{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "5a7c8083", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "887e602d", "metadata": {}, "source": [ "# load gobm result" ] }, { "cell_type": "code", "execution_count": 3, "id": "27269222", "metadata": {}, "outputs": [], "source": [ "def get_sumdf(df, col='gobm_sum', target_col = 'targets', prediction=None):\n", " # index = df[df.targets.isnull()==True].index\n", " # df.loc[index, 'predictions'] = np.nan\n", "\n", " sum_df = pd.DataFrame()\n", " s = df.groupby(['year','month'])[target_col].sum() \n", " s = s.reset_index()\n", " sum_df['year'] = s['year']\n", " sum_df['month'] = s['month']\n", " sum_df[col] = s[target_col]\n", " if prediction:\n", " s = df.groupby(['year','month'])[prediction].sum() \n", " s = s.reset_index()\n", " sum_df[prediction] = s[prediction]\n", "\n", " sum_df['time'] = sum_df.apply(lambda x: f'{int(x[\"year\"])}_{int(x[\"month\"])}', axis=1) \n", " sum_df = sum_df[sum_df.year>=2010]\n", " return sum_df\n", "\n", "import matplotlib.pyplot as plt \n", "import numpy as np \n", " \n", "# Assuming you have 12 sets of data, each of size 180x360. \n", "# For this example, I'll generate random data. \n", "def plot_year_data(data, target_col = 'NEMO-PlankTOM12',year = 2013, min=None, max=None):\n", " # Create a figure \n", " fig, axs = plt.subplots(4, 3, figsize=(12,8)) \n", " axs = axs.ravel() \n", " sel = data[(data.year == year)]\n", " data = sel[target_col].values.reshape(-1, 180,360)\n", " if not isinstance(min, int):\n", " max, min = np.nanmax(data), np.nanmin(data)\n", " print(min,max)\n", " # Loop over the data and create a subplot for each \n", " for i in range(len(data)): \n", " title = f'{year}_{i+1}_{target_col}'\n", " # plt.imshow(data[i], cmap='viridis') \n", " im = axs[i].imshow(data[i], cmap='coolwarm', interpolation='nearest', origin='lower', vmin=min, vmax=max) \n", " # im = axs[i].imshow(data[i], cmap='coolwarm', interpolation='nearest', origin='lower') \n", " \n", " plt.colorbar(im, ax=axs[i]) \n", " \n", " axs[i].set_title(title) \n", " # plt.title(f'Subplot {i+1}') \n", " \n", " plt.tight_layout() \n", " plt.show() \n", "\n", "def set_nan(df):\n", " # for year 2010-2021\n", " null_index = df[(df.year<=2021) & (df.targets.isnull())].index\n", " df.loc[null_index, 'predictions'] = np.nan\n", "\n", " # for year 2022 - 2023\n", " null_index = df[(df.year ==2021) & (df.targets.isnull())].index\n", " for year in [2022,2023, 2024]:\n", " df.loc[null_index + (12 * 180 * 360) * (year-2021), 'predictions'] = np.nan\n", " return df\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "ce74b128", "metadata": {}, "outputs": [], "source": [ "gobm = pd.read_csv('/data/angcb/home/kpiyu/ANGCB/result/202506_test_64/101_1.csv')\n", "gobm = set_nan(gobm)" ] }, { "cell_type": "code", "execution_count": 69, "id": "6deab1c5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
| \n", " | year | \n", "month | \n", "lat | \n", "lon | \n", "predictions | \n", "targets | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "NaN | \n", "NaN | \n", "
| 1 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "1.0 | \n", "NaN | \n", "NaN | \n", "
| 2 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "2.0 | \n", "NaN | \n", "NaN | \n", "
| 3 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "3.0 | \n", "NaN | \n", "NaN | \n", "
| 4 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "4.0 | \n", "NaN | \n", "NaN | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 12441595 | \n", "2025.0 | \n", "12.0 | \n", "179.0 | \n", "355.0 | \n", "0.402356 | \n", "NaN | \n", "
| 12441596 | \n", "2025.0 | \n", "12.0 | \n", "179.0 | \n", "356.0 | \n", "0.410615 | \n", "NaN | \n", "
| 12441597 | \n", "2025.0 | \n", "12.0 | \n", "179.0 | \n", "357.0 | \n", "0.413244 | \n", "NaN | \n", "
| 12441598 | \n", "2025.0 | \n", "12.0 | \n", "179.0 | \n", "358.0 | \n", "0.342602 | \n", "NaN | \n", "
| 12441599 | \n", "2025.0 | \n", "12.0 | \n", "179.0 | \n", "359.0 | \n", "0.468141 | \n", "NaN | \n", "
12441600 rows × 6 columns
\n", "| \n", " | year | \n", "month | \n", "gobm_sum | \n", "predictions | \n", "time | \n", "
|---|---|---|---|---|---|
| 0 | \n", "2010.0 | \n", "1.0 | \n", "97289.959628 | \n", "9.512860e+04 | \n", "2010_1 | \n", "
| 1 | \n", "2010.0 | \n", "2.0 | \n", "98032.624662 | \n", "9.529348e+04 | \n", "2010_2 | \n", "
| 2 | \n", "2010.0 | \n", "3.0 | \n", "130292.928464 | \n", "1.319498e+05 | \n", "2010_3 | \n", "
| 3 | \n", "2010.0 | \n", "4.0 | \n", "148708.714767 | \n", "1.501845e+05 | \n", "2010_4 | \n", "
| 4 | \n", "2010.0 | \n", "5.0 | \n", "135784.431322 | \n", "1.349942e+05 | \n", "2010_5 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 187 | \n", "2025.0 | \n", "8.0 | \n", "0.000000 | \n", "1.117881e+05 | \n", "2025_8 | \n", "
| 188 | \n", "2025.0 | \n", "9.0 | \n", "0.000000 | \n", "-3.437751e+05 | \n", "2025_9 | \n", "
| 189 | \n", "2025.0 | \n", "10.0 | \n", "0.000000 | \n", "3.974564e+06 | \n", "2025_10 | \n", "
| 190 | \n", "2025.0 | \n", "11.0 | \n", "0.000000 | \n", "6.162484e+07 | \n", "2025_11 | \n", "
| 191 | \n", "2025.0 | \n", "12.0 | \n", "0.000000 | \n", "7.141640e+07 | \n", "2025_12 | \n", "
192 rows × 5 columns
\n", "| \n", " | year | \n", "month | \n", "gobm_sum | \n", "predictions | \n", "time | \n", "
|---|---|---|---|---|---|
| 0 | \n", "2010.0 | \n", "1.0 | \n", "97289.959628 | \n", "96814.171124 | \n", "2010_1 | \n", "
| 1 | \n", "2010.0 | \n", "2.0 | \n", "98032.624662 | \n", "98083.563515 | \n", "2010_2 | \n", "
| 2 | \n", "2010.0 | \n", "3.0 | \n", "130292.928464 | \n", "131789.088589 | \n", "2010_3 | \n", "
| 3 | \n", "2010.0 | \n", "4.0 | \n", "148708.714767 | \n", "152074.478428 | \n", "2010_4 | \n", "
| 4 | \n", "2010.0 | \n", "5.0 | \n", "135784.431322 | \n", "133771.651031 | \n", "2010_5 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 183 | \n", "2025.0 | \n", "4.0 | \n", "171571.420273 | \n", "195286.916002 | \n", "2025_4 | \n", "
| 184 | \n", "2025.0 | \n", "5.0 | \n", "155357.661252 | \n", "176546.422412 | \n", "2025_5 | \n", "
| 185 | \n", "2025.0 | \n", "6.0 | \n", "128135.666575 | \n", "148862.267085 | \n", "2025_6 | \n", "
| 186 | \n", "2025.0 | \n", "7.0 | \n", "100428.411796 | \n", "116013.400070 | \n", "2025_7 | \n", "
| 187 | \n", "2025.0 | \n", "8.0 | \n", "0.000000 | \n", "117323.540872 | \n", "2025_8 | \n", "
188 rows × 5 columns
\n", "| \n", " | year | \n", "month | \n", "lat | \n", "lon | \n", "predictions | \n", "targets | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "379.00604 | \n", "NaN | \n", "
| 1 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "1.0 | \n", "379.10434 | \n", "NaN | \n", "
| 2 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "2.0 | \n", "379.15390 | \n", "NaN | \n", "
| 3 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "3.0 | \n", "379.03122 | \n", "NaN | \n", "
| 4 | \n", "2010.0 | \n", "1.0 | \n", "0.0 | \n", "4.0 | \n", "378.87787 | \n", "NaN | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 10886395 | \n", "2023.0 | \n", "12.0 | \n", "179.0 | \n", "355.0 | \n", "341.51486 | \n", "NaN | \n", "
| 10886396 | \n", "2023.0 | \n", "12.0 | \n", "179.0 | \n", "356.0 | \n", "341.55005 | \n", "NaN | \n", "
| 10886397 | \n", "2023.0 | \n", "12.0 | \n", "179.0 | \n", "357.0 | \n", "341.67987 | \n", "NaN | \n", "
| 10886398 | \n", "2023.0 | \n", "12.0 | \n", "179.0 | \n", "358.0 | \n", "341.70972 | \n", "NaN | \n", "
| 10886399 | \n", "2023.0 | \n", "12.0 | \n", "179.0 | \n", "359.0 | \n", "341.46094 | \n", "NaN | \n", "
10886400 rows × 6 columns
\n", "| \n", " | year | \n", "month | \n", "data_mean | \n", "time | \n", "
|---|---|---|---|---|
| 12 | \n", "2010 | \n", "1 | \n", "1.626907e+07 | \n", "2010_1 | \n", "
| 13 | \n", "2010 | \n", "2 | \n", "1.642760e+07 | \n", "2010_2 | \n", "
| 14 | \n", "2010 | \n", "3 | \n", "1.656783e+07 | \n", "2010_3 | \n", "
| 15 | \n", "2010 | \n", "4 | \n", "1.658076e+07 | \n", "2010_4 | \n", "
| 16 | \n", "2010 | \n", "5 | \n", "1.650887e+07 | \n", "2010_5 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 187 | \n", "2024 | \n", "8 | \n", "0.000000e+00 | \n", "2024_8 | \n", "
| 188 | \n", "2024 | \n", "9 | \n", "0.000000e+00 | \n", "2024_9 | \n", "
| 189 | \n", "2024 | \n", "10 | \n", "0.000000e+00 | \n", "2024_10 | \n", "
| 190 | \n", "2024 | \n", "11 | \n", "0.000000e+00 | \n", "2024_11 | \n", "
| 191 | \n", "2024 | \n", "12 | \n", "0.000000e+00 | \n", "2024_12 | \n", "
180 rows × 4 columns
\n", "