Merge pull request #80 from vincelhx/dev_streaks_vinc

update xsarsea

src/xsarsea/gradients.py

@@ -327,14 +327,31 @@ def histogram(self):
 
     @staticmethod
     def _sigma0_resample(sigma0, factor):
+
         if factor == 1:
             return sigma0
-        __sigma0 = sigma0.isel(line=slice(0, None, factor), sample=slice(0, None, factor)).copy(
-            True
-        )
-        __sigma0.values[::] = cv2.resize(sigma0.values, __sigma0.shape[::-1], cv2.INTER_AREA)
-        return __sigma0
 
+        def compute_coords(coords, factor):
+            n = (len(coords) // factor) * factor
+            coords_trimmed = coords[:n]
+            coords_reshaped = coords_trimmed.reshape(-1, factor)
+            return coords_reshaped.mean(axis=1)
+
+        target_line = int(np.floor(sigma0.sizes['line'] / factor))
+        target_sample = int(np.floor(sigma0.sizes['sample'] / factor))
+
+        resized_values = cv2.resize(
+            sigma0.values, (target_sample, target_line), interpolation=cv2.INTER_AREA)
+
+        line_coords = compute_coords(sigma0.line.values, factor)
+        sample_coords = compute_coords(sigma0.sample.values, factor)
+        other_coords = {coord: sigma0.coords[coord]
+                        for coord in sigma0.coords if coord not in ['line', 'sample']}
+
+        return xr.DataArray(resized_values, dims=['line', 'sample'],
+                            coords={'line': line_coords,
+                                    'sample': sample_coords, **other_coords},
+                            attrs=sigma0.attrs)
 
 class PlotGradients:
     """Plotting class"""
@@ -637,7 +654,8 @@ def _conv2d(in1, in2=in2, mode="same", boundary=boundary, fillvalue=fillvalue):
 def smoothing(image):
     # filtre gaussien
 
-    B2 = np.asmatrix("[1,2,1; 2,4,2; 1,2,1]", float) * 1 / 16
+    B2 = np.asmatrix('[1,2,1; 2,4,2; 1,2,1]', float) * 1 / 16
+
     B2 = np.array(B2)
 
     _image = convolve2d(image, B2, boundary="symm")
@@ -661,7 +679,8 @@ def R2(image):
         resampled
     """
 
-    B2 = np.asmatrix("[1,2,1; 2,4,2; 1,2,1]", float) * 1 / 16
+    B2 = np.asmatrix('[1,2,1; 2,4,2; 1,2,1]', float) * 1 / 16
+
     B2 = np.array(B2)
     B4 = signal.convolve(B2, B2)
 
@@ -694,11 +713,13 @@ def Mean(image):
     xarray.DataArray
         smoothed
     """
-    B2 = np.asmatrix("[1,2,1; 2,4,2; 1,2,1]", float) * 1 / 16
+    B2 = np.asmatrix('[1,2,1; 2,4,2; 1,2,1]', float) * 1 / 16
     B2 = np.array(B2)
     B4 = signal.convolve(B2, B2)
 
-    B22 = np.asmatrix("[1,0,2,0,1;0,0,0,0,0;2,0,4,0,2;0,0,0,0,0;1,0,2,0,1]", float) * 1 / 16
+    B22 = np.asmatrix(
+        '[1,0,2,0,1;0,0,0,0,0;2,0,4,0,2;0,0,0,0,0;1,0,2,0,1]', float) * 1/16
+
     B42 = signal.convolve(B22, B22)
 
     _image = convolve2d(image, B4, boundary="symm")

src/xsarsea/utils.py

@@ -7,12 +7,14 @@
 import aiohttp
 import fsspec
 import yaml
+
 try:
     from importlib_resources import files
 except:
     from importlib.resources import files # new syntaxe
 logger = logging.getLogger("xsarsea")
 logger.addHandler(logging.NullHandler())
+
 mem_monitor = True
 
 try:

src/xsarsea/windspeed/models.py

@@ -9,7 +9,7 @@
 import pandas as pd
 import xarray as xr
 
-logger = logging.getLogger("xsarsea.windspeed")
+logger = logging.getLogger('xsarsea.windspeed.models')
 
 
 class Model:

src/xsarsea/windspeed/utils.py

@@ -3,16 +3,17 @@
 import warnings
 
 import numpy as np
+
 import yaml
 try:
     from importlib_resources import files
 except:
     from importlib.resources import files # new syntaxe
 
-logger = logging.getLogger("xsarsea.windspeed")
-# logger.addHandler(logging.NullHandler())
-# logger must print
-logger.setLevel(logging.DEBUG)
+import logging
+logger = logging.getLogger('xsarsea.windspeed.utils')
+logger.setLevel(logging.INFO)
+
 
 
 def get_dsig(name, inc, sigma0_cr, nesz_cr):
@@ -131,26 +132,3 @@ def _noise_flattening_1row(noise_row, inc_row):
     # incidence is almost constant along line dim, so we can make it 1D
     return np.apply_along_axis(_noise_flattening_1row, 1, noise, np.nanmean(inc, axis=0))
 
-
-def _load_config_luts(config_path):
-    """
-    load config from default xsarsea/windspeed.config_luts_default_direct_01_01_10.yml file or user ~/.xsarsea/config.yml
-    Returns
-    -------
-    dict
-    """
-
-    user_config_file = open(config_path)
-    default_config_file = (
-        files("xsarsea").joinpath("windspeed").joinpath("config_luts_default_direct_01_01_10.yml")
-    )
-
-    if os.exists(user_config_file.exists):
-        config_file = user_config_file
-    else:
-        # logger.info(f"Using default config file {default_config_file}")
-        # config_file = default_config_file
-        raise FileNotFoundError(f"Config file {user_config_file} not found")
-    config = yaml.load(open(config_file), Loader=yaml.FullLoader)
-
-    return config

src/xsarsea/windspeed/windspeed.py

@@ -3,12 +3,16 @@
 
 import numpy as np
 import xarray as xr
+
+import logging
 from numba import complex128, float64, guvectorize, void
 
 from xsarsea.utils import timing
 from xsarsea.windspeed.models import get_model
 from xsarsea.windspeed.utils import logger
 
+logger = logging.getLogger('xsarsea.windspeed')
+logger.setLevel(logging.DEBUG)
 
 @timing(logger.debug)
 def invert_from_model(
@@ -295,6 +299,9 @@ def __invert_from_model_vect(*args):
                 return out_co.reshape(ori_shape), out_cr.reshape(ori_shape)
 
         else:
+            logger.debug(
+                'using guvectorize')
+
             # fastmath can be used, but we will need nan handling
             __invert_from_model_vect = timing(logger=logger.debug)(
                 guvectorize(
@@ -360,6 +367,8 @@ def _invert_from_model_any(inc, sigma0_co_db, sigma0_cr_db, dsig_cr, ancillary_w
                     raise TypeError
 
             except (ImportError, TypeError):
+                logger.debug('Finally invert with numpy')
+
                 # use numpy array, but store in xarray
                 da_ws_co.data, da_ws_cr.data = _invert_from_model_numpy(
                     np.asarray(inc),