diff --git a/edav/edav_make_subset_from_catalog_data.ipynb b/edav/edav_make_subset_from_catalog_data.ipynb
index 23ce68770c6e74b52cbfb33967f01c639e328465..bce99e9468023586ad11a9994b44b559f692aecb 100644
--- a/edav/edav_make_subset_from_catalog_data.ipynb
+++ b/edav/edav_make_subset_from_catalog_data.ipynb
@@ -132,11 +132,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -249,11 +254,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -317,7 +327,7 @@
},
{
"cell_type": "code",
- "execution_count": 288,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -326,7 +336,7 @@
},
{
"cell_type": "code",
- "execution_count": 289,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -342,7 +352,7 @@
},
{
"cell_type": "code",
- "execution_count": 290,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -358,7 +368,7 @@
},
{
"cell_type": "code",
- "execution_count": 291,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -366,11 +376,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -434,7 +449,7 @@
},
{
"cell_type": "code",
- "execution_count": 293,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -443,7 +458,7 @@
},
{
"cell_type": "code",
- "execution_count": 294,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -459,7 +474,7 @@
},
{
"cell_type": "code",
- "execution_count": 295,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -475,7 +490,7 @@
},
{
"cell_type": "code",
- "execution_count": 296,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -483,11 +498,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -499,22 +519,9 @@
},
{
"cell_type": "code",
- "execution_count": 297,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1440x1440 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"imgplot = plt.imshow(np.absolute(input_image))"
]
@@ -564,7 +571,7 @@
},
{
"cell_type": "code",
- "execution_count": 298,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -574,7 +581,7 @@
},
{
"cell_type": "code",
- "execution_count": 299,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -614,11 +621,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -682,7 +694,7 @@
},
{
"cell_type": "code",
- "execution_count": 301,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -691,7 +703,7 @@
},
{
"cell_type": "code",
- "execution_count": 302,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -707,7 +719,7 @@
},
{
"cell_type": "code",
- "execution_count": 303,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -723,7 +735,7 @@
},
{
"cell_type": "code",
- "execution_count": 304,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -731,11 +743,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -747,22 +764,9 @@
},
{
"cell_type": "code",
- "execution_count": 305,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 1440x1440 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"imgplot = plt.imshow(np.absolute(input_image))"
]
@@ -812,7 +816,7 @@
},
{
"cell_type": "code",
- "execution_count": 306,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -821,7 +825,7 @@
},
{
"cell_type": "code",
- "execution_count": 307,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -837,7 +841,7 @@
},
{
"cell_type": "code",
- "execution_count": 308,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -853,7 +857,7 @@
},
{
"cell_type": "code",
- "execution_count": 309,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -861,11 +865,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -877,22 +886,9 @@
},
{
"cell_type": "code",
- "execution_count": 310,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": "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\n",
- "text/plain": [
- "<Figure size 1440x1440 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"imgplot = plt.imshow(np.absolute(input_image))"
]
@@ -991,11 +987,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1116,11 +1117,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1193,7 +1199,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1233,11 +1239,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1357,11 +1368,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1474,11 +1490,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1549,7 +1570,7 @@
},
{
"cell_type": "code",
- "execution_count": 314,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1558,7 +1579,7 @@
},
{
"cell_type": "code",
- "execution_count": 315,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1574,7 +1595,7 @@
},
{
"cell_type": "code",
- "execution_count": 316,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1590,7 +1611,7 @@
},
{
"cell_type": "code",
- "execution_count": 317,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1598,11 +1619,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1715,11 +1741,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1832,11 +1863,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -1900,7 +1936,7 @@
},
{
"cell_type": "code",
- "execution_count": 321,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1909,7 +1945,7 @@
},
{
"cell_type": "code",
- "execution_count": 322,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1925,7 +1961,7 @@
},
{
"cell_type": "code",
- "execution_count": 323,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1941,7 +1977,7 @@
},
{
"cell_type": "code",
- "execution_count": 324,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -1949,11 +1985,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{
@@ -2017,7 +2058,7 @@
},
{
"cell_type": "code",
- "execution_count": 326,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2026,7 +2067,7 @@
},
{
"cell_type": "code",
- "execution_count": 327,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2042,7 +2083,7 @@
},
{
"cell_type": "code",
- "execution_count": 328,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2058,7 +2099,7 @@
},
{
"cell_type": "code",
- "execution_count": 329,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -2066,11 +2107,16 @@
" with open(inputFilename, 'wb') as f:\n",
" for chunk in subset:\n",
" f.write(chunk)\n",
- "input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
- "input_image = input_image_driver.ReadAsArray()\n",
- "RasterXSize = input_image_driver.RasterXSize\n",
- "RasterYSize = input_image_driver.RasterYSize\n",
- "input_image_driver = None"
+ " input_image_driver = gdal.Open(inputFilename, GA_ReadOnly)\n",
+ " input_image = input_image_driver.ReadAsArray()\n",
+ " RasterXSize = input_image_driver.RasterXSize\n",
+ " RasterYSize = input_image_driver.RasterYSize\n",
+ " input_image_driver = None\n",
+ "elif subset.status_code==413:\n",
+ " print(subset.text)\n",
+ " print(\"The area selected is too large\")\n",
+ "else:\n",
+ " print(subset.text)"
]
},
{