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-rw-r--r--tools/BER Plots.ipynb1215
-rw-r--r--tools/Phase Measurement Prototype.ipynb1803
-rw-r--r--tools/ROCOF test data generator.ipynb235
-rwxr-xr-xtools/dec_proto_am_ber_top.py220
-rwxr-xr-xtools/dec_proto_am_dc_ber_top.py191
-rwxr-xr-xtools/dec_proto_fm_ber_top.py183
-rw-r--r--tools/grid_frequency_spectra.ipynb4206
-rw-r--r--tools/rocof_test_data.py180
-rw-r--r--tools/sweep_gr_sims.py93
9 files changed, 8326 insertions, 0 deletions
diff --git a/tools/BER Plots.ipynb b/tools/BER Plots.ipynb
new file mode 100644
index 0000000..794872b
--- /dev/null
+++ b/tools/BER Plots.ipynb
@@ -0,0 +1,1215 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "import statistics\n",
+ "import json\n",
+ "\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "import matplotlib as mpl\n",
+ "import tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#from math import nan, inf\n",
+ "#data = {'dec_proto_am_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.1706362050026655, -1.193387892562896, -1.2494141100905836, -1.273546683602035, -1.3226867043413222, -1.3284842972643673, -1.4249085476621985, -2.4881654670462012, -2.9280282892286777, -1.8337596086785197, -3.4516299068927765, -3.6739503433927894, -3.85142894461751, -4.2109690103679895, -4.841764334589243, -5.121118910610676, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf, -inf], [0.000562024584446438, 0.002583366143280799, 0.003536123538459578, 0.0060136203314800725, 0.0017120634851061035, 0.01202664019209608, 0.009352711681458127, 0.010626429313400118, 0.0031605552412962345, 0.07580074150906693, 0.008303067934118849, 0.010968003992851543, 0.010921403354231309, 0.014436211616218221, 0.045257276108434545, 0.05063300417965297, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]), 'dec_proto_am_dc_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.208226392045617, -1.2001309534534812, -1.2082590111531317, -1.2057580375112593, -1.214704089694553, -1.231758143831406, -1.2328452042170934, -1.2342556988606688, -1.2555496906861663, -1.2633800823241472, -1.2801077286712825, -1.292429564986378, -1.2502315024699062, -1.2731027859982436, -1.3264964096914462, -1.350060076963517, -1.402916835230801, -1.6361557068303227, -1.3996004345826805, -2.025891115888953, -2.2259163050377957, -2.403329889470167, -2.5532801901852644, -2.6723825335502625, -2.7451475376985512, -2.7838943274880226, -2.7973828878928355, -2.8114503007382154, -2.7500487601808214, -2.7576294792325875, -2.7531131004032336, -2.771351588479543, -2.763352069271704, -2.7856492625232554, -2.8089246354122395, -2.805404500961304], [0.0006223969511333752, 0.001109700896962153, 0.00210398864758181, 0.0009171589283670842, 0.01005799259051457, 0.01198940071540007, 0.013730311872618627, 0.020358273695306007, 0.019376830251761356, 0.02698367824924875, 0.03015560422449139, 0.04189253434399468, 0.04626542022859063, 0.07217384274518368, 0.08584595043975161, 0.12539079396237413, 0.09791907379447246, 0.10581626829587948, 0.18250650933422224, 0.07591527055792387, 0.20120497031325296, 0.2529568393261202, 0.3140587593946733, 0.3626712973758648, 0.39454531783086805, 0.40694947364033235, 0.4101018950589088, 0.38136874448954844, 0.4108311426740005, 0.40839715897167816, 0.4083367927775933, 0.40823628264400785, 0.4080951641200549, 0.40959607776701595, 0.40969886669408834, 0.4099477409126599]), 'dec_proto_fm_ber_top.py': ([1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2, 10.0, 12.0, 15.0, 18.0, 22.0, 27.0, 33.0, 39.0, 47.0, 56.0, 68.0, 82.0, 100.0, 120.0, 150.0, 180.0, 220.00000000000003, 270.0, 330.0, 390.0, 470.0, 560.0, 680.0, 819.9999999999999], [-1.3057961403392255, -1.3484294968657196, -1.4667961434461176, -2.8690875116735697, -1.6547222812660038, -1.3891625558026135, -3.5982019547373056, -3.771391890011728, -4.029223203659058, -4.187133187428117, -4.5257152915000916, -4.8291374538093805, -4.9882102105766535, -4.988903861492872, -4.977243402972817, -4.991583617404103, -4.978662932291627, -4.995597720146179, -4.980234434828162, -4.898328188806772, -4.886065758764744, -4.892892232164741, -4.887955756857991, -4.894121825695038, -4.874834077432752, -4.881909834221005, -4.885749246925116, -4.879474958404899, -4.893610496073961, -4.893589161336422, -4.900892127305269, -4.89244575984776, -4.886744260787964, -4.895636919885874, -4.909515650942922, -4.8994301706552505], [0.014213245118859085, 0.001330722343276248, 0.013951488821076687, 0.0041134580502828425, 0.038365233682153145, 0.030733212747131068, 0.0091992661239188, 0.010529797577944408, 0.014647350039240111, 0.014036738695564741, 0.0201667482688038, 0.03195929762792339, 0.050554225347760565, 0.05155121488079693, 0.05696637316379902, 0.05194819962648275, 0.04815391425232906, 0.04198674248536032, 0.0531488148233794, 0.043095657257340825, 0.05140641385191975, 0.047935496094956176, 0.05329373773860191, 0.05040869503181174, 0.05644083328947176, 0.053389328604204575, 0.05074839526504205, 0.053625197798602975, 0.047252304573416753, 0.051310379811370974, 0.046438087027853785, 0.05365724267638675, 0.0534321058650641, 0.04956836848859283, 0.04218369035098332, 0.05032427561533336])}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "75ad09f7b6df4c1aa68ef78b1e4ceb0a",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "HBox(children=(IntProgress(value=0, max=380), HTML(value='')))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Will launch 450 simulation jobs in 38 batches of 12\n",
+ "Starting batch 1/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 2/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 3/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 4/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 5/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 6/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 7/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 8/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 9/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 10/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 11/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 12/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 13/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 14/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 15/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 16/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 17/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 18/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 19/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 20/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 21/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 22/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 23/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 24/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 25/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 26/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 27/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 28/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 29/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 30/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 31/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 32/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 33/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 34/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 35/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 36/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 37/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "Starting batch 38/38...\n",
+ "done.\n",
+ "Waiting for simulation:\n",
+ "Terminating processes...\n",
+ "done.\n",
+ "Processing simulation results\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "import sweep_gr_sims\n",
+ "data = sweep_gr_sims.run_simulation(\n",
+ " amplitudes = [10**x for x in np.linspace(0, 2.5, 30)],\n",
+ " #simulations=['dec_proto_am_ber_top.py'],\n",
+ " duration=10.0,\n",
+ " forklimit=12,\n",
+ " repeat_runs=5,\n",
+ " tqdm=tqdm.tqdm_notebook)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#with open('gr_sweep_results2.json', 'w') as f:\n",
+ "# f.write(json.dumps(data))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('results_digitalocean2.json') as f:\n",
+ " data = json.loads(f.read())\n",
+ " for sim in list(data):\n",
+ " data[sim] = {\n",
+ " float(a): entry for a, entry in data[sim].items()\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "labels = {\n",
+ " 'dec_proto_am_dc_ber_top.py': '\"DC\"',\n",
+ " 'dec_proto_am_ber_top.py': 'ASK',\n",
+ " 'dec_proto_fm_ber_top.py': 'FSK'\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"900\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Note on this plot: For the ASK trial, above a certain level no further errors were detected,\n",
+ "# thus the BER could not be calculated. The ASK line stops at this point.\n",
+ "\n",
+ "# Another note: The wobbliness of the plotted lines despite the low standard deviation of measurements\n",
+ "# lets us conclude there are some serious systematic errors in this simulation. Since we're only interested\n",
+ "# in a ballpark measurement here, this is not a significant issue.\n",
+ "\n",
+ "# A BER of 0.5 in dB\n",
+ "ber05 = 10*math.log10(0.5)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(9, 5))\n",
+ "ax.set_title('BERs for basic modulation types')\n",
+ "if data.keys() - labels.keys():\n",
+ " raise ValueError(f'Unmatched simulation labels: {data.keys() - labels.keys()}')\n",
+ "for sim, label in labels.items():\n",
+ " d = data[sim]\n",
+ " ampls = np.array(sorted(list(d.keys())))\n",
+ " filter_inf = lambda l: [ x for x in l if math.isfinite(x) ] or [-math.inf]\n",
+ " filter_nan = lambda l: [ x for x in l if math.isfinite(x) ] or [math.nan]\n",
+ " bers = np.array([ statistics.mean(filter_inf(d[a][0])) for a in ampls ])\n",
+ " #stdevs = np.array([statistics.stdev(filter_inf(d[a][0])) if len(filter_inf(d[a][0]))>1 else 0 for a in ampls])\n",
+ " #stdevs = np.array([math.sqrt(statistics.mean([x**2 for x in filter_nan(d[a][1])] + [0])) for a in ampls])\n",
+ " stdevs = np.array([statistics.stdev((np.array(filter_inf(d[a][0])) + math.log10(8))*10) if len(filter_inf(d[a][0]))>1 else 0 for a in ampls])\n",
+ " \n",
+ " # The Gnuradio BER block calculates bit error rate over whole bytes, but we only feed in bits casted\n",
+ " # to bytes. We correct for this by adding log10(8).\n",
+ " # Also convert log10 values to dB.\n",
+ " bers = (bers + math.log10(8))*10\n",
+ " #stdevs = (stdevs + math.log10(8)) *10\n",
+ " #ax.errorbar(ampls, bers, yerr=stdevs, label=label)\n",
+ " p, = ax.plot(ampls, bers, label=label)\n",
+ " \n",
+ " ax.fill_between(ampls, bers-stdevs, np.clip(bers+stdevs, a_min=None, a_max=ber05),\n",
+ " alpha=0.3, facecolor=p.get_color(), linewidth=0)\n",
+ "ax.grid()\n",
+ "ax.legend()\n",
+ "ax.set_xscale('log')\n",
+ "ax.set_xlabel('Amplitude Δf [mHz]')\n",
+ "ax.set_ylabel('BER [dB]')\n",
+ "ax.set_ylim([-50, 0])\n",
+ "ax.axhline(ber05, linestyle='--', color='red')\n",
+ "bbox = {'facecolor': 'black', 'alpha': 0.8, 'pad': 2}\n",
+ "xform = mpl.transforms.blended_transform_factory(ax.transAxes, ax.transData)\n",
+ "ax.text(0.9, ber05, f'BER=0.5', transform=xform, color='white', bbox=bbox, ha='center', va='center')\n",
+ "\n",
+ "None"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/Phase Measurement Prototype.ipynb b/tools/Phase Measurement Prototype.ipynb
new file mode 100644
index 0000000..750969d
--- /dev/null
+++ b/tools/Phase Measurement Prototype.ipynb
@@ -0,0 +1,1803 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 224,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "\n",
+ "import numpy as np\n",
+ "from scipy import signal, optimize\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import rocof_test_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 225,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 275,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "fs = 1000 # Hz\n",
+ "ff = 50 # Hz\n",
+ "duration = 60 # seconds\n",
+ "# test_data = rocof_test_data.sample_waveform(rocof_test_data.test_close_interharmonics_and_flicker(),\n",
+ "# duration=20,\n",
+ "# sampling_rate=fs,\n",
+ "# frequency=ff)[0]\n",
+ "# test_data = rocof_test_data.sample_waveform(rocof_test_data.gen_noise(fmin=10, amplitude=1),\n",
+ "# duration=20,\n",
+ "# sampling_rate=fs,\n",
+ "# frequency=ff)[0]\n",
+ "\n",
+ "\n",
+ "#gen = rocof_test_data.gen_noise(fmin=10, amplitude=1)\n",
+ "# gen = rocof_test_data.gen_noise(fmin=60, amplitude=0.2)\n",
+ "# gen = rocof_test_data.test_harmonics()\n",
+ "# gen = rocof_test_data.gen_interharmonic(*rocof_test_data.test_interharmonics)\n",
+ "# gen = rocof_test_data.test_amplitude_steps()\n",
+ "# gen = rocof_test_data.test_amplitude_and_phase_steps()\n",
+ "test_data = []\n",
+ "test_labels = [ fun.__name__.replace('test_', '') for fun in rocof_test_data.all_tests ]\n",
+ "for gen in rocof_test_data.all_tests:\n",
+ " test_data.append(rocof_test_data.sample_waveform(gen(),\n",
+ " duration=duration,\n",
+ " sampling_rate=fs,\n",
+ " frequency=ff)[0])\n",
+ "# d = 10 # seconds\n",
+ "# test_data = np.sin(2*np.pi * ff * np.linspace(0, d, int(d*fs)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 276,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "analysis_periods = 10\n",
+ "window_len = fs * analysis_periods/ff\n",
+ "nfft_factor = 4\n",
+ "sigma = window_len/8 # samples\n",
+ "\n",
+ "ffts = []\n",
+ "for item in test_data:\n",
+ " f, t, Zxx = signal.stft(item,\n",
+ " fs = fs,\n",
+ " window=('gaussian', sigma),\n",
+ " nperseg = window_len,\n",
+ " nfft = window_len * nfft_factor)\n",
+ " #boundary = 'zeros')\n",
+ " ffts.append((f, t, Zxx))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 277,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n",
+ "fig.tight_layout(pad=2, h_pad=0.1)\n",
+ "\n",
+ "for fft, ax, label in zip(ffts, ax.flatten(), test_labels):\n",
+ " f, t, Zxx = fft\n",
+ " ax.pcolormesh(t[1:], f[:250], np.abs(Zxx[:250,1:]))\n",
+ " ax.set_title(label, pad=-20, color='white')\n",
+ " ax.grid()\n",
+ " ax.set_ylabel('f [Hz]')\n",
+ " ax.set_ylim([30, 75]) # Hz\n",
+ "ax.set_xlabel('simulation time t [s]')\n",
+ "None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 278,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"800\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axs = plt.subplots(len(test_data), figsize=(8, 20), sharex=True)\n",
+ "fig.tight_layout(pad=2.2, h_pad=0, w_pad=1)\n",
+ "\n",
+ "for fft, ax, label in zip(ffts, axs.flatten(), test_labels):\n",
+ " f, f_t, Zxx = fft\n",
+ " \n",
+ " n_f, n_t = Zxx.shape\n",
+ " # start, stop = 180, 220\n",
+ " # start, stop = 90, 110\n",
+ " # start, stop = 15, 35\n",
+ " # bounds_f = slice(start // 4 * nfft_factor, stop // 4 * nfft_factor)\n",
+ " f_min, f_max = 30, 70 # Hz\n",
+ " bounds_f = slice(np.argmax(f > f_min), np.argmin(f < f_max))\n",
+ " \n",
+ "\n",
+ " f_mean = np.zeros(Zxx.shape[1])\n",
+ " for t in range(1, Zxx.shape[1] - 1):\n",
+ " frame_f = f[bounds_f]\n",
+ " frame_step = frame_f[1] - frame_f[0]\n",
+ " time_step = f_t[1] - f_t[0]\n",
+ " #if t == 10:\n",
+ " # axs[-1].plot(frame_f, frame_Z)\n",
+ " frame_Z = np.abs(Zxx[bounds_f, t])\n",
+ " # frame_f = f[180:220]\n",
+ " # frame_Z = np.abs(Zxx[180:220, 40])\n",
+ " # frame_f = f[15:35]\n",
+ " # frame_Z = np.abs(Zxx[15:35, 40])\n",
+ " # plt.plot(frame_f, frame_Z)\n",
+ "\n",
+ " # peak_f = frame_f[np.argmax(frame)]\n",
+ " # plt.axvline(peak_f, color='red')\n",
+ "\n",
+ "# def gauss(x, *p):\n",
+ "# A, mu, sigma, o = p\n",
+ "# return A*np.exp(-(x-mu)**2/(2.*sigma**2)) + o\n",
+ " \n",
+ " def gauss(x, *p):\n",
+ " A, mu, sigma = p\n",
+ " return A*np.exp(-(x-mu)**2/(2.*sigma**2))\n",
+ " \n",
+ " f_start = frame_f[np.argmax(frame_Z)]\n",
+ " A_start = np.max(frame_Z)\n",
+ " p0 = [A_start, f_start, 1.]\n",
+ " try:\n",
+ " coeff, var = optimize.curve_fit(gauss, frame_f, frame_Z, p0=p0)\n",
+ " # plt.plot(frame_f, gauss(frame_f, *coeff))\n",
+ " #print(coeff)\n",
+ " A, mu, sigma, *_ = coeff\n",
+ " f_mean[t] = mu\n",
+ " except RuntimeError:\n",
+ " f_mean[t] = np.nan\n",
+ " ax.plot(f_t[1:-1], f_mean[1:-1])\n",
+ " \n",
+ "# b, a = signal.butter(3,\n",
+ "# 1/5, # Hz\n",
+ "# btype='lowpass',\n",
+ "# fs=1/time_step)\n",
+ "# filtered = signal.lfilter(b, a, f_mean[1:-1], axis=0)\n",
+ "# ax.plot(f_t[1:-1], filtered)\n",
+ " \n",
+ " ax.set_title(label, pad=-20)\n",
+ " ax.set_ylabel('f [Hz]')\n",
+ " ax.grid()\n",
+ " if not label in ['off_frequency', 'sweep_phase_steps']:\n",
+ " ax.set_ylim([49.90, 50.10])\n",
+ " var = np.var(f_mean[1:-1])\n",
+ " ax.text(0.5, 0.1, f'σ²={var * 1e3:.3g} mHz²', transform=ax.transAxes, ha='center')\n",
+ " ax.text(0.5, 0.25, f'σ={np.sqrt(var) * 1e3:.3g} mHz', transform=ax.transAxes, ha='center')\n",
+ "# ax.text(0.5, 0.2, f'filt. σ²={np.var(filtered) * 1e3:.3g} mHz', transform=ax.transAxes, ha='center')\n",
+ " else:\n",
+ " f_min, f_max = min(f_mean[1:-1]), max(f_mean[1:-1])\n",
+ " delta = f_max - f_min\n",
+ " ax.set_ylim(f_min - delta * 0.1, f_max + delta * 0.3)\n",
+ " \n",
+ "ax.set_xlabel('simulation time t [s]')\n",
+ "None"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/ROCOF test data generator.ipynb b/tools/ROCOF test data generator.ipynb
new file mode 100644
index 0000000..df94a7b
--- /dev/null
+++ b/tools/ROCOF test data generator.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# ROCOF test waveform library\n",
+ "\n",
+ "This is a re-implementation of the ROCOF test waveforms described in https://zenodo.org/record/3559798\n",
+ "\n",
+ "**This file is exported as a python module and loaded from other notebooks here, so please make sure to re-export when changing it.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import math\n",
+ "import itertools\n",
+ "\n",
+ "import numpy as np\n",
+ "from scipy import signal\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def sample_waveform(generator, duration:\"s\"=10, sampling_rate:\"sp/s\"=10000, frequency:\"Hz\"=50):\n",
+ " samples = int(duration*sampling_rate)\n",
+ " phases = np.linspace(0, 2*np.pi, 6, endpoint=False)\n",
+ " omega_t = np.linspace(phases, phases + 2*np.pi*duration*frequency, samples)\n",
+ " fundamental = np.sin(omega_t)\n",
+ " return generator(omega_t, fundamental, sampling_rate=sampling_rate, duration=duration, frequency=frequency).swapaxes(0, 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_harmonics(amplitudes, phases=[]):\n",
+ " return lambda omega_t, fundamental, **_: fundamental + np.sum([\n",
+ " a*np.sin((p if p else 0) + i*omega_t)\n",
+ " for i, (a, p) in enumerate(itertools.zip_longest(amplitudes, phases), start=2)\n",
+ " ], axis=0)\n",
+ "\n",
+ "def test_harmonics():\n",
+ " return gen_harmonics([0.02, 0.05, 0.01, 0.06, 0.005, 0.05, 0.005, 0.015, 0.005, 0.035, 0.005, 0.003])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_interharmonic(amplitudes, ih=[], ih_phase=[]):\n",
+ " def gen(omega_t, fundamental, **_):\n",
+ " return fundamental + np.sum([\n",
+ " a*np.sin(omega_t * ih + (p if p else 0))\n",
+ " for a, ih, p in itertools.zip_longest(amplitudes, ih, ih_phase)\n",
+ " ], axis=0)\n",
+ " return gen\n",
+ "\n",
+ "def test_interharmonics():\n",
+ " return gen_interharmonic([0.1], [15.01401], [np.pi])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_noise(amplitude=0.2, fmax:'Hz'=4.9e3, fmin:'Hz'=100, filter_order=6):\n",
+ " def gen(omega_t, fundamental, sampling_rate, **_):\n",
+ " noise = np.random.normal(0, amplitude, fundamental.shape)\n",
+ " b, a = signal.butter(filter_order,\n",
+ " [fmin, min(fmax, sampling_rate//2-1)],\n",
+ " btype='bandpass',\n",
+ " fs=sampling_rate)\n",
+ " return fundamental + signal.lfilter(b, a, noise, axis=0)\n",
+ " return gen\n",
+ "\n",
+ "def test_noise():\n",
+ " return gen_noise()\n",
+ "\n",
+ "def test_noise_loud():\n",
+ " return gen_noise(amplitude=0.5, fmin=10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 406,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def gen_steps(size_amplitude=0.1, size_phase=0.1*np.pi, steps_per_sec=1):\n",
+ " def gen(omega_t, fundamental, duration, **_):\n",
+ " n = int(steps_per_sec * duration)\n",
+ " indices = np.random.randint(0, len(omega_t), n)\n",
+ " amplitudes = np.random.normal(1, size_amplitude, (n, 6))\n",
+ " phases = np.random.normal(0, size_phase, (n, 6))\n",
+ " amplitude = np.ones(omega_t.shape)\n",
+ " for start, end, a, p in zip(indices, indices[1:], amplitudes, phases):\n",
+ " omega_t[start:end] += p\n",
+ " amplitude[start:end] = a\n",
+ " return amplitude*np.sin(omega_t)\n",
+ " return gen\n",
+ "\n",
+ "def test_amplitude_steps():\n",
+ " return gen_steps(size_amplitude=0.4, size_phase=0)\n",
+ "\n",
+ "def test_phase_steps():\n",
+ " return gen_steps(size_amplitude=0, size_phase=0.1)\n",
+ "\n",
+ "def test_amplitude_and_phase_steps():\n",
+ " return gen_steps(size_amplitude=0.2, size_phase=0.07)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 418,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def step_gen(shape, stdev, duration, steps_per_sec=1.0, mean=0.0):\n",
+ " samples, channels = shape\n",
+ " n = int(steps_per_sec * duration)\n",
+ " indices = np.random.randint(0, samples, n)\n",
+ " phases = np.random.normal(mean, stdev, (n, 6))\n",
+ " amplitude = np.ones((samples, channels))\n",
+ " out = np.zeros(shape)\n",
+ " for start, end, a in zip(indices, indices[1:], amplitude):\n",
+ " out[start:end] = a\n",
+ " return out\n",
+ "\n",
+ "def gen_chirp(fmin, fmax, period, dwell_time=1.0, amplitude=None, phase_steps=None):\n",
+ " def gen(omega_t, fundamental, sampling_rate, duration, **_):\n",
+ " samples = int(duration*sampling_rate)\n",
+ " phases = np.linspace(0, 2*np.pi, 6, endpoint=False)\n",
+ " \n",
+ " c = (fmax-fmin)/period\n",
+ " t = np.linspace(0, duration, samples)\n",
+ " \n",
+ " x = np.repeat(np.reshape(2*np.pi*fmin*t, (-1,1)), 6, axis=1)\n",
+ " data = (phases + x)[:int(sampling_rate*dwell_time)]\n",
+ " current_phase = 2*np.pi*fmin*dwell_time\n",
+ " direction = 'up'\n",
+ " \n",
+ " for idx in range(int(dwell_time*sampling_rate), samples, int(2*period*sampling_rate)):\n",
+ " t1 = np.linspace(0, period, int(period*sampling_rate))\n",
+ " t2 = np.linspace(0, period, int(period*sampling_rate))\n",
+ " chirp_phase = np.hstack((\n",
+ " 2*np.pi*(c/2 * t1**2 + fmin * t1),\n",
+ " 2*np.pi*(-c/2 * t2**2 + fmax * t2 - (c/2 * period**2 + fmin * period))\n",
+ " ))\n",
+ " chirp_phase = np.repeat(np.reshape(chirp_phase, (-1, 1)), 6, axis=1)\n",
+ " new = phases + chirp_phase + current_phase\n",
+ " current_phase = chirp_phase[-1]\n",
+ " data = np.vstack((data, new))\n",
+ " \n",
+ " data = data[:len(fundamental)]\n",
+ " \n",
+ " if phase_steps:\n",
+ " (step_amplitude, steps_per_sec) = phase_steps\n",
+ " steps = step_gen(data.shape, step_amplitude, duration, steps_per_sec)\n",
+ " data += steps\n",
+ " \n",
+ " if amplitude is None:\n",
+ " return np.sin(data)\n",
+ " else:\n",
+ " return fundamental + amplitude*np.sin(data)\n",
+ " return gen\n",
+ "\n",
+ "def test_close_interharmonics_and_flicker():\n",
+ " return gen_chirp(90.0, 150.0, 10, 1, amplitude=0.1)\n",
+ "\n",
+ "def test_off_frequency():\n",
+ "# return gen_chirp(48.0, 52.0, 0.25, 1)\n",
+ " return gen_chirp(48.0, 52.0, 10, 1)\n",
+ "\n",
+ "def test_sweep_phase_steps():\n",
+ " return gen_chirp(48.0, 52.0, 10, 1, phase_steps=(0.1, 1))\n",
+ "# return gen_chirp(48.0, 52.0, 0.25, 1, phase_steps=(0.1, 1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_tests = [test_harmonics, test_interharmonics, test_noise, test_noise_loud, test_amplitude_steps, test_phase_steps, test_amplitude_and_phase_steps, test_close_interharmonics_and_flicker, test_off_frequency, test_sweep_phase_steps]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/dec_proto_am_ber_top.py b/tools/dec_proto_am_ber_top.py
new file mode 100755
index 0000000..d8543e1
--- /dev/null
+++ b/tools/dec_proto_am_ber_top.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+##################################################
+# GNU Radio Python Flow Graph
+# Title: Dec Proto Am Ber Top
+# GNU Radio version: 3.7.13.5
+##################################################
+
+from gnuradio import analog
+from gnuradio import blocks
+from gnuradio import digital
+from gnuradio import eng_notation
+from gnuradio import fec
+from gnuradio import filter
+from gnuradio import gr
+from gnuradio.eng_option import eng_option
+from gnuradio.filter import firdes
+from optparse import OptionParser
+import pmt
+
+
+class dec_proto_am_ber_top(gr.top_block):
+
+ def __init__(self, ber_file='0', carrier=1, mod_depth=0.8, signal_strength=2):
+ gr.top_block.__init__(self, "Dec Proto Am Ber Top")
+
+ ##################################################
+ # Parameters
+ ##################################################
+ self.ber_file = ber_file
+ self.carrier = carrier
+ self.mod_depth = mod_depth
+ self.signal_strength = signal_strength
+
+ ##################################################
+ # Variables
+ ##################################################
+ self.sim_mul = sim_mul = 1e4
+ self.actual_sampling_rate = actual_sampling_rate = 10
+ self.sync_tag = sync_tag = gr.tag_utils.python_to_tag((0, pmt.intern("sync"), pmt.from_double(0.0), pmt.intern("correlate_access_code")))
+ self.samp_rate = samp_rate = actual_sampling_rate*sim_mul
+ self.pi = pi = 3.141592653589793
+ self.packet_time_est_tag = packet_time_est_tag = gr.tag_utils.python_to_tag((0, pmt.intern("start"), pmt.from_double(0.0), pmt.intern("packet_vector_source")))
+
+ ##################################################
+ # Blocks
+ ##################################################
+ self.low_pass_filter_0 = filter.fir_filter_ccf(1, firdes.low_pass(
+ 1.0/signal_strength*1000 * 2, samp_rate, 0.2 * sim_mul, 0.05 * sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.fec_ber_bf_0 = fec.ber_bf(False, 0, -7.0)
+ self.digital_clock_recovery_mm_xx_0 = digital.clock_recovery_mm_ff(50, 0.001, 0, 0.01, 0.01)
+ self.digital_binary_slicer_fb_0 = digital.binary_slicer_fb()
+ self.blocks_vector_source_x_0_0_1_0 = blocks.vector_source_f([1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*128, True, 1, [packet_time_est_tag])
+ self.blocks_throttle_0 = blocks.throttle(gr.sizeof_float*1, samp_rate,True)
+ self.blocks_repeat_0 = blocks.repeat(gr.sizeof_float*1, 10*5)
+ self.blocks_null_source_1 = blocks.null_source(gr.sizeof_float*1)
+ self.blocks_multiply_xx_0_0 = blocks.multiply_vff(1)
+ self.blocks_multiply_xx_0 = blocks.multiply_vcc(1)
+ self.blocks_multiply_const_vxx_1 = blocks.multiply_const_vff((mod_depth, ))
+ self.blocks_multiply_const_vxx_0 = blocks.multiply_const_vcc((1.0/mod_depth, ))
+ self.blocks_float_to_complex_0 = blocks.float_to_complex(1)
+ self.blocks_float_to_char_0 = blocks.float_to_char(1, 1)
+ self.blocks_file_source_0 = blocks.file_source(gr.sizeof_float*1, '/home/user/research/smart_meter_reset/gm_platform/fw/raw_freq.bin', True)
+ self.blocks_file_source_0.set_begin_tag(pmt.PMT_NIL)
+ self.blocks_file_sink_0 = blocks.file_sink(gr.sizeof_float*1, ber_file, False)
+ self.blocks_file_sink_0.set_unbuffered(False)
+ self.blocks_delay_0 = blocks.delay(gr.sizeof_float*1, 5)
+ self.blocks_complex_to_mag_0 = blocks.complex_to_mag(1)
+ self.blocks_add_xx_0 = blocks.add_vff(1)
+ self.blocks_add_const_vxx_2_0 = blocks.add_const_vff((-0.5, ))
+ self.blocks_add_const_vxx_2 = blocks.add_const_vcc((-(1-mod_depth), ))
+ self.blocks_add_const_vxx_1 = blocks.add_const_vff((1-mod_depth, ))
+ self.blocks_add_const_vxx_0 = blocks.add_const_vff((-50, ))
+ self.analog_sig_source_x_0_0 = analog.sig_source_f(samp_rate, analog.GR_COS_WAVE, carrier * sim_mul, signal_strength*1e-3, 0)
+ self.analog_sig_source_x_0 = analog.sig_source_c(samp_rate, analog.GR_COS_WAVE, -carrier * sim_mul, 1, 0)
+
+
+
+ ##################################################
+ # Connections
+ ##################################################
+ self.connect((self.analog_sig_source_x_0, 0), (self.blocks_multiply_xx_0, 1))
+ self.connect((self.analog_sig_source_x_0_0, 0), (self.blocks_multiply_xx_0_0, 1))
+ self.connect((self.blocks_add_const_vxx_0, 0), (self.blocks_add_xx_0, 0))
+ self.connect((self.blocks_add_const_vxx_1, 0), (self.blocks_multiply_xx_0_0, 0))
+ self.connect((self.blocks_add_const_vxx_2, 0), (self.blocks_multiply_const_vxx_0, 0))
+ self.connect((self.blocks_add_const_vxx_2_0, 0), (self.digital_clock_recovery_mm_xx_0, 0))
+ self.connect((self.blocks_add_xx_0, 0), (self.blocks_float_to_complex_0, 0))
+ self.connect((self.blocks_complex_to_mag_0, 0), (self.blocks_add_const_vxx_2_0, 0))
+ self.connect((self.blocks_delay_0, 0), (self.blocks_float_to_char_0, 0))
+ self.connect((self.blocks_file_source_0, 0), (self.blocks_throttle_0, 0))
+ self.connect((self.blocks_float_to_char_0, 0), (self.fec_ber_bf_0, 1))
+ self.connect((self.blocks_float_to_complex_0, 0), (self.blocks_multiply_xx_0, 0))
+ self.connect((self.blocks_multiply_const_vxx_0, 0), (self.blocks_complex_to_mag_0, 0))
+ self.connect((self.blocks_multiply_const_vxx_1, 0), (self.blocks_add_const_vxx_1, 0))
+ self.connect((self.blocks_multiply_xx_0, 0), (self.low_pass_filter_0, 0))
+ self.connect((self.blocks_multiply_xx_0_0, 0), (self.blocks_add_xx_0, 1))
+ self.connect((self.blocks_null_source_1, 0), (self.blocks_float_to_complex_0, 1))
+ self.connect((self.blocks_repeat_0, 0), (self.blocks_multiply_const_vxx_1, 0))
+ self.connect((self.blocks_throttle_0, 0), (self.blocks_add_const_vxx_0, 0))
+ self.connect((self.blocks_vector_source_x_0_0_1_0, 0), (self.blocks_delay_0, 0))
+ self.connect((self.blocks_vector_source_x_0_0_1_0, 0), (self.blocks_repeat_0, 0))
+ self.connect((self.digital_binary_slicer_fb_0, 0), (self.fec_ber_bf_0, 0))
+ self.connect((self.digital_clock_recovery_mm_xx_0, 0), (self.digital_binary_slicer_fb_0, 0))
+ self.connect((self.fec_ber_bf_0, 0), (self.blocks_file_sink_0, 0))
+ self.connect((self.low_pass_filter_0, 0), (self.blocks_add_const_vxx_2, 0))
+
+ def get_ber_file(self):
+ return self.ber_file
+
+ def set_ber_file(self, ber_file):
+ self.ber_file = ber_file
+ self.blocks_file_sink_0.open(self.ber_file)
+
+ def get_carrier(self):
+ return self.carrier
+
+ def set_carrier(self, carrier):
+ self.carrier = carrier
+ self.analog_sig_source_x_0_0.set_frequency(self.carrier * self.sim_mul)
+ self.analog_sig_source_x_0.set_frequency(-self.carrier * self.sim_mul)
+
+ def get_mod_depth(self):
+ return self.mod_depth
+
+ def set_mod_depth(self, mod_depth):
+ self.mod_depth = mod_depth
+ self.blocks_multiply_const_vxx_1.set_k((self.mod_depth, ))
+ self.blocks_multiply_const_vxx_0.set_k((1.0/self.mod_depth, ))
+ self.blocks_add_const_vxx_2.set_k((-(1-self.mod_depth), ))
+ self.blocks_add_const_vxx_1.set_k((1-self.mod_depth, ))
+
+ def get_signal_strength(self):
+ return self.signal_strength
+
+ def set_signal_strength(self, signal_strength):
+ self.signal_strength = signal_strength
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1.0/self.signal_strength*1000 * 2, self.samp_rate, 0.2 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.analog_sig_source_x_0_0.set_amplitude(self.signal_strength*1e-3)
+
+ def get_sim_mul(self):
+ return self.sim_mul
+
+ def set_sim_mul(self, sim_mul):
+ self.sim_mul = sim_mul
+ self.set_samp_rate(self.actual_sampling_rate*self.sim_mul)
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1.0/self.signal_strength*1000 * 2, self.samp_rate, 0.2 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.analog_sig_source_x_0_0.set_frequency(self.carrier * self.sim_mul)
+ self.analog_sig_source_x_0.set_frequency(-self.carrier * self.sim_mul)
+
+ def get_actual_sampling_rate(self):
+ return self.actual_sampling_rate
+
+ def set_actual_sampling_rate(self, actual_sampling_rate):
+ self.actual_sampling_rate = actual_sampling_rate
+ self.set_samp_rate(self.actual_sampling_rate*self.sim_mul)
+
+ def get_sync_tag(self):
+ return self.sync_tag
+
+ def set_sync_tag(self, sync_tag):
+ self.sync_tag = sync_tag
+
+ def get_samp_rate(self):
+ return self.samp_rate
+
+ def set_samp_rate(self, samp_rate):
+ self.samp_rate = samp_rate
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1.0/self.signal_strength*1000 * 2, self.samp_rate, 0.2 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.blocks_throttle_0.set_sample_rate(self.samp_rate)
+ self.analog_sig_source_x_0_0.set_sampling_freq(self.samp_rate)
+ self.analog_sig_source_x_0.set_sampling_freq(self.samp_rate)
+
+ def get_pi(self):
+ return self.pi
+
+ def set_pi(self, pi):
+ self.pi = pi
+
+ def get_packet_time_est_tag(self):
+ return self.packet_time_est_tag
+
+ def set_packet_time_est_tag(self, packet_time_est_tag):
+ self.packet_time_est_tag = packet_time_est_tag
+ self.blocks_vector_source_x_0_0_1_0.set_data([1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*128, [self.packet_time_est_tag])
+
+
+def argument_parser():
+ parser = OptionParser(usage="%prog: [options]", option_class=eng_option)
+ parser.add_option(
+ "", "--ber-file", dest="ber_file", type="string", default='0',
+ help="Set BER data output file [default=%default]")
+ parser.add_option(
+ "", "--carrier", dest="carrier", type="eng_float", default=eng_notation.num_to_str(1),
+ help="Set Carrier frequency in Hz [default=%default]")
+ parser.add_option(
+ "", "--mod-depth", dest="mod_depth", type="eng_float", default=eng_notation.num_to_str(0.8),
+ help="Set Modulation depth (0-1) [default=%default]")
+ parser.add_option(
+ "", "--signal-strength", dest="signal_strength", type="eng_float", default=eng_notation.num_to_str(2),
+ help="Set signal strength in mHz [default=%default]")
+ return parser
+
+
+def main(top_block_cls=dec_proto_am_ber_top, options=None):
+ if options is None:
+ options, _ = argument_parser().parse_args()
+
+ tb = top_block_cls(ber_file=options.ber_file, carrier=options.carrier, mod_depth=options.mod_depth, signal_strength=options.signal_strength)
+ tb.start()
+ try:
+ raw_input('Press Enter to quit: ')
+ except EOFError:
+ pass
+ tb.stop()
+ tb.wait()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/tools/dec_proto_am_dc_ber_top.py b/tools/dec_proto_am_dc_ber_top.py
new file mode 100755
index 0000000..8202c5a
--- /dev/null
+++ b/tools/dec_proto_am_dc_ber_top.py
@@ -0,0 +1,191 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+##################################################
+# GNU Radio Python Flow Graph
+# Title: Dec Proto Am Dc Ber Top
+# GNU Radio version: 3.7.13.5
+##################################################
+
+from gnuradio import blocks
+from gnuradio import digital
+from gnuradio import eng_notation
+from gnuradio import fec
+from gnuradio import filter
+from gnuradio import gr
+from gnuradio.eng_option import eng_option
+from gnuradio.filter import firdes
+from optparse import OptionParser
+import pmt
+
+
+class dec_proto_am_dc_ber_top(gr.top_block):
+
+ def __init__(self, ber_file='0', signal_strength=50):
+ gr.top_block.__init__(self, "Dec Proto Am Dc Ber Top")
+
+ ##################################################
+ # Parameters
+ ##################################################
+ self.ber_file = ber_file
+ self.signal_strength = signal_strength
+
+ ##################################################
+ # Variables
+ ##################################################
+ self.sim_mul = sim_mul = 1e4
+ self.actual_sampling_rate = actual_sampling_rate = 10
+ self.sync_tag = sync_tag = gr.tag_utils.python_to_tag((0, pmt.intern("sync"), pmt.from_double(0.0), pmt.intern("correlate_access_code")))
+ self.samp_rate = samp_rate = actual_sampling_rate*sim_mul
+ self.pi = pi = 3.141592653589793
+ self.packet_time_est_tag = packet_time_est_tag = gr.tag_utils.python_to_tag((0, pmt.intern("start"), pmt.from_double(0.0), pmt.intern("packet_vector_source")))
+ self.ber_delay = ber_delay = 198
+
+ ##################################################
+ # Blocks
+ ##################################################
+ self.low_pass_filter_0 = filter.fir_filter_ccf(1, firdes.low_pass(
+ 1, samp_rate, 0.1 * sim_mul, 0.05 * sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.high_pass_filter_0 = filter.fir_filter_ccf(1, firdes.high_pass(
+ 1, samp_rate, sim_mul/200, sim_mul/800, firdes.WIN_HAMMING, 6.76))
+ self.fec_ber_bf_0 = fec.ber_bf(False, 0, -7.0)
+ self.digital_clock_recovery_mm_xx_0 = digital.clock_recovery_mm_ff(50, 0.001, 0, 0.01, 0.01)
+ self.digital_binary_slicer_fb_0 = digital.binary_slicer_fb()
+ self.blocks_vector_source_x_0_0_1_0 = blocks.vector_source_f([1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*8, True, 1, [packet_time_est_tag])
+ self.blocks_throttle_0 = blocks.throttle(gr.sizeof_float*1, samp_rate,True)
+ self.blocks_repeat_0 = blocks.repeat(gr.sizeof_float*1, 10*5)
+ self.blocks_null_source_1 = blocks.null_source(gr.sizeof_float*1)
+ self.blocks_multiply_const_vxx_1 = blocks.multiply_const_vff((signal_strength * 0.001, ))
+ self.blocks_multiply_const_vxx_0 = blocks.multiply_const_vff((1000.0/signal_strength, ))
+ self.blocks_float_to_complex_0 = blocks.float_to_complex(1)
+ self.blocks_float_to_char_0 = blocks.float_to_char(1, 1)
+ self.blocks_file_source_0 = blocks.file_source(gr.sizeof_float*1, '/home/user/research/smart_meter_reset/gm_platform/fw/raw_freq.bin', True)
+ self.blocks_file_source_0.set_begin_tag(pmt.PMT_NIL)
+ self.blocks_file_sink_0 = blocks.file_sink(gr.sizeof_float*1, ber_file, False)
+ self.blocks_file_sink_0.set_unbuffered(False)
+ self.blocks_delay_0 = blocks.delay(gr.sizeof_float*1, ber_delay)
+ self.blocks_complex_to_real_0 = blocks.complex_to_real(1)
+ self.blocks_add_xx_0 = blocks.add_vff(1)
+ self.blocks_add_const_vxx_1 = blocks.add_const_vff((-signal_strength*0.001/2.0, ))
+ self.blocks_add_const_vxx_0 = blocks.add_const_vff((-50, ))
+
+
+
+ ##################################################
+ # Connections
+ ##################################################
+ self.connect((self.blocks_add_const_vxx_0, 0), (self.blocks_add_xx_0, 0))
+ self.connect((self.blocks_add_const_vxx_1, 0), (self.blocks_add_xx_0, 1))
+ self.connect((self.blocks_add_xx_0, 0), (self.blocks_float_to_complex_0, 0))
+ self.connect((self.blocks_complex_to_real_0, 0), (self.blocks_multiply_const_vxx_0, 0))
+ self.connect((self.blocks_delay_0, 0), (self.blocks_float_to_char_0, 0))
+ self.connect((self.blocks_file_source_0, 0), (self.blocks_throttle_0, 0))
+ self.connect((self.blocks_float_to_char_0, 0), (self.fec_ber_bf_0, 1))
+ self.connect((self.blocks_float_to_complex_0, 0), (self.high_pass_filter_0, 0))
+ self.connect((self.blocks_multiply_const_vxx_0, 0), (self.digital_clock_recovery_mm_xx_0, 0))
+ self.connect((self.blocks_multiply_const_vxx_1, 0), (self.blocks_add_const_vxx_1, 0))
+ self.connect((self.blocks_null_source_1, 0), (self.blocks_float_to_complex_0, 1))
+ self.connect((self.blocks_repeat_0, 0), (self.blocks_multiply_const_vxx_1, 0))
+ self.connect((self.blocks_throttle_0, 0), (self.blocks_add_const_vxx_0, 0))
+ self.connect((self.blocks_vector_source_x_0_0_1_0, 0), (self.blocks_delay_0, 0))
+ self.connect((self.blocks_vector_source_x_0_0_1_0, 0), (self.blocks_repeat_0, 0))
+ self.connect((self.digital_binary_slicer_fb_0, 0), (self.fec_ber_bf_0, 0))
+ self.connect((self.digital_clock_recovery_mm_xx_0, 0), (self.digital_binary_slicer_fb_0, 0))
+ self.connect((self.fec_ber_bf_0, 0), (self.blocks_file_sink_0, 0))
+ self.connect((self.high_pass_filter_0, 0), (self.low_pass_filter_0, 0))
+ self.connect((self.low_pass_filter_0, 0), (self.blocks_complex_to_real_0, 0))
+
+ def get_ber_file(self):
+ return self.ber_file
+
+ def set_ber_file(self, ber_file):
+ self.ber_file = ber_file
+ self.blocks_file_sink_0.open(self.ber_file)
+
+ def get_signal_strength(self):
+ return self.signal_strength
+
+ def set_signal_strength(self, signal_strength):
+ self.signal_strength = signal_strength
+ self.blocks_multiply_const_vxx_1.set_k((self.signal_strength * 0.001, ))
+ self.blocks_multiply_const_vxx_0.set_k((1000.0/self.signal_strength, ))
+ self.blocks_add_const_vxx_1.set_k((-self.signal_strength*0.001/2.0, ))
+
+ def get_sim_mul(self):
+ return self.sim_mul
+
+ def set_sim_mul(self, sim_mul):
+ self.sim_mul = sim_mul
+ self.set_samp_rate(self.actual_sampling_rate*self.sim_mul)
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1, self.samp_rate, 0.1 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.high_pass_filter_0.set_taps(firdes.high_pass(1, self.samp_rate, self.sim_mul/200, self.sim_mul/800, firdes.WIN_HAMMING, 6.76))
+
+ def get_actual_sampling_rate(self):
+ return self.actual_sampling_rate
+
+ def set_actual_sampling_rate(self, actual_sampling_rate):
+ self.actual_sampling_rate = actual_sampling_rate
+ self.set_samp_rate(self.actual_sampling_rate*self.sim_mul)
+
+ def get_sync_tag(self):
+ return self.sync_tag
+
+ def set_sync_tag(self, sync_tag):
+ self.sync_tag = sync_tag
+
+ def get_samp_rate(self):
+ return self.samp_rate
+
+ def set_samp_rate(self, samp_rate):
+ self.samp_rate = samp_rate
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1, self.samp_rate, 0.1 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.high_pass_filter_0.set_taps(firdes.high_pass(1, self.samp_rate, self.sim_mul/200, self.sim_mul/800, firdes.WIN_HAMMING, 6.76))
+ self.blocks_throttle_0.set_sample_rate(self.samp_rate)
+
+ def get_pi(self):
+ return self.pi
+
+ def set_pi(self, pi):
+ self.pi = pi
+
+ def get_packet_time_est_tag(self):
+ return self.packet_time_est_tag
+
+ def set_packet_time_est_tag(self, packet_time_est_tag):
+ self.packet_time_est_tag = packet_time_est_tag
+ self.blocks_vector_source_x_0_0_1_0.set_data([1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*8, [self.packet_time_est_tag])
+
+ def get_ber_delay(self):
+ return self.ber_delay
+
+ def set_ber_delay(self, ber_delay):
+ self.ber_delay = ber_delay
+ self.blocks_delay_0.set_dly(self.ber_delay)
+
+
+def argument_parser():
+ parser = OptionParser(usage="%prog: [options]", option_class=eng_option)
+ parser.add_option(
+ "", "--ber-file", dest="ber_file", type="string", default='0',
+ help="Set BER data output file [default=%default]")
+ parser.add_option(
+ "", "--signal-strength", dest="signal_strength", type="eng_float", default=eng_notation.num_to_str(50),
+ help="Set signal strength in mHz [default=%default]")
+ return parser
+
+
+def main(top_block_cls=dec_proto_am_dc_ber_top, options=None):
+ if options is None:
+ options, _ = argument_parser().parse_args()
+
+ tb = top_block_cls(ber_file=options.ber_file, signal_strength=options.signal_strength)
+ tb.start()
+ try:
+ raw_input('Press Enter to quit: ')
+ except EOFError:
+ pass
+ tb.stop()
+ tb.wait()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/tools/dec_proto_fm_ber_top.py b/tools/dec_proto_fm_ber_top.py
new file mode 100755
index 0000000..74cf3ab
--- /dev/null
+++ b/tools/dec_proto_fm_ber_top.py
@@ -0,0 +1,183 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+##################################################
+# GNU Radio Python Flow Graph
+# Title: Dec Proto Fm Ber Top
+# GNU Radio version: 3.7.13.5
+##################################################
+
+from gnuradio import analog
+from gnuradio import blocks
+from gnuradio import digital
+from gnuradio import eng_notation
+from gnuradio import fec
+from gnuradio import filter
+from gnuradio import gr
+from gnuradio.eng_option import eng_option
+from gnuradio.filter import firdes
+from optparse import OptionParser
+import math
+import pmt
+
+
+class dec_proto_fm_ber_top(gr.top_block):
+
+ def __init__(self, ber_file='0', signal_strength=1):
+ gr.top_block.__init__(self, "Dec Proto Fm Ber Top")
+
+ ##################################################
+ # Parameters
+ ##################################################
+ self.ber_file = ber_file
+ self.signal_strength = signal_strength
+
+ ##################################################
+ # Variables
+ ##################################################
+ self.sim_mul = sim_mul = 1e4
+ self.actual_sampling_rate = actual_sampling_rate = 10
+ self.sync_tag = sync_tag = gr.tag_utils.python_to_tag((0, pmt.intern("sync"), pmt.from_double(0.0), pmt.intern("correlate_access_code")))
+ self.samp_rate = samp_rate = actual_sampling_rate*sim_mul
+ self.pi = pi = 3.141592653589793
+ self.packet_time_est_tag = packet_time_est_tag = gr.tag_utils.python_to_tag((0, pmt.intern("start"), pmt.from_double(0.0), pmt.intern("packet_vector_source")))
+
+ ##################################################
+ # Blocks
+ ##################################################
+ self.low_pass_filter_0 = filter.fir_filter_ccf(1, firdes.low_pass(
+ 1, samp_rate, 0.1 * sim_mul, 0.05 * sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.fec_ber_bf_0 = fec.ber_bf(False, 0, -7.0)
+ self.digital_clock_recovery_mm_xx_0 = digital.clock_recovery_mm_ff(50, 0.001, 0, 0.01, 0.01)
+ self.digital_binary_slicer_fb_0 = digital.binary_slicer_fb()
+ self.blocks_vector_source_x_0_0_1_0 = blocks.vector_source_f([1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*128, True, 1, [packet_time_est_tag])
+ self.blocks_vco_f_0 = blocks.vco_f(samp_rate, sim_mul*2*pi, signal_strength*1e-3)
+ self.blocks_throttle_0 = blocks.throttle(gr.sizeof_float*1, samp_rate,True)
+ self.blocks_repeat_0 = blocks.repeat(gr.sizeof_float*1, 10*5)
+ self.blocks_null_source_1 = blocks.null_source(gr.sizeof_float*1)
+ self.blocks_multiply_xx_0 = blocks.multiply_vcc(1)
+ self.blocks_multiply_const_vxx_1 = blocks.multiply_const_vff((0.2, ))
+ self.blocks_float_to_complex_0 = blocks.float_to_complex(1)
+ self.blocks_float_to_char_0 = blocks.float_to_char(1, 1)
+ self.blocks_file_source_0 = blocks.file_source(gr.sizeof_float*1, '/home/user/research/smart_meter_reset/gm_platform/fw/raw_freq.bin', True)
+ self.blocks_file_source_0.set_begin_tag(pmt.PMT_NIL)
+ self.blocks_file_sink_0 = blocks.file_sink(gr.sizeof_float*1, ber_file, False)
+ self.blocks_file_sink_0.set_unbuffered(False)
+ self.blocks_delay_0 = blocks.delay(gr.sizeof_float*1, 5)
+ self.blocks_add_xx_0 = blocks.add_vff(1)
+ self.blocks_add_const_vxx_1 = blocks.add_const_vff((1.0, ))
+ self.blocks_add_const_vxx_0 = blocks.add_const_vff((-50, ))
+ self.analog_sig_source_x_0 = analog.sig_source_c(samp_rate, analog.GR_COS_WAVE, -1.1 * sim_mul, 1, 0)
+ self.analog_quadrature_demod_cf_0 = analog.quadrature_demod_cf(8)
+
+
+
+ ##################################################
+ # Connections
+ ##################################################
+ self.connect((self.analog_quadrature_demod_cf_0, 0), (self.digital_clock_recovery_mm_xx_0, 0))
+ self.connect((self.analog_sig_source_x_0, 0), (self.blocks_multiply_xx_0, 1))
+ self.connect((self.blocks_add_const_vxx_0, 0), (self.blocks_add_xx_0, 0))
+ self.connect((self.blocks_add_const_vxx_1, 0), (self.blocks_vco_f_0, 0))
+ self.connect((self.blocks_add_xx_0, 0), (self.blocks_float_to_complex_0, 0))
+ self.connect((self.blocks_delay_0, 0), (self.blocks_float_to_char_0, 0))
+ self.connect((self.blocks_file_source_0, 0), (self.blocks_throttle_0, 0))
+ self.connect((self.blocks_float_to_char_0, 0), (self.fec_ber_bf_0, 1))
+ self.connect((self.blocks_float_to_complex_0, 0), (self.blocks_multiply_xx_0, 0))
+ self.connect((self.blocks_multiply_const_vxx_1, 0), (self.blocks_add_const_vxx_1, 0))
+ self.connect((self.blocks_multiply_xx_0, 0), (self.low_pass_filter_0, 0))
+ self.connect((self.blocks_null_source_1, 0), (self.blocks_float_to_complex_0, 1))
+ self.connect((self.blocks_repeat_0, 0), (self.blocks_multiply_const_vxx_1, 0))
+ self.connect((self.blocks_throttle_0, 0), (self.blocks_add_const_vxx_0, 0))
+ self.connect((self.blocks_vco_f_0, 0), (self.blocks_add_xx_0, 1))
+ self.connect((self.blocks_vector_source_x_0_0_1_0, 0), (self.blocks_delay_0, 0))
+ self.connect((self.blocks_vector_source_x_0_0_1_0, 0), (self.blocks_repeat_0, 0))
+ self.connect((self.digital_binary_slicer_fb_0, 0), (self.fec_ber_bf_0, 0))
+ self.connect((self.digital_clock_recovery_mm_xx_0, 0), (self.digital_binary_slicer_fb_0, 0))
+ self.connect((self.fec_ber_bf_0, 0), (self.blocks_file_sink_0, 0))
+ self.connect((self.low_pass_filter_0, 0), (self.analog_quadrature_demod_cf_0, 0))
+
+ def get_ber_file(self):
+ return self.ber_file
+
+ def set_ber_file(self, ber_file):
+ self.ber_file = ber_file
+ self.blocks_file_sink_0.open(self.ber_file)
+
+ def get_signal_strength(self):
+ return self.signal_strength
+
+ def set_signal_strength(self, signal_strength):
+ self.signal_strength = signal_strength
+
+ def get_sim_mul(self):
+ return self.sim_mul
+
+ def set_sim_mul(self, sim_mul):
+ self.sim_mul = sim_mul
+ self.set_samp_rate(self.actual_sampling_rate*self.sim_mul)
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1, self.samp_rate, 0.1 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.analog_sig_source_x_0.set_frequency(-1.1 * self.sim_mul)
+
+ def get_actual_sampling_rate(self):
+ return self.actual_sampling_rate
+
+ def set_actual_sampling_rate(self, actual_sampling_rate):
+ self.actual_sampling_rate = actual_sampling_rate
+ self.set_samp_rate(self.actual_sampling_rate*self.sim_mul)
+
+ def get_sync_tag(self):
+ return self.sync_tag
+
+ def set_sync_tag(self, sync_tag):
+ self.sync_tag = sync_tag
+
+ def get_samp_rate(self):
+ return self.samp_rate
+
+ def set_samp_rate(self, samp_rate):
+ self.samp_rate = samp_rate
+ self.low_pass_filter_0.set_taps(firdes.low_pass(1, self.samp_rate, 0.1 * self.sim_mul, 0.05 * self.sim_mul, firdes.WIN_HAMMING, 6.76))
+ self.blocks_throttle_0.set_sample_rate(self.samp_rate)
+ self.analog_sig_source_x_0.set_sampling_freq(self.samp_rate)
+
+ def get_pi(self):
+ return self.pi
+
+ def set_pi(self, pi):
+ self.pi = pi
+
+ def get_packet_time_est_tag(self):
+ return self.packet_time_est_tag
+
+ def set_packet_time_est_tag(self, packet_time_est_tag):
+ self.packet_time_est_tag = packet_time_est_tag
+ self.blocks_vector_source_x_0_0_1_0.set_data([1,0]*(4*12)+[1,1,0,1,0,1,0,1]*12+[1,0,1,1,1,1,1,0,0,1]+[1,1,1,1,0,1,1,0,0,1]+[1,0,1,1,1,1,1,0,0,1]+[0,1,1,1,0,1,1,0,1,0]+[0,0,0,0,0,1,0,1,0,1,1,0,0,1,1,1,0,0,0,0]+[0]*128, [self.packet_time_est_tag])
+
+
+def argument_parser():
+ parser = OptionParser(usage="%prog: [options]", option_class=eng_option)
+ parser.add_option(
+ "", "--ber-file", dest="ber_file", type="string", default='0',
+ help="Set BER data output file [default=%default]")
+ parser.add_option(
+ "", "--signal-strength", dest="signal_strength", type="eng_float", default=eng_notation.num_to_str(1),
+ help="Set signal strength in mHz [default=%default]")
+ return parser
+
+
+def main(top_block_cls=dec_proto_fm_ber_top, options=None):
+ if options is None:
+ options, _ = argument_parser().parse_args()
+
+ tb = top_block_cls(ber_file=options.ber_file, signal_strength=options.signal_strength)
+ tb.start()
+ try:
+ raw_input('Press Enter to quit: ')
+ except EOFError:
+ pass
+ tb.stop()
+ tb.wait()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/tools/grid_frequency_spectra.ipynb b/tools/grid_frequency_spectra.ipynb
new file mode 100644
index 0000000..ba7c48d
--- /dev/null
+++ b/tools/grid_frequency_spectra.ipynb
@@ -0,0 +1,4206 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import csv\n",
+ "\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt\n",
+ "import scipy.fftpack"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data = np.genfromtxt('Netzfrequenz_Sekundenwerte_2012_KW37.csv', delimiter=',')[1:,1:]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7608496bfed0>]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plt.plot(data[:3600*24, 0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.02051102806199375"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.std(data[:,0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
+ " import sys\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ys = scipy.fftpack.fft(data[:,0])\n",
+ "ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
+ "s = 60\n",
+ "\n",
+ "ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
+ "\n",
+ "xs = np.linspace(0, 1.0/2.0, len(data)/2)\n",
+ "#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.loglog(xs[s//2:-s//2+1], ys)\n",
+ "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
+ " import sys\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ys = scipy.fftpack.fft(data[:,0])\n",
+ "ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
+ "s = 1\n",
+ "\n",
+ "ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
+ "\n",
+ "xs = np.linspace(0, 1.0/2.0, len(data)/2)\n",
+ "#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.loglog(xs[s//2:-s//2+1 if s > 1 else None], ys)\n",
+ "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
+ " import sys\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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\" width=\"640\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ys = scipy.fftpack.fft(data[:,0])\n",
+ "ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
+ "s = 1\n",
+ "\n",
+ "ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
+ "\n",
+ "xs = np.linspace(0, 1.0/2.0, len(data)/2)\n",
+ "\n",
+ "ys *= 2*np.pi*xs\n",
+ "#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
+ "\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.loglog(xs[s//2:-s//2+1 if s > 1 else None], ys)\n",
+ "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/lib/python3.7/site-packages/ipykernel_launcher.py:7: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
+ " import sys\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\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('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",
+ "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 = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(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 (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.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 = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\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 nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\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",
+ "\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",
+ "\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]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.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, 0, fig.canvas.width, fig.canvas.height);\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",
+ " {\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.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",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\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",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\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",
+ " 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",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\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",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\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,\n",
+ " 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",
+ "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"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\";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 = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\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.get(0);\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",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\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) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('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",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\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",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " event.shiftKey = false;\n",
+ " // Send a \"J\" for go to next cell\n",
+ " event.which = 74;\n",
+ " event.keyCode = 74;\n",
+ " manager.command_mode();\n",
+ " manager.handle_keydown(event);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\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('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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\" width=\"900\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ys = scipy.fftpack.fft(data[:,0])\n",
+ "ys = 2.0/len(data) * np.abs(ys[:len(data)//2])\n",
+ "s = 30\n",
+ "\n",
+ "ys = np.convolve(ys, np.ones((s,))/s, mode='valid')\n",
+ "\n",
+ "xs = np.linspace(0, 1.0/2.0, len(data)/2)\n",
+ "\n",
+ "ys *= 2*np.pi*xs[s//2:-s//2+1]\n",
+ "\n",
+ "#xs = np.linspace(len(data)/2, 1, len(data)/2)\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(9,5))\n",
+ "ax.loglog(xs[s//2:-s//2+1], ys)\n",
+ "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _pos: 1/x))\n",
+ "ax.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "15.923566878980893"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "1/0.0628"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tools/rocof_test_data.py b/tools/rocof_test_data.py
new file mode 100644
index 0000000..91bee95
--- /dev/null
+++ b/tools/rocof_test_data.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # ROCOF test waveform library
+#
+# This is a re-implementation of the ROCOF test waveforms described in https://zenodo.org/record/3559798
+#
+# **This file is exported as a python module and loaded from other notebooks here, so please make sure to re-export when changing it.**
+
+# In[ ]:
+
+
+import math
+import itertools
+
+import numpy as np
+from scipy import signal
+from matplotlib import pyplot as plt
+
+
+# In[ ]:
+
+
+get_ipython().run_line_magic('matplotlib', 'notebook')
+
+
+# In[ ]:
+
+
+def sample_waveform(generator, duration:"s"=10, sampling_rate:"sp/s"=10000, frequency:"Hz"=50):
+ samples = int(duration*sampling_rate)
+ phases = np.linspace(0, 2*np.pi, 6, endpoint=False)
+ omega_t = np.linspace(phases, phases + 2*np.pi*duration*frequency, samples)
+ fundamental = np.sin(omega_t)
+ return generator(omega_t, fundamental, sampling_rate=sampling_rate, duration=duration, frequency=frequency).swapaxes(0, 1)
+
+
+# In[ ]:
+
+
+def gen_harmonics(amplitudes, phases=[]):
+ return lambda omega_t, fundamental, **_: fundamental + np.sum([
+ a*np.sin((p if p else 0) + i*omega_t)
+ for i, (a, p) in enumerate(itertools.zip_longest(amplitudes, phases), start=2)
+ ], axis=0)
+
+def test_harmonics():
+ return gen_harmonics([0.02, 0.05, 0.01, 0.06, 0.005, 0.05, 0.005, 0.015, 0.005, 0.035, 0.005, 0.003])
+
+
+# In[ ]:
+
+
+def gen_interharmonic(amplitudes, ih=[], ih_phase=[]):
+ def gen(omega_t, fundamental, **_):
+ return fundamental + np.sum([
+ a*np.sin(omega_t * ih + (p if p else 0))
+ for a, ih, p in itertools.zip_longest(amplitudes, ih, ih_phase)
+ ], axis=0)
+ return gen
+
+def test_interharmonics():
+ return gen_interharmonic([0.1], [15.01401], [np.pi])
+
+
+# In[ ]:
+
+
+def gen_noise(amplitude=0.2, fmax:'Hz'=4.9e3, fmin:'Hz'=100, filter_order=6):
+ def gen(omega_t, fundamental, sampling_rate, **_):
+ noise = np.random.normal(0, amplitude, fundamental.shape)
+ b, a = signal.butter(filter_order,
+ [fmin, min(fmax, sampling_rate//2-1)],
+ btype='bandpass',
+ fs=sampling_rate)
+ return fundamental + signal.lfilter(b, a, noise, axis=0)
+ return gen
+
+def test_noise():
+ return gen_noise()
+
+def test_noise_loud():
+ return gen_noise(amplitude=0.5, fmin=10)
+
+
+# In[406]:
+
+
+def gen_steps(size_amplitude=0.1, size_phase=0.1*np.pi, steps_per_sec=1):
+ def gen(omega_t, fundamental, duration, **_):
+ n = int(steps_per_sec * duration)
+ indices = np.random.randint(0, len(omega_t), n)
+ amplitudes = np.random.normal(1, size_amplitude, (n, 6))
+ phases = np.random.normal(0, size_phase, (n, 6))
+ amplitude = np.ones(omega_t.shape)
+ for start, end, a, p in zip(indices, indices[1:], amplitudes, phases):
+ omega_t[start:end] += p
+ amplitude[start:end] = a
+ return amplitude*np.sin(omega_t)
+ return gen
+
+def test_amplitude_steps():
+ return gen_steps(size_amplitude=0.4, size_phase=0)
+
+def test_phase_steps():
+ return gen_steps(size_amplitude=0, size_phase=0.1)
+
+def test_amplitude_and_phase_steps():
+ return gen_steps(size_amplitude=0.2, size_phase=0.07)
+
+
+# In[418]:
+
+
+def step_gen(shape, stdev, duration, steps_per_sec=1.0, mean=0.0):
+ samples, channels = shape
+ n = int(steps_per_sec * duration)
+ indices = np.random.randint(0, samples, n)
+ phases = np.random.normal(mean, stdev, (n, 6))
+ amplitude = np.ones((samples, channels))
+ out = np.zeros(shape)
+ for start, end, a in zip(indices, indices[1:], amplitude):
+ out[start:end] = a
+ return out
+
+def gen_chirp(fmin, fmax, period, dwell_time=1.0, amplitude=None, phase_steps=None):
+ def gen(omega_t, fundamental, sampling_rate, duration, **_):
+ samples = int(duration*sampling_rate)
+ phases = np.linspace(0, 2*np.pi, 6, endpoint=False)
+
+ c = (fmax-fmin)/period
+ t = np.linspace(0, duration, samples)
+
+ x = np.repeat(np.reshape(2*np.pi*fmin*t, (-1,1)), 6, axis=1)
+ data = (phases + x)[:int(sampling_rate*dwell_time)]
+ current_phase = 2*np.pi*fmin*dwell_time
+ direction = 'up'
+
+ for idx in range(int(dwell_time*sampling_rate), samples, int(2*period*sampling_rate)):
+ t1 = np.linspace(0, period, int(period*sampling_rate))
+ t2 = np.linspace(0, period, int(period*sampling_rate))
+ chirp_phase = np.hstack((
+ 2*np.pi*(c/2 * t1**2 + fmin * t1),
+ 2*np.pi*(-c/2 * t2**2 + fmax * t2 - (c/2 * period**2 + fmin * period))
+ ))
+ chirp_phase = np.repeat(np.reshape(chirp_phase, (-1, 1)), 6, axis=1)
+ new = phases + chirp_phase + current_phase
+ current_phase = chirp_phase[-1]
+ data = np.vstack((data, new))
+
+ data = data[:len(fundamental)]
+
+ if phase_steps:
+ (step_amplitude, steps_per_sec) = phase_steps
+ steps = step_gen(data.shape, step_amplitude, duration, steps_per_sec)
+ data += steps
+
+ if amplitude is None:
+ return np.sin(data)
+ else:
+ return fundamental + amplitude*np.sin(data)
+ return gen
+
+def test_close_interharmonics_and_flicker():
+ return gen_chirp(90.0, 150.0, 10, 1, amplitude=0.1)
+
+def test_off_frequency():
+# return gen_chirp(48.0, 52.0, 0.25, 1)
+ return gen_chirp(48.0, 52.0, 10, 1)
+
+def test_sweep_phase_steps():
+ return gen_chirp(48.0, 52.0, 10, 1, phase_steps=(0.1, 1))
+# return gen_chirp(48.0, 52.0, 0.25, 1, phase_steps=(0.1, 1))
+
+
+# In[ ]:
+
+
+all_tests = [test_harmonics, test_interharmonics, test_noise, test_noise_loud, test_amplitude_steps, test_phase_steps, test_amplitude_and_phase_steps, test_close_interharmonics_and_flicker, test_off_frequency, test_sweep_phase_steps]
+
diff --git a/tools/sweep_gr_sims.py b/tools/sweep_gr_sims.py
new file mode 100644
index 0000000..661af56
--- /dev/null
+++ b/tools/sweep_gr_sims.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+import subprocess
+import time
+import struct
+import random
+import math
+import statistics
+import tempfile
+import itertools
+from collections import defaultdict
+from os import path
+
+import tqdm
+
+
+SIMS = [ 'dec_proto_am_ber_top.py',
+ 'dec_proto_am_dc_ber_top.py',
+ 'dec_proto_fm_ber_top.py',
+ ]
+E12_SERIES = [1.0, 1.2, 1.5, 1.8, 2.2, 2.7, 3.3, 3.9, 4.7, 5.6, 6.8, 8.2]
+AMPLITUDES_MILLIHERTZ = [ x*y for y in [1, 10, 100] for x in E12_SERIES ]
+
+SIMULATION_DURATION = 30.0 # seconds realtime
+MAX_CONCURRENT_PROCESSES = 8
+
+
+def run_simulation(
+ duration = SIMULATION_DURATION,
+ simulations = SIMS,
+ forklimit = MAX_CONCURRENT_PROCESSES,
+ amplitudes:'list(millihertz)' = AMPLITUDES_MILLIHERTZ,
+ terminate_timeout:'s' = 5.0,
+ communicate_timeout:'s' = 10.0,
+ repeat_runs = 1,
+ shuffle = False,
+ tqdm = tqdm.tqdm
+ ):
+ with tempfile.TemporaryDirectory() as tmpdir:
+
+ jobs = list(enumerate(itertools.product(simulations, amplitudes * repeat_runs)))
+ if shuffle:
+ random.shuffle(jobs)
+ nchunks = int(math.ceil(len(jobs)/forklimit))
+
+ def start_processes(jobs):
+ for i, (sim, ampl_mhz) in jobs:
+ berfile = path.join(tmpdir, f'berfile_{i}')
+ proc = subprocess.Popen(['/usr/bin/python2', sim,
+ '--signal-strength', str(ampl_mhz),
+ '--ber-file', berfile],
+ stdin=subprocess.PIPE, stdout=subprocess.DEVNULL)
+ yield proc, sim, ampl_mhz, berfile
+
+ results = { sim: defaultdict(lambda: ([], [])) for sim in simulations }
+ with tqdm(total = nchunks * duration, bar_format='{l_bar}{bar}| {elapsed}<{remaining}') as tq:
+ tq.write(f'Will launch {len(jobs)} simulation jobs in {nchunks} batches of {forklimit}')
+ for n, i in enumerate(range(0, len(jobs), forklimit)):
+ batch = jobs[i:][:forklimit]
+ tq.write(f'Starting batch {n+1}/{nchunks}...')
+ processes = list(start_processes(batch))
+ tq.write('done.')
+
+ tq.write('Waiting for simulation:')
+ for _ in range(100):
+ time.sleep(duration/100)
+ tq.update(duration/100)
+
+ tq.write('Terminating processes...')
+ for proc, *_ in processes:
+ proc.communicate(b'\n', timeout=communicate_timeout)
+
+ for proc, *_ in processes:
+ proc.wait(terminate_timeout)
+ tq.write('done.')
+
+ tq.write('Processing simulation results')
+ for _proc, sim, ampl_mhz, berfile in processes:
+
+ with open(berfile, 'rb') as f:
+ data = f.read()
+
+ floats = struct.unpack(f'{len(data)//4}f', data)
+ ber = statistics.mean(floats[-256:])
+ stdev = statistics.stdev(floats[-256:])
+
+ bers, stdevs = results[sim][ampl_mhz]
+ bers.append(ber)
+ stdevs.append(stdev)
+
+ return results
+
+if __name__ == '__main__':
+ print(run_simulation())