diff --git a/_sources/examples/generated/Regional_Surface_Obs_Plot.rst.txt b/_sources/examples/generated/Regional_Surface_Obs_Plot.rst.txt index b989cb5..427f534 100644 --- a/_sources/examples/generated/Regional_Surface_Obs_Plot.rst.txt +++ b/_sources/examples/generated/Regional_Surface_Obs_Plot.rst.txt @@ -305,7 +305,7 @@ the maps database called **state**. We can take the results from that filter and get a geographic **envelope** based on the Florida polygon that was returned from the previous cell. - Warning: Without such a filter you may be requesting many tens of + **Warning**: Without such a filter you may be requesting many tens of thousands of records. .. code:: ipython3 @@ -354,9 +354,9 @@ Both the METAR and Synoptic datasets should be filtered by time to avoid requesting an unreasonable amount of data. By defining one filter now, we can use it in both of their data requests to EDEX. - Note: Here we will use the most recent hour as our default filter. - Try adjusting the timerange and see the difference in the final - plots. + **Note**: Here we will use the most recent hour as our default + filter. Try adjusting the timerange and see the difference in the + final plots. .. code:: ipython3 diff --git a/examples/generated/Regional_Surface_Obs_Plot.html b/examples/generated/Regional_Surface_Obs_Plot.html index 436804b..6f99ef8 100644 --- a/examples/generated/Regional_Surface_Obs_Plot.html +++ b/examples/generated/Regional_Surface_Obs_Plot.html @@ -381,7 +381,7 @@ the maps database called state. We can take the results from th filter and get a geographic envelope based on the Florida polygon that was returned from the previous cell.
-Warning: Without such a filter you may be requesting many tens of +
Warning: Without such a filter you may be requesting many tens of thousands of records.
# Append each geometry to a numpy array @@ -426,9 +426,9 @@ thousands of records. requesting an unreasonable amount of data. By defining one filter now, we can use it in both of their data requests to EDEX.-Note: Here we will use the most recent hour as our default filter. -Try adjusting the timerange and see the difference in the final -plots.
+Note: Here we will use the most recent hour as our default +filter. Try adjusting the timerange and see the difference in the +final plots.
# Filter for the last hour lastHourDateTime = datetime.utcnow() - timedelta(minutes = 60)