Merge 87a699c into 0a25564

benchmarks/track_model_results.py

@@ -85,13 +85,13 @@ def track_rectangular_ratio(self, gdir):
 
     def track_mustar(self, gdir):
         self.cfg_init()
-        df = pd.read_csv(gdir.get_filepath('local_mustar')).iloc[0]
+        df = gdir.read_json('local_mustar')
         assert df['mu_star_allsame']
         return df['mu_star_glacierwide']
 
     def track_bias(self, gdir):
         self.cfg_init()
-        df = pd.read_csv(gdir.get_filepath('local_mustar')).iloc[0]
+        df = gdir.read_json('local_mustar')
         return df['bias']
 
     def track_mb_1980_avg(self, gdir):

oggm/cfg.py

@@ -171,8 +171,8 @@ def __setitem__(self, key, value):
 _doc = 'The monthly GCM climate timeseries stored in a netCDF file.'
 BASENAMES['cesm_data'] = ('cesm_data.nc', _doc)
 
-_doc = "A csv containing the glacier's t*, bias, and the flowlines' mu*"
-BASENAMES['local_mustar'] = ('local_mustar.csv', _doc)
+_doc = "A dict containing the glacier's t*, bias, and the flowlines' mu*"
+BASENAMES['local_mustar'] = ('local_mustar.json', _doc)
 
 _doc = 'List of dicts containing the data needed for the inversion.'
 BASENAMES['inversion_input'] = ('inversion_input.pkl', _doc)

oggm/core/climate.py

@@ -952,8 +952,7 @@ def calving_mb(gdir):
 
 
 @entity_task(log, writes=['local_mustar'])
-def local_t_star(gdir, *, ref_df=None,
-                 tstar=None, bias=None):
+def local_t_star(gdir, *, ref_df=None, tstar=None, bias=None):
     """Compute the local t* and associated glacier-wide mu*.
 
     If ``tstar`` and ``bias`` are not provided, they will be interpolated from
@@ -1054,13 +1053,13 @@ def local_t_star(gdir, *, ref_df=None,
     if not (cfg.PARAMS['min_mu_star'] < mustar < cfg.PARAMS['max_mu_star']):
         raise MassBalanceCalibrationError('mu* out of specified bounds.')
 
-    # Scalars in a small dataframe for later
-    df = pd.DataFrame()
-    df['rgi_id'] = [gdir.rgi_id]
-    df['t_star'] = [tstar]
-    df['bias'] = [bias]
-    df['mu_star_glacierwide'] = [mustar]
-    df.to_csv(gdir.get_filepath('local_mustar'), index=False)
+    # Scalars in a small dict for later
+    df = dict()
+    df['rgi_id'] = gdir.rgi_id
+    df['t_star'] = int(tstar)
+    df['bias'] = bias
+    df['mu_star_glacierwide'] = mustar
+    gdir.write_json(df, 'local_mustar')
 
 
 def _mu_star_per_minimization(x, fls, cmb, temp, prcp, widths):
@@ -1168,9 +1167,9 @@ def mu_star_calibration(gdir):
     """
 
     # Interpolated data
-    df = pd.read_csv(gdir.get_filepath('local_mustar'))
-    t_star = df['t_star'].iloc[0]
-    bias = df['bias'].iloc[0]
+    df = gdir.read_json('local_mustar')
+    t_star = df['t_star']
+    bias = df['bias']
 
     # For each flowline compute the apparent MB
     fls = gdir.read_pickle('inversion_flowlines')
@@ -1220,10 +1219,10 @@ def mu_star_calibration(gdir):
     # Store diagnostics
     mus = []
     weights = []
-    for i, fl in enumerate(fls):
-        df['mustar_flowline_{:03d}'.format(i+1)] = fl.mu_star
+    for fl in fls:
         mus.append(fl.mu_star)
         weights.append(np.sum(fl.widths))
+    df['mu_star_per_flowline'] = mus
     df['mu_star_flowline_avg'] = np.average(mus, weights=weights)
     all_same = np.allclose(mus, mus[0], atol=1e-3)
     df['mu_star_allsame'] = all_same
@@ -1235,7 +1234,7 @@ def mu_star_calibration(gdir):
                                               'glacier wide mu* and the '
                                               'flowlines mu*.')
     # Write
-    df.to_csv(gdir.get_filepath('local_mustar'), index=False)
+    gdir.write_json(df, 'local_mustar')
 
 
 @entity_task(log, writes=['inversion_flowlines', 'linear_mb_params'])

oggm/core/massbalance.py

@@ -262,10 +262,10 @@ def __init__(self, gdir, mu_star=None, bias=None,
         super(PastMassBalance, self).__init__()
         self.valid_bounds = [-1e4, 2e4]  # in m
         if mu_star is None:
-            df = pd.read_csv(gdir.get_filepath('local_mustar'))
-            mu_star = df['mu_star_glacierwide'][0]
+            df = gdir.read_json('local_mustar')
+            mu_star = df['mu_star_glacierwide']
             if check_calib_params:
-                if not df['mu_star_allsame'][0]:
+                if not df['mu_star_allsame']:
                     raise RuntimeError('You seem to use the glacier-wide mu* '
                                        'to compute the mass-balance although '
                                        'this glacier has different mu* for '
@@ -275,8 +275,8 @@ def __init__(self, gdir, mu_star=None, bias=None,
 
         if bias is None:
             if cfg.PARAMS['use_bias_for_run']:
-                df = pd.read_csv(gdir.get_filepath('local_mustar'))
-                bias = df['bias'][0]
+                df = gdir.read_json('local_mustar')
+                bias = df['bias']
             else:
                 bias = 0.
 
@@ -478,8 +478,8 @@ def __init__(self, gdir, mu_star=None, bias=None,
                                      input_filesuffix=input_filesuffix)
 
         if y0 is None:
-            df = pd.read_csv(gdir.get_filepath('local_mustar'))
-            y0 = df['t_star'][0]
+            df = gdir.read_json('local_mustar')
+            y0 = df['t_star']
 
         # This is a quick'n dirty optimisation
         try:
@@ -658,8 +658,8 @@ def __init__(self, gdir, mu_star=None, bias=None,
             self.years = self.mbmod.years
         else:
             if y0 is None:
-                df = pd.read_csv(gdir.get_filepath('local_mustar'))
-                y0 = df['t_star'][0]
+                df = gdir.read_json('local_mustar')
+                y0 = df['t_star']
             self.years = np.arange(y0-halfsize, y0+halfsize+1)
         self.yr_range = (self.years[0], self.years[-1]+1)
         self.ny = len(self.years)

oggm/tests/test_models.py

@@ -292,9 +292,9 @@ def test_past_mb_model(self):
         gdir = self.gdir
         init_present_time_glacier(gdir)
 
-        df = pd.read_csv(gdir.get_filepath('local_mustar'))
-        mu_star = df['mu_star_glacierwide'][0]
-        bias = df['bias'][0]
+        df = gdir.read_json('local_mustar')
+        mu_star = df['mu_star_glacierwide']
+        bias = df['bias']
 
         # Climate period
         yrp = [1851, 2000]
@@ -488,8 +488,8 @@ def test_constant_mb_model(self):
         gdir = self.gdir
         init_present_time_glacier(gdir)
 
-        df = pd.read_csv(gdir.get_filepath('local_mustar'))
-        bias = df['bias'][0]
+        df = gdir.read_json('local_mustar')
+        bias = df['bias']
 
         h, w = gdir.get_inversion_flowline_hw()
 

oggm/tests/test_prepro.py

@@ -1272,7 +1272,7 @@ def test_find_tstars(self):
         climate.local_t_star(gdir)
         cfg.PARAMS['tstar_search_window'] = [0, 0]
 
-        df = pd.read_csv(gdir.get_filepath('local_mustar'))
+        df = gdir.read_json('local_mustar')
         np.testing.assert_allclose(df['t_star'], t_star)
         np.testing.assert_allclose(df['bias'], bias)
 
@@ -1363,7 +1363,7 @@ def test_local_t_star(self):
         np.testing.assert_allclose(tmb, 0., atol=0.01)
         np.testing.assert_allclose(fls[-1].flux[-1], 0., atol=0.01)
 
-        df = pd.read_csv(gdir.get_filepath('local_mustar')).iloc[0]
+        df = gdir.read_json('local_mustar')
         assert df['mu_star_allsame']
         np.testing.assert_allclose(mu_ref, df['mu_star_flowline_avg'],
                                    atol=1e-3)
@@ -1522,13 +1522,13 @@ def test_correct(self):
         mus = np.array([fl.mu_star for fl in fls])
         assert np.max(mus[[1, 2, 3]]) < (np.max(mus[[0, -1]]) / 2)
 
-        df = pd.read_csv(gdir.get_filepath('local_mustar'))
-        mu_star_gw = df['mu_star_glacierwide'][0]
+        df = gdir.read_json('local_mustar')
+        mu_star_gw = df['mu_star_glacierwide']
 
         assert np.max(mus[[1, 2, 3]]) < mu_star_gw
         assert np.min(mus[[0, -1]]) > mu_star_gw
 
-        bias = df['bias'][0]
+        bias = df['bias']
         np.testing.assert_allclose(bias, 0)
 
         from oggm.core.massbalance import (MultipleFlowlineMassBalance,
@@ -1573,7 +1573,6 @@ def test_and_compare_two_methods(self):
         fls = gdir.read_pickle('inversion_flowlines')
 
         # These are the params:
-        # pd.read_csv(gdir.get_filepath('local_mustar'))
         # rgi_id                RGI50-11.00666
         # t_star                          1931
         # bias                               0

oggm/utils.py

@@ -2566,7 +2566,7 @@ def glacier_characteristics(gdirs, filesuffix='', path=True,
             pass
         try:
             # MB calib
-            df = pd.read_csv(gdir.get_filepath('local_mustar')).iloc[0]
+            df = gdir.read_json('local_mustar')
             d['t_star'] = df['t_star']
             d['mu_star_glacierwide'] = df['mu_star_glacierwide']
             d['mu_star_flowline_avg'] = df['mu_star_flowline_avg']
@@ -3127,8 +3127,7 @@ def read_pickle(self, filename, use_compression=None, filesuffix=''):
 
         return out
 
-    def write_pickle(self, var, filename, use_compression=None,
-                     filesuffix=''):
+    def write_pickle(self, var, filename, use_compression=None, filesuffix=''):
         """ Writes a variable to a pickle on disk.
 
         Parameters
@@ -3150,6 +3149,42 @@ def write_pickle(self, var, filename, use_compression=None,
         with _open(fp, 'wb') as f:
             pickle.dump(var, f, protocol=-1)
 
+    def read_json(self, filename, filesuffix=''):
+        """Reads a JSON file located in the directory.
+
+        Parameters
+        ----------
+        filename : str
+            file name (must be listed in cfg.BASENAME)
+        filesuffix : str
+            append a suffix to the filename (useful for experiments).
+
+        Returns
+        -------
+        A dictionary read from the JSON file
+        """
+
+        fp = self.get_filepath(filename, filesuffix=filesuffix)
+        with open(fp, 'r') as f:
+            out = json.load(f)
+        return out
+
+    def write_json(self, var, filename, filesuffix=''):
+        """ Writes a variable to a pickle on disk.
+
+        Parameters
+        ----------
+        var : object
+            the variable to write to JSON (must be a dictionary)
+        filename : str
+            file name (must be listed in cfg.BASENAME)
+        filesuffix : str
+            append a suffix to the filename (useful for experiments).
+        """
+        fp = self.get_filepath(filename, filesuffix=filesuffix)
+        with open(fp, 'w') as f:
+            json.dump(var, f)
+
     def create_gridded_ncdf_file(self, fname):
         """Makes a gridded netcdf file template.