Renamed const.py

1 files changed, 0 insertions, 82 deletions

diff --git a/dpd/src/const.py b/dpd/src/const.py
deleted file mode 100644
index d95d0bf..0000000
--- a/dpd/src/const.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# DAB Frame constants
-# Sources:
-#   - etsi_EN_300_401_v010401p p145
-#   - Measured with USRP B200
-
-import numpy as np
-
-class const:
-    def __init__(self, sample_rate, target_median):
-        self.sample_rate = sample_rate
-
-        # Time domain
-        self.T_F = sample_rate / 2048000 * 196608  # Transmission frame duration
-        self.T_NULL = sample_rate / 2048000 * 2656  # Null symbol duration
-        self.T_S = sample_rate / 2048000 * 2552  # Duration of OFDM symbols of indices l = 1, 2, 3,... L;
-        self.T_U = sample_rate / 2048000 * 2048  # Inverse of carrier spacing
-        self.T_C = sample_rate / 2048000 * 504  # Duration of cyclic prefix
-
-        # Frequency Domain
-        # example: np.delete(fft[3328:4865], 768)
-        self.FFT_delete = 768
-        self.FFT_delta = 1536  # Number of carrier frequencies
-        if sample_rate == 2048000:
-            self.FFT_start = 256
-            self.FFT_end = 1793
-        elif sample_rate == 8192000:
-            self.FFT_start = 3328
-            self.FFT_end = 4865
-        else:
-            raise RuntimeError("Sample Rate '{}' not supported".format(
-                sample_rate
-            ))
-
-        # Calculate sample offset from phase rotation
-        # time per sample = 1 / sample_rate
-        # frequency per bin = 1kHz
-        # phase difference per sample offset = delta_t * 2 * pi * delta_freq
-        self.phase_offset_per_sample = 1. / sample_rate * 2 * np.pi * 1000
-
-        # Constants for ExtractStatistic
-        self.ES_plot = True
-        self.ES_start = 0.0
-        self.ES_end = 1.0
-        self.ES_n_bins = 64
-        self.ES_n_per_bin = 128
-
-        # Constants for TX_Agc
-        self.TAGC_max_txgain = 89
-        self.TAGC_tx_median_target = target_median
-        self.TAGC_tx_median_max = self.TAGC_tx_median_target*1.4
-        self.TAGC_tx_median_min = self.TAGC_tx_median_target/1.4
-
-        # Constants for Agc
-        self.RAGC_min_rxgain = 25
-        self.RAGC_rx_median_target = self.TAGC_tx_median_target
-
-        # Constants for Model
-        self.MDL_plot = True
-
-        # Constants for MER
-        self.MER_plot = True
-
-        # Constants for Model_PM
-        self.MPM_tx_min = 0.1
-
-        # Constants for Measure_Shoulder
-        self.MS_plot = True
-        assert sample_rate==8192000
-        meas_offset = 976 # Offset from center frequency to measure shoulder [kHz]
-        meas_width = 100 # Size of frequency delta to measure shoulder [kHz]
-        shoulder_offset_edge = np.abs(meas_offset - self.FFT_delta)
-        self.MS_shoulder_left_start = self.FFT_start - shoulder_offset_edge - meas_width / 2
-        self.MS_shoulder_left_end = self.FFT_start - shoulder_offset_edge + meas_width / 2
-        self.MS_shoulder_right_start = self.FFT_end + shoulder_offset_edge - meas_width / 2
-        self.MS_shoulder_right_end = self.FFT_end + shoulder_offset_edge + meas_width / 2
-        self.MS_peak_start = self.FFT_start + 100 # Ignore region near edges
-        self.MS_peak_end = self.FFT_end - 100
-
-        self.MS_FFT_size = 8192
-        self.MS_averaging_size = 4 * self.MS_FFT_size
-        self.MS_n_averaging = 40
-        self.MS_n_proc = 4