hdf5_lib.hdf5_lib Class Reference

Public Member Functions
def	__init__ (self, filename, n_frames_to_inspect=0, n_fr_insp_st=0, sub_sample=0)
def	open_hdf5 (self)
def	get_data (self)
def	get_metadata (self)
def	filter_pilots (cmpx_pilots, z_padding=150, fft_size=64, cp=16, pilot_type='lts', seq_length=[])
def	log2csi_hdf5 (self, filename, offset=0)

Static Public Member Functions
def	samps2csi (samps, num_users, samps_per_user=224, fft_size=64, offset=0, bound=94, cp=0, sub=1, legacy=False, pilot_f=[])
def	samps2csi_large (samps, num_users, samps_per_user=224, offset=47, chunk_size=1000, legacy=False)
def	frame_sanity (match_filt, k_lts, n_lts, st_frame=0, frame_to_plot=0, plt_ant=0, cp=16)

Public Attributes
	h5file
	filename
	dirpath
	h5struct
	data
	metadata
	pilot_samples
	uplink_samples
	downlink_samples
	noise_samples
	n_frm_st
	n_frm_end
	sub_sample

Constructor & Destructor Documentation

__init__()

def hdf5_lib.hdf5_lib.__init__	(		self,
			filename,
			n_frames_to_inspect = 0,
			n_fr_insp_st = 0,
			sub_sample = 0
	)

Member Function Documentation

filter_pilots()

def hdf5_lib.hdf5_lib.filter_pilots	(		cmpx_pilots,
			z_padding = 150,
			fft_size = 64,
			cp = 16,
			pilot_type = 'lts',
			seq_length = []
	)

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frame_sanity()

def hdf5_lib.hdf5_lib.frame_sanity (   match_filt,   k_lts,   n_lts,   st_frame = 0,   frame_to_plot = 0,   plt_ant = 0,   cp = 16  )

static

Creates a map of the frames per antenna. 3 categories: Good frames, bad frames, probably partial frames.
Good frames are those where all k_lts peaks are present and spaced n_lts samples apart.
Bad frames are those with random peaks.
Potentially partial frames are those with some peaks at the right positions.
This is a random event. Some frames may have accidentally some peaks at the right places.
First the largest peak is detected, peaks at +1/-1 (probably due to multipath) and +CP/-CP samples are cleared out.
Then, the positions of the largest k_lts peaks are determined.
Finally, the function checks if these k_lts peaks are at the correct n_lts offstes.
Disclaimer: This function is good only for a high SNR scenario!

get_data()

def hdf5_lib.hdf5_lib.get_data

(

self

)

Parse file to retrieve metadata and data.
HDF5 file has been written in DataRecorder.cpp (in Sounder folder)

Output:
    Data (hierarchy):
-Path
-Pilot_Samples
    --Samples
-UplinkData
    --Samples
Dimensions of input sample data (as shown in DataRecorder.cpp in Sounder):
    - Pilots
dims_pilot[0] = maxFrame
dims_pilot[1] = number of cells
dims_pilot[2] = number of UEs
dims_pilot[3] = number of antennas (at BS)
dims_pilot[4] = samples per symbol * 2 (IQ)

    - Uplink Data
dims_data[0] = maxFrame
dims_data[1] = number of cells
dims_data[2] = uplink symbols per frame
dims_data[3] = number of antennas (at BS)
dims_data[4] = samples per symbol * 2 (IQ)

get_metadata()

def hdf5_lib.hdf5_lib.get_metadata

(

self

)

-Attributes
        {FREQ, RATE, PREFIX_LEN, POSTFIX_LEN, SLOT_SAMP_LEN, FFT_SIZE, CP_LEN,
        BEACON_SEQ_TYPE, PILOT_SEQ_TYPE, BS_HUB_ID, BS_SDR_NUM_PER_CELL, BS_SDR_ID, BS_NUM_CELLS,
        BS_CH_PER_RADIO, BS_FRAME_SCHED, BS_RX_GAIN_A, BS_TX_GAIN_A, BS_RX_GAIN_B, BS_TX_GAIN_B,
        BS_BEAMSWEEP, BS_BEACON_ANT, BS_NUM_ANT, BS_FRAME_LEN, CL_NUM, CL_CH_PER_RADIO, CL_AGC_EN,
        CL_RX_GAIN_A, CL_TX_GAIN_A, CL_RX_GAIN_B, CL_TX_GAIN_B, CL_FRAME_SCHED, CL_SDR_ID,
        CL_MODULATION, UL_SLOTS}

log2csi_hdf5()

def hdf5_lib.hdf5_lib.log2csi_hdf5	(		self,
			filename,
			offset = 0
	)

Convert raw IQ log to CSI.

Converts input Argos HDF5 trace to frequency domain CSI and writes it to the same filename with -csi appended.

Args: filename of log

Returns: None

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open_hdf5()

def hdf5_lib.hdf5_lib.open_hdf5

(

self

)

Get the most recent log file, open it if necessary.

samps2csi()

def hdf5_lib.hdf5_lib.samps2csi (   samps,   num_users,   samps_per_user = 224,   fft_size = 64,   offset = 0,   bound = 94,   cp = 0,   sub = 1,   legacy = False,   pilot_f = []  )

static

Convert an Argos HDF5 log file with raw IQ in to CSI.
Asumes 802.11 style LTS used for trace collection.
    
Args:
    samps: The h5py or numpy array containing the raw IQ samples,
dims = [Frame, Cell, User, Antenna, Sample].
    num_users: Number of users used in trace collection. (Last 'user' is noise.)
    samps_per_user: Number of samples allocated to each user in each frame.
     
Returns:
    csi: Complex numpy array with [Frame, Cell, User, Pilot Rep, Antenna, Subcarrier]
    iq: Complex numpy array of raw IQ samples [Frame, Cell, User, Pilot Rep, Antenna, samples]
     
Example:
    h5log = h5py.File(filename,'r')
    csi,iq = samps2csi(h5log['Pilot_Samples'], h5log.attrs['num_mob_ant']+1, h5log.attrs['samples_per_user'])

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samps2csi_large()

def hdf5_lib.hdf5_lib.samps2csi_large (   samps,   num_users,   samps_per_user = 224,   offset = 47,   chunk_size = 1000,   legacy = False  )

static

Wrapper function for samps2csi_main for to speed up large logs by leveraging data-locality. Chunk_size may need to be adjusted based on your computer.

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Member Data Documentation

data

hdf5_lib.hdf5_lib.data

dirpath

hdf5_lib.hdf5_lib.dirpath

downlink_samples

hdf5_lib.hdf5_lib.downlink_samples

filename

hdf5_lib.hdf5_lib.filename

h5file

hdf5_lib.hdf5_lib.h5file

h5struct

hdf5_lib.hdf5_lib.h5struct

metadata

hdf5_lib.hdf5_lib.metadata

n_frm_end

hdf5_lib.hdf5_lib.n_frm_end

n_frm_st

hdf5_lib.hdf5_lib.n_frm_st

noise_samples

hdf5_lib.hdf5_lib.noise_samples

pilot_samples

hdf5_lib.hdf5_lib.pilot_samples

sub_sample

hdf5_lib.hdf5_lib.sub_sample

uplink_samples

hdf5_lib.hdf5_lib.uplink_samples

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The documentation for this class was generated from the following file:

• PYTHON/IrisUtils/hdf5_lib.py