From 55651f7a2e0185ea8035be56f7c3b2a08c63db9b Mon Sep 17 00:00:00 2001 From: Mostafa Farrag Date: Sun, 8 Dec 2024 21:38:06 +0100 Subject: [PATCH] update notebooks --- examples/notebooks/01dataset.ipynb | 92 ++-- .../02spatial-operation-methods.ipynb | 438 +++++++++++++++--- examples/notebooks/03convert-longitude.ipynb | 208 +++++++-- examples/notebooks/04datacube.ipynb | 78 ++-- 4 files changed, 631 insertions(+), 185 deletions(-) diff --git a/examples/notebooks/01dataset.ipynb b/examples/notebooks/01dataset.ipynb index 7320a070..029b2548 100644 --- a/examples/notebooks/01dataset.ipynb +++ b/examples/notebooks/01dataset.ipynb @@ -141,8 +141,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-12-08T20:16:40.786604Z", - "start_time": "2024-12-08T20:16:40.412851Z" + "end_time": "2024-12-08T20:31:42.787622Z", + "start_time": "2024-12-08T20:31:42.552875Z" } }, "cell_type": "code", @@ -207,7 +207,7 @@ " if await self.run_code(code, result, async_=asy):\n", " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", " exec(code_obj, self.user_global_ns, self.user_ns)\n", - " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_22316\\3843230610.py\", line 1, in \n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_22316\\1752666259.py\", line 1, in \n", " glyph = dataset.plot(\n", " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", @@ -225,7 +225,7 @@ " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", " _log.debug('locator: %r', locator)\n", "Message: 'locator: %r'\n", - "Arguments: (,)\n" + "Arguments: (,)\n" ] }, { @@ -233,13 +233,13 @@ "text/plain": [ "
" ], - "image/png": "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" + "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 23 + "execution_count": 35 }, { "metadata": {}, @@ -249,8 +249,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2024-12-08T20:16:54.197865Z", - "start_time": "2024-12-08T20:16:54.192044Z" + "end_time": "2024-12-08T20:31:48.548405Z", + "start_time": "2024-12-08T20:31:48.543167Z" } }, "cell_type": "code", @@ -272,7 +272,7 @@ ] } ], - "execution_count": 24 + "execution_count": 36 }, { "cell_type": "code", @@ -282,8 +282,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:16:57.183451Z", - "start_time": "2024-12-08T20:16:57.179239Z" + "end_time": "2024-12-08T20:31:50.954213Z", + "start_time": "2024-12-08T20:31:50.949182Z" } }, "source": [ @@ -298,7 +298,7 @@ ] } ], - "execution_count": 25 + "execution_count": 37 }, { "cell_type": "code", @@ -308,8 +308,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:00.588960Z", - "start_time": "2024-12-08T20:17:00.582820Z" + "end_time": "2024-12-08T20:31:52.790703Z", + "start_time": "2024-12-08T20:31:52.782252Z" } }, "source": [ @@ -322,12 +322,12 @@ "['Band_1', 'Band_2', 'Band_3', 'Band_4']" ] }, - "execution_count": 26, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 26 + "execution_count": 38 }, { "cell_type": "markdown", @@ -349,8 +349,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:03.127882Z", - "start_time": "2024-12-08T20:17:03.122365Z" + "end_time": "2024-12-08T20:31:56.823703Z", + "start_time": "2024-12-08T20:31:56.818931Z" } }, "source": [ @@ -367,7 +367,7 @@ ] } ], - "execution_count": 27 + "execution_count": 40 }, { "cell_type": "code", @@ -377,8 +377,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:06.916105Z", - "start_time": "2024-12-08T20:17:06.904275Z" + "end_time": "2024-12-08T20:31:58.654184Z", + "start_time": "2024-12-08T20:31:58.642057Z" } }, "source": [ @@ -534,12 +534,12 @@ " 3.5775e+02, 3.5825e+02, 3.5875e+02, 3.5925e+02, 3.5975e+02])" ] }, - "execution_count": 28, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 28 + "execution_count": 41 }, { "cell_type": "code", @@ -549,8 +549,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:08.416341Z", - "start_time": "2024-12-08T20:17:08.400147Z" + "end_time": "2024-12-08T20:32:00.405591Z", + "start_time": "2024-12-08T20:32:00.398022Z" } }, "source": [ @@ -607,12 +607,12 @@ " -86.25, -86.75, -87.25, -87.75, -88.25, -88.75, -89.25, -89.75])" ] }, - "execution_count": 29, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 29 + "execution_count": 42 }, { "cell_type": "markdown", @@ -634,8 +634,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:10.951105Z", - "start_time": "2024-12-08T20:17:10.945829Z" + "end_time": "2024-12-08T20:32:02.334150Z", + "start_time": "2024-12-08T20:32:02.327644Z" } }, "source": [ @@ -648,12 +648,12 @@ "[0.0, -90.0, 360.0, 90.0]" ] }, - "execution_count": 30, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 30 + "execution_count": 43 }, { "cell_type": "code", @@ -664,8 +664,8 @@ }, "scrolled": true, "ExecuteTime": { - "end_time": "2024-12-08T20:17:12.776110Z", - "start_time": "2024-12-08T20:17:12.740780Z" + "end_time": "2024-12-08T20:32:03.561716Z", + "start_time": "2024-12-08T20:32:03.550261Z" } }, "source": "print(dataset.bounds)", @@ -679,7 +679,7 @@ ] } ], - "execution_count": 31 + "execution_count": 44 }, { "cell_type": "code", @@ -689,8 +689,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:14.494322Z", - "start_time": "2024-12-08T20:17:14.384783Z" + "end_time": "2024-12-08T20:32:05.169931Z", + "start_time": "2024-12-08T20:32:05.019659Z" } }, "source": [ @@ -703,7 +703,7 @@ "" ] }, - "execution_count": 32, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, @@ -718,7 +718,7 @@ "output_type": "display_data" } ], - "execution_count": 32 + "execution_count": 45 }, { "cell_type": "code", @@ -728,8 +728,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:16.016810Z", - "start_time": "2024-12-08T20:17:16.010813Z" + "end_time": "2024-12-08T20:32:07.446750Z", + "start_time": "2024-12-08T20:32:07.441875Z" } }, "source": [ @@ -742,12 +742,12 @@ "(0.0, 0.5, 0.0, 90.0, 0.0, -0.5)" ] }, - "execution_count": 33, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 33 + "execution_count": 46 }, { "cell_type": "code", @@ -757,8 +757,8 @@ "outputs_hidden": false }, "ExecuteTime": { - "end_time": "2024-12-08T20:17:17.264389Z", - "start_time": "2024-12-08T20:17:17.259190Z" + "end_time": "2024-12-08T20:32:11.465608Z", + "start_time": "2024-12-08T20:32:11.459735Z" } }, "source": [ @@ -771,12 +771,12 @@ "(0.0, 90.0)" ] }, - "execution_count": 34, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 34 + "execution_count": 47 } ], "metadata": { diff --git a/examples/notebooks/02spatial-operation-methods.ipynb b/examples/notebooks/02spatial-operation-methods.ipynb index c4f915b6..b66b436f 100644 --- a/examples/notebooks/02spatial-operation-methods.ipynb +++ b/examples/notebooks/02spatial-operation-methods.ipynb @@ -29,12 +29,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T20:17:49.225515Z", - "start_time": "2024-12-08T20:17:49.154891Z" + "end_time": "2024-12-08T20:32:21.636790Z", + "start_time": "2024-12-08T20:32:21.632392Z" } }, "outputs": [], - "execution_count": 20 + "execution_count": 28 }, { "cell_type": "code", @@ -44,12 +44,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T20:17:51.535720Z", - "start_time": "2024-12-08T20:17:51.521895Z" + "end_time": "2024-12-08T20:32:24.634448Z", + "start_time": "2024-12-08T20:32:24.630873Z" } }, "outputs": [], - "execution_count": 21 + "execution_count": 29 }, { "cell_type": "code", @@ -59,8 +59,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T20:17:52.969370Z", - "start_time": "2024-12-08T20:17:52.957066Z" + "end_time": "2024-12-08T20:32:25.868536Z", + "start_time": "2024-12-08T20:32:25.863920Z" } }, "outputs": [ @@ -81,7 +81,7 @@ ] } ], - "execution_count": 22 + "execution_count": 30 }, { "cell_type": "code", @@ -91,8 +91,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T20:17:56.209937Z", - "start_time": "2024-12-08T20:17:55.692137Z" + "end_time": "2024-12-08T20:32:28.502622Z", + "start_time": "2024-12-08T20:32:28.191031Z" } }, "outputs": [ @@ -168,7 +168,7 @@ " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", " _log.debug('locator: %r', locator)\n", "Message: 'locator: %r'\n", - "Arguments: (,)\n" + "Arguments: (,)\n" ] }, { @@ -188,12 +188,12 @@ " )" ] }, - "execution_count": 23, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 23 + "execution_count": 31 }, { "cell_type": "markdown", @@ -212,8 +212,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T20:18:03.045076Z", - "start_time": "2024-12-08T20:18:03.039785Z" + "end_time": "2024-12-08T20:32:30.741018Z", + "start_time": "2024-12-08T20:32:30.737670Z" } }, "outputs": [ @@ -225,7 +225,7 @@ ] } ], - "execution_count": 24 + "execution_count": 32 }, { "cell_type": "code", @@ -235,12 +235,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T20:18:04.645280Z", - "start_time": "2024-12-08T20:18:04.632356Z" + "end_time": "2024-12-08T20:32:33.812328Z", + "start_time": "2024-12-08T20:32:33.804821Z" } }, "outputs": [], - "execution_count": 25 + "execution_count": 33 }, { "metadata": {}, @@ -268,8 +268,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:25.517442Z", - "start_time": "2024-12-08T15:35:25.503449Z" + "end_time": "2024-12-08T20:32:55.387917Z", + "start_time": "2024-12-08T20:32:55.382874Z" } }, "outputs": [ @@ -283,7 +283,7 @@ ] } ], - "execution_count": 8 + "execution_count": 34 }, { "cell_type": "code", @@ -293,12 +293,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:26.747011Z", - "start_time": "2024-12-08T15:35:26.720545Z" + "end_time": "2024-12-08T20:32:59.222373Z", + "start_time": "2024-12-08T20:32:59.190965Z" } }, "outputs": [], - "execution_count": 9 + "execution_count": 35 }, { "cell_type": "code", @@ -308,8 +308,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:28.387607Z", - "start_time": "2024-12-08T15:35:28.382385Z" + "end_time": "2024-12-08T20:33:00.329976Z", + "start_time": "2024-12-08T20:33:00.321966Z" } }, "outputs": [ @@ -330,7 +330,7 @@ ] } ], - "execution_count": 10 + "execution_count": 36 }, { "cell_type": "code", @@ -340,11 +340,86 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:29.894552Z", - "start_time": "2024-12-08T15:35:29.679892Z" + "end_time": "2024-12-08T20:33:01.891369Z", + "start_time": "2024-12-08T20:33:01.624304Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1164, in emit\n", + " self.flush()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1144, in flush\n", + " self.stream.flush()\n", + "OSError: [Errno 9] Bad file descriptor\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_2668\\39532551.py\", line 1, in \n", + " resampled_dataset.plot(vmin=0, title=\"Rhine River Basin (reprojected to WGS 84)\", cbar_label=\"Elevation(m)\")\n", + " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", + " self.create_color_bar(ax, im, cbar_kw)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 529, in create_color_bar\n", + " cbar = ax.figure.colorbar(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\figure.py\", line 1253, in colorbar\n", + " cb = cbar.Colorbar(cax, mappable, **{\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 407, in __init__\n", + " self._draw_all()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 550, in _draw_all\n", + " self.update_ticks()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 804, in update_ticks\n", + " self._get_ticker_locator_formatter()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", + " _log.debug('locator: %r', locator)\n", + "Message: 'locator: %r'\n", + "Arguments: (,)\n" + ] + }, { "data": { "text/plain": [ @@ -362,12 +437,12 @@ " )" ] }, - "execution_count": 11, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 11 + "execution_count": 37 }, { "cell_type": "markdown", @@ -387,12 +462,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:33.122145Z", - "start_time": "2024-12-08T15:35:33.102227Z" + "end_time": "2024-12-08T20:33:05.236466Z", + "start_time": "2024-12-08T20:33:05.230682Z" } }, "outputs": [], - "execution_count": 12 + "execution_count": 38 }, { "cell_type": "code", @@ -402,8 +477,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:35.284507Z", - "start_time": "2024-12-08T15:35:35.271271Z" + "end_time": "2024-12-08T20:33:07.708050Z", + "start_time": "2024-12-08T20:33:07.699951Z" } }, "outputs": [ @@ -424,24 +499,98 @@ ] } ], - "execution_count": 13 + "execution_count": 39 }, { + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-08T20:33:15.093964Z", + "start_time": "2024-12-08T20:33:14.877868Z" + } + }, "cell_type": "code", "source": [ - "fig, ax = meteo_data.plot(\n", + "glyph = meteo_data.plot(\n", " band=0, figsize=(10, 5), title=\"Noah daily Precipitation 1979-01-01\", cbar_label=\"Raindall mm/day\", vmax=30,\n", " cbar_length=0.85\n", ")" ], - "metadata": { - "collapsed": false, - "ExecuteTime": { - "end_time": "2024-12-08T15:35:36.807552Z", - "start_time": "2024-12-08T15:35:36.637237Z" - } - }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1164, in emit\n", + " self.flush()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1144, in flush\n", + " self.stream.flush()\n", + "OSError: [Errno 9] Bad file descriptor\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_2668\\34527337.py\", line 1, in \n", + " glyph = meteo_data.plot(\n", + " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", + " self.create_color_bar(ax, im, cbar_kw)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 529, in create_color_bar\n", + " cbar = ax.figure.colorbar(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\figure.py\", line 1253, in colorbar\n", + " cb = cbar.Colorbar(cax, mappable, **{\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 407, in __init__\n", + " self._draw_all()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 550, in _draw_all\n", + " self.update_ticks()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 804, in update_ticks\n", + " self._get_ticker_locator_formatter()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", + " _log.debug('locator: %r', locator)\n", + "Message: 'locator: %r'\n", + "Arguments: (,)\n" + ] + }, { "data": { "text/plain": [ @@ -453,22 +602,19 @@ "output_type": "display_data" } ], - "execution_count": 14 + "execution_count": 40 }, { - "cell_type": "code", - "source": [ - "meteo_data_r = meteo_data.to_crs(4647)" - ], "metadata": { - "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:37.902628Z", - "start_time": "2024-12-08T15:35:37.876822Z" + "end_time": "2024-12-08T20:33:22.135173Z", + "start_time": "2024-12-08T20:33:22.116937Z" } }, + "cell_type": "code", + "source": "meteo_data_r = meteo_data.to_crs(4647)", "outputs": [], - "execution_count": 15 + "execution_count": 41 }, { "cell_type": "code", @@ -478,11 +624,86 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:39.236862Z", - "start_time": "2024-12-08T15:35:39.043723Z" + "end_time": "2024-12-08T20:33:24.697928Z", + "start_time": "2024-12-08T20:33:23.938920Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1164, in emit\n", + " self.flush()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1144, in flush\n", + " self.stream.flush()\n", + "OSError: [Errno 9] Bad file descriptor\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_2668\\1972531582.py\", line 1, in \n", + " meteo_data_r.plot(band=0)\n", + " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", + " self.create_color_bar(ax, im, cbar_kw)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 529, in create_color_bar\n", + " cbar = ax.figure.colorbar(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\figure.py\", line 1253, in colorbar\n", + " cb = cbar.Colorbar(cax, mappable, **{\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 407, in __init__\n", + " self._draw_all()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 550, in _draw_all\n", + " self.update_ticks()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 804, in update_ticks\n", + " self._get_ticker_locator_formatter()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", + " _log.debug('locator: %r', locator)\n", + "Message: 'locator: %r'\n", + "Arguments: (,)\n" + ] + }, { "data": { "text/plain": [ @@ -499,12 +720,12 @@ "(
, )" ] }, - "execution_count": 16, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 16 + "execution_count": 42 }, { "cell_type": "code", @@ -514,12 +735,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:40.101682Z", - "start_time": "2024-12-08T15:35:40.065214Z" + "end_time": "2024-12-08T20:33:26.545294Z", + "start_time": "2024-12-08T20:33:26.510139Z" } }, "outputs": [], - "execution_count": 17 + "execution_count": 43 }, { "cell_type": "code", @@ -529,11 +750,86 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:41.256231Z", - "start_time": "2024-12-08T15:35:41.064795Z" + "end_time": "2024-12-08T20:33:29.130483Z", + "start_time": "2024-12-08T20:33:28.839446Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1164, in emit\n", + " self.flush()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1144, in flush\n", + " self.stream.flush()\n", + "OSError: [Errno 9] Bad file descriptor\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_2668\\335208165.py\", line 1, in \n", + " rhine_meteo_data.plot(band=0)\n", + " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", + " self.create_color_bar(ax, im, cbar_kw)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 529, in create_color_bar\n", + " cbar = ax.figure.colorbar(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\figure.py\", line 1253, in colorbar\n", + " cb = cbar.Colorbar(cax, mappable, **{\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 407, in __init__\n", + " self._draw_all()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 550, in _draw_all\n", + " self.update_ticks()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 804, in update_ticks\n", + " self._get_ticker_locator_formatter()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", + " _log.debug('locator: %r', locator)\n", + "Message: 'locator: %r'\n", + "Arguments: (,)\n" + ] + }, { "data": { "text/plain": [ @@ -550,12 +846,12 @@ "(
, )" ] }, - "execution_count": 18, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 18 + "execution_count": 44 }, { "cell_type": "code", @@ -565,8 +861,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:42.298240Z", - "start_time": "2024-12-08T15:35:42.290546Z" + "end_time": "2024-12-08T20:33:32.250782Z", + "start_time": "2024-12-08T20:33:32.243182Z" } }, "outputs": [ @@ -587,12 +883,12 @@ " " ] }, - "execution_count": 19, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 19 + "execution_count": 45 } ], "metadata": { diff --git a/examples/notebooks/03convert-longitude.ipynb b/examples/notebooks/03convert-longitude.ipynb index 40a69465..e1c74086 100644 --- a/examples/notebooks/03convert-longitude.ipynb +++ b/examples/notebooks/03convert-longitude.ipynb @@ -38,12 +38,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:54.037374Z", - "start_time": "2024-12-08T15:35:53.446767Z" + "end_time": "2024-12-08T20:33:55.299868Z", + "start_time": "2024-12-08T20:33:55.241320Z" } }, "outputs": [], - "execution_count": 1 + "execution_count": 10 }, { "cell_type": "markdown", @@ -62,12 +62,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:56.482833Z", - "start_time": "2024-12-08T15:35:56.468546Z" + "end_time": "2024-12-08T20:33:58.241963Z", + "start_time": "2024-12-08T20:33:58.234962Z" } }, "outputs": [], - "execution_count": 2 + "execution_count": 11 }, { "cell_type": "code", @@ -77,8 +77,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:58.168199Z", - "start_time": "2024-12-08T15:35:58.147556Z" + "end_time": "2024-12-08T20:33:59.963793Z", + "start_time": "2024-12-08T20:33:59.954258Z" } }, "outputs": [ @@ -99,7 +99,7 @@ ] } ], - "execution_count": 3 + "execution_count": 12 }, { "cell_type": "code", @@ -110,8 +110,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:35:59.140280Z", - "start_time": "2024-12-08T15:35:59.131222Z" + "end_time": "2024-12-08T20:34:01.354779Z", + "start_time": "2024-12-08T20:34:01.350410Z" } }, "outputs": [ @@ -124,7 +124,7 @@ ] } ], - "execution_count": 4 + "execution_count": 13 }, { "cell_type": "markdown", @@ -138,7 +138,7 @@ { "cell_type": "code", "source": [ - "fig, ax = dataset.plot(\n", + "glyph = dataset.plot(\n", " band=0, figsize=(10, 5), title=\"NOAH daily Precipitation 1979-01-01\", cbar_label=\"Rainfall mm/day\", vmax=30,\n", " cbar_length=0.85\n", ")" @@ -146,11 +146,86 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:36:01.849338Z", - "start_time": "2024-12-08T15:36:00.984381Z" + "end_time": "2024-12-08T20:34:14.419262Z", + "start_time": "2024-12-08T20:34:14.164096Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1164, in emit\n", + " self.flush()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1144, in flush\n", + " self.stream.flush()\n", + "OSError: [Errno 9] Bad file descriptor\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_3972\\3351097019.py\", line 1, in \n", + " glyph = dataset.plot(\n", + " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", + " self.create_color_bar(ax, im, cbar_kw)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 529, in create_color_bar\n", + " cbar = ax.figure.colorbar(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\figure.py\", line 1253, in colorbar\n", + " cb = cbar.Colorbar(cax, mappable, **{\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 407, in __init__\n", + " self._draw_all()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 550, in _draw_all\n", + " self.update_ticks()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 804, in update_ticks\n", + " self._get_ticker_locator_formatter()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", + " _log.debug('locator: %r', locator)\n", + "Message: 'locator: %r'\n", + "Arguments: (,)\n" + ] + }, { "data": { "text/plain": [ @@ -162,7 +237,7 @@ "output_type": "display_data" } ], - "execution_count": 5 + "execution_count": 15 }, { "cell_type": "markdown", @@ -181,12 +256,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:36:03.515033Z", - "start_time": "2024-12-08T15:36:03.497539Z" + "end_time": "2024-12-08T20:34:19.181257Z", + "start_time": "2024-12-08T20:34:19.162088Z" } }, "outputs": [], - "execution_count": 6 + "execution_count": 16 }, { "cell_type": "code", @@ -199,11 +274,86 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:36:05.230230Z", - "start_time": "2024-12-08T15:36:05.023895Z" + "end_time": "2024-12-08T20:34:21.252006Z", + "start_time": "2024-12-08T20:34:20.939309Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1164, in emit\n", + " self.flush()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\logging\\__init__.py\", line 1144, in flush\n", + " self.stream.flush()\n", + "OSError: [Errno 9] Bad file descriptor\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 640, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\base_events.py\", line 1992, in _run_once\n", + " handle._run()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\eng_m\\AppData\\Local\\Temp\\ipykernel_3972\\3839577220.py\", line 1, in \n", + " new_dataset.plot(\n", + " File \"C:\\gdrive\\algorithms\\gis\\pyramids\\pyramids\\dataset.py\", line 1784, in plot\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 878, in plot\n", + " self.create_color_bar(ax, im, cbar_kw)\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\cleopatra\\array_glyph.py\", line 529, in create_color_bar\n", + " cbar = ax.figure.colorbar(\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\figure.py\", line 1253, in colorbar\n", + " cb = cbar.Colorbar(cax, mappable, **{\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 407, in __init__\n", + " self._draw_all()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 550, in _draw_all\n", + " self.update_ticks()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 804, in update_ticks\n", + " self._get_ticker_locator_formatter()\n", + " File \"C:\\Miniconda3\\envs\\pyramids\\Lib\\site-packages\\matplotlib\\colorbar.py\", line 855, in _get_ticker_locator_formatter\n", + " _log.debug('locator: %r', locator)\n", + "Message: 'locator: %r'\n", + "Arguments: (,)\n" + ] + }, { "data": { "text/plain": [ @@ -221,12 +371,12 @@ " )" ] }, - "execution_count": 7, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 7 + "execution_count": 17 }, { "cell_type": "code", @@ -237,8 +387,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:36:06.713116Z", - "start_time": "2024-12-08T15:36:06.709603Z" + "end_time": "2024-12-08T20:34:25.417813Z", + "start_time": "2024-12-08T20:34:25.414189Z" } }, "outputs": [ @@ -251,7 +401,7 @@ ] } ], - "execution_count": 8 + "execution_count": 18 }, { "cell_type": "code", @@ -261,12 +411,12 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-08T15:36:08.554040Z", - "start_time": "2024-12-08T15:36:08.508617Z" + "end_time": "2024-12-08T20:34:29.780137Z", + "start_time": "2024-12-08T20:34:29.737546Z" } }, "outputs": [], - "execution_count": 9 + "execution_count": 19 } ], "metadata": { diff --git a/examples/notebooks/04datacube.ipynb b/examples/notebooks/04datacube.ipynb index 35f9c63b..1d6dfff7 100644 --- a/examples/notebooks/04datacube.ipynb +++ b/examples/notebooks/04datacube.ipynb @@ -11,8 +11,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:08.627802Z", - "start_time": "2024-12-07T16:02:07.416486Z" + "end_time": "2024-12-08T20:37:04.147924Z", + "start_time": "2024-12-08T20:37:02.769211Z" } }, "outputs": [], @@ -69,8 +69,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:13.175730Z", - "start_time": "2024-12-07T16:02:13.169865Z" + "end_time": "2024-12-08T20:37:13.935664Z", + "start_time": "2024-12-08T20:37:13.929418Z" } }, "outputs": [ @@ -146,8 +146,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:18.458682Z", - "start_time": "2024-12-07T16:02:18.407423Z" + "end_time": "2024-12-08T20:37:24.036360Z", + "start_time": "2024-12-08T20:37:24.008297Z" } }, "outputs": [], @@ -170,8 +170,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:21.751010Z", - "start_time": "2024-12-07T16:02:21.747506Z" + "end_time": "2024-12-08T20:37:30.279573Z", + "start_time": "2024-12-08T20:37:30.275177Z" } }, "outputs": [ @@ -218,8 +218,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:27.101317Z", - "start_time": "2024-12-07T16:02:27.087060Z" + "end_time": "2024-12-08T20:37:35.559644Z", + "start_time": "2024-12-08T20:37:35.545215Z" } }, "outputs": [], @@ -234,8 +234,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:29.209579Z", - "start_time": "2024-12-07T16:02:29.204396Z" + "end_time": "2024-12-08T20:37:39.074485Z", + "start_time": "2024-12-08T20:37:39.069794Z" } }, "outputs": [ @@ -325,8 +325,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:32.680144Z", - "start_time": "2024-12-07T16:02:32.305556Z" + "end_time": "2024-12-08T20:37:44.051301Z", + "start_time": "2024-12-08T20:37:43.624260Z" } }, "outputs": [ @@ -364,8 +364,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2024-12-07T16:02:36.533249Z", - "start_time": "2024-12-07T16:02:35.427552Z" + "end_time": "2024-12-08T20:37:48.356337Z", + "start_time": "2024-12-08T20:37:46.749771Z" } }, "outputs": [ @@ -560,42 +560,42 @@ "\n", "\n", "
\n", - " \n", + " \n", "
\n", - " \n", + " oninput=\"anim52905a1c27d74c108905537cd1aa9ba8.set_frame(parseInt(this.value));\">\n", "
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "
\n", - "
\n", - " \n", - " \n", - " Once\n", + " \n", - " \n", - " Loop\n", + " \n", - " \n", + " \n", "
\n", "
\n", "
\n", @@ -605,9 +605,9 @@ " /* Instantiate the Animation class. */\n", " /* The IDs given should match those used in the template above. */\n", " (function() {\n", - " var img_id = \"_anim_img627b453a0050482792ec28a0d8e3e3f3\";\n", - " var slider_id = \"_anim_slider627b453a0050482792ec28a0d8e3e3f3\";\n", - " var loop_select_id = \"_anim_loop_select627b453a0050482792ec28a0d8e3e3f3\";\n", + " var img_id = \"_anim_img52905a1c27d74c108905537cd1aa9ba8\";\n", + " var slider_id = \"_anim_slider52905a1c27d74c108905537cd1aa9ba8\";\n", + " var loop_select_id = \"_anim_loop_select52905a1c27d74c108905537cd1aa9ba8\";\n", " var frames = new Array(10);\n", " \n", " frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAMgCAYAAADbcAZoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n", @@ -4228,7 +4228,7 @@ " /* set a timeout to make sure all the above elements are created before\n", " the object is initialized. */\n", " setTimeout(function() {\n", - " anim627b453a0050482792ec28a0d8e3e3f3 = new Animation(frames, img_id, slider_id, 200.0,\n", + " anim52905a1c27d74c108905537cd1aa9ba8 = new Animation(frames, img_id, slider_id, 200.0,\n", " loop_select_id);\n", " }, 0);\n", " })()\n",