical analysis.

In addition to providing direct access to tensor components, this class includes methods for advanced data manipulation, such as recalculating resistivity and phase from the impedance tensor.

Variables:

z_yy (z_xx, z_xy, z_yx,) – The complex impedance for the respective tensor component.
z_yy_err (z_xx_err, z_xy_err, z_yx_err,) – The propagated error for the respective impedance component.
res_yy (res_xx, res_xy, res_yx,) – The apparent resistivity for the respective tensor component.
res_yy_err (res_xx_err, res_xy_err, res_yx_err,) – The percent error for the respective resistivity component.
phase_yy (phase_xx, phase_xy, phase_yx,) – The impedance phase for the respective tensor component.
phase_yy_err (phase_xx_err, phase_xy_err, phase_yx_err,) – The standard deviation of the respective phase component.

Parameters:

verbose (bool)

compute_resistivity_phase()[source]#

Calculates apparent resistivity and phase from the complex impedance tensor Z.

Parameters:: todeg (bool)
Return type:: tuple[Series, Series, Series, Series]

set_resistivity_phase(rho, phi, rho_err=None, phi_err=None)[source]#

Updates the dataset with new resistivity and phase values, triggering a recalculation of the impedance tensor.

Parameters:

rho (Series)
phi (Series)
rho_err (Series | None)
phi_err (Series | None)

get_tensor_by_station(station_id, var='z')[source]#

Fetches a 3D tensor for a single station using xarray.

Parameters:

station_id (int | float)
var (Literal['z', 'rho', 'phase'])

calculate_statistics()[source]#

Computes statistical QC metrics from repeated measurements.

Parameters:

drop_on_failure (bool)
update_components (bool)

Return type:

DataFrame

unwrap_phase()[source]#

Corrects for 2π phase wrapping in the impedance phase data.

Parameters:

todeg (bool)
update_components (bool)

Return type:

DataFrame

rotate(angle_deg)[source]#

Performs a 2D rotation of the impedance tensor.

Parameters:

angle (float)
update_components (bool)

calculate_tipper(hz_data)[source]#

Computes the Tipper transfer function from vertical field data.

Parameters:

hz_data (Series)
update_components (bool)

Return type:

DataFrame

Examples

>>> from pycsamt.zonge import AMTAVG
>>> amt_avg = AMTAVG.from_file('data/avg/K2.avg')
>>> # Rotate the data by 30 degrees clockwise
>>> amt_avg.rotate(30)
>>> # Access the newly rotated Z_xy component
>>> z_xy_rotated = amt_avg.z_xy

See also

AVG: The parent class providing the main data loading and exporting functionality.
Z: The component that handles impedance calculations.
Resistivity: The component that manages resistivity data.
Phase: The component that manages phase data.

compute_resistivity_phase(todeg=False)[source]#

Compute rho and phi from the complex impedance Z.

This method calculates the apparent resistivity ($\rho_a$) and impedance phase ($\phi$) from the complex impedance tensor ($\mathbf{Z}$). It also propagates the errors from $\mathbf{Z}$ to the new values.

Parameters:

todeg (bool, default False) – If True, the output impedance phase and its error will be converted from milliradians to degrees.

Returns:

rho (pandas.Series) – The calculated apparent resistivity in ohm-meters.
phi (pandas.Series) – The calculated impedance phase in milliradians or degrees.
rho_err (pandas.Series) – The propagated relative error in apparent resistivity (in percent).
phi_err (pandas.Series) – The propagated error in impedance phase (in the same units as phi).

Return type:

tuple[Series, Series, Series, Series]

Notes

The calculations are based on the fundamental MT relationships [1]_:

\[\rho_a = \frac{1}{\omega \mu_0} |Z|^2 \quad \text{and} \quad \phi = \arctan\left(\frac{\text{Im}(Z)}{\text{Re}(Z)}\right)\]

Error propagation for $\rho_a$ is given by:

\[\frac{\delta\rho_a}{\rho_a} = 2 \frac{\delta|Z|}{|Z|}\]

References

set_resistivity_phase(rho, phi, rho_err=None, phi_err=None)[source]#

Attach new rho/phi data and reconstruct Z.

This method updates the core DataFrame with new apparent resistivity and impedance phase values. After updating, it automatically re-initializes all data components, ensuring that dependent values (like the impedance tensor Z) are recalculated and the entire object state remains consistent.

Parameters:

rho (pandas.Series) – A Series containing the new apparent resistivity values. The index must align with the internal DataFrame.
phi (pandas.Series) – A Series containing the new impedance phase values, assumed to be in milliradians.
rho_err (pandas.Series, optional) – A Series containing the new relative error values for resistivity (in percent).
phi_err (pandas.Series, optional) – A Series containing the new error values for phase (in milliradians).

Notes

This is a powerful method for data manipulation, such as applying static shifts or corrections. By calling self.info.read at the end, it ensures that all changes are propagated correctly throughout the entire data model.

get_tensor_by_station(station_id, *, var='z')[source]#

Fetch a 3D tensor for a single station using xarray.

This method extracts the data for a single station from the full multi-station dataset and returns it as a 3D tensor in an xarray.DataArray.

Parameters:

station_id (int or float) – The unique identifier for the station to retrieve. This must match one of the values in the ‘station’ coordinate of the dataset.
var ({'z', 'rho', 'phase'}, default 'z') – The specific data variable to retrieve. - ‘z’: Complex impedance tensor. - ‘rho’: Apparent resistivity tensor. - ‘phase’: Impedance phase tensor.

Returns:

A 3D DataArray with dimensions (frequency, e, h) containing the tensor data for the requested station.

Return type:

xarray.DataArray

Raises:

KeyError – If the specified station_id is not found in the dataset’s station coordinates.
ValueError – If an unsupported var is requested.
NotReadError – If this method is called before data has been loaded.

Notes

This method leverages the powerful label-based indexing of xarray’s .sel() method. It first constructs the full multi-station tensor in memory and then selects the slice corresponding to the given station_id.

Examples

>>> from pycsamt.zonge import AMTAVG
>>> avg = AMTAVG.from_file('data/avg/K2.avg')
>>> # Get the resistivity tensor for station 25.0
>>> rho_tensor_25 = avg.get_tensor_by_station(25.0, var='rho')
>>> print(rho_tensor_25.shape)
(28, 2, 2)

calculate_statistics(*, drop_on_failure=True, update_components=True)[source]#

Calculate statistics from grouped data.

This method computes statistical metrics for repeated measurements, mirroring the process of the original Zonge AMTAVG program. It groups the data by station and frequency and calculates the mean, standard deviation (σ), and coefficient of variation (C-var) for key measurement columns.

Parameters:

drop_on_failure (bool, default True) – If True, rows where statistical calculations cannot be performed (e.g., only one measurement in a group) will be excluded from the output DataFrame.
update_components (bool, default True) – If True, after calculating the statistics, this method will automatically update the corresponding QC components (e.g., self.pc_rho, self.s_phz) with the newly computed values.

Returns:

A DataFrame containing the calculated statistical metrics, including mean, standard deviation, and coefficient of variation for rho, phase, emag, and hmag.

Return type:

pandas.DataFrame

Notes

The calculations are based on the formulas described in the AMTAVG manual [1]_. The coefficient of variation (C-var), which corresponds to the legacy % columns, is calculated as:

\[C_{var} = 100 \cdot \frac{\sigma}{\bar{x}}\]

where $\sigma$ is the standard deviation and $\bar{x}$ is the mean of the measurements.

References

unwrap_phase(*, todeg=False, update_components=True)[source]#

Unwraps the impedance phase to correct for 2π jumps.

This method implements a phase referencing/unwrapping algorithm, similar to the PHASEREF mode in the original Zonge AMTAVG program. It corrects for phase wrapping by ensuring that the absolute difference between consecutive phase values (sorted by frequency) does not exceed π radians (1000π milliradians).

Parameters:

todeg (bool, default False) – If True, the final unwrapped phase values in the DataFrame will be converted from milliradians to degrees.
update_components (bool, default True) – If True, the main DataFrame (self.info.df) is updated in place with the unwrapped phase values, and all data components are re-read to reflect this change.

Returns:

A new DataFrame with the unwrapped phase values. The main self.info.df is also updated if update_components is True.

Return type:

pandas.DataFrame

Notes

The unwrapping is performed independently for each unique station and component pair. The data is first sorted by frequency to ensure the correct sequence for unwrapping.

The core of the algorithm uses numpy.unwrap(), which operates in radians. The phase values, assumed to be in milliradians, are converted to radians before unwrapping and then converted back to the desired output unit.

Examples

>>> from pycsamt.zonge import AMTAVG
>>> avg = AMTAVG.from_file('data/avg/K2.avg')
>>> # Unwrap phase and update the object in place
>>> avg.unwrap_phase()
>>> # Get the unwrapped phase for the xy component
>>> unwrapped_phi_xy = avg.phase_xy

See also

numpy.unwrap: The underlying function used for unwrapping.

rotate(angle, *, update_components=True)[source]#

Rotate the impedance tensor by a given angle.

This method performs a 2D rotation of the impedance tensor $\mathbf{Z}$ for all measurements. The rotation is applied in a clockwise-positive direction, which is standard in many geophysical applications.

Parameters:

angle (float) – The rotation angle in degrees, where a positive angle corresponds to a clockwise rotation of the coordinate system.
update_components (bool, default True) – If True, the main DataFrame (self.info.df) is updated in place with the new, rotated impedance values, and all dependent components (like resistivity and phase) are recalculated and updated.

Returns:

A new DataFrame containing the four rotated complex impedance components (z_xx_rot, z_xy_rot, z_yx_rot, z_yy_rot). The main self.info.df is also updated if update_components is True.

Return type:

pandas.DataFrame

Notes

The rotation is performed using the standard 2D tensor rotation matrix [1]_:

\[\mathbf{Z'} = \mathbf{R}(\theta) \mathbf{Z} \mathbf{R}^T(\theta)\]

where $\mathbf{Z}$ is the original impedance tensor, $\mathbf{Z'}$ is the rotated tensor, $\theta$ is the rotation angle, and $\mathbf{R}(\theta)$ is the rotation matrix:

\[\begin{split}\mathbf{R}(\theta) = \begin{pmatrix} \cos\theta & \sin\theta \\ -\sin\theta & \cos\theta \end{pmatrix}\end{split}\]

This operation updates the z_xx, z_xy, z_yx, and z_yy components. If update_components is True, the apparent resistivity and phase are then recalculated from this new rotated impedance tensor.

References

See also

compute_resistivity_phase: Recalculates rho and phi from Z.

calculate_tipper(hz_data, *, update_components=True)[source]#

Calculate the Tipper transfer function.

This method computes the complex Tipper components (Tx, Ty) by solving the linear relationship: $H_z = T_x H_x + T_y H_y$

Parameters:

hz_data (pandas.Series) – A Series containing the complex vertical magnetic field ($H_z$) data. The index of this Series must match the index of the main DataFrame (self.info.df).
update_components (bool, default True) – If True, a new Tipper component will be created and attached to the self.info object, populated with the calculated Tx and Ty values.

Returns:

A new DataFrame containing the calculated complex Tipper components, tx and ty.

Return type:

pandas.DataFrame

Notes

The calculation is performed for each unique station and frequency using a least-squares approach to solve for $T_x$ and $T_y$. This requires at least two linearly independent horizontal field measurements for a stable solution.

This method requires the horizontal magnetic field data (hmag, hphz) to be present in the dataset.

property z_xx[source]#

property z_xy[source]#

property z_yx[source]#

property z_yy[source]#

property z_xx_err[source]#

property z_xy_err[source]#

property z_yx_err[source]#

property z_yy_err[source]#

property res_xx[source]#

property res_xy[source]#

property res_yx[source]#

property res_yy[source]#

property res_xx_err[source]#

property res_xy_err[source]#

property res_yx_err[source]#

property res_yy_err[source]#

property phase_xx[source]#

property phase_xy[source]#

property phase_yx[source]#

property phase_yy[source]#

property phase_xx_err[source]#

property phase_xy_err[source]#

property phase_yx_err[source]#

property phase_yy_err[source]#