Clean up commented out print statements throughout

ssrs/hrrr/hrrr.py

@@ -222,7 +222,6 @@ def get_xarray_for_regex(self, regex, remove_grib=False):
             Datasets if the requested variables have different
             coordinates.
         """
-        #print(f'regex is {regex}')
         if regex not in self.datasets:
             # Retrieve GRIB2 data with Herbie
             # Note: There is an issue with how Herbie handles regular
@@ -235,16 +234,11 @@ def get_xarray_for_regex(self, regex, remove_grib=False):
                     remove_grib=remove_grib
                 )
 
-            #print(f'ds in hrrr is {ds}')
-            #print(f'len of ds is {len(ds)}')
-            #print(f'type of ds is {type(ds)}')
             # Sometimes we have a list of xarray Dataset, depending on the
             # regex requested. To make sure the coords call below is available,
             # we get the first Dataset from the list
             if isinstance(ds, list):
-                #print(f'ds is a list')
                 for i, ds_curr in enumerate(ds):
-                    #print(f'\n\nthis is a single ds, number {i}: {ds_curr}\n\n')
                     # Convert longitude from degrees east with range (0,360) to
                     # degrees east/west for +/- values with range (-180,180)
                     lon = ds[i].coords['longitude'].values
@@ -499,7 +493,6 @@ def wind_velocity_direction_at_altitude(
             vs = uv_grd[v_data_var].where(mask, drop=True).values.flatten()
             us = us[~np.isnan(us)]
             vs = vs[~np.isnan(vs)]
-            #print(f'inside hrrr. us is {us}')
             #print(f'inside hrrr. shape of us is {np.shape(us)}')
 
             # Use linear B-spline interpolation to find U and V values
@@ -678,7 +671,6 @@ def mask_at_coordinates(
             The mask to be used with the coordinates of the xarray
             dataset.
         """
-        #print(f'inside mask_at_coordinate, southwest_lonlat is {southwest_lonlat}')
 
         try:
             southwest_lon, southwest_lat = southwest_lonlat
@@ -897,7 +889,6 @@ def get_albedo(self, southwest_lonlat=None, extent=None, res=50):
         )
 
         # Let's make sure the Su and Sd xarrays only really one data variable
-        #print(f'variables in Su are {list(Su.keys())}')
         if len(list(Su.keys())) != 1:
             raise ValueError(f"Computation of Su inside get_albedo returned more",\
                              f"than one data variable. This shouldn't occur")
@@ -910,8 +901,6 @@ def get_albedo(self, southwest_lonlat=None, extent=None, res=50):
         Su = Su[list(Su.keys())[0]]
         Sd = Sd[list(Sd.keys())[0]]
 
-        #print(f'Su is {Su}')
-        #print(f'mean of Su is {np.mean(Su)}')
         if np.mean(Su) == 0:
             alpha_surface_albedo = np.ones_like(Su) # night
             # TODO: this is a placeholder. We should just compute the
@@ -962,12 +951,8 @@ def interp_onto_regular_grid(
         xform_lat_sq = xform_lat_sq - yref
 
         # create grid. bounds is now [xmin, xmax, ymin, ymax]
-        #print(f'bounds is (should be xmin,xmax, ymin,ymax) {bounds}')
         x = np.arange(bounds[0], bounds[2], res)
         y = np.arange(bounds[1], bounds[3], res)
-        #print(f'inside interp_onto_regular_grid. x is {x}')
-        #print(f'inside interp_onto_regular_grid. y is {y}')
-        #print(f'inside interp_onto_regular_grid. res is {res}')
 
         # Sometimes when the division of the domain extent and resolution is not 
         # a round number, we end up with arrays that are 1 position too large.
@@ -1091,8 +1076,6 @@ def get_var_on_grid(self,
             data_interp[varname] = (('x','y'), fi)
 
         # Set dimension coordinate
-        #print(f'shape of xx is {np.shape(xx)}')
-        #print(f'shape of yy is {np.shape(yy)}')
         data_interp = data_interp.assign_coords(x=xx[:,0], y=yy[0,:])
 
         return data_interp, xx, yy

ssrs/layers.py

@@ -58,8 +58,6 @@ def calcOrographicUpdraft_original(
     w0_abovemin = np.maximum(min_updraft_val, w0)
     w0_abovemin_coarse = highRes2lowRes(w0_abovemin, res_terrain, res)
 
-    #np.savetxt('wo_orig.csv',w0_abovemin_coarse,fmt='%.2f', delimiter=',')
-
     return w0_abovemin_coarse
 
 
@@ -123,16 +121,9 @@ def highRes2lowRes(field, res_h, res_l, sigma_in_m=30):
     """
 
     ratio = res_h/res_l
-    #print(f'ratio to get a high res array to low res is {ratio}')
     if ratio == 1:
         # same resolution, nothing to do here
         return field
-    #elif ratio == 1/3:
-        #print(f'inside the if for ratio==1/3')
-        # This ratio results in the coarsened array having one position less
-        # It is due to fact that 4000/3 is, rounded, 1333. But in case such
-        # as this one, we want 1334. So we manually round the ratio.
-        #ratio = 0.3334
 
     sigma = sigma_in_m/res_h
     if sigma<=1:
@@ -619,7 +610,6 @@ def compute_thermals_3d(
     albedo = albedo.values
     zi = zi[list(zi.keys())[0]].values
 
-    #print(f'zi is {zi}')
     if np.mean(zi) == np.nan:
         raise ValueError(f'The value obtained for the boundary layer height contains NaNs.',\
                          f'HRRR data is imcomplete at the site and time of interest.')

ssrs/movmodel.py

@@ -309,8 +309,6 @@ def generate_heuristic_eagle_track(
         random_walk_step_range: tuple = (None,None) # when a random walk does occur, the number of random steps will occur in this range
 ):
 
-    #print(f'inside generate_heuristic_eagle_track. Shape of orographic w0 is {np.shape(wo)}; shape of thermal wt is {np.shape(wt)}')
-
     assert random_walk_freq >= 0
     assert (len(random_walk_step_range) == 2)
 
@@ -344,8 +342,6 @@ def generate_heuristic_eagle_track(
     yg = np.arange(num_rows) * res
     maxx = xg[-1]
     maxy = yg[-1]
-    #print(f'maxxg=',xg[-1],'maxyg=',yg[-1])
-    #print(f'shape of wo is',np.shape(wo))
 
     # setup updraft and elevation interpolation and smoothed wo for lookahead
     wo_interp = RectBivariateSpline(xg, yg, wo.T)
@@ -442,7 +438,6 @@ def generate_heuristic_eagle_track(
         last_pos = trajectory[-1]
         for cur_pos in new_pos:
             if not ((0 < cur_pos[0] < xg[-1]) and (0 < cur_pos[1] < yg[-1])): 
-                #print('ending after',imove,'moves')
                 break
             delta = tuple(map(lambda i,j:i-j, cur_pos, last_pos))
             #delta = cur_pos - last_pos (this gives an error, unsupported operand for tuple)

ssrs/simulator.py

@@ -515,7 +515,6 @@ def compute_orographic_updrafts_using_hrrr(self) -> None:
         aspect = self.get_terrain_aspect()
         elev = self.get_terrain_elevation()
 
-        #print(f'the entire hrrr object is {self.hrrr}')
         start_time = time.time()
         for t, (dtime, case_id) in enumerate( zip(self.dtimes, self.case_ids) ):
             #wtk_df = self.wtk.get_dataframe_for_this_time(dtime)
@@ -609,11 +608,6 @@ def compute_thermal_updrafts(self, case_id: str):
                     # We have a list of datetime (possibly of size 1), let's iterate through them
                     for time in self.dtimes:
                         print(f'Calculating thermals for {time}')
-                        #print(f'passing to compute_thermals_3d, southwest_lonlat is {self.southwest_lonlat}')
-                        #print(f'passing to compute_thermals_3d, extent is {self.extent}')
-                        # maybe the second argumetn should be self.lonlat_bounds[0:2] to get the xmin, ymin (southwest)
-                        # Somehow, SOMEHOW, the extent now is xmin,xmax,ymin,ymax, when it used to be xmin,ymin,xmax,ymax.
-                        # The thermal3d function used to get it in the old format. Changing it
                         extent_for_thermals_3d = [0, 0, self.region_width_km[0]*1000, self.region_width_km[1]*1000]  # xmin, ymin, xmax, ymax
                         thermals = compute_thermals_3d(aspect, self.southwest_lonlat, extent_for_thermals_3d, self.resolution,
                                                       time, self.h, wfipInformed=False)
@@ -630,7 +624,6 @@ def load_updrafts(self, case_id: str, apply_threshold=True):
         """ Computes updrafts for the particular case """
         fname = self._get_orographicupdraft_fname(case_id, self.mode_data_dir)
         orographicupdraft = np.load(f'{fname}.npy')
-        #print(f'inside load_updrafts, loaded {fname}.npy, with shape {np.shape(orographicupdraft)}.')
         print(f'Found orographic updraft {os.path.basename(fname)}. Loading it...')
 
         updrafts= orographicupdraft
@@ -1303,8 +1296,6 @@ def plot_updrafts(self, apply_threshold=True, plot_turbs=True,
                  # Single updraft field
                  updrafts = [updrafts]
 
-            #print(f'updrafts shape is {np.shape(updrafts)}')
-
             for real_id, updraft in enumerate(updrafts):
                 #print(f'inside plot_updraft. updraft shape is {np.shape(updraft)}')
                 fig, axs = plt.subplots(figsize=self.fig_size)
@@ -2034,8 +2025,6 @@ def plot_atmospheric_conditions_HRRR(self, show=False) -> None:
                 extent   = [0, 0, self.region_width_km[0]*1000, self.region_width_km[1]*1000]  # xmin, ymin, xmax, ymax
                 res      = self.resolution
 
-                #print(f'Extent is {extent}')
-
                 # Compute wstar
                 wstar, xx, yy = hrrr.get_convective_velocity(SWlonlat, extent, res)