python类nanpercentile()的实例源码

def test_mutation(self):
        # Check that passed array is not modified.
        ndat = _ndat.copy()
        np.nanpercentile(ndat, 30)
        assert_equal(ndat, _ndat)

def test_keepdims(self):
        mat = np.eye(3)
        for axis in [None, 0, 1]:
            tgt = np.percentile(mat, 70, axis=axis, out=None,
                                overwrite_input=False)
            res = np.nanpercentile(mat, 70, axis=axis, out=None,
                                   overwrite_input=False)
            assert_(res.ndim == tgt.ndim)

        d = np.ones((3, 5, 7, 11))
        # Randomly set some elements to NaN:
        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
        w = w.astype(np.intp)
        d[tuple(w)] = np.nan
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always', RuntimeWarning)
            res = np.nanpercentile(d, 90, axis=None, keepdims=True)
            assert_equal(res.shape, (1, 1, 1, 1))
            res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True)
            assert_equal(res.shape, (1, 1, 7, 11))
            res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True)
            assert_equal(res.shape, (1, 5, 7, 1))
            res = np.nanpercentile(d, 90, axis=(1,), keepdims=True)
            assert_equal(res.shape, (3, 1, 7, 11))
            res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True)
            assert_equal(res.shape, (1, 1, 1, 1))
            res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True)
            assert_equal(res.shape, (1, 1, 7, 1))

def test_empty(self):
        mat = np.zeros((0, 3))
        for axis in [0, None]:
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all())
                assert_(len(w) == 1)
                assert_(issubclass(w[0].category, RuntimeWarning))
        for axis in [1]:
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([]))
                assert_(len(w) == 0)

def test_scalar(self):
        assert_(np.nanpercentile(0., 100) == 0.)

def test_extended_axis_invalid(self):
        d = np.ones((3, 5, 7, 11))
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=-5)
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=(0, -5))
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=4)
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=(0, 4))
        assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1))

def _nanpercentile1d(arr1d, q, overwrite_input=False, interpolation='linear'):
    """
    Private function for rank 1 arrays. Compute percentile ignoring
    NaNs.

    See nanpercentile for parameter usage
    """
    c = np.isnan(arr1d)
    s = np.where(c)[0]
    if s.size == arr1d.size:
        warnings.warn("All-NaN slice encountered", RuntimeWarning)
        if q.ndim == 0:
            return np.nan
        else:
            return np.nan * np.ones((len(q),))
    elif s.size == 0:
        return np.percentile(arr1d, q, overwrite_input=overwrite_input,
                             interpolation=interpolation)
    else:
        if overwrite_input:
            x = arr1d
        else:
            x = arr1d.copy()
        # select non-nans at end of array
        enonan = arr1d[-s.size:][~c[-s.size:]]
        # fill nans in beginning of array with non-nans of end
        x[s[:enonan.size]] = enonan
        # slice nans away
        return np.percentile(x[:-s.size], q, overwrite_input=True,
                             interpolation=interpolation)

def test_mutation(self):
        # Check that passed array is not modified.
        ndat = _ndat.copy()
        np.nanpercentile(ndat, 30)
        assert_equal(ndat, _ndat)

def test_keepdims(self):
        mat = np.eye(3)
        for axis in [None, 0, 1]:
            tgt = np.percentile(mat, 70, axis=axis, out=None,
                                overwrite_input=False)
            res = np.nanpercentile(mat, 70, axis=axis, out=None,
                                   overwrite_input=False)
            assert_(res.ndim == tgt.ndim)

        d = np.ones((3, 5, 7, 11))
        # Randomly set some elements to NaN:
        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
        w = w.astype(np.intp)
        d[tuple(w)] = np.nan
        with warnings.catch_warnings(record=True) as w:
            warnings.simplefilter('always', RuntimeWarning)
            res = np.nanpercentile(d, 90, axis=None, keepdims=True)
            assert_equal(res.shape, (1, 1, 1, 1))
            res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True)
            assert_equal(res.shape, (1, 1, 7, 11))
            res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True)
            assert_equal(res.shape, (1, 5, 7, 1))
            res = np.nanpercentile(d, 90, axis=(1,), keepdims=True)
            assert_equal(res.shape, (3, 1, 7, 11))
            res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True)
            assert_equal(res.shape, (1, 1, 1, 1))
            res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True)
            assert_equal(res.shape, (1, 1, 7, 1))

def test_empty(self):
        mat = np.zeros((0, 3))
        for axis in [0, None]:
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all())
                assert_(len(w) == 1)
                assert_(issubclass(w[0].category, RuntimeWarning))
        for axis in [1]:
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([]))
                assert_(len(w) == 0)

def test_scalar(self):
        assert_(np.nanpercentile(0., 100) == 0.)

def test_extended_axis_invalid(self):
        d = np.ones((3, 5, 7, 11))
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=-5)
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=(0, -5))
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=4)
        assert_raises(IndexError, np.nanpercentile, d, q=5, axis=(0, 4))
        assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1))

def interactive(img, cmap='gray', window=(2, 98)):
    import ipywidgets as ipy

    # Get some information about the image
    bbox = Box.fromMask(img)
    window_vals = np.nanpercentile(img.get_data(), window)
    # Setup figure
    fig, axes = plt.subplots(1, 3, figsize=(8, 4))
    implots = [None, None, None]
    for i in range(3):
        sl = Slice(bbox, bbox.center, i, 256, orient='clin')
        sl_img = sl.sample(img, order=0)
        sl_color = colorize(sl_img, cmap, window_vals)
        implots[i] = axes[i].imshow(sl_color, origin='lower', extent=sl.extent, cmap=cmap, vmin = 0.1)
        axes[i].axis('off')

    def wrap_sections(x, y, z):
        for i in range(3):
            sl = Slice(bbox, np.array((x, y, z)), i, 256, orient='clin')
            sl_img = sl.sample(img, order=0)
            sl_color = colorize(sl_img, cmap, window_vals)
            implots[i].set_data(sl_color)
            plt.show()

    # Setup widgets
    slider_x = ipy.FloatSlider(min=bbox.start[0], max=bbox.end[0], value=bbox.center[0])
    slider_y = ipy.FloatSlider(min=bbox.start[1], max=bbox.end[1], value=bbox.center[1])
    slider_z = ipy.FloatSlider(min=bbox.start[2], max=bbox.end[2], value=bbox.center[2])
    widgets = ipy.interactive(wrap_sections, x=slider_x, y=slider_y, z=slider_z)

    # Now do some manual layout
    hbox = ipy.HBox(widgets.children[0:3]) # Set the sliders to horizontal layout
    vbox = ipy.VBox((hbox, widgets.children[3]))
    # iplot.widget.children[-1].layout.height = '350px'
    return vbox

def plot_aligned(data_all, transitions, label = 'data', dt = time_bin, phase = 0):
  phase_names = ['hatch', 'L1', 'L2', 'L3', 'L4', 'L5'];

  ttref0 = np.max(transitions[:, 0]);
  ntt = data_all.shape[1] + ttref0 + 500;
  nw = data_all.shape[0];

  tr = phase;
  data_algn = np.zeros((nw, ntt));
  tref = np.max(transitions[:, tr]);
  for wid in range(nworms):
    tt = transitions[wid, tr];
    ts =  transitions[wid, 0];
    te =  transitions[wid, -1];      
    nt = te - ts;
    t0 = ts + tref - tt;
    t1 = t0 + nt;
    #print 'tref=%s, ts =%d, te=%d, tt =%d, nt = %d, t0=%d, t1=%d, ntt=%d' % (tref, ts,te,tt, nt, t0,t1, ntt)
    data_algn[wid, t0 : t1] = data_all[wid,  ts:te];

  #rmax = np.nanpercentile(roam_24hr, 95); 
  rmax = 1;
  plt.imshow(data_algn, interpolation = 'none', aspect = 'auto', cmap = plt.cm.viridis, vmax = rmax )
  cb = plt.colorbar(fraction = 0.025, shrink = 0.5, pad = 0.01)
  cb.ax.set_ylabel(label + ' [au]', rotation=270, labelpad = 20)

  days = np.array(24. * 60 * 60 / time_bin * np.arange(6), dtype = int);
  labl = ['%d'%d for d  in 24 *np.linspace(0,5, 6)];  
  plt.xticks(days, labl);
  plt.xlabel('time [hrs]');   
  plt.ylabel('worm id');
  plt.tight_layout()

  plt.title(phase_names[tr]);
  plt.tight_layout()

def plot_aligned(data_all, transitions, label = 'data', dt = time_bin, phase = 0):
  phase_names = ['hatch', 'L1', 'L2', 'L3', 'L4', 'L5'];

  ttref0 = np.max(transitions[:, 0]);
  ntt = data_all.shape[1] + ttref0 + 500;
  nw = data_all.shape[0];

  tr = phase;
  data_algn = np.zeros((nw, ntt));
  tref = np.max(transitions[:, tr]);
  for wid in range(nworms):
    tt = transitions[wid, tr];
    ts =  transitions[wid, 0];
    te =  transitions[wid, -1];      
    nt = te - ts;
    t0 = ts + tref - tt;
    t1 = t0 + nt;
    #print 'tref=%s, ts =%d, te=%d, tt =%d, nt = %d, t0=%d, t1=%d, ntt=%d' % (tref, ts,te,tt, nt, t0,t1, ntt)
    data_algn[wid, t0 : t1] = data_all[wid,  ts:te];

  #rmax = np.nanpercentile(roam_24hr, 95); 
  rmax = 1;
  plt.imshow(data_algn, interpolation = 'none', aspect = 'auto', cmap = plt.cm.viridis, vmax = rmax )
  cb = plt.colorbar(fraction = 0.025, shrink = 0.5, pad = 0.01)
  cb.ax.set_ylabel(label + ' [au]', rotation=270, labelpad = 20)

  days = np.array(24. * 60 * 60 / time_bin * np.arange(6), dtype = int);
  labl = ['%d'%d for d  in 24 *np.linspace(0,5, 6)];  
  plt.xticks(days, labl);
  plt.xlabel('time [hrs]');   
  plt.ylabel('worm id');
  plt.tight_layout()

  plt.title(phase_names[tr]);
  plt.tight_layout()

def plot_aligned(data_all, transitions, label = 'data', dt = time_bin, phase = 0):
  phase_names = ['hatch', 'L1', 'L2', 'L3', 'L4', 'L5'];

  ttref0 = np.max(transitions[:, 0]);
  ntt = data_all.shape[1] + ttref0 + 500;
  nw = data_all.shape[0];

  tr = phase;
  data_algn = np.zeros((nw, ntt));
  tref = np.max(transitions[:, tr]);
  for wid in range(nworms):
    tt = transitions[wid, tr];
    ts =  transitions[wid, 0];
    te =  transitions[wid, -1];      
    nt = te - ts;
    t0 = ts + tref - tt;
    t1 = t0 + nt;
    #print 'tref=%s, ts =%d, te=%d, tt =%d, nt = %d, t0=%d, t1=%d, ntt=%d' % (tref, ts,te,tt, nt, t0,t1, ntt)
    data_algn[wid, t0 : t1] = data_all[wid,  ts:te];

  #rmax = np.nanpercentile(roam_24hr, 95); 
  rmax = 1;
  plt.imshow(data_algn, interpolation = 'none', aspect = 'auto', cmap = plt.cm.viridis, vmax = rmax )
  cb = plt.colorbar(fraction = 0.025, shrink = 0.5, pad = 0.01)
  cb.ax.set_ylabel(label + ' [au]', rotation=270, labelpad = 20)

  days = np.array(24. * 60 * 60 / time_bin * np.arange(6), dtype = int);
  labl = ['%d'%d for d  in 24 *np.linspace(0,5, 6)];  
  plt.xticks(days, labl);
  plt.xlabel('time [hrs]');   
  plt.ylabel('worm id');
  plt.tight_layout()

  plt.title(phase_names[tr]);
  plt.tight_layout()

def plot_aligned(data_all, transitions, label = 'data', dt = time_bin, phase = 0):
  phase_names = ['hatch', 'L1', 'L2', 'L3', 'L4', 'L5'];

  ttref0 = np.max(transitions[:, 0]);
  ntt = data_all.shape[1] + ttref0 + 500;
  nw = data_all.shape[0];

  tr = phase;
  data_algn = np.zeros((nw, ntt));
  tref = np.max(transitions[:, tr]);
  for wid in range(nworms):
    tt = transitions[wid, tr];
    ts =  transitions[wid, 0];
    te =  transitions[wid, -1];      
    nt = te - ts;
    t0 = ts + tref - tt;
    t1 = t0 + nt;
    #print 'tref=%s, ts =%d, te=%d, tt =%d, nt = %d, t0=%d, t1=%d, ntt=%d' % (tref, ts,te,tt, nt, t0,t1, ntt)
    data_algn[wid, t0 : t1] = data_all[wid,  ts:te];

  #rmax = np.nanpercentile(roam_24hr, 95); 
  rmax = 1;
  plt.imshow(data_algn, interpolation = 'none', aspect = 'auto', cmap = plt.cm.viridis, vmax = rmax )
  cb = plt.colorbar(fraction = 0.025, shrink = 0.5, pad = 0.01)
  cb.ax.set_ylabel(label + ' [au]', rotation=270, labelpad = 20)

  days = np.array(24. * 60 * 60 / time_bin * np.arange(6), dtype = int);
  labl = ['%d'%d for d  in 24 *np.linspace(0,5, 6)];  
  plt.xticks(days, labl);
  plt.xlabel('time [hrs]');   
  plt.ylabel('worm id');
  plt.tight_layout()

  plt.title(phase_names[tr]);
  plt.tight_layout()

def plot_aligned(data_all, transitions, label = 'data', dt = time_bin, phase = 0):
  phase_names = ['hatch', 'L1', 'L2', 'L3', 'L4', 'L5'];

  ttref0 = np.max(transitions[:, 0]);
  ntt = data_all.shape[1] + ttref0 + 500;
  nw = data_all.shape[0];

  tr = phase;
  data_algn = np.zeros((nw, ntt));
  tref = np.max(transitions[:, tr]);
  for wid in range(nworms):
    tt = transitions[wid, tr];
    ts =  transitions[wid, 0];
    te =  transitions[wid, -1];      
    nt = te - ts;
    t0 = ts + tref - tt;
    t1 = t0 + nt;
    #print 'tref=%s, ts =%d, te=%d, tt =%d, nt = %d, t0=%d, t1=%d, ntt=%d' % (tref, ts,te,tt, nt, t0,t1, ntt)
    data_algn[wid, t0 : t1] = data_all[wid,  ts:te];

  #rmax = np.nanpercentile(roam_24hr, 95); 
  rmax = 1;
  plt.imshow(data_algn, interpolation = 'none', aspect = 'auto', cmap = plt.cm.viridis, vmax = rmax )
  cb = plt.colorbar(fraction = 0.025, shrink = 0.5, pad = 0.01)
  cb.ax.set_ylabel(label + ' [au]', rotation=270, labelpad = 20)

  days = np.array(24. * 60 * 60 / time_bin * np.arange(6), dtype = int);
  labl = ['%d'%d for d  in 24 *np.linspace(0,5, 6)];  
  plt.xticks(days, labl);
  plt.xlabel('time [hrs]');   
  plt.ylabel('worm id');
  plt.tight_layout()

  plt.title(phase_names[tr]);
  plt.tight_layout()

def downstddv(x):
    if x.size < 4:
        return np.nan
    median = np.nanpercentile(x, 50)
    return np.nanstd(x[x < median])

def __get_stats(data, groupby=0, staton=1):
    import itertools
    data = sorted(data, key=lambda xx: xx[groupby])
    x = []
    y = []
    for k, itr in itertools.groupby(data, key=lambda xx: xx[groupby]):
        all_y = [d[staton] for d in itr]
        y.append([np.nanpercentile(all_y, 5),
                  np.nanpercentile(all_y, 50),
                  np.nanpercentile(all_y, 95)])
        x.append(k)
    return np.array(x), np.array(y)

def p16(s, v):
        try:
            return np.nanpercentile(v, 16)
        except AttributeError:
            return np.percentile(v, 16)