{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation and uncertainties -- Two approaches\n", "\n", "Marty Ligare, August 2020" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consider the oft-encountered problem of calibrating an instrument. To be specific, let's consider the simple case in which you are calibrating a spectrometer, using a set of \n", "spectral lines with known wavelengths $\\lambda_i$. You measure the pixel number $p_i$ on the CCD array of the spectrometer for each of the lines, and each of these measurements has an associated uncertainty $\\sigma_i$ (uncertainties in the known wavelenths is assumed to be negligible). Let's also assume that a preliminary analysis suggests that data is well-modeled by a linear relationship between $\\lambda$ and $p$ (it's straightforward to generalize to more complicated relationships).\n", "\n", "In an experiment in which this calibration data is to be used, the value of the pixel number measured for a spectral line of unkown wavelength. Let's call the measured value of the pixel number for this unknown line $p^\\ast$, and the associated uncertainty $\\alpha^\\ast$. \n", "\n", "The question is: How do we determine the best value, including the uncertainty, for the unknown wavelength $\\lambda^\\ast$?\n", "\n", "#### Naive (and incorrect ) approach:\n", "\n", "Since there is good evidence for a linear relationship between $p$ and $\\lambda$, why not\n", "simply fit $\\lambda$ as a function of $p$, and use the linear relationship: \n", "\n", "$$\n", "\\Large\n", "\\lambda^\\star = \\mbox{slope}\\times p^\\star + \\mbox{intercept}\\quad?\n", "$$\n", "\n", "Whle this approach can give a \"quick and dirty\" estimate for $\\lambda^\\ast$, it is fundamentally flawed. All of the standard fitting routines we have used are based on the assumption that the uncertainties are all in the dependent variable. They can't be expected to handle uncertainties in the independent variable corrrectly, and they can't give any information about the uncertainty in the slope, the intercept or $\\lambda^\\ast$. \n", "\n", "#### Discussion of better approach, and correlated uncertainties\n", "\n", "To get good information about the relationship between $\\lambda$ and $p$ we should\n", "fit the function\n", "\n", "$$ \n", "\\Large\n", "p = m\\lambda + b \n", "$$\n", "\n", "to find values of $m$ and $b$, and then invert this function to find\n", "\n", "$$\n", "\\Large\n", "\\lambda^\\ast = \\frac{1}{m}(p^\\ast - b). \n", "$$\n", "\n", "In determining the uncertainty $\\sigma^\\ast$ in the measurement of the unknown wavelength\n", "there is an additional complication: the values of $m$ and $b$ determined by the fitting function are correlated. To understand correlation, consider the following cartoon.\n", "\n", "\n", "\n", "The fact that there is an uncertainty in the slope and the intercept of the best-fit line is captured in the graphic by the fact that there is a range of \"reasonable\" lines from which we determine the \"best\" by minimizing the $\\chi^2$ statistic. In looking at the illustrated\n", "extreme cases of \"reasonable\" lines, we see that the teal line has a low slope, but a relatively high intercept, while the purple line has a high slope, but a relatively low intercept. It is extremely unlikely that the data is fit by a line with a slope as large as that of the purple line, and an intercept as large as the teal line; such a line would lie above all of \n", "the data points. It is in this sense that the uncertainties in the slope and intercept are said to be correlated.\n", "\n", "One other feature to deduce from the cartoon is that a measurement of a pixel value $p^\\ast$\n", "for the unkown spectral line near 1100 will give a relatively small range of \"reasonable\"\n", "values for $\\lambda^\\star$, while a $p^\\ast$ measurement of 1300 will give a much \n", "larger uncertainty in $\\lambda^\\star$.\n", "\n", "In this notebook we will explore two approaches to the quantitative determination of the \n", "uncertainty in values of the wavelength $\\lambda^\\star$ using a model data set.\n", "- In the first approach we will use Monte Carlo methods to simulate data sets that are \n", "statistically equivalent to the calibration data. We won't use any \n", "propagation-of-errors rules, or combination-of-uncertainty rules; we'll just simulate lots of \"experiments\" and look at the spread in the outcomes.\n", "- In the second approach we show how to generalize things when simple rules for uncorrelated uncertainties break down. For example, when uncertainties are correlated,\n", "\n", "$$ \n", "\\Large\n", "\\alpha_\\text{total} \\neq \\sqrt{\\alpha_1^2 + \\alpha_2^2 + \\alpha_3^2 +\\dots}. \n", "$$\n", "\n", "Simple cases of how to handle correlated uncertainties are discussed in Section 7.3 of Hughes and Hase. In this notebook we will use a computer to calculate autmatically quantities like those given in Table 7.2 using information returned by the `optimize.curve_fit` function.\n", "\n", "NOTE: In the notebook below I make the transition from wavelength and pixel to the more general $x$ and $y$:\n", "\n", "$$ \\lambda \\longrightarrow \\verb+x+,\\quad p \\longrightarrow \\verb+y+,\\quad \\alpha_p\\longrightarrow \\verb+u+ $$\n", "$$ p^\\star \\longrightarrow \\verb+ystar+, \\quad \\alpha_{p^\\star} \\longrightarrow \\verb+uystar+ $$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports and Function Definitions" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from scipy import optimize\n", "from scipy import stats\n", "\n", "import numdifftools as nd # Module for numerical evaluation of derivatives\n", " # Installed on Bucknell linux network\n", " # To install on other computers: \n", " # pip install numdifftools\n", "\n", "import matplotlib as mpl \n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Following is an Ipython magic command that puts figures in notebook.\n", "%matplotlib notebook\n", " \n", "# M.L. modifications of matplotlib defaults\n", "# Changes can also be put in matplotlibrc file, \n", "# or effected using mpl.rcParams[]\n", "mpl.style.use('classic') \n", "plt.rc('figure', figsize = (6, 4.5)) # Reduces overall size of figures\n", "plt.rc('axes', labelsize=16, titlesize=14)\n", "plt.rc('figure', autolayout = True) # Adjusts supblot params or new size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Define linear function" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def f(x,m,b):\n", " return x*m + b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear fit to data for $m$ and $b$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data to be fit:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Or: data = np.loadtxt(\"file.dat\") \n", "# Format: [[x1,y1,u1], [x2,y2,u2], ... ] where u1 is uncertainty in y1\n", "data = np.array([[1, 2.947032612427293, 0.5],\n", " [2, 6.168779380682309, 0.5],\n", " [3, 7.1618838821688, 0.5],\n", " [4, 9.590549514954866, 0.5],\n", " [5, 11.20657, 0.5]])\n", "x = data.T[0] # separate x values into single array\n", "y = data.T[1] # separate y alues into single array\n", "u = data.T[2] # separate uncertainties into single array\n", "\n", "ystar = 3.9 # measurement of \"unknown\" (pixel) \n", "uystar = 0.5 # uncertainty in \"unknown\"" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "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. 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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'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0,\n", " 0,\n", " fig.canvas.width / mpl.ratio,\n", " fig.canvas.height / mpl.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", " var cursor = msg['cursor'];\n", " switch (cursor) {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "};\n", "\n", "mpl.figure.prototype.handle_message = function (fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "};\n", "\n", "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "};\n", "\n", "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "};\n", "\n", "mpl.figure.prototype.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", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = 'image/png';\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src\n", " );\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data\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 http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function (e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e) {\n", " e = window.event;\n", " }\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", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys(original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object') {\n", " obj[key] = original[key];\n", " }\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function (event, name) {\n", " var canvas_pos = mpl.findpos(event);\n", "\n", " if (name === 'button_press') {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {\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", "\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.which === this._key) {\n", " return;\n", " } else {\n", " this._key = event.which;\n", " }\n", " }\n", " if (name === 'key_release') {\n", " this._key = null;\n", " }\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which !== 17) {\n", " value += 'ctrl+';\n", " }\n", " if (event.altKey && event.which !== 18) {\n", " value += 'alt+';\n", " }\n", " if (event.shiftKey && event.which !== 16) {\n", " value += 'shift+';\n", " }\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", " return false;\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", " if (name === 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message('toolbar_button', { name: name });\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";/* 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.close = function () {\n", " comm.close();\n", " };\n", " ws.send = function (m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function (msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data']);\n", " });\n", " return ws;\n", "};\n", "\n", "mpl.mpl_figure_comm = function (comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = 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", "};\n", "\n", "mpl.figure.prototype.handle_close = function (fig, msg) {\n", " var width = fig.canvas.width / mpl.ratio;\n", " fig.root.removeEventListener('remove', this._remove_fig_handler);\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", " 'Software | Version |
---|---|
Python | 3.7.8 64bit [GCC 7.5.0] |
IPython | 7.17.0 |
OS | Linux 3.10.0 1062.9.1.el7.x86_64 x86_64 with centos 7.8.2003 Core |
numpy | 1.19.1 |
scipy | 1.5.0 |
numdifftools | 0.9.39 |
matplotlib | 3.3.0 |
Thu Aug 20 16:22:57 2020 EDT |