mth5.io.phoenix.readers.native package
Submodules
mth5.io.phoenix.readers.native.native_reader module
Module to read and parse native Phoenix Geophysics data formats of the MTU-5C Family.
This module implements Streamed readers for segmented-decimated time series formats of the MTU-5C family.
- author
Jorge Torres-Solis
Revised 2022 by J. Peacock
- class mth5.io.phoenix.readers.native.native_reader.NativeReader(path, num_files=1, scale_to=1, header_length=128, last_frame=0, ad_plus_minus_range=5.0, channel_type='E', report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseNative sampling rate ‘Raw’ time series reader class, these are the .bin files. They are formatted with a header of 128 bytes then frames of 64.
Each frame is 20 x 3 byte (24-bit) data point then a 4 byte footer.
- property npts_per_frame
- read()[source]
Read the full data file.
Note
This uses
numpy.lib.stride_tricks.as_stridedwhich can be unstable if the bytes are not the correct length. See notes by numpy.Got this solution from: https://stackoverflow.com/questions/12080279/how-do-i-create-a-numpy-dtype-that-includes-24-bit-integers?msclkid=3398046ecd6511ec9a37394f28c5aaba
- Returns
scaled data and footer
- Return type
tuple (data, footer)
- read_frames(num_frames)[source]
Read the given amount of frames from the data.
Note
that seek is not reset so if you iterate this the stream reads from the last tell.
- Parameters
num_frames (integer) – Number of frames to read
- Returns
Scaled data from the given number of frames
- Return type
np.ndarray(dtype=float)
- read_sequence(start=0, end=None)[source]
Read sequence of files into a single array
- Parameters
start (integer, optional) – sequence start, defaults to 0
end (integer, optional) – sequence end, defaults to None
- Returns
scaled data
- Return type
np.ndarray(dtype=float32)
- Returns
footer
- Return type
np.ndarray(dtype=int32)
Module contents
- class mth5.io.phoenix.readers.native.NativeReader(path, num_files=1, scale_to=1, header_length=128, last_frame=0, ad_plus_minus_range=5.0, channel_type='E', report_hw_sat=False, **kwargs)[source]
Bases:
TSReaderBaseNative sampling rate ‘Raw’ time series reader class, these are the .bin files. They are formatted with a header of 128 bytes then frames of 64.
Each frame is 20 x 3 byte (24-bit) data point then a 4 byte footer.
- property npts_per_frame
- read()[source]
Read the full data file.
Note
This uses
numpy.lib.stride_tricks.as_stridedwhich can be unstable if the bytes are not the correct length. See notes by numpy.Got this solution from: https://stackoverflow.com/questions/12080279/how-do-i-create-a-numpy-dtype-that-includes-24-bit-integers?msclkid=3398046ecd6511ec9a37394f28c5aaba
- Returns
scaled data and footer
- Return type
tuple (data, footer)
- read_frames(num_frames)[source]
Read the given amount of frames from the data.
Note
that seek is not reset so if you iterate this the stream reads from the last tell.
- Parameters
num_frames (integer) – Number of frames to read
- Returns
Scaled data from the given number of frames
- Return type
np.ndarray(dtype=float)
- read_sequence(start=0, end=None)[source]
Read sequence of files into a single array
- Parameters
start (integer, optional) – sequence start, defaults to 0
end (integer, optional) – sequence end, defaults to None
- Returns
scaled data
- Return type
np.ndarray(dtype=float32)
- Returns
footer
- Return type
np.ndarray(dtype=int32)