Merge pull request #118 from umr-lops/pre-release-v0.9

release v0.9

docs/examples/mask.ipynb

@@ -113,7 +113,7 @@
     "# get the shapely polygon of the mask (on the footprint, lon/lat)\n",
     "land_poly = s1meta.get_mask('ocean')\n",
     "\n",
-    "# convert lon/lat to atrack/xtrack\n",
+    "# convert lon/lat to line/sample\n",
     "land_poly_coords = s1meta.ll2coords(s1meta.get_mask('ocean'))\n",
     "\n",
     "# plot masked sigma0, with coastline\n",
@@ -141,7 +141,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

docs/examples/projections.ipynb

@@ -74,7 +74,7 @@
    "id": "ab8a4bb1-6523-4d93-b2a8-068355328498",
    "metadata": {},
    "source": [
-    "To have it displayed the same way it was aquired, we need to pass `kdims=['xtrack', 'atrack']`.\n",
+    "To have it displayed the same way it was aquired, we need to pass `kdims=['sample', 'line']`.\n",
     "\n",
     "We can see here that the orbit pass is **descending**"
    ]
@@ -86,7 +86,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hv.Image(xsar_obj.dataset.sigma0.sel(pol='VV'), kdims=['xtrack', 'atrack']).opts(alpha=0.7, cmap='gray', clim=(0,0.05))"
+    "hv.Image(xsar_obj.dataset.sigma0.sel(pol='VV'), kdims=['sample', 'line']).opts(alpha=0.7, cmap='gray', clim=(0,0.05))"
    ]
   },
   {
@@ -111,6 +111,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import rasterio\n",
     "xsar_obj.recompute_attrs()\n",
     "sigma0_proj = xsar_obj.dataset['sigma0'].rio.reproject('epsg:4326',shape=(1000,1000),nodata=np.nan)\n",
     "sigma0_proj"
@@ -198,8 +199,8 @@
     "cmap.set_clim(vmin=0, vmax=0.05)\n",
     "rgb_sigma0 = xr.DataArray(\n",
     "    (cmap.to_rgba(xsar_obj.dataset['sigma0'].sel(pol='VV'), bytes=True)),  \n",
-    "    dims=['atrack', 'xtrack', 'band']\n",
-    ").transpose('band', 'atrack', 'xtrack')\n",
+    "    dims=['line', 'sample', 'band']\n",
+    ").transpose('band', 'line', 'sample')\n",
     "rgb_sigma0\n"
    ]
   },
@@ -208,7 +209,7 @@
    "id": "3570b206-ebac-4bc8-a58a-b3a24b4369a8",
    "metadata": {},
    "source": [
-    "`rgb_sigma0` is now an `xarray.DataArray`, with sames spatials dims `['atrack', 'xtrack']`, and an new dim `band` that hold color in R,G,B,A. \n",
+    "`rgb_sigma0` is now an `xarray.DataArray`, with sames spatials dims `['line', 'sample']`, and an new dim `band` that hold color in R,G,B,A. \n",
     "\n",
     "This dataarray is currently not georeferenced. To georefence it, we have **to store it into xsar_obj.dataset and call [xsar.Sentinel1Dataset.recompute_attrs()](../basic_api.rst#xsar.Sentinel1Dataset.recompute_attrs)**.\n"
    ]
@@ -220,7 +221,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xsar_obj.dataset['sigma0_rgba'] = rgb_sigma0 \n",
+    "#xsar_obj.dataset['sigma0_rgba'] = rgb_sigma0 \n",
+    "rgb_sigma0.name = 'sigma0_rgba'\n",
+    "xsar_obj.datatree['measurement'] = xsar_obj.datatree['measurement'].assign(xr.merge([xsar_obj.dataset,rgb_sigma0]))\n",
     "xsar_obj.recompute_attrs()\n",
     "xsar_obj.dataset['sigma0_rgba'].rio.reproject('epsg:4326',shape=(1000,1000),nodata=0).rio.to_raster('/tmp/sigma0_color.tiff')"
    ]
@@ -373,7 +376,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "xsar_obj.dataset['sigma0_rasterized'] = xsar_obj.map_raster(merged_grid['sigma0'])\n",
+    "#xsar_obj.dataset['sigma0_rasterized'] = \n",
+    "tmp_ds = xsar_obj.map_raster(merged_grid['sigma0'])\n",
+    "tmp_ds.name = 'sigma0_rasterized'\n",
+    "xsar_obj.datatree['measurement'] = xsar_obj.datatree['measurement'].assign(xr.merge([xsar_obj.dataset,tmp_ds]))\n",
     "xsar_obj.recompute_attrs()"
    ]
   },
@@ -385,8 +391,8 @@
    "outputs": [],
    "source": [
     "(\n",
-    "    hv.Image(xsar_obj.dataset['sigma0_rasterized'], kdims=['xtrack','atrack']).opts(title='from raster') \n",
-    "    + hv.Image(xsar_obj.dataset.sigma0.sel(pol='VV'), kdims=['xtrack','atrack']).opts(title='original') \n",
+    "    hv.Image(xsar_obj.dataset['sigma0_rasterized'], kdims=['sample','line']).opts(title='from raster') \n",
+    "    + hv.Image(xsar_obj.dataset.sigma0.sel(pol='VV'), kdims=['sample','line']).opts(title='original') \n",
     ").opts(hv.opts.Image(tools=['hover'],cmap='gray', clim=(0,0.05), alpha=0.7))"
    ]
   }
@@ -407,7 +413,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,

docs/examples/xsar.ipynb

@@ -43,25 +43,43 @@
     "filename"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# open the datatree with xarray\n",
+    "dt_xsar = xsar.open_datatree(filename,resolution='100m')\n",
+    "dt_xsar"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# open the dataset with xarray\n",
-    "sar_ds = xsar.open_dataset(filename, resolution='100m')\n",
-    "sar_ds"
+    "ds_xsar = xsar.open_dataset(filename, resolution='100m')\n",
+    "ds_xsar"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# we use here 'rasterize' to display the image, because the full image is really big\n",
-    "rasterize(hv.Image(sar_ds.sigma0.sel(pol='VH')).opts(cmap='gray',colorbar=True,tools=['hover'],title=\"xsar\",width=800,height=800,clim=(0,0.02)))"
+    "rasterize(hv.Image(ds_xsar.sigma0.sel(pol='VH')).opts(cmap='gray',colorbar=True,tools=['hover'],title=\"xsar\",width=800,height=800,clim=(0,0.02)))"
    ]
   }
  ],
@@ -81,7 +99,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,

docs/examples/xsar_advanced.ipynb

@@ -95,7 +95,7 @@
     "\n",
     "It might be:\n",
     "\n",
-    " * a dict `{'atrack': 20, 'xtrack': 20}`: 20*20 pixels. so if sensor resolution is 10m, the final resolution will be 100m\n",
+    " * a dict `{'line': 20, 'sample': 20}`: 20*20 pixels. so if sensor resolution is 10m, the final resolution will be 100m\n",
     " * a string like `'200m'`: Sensor resolution will be automatically used to compute the window size"
    ]
   },
@@ -122,8 +122,8 @@
    "source": [
     "## Extract a sub image of 10*10km around a lon/lat point\n",
     "\n",
-    "### Convert (lon,lat) to (atrack, xtrack)\n",
-    "we can use [sar_meta.ll2coords](../basic_api.rst#xsar.Sentinel1Meta.ll2coords) to convert (lon,lat) to (atrack, xtrack):"
+    "### Convert (lon,lat) to (line, sample)\n",
+    "we can use [sar_meta.ll2coords](../basic_api.rst#xsar.Sentinel1Meta.ll2coords) to convert (lon,lat) to (line, sample):"
    ]
   },
   {
@@ -144,7 +144,7 @@
    "source": [
     "The result is floating, because it is the position inside the pixel.\n",
     "If real indexes from existing dataset is needed, you'll have to use [sar_ds.ll2coords](../basic_api.rst#xsar.Sentinel1Dataset.ll2coords) \n",
-    "Result will be the nearest (atrack, xtrack) in the dataset"
+    "Result will be the nearest (line, sample) in the dataset"
    ]
   },
   {
@@ -171,7 +171,7 @@
    "outputs": [],
    "source": [
     "box_size = 10000 # 10km\n",
-    "dist = {'atrack' : int(np.round(box_size / 2 / sar_meta.pixel_atrack_m)), 'xtrack': int(np.round(box_size / 2 / sar_meta.pixel_xtrack_m))}\n",
+    "dist = {'line' : int(np.round(box_size / 2 / sar_meta.pixel_line_m)), 'sample': int(np.round(box_size / 2 / sar_meta.pixel_sample_m))}\n",
     "dist"
    ]
   },
@@ -190,7 +190,7 @@
    "outputs": [],
    "source": [
     "# select 10*10 km around point_coords\n",
-    "sar_ds.dataset = sar_ds.dataset.sel(atrack=slice(point_coords[0] - dist['atrack'], point_coords[0] + dist['atrack']), xtrack=slice(point_coords[1] - dist['xtrack'], point_coords[1] + dist['xtrack']))\n",
+    "sar_ds.dataset = sar_ds.dataset.sel(line=slice(point_coords[0] - dist['line'], point_coords[0] + dist['line']), sample=slice(point_coords[1] - dist['sample'], point_coords[1] + dist['sample']))\n",
     "sar_ds"
    ]
   },
@@ -205,6 +205,10 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "",
+   "name": ""
+  },
   "language_info": {
    "name": "python",
    "pygments_lexer": "ipython3"

docs/examples/xsar_batch_datarmor.ipynb

@@ -189,7 +189,7 @@
    "outputs": [],
    "source": [
     "def l1b(safe, outfile):\n",
-    "    ds = xsar.open_dataset(safe, resolution='1000m')\n",
+    "    ds = xsar.open_dataset(safe, resolution='1000m')['measurement'].ds\n",
     "    # make attributes to be str, so writable to file\n",
     "    to_str = ['start_date', 'stop_date', 'footprint']\n",
     "    for attr in to_str:\n",