diff options
| -rw-r--r-- | doc/paper/rotohsm_paper.pdf | bin | 1190616 -> 1191965 bytes | |||
| -rw-r--r-- | doc/paper/rotohsm_paper.tex | 2 | ||||
| -rw-r--r-- | doc/paper/rotohsm_tech_report.pdf | bin | 111459 -> 112307 bytes | |||
| -rw-r--r-- | prototype/sensor-analysis/Accelerometer Data Analysis.ipynb | 4632 |
4 files changed, 4376 insertions, 258 deletions
diff --git a/doc/paper/rotohsm_paper.pdf b/doc/paper/rotohsm_paper.pdf Binary files differindex f0ad0b6..2a52783 100644 --- a/doc/paper/rotohsm_paper.pdf +++ b/doc/paper/rotohsm_paper.pdf diff --git a/doc/paper/rotohsm_paper.tex b/doc/paper/rotohsm_paper.tex index e2f3928..bb9beaa 100644 --- a/doc/paper/rotohsm_paper.tex +++ b/doc/paper/rotohsm_paper.tex @@ -485,6 +485,8 @@ which allows us to reliably transfer several tens of bytes in each direction dur speed of rotation. As a result of our prototype experiments, we consider a larger-scale implementation of the inertial HSM concept practical. +\section{Using accelerometers as rotation sensors} + \begin{figure} \center \includegraphics[width=8cm]{prototype_early_comms_small.jpg} diff --git a/doc/paper/rotohsm_tech_report.pdf b/doc/paper/rotohsm_tech_report.pdf Binary files differindex d87bc8e..780bfb0 100644 --- a/doc/paper/rotohsm_tech_report.pdf +++ b/doc/paper/rotohsm_tech_report.pdf diff --git a/prototype/sensor-analysis/Accelerometer Data Analysis.ipynb b/prototype/sensor-analysis/Accelerometer Data Analysis.ipynb index b7cf70d..23f31b8 100644 --- a/prototype/sensor-analysis/Accelerometer Data Analysis.ipynb +++ b/prototype/sensor-analysis/Accelerometer Data Analysis.ipynb @@ -45,41 +45,18 @@ }, { "cell_type": "code", - "execution_count": 706, - "metadata": {}, + "execution_count": 836, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last run was ID #33 with 3178 packets total, 125 distinct over 99.94346904754639s\n" + "Loading run #28 with 4918 packets total, 178 distinct over 161.5061011314392s\n" ] - } - ], - "source": [ - "num_packets, = db.execute('SELECT COUNT(*) FROM packets WHERE run_id=?', (last_run,)).fetchone()\n", - "num_packets_distinct, = db.execute('SELECT COUNT(*) FROM (SELECT DISTINCT data FROM packets WHERE run_id=?)', (last_run,)).fetchone()\n", - "timespan_start, timespan_end = db.execute('SELECT MIN(timestamp_us)/1e6, MAX(timestamp_us)/1e6 FROM packets WHERE run_id=?', (last_run,)).fetchone()\n", - "timespan = timespan_end - timespan_start\n", - "print(f'Last run was ID #{last_run} with {num_packets} packets total, {num_packets_distinct} distinct over {timespan}s')" - ] - }, - { - "cell_type": "code", - "execution_count": 707, - "metadata": {}, - "outputs": [], - "source": [ - "timestamps = db.execute('SELECT timestamp_us/1e6 FROM packets WHERE run_id=? ORDER BY timestamp_us', (last_run,)).fetchall()\n", - "timestamps = [ ts - timespan_start for ts, in timestamps ]\n", - "deltas = [ b-a for a, b in zip(timestamps[:-1], timestamps[1:]) ]" - ] - }, - { - "cell_type": "code", - "execution_count": 708, - "metadata": {}, - "outputs": [ + }, { "data": { "application/javascript": [ @@ -1041,7 +1018,7 @@ { "data": { "text/html": [ - "<img 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\" 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" @@ -1051,133 +1028,107 @@ "output_type": "display_data" }, { - "data": { - "text/plain": [ - "[<matplotlib.lines.Line2D at 0x7f3dff0d7190>]" - ] - }, - "execution_count": 708, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(deltas)" - ] - }, - { - "cell_type": "code", - "execution_count": 709, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Packet length: 40\n" - ] - } - ], - "source": [ - "packet_lengths = db.execute('SELECT LENGTH(data) FROM packets WHERE run_id=? GROUP BY LENGTH(data)', (last_run,)).fetchall()\n", - "assert len(packet_lengths) == 1\n", - "packet_len, = packet_lengths[0]\n", - "print('Packet length:', packet_len)" - ] - }, - { - "cell_type": "code", - "execution_count": 710, - "metadata": {}, - "outputs": [ - { "name": "stdout", "output_type": "stream", "text": [ - "Very approximate lower bound on baudrate: 129032.25806451612 bd\n" + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 611 ... 813\n" ] } ], "source": [ - "#approx_baudrate = 1.0 / (np.mean([ x for x in deltas if x < interval*0.02]) / (packet_len*10))\n", - "approx_baudrate = 1.0 / (0.0031 / (packet_len*10))\n", - "print(f'Very approximate lower bound on baudrate: {approx_baudrate} bd')" - ] - }, - { - "cell_type": "code", - "execution_count": 711, - "metadata": {}, - "outputs": [], - "source": [ "def decode_packet(packet):\n", " seq, *data, _crc = struct.unpack('<I16hI', packet)\n", " return (seq, tuple(data))\n", "\n", - "packets = sorted([ decode_packet(data) for data, in db.execute('SELECT data FROM packets WHERE run_id=?', (last_run,)) ])" + "def plot_measurements(ax, t, y):\n", + " ax.grid()\n", + " ax.plot(t, y / mems_lsb_per_g, color='darkblue', alpha=0.2)\n", + " #ax.plot(ts, scipy.signal.savgol_filter(reassembled_values / mems_lsb_per_g, 21, 2) )\n", + " sos = scipy.signal.butter(8, 0.5, 'lp', fs=10, output='sos')\n", + " filtered = scipy.signal.sosfiltfilt(sos, y / mems_lsb_per_g)\n", + " ax.plot(t, filtered, color='darkblue')\n", + " \n", + " ax.set_ylabel(r'$a\\; [g]$')\n", + " secax_y = ax.secondary_yaxis(\n", + " 'right', functions=(g_to_ms, ms_to_g))\n", + " secax_y.set_ylabel(r'$a\\; [ms^{-1}]$')\n", + "\n", + " formatter = ticker.FuncFormatter(lambda tick, _pos: f'{int(tick):02d}:{tick*60%60:02.0f}')\n", + " ax.xaxis.set_major_formatter(formatter)\n", + "\n", + "def load_run(run_id, plot=True):\n", + " num_packets, = db.execute('SELECT COUNT(*) FROM packets WHERE run_id=?', (run_id,)).fetchone()\n", + " num_packets_distinct, = db.execute('SELECT COUNT(*) FROM (SELECT DISTINCT data FROM packets WHERE run_id=?)', (run_id,)).fetchone()\n", + " timespan_start, timespan_end = db.execute('SELECT MIN(timestamp_us)/1e6, MAX(timestamp_us)/1e6 FROM packets WHERE run_id=?', (run_id,)).fetchone()\n", + " timespan = timespan_end - timespan_start\n", + " print(f'Loading run #{run_id} with {num_packets} packets total, {num_packets_distinct} distinct over {timespan}s')\n", + " \n", + " packet_timestamps = db.execute('SELECT timestamp_us/1e6 FROM packets WHERE run_id=? ORDER BY timestamp_us', (run_id,)).fetchall()\n", + " packet_timestamps = [ ts - timespan_start for ts, in packet_timestamps ]\n", + " packet_delays = [ b-a for a, b in zip(packet_timestamps[:-1], packet_timestamps[1:]) ]\n", + " \n", + " if plot:\n", + " fig, (ax1, ax2) = plt.subplots(2)\n", + " ax1.grid()\n", + " ax1.plot(packet_delays)\n", + " \n", + " packet_lengths = db.execute('SELECT LENGTH(data) FROM packets WHERE run_id=? GROUP BY LENGTH(data)', (run_id,)).fetchall()\n", + " assert len(packet_lengths) == 1\n", + " packet_len, = packet_lengths[0]\n", + " print('Packet length:', packet_len)\n", + " \n", + " #approx_baudrate = 1.0 / (np.mean([ x for x in deltas if x < interval*0.02]) / (packet_len*10))\n", + " approx_baudrate = 1.0 / (0.0031 / (packet_len*10))\n", + " print(f'Very approximate lower bound on baudrate: {approx_baudrate} bd')\n", + " \n", + " packets = sorted([ decode_packet(data) for data, in db.execute('SELECT data FROM packets WHERE run_id=?', (run_id,)) ])\n", + " \n", + " # group packets by sequence number\n", + " by_seq = { k: list(g) for k, g in itertools.groupby(packets, key=lambda x: x[0]) }\n", + " for seq, le_packets in by_seq.items():\n", + " # make sure we only ever have one version of a packet with a particular sequence number (no CRC collisions)\n", + " if len(set(le_packets)) > 1:\n", + " # In test_run.sqlite3 run 2 this happens to coincide with the time I intentionally bumped the rotor... ?\n", + " warnings.warn(f'BUG: Duplicate sequence number {seq} for {len(set(le_packets))} payloads!')\n", + " print('BUG: Duplicate sequence number')\n", + " print('Sequence number:', seq)\n", + " for seq, data in set(le_packets):\n", + " print(' ', data)\n", + " \n", + " seqs = list(by_seq)\n", + " print(f'Sequence number range: {min(seqs)} ... {max(seqs)}')\n", + " \n", + " # FIXME this is only approximate, doesn't consider sequence numbers properly!!!\n", + " # Negate values: Our sensor is mounted such that -X points outwards,\n", + " # so by negating we get larger centrifugal force -> higher value\n", + " y = np.array([ -val for (_seq, values), *_rest in by_seq.values() for val in values[:8] ])\n", + " t = np.arange(0, len(y)) / sampling_rate / 60\n", + " if plot:\n", + " plot_measurements(ax2, t, y)\n", + " return t, y, packet_delays, packet_timestamps\n", + " \n", + "load_run(28);" ] }, { "cell_type": "code", - "execution_count": 712, + "execution_count": 1019, "metadata": { "scrolled": false }, - "outputs": [], - "source": [ - "# group packets by sequence number\n", - "by_seq = { k: list(g) for k, g in itertools.groupby(packets, key=lambda x: x[0]) }\n", - "for seq, le_packets in by_seq.items():\n", - " # make sure we only ever have one version of a packet with a particular sequence number (no CRC collisions)\n", - " if len(set(le_packets)) > 1:\n", - " # In test_run.sqlite3 run 2 this happens to coincide with the time I intentionally bumped the rotor... ?\n", - " warnings.warn(f'BUG: Duplicate sequence number {seq} for {len(set(le_packets))} payloads!')\n", - " print('BUG: Duplicate sequence number')\n", - " print('Sequence number:', seq)\n", - " for seq, data in set(le_packets):\n", - " print(' ', data)" - ] - }, - { - "cell_type": "code", - "execution_count": 713, - "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sequence number range: 35 ... 161\n" + "Loading run #40 with 4762 packets total, 682 distinct over 572.8108429908752s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 739 ... 1457\n" ] - } - ], - "source": [ - "seqs = list(by_seq)\n", - "print(f'Sequence number range: {min(seqs)} ... {max(seqs)}')" - ] - }, - { - "cell_type": "code", - "execution_count": 714, - "metadata": {}, - "outputs": [], - "source": [ - "# FIXME this is only approximate, doesn't consider sequence numbers properly!!!\n", - "# Negate values: Our sensor is mounted such that -X points outwards,\n", - "# so by negating we get larger centrifugal force -> higher value\n", - "reassembled_values = np.array([ -val for (_seq, values), *_rest in by_seq.values() for val in values[:8] ])" - ] - }, - { - "cell_type": "code", - "execution_count": 715, - "metadata": { - "scrolled": false - }, - "outputs": [ + }, { "data": { "application/javascript": [ @@ -2139,7 +2090,7 @@ { "data": { "text/html": [ - "<img 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\" 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" @@ -2152,76 +2103,68 @@ "name": "stdout", "output_type": "stream", "text": [ - "Found sensor offset: 1.00 g / 9.81 m/s^2\n", + "Found sensor offset: 0.57 g / 5.55 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 3.12 Hz:\n", " Theory: 2.16 g / 21.14 m/s^2\n", - " Measurement: 55.57 g / 544.9912192549019 m/s^2\n", - " Rel. Error: -96.12 %\n", - " Abs. Error: -53.42 g / -523.85 m/s^2\n", + " Measurement: 1.90 g / 18.586194313627637 m/s^2\n", + " Rel. Error: 13.72 %\n", + " Abs. Error: 0.26 g / 2.55 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 5.55 Hz:\n", " Theory: 6.82 g / 66.88 m/s^2\n", - " Measurement: -0.58 g / -5.674407202881151 m/s^2\n", - " Rel. Error: -1278.66 %\n", - " Abs. Error: 7.40 g / 72.56 m/s^2\n", + " Measurement: 6.60 g / 64.67817413725541 m/s^2\n", + " Rel. Error: 3.41 %\n", + " Abs. Error: 0.22 g / 2.20 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 8.20 Hz:\n", " Theory: 14.89 g / 146.00 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 14.82 g / 145.35641444809548 m/s^2\n", + " Rel. Error: 0.44 %\n", + " Abs. Error: 0.07 g / 0.64 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 10.20 Hz:\n", " Theory: 23.04 g / 225.90 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 23.11 g / 226.6766272456559 m/s^2\n", + " Rel. Error: -0.34 %\n", + " Abs. Error: -0.08 g / -0.77 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 12.50 Hz:\n", " Theory: 34.60 g / 339.27 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 34.80 g / 341.26606334638393 m/s^2\n", + " Rel. Error: -0.59 %\n", + " Abs. Error: -0.20 g / -2.00 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 15.60 Hz:\n", " Theory: 53.88 g / 528.41 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 51.75 g / 507.51233891199473 m/s^2\n", + " Rel. Error: 4.12 %\n", + " Abs. Error: 2.13 g / 20.90 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 19.20 Hz:\n", " Theory: 81.62 g / 800.43 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 82.09 g / 805.0345119987545 m/s^2\n", + " Rel. Error: -0.57 %\n", + " Abs. Error: -0.47 g / -4.60 m/s^2\n", "\n", - "Centrifugal acceleration at 6.49 Hz:\n", + "Centrifugal acceleration at 11.60 Hz:\n", " Theory: 29.79 g / 292.17 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 29.71 g / 291.36536349393447 m/s^2\n", + " Rel. Error: 0.28 %\n", + " Abs. Error: 0.08 g / 0.81 m/s^2\n", "\n", "Centrifugal acceleration at 6.49 Hz:\n", " Theory: 9.33 g / 91.46 m/s^2\n", - " Measurement: nan g / nan m/s^2\n", - " Rel. Error: nan %\n", - " Abs. Error: nan g / nan m/s^2\n", + " Measurement: 9.21 g / 90.28568973827844 m/s^2\n", + " Rel. Error: 1.30 %\n", + " Abs. Error: 0.12 g / 1.17 m/s^2\n", "\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<ipython-input-715-bd4d0359155e>:46: RuntimeWarning: Mean of empty slice.\n", - " ivl_avg = (reassembled_values / mems_lsb_per_g)[idx].mean()\n", - "/usr/lib/python3.9/site-packages/numpy/core/_methods.py:188: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] } ], "source": [ + "t, y, _1, _2 = load_run(40, plot=False)\n", + "\n", "sampling_rate = 10 # sps, set in firmware\n", "mems_lsb_per_g = 68 # LSBs per 1g for our accelerometer\n", "\n", @@ -2230,28 +2173,13 @@ "\n", "fig, ax = plt.subplots()\n", "#ax.axvspan(ivl_start/60/sampling_rate, ivl_end/60/sampling_rate, color='orange', alpha=0.5)\n", - "\n", - "ax.grid()\n", - "\n", - "ts = np.arange(0, len(reassembled_values)) / sampling_rate / 60\n", - "ax.plot(ts, reassembled_values / mems_lsb_per_g, color='darkblue', alpha=0.2)\n", - "#ax.plot(ts, scipy.signal.savgol_filter(reassembled_values / mems_lsb_per_g, 21, 2) )\n", - "sos = scipy.signal.butter(8, 0.5, 'lp', fs=10, output='sos')\n", - "filtered = scipy.signal.sosfiltfilt(sos, reassembled_values / mems_lsb_per_g)\n", - "ax.plot(ts, filtered, color='darkblue')\n", + " \n", + "plot_measurements(ax, t, y)\n", "\n", "g = 9.8066\n", "g_to_ms = lambda x: x * g\n", "ms_to_g = lambda x: x / g\n", "\n", - "ax.set_ylabel(r'$a\\; [g]$')\n", - "secax_y = ax.secondary_yaxis(\n", - " 'right', functions=(g_to_ms, ms_to_g))\n", - "secax_y.set_ylabel(r'$a\\; [ms^{-1}]$')\n", - "\n", - "formatter = ticker.FuncFormatter(lambda tick, _pos: f'{int(tick):02d}:{tick*60%60:02.0f}')\n", - "ax.xaxis.set_major_formatter(formatter)\n", - "\n", "r_mems = 55e-3 # radius of our sensor from the axis of rotation in m\n", "le_data = [(0, 50, 3.12), (1,50,5.55), (2,40, 8.2), (3, 30, 10.2), (4,15, 12.5), (5,10, 15.6),\n", " (6,10, 19.2), (7,11, 11.6), (8,15, 6.49)]\n", @@ -2263,11 +2191,12 @@ " omegan = 2*np.pi*f_actual # angular velocity\n", " acc = omegan**2 * r_mems # m/s^2\n", " acc_theory.append(acc / g)\n", + " ax.axvspan(ts_abs-ivl_w/2, ts_abs+ivl_w/2, zorder=1, color='red', alpha=0.1)\n", " \n", " ts_abs = ts_m + ts_s/60\n", " ivl_w = 0.5\n", - " idx = (ts_abs - ivl_w/2 < ts) & (ts < ts_abs + ivl_w/2)\n", - " ivl_avg = (reassembled_values / mems_lsb_per_g)[idx].mean()\n", + " idx = (ts_abs - ivl_w/2 < t) & (t < ts_abs + ivl_w/2)\n", + " ivl_avg = (y / mems_lsb_per_g)[idx].mean()\n", " acc_meas.append(ivl_avg)\n", "\n", "# Calculate offset correction. The offset is due to manufacturing imperfections inherent to the device.\n", @@ -2290,7 +2219,7 @@ "print(f'Found sensor offset: {sensor_offx:.2f} g / {sensor_offx*g:.2f} m/s^2')\n", "print()\n", "\n", - "for theory, meas, interval in zip(acc_theory, acc_meas, interval_speeds):\n", + "for theory, meas, interval, (_1, _2, f_actual) in zip(acc_theory, acc_meas, interval_speeds, le_data):\n", " ax.axhline(theory - sensor_offx, color='orange', alpha=1, zorder=1)\n", " meas += sensor_offx\n", " \n", @@ -2304,7 +2233,7 @@ }, { "cell_type": "code", - "execution_count": 716, + "execution_count": 1022, "metadata": {}, "outputs": [ { @@ -3268,7 +3197,7 @@ { "data": { "text/html": [ - "<img 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\" 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" @@ -3278,71 +3207,1096 @@ "output_type": "display_data" }, { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x7f3df6c2b3d0>" + ] + }, + "execution_count": 1022, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "freqs = np.array([ f_actual for (_1, _2, f_actual) in le_data ])\n", + "\n", + "acc_theory_sorted = [ acc for _f, acc in sorted(zip(freqs, acc_theory)) ]\n", + "acc_meas_sorted = [ acc for _f, acc in sorted(zip(freqs, acc_meas)) ]\n", + "freqs = sorted(freqs)\n", + "\n", + "ax.plot(freqs, acc_theory_sorted, label='Theory')\n", + "ax.plot(freqs, acc_meas_sorted, label='Measurements')\n", + " \n", + "ax.grid()\n", + "ax.set_xlabel('$f\\;[Hz]$')\n", + "ax2 = ax.twiny()\n", + "x1, x2 = ax.get_xlim()\n", + "ax2.set_xlim((x1*60, x2*60))\n", + "ax2.set_xlabel('$f\\;[rpm]$')\n", + "ax.set_ylabel('$a\\;[g]$')\n", + "ax3 = ax.twinx()\n", + "y1, y2 = ax.get_ylim()\n", + "ax3.set_ylim(y1*g, y2*g)\n", + "ax3.set_ylabel('$a\\;[ms^-1]$')\n", + "\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 957, + "metadata": { + "scrolled": false + }, + "outputs": [ + { "name": "stdout", "output_type": "stream", "text": [ - "Largest peak at 4.31 Hz / 259 rpm\n", - "Mixing product 1 at 5.69 Hz / 341 rpm\n", - "Mixing product 2 at 14.31 Hz / 859 rpm\n", - "Mixing product 3 at 15.69 Hz / 941 rpm\n", - "Mixing product 4 at 24.31 Hz / 1459 rpm\n", - "Mixing product 5 at 25.69 Hz / 1541 rpm\n", - "Mixing product 6 at 34.31 Hz / 2059 rpm\n" + "Loading run #33 with 3178 packets total, 125 distinct over 99.94346904754639s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 35 ... 161\n" + ] + }, + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "<IPython.core.display.Javascript object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<img 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\" width=\"640\">" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Largest peak at 4.33 Hz / 260 rpm\n", + "Mixing product 1 at 5.67 Hz / 340 rpm\n", + "Mixing product 2 at 14.33 Hz / 860 rpm\n", + "Mixing product 3 at 15.67 Hz / 940 rpm\n", + "Mixing product 4 at 24.33 Hz / 1460 rpm\n", + "Mixing product 5 at 25.67 Hz / 1540 rpm\n", + "Mixing product 6 at 34.33 Hz / 2060 rpm\n" ] }, { "data": { "text/plain": [ - "<matplotlib.lines.Line2D at 0x7f3dfe41dc70>" + "4.3273542600896855" ] }, - "execution_count": 716, + "execution_count": 957, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tsa = np.array(timestamps)\n", - "#s_min, s_max = 70, 120\n", - "s_min, s_max = 40, 90\n", - "speed_idx = (tsa > s_min) & (tsa < s_max)\n", - "\n", - "ts = np.arange(0, len(reassembled_values)) / sampling_rate\n", - "fft_idx = (ts > s_min) & (ts < s_max)\n", + "def estimate_freq_fft(t, y, interval):\n", + " s_min, s_max = interval\n", + " fft_idx = (t*60 > s_min) & (t*60 < s_max)\n", "\n", - "N = fft_idx.sum()\n", - "T = 1/sampling_rate\n", - "x = np.linspace(0.0, N*T, N)\n", - "y = reassembled_values[fft_idx] / mems_lsb_per_g # cut out beginning and that time we tapped the thing\n", - "y *= scipy.signal.windows.blackmanharris(len(y))\n", - "yf = scipy.fftpack.fft(y)\n", - "xf = np.linspace(0.0, 1/(2*T), N//2)\n", - "mag = 2/N * np.abs(yf[:N//2])\n", + " N = fft_idx.sum()\n", + " T = 1/sampling_rate\n", + " x = np.linspace(0.0, N*T, N)\n", + " y = y[fft_idx] / mems_lsb_per_g # cut out beginning and that time we tapped the thing\n", + " y *= scipy.signal.windows.blackmanharris(len(y))\n", + " yf = scipy.fftpack.fft(y)\n", + " xf = np.linspace(0.0, 1/(2*T), N//2)\n", + " mag = 2/N * np.abs(yf[:N//2])\n", "\n", - "fig, ax = plt.subplots()\n", - "ax.grid()\n", - "ax.plot(xf, mag)\n", - "ax.set_ylabel('Magnitude [g]')\n", - "ax.set_xlabel('Frequency [Hz]')\n", + " fig, ax = plt.subplots()\n", + " ax.grid()\n", + " ax.plot(xf, mag)\n", + " ax.set_ylabel('Magnitude [g]')\n", + " ax.set_xlabel('Frequency [Hz]')\n", "\n", - "peaks, _ = scipy.signal.find_peaks(mag, height=.1, distance=1/T)\n", - "assert peaks.any()\n", + " peaks, _ = scipy.signal.find_peaks(mag, height=.1, distance=1/T)\n", + " assert peaks.any()\n", "\n", - "peak_data = sorted([ (-mag[idx], xf[idx]) for idx in peaks ])\n", - "largest_peak_f = peak_data[0][1]\n", - "print(f'Largest peak at {largest_peak_f:.2f} Hz / {largest_peak_f * 60:.0f} rpm')\n", - "for i in range(1,4):\n", - " mix1 = i*sampling_rate - largest_peak_f\n", - " mix2 = i*sampling_rate + largest_peak_f\n", - " print(f'Mixing product {2*i-1} at {mix1:.2f} Hz / {mix1 * 60:.0f} rpm')\n", - " print(f'Mixing product {2*i} at {mix2:.2f} Hz / {mix2 * 60:.0f} rpm')\n", + " peak_data = sorted([ (-mag[idx], xf[idx]) for idx in peaks ])\n", + " largest_peak_f = peak_data[0][1]\n", + " print(f'Largest peak at {largest_peak_f:.2f} Hz / {largest_peak_f * 60:.0f} rpm')\n", + " for i in range(1,4):\n", + " mix1 = i*sampling_rate - largest_peak_f\n", + " mix2 = i*sampling_rate + largest_peak_f\n", + " print(f'Mixing product {2*i-1} at {mix1:.2f} Hz / {mix1 * 60:.0f} rpm')\n", + " print(f'Mixing product {2*i} at {mix2:.2f} Hz / {mix2 * 60:.0f} rpm')\n", "\n", - "ax.axvline(largest_peak_f, color='orange')" + " ax.axvline(largest_peak_f, color='orange')\n", + " return largest_peak_f\n", + " \n", + "t, y, packet_delays, packet_times = load_run(33, plot=False)\n", + "estimate_freq_fft(t, y, (30, 75))" ] }, { "cell_type": "code", - "execution_count": 717, + "execution_count": 958, "metadata": { "scrolled": false }, @@ -3351,10 +4305,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "interval: -0.06357678079289661\n", - "scores: [0.0005478345240435783, 0.003917745560629984, 0.00382343194410793, 0.006241784699377939, 0.0074863442856081385, 0.008964752044997151, 0.008813512329706083, 0.008780553883453538, 0.008530974440876703]\n", - "argmin: 1\n", - "Average speed of rotation: 15.73 Hz / 944 rpm\n" + "Loading run #33 with 3178 packets total, 125 distinct over 99.94346904754639s\n" ] }, { @@ -4318,7 +5269,996 @@ { "data": { "text/html": [ - "<img 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KK01jr480JPjetrx4a6kn4O3gAnAQPAhkHDBkQ2O/IEnGxzZ4MimNY4658QDcOee/Uk/Bx8CIQADQQCGC45scOQPPNngyAZHNn4dNTU1yXHjAVizlwBMBQRgAAjAcMGRDY78gScbHNngyMavo8bGTwLww48OJP0cBCABGAgCMFxwZIMjf+DJBkc2OLLx6+jgocZEANbWJ98nAUgABoIADBcc2eDIH3iywZENjmz8Ojpw8JMArNtHAKYCAjAABGC44MgGR/7Akw2ObHBk49fRvoZDiQDcu/9g0s9BABKAgSAAwwVHNjjyB55scGSDIxu/jj4+8EkA1h8gAFMBARgAAjBccGSDI3/gyQZHNjiy8evoo/0HEwG4r+FQ0s9BABKAgeBC0OlzQdG2OhzhCUc4itL8OtqztzYRgPv37Un6ObgQNAEYCG4FxxhjjCV/tRM6JgKwYVJG0l+fW8ERgIEgABljjLHkb/cz/5oIwEOT/iXpr08AEoCB4C3g9HgroS0PR3jCEY6iNL+OdtV+mAjApoa9ST8HbwETgIHgQyDhgiMbHPkDTzY4ssGRjV9HO/fsTwRgKuBDIARgIAjAcMGRDY78gScbHNngyMavox11++S4ns7MIQBTBQEYAAIwXHBkgyN/4MkGRzY4svHrqLo2HoBnDZyRknMQgARgIAjAcMGRDY78gScbHNngyMavo627P5bjejrnjpkpOQcBSAAGggAMFxzZ4MgfeLLBkQ2ObPw6qvqwXo7r6dxBBGCqIAADQACGC45scOQPPNngyAZHNn4dbdkVD8Cud85KyTkIQAIwEARguODIBkf+wJMNjmxwZOPX0eaaj+S4ns67iwBMFQRgAAjAcMGRDY78gScbHNngyMavo40fxAPw/Ltnp+QcBCABGAgCMFxwZIMjf+DJBkc2OLLx6+i9nXvluJ6+mksApgoCMADcCSQ9rijflocjPOEIR1GaX0frt+6Q43q6YPDslJyDO4EQgIHgXsCMMcZY8rd2fCc5rqev3zE5Ja/PvYAJwEAQgIwxxljyt/pJR47r6RuDJqbk9QlAAjAQvAWcHm8ltOXhCE84wlGU5tfRu1u2y3E9XTh0bkrOwVvABGAg+BBIuODIBkf+wJMNjmxwZOPX0ar3a+W4ni4aPi8l5+BDIARgIAjAcMGRDY78gScbHNngyMavo7er4gF4Sd7LKTkHAUgABoIADBcc2eDIH3iywZENjmz8Olq5Zbcc19O37n0lJecgAAnAQBCA4YIjGxz5A082OLLBkY1fRysqP5Tjevp2PgGYKgjAABCA4YIjGxz5A082OLLBkY1fR29sjgfgd0fMT8k5CEACMBAEYLjgyAZH/sCTDY5scGTj19Hrm3bJcT11H0kApgoCMAAEYLjgyAZH/sCTDY5scGTj19GyjfEAvOz+BSk5BwFIAAaCAAwXHNngyB94ssGRDY5s/DpasqFGjuvpB6MWpuQcBCABGAguBJ0eFxRty8MRnnCEoyjNr6PStVVyXE+Xj5qfknNwIWgCMBDcCo4xxhhL/l57/AI5rqcrc0en5PW5FVyaBOCiRYvUs2dPnXbaaYrFYiouLv7M50+bNk0//OEP9aUvfUmf//zndckll2j27NnNnlNYWKhYLPap7du3z/e5CEDGGGMs+Vv8+NfkuJ565D6cktcnANMkAGfOnKlBgwZp2rRpvgKwf//+GjFihJYvX65169Zp4MCByszM1IoVKxLPKSws1EknnaTq6upmaw28BZwebyW05eEITzjCUZTm19HCii1yXE8/enBhSs7BW8BpEoB/j58APBrnnXeehgwZkvjnwsJCnXzyyYHOwodAwgVHNjjyB55scGSDIxu/juav2SHH9dTz4VdTcg4+BNJGArCxsVFnnHGGRo8enXissLBQGRkZ6tSpk04//XRdffXVzX5CeDT279+vurq6xKqqqhSLxVRTU6OGhoakrr6+XiUlJaqvr0/6ax8vwxGO8ISjKA1HyXM0Z9VWOa6nax5enJJz1NTUEIBhH6C1/DMBOHLkSJ1yyinasWNH4rElS5Zo4sSJWrlypRYvXqxrr71WHTp00Lp161p8ndzc3KP+3mBRUZFKSkoYY4wxloQNe+pFOa6nS4e+lJLXLyoqIgDDPkBraW0AFhUV6cQTT9S8efM+83mNjY264IILdOutt7b4HH4CGK3hCEd4wlGUhqPkOZr51vtyXE//9cirKTkHPwE8zgNwypQp6tChgzzP8/X8G2+8UT169PB9Fn4HMFxwZIMjf+DJBkc2OLLx62jWqmo5rqdrHy1NyTn4HcDjOACLiop0wgkn+I7FpqYmXXjhherTp4/vsxCA4YIjGxz5A082OLLBkY1fRzPf3ibH9fTzx8pScg4CME0CcO/evSovL1d5eblisZgKCgpUXl6uyspKSVJOTo569eqVeH5RUZHatWunMWPGNLvES21tbeI5gwcP1uzZs7VhwwaVl5erT58+ateunZYtW+b7XARguODIBkf+wJMNjmxwZOPXkfdWPACve5wATBVpEYALFiw46ocvevfuLUnq3bu3unfvnnh+9+7dP/P5kjRgwAB16tRJWVlZOvXUU3XFFVeorKx132gEYLjgyAZH/sCTDY5scGTj19HfVsY/BXz9E0tScg4CME0CMKoQgOGCIxsc+QNPNjiywZGNX0cl5fEPgfxyHAGYKgjAAHAnkPS4onxbHo7whCMcRWl+HRW/vkGO6+mGsaUpOQd3AiEAA8G9gBljjLHkb9ojl8lxPfUaOiQlr8+9gAnAQBCAjDHGWPL3wujvy3E99R42OCWvTwASgIHgLeD0eCuhLQ9HeMIRjqI0v46eW7pejuupz1NlKTkHbwETgIHgQyDhgiMbHPkDTzY4ssGRjV9Hzy6rlON6+u3Ty1NyDj4EQgAGggAMFxzZ4MgfeLLBkQ2ObPw6mrw0HoA3PvN6Ss5BABKAgSAAwwVHNjjyB55scGSDIxu/jiYu2SzH9fT7CQRgqiAAA0AAhguObHDkDzzZ4MgGRzZ+HU0o2yTH9dR34hspOQcBSAAGggAMFxzZ4MgfeLLBkQ2ObPw6ero0HoC3THozJecgAAnAQBCA4YIjGxz5A082OLLBkY1fR0+9tlGO6+kPkwnAVEEABoAADBcc2eDIH3iywZENjmz8Onry1XgA3lq0IiXnIAAJwEAQgOGCIxsc+QNPNjiywZGNX0fjFsdvBdf/WQIwVRCAAeBC0OlxQdG2PBzhCUc4itL8OnpiQYUc19Ntz76eknNwIWgCMBDcCo4xxhhL/h574Fo5rqc/5Q9IyetzKzgCMBAEIGOMMZb8jXngZ3JcT3/J75+S1ycACcBA8BZweryV0JaHIzzhCEdRml9Hj8x7V47r6fbn30jJOXgLmAAMBB8CCRcc2eDIH3iywZENjmz8Onr45XVyXE85095KyTn4EAgBGAgCMFxwZIMjf+DJBkc2OLLx6+jBefEAHDj97ZScgwAkAANBAIYLjmxw5A882eDIBkc2fh0VzF0rx/U0qJgATBUEYAAIwHDBkQ2O/IEnGxzZ4MjGr6NRc9bIcT3dVbIqJecgAAnAQBCA4YIjGxz5A082OLLBkY1fR/fNjgdg7ovvpOQcBCABGAgCMFxwZIMjf+DJBkc2OLLx62jErNVyXE+D/0YApgoCMAAEYLjgyAZH/sCTDY5scGTj19G9M+MBeM9L76bkHAQgARgIAjBccGSDI3/gyQZHNjiy8esob0b8VnDDPAIwVRCAASAAwwVHNjjyB55scGSDIxu/joZ58QtB582oSMk5CEACMBDcCSQ9rijflocjPOEIR1GaX0f3vLhSjuvpXu+tlJyDO4EQgIHgXsCMMcZY8jf4vt/JcT2NuP/XKXl97gVMAAaCAGSMMcaSv9yRv5fjerpv1A0peX0CkAAMBG8Bp8dbCW15OMITjnAUpfl1dNf0FXJcT6NmrUrJOXgLmAAMBB8CCRcc2eDIH3iywZENjmz8OhpU/LYc11PB3LUpOQcfAiEAA0EAhguObHDkDzzZ4MgGRzZ+HQ2cHg/AB+etS8k5CEACMBAEYLjgyAZH/sCTDY5scGTj11HOtLfkuJ4efpkATBUEYAAIwHDBkQ2O/IEnGxzZ4MjGr6PbX4gH4CPz16fkHAQgARgIAjBccGSDI3/gyQZHNjiy8evoL8/HrwP46IL3UnIOApAADAQBGC44ssGRP/BkgyMbHNn4dfSn5+IB+PhCAjBVEIABIADDBUc2OPIHnmxwZIMjG7+ObptSLsf1NHbRhpScgwAkAANBAIYLjmxw5A882eDIBkc2fh31fzZ+HcBxiwnAVEEABoALQafHBUXb8nCEJxzhKErz6+jWycvluJ7GL1qTknNwIeg0CcBFixapZ8+eOu200xSLxVRcXGz+mYULF6pbt25q3769zjzzTD322GOfes7UqVOVnZ2trKwsZWdna/r06a06F7eCY4wxxpK/P+TdLsf1VPhQz5S8PreCS5MAnDlzpgYNGqRp06b5CsCNGzfqxBNPVP/+/VVRUaFx48YpMzNTU6dOTTynrKxMGRkZysvL0+rVq5WXl6d27dpp6dKlvs9FADLGGGPJ3y15rhzX0zMPXZ2S1ycA0yQA/x4/AXj77bera9euzR676aabdMkllyT++brrrlOPHj2aPefKK6/U9ddf7/ssvAWcHm8ltOXhCE84wlGU5tdR3wlL5bieJry2NiXn4C3g4zQAv/vd76pfv37NHps+fbratWuX+MXTM844QwUFBc2eU1BQoE6dOvk+Cx8CCRcc2eDIH3iywZENjmz8Ovr9hNfluJ4mLd2cknPwIZDjNADPOeccDR8+vNljpaWlisVi2rZtmyQpMzNTkydPbvacyZMnKysrq8XX3b9/v+rq6hKrqqpSLBZTTU2NGhoakrr6+nqVlJSovr4+6a99vAxHOMITjqI0HCXP0f8WLpPjeppYtjEl56ipqSEAwz5Aa/EbgHl5ec0ee+211xSLxVRdXS0pHoBFRUXNnjNp0iS1b9++xdfNzc1VLBb71IqKilRSUsIYY4yxJOxH+X+T43pyn3gxJa9fVFREAIZ9gNYS5lvA/AQwWsMRjvCEoygNR8lz9Ovx8d8BLFq6KSXn4CeAx2kA3n777crOzm72WN++fT/1IZCrrrqq2XN69Ojxz30IhN8BDAUc2eDIH3iywZENjmz8Ovr1+PhbwC+8UZWSc/A7gGkSgHv37lV5ebnKy8sVi8VUUFCg8vJyVVZWSpJycnLUq1evxPOPXAbmtttuU0VFhcaPH/+py8CUlpYqIyND+fn5Wr16tfLz8//5y8AQgKGAIxsc+QNPNjiywZGNX0c3PBn/CeC0NwnAVJEWAbhgwYKj/u5d7969JUm9e/dW9+7dm/2ZhQsX6utf/7qysrLUuXPno14I+oUXXlCXLl2UmZmprl27atq0aa06FwEYLjiywZE/8GSDIxsc2fh19Ktx8QAsXvF+Ss5BAKZJAEYVAjBccGSDI3/gyQZHNjiy8evof8YukeN6KiknAFMFARgAAjBccGSDI3/gyQZHNjiy8evoF0+UyXE9/W3l1pScgwAkAAPBnUDS44rybXk4whOOcBSl+XX088deleN68so3puQc3AmEAAwE9wJmjDHGkr+fDRkhx/U089FvpeT1uRcwARgIApAxxhhL/n46ZKQc19OsR7+ZktcnAAnAQPAWcHq8ldCWhyM84QhHUZpfR//1yGI5rqc5b29OyTl4C5gADAQfAgkXHNngyB94ssGRDY5s/Dr68SOvyXE9zXt3e0rOwYdACMBAEIDhgiMbHPkDTzY4ssGRjV9H14yOfwjkldUEYKogAANAAIYLjmxw5A882eDIBkc2fh1d/XD8LeD5a3ak5BwEIAEYCAIwXHBkgyN/4MkGRzY4svHr6KoH4wG4cO3OlJyDACQAA0EAhguObHDkDzzZ4MgGRzZ+HV35wCI5rqfF6wjAVEEABoAADBcc2eDIH3iywZENjmz8OrqiIB6Ar1lmu3EAACAASURBVK3/ICXnIAAJwEAQgOGCIxsc+QNPNjiywZGNX0c/HLVQjuup9D0CMFUQgAEgAMMFRzY48geebHBkgyMbv46+f/8COa6nJRtqUnIOApAADAQXgk6PC4q25eEITzjCUZTm19Fl970ix/W0bP37KTkHF4ImAAPBreAYY4yx5K/7XWPluJ5eH5udktfnVnAEYCAIQMYYYyz5++5d4+S4nt4Y2zUlr08AEoCB4C3g9HgroS0PR3jCEY6iNL+Ovn3vy3JcTys2Jv/vVx3kLWCJAAwEHwIJFxzZ4MgfeLLBkQ2ObPw6+ta98d8BXLlld0rOwYdACMBAEIDhgiMbHPkDTzY4ssGRjV9Hl+TFfwL4dlVtSs5BABKAgSAAwwVHNjjyB55scGSDIxu/ji4aPk+O62nV+wRgqiAAA0AAhguObHDkDzzZ4MgGRzZ+HV04LB6A725NTaARgARgIAjAcMGRDY78gScbHNngyMavo28MnSvH9bS6mgBMFQRgAAjAcMGRDY78gScbHNngyMavo6/fEw/Atdv3pOQcBCABGAgCMFxwZIMjf+DJBkc2OLLx6+iCIXPkuJ7W7yAAUwUBGAACMFxwZIMjf+DJBkc2OLLx6+irubPluJ7e27k3JecgAAnAQBCA4YIjGxz5A082OLLBkY1fR+ffHQ/AjR98lJJzEIAEYCC4E0h6XFG+LQ9HeMIRjqI0v47Ou2uWHNfT5u0fpOQc3AmEAAwE9wJmjDHGkr+uOVPluJ62FP5bSl6fewETgIEgABljjLHk79ycaXJcT1WFp6bk9QlAAjAQvAWcHm8ltOXhCE84wlGU5tfROXfMkON62vpBTUrOwVvABGAg+BBIuODIBkf+wJMNjmxwZOPX0VkD4wFYXbsvJefgQyAEYCAIwHDBkQ2O/IEnGxzZ4MjGr6POOZ4c19OOPQRgqiAAA0AAhguObHDkDzzZ4MgGRzZ+HDU1Nclx4wH4wd79KTkHAUgABoIADBcc2eDIH3iywZENjmz8OGps/CQAd310ICXnIAAJwEAQgOGCIxsc+QNPNjiywZGNH0cHDzUmAnB3PQGYKgjAABCA4YIjGxz5A082OLLBkY0fRwcOfhKAtR+nxiUBSAAGggAMFxzZ4MgfeLLBkQ2ObPw42tdwKBGAe/YRgKmCAAwAARguOLLBkT/wZIMjGxzZ+HH08YFPAvCj/QdTcg4CMI0CcMyYMercubPat2+vbt26afHixS0+t3fv3orFYp/aeeedl3hOYWHhUZ+zb5//j5xzIej0uKBoWx6O8IQjHEVpfhx99FFtIgA/rq9LyTm4EHSaBOCUKVOUmZmpcePGqaKiQv3791fHjh1VWVl51OfX1taquro6saqqKp1yyinKzc1NPKewsFAnnXRSs+dVV1e36lzcCo4xxhhL7rwx304E4L6JWSn5GtwKLk0C8KKLLlLfvn2bPda1a1fl5OT4+vPFxcX63Oc+p82bNyceKyws1MknnxzoXAQgY4wxlrw1TYrpO3c9Kcf1dO2QEWqalJqvQwCmQQAeOHBAGRkZmj59erPH+/Xrp0svvdTXa/Ts2VOXX355s8cKCwuVkZGhTp066fTTT9fVV1+tFStWtOpsvAUc/bcS2vpwhCcc4ShKsxxt2r5TjuvpzBxPdXtrU3YO3gJOgwDcunWrYrGYSktLmz0+fPhwnXvuueaf37ZtmzIyMvTcc881e3zJkiWaOHGiVq5cqcWLF+vaa69Vhw4dtG7duhZfa//+/aqrq0usqqpKsVhMNTU1amhoSOrq6+tVUlKi+vr6pL/28TIc4QhPOIrScBTcUeGr78lxPV33eGlKz1FTU0MAhn0AiyMBWFZW1uzxYcOGqUuXLuafz8vL0xe/+EUdOPDZF5NsbGzUBRdcoFtvvbXF5+Tm5h71gyNFRUUqKSlhjDHGWID9+sG/yXE99Xnobyn9OkVFRQRg2AewCPIWcFNTk84++2wNGDDA19e68cYb1aNHjxb/c34CGK3hCEd4wlGUhqPgjvoVvSnH9fTo/HUpPQc/AUyDAJTiHwK5+eabmz2WnZ1tfghkwYIFisViWrVqlfk1mpqadOGFF6pPnz6+z8V1AMMFRzY48geebHBkgyMby9Gvxy+T43p67vUtKT0H1wFMkwA8chmY8ePHq6KiQgMGDFDHjh0Tn+rNyclRr169PvXnbrjhBl188cVHfc3Bgwdr9uzZ2rBhg8rLy9WnTx+1a9dOy5Yt830uAjBccGSDI3/gyQZHNjiysRz9ePSrclxP897dntJzEIBpEoBS/ELQjuMoKytL3bp106JFixL/We/evdW9e/dmz6+trVWHDh00duzYo77egAED1KlTJ2VlZenUU0/VFVdc8anfM7QgAMMFRzY48geebHBkgyMby9F3Rrwix/X0xuZdKT0HAZhGARhFCMBwwZENjvyBJxsc2eDIxnJ0/t2z5bieNuzcm9JzEIAEYCAIwHDBkQ2O/IEnGxzZ4MjmsxwdONiYuAPIhx999pU7gkIAEoCBIADDBUc2OPIHnmxwZIMjm89ytGPPvsRFoA81NqX0HAQgARgI7gQS7SvKMxzhCUc4itY+y9GctzfLcT19bcjslJ+DO4EQgIHgXsCMMcZYcnbk7d/ud41N+dfiXsAEYCAIQMYYYyz4Dk76l0QAPj/6Byn/egQgARgI3gKO7lsJDEd4whGOoreWHNXX1yUCsL6+LuXn4C1gAjAQfAgkXHBkgyN/4MkGRzY4smnJ0e76A4kAPHioMeXn4EMgBGAgCMBwwZENjvyBJxsc2eDIpiVH2+vinwD+8sAZx+QcBCABGAgCMFxwZIMjf+DJBkc2OLJpydGWXfVyXE9d75x1TM5BABKAgSAAwwVHNjjyB55scGSDI5uWHK3fsUeO6+mCIXOOyTkIQAIwEARguODIBkf+wJMNjmxwZNOSo3e21spxPf3nsHnH5BwEIAEYCAIwXHBkgyN/4MkGRzY4smnJ0ZuVH8pxPX1nxCvH5BwEIAEYCAIwXHBkgyN/4MkGRzY4smnJ0ZINNXJcT9+/f8ExOQcBSAAGggAMFxzZ4MgfeLLBkQ2ObFpytHDtTjmup6seXHxMzkEAEoCB4ELQ0bygKMMRnnCEo2iuJUdzD98H+CejFx2Tc3AhaAIwENwKjjHGGAu+l8Z8R47r6bp77j0mX49bwRGAgSAAGWOMseCb9shlclxPNwy955h8PQKQAAwEbwFH860EhiM84QhH0VxLjorK1slxPf22cMkxOQdvAROAgeBDIOGCIxsc+QNPNjiywZFNS46eLt0kx/V0y6Q3j8k5+BAIARgIAjBccGSDI3/gyQZHNjiyacnRE4vek+N6um1K+TE5BwFIAAaCAAwXHNngyB94ssGRDY5sWnI0+pX4W8A50946JucgAAnAQBCA4YIjGxz5A082OLLBkU1Lju6fs0aO6+nuklXH5BwEIAEYCAIwXHBkgyN/4MkGRzY4smnJ0fAZFXJcT8NnVByTcxCABGAgCMBwwZENjvyBJxsc2eDIpiVHuS++I8f1dN/sNcfkHAQgARgIAjBccGSDI3/gyQZHNjiyaclRzrS35LieHn553TE5BwFIAAaCAAwXHNngyB94ssGRDY5sWnJ023PlclxPjy9875icgwAkAANBAIYLjmxw5A882eDIBkc2LTm6ZfKbclxPha9tPCbnIAAJwEBwJ5BoXlGe4QhPOMJRNPePjpoa9ur9D2p00bC5clxPk8vWHZNzcCcQAjAQ3AuYMcYYa93mPXaReg8brMvvHqOvDHxejuslVvzI947JGbgXMAEYCAKQMcYYa90uu+vxZtF3lluib935lG7Jc7X7mX89JmcgAAnAQPAWcLTeSmA4whOOcBTtNXy8W9mDXpLjenp+2Xq9t22n9n1cd8zPwVvABGAg+BBIuODIBkf+wJMNjmxwZHPgwAF1duMBuKNuX2jn4EMgBGAgCMBwwZENjvyBJxsc2eDI5sO9Hyfe+t3XcCi0cxCABGAgCMBwwZENjvyBJxsc2eDIpvKDPXJcT2ffMUNNTU2hnYMAJAADQQCGC45scOQPPNngyAZHNqu27JLjeup2z9xQz0EAEoCBIADDBUc2OPIHnmxwZIMjm9J1O+S4nr43cn6o5yAACcBAEIDhgiMbHPkDTzY4ssGRzay335fjerpm9OJQz0EAEoCBIADDBUc2OPIHnmxwZIMjm+eXb5bjevrVuCWhnoMAJAADQQCGC45scOQPPNngyAZHNk8ufk+O6+nmia+Heg4CMI0CcMyYMercubPat2+vbt26afHiln98vGDBAsVisU9t9erVzZ43depUZWdnKysrS9nZ2Zo+fXqrzsSFoMO/oCiOcIQnHEVlOLJXMOttOa6n2597PdRzcCHoNAnAKVOmKDMzU+PGjVNFRYX69++vjh07qrKy8qjPPxKAa9euVXV1dWKHDn1yzaGysjJlZGQoLy9Pq1evVl5entq1a6elS5f6Phe3gmOMMcZaXsOkDL3z5Jl6ccylGjXql4lrAObd/5tQz8Wt4NIkAC+66CL17du32WNdu3ZVTk7OUZ9/JAB3797d4mted9116tGjR7PHrrzySl1//fW+z0UAMsYYYy3vx4NHNbvv75HNevSboZ6LAEyDADxw4IAyMjI+9fZsv379dOmllx71zxwJwM6dO+vf//3f9f3vf1/z5zf/yPkZZ5yhgoKCZo8VFBSoU6dOLZ5l//79qqurS6yqqkqxWEw12yvV8PHupK6+bqdeKp6i+rqdSX/t42U4whGecBSl4aj5du3+IBF8//3IIv1lynI9MvctPTH5+dAd1WyvJADDPoDF1q1bFYvFVFpa2uzx4cOH69xzzz3qn1mzZo3Gjh2rN998U2VlZbr55pv1uc99TosWLUo8JzMzU5MnT2725yZPnqysrKwWz5Kbm3vU3y0sKipSSUkJY4wxxg7vsaISOa6n7DteCv0s/7iioiICMOwDWBwJwLKysmaPDxs2TF26dPH9Oj179tQ111yT+OfMzEwVFRU1e86kSZPUvn37Fl+jxZ8A1tSooaEhqauvr1dJSYnq6+uT/trHy3CEIzzhKErDUfO98u42Oa6nKwoWRs5RTU0NARj2ASz+mbeAj8awYcPUtWvXxD//M28B/yNcBiZccGSDI3/gyQZHNjhqzrPLKuW4nn7z1LLEY1FxxGVg0iAApfiHQG6++eZmj2VnZ7f4IZCjce211+qyyy5L/PN1112nq666qtlzevTo8c99CIQADAUc2eDIH3iywZENjppTMHetHNdTzrS3E49FxREBmCYBeOQyMOPHj1dFRYUGDBigjh07avPmzZKknJwc9erVK/H8Bx54QMXFxVq3bp3eeecd5eTkKBaLadq0aYnnlJaWKiMjQ/n5+Vq9erXy8/P/+cvAEIChgCMbHPkDTzY4ssFRc/76wko5rqeHX16XeCwqjgjANAlAKX4haMdxlJWVpW7dujX7QEfv3r3VvXv3xD+PGDFCZ511lk444QR94Qtf0He+8x3NmDHjU6/5wgsvqEuXLsrMzFTXrl2bBaIfCMBwwZENjvyBJxsc2eCoOb8ev0yO6+m55VsSj0XFEQGYRgEYRQjAcMGRDY78gScbHNngqDnXPloqx/U08+1ticei4ogAJAADQQCGC45scOQPPNngyAZHzbnqwcVyXE8L1uxIPBYVRwQgARgIAjBccGSDI3/gyQZHNjhqzvfuWyDH9bRs467EY1FxRAASgIFIfAPt2sZNxUMYjnCEJxxFaThqvouGzZXjelpVWR05R3W7thGAYR8gneFewIwxxtjR99U7pshxPb03/vTQz/KP417ABGAgCEDGGGPs6Ds7p1iO62lb4RdDP8s/jgAkAAPBW8DhDkc4whOOojQc/Z2L/XvkuJ4c19Puuk98RMURbwETgIHgQyDhgiMbHPkDTzY4ssHRJ9R+3JAIwP0HDyUej4ojPgRCAAaCAAwXHNngyB94ssGRDY4+obp2nxzX05cHzlBTU1Pi8ag4IgAJwEAQgOGCIxsc+QNPNjiywdEnbNi5V47r6fy7Zzd7PCqOCEACMBAEYLjgyAZH/sCTDY5scPQJq96vleN6+s9h85o9HhVHBCABGAgCMFxwZIMjf+DJBkc2OPqE5Zt2yXE9dR85v9njUXFEABKAgSAAwwVHNjjyB55scGSDo09YuHanHNdTjwcXN3s8Ko4IQAIwEARguODIBkf+wJMNjmxw9AmzVm2T43r66aOlzR6PiiMCkAAMBAEYLjiywZE/8GSDIxscfcK0N6vkuJ5ueHJps8ej4ogAJAADwYWgQ77QKI5whCccRWht1VHjgb2qr6/T1g9qtKqyWqVrq3Tn9BVyXE+/e3ppJB1xIWgCMBDcCo4xxtjxslmPflMPFVyve+67UX/N76eb83J0w9B79F9D7leP3If1/bsf07fvfFIXDpqgr94xRV1zpuostyRxweej7U/5A0L/9zrauBUcARgIApAxxtjxsFVPfvkzQ87Pzs4p1oWDJuiHd4/RT4eM1I3DB+mdJ88M/d/taCMACcBA8BZwuMMRjvCEoygtnR3NKN8ox/V08fC5ynvpLY2e966efnWNpi5/T7Pe2qRFFVu0dN37Kt+0TRVV27V+2w5t2fGBtu/apQ/rduujj2rV1LA3bRzxFjABGAg+BBIuOLLBkT/wZIMjm3R2NKFskxzX0+8nvJ7SrxMVR3wIhAAMBAEYLjiywZE/8GSDI5t0dvTAvLVyXE8Dp7+d0q8TFUcEIAEYCAIwXHBkgyN/4MkGRzbp7OjO4lVyXE/3z1mT0q8TFUcEIAEYCAIwXHBkgyN/4MkGRzbp7OjmSW/IcT0VvrYxpV8nKo4IQAIwEARguODIBkf+wJMNjmzS2dHPHy+T43r628qtKf06UXFEABKAgSAAwwVHNjjyB55scGSTzo5+MGqhHNdT6foPUvp1ouKIACQAA0EAhguObHDkDzzZ4MgmXR0damzSOXfMlON6WlO9J6VfKyqOCEACMBAEYLjgyAZH/sCTDY5s0tXRyNmr5bieut45S3v3H0zp14qKIwKQAAwEARguOLLBkT/wZIMjm3R19O38V+S4niYt3ZzyrxUVRwQgARgI7gQS7nCEIzzhKEpLR0frt+2Q43r68kBPH31U22YccScQAjAQ3AuYMcZYlLel8N/0h7zb9cuhw/SzISP048GjdGXuaHW/a6wuGvRM4j6+/zXk/tDPeizHvYAJwEAQgIwxxqK8kaN6JSKvpZ2bM03Pj/5B6Gc9liMACcBA8BZwuMMRjvCEoygtio7+8lz8As9/nLRcM8o3at6qzVpUsUWvv7dVqyqrVb1rV5t0xFvABGAg+BBIuODIBkf+wJMNjmyi6Oh/C5fLcT09u6wy7KNIio4jPgRCAAaCAAwXHNngyB94ssGRTRQd/fiR1+S4nua+uz3so0iKjiMCkAAMBAEYLjiywZE/8GSDI5soOjpyiZc3Nn8Y9lEkRccRAUgABoIADBcc2eDIH3iywZFNFB1l3zVLjutpc81HYR9FUnQcEYAEYCAIwHDBkQ2O/IEnGxzZRM1R/YGDiU/67tkXjTNFxREBSAAGggAMFxzZ4MgfeLLBkU3UHG3ZVS/H9XTOoJlqamoK+ziSouOIACQAA0EAhguObHDkDzzZ4Mgmao5WVH4ox/V0Sd7LYR8lQVQcEYBpFIBjxoxR586d1b59e3Xr1k2LFy9u8bnTpk3TD3/4Q33pS1/S5z//eV1yySWaPXt2s+cUFhYqFot9avv27fN9JgIwXHBkgyN/4MkGRzZRc/TI/PVyXE83PLk07KMkiIojAjBNAnDKlCnKzMzUuHHjVFFRof79+6tjx46qrDz6dY369++vESNGaPny5Vq3bp0GDhyozMxMrVixIvGcwsJCnXTSSaqurm621sCFoMMdjnCEJxxFaVFydHD/Hl0+an78GoBL1oV+nqg54kLQaRKAF110kfr27dvssa5duyonJ8f3a5x33nkaMmRI4p8LCwt18sknBzoXt4JjjDEWlW0t/JLy7v+N+gy7W9+6c7wc11P2wBe0+5l/Df1sURu3gkuDADxw4IAyMjI0ffr0Zo/369dPl156qa/XaGxs1BlnnKHRo0cnHissLFRGRoY6deqk008/XVdffXWznxAejf3796uuri6xqqoqApAxxlgk9tf8fs3u8XtOznTNfPRboZ8riiMA0yAAt27dqlgsptLS0maPDx8+XOeee66v1xg5cqROOeUU7dixI/HYkiVLNHHiRK1cuVKLFy/Wtddeqw4dOmjdunUtvk5ubu5Rf2/wuclP6aXiKYwxxlhou2J4iRzX0y9HFWvU0y/o2efDP1NU99zkpwjAsA9gcSQAy8rKmj0+bNgwdenSxfzzRUVFOvHEEzVv3rzPfF5jY6MuuOAC3XrrrS0+p6WfANbU1KihoSGpq6+vV0lJierr65P+2sfLcIQjPOEoSgvb0RUFC+W4nl6p2Ba6i6g6OrKamhoCMOwDWAR5C3jKlCnq0KGDPM/z9bVuvPFG9ejRw/fZ+BRwuODIBkf+wJMNjmzCdvSNofPkuJ7e2Vobytf3Q9iOjsCngNMgAKX4h0BuvvnmZo9lZ2d/5odAioqKdMIJJ6i4uNjX12hqatKFF16oPn36+D4XARguOLLBkT/wZIMjmzAdNTY26cyc+O/+ba/zfzmzY01Uvo8IwDQJwCOXgRk/frwqKio0YMAAdezYUZs3b5Yk5eTkqFevXonnFxUVqV27dhozZkyzS7zU1n7y/4oGDx6s2bNna8OGDSovL1efPn3Url07LVu2zPe5CMBwwZENjvyBJxsc2YTpaNdHBxIf/jhwsPGYf32/ROX7iABMkwCU4heCdhxHWVlZ6tatmxYtWpT4z3r37q3u3bsn/rl79+5H/bBG7969E88ZMGCAOnXqpKysLJ166qm64oorPvV7hhYEYLjgyAZH/sCTDY5swnS0bvseOa6n/xg855h/7dYQle8jAjCNAjCKEIDhgiMbHPkDTzY4sgnTUdl7NXJcT5fdv+CYf+3WEJXvIwKQAAwEARguOLLBkT/wZIMjmzAdvfTWVjmup58/1rp3so41Ufk+IgAJwEAQgOGCIxsc+QNPNjiyCdNRwdy1clxPt00pP+ZfuzVE5fuIACQAA0EAhguObHDkDzzZ4MgmTEe/Hr9MjuvpmbJNx/xrt4aofB8RgARgIBLfQLu2Hbc3zI7ycIQjPOEoSkulo0MH9mr/vj366KNa1e7ZrZraD7V91y69/0GN1m/bkbgEzFubk//3Ubo4as3qdm0jAMM+QDqTCEDuBcwYY+zvtuGp/6sJD/1I9426QXeMuEXuiFv15/z+GnDvn/THvL/qpuED9ethg/Xze/J19eAHddndj+mbdz6lCwdN0NfumKzzB05R15ypOjunWJ3dvzW7x29LO3/gczowsV3o/+7pMO4FTAAGggBkjDH2j2uaFNOFgyb4irYgO9N9UefkTFf2wBd02d2PaeHj3UL/d0+XEYAEYCB4C5i3EqI+HOEJR8fe0badO+S4njrneLpz+gqNmr1Ko+e9q0dfqdDYBav11OI1mvDaWr2w7D3NKN+o+e9Uasm697Vy0za9u2W71m3doQ3VO7Vlxwfa+kGNdny4S7tqP1Td3lrV19fpwL49ajywN/R/33T+PuItYAIwEHwIJFxwZIMjf+DJBkc2Rxwt27BTjuvpm3kvh32kyBGV7yM+BEIABoIADBcc2eDIH3iywZHNEUfT3qiMX5Pv8Whfky8MovJ9RAASgIEgAMMFRzY48geebHBkc8TRg3PXyHE9/em5lWEfKXJE5fuIACQAA0EAhguObHDkDzzZ4MjmiKO/PF8ux/X0wLy1YR8pckTl+4gAJAADQQCGC45scOQPPNngyOaIo5+OeU2O62n6iqqwjxQ5ovJ9RAASgIEgAMMFRzY48geebHBk09DQoGnFJepy50w5rqf1O/aEfaTIEZXvIwKQAAwEARguOLLBkT/wZIMjm4aGBj0yuUSO6+krd89WY2NT2EeKHFH5PiIACcBAEIDhgiMbHPkDTzY4svlw78f66p0vyXE9Xf/EkrCPE0mi8n1EABKAgeBC0FxQNOrDEZ5wlPo1NezVgX17NPTF8sRdOrzyjaGfK4qLyvcRF4ImAAPBreAYYyy99n7hqSp94qsqeaS7nnzwxxo5qpdyRvxBffMG6pdDh+nn9+TrJ0Pu19WDH9SVuaP1g7sf1ffuekLfvvNJXXJnob4xaKIuuKNI5w98Tl0O36v3H2/RVvTwlaH/e7LPHreCIwADQQAyxlj6bP5j30jpvXm75EzVX/P76cDEdqH/u7LPHgFIAAaCt4B5KyHqwxGecBRf44G9+sH98+W4nr6VN0+/ePw1/WHSMt1dXK6C2av0zKtrVfz6Bs1cuUlz396s+e9WavHqLSpbW6Xl69/Xio3btKqyWhVV27V+6w5trN6pLTs/0LaaGu3c/aF21+3W/voP09pRW/o+4i1gAjAQfAgkXHBkgyN/4Mkm3R1VfVgvx/V0zh0zVbcvNf8O6e7oWBAVR3wIhAAMBAEYLjiywZE/8GST7o7erqqV43q6ePjLKfsa6e7oWBAVRwQgARgIAjBccGSDI3/gySbdHS1cu1OO66nHg4tT9jXS3dGxICqOCEACMBAEYLjgyAZH/sCTTbo7Kl7xvhzX0/+MTd31+dLd0bEgKo4IQAIwEARguODIBkf+wJNNujt66rWNclxPt0x+M2VfI90dHQui4ogAJAADQQCGC45scOQPPNmku6NRc9bIcT0NKn47ZV8j3R0dC6LiiAAkAANBAIYLjmxw5A882aS7ozuLV8lxPY2asyZlXyPdHR0LouKIACQAA0EAhguObHDkDzzZpLujWya/Kcf19NRrG1P2NdLd0bEgKo4IQAIwEARguODIBkf+wJNN/lgT0wAAFsRJREFUujv6xRNlclxPxSveT9nXSHdHx4KoOCIACcBAcCcQrigf9eEIT23N0YF9e/TGhq2aVLpO4xau1uh572pA0euJ27UteLeyzTsKc1FxxJ1ACMBAcC9gxhgLZw2TMvTq4xfo2YevUO9hg3VF7iPqdsekz7xX75W5o1U34cTQz87CH/cCJgADQQAyxlg4G3H/r1sMvfMGPq8+w+5W/3v/rJwRf1TBqF9q8eNf08FJ/xL6uVk0RgASgIHgLWDeSoj6cISn49FR+aZtidj76ZjFynvpLS1evUXvbtmuXbUfqvHA3jbvKKqLiiPeAiYAA8GHQMIFRzY48geebKLi6IO9+5V91yw5rqfv3bdAjY1NoZ7n74mKoygTFUd8CIQADAQBGC44ssGRP/BkExVH9x++oPNX7p6typr6UM/yj0TFUZSJiiMCkAAMBAEYLjiywZE/8GQTBUdz3qlW55z4W78l5am7nMs/SxQcRZ2oOCIACcBAEIDhgiMbHPkDTzZhOdpW+7Fe37RLc9/drguHzZPjerr9hbci9dbvEfg+somKIwKQAAwEARguOLLBkT/wZHOsHTU1NemdrbU6d9DMZp/w7T5yvvY1HDomZ2gtfB/ZRMURAUgABoIADBcc2eDIH3iyacnRocYmraneozc279Jr6z/QyxXb5b21TVPfqNLkpZUa/+pGjVmwXqPmrlXejArdXbJKt7/wlm6Z/KZ+9NBiXTz8ZX1j6Dx9bcgcnZ87W+fdNUvnDpqZeKvXcT11u2eufjBqoW6a8Ibe3/1xSAZs+D6yiYojAjCNAnDMmDHq3Lmz2rdvr27dumnx4sWf+fyFCxeqW7duat++vc4880w99thjn3rO1KlTlZ2draysLGVnZ2v69OmtOhMBGC44ssGRP6Lg6eChRn184JD27j+o2o8b9OFHB/TB3v3asWefdtTt0/a6faqu3adttR9rW+3H2rr7Y72/+2NVfVivLbviq6yp1+aaj7Tpg4+08YOPtGHnXr23c6/W79ir9Tv2aP2OPVq3fY/Wbt+jNdV7tLq6ThXb6vTu1jq9s7VWq96P7+2qWr1VtVsrt+zW8k279NzyLRr84ipdde/f9D9PlOkXT5TpZ4+V6r/HvKZv5r38mRdfDrpv5r2sbbXRjb6/JwrfR1EnKo4IwDQJwClTpigzM1Pjxo1TRUWF+vfvr44dO6qysvKoz9+4caNOPPFE9e/fXxUVFRo3bpwyMzM1derUxHPKysqUkZGhvLw8rV69Wnl5eWrXrp2WLl3q+1wEYLikm6OmpiY1NTWpsbFJhw7v4KFGHTzUqIZDjTpwML79Bw9p/8FD2tfwyT4+EF/9gYP6aH98ew+v6sN6lb1Xo4Vrd2r+mh16ZfV2zXt3u+a8Uy1vZZUGj39RJW9u0Ysrt6qk/P1mK17xvqavqNL0FVWa9maVpr4R3wtvVOn517fo+de36LkjW75FU5ZXasrySj27rFJFhzd5aaUmLd2sSUs3a+KS+CYs2awJZZs0oWyTninbpKdL4yt8baOe+ruNfzW+Jw9v3OINzTZ2UXxPLHovsccXxvfY4T26IL4xC9ZrzIL1emT+pzf6lXUa/co6PfxyfA8d3oPz4hs1e7VuGv2i7p9doYK5a1Uwd61GHdmcNRo1Z43uP7z7Zsc3cvbqxEbMii//8O6dGV/ezIr4ZsQ3fEaF7nnpXd1Vsko5097Wn59fqX7PrtCvxi3VlwfOSGlIpXLnDJqp746Yr8sLFuqa0a/q54+V6YYnl+rGZ17XH4tW6M/Pr9Sg4rc19KV3dd/sNRr9yjqNW7xBc96p1qr3a1WxrU7rtu/Rhp17VVlTr6oP67Vzz359tP9gJH/XryXS7X+TwiAqjgjANAnAiy66SH379m32WNeuXZWTk3PU599+++3q2rVrs8duuukmXXLJJYl/vu6669SjR49mz7nyyit1/fXX+z5Xqi4EvWXHB1r07ibd//QLWvDOJi1evUWLKrZoYcUWLXi3UvPfqdQr71Tq5VWbNW/VZs19e7PmvL1Zs9/apFlvbdKslZs0c+UmzSjfKK98o15asVEvvrlBJW/EV/z6Bk1//T1NW/6epi5/Ty8se0/PL1uv55bGN2XJehWVrdPE0rV6+tU1Gr9ojcYtXK3H51dozMvv6uG57+iBOe9o1KxVGjnzbd3rvaVhf1upIS+uVG5xue6cvkIDp76p259/Q3+e8oZue/Z19Zu8XH+YtEx9JyzV755eqt8WLtFvxpep15Ol+tXYUv3i8df080df1U/HLNZPRi/SNQ8v0lUPLtSVDyzQFQULdPmo+frhqPn6/v3zddl9r+iy+15R9xEv68Lcv+m7+S/ru/kv69v3vqxv3TtP38qbp2/mzdMlw+fp4uFz9Z/D5urCoXP1jaFz9I175qjbPXP09SFzdMHg2fqPwbP11dzZOj93ls6/e5a+cvcsZd81U9l3zVTXO+PrcudMnTsovnMGzYjvjhk6+/DOGhjflwd6OjMnvs6HF/ZfzOz4WOfD31dfHujprIHx77tz7oh/Lx753uxy+Ps1+66ZOu+u+Pfy+XfP0vm5s/TV3Pj3+gWDZ+trQ2br60Pi/z34xj1z9I2hc3Th0Ph/Ty4aNlcXD5+rS4bH/zv0rbx5+m7+y/rV2FLlTn9T7uNTNXXZOv1txUbNKN+oWW9t0ivvVKp2Dxc+jtJFjqO8qDjiQtBpEIAHDhxQRkbGp96e7devny699NKj/pnvfve76tevX7PHpk+frnbt2iX+X8cZZ5yhgoKCZs8pKChQp06dWjzL/v37VVdXl1hVVVVKbgVXMOqXof+Fw9JnZ+cU67K7HtdVuQ/p6sEP6seDC/STIffrv4fcp2uHjNDP78nXdffcq+vvGa7/GTpcvxw6TL8cOky/GjpUvxo6VDcMvSexXkOH6NfDBuvXwwar97DB+s2wXP1mWK76DLtbfYbdrf8ddrd+O+yu+IbfqRuHD9KNwwfpd8MH6ffD79Dvh9+hm4YP1E3DB6pv3kDdnJejm/NydEueq1vybtctebfrD3m36495f03s1nv/olvv/Yv6HV7/e/+s/vf+WQPu/VNitx1Z/m26Lf82/Sl/gP6UP0B/zu+f2F8O76/5/fTX/H66Pf9W3Z5/q9wRnyxnxB8P7w/KGfEHDTy8O0bcktigkTdr0MibdefIvrpzZF/ddXh3j7wpsdyRv2+2wff9ToPv+52G3HdjYvcc3tD7fquh9/1Ww+77Xw2773917/29NWrUr/RQwfV69IFrNe7Bn+iZh67WqnFnae+EDvp4Ynvtn9hOhyb9i5omhX/LLMaOx3EruDQIwK1btyoWi6m0tLTZ48OHD9e555571D9zzjnnaPjw4c0eKy0tVSwW07Zt2yRJmZmZmjx5crPnTJ48WVlZWS2eJTc3V7FY7FNLdgBOeOhHuvzuMboi9xFdmTtaV+aOVo/chxN/wfcc/ICuGVygHw8u0I8Hj9JPhtyv/zr8F/5Ph4zUtUNG6GeH/+L/+T336hf35DX7y//v/9I/8hf+kb/sE3/JD79TNw0fqFvyXP0x76/qf++fdVv+bfpLfn/ljPij7hhxi+4a2Ve5I3+ve+67UcPv66P8+3vrvlE3aNSoX+rBgus1uuA6jXngZ3riwf/WuAd/oqceukbPPHS1Jj58lYoevlLPjb5cUx/5voof+Z5eHHOpvDHf1qxHv6k5j16sVx67UAse76ZFj39drz5+gV57/AKVPvFVlT3xVS154nwtfeIrWjb2K1o+9jy9PjZbb4ztqjfHdtGKceeqfNy5WjnuHL017mytGneWVj35Zb3z5Jl698kzVfFkZ61+0tHa8Z20bvwZWj/+/2n9+P+n98afrg1P/V9tfOr/atNTp6nyqX9T5VP/pi2FR/b/qarwVFUVnqr3C0/V1sIvaWvhl7St8IvaVvhFVT/9RW1/+hRtf/oU7Xj6C9rx9P/Rjqf/j3Ye3gfPnKyaZ05SzTMnadczJ+nDZz6vD5/5vHY/86/a/cy/qvaZjqqdEF/dhBO1Z0IH7ZnQQXsP76MJJ6h+YnvVT2yvjye2176JWYeXqUPc35Qxxlo1AjCNArCsrKzZ48OGDVOXLl2O+mfOOecc5eXlNXvstddeUywWU3V1taR4ABYVFTV7zqRJk9S+ffsWz9LSTwBrtleq4ePdSV193U69VDxF9XU7k/7ax8twhCM84ShKw1H6OKrZXkkAhn0Aiyi9BfyP8CGQcMGRDY78gScbHNngyCYqjvgQSBoEoBT/EMjNN9/c7LHs7OzP/BBIdnZ2s8f69u37qQ+BXHXVVc2e06NHj3/uQyAEYCjgyAZH/sCTDY5scGQTFUcEYJoE4JHLwIwfP14VFRUaMGCAOnbsqM2bN0uScnJy1KtXr8Tzj1wG5rbbblNFRYXGjx//qcvAlJaWKiMjQ/n5+Vq9erXy8/O5DEyagSMbHPkDTzY4ssGRTVQcEYBpEoBS/ELQjuMoKytL3bp106JFixL/We/evdW9e/dmz1+4cKG+/vWvKysrS507dz7qhaBfeOEFdenSRZmZmerataumTZvWqjMRgOGCIxsc+QNPNjiywZFNVBwRgGkUgFGEAAwXHNngyB94ssGRDY5souKIACQAA0EAhguObHDkDzzZ4MgGRzZRcUQAEoCBIADDBUc2OPIHnmxwZIMjm6g4IgAJwEAQgOGCIxsc+QNPNjiywZFNVBwRgARgIAjAcMGRDY78gScbHNngyCYqjghAAjAQBGC44MgGR/7Akw2ObHBkExVHBCABGAgCMFxwZIMjf+DJBkc2OLKJiiMCkAAMBAEYLjiywZE/8GSDIxsc2UTFEQFIAAaCAAwXHNngyB94ssGRDY5souKIACQAA0EAhguObHDkDzzZ4MgGRzZRcUQAEoCBqK2tVSwWU1VVlerq6pK6mpoaFRUVqaamJumvfbwMRzjCE46iNBylj6OqqirFYjHV1taGnRKhQQAG4Mg3EGOMMcbSb1VVVWGnRGgQgAFobGxUVVWVamtrU/b/TlLx08XjZTjCEZ5wFKXhKH0c1dbWqqqqSo2NjWGnRGgQgBGlro7fT7DAkQ2O/IEnGxzZ4MgGR9GBAIwo/JfEBkc2/3979xYSVdeAcXyXjVOaSAfzFOrNW0qWESIGkaRWF2lBBCKFRkctS6GLTtAQVNhF3SnSgQiKDEqhSKIDaVB0YhRHo/JUkQUREhY5QszzXWj7fbfpZzc1m/b/B3Oz1kLWPIzj48zsNWT0a8hpfGQ0PjIaHxnZBwXQpvglGR8ZjY+Mfg05jY+MxkdG4yMj+6AA2pTf75fH45Hf7w/2VmyLjMZHRr+GnMZHRuMjo/GRkX1QAAEAAByGAggAAOAwFEAAAACHoQACAAA4DAUQAADAYSiANlRVVaWkpCS53W4tWrRI9+/fD/aW/pimpibl5eUpNjZWhmGovr7eMh8IBOTxeBQbG6vJkycrKytLbW1tljV+v19lZWWaMWOGwsLClJ+f/1d93c+xY8eUnp6uqVOnKioqSmvWrNGLFy8sa5yeU3V1tebPn6+IiAhFREQoMzNTDQ0N5rzT8xnNsWPHZBiGysvLzTFykjwez09fHxYdHW3Ok9GQd+/eaf369Zo+fbqmTJmitLQ0PXv2zJwnJ/uhANpMbW2tXC6XTp8+refPn6u8vFzh4eF68+ZNsLf2RzQ0NOjgwYO6evXqqAWwsrJSERERunr1qnw+nwoKChQbG6v+/n5zTUlJieLj43X79m15vV4tW7ZMaWlp+v79+5++O7/FypUrde7cObW1tamlpUWrVq1SQkKCvn79aq5xek7Xrl3TjRs39PLlS718+VIHDhyQy+Uy/+A4PZ+Rnjx5oqSkJC1YsMBSAMlpqADOmzdPHz58MG8fP34058lI6uvrU2JiojZu3KjHjx+rp6dHd+7cUWdnp7mGnOyHAmgzGRkZKikpsYwlJydr3759QdpR8IwsgIFAQDExMaqsrDTH/H6/IiMjVVNTI0n6/PmzXC6XamtrzTW9vb2aOHGibt68+ec2/wd9/PhRhmGoqalJEjmNZdq0aTpz5gz5jPDlyxf9888/un37trKysswCSE5DPB6P0tLSRp0joyF79+7VkiVLxpwnJ3uiANrI4OCgQkJCVFdXZxnfvXu3li5dGqRdBc/IAtjV1SXDMOT1ei3rVq9eraKiIknS3bt3ZRiG+vr6LGsWLFigQ4cO/f5NB0FHR4cMw5DP55NETiN9//5dly5dUmhoqNrb28lnhKKiIlVUVEiSpQCS0xCPx6OwsDDFxsYqKSlJBQUF6urqkkRGP6SkpKiiokLr1q1TVFSUFi5cqFOnTpnz5GRPFEAb6e3tlWEYevDggWX86NGjmjNnTpB2FTwjC+CDBw9kGIZ6e3st67Zu3aoVK1ZIki5evKjQ0NCfftby5cu1bdu237vhIAgEAsrPz7f8901OQ1pbWxUeHq6QkBBFRkbqxo0bksjnvy5duqTU1FQNDAxIshZAchrS0NCgK1euqLW11XyVNDo6Wp8+fSKjYW63W263W/v375fX61VNTY0mT56s8+fPS+KxZFcUQBv5UQAfPnxoGT9y5Ijmzp0bpF0Fz1gF8P3795Z1W7Zs0cqVKyWN/SSSm5ur7du3/94NB8GOHTuUmJho+aA0OQ0ZHBxUR0eHnj59qn379mnmzJlqb28nn2Fv377VrFmz1NLSYo6NVgCdntNIX79+VXR0tE6cOEFGw1wulxYvXmwZ27VrlzIzMyXxWLIrCqCN8BawFW8B/39lZWWaPXu2uru7LePkNLqcnBxt27aNfIbV19fLMAyFhISYN8MwNGHCBIWEhKizs5OcxpCbm6uSkhIeS8MSEhK0efNmy1h1dbXi4uIk8ZxkVxRAm8nIyFBpaallLCUlhYtA9O8HiY8fP26ODQ4OjvpB4suXL5tr3r9//1d9kDgQCGjnzp2Ki4vTq1evRp0np59lZ2eruLiYfIb19/fL5/NZbunp6dqwYYN8Ph85jcHv9ys+Pl6HDx8mo2GFhYU/XQRSUVFhvipITvZEAbSZH8fAnD17Vs+fP1dFRYXCw8P1+vXrYG/tj/jy5Yuam5vV3NwswzB08uRJNTc3m8fgVFZWKjIyUnV1dfL5fCosLBz1KIHZs2frzp078nq9ys7O/quOEigtLVVkZKQaGxstR1N8+/bNXOP0nPbv36/79++rp6dHra2tOnDggCZOnKhbt25JIp+x/PctYImcJGnPnj1qbGxUd3e3Hj16pLy8PEVERJjPyWQ0dIzQpEmTdPToUXV0dOjixYsKCwvThQsXzDXkZD8UQBuqqqpSYmKiQkNDtWjRIvN4Dye4d+/eT4euGoah4uJiSf8eJhoTEyO3262lS5eaV7/+MDAwoLKyMvNA0ry8PL19+zYI9+b3GC0fwzB07tw5c43Tc9q0aZP5OxQVFaWcnByz/EnkM5aRBZCcZJ5X53K5FBcXp7Vr16q9vd2cJ6Mh169fV2pqqtxut5KTky1XAUvkZEcUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAIAADgMBRAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DD/AzYEcd8eGE+8AAAAAElFTkSuQmCC\" 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\" width=\"640\">" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 35 ... 161\n" + ] + } + ], + "source": [ + "load_run(33, plot=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 959, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "<IPython.core.display.Javascript object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" @@ -4330,21 +6270,1159 @@ { "data": { "text/plain": [ - "[<matplotlib.lines.Line2D at 0x7f3dfe402ca0>]" + "[<matplotlib.lines.Line2D at 0x7f3e0e5af160>]" ] }, - "execution_count": 717, + "execution_count": 959, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "fig, ax = plt.subplots()\n", + "x = np.linspace(0, 1, 100000)\n", + "y = 1 - (1 + np.tanh((x - 0.05)/0.05 * np.pi))/2\n", + "ax.plot(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 1000, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "<IPython.core.display.Javascript object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<img 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width=\"950\">" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ===== Run 29: =====\n", + "Loading run #29 with 2387 packets total, 206 distinct over 194.01968002319336s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 827 ... 1071\n", + "interval: 0.10719954325502887\n", + "scores: [0.0005558591029225243, 0.007937380545979303, -0.034688942841427636, 0.009128952125600649, 0.013406031099251599, 0.013555935298953624, 0.017543481008845643, 0.017016779430178704, 0.01657709203008989]\n", + "argmin: 3\n", + "min score -0.034688942841427636 0.006114450132147767\n", + "Average speed of rotation: 3.11 Hz / 187 rpm\n", + "\n", + " f_meas = 3.10 Hz; f_est = 3.11 Hz\n", + " Δabs = 0.01 Hz; Δrel = +0.3 %\n", + "\n", + " ===== Run 28: =====\n", + "Loading run #28 with 4918 packets total, 178 distinct over 161.5061011314392s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 611 ... 813\n", + "interval: 0.11439170940941061\n", + "scores: [-0.020534204449181033, -0.03948834332572777, 0.008701765576176747, 0.013951642455491747, 0.014883935285322263, 0.013919464173338898, 0.013597797966206103, 0.01283288815134905, 0.012482191795223349]\n", + "argmin: 2\n", + "min score -0.03948834332572777 0.003962460736427981\n", + "Average speed of rotation: 4.37 Hz / 262 rpm\n", + "\n", + " f_meas = 4.35 Hz; f_est = 4.37 Hz\n", + " Δabs = 0.02 Hz; Δrel = +0.5 %\n", + "\n", + " ===== Run 37: =====\n", + "Loading run #37 with 3070 packets total, 144 distinct over 117.43898510932922s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 326 ... 473\n", + "interval: 0.14865745248471265\n", + "scores: [-0.03883825341506492, 0.005310783081280478, 0.008339889910074037, 0.012661676804576305, 0.012709861511978487, 0.013085760738242736, 0.012322417269530055, 0.01163783853233394, 0.011025320714842681]\n", + "argmin: 1\n", + "min score -0.03883825341506492 -0.19352513539180108\n", + "Average speed of rotation: 6.73 Hz / 404 rpm\n", + "\n", + " f_meas = 6.67 Hz; f_est = 6.73 Hz\n", + " Δabs = 0.06 Hz; Δrel = +0.9 %\n", + "\n", + " ===== Run 30: =====\n", + "Loading run #30 with 2208 packets total, 198 distinct over 166.86816382408142s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 1079 ... 1288\n", + "interval: 0.14157585656540028\n", + "scores: [-0.027368924365523392, 0.0016046408793783343, 6.757317985323834e-05, 0.010033377011136338, 0.014202212278658977, 0.01690264817464519, 0.016602774129523788, 0.016489898206424946, 0.01562597856556622]\n", + "argmin: 1\n", + "min score -0.027368924365523392 -0.1252939758810193\n", + "Average speed of rotation: 7.06 Hz / 424 rpm\n", + "\n", + " f_meas = 7.04 Hz; f_est = 7.06 Hz\n", + " Δabs = 0.02 Hz; Δrel = +0.3 %\n", + "\n", + " ===== Run 31: =====\n", + "Loading run #31 with 2630 packets total, 157 distinct over 126.19650292396545s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 1316 ... 1474\n", + "interval: 0.13094824532824215\n", + "scores: [-0.03704675515805195, 0.006079322222570231, 0.006225731403203891, 0.007881091442639427, 0.010594872980404482, 0.01181994722739787, 0.011899512695214984, 0.012162509479493641, 0.011532909467429079]\n", + "argmin: 1\n", + "min score -0.03704675515805195 -0.19455134377560843\n", + "Average speed of rotation: 7.64 Hz / 458 rpm\n", + "\n", + " f_meas = 7.57 Hz; f_est = 7.64 Hz\n", + " Δabs = 0.07 Hz; Δrel = +0.9 %\n", + "\n", + " ===== Run 34: =====\n", + "Loading run #34 with 2714 packets total, 121 distinct over 97.30304408073425s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 165 ... 287\n", + "interval: 0.11474772130686393\n", + "scores: [-0.030846569646590577, -0.017939041958299002, 0.007585993694854823, 0.013487796252691883, 0.01286325763359304, 0.012345763316803614, 0.011619541945226932, 0.010974011837158768, 0.010396432266781988]\n", + "argmin: 1\n", + "min score -0.030846569646590577 -0.09545980709610284\n", + "Average speed of rotation: 8.71 Hz / 523 rpm\n", + "\n", + " f_meas = 8.62 Hz; f_est = 8.71 Hz\n", + " Δabs = 0.09 Hz; Δrel = +1.1 %\n", + "\n", + " ===== Run 38: =====\n", + "Loading run #38 with 2317 packets total, 122 distinct over 103.3755898475647s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 475 ... 604\n", + "interval: 0.10489854346020394\n", + "scores: [-0.029808429081348096, -0.022247814228522737, 0.007496451366422777, 0.012847680228334563, 0.012120698169116668, 0.01148795434824668, 0.0108151380653151, 0.010214297061686485, 0.009676702479492458]\n", + "argmin: 1\n", + "min score -0.029808429081348096 -0.08125594859438061\n", + "Average speed of rotation: 9.53 Hz / 572 rpm\n", + "\n", + " f_meas = 9.43 Hz; f_est = 9.53 Hz\n", + " Δabs = 0.10 Hz; Δrel = +1.1 %\n", + "\n", + " ===== Run 32: =====\n", + "Loading run #32 with 2643 packets total, 118 distinct over 98.81923913955688s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 3 ... 1583\n", + "interval: 0.09441178519979387\n", + "scores: [-0.013690197808912317, -0.008608471942849465, 0.00431677808670242, 0.007819524539326253, 0.007824114618254871, 0.007696014525605405, 0.007352130944008647, 0.007048798098339794, 0.006677808724742963]\n", + "argmin: 1\n", + "min score -0.013690197808912317 -0.038159149663930074\n", + "Average speed of rotation: 10.59 Hz / 636 rpm\n", + "\n", + " f_meas = 10.40 Hz; f_est = 10.59 Hz\n", + " Δabs = 0.19 Hz; Δrel = +1.8 %\n", + "\n", + " ===== Run 39: =====\n", + "Loading run #39 with 1429 packets total, 129 distinct over 104.99669194221497s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 606 ... 737\n", + "interval: -0.009543333906374466\n", + "scores: [0.00016699475726220442, 0.00015527347855214557, 0.0006494893315487613, 0.00013309155304469622, 0.0008010865133540054, 0.0010090902176491685, 0.001234839163051902, 0.00010351565236809708, 0.0014523737093152392]\n", + "argmin: 8\n", + "min score 0.00010351565236809708 0.0013446078569502677\n", + "Average speed of rotation: 13.10 Hz / 786 rpm\n", + "\n", + " f_meas = 13.10 Hz; f_est = 13.10 Hz\n", + " Δabs = -0.00 Hz; Δrel = -0.0 %\n", + "\n", + " ===== Run 33: =====\n", + "Loading run #33 with 3178 packets total, 125 distinct over 99.94346904754639s\n", + "Packet length: 40\n", + "Very approximate lower bound on baudrate: 129032.25806451612 bd\n", + "Sequence number range: 35 ... 161\n", + "interval: 0.09687252539136751\n", + "scores: [-0.004649921628008072, -0.012634045995330379, 0.0037816457952707543, 0.0017246285208280352, 0.009875890071400597, 0.011254914748918448, 0.012497966748572316, 0.013301204816616601, 0.012601141405215724]\n", + "argmin: 2\n", + "min score -0.012634045995330379 0.013042674363101692\n", + "Average speed of rotation: 5.16 Hz / 310 rpm\n", + "\n", + " f_meas = 16.10 Hz; f_est = 5.16 Hz\n", + " Δabs = -10.94 Hz; Δrel = -67.9 %\n" + ] + } + ], + "source": [ "def calc_rspeed_deltas(target_deltas):\n", " target_deltas = np.array(target_deltas)\n", - " target_deltas = target_deltas[0:-int(len(target_deltas)*0.1)]\n", - " target_deltas = target_deltas[target_deltas > 1/30]\n", + " target_deltas = target_deltas[target_deltas > 1/50]\n", + " target_deltas = target_deltas[0:int(len(target_deltas)*0.9)]\n", " def fun(x):\n", - " return np.sqrt(np.mean([ ((val + 0.5*x[0]) % x[0] - 0.5*x[0])**2 for val in target_deltas ]))\n", + " rms = np.sqrt(np.mean([ ((val + 0.5*x[0]) % x[0] - 0.5*x[0])**2 for val in target_deltas ]))\n", + " #matches = (target_deltas > 0.5*x[0]).mean()\n", + " matches1 = ((target_deltas > (0.9*x[0])) & (target_deltas < (1.1*x[0]))).mean()\n", + " matches2 = ((target_deltas > (1.9*x[0])) & (target_deltas < (2.1*x[0]))).mean()\n", + " #matchesh= ((target_deltas > (0.45*x[0])) & (target_deltas < (0.55*x[0]))).mean()\n", + " #matches3 = ((target_deltas > (2.9*x[0])) & (target_deltas < (3.1*x[0]))).mean()\n", + " #penalty = 1 - (1 + np.tanh((matches1 - 0.05)/0.05 * np.pi))/2\n", + " return rms - 0.5*matches1 - 0.25*matches2 #+ 0.125*matches3 #+ 0.1*penalty\n", "\n", " #def accept(x_old, x_new, **kwargs):\n", " # return 1/30 < x_new[0] and x_new[0] < 1\n", @@ -4357,17 +7435,1055 @@ " print('scores:', scores)\n", " argmin = np.argmin(scores)+1\n", " print('argmin:', argmin)\n", + " print('min score', min(scores), fun([interval*0.5]))\n", " interval = np.abs(interval) * argmin\n", " print(f'Average speed of rotation: {1/interval:.2f} Hz / {60 / interval:.0f} rpm')\n", " return interval\n", "\n", - "target_deltas = sorted(np.array(deltas)[speed_idx[:-1]])\n", - "interval = calc_rspeed_deltas(target_deltas)\n", + "def estimate_freq_delay(delays, times, interval, ax=None):\n", + " s_min, s_max = interval\n", + " tsa = np.array(times)\n", + " idx = (tsa > s_min) & (tsa < s_max)\n", + "\n", + " target_deltas = sorted(np.array(delays)[idx[:-1]])\n", + " interval = calc_rspeed_deltas(target_deltas)\n", + " \n", + " if ax is not None:\n", + " ax.grid()\n", + " for i in range(1, int(max(target_deltas)//interval)):\n", + " ax.axhline(i*interval, color='orange')\n", + " ax.plot(target_deltas)\n", + " return 1/interval\n", + " \n", + "run_spans = {\n", + " 28: (4.35, 70, 120),\n", + " 29: (3.10, 70, 120),\n", + " 30: (7.04, 40, 120),\n", + " 31: (7.57, 30, 100),\n", + " 32: (10.4, 40, 90),\n", + " 33: (16.1, 40, 75),\n", + " 34: (8.62, 20, 80),\n", + " 37: (6.67, 20, 90),\n", + " 38: (9.43, 20, 80),\n", + " 39: (13.1, 20, 85),\n", + " }\n", + "\n", + "deltas = []\n", + "fig, axs = plt.subplots(5, 2, figsize=(9.5, 14))\n", + "for (run_id, (freq_meas, t_start, t_end)), ax in zip(sorted(run_spans.items(), key=lambda x: x[1][0]), axs.flatten()):\n", + " \n", + " print()\n", + " print(f' ===== Run {run_id}: =====')\n", + " t, y, packet_delay, packet_times = load_run(run_id, plot=False)\n", + " freq_est = estimate_freq_delay(packet_delay, packet_times, (t_start, t_end), ax=ax)\n", + " ax.set_title(f'Run {run_id} @ $f_{{meas}} = {freq_meas:02.2f} Hz$ / $f_{{est}} = {freq_est:02.2f} Hz$')\n", + " print()\n", + " print(f' f_meas = {freq_meas:02.2f} Hz; f_est = {freq_est:02.2f} Hz')\n", + " delta_abs = freq_est - freq_meas\n", + " delta_rel = freq_est/freq_meas-1\n", + " deltas.append((delta_abs, delta_rel))\n", + " print(f' Δabs = {delta_abs:02.2f} Hz; Δrel = {delta_rel*100:+.1f} %')\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 995, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", + " if (this.ResizeObserver === undefined) {\n", + " if (window.ResizeObserver !== undefined) {\n", + " this.ResizeObserver = window.ResizeObserver;\n", + " } else {\n", + " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", + " this.ResizeObserver = obs.ResizeObserver;\n", + " }\n", + " }\n", + "\n", + " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " this.resizeObserverInstance.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "\n", + "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", + "// prettier-ignore\n", + "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.on(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " if (event.target !== this) {\n", + " // Ignore bubbled events from children.\n", + " return;\n", + " }\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "<IPython.core.display.Javascript object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "<img 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\" width=\"640\">" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Text(0.5, 0, '$f\\\\;[Hz]$')" + ] + }, + "execution_count": 995, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ "fig, ax = plt.subplots()\n", + "#freqs = sorted([ f for f, _1, _2 in run_spans.values() ])\n", + "#ax.plot(freqs[:-1], [ delta_abs for delta_abs, _delta_rel in deltas[:-1] ])\n", + "#ax.set_ylabel('$\\Delta_{abs}\\;[Hz]$')\n", + "\n", + "#ax = ax.twinx()\n", "ax.grid()\n", - "for i in range(int(max(target_deltas)//interval)):\n", - " ax.axhline(i*interval, color='orange')\n", - "ax.plot(target_deltas)" + "ax.plot(freqs[:-1], [ delta_rel*100 for _delta_abs, delta_rel in deltas[:-1] ])#, color='orange')\n", + "ax.set_ylabel('$\\Delta_{rel}\\;[\\%]$')\n", + "\n", + "ax.set_xlabel('$f\\;[Hz]$')" ] }, { |