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for i,train in enumerate(spike_trains): s = spikes_to_signal_1D(fs, train, tmax) signals[:,i] = s | signals = [_spikes_to_signal(fs, train, tmax) for train in spike_trains] signals = np.array(signals).T | def spikes_to_signal(fs, spike_trains, tmax=None): """ Convert spike trains to theirs time functions. fs: sampling frequency (Hz) spike_trains: trains of spikes to be converted (ms) tmax: length of the output signal (ms) return: time signal >>> spikes_to_signal(10, [np.array([100]), np.array([200, 300])]) array([[ 0... |
plot.yrange = (0, len(spike_trains)-1) | plot.yrange = (-0.5, len(spike_trains)-0.5) | def plot_raster(spike_trains, plot=None, **style): """ Plot raster plot. """ import biggles # Compute trial number L = [ len(train) for train in spike_trains ] r = np.arange(len(spike_trains)) n = np.repeat(r, L) # Spike timings s = np.concatenate(tuple(spike_trains)) c = biggles.Points(s, n, type='dot') c.style(**... |
(array([ 1., 1.]), array([ 1.5, 2.5])) | (array([ 1., 1.]), array([ 1., 2., 3.])) | def calc_isih(spike_trains, bin_size=1, trial_num=None): """ Calculate inter-spike interval histogram. >>> spikes = [np.array([1,2,3]), np.array([2,5,8])] >>> calc_isih(spikes) (array([ 1., 1.]), array([ 1.5, 2.5])) """ isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate(isi_trains) ... |
entrainment_window = (bins > stim_period/2) & (bins < stim_period*3/2) entrainment = sum(isih[entrainment_window])/sum(isih) | entrainment_window = (bins[:-1] > stim_period/2) & (bins[:-1] < stim_period*3/2) entrainment = np.sum(isih[entrainment_window]) / np.sum(isih) | def calc_entrainment(spike_trains, fstim, bin_size=1): """ Calculate entrainment of spike_trains. >>> spike_trains = [np.array([2, 4, 6]), np.array([0, 5, 10])] >>> calc_entrainment(spike_trains, fstim=500) 0.5 """ isih, bins = calc_isih(spike_trains, bin_size=bin_size) if len(isih) == 0: return 0 stim_period = 1000... |
sac = calc_shuffled_autocorrelation | calc_sac = calc_shuffled_autocorrelation | def calc_shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): """ Calculate Shuffled Autocorrelogram (Joris 2006) >>> a = [np.array([1, 2, 3]), np.array([1, 2.01, 2.5])] >>> calc_shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) (array([-1.2, -0... |
rcv_proc.join() | def run_workers(sender, worker, receiver, nproc=None): if nproc is None: nproc = multiprocessing.cpu_count() task_queue = multiprocessing.Queue() done_queue = multiprocessing.Queue() # Receiver process rcv_proc = multiprocessing.Process(target=receiver, args=(done_queue,)) rcv_proc.start() # Worker processes work_p... | |
def plot_raster(spike_trains, axis=None, **kwargs): """ Plot raster plot. """ | def plot_raster(spike_trains, axis=None, style='k,', **kwargs): """ Plot raster plot. """ | def plot_raster(spike_trains, axis=None, **kwargs): """ Plot raster plot. """ # Compute trial number L = [ len(train) for train in spike_trains ] r = np.arange(len(spike_trains)) n = np.repeat(r, L) # Spike timings s = np.concatenate(tuple(spike_trains)) if axis == None: axis = plt.gca() axis.plot(s, n, 'k,', **kwa... |
axis.plot(s, n, 'k,', **kwargs) | axis.plot(s, n, style, **kwargs) | def plot_raster(spike_trains, axis=None, **kwargs): """ Plot raster plot. """ # Compute trial number L = [ len(train) for train in spike_trains ] r = np.arange(len(spike_trains)) n = np.repeat(r, L) # Spike timings s = np.concatenate(tuple(spike_trains)) if axis == None: axis = plt.gca() axis.plot(s, n, 'k,', **kwa... |
def plot_psth(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): | def plot_psth(spike_trains, bin_size=1, trial_num=None, axis=None, style='k', drawstyle='steps-mid', **kwargs): | def plot_psth(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): """ Plots PSTH of spike_trains. spike_trains: list of spike trains bin_size: bin size in ms trial_num: total number of trials axis: axis to draw on **kwargs: plt.plot arguments """ all_spikes = np.concatenate(tuple(spike_trains)) nbins = np... |
axis.plot(bins[:-1], values, **kwargs) axis.set_xlabel("Time [ms]") axis.set_ylabel("Spikes per second") | do_show = True else: do_show = False axis.plot(bins[:-1], values, style, drawstyle=drawstyle, **kwargs) axis.set_xlabel("Time [ms]") axis.set_ylabel("Spikes per second") if do_show: | def plot_psth(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): """ Plots PSTH of spike_trains. spike_trains: list of spike trains bin_size: bin size in ms trial_num: total number of trials axis: axis to draw on **kwargs: plt.plot arguments """ all_spikes = np.concatenate(tuple(spike_trains)) nbins = np... |
else: axis.plot(bins[:-1], values, **kwargs) axis.set_xlabel("Time [ms]") axis.set_ylabel("Spikes per second") def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): """ Plot inter-spike interval histogram. """ | def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, style='k', drawstyle='steps-mid', **kwargs): """ Plot inter-spike interval histogram. """ | def plot_psth(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): """ Plots PSTH of spike_trains. spike_trains: list of spike trains bin_size: bin size in ms trial_num: total number of trials axis: axis to draw on **kwargs: plt.plot arguments """ all_spikes = np.concatenate(tuple(spike_trains)) nbins = np... |
axis.plot(bins[:-1], values, **kwargs) axis.set_xlabel("Inter-Spike Interval [ms]") axis.set_ylabel("Interval | do_show = True else: do_show = False axis.plot(bins[:-1], values, style, drawstyle=drawstyle, **kwargs) axis.set_xlabel("Inter-Spike Interval [ms]") axis.set_ylabel("Interval if do_show: | def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): """ Plot inter-spike interval histogram. """ isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate( isi_trains ) nbins = np.ceil((max(all_isi) - min(all_isi)) / bin_size) values, bins = np.histogram(all_isi, nb... |
else: axis.plot(bins[:-1], values, **kwargs) axis.set_xlabel("Inter-Spike Interval [ms]") axis.set_ylabel("Interval | def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, **kwargs): """ Plot inter-spike interval histogram. """ isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate( isi_trains ) nbins = np.ceil((max(all_isi) - min(all_isi)) / bin_size) values, bins = np.histogram(all_isi, nb... | |
def average_firing_rate(spike_trains, stimulus_duration=None): """ Calculates average firing rate. | def average_firing_rate(spike_trains, stimulus_duration=None, trial_num=None): """ Calculates average firing rate. | def average_firing_rate(spike_trains, stimulus_duration=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> average_firing_r... |
""" | """ if len(spike_trains) == 0: return 0 | def average_firing_rate(spike_trains, stimulus_duration=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> average_firing_r... |
trial_num = len(spike_trains) | if trial_num == None: trial_num = len(spike_trains) | def average_firing_rate(spike_trains, stimulus_duration=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> average_firing_r... |
rate = average_firing_rate | def average_firing_rate(spike_trains, stimulus_duration=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> average_firing_r... | |
almost_all_spikes = np.concatenate(tuple(other_trains)) | almost_all_spikes = np.concatenate(other_trains) | def shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): """ Calculate Shuffled Autocorrelogram (Joris 2006) >>> a = [np.array([1, 2, 3]), np.array([1, 2.01, 2.5])] >>> shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) (array([-1.2, -0.4, 0.4, ... |
cum = np.concatenate(tuple(cum)) | cum = np.concatenate(cum) | def shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): """ Calculate Shuffled Autocorrelogram (Joris 2006) >>> a = [np.array([1, 2, 3]), np.array([1, 2.01, 2.5])] >>> shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) (array([-1.2, -0.4, 0.4, ... |
def concat_and_fold(spike_trains, period): all_spikes = np.concatenate( tuple(spike_trains) ) return [ np.fmod(all_spikes, period) ] | def concat_and_fold(spike_trains, period): all_spikes = np.concatenate( tuple(spike_trains) ) return [ np.fmod(all_spikes, period) ] | |
""" Fold each of the spike trains. >>> spike_trains = [np.array([1,2,3,4]), np.array([3,4,5,6])] | """ Fold each of the spike trains. >>> spike_trains = [np.array([1,2,3,4]), np.array([2,3,4,5])] >>> fold_spikes(spike_trains, 3) [array([1, 2]), array([0, 1]), array([2]), array([0, 1, 2])] >>> spike_trains = [np.array([2.]), np.array([])] | def fold_spikes(spike_trains, period): """ Fold each of the spike trains. >>> spike_trains = [np.array([1,2,3,4]), np.array([3,4,5,6])] >>> fold_spikes(spike_trains, 2) [array([1]), array([0, 1]), array([1]), array([0, 1])] """ folded = [] for train in spike_trains: period_num = int( np.ceil(train.max() / period) ) fo... |
[array([1]), array([0, 1]), array([1]), array([0, 1])] """ | [array([], dtype=float64), array([ 0.]), array([], dtype=float64), array([], dtype=float64)] """ all_spikes = np.concatenate(tuple(spike_trains)) if len(all_spikes) == 0: return spike_trains max_spike = all_spikes.max() period_num = int(np.ceil((max_spike+1) / period)) | def fold_spikes(spike_trains, period): """ Fold each of the spike trains. >>> spike_trains = [np.array([1,2,3,4]), np.array([3,4,5,6])] >>> fold_spikes(spike_trains, 2) [array([1]), array([0, 1]), array([1]), array([0, 1])] """ folded = [] for train in spike_trains: period_num = int( np.ceil(train.max() / period) ) fo... |
period_num = int( np.ceil(train.max() / period) ) for idx in range( period_num ): | for idx in range(period_num): | def fold_spikes(spike_trains, period): """ Fold each of the spike trains. >>> spike_trains = [np.array([1,2,3,4]), np.array([3,4,5,6])] >>> fold_spikes(spike_trains, 2) [array([1]), array([0, 1]), array([1]), array([0, 1])] """ folded = [] for train in spike_trains: period_num = int( np.ceil(train.max() / period) ) fo... |
if len(sec) > 0: sec = np.fmod(sec, period) folded.append( sec ) | sec = np.fmod(sec, period) folded.append(sec) | def fold_spikes(spike_trains, period): """ Fold each of the spike trains. >>> spike_trains = [np.array([1,2,3,4]), np.array([3,4,5,6])] >>> fold_spikes(spike_trains, 2) [array([1]), array([0, 1]), array([1]), array([0, 1])] """ folded = [] for train in spike_trains: period_num = int( np.ceil(train.max() / period) ) fo... |
def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, style='', **kwargs): """ Plot inter-spike interval histogram. """ import matplotlib.pyplot as plt | def calc_isih(spike_trains, bin_size=1, trial_num=None): """ Calculate inter-spike interval histogram. >>> spikes = [np.array([1,2,3]), np.array([2,5,8])] >>> calc_isih(spikes) (array([ 1., 1.]), array([ 1.5, 2.5])) """ | def plot_psth(spike_trains, bin_size=1, trial_num=None, axis=None, style='', **kwargs): """ Plots PSTH of spike_trains. spike_trains: list of spike trains bin_size: bin size in ms trial_num: total number of trials axis: axis to draw on **kwargs: plt.plot arguments """ import matplotlib.pyplot as plt all_spikes = np.c... |
values = np.append(0, values) bins = np.append(bins[0]-bin_size, bins) | dbins = (bins[1]-bins[0])/2 bins = bins + dbins bins = bins[0:-1] | def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, style='', **kwargs): """ Plot inter-spike interval histogram. """ import matplotlib.pyplot as plt isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate(isi_trains) nbins = np.ceil((max(all_isi) - min(all_isi)) / bin_size)... |
axis.plot(bins[:-1], values, style, **kwargs) | axis.plot(bins, values, style, **kwargs) | def plot_isih(spike_trains, bin_size=1, trial_num=None, axis=None, style='', **kwargs): """ Plot inter-spike interval histogram. """ import matplotlib.pyplot as plt isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate(isi_trains) nbins = np.ceil((max(all_isi) - min(all_isi)) / bin_size)... |
def synchronization_index(Fstim, spike_trains): | def calc_synchronization_index(spike_trains, fstim): | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
Fstim: stimulus frequency in Hz | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... | |
min_max: range for SI calculation | fstim: stimulus frequency in Hz | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
>>> Fstim = 100.0 | >>> fstim = 100.0 | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
>>> si0 = synchronization_index(Fstim, test0) | >>> si0 = calc_synchronization_index(test0, fstim) | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
>>> si1 = synchronization_index(Fstim, test1) | >>> si1 = calc_synchronization_index(test1, fstim) | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
Fstim = Fstim / 1000 | fstim = fstim / 1000 | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
folded = np.fmod(all_spikes, 1/Fstim) | folded = np.fmod(all_spikes, 1/fstim) | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
si = synchronization_index vector_strength = synchronization_index vs = synchronization_index | calc_si = calc_synchronization_index calc_vector_strength = calc_synchronization_index calc_vs = calc_synchronization_index | def synchronization_index(Fstim, spike_trains): """ Calculate Synchronization Index. Fstim: stimulus frequency in Hz spike_trains: list of arrays of spiking times min_max: range for SI calculation return: synchronization index >>> fs = 36000.0 >>> Fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0... |
def average_firing_rate(spike_trains, stimulus_duration=None, trial_num=None): | def calc_average_firing_rate(spike_trains, stimulus_duration=None, trial_num=None): | def average_firing_rate(spike_trains, stimulus_duration=None, trial_num=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> ... |
>>> average_firing_rate(spike_trains, 1000) | >>> calc_average_firing_rate(spike_trains, 1000) | def average_firing_rate(spike_trains, stimulus_duration=None, trial_num=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> ... |
firing_rate = average_firing_rate rate = average_firing_rate | calc_firing_rate = calc_average_firing_rate calc_rate = calc_average_firing_rate | def average_firing_rate(spike_trains, stimulus_duration=None, trial_num=None): """ Calculates average firing rate. spike_trains: trains of spikes stimulus_duration: in ms, if None, then calculated from spike timeings return: average firing rate in spikes per second (Hz) >>> spike_trains = [range(20), range(10)] >>> ... |
def correlation_index(spike_trains, coincidence_window=0.05, stimulus_duration=None): | def calc_correlation_index(spike_trains, coincidence_window=0.05, stimulus_duration=None): | def correlation_index(spike_trains, coincidence_window=0.05, stimulus_duration=None): """ Compute correlation index (Joris 2006) """ if len(spike_trains) == 0: return 0 if stimulus_duration == None: all_spikes = np.concatenate(tuple(spike_trains)) stimulus_duration = all_spikes.max() - all_spikes.min() firing_rate = ... |
firing_rate = average_firing_rate(spike_trains, stimulus_duration) | firing_rate = calc_average_firing_rate(spike_trains, stimulus_duration) | def correlation_index(spike_trains, coincidence_window=0.05, stimulus_duration=None): """ Compute correlation index (Joris 2006) """ if len(spike_trains) == 0: return 0 if stimulus_duration == None: all_spikes = np.concatenate(tuple(spike_trains)) stimulus_duration = all_spikes.max() - all_spikes.min() firing_rate = ... |
ci = correlation_index def shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): | calc_ci = calc_correlation_index def calc_shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): | def correlation_index(spike_trains, coincidence_window=0.05, stimulus_duration=None): """ Compute correlation index (Joris 2006) """ if len(spike_trains) == 0: return 0 if stimulus_duration == None: all_spikes = np.concatenate(tuple(spike_trains)) stimulus_duration = all_spikes.max() - all_spikes.min() firing_rate = ... |
>>> shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) | >>> calc_shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) | def shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): """ Calculate Shuffled Autocorrelogram (Joris 2006) >>> a = [np.array([1, 2, 3]), np.array([1, 2.01, 2.5])] >>> shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) (array([-1.2, -0.4, 0.4, ... |
firing_rate = average_firing_rate(spike_trains, stimulus_duration) | firing_rate = calc_average_firing_rate(spike_trains, stimulus_duration) | def shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): """ Calculate Shuffled Autocorrelogram (Joris 2006) >>> a = [np.array([1, 2, 3]), np.array([1, 2.01, 2.5])] >>> shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) (array([-1.2, -0.4, 0.4, ... |
sac = shuffled_autocorrelation | sac = calc_shuffled_autocorrelation | def shuffled_autocorrelation(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None): """ Calculate Shuffled Autocorrelogram (Joris 2006) >>> a = [np.array([1, 2, 3]), np.array([1, 2.01, 2.5])] >>> shuffled_autocorrelation(a, coincidence_window=1, analysis_window=2) (array([-1.2, -0.4, 0.4, ... |
t, sac = shuffled_autocorrelation(spike_trains, coincidence_window, analysis_window, stimulus_duration) | t, sac = calc_shuffled_autocorrelation(spike_trains, coincidence_window, analysis_window, stimulus_duration) | def plot_sac(spike_trains, coincidence_window=0.05, analysis_window=5, stimulus_duration=None, axis=None, **kwargs): """ Plot shuffled autocorrelogram (SAC) (Joris 2006) """ import matplotlib.pyplot as plt t, sac = shuffled_autocorrelation(spike_trains, coincidence_window, analysis_window, stimulus_duration) if axis ... |
nbins = np.ceil((max(all_spikes) - min(all_spikes)) / bin_size) values, bins = np.histogram(all_spikes, nbins) | nbins = np.ceil(all_spikes.max() / bin_size) values, bins = np.histogram(all_spikes, nbins, range=(0, all_spikes.max())) | def plot_psth(spike_trains, bin_size=1, trial_num=None, plot=None, **style): """ Plots PSTH of spike_trains. spike_trains: list of spike trains bin_size: bin size in ms trial_num: total number of trials axis: axis to draw on **kwargs: plt.plot arguments """ import biggles all_spikes = np.concatenate(tuple(spike_train... |
(array([ 1., 1.]), array([ 1., 2., 3.])) | (array([ 0., 1., 1.]), array([ 0., 1., 2., 3.])) | def calc_isih(spike_trains, bin_size=1, trial_num=None): """ Calculate inter-spike interval histogram. >>> spikes = [np.array([1,2,3]), np.array([2,5,8])] >>> calc_isih(spikes) (array([ 1., 1.]), array([ 1., 2., 3.])) """ isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate(isi_train... |
nbins = np.ceil((max(all_isi) - min(all_isi)) / bin_size) | nbins = np.ceil(all_isi.max() / bin_size) | def calc_isih(spike_trains, bin_size=1, trial_num=None): """ Calculate inter-spike interval histogram. >>> spikes = [np.array([1,2,3]), np.array([2,5,8])] >>> calc_isih(spikes) (array([ 1., 1.]), array([ 1., 2., 3.])) """ isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate(isi_train... |
values, bins = np.histogram(all_isi, nbins) | values, bins = np.histogram(all_isi, nbins, range=(0,all_isi.max())) | def calc_isih(spike_trains, bin_size=1, trial_num=None): """ Calculate inter-spike interval histogram. >>> spikes = [np.array([1,2,3]), np.array([2,5,8])] >>> calc_isih(spikes) (array([ 1., 1.]), array([ 1., 2., 3.])) """ isi_trains = [ np.diff(train) for train in spike_trains ] all_isi = np.concatenate(isi_train... |
ph = np.histogram(folded, bins=nbins)[0] | ph,edges = np.histogram(folded, bins=nbins, range=(0,1/fstim)) | def plot_period_histogram(spike_trains, fstim, nbins=64, plot=None, **style): """ Plots period histogram. """ import biggles fstim = fstim / 1000 # Hz -> kHz; s -> ms if len(spike_trains) == 0: return 0 all_spikes = np.concatenate(tuple(spike_trains)) if len(all_spikes) == 0: return 0 folded = np.fmod(all_s... |
ph = np.histogram(folded, bins=180)[0] | ph,edges = np.histogram(folded, bins=1000, range=(0, 1/fstim)) | def calc_synchronization_index(spike_trains, fstim): """ Calculate Synchronization Index. spike_trains: list of arrays of spiking times fstim: stimulus frequency in Hz return: synchronization index >>> fs = 36000.0 >>> fstim = 100.0 >>> test0 = [np.arange(0, 0.1, 1/fs)*1000, np.arange(0, 0.1, 1/fs)*1000] >>> si0 = ... |
children=[]) | children=[], parents=[]) | def _parse(dag, dir): # A DAG syntax is pretty simple # JOB JOBNAME JOBSCRIPT # PARENT JOBNAME CHILD JOBNAME # We do not support DATA jobs quite yet. dag = os.path.join(dir, dag) lines = [l.strip() for l in dag.split('\n') if l.strip()] # Nodes. nodes = {} # {name, Node... |
parent.children = [nodes[n] for n in tokens[3:]] for child in parent.children: | children = [nodes[n] for n in tokens[3:]] parent.children = children for child in children: | def _parse(dag, dir): # A DAG syntax is pretty simple # JOB JOBNAME JOBSCRIPT # PARENT JOBNAME CHILD JOBNAME # We do not support DATA jobs quite yet. dag = os.path.join(dir, dag) lines = [l.strip() for l in dag.split('\n') if l.strip()] # Nodes. nodes = {} # {name, Node... |
html_refs = os.path.join(options.path, "docs", "html_refs") | html_refs = os.path.join(options.build, "docs", "html_refs") | def check_references(): """ Check if each [link] in the reference manual actually exists. Also fills in global variable "links". """ print("Checking References...") html_refs = os.path.join(options.path, "docs", "html_refs") for line in open(html_refs): mob = re.match(r"\[(.*?)\]", line) if mob: links[mob.group(1)] = ... |
const float du_dx = s->du_dx; const float dv_dx = s->dv_dx; const int w = s->w; const int h = s->h; | const al_fixed du_dx = al_ftofix(s->du_dx); const al_fixed dv_dx = al_ftofix(s->dv_dx); const al_fixed w = al_ftofix(s->w); const al_fixed h = al_ftofix(s->h); al_fixed uu = al_ftofix(u); al_fixed vv = al_ftofix(v); | def make_loop( op='op', src_mode='src_mode', dst_mode='dst_mode', op_alpha='op_alpha', src_alpha='src_alpha', dst_alpha='dst_alpha', src_format='src_format', dst_format='dst_format', if_format=None ): if if_format: src_format = if_format dst_format = if_format print interp("if (dst_format == #{dst_format}") if texture... |
const int src_x = _al_fast_float_to_int(u) + offset_x; const int src_y = _al_fast_float_to_int(v) + offset_y; | const int src_x = (uu >> 16) + offset_x; const int src_y = (vv >> 16) + offset_y; | def make_loop( op='op', src_mode='src_mode', dst_mode='dst_mode', op_alpha='op_alpha', src_alpha='src_alpha', dst_alpha='dst_alpha', src_format='src_format', dst_format='dst_format', if_format=None ): if if_format: src_format = if_format dst_format = if_format print interp("if (dst_format == #{dst_format}") if texture... |
u += du_dx; v += dv_dx; if (u < 0) u += w; else if (u >= w) u -= w; if (v < 0) v += h; else if (v >= h) v -= h; | uu += du_dx; vv += dv_dx; if (uu < 0) uu += w; else if (uu >= w) uu -= w; if (vv < 0) vv += h; else if (vv >= h) vv -= h; | def make_loop( op='op', src_mode='src_mode', dst_mode='dst_mode', op_alpha='op_alpha', src_alpha='src_alpha', dst_alpha='dst_alpha', src_format='src_format', dst_format='dst_format', if_format=None ): if if_format: src_format = if_format dst_format = if_format print interp("if (dst_format == #{dst_format}") if texture... |
const int src_format = s->texture->locked_region.format; | const int offset_x = s->texture->parent ? s->texture->xofs : 0; const int offset_y = s->texture->parent ? s->texture->yofs : 0; ALLEGRO_BITMAP* texture = s->texture->parent ? s->texture->parent : s->texture; const int src_format = texture->locked_region.format; | def make_drawer(name): global texture, grad, solid, shade, opaque, white texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: rais... |
const int src_x = _al_fast_float_to_int(u); const int src_y = _al_fast_float_to_int(v); uint8_t *src_data = (uint8_t *)s->texture->locked_region.data + (src_y - s->texture->lock_y) * s->texture->locked_region.pitch + (src_x - s->texture->lock_x) * src_size; | const int src_x = _al_fast_float_to_int(u) + offset_x; const int src_y = _al_fast_float_to_int(v) + offset_y; uint8_t *src_data = (uint8_t *)texture->locked_region.data + (src_y - texture->lock_y) * texture->locked_region.pitch + (src_x - texture->lock_x) * src_size; | def make_loop( op='op', src_mode='src_mode', dst_mode='dst_mode', op_alpha='op_alpha', src_alpha='src_alpha', dst_alpha='dst_alpha', src_format='src_format', dst_format='dst_format', if_format=None ): if if_format: src_format = if_format dst_format = if_format print interp("if (dst_format == #{dst_format}") if texture... |
print " | raise Exception("grad and solid") | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
print " | raise Exception("grad and white") | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
print " print "static void", name, "(uintptr_t state, int x1, int y, int x2) {" | raise Exception("shade and opaque") print interp("static void | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
print """\ | if shade: make_if_blender_loop( op='ALLEGRO_ADD', src_mode='ALLEGRO_ALPHA', src_alpha='ALLEGRO_ALPHA', op_alpha='ALLEGRO_ADD', dst_mode='ALLEGRO_INVERSE_ALPHA', dst_alpha='ALLEGRO_INVERSE_ALPHA', if_format='ALLEGRO_PIXEL_FORMAT_ARGB_8888' ) print "else" make_if_blender_loop( op='ALLEGRO_ADD', src_mode='ALLEGRO_ONE', sr... | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
print """\ | print interp("""\ | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
_AL_INLINE_GET_PIXEL(src_format, src_data, src_color, false); """ | _AL_INLINE_GET_PIXEL( """) | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
_AL_INLINE_GET_PIXEL(dst_format, dst_data, dst_color, false); | _AL_INLINE_GET_PIXEL( | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
op, src_mode, dst_mode, op_alpha, src_alpha, dst_alpha, &result); _AL_INLINE_PUT_PIXEL(dst_format, dst_data, result, true); | &result); _AL_INLINE_PUT_PIXEL( | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
""" | """) | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
print """\ _AL_INLINE_PUT_PIXEL(dst_format, dst_data, src_color, true); """ | print interp("""\ _AL_INLINE_PUT_PIXEL( """) | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
} } } | } | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
+ (y-1) * target->locked_region.pitch | + y * target->locked_region.pitch | def make_drawer(name): texture = (name.find("_texture_") != -1) grad = (name.find("_grad_") != -1) solid = (name.find("_solid_") != -1) shade = (name.find("_shade") != -1) opaque = (name.find("_opaque") != -1) white = (name.find("_white") != -1) if grad and solid: print "#error grad and solid" if grad and white: print... |
description = description + u'|description={{en|1=' + metadata.get('title') | informationDescription = metadata.get('title').strip() if informationDescription[-1:].isalnum(): informationDescription = informationDescription + u'.' | def getDescription(metadata): ''' Create the description of the image based on the metadata ''' description = u'' description = description + u'== {{int:filedesc}} ==\n' description = description + u'{{Information\n' description = description + u'|description={{en|1=' + metadata.get('title') if not metadata['comment']... |
description = description + u' ' + metadata['comment'] description = description + u'}}\n' | informationDescription = informationDescription + u' ' + metadata['comment'] description = description + u'|description={{en|1=' +informationDescription + u'}}\n' | def getDescription(metadata): ''' Create the description of the image based on the metadata ''' description = u'' description = description + u'== {{int:filedesc}} ==\n' description = description + u'{{Information\n' description = description + u'|description={{en|1=' + metadata.get('title') if not metadata['comment']... |
result = result.replace(u'http://www.news.navy.mil/management/photodb/photos/', u'') | result = result.replace(u'http://www.navy.mil/management/photodb/photos/', u'') | def getNavyIdentifier(url): result = url result = result.replace(u'http://www.news.navy.mil/management/photodb/photos/', u'') result = result.replace(u'.jpg', u'') return result |
def getDate(description): dateregex = u'\(([^\)]+\d\d\d\d)\)' matches = re.search(dateregex, description) if matches: return matches.group(1) else: return u'' | def getDate(description): dateregex = u'\(([^\)]+\d\d\d\d)\)' matches = re.search(dateregex, description) if matches: #Should probably parse it, but that didn't work out #descriptionformat = u'%b. %d, %Y' #isoformat = '%Y-%m-%d' #date = datetime.strptime(matches.group(1), descriptionformat) #print date.strftime(isoform... | |
def getLocation(description): locationregex = u'^([^\(^\r^\n]+)\(([^\)]+\d\d\d\d)\)' matches = re.search(locationregex, description, re.MULTILINE) | def getShip(description): shipRegex = u'USS [^\(]{1,25}\([^\)]+\)' matches = re.search(shipRegex, description, re.I) | def getLocation(description): # Assume that the location is before date. # Based on dateregex locationregex = u'^([^\(^\r^\n]+)\(([^\)]+\d\d\d\d)\)' matches = re.search(locationregex, description, re.MULTILINE) if matches: return matches.group(1) else: # Unknown location return u'unknown' |
return matches.group(1) | return matches.group(0) | def getLocation(description): # Assume that the location is before date. # Based on dateregex locationregex = u'^([^\(^\r^\n]+)\(([^\)]+\d\d\d\d)\)' matches = re.search(locationregex, description, re.MULTILINE) if matches: return matches.group(1) else: # Unknown location return u'unknown' |
return u'unknown' | return u'' | def getLocation(description): # Assume that the location is before date. # Based on dateregex locationregex = u'^([^\(^\r^\n]+)\(([^\)]+\d\d\d\d)\)' matches = re.search(locationregex, description, re.MULTILINE) if matches: return matches.group(1) else: # Unknown location return u'unknown' |
description = description + u'[[Category:Images from US Navy, location ' + metadata.get('location') + u']]\n' | if not metadata.get('ship')==u'': description = description + getShipCategory(metadata.get('ship')) + u'\n' else: description = description + u'[[Category:Images from US Navy, location ' + metadata.get('location') + u']]\n' | def buildDescription(photo_id, metadata): ''' Create the description of the image based on the metadata ''' description = u'' description = description + u'== {{int:filedesc}} ==\n' description = description + u'{{Information\n' description = description + u'|description={{en|1=' + metadata.get('description') + u'}}\n... |
wikipedia.output(title) | def processPhoto(photo_id): ''' Work on a single photo at http://www.navy.mil/view_single.asp?id=<photo_id> get the metadata, check for dupes, build description, upload the image ''' # Get all the metadata metadata = getMetadata(photo_id) if not metadata: #Incorrect photo_id return False photo = downloadPhoto(metada... | |
print len(locationList) | def getOpenStreetMapCategories(metadata, cursor, cursor2): ''' Get a list of location categories based on the extended find nearby tool ''' result = [] locationList = getOpenStreetMap(metadata.get('wgs84_lat'), metadata.get('wgs84_long')) print len(locationList) for i in range(0, len(locationList)): print 'Working on '... | |
conn = MySQLdb.connect('sql.toolserver.org', db='u_multichill', user = config.db_username, passwd = config.db_password, use_unicode=True) | conn = MySQLdb.connect('sql.toolserver.org', db='u_multichill', user = config.db_username, passwd = config.db_password, use_unicode=True, charset='utf8') | def connectDatabase(): ''' Connect to the mysql database, if it fails, go down in flames ''' conn = MySQLdb.connect('sql.toolserver.org', db='u_multichill', user = config.db_username, passwd = config.db_password, use_unicode=True) cursor = conn.cursor() return (conn, cursor) |
query = u"""REPLACE INTO monumenten(objrijksnr, woonplaats, adres, objectnaam, type_obj, oorspr_functie, bouwjaar, architect, cbs_tekst, RD_x, RD_y, lat, lon, source) VALUES ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')"""; | query = u"""REPLACE INTO monumenten(objrijksnr, woonplaats, adres, objectnaam, type_obj, oorspr_functie, bouwjaar, architect, cbs_tekst, RD_x, RD_y, lat, lon, image, source) VALUES ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')"""; | def updateMonument(contents, conn, cursor): query = u"""REPLACE INTO monumenten(objrijksnr, woonplaats, adres, objectnaam, type_obj, oorspr_functie, bouwjaar, architect, cbs_tekst, RD_x, RD_y, lat, lon, source) VALUES ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')"""; cursor.execu... |
contents['source'] = source | contents['source'] = source.replace("'", "\\'") | def processMonument(text, source, conn, cursor): ''' Process a single instance of the Tabelrij rijksmonument template ''' # First remove line breaks like \n and \r #text = text.replace(u'\n', u' ') #text = text.replace(u'\r', u' ') # The regexes to find all the fields fields = [u'objrijksnr', u'woonplaats', u'adres', ... |
matches = re.findall(regex, text) for match in matches: monument = match.group(0) | monuments = re.findall(regex, text) for monument in monuments: | def processText(text, source, conn, cursor): ''' Process a text containing one or multiple instances of the Tabelrij rijksmonument template ''' regex = u'\{\{Tabelrij rijksmonument[^}]+\}\}' matches = re.findall(regex, text) for match in matches: monument = match.group(0) processMonument(monument, source, conn, cursor)... |
processText(page.get(), page.permalink(), conn, cursor) | if page.exists() and not page.isRedirectPage(): processText(page.get(), page.permalink(), conn, cursor) | def main(): ''' The main loop ''' # First find out what to work on textfile = u'' genFactory = pagegenerators.GeneratorFactory() conn = None cursor = None (conn, cursor) = connectDatabase() for arg in wikipedia.handleArgs(): if arg.startswith('-textfile:'): textfile = arg [len('-textfile:'):] else: genFactory.handleA... |
(Null, week,Null) = monday.isocalendar() | week = monday.strftime('%W') | def createPage (monday = None): ''' Create a new week page for http://nl.wikipedia.org/wiki/Wikipedia:Te_verwijderen_sjablonen. Use http://nl.wikipedia.org/wiki/Sjabloon:Te_verwijderen_sjablonen_nieuwe_week as template. ''' day = monday.strftime('%d') # 01-31 month = monday.strftime('%m') # 01-12 year = monday.strftime... |
(year, week, Null) = monday.isocalendar() | year = monday.strftime('%Y') week = monday.strftime('%W') | def addWeek (monday = None): ''' Add another week to http://nl.wikipedia.org/wiki/Wikipedia:Te_verwijderen_sjablonen ''' page = wikipedia.Page(wikipedia.getSite(u'nl', u'wikipedia'), u'Wikipedia:Te verwijderen sjablonen') pagetext = page.get() (year, week, Null) = monday.isocalendar() qmonday = monday + timedelta(weeks... |
(qyear, qweek, Null) = qmonday.isocalendar() | qyear = qmonday.strftime('%Y') qweek = qmonday.strftime('%W') | def addWeek (monday = None): ''' Add another week to http://nl.wikipedia.org/wiki/Wikipedia:Te_verwijderen_sjablonen ''' page = wikipedia.Page(wikipedia.getSite(u'nl', u'wikipedia'), u'Wikipedia:Te verwijderen sjablonen') pagetext = page.get() (year, week, Null) = monday.isocalendar() qmonday = monday + timedelta(weeks... |
if categories: | if categories and not set(currentCategories)==set(categories): | def categorizeImage(page, id, cursor, cursor2): # get metadata metadata = getMetadata(id, cursor) # get current text oldtext = page.get() # get current categories currentCategories =[] for cat in page.categories(): currentCategories.append(cat.titleWithoutNamespace().strip().replace(u' ', u'_')) # remove templates clea... |
(geocat.cl_to='Images_from_Geograph_needing_category_review_as_of_30_January_2010' OR geocat.cl_to='Images_from_Geograph_needing_category_review_as_of_31_January_2010' OR geocat.cl_to='Images_from_Geograph_needing_category_review_as_of_1_February_2010' ) AND | geocat.cl_to LIKE 'Images\_from\_Geograph\_needing\_category\_review\_as\_of\_%%' AND | def getImagesWithTopic(cursor, topic): result = [] query = u"""SELECT DISTINCT page_title, REPLACE(el_to, 'http://www.geograph.org.uk/photo/', '') FROM page JOIN categorylinks AS geocat ON page_id=geocat.cl_from JOIN categorylinks AS topiccat ON page_id=topiccat.cl_from JOIN externallinks ON page_id=el_from WHERE page_... |
for page in generator: | pregenerator = pagegenerators.PreloadingGenerator(generator) for page in pregenerator: | def main(): ''' The main loop ''' # First find out what to work on textfile = u'' genFactory = pagegenerators.GeneratorFactory() conn = None cursor = None (conn, cursor) = connectDatabase() for arg in wikipedia.handleArgs(): if arg.startswith('-textfile:'): textfile = arg [len('-textfile:'):] else: genFactory.handleA... |
conn = MySQLdb.connect(config.db_hostname, db='u_multichill', user = config.db_username, passwd = config.db_password) | conn = MySQLdb.connect(u'sql-s2.toolserver.org', db='u_multichill_commons_categories_p', user = config.db_username, passwd = config.db_password) | def connectDatabase(): conn = MySQLdb.connect(config.db_hostname, db='u_multichill', user = config.db_username, passwd = config.db_password) cursor = conn.cursor() return (conn, cursor) |
if movieFile.info().get('Content-Length') > ogv_size: | if int(movieFile.info().get('Content-Length')) > ogv_size: | def getRightMovie(metadata): ''' Extract the right movie (biggest ogv file) from the list of files. ''' ogv_url = u'' ogv_size = 0 for medium in metadata.getField('medium'): if medium.endswith(u'.ogv'): movieFile=urllib.urlopen(medium) #print movieFile.info() #print movieFile.info().get('Content-Length') if movieFile.... |
ogv_size = movieFile.info().get('Content-Length') | ogv_size = int(movieFile.info().get('Content-Length')) | def getRightMovie(metadata): ''' Extract the right movie (biggest ogv file) from the list of files. ''' ogv_url = u'' ogv_size = 0 for medium in metadata.getField('medium'): if medium.endswith(u'.ogv'): movieFile=urllib.urlopen(medium) #print movieFile.info() #print movieFile.info().get('Content-Length') if movieFile.... |
bot = upload.UploadRobot(movieurl, description=description, useFilename=title, keepFilename=True, verifyDescription=False, targetSite = wikipedia.getSite('commons', 'commons')) | bot = upload.UploadRobot(movieurl, description=description, useFilename=title, keepFilename=True, verifyDescription=False, ignoreWarning=True, targetSite = wikipedia.getSite('commons', 'commons')) | def processItem(record): (header, metadata, about) = record identifier = header.identifier().replace(u'oai:openimages.eu:', u'') if not getLicenseTemplate(metadata): wikipedia.output(u'File doesn\'t contain a valid license') return False movieurl = getRightMovie(metadata) if not movieurl: wikipedia.output(u'No .ogv... |
topic = topic.replace(u'_', u' ').strip() | topic = topic.strip().replace(u' ', u'_') | def getCategories(metadata, cursor, cursor2, currentCategories=[]): ''' Produce one or more suitable Commons categories based on the metadata ''' result = u'' locationList =getExtendedFindNearby(metadata.get('wgs84_lat'), metadata.get('wgs84_long')) for i in range(0, len(locationList)): #First try to get the location ... |
categories = filterCategories(categories, cursor2) | def getCategories(metadata, cursor, cursor2, currentCategories=[]): ''' Produce one or more suitable Commons categories based on the metadata ''' result = u'' locationList =getExtendedFindNearby(metadata.get('wgs84_lat'), metadata.get('wgs84_long')) for i in range(0, len(locationList)): #First try to get the location ... | |
currentCategories.append(cat.titleWithoutNamespace().replace(u' ', u'_')) | currentCategories.append(cat.titleWithoutNamespace().strip().replace(u' ', u'_')) | def categorizeImage(page, id, cursor, cursor2): # get metadata metadata = getMetadata(id, cursor) # get current text oldtext = page.get() # get current categories currentCategories =[] for cat in page.categories(): currentCategories.append(cat.titleWithoutNamespace().replace(u' ', u'_')) # remove templates cleanDescrip... |
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