Add n_bins, n_per_bin, n_meas parameter; Cleanup import

1 files changed, 84 insertions, 0 deletions

diff --git a/dpd/src/Const.py b/dpd/src/Const.py
new file mode 100644
index 0000000..2504c1e
--- /dev/null
+++ b/dpd/src/Const.py
@@ -0,0 +1,84 @@
+# 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, n_bins, n_per_bin, n_meas):
+        self.sample_rate = sample_rate
+        self.n_meas = n_meas
+
+        # 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 = False
+        self.ES_start = 0.0
+        self.ES_end = 1.0
+        self.ES_n_bins = n_bins
+        self.ES_n_per_bin = n_per_bin
+
+        # 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
+
+
+        self.RAGC_min_rxgain = 25
+        self.RAGC_rx_median_target = self.TAGC_tx_median_target
+
+        # Constants for Model
+        self.MDL_plot = False
+
+        # Constants for MER
+        self.MER_plot = False
+
+        # Constants for Model_PM
+        self.MPM_tx_min = 0.1
+
+        # Constants for Measure_Shoulder
+        self.MS_enable = False
+        self.MS_plot = False
+        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