Start Part2

2_Vostok_measurements_images.ipynb

@@ -327,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 28,
    "id": "6d6b5a71-6dd3-4471-b081-f3606c7b6956",
    "metadata": {},
    "outputs": [],
@@ -343,7 +343,8 @@
     "    ]\n",
     ").swapaxes(0,1)\n",
     "\n",
-    "data_counter = data_counter.astype(int)"
+    "data_counter = data_counter.astype(int)\n",
+    "np.savez('./data/vostok_2006_2020_results', mean=data, counter=data_counter, sqr=data_sqr)"
    ]
   },
   {

3_WRF_T2_images.ipynb

@@ -40,7 +40,7 @@
     "# montly averaged 2-meter level temperature (T2m) for each 1° latitude band across a full 360° longitude\n",
     "# T2m are calculated with the Weather Research and Forecasting model (WRF) version 4.3.\n",
     "# shape = (180, 12)\n",
-    "wrf_mon_T2 = np.load(\"./data/WRF_T2_MONxLAT.npy\")"
+    "wrf_mon_T2 = np.load(\"./data/WRF/WRF_T2_LATxMON.npy\")"
    ]
   },
   {

readme.md

@@ -50,13 +50,15 @@ To construct Figure 2, using the prepared data and helper functions, we calculat
 2. The same series up to and including the year 2012.
 3. The same series after the year 2012.
 
+> **_Note:_** The data from this figure is saved in the temporary file `vostok_2006_2020_results.npz` for use in the second article. This helps avoid code duplication or merging code to build different entities in a single cumbersome file.
+
 ### Figure 3
 
 To construct Figure 3, we transform the Vostok data series into a matrix of 12 months x 24 hours. To do this, we group the original dataframe of fair weather hours by months and hours, and then find the mean value for all data points taken at a specific hour of a specific month (saved in dataframe `sd_df`).
 
 For clarity, we also present slices of this diurnal-seasonal diagram at 3, 9, 15, and 21 hours UTC.
 
-We note that renaming the axes of the multi-index resulting from grouping (`sd_df.index.set_names(['hour', 'month'], inplace=True)`) is not necessary for the code and can be commented out; however, it may be convenient for further work with the diurnal-seasonal dataframe `sd_df`.
+> **_Note:_**  Renaming the axes of the multi-index resulting from grouping (`sd_df.index.set_names(['hour', 'month'], inplace=True)`) is not necessary for the code and can be commented out; however, it may be convenient for further work with the diurnal-seasonal dataframe `sd_df`.
 
 ### Figure 4
 #### Removal of field anomalies associated with meteorological parameters