diff --git a/.gitignore b/.gitignore index a48eb4435..d6fd59c29 100644 --- a/.gitignore +++ b/.gitignore @@ -86,6 +86,9 @@ ipython_config.py # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version +.conda +bootstrap_requirements.txt +environment.yml # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. diff --git a/docs/examples/Pulse_Building_Tutorial.ipynb b/docs/examples/Pulse_Building_Tutorial.ipynb index ae0c1a8d8..737975f56 100644 --- a/docs/examples/Pulse_Building_Tutorial.ipynb +++ b/docs/examples/Pulse_Building_Tutorial.ipynb @@ -68,7 +68,15 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "NoTagError: `git describe --long --dirty --always --tags '--match=v*'` could not find a tag\n" + ] + } + ], "source": [ "#\n", "# IMPORTS\n", @@ -112,7 +120,7 @@ "Segment 1: \"ramp\", PulseAtoms.ramp, (0, 0.001), 3e-06\n", "Segment 2: \"mysine\", PulseAtoms.sine, (500000.0, 0.001, 0.001, 0), 2e-06\n", "Segment 3: \"ramp2\", PulseAtoms.ramp, (0.001, 0), 3e-06\n", - "Segment 4: \"myfunc\", PulseAtoms.arb_func, ( at 0x000001B2945D0280>, {'ampl': 500000000.0}), 2e-06\n", + "Segment 4: \"myfunc\", PulseAtoms.arb_func, ( at 0x0000012CDB1F7F60>, {'ampl': 500000000.0}), 2e-06\n", "----------\n" ] } @@ -156,7 +164,18 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" ] @@ -180,7 +199,18 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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"text/plain": [ "
" ] @@ -248,21 +278,20 @@ "outputs": [ { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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", 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" ] }, + "execution_count": 7, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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", 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" ] }, "metadata": {}, @@ -492,31 +483,20 @@ "outputs": [ { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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", 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AoxFmYffCHfoh0ak5s5RmAcAUhFlYjcU9AADYjTALwNmCVO8Z8QYA5iDMwiqEEAAAnIUwC7vRM4t2L5FgW4Bx2gDAFIRZ2IWdm9AdlxFpFgCMQZiFA1eqAwAAWxBmYXdFjTYDtCdomwF9BgBgCsIs7EIGAQDAUQizsBqFWbR7jdCKAgBGI8zCMpRmEaagXQZcRwBgCsIsrBIYQlgABgCAzQizABwt+JxZKrMAYArCLOwSUJilMgsAgM0Is7AaURaduUrYNAEAzEGYhVUIIeimCwkAYAjCLOxGmwHavUY4RQBgMsIs7OK3cCfo4h6g9TUSNM1SmgUAUxBmAcAPc2YBwByEWQDORvUeAIxGmIVVqKihe+bMch4BwBSEWdjFf9g9VTcAAKxGmAUAP4x4AwBzEGZh9wZgzF1CR1DBBwBjEWZhF9oM0B19s/7XEQAgZhFmAQAAYCzCLCzjv2lC1A4EAAD0AMIsLEeaRfgY8QYA5iDMwiqEEHQKPbMAYCzCLOxGnwE6cplQwQcAYxFmYRcWoaNbLiMuJAAwBWEWVocQOmbRIVTwAcBYhFkA8EdhFgCMQZiFXdg0AZ1AYRYAzEWYhbN2dgKCXyl+9ynNAoApCLMA4IcRbwBgDsIsrEIIQadQwQcAYxFmYTdCCjpymfg/QJcBABiDMAu7F4ABnbmMSLMAYAzCLKzGzk7o2IXCQkEAMBVhFlahLovuuZC4kgDAFPHRPgAgsnNmOb9oHyPcAHQ3r9cjtZUX5eqVS3Ktploar10VT1OTNDd7xR2fIPGJSZLcO11S0m+S3ll9JT4hkW9CJxFmAQAAukl9bY1cLD0l1RfKxOtpCvqcJo9HmuqvybXqK3L5XIludUrNzJasmwdL78xsvhdhIszCMr6VWSpuAICe0FBXK+dPHpfqivLwX9zcLLWXLuhbUu906T90uKRm9onEYVqJMAvL58zSZ4DwF4AxrxhAOO0EFac/k0ulp6TZ6+3yiauvqZLig7slLbu/DLh1hG5HQGiEWQDwxwIwAB1wraZKzh77r24taEtKRqakZmRJUu80iU9I0jUWb1OTfk1d9WWpqayQZo8n4HWqwqv6bVWgTe+bw/cjBMIs7BJQmKUyi/Yxwg1AuC6Xl0pZ0eGg1Vh3QoJk5gySzNxBbVZW1aIvRfXVXjl/Ti6VnpaGq76h2NPYKKVHP5arVZd160GciyFUwRBmYTWiLDrVZsCQNwBtUOG1/OQxqSw9HfijxOXSi7j65N2iJxZ0hMsdL5k5eXLTgIFypbxU9916Ght8nqPeq76mWm7OH0vbQRBEfFiFEIJuupAAIICqopYc3hc0yKoxW0MKpkq/ocM7HGT9FyyrQHvLhP8N2lagWg5OHfhILzSDL8Is7EabAbhMAHSDpsYGOf3f3VJbWRHwZ5k3D5Yh46ZIcmpal99HzZtVFdic4aMD2goar9XJqQP/0b26+AJhFnZh4Q6650LiPALwCZGnP96l58K2psKmCp0D/ie/2/tZb+p/swweO1nik5J9HlctCOpYai9f5Dv0OcIsrMacWXTwSvG5x2guAC2aGurl9MHdAYuz3AmJMnjMJB06IyUlLUOGFkyV5LSMwHaHQ3v1DmMgzAIAALTZWqBmvjbWXfV5PCE5RQaPnaS3oo00NQ1h8JiJAZsoqIVoJUf2UaElzMI2VNTQLb3VdBkAjqfGYhUf3BMwQzaxV2/98X9Sr949do7UxIOBIwslre8A3x9VHo9ekKYWhzkZbQawu2eWBWDoAEa4AWjN09QoJYf36t24WktMSdVV0gS/Ptae4HK55ebhY4IG2uJDe+XqlUpxqpgIsytWrJAhQ4ZIcnKyTJ48WXbt2hXy+X/961/ltttu088fPXq0vP322z12rADsx4g3wLm8n1c766ouB7QWDBo9IapzXtUis7YCbcmhvQHH7BRR3zRh/fr1smjRIlm5cqUOssuXL5dZs2bJsWPHpF+/fgHP37FjhzzwwAOydOlS+eY3vylr166Vu+++W/bt2yejRo2SWNuvmdX1Pau52XcnFnZ2Qqc2TfB49AILALHF6/HqdrJILe5V/98+c2Sf1PlVOdVEgUGjJ+pAG20tgba0uVlvedtC/cxSFVpVOVYzb50krjnKTYYqwE6cOFFeeuklfd/r9UpeXp48+eST8txzzwU8//7775fa2lrZtGnTjcemTJki48aN04G4PVVVVZKRkSFXrlyR9PTIfrPPHjuod/NAz1LX0LGjZ/TXU7/2v5Jz6wi+BQjpxL4dcuA/h/TXw/MHiostI4GY/fl+/NhZSevTT/7vO7PF7XZ3279bLag6c2S/1Fy64PO4+/MFWD3ZI9vR4y395GOfQNt6ykJSavcdr8fjkX0f7tdfF95e0K3nvTvyWlTbDBoaGmTv3r0yY8aMLw7I5dL3d+7cGfQ16vHWz1dUJbet59fX1+sT0voGAADMpEJc1YUyaait7fZgGBBkExJ0a0GsBdmWCm3ubWMkNTM7YA6tmsDgpJ3CohpmKyoqdNrv37+/z+PqfllZWdDXqMfDeb5qR1DJvuWmqr5wjoSkXtE+BBggISn6Hx0CCE+939zXzlIfUJ89fjCgwumKV0F2Yrfs6hXJRWEDRxRIr5v6BMzGVZMY1GYPThATC8AiafHixbpE3XIrKSmJ9iGhhySlpkl6v8D9rQF/2Xn/o7eQBGCO7uhrV0G2rOiwVJ0/5/N4nNsteaMKjeg9daljHVkQMPNW71qmZuTWXxPbRXUBWHZ2tu67KC/3/W1I3R8wwHelXgv1eDjPT0pK0rdo6HfLcOk7+NaovLfTFwhUew+KOz5e34D2JKamSs6XRukB6bdOHCsut/W/5wPGUetQaiorfMZndTnIfnpELpddX2PR+uP7vJGF0is9U0zhcsdL3qjxUvzf3XKt1TgxtdmDqtCqnt9oTmGItKj+nz4xMVHGjx8vW7du1RMJWpq71f0FCxYEfc3UqVP1nz/zzDM3HtuyZYt+PNboSk9CtI/CeVTrCiEWnf07G5+c3COLGwCEx53o++mJt6lrldnyz47K5XMlAUFWbU6Q6vexvQnc8QmSN3qCDrT1tdU3Hlfb8F4PtJN0D7CNol5+UGO5Vq1aJWvWrJGjR4/K/Pnz9bSCefPm6T+fO3eubhVo8fTTT8u7774ry5Ytk08++UReeOEF2bNnT5vhFwAAmM/t9q2/ebydD7OqIlt5tjhw5FX+OOntt6DKtF/IB42eoDd3aE2F2+JDe7pczY5VUf8MVo3aunDhgixZskQv4lIjtlRYbVnkVVxc7DMmZ9q0aXq27I9//GP50Y9+JMOGDZMNGzbE3IxZAADQfVx+bWOdqcyq1gJVkQ0aZG8bq0d+mS4+MUkGjZkopz/+j88CsGvVV/TGCnkjx1tXoY16mFVUVbWtyur7778f8Nh9992nbwAAwBnUx+hdWQCmxm+pvtuqC+cCNk1RI67Ssn0nJZksQW3yoAPtLmlqtQBM7RB2+r+7dH9tNLbktbbNAAAAoCOLnFrzhFGZVcFXbVEbLMiqimx6dvBF5CZLTO4VdOGXajlQVVub5tASZgEAgIFtBh3r/1Sh7dSBj6S21SSE1q0F6X3tC7ItVO+smpXrH2hV+8HJ/R8FbBJhKsIsAAAwbwFYB9oMVFhToa2+tiZwjuzI8VYH2RZqW9vB4yYHLApTvwyoHtqK4s90L7HJCLMAAMCqBWBej0fKPjuqw5p/BdedkKjHVKVmmjd+q0stB2MnBd0E4sKpIik5ZPZuYYRZAABgzQIwtS3tiX0fSmXp6aA7Qw4ZN0VS0jLEaeITk2Tw2MmSFqQaXVt5UU7s/VAunjkpXq9HTBMT0wwAAADCWQCmphOoCqzazlV9rVoKVBhTK/aDSe+bo3f6U893KpfbLQPzx8nF3ifl/KnjalaZzy8H508c02PLsm4eIjcNuDngnMcqM44SAAA4mn+bgXKp9JT+eLz64nnxNDYEfZ1a6NVv6HDJunlwDxylGfrkDdUtB2ePH/QZ3aWo86lm8Z4/eUx6Z/WVXhlZut+2qaFeXH7V8VhBmAUAAMYtAGvp9wwlJf0mXY1N6tU7gkdmptTMPnLL+Nul/MQxuVJ2JuDPVbVbtWyom9frlXNF158z7Es5kpWbJ7GEMAsAAGKeqrCqKQQdkZCcIv2Gfkm3FiB0H3Lul0ZJ5oCBUn7ikzZbNPy3zI01hFkAAGCE5NS00H+elqHbCdQmCCr8omNUBVstjLt65ZJcPHPq+vzZNsZ1+c+sjQWEWQAAYIT+Q2+T+P1Fun9TVWlVBVb1fqrpBGl9+ktiSq9oH6LRemVk6VtTY4PUXDwvV6suy7XqK3Lt6hdzet2EWQAAgM5JTs+QnGEj9ZD/4dMKxe3gyQSRFJ+QKDcNGKhvisfjkeqm3XoLYTWnN9ZQmQUAAEaJi4uL9iE4jssdr2+xeOppKAEAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiOWwCmVkAqVVVV0T4URIhadVlTW3Pj+8xqV3DNAHbg57tzznvV5zmtJbeF4rgwW11drf+ZlxdbW7EBAAAgMLdlZGRIKHHNHYm8FlH7C589e1bS0tJ6ZLSH+s1CBeeSkhJJT0+P+PsB0cT1DifheoeTVPVwnlHxVAXZ3NxccbWzm5vjKrPqhAwceH0IcE9S33jCLJyC6x1OwvUOJ0nvwTzTXkW2BQvAAAAAYCzCLAAAAIxFmI2wpKQkef755/U/AdtxvcNJuN7hJEkxnGcctwAMAAAA9qAyCwAAAGMRZgEAAGAswiwAAACMRZgFAACAsQizEbZixQoZMmSIJCcny+TJk2XXrl2Rfkugxy1dulQmTpyod9br16+f3H333XLs2DG+E3CEX/ziF3pHyWeeeSbahwJERGlpqTz44IPSp08fSUlJkdGjR8uePXskVhBmI2j9+vWyaNEiPcpi3759MnbsWJk1a5acP38+km8L9Lh///vf8sQTT8hHH30kW7ZskcbGRpk5c6bU1tby3YDVdu/eLa+++qqMGTMm2ocCRERlZaXcfvvtkpCQIO+8844cOXJEli1bJpmZmRIrGM0VQaoSq6pVL730kr7v9Xr1vsZPPvmkPPfcc5F8ayCqLly4oCu0KuTecccdfDdgpZqaGiksLJSXX35Zfv7zn8u4ceNk+fLl0T4soFupvPLhhx/Ktm3bJFZRmY2QhoYG2bt3r8yYMeOLk+1y6fs7d+6M1NsCMeHKlSv6n1lZWdE+FCBi1KcRs2fP9vk5D9jm73//u0yYMEHuu+8+XaQoKCiQVatWSSwhzEZIRUWFeDwe6d+/v8/j6n5ZWVmk3haIOvUJhOodVB9LjRo1KtqHA0TEunXrdPuY6hcHbHbixAl55ZVXZNiwYfLee+/J/Pnz5amnnpI1a9ZIrIiP9gEAsK9adejQIdm+fXu0DwWIiJKSEnn66ad1f7ha3AvYXqCYMGGCvPjii/q+qsyqn/ErV66Uhx56SGIBldkIyc7OFrfbLeXl5T6Pq/sDBgyI1NsCUbVgwQLZtGmT/Otf/5KBAwfy3YCVVAuZWsir+mXj4+P1TfWH/+53v9Nfq0/lAFvk5OTIiBEjfB7Lz8+X4uJiiRWE2QhJTEyU8ePHy9atW31+u1H3p06dGqm3BaKiublZB9m33npL/vnPf8rQoUP5TsBa06dPl4MHD8qBAwdu3FTlas6cOfprVcgAbHH77bcHjFo8fvy4DB48WGIFbQYRpMZyqRK8+iE3adIkvcpVjSqaN29eJN8WiEprwdq1a2Xjxo161mxLX3hGRoaeSQjYRF3j/v3gqampegYnfeKwzcKFC2XatGm6zeC73/2unpf/2muv6VusYDRXhKmxXL/61a/0/9zV2Bb1MZQa2QXYRA2MD2b16tXy/e9/v8ePB+hpd955J6O5YK1NmzbJ4sWLpaioSH/ypop1jz32mMQKwiwAAACMRc8sAAAAjEWYBQAAgLEIswAAADAWYRYAAADGIswCAADAWIRZAAAAGIswCwAAAGMRZgEAAGAswiwAGOTixYvSr18/OXXqVMjnVVRU6OedOXOmx44NAKKBHcAAwCBqG8nq6mpZtWpVu8/9wQ9+IJWVlfKHP/yhR44NAKKBMAsAhrh69ark5OTIe++9J1OmTGn3+YcPH5bx48fL2bNnJSsrq0eOEQB6Gm0GAGCIt99+W5KSkm4EWVV1nTNnjvTt21dSUlJk2LBhsnr16hvPHzlypOTm5spbb70VxaMGgMiKj/C/HwDQTbZt26YrrS1+8pOfyJEjR+Sdd96R7Oxs+fTTT6Wurs7nNZMmTdKve+SRR/g+ALASYRYADHH69GldaW1RXFwsBQUFMmHCBH1/yJAhAa9Rz9+/f3+PHicA9CTaDADAEKrqmpycfOP+/PnzZd26dTJu3Dh59tlnZceOHQGvUe0HqtcWAGxFmAUAQ6hWAtUn2+Kuu+7S1dqFCxfqRV7Tp0/XEwxau3Tpku6pBQBbEWYBwBCqpUD1yLamgupDDz0kb7zxhixfvlxee+01nz8/dOiQfh0A2IowCwCGmDVrlh631VKdXbJkiWzcuFEv/FKPb9q0SfLz8288X7UX7N27V2bOnBnFowaAyCLMAoAhRo8eLYWFhfLmm2/q+4mJibJ48WIZM2aM3HHHHeJ2u3UPbQsVdAcNGiRf/vKXo3jUABBZbJoAAAbZvHmz/PCHP9TtAy5X6HqEmkf71FNPyfe+970eOz4A6GmM5gIAg8yePVuKioqktLRU8vLy2nxeRUWF3HPPPfLAAw/06PEBQE+jMgsAAABj0TMLAAAAYxFmAQAAYCzCLAAAAIxFmAUAAICxCLMAAAAwFmEWAAAAxiLMAgAAwFiEWQAAABiLMAsAAAAx1f8DpStQ6NGYlPgAAAAASUVORK5CYII=", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -572,21 +550,20 @@ "outputs": [ { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -752,12 +728,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Help on method changeArg in module broadbean.broadbean:\n", + "Help on method changeArg in module broadbean.element:\n", "\n", - "changeArg(channel:Union[str, int], name:str, arg:Union[str, int], value:Union[int, float], replaceeverywhere:bool=False) -> None method of broadbean.broadbean.Element instance\n", + "changeArg(channel: 'str | int', name: 'str', arg: 'str | int', value: 'int | float', replaceeverywhere: 'bool' = False) -> 'None' method of broadbean.element.Element instance\n", " Change the argument of a function of the blueprint on the specified\n", " channel.\n", - " \n", + "\n", " Args:\n", " channel: The channel where the blueprint sits.\n", " name: The name of the segment in which to change an argument\n", @@ -768,7 +744,7 @@ " in ALL segments where the name matches. E.g. 'gaussian1' will\n", " match 'gaussian', 'gaussian2', etc. If False, only the segment\n", " with exact name match gets a replacement.\n", - " \n", + "\n", " Raises:\n", " ValueError: If the specified channel has no blueprint.\n", " ValueError: If the argument can not be matched (either the argument\n", @@ -788,21 +764,20 @@ "outputs": [ { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -976,21 +950,20 @@ "outputs": [ { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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" ] }, + "execution_count": 21, "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
');\n var titletext = $(\n '
');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_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 = $('');\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 = $('');\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\nmpl.figure.prototype._init_toolbar = function() {\n var fig = this;\n\n var nav_element = $('
')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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99VUZMWKE/O1vf0tquS+++KIMHjxYOnfuLBMnTnS6aySiBvipH9vyob7nBwbEIRAhHP8FhBMbG6A7NSVbMo9Ub/DhqsVYNVWrkX/f+c53nGRUzVXcv39/57m6I576++eff+60KP/ud7+TW2+9tcNlqr7KCxYskKVLlzpJ8XPPPef0GdmzZ4/06dOnze+oKTnU343uY8fIcgAAAF+5Sox79uwpS5YskV//+teycuVK2bhxoxw4cEBqamqkV69eTp9jldSq1uJkPfPMM87NQZruaa0SZLVsNcDvsccea/M7KhEuLCx0U3TAHJw0+cfEk25YhPW3pVg0KqcqDkv9+Rop6xaTSCTlibmMoFpVjx/4QjpfXiBhs27dOuemcps3b3YaSy/uuXDDDTfISy+9JDfeeKM/8xjn5+fL7bff7jzSUVdXJ9u2bZOFCxc2v6dWVDUPcmlpacLvqYmc1d33VFBV67RqxR4+fHibn1W3qW55q+ozZ86kVWYAgBlsO8e0vTtgxf92SlVlhfO8+mQliXEsJufPnnEep46USc+iwRIWqneBalS9OClWunXrJj/+8Y/l2WefTSkxDvR0qbKy0ukD0rdv31bvq9dHjx5t8zvXXHON05r83nvvOfMoq8CqM4NDhw61+fnFixc7ldT0KCoqyshvAQAA+jp3+mTQRdBW9ZkTEiaffvqp3HzzzQn/Pm3aNKfhNRWhu44wefJk5xZ/o0ePlilTpsjf//536d27tzMosC2qNVo1qzc9ysrKfC8zDGphCeDSeijvvhZWBrSo2d4qCCQSj8WonISVE679RkVFxSVTs7WUk5Mjx48fT2nZntwSOlWqX3J2drbzA1tSr5PtQ6wqZsyYMbJv3742/56Xl+c8bFxxAKTHyIG9SIxwW+WKfkWSm+/dHdPCpPrUieZuJWE0YMAA2bFjhwwZMqTNv//nP/+Rfv36hS8xzs3NdWa2WLt2bfO8dKprhHqtOlUnQ3XF+Oyzz5KaAUPnAyetgkgWN6EAAPcubtoq6FUoXbr3tLb1PMyJ8a233ipPPvmk053i4il71YQQTz31lMycOTN8ibGipmq78847Zfz48VJSUuJ0qK6urm6epUJ1m1BnBqqvcNMkzurue+os4dSpU/J///d/zswY9957b8C/BAACQKt26ky/CnjxumH4z3WNKwSh7YL1xBNPOF1phw4d6jSkqvFnyu7du517Y6hG01/84hfhTIzvuOMOpx/IokWLnAF3qu/w6tWrmwfkqbvttZxSRc2XrEYiqs+q21GrFudNmzbJddddF+CvgLFCtrMAkDqu3AHhoHJElfvNmzfPGUvWlNirq/hq2mCVHF88sUNoEmNFZfuJuk6sX7++1Ws1/YZ6mI50DEBqOw/2HgDbhvlXmq688kpZtWqV02Cqxpmp5Li4uNhpNE2HFokxAADwVvhTH/gmxCfU3bt3lwkTJni2vNBN1wYAgBdsG8hq2+8FUkFirMs5Oaf2oRBIH0QDLnkBgG6YjhFtITEGwibEl7zgPQaMAcli34mOkRi7wGUowGxhm7IIHuPqDCzBCXViJMa64gANBC+EiRIn8ECywrd9ZwpNAl8jMQbcCKSLMTtvICNMb4Bg32FVuOENEmMACDOSHw+r0vCTUDJDNDF8VU8HiTGQAP1NAQBW4KSpGYkxAJh0cDDgJ8AbxreAp4vqQRtIjN3ggAMAAGAsEmMAAGA+E64IeYSrCYmRGOty4zsueQHaYa5PwFxs3y1x0tCExBhIhNYFAACsQmIMuEDLPnTDxSYgOdz8ptWeg9UmARJjbXFZAwnWDFqyAW/2smxLwIVtgYpoQmIMAEY1qnCES5nhze+cCAAdIzEGAJIGwD6GnwghNSTG2oxqZQMNhwDixM4b7a8g1A/YdySD/gJIAomxK1yiBABj2JYo2fZ7kRgNLgmRGANhG8HMwQ3trh6arreAZphlqAX2G81IjDXFOgqw0QEA/EViDAAJ0KJkF+6EBlswMiExEmMAAACLadt1MAAkxgAQZgyiQaJVg3ZBwDUSYxcY1GIZTqABuxh+fZldGtAxEmNA92mMTT9aa426NxUNHfYh5i1wpSkhEmNdVi5WUgBeoFkQzccVqoLjbrL7DXYcTUiMAYDBJwAAEmMACDcaBZE021sFbf/9SAotxrpiA0a7qwc7eIB9LJAa5mhPjMQY0H0gHE2CcIH5SNPY1BjrAViPxBhIiFZZqxBuwCrM+IO2kBhr0sJAoyCgITZMhBgt4IB7JMZu0KIEAAYz/UyIgxjQERJjIIwYfIcm9ItNbRMiSbRKmwOWTT8PcoEB3V8jMdYUO20AQHrI/AC3SIyBBDiDRigH53A1AUBHuNKUEIkxoPnOhAE0fqH/Jcxm9ZVIThipnySRGANAiHHilCKLc0Q0YttBW0iMXbD6bBsATBfCnjMAvEViDAAAAJAYa4SO8HCBqxfeY7AljMNxpYN9pr2XCC4eWMz18K/RYgxo3yfN3p134EgsYBKyH6BDJMZAIhxEAKs28FBOzwfAUyTGumJqGQBAGkjzWx5TWZXaXTnIOZqRGAMAAAAkxkBI0foBwPVugx1HKzSpow20GGsyOIeJxgH9hHG7ZHYN6glA6kiM3aAPjlVoXbEMjWkI4YkQYMtJv19IjAHdsf9Cu+sHKwjQMWYhcVs/tiIxBgDARJw0Aa6RGAMAAFiFK02JkBgDgEl9yhkLkWQ9Wdjv0uJ1g0GpHdWPT4EIARJjIJTYiwFon/GJfrqoH7SBxBjQfEfKbWoBAPAHiTEAhBgnTgDgHRLjNHFQMhidrgBjGdOnHMmxsU95ey7+6RzvmpEYA0AiNh84AcBCJMZACDHCOiOVKiagJRQAUkdirCkSHwBJoVUb6PiYSteZ1ruNi/pSUD9fIzEGAAAASIyBEAy2pEUQ8KfrjOHbGlcigY7RYpzuTsWr/ajhO2QAPjGjqzS8wHEFcI3EGAAAACAxBoDEmKccRjFk5pWUWNh1pl0X/3ab142L0GKsK1ZStLt+UD0AAHiNxBhwI5Cxdxa3aqBDrB7e4QoBABJjAAAAi9DgkhiJsRt0b7AKUxshiCsEgFdoAad+4B6JMQAYdSJkyu8AAP+RGAMhxO078TWatbWbl15TnDIBHSMx1qSfDpe8AACeMjzRd8PGEyE7r5ilj8QYAAAAIDEG2tHGGTQjeQ1mSIsJLT8AkDpajDVlxiEaCDdOhAxmyImQKzb+5iY2//a2cOe7hEiMAQDgRAiALonxiy++KIMHD5bOnTvLxIkTZcuWLe1+/q9//atce+21zudHjhwpq1at8q2sAKAVbn0HpLjpMPoOl8qRgC1fvlwWLFggS5cudZLi5557TqZPny579uyRPn36XPL5TZs2yezZs2Xx4sUyc+ZMefvtt2XWrFny8ccfy4gRI1z/+/FYTBrqapP6bH3t+Tbe9WjDyrq0XPXna7xZtgViURXHOud5tKFBsrOz0493XVvx1kND7XmJR6NiK9vi7Ua0vt64fQfx9ibxi0UbQrFuEO9gxiYEtW7EktyfW5MYP/PMM3LffffJXXfd5bxWCfLKlSvltddek8cee+ySzz///PNy8803yyOPPOK8fvrpp2XNmjXywgsvON91q67mnHyxbaPopu7cWdm35YOgixEasVhMyvcecp6fvLqX9L16aIbiHUALQxutGvs/3iQ2Mzreaar43y7nYRL/4m22qsoKqT5xXHRHvP0Xra8LLOeIJbl9W9GVoq6uTrZt2yZTp079ukCRiPO6tLS0ze+o91t+XlEtzIk+X1tbK2fOnGn1AADAhBMhpMPeeHPvBE0T48rKSolGo9K3b99W76vXR48ebfM76n03n1ddLrp169b8KCoq8qz8Obl5EvGoyT+382WeLAeZ42W83cjO6STZnTr5/u/aLqh4u5Wbz77Dpni7kZvfJegiaMvEeLvBfkPzwXeZtHDhQjl9+nTzo6yszLNkpV+x+z7NieQXXCE9Bg6WrIjxIQklr+Pttp9g4ZDhEskhOfYz3qrOw6DPlUOlU15+0MUItSC370wq6N1PuvYuZJCZJfF2o1PnfOk9eCg5h259jHv16uV0sq6oqGj1vnpdWFjY5nfU+24+n5eX5zwSyb2si1zzjdZdM9oTjcbkrHwikUi2dOnRS7zU56prZMC11RKPx2ToDWMkO5sk2W1clJ5FV4ci3m507dVXBlwzUmKxKOuGT/G+vGdvCYNO+flSOGSY009v6A2jjdxvmL59Z4pqER1w7Sgpr2xw5vENy3GFePujZ9FVMuDak4HnHNEkt28rEuPc3FwZN26crF271plZQlE7d/V6/vz5bX5n8uTJzt8feuih5vfU4Dv1fqqtcVnZyVdDXKLOTjRTnPJkZTs7NJsv87jVMi5ZkazQxNstVRbWDXvi7YYan2HqukG80xPJijjdacOyfhBv/+iQc8ST3J9bMyuFmqrtzjvvlPHjx0tJSYkzXVt1dXXzLBVz586VAQMGOH2FlQcffFCmTJkiS5YskRkzZsiyZctk69at8sorrwT8SwAAABBmgSfGd9xxhxw/flwWLVrkDKAbPXq0rF69unmA3cGDB52WkCY33HCDM3fxE088IY8//rgUFxfLihUrUprDGAAAANAmMVZUt4lEXSfWr19/yXs/+MEPnAcAAADgFf174QMAAAC2tBj7fdtDJdUbfah5l89Wn21ehpe3Lszksk2XqO6a4qxjvMNYDl2YHm/Ty+wW8fa+7sIc7/Ly8pSXW3G8cVarQ4cOhaIuMkmX+ogmKEdTnNWkDH7KijdlipZQle7lTT4AAACQGVu2bJEJEyaIX6xLjNWZx5EjR6Rr166tJj1XZ6IqYVY3ACkoKJAwoMzJnYnu27dPhgwZ0upsmLrzh9/1TLyDRbzDU3dhLHN9fb2UlpY6g+1zcr6+4F1VVSXXXXed7Ny50zm2hwFlTi5fU/epGDNmTKt4Z5p1XSnUDBcDBw5M+He1cYdlp9SEMrevvTNN6s4fftYz8Q4e8Q5H3YWxzDNnzrzkvaYuFmpq17DUHWVOzqBBg8RvDL4DAAAASIwBAACARrQYX5CXlydPPfWU8/+woMzUne50WUd1KYcblJm6050u66gu5XCDMuvLusF3AAAAQFtoMQYAAABIjAEAAIBGtBgDAAAAJMYAAABAI1qMReTFF1+UwYMHS+fOnWXixInO7Qd1tnjxYucmBuoOP3369JFZs2bJnj17JCx++9vfOncdfOihhwL594m3v4i3O2zf6WH79hfbtzts3/qzPjFevny5LFiwwJnq5eOPP5ZRo0bJ9OnT5dixY6KrDz74QB544AHZvHmzrFmzxrlN5rRp06S6ulp099FHH8nLL78s119/fSD/PvH2F/F2j+07dWzf/mL7do/tOwTilispKYk/8MADza+j0Wi8f//+8cWLFwdaLjeOHTumptyLf/DBB3GdVVVVxYuLi+Nr1qyJT5kyJf7ggw/6Xgbi7R/i7Q227+SxffuH7dsbbN/6sbrFuK6uTrZt2yZTp05tfi8SiTivS0tLJSxOnz7t/L9Hjx6iM9XKPWPGjFb17Sfi7S/i7Q227+SwffuL7dsbbN/6yRGLVVZWSjQalb59+7Z6X73evXu3hEEsFnP66n7jG9+QESNGiK6WLVvmdFVRl96CQrz9Q7y9wfadPLZv/7B9e4PtW09WJ8YmUGftO3bskI0bN4quysrK5MEHH3T6Q6sBjkgd8bYL8bYL8bYL8daT1Ylxr169JDs7WyoqKlq9r14XFhaK7ubPny/vv/++bNiwQQYOHCi6Ut1V1GDGsWPHNr+nWupVuV944QWpra114pBpxNsfxNsbbN/usH37g+3bG2zf+rK6j3Fubq6MGzdO1q5d2+rShno9efJk0VU8Hnc2qnfffVfWrVsnV111lejspptuks8++0y2b9/e/Bg/frzMmTPHee5HUqwQb38Q7/SwfaeG7dsfbN/pYfsOgbjlli1bFs/Ly4u/8cYb8Z07d8Z/9KMfxa+44or40aNH47qaN29evFu3bvH169fHy8vLmx/nzp2Lh0VQs1IQ72AQ7+SxfaeO7TsYbN/JY/vWn/WJsfL73/8+PmjQoHhubq4z3c/mzZvjOlPnM209Xn/99XhYBLUjVYi3/4h38ti+08P27T+27+SxfesvS/0n6FZrAAAAIGhW9zEGAAAAmpAYAwAAACTGAAAAQCNajAEAAAASYwAAAKARLcYAAAAAiTEAAADQiBZjAAAAgMTYTF999ZX06dNHvvzyy3Y/V1lZ6Xzu0KFDvpUN3iPediHediHediHewePOdwZasGCBVFVVyauvvtrhZ3/2s5/JyZMn5U9/+pMvZYP3iLddiLddiLddiHfwSIwNc+7cOenXr5/8+9//lkmTJnX4+c8//1zGjRsnR44ckR49evhSRniHeNuFeNuFeNuFeOuBPsaGWbVqleTl5TUnxao1eM6cOdK7d2/Jz8+X4uJief3115s/P3z4cOnfv7+8++67AZYaqSLediHediHediHeesgJugDw1ocffui0ADd58sknZefOnfKvf/1LevXqJfv27ZOamppW3ykpKXG+d8899xCOkCHediHediHediHeeiAxNsyBAwecFuAmBw8elDFjxsj48eOd14MHD77kO+rzn3zyia/lhDeIt12It12It12Itx7oSmEY1RrcuXPn5tfz5s2TZcuWyejRo+XRRx+VTZs2XfId1cVC9W1C+BBvuxBvuxBvuxBvPZAYG0Z1l1D9ipvccsstzlnoww8/7Aywu+mmm5yZKFo6ceKE0wcZ4UO87UK87UK87UK89UBibBjVbUL1KW5JJb133nmnvPnmm/Lcc8/JK6+80urvO3bscL6H8CHediHediHediHeeiAxNsz06dOdKdiaWo0XLVok7733njPoTr3//vvvy7Bhw5o/r7pQbNu2TaZNmxZgqZEq4m0X4m0X4m0X4q0HEmPDjBw5UsaOHSvvvPOO8zo3N1cWLlwo119/vdx4442SnZ3t9DluopLmQYMGyTe/+c0AS41UEW+7EG+7EG+7EG9NxGGc999/Pz5s2LB4NBrt8LMTJ06Mv/XWW76UC5lBvO1CvO1CvO1CvIPHdG0GmjFjhuzdu1cOHz4sRUVFCT9XWVkpt912m8yePdvX8sFbxNsuxNsuxNsuxDt43BIaAAAAoI8xAAAA0IjBdwAAAACJMQAAANCIFmMAAACAxBgAAABoRIsxAAAAQGIMAAAANKLFGAAAACAxBgAAAMTx/2Y501o1aL1fAAAAAElFTkSuQmCC", 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -1153,45 +1112,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/william/sourcecodes/pulsebuilding/broadbean/broadbean.py:1425: UserWarning: Deprecation warning. This function is only compatible with AWG5014 output and will be removed. Please use the specific setSequencingXXX methods.\n", - " warnings.warn('Deprecation warning. This function is only compatible '\n" + "C:\\Users\\bethomse\\dev\\broadbean\\src\\broadbean\\sequence.py:190: UserWarning: Deprecation warning. This function is only compatible with AWG5014 output and will be removed. Please use the specific setSequencingXXX methods.\n", + " warnings.warn(\n" ] }, { "data": { - "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.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\nmpl.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 = $('
');\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 this.ws.close();\n }\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n var titlebar = $(\n '
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');\n titlebar.append(titletext)\n this.root.append(titlebar);\n this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n var fig = this;\n\n var canvas_div = $('
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')\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 = $('');\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 = $('');\n nav_element.append(status_bar);\n this.message = status_bar[0];\n\n // Add the close button to the window.\n var buttongrp = $('
');\n var 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\nmpl.figure.prototype._root_extra_style = function(el){\n var fig = this\n el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n });\n}\n\nmpl.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\nmpl.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 // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n fig.ondownload(fig, null);\n}\n\n\nmpl.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= 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.\nif (IPython.notebook.kernel != null) {\n IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n", + "image/png": 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", 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" ] }, "metadata": {}, @@ -1279,7 +1217,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.12.12" } }, "nbformat": 4, diff --git a/src/broadbean/blueprint.py b/src/broadbean/blueprint.py index 92d140c31..7bab50856 100644 --- a/src/broadbean/blueprint.py +++ b/src/broadbean/blueprint.py @@ -1,6 +1,7 @@ # This file is for defining the blueprint object import functools as ft +import inspect import json import re import warnings @@ -81,9 +82,15 @@ def __init__( # Make special functions live in the funlist but transfer their names # to the namelist # Infer names from signature if not given, i.e. allow for '' names + # for ii, name in enumerate(namelist): + # if isinstance(funlist[ii], str): + # namelist[ii] = funlist[ii] + # elif name == "": + # namelist[ii] = funlist[ii].__name__ for ii, name in enumerate(namelist): if isinstance(funlist[ii], str): - namelist[ii] = funlist[ii] + if name == "": + namelist[ii] = funlist[ii] elif name == "": namelist[ii] = funlist[ii].__name__ @@ -93,8 +100,8 @@ def __init__( argslist[ii] = (args,) self._argslist = argslist - self._namelist = namelist namelist = self._make_names_unique(namelist) + self._namelist = namelist # initialise markers if marker1 is None: @@ -265,6 +272,55 @@ def description(self): desc[segkey]["durations"] = self._durslist[sn] if desc[segkey]["function"] == "waituntil": desc[segkey]["arguments"] = {"waittime": self._argslist[sn]} + elif desc[segkey]["function"] == "function PulseAtoms.arb_func": + # Special handling for arb_func serialization + func_obj, kwargs_dict = self._argslist[sn] + + # Serialize the function + if hasattr(func_obj, "__name__") and func_obj.__name__ != "": + # Regular function - store name and try to get source + func_name = func_obj.__name__ + try: + func_source = inspect.getsource(func_obj) + except (OSError, TypeError): + func_source = None + desc[segkey]["arguments"] = { + "func_type": "named_function", + "func_name": func_name, + "func_source": func_source, + "kwargs": kwargs_dict, + } + else: + # Lambda function - store source code + # First check if the lambda has a __func_source__ attribute + # (for dynamically created lambdas) + if hasattr(func_obj, "__func_source__"): + func_source = func_obj.__func_source__ + else: + # Fall back to inspect.getsource() with regex parsing + try: + func_source = inspect.getsource(func_obj) + # Extract just the lambda part using regex + import re + + # Match 'lambda' followed by parameters, colon, and expression + # This handles nested parentheses and complex expressions + lambda_match = re.search( + r"lambda\s+[^:]*:\s*[^\n,;]+", func_source + ) + if lambda_match: + func_source = lambda_match.group(0).strip() + else: + func_source = "lambda t, **kwargs: 0" + except (OSError, TypeError): + # Fallback: create a generic lambda string + func_source = "lambda t, **kwargs: 0" # Default fallback + + desc[segkey]["arguments"] = { + "func_type": "lambda", + "func_source": func_source, + "kwargs": kwargs_dict, + } else: sig = signature(self._funlist[sn]) desc[segkey]["arguments"] = dict( @@ -275,6 +331,7 @@ def description(self): desc["marker2_abs"] = self.marker2 desc["marker1_rel"] = self._segmark1 desc["marker2_rel"] = self._segmark2 + desc["SR"] = self._SR return desc @@ -312,7 +369,76 @@ def blueprint_from_description(cls, blue_dict): if seg_dict["function"] == "waituntil": arguments = blue_dict[seg]["arguments"].values() arguments = (list(arguments)[0][0],) - bp_seg.insertSegment(i, "waituntil", arguments) + bp_seg.insertSegment(i, "waituntil", arguments, name=seg_dict["name"]) + elif seg_dict["function"] == "function PulseAtoms.arb_func": + # Special handling for arb_func reconstruction + args_dict = blue_dict[seg]["arguments"] + + if args_dict.get("func_type") == "lambda": + # Reconstruct lambda function + func_source = args_dict["func_source"] + try: + # Create lambda function from source + func_obj = eval(func_source) + except (SyntaxError, NameError) as e: + # Fallback: create a zero function + print( + f"Warning: Could not reconstruct lambda function '{func_source}'. Using zero function. Error: {e}" + ) + + def zero_function(t, **kwargs): + return 0 + + func_obj = zero_function + + kwargs_dict = args_dict["kwargs"] + arguments = (func_obj, kwargs_dict) + elif args_dict.get("func_type") == "named_function": + # Reconstruct named function + func_name = args_dict["func_name"] + func_source = args_dict.get("func_source") + kwargs_dict = args_dict["kwargs"] + + # Try to reconstruct from source first + func_obj = None + if func_source: + try: + # Execute the function source in a local namespace + local_ns = {} + exec(func_source, globals(), local_ns) + if func_name in local_ns: + func_obj = local_ns[func_name] + except Exception as e: + print( + f"Warning: Could not reconstruct named function '{func_name}' from source. Error: {e}" + ) + + # Fallback: try to find function in globals + if func_obj is None: + try: + func_obj = globals()[func_name] + except KeyError: + print( + f"Warning: Could not find function '{func_name}' in globals. Using zero function." + ) + + def zero_function(t, **kwargs): + return 0 + + func_obj = zero_function + + arguments = (func_obj, kwargs_dict) + else: + # Legacy format or fallback + arguments = tuple(blue_dict[seg]["arguments"].values()) + + bp_seg.insertSegment( + i, + knowfunctions[seg_dict["function"]], + arguments, + name=re.sub(r"\d", "", seg_dict["name"]), + dur=seg_dict["durations"], + ) else: arguments = tuple(blue_dict[seg]["arguments"].values()) bp_seg.insertSegment( @@ -329,6 +455,8 @@ def blueprint_from_description(cls, blue_dict): listmarker2 = blue_dict["marker2_rel"] bp_sum._segmark1 = [tuple(mark) for mark in listmarker1] bp_sum._segmark2 = [tuple(mark) for mark in listmarker2] + if "SR" in blue_dict: + bp_sum._SR = blue_dict["SR"] return bp_sum @classmethod @@ -664,7 +792,6 @@ def insertSegment(self, pos, func, args=(), dur=None, name=None, durs=None): if pos < -1: raise ValueError("Position must be strictly larger than -1") - if name is None or name == "": if func == "waituntil": name = "waituntil" @@ -674,7 +801,6 @@ def insertSegment(self, pos, func, args=(), dur=None, name=None, durs=None): if len(name) > 0: if name[-1].isdigit(): raise ValueError("Segment name must not end in a number") - if pos == -1: self._namelist.append(name) self._namelist = self._make_names_unique(self._namelist) diff --git a/tests/test_arb_func_serialization.py b/tests/test_arb_func_serialization.py new file mode 100644 index 000000000..b93a9cb2f --- /dev/null +++ b/tests/test_arb_func_serialization.py @@ -0,0 +1,263 @@ +""" +Test for arb_func serialization functionality in blueprint.py +""" + +import json +import os +import tempfile + +import numpy as np +import pytest + +import broadbean as bb + +################################################## +# FIXTURES + + +@pytest.fixture +def blueprint_with_lambda(): + """ + Return a blueprint with an arb_func using a lambda function + """ + bp = bb.BluePrint() + bp.setSR(1e9) + + bp.insertSegment( + 0, + bb.PulseAtoms.arb_func, + (lambda t, ampl: ampl * t * t, {"ampl": 2}), + dur=1e-6, + name="test_lambda", + ) + return bp + + +@pytest.fixture +def blueprint_with_sine_lambda(): + """ + Return a blueprint with an arb_func using a sine wave lambda function + """ + bp = bb.BluePrint() + bp.setSR(1e9) + + bp.insertSegment( + 0, + bb.PulseAtoms.arb_func, + ( + lambda t, freq, ampl: ampl * np.sin(2 * np.pi * freq * t), + {"freq": 1e6, "ampl": 1}, + ), + dur=2e-6, + name="sine_wave", + ) + return bp + + +@pytest.fixture +def blueprint_with_func_source(): + """ + Return a blueprint with a dynamically created lambda with __func_source__ attribute + """ + bp = bb.BluePrint() + bp.setSR(1e9) + + lambda_str = "lambda t, ampl, freq: ampl * np.sin(2 * np.pi * freq * t)" + eval_globals = {"np": np} + lambda_func = eval(lambda_str, eval_globals) + lambda_func.__func_source__ = lambda_str + + kwargs = {"ampl": 1.5, "freq": 1e6} + bp.insertSegment( + 0, bb.PulseAtoms.arb_func, (lambda_func, kwargs), dur=2e-6, name="custom_wave" + ) + return bp + + +################################################## +# HELPER FUNCTIONS + + +def get_arrays_from_blueprint(bp): + """Helper function to generate arrays from a blueprint""" + elem = bb.Element() + elem.addBluePrint(1, bp) + return elem.getArrays() + + +################################################## +# TEST ARB_FUNC LAMBDA SERIALIZATION + + +def test_arb_func_lambda_description_contains_func_type(blueprint_with_lambda): + """Test that description contains func_type for lambda""" + desc = blueprint_with_lambda.description + args = desc["segment_01"]["arguments"] + assert args["func_type"] == "lambda" + + +def test_arb_func_lambda_description_contains_source(blueprint_with_lambda): + """Test that description contains func_source for lambda""" + desc = blueprint_with_lambda.description + args = desc["segment_01"]["arguments"] + assert "lambda t, ampl: ampl * t * t" in args["func_source"] + + +def test_arb_func_lambda_description_contains_kwargs(blueprint_with_lambda): + """Test that description contains kwargs for lambda""" + desc = blueprint_with_lambda.description + args = desc["segment_01"]["arguments"] + assert args["kwargs"] == {"ampl": 2} + + +def test_arb_func_lambda_json_serialization(blueprint_with_lambda): + """Test that arb_func description can be serialized to JSON""" + desc = blueprint_with_lambda.description + json_str = json.dumps(desc) + desc_restored = json.loads(json_str) + assert desc_restored["segment_01"]["arguments"]["func_type"] == "lambda" + + +def test_arb_func_lambda_blueprint_reconstruction(blueprint_with_lambda): + """Test that blueprint can be reconstructed from description""" + arrays_orig = get_arrays_from_blueprint(blueprint_with_lambda) + + desc = blueprint_with_lambda.description + json_str = json.dumps(desc) + desc_restored = json.loads(json_str) + + bp_restored = bb.BluePrint.blueprint_from_description(desc_restored) + arrays_restored = get_arrays_from_blueprint(bp_restored) + + assert np.allclose(arrays_orig[1]["wfm"], arrays_restored[1]["wfm"]) + + +def test_arb_func_lambda_mathematical_correctness(blueprint_with_lambda): + """Test that restored waveform is mathematically correct""" + arrays_orig = get_arrays_from_blueprint(blueprint_with_lambda) + + desc = blueprint_with_lambda.description + bp_restored = bb.BluePrint.blueprint_from_description(desc) + arrays_restored = get_arrays_from_blueprint(bp_restored) + + time_test = 0.5e-6 + kwargs = {"ampl": 2} + expected = kwargs["ampl"] * time_test * time_test + actual_idx = int(time_test * blueprint_with_lambda.SR) + + assert np.isclose(expected, arrays_orig[1]["wfm"][actual_idx]) + assert np.isclose(expected, arrays_restored[1]["wfm"][actual_idx]) + + +################################################## +# TEST ARB_FUNC JSON FILE OPERATIONS + + +def test_arb_func_write_to_json(blueprint_with_sine_lambda): + """Test that arb_func blueprint can be written to JSON file""" + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json_file_path = f.name + + try: + blueprint_with_sine_lambda.write_to_json(json_file_path) + assert os.path.exists(json_file_path) + finally: + if os.path.exists(json_file_path): + os.unlink(json_file_path) + + +def test_arb_func_read_from_json(blueprint_with_sine_lambda): + """Test that arb_func blueprint can be read from JSON file""" + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json_file_path = f.name + + try: + blueprint_with_sine_lambda.write_to_json(json_file_path) + bp_restored = bb.BluePrint.init_from_json(json_file_path) + assert isinstance(bp_restored, bb.BluePrint) + finally: + if os.path.exists(json_file_path): + os.unlink(json_file_path) + + +def test_arb_func_json_roundtrip_waveform_match(blueprint_with_sine_lambda): + """Test that waveforms match after JSON file round-trip""" + arrays_orig = get_arrays_from_blueprint(blueprint_with_sine_lambda) + + with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f: + json_file_path = f.name + + try: + blueprint_with_sine_lambda.write_to_json(json_file_path) + bp_restored = bb.BluePrint.init_from_json(json_file_path) + arrays_restored = get_arrays_from_blueprint(bp_restored) + + assert np.allclose(arrays_orig[1]["wfm"], arrays_restored[1]["wfm"]) + finally: + if os.path.exists(json_file_path): + os.unlink(json_file_path) + + +################################################## +# TEST ARB_FUNC WITH __func_source__ ATTRIBUTE + + +def test_arb_func_func_source_attribute_func_type(blueprint_with_func_source): + """Test that func_type is lambda for functions with __func_source__""" + desc = blueprint_with_func_source.description + args = desc["segment_01"]["arguments"] + assert args["func_type"] == "lambda" + + +def test_arb_func_func_source_attribute_preserved(blueprint_with_func_source): + """Test that __func_source__ attribute is used exactly as provided""" + desc = blueprint_with_func_source.description + args = desc["segment_01"]["arguments"] + expected_source = "lambda t, ampl, freq: ampl * np.sin(2 * np.pi * freq * t)" + assert args["func_source"] == expected_source + + +def test_arb_func_func_source_attribute_kwargs(blueprint_with_func_source): + """Test that kwargs are preserved for functions with __func_source__""" + desc = blueprint_with_func_source.description + args = desc["segment_01"]["arguments"] + assert args["kwargs"] == {"ampl": 1.5, "freq": 1e6} + + +def test_arb_func_func_source_json_roundtrip(blueprint_with_func_source): + """Test JSON round-trip for functions with __func_source__""" + arrays_orig = get_arrays_from_blueprint(blueprint_with_func_source) + + desc = blueprint_with_func_source.description + json_str = json.dumps(desc) + desc_restored = json.loads(json_str) + bp_restored = bb.BluePrint.blueprint_from_description(desc_restored) + + arrays_restored = get_arrays_from_blueprint(bp_restored) + + assert np.allclose(arrays_orig[1]["wfm"], arrays_restored[1]["wfm"]) + + +@pytest.mark.parametrize( + "time_test, kwargs", + [ + (0.5e-6, {"ampl": 1.5, "freq": 1e6}), + (1.0e-6, {"ampl": 1.5, "freq": 1e6}), + (1.5e-6, {"ampl": 1.5, "freq": 1e6}), + ], +) +def test_arb_func_func_source_mathematical_correctness( + blueprint_with_func_source, time_test, kwargs +): + """Test mathematical correctness at various time points""" + arrays_orig = get_arrays_from_blueprint(blueprint_with_func_source) + + desc = blueprint_with_func_source.description + bp_restored = bb.BluePrint.blueprint_from_description(desc) + arrays_restored = get_arrays_from_blueprint(bp_restored) + + expected = kwargs["ampl"] * np.sin(2 * np.pi * kwargs["freq"] * time_test) + actual_idx = int(time_test * blueprint_with_func_source.SR) + + assert np.isclose(expected, arrays_orig[1]["wfm"][actual_idx], rtol=1e-3) + assert np.isclose(expected, arrays_restored[1]["wfm"][actual_idx], rtol=1e-3) diff --git a/tests/test_blueprint.py b/tests/test_blueprint.py index 19f99e71d..64859f53d 100644 --- a/tests/test_blueprint.py +++ b/tests/test_blueprint.py @@ -409,6 +409,7 @@ def test_description(blueprint_nasty, blueprint_tophat): desc2 = blueprint_tophat.description exp_keys = [ + "SR", "marker1_abs", "marker1_rel", "marker2_abs",