def _map(self, i): import maps log.debug('map of {}'.format([getattr(self, v)[i] for v in 'sxyzc'])) kwargs = self.opts(i) x, y, z = self.data(i) o = get_args_from(kwargs, margin = 0.05, width = 10e6, height = None, boundarylat = 50, projection = 'cyl', drawcoastline = 1, drawgrid = 1, drawspecgrid = 1, drawcountries = 0, bluemarble = 0, nightshade = None) m = maps.drawmap(y, x, **o) x, y = m(x, y) if z is None: l, = plt.plot(x, y, **kwargs) else: # linestyle must not be 'none' when plotting 3D if 'linestyle' in kwargs and kwargs['linestyle'] == 'none': kwargs['linestyle'] = ':' o = get_args_from(kwargs, markersize = 6, cbfrac = 0.04, cblabel = self.alabel('z')) p = set_defaults(kwargs, zorder = 100) l = plt.scatter(x, y, c = z, s = o.markersize ** 2, edgecolor = 'none', **p) m = 6.0 dmin, dmax = np.nanmin(z), np.nanmax(z) cticks = ticks.get_ticks(dmin, dmax, m, only_inside = 1) formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(x)) cb = plt.colorbar(fraction = o.cbfrac, pad = 0.01, aspect = 40, ticks = cticks, format = formatter) cb.set_label(o.cblabel) self.legend.append((l, self.llabel(i)))
def _profile(self, i): log.debug('profile of {}'.format([getattr(self, v)[i] for v in 'sxyzc'])) kwargs = self.opts(i) x, y, z = self.data(i) o = get_args_from(kwargs, xerr = 0, yerr = 0) # make x binning xedges, xcenters, xwidths = get_binning(self.bins(i, 'x'), x) # compute avg and std for each x bin xx = xcenters xerr = 0.5 * xwidths if o.xerr else None yy = [] yerr = [] for l, u in zip(xedges[:-1], xedges[1:]): bindata = y[(l <= x) & (x < u)] yy.append(np.mean(bindata)) yerr.append(np.std(bindata)) if not o.yerr: yerr = None pargs = set_defaults(kwargs, capsize = 3, marker = '.', linestyle = 'none') l, _d, _d = plt.errorbar(xx, yy, yerr, xerr, **pargs) self.legend.append((l, self.llabel(i))) self.fit(i, xx, yy, yerr)
_links['last'] = {'title': 'last page', 'href': '%s%s' % (resource_uri(resource), q)} if req.page > 1: q = querydef(req.max_results, req.where, req.sort, req.page - 1) _links['prev'] = {'title': 'previous page', 'href': '%s%s' % (resource_uri(resource), q)} """ return _links #class MyRestHandler(cyclone.web.RequestHandler, python_rest_handler.RestRequestHandler): # data_manager = MongoDBDataManager if __name__ == "__main__": handlers = [ #(r"/jobs/?", CycloneRestHandler, dict(rest_settings=set_defaults("/jobs"))), (r"/jobs/?([A-Za-z0-9]+/?)?", CycloneRestHandler, dict(rest_settings=set_defaults("/jobs"))), #(r"/jobs/?", CycloneRestHandler, dict(rest_settings=set_defaults("/jobs"))), #(r"/jobs/([A-Za-z0-9]+)/?", CycloneRestHandler, dict(rest_settings={})), #(r"/jobs/([A-Za-z0-9]+)/edit/?", CycloneRestHandler, dict(rest_settings={})) #(r"/jobs/?", CycloneRestHandler) ] application = cyclone.web.Application(handlers) log.startLogging(sys.stdout) reactor.listenTCP(8888, application, interface="127.0.0.1") reactor.run()
# IMPORT MIR LIBRARIES sys.path.append('../lib') import librosa import pymir import midiutil # IMPORT OUR MIR FUNCTIONS sys.path.append('functions') import utils import dictionaries import beatDetection import chordPrediction import midiConversion import midiFileCreation show_diagnostics, settings, save = utils.set_defaults() print('\nWelcome to Play With Yourself Music Accompaniment Tool.' '\nType help for a list of valid commands') while (True): cmd = raw_input('\nWhat would you like to do?\n') cmd, show_diagnostics, settings, save = utils.process_command( cmd, show_diagnostics, settings, save) if (cmd == 'load_yes'): UI_instrument_notes = int(settings['inst1']) UI_onset_threshold = float((10 - int(settings['busy'])) / 10.0) UI_instrument_chords = int(settings['inst2']) UI_dynamic_threshold = float(settings['dyn'] / 10.0) UI_instrument_beats = int(settings['inst3']) UI_beat_windowSize = float(settings['window'] / 10.0) #300 msec
def _hist2d(self, i): log.debug('2D histogram of {}'.format([getattr(self, v)[i] for v in 'sxyzc'])) kwargs = self.opts(i) x, y, z = self.data(i) o = get_args_from(kwargs, style = 'color', density = False, log = False, cbfrac = 0.04, cblabel = 'bincontent', levels = 10) filled = 'color' in o.style or ('fill' in o.style) o.update(get_args_from(kwargs, hidezero = o.log or filled, colorbar = filled, clabels = not filled)) # make binnings bins = self.bins(i, 'x') if bins == 0: bins = int(1 + np.log2(len(x))) xedges, xcenters, xwidths = get_binning(bins, x) bins = self.bins(i, 'y') if bins == 0: bins = int(1 + np.log2(len(y))) yedges, ycenters, ywidths = get_binning(bins, y) bincontents, _d1, _d2 = np.histogram2d(x, y, [xedges, yedges]) bincontents = np.transpose(bincontents) assert np.all(_d1 == xedges) assert np.all(_d2 == yedges) # statsbox self.stats_fields2d(i, bincontents, xcenters, ycenters) if o.density: bincontents = get_density2d(bincontents, xwidths, ywidths) if o.hidezero: bincontents[bincontents == 0] = np.nan if o.log: bincontents = np.log10(bincontents) formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(np.power(10, x))) else: formatter = mpl.ticker.FuncFormatter(func = lambda x, i:number_mathformat(x)) if 'color' in o.style: pargs = set_defaults(kwargs, cmap = 'jet', edgecolor = 'none') plt.pcolor(xedges, yedges, ma.array(bincontents, mask = np.isnan(bincontents)), **kwargs) elif 'box' in o.style: pargs = set_defaults(kwargs, color = (1, 1, 1, 0), marker = 's', edgecolor = 'k') n = bincontents.size s = bincontents.reshape(n) s = s / np.nanmax(s) * (72. / 2. * self.w / max(len(xcenters), len(ycenters))) ** 2 xcenters, ycenters = np.meshgrid(xcenters, ycenters) plt.scatter(xcenters.reshape(n), ycenters.reshape(n), s = s, **pargs) elif 'contour' in o.style: pargs = set_defaults(kwargs, cmap = 'jet') if not isinstance(pargs['cmap'], mpl.colors.Colormap): pargs['cmap'] = mpl.cm.get_cmap(pargs['cmap']) if filled: cs = plt.contourf(xcenters, ycenters, bincontents, o.levels, **pargs) else: cs = plt.contour(xcenters, ycenters, bincontents, o.levels, **pargs) if o.clabels: plt.clabel(cs, inline = 1) else: raise ValueError('unknown style ' + o.style) if o.colorbar: m = 6.0 dmin, dmax = np.nanmin(bincontents), np.nanmax(bincontents) if o.log: dmin, dmax = np.ceil(dmin), np.floor(dmax) + 1 step = max(1, np.floor((dmax - dmin) / m)) cticks = np.arange(dmin, dmax, step) else: cticks = ticks.get_ticks(dmin, dmax, m, only_inside = 1) cb = plt.colorbar(fraction = o.cbfrac, pad = 0.01, aspect = 40, ticks = cticks, format = formatter) cb.set_label(o.cblabel)
def _hist1d(self, i): self.plotted_lines = [] log.debug('1D histogram of {}'.format([getattr(self, v)[i] for v in 'sxyzc'])) kwargs = self.opts(i) x, y, z = self.data(i) o = get_args_from(kwargs, density = False, cumulative = 0) o.update(get_args_from(kwargs, style = 'histline' if o.density else 'hist')) err = 0 # o.style.startswith('s') o.update(get_args_from(kwargs, xerr = err, yerr = err, capsize = 3 if err else 0)) bins = self.bins(i, 'x') if bins == 0: bins = int(1 + np.log2(len(x))) binedges, bincenters, binwidths = get_binning(bins, x) bincontents, _d1 = np.histogram(x, binedges) assert np.all(binedges == _d1) binerrors = np.sqrt(bincontents) binerrors[binerrors == 0] = 1 # statsbox self.stats_fields1d(i, x, bincontents, binerrors, binedges) if o.density: bincontents, binerrors = get_density(bincontents, binerrors, binwidths) if o.cumulative: bincontents, binerrors = get_cumulative(bincontents, binerrors, o.cumulative, binwidths if o.density else 1) if 'line' in o.style: x = bincenters y = bincontents else: x, y = get_step_points(bincontents, binedges) if 'fill' in o.style: l, = plt.fill(x, y, **kwargs) elif 'hist' in o.style: l, = plt.plot(x, y, **kwargs) elif 'scat' in o.style: pargs = set_defaults(kwargs, linestyle = '', marker = '.') l, = plt.plot(bincenters, bincontents, **pargs) else: raise ValueError('unknown style: ' + o.style) if o.xerr or o.yerr: pargs = set_defaults(kwargs, capsize = o.capsize, ecolor = 'k' if 'fill' in o.style else l.get_c()) xerr = 0.5 * binwidths if o.xerr else None yerr = binerrors if o.yerr else None plt.errorbar(bincenters, bincontents, yerr, xerr, fmt = None, **pargs) adjust_limits('x', binedges) adjust_limits('y', bincontents + binerrors, marl = 0) self.legend.append((l, self.llabel(i))) self.fit(i, bincenters, bincontents, binerrors)
'parameters': Params.COUNT_PARAMS[:10] + Params.WORD2VEC_PARAMS + [Params.train_algorithm_w2v, Params.null_word, Params.cbow_mean], 'default': '', 'defaults': {} }, 'Doc2Vec': { 'class': Doc2VecVectorizer, 'parameters': Params.COUNT_PARAMS[:10] + Params.WORD2VEC_PARAMS + [ Params.train_algorithm_d2v, Params.dbow_words, Params.dm_mean, Params.dm_concat, Params.dm_tag_count, Params.retrain_count ], 'default': '', 'defaults': { 'token_pattern': r"(?u)[\b\w\w+\b]+|[.,!?();:\"{}\[\]]", 'vector_size': 300, 'window': 8 } } } for item, settings in TRANSFORMERS.iteritems(): TRANSFORMERS[item]['defaults'] = set_defaults(settings['defaults'], settings['parameters']) TRANSFORMERS[item]['parameters'] = set_params_defaults( settings['parameters'], settings['defaults'])