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"""This sub-module contains functions for importing and analyzing the IF
response.
These are hot/cold load measurements that were measured using a spectrum
analyzer. From this, we calculate the noise temperature versus IF frequency
(i.e., the IF response).
"""
import numpy as np
from qmix.exp.parameters import params as PARAMS
def if_response(if_data, **kw):
"""Calculate the noise temperature from hot/cold spectrum measurements.
This is the IF output power (versus IF frequency) that is measured from
hot and cold blackbody loads. This data is used to calculate the noise
temperature versus IF frequency, sometimes referred to as the IF
response.
Args:
if_data: IF response data. This can either be in the form of a CSV
file, or a Numpy array. Either way, the data should have 3
columns: frequency, in units [GHz], hot IF power, in units
[dBm], and cold IF power, in units [dBm].
Keyword Args:
t_hot: hot blackbody load temperature
t_cold: cold blackbody load temperature
ifresp_delimiter: delimiter for the IF response files
ifresp_usecols: which columns to import from IF response files
ifresp_skipheader: how many rows to skip at the beginning of IF
response files.
Returns:
ndarray: frequency, noise temp, hot power, cold power
"""
# Unpack keyword arguments
th = kw.get('t_hot', PARAMS['t_hot'])
tc = kw.get('t_cold', PARAMS['t_cold'])
ifresp_delimiter = kw.get('ifresp_delimiter', PARAMS['ifresp_delimiter'])
ifresp_usecols = kw.get('ifresp_usecols', PARAMS['ifresp_usecols'])
ifresp_skipheader = kw.get('ifresp_skipheader', PARAMS['ifresp_skipheader'])
ifresp_maxtn = kw.get('ifresp_maxtn', PARAMS['ifresp_maxtn'])
# Import IF spectrum measurements
if isinstance(if_data, str): # input is a CSV file
f, ph_db, pc_db = np.genfromtxt(if_data,
delimiter=ifresp_delimiter,
usecols=ifresp_usecols,
skip_header=ifresp_skipheader).T
elif isinstance(if_data, np.ndarray): # input is a Numpy array
assert if_data.ndim == 2, \
'IF response data should be 2-dimensional.'
if if_data.shape[1] != 3:
if_data = if_data.T
assert if_data.shape[1] == 3, 'IF response should have 3 columns.'
f, ph_db, pc_db = if_data.T
else:
raise ValueError("Input data type not recognized.")
# Y-factor
y = _db_to_lin(ph_db) / _db_to_lin(pc_db)
y[y <= 1] = 1 + 1e-6
# Noise temperature
tn = (th - tc * y) / (y - 1)
# Remove bad noise temperatures
mask = (tn < 0) | (tn > ifresp_maxtn)
tn[mask] = ifresp_maxtn
# Stack data for output
data = np.vstack((f, tn, ph_db, pc_db))
return data
def _db_to_lin(db):
"""dB to linear units.
Args:
db: value in decibels
Returns:
ndarray: value in linear units
"""
return 10 ** (db / 10.)
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