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@@ -34,12 +34,12 @@ | |
| "source": [ | ||
| "```{admonition} Submission Guide\n", | ||
| "\n", | ||
| "Deadline: **Sunday 11:59 pm, 19th November 2023** \n", | ||
| "Deadline: **Sunday 11:59 pm, 17th November 2024** \n", | ||
| "(Note: Late submissions will not be accepted). \n", | ||
| "\n", | ||
| "Please upload your solutions to [Canvas](https://canvas.nus.edu.sg/courses/47849/assignments) in a Jupyter Notebook format with the name \"Homework2_StudentID.ipynb\". Make sure to write down your student ID and full name in the cell below. \n", | ||
| "Please upload your solutions to [Canvas](https://canvas.nus.edu.sg/courses/61921/assignments) in a Jupyter Notebook format with the name \"Homework2_StudentID.ipynb\". Make sure to write down your student ID and full name in the cell below. \n", | ||
| "\n", | ||
| "For any questions, feel free to contact Prof. Xiaogang HE ([[email protected]](mailto:[email protected])), or Zhixiao NIU ([niu.zhixiao@u.nus.edu](mailto:niu.zhixiao@u.nus.edu)).\n", | ||
| "For any questions, feel free to contact Prof. Xiaogang HE ([[email protected]](mailto:[email protected])), Haoling CHEN ([h.chen@u.nus.edu](mailto:[email protected])) or Meilian LI ([[email protected]](mailto:limeilian@u.nus.edu)).\n", | ||
| "\n", | ||
| "```" | ||
| ] | ||
|
|
@@ -82,13 +82,26 @@ | |
| "source": [ | ||
| "## Task 1: Visualize the GEV distribution (30 marks)\n", | ||
| "To visualize the GEV distribution, you can:\n", | ||
| "1. Fit the Generalized Extreme Value (GEV) distribution to annual maximum daily temperature data and estimate the GEV parameters using the **Maximum Likelihood method**.\n", | ||
| "2. Plot the probability density function (PDF) curve to to represent the distribution directly.\n", | ||
| "3. Calculate the return level for a 20-year event and mark it on the plot.\n", | ||
| "4. Fill the figure with different colors regarding the calculated return level as the dividing line.\n", | ||
| "1. Fit the Generalized Extreme Value (GEV) distribution to annual maximum daily temperature data and estimate the GEV parameters using the **L-Moments method**.\n", | ||
| "2. Plot the probability density function (PDF) curve to represent the distribution directly.\n", | ||
| "3. Calculate the return level for a 10-year event and mark it on the plot.\n", | ||
| "4. Shade the plot with different colors to distinguish areas above and below the calculated return level.\n", | ||
| "5. Ensure that the necessary non-data elements are included, such as title, x/y axis labels, legend, etc. (you can check the [Matplotlib tutorial](https://xiaoganghe.github.io/python-climate-visuals/chapters/data-visuals/matplotlib-basic.html) for details)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "8f7a646f", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "```{admonition} Bonus: 10 marks\n", | ||
| ":class: tip\n", | ||
| "\n", | ||
| "Illustrate how return levels vary across different return periods (e.g., 2, 5, 10, 20, 50, 100 years) using interactive sliders or dashboards. Resources such as the [Plotly documentation](https://plotly.com/python/sliders/) and [this tutorial video](https://www.youtube.com/watch?v=gs4d0_AKQi8) may be helpful for this task.\n", | ||
| "\n", | ||
| "```" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
|
|
@@ -105,12 +118,12 @@ | |
| "id": "67c41c3b", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Task 2: Visualize the trend of temperature anomaly ( 70 marks )\n", | ||
| "## Task 2: Visualize the trend of temperature anomaly (70 marks)\n", | ||
| "### Q1: Visualize the time series and trend of historical temperature anomalies of Singapore (35 marks)\n", | ||
| "- Calculate the annual mean temperature from the daily data. This will result in a data set of 39 values — one per year. (5 marks)\n", | ||
| "- Calculate the annual temperature anomalies using the first 15-year period as the baseline. (Hint: the anomalies is calculated by substracting the mean temperature over year 1982 to 1996 from the yearly mean temperature.) (5 marks)\n", | ||
| "- Make a line graph of these annual temperature anomalies and make sure that the necessary non-data elements are included. (10 marks)\n", | ||
| "- Based on the annual temperature anomalies above, visualize the trend using a 10-year moving window. (15 marks)" | ||
| "- Calculate the annual mean temperature from the daily data. This will result in a dataset of 39 values — one per year. (5 marks)\n", | ||
| "- Calculate the annual temperature anomalies using the first 10-year period as the baseline. (Hint: subtract the mean temperature over 1982 to 1991 from the annual mean temperature for each year.) (5 marks)\n", | ||
| "- Visualize the change in these annual temperature anomalies over time, ensuring that you include essential non-data elements. (10 marks)\n", | ||
| "- Visualize the trend of the annual temperature anomalies using a 5-year moving window. (15 marks)" | ||
| ] | ||
| }, | ||
| { | ||
|
|
@@ -143,8 +156,8 @@ | |
| "- Use your generated climate stripes as the background and then overlay the time series and the trend of annual anomalies (from Q1) on top of the climate stripes. (15 marks)\n", | ||
| "\n", | ||
| "Tips: \n", | ||
| "- You can refer to [this GitHub repository](https://github.com/josephshea/ClimateStripes/blob/master/ClimateStripes-Canada.ipynb) to make the climate stripes. \n", | ||
| "- To make a pretty and accessible (colorblind safe) chart, you might want to fine tune the aesthetics (e.g., color palette of the diverging colorbar) of your chart. " | ||
| "- Refer to [this GitHub repository](https://github.com/josephshea/ClimateStripes/blob/master/ClimateStripes-Canada.ipynb) for guidance on creating climate stripes.\n", | ||
| "- Fine-tune the aesthetics of your chart (e.g., color palette of the diverging colorbar) to ensure it is visually appealing and accessible (colorblind safe)." | ||
| ] | ||
| }, | ||
| { | ||
|
|
@@ -161,11 +174,24 @@ | |
| "# Your solutions go here.\n", | ||
| "# Use the + icon in the toolbar to add a cell." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "14d1fb06", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "```{admonition} Bonus: 10 marks\n", | ||
| ":class: tip\n", | ||
| "\n", | ||
| "Create an interactive plot where users can hover over climate stripes and trend lines to view detailed information. For inspiration, you can refer to [this example](https://ourworldindata.org/un-population-2024-revision).\n", | ||
| "\n", | ||
| "```" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "display_name": "PytorchCPU", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
|
|
@@ -179,7 +205,7 @@ | |
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.6" | ||
| "version": "3.8.13" | ||
| }, | ||
| "toc": { | ||
| "base_numbering": 1, | ||
|
|
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