def show_results(x: TensorSeq, y, samples, outs, ctxs=None, max_n=9, nrows=None, ncols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize) for i in range(len(outs[0])): ctxs = [ TSTensorSeqy(b, m='*r', label='pred').show(ctx=c, **kwargs) for b, c, _ in zip(outs.itemgot(i), ctxs, range(max_n)) ] for i in range(len(samples[0])): ctxs = [ b.show(ctx=c, **kwargs) for b, c, _ in zip(samples.itemgot(i), ctxs, range(max_n)) ] return ctxs
def show_results(x: TensorTS, y, samples, outs, ctxs=None, max_n=9, nrows=None, ncols=None, figsize=(14, 12), **kwargs): if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize) # outs = [('6.0',),('2.0',)] outs = [detuplify(o) for o in outs] # outs = ['6.0', '2.0'] ctxs = default_show_results(x, y, samples, outs, ctxs=ctxs, max_n=max_n, figsize=figsize, **kwargs) return ctxs
def show_batch(x:TensorTS, y, samples, ctxs=None, max_n=9, rows=None, cols=None, figsize=None, title=None, **kwargs): if ctxs is None: ctxs = get_grid(max_n, rows=rows, cols=cols, figsize=figsize) ctxs = default_show_batch(x, y, samples, ctxs=ctxs, max_n=max_n, **kwargs) if title: plt.suptitle(title, fontsize=16) plt.subplots_adjust() plt.subplots_adjust(left=0.0, wspace=0.4, top=0.9, bottom=0.5) return ctxs
def show_batch(x: TensorTS, y, samples, ctxs=None, max_n=9, nrows=None, ncols=None, figsize=(14, 12), **kwargs): if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, figsize=figsize) ctxs = default_show_batch(x, y, samples, ctxs=ctxs, max_n=max_n, **kwargs) return ctxs
def show_graphs(arrays, rows=None, cols=None, figsize=None, titles=None, **kwargs): "Show all images `arrays` as subplots with `rows` using `titles`" if titles is None: titles = [None] * len(arrays) axs = get_grid(len(arrays), rows=rows, cols=cols, add_vert=1, figsize=figsize) for a, t, ax in zip(arrays, titles, axs): ctx = show_graph(a[0], ax=ax, title=t) for y in a[1:]: ctx = y.show(ctx=ctx) return axs
def show_batch(x: TSMulti, y: TensorSeq, its, *args, ctxs=None, max_n=10, rows=None, cols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(x[0].shape[0], max_n), add_vert=1, figsize=figsize, **kwargs) for i, ctx in enumerate(ctxs): o = TSMulti([type(o)(o, **o._meta) for o in its[i] if o.shape[-1] > 0]) ctx = o.show(ctx=ctx) return ctxs
def show_batch(x: TSMulti, none_1, none_2, *args, ctxs=None, max_n=10, rows=None, cols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(x[0].shape[0], max_n), add_vert=1, figsize=figsize, **kwargs) for i, ctx in enumerate(ctxs): o = TSMulti([type(o)(o[i], **o._meta) for o in x]) ctx = o.show(ctx=ctx) return ctxs
def show_results(x:TensorTS, y, samples, outs, ctxs=None, max_n=9, rows=None, cols=None, figsize=None, **kwargs): # if ctxs is None: ctxs = get_grid(min(len(samples), max_n), rows=rows, cols=cols, add_vert=1, figsize=figsize) s = len(samples) # min(len(samples), max_n) # max_n = min(s, max_n) if ctxs is None: ctxs = get_grid(max_n, rows=rows, cols=cols, add_vert=1, figsize=figsize) # print(len(samples), max_n) # print(samples) # print(type(y)) # outs = [('6.0',),('2.0',)] # print(f'outs - before detuplify : {outs}') outs = [detuplify(o) for o in outs] # outs = ['6.0', '2.0'] # print(f'outs : {outs}) ctxs = [b[0].show(ctx=c, title=f'{o} / {b[1]}', **kwargs) for b,o,c,_ in zip(samples,outs,ctxs,range(max_n))] # if title: # plt.suptitle(title, fontsize=16) # plt.subplots_adjust(left=0.0, wspace=0.4, top=0.9) plt.tight_layout() return ctxs
def show_results(x: TSMulti, y, its, outs, ctxs=None, max_n=9, rows=None, cols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(x[0].shape[0], max_n), add_vert=1, figsize=figsize, **kwargs) for i, ctx in enumerate(ctxs): r = [type(o)(o, **o._meta) for o in its[i] if o.shape[-1] > 0] r.append(type(its[i][-1])(outs[i][0], label=['pred_y'], m=['r'])) o = TSMulti(r) ctx = o.show(ctx=ctx)
def show_results(x: TSMulti, x1, none_1, none_2, ctxs=None, max_n=9, rows=None, cols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(x[0].shape[0], max_n), add_vert=1, figsize=figsize, **kwargs) for i, ctx in enumerate(ctxs): r = [type(o)(o[i], **o._meta) for o in x] r.append(type(x1[-1])(x1[-1][i], label=['pred'], m=['r'])) o = TSMulti(r) ctx = o.show(ctx=ctx)
def show_batch(x: TensorSeq, y, samples, ctxs=None, max_n=10, nrows=None, ncols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize) ctxs = show_batch[object](x, y, samples=samples, ctxs=ctxs, max_n=max_n, **kwargs) return ctxs
def show_batch(x: TSeries, y, samples, ctxs=None, max_n=10,rows=None, cols=None, figsize=None, **kwargs): "Show batch for TSeries objects" if ctxs is None: ctxs = get_grid(min(len(samples), max_n), rows=rows, cols=cols, add_vert=1, figsize=figsize) ctxs = show_batch[object](x, y, samples=samples, ctxs=ctxs, max_n=max_n, **kwargs) return ctxs