Up plots

Plots/Hists/all_zones_median_cli.py

@@ -2,7 +2,7 @@
 from os import path
 
 import matplotlib.pyplot as plt
-import tol_colors as tc
+import xarray as xr
 
 import lib
 import lib.data.hists as lh
@@ -10,36 +10,51 @@ import Plots.util as plot_util
 
 plot_util.use_tex(True)
 
-args = dict(region='GS', days=1, kind='2thr', mask=True)
+args = dict(region='GS', days=1, kind='2thr')
 fixes = dict(
     scale=30.,
     number=2,
     coef=0,
     thr_lo=5.,
-    thr_hi=15.
+    thr_hi=10.
 )
 
 
-ds = lh.get_data(args, fixes=fixes)
+ds = lh.get_data(args, fixes=fixes, mask=True)
 ds = ds.squeeze()
 ds = lh.resample_8D_multiple_years(ds)
 ds = ds.load()
 
-# join = ['hi' if m == 'low' else 'low' for m in ds.mask]
-# ds = xr.core.groupby.DatasetGroupBy(
-#     ds, xr.DataArray(join, dims=['mask'], name='mask')
-# ).sum(keep_attrs=True)
+pdf = lh.get_pdf(ds)
+ds['med'] = lh.get_median(pdf).load()
 
-N = lh.get_N(ds.hist_CHL)
+ds = ds.drop_vars(['hist_CHL', 'hist_sst'])
+
+ds_nm = lh.get_data(args, fixes=fixes, mask=False)
+ds_nm = ds_nm.squeeze()
+ds_nm = lh.resample_8D_multiple_years(ds_nm)
+ds_nm = ds_nm.load()
+N = lh.get_N(ds_nm.hist_CHL)
 frac = N / N.sum('mask') * 100.
 ds['frac'] = frac
 
-pdf = lh.get_pdf(ds)
-ds['med'] = lh.get_median(pdf).load()
+T = lh.get_total(ds_nm.hist_CHL)
 
-ds = ds.drop_vars(['hist_CHL', 'hist_sst'])
+def get_C_mean(T, N, masks):
+    Ts, Ns = [da.sel(mask=masks).sum('mask') for da in [T, N]]
+    return Ts/Ns
+
+
+# Chl moyenne
+C = get_C_mean(T, N, ['low', 'mid', 'hi'])
+
+C_nf_hi = get_C_mean(T, N, ['low', 'mid'])
+C_nf_mid = get_C_mean(T, N, ['low', 'hi'])
+C_nf = xr.concat([C_nf_mid, C_nf_hi], dim=xr.DataArray(['mid', 'hi'],
+                                                       dims=['mask']))
+ds['surplus'] = (C-C_nf)/C_nf * 100.
 
-clis = lh.get_climatology(ds)
+clis = lh.get_climatology(ds.load())
 
 
 def loop_zones():
@@ -57,10 +72,10 @@ def plot(cli, cli_d1, cli_d9, ax, var, sel, color='k', ls='-'):
                     color=color, alpha=.3)
 
 
-colors = plot_util.get_mask_colors('vibrant', 'red', 'blue', 'teal')
+colors = plot_util.get_mask_colors()
 
-fig, axes = plt.subplots(3, 3, figsize=(10, 6), sharex=True)
-fig.subplots_adjust(left=0.06, bottom=0.05, right=0.98, top=0.95,
+fig, axes = plt.subplots(4, 3, figsize=(8, 6), sharex=True)
+fig.subplots_adjust(left=0.08, bottom=0.05, right=0.98, top=0.95,
                     hspace=0.1, wspace=0.15)
 
 for ax in axes.flat:
@@ -80,8 +95,10 @@ for j, zone in enumerate(loop_zones()):
     for m in ['mid', 'hi']:
         sel['mask'] = m
         plot(*clis, axes[2, j], 'frac', sel, color=colors[m])
+        plot(*clis, axes[3, j], 'surplus', sel, color=colors[m])
     axes[2, j].set_ylim(0, 60)
-    plt.setp(axes[2, j].xaxis.get_majorticklabels(), ha='left')
+    axes[3, j].set_ylim(-6, 23)
+    plt.setp(axes[3, j].xaxis.get_majorticklabels(), ha='left')
 
 
 
@@ -90,6 +107,7 @@ axes[0, 0].set_xlim(*(clis[0].time[[0, -1]].values))
 axes[0, 0].set_ylabel(plot_util.SST_LABEL)
 axes[1, 0].set_ylabel(plot_util.CHL_LABEL)
 axes[2, 0].set_ylabel('Fronts surface [\%]')
+axes[3, 0].set_ylabel('Surplus [\%]')
 
 axes[0, 0].set_title('South')
 axes[0, 1].set_title('Jet')

Plots/Hists/article_hist_sensitivity.py

 """Sensitivity to parameters.
 
-4 subplots. Each showing Chl histogram for a full year in background and front
-for different values of parameters. One parameter is changed each time. The
-threshold for each parameter is choosen so that the max surface fraction of
-fronts is ~10%.
+2 tests: varying rolling window size (scale) and coefficients weights.
+Each test is done for all 3 GS3 zones.
 
-Take zones south of jet (GS3_I and GS3_S).
+Threshold is not changed between tests.
 
+Test only done with ostia data.
 """
 
 from os import path
@@ -38,34 +37,13 @@ fixes_def = dict(
 
 
 def get_data(fixes):
-    def preprocess(ds):
-        d = 'threshold'
-        if d in ds.coords and ds.coords[d].size <= 1:
-            ds = ds.isel({d: 0})
-        return ds.reset_coords([d], drop=True)
-
-    ds = lh.get_data(args, fixes=fixes, open_mf_kw=dict(process=preprocess))
-    if 'CHL' in ds.VARS:
-        var = 'CHL'
-    else:
-        var = 'chl_ocx'
-    hist = ds[lh.var_name('hist', var)].squeeze()
-    # if 'zone' in hist.dims:
-    #     hist = hist.sel(zone=fixes['zone'])
-
+    ds = lh.get_data(args, fixes=fixes)
+    hist = ds.hist_CHL.squeeze()
     hist = hist.sum('time', keep_attrs=True)
     hist.load()
     pdf = lh.get_pdf(hist)
     med = lh.get_median(pdf)
-    return med, pdf, pdf[lh.var_name('bins', var)]
-
-
-def loop_thr(fixes_iter):
-    for values in zip(*fixes_iter.values()):
-        fixes = fixes_def.copy()
-        for k, v in zip(fixes_iter.keys(), values):
-            fixes[k] = v
-        yield fixes
+    return med, pdf, pdf.bins_CHL
 
 
 cset = tc.tol_cset('high-contrast')
@@ -81,71 +59,59 @@ med_bkg_tot = []
 ## Subplot Scale
 print("Scale", flush=True)
 colors = [cset.yellow, cset.red, cset.blue]
-med_frt = []
-med_bkg = []
 
-fixes_iter = dict(
-    scale=[20., 30., 40.],
-    threshold=[5., 5., 5.]
-)
+fixes = fixes_def.copy()
+fixes['scale'] = [20., 30., 40.]
+
+med, pdf, bins = get_data(fixes)
 
 labels = []
 handles = []
-for i, fixes in enumerate(loop_thr(fixes_iter)):
-    med, pdf, bins = get_data(fixes)
-
-    for z, ax in zip('NIS', axes[:, 0]):
-        zone = 'GS3_' + z
-        ax.plot(bins, pdf.sel(mask='frt', zone=zone), color=colors[i], ls='-')
-        ax.plot(bins, pdf.sel(mask='bkg', zone=zone), color=colors[i], ls='--')
-        med_frt.append(med.sel(mask='frt', zone=zone).values)
-        med_bkg.append(med.sel(mask='bkg', zone=zone).values)
-        ax.axvline(med_frt[-1], color=colors[i], ls='-')
-        ax.axvline(med_bkg[-1], color=colors[i], ls='--')
+for z, ax in zip('NIS', axes[:, 0]):
+    zone = 'GS3_' + z
+    for i, s in enumerate(fixes['scale']):
+        sel = dict(zone=zone, scale=s)
+        ax.plot(bins, pdf.sel(mask='frt', **sel), color=colors[i], ls='-')
+        ax.plot(bins, pdf.sel(mask='bkg', **sel), color=colors[i], ls='--')
+        ax.axvline(med.sel(mask='frt', **sel), color=colors[i], ls='-')
+        ax.axvline(med.sel(mask='bkg', **sel), color=colors[i], ls='--')
 
-    labels.append(r'\SI{{{:.0f}}}{{\kilo\meter}}'.format(fixes['scale']))
-    handles.append(plt.Line2D((), (), color=colors[i], ls='-'))
 
+labels = [r'\SI{{{:.0f}}}{{\kilo\meter}}'.format(s) for s in fixes['scale']]
+handles = [plt.Line2D((), (), color=c) for c in colors]
 axes[1, 0].legend(labels=labels, handles=handles, framealpha=1)
 
-print("FRT std: ", np.std(med_frt))
-med_frt_tot += list(np.array(med_frt) - np.mean(med_frt))
-med_bkg_tot += list(np.array(med_bkg) - np.mean(med_bkg))
+med_norm = med - med.mean('scale')
+print("FRT std: ", med_norm.sel(mask='frt').std().values)
+med_frt_tot += list(med_norm.sel(mask='frt').values.reshape(-1))
+med_bkg_tot += list(med_norm.sel(mask='bkg').values.reshape(-1))
 
 
 ## Subplot Variant
 print("Variant")
-ax = axes[0, 1]
 colors = [cset.black, cset.yellow, cset.red, cset.blue]
-med_frt = []
-med_bkg = []
-
-fixes_iter = dict(
-    coef=[0, 1, 2, 3],
-    threshold=[5., 5., 5., 5.]
-)
 
-for i, fixes in enumerate(loop_thr(fixes_iter)):
-    med, pdf, bins = get_data(fixes)
+fixes = fixes_def.copy()
+fixes['coef'] = [0, 1, 2, 3]
+med, pdf, bins = get_data(fixes)
 
-    for z, ax in zip('NIS', axes[:, 1]):
-        zone = 'GS3_' + z
-        ax.plot(bins, pdf.sel(mask='frt', zone=zone), color=colors[i], ls='-')
-        ax.plot(bins, pdf.sel(mask='bkg', zone=zone), color=colors[i], ls='--')
-
-        med_frt.append(med.sel(mask='frt', zone=zone).values)
-        med_bkg.append(med.sel(mask='bkg', zone=zone).values)
-        ax.axvline(med_frt[-1], color=colors[i], ls='-')
-        ax.axvline(med_bkg[-1], color=colors[i], ls='--')
+for z, ax in zip('NIS', axes[:, 1]):
+    zone = 'GS3_' + z
+    for i, coef in enumerate(fixes['coef']):
+        sel = dict(zone=zone, coef=coef)
+        ax.plot(bins, pdf.sel(mask='frt', **sel), color=colors[i], ls='-')
+        ax.plot(bins, pdf.sel(mask='bkg', **sel), color=colors[i], ls='--')
+        ax.axvline(med.sel(mask='frt', **sel), color=colors[i], ls='-')
+        ax.axvline(med.sel(mask='bkg', **sel), color=colors[i], ls='--')
 
 labels = ['Balanced', 'More skewness', 'More variance', 'More bimodality']
 handles = [plt.Line2D((), (), color=c) for c in colors]
 axes[1, 1].legend(labels=labels, handles=handles, framealpha=1)
 
-print("FRT std: ", np.std(med_frt))
-med_frt_tot += list(np.array(med_frt) - np.mean(med_frt))
-med_bkg_tot += list(np.array(med_bkg) - np.mean(med_bkg))
-
+med_norm = med - med.mean('coef')
+print("FRT std: ", med_norm.sel(mask='frt').std().values)
+med_frt_tot += list(med_norm.sel(mask='frt').values.reshape(-1))
+med_bkg_tot += list(med_norm.sel(mask='bkg').values.reshape(-1))
 
 print("TOTAL FRT std: ", np.std(med_frt_tot))
 print("TOTAL BKG std: ", np.std(med_bkg_tot))

Plots/Hists/article_median.py

@@ -25,7 +25,7 @@ fixes = dict(
 ds = lh.get_data(args, fixes=fixes)
 ds = ds.squeeze()
 ds = lh.resample_8D_multiple_years(ds, days=3)
-pdf = lh.get_pdf(ds, variables='CHL')
+pdf = lh.get_pdf(ds, variables='CHL').load()
 med = lh.get_median(pdf)
 med = med.load()
 
@@ -38,11 +38,23 @@ right = 0.96
 fig.subplots_adjust(left=0.08, bottom=0.05, right=right, top=0.93,
                     hspace=0.1)
 
+
+med_frt_std = 0.0041
+med_bkg_std = 0.0020
+error = dict(
+    low=med_bkg_std*2,
+    mid=med_frt_std*2,
+    hi=med_frt_std*2
+)
+
+
 for ax, z in zip(axes, 'NIS'):
     zone = lh.get_GS3_name(z)
     for m, c in colors.items():
-        ax.plot(med.time, med.sel(zone=zone, mask=m),
-                color=c)
+        M = med.sel(zone=zone, mask=m)
+        ax.plot(med.time, M, color=c)
+        ax.fill_between(med.time.values, M-error[m], M+error[m],
+                        color=c, alpha=0.3)
 
     if z != 'N':
         ax.set_ylim(None, ymax.sel(zone=zone))
@@ -52,8 +64,8 @@ for ax, z in zip(axes, 'NIS'):
 
 for i, ax in enumerate(axes):
     ax.annotate(chr(97+i)+')', (0., 1.), xycoords='axes fraction',
-                xytext=(-40, 2), textcoords='offset points',
-                ha='left', va='bottom')
+                xytext=(-5, -5), textcoords='offset points',
+                ha='right', va='bottom')
     ax.xaxis.axis_date()
     ax.set_ylabel(plot_util.CHL_LABEL, labelpad=10.0)
 

Plots/Hists/article_median_cli.py

@@ -16,19 +16,13 @@ plot_util.set_style()
 plot_util.use_tex(True)
 
 
-args = dict(
-    region='GS',
-    days=1
-)
+args = dict(region='GS', days=1, kind='2thr')
 
-zone = 'box7'
 fixes = dict(
     scale=30.,
     number=2,
     coef=0,
-    zone=zone,
-    threshold=15.0
-    # threshold=dict(S=6.0, N=15.0)[zone[-1]]
+    thr_lo=15.0
 )
 
 ds = lib.data.hists.get_data(args, fixes=fixes)
@@ -83,23 +77,20 @@ def plot(ax, var, sel, color='k', ls='-'):
 
 
 ## Plot
-fig, axes = plt.subplots(3, 1, figsize=(6, 4), sharex=True)
+fig, axes = plt.subplots(4, 1, figsize=(6, 4), sharex=True)
 fig.subplots_adjust(left=0.11, bottom=0.07, right=0.98, top=0.935,
                     hspace=0.1)
 
-ax1, ax2, ax3 = axes
+ax1, ax2, ax3, ax4 = axes
 
 cset = tc.tol_cset('vibrant')
 
-plot(ax1, 'med', dict(variable='CHL', mask='frt'),
-     color=cset.orange)
-plot(ax1, 'med', dict(variable='CHL', mask='bkg'),
-     color=cset.teal)
-plot(ax2, 'med', dict(variable='sst', mask='frt'),
-     color=cset.orange)
-plot(ax2, 'med', dict(variable='sst', mask='bkg'),
-     color=cset.teal)
+plot(ax1, 'med', dict(variable='CHL', mask='frt'), color=cset.orange)
+plot(ax1, 'med', dict(variable='CHL', mask='bkg'), color=cset.teal)
+plot(ax2, 'med', dict(variable='sst', mask='frt'), color=cset.orange)
+plot(ax2, 'med', dict(variable='sst', mask='bkg'), color=cset.teal)
 plot(ax3, 'frac', dict())
+plot(ax4, 'surplus', dict())
 
 
 labelpad = 11

Plots/Hists/article_timeline.py

@@ -15,12 +15,7 @@ plot_util.set_style()
 plot_util.use_tex(True)
 
 
-args = dict(
-    region='GS',
-    days=1
-)
-
-zone = 'GS3_N'
+args = dict(region='GS', days=1, kind='2thr')
 fixes = dict(
     scale=30.,
     number=2,
@@ -29,11 +24,10 @@ fixes = dict(
     var='CHL'
 )
 
-start = 2005
-# end = start + 4
-end = 2015
+start = 2007
+end = start + 4
 
-ds = lib.data.hists.get_data(args, remove_dims=['threshold'], fixes=fixes)
+ds = lib.data.hists.get_data(args, fixes=fixes)
 ds = ds.squeeze()
 ds = ds.sel(time=slice(datetime(start, 1, 1), datetime(end, 12, 31)))
 ds = ds.resample(time='8D').sum()

Plots/Hists/hists_seasons.py

@@ -7,9 +7,14 @@ import tol_colors as tc
 
 import lib
 import lib.data.hists as lh
+import Plots.util as plot_util
 
 
+plot_util.set_style()
+plot_util.use_tex(True)
+
 args = lib.get_args(['region', 'days', 'fixes'])
+args['kind'] = '2thr'
 fixes = dict(
     scale=30.,
     number=2,
@@ -22,39 +27,41 @@ args['fixes'] = fixes
 
 ds = lh.get_data(args)
 ds = ds.squeeze()
-
 ds = ds.groupby('time.season').sum(keep_attrs=True)
 ds.load()
 pdf = lh.get_pdf(ds)
 
-cset = tc.tol_cset('vibrant')
+colors = plot_util.get_mask_colors()
 
 for var in pdf.VARS:
-    fig, axes = plt.subplots(3, 4, figsize=(10, 8), sharex=True)
-    fig.subplots_adjust(left=0.05, bottom=0.05, right=0.98, top=0.94,
+    fig, axes = plt.subplots(3, 4, figsize=(8, 6), sharex=True)
+    fig.subplots_adjust(left=0.05, bottom=0.08, right=0.98, top=0.95,
                         wspace=0.1, hspace=0.1)
 
-    fig.suptitle(var)
-
     for i, (z, z_title) in enumerate(zip('NIS', ['North', 'Jet', 'South'])):
         axes[i, 0].set_ylabel(z_title)
         zone = 'GS3_{}'.format(z)
         for j, season in enumerate(['DJF', 'MAM', 'JJA', 'SON']):
             axes[0, j].set_title(season)
             p = pdf[lh.var_name('pdf', var)].sel(zone=zone, season=season)
-            for mask, color in zip(['low', 'mid', 'hi'],
-                                   ['cyan', 'orange', 'red']):
+            for mask, color in colors.items():
                 axes[i, j].plot(p[lh.var_name('bins', var)],
-                                p.sel(mask=mask), color=getattr(cset, color))
+                                p.sel(mask=mask), color=color)
 
     for ax in axes.flat:
         ax.tick_params(axis='y', labelleft=False)
 
+    for ax in axes[-1, :]:
+        ax.set_xlabel('--')
     if var == 'CHL':
+        axes[-1, 0].set_xlabel(plot_util.CHL_LABEL)
         axes[0, 0].set_xlim(0, 1.1)
     elif var == 'sst':
+        axes[-1, 0].set_xlabel(plot_util.SST_LABEL)
         axes[0, 0].set_xlim(-4, 32)
 
+    axes[1, 2].legend(*plot_util.get_mask_legend(colors))
+
     fig.canvas.draw()
     fig.savefig(path.join(lib.root_plot, 'Hists', 'hists_season',
                           'fig_{}.png'.format(var)),

Plots/Hists/hists_seasons_cli.py

@@ -14,7 +14,6 @@ fixes = dict(
     scale=30.,
     number=2,
     coef=0,
-    zone='GS3_N',
     var='CHL'
 )
 

Plots/Images/article_example_zoom.py

@@ -61,7 +61,7 @@ fig.delaxes(axes[0, -1])
 
 def add_cbar(im, ax):
     bbox = ax.bbox.transformed(fig.transFigure.inverted())
-    cax = fig.add_axes([bbox.xmax, bbox.ymin, 0.01, bbox.ymax-bbox.ymin])
+    cax = fig.add_axes([bbox.xmax, bbox.ymin, 0.01, bbox.height])
     cbar = fig.colorbar(im, cax=cax)
     return cbar
 

Plots/article_zone_separation.py

+import itertools
 from os import path
 
 import cartopy.crs as ccrs
@@ -10,10 +11,12 @@ from matplotlib.colors import LogNorm
 import lib
 import lib.data.ostia as lo
 import lib.data.globcolour as lg
-import lib.data.SN_separation as lthr
 import Plots.util as plot_util
 import Compute.compute_temp_separation_zones as csep
 
+plot_util.set_style()
+plot_util.use_tex(True)
+
 args = dict(
     region='GS',
     level='L3',
@@ -36,33 +39,98 @@ def normalize_hist(bins, hist):
 sst = lo.get_data(args).sst.squeeze()
 chl = lg.get_data(args).CHL.squeeze()
 
+sep = csep._get_separation(sst.sel(lat=slice(None, 32)).to_numpy())
+thr = sep['threshold']
+
+## Plot
 var_range = dict(sst=[-4, 30], CHL=[1e-1, 5])
 var_range_kw = {v: dict(zip(['vmin', 'vmax'], r)) for v, r in var_range.items()}
 
 cset_sep = tc.tol_cset('bright')
+zone_names = dict(N='North', I='Jet', S='South')
+
+fig = plt.figure(figsize=(7.6, 6))
+gs = fig.add_gridspec(2, 1, left=0.04, bottom=0.07, right=0.94, top=0.97,
+                      hspace=0.1)
+
+## Maps
+axes_map = gs[0, 0].subgridspec(1, 2, wspace=0.2).subplots(
+    subplot_kw=dict(projection=ccrs.PlateCarree()))
+axes_map[0].sharex(axes_map[1])
+axes_map[0].sharey(axes_map[1])
+
+mult_sst = 9./0.150  # So that hist is 9 at max
+im_sst = sst.plot.imshow(
+    ax=axes_map[0], cmap=cm.thermal, **var_range_kw['sst'],
+    add_labels=False, add_colorbar=False
+)
+im_chl = chl.plot.imshow(
+    ax=axes_map[1], cmap=cm.algae, **var_range_kw['CHL'],
+    add_labels=False, add_colorbar=False, norm=LogNorm()
+)
+
+for ax in axes_map:
+    ax.xaxis.set_major_formatter(plot_util.LonFormatter('.0f'))
+    ax.yaxis.set_major_formatter(plot_util.LatFormatter('.0f'))
+    ax.set_aspect('equal')
 
-fig, axes = plt.subplots(2, 2, figsize=(8, 6))
-fig.subplots_adjust(left=0.06, bottom=0.05, right=0.97, top=0.95)
+    ax.coastlines(color='k', lw=0.7)
+    gl = ax.gridlines(draw_labels=True, lw=0.7)
+    gl.top_labels = False
+    gl.right_labels = False
+    gl.xformatter = plot_util.LonFormatter('.0f')
+    gl.yformatter = plot_util.LatFormatter('.0f')
+
+    sst.plot.contour(ax=ax, levels=[thr], colors=cset_sep.purple,
+                     add_labels=False)
+    ax.axhline(32, color=cset_sep.purple, ls='--')
 
-axes[0, 0].sharex(axes[0, 1])
-axes[0, 0].sharey(axes[0, 1])
 
-sst.plot.imshow(ax=axes[0, 0], cmap=cm.thermal, **var_range_kw['sst'],
-                cbar_kwargs=dict(label='SST', pad=0.02))
-chl.plot.imshow(ax=axes[0, 1], cmap=cm.algae, **var_range_kw['CHL'],
-                cbar_kwargs=dict(label='CHL', pad=0.02), norm=LogNorm())
+ax = axes_map[0]
+kw = dict(color='#E2E2E2', ha='center', size='large')
+lat_str = r'\textbf{{{}}}'
+ax.annotate(lat_str.format(zone_names['N']), (-46, 48), **kw)
+ax.annotate(lat_str.format(zone_names['I']), (-50, 35), **kw)
+ax.annotate(lat_str.format(zone_names['S']), (-55, 24), **kw)
+
+var_labels = [plot_util.SST_LABEL, plot_util.CHL_LABEL]
+caxes = []
+for ax, im, lab in zip(axes_map, [im_sst, im_chl], var_labels):
+    cax = plot_util.add_cax(ax, axes_class=plt.Axes)
+    plt.colorbar(im, cax=cax, label=lab)
+    caxes.append(cax)
+
+
+## Hists
+fig.canvas.draw()
+# Impose positions of axes from axes_map
+axes_hist = []
+for ax, cax in zip(axes_map, caxes):
+    bbox = ax.bbox.transformed(fig.transFigure.inverted())
+    bbox_cax = cax.bbox.transformed(fig.transFigure.inverted())
+    axes_hist.append(fig.add_axes(
+        (bbox.xmin, gs.bottom,