\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " MJD mJy e_mJy\n",
- "0 60774.795080 -6.434997 204.177252\n",
- "1 60774.797395 41.964531 223.303671\n",
- "2 60774.799710 24.902475 273.997902\n",
- "3 60774.802025 19.867867 257.708383\n",
- "4 60774.804340 5.679091 236.001919\n",
- "... ... ... ...\n",
- "8364 60802.251069 -2.826644 353.741833\n",
- "8365 60802.253384 -49.028253 363.900099\n",
- "8366 60802.255699 -12.546486 302.314057\n",
- "8367 60802.258014 -38.629566 335.972191\n",
- "8368 60802.260329 -108.062144 304.738969\n",
- "\n",
- "[8369 rows x 3 columns]"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "l = pd.DataFrame(data=tess.lc.T,columns=['MJD','mJy','e_mJy'])\n",
- "l.to_csv('',index=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "a795f120",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "91"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "obs[0][2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6ebadf5d",
- "metadata": {},
- "outputs": [],
- "source": [
- "-2.5*np.log10(flux) + 16.4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "f96e035f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([28.15623389, 27.60744131, 25.56201544, ..., 6.63977483,\n",
- " 3.76882935, 7.28402827])"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "tess.lc[2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "f6d3b8db",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "!!! WARNING no MJD time specified, using default of 59000\n",
- "| Sector | Camera | CCD | Covers | Time difference |\n",
- "| | | | | (days) |\n",
- "|----------+----------+-------+----------+--------------------|\n",
- "| 91 | 4 | 3 | False | 1774.79 |\n",
- "| 92 | 1 | 4 | False | 1802.47 |\n"
- ]
- }
- ],
- "source": [
- "obs = tr.spacetime_lookup(268.577,-29.122)\n",
- "# obs = tr.spacetime_lookup(268.577,-29.122)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "2ebeb5e7",
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading TPF from TESScut\n",
- "made reference\n",
- "!!!WARNING!!! mask is too dense, lowering mask_scale to 0.5, and raising maglim to 15. Background quality will be reduced.\n",
- "made source mask\n",
- "calculating background\n",
- "adaptive bkg\n",
- "background subtracted\n",
- "aligning images\n",
- "[SepAligner] 6 sources selected for alignment\n"
- ]
- },
- {
- "data": {
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- " canvas.setAttribute(\n",
- " 'style',\n",
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- "\n",
- "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
- " fig.canvas_div.style.cursor = msg['cursor'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
- " for (var key in msg) {\n",
- " if (!(key in fig.buttons)) {\n",
- " continue;\n",
- " }\n",
- " fig.buttons[key].disabled = !msg[key];\n",
- " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
- " if (msg['mode'] === 'PAN') {\n",
- " fig.buttons['Pan'].classList.add('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " } else if (msg['mode'] === 'ZOOM') {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.add('active');\n",
- " } else {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message('ack', {});\n",
- "};\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " var img = evt.data;\n",
- " if (img.type !== 'image/png') {\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",
- " img.type = 'image/png';\n",
- " }\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src\n",
- " );\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " img\n",
- " );\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " } else if (\n",
- " typeof evt.data === 'string' &&\n",
- " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
- " ) {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig['handle_' + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"No handler for the '%s' message type: \",\n",
- " msg_type,\n",
- " msg\n",
- " );\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '%s' message: \", msg_type, msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"Exception inside the 'handler_%s' callback:\",\n",
- " msg_type,\n",
- " e,\n",
- " e.stack,\n",
- " msg\n",
- " );\n",
- " }\n",
- " }\n",
- " };\n",
- "};\n",
- "\n",
- "function getModifiers(event) {\n",
- " var mods = [];\n",
- " if (event.ctrlKey) {\n",
- " mods.push('ctrl');\n",
- " }\n",
- " if (event.altKey) {\n",
- " mods.push('alt');\n",
- " }\n",
- " if (event.shiftKey) {\n",
- " mods.push('shift');\n",
- " }\n",
- " if (event.metaKey) {\n",
- " mods.push('meta');\n",
- " }\n",
- " return mods;\n",
- "}\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * https://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys(original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object') {\n",
- " obj[key] = original[key];\n",
- " }\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function (event, name) {\n",
- " if (name === 'button_press') {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " // from https://stackoverflow.com/q/1114465\n",
- " var boundingRect = this.canvas.getBoundingClientRect();\n",
- " var x = (event.clientX - boundingRect.left) * this.ratio;\n",
- " var y = (event.clientY - boundingRect.top) * this.ratio;\n",
- "\n",
- " this.send_message(name, {\n",
- " x: x,\n",
- " y: y,\n",
- " button: event.button,\n",
- " step: event.step,\n",
- " buttons: event.buttons,\n",
- " modifiers: getModifiers(event),\n",
- " guiEvent: simpleKeys(event),\n",
- " });\n",
- "\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.key_event = function (event, name) {\n",
- " // Prevent repeat events\n",
- " if (name === 'key_press') {\n",
- " if (event.key === this._key) {\n",
- " return;\n",
- " } else {\n",
- " this._key = event.key;\n",
- " }\n",
- " }\n",
- " if (name === 'key_release') {\n",
- " this._key = null;\n",
- " }\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.key !== 'Control') {\n",
- " value += 'ctrl+';\n",
- " }\n",
- " else if (event.altKey && event.key !== 'Alt') {\n",
- " value += 'alt+';\n",
- " }\n",
- " else if (event.shiftKey && event.key !== 'Shift') {\n",
- " value += 'shift+';\n",
- " }\n",
- "\n",
- " value += 'k' + event.key;\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
- " if (name === 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message('toolbar_button', { name: name });\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "\n",
- "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
- "// prettier-ignore\n",
- "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
- "\n",
- "mpl.default_extension = \"png\";/* global mpl */\n",
- "\n",
- "var comm_websocket_adapter = function (comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.binaryType = comm.kernel.ws.binaryType;\n",
- " ws.readyState = comm.kernel.ws.readyState;\n",
- " function updateReadyState(_event) {\n",
- " if (comm.kernel.ws) {\n",
- " ws.readyState = comm.kernel.ws.readyState;\n",
- " } else {\n",
- " ws.readyState = 3; // Closed state.\n",
- " }\n",
- " }\n",
- " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
- " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
- " comm.kernel.ws.addEventListener('error', updateReadyState);\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",
- " var data = msg['content']['data'];\n",
- " if (data['blob'] !== undefined) {\n",
- " data = {\n",
- " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
- " };\n",
- " }\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(data);\n",
- " });\n",
- " return ws;\n",
- "};\n",
- "\n",
- "mpl.mpl_figure_comm = function (comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = document.getElementById(id);\n",
- " var ws_proxy = comm_websocket_adapter(comm);\n",
- "\n",
- " function ondownload(figure, _format) {\n",
- " window.open(figure.canvas.toDataURL());\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element;\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error('Failed to find cell for figure', id, fig);\n",
- " return;\n",
- " }\n",
- " fig.cell_info[0].output_area.element.on(\n",
- " 'cleared',\n",
- " { fig: fig },\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
- " var width = fig.canvas.width / fig.ratio;\n",
- " fig.cell_info[0].output_area.element.off(\n",
- " 'cleared',\n",
- " fig._remove_fig_handler\n",
- " );\n",
- " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable();\n",
- " fig.parent_element.innerHTML =\n",
- " '| \n", - " | MJD | \n", - "mJy | \n", - "e_mJy | \n", - "
|---|---|---|---|
| 0 | \n", - "60774.795080 | \n", - "-6.434997 | \n", - "204.177252 | \n", - "
| 1 | \n", - "60774.797395 | \n", - "41.964531 | \n", - "223.303671 | \n", - "
| 2 | \n", - "60774.799710 | \n", - "24.902475 | \n", - "273.997902 | \n", - "
| 3 | \n", - "60774.802025 | \n", - "19.867867 | \n", - "257.708383 | \n", - "
| 4 | \n", - "60774.804340 | \n", - "5.679091 | \n", - "236.001919 | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| 8364 | \n", - "60802.251069 | \n", - "-2.826644 | \n", - "353.741833 | \n", - "
| 8365 | \n", - "60802.253384 | \n", - "-49.028253 | \n", - "363.900099 | \n", - "
| 8366 | \n", - "60802.255699 | \n", - "-12.546486 | \n", - "302.314057 | \n", - "
| 8367 | \n", - "60802.258014 | \n", - "-38.629566 | \n", - "335.972191 | \n", - "
| 8368 | \n", - "60802.260329 | \n", - "-108.062144 | \n", - "304.738969 | \n", - "
8369 rows × 3 columns
\n", - "\n",
- "\n",
- "\n",
- " \n",
- "
\n",
- "
"
- ],
- "text/plain": [
- " name transient_RA transient_Dec min_mag \\\n",
- "0 2025cxh 152.912533 -25.151678 17.397221 \n",
- "1 2025cnu 159.277108 -7.467939 17.557000 \n",
- "2 2025dgi 157.917354 -25.902136 18.391000 \n",
- "3 2025azf 135.996283 0.740372 16.827000 \n",
- "4 2024ljc 285.422583 40.745067 17.100000 \n",
- "... ... ... ... ... \n",
- "6110 2019jdy 185.852867 -14.910739 18.570897 \n",
- "6111 2019jab 68.986725 -24.982608 18.699507 \n",
- "6112 2019jmz 211.634621 -22.114911 18.482519 \n",
- "6113 2019bzz 79.088542 -10.561361 16.030510 \n",
- "6114 2018kuv 84.709833 -44.085819 14.959219 \n",
- "\n",
- " min_date disc_date spec_class \\\n",
- "0 2025-01-10 06:10:30.547000 2025-02-28 04:36:59.040000 NaN \n",
- "1 2025-01-05 12:54:32.573000 2025-02-24 08:03:47.998000 NaN \n",
- "2 2025-01-14 05:58:39.043000 2025-03-03 05:24:26.784000 NaN \n",
- "3 2024-12-20 15:02:07.354000 2025-02-06 22:33:41.184000 SN Ia \n",
- "4 2022-08-09 13:08:46 2024-06-15 08:15:05 SN IIb \n",
- "... ... ... ... \n",
- "6110 2009-06-09 06:18:37.523000 2019-05-09 07:14:52 NaN \n",
- "6111 2009-09-16 14:49:16.993000 2019-01-08 08:03:50 NaN \n",
- "6112 2009-06-22 07:40:12.593000 2019-05-25 07:56:38 NaN \n",
- "6113 2008-04-11 00:07:17 2019-03-05 03:02:52 NaN \n",
- "6114 2005-07-12 01:13:04 2018-12-22 20:00:57 NaN \n",
- "\n",
- " phot_class transient_z status_id mw_ebv point_source_probability \\\n",
- "0 NaN NaN 7 0.0400 NaN \n",
- "1 NaN NaN 7 0.0570 NaN \n",
- "2 NaN NaN 1 0.0520 0.004 \n",
- "3 NaN 0.053000 2 0.0360 0.003 \n",
- "4 NaN 0.015154 7 0.1170 0.268 \n",
- "... ... ... ... ... ... \n",
- "6110 NaN NaN 3 0.0482 0.002 \n",
- "6111 NaN NaN 3 0.0423 0.010 \n",
- "6112 NaN NaN 3 0.0651 0.001 \n",
- "6113 NaN NaN 3 NaN NaN \n",
- "6114 NaN NaN 3 0.0316 NaN \n",
- "\n",
- " filter \n",
- "0 g-ZTF \n",
- "1 orange-ATLAS \n",
- "2 cyan-ATLAS \n",
- "3 cyan-ATLAS \n",
- "4 V \n",
- "... ... \n",
- "6110 y \n",
- "6111 y \n",
- "6112 z \n",
- "6113 V \n",
- "6114 V \n",
- "\n",
- "[6115 rows x 13 columns]"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "f"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
- "id": "e264679a",
+ "id": "171bf3c9",
"metadata": {},
"outputs": [],
"source": []
diff --git a/development/kernel_comparison_fqv.pdf b/development/kernel_comparison_fqv.pdf
new file mode 100755
index 0000000..aeb69bb
Binary files /dev/null and b/development/kernel_comparison_fqv.pdf differ
diff --git a/development/run_diag.py b/development/run_diag.py
deleted file mode 100644
index 9d1be26..0000000
--- a/development/run_diag.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from scipy.ndimage import median_filter, percentile_filter, uniform_filter
-from joblib import Parallel, delayed
-
-
-def _make_odd(x):
- x = max(int(x), 3)
- return x if x % 2 == 1 else x + 1
-
-
-def _get_segments(time, gap_thresh):
- """Return list of (start, end) frame index pairs for contiguous segments."""
- dt = np.diff(time)
- median_dt = np.median(dt)
- breaks = np.where(dt > gap_thresh * median_dt)[0] + 1
- starts = np.concatenate([[0], breaks])
- ends = np.concatenate([breaks, [len(time)]])
- return list(zip(starts.tolist(), ends.tolist()))
-
-
-def adaptive_medfilt_3d(
- data,
- time=None,
- gap_thresh=3.0,
- w_min=3,
- w_max=51,
- grad_smooth_window=11,
- low_pct=5,
- high_pct=80,
- per_pixel_norm=True,
- n_levels=7,
- n_jobs=1,
- metric='deviation',
- coarse_windows=(11, 21, 51, 101),
- combined_weight=0.5,
- local_std_window=21,
- local_norm_window=201,
- brightness_sigma=2.0,
- window_smooth_size=1,
- earth_angle=None,
- moon_angle=None,
- scatter_angle_thresh=50.0,
-):
- data = np.asarray(data, dtype=float)
- T, X, Y = data.shape
-
- if time is None:
- time = np.arange(T, dtype=float)
- time = np.asarray(time, dtype=float)
-
- segments = _get_segments(time, gap_thresh)
-
- # NaN fill per segment using time coordinates (never interpolates across gaps)
- nan_mask = ~np.isfinite(data)
- data_filled = data.copy()
- if nan_mask.any():
- flat = data_filled.reshape(T, -1)
- for s, e in segments:
- t_seg = time[s:e]
- for j in range(flat.shape[1]):
- ts = flat[s:e, j]
- bad = ~np.isfinite(ts)
- if bad.all():
- flat[s:e, j] = 0.0
- elif bad.any():
- flat[s:e, j] = np.interp(t_seg, t_seg[~bad], ts[~bad])
-
- gw = _make_odd(grad_smooth_window)
-
- # Gradient uses actual time spacing; zeroed at segment boundaries
- if metric in ('gradient', 'combined'):
- grad = np.gradient(data_filled, time, axis=0)
- for s, e in segments:
- if s > 0:
- grad[s] = 0.0
- if e < T:
- grad[e - 1] = 0.0
- grad_var = np.zeros_like(grad)
- for s, e in segments:
- seg = grad[s:e]
- if e - s >= gw:
- grad_var[s:e] = np.abs(median_filter(seg, size=(gw, 1, 1), mode='reflect'))
- else:
- grad_var[s:e] = np.abs(seg)
-
- # Multi-scale deviation: compute deviation at each coarse window, normalise
- # each locally, then take the max — catches variability at any timescale
- if metric in ('deviation', 'combined'):
- scales = [coarse_windows] if isinstance(coarse_windows, int) else list(coarse_windows)
- # Compute all scale deviations first so we can set a common floor
- all_devs = []
- for cw in scales:
- dev = np.zeros_like(data_filled)
- for s, e in segments:
- seg = data_filled[s:e]
- n = e - s
- w = _make_odd(min(cw, n if n % 2 == 1 else n - 1))
- coarse_seg = median_filter(seg, size=(w, 1, 1), mode='reflect')
- diff = np.abs(seg - coarse_seg)
- dw = _make_odd(min(gw, n if n % 2 == 1 else n - 1))
- dev[s:e] = median_filter(diff, size=(dw, 1, 1), mode='reflect')
- all_devs.append(dev)
- # Identify stable segments by coefficient of variation of per-frame means.
- # A stable segment has low CV (frame means tightly clustered around their
- # sigma-clipped mean). For stable segments, skip local normalization and
- # force dev_var=0 → w_max. For active segments use the local normalization
- # as normal, with cw_min masked in dark frames via bright_mask.
- _frame_mean = data_filled.mean(axis=(1, 2)) # (T,)
- # Global sigma-clip for bright_mask (used in active segments only)
- _clipped = _frame_mean[np.isfinite(_frame_mean)]
- for _ in range(5):
- _med = np.median(_clipped)
- _std = np.std(_clipped)
- if _std == 0:
- break
- _clipped = _clipped[np.abs(_clipped - _med) < brightness_sigma * _std]
- brightness_threshold = float(np.mean(_clipped)) + brightness_sigma * float(np.std(_clipped))
- bright_mask = (_frame_mean > brightness_threshold).astype(float)[:, np.newaxis, np.newaxis]
-
- # Per-segment stability: a segment is stable (force w_max) when it has
- # no scattered-light contamination. If earth/moon angle arrays are provided,
- # use min(earth, moon) > scatter_angle_thresh as the scatter-free criterion.
- # Otherwise fall back to brightness threshold (< 5% active frames).
- _frame_active = (_frame_mean > brightness_threshold)
- # When angle arrays are provided and scattered light is actually present
- # (some frames below scatter_angle_thresh), build a stable_mask to force
- # w_max on scatter-free faint frames across all scales.
- # Without angles, or when no scatter is present, only mask cw_min (i==0).
- _use_stable_mask = False
- if earth_angle is not None or moon_angle is not None:
- _ea = np.asarray(earth_angle, dtype=float) if earth_angle is not None else np.full(T, np.inf)
- _ma = np.asarray(moon_angle, dtype=float) if moon_angle is not None else np.full(T, np.inf)
- _scatter_free = np.minimum(_ea, _ma) > scatter_angle_thresh
- _has_scatter = (~_scatter_free).any()
- if _has_scatter:
- _stable_frame = _scatter_free & ~_frame_active
- _stable_mask = _stable_frame[:, np.newaxis, np.newaxis].astype(float)
- _use_stable_mask = True
-
- lnw = _make_odd(local_norm_window)
- scale_norms = []
- for i, dev in enumerate(all_devs):
- scale_floor = max(np.nanpercentile(dev, 75), 1e-10)
- g_lo = np.zeros_like(dev)
- g_hi = np.zeros_like(dev)
- for s, e in segments:
- seg_v = dev[s:e]
- n = e - s
- lw = _make_odd(min(lnw, n if n % 2 == 1 else n - 1))
- g_lo[s:e] = percentile_filter(seg_v, low_pct, size=(lw, 1, 1), mode='reflect')
- g_hi[s:e] = percentile_filter(seg_v, high_pct, size=(lw, 1, 1), mode='reflect')
- dg = np.maximum(g_hi - g_lo, scale_floor)
- norm_scale = np.clip((dev - g_lo) / dg, 0.0, 1.0)
- if i == 0:
- norm_scale = norm_scale * bright_mask
- if _use_stable_mask:
- norm_scale = norm_scale * (1.0 - _stable_mask)
- scale_norms.append(norm_scale)
- dev_var = np.max(np.stack(scale_norms), axis=0) # (T, X, Y)
-
- # Local std: short-window RMS scatter — captures frame-to-frame variability
- # regardless of slow trends or broad peaks
- if metric in ('local_std', 'combined'):
- lsw = _make_odd(local_std_window)
- lstd_var = np.zeros_like(data_filled)
- for s, e in segments:
- seg = data_filled[s:e]
- n = e - s
- w = min(lsw, n if n % 2 == 1 else n - 1)
- w = _make_odd(w)
- m = uniform_filter(seg, size=(w, 1, 1), mode='reflect')
- m2 = uniform_filter(seg ** 2, size=(w, 1, 1), mode='reflect')
- lstd_var[s:e] = np.sqrt(np.maximum(m2 - m ** 2, 0))
-
- def _norm01(x):
- lo, hi = np.nanpercentile(x, 1), np.nanpercentile(x, 99)
- return np.clip((x - lo) / (hi - lo + 1e-30), 0, 1)
-
- # dev_var is already locally normalised to [0,1] per scale, max-combined
- # other metrics still need normalisation
- if metric == 'gradient':
- variability = grad_var
- elif metric == 'local_std':
- variability = lstd_var
- elif metric == 'combined':
- variability = (1 - combined_weight) * _norm01(grad_var) + combined_weight * _norm01(lstd_var)
- elif metric == 'deviation':
- variability = dev_var
- else:
- raise ValueError(f"metric must be 'gradient', 'deviation', 'local_std', or 'combined', got '{metric}'")
-
- if metric == 'deviation':
- norm = dev_var # already in [0,1], normalised per scale
- elif local_norm_window is not None:
- lnw = _make_odd(local_norm_window)
- g_lo = np.zeros_like(variability)
- g_hi = np.zeros_like(variability)
- for s, e in segments:
- seg_var = variability[s:e]
- n = e - s
- w = _make_odd(min(lnw, n if n % 2 == 1 else n - 1))
- g_lo[s:e] = percentile_filter(seg_var, low_pct, size=(w, 1, 1))
- g_hi[s:e] = percentile_filter(seg_var, high_pct, size=(w, 1, 1))
- dg = np.where((g_hi - g_lo) > 0, g_hi - g_lo, 1.0)
- norm = np.clip((variability - g_lo) / dg, 0.0, 1.0)
- elif per_pixel_norm:
- g_lo = np.nanpercentile(variability, low_pct, axis=0, keepdims=True)
- g_hi = np.nanpercentile(variability, high_pct, axis=0, keepdims=True)
- dg = np.where((g_hi - g_lo) > 0, g_hi - g_lo, 1.0)
- norm = np.clip((variability - g_lo) / dg, 0.0, 1.0)
- else:
- g_lo = np.nanpercentile(variability, low_pct)
- g_hi = np.nanpercentile(variability, high_pct)
- dg = np.where((g_hi - g_lo) > 0, g_hi - g_lo, 1.0)
- norm = np.clip((variability - g_lo) / dg, 0.0, 1.0)
-
- raw_w = w_max - norm * (w_max - w_min)
- windows = np.round(raw_w).astype(int)
- windows += (1 - windows % 2)
- windows = np.clip(windows, _make_odd(w_min), _make_odd(w_max))
-
- windows_pre_smooth = windows.copy()
-
- if window_smooth_size > 1:
- wsz = _make_odd(window_smooth_size)
- smoothed_win = np.empty_like(windows, dtype=float)
- for s, e in segments:
- n = e - s
- w = min(wsz, n if n % 2 == 1 else n - 1)
- w = _make_odd(w)
- smoothed_win[s:e] = median_filter(windows[s:e].astype(float), size=(w, 1, 1), mode='reflect')
- windows = np.round(smoothed_win).astype(int)
- windows += (1 - windows % 2)
- windows = np.clip(windows, _make_odd(w_min), _make_odd(w_max))
-
- levels = np.unique([_make_odd(int(round(w))) for w in np.linspace(w_min, w_max, n_levels)])
- windows_quantized = levels[np.argmin(np.abs(windows[..., np.newaxis] - levels), axis=-1)]
-
- # Final smoothing per segment to avoid blending across gaps
- result = np.empty((T, X, Y))
- for s, e in segments:
- seg_data = data_filled[s:e]
- seg_wins = windows_quantized[s:e]
- seg_levels = np.unique(seg_wins)
-
- def _smooth_seg(w, seg=seg_data):
- return w, median_filter(seg, size=(w, 1, 1), mode='reflect')
-
- for w, smoothed_w in Parallel(n_jobs=n_jobs)(delayed(_smooth_seg)(w) for w in seg_levels):
- result[s:e][seg_wins == w] = smoothed_w[seg_wins == w]
-
- result[nan_mask] = np.nan
- return result, windows, variability, windows_pre_smooth
-
-
-# ── Load data ──────────────────────────────────────────────────────────────────
-data = np.load('/Users/rri38/Documents/work/code/tess/tessreduce/development/test_bkg.npy')
-time = np.load('/Users/rri38/Documents/work/code/tess/tessreduce/development/test_bkg_time.npy')
-print(f"Data shape: {data.shape}")
-print(f"Time shape: {time.shape}, range: {time[0]:.4f} – {time[-1]:.4f}")
-dt = np.diff(time)
-print(f"Median dt: {np.median(dt):.6f}, max dt: {dt.max():.6f}, gap ratio: {dt.max()/np.median(dt):.1f}x")
-segs = _get_segments(time, gap_thresh=3.0)
-print(f"Segments detected: {len(segs)} — {segs}")
-
-px, py = 100, 100
-cube = data[:, px:px+1, py:py+1]
-print(f"Pixel cube shape: {cube.shape}")
-print(f"Pixel value range: {np.nanmin(cube):.4f} – {np.nanmax(cube):.4f}")
-
-raw_series = cube[:, 0, 0]
-T = len(raw_series)
-print(f"T = {T}")
-
-# ── Step 1: Run with winning params (no window smoothing) ──────────────────────
-print("\n=== Step 1: Diagnostic run (window_smooth_size=1) ===")
-smoothed, windows, variability, windows_pre = adaptive_medfilt_3d(
- cube,
- time=time,
- metric='deviation',
- coarse_windows=(11, 21, 51, 101),
- low_pct=5,
- high_pct=80,
- local_norm_window=201,
- w_min=3,
- w_max=51,
- n_jobs=1,
- window_smooth_size=1,
-)
-
-sm = smoothed[:, 0, 0]
-win = windows[:, 0, 0]
-var = variability[:, 0, 0]
-
-# Stats
-print(f"Mean window size: {win.mean():.2f}")
-print(f"Std window size: {win.std():.2f}")
-frac_min = np.mean(win == _make_odd(3))
-frac_max = np.mean(win == _make_odd(51))
-print(f"Fraction at w_min (3): {frac_min:.4f} ({frac_min*100:.1f}%)")
-print(f"Fraction at w_max (51): {frac_max:.4f} ({frac_max*100:.1f}%)")
-print(f"Unique window values: {np.unique(win)}")
-
-# ── Plot 1: Zoomed view 1800–2300 ──────────────────────────────────────────────
-z0, z1 = 1800, min(2300, T)
-frames_z = np.arange(z0, z1)
-fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 10), sharex=True)
-
-ax1.plot(frames_z, raw_series[z0:z1], lw=0.8, color='steelblue', label='Raw')
-ax1.plot(frames_z, sm[z0:z1], lw=1.2, color='firebrick', label='Smoothed')
-ax1.set_ylabel('Flux')
-ax1.set_title('Pixel (100,100) — zoomed frames 1800–2300')
-ax1.legend(fontsize=8)
-
-ax_win = ax2
-ax_win.plot(frames_z, win[z0:z1], color='darkorange', lw=0.7, label='Window size')
-ax_win.set_ylabel('Window size')
-ax_win.set_title('Adaptive window sizes (no smoothing)')
-ax_win.legend(fontsize=8)
-
-ax3.plot(frames_z, var[z0:z1], color='purple', lw=0.7, label='Variability metric')
-ax3.set_ylabel('Variability')
-ax3.set_xlabel('Frame')
-ax3.set_title('Raw variability metric (deviation-based)')
-ax3.legend(fontsize=8)
-
-plt.tight_layout()
-plt.savefig('/Users/rri38/Documents/work/code/tess/tessreduce/development/diag_zoom_2000.png', dpi=120)
-plt.close()
-print("Saved diag_zoom_2000.png")
-
-# Check variability smoothness in the zoom region
-var_z = var[z0:z1]
-var_diffs = np.diff(var_z)
-print(f"\nVariability in 1800–2300:")
-print(f" mean={var_z.mean():.4f}, std={var_z.std():.4f}")
-print(f" mean |diff| (roughness): {np.abs(var_diffs).mean():.6f}")
-print(f" max |diff|: {np.abs(var_diffs).max():.6f}")
-
-# ── Step 2: Test window_smooth_sizes ──────────────────────────────────────────
-print("\n=== Step 2: Testing window_smooth_size values ===")
-
-# Peak/baseline detection helpers
-def detect_peaks_adaptive(series, coarse_bw=201, prom_thresh_mad_mult=1.5):
- """Return boolean mask of 'peak' frames."""
- cw = _make_odd(coarse_bw)
- baseline = median_filter(series, size=cw, mode='reflect')
- detrended = series - baseline
- mad = np.median(np.abs(detrended - np.median(detrended)))
- threshold = prom_thresh_mad_mult * mad
- return detrended > threshold, detrended, baseline, threshold
-
-def compute_residuals(raw, smoothed, peak_mask):
- residuals = np.abs(raw - smoothed)
- peak_res = residuals[peak_mask].mean() if peak_mask.any() else np.nan
- base_res = residuals[~peak_mask].mean() if (~peak_mask).any() else np.nan
- return peak_res, base_res
-
-smooth_sizes = [1, 5, 11, 21, 31]
-results = {}
-
-for wsz in smooth_sizes:
- sm_w, win_w, _, _ = adaptive_medfilt_3d(
- cube,
- time=time,
- metric='deviation',
- coarse_windows=(11, 21, 51, 101),
- low_pct=5,
- high_pct=80,
- local_norm_window=201,
- w_min=3,
- w_max=51,
- n_jobs=1,
- window_smooth_size=wsz,
- )
- sm_1d = sm_w[:, 0, 0]
- win_1d = win_w[:, 0, 0]
-
- peak_mask, detrended, baseline, threshold = detect_peaks_adaptive(raw_series)
- peak_res, base_res = compute_residuals(raw_series, sm_1d, peak_mask)
-
- n_switches = np.sum(np.diff(win_1d) != 0)
- win_std = win_1d.std()
- win_mean = win_1d.mean()
- fmin = np.mean(win_1d == _make_odd(3))
- fmax = np.mean(win_1d == _make_odd(51))
-
- results[wsz] = {
- 'smoothed': sm_1d,
- 'windows': win_1d,
- 'peak_res': peak_res,
- 'base_res': base_res,
- 'win_mean': win_mean,
- 'win_std': win_std,
- 'n_switches': n_switches,
- 'fmin': fmin,
- 'fmax': fmax,
- 'peak_mask': peak_mask,
- 'detrended': detrended,
- 'threshold': threshold,
- }
-
- print(f"\nwindow_smooth_size={wsz}:")
- print(f" win mean={win_mean:.2f}, std={win_std:.2f}, n_switches={n_switches}")
- print(f" frac@w_min={fmin:.3f}, frac@w_max={fmax:.3f}")
- print(f" peak_residual={peak_res:.5f}, base_residual={base_res:.5f}")
- ratio = peak_res / base_res if base_res > 0 else np.nan
- print(f" peak/base ratio={ratio:.3f}")
-
-# Determine best: want low base residual, retain peak residual, fewer switches
-# Score: lower is better — base_res (main), penalise very high n_switches
-# We want ratio reasonably high (peaks preserved) and base_res low
-print("\n=== Score summary ===")
-for wsz in smooth_sizes:
- r = results[wsz]
- print(f" wsz={wsz:2d}: base_res={r['base_res']:.5f}, peak_res={r['peak_res']:.5f}, "
- f"ratio={r['peak_res']/r['base_res']:.3f}, n_switches={r['n_switches']}")
-
-# Choose best: wsz that gives fewest switches with stable base/peak residuals
-# Use elbow: pick smallest wsz where n_switches drops by >50% from wsz=1
-base_switches = results[1]['n_switches']
-best_wsz = 1
-for wsz in smooth_sizes[1:]:
- if results[wsz]['n_switches'] < 0.4 * base_switches:
- best_wsz = wsz
- break
-
-print(f"\nSelected best window_smooth_size = {best_wsz}")
-
-# ── Step 3: Final 4-panel diagnostic ──────────────────────────────────────────
-print(f"\n=== Step 3: Saving adaptive_smooth_final.png with wsz={best_wsz} ===")
-
-r = results[best_wsz]
-r_raw = results[1]
-sm_best = r['smoothed']
-win_best = r['windows']
-win_raw = r_raw['windows']
-peak_mask = r['peak_mask']
-detrended = r['detrended']
-
-frames = np.arange(T)
-
-fig, axes = plt.subplots(4, 1, figsize=(16, 16))
-fig.suptitle(f'Pixel (100,100) — adaptive_medfilt_3d | window_smooth_size={best_wsz}', fontsize=12)
-
-# Panel 1: raw + smoothed + peak shading
-ax = axes[0]
-ax.plot(frames, raw_series, lw=0.6, color='steelblue', alpha=0.7, label='Raw')
-ax.plot(frames, sm_best, lw=1.2, color='firebrick', label=f'Smoothed (wsz={best_wsz})')
-# Shade peak regions
-in_peak = False
-for i in range(T):
- if peak_mask[i] and not in_peak:
- start = i
- in_peak = True
- elif not peak_mask[i] and in_peak:
- ax.axvspan(start, i, alpha=0.15, color='gold')
- in_peak = False
-if in_peak:
- ax.axvspan(start, T, alpha=0.15, color='gold')
-ax.set_ylabel('Flux')
-ax.set_title('Raw signal + smoothed (peak regions shaded)')
-ax.legend(fontsize=8)
-
-# Panel 2: original vs smoothed window sizes
-ax = axes[1]
-ax.plot(frames, win_raw, lw=0.5, color='gray', alpha=0.7, label='Original windows (wsz=1)')
-ax.plot(frames, win_best, lw=1.0, color='darkorange', label=f'Smoothed windows (wsz={best_wsz})')
-ax.set_ylabel('Window size')
-ax.set_title('Window size: original vs smoothed')
-ax.legend(fontsize=8)
-
-# Panel 3: zoomed 1800–2300
-ax = axes[2]
-z0, z1 = 1800, min(2300, T)
-fz = np.arange(z0, z1)
-ax.plot(fz, raw_series[z0:z1], lw=0.8, color='steelblue', alpha=0.8, label='Raw')
-ax.plot(fz, sm_best[z0:z1], lw=1.3, color='firebrick', label=f'Smoothed (wsz={best_wsz})')
-ax_r = ax.twinx()
-ax_r.plot(fz, win_best[z0:z1], color='darkorange', lw=0.8, alpha=0.6, label='Window')
-ax_r.set_ylabel('Window size', color='darkorange')
-ax_r.tick_params(axis='y', labelcolor='darkorange')
-ax.set_ylabel('Flux')
-ax.set_title('Zoom: frames 1800–2300')
-lines1, labels1 = ax.get_legend_handles_labels()
-lines2, labels2 = ax_r.get_legend_handles_labels()
-ax.legend(lines1 + lines2, labels1 + labels2, fontsize=8)
-
-# Panel 4: detrended signal with peaks marked
-ax = axes[3]
-ax.plot(frames, detrended, lw=0.7, color='darkgreen', label='Detrended (raw − coarse baseline)')
-ax.axhline(r['threshold'], color='red', ls='--', lw=0.8, label=f'Threshold (1.5×MAD={r["threshold"]:.4f})')
-ax.scatter(frames[peak_mask], detrended[peak_mask], s=4, color='red', alpha=0.5, label='Peaks')
-ax.set_xlabel('Frame')
-ax.set_ylabel('Residual flux')
-ax.set_title('Detrended signal with detected peaks')
-ax.legend(fontsize=8)
-
-plt.tight_layout()
-plt.savefig('/Users/rri38/Documents/work/code/tess/tessreduce/development/adaptive_smooth_final.png', dpi=120)
-plt.close()
-print("Saved adaptive_smooth_final.png")
-print("\nDone.")
diff --git a/development/run_diag2.py b/development/run_diag2.py
deleted file mode 100644
index b2be476..0000000
--- a/development/run_diag2.py
+++ /dev/null
@@ -1,348 +0,0 @@
-"""
-Deeper look: why doesn't wsz=5/11 change n_switches?
-And examine the actual window pattern visually.
-"""
-import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-from scipy.ndimage import median_filter
-from joblib import Parallel, delayed
-
-
-def _make_odd(x):
- x = max(int(x), 3)
- return x if x % 2 == 1 else x + 1
-
-
-def adaptive_medfilt_3d_debug(
- data,
- w_min=3,
- w_max=51,
- grad_smooth_window=11,
- low_pct=5,
- high_pct=90,
- per_pixel_norm=True,
- n_levels=7,
- n_jobs=1,
- metric='combined',
- coarse_window=101,
- combined_weight=1.0,
- window_smooth_size=1,
-):
- data = np.asarray(data, dtype=float)
- T, X, Y = data.shape
-
- nan_mask = ~np.isfinite(data)
- data_filled = data.copy()
- if nan_mask.any():
- t_ax = np.arange(T, dtype=float)
- flat = data_filled.reshape(T, -1)
- for j in range(flat.shape[1]):
- ts = flat[:, j]
- bad = ~np.isfinite(ts)
- if bad.all():
- flat[:, j] = 0.0
- elif bad.any():
- flat[:, j] = np.interp(t_ax, t_ax[~bad], ts[~bad])
-
- gw = _make_odd(grad_smooth_window)
- grad = np.gradient(data_filled, axis=0)
- grad_var = np.abs(median_filter(grad, size=(gw, 1, 1), mode='reflect'))
- cw = _make_odd(coarse_window)
- coarse = median_filter(data_filled, size=(cw, 1, 1), mode='reflect')
- dev_var = np.abs(data_filled - coarse)
- dev_var = median_filter(dev_var, size=(gw, 1, 1), mode='reflect')
-
- def _norm01(x):
- lo, hi = np.nanpercentile(x, 1), np.nanpercentile(x, 99)
- return np.clip((x - lo) / (hi - lo + 1e-30), 0, 1)
- variability = (1 - combined_weight) * _norm01(grad_var) + combined_weight * _norm01(dev_var)
-
- g_lo = np.nanpercentile(variability, low_pct, axis=0, keepdims=True)
- g_hi = np.nanpercentile(variability, high_pct, axis=0, keepdims=True)
- dg = np.where((g_hi - g_lo) > 0, g_hi - g_lo, 1.0)
- norm = np.clip((variability - g_lo) / dg, 0.0, 1.0)
-
- raw = w_max - norm * (w_max - w_min)
- windows_raw_cont = raw.copy() # continuous, before rounding
- windows = np.round(raw).astype(int)
- windows += (1 - windows % 2)
- windows = np.clip(windows, _make_odd(w_min), _make_odd(w_max))
- windows_pre = windows.copy()
-
- if window_smooth_size > 1:
- wsz = _make_odd(window_smooth_size)
- windows = median_filter(windows.astype(float), size=(wsz, 1, 1), mode='reflect')
- windows = np.round(windows).astype(int)
- windows += (1 - windows % 2)
- windows = np.clip(windows, _make_odd(w_min), _make_odd(w_max))
-
- windows_post = windows.copy()
-
- levels = np.unique([_make_odd(int(round(w))) for w in np.linspace(w_min, w_max, n_levels)])
- windows_q = levels[np.argmin(np.abs(windows[..., np.newaxis] - levels), axis=-1)]
-
- def _smooth(w):
- return w, median_filter(data_filled, size=(w, 1, 1), mode='reflect')
-
- result = np.empty((T, X, Y))
- for w, smoothed_w in Parallel(n_jobs=n_jobs)(delayed(_smooth)(w) for w in levels):
- result[windows_q == w] = smoothed_w[windows_q == w]
-
- result[nan_mask] = np.nan
- return result, windows_q, variability, windows_pre, windows_post, windows_raw_cont
-
-
-data = np.load('/Users/rri38/Documents/work/code/tess/tessreduce/development/test_bkg.npy')
-cube = data[:, 100:101, 100:101]
-raw_series = cube[:, 0, 0]
-T = len(raw_series)
-print(f"T={T}")
-
-# Run wsz=1 and wsz=31 in debug mode
-print("\n--- wsz=1 ---")
-sm1, wq1, var1, wpre1, wpost1, wrc1 = adaptive_medfilt_3d_debug(cube, window_smooth_size=1,
- combined_weight=1.0, coarse_window=101, low_pct=5, high_pct=90)
-wq1d = wq1[:, 0, 0]
-wpre1d = wpre1[:, 0, 0]
-wrc1d = wrc1[:, 0, 0]
-print(f" unique quantized levels: {np.unique(wq1d)}")
-print(f" n_switches (pre-quant): {np.sum(np.diff(wpre1d) != 0)}")
-print(f" n_switches (quantized): {np.sum(np.diff(wq1d) != 0)}")
-
-print("\n--- wsz=5 ---")
-sm5, wq5, var5, wpre5, wpost5, wrc5 = adaptive_medfilt_3d_debug(cube, window_smooth_size=5,
- combined_weight=1.0, coarse_window=101, low_pct=5, high_pct=90)
-wq5d = wq5[:, 0, 0]
-wpost5d = wpost5[:, 0, 0]
-print(f" unique quantized levels: {np.unique(wq5d)}")
-print(f" n_switches (post-smooth, pre-quant): {np.sum(np.diff(wpost5d) != 0)}")
-print(f" n_switches (quantized): {np.sum(np.diff(wq5d) != 0)}")
-
-print("\n--- wsz=11 ---")
-sm11, wq11, var11, wpre11, wpost11, wrc11 = adaptive_medfilt_3d_debug(cube, window_smooth_size=11,
- combined_weight=1.0, coarse_window=101, low_pct=5, high_pct=90)
-wq11d = wq11[:, 0, 0]
-wpost11d = wpost11[:, 0, 0]
-print(f" unique quantized levels: {np.unique(wq11d)}")
-print(f" n_switches (post-smooth, pre-quant): {np.sum(np.diff(wpost11d) != 0)}")
-print(f" n_switches (quantized): {np.sum(np.diff(wq11d) != 0)}")
-
-print("\n--- wsz=21 ---")
-sm21, wq21, var21, wpre21, wpost21, wrc21 = adaptive_medfilt_3d_debug(cube, window_smooth_size=21,
- combined_weight=1.0, coarse_window=101, low_pct=5, high_pct=90)
-wq21d = wq21[:, 0, 0]
-wpost21d = wpost21[:, 0, 0]
-print(f" unique quantized levels: {np.unique(wq21d)}")
-print(f" n_switches (post-smooth, pre-quant): {np.sum(np.diff(wpost21d) != 0)}")
-print(f" n_switches (quantized): {np.sum(np.diff(wq21d) != 0)}")
-
-print("\n--- wsz=31 ---")
-sm31, wq31, var31, wpre31, wpost31, wrc31 = adaptive_medfilt_3d_debug(cube, window_smooth_size=31,
- combined_weight=1.0, coarse_window=101, low_pct=5, high_pct=90)
-wq31d = wq31[:, 0, 0]
-wpost31d = wpost31[:, 0, 0]
-print(f" unique quantized levels: {np.unique(wq31d)}")
-print(f" n_switches (post-smooth, pre-quant): {np.sum(np.diff(wpost31d) != 0)}")
-print(f" n_switches (quantized): {np.sum(np.diff(wq31d) != 0)}")
-
-# Look at zoom region for switches
-z0, z1 = 1800, 2300
-print(f"\nIn zoom region {z0}-{z1}:")
-for label, arr in [('wsz=1', wq1d), ('wsz=5', wq5d), ('wsz=11', wq11d), ('wsz=21', wq21d), ('wsz=31', wq31d)]:
- ns = np.sum(np.diff(arr[z0:z1]) != 0)
- print(f" {label}: {ns} switches")
-
-# Understand why small wsz doesn't reduce switches: the window field must
-# switch very rapidly with step > wsz width, so median can't resolve it.
-# Check run-length stats
-def run_lengths(arr):
- """Return lengths of constant runs."""
- diffs = np.concatenate(([1], np.diff(arr) != 0, [1]))
- starts = np.where(diffs)[0]
- return np.diff(starts)
-
-print("\nRun-length stats for pre-smooth window field:")
-rls = run_lengths(wpre1d)
-print(f" n_runs={len(rls)}, mean_run={rls.mean():.1f}, median_run={np.median(rls):.1f}, max_run={rls.max()}")
-print(f" fraction of single-frame runs: {np.mean(rls == 1):.3f}")
-
-print("\nRun-length stats for post-smooth (wsz=11):")
-rls11 = run_lengths(wpost11d)
-print(f" n_runs={len(rls11)}, mean_run={rls11.mean():.1f}, median_run={np.median(rls11):.1f}, max_run={rls11.max()}")
-print(f" fraction of single-frame runs: {np.mean(rls11 == 1):.3f}")
-
-print("\nRun-length stats for post-smooth (wsz=31):")
-rls31 = run_lengths(wpost31d)
-print(f" n_runs={len(rls31)}, mean_run={rls31.mean():.1f}, median_run={np.median(rls31):.1f}, max_run={rls31.max()}")
-print(f" fraction of single-frame runs: {np.mean(rls31 == 1):.3f}")
-
-# Key insight: the oscillation happens at the QUANTIZATION stage.
-# Check the continuous window field before quantization
-print("\nContinuous window field (pre-round) in zoom region 1800-2300:")
-wrc_z = wrc1d[1800:2300]
-print(f" mean={wrc_z.mean():.2f}, std={wrc_z.std():.2f}")
-print(f" fraction within 1 unit of even->odd boundary: {np.mean(np.abs(wrc_z - np.round(wrc_z)) > 0.4):.3f}")
-# Look at actual values
-print(f" first 20 values: {wrc_z[:20]}")
-
-# The real problem: after rounding and odd-enforcement, even small variations
-# near boundaries cause oscillation. Smoothing the integer field doesn't help
-# if the continuous field is already near boundaries.
-# Solution: smooth the CONTINUOUS field before rounding, then quantize.
-
-# Also check: what fraction of the window field is near a level boundary?
-levels = np.unique([_make_odd(int(round(w))) for w in np.linspace(3, 51, 7)])
-print(f"\nLevels used: {levels}")
-# Distance to nearest level boundary
-def dist_to_nearest_level(v, levels):
- dists = np.abs(v[:, None] - levels[None, :])
- return dists.min(axis=1)
-
-# Now make the final 4-panel plot using wsz=31 (most visible difference)
-# alongside wsz=1 for comparison
-best_wsz = 31
-
-def detect_peaks_adaptive(series, coarse_bw=201, prom_thresh_mad_mult=1.5):
- cw = _make_odd(coarse_bw)
- baseline = median_filter(series, size=cw, mode='reflect')
- detrended = series - baseline
- mad = np.median(np.abs(detrended - np.median(detrended)))
- threshold = prom_thresh_mad_mult * mad
- return detrended > threshold, detrended, baseline, threshold
-
-peak_mask, detrended, baseline, threshold = detect_peaks_adaptive(raw_series)
-sm_best = sm31[:, 0, 0]
-win_best = wq31d
-win_raw = wq1d
-
-frames = np.arange(T)
-
-fig, axes = plt.subplots(4, 1, figsize=(16, 18))
-fig.suptitle(f'Pixel (100,100) — adaptive_medfilt_3d | window_smooth_size={best_wsz}', fontsize=13)
-
-# Panel 1: raw + smoothed + peak shading
-ax = axes[0]
-ax.plot(frames, raw_series, lw=0.5, color='steelblue', alpha=0.7, label='Raw')
-ax.plot(frames, sm_best, lw=1.2, color='firebrick', label=f'Smoothed (wsz={best_wsz})')
-in_peak = False
-for i in range(T):
- if peak_mask[i] and not in_peak:
- start = i
- in_peak = True
- elif not peak_mask[i] and in_peak:
- ax.axvspan(start, i, alpha=0.12, color='gold')
- in_peak = False
-if in_peak:
- ax.axvspan(start, T, alpha=0.12, color='gold')
-ax.set_ylabel('Flux')
-ax.set_title('Raw signal + smoothed (peak regions shaded in gold)')
-ax.legend(fontsize=8)
-
-# Panel 2: original vs smoothed window sizes
-ax = axes[1]
-ax.plot(frames, win_raw, lw=0.5, color='gray', alpha=0.6, label='Window sizes (wsz=1, no smoothing)')
-ax.plot(frames, win_best, lw=1.0, color='darkorange', alpha=0.9, label=f'Window sizes (wsz={best_wsz})')
-ax.set_ylabel('Window size')
-ax.set_title('Window size: original (noisy) vs smoothed')
-ax.legend(fontsize=8)
-
-# Panel 3: zoomed 1800–2300
-ax = axes[2]
-fz = np.arange(z0, z1)
-ax.plot(fz, raw_series[z0:z1], lw=0.8, color='steelblue', alpha=0.8, label='Raw')
-ax.plot(fz, sm_best[z0:z1], lw=1.4, color='firebrick', label=f'Smoothed (wsz={best_wsz})')
-ax_r = ax.twinx()
-ax_r.plot(fz, win_raw[z0:z1], lw=0.6, color='gray', alpha=0.5, label='win (wsz=1)')
-ax_r.plot(fz, win_best[z0:z1], lw=1.0, color='darkorange', alpha=0.8, label=f'win (wsz={best_wsz})')
-ax_r.set_ylabel('Window size', color='darkorange')
-ax_r.tick_params(axis='y', labelcolor='darkorange')
-ax.set_ylabel('Flux')
-ax.set_title('Zoom: frames 1800–2300')
-lines1, labels1 = ax.get_legend_handles_labels()
-lines2, labels2 = ax_r.get_legend_handles_labels()
-ax.legend(lines1 + lines2, labels1 + labels2, fontsize=7, loc='upper left')
-
-# Panel 4: detrended signal with peaks marked
-ax = axes[3]
-ax.plot(frames, detrended, lw=0.6, color='darkgreen', label='Detrended (raw − coarse baseline)')
-ax.axhline(threshold, color='red', ls='--', lw=0.9, label=f'Threshold (1.5×MAD = {threshold:.4f})')
-ax.scatter(frames[peak_mask], detrended[peak_mask], s=3, color='red', alpha=0.5, label=f'Peaks ({peak_mask.sum()} frames)')
-ax.set_xlabel('Frame')
-ax.set_ylabel('Residual flux')
-ax.set_title('Detrended signal with detected peaks')
-ax.legend(fontsize=8)
-
-plt.tight_layout()
-plt.savefig('/Users/rri38/Documents/work/code/tess/tessreduce/development/adaptive_smooth_final.png', dpi=120)
-plt.close()
-print("\nSaved adaptive_smooth_final.png")
-
-# Also save the zoom diagnostic as requested
-fig, axes = plt.subplots(3, 1, figsize=(14, 12), sharex=True)
-var1d = var1[:, 0, 0]
-fz = np.arange(z0, z1)
-
-ax = axes[0]
-ax.plot(fz, raw_series[z0:z1], lw=0.7, color='steelblue', label='Raw')
-ax.plot(fz, sm1[:, 0, 0][z0:z1], lw=1.1, color='firebrick', label='Smoothed (wsz=1)')
-ax.set_ylabel('Flux')
-ax.set_title('Pixel (100,100): frames 1800–2300 — raw and smoothed')
-ax.legend(fontsize=8)
-
-ax = axes[1]
-ax.plot(fz, win_raw[z0:z1], lw=0.6, color='gray', alpha=0.7, label='Quantized windows (wsz=1)')
-ax_w = ax.twinx()
-ax_w.plot(fz, wrc1d[z0:z1], lw=0.8, color='navy', alpha=0.6, label='Continuous raw (pre-round)')
-ax_w.set_ylabel('Continuous window value', color='navy')
-ax_w.tick_params(axis='y', labelcolor='navy')
-ax.set_ylabel('Quantized window size', color='gray')
-ax.set_title('Window sizes (quantized) and continuous field')
-lines1, labels1 = ax.get_legend_handles_labels()
-lines2, labels2 = ax_w.get_legend_handles_labels()
-ax.legend(lines1 + lines2, labels1 + labels2, fontsize=7)
-
-ax = axes[2]
-ax.plot(fz, var1d[z0:z1], lw=0.7, color='purple', label='Variability metric')
-ax.set_ylabel('Variability')
-ax.set_xlabel('Frame')
-ax.set_title('Raw variability metric in zoom region')
-ax.legend(fontsize=8)
-
-plt.tight_layout()
-plt.savefig('/Users/rri38/Documents/work/code/tess/tessreduce/development/diag_zoom_2000.png', dpi=120)
-plt.close()
-print("Saved diag_zoom_2000.png (updated)")
-
-print("\n=== FINAL REPORT ===")
-print(f"Pixel (100,100), T={T}")
-print(f"\nWindow size stats (wsz=1, no smoothing):")
-print(f" mean={wq1d.mean():.2f}, std={wq1d.std():.2f}")
-print(f" n_switches={np.sum(np.diff(wq1d)!=0)}, mean run={run_lengths(wq1d).mean():.1f} frames")
-print(f" frac@w_min={np.mean(wq1d==3):.3f}, frac@w_max={np.mean(wq1d==51):.3f}")
-
-for wsz, wqd in [(5, wq5d), (11, wq11d), (21, wq21d), (31, wq31d)]:
- print(f"\nWindow size stats (wsz={wsz}):")
- print(f" mean={wqd.mean():.2f}, std={wqd.std():.2f}")
- print(f" n_switches={np.sum(np.diff(wqd)!=0)}, mean run={run_lengths(wqd).mean():.1f} frames")
- print(f" frac@w_min={np.mean(wqd==3):.3f}, frac@w_max={np.mean(wqd==51):.3f}")
-
-print(f"\nPeak/baseline residuals:")
-def compute_residuals(raw, sm, pm):
- r = np.abs(raw - sm)
- return r[pm].mean() if pm.any() else np.nan, r[~pm].mean() if (~pm).any() else np.nan
-
-for wsz, smd in [(1, sm1[:, 0, 0]), (5, sm5[:, 0, 0]), (11, sm11[:, 0, 0]),
- (21, sm21[:, 0, 0]), (31, sm31[:, 0, 0])]:
- pr, br = compute_residuals(raw_series, smd, peak_mask)
- print(f" wsz={wsz:2d}: peak_res={pr:.5f}, base_res={br:.5f}, ratio={pr/br:.3f}")
-
-print(f"\nDiagnosis of oscillation:")
-rls = run_lengths(wpre1d)
-print(f" Pre-smooth window field: {len(rls)} runs, mean={rls.mean():.1f}, median={np.median(rls):.0f} frames/run")
-print(f" Fraction single-frame runs: {np.mean(rls==1):.3f}")
-print(f" Root cause: window field oscillates at period ~{2*np.median(rls):.0f} frames")
-print(f" Smoothing is partially absorbed by quantization (n_levels=7 with step-size ~8)")
-print(f" wsz=31 gives best switch reduction: {np.sum(np.diff(wq1d)!=0)} -> {np.sum(np.diff(wq31d)!=0)} ({100*(1-np.sum(np.diff(wq31d)!=0)/np.sum(np.diff(wq1d)!=0)):.0f}% reduction)")
-print(f"\nBest window_smooth_size = 31 (largest switch reduction with acceptable residual increase)")
diff --git a/development/sn2020aoi_cal.pdf b/development/sn2020aoi_cal.pdf
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diff --git a/development/sn2020aoi_cal.png b/development/sn2020aoi_cal.png
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diff --git a/development/stack_ref.ipynb b/development/stack_ref.ipynb
deleted file mode 100644
index 3195c2a..0000000
--- a/development/stack_ref.ipynb
+++ /dev/null
@@ -1,21739 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "9258d3e2",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import tessreduce as tr\n",
- "%matplotlib notebook"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "f00f5ed5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "made reference\n",
- "!!!WARNING!!! mask is too dense, lowering mask_scale to 0.5, and raising maglim to 15. Background quality will be reduced.\n",
- "made source mask\n",
- "calculating background\n",
- "background subtracted\n",
- "aligning images\n",
- "!!Re-running for difference image!!\n",
- "shifting images\n",
- "remade mask\n",
- "background\n",
- "background correlation correction\n"
- ]
- },
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "/* global mpl */\n",
- "window.mpl = {};\n",
- "\n",
- "mpl.get_websocket_type = function () {\n",
- " if (typeof WebSocket !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof MozWebSocket !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert(\n",
- " 'Your browser does not have WebSocket support. ' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.'\n",
- " );\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = this.ws.binaryType !== undefined;\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById('mpl-warnings');\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent =\n",
- " 'This browser does not support binary websocket messages. ' +\n",
- " 'Performance may be slow.';\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = document.createElement('div');\n",
- " this.root.setAttribute('style', 'display: inline-block');\n",
- " this._root_extra_style(this.root);\n",
- "\n",
- " parent_element.appendChild(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message('supports_binary', { value: fig.supports_binary });\n",
- " fig.send_message('send_image_mode', {});\n",
- " if (fig.ratio !== 1) {\n",
- " fig.send_message('set_device_pixel_ratio', {\n",
- " device_pixel_ratio: fig.ratio,\n",
- " });\n",
- " }\n",
- " fig.send_message('refresh', {});\n",
- " };\n",
- "\n",
- " this.imageObj.onload = function () {\n",
- " if (fig.image_mode === 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function () {\n",
- " fig.ws.close();\n",
- " };\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_header = function () {\n",
- " var titlebar = document.createElement('div');\n",
- " titlebar.classList =\n",
- " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
- " var titletext = document.createElement('div');\n",
- " titletext.classList = 'ui-dialog-title';\n",
- " titletext.setAttribute(\n",
- " 'style',\n",
- " 'width: 100%; text-align: center; padding: 3px;'\n",
- " );\n",
- " titlebar.appendChild(titletext);\n",
- " this.root.appendChild(titlebar);\n",
- " this.header = titletext;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function () {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = (this.canvas_div = document.createElement('div'));\n",
- " canvas_div.setAttribute('tabindex', '0');\n",
- " canvas_div.setAttribute(\n",
- " 'style',\n",
- " 'border: 1px solid #ddd;' +\n",
- " 'box-sizing: content-box;' +\n",
- " 'clear: both;' +\n",
- " 'min-height: 1px;' +\n",
- " 'min-width: 1px;' +\n",
- " 'outline: 0;' +\n",
- " 'overflow: hidden;' +\n",
- " 'position: relative;' +\n",
- " 'resize: both;' +\n",
- " 'z-index: 2;'\n",
- " );\n",
- "\n",
- " function on_keyboard_event_closure(name) {\n",
- " return function (event) {\n",
- " return fig.key_event(event, name);\n",
- " };\n",
- " }\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'keydown',\n",
- " on_keyboard_event_closure('key_press')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'keyup',\n",
- " on_keyboard_event_closure('key_release')\n",
- " );\n",
- "\n",
- " this._canvas_extra_style(canvas_div);\n",
- " this.root.appendChild(canvas_div);\n",
- "\n",
- " var canvas = (this.canvas = document.createElement('canvas'));\n",
- " canvas.classList.add('mpl-canvas');\n",
- " canvas.setAttribute(\n",
- " 'style',\n",
- " 'box-sizing: content-box;' +\n",
- " 'pointer-events: none;' +\n",
- " 'position: relative;' +\n",
- " 'z-index: 0;'\n",
- " );\n",
- "\n",
- " this.context = canvas.getContext('2d');\n",
- "\n",
- " var backingStore =\n",
- " this.context.backingStorePixelRatio ||\n",
- " this.context.webkitBackingStorePixelRatio ||\n",
- " this.context.mozBackingStorePixelRatio ||\n",
- " this.context.msBackingStorePixelRatio ||\n",
- " this.context.oBackingStorePixelRatio ||\n",
- " this.context.backingStorePixelRatio ||\n",
- " 1;\n",
- "\n",
- " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
- " 'canvas'\n",
- " ));\n",
- " rubberband_canvas.setAttribute(\n",
- " 'style',\n",
- " 'box-sizing: content-box;' +\n",
- " 'left: 0;' +\n",
- " 'pointer-events: none;' +\n",
- " 'position: absolute;' +\n",
- " 'top: 0;' +\n",
- " 'z-index: 1;'\n",
- " );\n",
- "\n",
- " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
- " if (this.ResizeObserver === undefined) {\n",
- " if (window.ResizeObserver !== undefined) {\n",
- " this.ResizeObserver = window.ResizeObserver;\n",
- " } else {\n",
- " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
- " this.ResizeObserver = obs.ResizeObserver;\n",
- " }\n",
- " }\n",
- "\n",
- " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
- " // There's no need to resize if the WebSocket is not connected:\n",
- " // - If it is still connecting, then we will get an initial resize from\n",
- " // Python once it connects.\n",
- " // - If it has disconnected, then resizing will clear the canvas and\n",
- " // never get anything back to refill it, so better to not resize and\n",
- " // keep something visible.\n",
- " if (fig.ws.readyState != 1) {\n",
- " return;\n",
- " }\n",
- " var nentries = entries.length;\n",
- " for (var i = 0; i < nentries; i++) {\n",
- " var entry = entries[i];\n",
- " var width, height;\n",
- " if (entry.contentBoxSize) {\n",
- " if (entry.contentBoxSize instanceof Array) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " width = entry.contentBoxSize[0].inlineSize;\n",
- " height = entry.contentBoxSize[0].blockSize;\n",
- " } else {\n",
- " // Firefox implements old version of spec.\n",
- " width = entry.contentBoxSize.inlineSize;\n",
- " height = entry.contentBoxSize.blockSize;\n",
- " }\n",
- " } else {\n",
- " // Chrome <84 implements even older version of spec.\n",
- " width = entry.contentRect.width;\n",
- " height = entry.contentRect.height;\n",
- " }\n",
- "\n",
- " // Keep the size of the canvas and rubber band canvas in sync with\n",
- " // the canvas container.\n",
- " if (entry.devicePixelContentBoxSize) {\n",
- " // Chrome 84 implements new version of spec.\n",
- " canvas.setAttribute(\n",
- " 'width',\n",
- " entry.devicePixelContentBoxSize[0].inlineSize\n",
- " );\n",
- " canvas.setAttribute(\n",
- " 'height',\n",
- " entry.devicePixelContentBoxSize[0].blockSize\n",
- " );\n",
- " } else {\n",
- " canvas.setAttribute('width', width * fig.ratio);\n",
- " canvas.setAttribute('height', height * fig.ratio);\n",
- " }\n",
- " /* This rescales the canvas back to display pixels, so that it\n",
- " * appears correct on HiDPI screens. */\n",
- " canvas.style.width = width + 'px';\n",
- " canvas.style.height = height + 'px';\n",
- "\n",
- " rubberband_canvas.setAttribute('width', width);\n",
- " rubberband_canvas.setAttribute('height', height);\n",
- "\n",
- " // And update the size in Python. We ignore the initial 0/0 size\n",
- " // that occurs as the element is placed into the DOM, which should\n",
- " // otherwise not happen due to the minimum size styling.\n",
- " if (width != 0 && height != 0) {\n",
- " fig.request_resize(width, height);\n",
- " }\n",
- " }\n",
- " });\n",
- " this.resizeObserverInstance.observe(canvas_div);\n",
- "\n",
- " function on_mouse_event_closure(name) {\n",
- " /* User Agent sniffing is bad, but WebKit is busted:\n",
- " * https://bugs.webkit.org/show_bug.cgi?id=144526\n",
- " * https://bugs.webkit.org/show_bug.cgi?id=181818\n",
- " * The worst that happens here is that they get an extra browser\n",
- " * selection when dragging, if this check fails to catch them.\n",
- " */\n",
- " var UA = navigator.userAgent;\n",
- " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n",
- " if(isWebKit) {\n",
- " return function (event) {\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We\n",
- " * want to control all of the cursor setting manually through\n",
- " * the 'cursor' event from matplotlib */\n",
- " event.preventDefault()\n",
- " return fig.mouse_event(event, name);\n",
- " };\n",
- " } else {\n",
- " return function (event) {\n",
- " return fig.mouse_event(event, name);\n",
- " };\n",
- " }\n",
- " }\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'mousedown',\n",
- " on_mouse_event_closure('button_press')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'mouseup',\n",
- " on_mouse_event_closure('button_release')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'dblclick',\n",
- " on_mouse_event_closure('dblclick')\n",
- " );\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " canvas_div.addEventListener(\n",
- " 'mousemove',\n",
- " on_mouse_event_closure('motion_notify')\n",
- " );\n",
- "\n",
- " canvas_div.addEventListener(\n",
- " 'mouseenter',\n",
- " on_mouse_event_closure('figure_enter')\n",
- " );\n",
- " canvas_div.addEventListener(\n",
- " 'mouseleave',\n",
- " on_mouse_event_closure('figure_leave')\n",
- " );\n",
- "\n",
- " canvas_div.addEventListener('wheel', function (event) {\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " on_mouse_event_closure('scroll')(event);\n",
- " });\n",
- "\n",
- " canvas_div.appendChild(canvas);\n",
- " canvas_div.appendChild(rubberband_canvas);\n",
- "\n",
- " this.rubberband_context = rubberband_canvas.getContext('2d');\n",
- " this.rubberband_context.strokeStyle = '#000000';\n",
- "\n",
- " this._resize_canvas = function (width, height, forward) {\n",
- " if (forward) {\n",
- " canvas_div.style.width = width + 'px';\n",
- " canvas_div.style.height = height + 'px';\n",
- " }\n",
- " };\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " canvas_div.addEventListener('contextmenu', function (_e) {\n",
- " event.preventDefault();\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus() {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function () {\n",
- " var fig = this;\n",
- "\n",
- " var toolbar = document.createElement('div');\n",
- " toolbar.classList = 'mpl-toolbar';\n",
- " this.root.appendChild(toolbar);\n",
- "\n",
- " function on_click_closure(name) {\n",
- " return function (_event) {\n",
- " return fig.toolbar_button_onclick(name);\n",
- " };\n",
- " }\n",
- "\n",
- " function on_mouseover_closure(tooltip) {\n",
- " return function (event) {\n",
- " if (!event.currentTarget.disabled) {\n",
- " return fig.toolbar_button_onmouseover(tooltip);\n",
- " }\n",
- " };\n",
- " }\n",
- "\n",
- " fig.buttons = {};\n",
- " var buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " for (var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " /* Instead of a spacer, we start a new button group. */\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- " buttonGroup = document.createElement('div');\n",
- " buttonGroup.classList = 'mpl-button-group';\n",
- " continue;\n",
- " }\n",
- "\n",
- " var button = (fig.buttons[name] = document.createElement('button'));\n",
- " button.classList = 'mpl-widget';\n",
- " button.setAttribute('role', 'button');\n",
- " button.setAttribute('aria-disabled', 'false');\n",
- " button.addEventListener('click', on_click_closure(method_name));\n",
- " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
- "\n",
- " var icon_img = document.createElement('img');\n",
- " icon_img.src = '_images/' + image + '.png';\n",
- " icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
- " icon_img.alt = tooltip;\n",
- " button.appendChild(icon_img);\n",
- "\n",
- " buttonGroup.appendChild(button);\n",
- " }\n",
- "\n",
- " if (buttonGroup.hasChildNodes()) {\n",
- " toolbar.appendChild(buttonGroup);\n",
- " }\n",
- "\n",
- " var fmt_picker = document.createElement('select');\n",
- " fmt_picker.classList = 'mpl-widget';\n",
- " toolbar.appendChild(fmt_picker);\n",
- " this.format_dropdown = fmt_picker;\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = document.createElement('option');\n",
- " option.selected = fmt === mpl.default_extension;\n",
- " option.innerHTML = fmt;\n",
- " fmt_picker.appendChild(option);\n",
- " }\n",
- "\n",
- " var status_bar = document.createElement('span');\n",
- " status_bar.classList = 'mpl-message';\n",
- " toolbar.appendChild(status_bar);\n",
- " this.message = status_bar;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_message = function (type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function () {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1], msg['forward']);\n",
- " fig.send_message('refresh', {});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
- " var x0 = msg['x0'] / fig.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
- " var x1 = msg['x1'] / fig.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0,\n",
- " 0,\n",
- " fig.canvas.width / fig.ratio,\n",
- " fig.canvas.height / fig.ratio\n",
- " );\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
- " fig.canvas_div.style.cursor = msg['cursor'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
- " for (var key in msg) {\n",
- " if (!(key in fig.buttons)) {\n",
- " continue;\n",
- " }\n",
- " fig.buttons[key].disabled = !msg[key];\n",
- " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
- " if (msg['mode'] === 'PAN') {\n",
- " fig.buttons['Pan'].classList.add('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " } else if (msg['mode'] === 'ZOOM') {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.add('active');\n",
- " } else {\n",
- " fig.buttons['Pan'].classList.remove('active');\n",
- " fig.buttons['Zoom'].classList.remove('active');\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function () {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message('ack', {});\n",
- "};\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " var img = evt.data;\n",
- " if (img.type !== 'image/png') {\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",
- " img.type = 'image/png';\n",
- " }\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src\n",
- " );\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " img\n",
- " );\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " } else if (\n",
- " typeof evt.data === 'string' &&\n",
- " evt.data.slice(0, 21) === 'data:image/png;base64'\n",
- " ) {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig['handle_' + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"No handler for the '%s' message type: \",\n",
- " msg_type,\n",
- " msg\n",
- " );\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '%s' message: \", msg_type, msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\n",
- " \"Exception inside the 'handler_%s' callback:\",\n",
- " msg_type,\n",
- " e,\n",
- " e.stack,\n",
- " msg\n",
- " );\n",
- " }\n",
- " }\n",
- " };\n",
- "};\n",
- "\n",
- "function getModifiers(event) {\n",
- " var mods = [];\n",
- " if (event.ctrlKey) {\n",
- " mods.push('ctrl');\n",
- " }\n",
- " if (event.altKey) {\n",
- " mods.push('alt');\n",
- " }\n",
- " if (event.shiftKey) {\n",
- " mods.push('shift');\n",
- " }\n",
- " if (event.metaKey) {\n",
- " mods.push('meta');\n",
- " }\n",
- " return mods;\n",
- "}\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * https://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys(original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object') {\n",
- " obj[key] = original[key];\n",
- " }\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function (event, name) {\n",
- " if (name === 'button_press') {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " // from https://stackoverflow.com/q/1114465\n",
- " var boundingRect = this.canvas.getBoundingClientRect();\n",
- " var x = (event.clientX - boundingRect.left) * this.ratio;\n",
- " var y = (event.clientY - boundingRect.top) * this.ratio;\n",
- "\n",
- " this.send_message(name, {\n",
- " x: x,\n",
- " y: y,\n",
- " button: event.button,\n",
- " step: event.step,\n",
- " buttons: event.buttons,\n",
- " modifiers: getModifiers(event),\n",
- " guiEvent: simpleKeys(event),\n",
- " });\n",
- "\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.key_event = function (event, name) {\n",
- " // Prevent repeat events\n",
- " if (name === 'key_press') {\n",
- " if (event.key === this._key) {\n",
- " return;\n",
- " } else {\n",
- " this._key = event.key;\n",
- " }\n",
- " }\n",
- " if (name === 'key_release') {\n",
- " this._key = null;\n",
- " }\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.key !== 'Control') {\n",
- " value += 'ctrl+';\n",
- " }\n",
- " else if (event.altKey && event.key !== 'Alt') {\n",
- " value += 'alt+';\n",
- " }\n",
- " else if (event.shiftKey && event.key !== 'Shift') {\n",
- " value += 'shift+';\n",
- " }\n",
- "\n",
- " value += 'k' + event.key;\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
- " return false;\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
- " if (name === 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message('toolbar_button', { name: name });\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "\n",
- "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
- "// prettier-ignore\n",
- "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
- "\n",
- "mpl.default_extension = \"png\";/* global mpl */\n",
- "\n",
- "var comm_websocket_adapter = function (comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.binaryType = comm.kernel.ws.binaryType;\n",
- " ws.readyState = comm.kernel.ws.readyState;\n",
- " function updateReadyState(_event) {\n",
- " if (comm.kernel.ws) {\n",
- " ws.readyState = comm.kernel.ws.readyState;\n",
- " } else {\n",
- " ws.readyState = 3; // Closed state.\n",
- " }\n",
- " }\n",
- " comm.kernel.ws.addEventListener('open', updateReadyState);\n",
- " comm.kernel.ws.addEventListener('close', updateReadyState);\n",
- " comm.kernel.ws.addEventListener('error', updateReadyState);\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",
- " var data = msg['content']['data'];\n",
- " if (data['blob'] !== undefined) {\n",
- " data = {\n",
- " data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
- " };\n",
- " }\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(data);\n",
- " });\n",
- " return ws;\n",
- "};\n",
- "\n",
- "mpl.mpl_figure_comm = function (comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = document.getElementById(id);\n",
- " var ws_proxy = comm_websocket_adapter(comm);\n",
- "\n",
- " function ondownload(figure, _format) {\n",
- " window.open(figure.canvas.toDataURL());\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element;\n",
- " fig.cell_info = mpl.find_output_cell(\"\");\n",
- " if (!fig.cell_info) {\n",
- " console.error('Failed to find cell for figure', id, fig);\n",
- " return;\n",
- " }\n",
- " fig.cell_info[0].output_area.element.on(\n",
- " 'cleared',\n",
- " { fig: fig },\n",
- " fig._remove_fig_handler\n",
- " );\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
- " var width = fig.canvas.width / fig.ratio;\n",
- " fig.cell_info[0].output_area.element.off(\n",
- " 'cleared',\n",
- " fig._remove_fig_handler\n",
- " );\n",
- " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable();\n",
- " fig.parent_element.innerHTML =\n",
- " '| \n", - " | name | \n", - "transient_RA | \n", - "transient_Dec | \n", - "min_mag | \n", - "min_date | \n", - "disc_date | \n", - "spec_class | \n", - "phot_class | \n", - "transient_z | \n", - "status_id | \n", - "mw_ebv | \n", - "point_source_probability | \n", - "filter | \n", - "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", - "2025cxh | \n", - "152.912533 | \n", - "-25.151678 | \n", - "17.397221 | \n", - "2025-01-10 06:10:30.547000 | \n", - "2025-02-28 04:36:59.040000 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "7 | \n", - "0.0400 | \n", - "NaN | \n", - "g-ZTF | \n", - "
| 1 | \n", - "2025cnu | \n", - "159.277108 | \n", - "-7.467939 | \n", - "17.557000 | \n", - "2025-01-05 12:54:32.573000 | \n", - "2025-02-24 08:03:47.998000 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "7 | \n", - "0.0570 | \n", - "NaN | \n", - "orange-ATLAS | \n", - "
| 2 | \n", - "2025dgi | \n", - "157.917354 | \n", - "-25.902136 | \n", - "18.391000 | \n", - "2025-01-14 05:58:39.043000 | \n", - "2025-03-03 05:24:26.784000 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "1 | \n", - "0.0520 | \n", - "0.004 | \n", - "cyan-ATLAS | \n", - "
| 3 | \n", - "2025azf | \n", - "135.996283 | \n", - "0.740372 | \n", - "16.827000 | \n", - "2024-12-20 15:02:07.354000 | \n", - "2025-02-06 22:33:41.184000 | \n", - "SN Ia | \n", - "NaN | \n", - "0.053000 | \n", - "2 | \n", - "0.0360 | \n", - "0.003 | \n", - "cyan-ATLAS | \n", - "
| 4 | \n", - "2024ljc | \n", - "285.422583 | \n", - "40.745067 | \n", - "17.100000 | \n", - "2022-08-09 13:08:46 | \n", - "2024-06-15 08:15:05 | \n", - "SN IIb | \n", - "NaN | \n", - "0.015154 | \n", - "7 | \n", - "0.1170 | \n", - "0.268 | \n", - "V | \n", - "
| ... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "... | \n", - "
| 6110 | \n", - "2019jdy | \n", - "185.852867 | \n", - "-14.910739 | \n", - "18.570897 | \n", - "2009-06-09 06:18:37.523000 | \n", - "2019-05-09 07:14:52 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "3 | \n", - "0.0482 | \n", - "0.002 | \n", - "y | \n", - "
| 6111 | \n", - "2019jab | \n", - "68.986725 | \n", - "-24.982608 | \n", - "18.699507 | \n", - "2009-09-16 14:49:16.993000 | \n", - "2019-01-08 08:03:50 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "3 | \n", - "0.0423 | \n", - "0.010 | \n", - "y | \n", - "
| 6112 | \n", - "2019jmz | \n", - "211.634621 | \n", - "-22.114911 | \n", - "18.482519 | \n", - "2009-06-22 07:40:12.593000 | \n", - "2019-05-25 07:56:38 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "3 | \n", - "0.0651 | \n", - "0.001 | \n", - "z | \n", - "
| 6113 | \n", - "2019bzz | \n", - "79.088542 | \n", - "-10.561361 | \n", - "16.030510 | \n", - "2008-04-11 00:07:17 | \n", - "2019-03-05 03:02:52 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "3 | \n", - "NaN | \n", - "NaN | \n", - "V | \n", - "
| 6114 | \n", - "2018kuv | \n", - "84.709833 | \n", - "-44.085819 | \n", - "14.959219 | \n", - "2005-07-12 01:13:04 | \n", - "2018-12-22 20:00:57 | \n", - "NaN | \n", - "NaN | \n", - "NaN | \n", - "3 | \n", - "0.0316 | \n", - "NaN | \n", - "V | \n", - "
6115 rows × 13 columns
\n", - "