mth5.io.nims package

Submodules

mth5.io.nims.gps module

Created on Thu Sep 1 11:43:56 2022

@author: jpeacock

class mth5.io.nims.gps.GPS(gps_string, index=0)[source]

Bases: object

class to parse GPS stamp from the NIMS

Depending on the type of Stamp different attributes will be filled.

GPRMC has full date and time information and declination GPGGA has elevation data

Note

GPGGA date is set to 1980-01-01 so that the time can be estimated. Should use GPRMC for accurate date/time information.

property declination

geomagnetic declination in degrees from north

property elevation

elevation in meters

property fix

GPS fixed

property gps_type

GPRMC or GPGGA

property latitude

Latitude in decimal degrees, WGS84

property longitude

Latitude in decimal degrees, WGS84

parse_gps_string(gps_string)[source]

Parse a raw gps string from the NIMS and set appropriate attributes. GPS string will first be validated, then parsed.

Parameters

gps_string (string) – raw GPS string to be parsed

property time_stamp

return a datetime object of the time stamp

validate_gps_list(gps_list)[source]

check to make sure the gps stamp is the correct format, checks each element for the proper format

Parameters

gps_list (list) – a parsed gps string from a NIMS

Raises

mth5.io.nims.GPSError if anything is wrong.

validate_gps_string(gps_string)[source]

make sure the string is valid, remove any binary numbers and find the end of the string as ‘*’

Parameters

gps_string (string) – raw GPS string to be validated

Returns

validated string or None if there is something wrong

exception mth5.io.nims.gps.GPSError[source]

Bases: Exception

mth5.io.nims.header module

Created on Thu Sep 1 12:57:32 2022

@author: jpeacock

exception mth5.io.nims.header.NIMSError[source]

Bases: Exception

class mth5.io.nims.header.NIMSHeader(fn=None)[source]

Bases: object

class to hold the NIMS header information.

A typical header looks like

'''
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>user field>>>>>>>>>>>>>>>>>>>>>>>>>>>>
SITE NAME: Budwieser Spring
STATE/PROVINCE: CA
COUNTRY: USA
>>> The following code in double quotes is REQUIRED to start the NIMS <<
>>> The next 3 lines contain values required for processing <<<<<<<<<<<<
>>> The lines after that are optional <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
"300b"  <-- 2CHAR EXPERIMENT CODE + 3 CHAR SITE CODE + RUN LETTER
1105-3; 1305-3  <-- SYSTEM BOX I.D.; MAG HEAD ID (if different)
106  0 <-- N-S Ex WIRE LENGTH (m); HEADING (deg E mag N)
109  90 <-- E-W Ey WIRE LENGTH (m); HEADING (deg E mag N)
1         <-- N ELECTRODE ID
3         <-- E ELECTRODE ID
2         <-- S ELECTRODE ID
4         <-- W ELECTRODE ID
Cu        <-- GROUND ELECTRODE INFO
GPS INFO: 01/10/19 16:16:42 1616.7000 3443.6088 115.7350 W 946.6
OPERATOR: KP
COMMENT: N/S CRS: .95/.96 DCV: 3.5 ACV:1
E/W CRS: .85/.86 DCV: 1.5 ACV: 1
Redeployed site for run b b/c possible animal disturbance
'''
property file_size

Size of the file

property fn

Full path to NIMS file

parse_header_dict(header_dict=None)[source]

parse the header dictionary into something useful

read_header(fn=None)[source]

read header information

Parameters

fn (string or pathlib.Path) – full path to file to read

Raises

mth5.io.nims.NIMSError if something is not right.

property station

Station ID

mth5.io.nims.nims module

NIMS

  • deals with reading in NIMS DATA.BIN files

This is a translation from Matlab codes written and edited by:

  • Anna Kelbert

  • Paul Bedrosian

  • Esteban Bowles-Martinez

  • Possibly others.

I’ve tested it against a version, and it matches. The data/GPS gaps I still don’t understand so for now the time series is just made continuous and the number of missing seconds is clipped from the end of the time series.

Note

this only works for 8Hz data for now

copyright

Jared Peacock (jpeacock@usgs.gov)

license

MIT

class mth5.io.nims.nims.NIMS(fn=None)[source]

Bases: NIMSHeader

NIMS Class will read in a NIMS DATA.BIN file.

A fast way to read the binary files are to first read in the GPS strings, the third byte in each block as a character and parse that into valid GPS stamps.

Then read in the entire data set as unsigned 8 bit integers and reshape the data to be n seconds x block size. Then parse that array into the status information and data.

I only have a limited amount of .BIN files to test so this will likely break if there are issues such as data gaps. This has been tested against the matlab program loadNIMS by Anna Kelbert and the match for all the .bin files I have. If something looks weird check it against that program.

Warning

Currently Only 8 Hz data is supported

align_data(data_array, stamps)[source]

Need to match up the first good GPS stamp with the data

Do this by using the first GPS stamp and assuming that the time from the first time stamp to the start is the index value.

put the data into a pandas data frame that is indexed by time

Parameters

  • data_array (array) – structure array with columns for each component [hx, hy, hz, ex, ey]

  • stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]

Returns

pandas DataFrame with colums of components and indexed by time initialized by the start time.

Note

Data gaps are squeezed cause not sure what a gap actually means.

property box_temperature

data logger temperature, sampled at 1 second

check_timing(stamps)[source]

make sure that there are the correct number of seconds in between the first and last GPS GPRMC stamps

Parameters

stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]

Returns

[ True | False ] if data is valid or not.

Returns

gap index locations

Note

currently it is assumed that if a data gap occurs the data can be squeezed to remove them. Probably a more elegant way of doing it.

property declination

median elevation value from all the GPS stamps in decimal degrees WGS84

Only get from the first stamp within the sets

property elevation

median elevation value from all the GPS stamps in decimal degrees WGS84

Only get from the first stamp within the sets

property end_time

start time is the first good GPS time stamp minus the seconds to the beginning of the time series.

property ex

EX

property ex_metadata
property ey

EY

property ey_metadata
find_sequence(data_array, block_sequence=None)[source]

find a sequence in a given array

Parameters

  • data_array (array) – array of the data with shape [n, m] where n is the number of seconds recorded m is the block length for a given sampling rate.

  • block_sequence (list) – sequence pattern to locate default is [1, 131] the start of a data block.

Returns

array of index locations where the sequence is found.

get_channel_response(channel, dipole_length=1)[source]

Get the channel response for a given channel

Parameters

  • channel (TYPE) – DESCRIPTION

  • dipole_length (TYPE, optional) – DESCRIPTION, defaults to 1

Returns

DESCRIPTION

Return type

TYPE

get_stamps(nims_string)[source]

get a list of valid GPS strings and match synchronous GPRMC with GPGGA stamps if possible.

Parameters

nims_string (str) – raw GPS string output by NIMS

has_data()[source]
property hx

HX

property hx_metadata
property hy

HY

property hy_metadata
property hz

HZ

property hz_metadata
property latitude

median latitude value from all the GPS stamps in decimal degrees WGS84

Only get from the GPRMC stamp as they should be duplicates

property longitude

median longitude value from all the GPS stamps in decimal degrees WGS84

Only get from the first stamp within the sets

make_dt_index(start_time, sample_rate, stop_time=None, n_samples=None)[source]

make time index array

Note

date-time format should be YYYY-M-DDThh:mm:ss.ms UTC

Parameters

  • start_time (string) – start time

  • end_time (string) – end time

  • sample_rate (float) – sample_rate in samples/second

match_status_with_gps_stamps(status_array, gps_list)[source]

Match the index values from the status array with the index values of the GPS stamps. There appears to be a bit of wiggle room between when the lock is recorded and the stamp was actually recorded. This is typically 1 second and sometimes 2.

Parameters

  • status_array (array) – array of status values from each data block

  • gps_list (list) – list of valid GPS stamps [[GPRMC, GPGGA], …]

Note

I think there is a 2 second gap between the lock and the first stamp character.

property n_samples
read_nims(fn=None)[source]

Read NIMS DATA.BIN file.

  1. Read in the header information and stores those as attributes with the same names as in the header file.

  2. Locate the beginning of the data blocks by looking for the first [1, 131, …] combo. Anything before that is cut out.

  3. Make sure the data is a multiple of the block length, if the data is longer the extra bits are cut off.

  4. Read in the GPS data (3rd byte of each block) as characters. Parses those into valid GPS stamps with appropriate index locations of where the ‘$’ was found.

  5. Read in the data as unsigned 8-bit integers and reshape the array into [N, data_block_length]. Parse this array into the status information and the data.

  6. Remove duplicate blocks, by removing the first of the duplicates as suggested by Anna and Paul.

  7. Match the GPS locks from the status with valid GPS stamps.

  8. Check to make sure that there is the correct number of seconds between the first and last GPS stamp. The extra seconds are cut off from the end of the time series. Not sure if this is the best way to accommodate gaps in the data.

Note

The data and information array returned have the duplicates removed and the sequence reset to be monotonic.

Parameters

fn (str) – full path to DATA.BIN file

Example

>>> from mth5.io import nims
>>> n = nims.NIMS(r"/home/mt_data/nims/mt001.bin")
remove_duplicates(info_array, data_array)[source]

remove duplicate blocks, removing the first duplicate as suggested by Paul and Anna. Checks to make sure that the mag data are identical for the duplicate blocks. Removes the blocks from the information and data arrays and returns the reduced arrays. This should sync up the timing of GPS stamps and index values.

Parameters

  • info_array (np.array) – structured array of block information

  • data_array (np.array) – structured array of the data

Returns

reduced information array

Returns

reduced data array

Returns

index of duplicates in raw data

property run_metadata

Run metadata

property start_time

start time is the first good GPS time stamp minus the seconds to the beginning of the time series.

property station_metadata

Station metadata from nims file

to_runts(calibrate=False)[source]

Get xarray for run

unwrap_sequence(sequence)[source]

unwrap the sequence to be sequential numbers instead of modulated by 256. sets the first number to 0

Parameters

sequence (list) – sequence of bytes numbers

Returns

unwrapped number of counts

mth5.io.nims.nims.read_nims(fn)[source]
Parameters

fn (TYPE) – DESCRIPTION

Returns

DESCRIPTION

Return type

TYPE

mth5.io.nims.nims_collection module

LEMI 424 Collection

Collection of TXT files combined into runs

Created on Wed Aug 31 10:32:44 2022

@author: jpeacock

class mth5.io.nims.nims_collection.NIMSCollection(file_path=None, **kwargs)[source]

Bases: Collection

Collection of NIMS files into runs.

>>> from mth5.io.nims import LEMICollection
>>> lc = NIMSCollection(r"/path/to/single/lemi/station")
>>> lc.station_id = "mt001"
>>> lc.survey_id = "test_survey"
>>> run_dict = lc.get_runs(1)
assign_run_names(df, zeros=2)[source]

Assign run names assuming a row represents single station

Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.

Parameters

  • df (pandas.DataFrame) – Dataframe with the appropriate columns

  • zeros (int, optional) – number of zeros in run name, defaults to 4

Returns

Dataframe with run names

Return type

pandas.DataFrame

to_dataframe(sample_rates=[1], run_name_zeros=2, calibration_path=None)[source]

Create a data frame of each TXT file in a given directory.

Note

This assumes the given directory contains a single station

Parameters

  • sample_rates (int or list, optional) – sample rate to get, will always be 1 for LEMI data defaults to [1]

  • run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4

  • calibration_path (string or Path, optional) – path to calibration files, defaults to None

Returns

Dataframe with information of each TXT file in the given directory.

Return type

pandas.DataFrame

Example
>>> from mth5.io.lemi import LEMICollection
>>> lc = LEMICollection("/path/to/single/lemi/station")
>>> lemi_df = lc.to_dataframe()

mth5.io.nims.response_filters module

Created on Fri Sep 2 13:50:51 2022

@author: jpeacock

class mth5.io.nims.response_filters.Response(system_id=None, **kwargs)[source]

Bases: object

Common NIMS response filters for electric and magnetic channels

dipole_filter(length)[source]

Make a dipole filter

Parameters

length (TYPE) – dipole length in meters

Returns

DESCRIPTION

Return type

TYPE

property electric_conversion

electric channel conversion from counts to volts :return: DESCRIPTION :rtype: TYPE

property electric_high_pass_hp

1-pole low pass for 1 hz instuments :return: DESCRIPTION :rtype: TYPE

property electric_high_pass_pc

1-pole low pass filter for 8 hz instruments :return: DESCRIPTION :rtype: TYPE

property electric_low_pass

5 pole electric low pass filter :return: DESCRIPTION :rtype: TYPE

property electric_physical_units

DESCRIPTION :rtype: TYPE

Type

return

get_channel_response(channel, dipole_length=1)[source]

Get the full channel response filter :param channel: DESCRIPTION :type channel: TYPE :param dipole_length: DESCRIPTION, defaults to 1 :type dipole_length: TYPE, optional :return: DESCRIPTION :rtype: TYPE

get_electric_high_pass(hardware='pc')[source]

get the electric high pass filter based on the hardware

property magnetic_conversion

DESCRIPTION :rtype: TYPE

Type

return

property magnetic_low_pass

Low pass 3 pole filter

Returns

DESCRIPTION

Return type

TYPE

exception mth5.io.nims.response_filters.ResponseError[source]

Bases: Exception

Module contents

class mth5.io.nims.GPS(gps_string, index=0)[source]

Bases: object

class to parse GPS stamp from the NIMS

Depending on the type of Stamp different attributes will be filled.

GPRMC has full date and time information and declination GPGGA has elevation data

Note

GPGGA date is set to 1980-01-01 so that the time can be estimated. Should use GPRMC for accurate date/time information.

property declination

geomagnetic declination in degrees from north

property elevation

elevation in meters

property fix

GPS fixed

property gps_type

GPRMC or GPGGA

property latitude

Latitude in decimal degrees, WGS84

property longitude

Latitude in decimal degrees, WGS84

parse_gps_string(gps_string)[source]

Parse a raw gps string from the NIMS and set appropriate attributes. GPS string will first be validated, then parsed.

Parameters

gps_string (string) – raw GPS string to be parsed

property time_stamp

return a datetime object of the time stamp

validate_gps_list(gps_list)[source]

check to make sure the gps stamp is the correct format, checks each element for the proper format

Parameters

gps_list (list) – a parsed gps string from a NIMS

Raises

mth5.io.nims.GPSError if anything is wrong.

validate_gps_string(gps_string)[source]

make sure the string is valid, remove any binary numbers and find the end of the string as ‘*’

Parameters

gps_string (string) – raw GPS string to be validated

Returns

validated string or None if there is something wrong

exception mth5.io.nims.GPSError[source]

Bases: Exception

class mth5.io.nims.NIMS(fn=None)[source]

Bases: NIMSHeader

NIMS Class will read in a NIMS DATA.BIN file.

A fast way to read the binary files are to first read in the GPS strings, the third byte in each block as a character and parse that into valid GPS stamps.

Then read in the entire data set as unsigned 8 bit integers and reshape the data to be n seconds x block size. Then parse that array into the status information and data.

I only have a limited amount of .BIN files to test so this will likely break if there are issues such as data gaps. This has been tested against the matlab program loadNIMS by Anna Kelbert and the match for all the .bin files I have. If something looks weird check it against that program.

Warning

Currently Only 8 Hz data is supported

align_data(data_array, stamps)[source]

Need to match up the first good GPS stamp with the data

Do this by using the first GPS stamp and assuming that the time from the first time stamp to the start is the index value.

put the data into a pandas data frame that is indexed by time

Parameters

  • data_array (array) – structure array with columns for each component [hx, hy, hz, ex, ey]

  • stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]

Returns

pandas DataFrame with colums of components and indexed by time initialized by the start time.

Note

Data gaps are squeezed cause not sure what a gap actually means.

property box_temperature

data logger temperature, sampled at 1 second

check_timing(stamps)[source]

make sure that there are the correct number of seconds in between the first and last GPS GPRMC stamps

Parameters

stamps (list) – list of GPS stamps [[status_index, [GPRMC, GPGGA]]]

Returns

[ True | False ] if data is valid or not.

Returns

gap index locations

Note

currently it is assumed that if a data gap occurs the data can be squeezed to remove them. Probably a more elegant way of doing it.

property declination

median elevation value from all the GPS stamps in decimal degrees WGS84

Only get from the first stamp within the sets

property elevation

median elevation value from all the GPS stamps in decimal degrees WGS84

Only get from the first stamp within the sets

property end_time

start time is the first good GPS time stamp minus the seconds to the beginning of the time series.

property ex

EX

property ex_metadata
property ey

EY

property ey_metadata
find_sequence(data_array, block_sequence=None)[source]

find a sequence in a given array

Parameters

  • data_array (array) – array of the data with shape [n, m] where n is the number of seconds recorded m is the block length for a given sampling rate.

  • block_sequence (list) – sequence pattern to locate default is [1, 131] the start of a data block.

Returns

array of index locations where the sequence is found.

get_channel_response(channel, dipole_length=1)[source]

Get the channel response for a given channel

Parameters

  • channel (TYPE) – DESCRIPTION

  • dipole_length (TYPE, optional) – DESCRIPTION, defaults to 1

Returns

DESCRIPTION

Return type

TYPE

get_stamps(nims_string)[source]

get a list of valid GPS strings and match synchronous GPRMC with GPGGA stamps if possible.

Parameters

nims_string (str) – raw GPS string output by NIMS

has_data()[source]
property hx

HX

property hx_metadata
property hy

HY

property hy_metadata
property hz

HZ

property hz_metadata
property latitude

median latitude value from all the GPS stamps in decimal degrees WGS84

Only get from the GPRMC stamp as they should be duplicates

property longitude

median longitude value from all the GPS stamps in decimal degrees WGS84

Only get from the first stamp within the sets

make_dt_index(start_time, sample_rate, stop_time=None, n_samples=None)[source]

make time index array

Note

date-time format should be YYYY-M-DDThh:mm:ss.ms UTC

Parameters

  • start_time (string) – start time

  • end_time (string) – end time

  • sample_rate (float) – sample_rate in samples/second

match_status_with_gps_stamps(status_array, gps_list)[source]

Match the index values from the status array with the index values of the GPS stamps. There appears to be a bit of wiggle room between when the lock is recorded and the stamp was actually recorded. This is typically 1 second and sometimes 2.

Parameters

  • status_array (array) – array of status values from each data block

  • gps_list (list) – list of valid GPS stamps [[GPRMC, GPGGA], …]

Note

I think there is a 2 second gap between the lock and the first stamp character.

property n_samples
read_nims(fn=None)[source]

Read NIMS DATA.BIN file.

  1. Read in the header information and stores those as attributes with the same names as in the header file.

  2. Locate the beginning of the data blocks by looking for the first [1, 131, …] combo. Anything before that is cut out.

  3. Make sure the data is a multiple of the block length, if the data is longer the extra bits are cut off.

  4. Read in the GPS data (3rd byte of each block) as characters. Parses those into valid GPS stamps with appropriate index locations of where the ‘$’ was found.

  5. Read in the data as unsigned 8-bit integers and reshape the array into [N, data_block_length]. Parse this array into the status information and the data.

  6. Remove duplicate blocks, by removing the first of the duplicates as suggested by Anna and Paul.

  7. Match the GPS locks from the status with valid GPS stamps.

  8. Check to make sure that there is the correct number of seconds between the first and last GPS stamp. The extra seconds are cut off from the end of the time series. Not sure if this is the best way to accommodate gaps in the data.

Note

The data and information array returned have the duplicates removed and the sequence reset to be monotonic.

Parameters

fn (str) – full path to DATA.BIN file

Example

>>> from mth5.io import nims
>>> n = nims.NIMS(r"/home/mt_data/nims/mt001.bin")
remove_duplicates(info_array, data_array)[source]

remove duplicate blocks, removing the first duplicate as suggested by Paul and Anna. Checks to make sure that the mag data are identical for the duplicate blocks. Removes the blocks from the information and data arrays and returns the reduced arrays. This should sync up the timing of GPS stamps and index values.

Parameters

  • info_array (np.array) – structured array of block information

  • data_array (np.array) – structured array of the data

Returns

reduced information array

Returns

reduced data array

Returns

index of duplicates in raw data

property run_metadata

Run metadata

property start_time

start time is the first good GPS time stamp minus the seconds to the beginning of the time series.

property station_metadata

Station metadata from nims file

to_runts(calibrate=False)[source]

Get xarray for run

unwrap_sequence(sequence)[source]

unwrap the sequence to be sequential numbers instead of modulated by 256. sets the first number to 0

Parameters

sequence (list) – sequence of bytes numbers

Returns

unwrapped number of counts

class mth5.io.nims.NIMSCollection(file_path=None, **kwargs)[source]

Bases: Collection

Collection of NIMS files into runs.

>>> from mth5.io.nims import LEMICollection
>>> lc = NIMSCollection(r"/path/to/single/lemi/station")
>>> lc.station_id = "mt001"
>>> lc.survey_id = "test_survey"
>>> run_dict = lc.get_runs(1)
assign_run_names(df, zeros=2)[source]

Assign run names assuming a row represents single station

Run names are assigned as sr{sample_rate}_{run_number:0{zeros}}.

Parameters

  • df (pandas.DataFrame) – Dataframe with the appropriate columns

  • zeros (int, optional) – number of zeros in run name, defaults to 4

Returns

Dataframe with run names

Return type

pandas.DataFrame

to_dataframe(sample_rates=[1], run_name_zeros=2, calibration_path=None)[source]

Create a data frame of each TXT file in a given directory.

Note

This assumes the given directory contains a single station

Parameters

  • sample_rates (int or list, optional) – sample rate to get, will always be 1 for LEMI data defaults to [1]

  • run_name_zeros (int, optional) – number of zeros to assing to the run name, defaults to 4

  • calibration_path (string or Path, optional) – path to calibration files, defaults to None

Returns

Dataframe with information of each TXT file in the given directory.

Return type

pandas.DataFrame

Example
>>> from mth5.io.lemi import LEMICollection
>>> lc = LEMICollection("/path/to/single/lemi/station")
>>> lemi_df = lc.to_dataframe()
class mth5.io.nims.NIMSHeader(fn=None)[source]

Bases: object

class to hold the NIMS header information.

A typical header looks like

'''
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
>>>user field>>>>>>>>>>>>>>>>>>>>>>>>>>>>
SITE NAME: Budwieser Spring
STATE/PROVINCE: CA
COUNTRY: USA
>>> The following code in double quotes is REQUIRED to start the NIMS <<
>>> The next 3 lines contain values required for processing <<<<<<<<<<<<
>>> The lines after that are optional <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
"300b"  <-- 2CHAR EXPERIMENT CODE + 3 CHAR SITE CODE + RUN LETTER
1105-3; 1305-3  <-- SYSTEM BOX I.D.; MAG HEAD ID (if different)
106  0 <-- N-S Ex WIRE LENGTH (m); HEADING (deg E mag N)
109  90 <-- E-W Ey WIRE LENGTH (m); HEADING (deg E mag N)
1         <-- N ELECTRODE ID
3         <-- E ELECTRODE ID
2         <-- S ELECTRODE ID
4         <-- W ELECTRODE ID
Cu        <-- GROUND ELECTRODE INFO
GPS INFO: 01/10/19 16:16:42 1616.7000 3443.6088 115.7350 W 946.6
OPERATOR: KP
COMMENT: N/S CRS: .95/.96 DCV: 3.5 ACV:1
E/W CRS: .85/.86 DCV: 1.5 ACV: 1
Redeployed site for run b b/c possible animal disturbance
'''
property file_size

Size of the file

property fn

Full path to NIMS file

parse_header_dict(header_dict=None)[source]

parse the header dictionary into something useful

read_header(fn=None)[source]

read header information

Parameters

fn (string or pathlib.Path) – full path to file to read

Raises

mth5.io.nims.NIMSError if something is not right.

property station

Station ID

class mth5.io.nims.Response(system_id=None, **kwargs)[source]

Bases: object

Common NIMS response filters for electric and magnetic channels

dipole_filter(length)[source]

Make a dipole filter

Parameters

length (TYPE) – dipole length in meters

Returns

DESCRIPTION

Return type

TYPE

property electric_conversion

electric channel conversion from counts to volts :return: DESCRIPTION :rtype: TYPE

property electric_high_pass_hp

1-pole low pass for 1 hz instuments :return: DESCRIPTION :rtype: TYPE

property electric_high_pass_pc

1-pole low pass filter for 8 hz instruments :return: DESCRIPTION :rtype: TYPE

property electric_low_pass

5 pole electric low pass filter :return: DESCRIPTION :rtype: TYPE

property electric_physical_units

DESCRIPTION :rtype: TYPE

Type

return

get_channel_response(channel, dipole_length=1)[source]

Get the full channel response filter :param channel: DESCRIPTION :type channel: TYPE :param dipole_length: DESCRIPTION, defaults to 1 :type dipole_length: TYPE, optional :return: DESCRIPTION :rtype: TYPE

get_electric_high_pass(hardware='pc')[source]

get the electric high pass filter based on the hardware

property magnetic_conversion

DESCRIPTION :rtype: TYPE

Type

return

property magnetic_low_pass

Low pass 3 pole filter

Returns

DESCRIPTION

Return type

TYPE

mth5.io.nims.read_nims(fn)[source]
Parameters

fn (TYPE) – DESCRIPTION

Returns

DESCRIPTION

Return type

TYPE