Source code for openquake.commands.plot

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2022 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
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import os
import gzip
import json
import logging
import shapely
import numpy
from scipy.stats import linregress
from openquake.hazardlib.geo.utils import cross_idl
from openquake.hazardlib.contexts import Effect, get_effect_by_mag
from openquake.hazardlib.calc.filters import getdefault, IntegrationDistance
from openquake.calculators.extract import Extractor, WebExtractor, clusterize


[docs]def import_plt(): if os.environ.get('TEXT'): import plotext as plt else: import matplotlib.pyplot as plt return plt
[docs]def make_figure_hcurves(extractors, what): """ $ oq plot "hcurves?kind=mean&imt=PGA&site_id=0" """ plt = import_plt() fig = plt.figure() got = {} # (calc_id, kind) -> curves for i, ex in enumerate(extractors): hcurves = ex.get(what) for kind in hcurves.kind: arr = getattr(hcurves, kind) got[ex.calc_id, kind] = arr oq = ex.oqparam n_imts = len(hcurves.imt) [site] = hcurves.site_id for j, imt in enumerate(hcurves.imt): imls = oq.imtls[imt] ax = fig.add_subplot(n_imts, 1, j + 1) ax.set_xlabel('%s, site %s, inv_time=%dy' % (imt, site, oq.investigation_time)) ax.set_ylabel('PoE') for ck, arr in got.items(): if (arr == 0).all(): logging.warning('There is a zero curve %s_%s', *ck) ax.loglog(imls, arr.flat, '-', label='%s_%s' % ck) ax.grid(True) ax.legend() return plt
[docs]def make_figure_uhs_cluster(extractors, what): """ $ oq plot "uhs_cluster?k=12" """ plt = import_plt() import matplotlib.cm as cm kstr = what.split('?')[1] k = int(kstr.split('=')[1]) fig, ax = plt.subplots() [ex] = extractors hmaps = ex.get('hmaps?kind=rlzs') rlzs = ex.get('realizations').array labels = [] for p, poe in enumerate(ex.oqparam.poes): for imt in ex.oqparam.imtls: labels.append('%s' % imt) xs = numpy.arange(len(labels)) ax.set_xticks(xs) ax.set_xticklabels(labels) ax.set_ylabel('IML') obs = [getattr(hmaps, 'rlz-%03d' % rlz)[0] for rlz in range(len(rlzs))] arr, cluster = clusterize(numpy.array(obs), rlzs, k) colors = cm.rainbow(numpy.linspace(0, 1, len(arr))) paths = [p.decode('ascii') for p in arr['branch_paths']] for rlz in range(len(rlzs)): # ush-rlz has shape NMP ys = getattr(hmaps, 'rlz-%03d' % rlz)[0].T.flatten() ax.plot(xs, ys, '-', color=colors[cluster[rlz]]) for c, curve in enumerate(arr['centroid']): lbl = '%s:%s' % (c + 1, paths[c]) print(lbl) ax.plot(xs, curve, '--', color=colors[c], label=lbl) ax.grid(True) ax.legend() return plt
[docs]def make_figure_avg_gmf(extractors, what): """ $ oq plot "avg_gmf?imt=PGA" """ plt = import_plt() fig = plt.figure() imt = what.split('=')[1] ax = fig.add_subplot(1, 1, 1) ax.grid(True) ax.set_title('Avg GMF for %s' % imt) ax.set_xlabel('Lon') ax.set_ylabel('Lat') if len(extractors) == 2: # compare two avg_gmf ex1, ex2 = extractors avg_gmf = ex1.get(what) avg_gmf2 = ex2.get(what) gmf = avg_gmf[imt] - avg_gmf2[imt] else: # plot a single avg_gmf [ex] = extractors avg_gmf = ex.get(what) gmf = avg_gmf[imt] coll = ax.scatter(avg_gmf['lons'], avg_gmf['lats'], c=gmf, cmap='jet') plt.colorbar(coll) return plt
[docs]def make_figure_vs30(extractors, what): """ $ oq plot "vs30?" """ plt = import_plt() fig = plt.figure() [ex] = extractors sitecol = ex.get('sitecol') ax = fig.add_subplot(111) ax.grid(True) ax.set_xlabel('vs30 for calculation %d' % ex.calc_id) vs30 = sitecol['vs30'] vs30[numpy.isnan(vs30)] = 0 ax.scatter(sitecol['lon'], sitecol['lat'], c=vs30, cmap='jet') return plt
[docs]def make_figure_hmaps(extractors, what): """ $ oq plot "hmaps?kind=mean&imt=PGA" """ plt = import_plt() fig = plt.figure() ncalcs = len(extractors) if ncalcs > 2: raise RuntimeError('Could not plot more than two calculations at once') elif ncalcs == 2: # plot the differences ex1, ex2 = extractors oq1 = ex1.oqparam oq2 = ex2.oqparam n_poes = len(oq1.poes) assert n_poes == len(oq2.poes) itime = oq1.investigation_time assert oq2.investigation_time == itime sitecol = ex1.get('sitecol') array2 = ex2.get('sitecol').array for name in ('lon', 'lat'): numpy.testing.assert_equal(array2[name], sitecol.array[name]) hmaps1 = ex1.get(what) hmaps2 = ex2.get(what) [imt] = hmaps1.imt assert [imt] == hmaps2.imt [kind] = hmaps1.kind assert hmaps1.kind == [kind] for j, poe in enumerate(oq1.poes): diff = hmaps1[kind][:, 0, j] - hmaps2[kind][:, 0, j] maxdiff = numpy.abs(diff).max() ax = fig.add_subplot(1, n_poes, j + 1) ax.grid(True) ax.set_xlabel('IMT=%s, kind=%s, poe=%s\ncalcs %d-%d, ' 'inv_time=%dy\nmaxdiff=%s' % (imt, kind, poe, ex1.calc_id, ex2.calc_id, itime, maxdiff)) coll = ax.scatter(sitecol['lon'], sitecol['lat'], c=diff, cmap='jet') plt.colorbar(coll) elif ncalcs == 1: # plot the hmap [ex] = extractors oq = ex.oqparam n_poes = len(oq.poes) sitecol = ex.get('sitecol') hmaps = ex.get(what) [imt] = hmaps.imt [kind] = hmaps.kind for j, poe in enumerate(oq.poes): ax = fig.add_subplot(1, n_poes, j + 1) ax.grid(True) ax.set_xlabel('hmap for IMT=%s, kind=%s, poe=%s\ncalculation %d, ' 'inv_time=%dy' % (imt, kind, poe, ex.calc_id, oq.investigation_time)) coll = ax.scatter(sitecol['lon'], sitecol['lat'], c=hmaps[kind][:, 0, j], cmap='jet') plt.colorbar(coll) return plt
[docs]def make_figure_uhs(extractors, what): """ $ oq plot "uhs?kind=mean&site_id=0" """ plt = import_plt() fig = plt.figure() got = {} # (calc_id, kind) -> curves for i, ex in enumerate(extractors): uhs = ex.get(what) for kind in uhs.kind: got[ex.calc_id, kind] = uhs[kind][0] # 1 site oq = ex.oqparam n_poes = len(oq.poes) periods = [imt.period for imt in oq.imt_periods()] imts = [imt.string for imt in oq.imt_periods()] [site] = uhs.site_id for j, poe in enumerate(oq.poes): ax = fig.add_subplot(n_poes, 1, j + 1) ax.set_xlabel('UHS on site %s, poe=%s, inv_time=%dy' % (site, poe, oq.investigation_time)) ax.set_ylabel('g') for ck, arr in got.items(): curve = list(arr[str(poe)][imts]) ax.plot(periods, curve, '-', label='%s_%s' % ck) ax.plot(periods, curve, '.') ax.grid(True) ax.legend() return plt
[docs]def middle(x): # [1, 2, 3] => [1.5, 2.5] return (x[:-1] + x[1:]) / 2.
[docs]def stacked_bar(ax, x, ys, width): cumsum = ys.cumsum(axis=0) for i, y in enumerate(ys): if i > 0: ax.bar(x, y, width, bottom=cumsum[i-1]) else: ax.bar(x, y, width)
[docs]def make_figure_disagg(extractors, what): """ $ oq plot "disagg?kind=Mag&imt=PGA&poe_id=0" """ plt = import_plt() from matplotlib import cm fig = plt.figure() oq = extractors[0].oqparam disagg = extractors[0].get(what) kind = disagg.kind # ex. ('Mag', 'Dist') ndims = len(kind) [sid] = disagg.site_id [imt] = disagg.imt [poe_id] = disagg.poe_id poes = disagg.array[:, ndims:] # from the right columns y = numpy.average(poes, weights=disagg.weights, axis=-1) print(y) bins = getattr(disagg, kind[0]) ncalcs = len(extractors) width = (bins[1] - bins[0]) * 0.5 x = middle(bins) if ncalcs == 1 else middle(bins) - width if ndims == 1: # simple bar chart ax = fig.add_subplot(1, 1, 1) ax.set_xlabel('Disagg%s on site %s, imt=%s, poe_id=%d, inv_time=%dy' % (disagg.kind, sid, imt, poe_id, oq.investigation_time)) ax.set_xlabel(kind[0]) ax.set_xticks(bins) ax.bar(x, y, width) for ex in extractors[1:]: ax.bar(x + width, ex.get(what).array, width) return plt if ncalcs > 1: raise NotImplementedError('Comparison for %s' % disagg.kind) shape = [len(getattr(disagg, ax)) - 1 for ax in kind] # 2D images if ndims == 2: y = y.reshape(shape + [1]) zbins = [''] else: # ndims == 3 y = y.reshape(shape) zbins = getattr(disagg, kind[2]) Z = y.shape[-1] axes = [] for z in range(Z): arr = y[:, :, z] ax = fig.add_subplot(Z, 1, z + 1) axes.append(ax) ax.set_xlabel('%s' % kind[0]) ax.set_ylabel(kind[1]) vbins = getattr(disagg, kind[1]) # vertical bins cmap = cm.get_cmap('jet', 100) extent = bins[0], bins[-1], vbins[0], vbins[-1] im = ax.imshow(arr, cmap=cmap, extent=extent, aspect='auto', vmin=y.min(), vmax=y.max()) # stacked bar chart # stacked_bar(ax, x, y.T, width) # ys = ['%.1f' % y for y in getattr(disagg, kind[1])] # ax.legend(ys) fig.colorbar(im, ax=axes) return plt
[docs]def make_figure_task_info(extractors, what): """ $ oq plot "task_info?kind=classical" """ plt = import_plt() if plt.__name__ == 'plotext': [ex] = extractors [(task_name, task_info)] = ex.get(what).to_dict().items() x = task_info['duration'] mean, std, med = x.mean(), x.std(ddof=1), numpy.median(x) plt.hist(x, bins=50) plt.title("mean=%d+-%d seconds, median=%d" % (mean, std, med)) return plt fig = plt.figure() [ex] = extractors [(task_name, task_info)] = ex.get(what).to_dict().items() x = task_info['duration'] ax = fig.add_subplot(2, 1, 1) mean, std = x.mean(), x.std(ddof=1) ax.hist(x, bins=50, rwidth=0.9) ax.set_title("mean=%d+-%d seconds" % (mean, std)) ax.set_ylabel("tasks=%d" % len(x)) ax = fig.add_subplot(2, 1, 2) arr = numpy.sort(task_info, order='duration') x, y = arr['duration'], arr['weight'] reg = linregress(x, y) ax.plot(x, reg.intercept + reg.slope * x) ax.plot(x, y) ax.set_ylabel("weight") ax.set_xlabel("duration") return plt
[docs]def make_figure_source_data(extractors, what): """ $ oq plot "source_data?taskno=XX" """ plt = import_plt() fig, ax = plt.subplots() [ex] = extractors aw = ex.get(what) x, y = aw.ctimes, aw.weight reg = linregress(x, y) ax.plot(x, reg.intercept + reg.slope * x) ax.plot(x, y) ax.set_xlabel("duration") ax.set_ylabel("weight") return plt
[docs]def make_figure_memory(extractors, what): """ $ oq plot "memory?" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() [ex] = extractors task_info = ex.get('task_info').to_dict() fig, ax = plt.subplots() ax.grid(True) ax.set_xlabel('tasks') ax.set_ylabel('GB') start = 0 for task_name in task_info: mem = task_info[task_name]['mem_gb'] ax.plot(range(start, start + len(mem)), mem, label=task_name) start += len(mem) ax.legend() return plt
[docs]class PolygonPlotter(): """ Add polygons to a given axis object """ def __init__(self, ax): self.ax = ax self.minxs = [] self.maxxs = [] self.minys = [] self.maxys = []
[docs] def add(self, poly, **kw): from openquake.hmtk.plotting.patch import PolygonPatch minx, miny, maxx, maxy = poly.bounds self.minxs.append(minx) self.maxxs.append(maxx) self.minys.append(miny) self.maxys.append(maxy) try: self.ax.add_patch(PolygonPatch(poly, **kw)) except ValueError: # LINESTRING, not POLYGON pass
[docs] def set_lim(self, xs=(), ys=()): if len(xs): self.minxs.append(min(xs)) self.maxxs.append(max(xs)) if len(ys): self.minys.append(min(ys)) self.maxys.append(max(ys)) if self.minxs and self.maxxs: self.ax.set_xlim(min(self.minxs), max(self.maxxs)) if self.minys and self.maxys: self.ax.set_ylim(min(self.minys), max(self.maxys))
[docs]def make_figure_sources(extractors, what): """ $ oq plot "sources?limit=100" $ oq plot "sources?source_id=1&source_id=2" $ oq plot "sources?code=A&code=N" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() [ex] = extractors info = ex.get(what) wkts = gzip.decompress(info.wkt_gz).decode('utf8').split(';') srcs = gzip.decompress(info.src_gz).decode('utf8').split(';') fig, ax = plt.subplots() ax.grid(True) sitecol = ex.get('sitecol') pp = PolygonPlotter(ax) n = 0 tot = 0 psources = [] for rec, srcid, wkt in zip(info, srcs, wkts): if not wkt: logging.warning('No geometries for source id %s', srcid) continue if rec['eff_ruptures']: # not filtered out color = 'green' alpha = .3 n += 1 else: color = 'yellow' alpha = .1 if wkt.startswith('POINT'): psources.append(shapely.wkt.loads(wkt)) else: pp.add(shapely.wkt.loads(wkt), alpha=alpha, color=color) tot += 1 lons = [p.x for p in psources] lats = [p.y for p in psources] ss_lons = lons + list(sitecol['lon']) # sites + sources longitudes ss_lats = lats + list(sitecol['lat']) # sites + sources latitudes if len(ss_lons) > 1 and cross_idl(*ss_lons): ss_lons = [lon % 360 for lon in ss_lons] lons = [lon % 360 for lon in lons] sitecol['lon'] = sitecol['lon'] % 360 ax.plot(sitecol['lon'], sitecol['lat'], '.') ax.plot(lons, lats, 'o') pp.set_lim(ss_lons, ss_lats) ax.set_title('%d/%d sources' % (n, tot)) return plt
[docs]def make_figure_gridded_sources(extractors, what): """ $ oq plot "gridded_sources?task_no=0" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() [ex] = extractors dic = json.loads(ex.get(what).json) # id -> lonlats fig, ax = plt.subplots() ax.grid(True) sitecol = ex.get('sitecol') tot = 0 for lonlats in dic.values(): if len(lonlats) == 2: # not collapsed tot += 1 else: # collapsed tot += len(lonlats) / 2 - 1 ax.plot([lonlats[0]], [lonlats[1]], '*') lons = lonlats[2::2] lats = lonlats[3::2] ax.scatter(lons, lats) ax.plot(sitecol['lon'], sitecol['lat'], '.') ax.set_title('Reduced %d->%d sources' % (tot, len(dic))) # TODO: fix plot around the IDL return plt
[docs]def make_figure_rupture_info(extractors, what): """ $ oq plot "rupture_info?min_mag=6" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() [ex] = extractors info = ex.get(what) fig, ax = plt.subplots() ax.grid(True) n = 0 tot = 0 pp = PolygonPlotter(ax) geoms = gzip.decompress(info['boundaries']).decode('utf8').split('\n') for rec, wkt in zip(info, geoms): poly = shapely.wkt.loads(wkt) if poly.is_valid: pp.add(poly) n += 1 else: print('Invalid %s' % wkt) tot += 1 pp.set_lim() ax.set_title('%d/%d valid ruptures' % (n, tot)) if tot == 1: # print the full geometry print(ex.get('rupture/%d' % rec['rupid']).toml()) return plt
[docs]def make_figure_effect(extractors, what): """ $ oq plot "effect?" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() from matplotlib import cm [ex] = extractors effect = ex.get(what) trts = ex.get('full_lt').trts mag_ticks = effect.mags[::-5] fig = plt.figure() cmap = cm.get_cmap('jet', 100) axes = [] vmin = numpy.log10(effect.array.min()) vmax = numpy.log10(effect.array.max()) for trti, trt in enumerate(trts): ax = fig.add_subplot(len(trts), 1, trti + 1) axes.append(ax) ax.set_xticks(mag_ticks) ax.set_xlabel('Mag') dist_ticks = effect.dist_bins[trt][::10] ax.set_yticks(dist_ticks) ax.set_ylabel(trt) extent = mag_ticks[0], mag_ticks[-1], dist_ticks[0], dist_ticks[-1] im = ax.imshow(numpy.log10(effect[:, :, trti]), cmap=cmap, extent=extent, aspect='auto', vmin=vmin, vmax=vmax) fig.colorbar(im, ax=axes) return plt
[docs]def make_figure_rups_by_mag_dist(extractors, what): """ $ oq plot "rups_by_mag_dist?" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() from matplotlib import cm [ex] = extractors counts = ex.get(what) counts.array = numpy.log10(counts.array + 1) trts = ex.get('full_lt').trts mag_ticks = counts.mags[::-5] fig = plt.figure() cmap = cm.get_cmap('jet', 100) axes = [] vmax = counts.array.max() for trti, trt in enumerate(trts): ax = fig.add_subplot(len(trts), 1, trti + 1) axes.append(ax) ax.set_xticks(mag_ticks) ax.set_xlabel('Mag') dist_ticks = counts.dist_bins[trt][::10] ax.set_yticks(dist_ticks) ax.set_ylabel(trt) extent = mag_ticks[0], mag_ticks[-1], dist_ticks[0], dist_ticks[-1] im = ax.imshow(counts[:, :, trti], cmap=cmap, extent=extent, aspect='auto', vmin=0, vmax=vmax) fig.colorbar(im, ax=axes) return plt
[docs]def make_figure_dist_by_mag(extractors, what): """ $ oq plot "dist_by_mag?" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() [ex] = extractors effect = ex.get('effect') mags = ['%.2f' % mag for mag in effect.mags] fig, ax = plt.subplots() trti = 0 for trt, dists in effect.dist_bins.items(): dic = dict(zip(mags, effect[:, :, trti])) if ex.oqparam.pointsource_distance: pdist = getdefault(ex.oqparam.pointsource_distance, trt) else: pdist = None eff = Effect(dic, dists, pdist) dist_by_mag = eff.dist_by_mag() ax.plot(effect.mags, list(dist_by_mag.values()), label=trt, color='red') if pdist: dist_by_mag = eff.dist_by_mag(eff.collapse_value) ax.plot(effect.mags, list(dist_by_mag.values()), label=trt, color='green') ax.set_xlabel('Mag') ax.set_ylabel('Dist') ax.set_title('Integration Distance at intensity=%s' % eff.zero_value) trti += 1 ax.legend() return plt
[docs]def make_figure_effect_by_mag(extractors, what): """ $ oq plot "effect_by_mag?" """ # NB: matplotlib is imported inside since it is a costly import plt = import_plt() [ex] = extractors gsims_by_trt = ex.get('gsims_by_trt', asdict=True) mags = ex.get('source_mags').array try: effect = ex.get('effect') except KeyError: onesite = ex.get('sitecol').one() maximum_distance = IntegrationDistance(ex.oqparam.maximum_distance) imtls = ex.oqparam.imtls ebm = get_effect_by_mag( mags, onesite, gsims_by_trt, maximum_distance, imtls) effect = numpy.array(list(ebm.values())) fig, ax = plt.subplots() trti = 0 for trt in gsims_by_trt: ax.plot(mags, effect[:, -1, trti], label=trt) ax.set_xlabel('Mag') ax.set_ylabel('Intensity') ax.set_title('Effect at maximum distance') trti += 1 ax.legend() return plt
[docs]def make_figure_agg_curves(extractors, what): """ $ oq plot "agg_curves?kind=mean&loss_type=structural" -1 """ plt = import_plt() fig = plt.figure() got = {} # (calc_id, kind) -> curves for i, ex in enumerate(extractors): aw = ex.get(what + '&absolute=1') if isinstance(aw.json, numpy.ndarray): # from webui js = bytes(aw.json).decode('utf8') else: js = aw.json vars(aw).update(json.loads(js)) agg_curve = aw.array.squeeze() got[ex.calc_id, aw.kind[0]] = agg_curve oq = ex.oqparam periods = aw.return_period ax = fig.add_subplot(1, 1, 1) ax.set_xlabel('risk_inv_time=%dy' % oq.risk_investigation_time) ax.set_ylabel('loss') for ck, arr in got.items(): ax.loglog(periods, agg_curve, '-', label='%s_%s' % ck) ax.loglog(periods, agg_curve, '.') ax.grid(True) ax.legend() return plt
[docs]def make_figure_csq_curves(extractors, what): """ $ oq plot "csq_curves?agg_id=0&loss_type=structural&consequence=losses" -1 """ plt = import_plt() fig = plt.figure() got = {} # (calc_id, limit_state) -> curve for i, ex in enumerate(extractors): aw = ex.get(what) P, C = aw.shape if P < 2: raise RuntimeError('Not enough return periods: %d' % P) for c, csq in enumerate(aw.consequences): if csq in what: got[ex.calc_id, csq] = aw[:, c] oq = ex.oqparam periods = aw.return_period ax = fig.add_subplot(1, 1, 1) ax.set_xlabel('risk_inv_time=%dy' % oq.risk_investigation_time) ax.set_ylabel(csq) for ck, arr in got.items(): ax.loglog(periods, arr, '-', label=ck[0]) ax.loglog(periods, arr, '.') ax.grid(True) ax.legend() return plt
[docs]def make_figure_tot_curves(extractors, what): """ $ oq plot "tot_curves?loss_type=structural&kind=rlz-000&absolute=1" """ return make_figure_agg_curves(extractors, what)
[docs]def plot_wkt(wkt_string): """ Plot a WKT string describing a polygon """ from shapely import wkt plt = import_plt() poly = wkt.loads(wkt_string) coo = numpy.array(poly.exterior.coords) plt.plot(coo[:, 0], coo[:, 1], '-') return plt
[docs]def main(what, calc_id: int = -1, others: int = [], webapi=False, local=False): """ Generic plotter for local and remote calculations. """ if what.startswith('POLYGON'): plt = plot_wkt(what) plt.show() return if what == 'examples': help_msg = ['Examples of possible plots:'] for k, v in globals().items(): if k.startswith('make_figure_'): help_msg.append(v.__doc__) raise SystemExit(''.join(help_msg)) if '?' not in what: raise SystemExit('Missing ? in %r' % what) prefix, rest = what.split('?', 1) if prefix in 'hcurves hmaps' and 'imt=' not in rest: raise SystemExit('Missing imt= in %r' % what) elif prefix == 'uhs' and 'imt=' in rest: raise SystemExit('Invalid IMT in %r' % what) elif prefix in 'hcurves uhs disagg' and 'site_id=' not in rest: what += '&site_id=0' if prefix == 'disagg' and 'poe_id=' not in rest: what += '&poe_id=0' if local: xs = [WebExtractor(calc_id, 'http://localhost:8800', '')] for other_id in others: xs.append(WebExtractor(other_id), 'http://localhost:8800', '') elif webapi: xs = [WebExtractor(calc_id)] for other_id in others: xs.append(WebExtractor(other_id)) else: xs = [Extractor(calc_id)] for other_id in others: xs.append(Extractor(other_id)) make_figure = globals()['make_figure_' + prefix] plt = make_figure(xs, what) plt.show()
main.what = 'what to extract (try examples)' main.calc_id = 'computation ID' main.others = dict(help='IDs of other computations', nargs='*') main.webapi = 'if given, pass through the WebAPI' main.local = 'if passed, use the local WebAPI'