avg = weighted_average(vs, ws)
values = [mn, avg] + list(ps) + [mx]
- row = [end, ss_cnt] + map(lambda x: float(x) / ctx.divisor, values)
+ row = [end, ss_cnt] + [float(x) / ctx.divisor for x in values]
fmt = "%d, %d, %d, " + fmt_float_list(ctx, 5) + ", %d"
print (fmt % tuple(row))
max_cols = guess_max_from_bins(ctx, __HIST_COLUMNS)
coarseness = int(np.log2(float(max_cols) / __HIST_COLUMNS))
- bin_vals = np.array(map(lambda x: plat_idx_to_val_coarse(x, coarseness), np.arange(__HIST_COLUMNS)), dtype=float)
- lower_bin_vals = np.array(map(lambda x: plat_idx_to_val_coarse(x, coarseness, 0.0), np.arange(__HIST_COLUMNS)), dtype=float)
- upper_bin_vals = np.array(map(lambda x: plat_idx_to_val_coarse(x, coarseness, 1.0), np.arange(__HIST_COLUMNS)), dtype=float)
+ bin_vals = np.array([plat_idx_to_val_coarse(x, coarseness) for x in np.arange(__HIST_COLUMNS)], dtype=float)
+ lower_bin_vals = np.array([plat_idx_to_val_coarse(x, coarseness, 0.0) for x in np.arange(__HIST_COLUMNS)], dtype=float)
+ upper_bin_vals = np.array([plat_idx_to_val_coarse(x, coarseness, 1.0) for x in np.arange(__HIST_COLUMNS)], dtype=float)
fps = [open(f, 'r') for f in ctx.FILE]
gen = histogram_generator(ctx, fps, ctx.buff_size)
start += ctx.interval
end = start + ctx.interval
finally:
- map(lambda f: f.close(), fps)
+ for fp in fps:
+ fp.close()
if __name__ == '__main__':