diff --git a/applications/lofar2/model/apertif_arts_firmware_model.py b/applications/lofar2/model/apertif_arts_firmware_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..1aae4143d01720dc1d28d0d342bbaca81f7c98ef
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+++ b/applications/lofar2/model/apertif_arts_firmware_model.py
@@ -0,0 +1,1282 @@
+###############################################################################
+#
+# Copyright (C) 2018
+# ASTRON (Netherlands Institute for Radio Astronomy) <http://www.astron.nl/>
+# P.O.Box 2, 7990 AA Dwingeloo, The Netherlands
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+#
+###############################################################################
+
+# Author: Eric Kooistra, 28 Oct 2018,
+
+"""Model the Apertif BF, XC / Arts TAB, IAB firmware data path signal levels
+
+The Apertif / Arts data path processing is described in:
+
+ ASTRON_SP_062_Detailed_Design_Arts_FPGA_Beamformer.pdf
+
+The purpose of the apertif_arts_firmware_model is to understand signal levels
+in the data path processing and to prove that the internal requantization in
+the firmware implementation is does not increase the system noise while
+providing the maximum dynamic range, given data widths at the external
+interfaces (i.e. W_adc = 8, W_beamlet = 6, W_vis = 32, W_volt = 8 and
+W_pow = 8).
+
+This apertif_arts_firmware_model.py model consists of the following sections:
+
+- User parameters
+- Firmware parameters
+- A static model to determine the expected internal signal levels as function
+ of the WG amplitude or the ADC noise sigma level.
+- A dynamic model using FFT, BF and ()**2 to model the data path signal
+ processing for a WG input signal or a system noise input signal. The FIR
+ part of the F_sub and F_chan is not modelled.
+ . WG input for test purposes to verify the linearity and dynamic range
+ as function of the WG amplitude and the number of dishes.
+ . Noise input for operational purposes to verify that the system noise
+ maintains represented by suffcient LSbits throughout the data path
+ processing chain from ADC via beamlet and fine channel to correlator
+ visibility, Arts SC beamlet TAB, Arts SC4 power TAB and IAB.
+- Plot results of the static and the dynamic model.
+
+The model matches the drawings of the firmware parameters and signal levels in:
+
+ apertif_arts_firmware_model.vsd
+ apertif_arts_firmware_model.pdf
+
+More information and a log an plots are also reported in:
+
+ ASTRON_RP_010888_Apertif_Arts_firmware_model.pdf
+
+Parameters:
+* wgEnable
+ When True then a WG input is used for the dynamic model, else a noise input.
+* wgSweep
+ The WG makes a sweep through wgSweep number of subbands if wgSweep != 0. The
+ wgSweep can be < 1 and negative. If wgSweep != 0 then an image plot is made
+ of the subbands spectrum and the channels spectrum. The expected signal
+ levels that are reported by the dynamic model are only relevant when wgSweep
+ = 0.
+* quantize
+ The quantize parameter provides rounding of internal signal levels, weights
+ and gain factors. For the dynamic model clipping is not supported, because
+ the static model already shows when the signals clip. In the static model the
+ clipping can be controlled via the adjust parameter.
+
+Remark:
+- power = rms**2
+- mean powers = (rms voltages)**2
+- rms**2 = std**2 + mean**2 = std**2 when mean = 0
+- rms = std = ampl / sqrt(2) when mean is 0
+ --> pi_apr.wgScale = sqrt(2)
+
+- power complex = power real + power imag
+ = (rms real)**2 + (rms imag)**2
+ = (ampl real/sqrt(2))**2 + (ampl imag/sqrt(2))**2
+- power real = power imag = power complex / 2, when signal is random or periodic
+- rms real = rms imag = rms complex / sqrt(2), when signal is random or periodic
+ --> pi_apr.rcScale = sqrt(2) = sqrt(nof_complex)
+- rms complex = sqrt(power complex)
+- ampl real = ampl imag = sqrt(power complex / 2) * sqrt(2)
+ = sqrt(power complex)
+ = rms complex
+ = (rms real) * sqrt(2)
+ = (rms imag) * sqrt(2)
+
+A = 1.0
+N = 100000
+realSamples = A * np.random.randn(N)
+realPowers = realSamples * realSamples
+x = rms(realSamples)**2
+y = np.mean(realPowers)
+z = np.std(realPowers)
+x # = A * 1.0
+y # = A * 1.0
+z # = A * sqrt(2)
+complexSamples = A * np.random.randn(N) + A * np.random.randn(N) * 1j
+complexPowers = complexSamples * np.conjugate(complexSamples)
+complexPowers = complexPowers.real
+x = rms(complexSamples)**2
+xr = rms(complexSamples.real)**2
+xi = rms(complexSamples.imag)**2
+y = np.mean(complexPowers)
+z = np.std(complexPowers)
+x # = A * 2.0
+xr
+xi
+y # = A * 2.0
+z # = A * 2.0
+
+Use prefix rmsComplexY for rms of complex signal Y
+Use prefix rmsY for rms of real signal Y or of Y.real or of Y.imag, so
+ for complex signal Y then rmsY = rmsComplexY / pi_apr.rcScale,
+ for real signal Y then rmsY = rmsY.
+
+Usage:
+# in iPython:
+import os
+filepath='E:\\svn_radiohdl\\trunk\\applications\\apertif\\matlab'
+os.chdir(filepath)
+run apertif_arts_firmware_model.py
+
+# on command line
+python apertif_arts_firmware_model.py
+
+"""
+
+import argparse
+import math
+import numpy as np
+import common as cm
+import test_plot
+import pi_apertif_system as pi_apr
+
+################################################################################
+# General
+################################################################################
+
+def rms(arr):
+ """Root mean square of values in arr
+ Use this root-mean-square rms() instead of std() for CW signals that are at
+ center of bin and result in static phasor with std() = 0. For noise
+ signals with zero mean rms() is equal to std(). This rms() also works for
+ complex input thanks to using np.abs().
+ """
+ return np.sqrt(np.mean(np.abs(arr)**2.0))
+
+nofSigma = 4 # Number of sigma to fit in full scale
+
+sc1_use_qua = True # use SC1 TAB bf quantized and clipped output
+sc1_use_qua = False # use SC1 TAB bf raw LSbits and wrapped MSbits output (as implemented in arts_unb1_sc1)
+
+################################################################################
+# Parse arguments to derive user settings
+################################################################################
+
+_parser = argparse.ArgumentParser('apertif_arts_firmware_model')
+_parser.add_argument('--app', default='all', type=str, help='Application to model: dish, apertif, arts_sc1, arts_sc4 or all')
+_parser.add_argument('--model', default='all', type=str, help='Model: static, dynamic or all')
+_parser.add_argument('--sky_sigma', default=4.0, type=float, help='Signal sigma level at ADC input')
+_parser.add_argument('--wg_enable', default=False, action='store_true', help='Model coherent ADC input using WG sinus or model incoherent ADC input using Gaussian white noise')
+_parser.add_argument('--wg_ampl', default=4.0 * 2**0.5, type=float, help='WG amplitude at ADC input')
+_parser.add_argument('--wg_sweep', default=0.0, type=float, help='Number of subbands fsub to sweep with WG in dynamic model')
+_parser.add_argument('--nof_periods', default=250, type=int, help='Number of channel periods in dynamic model')
+_parser.add_argument('--nof_dishes', default=1, type=int, help='Number of dishes')
+_parser.add_argument('--quantize', default=False, action='store_true', help='Use fixed point rounding or no rounding of weights, gains and internal signals')
+_parser.add_argument('--useplot', default=False, action='store_true', help='Use plots or skip plotting')
+_parser.add_argument('--cb_weight_adjust', default=1.0, type=float, help='Factor to adjust unit CB weight')
+_parser.add_argument('--tab_weight_adjust_sc1', default=1.0, type=float, help='Factor to adjust unit TAB weight for SC1')
+_parser.add_argument('--tab_weight_adjust_sc4', default=1.0, type=float, help='Factor to adjust unit TAB weight for SC4')
+_parser.add_argument('--iab_gain_adjust', default=1.0, type=float, help='Factor to adjust unit IAB gain for SC4')
+args = _parser.parse_args()
+
+if args.app=='all':
+ apps = ['dish', 'apertif', 'arts_sc1', 'arts_sc4']
+else:
+ apps = [args.app]
+if args.model=='all':
+ models = ['static', 'dynamic']
+else:
+ models = [args.model]
+
+skySigma = args.sky_sigma
+#skySigma = 4
+#skySigma = 89.8 # 89.8 * 2**0.5 = 127 for full scale WG amplitude
+#skySigma = 26.1 # 26.1 * 2**0.5 = 37
+#skySigma = 11.3 # 11.3 * 2**0.5 = 16 for full scale 8 bit beamlet amplitude
+
+wgAmpl = args.wg_ampl
+wgSigma = wgAmpl / np.sqrt(2)
+
+wgEnable = args.wg_enable
+wgSweep = args.wg_sweep
+#wgSweep = 5.0 # number of fsub to sweep over nofTchanx
+#wgSweep = -5.0 # number of fsub to sweep over nofTchanx
+#wgSweep = 2.0 # number of fsub to sweep over nofTchanx
+#wgSweep = 1.0/32 # number of fsub to sweep over nofTchanx
+if wgSweep != 0:
+ wgEnable = True # force WG
+ apps = ['dish'] # only support sweep for dish
+
+quantize = args.quantize
+
+useplot = args.useplot
+
+cbWeightAdjust = args.cb_weight_adjust
+tabWeightAdjustSc1 = args.tab_weight_adjust_sc1
+tabWeightAdjustSc4 = args.tab_weight_adjust_sc4
+iabGainAdjust = args.iab_gain_adjust
+#cbWeightAdjust = 1.0/8 # use <= 1/8 to have no beamlet (8 bit) clipping
+#cbWeightAdjust = 1.0/32 # use <= 1/32 to have no fine channel_x (9 bit) clipping
+#tabWeightAdjustSc1 = 1.0 # = 1.0 to use unit weight
+#tabWeightAdjustSc4 = 1.0/4 # use <= 1/4 and cbWeightAdjust = 1/32 to have no TAB Stokes I (8 bit) clipping
+#iabGainAdjust = 1.0/16 # use <= 1/16 and cbWeightAdjust = 1/32 to have no IAB Stokes I (8 bit) clipping
+
+# Number of dishes
+# - not used for Apertif correlator, because it only models auto visibilities
+# - only used for Arts array beamformers:
+# . use 1 to verify the expected TAB, IAB output signal level
+# . use 12 is all dishes for Arts SC1 or for IAB
+# . use 10 is equidistant dishes for Arts SC4
+# . use 8 is equidistant and fixed dishes for Arts SC3
+# The actual number of dishes does not alter the TAB or IAB output level,
+# because the number of dishes is compensated via the TAB weights in case of
+# SC1 when sc1_use_qua = True and SC4 or via the IAB gain in case of the IAB.
+# When sc1_use_qua = False then the TAB output does increase with the number
+# of dishes, because then the TAB weigth is fixed at 1. Still for SC1 the
+# maximum gain with N_dish = 12 dishes is log2(sqrt(12)) = 1.8 bits, so with
+# 6 bit beamlet input and 8 bit voltage TAB data this gain still fits, and
+# therefore it is fine to use sc1_use_qua = False for SC1.
+# Typical values:
+# . nofDishes = 16 # model only
+# . nofDishes = 12 # RT2-9,a-d, all for Apertif XC and Arts SC1
+# . nofDishes = 10 # RT2-9,a,b at equidistant for Arts SC4
+# . nofDishes = 8 # RT2-9 fixed at equidistant for commensal Arts SC3
+# . nofDishes = 1 # model default, because tabWeightSc1, tabWeightSc4 and
+# # iabGainReg nomalize for nofDishes. Therefore use
+# # nofDishes = 1 to speed up simulation, except when modeling
+# # Arts SC1 with fixed tabWeightSc1 = 1 (when sc1_use_qua =
+# # False).
+nofDishes = args.nof_dishes
+
+# Time series
+if 'dynamic' in models:
+ # use args.nof_periods for number of channel periods to simulate:
+ if wgEnable:
+ nofTchanx = args.nof_periods
+ #nofTchanx = 10 # use >~10 for WG
+ #nofTchanx = 100
+ else:
+ nofTchanx = args.nof_periods
+ #nofTchanx = 100 # number of channel periods to simulate, use >~ 100 for noise
+ #nofTchanx = 1000 # use >= 1000 for more accuracy
+ #nofTchanx = 250 # use < 1000 to speed up simulation
+ nofTStokes = nofTchanx
+
+ # also use args.nof_periods for number of Ts, Tsub, Tchanx periods to plot
+ # . to speed up plotting for Ts and Tsub
+ # . to have same density of samples per plot
+ nof_periods = args.nof_periods
+
+# Frequency selection
+if 'dynamic' in models:
+ # Select frequency for WG, SST, BST, XC
+ selSub = 65
+ selChanx = 33 # N_chan_x / 2 = center of subband
+ selChanxFraction = 1 * 1.0/nofTchanx # yields 1 rotation in fine channel
+ #selChanxFraction = 0.0 # center channel, yields constant in fine channel
+ if wgSweep:
+ selChanx = 32 # N_chan_x / 2 = center of subband
+ selChanxFraction = 0.0 # center channel, yields constant in fine channel
+
+################################################################################
+# Derived gains
+
+# Aperif BF subband CB voltage weight
+cbWeight = pi_apr.cbUnitWeight * cbWeightAdjust # = 8192 = 2**13 * 1.0
+if quantize:
+ cbWeight = np.round(cbWeight)
+cbGain = cbWeight/pi_apr.cbMaxWeight # normalized gain relative to maximum gain 1.0
+
+# Arts SC1 beamlet TAB voltage weight
+if sc1_use_qua:
+ # Intended implementation using quantized and clipped BF data with
+ # TAB weights that are scaled by 1/nofDishes for coherent WG input
+ # and by 1/sqrt(nofDishes) for incoherent noise input.
+ W_tab_unit_scale_sc1 = -2 # = -2
+ W_tab_weight_fraction_sc1 = pi_apr.W_tab_max_weight_sc1 + W_tab_unit_scale_sc1 # =
+ tabUnitWeightSc1 = 2.0**W_tab_weight_fraction_sc1 # = 2**13 = 8192
+ if wgEnable:
+ tabWeightSc1 = tabUnitWeightSc1 / nofDishes # coherent TAB input, like WG
+ else:
+ tabWeightSc1 = tabUnitWeightSc1 / math.sqrt(nofDishes) # incoherent TAB input, like sky noise
+ tabWeightSc1 *= tabWeightAdjustSc1
+else:
+ # Current implementation using LSbits and wrapped MSbits of raw BF data
+ # and unit weight = 1 independent of nofDishes. The TAB weight
+ # tabWeightSc1 = 1 is the smallest weight value, so effectively the range
+ # of W_tab_weight_sc1 is not used.
+ W_tab_unit_scale_sc1 = -pi_apr.W_tab_max_weight_sc1 # = -15
+ W_tab_weight_fraction_sc1 = pi_apr.W_tab_max_weight_sc1 + W_tab_unit_scale_sc1 # = 0 = 15 + -15
+ tabUnitWeightSc1 = 1.0 # = 1 = 2.0**W_tab_weight_fraction_sc1 = 2**0
+ tabWeightSc1 = tabUnitWeightSc1 # = 1 independent of nofDishes
+if quantize:
+ tabWeightSc1 = np.round(tabWeightSc1)
+tabGainSc1 = tabWeightSc1/pi_apr.tabMaxWeightSc1 # Normalized gain relative to maximum gain 1.0
+
+# Arts SC4 channel TAB voltage weight
+if wgEnable:
+ tabWeightSc4 = pi_apr.tabUnitWeightSc4 / nofDishes # coherent TAB input, like WG
+else:
+ tabWeightSc4 = pi_apr.tabUnitWeightSc4 / math.sqrt(nofDishes) # incoherent TAB input, like sky noise
+tabWeightSc4 = tabWeightSc4 * tabWeightAdjustSc4
+if quantize:
+ tabWeightSc4 = np.round(tabWeightSc4)
+tabGainSc4 = tabWeightSc4/pi_apr.tabMaxWeightSc4 # Normalized gain relative to maximum gain 1.0
+
+# Arts SC4 power IAB power gain
+iabGainReg = pi_apr.iabUnitGain * iabGainAdjust / nofDishes # IAB input powers add coherently
+if quantize:
+ iabGainReg = np.round(iabGainReg)
+iabGain = iabGainReg/pi_apr.iabMaxGain # Normalized gain relative to maximum gain 1.0
+
+
+################################################################################
+# Apertif / Arts settings
+# . BF
+N_fft = pi_apr.N_FFT # = 1024
+N_sub = pi_apr.N_sub # = 512
+N_int_sub = pi_apr.N_int_x # = 800000
+
+# . XC, SC4
+N_chan_x = pi_apr.N_chan_x # = 64
+N_chan_bf = pi_apr.N_chan_bf # = 4
+N_int_chan_x = pi_apr.N_int_chan_x # = 12500 = N_int_x / N_chan_x
+
+if 'static' in models:
+ ################################################################################
+ # Static model of signal level as function of:
+ # . WG input amplitude level
+ # . Noise input sigma level
+ ################################################################################
+
+ # Input axis
+ inputResolution = 0.05
+
+ # Dish
+ A_wg = np.arange(inputResolution, 2**pi_apr.W_adc_max, inputResolution) # 0:127
+ S_wg = A_wg / pi_apr.wgScale
+ S_wg_complex = S_wg
+ S_wg_complex_phase = S_wg_complex / pi_apr.rcScale
+ A_wg_complex_phase = S_wg_complex
+ S_noise = np.arange(inputResolution, 2**pi_apr.W_adc_max / nofSigma, inputResolution) # 0:31
+ S_noise_complex = S_noise
+ S_noise_complex_phase = S_noise / pi_apr.rcScale
+
+ SST_wg_inputs = S_wg_complex / pi_apr.wgSstScale
+ SST_wg_values = SST_wg_inputs**2 * N_int_sub
+ SST_wg_dBs = 10*np.log10(SST_wg_values)
+ SST_noise_inputs = S_noise_complex / pi_apr.noiseSstScale
+ SST_noise_values = SST_noise_inputs**2 * N_int_sub
+ SST_noise_dBs = 10*np.log10(SST_noise_values)
+
+ BST_wg_inputs = S_wg_complex * cbWeightAdjust / pi_apr.wgBstScale
+ BST_wg_values = BST_wg_inputs**2 * N_int_sub
+ BST_wg_dBs = 10*np.log10(BST_wg_values)
+ BST_noise_inputs = S_noise_complex * cbWeightAdjust / pi_apr.noiseBstScale
+ BST_noise_values = BST_noise_inputs**2 * N_int_sub
+ BST_noise_dBs = 10*np.log10(BST_noise_values)
+
+ clip = 2.0**pi_apr.W_beamlet_max - 1
+ beamlet_wg_complex_phase_values = S_wg_complex_phase * cbWeightAdjust / pi_apr.wgBeamletScale
+ beamlet_wg_complex_phase_values = np.clip(beamlet_wg_complex_phase_values, -clip, clip)
+ beamlet_noise_complex_phase_values = S_noise_complex_phase * cbWeightAdjust / pi_apr.noiseBeamletScale
+ beamlet_noise_complex_phase_values = np.clip(beamlet_noise_complex_phase_values, -clip, clip)
+
+ # Central
+ clip = 2.0**pi_apr.W_channel_x_max - 1
+ channel_x_wg_complex_phase_values = S_wg_complex_phase * cbWeightAdjust / pi_apr.wgChannelXcorScale
+ channel_x_wg_complex_phase_values = np.clip(channel_x_wg_complex_phase_values, -clip, clip)
+ channel_x_wg_complex_values = channel_x_wg_complex_phase_values * pi_apr.rcScale
+ channel_x_noise_complex_phase_values = S_noise_complex_phase * cbWeightAdjust / pi_apr.noiseChannelXcorScale
+ channel_x_noise_complex_phase_values = np.clip(channel_x_noise_complex_phase_values, -clip, clip)
+ channel_x_noise_complex_values = channel_x_noise_complex_phase_values * pi_apr.rcScale
+
+ if 'apertif' in apps:
+ # Correlator
+ XC_wg_auto_visibilities = channel_x_wg_complex_values**2 * N_int_chan_x
+ XC_wg_auto_visibilities_dB = 10*np.log10(XC_wg_auto_visibilities)
+ XC_noise_auto_visibilities = channel_x_noise_complex_values**2 * N_int_chan_x
+ XC_noise_auto_visibilities_dB = 10*np.log10(XC_noise_auto_visibilities)
+
+ if 'arts_sc1' in apps:
+ # Arts SC1 beamlet voltage TAB
+ if sc1_use_qua:
+ beamlet_tab_wg_complex_phase_values_sc1 = beamlet_wg_complex_phase_values * tabWeightAdjustSc1
+ beamlet_tab_noise_complex_phase_values_sc1 = beamlet_noise_complex_phase_values * tabWeightAdjustSc1
+ else:
+ beamlet_tab_wg_complex_phase_values_sc1 = beamlet_wg_complex_phase_values * nofDishes
+ beamlet_tab_noise_complex_phase_values_sc1 = beamlet_noise_complex_phase_values * nofDishes
+ clip = 2.0**pi_apr.W_volt_max - 1
+ beamlet_tab_wg_complex_phase_values_sc1 = np.clip(beamlet_tab_wg_complex_phase_values_sc1, -clip, clip)
+ beamlet_tab_noise_complex_phase_values_sc1 = np.clip(beamlet_tab_noise_complex_phase_values_sc1, -clip, clip)
+
+ if 'arts_sc4' in apps:
+ # Arts SC4 channel power TAB
+ clip = 2.0**pi_apr.W_volt - 1
+ power_tab_xx_wg_complex_values_sc4 = (channel_x_wg_complex_values * tabWeightAdjustSc4)**2 # Stokes I = XX* + 0
+ power_tab_xx_wg_complex_values_sc4 = np.clip(power_tab_xx_wg_complex_values_sc4, 0, clip) # Stokes I is unsigned >= 0
+ power_tab_xx_noise_values_sc4 = (channel_x_noise_complex_values * tabWeightAdjustSc4)**2 # Stokes I = XX* + 0
+ power_tab_xx_noise_values_sc4 = np.clip(power_tab_xx_noise_values_sc4, 0, clip) # Stokes I is unsigned >= 0
+
+ # Arts SC4 channel IAB
+ clip = 2.0**pi_apr.W_pow - 1
+ iab_xx_wg_complex_values_sc4 = channel_x_wg_complex_values**2 * iabGainAdjust # Stokes I = XX* + 0
+ iab_xx_wg_complex_values_sc4 = np.clip(iab_xx_wg_complex_values_sc4, 0, clip) # Stokes I is unsigned >= 0
+ iab_xx_noise_values_sc4 = channel_x_noise_complex_values**2 * iabGainAdjust # Stokes I = XX* + 0
+ iab_xx_noise_values_sc4 = np.clip(iab_xx_noise_values_sc4, 0, clip) # Stokes I is unsigned >= 0
+
+
+if 'dynamic' in models:
+ ################################################################################
+ # Dynamic model parameters
+ ################################################################################
+
+ ################################################################################
+ # Apertif / Arts settings
+ # . BF
+ fs = 800e6
+ fsub = fs / N_fft
+ Ts = 1/fs
+ Tsub = 1/fsub
+
+ # . XC, SC4
+ fchan_x = fsub / N_chan_x
+ Tchanx = 1/fchan_x
+
+ # . SC4
+ Tstokesx = Tchanx
+ M_int_ax = pi_apr.M_int_ax # = 16
+ W_int_ax = pi_apr.W_int_ax # = 4 = log2(16)
+ #M_int_ax = 1
+ #W_int_ax = cm.ceil_log2(M_int_ax)
+
+ ################################################################################
+ # Dish CB
+
+ # Number of bits for internal product
+ W_cb_product = pi_apr.W_cb_in + pi_apr.W_cb_weight - 1 # = 31 = 16 + 16 -1, do -1 to skip double sign bit of product
+
+ # Number of bits for fraction < 1
+ W_beamlet_fraction = pi_apr.W_beamlet_fraction # number of bits for fraction < 1
+
+ if 'arts_sc1' in apps:
+ ################################################################################
+ # Arts SC1 beamlet TAB
+
+ # Number of bits for internal product
+ W_tab_product_sc1 = pi_apr.W_beamlet + pi_apr.W_tab_weight_sc1 - 1 # = 23 = 8 + 16 -1, do -1 to skip double sign bit of product
+
+ # Number of bits for fraction < 1
+ W_tab_fraction_sc1 = W_beamlet_fraction - W_tab_unit_scale_sc1 # = 8 = 6 - -2 when sc1_use_qua = True
+ # = 21 = 6 - -15 when sc1_use_qua = False
+
+ if 'arts_sc4' in apps:
+ ################################################################################
+ # Arts SC4 channel TAB
+
+ # Fine channel TAB
+ # Number of bits for internal product
+ W_tab_voltage_product_sc4 = pi_apr.W_channel_x + pi_apr.W_tab_weight_sc4 - 1 # = 17 = 9 + 9 -1, do -1 to skip double sign bit of product
+ W_tab_power_product_sc4 = 2*W_tab_voltage_product_sc4 - 1 # = 33 = 2*17 -1, do -1 to skip double sign bit of product
+
+ # For SC4 it is possible to use W_channel_x_fraction > 9, because the F_chan is internal
+ # For SC3 it W_channel_x_fraction = 9, because the F_chan is in Apertif XC
+ # Number of bits for fraction < 1
+ W_tab_voltage_fraction_sc4 = pi_apr.W_channel_x_fraction - pi_apr.W_tab_unit_scale_sc4 # = 10 = 9 - -1
+
+ # Arts channel TAB
+ # Number of bits for fraction < 1
+ W_tab_power_fraction_sc4 = 2*W_tab_voltage_fraction_sc4 # = 20 = 2*10
+ W_tab_power_int_fraction_sc4 = W_tab_power_fraction_sc4 - W_int_ax # = 16 = 20 - 4
+
+ ################################################################################
+ # Arts SC4 channel IAB
+
+ # Number of bits for internal product
+ W_iab_power_product = 2*pi_apr.W_channel_x_fraction - 1 # = 17 = 2*9 -1, do -1 to skip double sign bit of product
+ W_iab_gain_product = W_iab_power_product + pi_apr.W_iab_gain - 1 # = 32 = 17 + 16 - 1, do -1 to skip double sign bit of product
+
+ # Number of bits for fraction < 1
+ W_iab_power_fraction = 2*pi_apr.W_channel_x_fraction # = 18 = 2*9
+ W_iab_gain_fraction = W_iab_power_fraction - W_int_ax - pi_apr.W_iab_unit_scale # = 16 = 18 - 4 - -2
+
+ ################################################################################
+ # Dynamic model time series
+ ################################################################################
+
+ # Time, frequency axis
+ nofTsub = nofTchanx * N_chan_x
+ nofTs = nofTsub * N_fft
+ t = np.arange(nofTs) * Ts
+
+ ################################################################################
+ # Dish Compound Beamformer
+
+ # ADC input
+ if wgEnable:
+ wgFreq = (selSub + (selChanx + selChanxFraction - N_chan_x/2.0) / N_chan_x) * fsub
+ chirpFreq = 0
+ if wgSweep:
+ chirpFreq = wgSweep * fsub * np.arange(nofTs)/nofTs/2.0
+ spSamples = wgAmpl * np.sin(2*np.pi*(wgFreq + chirpFreq) * t)
+ else:
+ spSamples = np.random.randn(nofTs)
+ spSamples *= skySigma / rms(spSamples)
+
+ if quantize:
+ # The unit level is at 1 lsb of the ADC input, so the W_adc = 8 bit input
+ # has no fraction and can thus directly be rounded to model quantization.
+ spSamples = np.round(spSamples)
+ rmsSp = rms(spSamples) # real signal
+ amplSp = rmsSp * pi_apr.wgScale
+
+ # Fsub subbands full bandwidth
+ spBlocks = spSamples.reshape((nofTsub, N_fft)) # [nofTsub][N_fft]
+ fftSamples = np.fft.fft(spBlocks) / N_fft # [nofTsub][N_fft]
+ fSubSamples = fftSamples[:, 0:N_sub] # [nofTsub][N_sub], real input, so only keep positive frequencies
+
+ # SST for selSub
+ sstSamples = fSubSamples[:, selSub].copy()
+ if quantize:
+ sstSamples *= 2.0**pi_apr.W_sst_fraction # fixed point fraction
+ sstSamples = np.round(sstSamples)
+ sstSamples /= 2.0**pi_apr.W_sst_fraction # back to normalized fraction
+ rmsComplexSST = rms(sstSamples) # complex signal
+ rmsSST = rmsComplexSST / pi_apr.rcScale
+ amplSST = rmsSST * pi_apr.wgScale
+ SSTpower = N_int_sub * rmsComplexSST**2
+
+ # BF samples full bandwidth
+ bfSamples = fSubSamples * cbGain
+
+ # BST for selSub
+ bstSamples = bfSamples[:, selSub].copy()
+ if quantize:
+ bstSamples *= 2.0**pi_apr.W_bst_fraction # fixed point fraction
+ bstSamples = np.round(bstSamples)
+ bstSamples /= 2.0**pi_apr.W_bst_fraction # back to normalized fraction
+ rmsComplexBST = rms(bstSamples) # complex signal
+ rmsBST = rmsComplexBST / pi_apr.rcScale
+ amplBST = rmsBST * pi_apr.wgScale
+ BSTpower = N_int_sub * rmsComplexBST**2
+
+ # CB beamlet for selSub
+ beamletSamples = bfSamples[:, selSub].copy()
+ if quantize:
+ beamletSamples *= 2.0**pi_apr.W_beamlet_fraction # fixed point fraction
+ beamletSamples = np.round(beamletSamples)
+ beamletSamples /= 2.0**pi_apr.W_beamlet_fraction # back to normalized fraction
+ rmsComplexBeamlet = rms(beamletSamples) # complex signal
+ rmsBeamlet = rmsComplexBeamlet / pi_apr.rcScale
+ amplBeamlet = rmsBeamlet * pi_apr.wgScale
+
+ ################################################################################
+ # Beamlet channelizer
+ # . Fchan output
+ transposeSamples = bfSamples.transpose() # [nofTsub][N_sub] --> [N_sub][nofTsub]
+ transposeBlocks = transposeSamples.reshape(N_sub, nofTchanx, N_chan_x) # [N_sub][nofTchanx][N_chan_x]
+ fSubChanSamples = np.fft.fft(transposeBlocks) / N_chan_x # [N_sub][nofTchanx][N_chan_x], complex input and output
+ fSubChanSamples = np.fft.fftshift(fSubChanSamples, 2)
+ fSubChanSamples = fSubChanSamples.transpose(1, 0, 2) # [nofTchanx][N_sub][N_chan_x]
+
+ # . CB fine channel for selSub, selChanx
+ fineChannelSamples = fSubChanSamples[:, selSub, selChanx].copy()
+ if quantize:
+ fineChannelSamples *= 2.0**pi_apr.W_channel_x_fraction # fixed point fraction
+ fineChannelSamples = np.round(fineChannelSamples)
+ fineChannelSamples /= 2.0**pi_apr.W_channel_x_fraction # back to normalized fraction
+ rmsComplexFineChannel = rms(fineChannelSamples) # complex signal
+ rmsFineChannel = rmsComplexFineChannel / pi_apr.rcScale
+ amplFineChannel = rmsFineChannel * pi_apr.wgScale
+
+ if 'apertif' in apps:
+ ################################################################################
+ # Aperif XC channel auto-visibility
+ XCpower = N_int_chan_x * rmsComplexFineChannel**2
+
+ if 'arts_sc1' in apps:
+ ################################################################################
+ # Arts SC1 beamlet voltage TAB
+ if wgEnable:
+ # Coherent sum using beamlets from original ADC input
+ beamletTabSamples = nofDishes * beamletSamples.copy()
+ else:
+ # Incoherent sum using copies with the same rms beamlet as for the original ADC input
+ beamletTabSamples = np.zeros(nofTsub) + np.zeros(nofTsub) * 1j
+ for tel in range(nofDishes):
+ complexSamples = np.random.randn(nofTsub) + np.random.randn(nofTsub) * 1j
+ complexSamples *= rmsComplexBeamlet / rms(complexSamples)
+ beamletTabSamples += complexSamples
+ beamletTabSamples *= tabGainSc1
+ if quantize:
+ beamletTabSamples *= 2.0**W_tab_fraction_sc1 # fixed point fraction
+ beamletTabSamples = np.round(beamletTabSamples)
+ beamletTabSamples /= 2.0**W_tab_fraction_sc1 # back to normalized fraction
+ rmsComplexBeamletTab = rms(beamletTabSamples) # complex signal
+ rmsBeamletTab = rmsComplexBeamletTab / pi_apr.rcScale
+ amplBeamletTab = rmsBeamletTab * pi_apr.wgScale
+
+ if 'arts_sc4' in apps:
+ ################################################################################
+ # Arts SC4 Power TAB for single polarization Stokes I = XX* + 0
+ # . Fine channel voltage TAB
+ if wgEnable:
+ # Coherent sum using channel from original ADC input
+ fineChannelTabSamples = nofDishes * fineChannelSamples.copy()
+ else:
+ # Incoherent sum using copies with the same rms channel as for the original ADC input
+ fineChannelTabSamples = np.zeros(nofTchanx) + np.zeros(nofTchanx) * 1j
+ for tel in range(nofDishes):
+ complexSamples = np.random.randn(nofTchanx) + np.random.randn(nofTchanx) * 1j
+ complexSamples *= rmsComplexFineChannel / rms(complexSamples)
+ fineChannelTabSamples += complexSamples # voltage sum
+ fineChannelTabSamples *= tabGainSc4
+ if quantize:
+ fineChannelTabSamples *= 2.0**W_tab_voltage_fraction_sc4 # fixed point fraction
+ fineChannelTabSamples = np.round(fineChannelTabSamples)
+ fineChannelTabSamples /= 2.0**W_tab_voltage_fraction_sc4 # back to normalized fraction
+ rmsComplexFineChannelTab = rms(fineChannelTabSamples) # complex signal
+ rmsFineChannelTab = rmsComplexFineChannelTab / pi_apr.rcScale
+ amplFineChannelTab = rmsFineChannelTab * pi_apr.wgScale
+
+ # . Fine channel power TAB
+ fineChannelTabPowers = fineChannelTabSamples * np.conjugate(fineChannelTabSamples) # powers
+ fineChannelTabPowers = fineChannelTabPowers.real # keep real, imag = 0
+ meanFineChannelTabPower = np.mean(fineChannelTabPowers) # = rmsComplexFineChannelTab**2, use mean for power data and rms for voltage data
+ stdFineChannelTabPower = np.std(fineChannelTabPowers)
+
+ # . Integrated channels power TAB
+ if wgEnable:
+ # The WG sinus only occurs in one fine channel if it is close to the center fine channel frequency, so
+ # the other fine channels in N_chan_bf fine channels carry almost zero and do not contribute much
+ integratedTabPowers = fineChannelTabPowers.copy()
+ else:
+ # Integrate using copies with the same rmsComplexFineChannelTab as for the orginal ADC input
+ integratedTabPowers = np.zeros(nofTchanx)
+ for ci in range(M_int_ax):
+ complexSamples = np.random.randn(nofTchanx) + np.random.randn(nofTchanx) * 1j
+ complexSamples *= rmsComplexFineChannelTab / rms(complexSamples)
+ powerSamples = complexSamples * np.conjugate(complexSamples)
+ powerSamples = powerSamples.real # keep real, imag = 0
+ integratedTabPowers += powerSamples
+ if quantize:
+ integratedTabPowers *= 2.0**W_tab_power_int_fraction_sc4 # fixed point fraction
+ integratedTabPowers = np.round(integratedTabPowers)
+ integratedTabPowers /= 2.0**W_tab_power_int_fraction_sc4 # back to normalized fraction
+ meanIntegratedTabPower = np.mean(integratedTabPowers) # use mean for power data and rms for voltage data
+ stdIntegratedTabPower = np.std(integratedTabPowers)
+
+ ################################################################################
+ # Arts SC4 IAB for single polarization Stokes I = XX* + 0
+
+ # . Fine channel powers
+ fineChannelPowers = fineChannelSamples * np.conjugate(fineChannelSamples)
+ fineChannelPowers = fineChannelPowers.real # keep real, imag = 0
+ meanFineChannelPower = np.mean(fineChannelPowers) # = rmsComplexFineChannel**2, use mean for power data and rms for voltage data
+
+ # . Integrated channel powers
+ if wgEnable:
+ # The WG sinus only occurs in one fine channel if it is close to the center fine channel frequency, so
+ # the other fine channels in N_chan_bf fine channels carry almost zero and do not contribute much
+ integratedChannelPowers = fineChannelPowers
+ else:
+ # Integrate using copies with the same rmsComplexFineChannel as for the orginal ADC input
+ integratedChannelPowers = np.zeros(nofTchanx)
+ for ci in range(M_int_ax):
+ complexSamples = np.random.randn(nofTchanx) + np.random.randn(nofTchanx) * 1j
+ complexSamples *= rmsComplexFineChannel / rms(complexSamples)
+ powerSamples = complexSamples * np.conjugate(complexSamples)
+ powerSamples = powerSamples.real # keep real, imag = 0
+ integratedChannelPowers += powerSamples
+ meanIntegratedChannelPower = np.mean(integratedChannelPowers) # use mean for power data and rms for voltage data
+
+ # . Sum of powers is coherent sum
+ if wgEnable:
+ # Sum of powers using integrated channel from original ADC input
+ integratedIabPowers = nofDishes * integratedChannelPowers
+ else:
+ # Sum of powers using copies with the same rmsComplexFineChannel as for the original ADC input
+ integratedIabPowers = np.zeros(nofTchanx)
+ for tel in range(nofDishes):
+ telPowerSamples = np.zeros(nofTchanx)
+ for ci in range(M_int_ax):
+ complexSamples = np.random.randn(nofTchanx) + np.random.randn(nofTchanx) * 1j
+ complexSamples *= rmsComplexFineChannel / rms(complexSamples)
+ powerSamples = complexSamples * np.conjugate(complexSamples)
+ powerSamples = powerSamples.real # keep real, imag = 0
+ telPowerSamples += powerSamples # powers sum integration
+ integratedIabPowers += telPowerSamples # powers sum IAB
+ meanIntegratedIabPower = np.mean(integratedIabPowers) # use mean for power data and rms for voltage data
+
+ # . Output gain
+ outputIabPowers = integratedIabPowers * iabGain
+ if quantize:
+ outputIabPowers *= 2.0**W_iab_gain_fraction # fixed point fraction
+ outputIabPowers = np.round(outputIabPowers)
+ outputIabPowers /= 2.0**W_iab_gain_fraction # back to normalized fraction
+ meanOutputIabPower = np.mean(outputIabPowers) # use mean for power data and rms for voltage data
+ stdOutputIabPower = np.std(outputIabPowers)
+
+ ################################################################################
+ # Dynamic model: Normalized floating point fraction to fixed point fraction
+ ################################################################################
+
+ # SST
+ rmsComplexSST *= 2.0**pi_apr.W_sst_fraction
+ rmsSST *= 2.0**pi_apr.W_sst_fraction
+ amplSST *= 2.0**pi_apr.W_sst_fraction
+ SSTpower *= 2.0**(2*pi_apr.W_sst_fraction)
+
+ # BST
+ rmsComplexBST *= 2.0**pi_apr.W_bst_fraction
+ rmsBST *= 2.0**pi_apr.W_bst_fraction
+ amplBST *= 2.0**pi_apr.W_bst_fraction
+ BSTpower *= 2.0**(2*pi_apr.W_bst_fraction)
+
+ # CB beamlet for selSub
+ beamletSamples *= 2.0**pi_apr.W_beamlet_fraction
+ rmsComplexBeamlet *= 2.0**pi_apr.W_beamlet_fraction
+ rmsBeamlet *= 2.0**pi_apr.W_beamlet_fraction
+ amplBeamlet *= 2.0**pi_apr.W_beamlet_fraction
+
+ # CB fine channel for selSub, selChanx
+ fineChannelSamples *= 2.0**pi_apr.W_channel_x_fraction
+ rmsComplexFineChannel *= 2.0**pi_apr.W_channel_x_fraction
+ rmsFineChannel *= 2.0**pi_apr.W_channel_x_fraction
+ amplFineChannel *= 2.0**pi_apr.W_channel_x_fraction
+
+ if 'apertif' in apps:
+ # Aperif XC channel auto-visibility
+ XCpower *= 2.0**(2*pi_apr.W_channel_x_fraction)
+
+ if 'arts_sc1' in apps:
+ # Arts SC1 beamlet voltage TAB
+ beamletTabSamples *= 2.0**W_tab_fraction_sc1
+ rmsComplexBeamletTab *= 2.0**W_tab_fraction_sc1
+ rmsBeamletTab *= 2.0**W_tab_fraction_sc1
+ amplBeamletTab *= 2.0**W_tab_fraction_sc1
+
+ if 'arts_sc4' in apps:
+ # Arts SC4 Power TAB for single polarization Stokes I = XX* + 0
+ # . Fine channel voltage TAB
+ fineChannelTabSamples *= 2.0**W_tab_voltage_fraction_sc4
+ rmsComplexFineChannelTab *= 2.0**W_tab_voltage_fraction_sc4
+ rmsFineChannelTab *= 2.0**W_tab_voltage_fraction_sc4
+ amplFineChannelTab *= 2.0**W_tab_voltage_fraction_sc4
+
+ # . Fine channel power TAB
+ fineChannelTabPowers *= 2.0**W_tab_power_fraction_sc4
+ meanFineChannelTabPower *= 2.0**W_tab_power_fraction_sc4
+ stdFineChannelTabPower *= 2.0**W_tab_power_fraction_sc4
+
+ # . Integrated channels power TAB
+ integratedTabPowers *= 2.0**W_tab_power_int_fraction_sc4
+ meanIntegratedTabPower *= 2.0**W_tab_power_int_fraction_sc4
+ stdIntegratedTabPower *= 2.0**W_tab_power_int_fraction_sc4
+
+ # Arts SC4 IAB for single polarization Stokes I = XX* + 0
+ # . Fine channel powers
+ fineChannelPowers *= 2.0**W_iab_power_fraction
+ meanFineChannelPower *= 2.0**W_iab_power_fraction
+
+ # . Integrated channel powers
+ integratedChannelPowers *= 2.0**W_iab_power_fraction
+ meanIntegratedChannelPower *= 2.0**W_iab_power_fraction
+
+ # . Sum of powers is coherent sum
+ integratedIabPowers *= 2.0**W_iab_power_fraction
+ meanIntegratedIabPower *= 2.0**W_iab_power_fraction
+
+ # . Output gain
+ outputIabPowers *= 2.0**W_iab_gain_fraction
+ meanOutputIabPower *= 2.0**W_iab_gain_fraction
+ stdOutputIabPower *= 2.0**W_iab_gain_fraction
+
+################################################################################
+# Logging
+################################################################################
+print '--------------------------------------------------------------------'
+print '-- User settings'
+print '--------------------------------------------------------------------'
+if wgEnable:
+ print 'WG sinus input:'
+ print '. wgSigma = %f' % wgSigma
+ print '. wgAmpl = %f [fsub]' % wgAmpl
+ print '. wgSweep = %f [fsub]' % wgSweep
+else:
+ print 'ADC noise input:'
+ print '. skySigma = %f' % skySigma
+print '. quantize = %s' % quantize
+print '. nofDishes = %d (= %.1f bit)' % (nofDishes, math.log(nofDishes, 2))
+print ''
+
+if 'dynamic' in models:
+ print 'Dynamic model:'
+ print '. selSub = %d' % selSub
+ print '. selChanx = %d' % selChanx
+ print '. selChanxFraction = %d' % selChanxFraction
+ print '. nofTchanx = %d' % nofTchanx
+ print '. nofTsub = %d' % nofTsub
+ print '. nofTs = %d' % nofTs
+ print ''
+
+print '--------------------------------------------------------------------'
+print '-- Design settings'
+print '--------------------------------------------------------------------'
+print 'Apertif BF:'
+print '. N_fft = %d' % N_fft
+print '. N_sub = %d' % N_sub
+print '. N_int_sub = %d' % N_int_sub
+print ''
+if 'apertif' in apps:
+ print 'Apertif XC:'
+ print '. N_chan_x = %d' % N_chan_x
+ print '. N_chan_bf = %d' % N_chan_bf
+ print '. N_int_chan_x = %d' % N_int_chan_x
+ print '. cbWeightAdjust = %f' % cbWeightAdjust
+ print '. cbMaxWeight = %d' % pi_apr.cbMaxWeight
+ print '. cbUnitWeight = %d' % pi_apr.cbUnitWeight
+ print '. cbWeight = %d' % cbWeight
+ print '. cbGain = %f' % cbGain
+ print ''
+if 'arts_sc1' in apps:
+ print 'Arts SC1:'
+ print '. sc1_use_qua = %s' % sc1_use_qua
+ print '. tabWeightAdjustSc1 = %f' % tabWeightAdjustSc1
+ print '. tabUnitWeightSc1 = %d' % tabUnitWeightSc1
+ print '. tabWeightSc1 = %d' % tabWeightSc1
+ print '. tabGainSc1 = %.8f' % tabGainSc1
+ print ''
+if 'arts_sc4' in apps:
+ print 'Arts SC4:'
+ print '. tabWeightAdjustSc4 = %f' % tabWeightAdjustSc4
+ print '. iabGainAdjust = %f' % iabGainAdjust
+ print '. iabMaxGain = %d' % pi_apr.iabMaxGain
+ print '. iabUnitGain = %d' % pi_apr.iabUnitGain
+ print '. iabGainReg = %d' % iabGainReg
+ print '. iabGain = %.8f' % iabGain
+ print ''
+
+if 'dynamic' in models:
+ print '--------------------------------------------------------------------'
+ print '-- Dynamic model settings'
+ print '--------------------------------------------------------------------'
+ print 'Design:'
+ print '. fs = %f [Hz]' % fs
+ print '. Ts = %.2f [ns]' % (Ts * 1e9)
+ print '. fsub = %f [Hz]' % fsub
+ print '. Tsub = %.2f [us]' % (Tsub * 1e6)
+ print '. W_fsub_gain = %d [bit]' % pi_apr.W_fsub_gain
+ if 'apertif' in apps:
+ print '. fchan_x = %f [Hz]' % fchan_x
+ print '. Tchanx = %.2f [us]' % (Tchanx * 1e6)
+ print '. W_fchan_x_gain = %d [bit]' % pi_apr.W_fchan_x_gain
+ if 'arts_sc4' in apps:
+ print '. Tstokesx = %.2f [us]' % (Tstokesx * 1e6)
+ print ''
+
+ print 'Dish:'
+ print '. W_adc = %d [bit]' % pi_apr.W_adc
+ print '. W_adc_max = %d [bit], adc max = %d' % (pi_apr.W_adc_max, 2**pi_apr.W_adc_max-1)
+ print '. W_sst_in = %d [bit]' % pi_apr.W_sst_in
+ print '. W_sst_fraction = %d [bit]' % pi_apr.W_sst_fraction
+ print '. W_cb_weight = %d [bit]' % pi_apr.W_cb_weight
+ print '. W_cb_unit_scale = %d [bit]' % pi_apr.W_cb_unit_scale
+ print '. W_cb_weight_fraction = %d [bit]' % pi_apr.W_cb_weight_fraction
+ print '. W_cb_in = %d [bit]' % pi_apr.W_cb_in
+ print '. W_cb_product = %d [bit]' % W_cb_product
+ print '. W_bst_in = %d [bit]' % pi_apr.W_bst_in
+ print '. W_bst_fraction = %d [bit]' % pi_apr.W_bst_fraction
+ print '. W_beamlet_sc1 = %d [bit]' % pi_apr.W_beamlet_sc1
+ print '. W_beamlet = %d [bit]' % pi_apr.W_beamlet
+ print '. W_beamlet_fraction = %d [bit]' % pi_apr.W_beamlet_fraction
+ print ''
+
+ if 'apertif' in apps:
+ print 'Apertif XC:'
+ print '. W_channel_x = %d [bit]' % pi_apr.W_channel_x
+ print '. W_channel_x_fraction = %d [bit]' % pi_apr.W_channel_x_fraction
+ print ''
+
+ if 'arts_sc1' in apps:
+ print 'Arts SC1 beamlet TAB:'
+ print '. W_tab_weight_sc1 = %d [bit]' % pi_apr.W_tab_weight_sc1
+ print '. W_tab_unit_scale_sc1 = %d [bit]' % W_tab_unit_scale_sc1
+ print '. W_tab_weight_fraction_sc1 = %d [bit]' % W_tab_weight_fraction_sc1
+ print '. W_beamlet_sc1 = %d [bit]' % pi_apr.W_beamlet_sc1
+ print '. W_tab_product_sc1 = %d [bit]' % W_tab_product_sc1
+ print '. W_tab_fraction_sc1 = %d [bit]' % W_tab_fraction_sc1
+ print ''
+
+ if 'arts_sc4' in apps:
+ print 'Arts SC4 TAB:'
+ print '. W_channel_x = %d [bit]' % pi_apr.W_channel_x
+ print '. W_channel_x_fraction = %d [bit]' % pi_apr.W_channel_x_fraction
+ print '. W_tab_weight_sc4 = %d [bit]' % pi_apr.W_tab_weight_sc4
+ print '. W_tab_unit_scale_sc4 = %d [bit]' % pi_apr.W_tab_unit_scale_sc4
+ print '. W_tab_weight_fraction_sc4 = %d [bit]' % pi_apr.W_tab_weight_fraction_sc4
+ print '. tabUnitWeightSc4 = %d' % pi_apr.tabUnitWeightSc4
+ print '. tabWeightSc4 = %d' % tabWeightSc4
+ print '. tabGainSc4 = %.8f' % tabGainSc4
+ print '. W_beamlet = %d [bit]' % pi_apr.W_beamlet
+ print '. W_tab_voltage_product_sc4 = %d [bit]' % W_tab_voltage_product_sc4
+ print '. W_tab_voltage_fraction_sc4 = %d [bit]' % W_tab_voltage_fraction_sc4
+ print '. W_tab_power_product_sc4 = %d [bit]' % W_tab_power_product_sc4
+ print '. W_tab_power_fraction_sc4 = %d [bit]' % W_tab_power_fraction_sc4
+ print '. W_tab_power_int_fraction_sc4 = %d [bit]' % W_tab_power_int_fraction_sc4
+ print '. W_int_ax = %d [bit]' % W_int_ax
+ print '. M_int_ax = %d' % M_int_ax
+ print ''
+
+ print 'Arts SC4 IAB:'
+ print '. W_channel_x = %d [bit]' % pi_apr.W_channel_x
+ print '. W_channel_x_fraction = %d [bit]' % pi_apr.W_channel_x_fraction
+ print '. W_int_ax = %d [bit]' % W_int_ax
+ print '. M_int_ax = %d' % M_int_ax
+ print '. W_iab_gain = %d [bit]' % pi_apr.W_iab_gain
+ print '. W_iab_unit_scale = %d [bit]' % pi_apr.W_iab_unit_scale
+ print '. W_iab_gain_fraction = %d [bit]' % pi_apr.W_iab_gain_fraction
+ print '. W_iab_power_product = %d [bit]' % W_iab_power_product
+ print '. W_iab_power_fraction = %d [bit]' % W_iab_power_fraction
+ print '. W_iab_gain_product = %d [bit]' % W_iab_gain_product
+ print '. W_iab_gain_fraction = %d [bit]' % W_iab_gain_fraction
+ print ''
+
+if 'dynamic' in models and wgSweep == 0:
+ print '--------------------------------------------------------------------'
+ print '-- Dynamic model results'
+ print '--------------------------------------------------------------------'
+ if wgEnable:
+ print 'Apertif BF internal levels (excluding sign bit) for coherent WG sinus input:'
+ print '. ADC input rms = %20.2f (= %4.1f [bit])' % (rmsSp, math.log(rmsSp, 2))
+ print '. ADC input ampl = %20.2f (= %4.1f [bit])' % (amplSp, math.log(amplSp, 2))
+ print '. SST input rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexSST, math.log(rmsComplexSST, 2))
+ print '. SST input ampl = %20.2f (= %4.1f [bit])' % (amplSST, math.log(amplSST, 2))
+ print '. BST input rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexBST, math.log(rmsComplexBST, 2))
+ print '. BST input ampl = %20.2f (= %4.1f [bit])' % (amplBST, math.log(amplBST, 2))
+ print '. CB beamlet rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexBeamlet, math.log(rmsComplexBeamlet, 2))
+ print '. CB beamlet ampl = %20.2f (= %4.1f [bit])' % (amplBeamlet, math.log(amplBeamlet, 2))
+ else:
+ print 'Apertif BF internal levels (excluding sign bit) for incoherent ADC noise input:'
+ print '. ADC input rms = %20.2f (= %4.1f [bit])' % (rmsSp, math.log(rmsSp, 2))
+ print '. SST input rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexSST, math.log(rmsComplexSST, 2))
+ print '. SST input rms = %20.2f (= %4.1f [bit])' % (rmsSST, math.log(rmsSST, 2))
+ print '. BST input rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexBST, math.log(rmsComplexBST, 2))
+ print '. BST input rms = %20.2f (= %4.1f [bit])' % (rmsBST, math.log(rmsBST, 2))
+ print '. CB beamlet rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexBeamlet, math.log(rmsComplexBeamlet, 2))
+ print '. CB beamlet rms = %20.2f (= %4.1f [bit])' % (rmsBeamlet, math.log(rmsBeamlet, 2))
+ print '. SST power = %20.2f = %7.2f dB (= %4.1f [bit])' % (SSTpower, 10*math.log(SSTpower, 10), math.log(SSTpower, 2))
+ print '. BST power = %20.2f = %7.2f dB (= %4.1f [bit])' % (BSTpower, 10*math.log(BSTpower, 10), math.log(BSTpower, 2))
+ print ''
+
+ if 'apertif' in apps:
+ if wgEnable:
+ print 'Apertif XC output for coherent WG sinus input:'
+ print '. Fine channel rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannel, math.log(rmsComplexFineChannel, 2))
+ print '. Fine channel ampl = %20.2f (= %4.1f [bit])' % (amplFineChannel, math.log(amplFineChannel, 2))
+ else:
+ print 'Apertif XC output for incoherent ADC noise input:'
+ print '. Fine channel rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannel, math.log(rmsComplexFineChannel, 2))
+ print '. Fine channel rms = %20.2f (= %4.1f [bit])' % (rmsFineChannel, math.log(rmsFineChannel, 2))
+ print '. XC auto power = %20.2f = %7.2f dB (= %4.1f [bit])' % (XCpower, 10*math.log(XCpower, 10), math.log(XCpower, 2))
+ print ''
+
+ if 'arts_sc1' in apps:
+ if wgEnable:
+ print 'Arts SC1 beamlet TAB internal levels (excluding sign bit) for coherent WG sinus input:'
+ print '. Output TAB rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexBeamletTab, math.log(rmsComplexBeamletTab, 2))
+ print '. Output TAB ampl = %20.2f (= %4.1f [bit])' % (amplBeamletTab, math.log(amplBeamletTab, 2))
+ else:
+ print 'Arts SC1 beamlet TAB internal levels (excluding sign bit) for incoherent ADC noise input:'
+ print '. Output TAB rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexBeamletTab, math.log(rmsComplexBeamletTab, 2))
+ print '. Output TAB rms = %20.2f (= %4.1f [bit])' % (rmsBeamletTab, math.log(rmsBeamletTab, 2))
+ print ''
+
+ if 'arts_sc4' in apps:
+ if wgEnable:
+ print 'Arts SC4 TAB internal levels (excluding sign bit) for coherent WG sinus input:'
+ print '. Fine channel rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannel, math.log(rmsComplexFineChannel, 2))
+ print '. Fine channel ampl = %20.2f (= %4.1f [bit])' % (amplFineChannel, math.log(amplFineChannel, 2))
+ print '. Fine channel TAB rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannelTab, math.log(rmsComplexFineChannelTab, 2))
+ print '. Fine channel TAB ampl = %20.2f (= %4.1f [bit])' % (amplFineChannelTab, math.log(amplFineChannelTab, 2))
+ print '. Fine channel TAB power = %20.2f (= %4.1f [bit])' % (meanFineChannelTabPower, math.log(meanFineChannelTabPower, 2))
+ print '. Output TAB power = %20.2f (= %4.1f [bit])' % (meanIntegratedTabPower, math.log(meanIntegratedTabPower, 2))
+ else:
+ print 'Arts SC4 TAB internal levels (excluding sign bit) for incoherent ADC noise input:'
+ print '. Fine channel rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannel, math.log(rmsComplexFineChannel, 2))
+ print '. Fine channel rms = %20.2f (= %4.1f [bit])' % (rmsFineChannel, math.log(rmsFineChannel, 2))
+ print '. Fine channel TAB rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannelTab, math.log(rmsComplexFineChannelTab, 2))
+ print '. Fine channel TAB rms = %20.2f (= %4.1f [bit])' % (rmsFineChannelTab, math.log(rmsFineChannelTab, 2))
+ print '. Fine channel TAB power = %20.2f (= %4.1f [bit])' % (meanFineChannelTabPower, math.log(meanFineChannelTabPower, 2))
+ print '. Fine channel TAB power std = %20.2f (= %4.1f [bit])' % (stdFineChannelTabPower, math.log(stdFineChannelTabPower, 2))
+ print '. Output TAB power = %20.2f (= %4.1f [bit])' % (meanIntegratedTabPower, math.log(meanIntegratedTabPower, 2))
+ print '. Output TAB power std = %20.2f (= %4.1f [bit])' % (stdIntegratedTabPower, math.log(stdIntegratedTabPower, 2))
+ print ''
+
+ if wgEnable:
+ print 'Arts SC4 IAB internal levels (excluding sign bit) for coherent WG sinus input:'
+ print '. Fine channel rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannel, math.log(rmsComplexFineChannel, 2))
+ print '. Fine channel ampl = %20.2f (= %4.1f [bit])' % (amplFineChannel, math.log(amplFineChannel, 2))
+ print '. Fine channel power = %20.2f (= %4.1f [bit])' % (meanFineChannelPower, math.log(meanFineChannelPower, 2))
+ print '. Integrated channel power = %20.2f (= %4.1f [bit])' % (meanIntegratedChannelPower, math.log(meanIntegratedChannelPower, 2))
+ print '. Integrated IAB power = %20.2f (= %4.1f [bit])' % (meanIntegratedIabPower, math.log(meanIntegratedIabPower, 2))
+ print '. Output IAB power = %20.2f (= %4.1f [bit])' % (meanOutputIabPower, math.log(meanOutputIabPower, 2))
+ else:
+ print 'Arts SC4 IAB internal levels (excluding sign bit) for incoherent ADC noise input:'
+ print '. Fine channel rms complex = %20.2f (= %4.1f [bit])' % (rmsComplexFineChannel, math.log(rmsComplexFineChannel, 2))
+ print '. Fine channel rms = %20.2f (= %4.1f [bit])' % (rmsFineChannel, math.log(rmsFineChannel, 2))
+ print '. Fine channel power = %20.2f (= %4.1f [bit])' % (meanFineChannelPower, math.log(meanFineChannelPower, 2))
+ print '. Integrated channel power = %20.2f (= %4.1f [bit])' % (meanIntegratedChannelPower, math.log(meanIntegratedChannelPower, 2))
+ print '. Integrated IAB power = %20.2f (= %4.1f [bit])' % (meanIntegratedIabPower, math.log(meanIntegratedIabPower, 2))
+ print '. Output IAB power = %20.2f (= %4.1f [bit])' % (meanOutputIabPower, math.log(meanOutputIabPower, 2))
+ print '. Output IAB power std = %20.2f (= %4.1f [bit])' % (stdOutputIabPower, math.log(stdOutputIabPower, 2))
+ print ''
+
+################################################################################
+# Plotting
+################################################################################
+if useplot:
+ tplot = test_plot.Testplot()
+ tplot.close_plots()
+
+ if 'static' in models:
+ ################################################################################
+ # Static model:
+ # . WG, coherent input
+ # . Noise, incoherent input
+ ################################################################################
+
+ if wgEnable:
+ # WG, coherent input
+ # Plot internal levels as function of WG amplitude
+ Lx = A_wg
+ if 'apertif' in apps or 'arts_sc4' in apps:
+ A = []
+ A.append(A_wg)
+ A.append(beamlet_wg_complex_phase_values * pi_apr.wgScale)
+ A.append(channel_x_wg_complex_phase_values * pi_apr.wgScale)
+ Alegend = ['adc', 'beamlet', 'channel_x']
+ lineFormats = ['-', '-', '-']
+ Title = 'Amplitudes for cbWeight=%d' % cbWeight
+ Ylim = None
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='WG amplitude', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=lineFormats)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_wg_amplitudes.png') # Save current figure
+
+ if 'arts_sc1' in apps:
+ A = []
+ A.append(A_wg)
+ A.append(beamlet_wg_complex_phase_values * pi_apr.wgScale)
+ A.append(beamlet_tab_wg_complex_phase_values_sc1 * pi_apr.wgScale)
+ Alegend = ['adc', 'beamlet', 'tab sc1']
+ lineFormats = ['-', '-', '*']
+ Title = 'Amplitudes for cbWeight=%d, tabWeightSc1=%d, nofDishes=%d' % (cbWeight, tabWeightSc1, nofDishes)
+ Ylim = None
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='WG amplitude', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=lineFormats)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_wg_amplitudes_sc1.png') # Save current figure
+
+ if 'arts_sc4' in apps:
+ A = []
+ A.append(power_tab_xx_wg_complex_values_sc4)
+ A.append(iab_xx_wg_complex_values_sc4)
+ Alegend = ['TAB XX* power', 'IAB XX* power']
+ lineFormats = ['-', '*']
+ Title = 'Powers for cbWeight=%d, tabWeightSc4=%d, iabGainReg=%d, nofDishes=%d' % (cbWeight, tabWeightSc4, iabGainReg, nofDishes)
+ Ylim = [0, 1.1 * 2.0**pi_apr.W_pow]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='WG amplitude', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=lineFormats)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_wg_powers_sc4.png') # Save current figure
+
+ # Plot power statistics as function of WG amplitude
+ A = []
+ A.append(SST_wg_dBs)
+ A.append(BST_wg_dBs)
+ if 'apertif' in apps:
+ A.append(XC_wg_auto_visibilities_dB)
+ Alegend = ['SST', 'BST', 'XC']
+ else:
+ Alegend = ['SST', 'BST']
+ Title = 'Auto correlation power statistics for CB weight = %d' % cbWeight
+ Ylim = None
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='WG amplitude', Ylabel='Level [dB]', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_wg_auto_powers.png') # Save current figure
+
+ else:
+ # Noise, incoherent input
+ # Plot internal levels as function of WG amplitude
+ Lx = S_noise
+ if 'apertif' in apps or 'arts_sc4' in apps:
+ A = []
+ A.append(S_noise)
+ A.append(beamlet_noise_complex_phase_values)
+ A.append(channel_x_noise_complex_phase_values)
+ Alegend = ['adc', 'beamlet', 'channel_x']
+ lineFormats = ['-', '*', '-']
+ Title = 'Sigmas for cbWeight=%d' % cbWeight
+ Ylim = None
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='ADC noise', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=lineFormats)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_noise_sigmas.png') # Save current figure
+
+ if 'arts_sc1' in apps:
+ A = []
+ A.append(S_noise)
+ A.append(beamlet_noise_complex_phase_values)
+ A.append(beamlet_tab_noise_complex_phase_values_sc1)
+ Alegend = ['adc', 'beamlet', 'tab sc1']
+ lineFormats = ['-', '-', '*']
+ Title = 'Sigmas for cbWeight=%d, tabWeightSc1=%d, nofDishes=%d' % (cbWeight, tabWeightSc1, nofDishes)
+ Ylim = None
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='ADC noise', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=lineFormats)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_noise_sigmas_sc1.png') # Save current figure
+
+ if 'arts_sc4' in apps:
+ A = []
+ A.append(power_tab_xx_noise_values_sc4)
+ A.append(iab_xx_noise_values_sc4)
+ Alegend = ['TAB XX* power', 'IAB XX* power']
+ lineFormats = ['-', '*']
+ Title = 'Powers for cbWeight=%d, tabWeightSc4=%d, iabGainReg=%d, nofDishes=%d' % (cbWeight, tabWeightSc4, iabGainReg, nofDishes)
+ Ylim = [0, 1.1 * 2.0**pi_apr.W_pow]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='ADC noise', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=lineFormats)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_noise_powers_sc4.png') # Save current figure
+
+ # Plot power statistics as function of WG amplitude
+ A = []
+ A.append(SST_noise_dBs)
+ A.append(BST_noise_dBs)
+ A.append(XC_noise_auto_visibilities_dB)
+ Alegend = ['SST', 'BST', 'XC']
+ Title = 'Auto correlation power statistics for CB weight = %d' % cbWeight
+ Ylim = None
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=Lx, representation='', offset=0, Title=Title, Xlabel='ADC noise', Ylabel='Level [dB]', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_static_noise_auto_powers.png') # Save current figure
+
+
+ if 'dynamic' in models and wgSweep == 0:
+ ###############################################################################
+ # Dynamic model: Processing
+ ###############################################################################
+
+ # Plot dish ADC
+ L = spSamples[0:nof_periods] # do not plot all to save time
+ if wgEnable:
+ Title = 'Dish ADC input (sigma = %.2f, ampl = %.2f)' % (rmsSp, wgAmpl)
+ ylim = 5 * math.ceil(0.2 * 1.1 * amplSp)
+ else:
+ Title = 'ADC input (sigma = %.2f)' % rmsSp
+ ylim = 5 * math.ceil(0.2 * 1.1 * rmsSp * nofSigma)
+ Ylim = [-ylim, ylim]
+ tplot.plot_one_dimensional_list(3, L, Lx=None, representation='', offset=0, Title=Title, Xlabel='Time [Tsub]', Ylabel='Voltage', Xlim=None, Ylim=Ylim)
+ tplot.save_figure('plots/apertif_arts_firmware_model_dynamic_adc.png') # Save current figure
+
+ # Plot dish output beamlets for selSub
+ A = []
+ A.append(beamletSamples.real[0:nof_periods])
+ A.append(beamletSamples.imag[0:nof_periods])
+ Alegend = ['real', 'imag']
+ if wgEnable:
+ Title = 'Dish CB output samples (ampl = %.2f)' % amplBeamlet
+ ylim = 5 * math.ceil(0.2 * 1.1 * amplBeamlet)
+ else:
+ Title = 'Dish CB output samples (sigma = %.2f)' % rmsBeamlet
+ ylim = 5 * math.ceil(0.2 * 1.1 * rmsBeamlet * nofSigma)
+ Ylim = [-ylim, ylim]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=None, representation='', offset=0, Title=Title, Xlabel='time [Tsub]', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_dynamic_beamlet.png') # Save current figure
+
+ if 'apertif' in apps or 'arts_sc4' in apps:
+ # Plot correlator fine channels for selSub, selChanx
+ A = []
+ A.append(fineChannelSamples.real[0:nof_periods])
+ A.append(fineChannelSamples.imag[0:nof_periods])
+ Alegend = ['real', 'imag']
+ if wgEnable:
+ Title = 'Central fine channel samples (ampl = %.2f)' % amplFineChannel
+ ylim = 5 * math.ceil(0.2 * 1.1 * amplFineChannel)
+ else:
+ Title = 'Central fine channel samples (sigma = %.2f)' % rmsFineChannel
+ ylim = 5 * math.ceil(0.2 * 1.1 * rmsFineChannel * nofSigma)
+ Ylim = [-ylim, ylim]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=None, representation='', offset=0, Title=Title, Xlabel='time [Tsub]', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_dynamic_fine_channel.png') # Save current figure
+
+ if 'arts_sc1' in apps:
+ # Plot Arts SC1 output TAB samples for selSub and nofDishes
+ # . Independent of nofDishes when tabWeightSc1 is scaled by nofDishes or sqrt(nofDishes)
+ A = []
+ A.append(beamletTabSamples.real[0:nof_periods])
+ A.append(beamletTabSamples.imag[0:nof_periods])
+ Alegend = ['real', 'imag']
+ if wgEnable:
+ Title = 'Arts SC1 output TAB samples (ampl = %.2f, nofDishes = %d)' % (amplBeamletTab, nofDishes)
+ ylim = 5 * math.ceil(0.2 * 1.1 * amplBeamletTab)
+ else:
+ Title = 'Arts SC1 output TAB samples (sigma = %.2f, nofDishes = %d)' % (rmsBeamletTab, nofDishes)
+ ylim = 5 * math.ceil(0.2 * 1.1 * rmsBeamletTab * nofSigma)
+ Ylim = [-ylim, ylim]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=None, representation='', offset=0, Title=Title, Xlabel='time [Tsub]', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_dynamic_beamlet_tab.png') # Save current figure
+
+ if 'arts_sc4' in apps:
+ # Plot Arts SC4 output TAB powers for selSub, selChanx and nofDishes
+ # . Independent of nofDishes, because tabWeightSc4 is scaled by nofDishes or sqrt(nofDishes)
+ A = []
+ A.append(integratedTabPowers[0:nof_periods])
+ Alegend = None
+ Title = 'Arts SC4 output TAB powers (mean = %.2f = %.1f [bit], nofDishes = %d)' % (meanIntegratedTabPower, math.log(meanIntegratedTabPower, 2), nofDishes)
+ if wgEnable:
+ ylim = 5 * math.ceil(0.2 * 1.1 * meanIntegratedTabPower)
+ else:
+ ylim = 5 * math.ceil(0.2 * 1.1 * meanIntegratedTabPower * nofSigma)
+ Ylim = [0, ylim]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=None, representation='', offset=0, Title=Title, Xlabel='time [Tchanx]', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_dynamic_power_tab.png') # Save current figure
+
+ # Plot Arts SC4 output IAB powers for selSub, selChanx and nofDishes
+ # . Independent of nofDishes, because tabWeightSc4 is scaled by nofDishes or sqrt(nofDishes)
+ A = []
+ A.append(outputIabPowers)
+ Alegend = None
+ Title = 'Arts SC4 output IAB powers (mean = %.2f = %.1f [bit], nofDishes = %d)' % (meanOutputIabPower, math.log(meanOutputIabPower, 2), nofDishes)
+ if wgEnable:
+ ylim = 5 * math.ceil(0.2 * 1.1 * meanOutputIabPower)
+ else:
+ ylim = 5 * math.ceil(0.2 * 1.1 * meanOutputIabPower * nofSigma)
+ Ylim = [0, ylim]
+ tplot.plot_two_dimensional_list(3, A, Alegend=Alegend, Lx=None, representation='', offset=0, Title=Title, Xlabel='time [Tchanx]', Ylabel='Level', Xlim=None, Ylim=Ylim, lineFormats=None)
+ tplot.save_figure('plots/apertif_arts_firmware_model_dynamic_power_iab.png') # Save current figure
+
+
+ if 'dynamic' in models and wgSweep != 0:
+ ###############################################################################
+ # Dynamic model: WG frequency sweep
+ ###############################################################################
+
+ # Plot subband spectrum abs(fSubSamples[nofTsub][N_sub])
+ A = np.abs(fSubSamples)
+ tplot.image_two_dimensional_list(3, A, transpose=True, grid=True, Title='Subband magnitudes', Xlabel='time [Tsub]', Ylabel='freq [fsub]')
+ tplot.save_figure('plots/apertif_arts_firmware_model_wg_sweep_subband_spectrum.png') # Save current figure
+
+ # Plot fine channels spectrum abs(fSubChanSamples[N_sub][nofTchanx][N_chan_x])
+ A = np.abs(fSubChanSamples.reshape(nofTchanx, N_sub*N_chan_x)) # [nofTchanx][N_sub*N_chan_x]
+ chanLo = 0
+ chanHi = N_sub*N_chan_x - 1
+ nofSubBorder = 0
+ nofSubBorder = 1 + int(abs(wgSweep))
+ if nofSubBorder>0:
+ if wgSweep>0:
+ chanLo = N_chan_x * (selSub - nofSubBorder)
+ chanHi = N_chan_x * (selSub + nofSubBorder + wgSweep + 1) - 1
+ else:
+ chanLo = N_chan_x * (selSub - nofSubBorder + wgSweep)
+ chanHi = N_chan_x * (selSub + nofSubBorder + 1) - 1
+ A = A[:, chanLo:chanHi+1]
+ extent = [0, nofTchanx-1, 1.0 * chanLo / N_chan_x - 0.5, 1.0 * chanHi / N_chan_x - 0.5]
+ #print chanLo, chanHi, extent
+ #extent = None
+ tplot.image_two_dimensional_list(3, A, transpose=True, grid=True, Title='Channel magnitudes', Xlabel='time [Tchanx]', Ylabel='freq [fsub]', extent=extent)
+ tplot.save_figure('plots/apertif_arts_firmware_model_wg_sweep_fine_channel_spectrum.png') # Save current figure
+
+ tplot.show_plots()
+
diff --git a/applications/lofar2/doc/lofar_station_firmware_model.drawio b/applications/lofar2/model/lofar_station_firmware_model.drawio
similarity index 100%
rename from applications/lofar2/doc/lofar_station_firmware_model.drawio
rename to applications/lofar2/model/lofar_station_firmware_model.drawio
diff --git a/applications/lofar2/doc/lofar_station_firmware_model.vsd b/applications/lofar2/model/lofar_station_firmware_model.vsd
similarity index 100%
rename from applications/lofar2/doc/lofar_station_firmware_model.vsd
rename to applications/lofar2/model/lofar_station_firmware_model.vsd
diff --git a/applications/lofar2/doc/lofar_station_firmware_model.vsdx b/applications/lofar2/model/lofar_station_firmware_model.vsdx
similarity index 100%
rename from applications/lofar2/doc/lofar_station_firmware_model.vsdx
rename to applications/lofar2/model/lofar_station_firmware_model.vsdx
diff --git a/applications/lofar2/model/pfb_pk/PFB.ipynb b/applications/lofar2/model/pfb_pk/PFB.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8c79074ef13760a20c267381526519973283a846
--- /dev/null
+++ b/applications/lofar2/model/pfb_pk/PFB.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Calculate the weight of the filter bank\n",
+ "def GetW(Nfft,P=4):\n",
+ " M=Nfft*P\n",
+ " W=np.arange(0,M,1)\n",
+ " flt=np.zeros([M])\n",
+ " #P=P-1\n",
+ " flt[0:P-2]=1;\n",
+ " flt[P-2]=0.5;\n",
+ " flt[-P+3:]=1;\n",
+ " flt[-P+2]=0.5;\n",
+ " W=np.fft.ifft(flt).real #Imag part should be zero\n",
+ " W=np.roll(W,M//2)\n",
+ " return W*Nfft\n",
+ "\n",
+ "#Apply the PFB to data\n",
+ "def fftw(A,W,P=4):\n",
+ " Nfft=A.shape[-1]\n",
+ " Dx=A[:,:-P,:]*W[:Nfft]\n",
+ " for i in range(P-1):\n",
+ " S=(i+1)*Nfft\n",
+ " Dx+=A[:,i+1:-P+i+1,:]*W[S:S+Nfft]\n",
+ " return (np.fft.fft(Dx,axis=2))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fb39bf1bf98>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "P=4\n",
+ "Nfft=1024; #FFT length\n",
+ "W=GetW(Nfft,P=P)\n",
+ "plt.plot(W);\n",
+ "plt.title(\"PFB coefficients\");"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(192, 1, 131072)\n",
+ "(192, 128, 1024)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Load Data\n",
+ "Nc=3; #Number of channels\n",
+ "\n",
+ "A=np.array(np.load(\"../LabTest8/D6b.npy\")) #Raw ADC data\n",
+ "A-=65536*(A>65536//2)\n",
+ "print(A.shape)\n",
+ "[n1,n2,n3]=A.shape\n",
+ "\n",
+ "A=A.reshape([n1,n3//Nfft,Nfft])\n",
+ "print(A.shape) #Channels*seconds, blocks, fft length"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(3, 7936, 1024)\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Do FFT\n",
+ "F=fftw(A,W,P=P)\n",
+ "[n1,n2,n3]=F.shape\n",
+ "F=F.reshape([n1//Nc,Nc,n2,n3])\n",
+ "F=F.swapaxes(0,1)\n",
+ "F=F.reshape([Nc,n1//Nc*n2,n3])\n",
+ "print(F.shape) #channels, blocks, ffts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.text.Text at 0x7fb3fbe9f748>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fb3fbf94a58>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plot power\n",
+ "FP=np.sum((F*F.conj()).real,axis=1)[:,:Nfft//2]\n",
+ "FP=10*np.log10(FP)\n",
+ "freq=np.arange(0,Nfft//2,1)/(Nfft//2)*100\n",
+ "plt.figure(dpi=300)\n",
+ "plt.plot(freq,FP.T,linewidth=0.5);\n",
+ "plt.xlabel(\"Frequency (MHz)\")\n",
+ "plt.ylabel(\"Power (dB)\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/applications/lofar2/model/pfb_pk/PFB.pdf b/applications/lofar2/model/pfb_pk/PFB.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..5deb50c49e06202c2310a2d8839b52430da5d6c6
Binary files /dev/null and b/applications/lofar2/model/pfb_pk/PFB.pdf differ
diff --git a/applications/lofar2/model/pfb_pk/PFB.py b/applications/lofar2/model/pfb_pk/PFB.py
new file mode 100644
index 0000000000000000000000000000000000000000..566bc05a594d4ae081b6424ea857f3fa08f084c0
--- /dev/null
+++ b/applications/lofar2/model/pfb_pk/PFB.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# In[8]:
+
+
+#Calculate the weight of the filter bank
+def GetW(Nfft,P=4):
+ M=Nfft*P
+ W=np.arange(0,M,1)
+ flt=np.zeros([M])
+ #P=P-1
+ flt[0:P-2]=1;
+ flt[P-2]=0.5;
+ flt[-P+3:]=1;
+ flt[-P+2]=0.5;
+ W=np.fft.ifft(flt).real #Imag part should be zero
+ W=np.roll(W,M//2)
+ return W*Nfft
+
+#Apply the PFB to data
+def fftw(A,W,P=4):
+ Nfft=A.shape[-1]
+ Dx=A[:,:-P,:]*W[:Nfft]
+ for i in range(P-1):
+ S=(i+1)*Nfft
+ Dx+=A[:,i+1:-P+i+1,:]*W[S:S+Nfft]
+ return (np.fft.fft(Dx,axis=2))
+
+
+# In[11]:
+
+
+P=4
+Nfft=1024; #FFT length
+W=GetW(Nfft,P=P)
+plt.plot(W);
+plt.title("PFB coefficients");
+
+
+# In[22]:
+
+
+#Load Data
+Nc=3; #Number of channels
+
+A=np.array(np.load("../LabTest8/D6b.npy")) #Raw ADC data
+A-=65536*(A>65536//2)
+print(A.shape)
+[n1,n2,n3]=A.shape
+
+A=A.reshape([n1,n3//Nfft,Nfft])
+print(A.shape) #Channels*seconds, blocks, fft length
+
+
+# In[ ]:
+
+
+
+
+
+# In[23]:
+
+
+#Do FFT
+F=fftw(A,W,P=P)
+[n1,n2,n3]=F.shape
+F=F.reshape([n1//Nc,Nc,n2,n3])
+F=F.swapaxes(0,1)
+F=F.reshape([Nc,n1//Nc*n2,n3])
+print(F.shape) #channels, blocks, ffts
+
+
+# In[26]:
+
+
+#plot power
+FP=np.sum((F*F.conj()).real,axis=1)[:,:Nfft//2]
+FP=10*np.log10(FP)
+freq=np.arange(0,Nfft//2,1)/(Nfft//2)*100
+plt.figure(dpi=300)
+plt.plot(freq,FP.T,linewidth=0.5);
+plt.xlabel("Frequency (MHz)")
+plt.ylabel("Power (dB)")
+
+
+# In[ ]:
+
+
+
+
diff --git a/applications/lofar2/model/pfb_pk/readme.txt b/applications/lofar2/model/pfb_pk/readme.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2f70e46afbdc3ad2046a61c45d22df7c70af2c32
--- /dev/null
+++ b/applications/lofar2/model/pfb_pk/readme.txt
@@ -0,0 +1,3 @@
+2021-01-20 Paulus Kruger script for offline analysis of LTS raw data and comparison with SST data.
+Use Jupyter notebook: https://jupyter.org/
+LTS results: https://support.astron.nl/confluence/display/RCU/2020-11-23+Stati+measurement+vs+raw+data