update lake level and lake temp

.ssh_tunnel/aws_emr.json

@@ -0,0 +1 @@
+{}

climate_conditions.py

@@ -491,15 +491,13 @@ def plot_air_quality(df):
             ),
     )
 
-
 def calcAQI(Cp, Ih, Il, BPh, BPl):
     a = Ih - Il
     b = BPh - BPl
     c = Cp - BPl
     val = round((a / b) * c + Il)
     return val
 
-
 def get_data_lake_level(days):
     site_number = 10337000
 
@@ -524,7 +522,6 @@ def get_data_lake_level(days):
     weekly = df.groupby(pd.Grouper(key="dateTime", freq="W"))["value"].mean().reset_index()
     return weekly
 
-
 def plot_lake_level(df):
     trendline(
         df,
@@ -548,7 +545,6 @@ def plot_lake_level(df):
         hovermode="x",
     )
 
-
 def plot_lake_level_with_high_water_mark(df):
     path_html = "html/1.3.a_Lake_Level.html"
     div_id = "1.3.a_Lake_Level"
@@ -644,18 +640,16 @@ def plot_lake_level_with_high_water_mark(df):
         div_id=div_id,
     )
 
-
 def get_data_lake_temp():
     lakeTempURL = "https://tepfsail50.execute-api.us-west-2.amazonaws.com/v1/report/ns-station-range?rptdate=20240130&rptend=20240202&id=4"
     response = requests.get(lakeTempURL)
-    df = pd.DataFrame(response.json())
+    df = pd.DataFrame(response.json()).reset_index()
     df["LS_Temp_Avg"] = df["LS_Temp_Avg"].astype(float)
     return df
 
-
 def get_all_temp_midlake():
     # get start/end dates for the last year
-    start = datetime.now() - timedelta(days=365)
+    start = datetime.now() - timedelta(days=400)
     start = start.strftime("%Y%m%d")
     end = datetime.now().strftime("%Y%m%d")
     dfMerge = pd.DataFrame()
@@ -680,7 +674,6 @@ def get_all_temp_midlake():
     df["RBR_0p5_F_7_day_avg"] = df["RBR_0p5_F"].rolling(window=7).mean()
     return df
 
-
 def get_all_temp_shore():
     # get all data from lake temp URL
     start = datetime.now() - timedelta(days=365)
@@ -704,7 +697,6 @@ def get_all_temp_shore():
     df = dfMerge.groupby("TmStamp")["LS_Temp_Avg_F"].mean().reset_index()
     return df
 
-
 def plot_lake_temp_midlake(df):
     trendline(
         df,
@@ -741,7 +733,6 @@ def plot_lake_temp_midlake(df):
         ),
     )
 
-
 def get_data_precip():
     # snowlab precip data
     url = "https://maps.trpa.org/server/rest/services/LTinfo_Climate_Resilience_Dashboard/MapServer/145"
@@ -780,7 +771,6 @@ def get_data_precip():
     df = dfYearly[dfYearly["Year"] >= 1987]
     return df
 
-
 def plot_precip(df):
     stackedbar(
         df,
@@ -814,7 +804,6 @@ def plot_precip(df):
         ),
     )
 
-
 def get_data_temp():
     # meteostat data
     # Set time period
@@ -838,7 +827,6 @@ def get_data_temp():
     df = df.assign(AvgTemp=lambda x: (9 / 5) * x["tavg"] + 32)
     return df
 
-
 def plot_temp(df):
     # Plot daily temperature data
     fig = px.scatter(
@@ -863,7 +851,6 @@ def plot_temp(df):
         div_id=div_id,
     )
 
-
 def plot_extremeheat(df):
     path_html = "html/1.2.a_ExtremeHeatDays.html"
     div_id = "1.3.d_Precip"

metadata.yaml

@@ -0,0 +1 @@
+project_uuid: f00582d0dcfc4343b2a57d5724cbcb96

notebook.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +137,7 @@
    "source": [
     "# html\\1.3.b_Lake_Temp.html ### days is hard coded to 730\n",
     "df = get_all_temp_midlake()\n",
-    "plot_lake_temp_midlake(df)"
+    "plot_lake_temp_midlake()"
    ]
   },
   {
@@ -481,7 +481,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "trpa",
+   "display_name": "arcgispro-py3",
    "language": "python",
    "name": "python3"
   },
@@ -495,7 +495,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,

requirements.txt

@@ -1,11 +1,11 @@
-pandas==2.1.3
-numpy==1.26.2
-plotly==5.18.0
-arcgis==2.2.0.2
-statsmodels==0.14.1
-meteostat==1.6.7
-pydeck==0.8.0
+# pandas==2.1.3
+# numpy==1.26.2
+# plotly==5.18.0
+# arcgis==2.2.0.2
+# statsmodels==0.14.1
+# meteostat==1.6.7
+# pydeck==0.8.0
 
-pre-commit==3.6.0
-black==23.12.0
-tqdm==4.66.1
+# pre-commit==3.6.0
+# black==23.12.0
+# tqdm==4.66.1