diff --git a/tutorials/W1D2_Ocean-AtmosphereReanalysis/W1D2_Tutorial6.ipynb b/tutorials/W1D2_Ocean-AtmosphereReanalysis/W1D2_Tutorial6.ipynb index 040173f43..575b000b0 100644 --- a/tutorials/W1D2_Ocean-AtmosphereReanalysis/W1D2_Tutorial6.ipynb +++ b/tutorials/W1D2_Ocean-AtmosphereReanalysis/W1D2_Tutorial6.ipynb @@ -66,6 +66,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [ "colab" ] @@ -81,7 +82,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "import xarray as xr\n", @@ -98,7 +101,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -126,7 +130,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -153,7 +158,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -171,6 +177,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -226,7 +233,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -238,6 +248,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "pycharm": { "name": "#%%\n" }, @@ -260,7 +271,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -283,7 +297,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# import preprocessed ECCO data. This data is full depth temperature data over 1992 to 2016 (annual mean)\n", @@ -311,7 +327,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Quick plot of the ocean temperature in Kelvin\n", @@ -332,7 +350,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# take the temporal mean over the period 1992 to 1994\n", @@ -354,7 +374,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# plot the zonal mean section of this data\n", @@ -394,7 +416,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "theta_area_int = (\n", @@ -405,7 +429,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# define reference density of salt water and the specific heat capacity\n", @@ -460,7 +486,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "area_of_ocean = (\n", @@ -490,7 +518,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -504,7 +534,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -535,7 +566,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# this cell may take a while to run!\n", @@ -617,7 +650,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", @@ -658,7 +693,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -672,7 +709,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -701,7 +739,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# first let's plot where heat is stored in the mean\n", @@ -755,7 +795,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# we already defined an object that's the mean over years 1992 to 1994 (subset_theta)\n", @@ -783,7 +825,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# plot 2 maps to compare changes in heat content in those two layers\n", @@ -902,7 +946,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D2_Ocean-AtmosphereReanalysis/instructor/W1D2_Tutorial6.ipynb b/tutorials/W1D2_Ocean-AtmosphereReanalysis/instructor/W1D2_Tutorial6.ipynb index 3091a8933..575b000b0 100644 --- a/tutorials/W1D2_Ocean-AtmosphereReanalysis/instructor/W1D2_Tutorial6.ipynb +++ b/tutorials/W1D2_Ocean-AtmosphereReanalysis/instructor/W1D2_Tutorial6.ipynb @@ -226,10 +226,23 @@ "tabs = widgets.Tab()\n", "tabs.children = tab_contents\n", "for i in range(len(tab_contents)):\n", - " tabs.set_title(i, video_ids[i][0])\n", + " tabs.set_title(i, video_ids[i][0])\n", "display(tabs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Oceans_Role_in_Climate_Video\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -255,6 +268,19 @@ "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Oceans_Role_in_Climate_Slides\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -920,7 +946,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D2_Ocean-AtmosphereReanalysis/student/W1D2_Tutorial6.ipynb b/tutorials/W1D2_Ocean-AtmosphereReanalysis/student/W1D2_Tutorial6.ipynb index f7b455f6d..7e5c426a6 100644 --- a/tutorials/W1D2_Ocean-AtmosphereReanalysis/student/W1D2_Tutorial6.ipynb +++ b/tutorials/W1D2_Ocean-AtmosphereReanalysis/student/W1D2_Tutorial6.ipynb @@ -226,10 +226,23 @@ "tabs = widgets.Tab()\n", "tabs.children = tab_contents\n", "for i in range(len(tab_contents)):\n", - " tabs.set_title(i, video_ids[i][0])\n", + " tabs.set_title(i, video_ids[i][0])\n", "display(tabs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Oceans_Role_in_Climate_Video\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -255,6 +268,19 @@ "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Oceans_Role_in_Climate_Slides\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -912,7 +938,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb index 1efc35070..6be5170b8 100644 --- a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb +++ b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial1.ipynb @@ -364,7 +364,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -559,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial2.ipynb b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial2.ipynb index df1dfaf37..96515c1b3 100644 --- a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial2.ipynb +++ b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial2.ipynb @@ -318,7 +318,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -504,7 +507,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -556,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial3.ipynb b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial3.ipynb index d81f00c6f..00d8eb08a 100644 --- a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial3.ipynb +++ b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial3.ipynb @@ -979,7 +979,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial4.ipynb b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial4.ipynb index aec2dcc5d..e8cdccd7c 100644 --- a/tutorials/W1D3_RemoteSensing/W1D3_Tutorial4.ipynb +++ b/tutorials/W1D3_RemoteSensing/W1D3_Tutorial4.ipynb @@ -64,6 +64,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [ "colab" ] @@ -93,6 +94,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -113,7 +115,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -142,6 +145,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -159,7 +163,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -171,7 +176,7 @@ " #print(user_temp_cache)\n", " # set pooch logger to print only warnings such that redundant downloading is avoided\n", " pooch.get_logger().setLevel(\"WARNING\")\n", - " \n", + "\n", " if os.path.exists(os.path.join(shared_location, filename)):\n", " file = os.path.join(shared_location, filename)\n", " else:\n", @@ -190,6 +195,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -246,7 +252,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -259,6 +266,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "pycharm": { "name": "#%%\n" }, @@ -282,7 +290,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -328,6 +337,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -345,6 +355,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -366,6 +377,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -400,6 +412,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -421,6 +434,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -440,6 +454,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -469,6 +484,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -509,6 +525,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -533,6 +550,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -592,6 +610,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -616,6 +635,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -647,7 +667,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -678,6 +699,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -694,7 +716,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -734,6 +757,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -782,6 +806,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -806,6 +831,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -828,6 +854,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -882,6 +909,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -900,7 +928,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -921,6 +950,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -967,6 +997,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -1000,6 +1031,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -1047,7 +1079,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -1118,7 +1151,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial1.ipynb b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial1.ipynb index dd6580db5..4a2c7499b 100644 --- a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial1.ipynb +++ b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial1.ipynb @@ -281,7 +281,7 @@ "- **Medium-Earth orbit** (approximately 2,000 to 35,500 km above Earth): this type of orbit is often used by the [Global Positioning System (GPS)](https://en.wikipedia.org/wiki/Global_Positioning_System) satellites.\n", "- **High-Earth orbit** (above 35,500 km above Earth): Satellites in this orbit are typically geostationary. They maintain a fixed position above a specific location on Earth's surface. NOAA, for instance, operates two geostationary satellites that provide observations of the western hemisphere every five minutes and targeted observations of severe weather events every 30 seconds.\n", "\n", - " ## ![Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022.](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg) |\n", + "![Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022.](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg)\n", "\n", " Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022. (Credit: [NOAA](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg) )\n" ] @@ -314,13 +314,13 @@ "\n", "For instance, the Airborne Visible/Infrared Imaging Spectrometer ([AVIRIS](https://aviris.jpl.nasa.gov/)) shown below records data in 224 spectral channels. These sensors enable us to differentiate between various rock and mineral types, vegetation types, water quality, and other features.\n", "\n", - "## ![Sample image of AVIRIS over a lake and evaporation pond.](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa) |\n", + "![Sample image of AVIRIS over a lake and evaporation pond.](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa)\n", "\n", "(Credit: [NASA JPL](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa))\n", "\n", "**Radiometric resolution** quantifies the amount of information contained in each pixel and is often expressed as the number of bits representing the recorded energy. Each bit records an exponent of power 2. For instance, an 8-bit resolution equals $2^8$, implying that the sensor can utilize 256 potential digital values (0-255) to store information. The image below from NASA Earth Observatory illustrates how radiometric resolution affects the level of detail visible in remotely sensed data.\n", "\n", - "## ![Illustration of the impact of radiometric resoultion on remotely sensed data.](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ) |\n", + "![Illustration of the impact of radiometric resoultion on remotely sensed data.](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ)\n", "\n", "Credit: [NASA](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ)\n", "\n", @@ -361,6 +361,19 @@ "\"\"\"" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Questions_1_3\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -395,7 +408,7 @@ "\n", "The answers to these questions bear significant implications for local water availability, thereby influencing crucial areas such as agriculture, wildlife conservation, and energy consumption.\n", "\n", - "## ![modis snow](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_snow.gif?raw=true)|\n", + "![modis snow](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_snow.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MOD10C1_M_SNOW)\n" ] @@ -416,7 +429,7 @@ "\n", "These applications have strong implications on communities through food security, culture activities, and other economic activities.\n", "\n", - "## ![modis ndvi](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_ndvi.gif?raw=true)|\n", + "![modis ndvi](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_ndvi.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MOD_NDVI_M)\n" ] @@ -439,7 +452,7 @@ "\n", "Clouds are one of the major sources of uncertainty in future climate projections. Satellite data is valuable to help us deepen our understanding of cloud processes to better incorporate the effects of clouds in climate models.\n", "\n", - "## ![modis cloud](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_cloud.gif?raw=true)|\n", + "![modis cloud](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_cloud.gif?raw=true)\n", "\n", "Cloud fraction, or the portion of a pixel covered by clouds. Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MODAL2_M_CLD_FR)\n" ] @@ -459,7 +472,7 @@ "- Quantify the variations in rainfall rates across different regions over time. This information can support water resource planning and management to meet the needs of local communities.\n", "- Investigate the frequency and potential causes of extreme rainfall events and examine their impacts on societal and natural systems. Examples include studying hurricanes and extreme rainfall events.\n", "\n", - "## ![rainfall](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_gpm_imerg_rainfall.gif?raw=true)|\n", + "![rainfall](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_gpm_imerg_rainfall.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/GPM_3IMERGM)\n" ] @@ -480,7 +493,7 @@ "- Monitoring SST is essential for studying large-scale ocean circulation and climate variability, such as the El Niño-Southern Oscillation (ENSO). Changes in SST patterns can provide valuable information about the occurrence and strength of ENSO events, which have significant impacts on global weather patterns.\n", "- SST plays a pivotal role in the study and prediction of tropical cyclones. Warm SST provides the necessary energy for the formation and intensification of tropical cyclones, making SST data critical for understanding and forecasting these powerful storm systems.\n", "\n", - "## ![modis sst](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_sst.gif?raw=true) |\n", + "## ![modis sst](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_sst.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MYD28M)\n" ] @@ -500,7 +513,7 @@ "- How have phytoplankton populations changed in response to shifting sea surface temperatures in different ocean basins?\n", "- By combining phytoplankton data with socioeconomic information, can we gain a comprehensive understanding of how the changing climate affects the fishing industry and the communities reliant on it.\n", "\n", - "## ![modis chl](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_chl.gif?raw=true) |\n", + "![modis chl](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_chl.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MY1DMM_CHLORA)\n" ] @@ -549,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial2.ipynb b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial2.ipynb index 92028a202..8596888e4 100644 --- a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial2.ipynb +++ b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial2.ipynb @@ -315,6 +315,19 @@ "\"\"\"" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Questions_1_2\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -491,6 +504,19 @@ "\"\"\"" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Questions_2_3\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -536,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial3.ipynb b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial3.ipynb index cfa341433..4b03da10d 100644 --- a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial3.ipynb +++ b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial3.ipynb @@ -331,7 +331,7 @@ "source": [ "If we go to the [cloud storage space (or a S3 bucket)](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html#data/) that hosts NOAA NDVI CDR data, you will see the pattern of how the NOAA NDVI CDR is organized:\n", "\n", - "`s3://noaa-cdr-ndvi-pds/data/2024/VIIRS-Land_v001_NPP13C1_S-NPP_20220101_c20240126162652.nc`\n", + "`s3://noaa-cdr-ndvi-pds/data/2022/VIIRS-Land_v001_NPP13C1_S-NPP_20220101_c20240126162652.nc`\n", "\n", "We can take advantage of the pattern to search for the data file systematically. \n", "\n", @@ -348,7 +348,7 @@ "> Satellite platform: `S-NPP` (*NOAA-20 in 2024, so dependent on the satellite era*)\n", "> Date of the data: `20220101` \n", "> Processing time: `c20240126162652` (*This will change for each file based on when the file was processed*) \n", - "> File format: `.nc` (*netCDR-4 format*)\n", + "> File format: `.nc` (*netCDF-4 format*)\n", "\n", "In other words, if we are looking for the data of a specific day, we can easily locate where the file might be. \n", "\n", @@ -983,7 +983,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial4.ipynb b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial4.ipynb index c6e984e4d..230370f04 100644 --- a/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial4.ipynb +++ b/tutorials/W1D3_RemoteSensing/instructor/W1D3_Tutorial4.ipynb @@ -173,6 +173,9 @@ "def pooch_load(filelocation=None, filename=None, processor=None):\n", " shared_location = \"/home/jovyan/shared/data/tutorials/W1D3_RemoteSensing\" # this is different for each day\n", " user_temp_cache = tempfile.gettempdir()\n", + " #print(user_temp_cache)\n", + " # set pooch logger to print only warnings such that redundant downloading is avoided\n", + " pooch.get_logger().setLevel(\"WARNING\")\n", "\n", " if os.path.exists(os.path.join(shared_location, filename)):\n", " file = os.path.join(shared_location, filename)\n", @@ -440,6 +443,7 @@ "# this process will take ~ 5 minute to complete due to the number of data files.\n", "\n", "# file_ob = [pooch.retrieve('http://s3.amazonaws.com/'+file,known_hash=None ) for file in file_location]\n", + "\n", "file_ob = [\n", " pooch_load(filelocation=\"http://s3.amazonaws.com/\" + file, filename=file)\n", " for file in file_location\n", @@ -1151,7 +1155,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial1.ipynb b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial1.ipynb index 84bd06e07..a1d14e455 100644 --- a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial1.ipynb +++ b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial1.ipynb @@ -281,7 +281,7 @@ "- **Medium-Earth orbit** (approximately 2,000 to 35,500 km above Earth): this type of orbit is often used by the [Global Positioning System (GPS)](https://en.wikipedia.org/wiki/Global_Positioning_System) satellites.\n", "- **High-Earth orbit** (above 35,500 km above Earth): Satellites in this orbit are typically geostationary. They maintain a fixed position above a specific location on Earth's surface. NOAA, for instance, operates two geostationary satellites that provide observations of the western hemisphere every five minutes and targeted observations of severe weather events every 30 seconds.\n", "\n", - " ## ![Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022.](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg) |\n", + "![Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022.](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg)\n", "\n", " Satellite images of NOAA's GOES-West (on the left) and GOES-East (on the right) full disk data on the day of summer solstice of 2022. (Credit: [NOAA](https://live.staticflickr.com/65535/48127127191_911a25f659_b.jpg) )\n" ] @@ -314,13 +314,13 @@ "\n", "For instance, the Airborne Visible/Infrared Imaging Spectrometer ([AVIRIS](https://aviris.jpl.nasa.gov/)) shown below records data in 224 spectral channels. These sensors enable us to differentiate between various rock and mineral types, vegetation types, water quality, and other features.\n", "\n", - "## ![Sample image of AVIRIS over a lake and evaporation pond.](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa) |\n", + "![Sample image of AVIRIS over a lake and evaporation pond.](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa)\n", "\n", "(Credit: [NASA JPL](https://www.earthdata.nasa.gov/s3fs-public/imported/avcubebig.gif?VersionId=vB8vyHe0veiLvzKN37vSwnXUpe4WpOHa))\n", "\n", "**Radiometric resolution** quantifies the amount of information contained in each pixel and is often expressed as the number of bits representing the recorded energy. Each bit records an exponent of power 2. For instance, an 8-bit resolution equals $2^8$, implying that the sensor can utilize 256 potential digital values (0-255) to store information. The image below from NASA Earth Observatory illustrates how radiometric resolution affects the level of detail visible in remotely sensed data.\n", "\n", - "## ![Illustration of the impact of radiometric resoultion on remotely sensed data.](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ) |\n", + "![Illustration of the impact of radiometric resoultion on remotely sensed data.](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ)\n", "\n", "Credit: [NASA](https://www.earthdata.nasa.gov/s3fs-public/2022-02/radiometric_resolution.png?VersionId=SUfbvvyRgjUqC1C5CoB2Br52GvwKq9iZ)\n", "\n", @@ -356,6 +356,19 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Questions_1_3\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -390,7 +403,7 @@ "\n", "The answers to these questions bear significant implications for local water availability, thereby influencing crucial areas such as agriculture, wildlife conservation, and energy consumption.\n", "\n", - "## ![modis snow](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_snow.gif?raw=true)|\n", + "![modis snow](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_snow.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MOD10C1_M_SNOW)\n" ] @@ -411,7 +424,7 @@ "\n", "These applications have strong implications on communities through food security, culture activities, and other economic activities.\n", "\n", - "## ![modis ndvi](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_ndvi.gif?raw=true)|\n", + "![modis ndvi](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_ndvi.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MOD_NDVI_M)\n" ] @@ -434,7 +447,7 @@ "\n", "Clouds are one of the major sources of uncertainty in future climate projections. Satellite data is valuable to help us deepen our understanding of cloud processes to better incorporate the effects of clouds in climate models.\n", "\n", - "## ![modis cloud](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_cloud.gif?raw=true)|\n", + "![modis cloud](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_cloud.gif?raw=true)\n", "\n", "Cloud fraction, or the portion of a pixel covered by clouds. Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MODAL2_M_CLD_FR)\n" ] @@ -454,7 +467,7 @@ "- Quantify the variations in rainfall rates across different regions over time. This information can support water resource planning and management to meet the needs of local communities.\n", "- Investigate the frequency and potential causes of extreme rainfall events and examine their impacts on societal and natural systems. Examples include studying hurricanes and extreme rainfall events.\n", "\n", - "## ![rainfall](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_gpm_imerg_rainfall.gif?raw=true)|\n", + "![rainfall](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_gpm_imerg_rainfall.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/GPM_3IMERGM)\n" ] @@ -475,7 +488,7 @@ "- Monitoring SST is essential for studying large-scale ocean circulation and climate variability, such as the El Niño-Southern Oscillation (ENSO). Changes in SST patterns can provide valuable information about the occurrence and strength of ENSO events, which have significant impacts on global weather patterns.\n", "- SST plays a pivotal role in the study and prediction of tropical cyclones. Warm SST provides the necessary energy for the formation and intensification of tropical cyclones, making SST data critical for understanding and forecasting these powerful storm systems.\n", "\n", - "## ![modis sst](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_sst.gif?raw=true) |\n", + "## ![modis sst](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_sst.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MYD28M)\n" ] @@ -495,7 +508,7 @@ "- How have phytoplankton populations changed in response to shifting sea surface temperatures in different ocean basins?\n", "- By combining phytoplankton data with socioeconomic information, can we gain a comprehensive understanding of how the changing climate affects the fishing industry and the communities reliant on it.\n", "\n", - "## ![modis chl](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_chl.gif?raw=true) |\n", + "![modis chl](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/W1D3_RemoteSensing/asset/img/t1_modis_chl.gif?raw=true)\n", "\n", "Credit: [NASA](https://earthobservatory.nasa.gov/global-maps/MY1DMM_CHLORA)\n" ] @@ -544,7 +557,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial2.ipynb b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial2.ipynb index 632193e29..0f5845f03 100644 --- a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial2.ipynb +++ b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial2.ipynb @@ -309,6 +309,19 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Questions_1_2\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -485,6 +498,19 @@ "\"\"\"" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Questions_2_3\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -530,7 +556,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial3.ipynb b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial3.ipynb index 0c0301e1c..2ab14949d 100644 --- a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial3.ipynb +++ b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial3.ipynb @@ -331,7 +331,7 @@ "source": [ "If we go to the [cloud storage space (or a S3 bucket)](https://noaa-cdr-ndvi-pds.s3.amazonaws.com/index.html#data/) that hosts NOAA NDVI CDR data, you will see the pattern of how the NOAA NDVI CDR is organized:\n", "\n", - "`s3://noaa-cdr-ndvi-pds/data/2024/VIIRS-Land_v001_NPP13C1_S-NPP_20220101_c20240126162652.nc`\n", + "`s3://noaa-cdr-ndvi-pds/data/2022/VIIRS-Land_v001_NPP13C1_S-NPP_20220101_c20240126162652.nc`\n", "\n", "We can take advantage of the pattern to search for the data file systematically. \n", "\n", @@ -348,7 +348,7 @@ "> Satellite platform: `S-NPP` (*NOAA-20 in 2024, so dependent on the satellite era*)\n", "> Date of the data: `20220101` \n", "> Processing time: `c20240126162652` (*This will change for each file based on when the file was processed*) \n", - "> File format: `.nc` (*netCDR-4 format*)\n", + "> File format: `.nc` (*netCDF-4 format*)\n", "\n", "In other words, if we are looking for the data of a specific day, we can easily locate where the file might be. \n", "\n", @@ -927,7 +927,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial4.ipynb b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial4.ipynb index 40b3f23ed..d9778ad65 100644 --- a/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial4.ipynb +++ b/tutorials/W1D3_RemoteSensing/student/W1D3_Tutorial4.ipynb @@ -173,6 +173,9 @@ "def pooch_load(filelocation=None, filename=None, processor=None):\n", " shared_location = \"/home/jovyan/shared/data/tutorials/W1D3_RemoteSensing\" # this is different for each day\n", " user_temp_cache = tempfile.gettempdir()\n", + " #print(user_temp_cache)\n", + " # set pooch logger to print only warnings such that redundant downloading is avoided\n", + " pooch.get_logger().setLevel(\"WARNING\")\n", "\n", " if os.path.exists(os.path.join(shared_location, filename)):\n", " file = os.path.join(shared_location, filename)\n", @@ -440,6 +443,7 @@ "# this process will take ~ 5 minute to complete due to the number of data files.\n", "\n", "# file_ob = [pooch.retrieve('http://s3.amazonaws.com/'+file,known_hash=None ) for file in file_location]\n", + "\n", "file_ob = [\n", " pooch_load(filelocation=\"http://s3.amazonaws.com/\" + file, filename=file)\n", " for file in file_location\n", @@ -1087,7 +1091,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D5_IntroductiontoClimateModeling/W1D5_Tutorial3.ipynb b/tutorials/W1D5_IntroductiontoClimateModeling/W1D5_Tutorial3.ipynb index 48edcfbbe..9e378a32d 100644 --- a/tutorials/W1D5_IntroductiontoClimateModeling/W1D5_Tutorial3.ipynb +++ b/tutorials/W1D5_IntroductiontoClimateModeling/W1D5_Tutorial3.ipynb @@ -1528,7 +1528,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D5_IntroductiontoClimateModeling/instructor/W1D5_Tutorial3.ipynb b/tutorials/W1D5_IntroductiontoClimateModeling/instructor/W1D5_Tutorial3.ipynb index da1904923..3dc2b95c6 100644 --- a/tutorials/W1D5_IntroductiontoClimateModeling/instructor/W1D5_Tutorial3.ipynb +++ b/tutorials/W1D5_IntroductiontoClimateModeling/instructor/W1D5_Tutorial3.ipynb @@ -857,7 +857,7 @@ "execution": {} }, "source": [ - "# Section 2: Revisiting the Climate Change Scenario from Tutorial 3\n" + "# Section 2: Revisiting the Climate Change Scenario from Tutorial 2\n" ] }, { @@ -1542,7 +1542,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W1D5_IntroductiontoClimateModeling/student/W1D5_Tutorial3.ipynb b/tutorials/W1D5_IntroductiontoClimateModeling/student/W1D5_Tutorial3.ipynb index a6bc8ea2b..fcca10b8d 100644 --- a/tutorials/W1D5_IntroductiontoClimateModeling/student/W1D5_Tutorial3.ipynb +++ b/tutorials/W1D5_IntroductiontoClimateModeling/student/W1D5_Tutorial3.ipynb @@ -800,7 +800,7 @@ "execution": {} }, "source": [ - "# Section 2: Revisiting the Climate Change Scenario from Tutorial 3\n" + "# Section 2: Revisiting the Climate Change Scenario from Tutorial 2\n" ] }, { @@ -1355,7 +1355,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial3.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial3.ipynb index f3206185c..8f309e248 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial3.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial3.ipynb @@ -56,7 +56,9 @@ "cell_type": "code", "execution_count": null, "id": "062c4b1a-6239-4644-a0dd-03fe1342fe99", - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# import\n", @@ -68,7 +70,8 @@ "execution_count": null, "id": "33ac31c0-ac52-447a-82d7-aec0b98dc6e6", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -97,7 +100,8 @@ "execution_count": null, "id": "8b4722fe-cbd5-484a-be6d-96ed7aafbf62", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -115,7 +119,8 @@ "execution_count": null, "id": "053ee0ea-d704-45c8-8b4b-aad17216710d", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -143,7 +148,8 @@ "execution_count": null, "id": "5b50c362", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -202,7 +208,8 @@ "execution_count": null, "id": "74b1c5e6-ed8f-4ca8-bbab-a0a6c417dced", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -215,7 +222,8 @@ "execution_count": null, "id": "703ee8f2", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -234,7 +242,8 @@ "execution_count": null, "id": "9fd96992-bab2-4494-aeb5-997cc8689746", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -288,7 +297,9 @@ "cell_type": "code", "execution_count": null, "id": "c97e6d3d-1718-479b-a19a-ffc6a082347b", - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -309,7 +320,8 @@ "execution_count": null, "id": "867fc0d6-6802-4d3d-888b-be960ffd54e8", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -374,7 +386,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb index 31aef30e2..d80afbecf 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/W2D2_Tutorial4.ipynb @@ -63,6 +63,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 1460, "status": "ok", @@ -93,7 +94,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -121,7 +123,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -141,6 +144,7 @@ "code_folding": [ 0 ], + "execution": {}, "tags": [] }, "outputs": [], @@ -175,6 +179,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -232,7 +237,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -245,6 +251,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "pycharm": { "name": "#%%\n" }, @@ -268,7 +275,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -315,7 +323,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Load SSP data from .csv file\n", @@ -340,7 +350,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "print(df.SCENARIO.unique()) # print all scenarios\n", @@ -374,7 +386,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -425,7 +438,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# put variables of interest in a list\n", @@ -542,7 +557,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# put variables of interest in a list\n", @@ -614,7 +631,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# put two variables of interest in a list\n", @@ -663,7 +682,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove solution\n", @@ -706,7 +727,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -726,7 +749,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -792,7 +816,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial3.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial3.ipynb index 227664d5a..69213a0ed 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial3.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial3.ipynb @@ -153,7 +153,7 @@ }, "outputs": [], "source": [ - "# @title Video 1: The IPCC's Transition Narratives and Project Modelling\n", + "# @title Video 1: Orienting inside a 'Climate Solution' Simulator\n", "\n", "from ipywidgets import widgets\n", "from IPython.display import YouTubeVideo\n", @@ -386,7 +386,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial4.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial4.ipynb index f4fa9fbd0..540692236 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial4.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/instructor/W2D2_Tutorial4.ipynb @@ -59,17 +59,6 @@ "# Setup" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# installations ( uncomment and run this cell ONLY when using google colab or kaggle )" - ] - }, { "cell_type": "code", "execution_count": null, @@ -195,7 +184,7 @@ }, "outputs": [], "source": [ - "# @title Video 1: Transition Goals and Integrated Assessment Models\n", + "# @title Video 1: Shared Socioeconomic Pathways\n", "\n", "from ipywidgets import widgets\n", "from IPython.display import YouTubeVideo\n", @@ -829,7 +818,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial3.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial3.ipynb index b459f4e8f..a689db851 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial3.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial3.ipynb @@ -153,7 +153,7 @@ }, "outputs": [], "source": [ - "# @title Video 1: The IPCC's Transition Narratives and Project Modelling\n", + "# @title Video 1: Orienting inside a 'Climate Solution' Simulator\n", "\n", "from ipywidgets import widgets\n", "from IPython.display import YouTubeVideo\n", @@ -376,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial4.ipynb b/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial4.ipynb index 6c1a1856c..f75ebb255 100644 --- a/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial4.ipynb +++ b/tutorials/W2D2_TheSocioeconomicsofClimateChange/student/W2D2_Tutorial4.ipynb @@ -59,17 +59,6 @@ "# Setup" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# installations ( uncomment and run this cell ONLY when using google colab or kaggle )" - ] - }, { "cell_type": "code", "execution_count": null, @@ -195,7 +184,7 @@ }, "outputs": [], "source": [ - "# @title Video 1: Transition Goals and Integrated Assessment Models\n", + "# @title Video 1: Shared Socioeconomic Pathways\n", "\n", "from ipywidgets import widgets\n", "from IPython.display import YouTubeVideo\n", @@ -787,7 +776,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/W2D3_Intro.ipynb b/tutorials/W2D3_ExtremesandVariability/W2D3_Intro.ipynb index 0562b46cd..2de2d8b90 100644 --- a/tutorials/W2D3_ExtremesandVariability/W2D3_Intro.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/W2D3_Intro.ipynb @@ -67,7 +67,10 @@ "cell_type": "code", "execution_count": null, "id": "abc21da9-5eec-41e0-ba56-271a511ef335", - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Install and import feedback gadget\n", @@ -105,7 +108,8 @@ "execution_count": null, "id": "63305b4b", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -161,7 +165,10 @@ "cell_type": "code", "execution_count": null, "id": "8dc8fe9e-bf63-4bf1-b9e2-1bbaf9d65648", - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -183,7 +190,8 @@ "execution_count": null, "id": "4e34db65", "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -201,7 +209,10 @@ "cell_type": "code", "execution_count": null, "id": "996b5336-20a1-43df-8c49-005d1a307a90", - "metadata": {}, + "metadata": { + "cellView": "form", + "execution": {} + }, "outputs": [], "source": [ "# @title Submit your feedback\n", @@ -236,7 +247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial1.ipynb b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial1.ipynb index 7d46a5ba4..1a525691b 100644 --- a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial1.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial1.ipynb @@ -67,6 +67,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -86,7 +87,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -115,6 +117,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -132,7 +135,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -161,6 +165,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -217,7 +222,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -230,6 +236,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "pycharm": { "name": "#%%\n" }, @@ -253,7 +260,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -285,6 +293,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -303,6 +312,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -332,6 +342,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -366,6 +377,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -412,6 +424,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -430,6 +443,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -441,6 +455,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -484,6 +499,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -496,6 +512,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -508,6 +525,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -535,6 +553,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -598,6 +617,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -621,6 +641,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -647,7 +668,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", @@ -681,6 +704,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -730,7 +754,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -751,7 +776,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -764,7 +791,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -824,7 +852,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial4.ipynb b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial4.ipynb index c291cae98..02069be70 100644 --- a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial4.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial4.ipynb @@ -70,6 +70,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 2204, "status": "ok", @@ -100,7 +101,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -129,6 +131,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -146,7 +149,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -175,6 +179,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "tags": [] }, "outputs": [], @@ -231,7 +236,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -244,6 +250,7 @@ "execution_count": null, "metadata": { "cellView": "form", + "execution": {}, "pycharm": { "name": "#%%\n" }, @@ -267,7 +274,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -297,6 +305,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 530, "status": "ok", @@ -337,6 +346,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 267, "status": "ok", @@ -370,6 +380,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -392,6 +403,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 7, "status": "ok", @@ -422,6 +434,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -444,6 +457,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 188, "status": "ok", @@ -465,6 +479,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 2, "status": "ok", @@ -498,6 +513,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 784, "status": "ok", @@ -558,6 +574,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -591,7 +608,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# setup plots\n", @@ -620,6 +639,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "executionInfo": { "elapsed": 357, "status": "ok", @@ -664,7 +684,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -686,7 +707,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -699,7 +722,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -732,7 +756,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# initalize list to store parameters from samples\n", @@ -785,6 +811,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "tags": [] }, "outputs": [], @@ -843,7 +870,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -903,7 +931,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial5.ipynb b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial5.ipynb index 59fdf966e..2cacad236 100644 --- a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial5.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial5.ipynb @@ -755,7 +755,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial6.ipynb b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial6.ipynb index a2a20897d..cd1eb1b2d 100644 --- a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial6.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial6.ipynb @@ -1507,7 +1507,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial8.ipynb b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial8.ipynb index 25789286a..92e03daa2 100644 --- a/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial8.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/W2D3_Tutorial8.ipynb @@ -1895,7 +1895,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Intro.ipynb b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Intro.ipynb index 0475d4b2e..4f57ae4a0 100644 --- a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Intro.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Intro.ipynb @@ -63,6 +63,36 @@ "4. Characterize extreme events (e.g. precipitation, sea level height, and heat) by these probabilities and prescribed thresholds.\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "abc21da9-5eec-41e0-ba56-271a511ef335", + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Install and import feedback gadget\n", + "\n", + "!pip3 install vibecheck datatops --quiet\n", + "\n", + "from vibecheck import DatatopsContentReviewContainer\n", + "def content_review(notebook_section: str):\n", + " return DatatopsContentReviewContainer(\n", + " \"\", # No text prompt\n", + " notebook_section,\n", + " {\n", + " \"url\": \"https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab\",\n", + " \"name\": \"comptools_4clim\",\n", + " \"user_key\": \"l5jpxuee\",\n", + " },\n", + " ).render()\n", + "\n", + "\n", + "feedback_prefix = \"W2D3_Intro\"" + ] + }, { "cell_type": "markdown", "id": "061742f3", @@ -131,6 +161,20 @@ "display(tabs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dc8fe9e-bf63-4bf1-b9e2-1bbaf9d65648", + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Extremes_in_a_Changing_Climate_Video\")" + ] + }, { "cell_type": "markdown", "id": "205d09de", @@ -160,6 +204,20 @@ "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n", "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "996b5336-20a1-43df-8c49-005d1a307a90", + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Extremes_in_a_Changing_Climate_Slides\")" + ] } ], "metadata": { @@ -189,7 +247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial1.ipynb b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial1.ipynb index d042cc944..221bc5869 100644 --- a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial1.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial1.ipynb @@ -404,7 +404,7 @@ " Click here for a description of the plot \n", "Histogram plot of the annual maximum daily precipitation in millimeters per day. The horizontal axis of the histogram represents the data's range via bins, while the vertical axis represents the frequency of occurrences within each interval by counting the number of data points that fall into each bin. \n", "By visually inspecting the histogram we get an impression of the shape, central tendency, and spread of the dataset. \n", - "The bin with the largest amount of occurrences shows 12 counts of annual maximum daily precipitation between 14 and 16 millimeters per day. \n", + "The bin with the largest amount of occurrences shows 12 counts of annual maximum daily precipitation between 20 and 22 millimeters per day. \n", "Bins that show at least one count range from 14 millimeters per day to 60 millimeters per day. \n", "" ] @@ -856,7 +856,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial4.ipynb b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial4.ipynb index 9069a07d1..1b0fca083 100644 --- a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial4.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial4.ipynb @@ -548,7 +548,7 @@ "source": [ "
\n", " Click to see a description of the plot \n", - "Return levels versus return periods calculated from fitted GEV parameters and equal-spaced periods, log scaled on the x-axis. Hence, we can easily access the return level for an X-year event, e.g. 20 year-event corresponds to an annual maximum precipitation of 70 millimeters per day.\n", + "Return levels versus return periods calculated from fitted GEV parameters and equal-spaced periods, log scaled on the x-axis. Hence, we can easily access the return level for an X-year event, e.g. 200-year event corresponds to an annual maximum precipitation of 70 millimeters per day.\n", "
" ] }, @@ -935,7 +935,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial5.ipynb b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial5.ipynb index 5d7531b83..3e14eb52f 100644 --- a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial5.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial5.ipynb @@ -559,36 +559,6 @@ "ax.set_ylabel(\"Density\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "data_periods = []\n", - "for ind, yr in enumerate(range(30,120,30)):\n", - " data_periods.append(data.iloc[yr-30:yr])\n", - "\n", - "fig, ax = plt.subplots()\n", - "x = np.linspace(-200, 600, 1000)\n", - "\n", - "for ind, yr in enumerate(range(30,120,30)):\n", - " shape_period, loc_period, scale_period = gev.fit(data_periods[ind].ssh.values, 0)\n", - " ax.plot(\n", - " x,\n", - " gev.pdf(x, shape_period, loc=loc_period, scale=scale_period),\n", - " c=colors[ind],\n", - " lw=3,\n", - " label=f\"{1901+yr}-{1930+yr}\"\n", - ")\n", - "\n", - "ax.legend()\n", - "ax.set_xlabel(\"Annual Maximum Sea Surface Height (mm)\")\n", - "ax.set_ylabel(\"Density\")" - ] - }, { "cell_type": "markdown", "metadata": { @@ -787,7 +757,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial6.ipynb b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial6.ipynb index 2a1d789b9..54cb5f3d4 100644 --- a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial6.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial6.ipynb @@ -394,7 +394,7 @@ "# Section 1: Load CMIP6 Data\n", "\n", "\n", - "As in W2D1 (IPCC Physical Basis), you will be loading CMIP6 data from [Pangeo](https://pangeo.io/). In this way, you can access large amounts of climate model output that has been stored in the cloud. Here, we have already accessed the data of interest and collected it into a [.nc](https://en.wikipedia.org/wiki/NetCDF) file for you. However, the information on how to access this data directly is provided in the Resources section at the end of this notebook.\n", + "As in W1D5 and W2D1, you will be loading CMIP6 data from [Pangeo](https://pangeo.io/). In this way, you can access large amounts of climate model output that has been stored in the cloud. Here, we have already accessed the data of interest and collected it into a [.nc](https://en.wikipedia.org/wiki/NetCDF) file for you. However, the information on how to access this data directly is provided in the Resources section at the end of this notebook.\n", "\n", "You can learn more about CMIP, including additional methods to access CMIP data, through our [CMIP Resource Bank](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md) and the [CMIP website](https://wcrp-cmip.org/)." ] @@ -1513,7 +1513,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial8.ipynb b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial8.ipynb index 2cb62330b..0c34bb60b 100644 --- a/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial8.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/instructor/W2D3_Tutorial8.ipynb @@ -1201,7 +1201,7 @@ "source": [ "
\n", " Click here for a detailed description of the plot.\n", - "Time series of the wet-bulb globe temperature in $\\degree$C in India, New Delhi for three scenarios and the historical period, overlaid by the 100-year return level of certain periods, as computed over 1950-2014 (dot-dashed), 2015-2050 (dashed), and 2070-2100 (dotted). In summary, the return levels increase over time, only the blue scenario (SSP1-2.6) keeps it at a temporally constant level at the end of the century, while the more severe scenarios still show rising return levels. The SSP5-8.5, often called the 'business-as-usual' scenario due to omitted implementation of policies, shows a return level of 36 $\\degree$C for 100-year events, 3 years more than the SSP2-4.5 scenario.\n", + "Time series of the wet-bulb globe temperature in $\\degree$C in India, New Delhi for three scenarios and the historical period, overlaid by the 100-year return level of certain periods, as computed over 1950-2014 (dot-dashed), 2015-2050 (dashed), and 2070-2100 (dotted). In summary, the return levels increase over time, only the blue scenario (SSP1-2.6) keeps it at a temporally constant level at the end of the century, while the more severe scenarios still show rising return levels. The SSP5-8.5, often called the 'business-as-usual' scenario due to omitted implementation of policies, shows a return level of 36 $\\degree$C for 100-year events, 3 degrees Celsius more than the SSP2-4.5 scenario.\n", "
" ] }, @@ -1895,7 +1895,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial1_Solution_e35d1bc8_2.png b/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial1_Solution_e35d1bc8_2.png index d8e223623..c17ee40e4 100644 Binary files a/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial1_Solution_e35d1bc8_2.png and b/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial1_Solution_e35d1bc8_2.png differ diff --git a/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial4_Solution_c4127f14_2.png b/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial4_Solution_c4127f14_2.png index 106e93c2e..00914e5c0 100644 Binary files a/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial4_Solution_c4127f14_2.png and b/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial4_Solution_c4127f14_2.png differ diff --git a/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial6_Solution_b99eab92_6.png b/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial6_Solution_b99eab92_6.png index 7b3a8bfa5..a87a5e6d3 100644 Binary files a/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial6_Solution_b99eab92_6.png and b/tutorials/W2D3_ExtremesandVariability/static/W2D3_Tutorial6_Solution_b99eab92_6.png differ diff --git a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Intro.ipynb b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Intro.ipynb index ba916e673..1061e2038 100644 --- a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Intro.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Intro.ipynb @@ -63,6 +63,36 @@ "4. Characterize extreme events (e.g. precipitation, sea level height, and heat) by these probabilities and prescribed thresholds.\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "abc21da9-5eec-41e0-ba56-271a511ef335", + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Install and import feedback gadget\n", + "\n", + "!pip3 install vibecheck datatops --quiet\n", + "\n", + "from vibecheck import DatatopsContentReviewContainer\n", + "def content_review(notebook_section: str):\n", + " return DatatopsContentReviewContainer(\n", + " \"\", # No text prompt\n", + " notebook_section,\n", + " {\n", + " \"url\": \"https://pmyvdlilci.execute-api.us-east-1.amazonaws.com/klab\",\n", + " \"name\": \"comptools_4clim\",\n", + " \"user_key\": \"l5jpxuee\",\n", + " },\n", + " ).render()\n", + "\n", + "\n", + "feedback_prefix = \"W2D3_Intro\"" + ] + }, { "cell_type": "markdown", "id": "061742f3", @@ -131,6 +161,20 @@ "display(tabs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dc8fe9e-bf63-4bf1-b9e2-1bbaf9d65648", + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Extremes_in_a_Changing_Climate_Video\")" + ] + }, { "cell_type": "markdown", "id": "205d09de", @@ -160,6 +204,20 @@ "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n", "IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=854, height=480)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "996b5336-20a1-43df-8c49-005d1a307a90", + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Submit your feedback\n", + "content_review(f\"{feedback_prefix}_Extremes_in_a_Changing_Climate_Slides\")" + ] } ], "metadata": { @@ -189,7 +247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial1.ipynb b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial1.ipynb index 11fd81ea9..568a75736 100644 --- a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial1.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial1.ipynb @@ -404,7 +404,7 @@ " Click here for a description of the plot \n", "Histogram plot of the annual maximum daily precipitation in millimeters per day. The horizontal axis of the histogram represents the data's range via bins, while the vertical axis represents the frequency of occurrences within each interval by counting the number of data points that fall into each bin. \n", "By visually inspecting the histogram we get an impression of the shape, central tendency, and spread of the dataset. \n", - "The bin with the largest amount of occurrences shows 12 counts of annual maximum daily precipitation between 14 and 16 millimeters per day. \n", + "The bin with the largest amount of occurrences shows 12 counts of annual maximum daily precipitation between 20 and 22 millimeters per day. \n", "Bins that show at least one count range from 14 millimeters per day to 60 millimeters per day. \n", "" ] @@ -811,7 +811,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial4.ipynb b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial4.ipynb index 1139a8382..838771898 100644 --- a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial4.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial4.ipynb @@ -548,7 +548,7 @@ "source": [ "
\n", " Click to see a description of the plot \n", - "Return levels versus return periods calculated from fitted GEV parameters and equal-spaced periods, log scaled on the x-axis. Hence, we can easily access the return level for an X-year event, e.g. 20 year-event corresponds to an annual maximum precipitation of 70 millimeters per day.\n", + "Return levels versus return periods calculated from fitted GEV parameters and equal-spaced periods, log scaled on the x-axis. Hence, we can easily access the return level for an X-year event, e.g. 200-year event corresponds to an annual maximum precipitation of 70 millimeters per day.\n", "
" ] }, @@ -866,7 +866,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial5.ipynb b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial5.ipynb index 4173c3430..9a4bcbb75 100644 --- a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial5.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial5.ipynb @@ -559,36 +559,6 @@ "ax.set_ylabel(\"Density\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "data_periods = []\n", - "for ind, yr in enumerate(range(30,120,30)):\n", - " data_periods.append(data.iloc[yr-30:yr])\n", - "\n", - "fig, ax = plt.subplots()\n", - "x = np.linspace(-200, 600, 1000)\n", - "\n", - "for ind, yr in enumerate(range(30,120,30)):\n", - " shape_period, loc_period, scale_period = gev.fit(data_periods[ind].ssh.values, 0)\n", - " ax.plot(\n", - " x,\n", - " gev.pdf(x, shape_period, loc=loc_period, scale=scale_period),\n", - " c=colors[ind],\n", - " lw=3,\n", - " label=f\"{1901+yr}-{1930+yr}\"\n", - ")\n", - "\n", - "ax.legend()\n", - "ax.set_xlabel(\"Annual Maximum Sea Surface Height (mm)\")\n", - "ax.set_ylabel(\"Density\")" - ] - }, { "cell_type": "markdown", "metadata": { @@ -761,7 +731,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial6.ipynb b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial6.ipynb index 63ea50a4f..e00dde111 100644 --- a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial6.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial6.ipynb @@ -394,7 +394,7 @@ "# Section 1: Load CMIP6 Data\n", "\n", "\n", - "As in W2D1 (IPCC Physical Basis), you will be loading CMIP6 data from [Pangeo](https://pangeo.io/). In this way, you can access large amounts of climate model output that has been stored in the cloud. Here, we have already accessed the data of interest and collected it into a [.nc](https://en.wikipedia.org/wiki/NetCDF) file for you. However, the information on how to access this data directly is provided in the Resources section at the end of this notebook.\n", + "As in W1D5 and W2D1, you will be loading CMIP6 data from [Pangeo](https://pangeo.io/). In this way, you can access large amounts of climate model output that has been stored in the cloud. Here, we have already accessed the data of interest and collected it into a [.nc](https://en.wikipedia.org/wiki/NetCDF) file for you. However, the information on how to access this data directly is provided in the Resources section at the end of this notebook.\n", "\n", "You can learn more about CMIP, including additional methods to access CMIP data, through our [CMIP Resource Bank](https://github.com/neuromatch/climate-course-content/blob/main/tutorials/CMIP/CMIP_resource_bank.md) and the [CMIP website](https://wcrp-cmip.org/)." ] @@ -1413,7 +1413,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial8.ipynb b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial8.ipynb index 3779d3b4b..3f0902672 100644 --- a/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial8.ipynb +++ b/tutorials/W2D3_ExtremesandVariability/student/W2D3_Tutorial8.ipynb @@ -1196,7 +1196,7 @@ "source": [ "
\n", " Click here for a detailed description of the plot.\n", - "Time series of the wet-bulb globe temperature in $\\degree$C in India, New Delhi for three scenarios and the historical period, overlaid by the 100-year return level of certain periods, as computed over 1950-2014 (dot-dashed), 2015-2050 (dashed), and 2070-2100 (dotted). In summary, the return levels increase over time, only the blue scenario (SSP1-2.6) keeps it at a temporally constant level at the end of the century, while the more severe scenarios still show rising return levels. The SSP5-8.5, often called the 'business-as-usual' scenario due to omitted implementation of policies, shows a return level of 36 $\\degree$C for 100-year events, 3 years more than the SSP2-4.5 scenario.\n", + "Time series of the wet-bulb globe temperature in $\\degree$C in India, New Delhi for three scenarios and the historical period, overlaid by the 100-year return level of certain periods, as computed over 1950-2014 (dot-dashed), 2015-2050 (dashed), and 2070-2100 (dotted). In summary, the return levels increase over time, only the blue scenario (SSP1-2.6) keeps it at a temporally constant level at the end of the century, while the more severe scenarios still show rising return levels. The SSP5-8.5, often called the 'business-as-usual' scenario due to omitted implementation of policies, shows a return level of 36 $\\degree$C for 100-year events, 3 degrees Celsius more than the SSP2-4.5 scenario.\n", "
" ] }, @@ -1885,7 +1885,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial1.ipynb b/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial1.ipynb index 5f86753d1..bf515d62a 100644 --- a/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial1.ipynb +++ b/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial1.ipynb @@ -67,6 +67,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "pycharm": { "name": "#%%\n" } @@ -85,7 +86,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -113,7 +115,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -130,7 +133,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -194,7 +198,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -208,6 +213,7 @@ "metadata": { "cellView": "form", "editable": true, + "execution": {}, "slideshow": { "slide_type": "" }, @@ -231,7 +237,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -384,7 +391,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "#Load Dataset\n", @@ -413,7 +422,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "print(training_data.shape)" @@ -433,7 +444,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "training_data" @@ -463,7 +476,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "training_data.isnull().sum()" @@ -501,7 +516,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Create a xarray dataset from the pandas dataframe\n", @@ -516,7 +533,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# create geoaxes\n", @@ -577,7 +596,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "def plot_spatial_distribution(data, col_name, c_label):\n", @@ -626,7 +647,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove solution\n", @@ -690,7 +713,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -820,7 +844,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial4.ipynb b/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial4.ipynb index 9fd2d6708..f98b840c2 100644 --- a/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial4.ipynb +++ b/tutorials/W2D4_AIandClimateChange/W2D4_Tutorial4.ipynb @@ -58,6 +58,7 @@ "cell_type": "code", "execution_count": null, "metadata": { + "execution": {}, "pycharm": { "name": "#%%\n" } @@ -81,7 +82,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -109,7 +111,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -126,7 +129,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -146,7 +150,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -175,7 +180,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -278,7 +284,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -342,7 +349,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -354,7 +362,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -374,7 +383,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -418,7 +428,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Loading the new Spatial test data\n", @@ -443,7 +455,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# plot spatial distribution of temperature anomalies for 2015\n", @@ -473,7 +487,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "rf_regressor = RandomForestRegressor(random_state=42, n_estimators=80, max_depth=50)\n", @@ -497,7 +513,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "spatial_test_score = rf_regressor.score(spatial_test_data,spatial_test_target)\n", @@ -527,7 +545,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "def scatter_plot_predicted_vs_true(spatial_test_data, true_values):\n", @@ -570,7 +590,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove solution\n", @@ -616,7 +638,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -640,7 +663,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# to_remove explanation\n", @@ -659,7 +684,8 @@ "cell_type": "code", "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -725,7 +751,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial1.ipynb b/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial1.ipynb index ed9494954..662f6b88b 100644 --- a/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial1.ipynb +++ b/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial1.ipynb @@ -129,36 +129,6 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [], - "source": [ - "# @title Set random seed\n", - "\n", - "# @markdown Executing `set_seed(seed=seed)` you are setting the seed\n", - "\n", - "# Call `set_seed` function in the exercises to ensure reproducibility.\n", - "import random\n", - "import numpy as np\n", - "\n", - "def set_seed(seed=None):\n", - " if seed is None:\n", - " seed = np.random.choice(2 ** 32)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " print(f'Random seed {seed} has been set.')\n", - "\n", - "# Set a global seed value for reproducibility\n", - "random_state = 42 # change 42 with any number you like\n", - "\n", - "set_seed(seed=random_state)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -388,7 +358,7 @@ "execution": {} }, "source": [ - "

W2D4_Tutorial1_climatebench_Scenario

" + "

W2D4_Tutorial1_climatebench_Scenario

" ] }, { @@ -876,7 +846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial4.ipynb b/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial4.ipynb index 767c431d7..6241ced77 100644 --- a/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial4.ipynb +++ b/tutorials/W2D4_AIandClimateChange/instructor/W2D4_Tutorial4.ipynb @@ -422,7 +422,7 @@ "\n", "We will take our random forest model that was trained on data from the region in the blue box and see if it can work well using lat/lon locations that come from the red box. We already have the data from the blue box region loaded, so now we just need to load the data from the red box.\n", "\n", - "

W2D4_Tutorial4_map

" + "

W2D4_Tutorial4_map

" ] }, { @@ -753,7 +753,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial1.ipynb b/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial1.ipynb index 4343d0b17..498be4a53 100644 --- a/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial1.ipynb +++ b/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial1.ipynb @@ -129,36 +129,6 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [], - "source": [ - "# @title Set random seed\n", - "\n", - "# @markdown Executing `set_seed(seed=seed)` you are setting the seed\n", - "\n", - "# Call `set_seed` function in the exercises to ensure reproducibility.\n", - "import random\n", - "import numpy as np\n", - "\n", - "def set_seed(seed=None):\n", - " if seed is None:\n", - " seed = np.random.choice(2 ** 32)\n", - " random.seed(seed)\n", - " np.random.seed(seed)\n", - " print(f'Random seed {seed} has been set.')\n", - "\n", - "# Set a global seed value for reproducibility\n", - "random_state = 42 # change 42 with any number you like\n", - "\n", - "set_seed(seed=random_state)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -388,7 +358,7 @@ "execution": {} }, "source": [ - "

W2D4_Tutorial1_climatebench_Scenario

" + "

W2D4_Tutorial1_climatebench_Scenario

" ] }, { @@ -836,7 +806,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial4.ipynb b/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial4.ipynb index 9b260f188..a2e02532e 100644 --- a/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial4.ipynb +++ b/tutorials/W2D4_AIandClimateChange/student/W2D4_Tutorial4.ipynb @@ -422,7 +422,7 @@ "\n", "We will take our random forest model that was trained on data from the region in the blue box and see if it can work well using lat/lon locations that come from the red box. We already have the data from the blue box region loaded, so now we just need to load the data from the red box.\n", "\n", - "

W2D4_Tutorial4_map

" + "

W2D4_Tutorial4_map

" ] }, { @@ -710,7 +710,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.9.20" } }, "nbformat": 4,