{"action":"created","comment":{"url":"https://api.github.com/repos/lbl-anp/becquerel/pulls/comments/204492959","pull_request_review_id":139580066,"id":204492959,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIwNDQ5Mjk1OQ==","diff_hunk":"@@ -0,0 +1,367 @@\n+# -*- coding: utf-8 -*-\n+\n+\"\"\"Spectral peak search using convolutions.\"\"\"\n+\n+from __future__ import print_function\n+import matplotlib.pyplot as plt\n+import numpy as np\n+from .spectrum import Spectrum\n+\n+\n+class PeakFilter(object):\n+    \"\"\"An energy-dependent kernel that can be convolved with a spectrum.\n+\n+    To detect lines, a kernel should have a positive component in the center\n+    and negative wings to subtract the continuum, e.g., a boxcar:\n+\n+    +2|     ┌───┐     |\n+      |     │   │     |\n+     0|─┬───┼───┼───┬─|\n+    -1| └───┘   └───┘ |\n+\n+    The positive part is meant to detect the peak, and the negative part to\n+    sample the continuum under the peak.\n+\n+    The kernel should sum to 0.\n+\n+    The width of the kernel scales proportionally to the square root\n+    of the channels, with a minimum value set by 'fwhm_at_0'.\n+\n+    \"\"\"\n+\n+    def __init__(self, ref_channel, ref_fwhm, fwhm_at_0=1.):\n+        \"\"\"Initialize with a reference line position and FWHM in channels.\"\"\"\n+        assert ref_channel > 0\n+        assert ref_fwhm > 0\n+        self.ref_channel = float(ref_channel)\n+        self.ref_fwhm = float(ref_fwhm)\n+        self.fwhm_at_0 = float(fwhm_at_0)\n+\n+    def fwhm(self, channel):\n+        \"\"\"Calculate the expected FWHM at the given channel.\"\"\"\n+        # f(x)^2 = f0^2 + k x^2\n+        # f1^2 = f0^2 + k x1^2\n+        # k = (f1^2 - f0^2) / x1^2\n+        # f(x)^2 = f0^2 + (f1^2 - f0^2) (x/x1)^2\n+        f0 = self.fwhm_at_0\n+        f1 = self.ref_fwhm\n+        x1 = self.ref_channel\n+        fwhm_sqr = f0**2 + (f1**2 - f0**2) * (channel / x1)**2\n+        return np.sqrt(fwhm_sqr)\n+\n+    def kernel(self, channel, n_channels):\n+        \"\"\"Generate the kernel for the given channel.\"\"\"\n+        raise NotImplementedError\n+\n+    def kernel_matrix(self, n_channels):\n+        \"\"\"Build a matrix of the kernel evaluated at each channel.\"\"\"\n+        kern = np.zeros((n_channels, n_channels))\n+        for j in range(n_channels):\n+            kern[:, j] = self.kernel(j, n_channels)\n+        kern_pos = +1 * kern.clip(0, np.inf)\n+        kern_neg = -1 * kern.clip(-np.inf, 0)\n+        # normalize negative part to be equal to the positive part\n+        kern_neg *= kern_pos.sum(axis=0) / kern_neg.sum(axis=0)\n+        return kern_pos - kern_neg\n+\n+    def plot_matrix(self, n_channels):\n+        \"\"\"Plot the matrix of kernels evaluated across the channels.\"\"\"\n+        kern_mat = self.kernel_matrix(n_channels)\n+        kern_min = kern_mat.min()\n+        kern_max = kern_mat.max()\n+        kern_min = min(kern_min, -1 * kern_max)\n+        kern_max = max(kern_max, -1 * kern_min)\n+\n+        plt.imshow(\n+            kern_mat.T[::-1, :], cmap=plt.get_cmap('bwr'),\n+            vmin=kern_min, vmax=kern_max,\n+            extent=[n_channels, 0, 0, n_channels])\n+        plt.colorbar()\n+        plt.xlabel('Input channel')\n+        plt.ylabel('Output channel')\n+        plt.gca().set_aspect('equal')\n+\n+    def convolve(self, data):\n+        \"\"\"Convolve this kernel with the data.\"\"\"\n+        kern_mat = self.kernel_matrix(len(data))\n+        kern_mat_pos = +1 * kern_mat.clip(0, np.inf)\n+        kern_mat_neg = -1 * kern_mat.clip(-np.inf, 0)\n+        peak_plus_bkg = np.dot(kern_mat_pos, data)\n+        bkg = np.dot(kern_mat_neg, data)\n+        signal = np.dot(kern_mat, data)\n+        noise = np.sqrt(np.dot(kern_mat**2, data))\n+        snr = np.zeros_like(signal)\n+        snr[noise > 0] = signal[noise > 0] / noise[noise > 0]\n+        return peak_plus_bkg, bkg, signal, noise, snr\n+\n+\n+class BoxcarPeakFilter(PeakFilter):\n+    \"\"\"A spectral kernel that is a boxcar with negative wings.\n+\n+    The kernel is proportional to this form:\n+\n+    +2|     ┌───┐     |\n+      |     │   │     |\n+     0|─┬───┼───┼───┬─|\n+    -1| └───┘   └───┘ |\n+\n+    \"\"\"\n+\n+    def kernel(self, channel, n_channels):\n+        \"\"\"Generate the kernel for the given channel.\"\"\"\n+        n_center = int(np.ceil(self.fwhm(channel)))\n+        if n_center % 2 == 0:\n+            n_center += 1\n+        kernel0 = -0.5 * np.ones(n_center)\n+        kernel0 = np.append(kernel0, np.ones(n_center))\n+        kernel0 = np.append(kernel0, -0.5 * np.ones(n_center))\n+        n_side = len(kernel0) // 2\n+        kernel = np.zeros(2 * n_side + n_channels)\n+        kernel[channel:channel + len(kernel0)] = kernel0[:]\n+        kernel = kernel[n_side:-n_side]\n+        assert len(kernel) == n_channels\n+        positive = kernel > 0\n+        negative = kernel < 0\n+        kernel[positive] /= sum(kernel[positive])\n+        kernel[negative] /= -1 * sum(kernel[negative])\n+        return kernel\n+\n+\n+def _gaussian0(x, mean, sigma):\n+    \"\"\"Gaussian function.\"\"\"\n+    z = (x - mean) / sigma\n+    return np.exp(-z**2 / 2.)\n+\n+\n+def _gaussian1(x, mean, sigma):\n+    \"\"\"First derivative of a gaussian.\"\"\"\n+    z = (x - mean)\n+    return -1 * z * _gaussian0(x, mean, sigma)\n+\n+\n+class GaussianPeakFilter(PeakFilter):\n+    \"\"\"A spectral kernel that is the second derivative of a Gaussian.\n+\n+    g0(x, u, s) = 1 / (sqrt(2 pi) s) * exp(-(x-u)^2/(2 s^2))\n+    g1(x, u, s) = -(x - u) / (s^2) * g0\n+    g2(x, u, s) = (x - u)^2 / (s^4) * g0 - (1/s^2) * g0\n+                = g0(x,u,s) ((x-u)^2/s^2 - 1) / s^2\n+\n+    integral(-g2, x=x0..x1)\n+        = (-g1) for x=x0..x1\n+        = g1(x0) - g1(x1)\n+\n+    \"\"\"\n+\n+    def kernel(self, channel, n_channels):\n+        \"\"\"Generate the kernel for the given channel.\"\"\"\n+        fwhm = self.fwhm(channel)\n+        sigma = fwhm / 2.355\n+        edges = np.arange(n_channels + 1)\n+        g1_x0 = _gaussian1(edges[:-1], channel + 0.5, sigma)\n+        g1_x1 = _gaussian1(edges[1:], channel + 0.5, sigma)\n+        kernel = g1_x0 - g1_x1\n+        return kernel\n+\n+\n+class PeakFinderError(Exception):\n+    \"\"\"Base class for errors in PeakFinder.\"\"\"\n+\n+    pass\n+\n+\n+class PeakFinder(object):\n+    \"\"\"Find peaks in a spectrum after convolving it with a kernel.\"\"\"\n+\n+    def __init__(self, spectrum, kernel, min_sep=5, fwhm_tol=(0.5, 1.5)):\n+        \"\"\"Initialize with a spectrum and kernel.\"\"\"\n+        assert min_sep > 0\n+        self.min_sep = min_sep\n+        self.fwhm_tol = tuple(fwhm_tol)\n+        self.spectrum = None\n+        self.kernel = None\n+        self.snr = []\n+        self._peak_plus_bkg = []\n+        self._bkg = []\n+        self._signal = []\n+        self._noise = []\n+        self.channels = []\n+        self.snrs = []\n+        self.fwhms = []\n+        self.integrals = []\n+        self.backgrounds = []\n+        self.calculate(spectrum, kernel)\n+\n+    def reset(self):\n+        \"\"\"Restore peak finder to pristine starting condition.\"\"\"\n+        self.channels = []\n+        self.snrs = []\n+        self.fwhms = []\n+        self.integrals = []\n+        self.backgrounds = []\n+\n+    def sort_by(self, arr):\n+        \"\"\"Sort peaks by the provided array.\"\"\"\n+        assert len(arr) == len(self.channels)\n+        self.channels = np.array(self.channels)\n+        self.snrs = np.array(self.snrs)\n+        self.fwhms = np.array(self.fwhms)\n+        self.integrals = np.array(self.integrals)\n+        self.backgrounds = np.array(self.backgrounds)\n+        i = np.argsort(arr)\n+        self.channels = list(self.channels[i])\n+        self.snrs = list(self.snrs[i])\n+        self.fwhms = list(self.fwhms[i])\n+        self.integrals = list(self.integrals[i])\n+        self.backgrounds = list(self.backgrounds[i])\n+\n+    def calculate(self, spectrum, kernel):\n+        \"\"\"Calculate the convolution of the spectrum with the kernel.\"\"\"\n+        assert isinstance(spectrum, Spectrum)\n+        assert isinstance(kernel, PeakFilter)","path":"becquerel/core/peakfinder.py","position":221,"original_position":221,"commit_id":"db147e675f55c4b9e2fb3573c265961406894096","original_commit_id":"db147e675f55c4b9e2fb3573c265961406894096","user":{"login":"jccurtis","id":1504659,"node_id":"MDQ6VXNlcjE1MDQ2NTk=","avatar_url":"https://avatars1.githubusercontent.com/u/1504659?v=4","gravatar_id":"","url":"https://api.github.com/users/jccurtis","html_url":"https://github.com/jccurtis","followers_url":"https://api.github.com/users/jccurtis/followers","following_url":"https://api.github.com/users/jccurtis/following{/other_user}","gists_url":"https://api.github.com/users/jccurtis/gists{/gist_id}","starred_url":"https://api.github.com/users/jccurtis/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jccurtis/subscriptions","organizations_url":"https://api.github.com/users/jccurtis/orgs","repos_url":"https://api.github.com/users/jccurtis/repos","events_url":"https://api.github.com/users/jccurtis/events{/privacy}","received_events_url":"https://api.github.com/users/jccurtis/received_events","type":"User","site_admin":false},"body":"@markbandstra can you change these from `assert` to raise a `PeakFinderError`?","created_at":"2018-07-23T17:38:26Z","updated_at":"2018-07-23T17:38:27Z","html_url":"https://github.com/lbl-anp/becquerel/pull/93#discussion_r204492959","pull_request_url":"https://api.github.com/repos/lbl-anp/becquerel/pulls/93","author_association":"MEMBER","_links":{"self":{"href":"https://api.github.com/repos/lbl-anp/becquerel/pulls/comments/204492959"},"html":{"href":"https://github.com/lbl-anp/becquerel/pull/93#discussion_r204492959"},"pull_request":{"href":"https://api.github.com/repos/lbl-anp/becquerel/pulls/93"}}},"pull_request":{"url":"https://api.github.com/repos/lbl-anp/becquerel/pulls/93","id":177610882,"node_id":"MDExOlB1bGxSZXF1ZXN0MTc3NjEwODgy","html_url":"https://github.com/lbl-anp/becquerel/pull/93","diff_url":"https://github.com/lbl-anp/becquerel/pull/93.diff","patch_url":"https://github.com/lbl-anp/becquerel/pull/93.patch","issue_url":"https://api.github.com/repos/lbl-anp/becquerel/issues/93","number":93,"state":"open","locked":false,"title":"Autocalibration tools","user":{"login":"markbandstra","id":1197880,"node_id":"MDQ6VXNlcjExOTc4ODA=","avatar_url":"https://avatars1.githubusercontent.com/u/1197880?v=4","gravatar_id":"","url":"https://api.github.com/users/markbandstra","html_url":"https://github.com/markbandstra","followers_url":"https://api.github.com/users/markbandstra/followers","following_url":"https://api.github.com/users/markbandstra/following{/other_user}","gists_url":"https://api.github.com/users/markbandstra/gists{/gist_id}","starred_url":"https://api.github.com/users/markbandstra/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/markbandstra/subscriptions","organizations_url":"https://api.github.com/users/markbandstra/orgs","repos_url":"https://api.github.com/users/markbandstra/repos","events_url":"https://api.github.com/users/markbandstra/events{/privacy}","received_events_url":"https://api.github.com/users/markbandstra/received_events","type":"User","site_admin":false},"body":"This commit adds some basic tools to automatically calibrate a `Spectrum` using minimal information about its gain. The notebook `autocal.ipynb` explains the process, but it amounts to:\r\n\r\n1. Create a `PeakFilter` using a rough FWHM (in channels) at one channel, and use it to instantiate a `PeakFinder`.\r\n2. Use `PeakFinder.find_peaks` to find the highest signal-to-noise peaks in the spectrum.\r\n3. Instantiate an `AutoCalibrator` with a `PeakFinder`.\r\n4. Use `AutoCalibrator.fit` with a list of energies to match to the peak candidates.\r\n\r\nRemaining TODO items:\r\n- [x] Split peak-finding functionality out of `AutoCalibrator` into `PeakFinder`\r\n- [x] Use `PeakFinder` to estimate both mean and FWHM of each line.\r\n- [x] Add tests.\r\n- [x] Update readme for `sim_spec`\r\n- [x] Ensure usability of the API and robustness with other real-world examples.\r\n- [x] Organization: should `autocal` remain in `core` or move to `tools`?\r\n- ~[ ] Integration: integrate autocalibration into a `Spectrum` method?~\r\n","created_at":"2018-03-27T01:45:43Z","updated_at":"2018-07-23T17:38:27Z","closed_at":null,"merged_at":null,"merge_commit_sha":"f3047dc838288e7a1fcc6ad1ce18f3d69c8b59c6","assignee":{"login":"markbandstra","id":1197880,"node_id":"MDQ6VXNlcjExOTc4ODA=","avatar_url":"https://avatars1.githubusercontent.com/u/1197880?v=4","gravatar_id":"","url":"https://api.github.com/users/markbandstra","html_url":"https://github.com/markbandstra","followers_url":"https://api.github.com/users/markbandstra/followers","following_url":"https://api.github.com/users/markbandstra/following{/other_user}","gists_url":"https://api.github.com/users/markbandstra/gists{/gist_id}","starred_url":"https://api.github.com/users/markbandstra/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/markbandstra/subscriptions","organizations_url":"https://api.github.com/users/markbandstra/orgs","repos_url":"https://api.github.com/users/markbandstra/repos","events_url":"https://api.github.com/users/markbandstra/events{/privacy}","received_events_url":"https://api.github.com/users/markbandstra/received_events","type":"User","site_admin":false},"assignees":[{"login":"markbandstra","id":1197880,"node_id":"MDQ6VXNlcjExOTc4ODA=","avatar_url":"https://avatars1.githubusercontent.com/u/1197880?v=4","gravatar_id":"","url":"https://api.github.com/users/markbandstra","html_url":"https://github.com/markbandstra","followers_url":"https://api.github.com/users/markbandstra/followers","following_url":"https://api.github.com/users/markbandstra/following{/other_user}","gists_url":"https://api.github.com/users/markbandstra/gists{/gist_id}","starred_url":"https://api.github.com/users/markbandstra/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/markbandstra/subscriptions","organizations_url":"https://api.github.com/users/markbandstra/orgs","repos_url":"https://api.github.com/users/markbandstra/repos","events_url":"https://api.github.com/users/markbandstra/events{/privacy}","received_events_url":"https://api.github.com/users/markbandstra/received_events","type":"User","site_admin":false}],"requested_reviewers":[],"requested_teams":[],"labels":[{"id":555569345,"node_id":"MDU6TGFiZWw1NTU1NjkzNDU=","url":"https://api.github.com/repos/lbl-anp/becquerel/labels/new%20feature","name":"new 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