Merge pull request #590 from akrherz/231117

Omnibus

htdocs/plotting/auto/scripts/p39.py

@@ -12,7 +12,13 @@
 import numpy as np
 from pyiem.exceptions import NoDataFound
 from pyiem.plot import figure_axes
-from pyiem.util import get_autoplot_context, get_dbconn
+from pyiem.util import get_autoplot_context, get_dbconnc
+
+PDICT = {
+    "manual": "Select comparison month manually",
+    "high": "Based on effective date, find warmest same month on record",
+    "low": "Based on effective date, find coldest same month on record",
+}
 
 
 def get_description():
@@ -28,6 +34,13 @@ def get_description():
             network="IACLIMATE",
             label="Select Station:",
         ),
+        {
+            "type": "select",
+            "name": "compare",
+            "default": "manual",
+            "label": "How to compare?",
+            "options": PDICT,
+        },
         dict(
             type="year",
             name="year",
@@ -52,16 +65,35 @@ def get_description():
     return desc
 
 
+def compute_compare_month(ctx, cursor):
+    """Figure out what the user wants."""
+    year = ctx["year"]
+    month = ctx["month"]
+    compare = ctx["compare"]
+    if compare == "manual":
+        return year, month
+    station = ctx["station"]
+    effective_date = ctx["date"]
+    cursor.execute(
+        f"""
+        select year, avg((high+low)/2) from alldata
+        where station = %s and month = %s and year != %s
+        GROUP by year
+        ORDER by avg {'desc' if compare == 'high' else 'asc'} LIMIT 1
+        """,
+        (station, effective_date.month, effective_date.year),
+    )
+    return cursor.fetchone()["year"], effective_date.month
+
+
 def plotter(fdict):
     """Go"""
-    pgconn = get_dbconn("coop")
-    cursor = pgconn.cursor()
+    pgconn, cursor = get_dbconnc("coop")
 
     ctx = get_autoplot_context(fdict, get_description())
     station = ctx["station"]
-    year = ctx["year"]
-    month = ctx["month"]
     effective_date = ctx["date"]
+    year, month = compute_compare_month(ctx, cursor)
 
     oldmonth = datetime.date(year, month, 1)
     sts = datetime.date(effective_date.year, effective_date.month, 1)
@@ -70,32 +102,35 @@ def plotter(fdict):
 
     # beat month
     cursor.execute(
-        "SELECT extract(day from day), (high+low)/2. from "
+        "SELECT extract(day from day), (high+low)/2. as t from "
         "alldata WHERE station = %s and year = %s and month = %s "
         "ORDER by day ASC",
         (station, year, month),
     )
     if cursor.rowcount == 0:
+        pgconn.close()
         raise NoDataFound("No Data Found.")
 
     prevmonth = []
     for row in cursor:
-        prevmonth.append(float(row[1]))
+        prevmonth.append(float(row["t"]))
 
     # build history
     cursor.execute(
-        "SELECT year, day, (high+low)/2. from alldata "
+        "SELECT year, day, (high+low)/2. as t from alldata "
         "WHERE station = %s and month = %s and extract(day from day) <= %s "
         "and day < %s ORDER by day ASC",
         (station, effective_date.month, days, ets),
     )
 
     for i, row in enumerate(cursor):
         if i == 0:
-            baseyear = row[0]
-            data = np.ma.ones((effective_date.year - row[0] + 1, days)) * -99
-        data[row[0] - baseyear, row[1].day - 1] = row[2]
-
+            baseyear = row["year"]
+            data = (
+                np.ma.ones((effective_date.year - row["year"] + 1, days)) * -99
+            )
+        data[row["year"] - baseyear, row["day"].day - 1] = row["t"]
+    pgconn.close()
     # Do we have data for the effective_date ?
     pos = (
         effective_date.day

htdocs/plotting/auto/scripts100/p192.py

@@ -97,7 +97,7 @@ def get_df(ctx, bnds, buf=2.25):
                 select id, st_x(geom) as lon, st_y(geom) as lat,
                 state, wfo from stations
                 where network ~* 'ASOS' and
-                ST_contains(ST_geomfromtext(%s), geom)
+                ST_contains(ST_GeomFromEWKT(%s), geom)
             )
             SELECT station, vsby, tmpf, dwpf, sknt, state, wfo, lat, lon, relh,
             abs(extract(epoch from (%s - valid))) as tdiff from
@@ -125,7 +125,7 @@ def get_df(ctx, bnds, buf=2.25):
         WHERE s.network ~* 'ASOS' and s.country = 'US' and
         valid + '80 minutes'::interval > now() and
         vsby >= 0 and vsby <= 10 and
-        ST_contains(ST_geomfromtext(%s), geom)
+        ST_contains(ST_GeomFromEWKT(%s), geom)
             """,
                 conn,
                 params=(giswkt,),

htdocs/plotting/auto/scripts200/p206.py

@@ -141,7 +141,7 @@ def get_df(ctx, buf=2.25):
                 select id, st_x(geom) as lon, st_y(geom) as lat,
                 state, wfo, iemid, country, geom from stations
                 where network ~* 'ASOS' and
-                ST_contains(ST_geomfromtext(%s), geom)
+                ST_contains(ST_GeomFromEWKT(%s), geom)
             )
             SELECT s.day, s.max_tmpf, s.min_tmpf, s.max_dwpf, s.min_dwpf,
             s.min_rh, s.max_rh, s.min_feel, s.max_feel,

htdocs/vtec/search.php

@@ -33,7 +33,7 @@
 
 $t->content = <<<EOF
 <p>This application allows you to search for National Weather Service Watch,
-Warning, and Advisories.  There are currently two options:
+Warning, and Advisories.  There are currently three options:
 <ul>
     <li><a href="#bypoint">1. Search for Storm Based Warnings by Point</a></li>
     <li><a href="#byugc">2. Search of Watch/Warning/Advisories by County/Zone or by Point</a></li>

scripts/00z/generate_rtp.py

@@ -26,7 +26,7 @@
     },
     {
         "filename": "awos_rtp_00z_dvn.shef",
-        "networks": "IA_ASOS ISUSM IA_DCP IA_RWIS IL_RWIS IL_ASOS".split(),
+        "networks": "IL_ASOS IA_ASOS ISUSM IA_DCP IL_RWIS IA_RWIS".split(),
         "precip_works": ["IA_ASOS", "IL_ASOS", "ISUSM"],
         "limiter": "wfo = :wfo",
         "filter": ["wfo", "DVN"],

scripts/12z/generate_rtp.py

@@ -24,7 +24,7 @@
     },
     {
         "filename": "awos_rtp_dvn.shef",
-        "networks": "IA_ASOS ISUSM IA_DCP IA_RWIS IL_RWIS IL_ASOS".split(),
+        "networks": "IL_ASOS IA_ASOS ISUSM IA_DCP IL_RWIS IA_RWIS".split(),
         "precip_works": ["IA_ASOS", "IL_ASOS", "ISUSM"],
         "limiter": "wfo = :wfo",
         "filter": ["wfo", "DVN"],

scripts/iemre/grid_rsds.py

@@ -12,6 +12,8 @@
 import numpy as np
 import pygrib
 import pyproj
+import xarray as xr
+from affine import Affine
 from pyiem import iemre
 from pyiem.util import get_dbconn, logger, ncopen, utc
 from scipy.interpolate import NearestNDInterpolator
@@ -105,8 +107,6 @@ def do_hrrr(ts):
         "+units=m +lat_2=38.5 +lat_1=38.5 +lat_0=38.5"
     )
     total = None
-    xaxis = None
-    yaxis = None
     # So IEMRE is storing data from coast to coast, so we should be
     # aggressive about running for an entire calendar date
     for hr in range(24):
@@ -136,12 +136,8 @@ def do_hrrr(ts):
                         lat1 = g["latitudeOfFirstGridPointInDegrees"]
                         lon1 = g["longitudeOfFirstGridPointInDegrees"]
                         llcrnrx, llcrnry = LCC(lon1, lat1)
-                        nx = g["Nx"]
-                        ny = g["Ny"]
                         dx = g["DxInMetres"]
                         dy = g["DyInMetres"]
-                        xaxis = llcrnrx + dx * np.arange(nx)
-                        yaxis = llcrnry + dy * np.arange(ny)
                     if subtotal is None:
                         subtotal = g.values
                     else:
@@ -179,12 +175,8 @@ def do_hrrr(ts):
             lat1 = g["latitudeOfFirstGridPointInDegrees"]
             lon1 = g["longitudeOfFirstGridPointInDegrees"]
             llcrnrx, llcrnry = LCC(lon1, lat1)
-            nx = g["Nx"]
-            ny = g["Ny"]
             dx = g["DxInMetres"]
             dy = g["DyInMetres"]
-            xaxis = llcrnrx + dx * np.arange(nx)
-            yaxis = llcrnry + dy * np.arange(ny)
         else:
             total += g.values
 
@@ -194,18 +186,16 @@ def do_hrrr(ts):
 
     # We wanna store as W m-2, so we just average out the data by hour
     total = total / 24.0
-
-    ds = iemre.get_grids(ts.date(), varnames="rsds")
-    for i, lon in enumerate(iemre.XAXIS):
-        for j, lat in enumerate(iemre.YAXIS):
-            (x, y) = LCC(lon, lat)
-            i2 = np.digitize([x], xaxis)[0]
-            j2 = np.digitize([y], yaxis)[0]
-            try:
-                ds["rsds"].values[j, i] = total[j2, i2]
-            except IndexError:
-                continue
-
+    affine_in = Affine(dx, 0.0, llcrnrx, 0.0, dy, llcrnry)
+
+    ds = xr.Dataset(
+        {
+            "rsds": xr.DataArray(
+                iemre.reproject2iemre(total, affine_in, LCC.crs),
+                dims=("y", "x"),
+            )
+        }
+    )
     iemre.set_grids(ts.date(), ds)
     subprocess.call(
         ["python", "db_to_netcdf.py", f"{ts:%Y}", f"{ts:%m}", f"{ts:%d}"]