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authorjaseg <git@jaseg.net>2018-04-24 10:07:25 +0200
committerjaseg <git@jaseg.net>2018-04-24 10:07:25 +0200
commitca6a4353cfe9c36350bd9c3b6abe96eaa5d38e89 (patch)
tree489ba01b1529f5b33569f5486a2ad5c98e39775e
parent6936655d451a1d5b7160b0d06e235b313b3527d2 (diff)
downloadolsndot-ca6a4353cfe9c36350bd9c3b6abe96eaa5d38e89.tar.gz
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More pretty graphs, and initial correction calculation
-rw-r--r--firmware/Run_analysis.ipynb274
-rw-r--r--firmware/main.c30
-rw-r--r--firmware/offset_test.py51
-rw-r--r--firmware/olsndot.py4
-rw-r--r--firmware/results.sqlite3bin45056 -> 86016 bytes
5 files changed, 273 insertions, 86 deletions
diff --git a/firmware/Run_analysis.ipynb b/firmware/Run_analysis.ipynb
index 3c4be49..b900f71 100644
--- a/firmware/Run_analysis.ipynb
+++ b/firmware/Run_analysis.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 193,
+ "execution_count": 44,
"metadata": {
"collapsed": true
},
@@ -13,13 +13,15 @@
"import numpy as np\n",
"import numpy.polynomial.polynomial as poly\n",
"\n",
+ "import statistics\n",
+ "import warnings\n",
"import itertools\n",
"import sqlite3"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {
"collapsed": true
},
@@ -30,88 +32,198 @@
},
{
"cell_type": "code",
- "execution_count": 222,
+ "execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
- "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",
- " data = db.execute('''\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",
+ "def fetch_run(names_or_ids):\n",
+ " run_info, grouped, cals = [], {}, {}\n",
+ " for name_or_id in names_or_ids:\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",
+ " run_info.append((run_id, run_name))\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]) if ch != -1}\n",
- " return (run_id, run_name), grouped, cal"
+ " data = db.execute('''\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",
+ " _ch, cal_duty, *cal = data[0]\n",
+ " assert cal_duty == 0\n",
+ " cals[run_id] = cal\n",
+ " for ch, data in itertools.groupby(data, lambda elem: elem[0]):\n",
+ " if ch == -1: # skip cal data\n",
+ " continue\n",
+ " if ch in grouped:\n",
+ " warnings.warn('Duplicate data: Channel {} found in more than one run!'.format(ch))\n",
+ " grouped[ch] = [(duty, volt, stdev) for _ch, duty, volt, stdev in data]\n",
+ " return run_info, grouped, next(iter(cals.values())) # for now just use some random cal value"
]
},
{
"cell_type": "code",
- "execution_count": 243,
+ "execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
- "def plot_run(name_or_id):\n",
- " (run_id, run_name), data, cal = fetch_run(name_or_id)\n",
+ "def plot_run(figtitle, *names_or_ids, combine_plots=False):\n",
+ " run_info, data, cal = fetch_run(names_or_ids)\n",
" \n",
- " fig, ax = plt.subplots(figsize=(12,8))\n",
- " ax.set_title('Run {} id {}'.format(run_name, run_id))\n",
- " ax.set_xscale('log')\n",
- " ax.set_yscale('log')\n",
+ " if combine_plots:\n",
+ " rows, cols = 1, 1\n",
+ " else:\n",
+ " rows = (len(data)+1)//2\n",
+ " cols = 2 if len(data) > 1 else 1\n",
+ " fig, axs = plt.subplots(rows, cols, figsize=(16,5*max(2, rows)), squeeze=False)\n",
+ " fig.suptitle(figtitle)\n",
+ " if combine_plots:\n",
+ " axs = np.array([axs[0,0]] * len(names_or_ids))\n",
"\n",
- " min_x = 1e9\n",
- " max_y = 0\n",
" cal_volt, cal_stdev = cal\n",
- " for ch in data:\n",
+ " offsets = []\n",
+ " for ch, ax in zip(data, axs.flat):\n",
" ch_data = data[ch]\n",
" duty, volt, stdev = zip(*ch_data)\n",
- " 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",
+ " duty = np.array(duty) / duty[0]\n",
+ " volt = np.array(volt) - cal_volt\n",
+ " vref = volt[0]\n",
+ " stdev = np.array(stdev)\n",
+ " \n",
+ " max_y = max(volt)/vref\n",
+ " min_x, max_x = min(duty), max(duty)\n",
+ " \n",
+ " offx, slope = fit_coefs = poly.polyfit(duty, volt, 1)\n",
+ " fit_func = poly.polyval(duty, fit_coefs)\n",
+ " ax.errorbar(duty, volt/vref, yerr=stdev/vref)\n",
+ " ax.plot(duty, fit_func/vref)\n",
+ " \n",
+ " ax.set_xscale('log')\n",
+ " ax.set_yscale('log')\n",
+ " bit_offx = offx/slope\n",
+ " offsets.append(bit_offx)\n",
+ " print('Channel {} offset: {:6.3f}lsb'.format(ch, bit_offx))\n",
+ " ax.set_title('Channel {}, offset={:.3f}lsb'.format(ch, bit_offx))\n",
" \n",
- " ax.set_xlim([min_x*0.9, 1.1])\n",
- " ax.set_ylim([0, max_y*1.1])"
+ " # 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, max_x*1.1])\n",
+ " ax.set_ylim([0, max_y*1.1])\n",
+ " print('Offset statistics: mean={:.4f}lsb, stdev={:.4f}lsb'.format(\n",
+ " statistics.mean(offsets), statistics.stdev(offsets)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "def fetch_runs(*names):\n",
+ " return [run_id for name in names for run_id, in db.execute('''\n",
+ " SELECT DISTINCT runs.run_id\n",
+ " FROM runs JOIN measurements USING (run_id)\n",
+ " WHERE name LIKE ? AND channel != -1\n",
+ " ''', (name,)).fetchall() ]"
]
},
{
"cell_type": "code",
- "execution_count": 245,
+ "execution_count": 67,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Channel 28 offset: 2.643lsb\n",
+ "Channel 29 offset: 2.616lsb\n",
+ "Channel 30 offset: 2.633lsb\n",
+ "Channel 31 offset: 2.681lsb\n",
+ "Offset statistics: mean=2.6432lsb, stdev=0.0276lsb\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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Wvfsr+XKl9zukyybldhAWEQkxkxcu5o6+pbjVHmBrny9UaIqIiPhs+vLF3N6r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\n2RQRERERERGPU7MpIiIiIiIiHvf/AAPBHdD/NXvcAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x760551543fd0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_run('All channels, blue runs', *fetch_runs('green1', 'green2', 'green3', 'green4'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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SJKl2XHUVLLcc9O0LZ589rQfo2mvD0KHQvj0ssEBs0YFYtqv6yxlRSWVjyJB4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\nc2cixYPLAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x760558235630>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def bitslide(nbits, offx_lsb):\n",
+ " return [ sum((2**n + offx_lsb) if i&(2**n) else 0 for n in range(nbits)) for i in range(2**nbits) ]\n",
+ "\n",
+ "def plot_bitslide(data):\n",
+ " fig, (axl, axr) = plt.subplots(1, 2, figsize=(16, 6))\n",
+ " axl.plot(data)\n",
+ " axr.plot(data)\n",
+ " axr.set_yscale('log')\n",
+ " axr.set_xscale('log')\n",
+ " axl.set_xlim((0, len(data)))\n",
+ " axr.set_xlim((0, len(data)))\n",
+ "\n",
+ "plot_bitslide(bitslide(8, 2.5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
"data": {
- "image/png": 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pbY40SOqUuXOLDSZ+/GOLXUlSfbLglbRIF14IM2YUBa8kSfWoKjO8ra2ttLS0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\n9Ej5/NXAFzp4/5jv/P+llObN5z4J9Cv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k2vz613DvvfDNbw7f5oAD8suj2liISjlwWK4kSVJzXHZZ1iN61FFFJ9FYWIhK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\nAABJRU5ErkJggg==\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7880f51ae3c8>"
+ "<matplotlib.figure.Figure at 0x76055d996a58>"
]
},
"metadata": {},
@@ -119,7 +231,65 @@
}
],
"source": [
- "plot_run(37)"
+ "def cutoff_reference(nbits, offx_lsb, cutoff):\n",
+ " return [ sum((2**n + offx_lsb) if i&(2**n) else 0 for n in range(cutoff, nbits))\n",
+ " for i in range(2**nbits) ]\n",
+ "\n",
+ "plot_bitslide(cutoff_reference(8, 2.5, 3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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DYcUVYdNNoV27dDTa5puns7ollb76xmaX5krA6aenJbjPPQdt2mRd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\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x76055c402f28>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def improved_bitslide1(nbits, offx_lsb, cutoff):\n",
+ " bs = sorted(bitslide(cutoff, offx_lsb))\n",
+ " return [ sum((2**n + offx_lsb) if i&(2**n) else 0 for n in range(cutoff, nbits))\n",
+ " + bs[i%(2**cutoff)] for i in range(2**nbits) ]\n",
+ "\n",
+ "plot_bitslide(improved_bitslide1(8, 2.5, 5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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cm6X69dVXqdjs3x822yzrNKoVzohKKqrXXoObboJx49JM6LBhsPjiWaeSJElt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\nRTkQlSRJkiQV5UBUkiRJklTUv2fCWd3jCpNQAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x76055c5a7f28>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def improved_bitslide2(nbits, offx_lsb, cutoff):\n",
+ " bs = lambda x: min(bitslide(cutoff, offx_lsb), key=lambda y: abs(x-y))\n",
+ " return [ sum((2**n + offx_lsb) if i&(2**n) else 0 for n in range(cutoff, nbits))\n",
+ " + bs(i%(2**cutoff)) for i in range(2**nbits) ]\n",
+ "\n",
+ "plot_bitslide(improved_bitslide2(8, 2.5, 5))"
]
}
],
diff --git a/firmware/main.c b/firmware/main.c
index ec1e576..7b9a627 100644
--- a/firmware/main.c
+++ b/firmware/main.c
@@ -77,20 +77,20 @@ uint32_t sys_time_seconds = 0;
*/
static uint16_t timer_period_lookup[NBITS] = {
/* LSB here */
- A - C + (B<< 0),
- A - C + (B<< 1),
- A - C + (B<< 2),
- A - C + (B<< 3),
- A - C + (B<< 4),
- A - C + (B<< 5),
- A - C + (B<< 6),
- A - C + (B<< 7),
- A - C + (B<< 8),
- A - C + (B<< 9),
- A - C + (B<<10),
- A - C + (B<<11),
- A - C + (B<<12),
- A - C + (B<<13),
+ A + (B<< 0) - C,
+ A + (B<< 1) - C,
+ A + (B<< 2) - C,
+ A + (B<< 3) - C,
+ A + (B<< 4) - C,
+ A + (B<< 5) - C,
+ A + (B<< 6) - C,
+ A + (B<< 7) - C,
+ A + (B<< 8) - C,
+ A + (B<< 9) - C,
+ A + (B<<10) - C,
+ A + (B<<11) - C,
+ A + (B<<12) - C,
+ A + (B<<13) - C,
/* MSB here */
};
@@ -182,7 +182,7 @@ int main(void) {
/* Configure TIM1 for display strobe generation */
TIM1->CR1 = TIM_CR1_ARPE;
- TIM1->PSC = 1; /* Prescale by 2, resulting in a 16MHz timer frequency and 62.5ns timer step size. */
+ TIM1->PSC = 1; /* Prescale by 2, resulting in a 15MHz timer frequency and 66.7ns timer step size. */
/* CH2 - clear/!MR, CH3 - strobe/STCP */
TIM1->CCMR2 = (6<<TIM_CCMR2_OC3M_Pos) | TIM_CCMR2_OC3PE | (6<<TIM_CCMR2_OC4M_Pos);
TIM1->CCER |= TIM_CCER_CC3E | TIM_CCER_CC3NE | TIM_CCER_CC3P | TIM_CCER_CC3NP | TIM_CCER_CC4E;
diff --git a/firmware/offset_test.py b/firmware/offset_test.py
index fca565b..63fd6ba 100644
--- a/firmware/offset_test.py
+++ b/firmware/offset_test.py
@@ -11,25 +11,17 @@ 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', nargs='?', default=None)
+ parser.add_argument('run_name', nargs='?', default='auto')
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')
+ parser.add_argument('-c', '--channels', nargs='?', default='auto', help='olsndot channels to test, format: 0-3,5,7,8-10')
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')
+ parser.add_argument('-b', '--bits', type=int, default=None, help='number of bits to sample')
args = parser.parse_args()
- def parse_channels(channels):
- for spec in channels.split(','):
- if str.isnumeric(spec):
- yield int(spec)
- else:
- low, high = spec.split('-')
- yield from range(int(low), int(high)+1)
- channels = list(parse_channels(args.channels))
-
db = sqlite3.connect(args.database)
db.execute("""
CREATE TABLE IF NOT EXISTS runs (
@@ -55,18 +47,43 @@ 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))
+ if not str.isnumeric(args.run_name[-1]):
+ names = [ n[len(run_name):] for n, in db.execute(
+ 'SELECT name FROM runs WHERE name LIKE ?||"%"', (run_name,)).fetchall() ]
+ names.append('0') # in case we get no results
+ run_name += str(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 (?, ?, ?)',
(run_name, args.mac, time.time()))
run_id = cur.lastrowid
+
+ nbits = args.bits if args.bits is not None else uut.nbits
+
+ def parse_channels(channels):
+ for spec in channels.split(','):
+ if str.isnumeric(spec):
+ yield int(spec)
+ else:
+ low, high = spec.split('-')
+ yield from range(int(low), int(high)+1)
+ if args.channels == 'auto':
+ for i in range(uut.nchannels):
+ fb = [0]*uut.nchannels
+ fb[i] = 0xffff;
+ uut.send_framebuf(fb)
+ time.sleep(0.2)
+ if bp.adc_value > 0.5:
+ break;
+ else:
+ raise ValueError('Cannot find active channel')
+ channels = [i]
+ else:
+ channels = list(parse_channels(args.channels))
+
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)]')
@@ -84,7 +101,7 @@ if __name__ == '__main__':
print('Zero cal: {:5.4f}V stdev={:5.4f}V'.format(mean, stdev))
for ch in channels:
- for i in range(uut.nbits):
+ for i in range(nbits):
fb = [0]*uut.nchannels
val = 1<<i
duty_cycle = val/(2**uut.nbits)
@@ -105,7 +122,7 @@ if __name__ == '__main__':
run_id, channel, duty_cycle, voltage, voltage_stdev, timestamp
) VALUES (?, ?, ?, ?, ?, ?)''',
(run_id, ch, duty_cycle, mean, stdev, time.time()))
- print('{:08d} ch={} {:04x}({:6.5f}): {:5.4f} stdev {:5.4}'.format(
+ print('{:08d} ch={} {:04x}({:6.5f}): {:5.4f} stdev {:5.4f}'.format(
cur.lastrowid, ch, val, duty_cycle, mean, stdev))
uut.send_framebuf([0]*uut.nchannels)
diff --git a/firmware/olsndot.py b/firmware/olsndot.py
index 016ac3a..557226a 100644
--- a/firmware/olsndot.py
+++ b/firmware/olsndot.py
@@ -112,8 +112,8 @@ class Olsndot:
def __str__(self):
st = self.fetch_status()
- return '<Olsndot@{} {}.{} {}ch*{} up {}s vcc {:4.3}V temp {}C>'.format(
- self.addr, self.fw_ver, self.hw_ver, self.nchannels, self.channel_format,
+ return '<Olsndot {}.{}@{} {}ch*{} up {}s vcc {:4.3}V temp {}C>'.format(
+ self.fw_ver, self.hw_ver, self.addr, self.nchannels, self.channel_format,
st.uptime_s, st.vcc_mv/1000, st.temp_celsius)
@property
diff --git a/firmware/results.sqlite3 b/firmware/results.sqlite3
index 8b61d71..b7d9dd1 100644
--- a/firmware/results.sqlite3
+++ b/firmware/results.sqlite3
Binary files differ