Merge pull request #1039 from akrherz/241222

📝 Sundry updates per review

data/wms/nexrad/n0q-t.map

@@ -56,7 +56,7 @@ LAYER
     "wms_title" "NEXRAD BASE REFLECT"
     "wms_srs"   "EPSG:4326 EPSG:900913 EPSG:102100 EPSG:3857"
     "wms_extent" "-171 15 -66 70"
-    "wms_timeextent" "2011-02-16/2024-12-31/PT5M"
+    "wms_timeextent" "2011-02-16/2025-12-31/PT5M"
     "wms_timeitem" "datetime"
     "wms_timedefault" "2011-02-21T19:30:00Z"
   END
@@ -78,7 +78,7 @@ LAYER
     "wms_title" "NEXRAD BASE REFLECT"
     "wms_srs"   "EPSG:4326 EPSG:900913 EPSG:102100 EPSG:3857"
     "wms_extent" "-171 15 -66 70"
-    "wms_timeextent" "2011-02-16/2024-12-31/PT5M"
+    "wms_timeextent" "2011-02-16/2025-12-31/PT5M"
     "wms_timeitem" "datetime"
     "wms_timedefault" "2011-02-21T19:30:00Z"
   END

data/wms/nexrad/n0r-t.map

@@ -58,7 +58,7 @@ LAYER
     "wms_title" "NEXRAD BASE REFLECT"
     "wms_srs"   "EPSG:4326 EPSG:900913 EPSG:102100 EPSG:3857"
     "wms_extent" "-126 24 -66 50"
-    "wms_timeextent" "1995-01-01/2024-12-31/PT5M"
+    "wms_timeextent" "1995-01-01/2025-12-31/PT5M"
     "wms_timeitem" "datetime"
     "wms_timedefault" "2006-06-23T03:10:00Z"
   END
@@ -80,7 +80,7 @@ LAYER
     "wms_title" "NEXRAD BASE REFLECT"
     "wms_srs"   "EPSG:4326 EPSG:900913 EPSG:102100 EPSG:3857"
     "wms_extent" "-126 24 -66 50"
-    "wms_timeextent" "1995-01-01/2024-12-31/PT5M"
+    "wms_timeextent" "1995-01-01/2025-12-31/PT5M"
     "wms_timeitem" "datetime"
     "wms_timedefault" "2006-06-23T03:10:00Z"
   END

htdocs/js/iemss.js

@@ -52,7 +52,7 @@ var htmlInterface = ['<div class="panel panel-default">',
     '</div><!-- End of panel-body -->',
     '</div><!-- End of panel -->'];
 
-var map, selectedFeature, selectControl, geojson, geojsonSource, network;
+var map, selectedFeature, geojson, geojsonSource, network;
 
 //http://www.lessanvaezi.com/filter-select-list-options/
 jQuery.fn.filterByText = function (textbox, selectSingleMatch) {
@@ -146,26 +146,26 @@ $().ready(() => {
     $('#stations_add').click(function () {
         return !$('#stations_in option:selected').remove().appendTo('#stations_out');
     });
-    $('#stations_addall').click(function () {
+    $('#stations_addall').click(() => {
         var ret = !$('#stations_in option').remove().appendTo('#stations_out');
         $('#stations_out option').prop('selected', true);
         return ret;
     });
-    $('#stations_delall').click(function () {
+    $('#stations_delall').click(() => {
         return !$('#stations_out option').remove().appendTo('#stations_in');
     });
     $('#stations_del').click(function () {
         $('#stations_out option:selected').remove().appendTo('#stations_in');
-        $('#stations_out option').each(function (i) {
+        $('#stations_out option').each(function () { // this
             $(this).attr("selected", "selected");
         });
         return false;
     });
 
-    $('#iemss-sortbyid').click(function () {
+    $('#iemss-sortbyid').click(() => {
         sortListing("id");
     });
-    $('#iemss-sortbyname').click(function () {
+    $('#iemss-sortbyname').click(() => {
         sortListing("name");
     });
 
@@ -200,7 +200,7 @@ $().ready(() => {
         })
     });
 
-    geojsonSource.on('change', function (e) {
+    geojsonSource.on('change', () => {
         if (geojsonSource.getState() == 'ready') {
             $.each(geojsonSource.getFeatures(), (_index, feat) => {
                 var lbl = `[${feat.get('sid')}] ${feat.get('sname')}`;

pylib/iemweb/autoplot/scripts/p47.py

@@ -11,9 +11,10 @@
 
 import pandas as pd
 import seaborn as sns
+from pyiem.database import get_sqlalchemy_conn
 from pyiem.exceptions import NoDataFound
 from pyiem.plot import figure
-from pyiem.util import get_autoplot_context, get_sqlalchemy_conn
+from pyiem.util import get_autoplot_context, utc
 
 from iemweb.autoplot import ARG_STATION
 
@@ -33,7 +34,7 @@ def get_description():
         dict(
             type="year",
             name="year",
-            default="2014",
+            default=utc().year,
             label="Select Year to Highlight:",
         ),
     ]
@@ -70,8 +71,8 @@ def plotter(fdict):
         f"{calendar.month_name[month]} Snowfall vs Precipitation Totals"
     )
     figure(title=title, apctx=ctx, fig=g.figure)
-    g.fig.tight_layout()
-    g.fig.subplots_adjust(top=0.91)
+    g.figure.tight_layout()
+    g.figure.subplots_adjust(top=0.91)
     if year in df.index:
         row = df.loc[year]
         g.ax_joint.scatter(
@@ -112,4 +113,4 @@ def plotter(fdict):
     g.ax_joint.set_ylabel("Snowfall Total [inch]")
     g.ax_joint.set_xlabel("Precipitation Total (liquid + melted) [inch]")
     g.ax_joint.legend(loc=1, scatterpoints=1)
-    return g.fig, df
+    return g.figure, df

pylib/iemweb/autoplot/scripts100/p101.py

@@ -7,7 +7,7 @@
 most frequent product.
 """
 
-import datetime
+from datetime import date
 
 import numpy as np
 import pandas as pd
@@ -41,7 +41,7 @@ def get_description():
         dict(
             type="year",
             name="eyear",
-            default=datetime.date.today().year,
+            default=date.today().year,
             label="End Year (inclusive):",
             min=2009,
         ),
@@ -56,8 +56,8 @@ def plotter(fdict):
     syear = ctx["syear"]
     eyear = ctx["eyear"] + 1
     station = ctx["station"][:4]
-    sts = datetime.date(syear, 1, 1)
-    ets = datetime.date(eyear, 1, 1)
+    sts = date(syear, 1, 1)
+    ets = date(eyear, 1, 1)
     params = {
         "sts": sts,
         "ets": ets,

pylib/iemweb/autoplot/scripts100/p109.py

@@ -155,7 +155,7 @@ def get_description():
     return desc
 
 
-def get_count_df(ctx, varname, pstr, sts, ets):
+def get_count_df(ctx, varname: str, pstr, sts, ets):
     """Oh boy, do complex things."""
 
     emerg_extra = ""

pylib/iemweb/autoplot/scripts100/p126.py

@@ -10,7 +10,7 @@
 """
 
 import calendar
-import datetime
+from datetime import date
 
 import metpy.calc as mcalc
 import pandas as pd
@@ -19,6 +19,7 @@
 from pyiem.exceptions import NoDataFound
 from pyiem.plot import figure
 from pyiem.util import get_autoplot_context
+from sqlalchemy import text
 
 PDICT = {
     "mixing_ratio": "Mixing Ratio [g/kg]",
@@ -34,7 +35,7 @@
 def get_description():
     """Return a dict describing how to call this plotter"""
     desc = {"description": __doc__, "data": True}
-    today = datetime.date.today()
+    today = date.today()
     desc["arguments"] = [
         dict(
             type="zstation",
@@ -75,16 +76,16 @@ def plotter(fdict):
     varname = ctx["var"]
     with get_sqlalchemy_conn("asos") as conn:
         df = pd.read_sql(
-            """
+            text("""
             SELECT extract(year from valid) as year,
             coalesce(mslp, alti * 33.8639, 1013.25) as slp,
             extract(doy from valid) as doy, tmpf, dwpf, relh from alldata
-            where station = %s and dwpf > -50 and dwpf < 90 and
+            where station = :station and dwpf > -50 and dwpf < 90 and
             tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
             and report_type = 3
-        """,
+        """),
             conn,
-            params=(station,),
+            params={"station": station},
             index_col=None,
         )
     if df.empty:

pylib/iemweb/autoplot/scripts100/p129.py

@@ -7,7 +7,7 @@
 """
 
 import calendar
-import datetime
+from datetime import date
 
 import pandas as pd
 from pyiem.database import get_sqlalchemy_conn
@@ -79,7 +79,7 @@ def add_ctx(ctx):
         GROUP by year, month ORDER by year desc, month ASC
         """,
             conn,
-            params=(level, station, datetime.date.today().replace(day=1)),
+            params=(level, station, date.today().replace(day=1)),
             index_col=None,
         )
     if monthly.empty:
@@ -116,7 +116,7 @@ def add_ctx(ctx):
         station,
         ctx["_nt"].sts[station]["name"],
         bs.year,
-        datetime.date.today().year,
+        date.today().year,
     )
     return ctx
 
@@ -229,7 +229,7 @@ def plotter(fdict):
         ax.text(
             idx,
             row["rank"],
-            "%.1f" % (row["rank"],),
+            f"{row['rank']:.1f}",
             ha="left",
             color="k",
             zorder=5,

pylib/iemweb/autoplot/scripts100/p138.py

@@ -8,14 +8,10 @@
 import pandas as pd
 from metpy.calc import wind_components, wind_direction
 from metpy.units import units as munits
+from pyiem.database import get_sqlalchemy_conn
 from pyiem.exceptions import NoDataFound
 from pyiem.plot import figure_axes
-from pyiem.util import (
-    convert_value,
-    drct2text,
-    get_autoplot_context,
-    get_sqlalchemy_conn,
-)
+from pyiem.util import convert_value, drct2text, get_autoplot_context
 
 UNITS = {"mph": "miles per hour", "kt": "knots", "mps": "meters per second"}
 UNITCONV = {"mph": "miles / hour", "kt": "knot", "mps": "meter / second"}

pylib/iemweb/autoplot/scripts100/p142.py

@@ -11,8 +11,8 @@
 If you plot a statewide average, you get no USDM included.
 """
 
-import datetime
 import sys
+from datetime import date, datetime, timedelta
 
 import matplotlib.dates as mdates
 import pandas as pd
@@ -38,8 +38,8 @@
 def get_description():
     """Return a dict describing how to call this plotter"""
     desc = {"description": __doc__, "data": True, "cache": 86400}
-    today = datetime.date.today()
-    sts = today - datetime.timedelta(days=720)
+    today = date.today()
+    sts = today - timedelta(days=720)
     desc["arguments"] = [
         dict(
             type="station",
@@ -85,7 +85,7 @@ def get_description():
 
 def underlay_usdm(axis, sts, ets, lon, lat):
     """Underlay the USDM as pretty bars, somehow"""
-    if ets < datetime.date(2000, 1, 1):
+    if ets < date(2000, 1, 1):
         axis.text(
             0.0,
             1.03,
@@ -115,11 +115,11 @@ def underlay_usdm(axis, sts, ets, lon, lat):
     for row in data["data"]:
         if row["category"] is None:
             continue
-        ts = datetime.datetime.strptime(row["valid"], "%Y-%m-%d")
-        date = datetime.date(ts.year, ts.month, ts.day)
+        ts = datetime.strptime(row["valid"], "%Y-%m-%d")
+        dt = date(ts.year, ts.month, ts.day)
         axis.axvspan(
-            date,
-            date + datetime.timedelta(days=7),
+            dt,
+            dt + timedelta(days=7),
             color=COLORS[row["category"]],
             zorder=-3,
         )
@@ -241,9 +241,9 @@ def plotter(fdict):
 
     (l1,) = ax.plot(
         df.index.values,
-        df["p1_" + pvar + "_" + how],
+        df[f"p1_{pvar}_{how}"],
         lw=2,
-        label="%s Day" % (p1,),
+        label=f"{p1} Day",
         zorder=5,
     )
     (l2,) = ax.plot(
@@ -301,7 +301,7 @@ def plotter(fdict):
             )
         except Exception as exp:
             sys.stderr.write(str(exp))
-    offset = datetime.timedelta(days=2)
+    offset = timedelta(days=2)
     ax.set_xlim(df.index.min() - offset, df.index.max() + offset)
 
     return fig, df

tests/iemweb/autoplot/urllist.txt

@@ -132,6 +132,8 @@
 /plotting/auto/plot/109/by:wfo::sdate:2023-10-01%200000::edate:2024-07-04%202359::var:count::w:set::phenomenav1:TO::significancev1:W::e:yes::cmap:jet::_r:t::dpi:100.png
 /plotting/auto/plot/109/by:state::sdate:2023-10-01%200000::edate:2024-07-04%202359::var:days::w:set::phenomenav1:TO::significancev1:W::e:yes::cmap:jet::_r:t::dpi:100.png
 /plotting/auto/plot/109/by:state::sdate:2023-10-01%200000::edate:2024-07-04%202359::var:days::w:all::phenomenav1:TO::significancev1:W::e:yes::cmap:jet::_r:t::dpi:100.png
+/plotting/auto/plot/109/by:wfo::sdate:2024-01-01%200000::edate:2024-12-24%202359::var:days::w:set::phenomenav1:SV::significancev1:W::e:all::cmap:jet::_r:t::dpi:100.png
+/plotting/auto/plot/109/by:wfo::sdate:2024-01-01%200000::edate:2024-12-24%202359::var:tpercent::w:set::phenomenav1:SV::significancev1:W::e:all::cmap:jet::_r:t::dpi:100.png
 /plotting/auto/plot/111/_cb:1.txt
 /plotting/auto/plot/112/_cb:1.txt
 /plotting/auto/plot/113/network:IACLIMATE::station:IATAME::var:maxmin::year1:2023::sday:0101::eday:1231::_r:43::dpi:100.png