diff --git a/firmware/inc/rf_tests.h b/firmware/inc/rf_tests.h
index 658ac1b..4193fcd 100644
--- a/firmware/inc/rf_tests.h
+++ b/firmware/inc/rf_tests.h
@@ -31,6 +31,7 @@ enum rf_tests {
   RF_TEST_APRS_HIGH_FM_TONE,
   RF_TEST_APRS_TONE,
   RF_TEST_TELEMETRY_TONE,
+  RF_TEST_RSID,
 };
 
 #define RF_TEST	RF_TEST_NONE
diff --git a/firmware/inc/si_trx.h b/firmware/inc/si_trx.h
index a53753d..460b514 100644
--- a/firmware/inc/si_trx.h
+++ b/firmware/inc/si_trx.h
@@ -27,12 +27,18 @@
 
 #include "samd20.h"
 
+enum si_filter_model {
+  SI_FILTER_DEFAULT,
+  SI_FILTER_APRS,
+  SI_FILTER_RSID
+};
+
 float si_trx_get_temperature(void);
 
 void si_trx_modem_set_deviation(uint32_t deviation);
 
 void si_trx_on(uint8_t modulation_type, uint32_t frequency,
-               uint16_t deviation, uint8_t power);
+                 uint16_t deviation, uint8_t power, enum si_filter_model filter);
 void si_trx_off(void);
 
 void si_trx_modem_set_offset(int16_t channel);
diff --git a/firmware/src/rf_tests.c b/firmware/src/rf_tests.c
index cbd056d..cc91597 100644
--- a/firmware/src/rf_tests.c
+++ b/firmware/src/rf_tests.c
@@ -39,12 +39,12 @@
 void telemetry_tone(void)
 {
   while (1) {
-    si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 0, 36);
+    si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 0, 36, SI_FILTER_DEFAULT);
     for (int i = 0; i < 200*1000; i++) {
       idle(IDLE_TELEMETRY_ACTIVE);
     }
     si_trx_off();
-    si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 0, 0x7f);
+    si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 0, 0x7f, SI_FILTER_DEFAULT);
     for (int i = 0; i < 200*1000; i++) {
       idle(IDLE_TELEMETRY_ACTIVE);
     }
@@ -54,12 +54,12 @@ void telemetry_tone(void)
 void aprs_tone(void)
 {
   while (1) {
-    si_trx_on(SI_MODEM_MOD_TYPE_CW, APRS_TEST_FREQUENCY, 0, 36);
+    si_trx_on(SI_MODEM_MOD_TYPE_CW, APRS_TEST_FREQUENCY, 0, 36, SI_FILTER_DEFAULT);
     for (int i = 0; i < 200*1000; i++) {
       idle(IDLE_TELEMETRY_ACTIVE);
     }
     si_trx_off();
-    si_trx_on(SI_MODEM_MOD_TYPE_CW, APRS_TEST_FREQUENCY, 0, 0x7f);
+    si_trx_on(SI_MODEM_MOD_TYPE_CW, APRS_TEST_FREQUENCY, 0, 0x7f, SI_FILTER_DEFAULT);
     for (int i = 0; i < 200*1000; i++) {
       idle(IDLE_TELEMETRY_ACTIVE);
     }
@@ -83,7 +83,7 @@ void aprs_high_fm_tone(void)
                              false);		/* Output Pin Enable	*/
 
   si_trx_on(SI_MODEM_MOD_TYPE_2GFSK, APRS_TEST_FREQUENCY,
-            AX25_DEVIATION, APRS_POWER);
+            AX25_DEVIATION, APRS_POWER, SI_FILTER_APRS);
   while (1) {
     idle(IDLE_TELEMETRY_ACTIVE);
   }
@@ -109,6 +109,23 @@ void aprs_test(void)
   }
 }
 
+/**
+ * RSID function for use during testing. Not for flight
+ */
+void rsid_test(void)
+{
+  while (1) {
+
+   telemetry_start_rsid(RSID_CONTESTIA_32_1000);
+
+//    Sleep wait for RSID
+   while (telemetry_active()) {
+     idle(IDLE_TELEMETRY_ACTIVE);
+   }
+
+    for (int i = 3*200*1000; i; i--);
+  }
+}
 
 void rf_tests(void)
 {
@@ -125,6 +142,9 @@ void rf_tests(void)
     case RF_TEST_TELEMETRY_TONE:
       telemetry_tone();
       break;
+    case RF_TEST_RSID:
+      rsid_test();
+      break;
     default:
       break;
   }
diff --git a/firmware/src/rsid.c b/firmware/src/rsid.c
index f379572..061dff1 100644
--- a/firmware/src/rsid.c
+++ b/firmware/src/rsid.c
@@ -100,6 +100,7 @@ void rsid_encode(rsid_code_t rsid_code, int8_t* rsid)
  */
 
 uint8_t rsid_index = 0xFE;
+uint8_t rsid_subtick;
 int8_t rsid[RSID_NSYMBOLS];
 
 #define MODEM_TONE_SPACING 7.805
@@ -118,6 +119,7 @@ void rsid_start(rsid_code_t rsid_code)
 {
   /* Start transmission */
   rsid_index = 0;
+  rsid_subtick = 0;
   rsid_encode(rsid_code, rsid);
 }
 
@@ -147,24 +149,35 @@ void rsid_tone(uint8_t tone)
   deviation = tone_offset - (float)channel;
 #endif
 
-  float duty_cycle = 0.5 + (deviation / 2); // FSK only provides a marginal improvement in performance!
+  float duty_cycle = 0.5 + deviation;
 
-  si_trx_switch_channel(channel);
+  si_trx_switch_channel(channel-2); /* -2 offset to line up with contestia signal */
   telemetry_gpio1_pwm_duty(duty_cycle);
 }
 
 /**
  * Called at the rsid baud rate
+ *
+ * We mantain the first tone longer than the others to give the radio
+ * time to start up. Oh so hacky but hey
  */
 uint8_t rsid_tick(void)
 {
+  if (rsid_index < RSID_NSYMBOLS || rsid_subtick) {
 
-  if (rsid_index < RSID_NSYMBOLS) {
+    /* Actual tick */
+    if (rsid_subtick == 0) {
+      /* Transmit this tone */
+      rsid_tone(rsid[rsid_index]);
+      rsid_index++;
+    }
 
-    /* Transmit this tone */
-    rsid_tone(rsid[rsid_index]);
+    rsid_subtick++;
+    if ((rsid_index-1 == 0 && rsid_subtick == 3) || /* First tone */
+        (rsid_index-1 != 0 && rsid_subtick == 2)) { /* Other tones */
+      rsid_subtick = 0;
+    }
 
-    rsid_index++;
     return 1;
   }
 
diff --git a/firmware/src/si_trx.c b/firmware/src/si_trx.c
index e02c013..17bd978 100644
--- a/firmware/src/si_trx.c
+++ b/firmware/src/si_trx.c
@@ -427,7 +427,7 @@ static float si_trx_set_frequency(uint32_t frequency, uint16_t deviation)
  * Resets the transceiver
  */
 void si_trx_reset(uint8_t modulation_type, uint32_t frequency,
-                  uint16_t deviation, uint8_t power)
+                  uint16_t deviation, uint8_t power, enum si_filter_model filter)
 {
   _si_trx_sdn_enable();  /* active high shutdown = reset */
 
@@ -459,22 +459,40 @@ void si_trx_reset(uint8_t modulation_type, uint32_t frequency,
   si_trx_set_frequency(frequency, deviation);
   si_trx_set_tx_power(power);
 
-  /**
-   * Modem tx filter coefficients for APRS
-   * 0dB @ 2.2kHz
-   * -6dB @ 1.2kHz (for pre-emphasis)
-   * < -30dB from 3.6KHz
-   */
+  /* Modem tx filter coefficients */
+  uint8_t rsid_si_coeff[] = {0x1, 0x4, 0x9, 0x11, 0x19, 0x22, 0x2a, 0x30, 0x31};
   uint8_t p_si_coeff[] = {0x6, 0x8, 0x1, 0xf2, 0xe4, 0xe7, 0xff, 0x1d, 0x2b};
-  si_trx_modem_set_tx_datarate(1600); /* Filter sampling rate 1600*10 = 16kHz */
-  si_trx_modem_tx_filter_coefficients(p_si_coeff);
+
+  switch (filter) {
+    case SI_FILTER_APRS:
+      /**
+       * Modem tx filter coefficients for APRS
+       * 0dB @ 2.2kHz
+       * -6dB @ 1.2kHz (for pre-emphasis)
+       * < -30dB from 3.6KHz
+       */
+      si_trx_modem_set_tx_datarate(1600); /* Filter sampling rate 1600*10 = 16kHz */
+      si_trx_modem_tx_filter_coefficients(p_si_coeff);
+      break;
+    case SI_FILTER_RSID:
+      /**
+       * Modem tx filter coefficients for RSID
+       * -6dB @ dc (for ±0.5 deviation max)
+       * < -50dB from 1.8KHz
+       */
+      si_trx_modem_set_tx_datarate(1600); /* Filter sampling rate 1600*10 = 16kHz */
+      si_trx_modem_tx_filter_coefficients(rsid_si_coeff);
+      break;
+    default:                      /* Just leave the defaults from startup in place */
+      break;
+  }
 
 
   /* RTTY from GPIO1 */
   si_trx_modem_set_modulation(SI_MODEM_MOD_DIRECT_MODE_SYNC, // ASYNC
-			      SI_MODEM_MOD_GPIO_1,
-			      SI_MODEM_MOD_SOURCE_DIRECT,
-			      modulation_type);
+                              SI_MODEM_MOD_GPIO_1,
+                              SI_MODEM_MOD_SOURCE_DIRECT,
+                              modulation_type);
 
   si_trx_state_tx_tune();
 }
@@ -483,9 +501,9 @@ void si_trx_reset(uint8_t modulation_type, uint32_t frequency,
  * Enables the radio and starts transmitting
  */
 void si_trx_on(uint8_t modulation_type, uint32_t frequency,
-               uint16_t deviation, uint8_t power)
+    uint16_t deviation, uint8_t power, enum si_filter_model filter)
 {
-  si_trx_reset(modulation_type, frequency, deviation, power);
+  si_trx_reset(modulation_type, frequency, deviation, power, filter);
   si_trx_start_tx(0);
 }
 /**
diff --git a/firmware/src/telemetry.c b/firmware/src/telemetry.c
index 1a1e156..e837c93 100644
--- a/firmware/src/telemetry.c
+++ b/firmware/src/telemetry.c
@@ -238,7 +238,8 @@ void telemetry_tick(void) {
 
       if (!radio_on) {
         /* Contestia: We use the modem offset to modulate */
-        si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER);
+        si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER,
+                  SI_FILTER_DEFAULT);
         radio_on = 1;
         contestia_preamble();
       }
@@ -259,7 +260,8 @@ void telemetry_tick(void) {
 
       if (!radio_on) {
         /* RTTY: We use the modem offset to modulate */
-        si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER);
+        si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER,
+                  SI_FILTER_DEFAULT);
         radio_on = 1;
         rtty_preamble();
       }
@@ -290,10 +292,9 @@ void telemetry_tick(void) {
         /* RSID: We PWM frequencies with the external pin */
         telemetry_gpio1_pwm_init();
 
-        si_trx_on(SI_MODEM_MOD_TYPE_2GFSK, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER);
+        si_trx_on(SI_MODEM_MOD_TYPE_2GFSK, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER,
+                  SI_FILTER_RSID);
         radio_on = 1;
-
-        return;
       }
 
       /* Do Tx */
@@ -304,6 +305,7 @@ void telemetry_tick(void) {
         telemetry_gpio1_pwm_deinit();
         return;
       }
+
       break;
 
     case TELEMETRY_APRS: /* ---- ---- APRS */
@@ -313,8 +315,8 @@ void telemetry_tick(void) {
         if (aprs_start() && _aprs_frequency) {
 
           /* Radio on */
-          si_trx_on(SI_MODEM_MOD_TYPE_2GFSK, _aprs_frequency,
-                    AX25_DEVIATION, APRS_POWER);
+          si_trx_on(SI_MODEM_MOD_TYPE_2GFSK, _aprs_frequency, AX25_DEVIATION,
+                    APRS_POWER, SI_FILTER_APRS);
           radio_on = 1;
         } else {
           /* Stop immediately */
@@ -333,7 +335,8 @@ void telemetry_tick(void) {
 
       if (!radio_on) { /* Turn on */
         /* Pips: Cw */
-        si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER);
+        si_trx_on(SI_MODEM_MOD_TYPE_CW, TELEMETRY_FREQUENCY, 1, TELEMETRY_POWER,
+                  SI_FILTER_DEFAULT);
         radio_on = 1;
 
       } else { /* Turn off */
@@ -430,7 +433,7 @@ void telemetry_init(void)
   rtty_timer_count	= timer0_get_count_value(RTTY_BITRATE);
   pips_timer_count	= timer0_get_count_value(PIPS_FREQUENCY);
   ax25_timer_count      = timer0_get_count_value(AX25_TICK_RATE);
-  rsid_timer_count	= timer0_get_count_value(RSID_SYMBOL_RATE);
+  rsid_timer_count	= timer0_get_count_value(RSID_SYMBOL_RATE*2); /* 2x Subtick */
 }
 
 
diff --git a/sim/si_fir_filter/si_fir_filter_rsid.ipynb b/sim/si_fir_filter/si_fir_filter_rsid.ipynb
new file mode 100644
index 0000000..8f9363d
--- /dev/null
+++ b/sim/si_fir_filter/si_fir_filter_rsid.ipynb
@@ -0,0 +1,534 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Reflects an array of coefficients about the last element                                                                                                                                                                                                     \n",
+      "def sym_fir_coeff(coeff):\n",
+      "    for i in range(len(coeff)-2, -1, -1):\n",
+      "        coeff.append(coeff[i])\n",
+      "\n",
+      "    return coeff\n",
+      "\n",
+      "# Normalises the si's 8-bit coefficients to 0 - 1                                                                                                                                                                                                              \n",
+      "def si_normalise_coeff(coeff):\n",
+      "    coeff = [x-0x100 if x > 0x7f else x for x in coeff]\n",
+      "    return [x/0x300 for x in coeff] # 0x300 seems to be the value required to get 0 gain at dc.. Not sure about this: Test!\n",
+      "\n",
+      "def si_coeff(coeff):\n",
+      "    norm = [int(x*0x300) for x in coeff]\n",
+      "    return [x+0x100 if x < 0 else x for x in norm]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "These are the default si coefficients"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Default si coefficients                                                                                                                                                                                                                                      \n",
+      "si_default_fir_coeff = [0x1, 0x3, 0x8, 0x11, 0x21, 0x36, 0x4d, 0x60, 0x67]\n",
+      "si_fir_coeff = si_normalise_coeff(sym_fir_coeff(si_default_fir_coeff))\n",
+      "print si_fir_coeff"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0.0013020833333333333, 0.00390625, 0.010416666666666666, 0.022135416666666668, 0.04296875, 0.0703125, 0.10026041666666667, 0.125, 0.13411458333333334, 0.125, 0.10026041666666667, 0.0703125, 0.04296875, 0.022135416666666668, 0.010416666666666666, 0.00390625, 0.0013020833333333333]\n"
+       ]
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Setup graphs"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline\n",
+      "from scipy import signal\n",
+      "from pylab import *\n",
+      "from __future__ import division\n",
+      "import matplotlib.pyplot as plt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "w,h=signal.freqz(si_fir_coeff,1) # Compute impulse response                                                                                                                                                                                                       \n",
+      "\n",
+      "plt.plot(w/pi,20*log10(abs(h)))\n",
+      "plt.grid()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ufCUwZ84c9OnTB9OmTUNlZSV27NgBADh58iR69epV/Tyj0QhTzalzRORQnp5y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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f62308f7cd0>"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Passband filter"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\"\"\"\n",
+      "sampling frequency: 16000 Hz\n",
+      "\n",
+      "* 0 Hz - 500 Hz\n",
+      "  gain = 0\n",
+      "  desired attenuation = -40 dB\n",
+      "  actual attenuation = -33.20090552393641 dB\n",
+      "\n",
+      "* 1200 Hz - 2500 Hz\n",
+      "  gain = 1\n",
+      "  desired ripple = 5 dB\n",
+      "  actual ripple = 7.628523622719886 dB\n",
+      "\n",
+      "* 3500 Hz - 8000 Hz\n",
+      "  gain = 0\n",
+      "  desired attenuation = -40 dB\n",
+      "  actual attenuation = -33.20090552393641 dB\n",
+      "\"\"\"\n",
+      "\n",
+      "p_fir_coeff = [\n",
+      "0.03315273058205705,\n",
+      "0.04369271452900486,\n",
+      "0.010060937885865288,\n",
+      "-0.07363298137328711,\n",
+      "-0.14853046126761885,\n",
+      "-0.13375882936132458,\n",
+      "-0.007516054328839414,\n",
+      "0.15276142842227725,\n",
+      "0.2256656942570718,\n",
+      "0.15276142842227725,\n",
+      "-0.007516054328839414,\n",
+      "-0.13375882936132458,\n",
+      "-0.14853046126761885,\n",
+      "-0.07363298137328711,\n",
+      "0.010060937885865288,\n",
+      "0.04369271452900486,\n",
+      "0.03315273058205705,\n",
+      "]\n",
+      "\n",
+      "w,h=signal.freqz([x/2 for x in p_fir_coeff],1) # Compute impulse response                                                                                                                                                                                                       \n",
+      "\n",
+      "plt.plot(w/pi,20*log10(abs(h)))\n",
+      "plt.grid()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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ZUntOREQEynTsM7d6NTBoENChg24mEAoxcCBQXAycOAF07Ki3NQThW3gM9klJSaioqGiw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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f62308ab190>"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Another filter design, low pass\n",
+      "rsid_fir_coeff = [x*5.2 for x in [\n",
+      "0.000306, \n",
+      "0.001117, \n",
+      "0.002461, \n",
+      "0.004306, \n",
+      "0.006491, \n",
+      "0.008734, \n",
+      "0.010690, \n",
+      "0.012026, \n",
+      "0.012500, \n",
+      "0.012026, \n",
+      "0.010690, \n",
+      "0.008734, \n",
+      "0.006491, \n",
+      "0.004306, \n",
+      "0.002461, \n",
+      "0.001117, \n",
+      "0.000306]]\n",
+      "\n",
+      "w,h=signal.freqz(rsid_fir_coeff,1) # Compute impulse response                                                                                                                                                                                                       \n",
+      "\n",
+      "plt.plot(w/pi,20*log10(abs(h)))\n",
+      "plt.grid()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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oWT1+PJCa2rSqpdkIGBwl5kBraQRsxWJqeAKu6qJdO7FdghGMgP0baF6eMm/b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\nYtgIMAzDeDFsBBiGYbwYNgIMwzBezP8HM2U9/JR3fLEAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f623070ea50>"
+       ]
+      }
+     ],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Convert to si hex"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "rsid_si_coeff_values = si_coeff(rsid_fir_coeff)[0:9]\n",
+      "\n",
+      "print rsid_si_coeff_values\n",
+      "print \", \".join(hex(n) for n in rsid_si_coeff_values)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 4, 9, 17, 25, 34, 42, 48, 49]\n",
+        "0x1, 0x4, 0x9, 0x11, 0x19, 0x22, 0x2a, 0x30, 0x31\n"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "and back again (to simulate precision loss)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "rsid_si_fir_coeff = si_normalise_coeff(sym_fir_coeff(rsid_si_coeff_values))\n",
+      "print rsid_si_fir_coeff"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0.0013020833333333333, 0.005208333333333333, 0.01171875, 0.022135416666666668, 0.032552083333333336, 0.044270833333333336, 0.0546875, 0.0625, 0.06380208333333333, 0.0625, 0.0546875, 0.044270833333333336, 0.032552083333333336, 0.022135416666666668, 0.01171875, 0.005208333333333333, 0.0013020833333333333]\n"
+       ]
+      }
+     ],
+     "prompt_number": 72
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "w,h=signal.freqz(rsid_si_fir_coeff,1) # Compute impulse response                                                                                                                                                                                                       \n",
+      "\n",
+      "plt.plot(w/pi,20*log10(abs(h)))\n",
+      "plt.grid()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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AOnU///zK73vhBdm4/uIvGFeKRfPmsgzI1KlXvsbGjeZvieprTAI24awBGFUT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\nAkRENvb/AY0p+cIYNG7VAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6230b704d0>"
+       ]
+      }
+     ],
+     "prompt_number": 73
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Test RSID data"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "rsid_symbol_rate = 11025.0/1024\n",
+      "sqw_frequency = 2000 # approx 2kHz\n",
+      "\n",
+      "# The ratio between the rsid frequency step and the channel frequency step\n",
+      "# In our case the channel frequency step is bigger than the rsid frequency step so we try to \"interpolate\"\n",
+      "ratio = rsid_symbol_rate/7.805\n",
+      "\n",
+      "rsid_data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 5, 2, 12, 0, 11, 4, 6, 2, 10, 11,1 ,8, 12, 13, 7, 4, 15, 12]\n",
+      "\n",
+      "# The number of sqw periods per symbol period\n",
+      "sqw_periods = int(round(sqw_frequency/rsid_symbol_rate))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 74
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "tones = [(round(t), t-round(t)) for t in [x*ratio for x in rsid_data]]\n",
+      "print tones"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[(0.0, 0.0), (1.0, 0.3794492713004485), (3.0, -0.24110145739910305), (4.0, 0.13834781390134587), (6.0, -0.4822029147982061), (7.0, -0.10275364349775806), (8.0, 0.27669562780269175), (10.0, -0.3438551008968602), (11.0, 0.03559417040358781), (12.0, 0.41504344170403584), (14.0, -0.20550728699551613), (15.0, 0.17394198430493368), (17.0, -0.4466087443946165), (18.0, -0.06715947309416848), (19.0, 0.31228979820627956), (21.0, -0.3082609304932724), (7.0, -0.10275364349775806), (3.0, -0.24110145739910305), (17.0, -0.4466087443946165), (0.0, 0.0), (15.0, 0.17394198430493368), (6.0, -0.4822029147982061), (8.0, 0.27669562780269175), (3.0, -0.24110145739910305), (14.0, -0.20550728699551613), (15.0, 0.17394198430493368), (1.0, 0.3794492713004485), (11.0, 0.03559417040358781), (17.0, -0.4466087443946165), (18.0, -0.06715947309416848), (10.0, -0.3438551008968602), (6.0, -0.4822029147982061), (21.0, -0.3082609304932724), (17.0, -0.4466087443946165)]\n"
+       ]
+      }
+     ],
+     "prompt_number": 75
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def get_frac_signal(sr=16000):\n",
+      "    signal = []\n",
+      "    sqw_width=int(round(sr/sqw_frequency))\n",
+      "    \n",
+      "    #print \"Width: \"+str(sqw_width)\n",
+      "    \n",
+      "    for t in tones:\n",
+      "        (inte, frac) = t\n",
+      "        for i in range(sqw_periods):\n",
+      "            high = int(round(sqw_width*(0.5+frac)))    #(1+frac)/2)\n",
+      "            #print \"High: \"+str(high)\n",
+      "            for j in range(high):\n",
+      "                signal.append(1)\n",
+      "            for j in range(sqw_width-high):\n",
+      "                signal.append(-1)\n",
+      "    return signal\n",
+      "\n",
+      "def get_inte_signal(sr=16000):\n",
+      "    signal = []\n",
+      "    \n",
+      "    for t in tones:\n",
+      "        (inte, frac) = t\n",
+      "        for i in range(int(round(sr/rsid_symbol_rate))):\n",
+      "            signal.append(inte)\n",
+      "            \n",
+      "    return signal\n",
+      "\n",
+      "def get_perfect_signal(sr=16000):\n",
+      "    signal = []\n",
+      "    \n",
+      "    for t in rsid_data:\n",
+      "        for i in range(int(round(sr/rsid_symbol_rate))):\n",
+      "            signal.append(t*ratio)\n",
+      "            \n",
+      "    return signal"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 76
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Returns rsid-like signal as a list of modem output channels over time\n",
+      "def get_rsid_channel_series(sr=16000):\n",
+      "    inte = get_inte_signal(sr)\n",
+      "    frac = get_frac_signal(sr)\n",
+      "    return [x + y for x, y in zip(inte,frac)]\n",
+      "    \n",
+      "\n",
+      "channel_data = get_rsid_channel_series(16000)\n",
+      "plt.plot(channel_data)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 77,
+       "text": [
+        "[<matplotlib.lines.Line2D at 0x7f6230755150>]"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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IPn364MCBAxBC4PXXX8eDDz7YYV1vv/02CgoKLGuHEALFxcUYPnw4nnzySc+1\n7+zZs+GZD1999RW2b9+OQCDgmfYtW7YMNTU1qKqqwsaNG3Hvvffi9ddf90z7Ll26hAsXLgAAmpqa\nsG3bNuTk5HimfYmJiUhOTsbx48cBADt27EBWVhaKiorsa1/3Tk/E9te//lVkZGSItLQ0sWzZMrM2\nI8WMGTNEUlKSiI+PF36/X7zyyivi3LlzoqCgQKSnp4vCwkLxn//8J7z8L3/5S5GWliaGDh0qysvL\nw49/9NFHIjs7W6SlpYnHH388/Pjly5fFD3/4QzFkyBAxZswYUVVVZVnb9uzZIzRNE7m5uSIvL0/k\n5eWJrVu3eqZ9H3/8sQgEAiI3N1fk5OSIlStXCiGEZ9p3vWAwGJ6945X2nTx5UuTm5orc3FyRlZUV\nzgqvtE8IISorK8XIkSPFiBEjxJQpU0RjY6Ot7ePFWURECuF/l0hEpBCGPhGRQhj6REQKYegTESmE\noU9EpBCGPhGRQhj6REQKYegTESnk/wEQouHRueKR6AAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6230a39390>"
+       ]
+      }
+     ],
+     "prompt_number": 77
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## What happens when we add a filter"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Which fir filter to use\n",
+      "fir = rsid_si_fir_coeff"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 78
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Returns rsid-like signal as a list of modem output channels over time\n",
+      "def get_rsid_channel_series_filtered_frac(sr=16000):\n",
+      "    inte = get_inte_signal(sr)\n",
+      "    frac = get_frac_signal(sr)\n",
+      "    filtered_frac = convolve(frac, fir, mode='valid')\n",
+      "    return [x+y for x, y in zip(inte,filtered_frac)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 79
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sr = 16000\n",
+      "\n",
+      "filtered_channel_data = get_rsid_channel_series_filtered_frac(sr)\n",
+      "plt.plot(filtered_channel_data)\n",
+      "\n",
+      "perfect = get_perfect_signal(sr)\n",
+      "plt.plot(perfect)\n",
+      "\n",
+      "inte = get_inte_signal(sr)\n",
+      "#plt.plot(inte)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7f6230509090>"
+       ]
+      }
+     ],
+     "prompt_number": 80
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file