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Double-Couple-Waveform-Inversion.ipynb 245 KiB

3 years ago
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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 270,
  6. "metadata": {
  7. "collapsed": true
  8. },
  9. "outputs": [],
  10. "source": [
  11. "import numpy as num\n",
  12. "from pyrocko import gf, moment_tensor as mtm, trace\n",
  13. "import scipy\n",
  14. "import matplotlib.pyplot as plt\n",
  15. "from pyrocko.gf import ws, LocalEngine, Target, DCSource\n",
  16. "from pyrocko.guts import Object, Float, String, Dict, Int\n",
  17. "from pyrocko import util, pile, model, config, trace, io, pile, catalog\n",
  18. "import time\n",
  19. "import os\n",
  20. "km = 1000."
  21. ]
  22. },
  23. {
  24. "cell_type": "markdown",
  25. "metadata": {},
  26. "source": [
  27. "We load the seismic data as a pyrocko pile and transform it to an array of seismic traces:"
  28. ]
  29. },
  30. {
  31. "cell_type": "code",
  32. "execution_count": 271,
  33. "metadata": {},
  34. "outputs": [
  35. {
  36. "name": "stderr",
  37. "output_type": "stream",
  38. "text": [
  39. "selecting files... done. 57 files selected.\n",
  40. "Looking at files [------------------------------------------------------] 100% \n",
  41. "Scanning files [--------------------------------------------------------] 100% \n"
  42. ]
  43. }
  44. ],
  45. "source": [
  46. "data = pile.make_pile(['aquila_realdata/'])\n",
  47. "traces = data.all()"
  48. ]
  49. },
  50. {
  51. "cell_type": "markdown",
  52. "metadata": {},
  53. "source": [
  54. "The precalculated greensfunction store with the id \"global_2s_25km\" needs to be downloaded in case it doesn't exist in the current directory:"
  55. ]
  56. },
  57. {
  58. "cell_type": "code",
  59. "execution_count": 272,
  60. "metadata": {
  61. "collapsed": true
  62. },
  63. "outputs": [],
  64. "source": [
  65. "store_id = 'global_2s_25km'\n",
  66. "if not os.path.exists(store_id):\n",
  67. " ws.download_gf_store(site='kinherd', store_id=store_id)"
  68. ]
  69. },
  70. {
  71. "cell_type": "markdown",
  72. "metadata": {},
  73. "source": [
  74. "Now we fire up the engine that is the main object to produce the synthetic traces based on the Green's Functions that are safed in the store."
  75. ]
  76. },
  77. {
  78. "cell_type": "code",
  79. "execution_count": 273,
  80. "metadata": {
  81. "collapsed": true
  82. },
  83. "outputs": [],
  84. "source": [
  85. "engine = gf.LocalEngine(store_superdirs=['.']) # The Path to where the gf_store(s) are saved is given to the Engine:\n",
  86. "store = engine.get_store(store_id) # Load the store."
  87. ]
  88. },
  89. {
  90. "cell_type": "markdown",
  91. "metadata": {},
  92. "source": [
  93. "We use the snuffler catalog search for the eq. and initalize a source. This source will be used to retrieve the expected arrival times."
  94. ]
  95. },
  96. {
  97. "cell_type": "code",
  98. "execution_count": 274,
  99. "metadata": {
  100. "collapsed": true
  101. },
  102. "outputs": [],
  103. "source": [
  104. "global_cmt_catalog = catalog.GlobalCMT()\n",
  105. "tmin = util.str_to_time('2009-04-06 00:00:00') # beginning time of query\n",
  106. "tmax = util.str_to_time('2009-04-06 05:59:59') # ending time of query\n",
  107. "events = global_cmt_catalog.get_events(\n",
  108. " time_range=(tmin, tmax),\n",
  109. " magmin=6.)\n",
  110. "\n",
  111. "event = events[0]\n",
  112. "origin = gf.Source(\n",
  113. " lat=event.lat,\n",
  114. " lon=event.lon)\n",
  115. "base_source = gf.MTSource.from_pyrocko_event(event)\n",
  116. "base_source.set_origin(origin.lat, origin.lon)"
  117. ]
  118. },
  119. {
  120. "cell_type": "markdown",
  121. "metadata": {},
  122. "source": [
  123. "Now we setup the optimization and setup a taper and filter, which will be applied to the data and synthetics.\n",
  124. "Also we define the boundaries for the source parameters. "
  125. ]
  126. },
  127. {
  128. "cell_type": "code",
  129. "execution_count": 275,
  130. "metadata": {
  131. "collapsed": true
  132. },
  133. "outputs": [],
  134. "source": [
  135. "taperer = trace.CosFader(xfade=2.0) # Cosine taper with fade in and out of 2s.\n",
  136. "ffreq = 0.05 # Hz, Define Filterfrequency for a lowpass filter\n",
  137. "phase = 'P' # Phase onset to fit\n",
  138. "tmin_fit = 15. # [s] to fit before theo. Phase onset\n",
  139. "tmax_fit = 35. # [s] to fit after theo. Phase onset\n",
  140. "component ='Z'\n",
  141. "# bounds given as (min,max)\n",
  142. "bounds = ((6.2, 6.4), # magnitude\n",
  143. " (100., 140.), # strike [deg.]\n",
  144. " (40., 60.), # dip [deg.]\n",
  145. " (-100, -150.), # rake [deg.]\n",
  146. " (3.*km, 8.*km), # depth [km]\n",
  147. " (-20.*km, 20.*km), # north shift from GCMT [km]\n",
  148. " (-20.*km, 20.*km), # east shift from GCMT [km]\n",
  149. " (-20., 20.)) # timeshift from GCMT [s]\n"
  150. ]
  151. },
  152. {
  153. "cell_type": "markdown",
  154. "metadata": {},
  155. "source": [
  156. "Next follows the loading of the stations and setup of targets.\n",
  157. "We use the term target for a single component of a single station."
  158. ]
  159. },
  160. {
  161. "cell_type": "code",
  162. "execution_count": 276,
  163. "metadata": {
  164. "collapsed": true
  165. },
  166. "outputs": [],
  167. "source": [
  168. "fn_stations = 'aquila_realdata/stations_short.txt'\n",
  169. "stations_list = model.load_stations(fn_stations) # load the stations file\n",
  170. "stations = {}\n",
  171. "for s in stations_list:\n",
  172. " stations[s.network, s.station, s.location] = s\n",
  173. " s.set_channels_by_name(*component.split())\n",
  174. "targets = []\n",
  175. "stations = {}"
  176. ]
  177. },
  178. {
  179. "cell_type": "markdown",
  180. "metadata": {},
  181. "source": [
  182. "Next we define the target, if we would have several components (here only BHZ), we would also iterate over the components for each station."
  183. ]
  184. },
  185. {
  186. "cell_type": "code",
  187. "execution_count": 277,
  188. "metadata": {
  189. "collapsed": true
  190. },
  191. "outputs": [],
  192. "source": [
  193. "stat_lats=[]\n",
  194. "stat_lons=[]\n",
  195. "for station in stations_list: # iterate over all stations\n",
  196. " target = Target(\n",
  197. " lat=station.lat, # station lat.\n",
  198. " lon=station.lon, # station lon.\n",
  199. " store_id=store_id, # The gf-store to be used for this target,\n",
  200. " # we can also employ different gf-stores for different targets.\n",
  201. " interpolation='multilinear', # interp. method between gf cells\n",
  202. " quantity='displacement', # wanted retrieved quantity\n",
  203. " codes=station.nsl() + ('BH'+component,)) # Station and network code\n",
  204. " stat_lats.append(station.lat), stat_lons.append(station.lon)\n",
  205. " targets.append(target) # append all singular targets in a list"
  206. ]
  207. },
  208. {
  209. "cell_type": "markdown",
  210. "metadata": {},
  211. "source": [
  212. "Now the objective function that will be called in the scipy.optimize function\n",
  213. "is defined:"
  214. ]
  215. },
  216. {
  217. "cell_type": "code",
  218. "execution_count": 278,
  219. "metadata": {
  220. "collapsed": true
  221. },
  222. "outputs": [],
  223. "source": [
  224. "def optimization(*params):\n",
  225. " params = num.asarray(params)\n",
  226. " parameter = num.ndarray.tolist(params)\n",
  227. " parameter = [val for sublist in parameter for val in sublist]\n",
  228. " source = DCSource(\n",
  229. " lat=event.lat, # Lat and lon are set to GCMT origin.\n",
  230. " lon=event.lon,\n",
  231. " north_shift=parameter[5], # Optimize for the shift from the GCMT origin\n",
  232. " east_shift=parameter[6], # in east and north direction.\n",
  233. " depth=parameter[4],\n",
  234. " strike=parameter[1],\n",
  235. " dip=parameter[2],\n",
  236. " rake=parameter[3],\n",
  237. " time=event.time-parameter[7],\n",
  238. " magnitude=parameter[0])\n",
  239. " engine = gf.get_engine() # init. the engine\n",
  240. " # The engine is now given a source and the targets:\n",
  241. " response = engine.process(source, targets)\n",
  242. " # And then we reform the response into traces:\n",
  243. " synthetic_traces = response.pyrocko_traces()\n",
  244. " misfit_list = [] # init a list for a all the singular misfits\n",
  245. " norm_list = [] # init a list for a all the singular normalizations\n",
  246. " for to,syn,target in zip(traces, synthetic_traces,targets):\n",
  247. " tp = store.t(Phase, base_source, target)\n",
  248. " tr = to.copy()\n",
  249. " tr.taper(taperer), syn.taper(taperer)\n",
  250. " tr.lowpass(4, ffreq), syn.lowpass(4, ffreq)\n",
  251. " tmin = base_source.time+tp-tmin_fit # start before theor. arrival\n",
  252. " tmax = base_source.time+tp+tmax_fit # end after theor. arrival\n",
  253. " tr.chop(tmin=tmin, tmax=tmax), syn.chop(tmin=tmin, tmax=tmax) # cut the traces to the window that should be optimized\n",
  254. " misfit = num.sqrt(num.sum((tr.ydata-syn.ydata)**2))\n",
  255. " norm = num.sqrt(num.sum(tr.ydata**2))\n",
  256. " misfit_list.append(misfit), norm_list.append(norm) # append the misfit into a list\n",
  257. " global_misfit_normed = num.sqrt(num.nansum((num.asarray(misfit_list))**2) / # sum all the misfits and normalize to get a single minimizable value\n",
  258. " num.nansum((num.asarray(norm_list))**2))\n",
  259. " print global_misfit_normed\n",
  260. " return global_misfit_normed"
  261. ]
  262. },
  263. {
  264. "cell_type": "markdown",
  265. "metadata": {},
  266. "source": [
  267. "Next the the main call is defined.\n",
  268. "Optimize.differential_evolution of scipy is used from scipy. Differential Evolution is stochastic in nature (does not use gradient methods) to find the minimium, and can search large areas of candidate space, but often requires\n",
  269. "larger numbers of function evaluations than conventional gradient based techniques. The scipy solver can easily be exchanged for a method of your favor."
  270. ]
  271. },
  272. {
  273. "cell_type": "code",
  274. "execution_count": 284,
  275. "metadata": {
  276. "collapsed": true
  277. },
  278. "outputs": [],
  279. "source": [
  280. "def solve():\n",
  281. " t = time.time() # start timing\n",
  282. " # bounds given as (min,max)\n",
  283. " bounds = ((6.2, 6.4), # magnitude\n",
  284. " (100., 140.), # strike [deg.]\n",
  285. " (40., 60.), # dip [deg.]\n",
  286. " (-100, -150.), # rake [deg.]\n",
  287. " (3.*km, 8.*km), # depth [km]\n",
  288. " (-20.*km, 20.*km), # north shift from GCMT [km]\n",
  289. " (-20.*km, 20.*km), # east shift from GCMT [km]\n",
  290. " (-20., 20.)) # timeshift from GCMT [s]\n",
  291. " # optimize.differential_evolution of scipy is used for the optim.\n",
  292. " # Differential Evolution is stochastic in nature (does not use gradient methods)\n",
  293. " #to find the minimium, and can search large areas of candidate space, but often requires\n",
  294. " #larger numbers of function evaluations than conventional gradient based techniques.\n",
  295. " # The scipy solver can easily be exchanged.\n",
  296. " result = scipy.optimize.differential_evolution(optimization, bounds=bounds, maxiter=15000,\n",
  297. " tol=0.01)\n",
  298. " elapsed = time.time() - t # get the processing time\n",
  299. " # Now we just print out all information that we like:\n",
  300. " print \"Time elapsed:\", elapsed\n",
  301. " print \"Best model: \"\n",
  302. " print \"magnitude:\", result.x[0], \"strike:\", result.x[1]\n",
  303. " print \"dip:\", result.x[2], \"rake:\", result.x[3], \"depth:\", result.x[4]\n",
  304. " print \"north shift from GCMT in m\", result.x[5], \"east shift from GCMT in m:\"\n",
  305. " print result.x[6], \"time shift from GCMT in s:\", result.x[7]\n",
  306. " return result"
  307. ]
  308. },
  309. {
  310. "cell_type": "markdown",
  311. "metadata": {},
  312. "source": [
  313. "We want to plot the synthetics produced by the best model vs. the data. Therefore we use the result and\n",
  314. "forward calculate synthetics. "
  315. ]
  316. },
  317. {
  318. "cell_type": "code",
  319. "execution_count": 280,
  320. "metadata": {
  321. "collapsed": true
  322. },
  323. "outputs": [],
  324. "source": [
  325. "def plot_traces(result):\n",
  326. " source = DCSource(\n",
  327. " lat=event.lat, # Lat and lon are set to GCMT origin.\n",
  328. " lon=event.lon,\n",
  329. " north_shift=result.x[5], # Optimize for the shift from the GCMT origin\n",
  330. " east_shift=result.x[6], # in east and north direction.\n",
  331. " depth=result.x[4],\n",
  332. " strike=result.x[1],\n",
  333. " dip=result.x[2],\n",
  334. " rake=result.x[3],\n",
  335. " time=event.time-result.x[7],\n",
  336. " magnitude=result.x[0])\n",
  337. " engine = gf.get_engine() # init. the engine\n",
  338. " response = engine.process(source, targets)\n",
  339. " synthetic_traces = response.pyrocko_traces()\n",
  340. " fig, axes1 = plt.subplots(10, squeeze=True, sharex=True)\n",
  341. " fig.subplots_adjust(hspace=0)\n",
  342. " plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)\n",
  343. " i=0\n",
  344. " k=0\n",
  345. " mod = (len(stat_lats) % 10)\n",
  346. " for to,syn,target in zip(traces, synthetic_traces,targets):\n",
  347. " tp = store.t(phase, base_source, target)\n",
  348. " tp_onset = base_source.time+tp\n",
  349. " tr = to.copy() \n",
  350. " tr.taper(taperer), syn.taper(taperer)\n",
  351. " tr.lowpass(4, ffreq), syn.lowpass(4, ffreq)\n",
  352. " tmin = tp_onset-tmin_fit \n",
  353. " tmax = tp_onset+tmax_fit \n",
  354. " if k % 10 == 0 and not k ==0:\n",
  355. " if k==(len(stat_lats)-mod):\n",
  356. " i=mod\n",
  357. " fig, axes1 = plt.subplots(i, squeeze=True, sharex=True)\n",
  358. " fig.subplots_adjust(hspace=0)\n",
  359. " plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)\n",
  360. " i = 0\n",
  361. " tr.chop(tmin=tmin, tmax=tmax),syn.chop(tmin=tmin, tmax=tmax)\n",
  362. " s1=axes1[i].plot(tr.get_xdata(), tr.ydata, color='b')\n",
  363. " s2=axes1[i].plot(syn.get_xdata(),syn.ydata, color='r')\n",
  364. " s3=axes1[i].plot([tp_onset, tp_onset], [num.min(tr.ydata), num.max(tr.ydata)], 'k-', lw=2)\n",
  365. " axes1[i].text(-.2,0.5,str(stations_list[k].nsl()[0])+'.'+str(stations_list[k].nsl()[1]),\n",
  366. " transform=axes1[i].transAxes)\n",
  367. " axes1[i].set_yticklabels([], visible=False)\n",
  368. " axes1[-1].set_xlabel('Time [s]')\n",
  369. " plt.suptitle('Waveform fits for' +' '+ str(phase) +'-Phase and component' +' ' + str(component))\n",
  370. " lgd = plt.legend((s1[0], s2[0], s3[0]), ('Data','Synthetic',str(phase)+'-onset'), loc='upper center', bbox_to_anchor=(0.5, -1.6),\n",
  371. " fancybox=True, shadow=True, ncol=5)\n",
  372. " i = i+1\n",
  373. " k = k+1\n",
  374. " \n",
  375. " plt.show()"
  376. ]
  377. },
  378. {
  379. "cell_type": "code",
  380. "execution_count": 281,
  381. "metadata": {
  382. "collapsed": true
  383. },
  384. "outputs": [],
  385. "source": [
  386. "def plot_snuffler(result):\n",
  387. " source = DCSource(\n",
  388. " lat=event.lat, # Lat and lon are set to GCMT origin.\n",
  389. " lon=event.lon,\n",
  390. " north_shift=result.x[5], # Optimize for the shift from the GCMT origin\n",
  391. " east_shift=result.x[6], # in east and north direction.\n",
  392. " depth=result.x[4],\n",
  393. " strike=result.x[1],\n",
  394. " dip=result.x[2],\n",
  395. " rake=result.x[3],\n",
  396. " time=event.time-result.x[7],\n",
  397. " magnitude=result.x[0])\n",
  398. " engine = gf.get_engine() # init. the engine\n",
  399. " response = engine.process(source, targets)\n",
  400. " synthetic_traces = response.pyrocko_traces()\n",
  401. " syns = []\n",
  402. " trs = []\n",
  403. " \n",
  404. " for to,syn,target in zip(traces, synthetic_traces,targets):\n",
  405. " tp = store.t('P', base_source, target)\n",
  406. " tr = to.copy()\n",
  407. " tr.taper(taperer), syn.taper(taperer)\n",
  408. " tr.lowpass(4, ffreq), syn.lowpass(4, ffreq)\n",
  409. " tmin = base_source.time+tp-tmin_fit # start 15s before theor. arrival\n",
  410. " tmax = base_source.time+tp+tmax_fit # end 15s after theor. arrival\n",
  411. " tr.chop(tmin=tmin, tmax=tmax), syn.chop(tmin=tmin, tmax=tmax)\n",
  412. " trs.append(tr)\n",
  413. " syns.append(syn)\n",
  414. "\n",
  415. " trace.snuffle(trs + syns, stations=stations_list, events=events)"
  416. ]
  417. },
  418. {
  419. "cell_type": "markdown",
  420. "metadata": {},
  421. "source": [
  422. "Next we plot the station distribution with https://matplotlib.org/basemap/"
  423. ]
  424. },
  425. {
  426. "cell_type": "code",
  427. "execution_count": 282,
  428. "metadata": {
  429. "collapsed": true
  430. },
  431. "outputs": [],
  432. "source": [
  433. "def plot_stations():\n",
  434. " from mpl_toolkits.basemap import Basemap\n",
  435. " width = 22000000\n",
  436. " m = Basemap(width=width,height=width,projection='aeqd',\n",
  437. " lat_0=event.lat,lon_0=event.lon)\n",
  438. " stat_x, stat_y = m(stat_lons,stat_lats)\n",
  439. " event_x, event_y = m(event.lon,event.lat)\n",
  440. " m.drawmapboundary(fill_color='#99ffff')\n",
  441. " m.fillcontinents(color='lightgray',zorder=0)\n",
  442. " m.scatter(stat_x,stat_y,10,marker='o',color='k')\n",
  443. " m.scatter(event_x,event_y,30,marker='*',color='r')\n",
  444. " plt.title('Stations (black) for Event(red)', fontsize=12)\n",
  445. " plt.show()"
  446. ]
  447. },
  448. {
  449. "cell_type": "code",
  450. "execution_count": 283,
  451. "metadata": {},
  452. "outputs": [
  453. {
  454. "name": "stdout",
  455. "output_type": "stream",
  456. "text": [
  457. "1.91027214118\n",
  458. "1.00999593342\n",
  459. "2.06274950913\n",
  460. "2.17004613154\n",
  461. "2.0719496048\n",
  462. "1.69690376731\n",
  463. "1.50146345907\n",
  464. "1.85113286929\n",
  465. "1.83859313118\n",
  466. "1.87930201421\n",
  467. "2.29965985152\n",
  468. "1.29076327838\n",
  469. "1.4210761196\n",
  470. "1.99386930416\n",
  471. "1.50131944248\n",
  472. "1.75506743758\n",
  473. "1.34159152874\n",
  474. "1.0835004672\n",
  475. "1.96619047074\n",
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  477. "2.20331687569\n",
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  633. "1.61555754771\n",
  634. "1.53586961335\n",
  635. "1.58898364217\n",
  636. "2.31445695374\n",
  637. "1.84361154195\n",
  638. "0.831681455989\n",
  639. "1.6292933419\n",
  640. "1.58905054385\n",
  641. "1.25497719513\n",
  642. "0.596166330167\n",
  643. "1.73217457777\n",
  644. "0.908919852447\n",
  645. "1.52861851551\n",
  646. "2.10644543269\n",
  647. "1.5689790466\n",
  648. "1.50667076689\n",
  649. "1.78290374993\n",
  650. "1.58814613795\n",
  651. "1.47295886756\n",
  652. "0.704556172691\n",
  653. "1.41536029923\n",
  654. "0.6473601226\n",
  655. "1.83752550752\n",
  656. "0.735607362365\n",
  657. "0.934407870471\n",
  658. "1.46838352376\n",
  659. "1.57231620003\n",
  660. "0.890284167656\n",
  661. "1.35408402013\n",
  662. "1.75024176664\n",
  663. "1.34242139429\n",
  664. "1.44959619049\n",
  665. "0.954733555713\n",
  666. "0.990825219326\n",
  667. "1.55773873101\n",
  668. "1.05413344401\n",
  669. "1.57471714456\n",
  670. "1.46585700746\n",
  671. "1.8974835781\n",
  672. "0.984208223904\n",
  673. "2.03973055456\n",
  674. "1.03483433519\n",
  675. "1.63812067162\n",
  676. "0.823949884464\n",
  677. "1.42147296748\n",
  678. "1.51292038357\n",
  679. "1.20593766847\n",
  680. "2.76728194353\n",
  681. "0.845634244749\n",
  682. "0.716689073316\n",
  683. "0.937392846429\n",
  684. "1.25506884714\n",
  685. "1.36170934834\n",
  686. "1.73234173138\n",
  687. "1.09903604916\n",
  688. "1.57646422194\n",
  689. "1.32676604693\n",
  690. "1.37004658901\n",
  691. "1.08822593262\n",
  692. "1.62131983788\n",
  693. "0.875101827064\n",
  694. "1.77332579848\n",
  695. "1.13502667697\n",
  696. "1.00267568452\n",
  697. "Time elapsed: 8.30075907707\n",
  698. "Best model: \n",
  699. "magnitude: 6.23806228649 strike: 101.654687903\n",
  700. "dip: 46.4696281733 rake: -128.203075297 depth: 3808.8681407\n",
  701. "north shift from GCMT in m 9131.81898409 east shift from GCMT in m:\n",
  702. "2577.63723609 time shift from GCMT in s: 3.71132589151\n"
  703. ]
  704. }
  705. ],
  706. "source": [
  707. "result = solve() # Call of the main function to start optimizing."
  708. ]
  709. },
  710. {
  711. "cell_type": "code",
  712. "execution_count": 285,
  713. "metadata": {},
  714. "outputs": [
  715. {
  716. "data": {
  717. 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nSlO+PFx6KfV2TARcQypDffpATAx8/DFUqnQqap0jiaoGuw65Qu3atdVmYDDm7JVyw+sx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\nK3LNjeW8Xs20eDEcOsTWP9/Fe+/ByJHQsyf85e2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  718. "text/plain": [
  719. "<matplotlib.figure.Figure at 0x7fa92f863490>"
  720. ]
  721. },
  722. "metadata": {},
  723. "output_type": "display_data"
  724. },
  725. {
  726. "data": {
  727. "image/png": 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PlSc1Ffr1g3r1MvSw3X47vPkmrfd+zT/X3p9lIAKnh2/R9irs6NYPvvkGfvwR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\nL1++PB0gIyPjuHZnxcXFNTuer1dkz549v1SkXH5+PgsXLgy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  728. "text/plain": [
  729. "<matplotlib.figure.Figure at 0x7fa92fe6ea10>"
  730. ]
  731. },
  732. "metadata": {},
  733. "output_type": "display_data"
  734. },
  735. {
  736. "data": {
  737. "image/png": 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Ic2nIZKRp06ZqM04Yk7ckzzihCQkcq1qHXw/UoOK6xTRIeSUcoFMn9i/fyqVBW/gnWghO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\n3b4UeML9ZJsNxLufwBuzHpgqIncD3d2hPhMiRBp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  738. "text/plain": [
  739. "<matplotlib.figure.Figure at 0x7fa92ed5df90>"
  740. ]
  741. },
  742. "metadata": {},
  743. "output_type": "display_data"
  744. },
  745. {
  746. "data": {
  747. "image/png": 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qUDADS7/BxNJRLFsGduk38Ow+JGNMoXHttSe2/a0/RnFX+0v4a9k+/kytQ7ly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\nZAAAAABJRU5ErkJggg==\n",
  748. "text/plain": [
  749. "<matplotlib.figure.Figure at 0x7fa92e928e50>"
  750. ]
  751. },
  752. "metadata": {},
  753. "output_type": "display_data"
  754. },
  755. {
  756. "data": {
  757. "image/png": 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6pGHDhmojMJj8KPWGV+/Pgr3bEgmNrsCfZW+i6Z7RmW7/VYNBdFvWk+PLVhNW/5KsD7hk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\n//zjTDbVt69TS8qjLBkZY0x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kxAgMHg80aQIbNvDmvRt544Mi/Pkn1KmTwYajRkGnTnze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\nhQ0boLDwdN+IG26AFSug/lm3mruxeTO0b8/Ulm/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  758. "text/plain": [
  759. "<matplotlib.figure.Figure at 0x7fa92f7b6dd0>"
  760. ]
  761. },
  762. "metadata": {},
  763. "output_type": "display_data"
  764. },
  765. {
  766. "data": {
  767. "image/png": 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MvaJmJDNB6nPgeVVd6JsoIm1FJMZ7XwKoAawNtCOvE8RZwJpgD+7Vzj4C3tfs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\nEqSEEEJolgQpIYQQmvX/1EmR0v+v/tgAAAAASUVORK5CYII=\n",
  768. "text/plain": [
  769. "<matplotlib.figure.Figure at 0x7fa92dd489d0>"
  770. ]
  771. },
  772. "metadata": {},
  773. "output_type": "display_data"
  774. }
  775. ],
  776. "source": [
  777. "plot_traces(result)"
  778. ]
  779. },
  780. {
  781. "cell_type": "code",
  782. "execution_count": 193,
  783. "metadata": {},
  784. "outputs": [
  785. {
  786. "data": {
  787. "image/png": 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Il8y+hJQMhsNV93vruXPFZeVaVxdpp5NQLEZPLHbZtnfeeScf+chH+PZ3vsPwm9/Mkbe+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\nBRtCiCX+B2ZWwSMuwsBlAAAAAElFTkSuQmCC\n",
  788. "text/plain": [
  789. "<matplotlib.figure.Figure at 0x7fa9300c6ed0>"
  790. ]
  791. },
  792. "metadata": {},
  793. "output_type": "display_data"
  794. }
  795. ],
  796. "source": [
  797. "plot_stations()"
  798. ]
  799. },
  800. {
  801. "cell_type": "markdown",
  802. "metadata": {},
  803. "source": [
  804. "Alternatively we can plot using snuffler, which will open in a seperate window."
  805. ]
  806. },
  807. {
  808. "cell_type": "code",
  809. "execution_count": 139,
  810. "metadata": {},
  811. "outputs": [
  812. {
  813. "name": "stderr",
  814. "output_type": "stream",
  815. "text": [
  816. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  817. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  818. " self._module = __import__(self._name)\n",
  819. " File \"/home/asteinbe/.snufflings/test_gradients.py\", line 88, in <module>\n",
  820. " main()\n",
  821. "NameError: name 'main' is not defined\n",
  822. "\n",
  823. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/test_gradients.py\" is broken\n",
  824. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  825. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1678, in load_if_needed\n",
  826. " raise InvalidSnufflingFilename(self._name)\n",
  827. "InvalidSnufflingFilename: cmt\n",
  828. "\n",
  829. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/cmt.py\" is broken\n",
  830. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  831. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  832. " self._module = __import__(self._name)\n",
  833. " File \"/home/asteinbe/.snufflings/setup.py\", line 101, in <module>\n",
  834. " 'install': PassSetup}\n",
  835. " File \"/usr/lib/python2.7/distutils/core.py\", line 139, in setup\n",
  836. " raise SystemExit, gen_usage(dist.script_name) + \"\\nerror: %s\" % msg\n",
  837. "SystemExit: usage: ipykernel_launcher.py [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]\n",
  838. " or: ipykernel_launcher.py --help [cmd1 cmd2 ...]\n",
  839. " or: ipykernel_launcher.py --help-commands\n",
  840. " or: ipykernel_launcher.py cmd --help\n",
  841. "\n",
  842. "error: option -f not recognized\n",
  843. "\n",
  844. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/setup.py\" is broken\n",
  845. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  846. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  847. " self._module = __import__(self._name)\n",
  848. " File \"/home/asteinbe/.snufflings/vtktest/snuffling.py\", line 3, in <module>\n",
  849. " import vtk\n",
  850. "ImportError: No module named vtk\n",
  851. "\n",
  852. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/vtktest/snuffling.py\" is broken\n",
  853. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  854. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  855. " self._module = __import__(self._name)\n",
  856. " File \"/home/asteinbe/.snufflings/grond_insar_okada/snuffling.py\", line 4, in <module>\n",
  857. " import cmt, grondles\n",
  858. " File \"/home/asteinbe/.snufflings/grond_insar_okada/grondles.py\", line 18, in <module>\n",
  859. " import cmtles # noqa\n",
  860. " File \"/home/asteinbe/.snufflings/grond_insar_okada/cmtles.py\", line 119, in <module>\n",
  861. " class OkadaProblem(Problem):\n",
  862. " File \"/home/asteinbe/.snufflings/grond_insar_okada/cmtles.py\", line 140, in OkadaProblem\n",
  863. " base_source = gf.OkadaSource.T()\n",
  864. "AttributeError: 'module' object has no attribute 'OkadaSource'\n",
  865. "\n",
  866. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/grond_insar_okada/snuffling.py\" is broken\n",
  867. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  868. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  869. " self._module = __import__(self._name)\n",
  870. " File \"/home/asteinbe/.snufflings/plotstations.py\", line 2\n",
  871. " f = open('Bmean.txt', 'r')\n",
  872. " ^\n",
  873. "IndentationError: unexpected indent\n",
  874. "\n",
  875. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/plotstations.py\" is broken\n",
  876. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  877. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  878. " self._module = __import__(self._name)\n",
  879. " File \"/home/asteinbe/.snufflings/grond3/snuffling.py\", line 7, in <module>\n",
  880. " reload(cmt)\n",
  881. " File \"/home/asteinbe/.snufflings/grond3/cmt.py\", line 291, in <module>\n",
  882. " class OkadaProblem(Problem):\n",
  883. " File \"/home/asteinbe/.snufflings/grond3/cmt.py\", line 312, in OkadaProblem\n",
  884. " base_source = gf.OkadaSource.T()\n",
  885. "AttributeError: 'module' object has no attribute 'OkadaSource'\n",
  886. "\n",
  887. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/grond3/snuffling.py\" is broken\n",
  888. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  889. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  890. " self._module = __import__(self._name)\n",
  891. " File \"/home/asteinbe/.snufflings/cc_matrix/snuffling.py\", line 8, in <module>\n",
  892. " from pyrocko.gf.seismosizer import Target, SeismosizerTrace\n",
  893. "ImportError: cannot import name SeismosizerTrace\n",
  894. "\n",
  895. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/cc_matrix/snuffling.py\" is broken\n",
  896. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  897. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  898. " self._module = __import__(self._name)\n",
  899. " File \"/home/asteinbe/.snufflings/spec.py\", line 264\n",
  900. " \n",
  901. " ^\n",
  902. "SyntaxError: invalid syntax\n",
  903. "\n",
  904. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/spec.py\" is broken\n",
  905. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  906. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  907. " self._module = __import__(self._name)\n",
  908. " File \"/home/asteinbe/.snufflings/opt.py\", line 20, in <module>\n",
  909. " import bcs\n",
  910. "ImportError: No module named bcs\n",
  911. "\n",
  912. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/opt.py\" is broken\n",
  913. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  914. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1683, in load_if_needed\n",
  915. " for snuffling in self._module.__snufflings__():\n",
  916. " File \"/home/asteinbe/.snufflings/synseis.py\", line 230, in __snufflings__\n",
  917. " return [ Seismosizer_plot() ]\n",
  918. "NameError: global name 'Seismosizer_plot' is not defined\n",
  919. "\n",
  920. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/synseis.py\" is broken\n",
  921. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  922. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1680, in load_if_needed\n",
  923. " self._module = __import__(self._name)\n",
  924. " File \"/home/asteinbe/.snufflings/ST2.py\", line 30, in <module>\n",
  925. " class ParaEditCp_TF_GTTG(Snuffling):\n",
  926. "NameError: name 'Snuffling' is not defined\n",
  927. "\n",
  928. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/ST2.py\" is broken\n",
  929. "opt.py:pyrocko.snuffling - ERROR - Traceback (most recent call last):\n",
  930. " File \"/usr/local/lib/python2.7/dist-packages/pyrocko/snuffling.py\", line 1683, in load_if_needed\n",
  931. " for snuffling in self._module.__snufflings__():\n",
  932. "AttributeError: 'module' object has no attribute '__snufflings__'\n",
  933. "\n",
  934. "opt.py:pyrocko.pile_viewer - WARNING - Snuffling module \"/home/asteinbe/.snufflings/__init__.py\" is broken\n"
  935. ]
  936. }
  937. ],
  938. "source": [
  939. "plot_snuffler(result)"
  940. ]
  941. }
  942. ],
  943. "metadata": {
  944. "kernelspec": {
  945. "display_name": "Python 2",
  946. "language": "python",
  947. "name": "python2"
  948. },
  949. "language_info": {
  950. "codemirror_mode": {
  951. "name": "ipython",
  952. "version": 2
  953. },
  954. "file_extension": ".py",
  955. "mimetype": "text/x-python",
  956. "name": "python",
  957. "nbconvert_exporter": "python",
  958. "pygments_lexer": "ipython2",
  959. "version": "2.7.6"
  960. }
  961. },
  962. "nbformat": 4,
  963. "nbformat_minor": 2
  964. }