diff --git a/GraphTest1-Copy1.ipynb b/GraphTest1-Copy1.ipynb deleted file mode 100644 index 66ac4554..00000000 --- a/GraphTest1-Copy1.ipynb +++ /dev/null @@ -1,3486 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 71, - "id": "quality-zimbabwe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(14, 21)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from papyri.crosslink import IngestedBlobs\n", - "\n", - "from papyri.config import ingest_dir\n", - "from papyri.crosslink import TreeReplacer\n", - "from papyri.graphstore import GraphStore\n", - "from json import loads, dumps\n", - "from papyri.take2 import RefInfo\n", - "import json\n", - "\n", - "gs = GraphStore(ingest_dir)\n", - "\n", - "\n", - "key = (\"scipy\", \"1.5.0\", \"module\", \"scipy.signal.filter_design.cheb2ord\")\n", - "key = \"numpy/1.20.0/api/numpy.polyfit\".replace(\"api\", \"module\").split(\"/\")\n", - "data = gs.get(key)\n", - "data = json.loads(data.decode())\n", - "gbr_data = gs.get_backref(key)\n", - "data[\"backrefs\"] = [RefInfo(*x).to_json() for x in gbr_data]\n", - "blob = IngestedBlobs.from_json(data)\n", - "\n", - "\n", - "def cgraph(gs, blob, key):\n", - " nodes_names = [b.path for b in blob.backrefs + blob.refs] + [key[3]]\n", - " # nodes_names = [n for n in nodes_names if n.startswith('numpy')]\n", - "\n", - " raw_edges = []\n", - " for k in blob.backrefs + blob.refs:\n", - " orig = [x[3] for x in gs.get_backref(tuple(k))]\n", - " for o in orig:\n", - " raw_edges.append((k.path, o))\n", - "\n", - " data = {\"nodes\": [], \"links\": []}\n", - "\n", - " nums_ = set()\n", - " edges = list(raw_edges)\n", - " nodes = list(set(nodes_names))\n", - " for a, b in edges:\n", - " if (a not in nodes) or (b not in nodes):\n", - " continue\n", - " nums_.add(a)\n", - " nums_.add(b)\n", - " nums = {x: i for i, x in enumerate(nodes, start=1)}\n", - "\n", - " for i, (from_, to) in enumerate(edges[:N]):\n", - " if from_ not in nodes:\n", - " continue\n", - " if to not in nodes:\n", - " continue\n", - " if key[3] in (to, from_):\n", - " continue\n", - "\n", - " data[\"links\"].append({\"source\": nums[from_], \"target\": nums[to], \"id\": i})\n", - " x = nums.keys()\n", - "\n", - " for node in nodes:\n", - " diam = 8\n", - " if node == key[3]:\n", - " diam = 18\n", - " data[\"nodes\"].append(\n", - " {\n", - " \"id\": nums[node],\n", - " \"val\": diam,\n", - " \"label\": node,\n", - " \"mod\": \".\".join(node.split(\".\")[0:1]),\n", - " }\n", - " )\n", - " return data\n", - "\n", - "\n", - "data = cgraph(gs, blob, key)\n", - "\n", - "\n", - "with open(\"d3.json\", \"w\") as f:\n", - " f.write(dumps(data))\n", - "len(data[\"nodes\"]), len(data[\"links\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "precise-nurse", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\"nodes\": [{\"id\": 1, \"val\": 8, \"label\": \"numpy.polynomial.polynomial.polyfit\", \"mod\": \"numpy\"}, {\"id\": 2, \"val\": 8, \"label\": \"matplotlib.pyplot.show\", \"mod\": \"matplotlib\"}, {\"id\": 3, \"val\": 18, \"label\": \"numpy.polyfit\", \"mod\": \"numpy\"}, {\"id\": 4, \"val\": 8, \"label\": \"matplotlib.pyplot.ylim\", \"mod\": \"matplotlib\"}, {\"id\": 5, \"val\": 8, \"label\": \"numpy.RankWarning\", \"mod\": \"numpy\"}, {\"id\": 6, \"val\": 8, \"label\": \"numpy.polynomial\", \"mod\": \"numpy\"}, {\"id\": 7, \"val\": 8, \"label\": \"papyri\", \"mod\": \"papyri\"}, {\"id\": 8, \"val\": 8, \"label\": \"matplotlib.pyplot\", \"mod\": \"matplotlib\"}, {\"id\": 9, \"val\": 8, \"label\": \"matplotlib\", \"mod\": \"matplotlib\"}, {\"id\": 10, \"val\": 8, \"label\": \"matplotlib.pyplot.plot\", \"mod\": \"matplotlib\"}, {\"id\": 11, \"val\": 8, \"label\": \"fig-numpy.polyfit-1.png\", \"mod\": \"fig-numpy\"}, {\"id\": 12, \"val\": 8, \"label\": \"numpy.poly1d\", \"mod\": \"numpy\"}, {\"id\": 13, \"val\": 8, \"label\": \"numpy.ma.extras.polyfit\", \"mod\": \"numpy\"}, {\"id\": 14, \"val\": 8, \"label\": \"numpy.linalg.lstsq\", \"mod\": \"numpy\"}], \"links\": [{\"source\": 10, \"target\": 10, \"id\": 5}, {\"source\": 10, \"target\": 14, \"id\": 17}, {\"source\": 10, \"target\": 13, \"id\": 25}, {\"source\": 14, \"target\": 14, \"id\": 149}, {\"source\": 14, \"target\": 13, \"id\": 152}, {\"source\": 14, \"target\": 1, \"id\": 159}, {\"source\": 4, \"target\": 13, \"id\": 166}, {\"source\": 12, \"target\": 13, \"id\": 177}, {\"source\": 5, \"target\": 5, \"id\": 187}, {\"source\": 5, \"target\": 13, \"id\": 188}, {\"source\": 5, \"target\": 1, \"id\": 195}, {\"source\": 6, \"target\": 13, \"id\": 196}, {\"source\": 6, \"target\": 12, \"id\": 198}, {\"source\": 6, \"target\": 6, \"id\": 205}, {\"source\": 2, \"target\": 14, \"id\": 234}, {\"source\": 2, \"target\": 13, \"id\": 243}, {\"source\": 8, \"target\": 8, \"id\": 407}, {\"source\": 8, \"target\": 14, \"id\": 422}, {\"source\": 8, \"target\": 13, \"id\": 430}, {\"source\": 9, \"target\": 14, \"id\": 676}, {\"source\": 9, \"target\": 13, \"id\": 685}]}" - ] - } - ], - "source": [ - "cat d3.json" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "postal-seeker", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/bussonniermatthias/dev/papyri'" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwd" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "contained-sixth", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('numpy.fft.fft2', 'numpy.fft.fftn'),\n", - " ('numpy.fft.fft2', 'numpy.fft.fft2'),\n", - " ('numpy.fft.fft2', 'numpy.fft.ifft2'),\n", - " ('numpy.fft.fftn', 'numpy.fft.fftn'),\n", - " ('numpy.fft.fftn', 'numpy.fft.fft2'),\n", - " ('numpy.fft.fftn', 'numpy.fft.fftn'),\n", - " ('numpy.fft.fftn', 'numpy.fft.ifftn'),\n", - " ('numpy.fft.fftn', 'numpy.fft.rfftn'),\n", - " ('numpy.fft.ifftn', 'numpy.fft.fftn'),\n", - " ('numpy.fft.ifftn', 'numpy.fft.ifft2'),\n", - " ('numpy.fft.ifftn', 'numpy.fft.ifftn'),\n", - 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" ('matplotlib', 'scipy.stats._distn_infrastructure.rv_discrete'),\n", - " ('matplotlib', 'scipy.stats._multivariate.invwishart_gen'),\n", - " ('matplotlib', 'scipy.stats._multivariate.multivariate_normal_gen'),\n", - " ('matplotlib', 'scipy.stats._multivariate.wishart_gen'),\n", - " ('matplotlib', 'scipy.stats._stats_mstats_common.linregress'),\n", - " ('matplotlib', 'scipy.stats._stats_mstats_common.siegelslopes'),\n", - " ('matplotlib', 'scipy.stats._stats_mstats_common.theilslopes'),\n", - " ('matplotlib', 'scipy.stats.kde.gaussian_kde'),\n", - " ('matplotlib', 'scipy.stats.kde.gaussian_kde.set_bandwidth'),\n", - " ('matplotlib', 'scipy.stats.morestats._calc_uniform_order_statistic_medians'),\n", - " ('matplotlib', 'scipy.stats.morestats.bayes_mvs'),\n", - " ('matplotlib', 'scipy.stats.morestats.boxcox'),\n", - " ('matplotlib', 'scipy.stats.morestats.boxcox_llf'),\n", - " ('matplotlib', 'scipy.stats.morestats.boxcox_normmax'),\n", - " ('matplotlib', 'scipy.stats.morestats.boxcox_normplot'),\n", - " ('matplotlib', 'scipy.stats.morestats.ppcc_max'),\n", - " ('matplotlib', 'scipy.stats.morestats.ppcc_plot'),\n", - " ('matplotlib', 'scipy.stats.morestats.probplot'),\n", - " ('matplotlib', 'scipy.stats.morestats.yeojohnson'),\n", - " ('matplotlib', 'scipy.stats.morestats.yeojohnson_llf'),\n", - " ('matplotlib', 'scipy.stats.morestats.yeojohnson_normmax'),\n", - " ('matplotlib', 'scipy.stats.morestats.yeojohnson_normplot'),\n", - " ('matplotlib', 'scipy.stats.stats.cumfreq'),\n", - " ('matplotlib', 'scipy.stats.stats.kurtosis'),\n", - " ('matplotlib', 'scipy.stats.stats.relfreq')]" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "raw_edges" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "pleased-motel", - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'SeeAlsoItem' object has no attribute 'ref'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - 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] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\"numpy.ma.core.asanyarray\" in nodes, nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "under-scanner", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'edges'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m nid \u001b[38;5;241m=\u001b[39m [n[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnodes\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m----> 2\u001b[0m tid \u001b[38;5;241m=\u001b[39m [e[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m \u001b[48;5;17mdata\u001b[49m\u001b[48;5;17m[\u001b[49m\u001b[38;5;124;48;5;17m'\u001b[39;49m\u001b[38;5;124;48;5;17medges\u001b[39;49m\u001b[38;5;124;48;5;17m'\u001b[39;49m\u001b[48;5;17m]\u001b[49m]\n\u001b[1;32m 3\u001b[0m sid \u001b[38;5;241m=\u001b[39m [e[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m data[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medges\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mset\u001b[39m(tid) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mset\u001b[39m(nid)\n", - "\u001b[0;31mKeyError\u001b[0m: 'edges'" - ] - } - ], - "source": [ - "nid = [n[\"id\"] for n in data[\"nodes\"]]\n", - "tid = [e[\"target\"] for e in data[\"edges\"]]\n", - "sid = [e[\"source\"] for e in data[\"edges\"]]\n", - "set(tid) - set(nid)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "cheap-transmission", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "edges[:N]" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "authorized-finance", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(50, 82)" - ] - }, - "execution_count": 140, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(data[\"edges\"]), len(data[\"nodes\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "id": "specialized-frame", - "metadata": {}, - "outputs": [], - "source": [ - 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" 'id': 70,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.1187,\n", - " 'x': 30.16653859437014,\n", - " 'location': 'Nashville',\n", - " 'label': 'Roxanne',\n", - " 'geo_cd': '0500000US47037',\n", - " 'size': 100,\n", - " 'company': 'New Age Records',\n", - " 'y': -25.21939895836587},\n", - " {'job_title': 'Historian',\n", - " 'skills': 'Art/History/Writing',\n", - " 'industry': 'Local Government',\n", - " 'betwc': 0.0047,\n", - " 'id': 71,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.0926,\n", - " 'x': 40.88762882994431,\n", - " 'location': 'Charleston',\n", - " 'label': 'Sandy',\n", - " 'geo_cd': '0500000US45019',\n", - " 'size': 100,\n", - " 'company': 'Charleston Historical Society',\n", - " 'y': -25.173956588721147},\n", - " {'job_title': 'Manager',\n", - " 'skills': 'Retail/Management/Sales',\n", - " 'industry': 'Retail',\n", - " 'betwc': 0.0031,\n", - " 'id': 72,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.0877,\n", - " 'x': 18.398359378493687,\n", - " 'location': 'Richmond',\n", - " 'label': 'Tom',\n", - " 'geo_cd': '0500000US51041',\n", - " 'size': 100,\n", - " 'company': 'Marlboro',\n", - " 'y': -35.355053680403245},\n", - " {'job_title': 'Biologist',\n", - " 'skills': 'Biology/Math/Engineering',\n", - " 'industry': 'Pharmaceuticals & Health',\n", - " 'betwc': 0.0147,\n", - " 'id': 73,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.1414,\n", - " 'x': 31.26084494132506,\n", - " 'location': 'Portland',\n", - " 'label': 'Urelia',\n", - " 'geo_cd': '0500000US41051',\n", - " 'size': 100,\n", - " 'company': 'Johnson & Johnson',\n", - " 'y': -33.151793329173834},\n", - " {'job_title': 'Congressman',\n", - " 'skills': 'Leadership/Writing/Law',\n", - " 'industry': 'Federal Government',\n", - " 'betwc': 0.0114,\n", - " 'id': 74,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.1443,\n", - " 'x': 24.17827438023363,\n", - " 'location': 'Denver',\n", - " 'label': 'Vladimir',\n", - " 'geo_cd': '0500000US08001',\n", - " 'size': 100,\n", - " 'company': 'US Government',\n", - " 'y': -29.882111653000244},\n", - " {'job_title': 'Manager',\n", - " 'skills': 'Management/Big Data/SQL',\n", - " 'industry': 'Defense & Aerospace',\n", - " 'betwc': 0.0143,\n", - " 'id': 75,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.1511,\n", - " 'x': 30.43421966420915,\n", - " 'location': 'Denver',\n", - " 'label': 'Wendy',\n", - " 'geo_cd': '0500000US08001',\n", - " 'size': 100,\n", - " 'company': 'Leidos',\n", - " 'y': -29.01820773857611},\n", - " {'job_title': 'College Professor',\n", - " 'skills': 'Leadership/History/Psychic Powers',\n", - " 'industry': 'Education',\n", - " 'betwc': 0.0212,\n", - " 'id': 76,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.1688,\n", - " 'x': 32.96216566809193,\n", - " 'location': 'Phoenix',\n", - " 'label': 'Xavier',\n", - " 'geo_cd': '0500000US04013',\n", - " 'size': 100,\n", - " 'company': 'University of Phoenix',\n", - " 'y': -39.96947152704902},\n", - " {'job_title': 'Data Scientist',\n", - " 'skills': 'Writing/Leadership/Math',\n", - " 'industry': 'Software & Technology',\n", - " 'betwc': 0.0062,\n", - " 'id': 77,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.1077,\n", - " 'x': 41.480566160541855,\n", - " 'location': 'Oakland',\n", - " 'label': 'Yang',\n", - " 'geo_cd': '0500000US06001',\n", - " 'size': 100,\n", - " 'company': 'Yelp',\n", - " 'y': -35.44566672578274},\n", - " {'job_title': 'Web Developer',\n", - " 'skills': 'Python/Biology/Creating Memes',\n", - " 'industry': 'Software & Technology',\n", - " 'betwc': 0.0202,\n", - " 'id': 78,\n", - " 'color': 'rgb(45,45,60)',\n", - " 'eigenc': 0.166,\n", - " 'x': 26.92040839198358,\n", - " 'location': 'Los Angeles',\n", - " 'label': 'Zed',\n", - " 'geo_cd': '0500000US06037',\n", - " 'size': 100,\n", - " 'company': 'Reddit',\n", - " 'y': -31.85508077812874}]}" - ] - }, - "execution_count": 114, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dd" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "continuous-quilt", - 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" },\n", - " {\n", - " \"id\": \"scipy.optimize.tnc.fmin_tnc\",\n", - " \"label\": \"fmin_tnc\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.signal.signaltools.correlate\",\n", - " \"label\": \"correlate\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.optimize.zeros.toms748\",\n", - " \"label\": \"toms748\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.signal.signaltools.choose_conv_method\",\n", - " \"label\": \"choose_conv_method\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.stats._continuous_distns.weibull_min_gen\",\n", - " \"label\": \"weibull_min_gen\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.optimize.nonlin\",\n", - " \"label\": \"nonlin\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.optimize.zeros.RootResults\",\n", - " \"label\": \"RootResults\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " },\n", - " {\n", - " \"id\": \"scipy.signal.filter_design.cheby2\",\n", - " \"label\": \"cheby2\",\n", - " \"x\": 0,\n", - " \"y\": 0,\n", - " \"size\": 1\n", - " }\n", - " ],\n", - " \"edges\": [\n", - " {\n", - " \"source\": \"scipy.signal.signaltools.fftconvolve\",\n", - " \"target\": \"scipy.signal.signaltools.convolve\",\n", - " \"id\": \"scipy.signal.signaltools.fftconvolvescipy.signal.signaltools.convolve\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.optimize.zeros.brentq\",\n", - " \"target\": \"scipy.optimize.tnc.fmin_tnc\",\n", - " \"id\": \"scipy.optimize.zeros.brentqscipy.optimize.tnc.fmin_tnc\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.signal.signaltools.correlate2d\",\n", - " \"target\": \"scipy.signal.signaltools.correlate\",\n", - " \"id\": \"scipy.signal.signaltools.correlate2dscipy.signal.signaltools.correlate\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.signal.signaltools.invres\",\n", - " \"target\": \"scipy.signal.signaltools.unique_roots\",\n", - " \"id\": \"scipy.signal.signaltools.invresscipy.signal.signaltools.unique_roots\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.stats.morestats.levene\",\n", - " \"target\": \"scipy.stats.stats.trim_mean\",\n", - " \"id\": \"scipy.stats.morestats.levenescipy.stats.stats.trim_mean\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.signal.wavelets.morlet\",\n", - " \"target\": \"scipy.signal.wavelets.cwt\",\n", - " \"id\": \"scipy.signal.wavelets.morletscipy.signal.wavelets.cwt\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.optimize.zeros.brentq\",\n", - " \"target\": \"scipy.optimize.optimize.fmin_bfgs\",\n", - " \"id\": \"scipy.optimize.zeros.brentqscipy.optimize.optimize.fmin_bfgs\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.stats._continuous_distns.weibull_max_gen\",\n", - " \"target\": \"scipy.stats._continuous_distns.weibull_min_gen\",\n", - " \"id\": \"scipy.stats._continuous_distns.weibull_max_genscipy.stats._continuous_distns.weibull_min_gen\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.signal.filter_design.cheby1\",\n", - " \"target\": \"scipy.signal.filter_design.cheby2\",\n", - " \"id\": \"scipy.signal.filter_design.cheby1scipy.signal.filter_design.cheby2\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.stats.mstats_basic.moment\",\n", - " \"target\": \"scipy.stats.stats.moment\",\n", - " \"id\": \"scipy.stats.mstats_basic.momentscipy.stats.stats.moment\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.signal.signaltools.choose_conv_method\",\n", - " \"target\": \"scipy.signal.signaltools.convolve\",\n", - " \"id\": \"scipy.signal.signaltools.choose_conv_methodscipy.signal.signaltools.convolve\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.optimize.zeros.toms748\",\n", - " \"target\": \"scipy.optimize.zeros.RootResults\",\n", - " \"id\": \"scipy.optimize.zeros.toms748scipy.optimize.zeros.RootResults\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.optimize._root.root\",\n", - " \"target\": \"scipy.optimize.nonlin\",\n", - " \"id\": \"scipy.optimize._root.rootscipy.optimize.nonlin\"\n", - " },\n", - " {\n", - " \"source\": \"scipy.spatial.distance.wminkowski\",\n", - " \"target\": \"scipy.spatial.distance.minkowski\",\n", - " \"id\": \"scipy.spatial.distance.wminkowskiscipy.spatial.distance.minkowski\"\n", - " }\n", - " ]\n", - "}" - ] - } - ], - "source": [ - "!cat npg.json" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "sacred-vietnamese", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Papyri Prototype.ipynb b/Papyri Prototype.ipynb deleted file mode 100644 index 13fc3dbd..00000000 --- a/Papyri Prototype.ipynb +++ /dev/null @@ -1,869 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 7, - "id": "f5fab7fd-768e-4ec1-afb8-a2b2cd2e137f", - "metadata": {}, - "outputs": [], - "source": [ - "%papyri off" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "42e7f541-1399-4fd3-829a-e8db24c4930c", - "metadata": {}, - "outputs": [], - "source": [ - "import scipy\n", - "import numpy" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3b4f1e1b-a0dc-432e-931b-5914ee197438", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mType:\u001b[0m module\n", - "\u001b[0;31mString form:\u001b[0m \n", - "\u001b[0;31mFile:\u001b[0m ~/miniforge3/envs/papyri/lib/python3.9/site-packages/scipy/__init__.py\n", - "\u001b[0;31mDocstring:\u001b[0m \n", - "SciPy: A scientific computing package for Python\n", - "================================================\n", - "\n", - "Documentation is available in the docstrings and\n", - "online at https://docs.scipy.org.\n", - "\n", - "Contents\n", - "--------\n", - "SciPy imports all the functions from the NumPy namespace, and in\n", - "addition provides:\n", - "\n", - "Subpackages\n", - "-----------\n", - "Using any of these subpackages requires an explicit import. For example,\n", - "``import scipy.cluster``.\n", - "\n", - "::\n", - "\n", - " cluster --- Vector Quantization / Kmeans\n", - " fft --- Discrete Fourier transforms\n", - " fftpack --- Legacy discrete Fourier transforms\n", - " integrate --- Integration routines\n", - " interpolate --- Interpolation Tools\n", - " io --- Data input and output\n", - " linalg --- Linear algebra routines\n", - " linalg.blas --- Wrappers to BLAS library\n", - " linalg.lapack --- Wrappers to LAPACK library\n", - " misc --- Various utilities that don't have\n", - " another home.\n", - " ndimage --- N-D image package\n", - " odr --- Orthogonal Distance Regression\n", - " optimize --- Optimization Tools\n", - " signal --- Signal Processing Tools\n", - " signal.windows --- Window functions\n", - " sparse --- Sparse Matrices\n", - " sparse.linalg --- Sparse Linear Algebra\n", - " sparse.linalg.dsolve --- Linear Solvers\n", - " sparse.linalg.dsolve.umfpack --- :Interface to the UMFPACK library:\n", - " Conjugate Gradient Method (LOBPCG)\n", - " sparse.linalg.eigen --- Sparse Eigenvalue Solvers\n", - " sparse.linalg.eigen.lobpcg --- Locally Optimal Block Preconditioned\n", - " Conjugate Gradient Method (LOBPCG)\n", - " spatial --- Spatial data structures and algorithms\n", - " special --- Special functions\n", - " stats --- Statistical Functions\n", - "\n", - "Utility tools\n", - "-------------\n", - "::\n", - "\n", - " test --- Run scipy unittests\n", - " show_config --- Show scipy build configuration\n", - " show_numpy_config --- Show numpy build configuration\n", - " __version__ --- SciPy version string\n", - " __numpy_version__ --- Numpy version string\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "?scipy" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "53d60ec6-12f3-496f-a12c-055f2d75b5ee", - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.signal.windows import dpss" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "e53a6098-9002-4e55-b29e-ed981aec3f29", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mdpss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msym\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_ratios\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Compute the Discrete Prolate Spheroidal Sequences (DPSS).\n", - "\n", - "DPSS (or Slepian sequences) are often used in multitaper power spectral\n", - "density estimation (see [1]_). The first window in the sequence can be\n", - "used to maximize the energy concentration in the main lobe, and is also\n", - "called the Slepian window.\n", - "\n", - "Parameters\n", - "----------\n", - "M : int\n", - " Window length.\n", - "NW : float\n", - " Standardized half bandwidth corresponding to ``2*NW = BW/f0 = BW*M*dt``\n", - " where ``dt`` is taken as 1.\n", - "Kmax : int | None, optional\n", - " Number of DPSS windows to return (orders ``0`` through ``Kmax-1``).\n", - " If None (default), return only a single window of shape ``(M,)``\n", - " instead of an array of windows of shape ``(Kmax, M)``.\n", - "sym : bool, optional\n", - " When True (default), generates a symmetric window, for use in filter\n", - " design.\n", - " When False, generates a periodic window, for use in spectral analysis.\n", - "norm : {2, 'approximate', 'subsample'} | None, optional\n", - " If 'approximate' or 'subsample', then the windows are normalized by the\n", - " maximum, and a correction scale-factor for even-length windows\n", - " is applied either using ``M**2/(M**2+NW)`` (\"approximate\") or\n", - " a FFT-based subsample shift (\"subsample\"), see Notes for details.\n", - " If None, then \"approximate\" is used when ``Kmax=None`` and 2 otherwise\n", - " (which uses the l2 norm).\n", - "return_ratios : bool, optional\n", - " If True, also return the concentration ratios in addition to the\n", - " windows.\n", - "\n", - "Returns\n", - "-------\n", - "v : ndarray, shape (Kmax, M) or (M,)\n", - " The DPSS windows. Will be 1D if `Kmax` is None.\n", - "r : ndarray, shape (Kmax,) or float, optional\n", - " The concentration ratios for the windows. Only returned if\n", - " `return_ratios` evaluates to True. Will be 0D if `Kmax` is None.\n", - "\n", - "Notes\n", - "-----\n", - "This computation uses the tridiagonal eigenvector formulation given\n", - "in [2]_.\n", - "\n", - "The default normalization for ``Kmax=None``, i.e. window-generation mode,\n", - "simply using the l-infinity norm would create a window with two unity\n", - "values, which creates slight normalization differences between even and odd\n", - "orders. The approximate correction of ``M**2/float(M**2+NW)`` for even\n", - "sample numbers is used to counteract this effect (see Examples below).\n", - "\n", - "For very long signals (e.g., 1e6 elements), it can be useful to compute\n", - "windows orders of magnitude shorter and use interpolation (e.g.,\n", - "`scipy.interpolate.interp1d`) to obtain tapers of length `M`,\n", - "but this in general will not preserve orthogonality between the tapers.\n", - "\n", - ".. versionadded:: 1.1\n", - "\n", - "References\n", - "----------\n", - ".. [1] Percival DB, Walden WT. Spectral Analysis for Physical Applications:\n", - " Multitaper and Conventional Univariate Techniques.\n", - " Cambridge University Press; 1993.\n", - ".. [2] Slepian, D. Prolate spheroidal wave functions, Fourier analysis, and\n", - " uncertainty V: The discrete case. Bell System Technical Journal,\n", - " Volume 57 (1978), 1371430.\n", - ".. [3] Kaiser, JF, Schafer RW. On the Use of the I0-Sinh Window for\n", - " Spectrum Analysis. IEEE Transactions on Acoustics, Speech and\n", - " Signal Processing. ASSP-28 (1): 105-107; 1980.\n", - "\n", - "Examples\n", - "--------\n", - "We can compare the window to `kaiser`, which was invented as an alternative\n", - "that was easier to calculate [3]_ (example adapted from\n", - "`here `_):\n", - "\n", - ">>> import numpy as np\n", - ">>> import matplotlib.pyplot as plt\n", - ">>> from scipy.signal import windows, freqz\n", - ">>> M = 51\n", - ">>> fig, axes = plt.subplots(3, 2, figsize=(5, 7))\n", - ">>> for ai, alpha in enumerate((1, 3, 5)):\n", - "... win_dpss = windows.dpss(M, alpha)\n", - "... beta = alpha*np.pi\n", - "... win_kaiser = windows.kaiser(M, beta)\n", - "... for win, c in ((win_dpss, 'k'), (win_kaiser, 'r')):\n", - "... win /= win.sum()\n", - "... axes[ai, 0].plot(win, color=c, lw=1.)\n", - "... axes[ai, 0].set(xlim=[0, M-1], title=r'$\\alpha$ = %s' % alpha,\n", - "... ylabel='Amplitude')\n", - "... w, h = freqz(win)\n", - "... axes[ai, 1].plot(w, 20 * np.log10(np.abs(h)), color=c, lw=1.)\n", - "... axes[ai, 1].set(xlim=[0, np.pi],\n", - "... title=r'$\\beta$ = %0.2f' % beta,\n", - "... ylabel='Magnitude (dB)')\n", - ">>> for ax in axes.ravel():\n", - "... ax.grid(True)\n", - ">>> axes[2, 1].legend(['DPSS', 'Kaiser'])\n", - ">>> fig.tight_layout()\n", - ">>> plt.show()\n", - "\n", - "And here are examples of the first four windows, along with their\n", - "concentration ratios:\n", - "\n", - ">>> M = 512\n", - ">>> NW = 2.5\n", - ">>> win, eigvals = windows.dpss(M, NW, 4, return_ratios=True)\n", - ">>> fig, ax = plt.subplots(1)\n", - ">>> ax.plot(win.T, linewidth=1.)\n", - ">>> ax.set(xlim=[0, M-1], ylim=[-0.1, 0.1], xlabel='Samples',\n", - "... title='DPSS, M=%d, NW=%0.1f' % (M, NW))\n", - ">>> ax.legend(['win[%d] (%0.4f)' % (ii, ratio)\n", - "... for ii, ratio in enumerate(eigvals)])\n", - ">>> fig.tight_layout()\n", - ">>> plt.show()\n", - "\n", - "Using a standard :math:`l_{\\infty}` norm would produce two unity values\n", - "for even `M`, but only one unity value for odd `M`. This produces uneven\n", - "window power that can be counteracted by the approximate correction\n", - "``M**2/float(M**2+NW)``, which can be selected by using\n", - "``norm='approximate'`` (which is the same as ``norm=None`` when\n", - "``Kmax=None``, as is the case here). Alternatively, the slower\n", - "``norm='subsample'`` can be used, which uses subsample shifting in the\n", - "frequency domain (FFT) to compute the correction:\n", - "\n", - ">>> Ms = np.arange(1, 41)\n", - ">>> factors = (50, 20, 10, 5, 2.0001)\n", - ">>> energy = np.empty((3, len(Ms), len(factors)))\n", - ">>> for mi, M in enumerate(Ms):\n", - "... for fi, factor in enumerate(factors):\n", - "... NW = M / float(factor)\n", - "... # Corrected using empirical approximation (default)\n", - "... win = windows.dpss(M, NW)\n", - "... energy[0, mi, fi] = np.sum(win ** 2) / np.sqrt(M)\n", - "... # Corrected using subsample shifting\n", - "... win = windows.dpss(M, NW, norm='subsample')\n", - "... energy[1, mi, fi] = np.sum(win ** 2) / np.sqrt(M)\n", - "... # Uncorrected (using l-infinity norm)\n", - "... win /= win.max()\n", - "... energy[2, mi, fi] = np.sum(win ** 2) / np.sqrt(M)\n", - ">>> fig, ax = plt.subplots(1)\n", - ">>> hs = ax.plot(Ms, energy[2], '-o', markersize=4,\n", - "... markeredgecolor='none')\n", - ">>> leg = [hs[-1]]\n", - ">>> for hi, hh in enumerate(hs):\n", - "... h1 = ax.plot(Ms, energy[0, :, hi], '-o', markersize=4,\n", - "... color=hh.get_color(), markeredgecolor='none',\n", - "... alpha=0.66)\n", - "... h2 = ax.plot(Ms, energy[1, :, hi], '-o', markersize=4,\n", - "... color=hh.get_color(), markeredgecolor='none',\n", - "... alpha=0.33)\n", - "... if hi == len(hs) - 1:\n", - "... leg.insert(0, h1[0])\n", - "... leg.insert(0, h2[0])\n", - ">>> ax.set(xlabel='M (samples)', ylabel=r'Power / $\\sqrt{M}$')\n", - ">>> ax.legend(leg, ['Uncorrected', r'Corrected: $\\frac{M^2}{M^2+NW}$',\n", - "... 'Corrected (subsample)'])\n", - ">>> fig.tight_layout()\n", - "\u001b[0;31mFile:\u001b[0m ~/miniforge3/envs/papyri/lib/python3.9/site-packages/scipy/signal/windows/_windows.py\n", - "\u001b[0;31mType:\u001b[0m function\n" - ], - "x-vendor/papyri": { - "qualname": "scipy.signal.windows._windows.dpss" - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "?dpss" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "95d6d27b-457c-4fd4-9b25-f8354f7e2e7c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVideo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0membed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmimetype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mhtml_attributes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'controls'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Create a video object given raw data or an URL.\n", - "\n", - "When this object is returned by an input cell or passed to the\n", - "display function, it will result in the video being displayed\n", - "in the frontend.\n", - "\n", - "Parameters\n", - "----------\n", - "data : unicode, str or bytes\n", - " The raw video data or a URL or filename to load the data from.\n", - " Raw data will require passing ``embed=True``.\n", - "\n", - "url : unicode\n", - " A URL for the video. If you specify ``url=``,\n", - " the image data will not be embedded.\n", - "\n", - "filename : unicode\n", - " Path to a local file containing the video.\n", - " Will be interpreted as a local URL unless ``embed=True``.\n", - "\n", - "embed : bool\n", - " Should the video be embedded using a data URI (True) or be\n", - " loaded using a