diff --git a/doc/DSSC fine delay with SCS toolbox.ipynb b/doc/DSSC fine delay with SCS toolbox.ipynb
index 0b40ec3cf8abbbca0a6f153779732cc773b5f42d..270324a3ff45821c1cc7200341723b19962817c1 100644
--- a/doc/DSSC fine delay with SCS toolbox.ipynb
+++ b/doc/DSSC fine delay with SCS toolbox.ipynb
@@ -17,7 +17,7 @@
],
"source": [
"import numpy as np\n",
- "%matplotlib notebook\n",
+ "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['figure.constrained_layout.use'] = True\n",
"\n",
@@ -29,865 +29,1138 @@
"import toolbox_scs.routines.boz as boz"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "e94a5c90",
+ "metadata": {},
+ "source": [
+ "# Improved method\n",
+ "\n",
+ "The delay is scan and saved in the DAQ while recording a single run."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 4,
- "id": "69c17cda",
+ "execution_count": 2,
+ "id": "975a36d1",
"metadata": {},
"outputs": [],
"source": [
- "runs = np.arange(775, 814+1)\n",
- "delay = np.arange(4756930, 4756969+1)"
+ "proposal = 900257\n",
+ "dNB = 67\n",
+ "rNB = 68"
]
},
{
"cell_type": "code",
- "execution_count": 5,
- "id": "b436e44e",
+ "execution_count": 3,
+ "id": "23144fcb",
"metadata": {},
"outputs": [],
"source": [
- "proposal = 2937"
+ "arr_dark, tid_dark = boz.load_dssc_module(proposal, dNB, drop_intra_darks=False)"
]
},
{
- "cell_type": "markdown",
- "id": "fdec2470",
+ "cell_type": "code",
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"source": [
- "use first run as dark"
+ "arr_dark"
]
},
{
"cell_type": "code",
- "execution_count": 9,
- "id": "6d5673f2",
+ "execution_count": 5,
+ "id": "6d233216",
"metadata": {},
"outputs": [],
"source": [
- "arr_dark, tid_dark = boz.load_dssc_module(proposal, runs[0])\n",
- "dark = boz.average_module(arr_dark).compute()"
+ "dark = arr_dark.mean(axis=0).compute()"
]
},
{
"cell_type": "code",
- "execution_count": 22,
- "id": "542d140c",
+ "execution_count": 6,
+ "id": "43934f12",
"metadata": {},
"outputs": [],
"source": [
- "intensity = np.zeros((len(runs)))\n",
- "for k,r in enumerate(runs):\n",
- " arr, tid = boz.load_dssc_module(proposal, r)\n",
- " data = boz.average_module(arr, dark=dark).compute()\n",
- " sensor = data[:,:,:256]\n",
- " intensity[k] = sensor.mean(axis=(0,1,2))"
+ "arr, tid = boz.load_dssc_module(proposal, rNB, drop_intra_darks=False)\n",
+ "arr = arr - dark"
]
},
{
"cell_type": "code",
- "execution_count": 23,
- "id": "eab988af",
+ "execution_count": 7,
+ "id": "300ce9c6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Bunch pattern table not found in run. Skipping!\n"
+ ]
+ }
+ ],
+ "source": [
+ "fields = [\"DSSC_delay\"]\n",
+ "\n",
+ "run, data = tb.load(proposal, rNB, fields)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "555a7a99",
"metadata": {},
"outputs": [
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+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
"\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\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",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
"\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
"\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
"\n",
- " var pass_mouse_events = true;\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\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",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\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",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
"\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\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",
+ ".xr-var-name {\n",
+ " grid-column: 1;\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",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\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",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
"\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option);\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
"\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
"}\n",
"\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
"}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>');\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " // select the cell after this one\n",
- " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
- " IPython.notebook.select(index + 1);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt, dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (trainId: 3987)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 1356431136 1356431137 ... 1356435121 1356435122\n",
+ "Data variables:\n",
+ " DSSC_delay (trainId) int32 4756934 4756934 4756934 ... 4756963 4756963\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202230/p900257/raw/r0068</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bfa04272-0b47-4c09-8ffe-fb916b03714c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bfa04272-0b47-4c09-8ffe-fb916b03714c' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 3987</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5cc36342-fcf0-41cc-8e60-cca0029ebe4c' class='xr-section-summary-in' type='checkbox' checked><label for='section-5cc36342-fcf0-41cc-8e60-cca0029ebe4c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1356431136 ... 1356435122</div><input id='attrs-b819dd08-6550-45cc-842c-3756b1cbc1f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b819dd08-6550-45cc-842c-3756b1cbc1f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-177439a1-f769-4844-a43c-b220fb7700a8' class='xr-var-data-in' type='checkbox'><label for='data-177439a1-f769-4844-a43c-b220fb7700a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1356431136, 1356431137, 1356431138, ..., 1356435120, 1356435121,\n",
+ " 1356435122], dtype=uint64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ba898b9-af16-4c92-ba3a-7ee1e61177d6' class='xr-section-summary-in' type='checkbox' checked><label for='section-3ba898b9-af16-4c92-ba3a-7ee1e61177d6' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>DSSC_delay</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>4756934 4756934 ... 4756963 4756963</div><input id='attrs-8e450f6c-0293-4711-b2a8-5196b3291fce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e450f6c-0293-4711-b2a8-5196b3291fce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34b9c4b0-ab3a-4159-ae6c-243b73a702c8' class='xr-var-data-in' type='checkbox'><label for='data-34b9c4b0-ab3a-4159-ae6c-243b73a702c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([4756934, 4756934, 4756934, ..., 4756963, 4756963, 4756963],\n",
+ " dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea3a3bf7-ae2b-4f54-b08c-478b479f7edb' class='xr-section-summary-in' type='checkbox' checked><label for='section-ea3a3bf7-ae2b-4f54-b08c-478b479f7edb' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>runFolder :</span></dt><dd>/gpfs/exfel/exp/SCS/202230/p900257/raw/r0068</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
- "<IPython.core.display.Javascript object>"
+ "<xarray.Dataset>\n",
+ "Dimensions: (trainId: 3987)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 1356431136 1356431137 ... 1356435121 1356435122\n",
+ "Data variables:\n",
+ " DSSC_delay (trainId) int32 4756934 4756934 4756934 ... 4756963 4756963\n",
+ "Attributes:\n",
+ " runFolder: /gpfs/exfel/exp/SCS/202230/p900257/raw/r0068"
]
},
+ "execution_count": 8,
"metadata": {},
- "output_type": "display_data"
- },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "133621fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "intensity = (arr[:,::2,:,:] - arr[:,1::2,:,:]).mean(axis=(1,2,3)).compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "aa37ecee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "b2ed51a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "xrintensity = xr.DataArray(intensity, coords={'trainId':tid}, dims=['trainId']).to_dataset(name='intensity')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "40ecf913",
+ "metadata": {},
+ "outputs": [
{
"data": {
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\" width=\"640\">"
+ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+ "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+ "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+ "</symbol>\n",
+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+ "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
+ ":root {\n",
+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+ " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+ " --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+ " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+ " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+ " --xr-background-color: var(--jp-layout-color0, white);\n",
+ " --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+ "}\n",
+ "\n",
+ "html[theme=dark],\n",
+ "body.vscode-dark {\n",
+ " --xr-font-color0: rgba(255, 255, 255, 1);\n",
+ " --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+ " --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+ " --xr-border-color: #1F1F1F;\n",
+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
+ " --xr-background-color-row-even: #111111;\n",
+ " --xr-background-color-row-odd: #313131;\n",
+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
+ "}\n",
+ "\n",
+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
+ " border-bottom: solid 1px var(--xr-border-color);\n",
+ "}\n",
+ "\n",
+ ".xr-header > div,\n",
+ ".xr-header > ul {\n",
+ " display: inline;\n",
+ " margin-top: 0;\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type,\n",
+ ".xr-array-name {\n",
+ " margin-left: 2px;\n",
+ " margin-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-sections {\n",
+ " padding-left: 0 !important;\n",
+ " display: grid;\n",
+ " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input + label {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label {\n",
+ " cursor: pointer;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label:hover {\n",
+ " color: var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary {\n",
+ " grid-column: 1;\n",
+ " color: var(--xr-font-color2);\n",
+ " font-weight: 500;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label:before {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
+ " padding-bottom: 4px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt, dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
+ "Dimensions: (trainId: 3987)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 1356431136 1356431137 ... 1356435121 1356435122\n",
+ "Data variables:\n",
+ " intensity (trainId) float64 -0.05101 -0.05173 -0.04971 ... 0.1101 0.09043</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b2b4ce51-a78c-465d-af44-bcf41e7b7e26' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b2b4ce51-a78c-465d-af44-bcf41e7b7e26' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>trainId</span>: 3987</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0ab9160a-b3c6-4a8e-bc60-82107d5e969c' class='xr-section-summary-in' type='checkbox' checked><label for='section-0ab9160a-b3c6-4a8e-bc60-82107d5e969c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trainId</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>uint64</div><div class='xr-var-preview xr-preview'>1356431136 ... 1356435122</div><input id='attrs-9b99e257-1d49-421f-99ce-82e91f73afd9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b99e257-1d49-421f-99ce-82e91f73afd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-734a9766-a051-4d1c-84a7-d0fc4dc46967' class='xr-var-data-in' type='checkbox'><label for='data-734a9766-a051-4d1c-84a7-d0fc4dc46967' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1356431136, 1356431137, 1356431138, ..., 1356435120, 1356435121,\n",
+ " 1356435122], dtype=uint64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee25f1d2-8e07-4c9c-a570-cbd048a10c42' class='xr-section-summary-in' type='checkbox' checked><label for='section-ee25f1d2-8e07-4c9c-a570-cbd048a10c42' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>intensity</span></div><div class='xr-var-dims'>(trainId)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.05101 -0.05173 ... 0.09043</div><input id='attrs-f0fd2f61-15b0-44d8-8f7b-bf449db5676a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0fd2f61-15b0-44d8-8f7b-bf449db5676a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca51ecb5-0f1f-48ec-b623-367016ff5138' class='xr-var-data-in' type='checkbox'><label for='data-ca51ecb5-0f1f-48ec-b623-367016ff5138' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.05101078, -0.05172943, -0.04970783, ..., 0.09228413,\n",
+ " 0.11005988, 0.09043484])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77c89a11-f801-4bfa-85a2-4fdacf30421f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-77c89a11-f801-4bfa-85a2-4fdacf30421f' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
- "<IPython.core.display.HTML object>"
+ "<xarray.Dataset>\n",
+ "Dimensions: (trainId: 3987)\n",
+ "Coordinates:\n",
+ " * trainId (trainId) uint64 1356431136 1356431137 ... 1356435121 1356435122\n",
+ "Data variables:\n",
+ " intensity (trainId) float64 -0.05101 -0.05173 -0.04971 ... 0.1101 0.09043"
]
},
+ "execution_count": 12,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "xrintensity"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "b12050ba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "r = xr.merge([data, xrintensity], join='inner', combine_attrs='no_conflicts')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "6c222b5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rbin = r.groupby('DSSC_delay').mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "be0a0b31",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Intensity')"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
},
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "offset = 4756949\n",
+ "plt.figure()\n",
+ "plt.plot(rbin['DSSC_delay'] - offset, rbin['intensity'], marker='o')\n",
+ "plt.title(f'{proposal} r:{rNB} d:{dNB}')\n",
+ "plt.xlabel(f'DSSC fine delay - {offset} (delay unit)')\n",
+ "plt.ylabel('Intensity')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1e47f42",
+ "metadata": {},
+ "source": [
+ "# Legacy\n",
+ "\n",
+ "One run is recorded for one fine delay in sequence, the delay itself is not saved in the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "69c17cda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs = np.arange(775, 814+1)\n",
+ "delay = np.arange(4756930, 4756969+1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "b436e44e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "proposal = 2937"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fdec2470",
+ "metadata": {},
+ "source": [
+ "use first run as dark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "6d5673f2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "arr_dark, tid_dark = boz.load_dssc_module(proposal, runs[0])\n",
+ "dark = boz.average_module(arr_dark).compute()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "542d140c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "intensity = np.zeros((len(runs)))\n",
+ "for k,r in enumerate(runs):\n",
+ " arr, tid = boz.load_dssc_module(proposal, r)\n",
+ " data = boz.average_module(arr, dark=dark).compute()\n",
+ " sensor = data[:,:,:256]\n",
+ " intensity[k] = sensor.mean(axis=(0,1,2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "eab988af",
+ "metadata": {},
+ "outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, '<intensity>')"
]
},
- "execution_count": 23,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
diff --git a/doc/changelog.rst b/doc/changelog.rst
index 49da25044d5f58c398e1047ae4b253fcd15a2ad8..6a83bdeb4c5b4dcd2d33b4719399b361e38d564a 100644
--- a/doc/changelog.rst
+++ b/doc/changelog.rst
@@ -18,7 +18,7 @@ unreleased
- update version GATT-related mnemonics, add `transmission_col2` :mr:`172`
- reorganize the Howto section :mr:`169`
- improve SLURM scripts with named arguments :mr:`176`
- - adds notebook for DSSC fine timing analysis
+ - adds notebook for DSSC fine timing analysis :mr:`184` and :mr:`185`
- **New Features**
- add routine for fluence calibration :mr:`180`
diff --git a/src/toolbox_scs/constants.py b/src/toolbox_scs/constants.py
index c3c2deba425ec5f4941fbe1c082f0bcbb9927a7a..8d4a75bc5cd2cdfe20da9901d5305202971142ee 100644
--- a/src/toolbox_scs/constants.py
+++ b/src/toolbox_scs/constants.py
@@ -446,6 +446,11 @@ mnemonics = {
'key': 'data.image.pixels',
'dim': ['llc1_y', 'llc1_x']},),
+ # DSSC
+ "DSSC_delay": ({'source': 'SCS_RR_SYS/TSYS/UTC-1-S2',
+ 'key': 'backTrg3.delay.value',
+ 'dim': None},),
+
# PI-MTE3 CCD camera
"MTE3": ({'source': 'SCS_CDIDET_MTE3/CAM/CAMERA:daqOutput',
'key': 'data.image.pixels',