added links to code

notebooks/computing_time_series.ipynb

@@ -182,7 +182,7 @@
     "\n",
     "Count how many grid-cells in `sst_at_buoy` have `analysed_sst` data. \n",
     "\n",
-    "For this we need to define a function to count non-NaN elements in a 2d array `(lat, lon)` of `analysed_sst` and then apply the function to the whole dask array. The function can be found [here]()"
+    "For this we need to define a function to count non-NaN elements in a 2d array `(lat, lon)` of `analysed_sst` and then apply the function to the whole dask array. The function can be found [here](https://git.noc.ac.uk/brecinosrivas/orchestra-sst/-/blob/master/sst_tools/workflow_sst.py#L413-415)"
    ]
   },
   {
@@ -262,7 +262,7 @@
    "metadata": {},
    "source": [
     "**4.2** All variables at `sst_at_buoy` need to be resampled but by calculating a time moving weighted average. Where the weights are the number of buoy points (`buoy_count`) on each grid cell. \n",
-    "Find code in [workflow.calculate_time_moving_weighted_average]()"
+    "Find code in [workflow.calculate_time_moving_weighted_average](https://git.noc.ac.uk/brecinosrivas/orchestra-sst/-/blob/master/sst_tools/workflow_sst.py#L423-450)"
    ]
   },
   {
@@ -431,7 +431,7 @@
    "source": [
     "**5.1** Spatial averages for **SST differences** between `sst_coarse` and `sst_at_buoy`. For the sst difference I also calculate some statisctis (std, median, and standard error)\n",
     "\n",
-    "The code for this can be found in [workflow.calculate_time_series_diff](). \n",
+    "The code for this can be found in [workflow.calculate_time_series_diff](https://git.noc.ac.uk/brecinosrivas/orchestra-sst/-/blob/master/sst_tools/workflow_sst.py#L295-350). \n",
     "\n",
     "> This might need some checking from Dave. "
    ]
@@ -535,7 +535,7 @@
    "source": [
     "**5.2** Spatial average of **bin statistics** only for `sst_at_buoy` variables mean estimated in 4.2. \n",
     "\n",
-    "See code in [workflow.calculate_spatial_avg_of_stats](). We also end up with a pandas.dataframe \n",
+    "See code in [workflow.calculate_spatial_avg_of_stats](https://git.noc.ac.uk/brecinosrivas/orchestra-sst/-/blob/master/sst_tools/workflow_sst.py#L295-350). We also end up with a pandas.dataframe \n",
     "\n",
     "> This is also might need checking"
    ]
@@ -641,7 +641,7 @@
    "source": [
     "**5.3** Total count of all buoy points. Only for `sst_at_buoy_buoy_count_sum` calculated in 4.3\n",
     "\n",
-    "See code in [workflow.calculate_total_buoy_density](). We also end up with a pandas.dataframe. "
+    "See code in [workflow.calculate_total_buoy_density](https://git.noc.ac.uk/brecinosrivas/orchestra-sst/-/blob/master/sst_tools/workflow_sst.py#L384-410). We also end up with a pandas.dataframe. "
    ]
   },
   {