diff --git a/applications/apertif/designs/apertif_unb1_correlator/tb/python/tc_apertif_unb1_correlator_lite_bg_ram.py b/applications/apertif/designs/apertif_unb1_correlator/tb/python/tc_apertif_unb1_correlator_lite_bg_ram.py
index 1eca6f8c87d1c3c9f070f2c7b7607e54a0e0e135..f464296e4f94e371bd55069c6f361bdcd10cb92f 100644
--- a/applications/apertif/designs/apertif_unb1_correlator/tb/python/tc_apertif_unb1_correlator_lite_bg_ram.py
+++ b/applications/apertif/designs/apertif_unb1_correlator/tb/python/tc_apertif_unb1_correlator_lite_bg_ram.py
@@ -25,36 +25,70 @@ bg = pi_diag_block_gen.PiDiagBlockGen(tc, io, nofChannels=NOF_BG_OUTPUTS, ramSiz
###############################################################################
# Create an example 2d list of [24 inputs]*[64 channels]
-# . All zeros except one input
-# . As input 9 contains 64 channels with complex(1); we should see visibility
-# 180 (autocorrelation of input 9) containing the value 12500.
# . 12500 = number of accumulations done per channel per integration period (1.024s)
# . 12500*64 channels = 800000 accumulations per integration period in total.
###############################################################################
-example_list = [ NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(1)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)],\
- NOF_CHANNELS*[complex(0)], ]
+#
+# list with only prime values on 2nd channel(channel 0 is alwasy flagged), rest channels all zero
+# this way the visibilities are at least unique, disadvantage is they will be huge at a certain point
+# the lines in list represents RT2 X, RT2 Y, RT3 X, RT3 Y ...... RTD X, RTD Y
+# and each line has NOF_CHANNELS inputchannels
+#
+#example_list = [ (NOF_CHANNELS)*[complex(2)],\
+# (NOF_CHANNELS)*[complex(3)],\
+# (NOF_CHANNELS)*[complex(4)],\
+# (NOF_CHANNELS)*[complex(5)],\
+# (NOF_CHANNELS)*[complex(11)],\
+# (NOF_CHANNELS)*[complex(13)],\
+# (NOF_CHANNELS)*[complex(17)],\
+# (NOF_CHANNELS)*[complex(19)],\
+# (NOF_CHANNELS)*[complex(23)],\
+# (NOF_CHANNELS)*[complex(29)],\
+# (NOF_CHANNELS)*[complex(31)],\
+# (NOF_CHANNELS)*[complex(37)],\
+# (NOF_CHANNELS)*[complex(41)],\
+# (NOF_CHANNELS)*[complex(43)],\
+# (NOF_CHANNELS)*[complex(47)],\
+# (NOF_CHANNELS)*[complex(53)],\
+# (NOF_CHANNELS)*[complex(59)],\
+# (NOF_CHANNELS)*[complex(61)],\
+# (NOF_CHANNELS)*[complex(67)],\
+# (NOF_CHANNELS)*[complex(71)],\
+# (NOF_CHANNELS)*[complex(73)],\
+# (NOF_CHANNELS)*[complex(79)],\
+# (NOF_CHANNELS)*[complex(83)],\
+# (NOF_CHANNELS)*[complex(89)] ]
+
+#
+# List with 1-24 on the first channel rest channels 0, disadvantage is that cross correlations are sometimes the same (p.e. 2x2 is same as 1x4)
+# Advantage is that the values will stay small. this might be nicer when you make a plot to look for anomalies
+# the lines in list represents RT2 X, RT2 Y, RT3 X, RT3 Y ...... RTD X, RTD Y
+# and each line has NOF_CHANNELS inputchannels
+#
+example_list = [ (NOF_CHANNELS)*[complex(1)],\
+ (NOF_CHANNELS)*[complex(2)],\
+ (NOF_CHANNELS)*[complex(3)],\
+ (NOF_CHANNELS)*[complex(4)],\
+ (NOF_CHANNELS)*[complex(5)],\
+ (NOF_CHANNELS)*[complex(6)],\
+ (NOF_CHANNELS)*[complex(7)],\
+ (NOF_CHANNELS)*[complex(8)],\
+ (NOF_CHANNELS)*[complex(9)],\
+ (NOF_CHANNELS)*[complex(10)],\
+ (NOF_CHANNELS)*[complex(11)],\
+ (NOF_CHANNELS)*[complex(12)],\
+ (NOF_CHANNELS)*[complex(13)],\
+ (NOF_CHANNELS)*[complex(14)],\
+ (NOF_CHANNELS)*[complex(15)],\
+ (NOF_CHANNELS)*[complex(16)],\
+ (NOF_CHANNELS)*[complex(17)],\
+ (NOF_CHANNELS)*[complex(18)],\
+ (NOF_CHANNELS)*[complex(19)],\
+ (NOF_CHANNELS)*[complex(20)],\
+ (NOF_CHANNELS)*[complex(21)],\
+ (NOF_CHANNELS)*[complex(22)],\
+ (NOF_CHANNELS)*[complex(23)],\
+ (NOF_CHANNELS)*[complex(24)] ]
bg_list_complex = example_list
@@ -121,12 +155,14 @@ bg_list = []
for input_signal in bg_list_complex_rewired:
bg_list.append( concat_complex(input_signal, COMPLEX_WIDTH) )
+#disable the block generator
+bg.write_disable()
+
###############################################################################
# OVerwrite block gen settings
# . Limit RAM high address to 2*64 channels
###############################################################################
bg.write_block_gen_settings(samplesPerPacket=NOF_BG_SAMPLES_PER_BLOCK, blocksPerSync=800000, gapSize=256-NOF_BG_SAMPLES_PER_BLOCK, memLowAddr=0, memHighAddr=2*NOF_CHANNELS-1, BSNInit=0)
-
###############################################################################
# Write the 12 waveform RAMs, one stream at a time
# . Our block generators has 12 output streams that carry 24 interleaved correlator inputs streams.
@@ -137,4 +173,6 @@ for i in range(NOF_BG_OUTPUTS):
# Write this to the BG as RAM contents
bg.write_waveform_ram(ram_contents, i)
+#Enable the block generator
+bg.write_enable()