{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5a38ed25", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T14:16:01.928614Z", "start_time": "2023-03-24T14:15:34.242247Z" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "../csiborgtools/field/__init__.py:20: UserWarning: MAS_library not found, `DensityField` will not be available\n", " warn(\"MAS_library not found, `DensityField` will not be available\", UserWarning) # noqa\n" ] } ], "source": [ "import sys\n", "from os.path import join\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from numba import jit\n", "from tqdm import tqdm, trange\n", "import joblib\n", "sys.path.append(\"../\")\n", "import csiborgtools\n", "\n", "%matplotlib widget\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "id": "6f3ae40e", "metadata": {}, "outputs": [], "source": [ "srcdir = \"/cosma8/data/dp016/dc-stis1/csiborg_new\"\n", "dumpdir = \"/cosma8/data/dp016/dc-stis1/csiborg_dump\"\n", "paths = csiborgtools.read.CSiBORGPaths(srcdir=srcdir, dumpdir=dumpdir)\n", "\n", "\n", "reader = csiborgtools.read.ParticleReader(paths)" ] }, { "cell_type": "code", "execution_count": 3, "id": "33abfe57", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading in output `00001` with ncpu = `324`.\n", "Opened 324 particle files.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 324/324 [03:57<00:00, 1.36it/s]\n" ] } ], "source": [ "part = reader.read_particle(1, 7444, [\"x\", \"y\", \"z\", \"M\", \"ID\"], verbose=True)" ] }, { "cell_type": "code", "execution_count": 4, "id": "32ab40ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ls: cannot access /cosma8/data/dp016/dc-stis1/csiborg_new/ramsess_out_7444/outputs_00001/: No such file or directory\n" ] } ], "source": [ "/cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444_new/output_00001/info_00001.txt" ] }, { "cell_type": "code", "execution_count": 9, "id": "fc116dff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cat: /cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444/output_00001/info_00001.txt: No such file or directory\n" ] } ], "source": [ "!cat /cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444/output_00001/info_00001.txt" ] }, { "cell_type": "code", "execution_count": 8, "id": "e6b05ceb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ncpu = 324\n", "ndim = 3\n", "levelmin = 8\n", "levelmax = 19\n", "ngridmax = 2800000\n", "nstep_coarse= 0\n", "\n", "boxlen = 0.100000000000000E+01\n", "time = -0.260788354371079E+02\n", "aexp = 0.142857000000000E-01\n", "H0 = 0.705000000000000E+02\n", "omega_m = 0.307000011205673E+00\n", "omega_l = 0.693000018596649E+00\n", "omega_k = -0.298023223876953E-07\n", "omega_b = 0.000000000000000E+00\n", "unit_l = 0.423740962969913E+26\n", "unit_d = 0.983942902810118E-24\n", "unit_t = 0.893232429772148E+14\n", "\n", "ordering type=hilbert \n", " DOMAIN ind_min ind_max\n", " 1 0.000000000000000E+00 0.890921902481080E+17\n", " 2 0.890921902481080E+17 0.903741664479150E+17\n", " 3 0.903741664479150E+17 0.905655754235576E+17\n", " 4 0.905655754235576E+17 0.907569842649825E+17\n", " 5 0.907569842649825E+17 0.910375727335997E+17\n", " 6 0.910375727335997E+17 0.916149055929713E+17\n", " 7 0.916149055929713E+17 0.918096761858294E+17\n", " 8 0.918096761858294E+17 0.920010850272543E+17\n", " 9 0.920010850272543E+17 0.942688305781146E+17\n", " 10 0.942688305781146E+17 0.945672973849723E+17\n", " 11 0.945672973849723E+17 0.947587062263972E+17\n", " 12 0.947587062263972E+17 0.949501751973642E+17\n", " 13 0.949501751973642E+17 0.955129510703923E+17\n", " 14 0.955129510703923E+17 0.958726141818962E+17\n", " 15 0.958726141818962E+17 0.963097707172332E+17\n", " 16 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275 0.892138464181486E+18 0.892329873022910E+18\n", " 276 0.892329873022910E+18 0.892521312063324E+18\n", " 277 0.892521312063324E+18 0.892712833647641E+18\n", " 278 0.892712833647641E+18 0.893333954101248E+18\n", " 279 0.893333954101248E+18 0.893653827830415E+18\n", " 280 0.893653827830415E+18 0.894359090988319E+18\n", " 281 0.894359090988319E+18 0.899130547593282E+18\n", " 282 0.899130547593282E+18 0.900009270521627E+18\n", " 283 0.900009270521627E+18 0.958721087984632E+18\n", " 284 0.958721087984632E+18 0.104545954810770E+19\n", " 285 0.104545954810770E+19 0.104631566863643E+19\n", " 286 0.104631566863643E+19 0.105113412305525E+19\n", " 287 0.105113412305525E+19 0.105174324551772E+19\n", " 288 0.105174324551772E+19 0.105209241884269E+19\n", " 289 0.105209241884269E+19 0.105278706349926E+19\n", " 290 0.105278706349926E+19 0.105298166296451E+19\n", " 291 0.105298166296451E+19 0.105317312455351E+19\n", " 292 0.105317312455351E+19 0.105336453339493E+19\n", " 293 0.105336453339493E+19 0.105355594223636E+19\n", " 294 0.105355594223636E+19 0.105374735121200E+19\n", " 295 0.105374735121200E+19 0.105393881266677E+19\n", " 296 0.105393881266677E+19 0.105413022150820E+19\n", " 297 0.105413022150820E+19 0.105432163048384E+19\n", " 298 0.105432163048384E+19 0.105451303932527E+19\n", " 299 0.105451303932527E+19 0.105470444830091E+19\n", " 300 0.105470444830091E+19 0.105489585714233E+19\n", " 301 0.105489585714233E+19 0.105508726598376E+19\n", " 302 0.105508726598376E+19 0.105527867495940E+19\n", " 303 0.105527867495940E+19 0.105547008380083E+19\n", " 304 0.105547008380083E+19 0.105566149264225E+19\n", " 305 0.105566149264225E+19 0.105585290161789E+19\n", " 306 0.105585290161789E+19 0.105604431045932E+19\n", " 307 0.105604431045932E+19 0.105623571930074E+19\n", " 308 0.105623571930074E+19 0.105642712827639E+19\n", " 309 0.105642712827639E+19 0.105661858973116E+19\n", " 310 0.105661858973116E+19 0.105705459105451E+19\n", " 311 0.105705459105451E+19 0.105742519707160E+19\n", " 312 0.105742519707160E+19 0.105797542036583E+19\n", " 313 0.105797542036583E+19 0.105816688182061E+19\n", " 314 0.105816688182061E+19 0.105835829066203E+19\n", " 315 0.105835829066203E+19 0.105865633938067E+19\n", " 316 0.105865633938067E+19 0.106092287187170E+19\n", " 317 0.106092287187170E+19 0.106111428071313E+19\n", " 318 0.106111428071313E+19 0.106130893198642E+19\n", " 319 0.106130893198642E+19 0.106188462242346E+19\n", " 320 0.106188462242346E+19 0.106216483535087E+19\n", " 321 0.106216483535087E+19 0.106235624432651E+19\n", " 322 0.106235624432651E+19 0.106254765316794E+19\n", " 323 0.106254765316794E+19 0.106382938307822E+19\n", " 324 0.106382938307822E+19 0.115292150460685E+19\n" ] } ], "source": [ "!cat /cosma8/data/dp016/dc-stis1/csiborg_new/ramses_out_7444_new/output_00001/info_00001.txt" ] }, { "cell_type": "code", "execution_count": 10, "id": "dc8ade85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/cosma8/data/dp016/dc-stis1/csiborg_dump'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "paths.dumpdir\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "1891c6ff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "101" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(paths.get_ics(tonew=True))" ] }, { "cell_type": "code", "execution_count": null, "id": "7101a155", "metadata": {}, "outputs": [], "source": [ " fname = \"ramses_out_{}_{}.npy\".format(\n", " str(self.nsim).zfill(5), str(self.nsnap).zfill(5))\n", " data = numpy.load(join(self.paths.dumpdir, fname))\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "190d39e6", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T14:16:12.485845Z", "start_time": "2023-03-24T14:16:01.930739Z" } }, "outputs": [], "source": [ "cat0 = csiborgtools.read.ClumpsCatalogue(7468)\n", "catx = csiborgtools.read.ClumpsCatalogue(7588)" ] }, { "cell_type": "code", "execution_count": 3, "id": "09c93ab0", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T14:16:31.435607Z", "start_time": "2023-03-24T14:16:12.487458Z" }, "scrolled": true }, "outputs": [], "source": [ "reader = csiborgtools.read.PairOverlap(cat0, catx, max_dist=150 / 0.705)" ] }, { "cell_type": "code", "execution_count": 31, "id": "650cbe8a", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T14:27:04.213308Z", "start_time": "2023-03-24T14:27:00.679174Z" }, "scrolled": false }, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\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\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = 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\nmpl.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\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key_event(event, name);\n };\n }\n\n canvas_div.addEventListener(\n 'keydown',\n on_keyboard_event_closure('key_press')\n );\n canvas_div.addEventListener(\n 'keyup',\n on_keyboard_event_closure('key_release')\n );\n\n this._canvas_extra_style(canvas_div);\n this.root.appendChild(canvas_div);\n\n var canvas = (this.canvas = document.createElement('canvas'));\n canvas.classList.add('mpl-canvas');\n canvas.setAttribute('style', 'box-sizing: content-box;');\n\n this.context = canvas.getContext('2d');\n\n var backingStore =\n this.context.backingStorePixelRatio ||\n this.context.webkitBackingStorePixelRatio ||\n this.context.mozBackingStorePixelRatio ||\n this.context.msBackingStorePixelRatio ||\n this.context.oBackingStorePixelRatio ||\n this.context.backingStorePixelRatio ||\n 1;\n\n this.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n 'canvas'\n ));\n rubberband_canvas.setAttribute(\n 'style',\n 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n );\n\n // Apply a ponyfill if ResizeObserver is not implemented by browser.\n if (this.ResizeObserver === undefined) {\n if (window.ResizeObserver !== undefined) {\n this.ResizeObserver = window.ResizeObserver;\n } else {\n var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n this.ResizeObserver = obs.ResizeObserver;\n }\n }\n\n this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n var nentries = entries.length;\n for (var i = 0; i < nentries; i++) {\n var entry = entries[i];\n var width, height;\n if (entry.contentBoxSize) {\n if (entry.contentBoxSize instanceof Array) {\n // Chrome 84 implements new version of spec.\n width = entry.contentBoxSize[0].inlineSize;\n height = entry.contentBoxSize[0].blockSize;\n } else {\n // Firefox implements old version of spec.\n width = entry.contentBoxSize.inlineSize;\n height = entry.contentBoxSize.blockSize;\n }\n } else {\n // Chrome <84 implements even older version of spec.\n width = entry.contentRect.width;\n height = entry.contentRect.height;\n }\n\n // Keep the size of the canvas and rubber band canvas in sync with\n // the canvas container.\n if (entry.devicePixelContentBoxSize) {\n // Chrome 84 implements new version of spec.\n canvas.setAttribute(\n 'width',\n entry.devicePixelContentBoxSize[0].inlineSize\n );\n canvas.setAttribute(\n 'height',\n entry.devicePixelContentBoxSize[0].blockSize\n );\n } else {\n canvas.setAttribute('width', width * fig.ratio);\n canvas.setAttribute('height', height * fig.ratio);\n }\n canvas.setAttribute(\n 'style',\n 'width: ' + width + 'px; height: ' + height + 'px;'\n );\n\n rubberband_canvas.setAttribute('width', width);\n rubberband_canvas.setAttribute('height', height);\n\n // And update the size in Python. We ignore the initial 0/0 size\n // that occurs as the element is placed into the DOM, which should\n // otherwise not happen due to the minimum size styling.\n if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n fig.request_resize(width, height);\n }\n }\n });\n this.resizeObserverInstance.observe(canvas_div);\n\n function on_mouse_event_closure(name) {\n return function (event) {\n return fig.mouse_event(event, name);\n };\n }\n\n rubberband_canvas.addEventListener(\n 'mousedown',\n on_mouse_event_closure('button_press')\n );\n rubberband_canvas.addEventListener(\n 'mouseup',\n on_mouse_event_closure('button_release')\n );\n rubberband_canvas.addEventListener(\n 'dblclick',\n on_mouse_event_closure('dblclick')\n );\n // Throttle sequential mouse events to 1 every 20ms.\n rubberband_canvas.addEventListener(\n 'mousemove',\n on_mouse_event_closure('motion_notify')\n );\n\n rubberband_canvas.addEventListener(\n 'mouseenter',\n on_mouse_event_closure('figure_enter')\n );\n rubberband_canvas.addEventListener(\n 'mouseleave',\n on_mouse_event_closure('figure_leave')\n );\n\n canvas_div.addEventListener('wheel', function (event) {\n if (event.deltaY < 0) {\n event.step = 1;\n } else {\n event.step = -1;\n }\n on_mouse_event_closure('scroll')(event);\n });\n\n canvas_div.appendChild(canvas);\n canvas_div.appendChild(rubberband_canvas);\n\n this.rubberband_context = rubberband_canvas.getContext('2d');\n this.rubberband_context.strokeStyle = '#000000';\n\n this._resize_canvas = function (width, height, forward) {\n if (forward) {\n canvas_div.style.width = width + 'px';\n canvas_div.style.height = height + 'px';\n }\n };\n\n // Disable right mouse context menu.\n this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n event.preventDefault();\n return false;\n });\n\n function set_focus() {\n canvas.focus();\n canvas_div.focus();\n }\n\n window.setTimeout(set_focus, 100);\n};\n\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.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\nmpl.figure.prototype.handle_figure_label = function (fig, msg) {\n // Updates the figure title.\n fig.header.textContent = msg['label'];\n};\n\nmpl.figure.prototype.handle_cursor = function (fig, msg) {\n fig.rubberband_canvas.style.cursor = msg['cursor'];\n};\n\nmpl.figure.prototype.handle_message = function (fig, msg) {\n fig.message.textContent = msg['message'];\n};\n\nmpl.figure.prototype.handle_draw = function (fig, _msg) {\n // Request the server to send over a new figure.\n fig.send_draw_message();\n};\n\nmpl.figure.prototype.handle_image_mode = function (fig, msg) {\n fig.image_mode = msg['mode'];\n};\n\nmpl.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\nmpl.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\nmpl.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.\nmpl.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 '\" + msg_type + \"' message type: \",\n msg\n );\n return;\n }\n\n if (callback) {\n try {\n // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n callback(fig, msg);\n } catch (e) {\n console.log(\n \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n e,\n e.stack,\n msg\n );\n }\n }\n };\n};\n\n// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function (e) {\n //this section is from http://www.quirksmode.org/js/events_properties.html\n var targ;\n if (!e) {\n e = window.event;\n }\n if (e.target) {\n targ = e.target;\n } else if (e.srcElement) {\n targ = e.srcElement;\n }\n if (targ.nodeType === 3) {\n // defeat Safari bug\n targ = targ.parentNode;\n }\n\n // pageX,Y are the mouse positions relative to the document\n var boundingRect = targ.getBoundingClientRect();\n var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n\n return { x: x, y: y };\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * https://stackoverflow.com/a/24161582/3208463\n */\nfunction 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\nmpl.figure.prototype.mouse_event = function (event, name) {\n var canvas_pos = mpl.findpos(event);\n\n if (name === 'button_press') {\n this.canvas.focus();\n this.canvas_div.focus();\n }\n\n var x = canvas_pos.x * this.ratio;\n var y = canvas_pos.y * this.ratio;\n\n this.send_message(name, {\n x: x,\n y: y,\n button: event.button,\n step: event.step,\n guiEvent: simpleKeys(event),\n });\n\n /* This prevents the web browser from automatically changing to\n * the text insertion cursor when the button is pressed. We want\n * to control all of the cursor setting manually through the\n * 'cursor' event from matplotlib */\n event.preventDefault();\n return false;\n};\n\nmpl.figure.prototype._key_event_extra = function (_event, _name) {\n // Handle any extra behaviour associated with a key event\n};\n\nmpl.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\nmpl.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\nmpl.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\nvar _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\nmpl.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\nmpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n\nmpl.default_extension = \"png\";/* global mpl */\n\nvar 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\nmpl.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\nmpl.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 fig.close_ws(fig, msg);\n};\n\nmpl.figure.prototype.close_ws = function (fig, msg) {\n fig.send_message('closing', msg);\n // fig.ws.close()\n};\n\nmpl.figure.prototype.push_to_output = function (_remove_interactive) {\n // Turn the data on the canvas into data in the output cell.\n var width = this.canvas.width / this.ratio;\n var dataURL = this.canvas.toDataURL();\n this.cell_info[1]['text/html'] =\n '';\n};\n\nmpl.figure.prototype.updated_canvas_event = function () {\n // Tell IPython that the notebook contents must change.\n IPython.notebook.set_dirty(true);\n this.send_message('ack', {});\n var fig = this;\n // Wait a second, then push the new image to the DOM so\n // that it is saved nicely (might be nice to debounce this).\n setTimeout(function () {\n fig.push_to_output();\n }, 1000);\n};\n\nmpl.figure.prototype._init_toolbar = function () {\n var fig = this;\n\n var toolbar = document.createElement('div');\n toolbar.classList = 'btn-toolbar';\n this.root.appendChild(toolbar);\n\n function on_click_closure(name) {\n return function (_event) {\n return fig.toolbar_button_onclick(name);\n };\n }\n\n function on_mouseover_closure(tooltip) {\n return function (event) {\n if (!event.currentTarget.disabled) {\n return fig.toolbar_button_onmouseover(tooltip);\n }\n };\n }\n\n fig.buttons = {};\n var buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n var button;\n for (var toolbar_ind in mpl.toolbar_items) {\n var name = mpl.toolbar_items[toolbar_ind][0];\n var tooltip = mpl.toolbar_items[toolbar_ind][1];\n var image = mpl.toolbar_items[toolbar_ind][2];\n var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n if (!name) {\n /* Instead of a spacer, we start a new button group. */\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n buttonGroup = document.createElement('div');\n buttonGroup.classList = 'btn-group';\n continue;\n }\n\n button = fig.buttons[name] = document.createElement('button');\n button.classList = 'btn btn-default';\n button.href = '#';\n button.title = name;\n button.innerHTML = '';\n button.addEventListener('click', on_click_closure(method_name));\n button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n buttonGroup.appendChild(button);\n }\n\n if (buttonGroup.hasChildNodes()) {\n toolbar.appendChild(buttonGroup);\n }\n\n // Add the status bar.\n var status_bar = document.createElement('span');\n status_bar.classList = 'mpl-message pull-right';\n toolbar.appendChild(status_bar);\n this.message = status_bar;\n\n // Add the close button to the window.\n var buttongrp = document.createElement('div');\n buttongrp.classList = 'btn-group inline pull-right';\n button = document.createElement('button');\n button.classList = 'btn btn-mini btn-primary';\n button.href = '#';\n button.title = 'Stop Interaction';\n button.innerHTML = '';\n button.addEventListener('click', function (_evt) {\n fig.handle_close(fig, {});\n });\n button.addEventListener(\n 'mouseover',\n on_mouseover_closure('Stop Interaction')\n );\n buttongrp.appendChild(button);\n var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n titlebar.insertBefore(buttongrp, titlebar.firstChild);\n};\n\nmpl.figure.prototype._remove_fig_handler = function (event) {\n var fig = event.data.fig;\n if (event.target !== this) {\n // Ignore bubbled events from children.\n return;\n }\n fig.close_ws(fig, {});\n};\n\nmpl.figure.prototype._root_extra_style = function (el) {\n el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n};\n\nmpl.figure.prototype._canvas_extra_style = function (el) {\n // this is important to make the div 'focusable\n el.setAttribute('tabindex', 0);\n // reach out to IPython and tell the keyboard manager to turn it's self\n // off when our div gets focus\n\n // location in version 3\n if (IPython.notebook.keyboard_manager) {\n IPython.notebook.keyboard_manager.register_events(el);\n } else {\n // location in version 2\n IPython.keyboard_manager.register_events(el);\n }\n};\n\nmpl.figure.prototype._key_event_extra = function (event, _name) {\n // Check for shift+enter\n if (event.shiftKey && event.which === 13) {\n this.canvas_div.blur();\n // select the cell after this one\n var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n IPython.notebook.select(index + 1);\n }\n};\n\nmpl.figure.prototype.handle_save = function (fig, _msg) {\n fig.ondownload(fig, null);\n};\n\nmpl.find_output_cell = function (html_output) {\n // Return the cell and output element which can be found *uniquely* in the notebook.\n // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n // IPython event is triggered only after the cells have been serialised, which for\n // our purposes (turning an active figure into a static one), is too late.\n var cells = IPython.notebook.get_cells();\n var ncells = cells.length;\n for (var i = 0; i < 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.\nif (IPython.notebook.kernel !== null) {\n IPython.notebook.kernel.comm_manager.register_target(\n 'matplotlib',\n mpl.mpl_figure_comm\n );\n}\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ks = np.argsort(reader.cat0(\"totpartmass\"))[::-1]\n", "k = ks[1]\n", "\n", "\n", "plt.figure()\n", "plt.scatter(reader.dist(False, \"r200\")[k], reader.mass_ratio()[k], c=reader.overlap(False)[k])\n", "plt.colorbar(label=\"Overlap\")\n", "\n", "plt.title(r\"$\\log M_{{\\rm tot}} / M_\\odot = {:.4f}$\".format(np.log10(reader.cat0(\"totpartmass\")[k])))\n", "plt.xlabel(r\"$\\Delta r_i / R_{200c}$\")\n", "plt.ylabel(r\"$|\\log \\dfrac{M_i}{M_{\\rm tot}}|$\")\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 32, "id": "51dd52f0", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T14:49:25.961273Z", "start_time": "2023-03-24T14:27:05.607189Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting: 2023-03-24 14:27:05.644524.\n", "Loaded `clump0`: 2023-03-24 14:41:48.868024.\n", "Loaded `clumpx`: 2023-03-24 14:49:25.871648.\n" ] } ], "source": [ "print(\"Starting: {}.\".format(datetime.now()))\n", "clumps0 = np.load(\"/mnt/extraspace/rstiskalek/csiborg/initmatch/clump_7468_particles.npy\", allow_pickle=True)\n", "print(\"Loaded `clump0`: {}.\".format(datetime.now()))\n", "clumpsx = np.load(\"/mnt/extraspace/rstiskalek/csiborg/initmatch/clump_7588_particles.npy\", allow_pickle=True)\n", "print(\"Loaded `clumpx`: {}.\".format(datetime.now()))\n", "\n", "overlapper = csiborgtools.match.ParticleOverlap()\n", "\n", "hid2clumps0 = {hid: n for n, hid in enumerate(clumps0[\"ID\"])}\n", "hid2clumpsx = {hid: n for n, hid in enumerate(clumpsx[\"ID\"])}" ] }, { "cell_type": "code", "execution_count": 33, "id": "bcef2505", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T15:09:47.965132Z", "start_time": "2023-03-24T15:08:50.141995Z" } }, "outputs": [], "source": [ "# Convert positions to cell IDs\n", "overlapper.clumps_pos2cell(clumps0)\n", "overlapper.clumps_pos2cell(clumpsx)\n", "\n", "mins0, maxs0 = csiborgtools.match.get_halolims(clumps0, overlapper.inv_clength, overlapper.nshift)\n", "minsx, maxsx = csiborgtools.match.get_halolims(clumpsx, overlapper.inv_clength, overlapper.nshift)" ] }, { "cell_type": "code", "execution_count": 34, "id": "e2c24b54", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T15:10:35.352871Z", "start_time": "2023-03-24T15:09:47.966986Z" } }, "outputs": [], "source": [ "delta_bckg = overlapper.make_bckg_delta(clumps0)\n", "delta_bckg = overlapper.make_bckg_delta(clumpsx, delta=delta_bckg)" ] }, { "cell_type": "code", "execution_count": 369, "id": "fb4e8c0a", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T17:04:22.358413Z", "start_time": "2023-03-24T17:01:54.600744Z" } }, "outputs": [], "source": [ "smooth_kwargs = {\"sigma\": 1, \"truncate\": 4, \"mode\": \"constant\", \"cval\": 0.0}\n", "\n", "delta_bckg_smooth = gaussian_filter(delta_bckg, **smooth_kwargs)" ] }, { "cell_type": "code", "execution_count": 363, "id": "bb707fb2", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T17:00:16.973318Z", "start_time": "2023-03-24T17:00:16.011637Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ratio is 0.9820815\n", "Original overlap is 0.6785714\n", "Smoothed overlap is 0.6664124\n" ] }, { "data": { "text/plain": [ "0.32628544480462635" ] }, "execution_count": 363, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# k = 24734 # skull!\n", "\n", "ks = np.argsort(reader.cat0(\"totpartmass\"))[::-1]\n", "# k = ks[1]\n", "k = 331\n", "n = 0\n", "\n", "print(\"Ratio is \", summed_ratio[k])\n", "\n", "print(\"Original overlap is \", overlap_raw[k][n])\n", "print(\"Smoothed overlap is \", overlap_smoothed[k][n])\n", "\n", "index_cl0 = hid2clumps0[reader.cat0(\"index\", k)]\n", "cl0 = clumps0[index_cl0][0]\n", "mins_cl0, maxs_cl0 = mins0[index_cl0], maxs0[index_cl0]\n", "\n", "index_clx = hid2clumpsx[reader.catx(\"index\", reader[\"match_indxs\"][k][n])]\n", "clx = clumpsx[index_clx][0]\n", "mins_clx, maxs_clx = minsx[index_clx], maxsx[index_clx]\n", "\n", "\n", "\n", "delta1, delta2, cellmins, nonzero = overlapper.make_deltas(\n", " cl0, clx, mins_cl0, maxs_cl0, mins_clx, maxs_clx, smooth_kwargs=smooth_kwargs)\n", "\n", "csiborgtools.match.calculate_overlap(delta1, delta2, cellmins, delta_bckg_smooth)" ] }, { "cell_type": "code", "execution_count": 364, "id": "5eeed44f", "metadata": { "ExecuteTime": { "end_time": "2023-03-24T17:00:19.825171Z", "start_time": "2023-03-24T17:00:19.787750Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NGP/smoothed overlap 0.6785714 0.6664124\n", "0.32628544480462635\n", "Sum is 0.32628544480462635\n", "Originally NGP/smoothed was 0.6785714 0.6664124\n" ] } ], "source": [ "xs = []\n", "for n in range(reader[\"match_indxs\"][k].size):\n", "\n", " index_clx = hid2clumpsx[reader.catx(\"index\", reader[\"match_indxs\"][k][n])]\n", " clx = clumpsx[index_clx][0]\n", " mins_clx, maxs_clx = minsx[index_clx], maxsx[index_clx]\n", " \n", " print(\"NGP/smoothed overlap \", overlap_raw[k][n], overlap_smoothed[k][n])\n", " delta1, delta2, cellmins, nonzero1 = overlapper.make_deltas(\n", " cl0, clx, mins_cl0, maxs_cl0, mins_clx, maxs_clx, smooth_kwargs=smooth_kwargs)\n", " \n", " x = csiborgtools.match.calculate_overlap(delta1, delta2, cellmins, delta_bckg_smooth)\n", " print(x)\n", " xs.append(x)\n", " \n", "print(\"Sum is \", sum(xs))\n", "print(\"Originally NGP/smoothed was \", summed_raw[k], summed_smoothed[k])" ] }, { "cell_type": "code", "execution_count": null, "id": "1db1bc57", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5883ecc7", "metadata": { "ExecuteTime": { "end_time": "2023-01-31T17:51:03.510067Z", "start_time": "2023-01-31T17:51:03.469080Z" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a58b300c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "56b90375", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T18:54:51.064154Z", "start_time": "2023-01-28T18:54:47.314086Z" } }, "outputs": [], "source": [ "dlogm = [None] * len(indxs)\n", "mass = [None] * len(indxs)\n", "for k in trange(len(indxs)):\n", " dlogm[k] = np.abs(np.log10(cat[0][\"totpartmass\"][k]) - np.log10(cat[1][\"totpartmass\"][indxs[k]]))\n", " mass[k] = np.ones(indxs[k].size) * cat[0][\"totpartmass\"][k]\n", "dlogm = np.asanyarray(dlogm)\n", "mass = np.asanyarray(mass)" ] }, { "cell_type": "code", "execution_count": null, "id": "e44414b7", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T18:56:19.841434Z", "start_time": "2023-01-28T18:56:19.041227Z" } }, "outputs": [], "source": [ "plt.figure()\n", "plt.scatter(np.concatenate(dlogm), np.concatenate(overlap), s=1, rasterized=True)\n", "t = np.linspace(0, 2)\n", "plt.plot(t, 10**(-t), c=\"red\", label=r\"$10^{-|\\log M_1 / M_2|}$\")\n", "plt.xlabel(r\"$|\\log M_1 / M_2|$\")\n", "plt.ylabel(r\"$\\mathcal{O}$\")\n", "plt.legend()\n", "plt.tight_layout()\n", "# plt.savefig(\"../plots/mass_comparison.png\", dpi=450)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "03cec1b7", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "1cde4797", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T15:19:36.573717Z", "start_time": "2023-01-28T15:19:34.985074Z" } }, "outputs": [], "source": [ "for k in trange(len(indxs)):\n", " if np.any((dlogm[k] > 1.75) & (overlap[k] > 0.15)):\n", " print(k)" ] }, { "cell_type": "code", "execution_count": null, "id": "58b2cb87", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T15:27:57.709539Z", "start_time": "2023-01-28T15:27:57.131915Z" } }, "outputs": [], "source": [ "k = 97788\n", "print(dlogm[k])\n", "print(overlap[k])\n", "n = np.argmax(overlap[k])\n", "\n", "index_cl0 = [cl[1] for cl in clumps0].index(cat[0][k][\"index\"])\n", "cl0 = clumps0[index_cl0][0]\n", "mins_cl0, maxs_cl0 = mins0[index_cl0], maxs0[index_cl0]\n", "\n", "index_clx = [cl[1] for cl in clumpsx].index(cat[1][\"index\"][indxs[k]][n])\n", "clx = clumpsx[index_clx][0]\n", "mins_clx, maxs_clx = minsx[index_clx], maxsx[index_clx]" ] }, { "cell_type": "code", "execution_count": null, "id": "f5193d37", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T15:28:02.049121Z", "start_time": "2023-01-28T15:28:02.016020Z" } }, "outputs": [], "source": [ "delta1, delta2, cellmins = overlapper.make_deltas(cl0, clx, mins_cl0, maxs_cl0, mins_clx, maxs_clx)" ] }, { "cell_type": "code", "execution_count": null, "id": "6e0176db", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T15:28:03.044781Z", "start_time": "2023-01-28T15:28:03.010050Z" } }, "outputs": [], "source": [ "overlapper.overlap(delta1, delta2, cellmins, delta)" ] }, { "cell_type": "code", "execution_count": null, "id": "a3993216", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T15:28:03.662552Z", "start_time": "2023-01-28T15:28:03.630680Z" } }, "outputs": [], "source": [ "delta1.sum() / delta2.sum()" ] }, { "cell_type": "code", "execution_count": null, "id": "120b0b61", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "5170b359", "metadata": { "ExecuteTime": { "end_time": "2023-01-28T15:28:05.961500Z", "start_time": "2023-01-28T15:28:05.857277Z" } }, "outputs": [], "source": [ "plt.figure()\n", "plt.imshow(np.sum(delta1, axis=2))\n", "plt.show()\n", "\n", "plt.figure()\n", "plt.imshow(np.sum(delta2, axis=2))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "adcca1e1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "d74da689", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0989f96e", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.667Z" } }, "outputs": [], "source": [ "ncounter = len(indxs[k])\n", "true_overlap = np.full(ncounter, np.nan)\n", "spherical_overlap = np.full(ncounter, np.nan)\n", "\n", "for n in trange(len(indxs[k])):\n", " clx = clumpsx[[cl[1] for cl in clumpsx].index(cat[1][\"index\"][indxs[k]][n])][0]\n", " \n", " R1 = (3 * cl0.size / (4 * np.pi))**(1./3) * 1 / 2048\n", " R2 = (3 * clx.size / (4 * np.pi))**(1./3) * 1 / 2048\n", " d = np.linalg.norm([np.mean(cl0[p]) - np.mean(clx[p]) for p in ('x', 'y', 'z')])\n", " \n", " spherical_overlap[n] = csiborgtools.match.spherical_overlap(R1, R2, d)\n", " true_overlap[n] = overlapper(cl0, clx, delta)\n", " \n", "# print(true_overlap, spherical_overlap)\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "6007e537", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.668Z" } }, "outputs": [], "source": [ "plt.figure()\n", "plt.scatter(true_overlap, spherical_overlap)\n", "\n", "t = np.linspace(0, 1, 100)\n", "plt.plot(t, t, c=\"k\", ls=\"--\")\n", "\n", "plt.xlabel(\"True overlap\")\n", "plt.ylabel(\"Spherical overlap\")\n", "# plt.xscale(\"log\")\n", "# plt.yscale(\"log\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "fd1a9591", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "2a4062f2", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.670Z" } }, "outputs": [], "source": [ "R1 = (3 * cl0.size / (4 * np.pi))**(1./3) * 1 / 2048\n", "R2 = (3 * clx.size / (4 * np.pi))**(1./3) * 1 / 2048\n", "d = np.linalg.norm([np.mean(cl0[p]) - np.mean(clx[p]) for p in ('x', 'y', 'z')])" ] }, { "cell_type": "code", "execution_count": null, "id": "d2b0dcd5", "metadata": { "ExecuteTime": { "end_time": "2023-01-23T20:52:54.565480Z", "start_time": "2023-01-23T20:52:54.534775Z" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "64634315", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "9cfcc924", "metadata": { "ExecuteTime": { "end_time": "2023-01-23T19:00:54.795153Z", "start_time": "2023-01-23T19:00:54.447475Z" } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a747a632", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.686Z" } }, "outputs": [], "source": [ "box = cat[0].box\n", "maverage = box.box2solarmass(clumps0[2][0][\"M\"][0])\n", "cell = box.box2mpc(1/2048)" ] }, { "cell_type": "code", "execution_count": null, "id": "27bb5c36", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.686Z" } }, "outputs": [], "source": [ "n_sim = 0\n", "import numpy\n", "\n", "R = (3 * cat.cats[n_sim][\"npart\"] / (4 * numpy.pi))**(1./3) * 1 / 2048\n", "R = cat.cats[n_sim].box.box2mpc(R)" ] }, { "cell_type": "code", "execution_count": null, "id": "03a7825f", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.687Z" } }, "outputs": [], "source": [ "# dlogm = [None] * len(indxs)\n", "# for k in trange(len(indxs)):\n", "# dlogm[k] = np.abs(np.log10(cat[0][\"totpartmass\"][k]) - np.log10(cat[1][\"totpartmass\"][indxs[k]]))\n", "# dlogm = np.asanyarray(dlogm)\n", "\n", "normdist = [None] * len(indxs)\n", "masses = [None] * len(indxs)\n", "for k in trange(len(indxs)):\n", " normdist[k] = dist0[k] / ((3 * cat[0][\"totpartmass\"][k] / (4 * np.pi * maverage))**(1/3) * cell)\n", " masses[k] = np.log10(np.ones(indxs[k].size) * cat[0][\"totpartmass\"][k])\n", " \n", "normdist = np.asanyarray(normdist)\n", "masses = np.asanyarray(masses)" ] }, { "cell_type": "code", "execution_count": null, "id": "e0330ca5", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.688Z" }, "scrolled": false }, "outputs": [], "source": [ "plt.figure()\n", "\n", "# plt.scatter(np.concatenate(normdist), np.concatenate(overlap), c=np.concatenate(masses), s=4)\n", "\n", "plt.scatter(np.concatenate(normdist), np.concatenate(masses), c=np.concatenate(overlap), s=4)\n", "\n", "\n", "plt.colorbar()\n", "# plt.xlabel(r\"$z = 0$ normalised separation by $\\hat{R}$\")\n", "# plt.xlabel(r\"Absolute difference in total mass [dex]\")\n", "# plt.xscale(\"log\")\n", "# plt.ylabel(r\"$\\mathcal{O}$\")\n", "plt.xscale(\"log\")\n", "plt.tight_layout()\n", "# plt.savefig(\"../plots/another_view.png\", dpi=450)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "de23a8a1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a6b5e4f8", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "43bc17db", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b4df25af", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0d25a16d", "metadata": { "ExecuteTime": { "start_time": "2023-01-26T09:49:07.690Z" } }, "outputs": [], "source": [ "cl0 = clumps0[[cl[1] for cl in clumps0].index(cat[0][k][\"index\"])][0]\n", "\n", "\n", "\n", "\n", "\n", "\n", "clx = clumpsx[[cl[1] for cl in clumpsx].index(cat[1][\"index\"][indxs[k]][n])][0]" ] }, { "cell_type": "code", "execution_count": null, "id": "be26cbcc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 5 }