Pretty graphs and stable results

3 files changed, 78 insertions, 41 deletions

diff --git a/firmware/Run_analysis.ipynb b/firmware/Run_analysis.ipynb
index ce6bab4..3c4be49 100644
--- a/firmware/Run_analysis.ipynb
+++ b/firmware/Run_analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 193,
    "metadata": {
     "collapsed": true
    },
@@ -11,6 +11,7 @@
     "from matplotlib import pyplot as plt\n",
     "%matplotlib inline\n",
     "import numpy as np\n",
+    "import numpy.polynomial.polynomial as poly\n",
     "\n",
     "import itertools\n",
     "import sqlite3"
@@ -29,65 +30,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 222,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "def fetch_run(run_name):\n",
-    "    runs = db.execute('SELECT run_id FROM runs WHERE name LIKE ?', (run_name,)).fetchall()\n",
-    "    if len(runs) > 1:\n",
-    "        raise ValueError('Ambiguous run name {} matches run ids {}'.format(run_name, runs))\n",
+    "def fetch_run(name_or_id):\n",
+    "    if type(name_or_id) is str:\n",
+    "        runs = db.execute('SELECT run_id FROM runs WHERE name LIKE ?', (run_name,)).fetchall()\n",
+    "        if len(runs) > 1:\n",
+    "            raise ValueError('Ambiguous run name {} matches run ids {}'.format(run_name, runs))\n",
+    "    \n",
+    "        ((run_id,),), run_name = runs, name_or_id\n",
+    "    else:\n",
+    "        run_id, (run_name,) = name_or_id, db.execute('SELECT name FROM runs WHERE run_id == ?', (name_or_id,)).fetchone()\n",
     "    \n",
-    "    (run_id,), = runs\n",
     "    data = db.execute('''\n",
-    "        SELECT channel, duty_cycle, voltage, voltage_stdev FROM measurements WHERE run_id == ?\n",
+    "        SELECT channel, duty_cycle, voltage, voltage_stdev FROM measurements\n",
+    "        WHERE run_id == ?\n",
+    "        ORDER BY channel ASC, duty_cycle ASC;\n",
     "        ''', (run_id,)).fetchall()\n",
     "    \n",
+    "    _ch, cal_duty, *cal = data[0]\n",
+    "    assert cal_duty == 0\n",
     "    grouped = {ch: [(duty, volt, stdev) for _ch, duty, volt, stdev in data]\n",
-    "               for ch, data in itertools.groupby(data, lambda elem: elem[0])}\n",
-    "    return grouped"
+    "               for ch, data in itertools.groupby(data, lambda elem: elem[0]) if ch != -1}\n",
+    "    return (run_id, run_name), grouped, cal"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 243,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
-    "def plot_run(name):\n",
-    "    data = fetch_run(name)\n",
+    "def plot_run(name_or_id):\n",
+    "    (run_id, run_name), data, cal = fetch_run(name_or_id)\n",
     "    \n",
     "    fig, ax = plt.subplots(figsize=(12,8))\n",
-    "    ax.set_title('Run {}'.format(name))\n",
+    "    ax.set_title('Run {} id {}'.format(run_name, run_id))\n",
     "    ax.set_xscale('log')\n",
-    "    min_x = min(duty for vals in data.values() for duty, volt, stdev in vals if duty > 0)\n",
-    "    ax.set_xlim([min_x*0.9, 1.1])\n",
     "    ax.set_yscale('log')\n",
     "\n",
+    "    min_x = 1e9\n",
+    "    max_y = 0\n",
+    "    cal_volt, cal_stdev = cal\n",
     "    for ch in data:\n",
-    "        (cal_duty, cal_volt, _cal_stdev), *ch_data = data[ch]\n",
-    "        assert cal_duty == 0\n",
+    "        ch_data = data[ch]\n",
     "        duty, volt, stdev = zip(*ch_data)\n",
-    "        #volt = np.array(volt) - cal_volt\n",
-    "        ax.errorbar(duty, volt, yerr=stdev)"
+    "        vref = volt[0] - cal_volt\n",
+    "        volt = (np.array(volt) - cal_volt) / vref\n",
+    "        stdev = np.array(stdev) / vref\n",
+    "        max_y = max(max(volt), max_y)\n",
+    "        min_x = min(min(duty), min_x)\n",
+    "        ax.errorbar(duty, volt, yerr=stdev)\n",
+    "    \n",
+    "    # reuse latest duty cycles here\n",
+    "    ax.set_xticks(duty)\n",
+    "    ax.set_xticklabels([str(i) for i in range(len(duty))])\n",
+    "    ax.set_xlabel('bit index')\n",
+    "    ax.set_yticks([2**i for i in range(len(duty))])\n",
+    "    ax.set_yticklabels([str(2**i) for i in range(len(duty))])\n",
+    "    \n",
+    "    ax.set_xlim([min_x*0.9, 1.1])\n",
+    "    ax.set_ylim([0, max_y*1.1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 245,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": false
    },
    "outputs": [
     {
      "data": {
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Ite85HPZNpDKpB1lERKQEnmFtXHw8YwsqqN4W1syCZUw4OZB/TDjAgc5/xT2g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\nBwEZAAAA8CAgAwAAAB4EZAAAAMCDgAwAAAB4EJABAAAADwIyAAAA4EF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pbY40SOqUuXOLDSZ+/GOLXUlSfbLglbRIF14IM2YUBa8kSfWoKjO8ra2ttLS0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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7880f57ba5c0>"
+       "<matplotlib.figure.Figure at 0x7880f51ae3c8>"
       ]
      },
      "metadata": {},
@@ -95,7 +119,7 @@
     }
    ],
    "source": [
-    "plot_run('test02')"
+    "plot_run(37)"
    ]
   }
  ],
diff --git a/firmware/offset_test.py b/firmware/offset_test.py
index 5f87d20..fca565b 100644
--- a/firmware/offset_test.py
+++ b/firmware/offset_test.py
@@ -7,18 +7,18 @@ from datetime import datetime
 
 from pyBusPirateLite import Buspirate
 
-AVERAGE_READINGS = 16
-
 if __name__ == '__main__':
     import argparse
 
     parser = argparse.ArgumentParser()
     parser.add_argument('channels', type=str, help='olsndot channels to test, format: 0-3,5,7,8-10')
-    parser.add_argument('run_name')
+    parser.add_argument('run_name', nargs='?', default=None)
     parser.add_argument('olsndot_port', nargs='?', default='/dev/serial/by-id/usb-FTDI_FT232R_USB_UART_A50285BI-if00-port0')
-    parser.add_argument('buspirate_port', nargs='?', default='/dev/serial/by-id/usb-FTDI_FT232R_USB_UART_AD01W1RF-if00-port0') # FIXME
-    parser.add_argument('-d', '--database', nargs='?', default='results.sqlite3', help='sqlite3 database file to store results in')
-    parser.add_argument('-m', '--mac', type=int, nargs='?', default=0xDEBE10BB, help='olsndot MAC address')
+    parser.add_argument('buspirate_port', nargs='?', default='/dev/serial/by-id/usb-FTDI_FT232R_USB_UART_AD01W1RF-if00-port0')
+    parser.add_argument('-d', '--database', default='results.sqlite3', help='sqlite3 database file to store results in')
+    parser.add_argument('-m', '--mac', type=int, default=0xDEBE10BB, help='olsndot MAC address')
+    parser.add_argument('-w', '--wait', type=float, default=0.1, help='time to wait between samples in seconds')
+    parser.add_argument('-o', '--oversample', type=int, default=16, help='oversampling ratio')
     args = parser.parse_args()
 
     def parse_channels(channels):
@@ -55,25 +55,38 @@ if __name__ == '__main__':
 
     uut = Olsndot(args.mac)
     d = Driver(args.olsndot_port, devices=[uut])
-
+    uut.send_framebuf([0]*uut.nchannels)
     print('Connected to uut:', uut)
 
+    run_name = args.run_name
+    if args.run_name is None:
+        names = [ n[4:] for n, in db.execute('SELECT name FROM runs WHERE name LIKE "test%"').fetchall() ]
+        run_name = 'test{}'.format(1+max(int(n) if str.isnumeric(n) else 0 for n in names))
     with db:
         cur = db.cursor()
         cur.execute('INSERT INTO runs(name, uut_mac, timestamp) VALUES (?, ?, ?)',
-                (args.run_name, args.mac, time.time()))
+                (run_name, args.mac, time.time()))
         run_id = cur.lastrowid
-    print('Starting run {} at {:%y-%m-%d %H:%M:%S:%f}'.format(run_id, datetime.now()))
+    print('Starting run {} "{}" at {:%y-%m-%d %H:%M:%S:%f}'.format(run_id, run_name, datetime.now()))
     print('mac={:08x} channels={}'.format(args.mac, ','.join('{:02d}'.format(ch) for ch in channels)))
     print('[measurement id] " " [hex setpoint value] "(" [float duty cycle] ")" " " [reading (V)]')
 
+    # zero cal
+    uut.send_framebuf([0]*uut.nchannels)
+    time.sleep(args.wait)
+    readings = [ bp.adc_value for _ in range(args.oversample) ]
+    mean, stdev = statistics.mean(readings), statistics.stdev(readings)
+    cur.execute('''
+        INSERT INTO measurements (
+                run_id, channel, duty_cycle, voltage, voltage_stdev, timestamp
+            ) VALUES (?, -1, 0, ?, ?, ?)''',
+            (run_id, mean, stdev, time.time()))
+    print('Zero cal: {:5.4f}V stdev={:5.4f}V'.format(mean, stdev))
+
     for ch in channels:
-        for i in range(-1, uut.nbits):
+        for i in range(uut.nbits):
             fb = [0]*uut.nchannels
-            if i == -1:
-                val = 0
-            else:
-                val = 1<<i
+            val = 1<<i
             duty_cycle = val/(2**uut.nbits)
             extra_shift = 16-uut.nbits
             val <<= extra_shift
@@ -81,8 +94,8 @@ if __name__ == '__main__':
             fb[ch] = val
             uut.send_framebuf(fb)
             
-            time.sleep(0.2)
-            readings = [ bp.adc_value for _ in range(AVERAGE_READINGS) ]
+            time.sleep(args.wait)
+            readings = [ bp.adc_value for _ in range(args.oversample) ]
             mean, stdev = statistics.mean(readings), statistics.stdev(readings)
 
             with db:
diff --git a/firmware/results.sqlite3 b/firmware/results.sqlite3
new file mode 100644
index 0000000..8b61d71
--- /dev/null
+++ b/firmware/results.sqlite3