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construct_sites.py
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import os
import dash_bootstrap_components as dbc
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import pyproj
import xmltodict
from dash import dcc, html
from server_requests import get_data, get_latest_data
from dash_bootstrap_templates import load_figure_template
from color_utils import get_colors
colors = get_colors()
# load_figure_template(["darkly", "sketchy", "slate"])
MAPBOX_TOKEN = "pk.eyJ1IjoibmljbW9zdGVydCIsImEiOiJjbGx3eDZ5ZHIxbzI0M2ZwaGR1ZHN5NnZzIn0.KDSOloWKwP8T6Uso9LEtcQ"
script_directory = os.path.dirname(os.path.abspath(__file__))
survey_path = os.path.join(script_directory, "23021-20230824-COORDINATES.xlsx")
DURATIONS = [
{"label": "1 Year", "value": "P1Y"},
{"label": "3 Months", "value": "P3M"},
{"label": "1 Month", "value": "P1M"},
{"label": "7 Days", "value": "P7D"},
{"label": "3 Days", "value": "P3D"},
{"label": "1 Day", "value": "P1D"},
]
def add_survey_data(sites):
df = pd.read_excel(survey_path, header=None)
split_rows = list(df[df[0] == "X"].index.values)
split_rows.append(len(df))
dams = []
bottom = 0
for site, row in zip(sites, split_rows[1:]):
split_df = df[bottom:row]
split_df.columns = split_df.iloc[0]
split_df = split_df[1:]
dams += [split_df]
bottom = row
sites[site]["name"] = site
sites[site]["distance"] = list(split_df["running distance"].values)
sites[site]["elevation"] = list(split_df["Z"].values)
sites[site]["outflow"] = min(list(split_df["Z"].values))
sites[site]["X"] = list(split_df["X"].values)
sites[site]["Y"] = list(split_df["Y"].values)
lats = []
lons = []
for nztm_x, nztm_y in zip(sites[site]["X"], sites[site]["Y"]):
lat, lon = convert_nztm_to_latlon(nztm_x, nztm_y)
lats += [lat]
lons += [lon]
sites[site]["lats"] = lats
sites[site]["lons"] = lons
sites[site]["centroid_nztm"] = calculate_centroid(
list(zip(split_df["X"].values, split_df["Y"].values))
)
sites[site]["centroid_wgs84"] = calculate_centroid(list(zip(lats, lons)))
sites[site]["min_height"] = split_df["Z"].min()
sites[site]["max_height"] = split_df["Z"].max()
sites[site]["radar_level"] = split_df["RADAR LEVEL : "].iloc[0]
sites[site]["paver_level"] = split_df["PAVER LEVEL"].iloc[0]
sites[site]["culvert_invert"] = split_df["CULVERT INVERT"].iloc[0]
all_ys = [
sites[site]["min_height"],
sites[site]["max_height"],
sites[site]["radar_level"],
sites[site]["paver_level"],
]
sites[site]["ymin"] = min(all_ys)
sites[site]["ymax"] = max(all_ys)
sites[site]["yrange"] = sites[site]["ymax"] - sites[site]["ymin"]
sites[site]["ylims"] = (
sites[site]["ymin"] - 0.1 * sites[site]["yrange"],
sites[site]["ymax"] + 0.1 * sites[site]["yrange"],
)
def fetch_duration_df(site, dur):
ts_response = get_data(site, "Stage", dur)
ts_data = xmltodict.parse(ts_response.content)
measurement = ts_data["Hilltop"]["Measurement"]["Data"]["E"]
dur_df = pd.DataFrame(measurement)
dur_df.rename(columns={"T": "Timestamp", "I1": "Stage (mm)"}, inplace=True)
dur_df["Stage (mm)"] = dur_df["Stage (mm)"].apply(pd.to_numeric)
dur_df["Timestamp"] = pd.to_datetime(dur_df["Timestamp"])
return dur_df
def add_stage_data(sites):
for site in sites:
data_xml = get_latest_data(site, "Stage")
data_dict = xmltodict.parse(data_xml.content)
sites[site]["last_updated"] = data_dict["Hilltop"]["Measurement"]["Data"]["E"][
"T"
]
sites[site]["last_raw_val"] = data_dict["Hilltop"]["Measurement"]["Data"]["E"][
"I1"
]
sites[site]["m_from_paver"] = (
float(sites[site]["last_raw_val"]) + float(sites[site]["offset"])
) / 1000
def convert_nztm_to_latlon(nztm_x, nztm_y):
nztm_epsg = "EPSG:2193"
wgs84_epsg = "EPSG:4326"
transformer = pyproj.Transformer.from_crs(nztm_epsg, wgs84_epsg)
lat, lon = transformer.transform(nztm_y, nztm_x)
return lat, lon
def calculate_centroid(coords):
total_x = 0
total_y = 0
num_coords = len(coords)
for x, y in coords:
total_x += x
total_y += y
centroid_x = total_x / num_coords
centroid_y = total_y / num_coords
return centroid_x, centroid_y
def plot_cross_section(data):
crossec_fig = go.Figure(
go.Scatter(
# data,
x=data["distance"],
y=data["elevation"],
showlegend=False,
fill="tozeroy",
marker=dict(
opacity=0,
color=colors["horizons_moss"],
),
hovertemplate=None,
name="Dam height",
)
)
level_df = fetch_duration_df(data["name"], "P1D")
latest_level = (level_df.iloc[-1]["Stage (mm)"] + data["offset"]) / 1000
crossec_fig.add_trace(
go.Scatter(
x=data["distance"],
y=[latest_level] * len(data["distance"]),
showlegend=False,
fill="tozeroy",
marker=dict(
opacity=0,
color=colors["horizons_teal"],
),
name="Radar Reading",
)
)
crossec_fig.add_hline(
y=latest_level,
annotation_text="RADAR READING",
annotation_position="top left",
annotation_font=dict(color=colors["horizons_blue"], size=16),
line={"color": colors["horizons_blue"], "width": 4},
)
crossec_fig.add_hline(
y=data["radar_level"],
annotation_text="RADAR HEIGHT",
line_dash="dot",
line_color=colors["horizons_slate_25"],
)
crossec_fig.add_hline(
y=data["paver_level"],
annotation_text="PAVER LEVEL",
line_dash="dot",
line_color=colors["horizons_slate_25"],
)
crossec_fig.add_hline(
y=data["outflow"],
annotation_text="OUTFLOW LEVEL",
line_dash="dot",
line_color=colors["horizons_slate_25"],
)
crossec_fig.add_hline(
y=data["culvert_invert"],
annotation_text="CULVERT LEVEL",
line_dash="dot",
)
crossec_fig.update_traces(hovertemplate=None)
crossec_fig.update_layout(
yaxis_range=data["ylims"],
xaxis_title="Distance along dam wall (m)",
yaxis_title="Elevation (m)",
hovermode="x",
plot_bgcolor=colors["horizons_slate"],
paper_bgcolor=colors["horizons_slate_75"],
xaxis_gridcolor=colors["horizons_slate_75"],
yaxis_gridcolor=colors["horizons_slate_75"],
font={"color": colors["horizons_light_grey"]},
)
return crossec_fig
def map_sat_image(name, data):
map_url = "https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}"
# response = requests.get(map_url)
map_layout = go.Layout(
mapbox=dict(
style="open-street-map", # Use 'mapbox://styles/mapbox/satellite-streets-v11' for satellite view
# accesstoken=mapbox_token,
center=dict(lat=data["centroid_wgs84"][0], lon=data["centroid_wgs84"][1]),
zoom=16,
layers=[
{
"below": "traces",
"sourcetype": "raster",
"sourceattribution": "Tiles © Esri — Source: Esri, i-cubed, USDA, USGS, AEX, GeoEye, Getmapping, Aerogrid, IGN, IGP, UPR-EGP, and the GIS User Community",
"source": [map_url],
}
],
),
showlegend=False,
)
map_fig = go.Figure(layout=map_layout)
map_fig.add_trace(
go.Scattermapbox(
lat=data["lats"],
lon=data["lons"],
mode="markers",
hoverlabel=None,
marker=dict(
size=8,
color=colors["horizons_slate"],
),
text=name,
)
)
map_fig.add_trace(
go.Scattermapbox(
lat=data["lats"],
lon=data["lons"],
mode="markers",
hoverlabel=None,
marker=dict(
size=6,
color=colors["horizons_moss"],
),
text=name,
)
)
map_fig.update_layout(
margin={"r": 0, "t": 0, "l": 0, "b": 0},
)
return map_fig
def map_overview(sites):
centroid_lat = []
centroid_lon = []
map_fig = go.Figure()
for i, (name, data) in enumerate(sites.items()):
centroid_lat += [data["centroid_wgs84"][0]]
centroid_lon += [data["centroid_wgs84"][1]]
map_fig.add_trace(
go.Scattermapbox(
lat=centroid_lat,
lon=centroid_lon,
mode="markers",
marker=go.scattermapbox.Marker(
size=20,
color=colors["horizons_slate"],
),
hoverinfo="none",
),
)
map_fig.add_trace(
go.Scattermapbox(
lat=centroid_lat,
lon=centroid_lon,
mode="markers",
marker=go.scattermapbox.Marker(
size=18,
color=colors["horizons_moss_50"],
),
hoverinfo="none",
),
)
map_fig.add_trace(
go.Scattermapbox(
lat=centroid_lat,
lon=centroid_lon,
mode="markers",
marker=go.scattermapbox.Marker(
size=12,
color=colors["horizons_moss"],
),
hoverinfo="lat+lon+text",
),
)
mid_lat = sum(centroid_lat) / len(centroid_lat)
mid_lon = sum(centroid_lon) / len(centroid_lon)
map_fig.update_traces(
customdata=list(sites.keys()),
text=list(sites.keys()),
selector=dict(type="scattermapbox"),
)
map_fig.update_layout(
margin={"r": 0, "t": 0, "l": 0, "b": 0},
mapbox=dict(
style="mapbox://styles/mapbox/satellite-streets-v12", # Use 'mapbox://styles/mapbox/satellite-streets-v11' for satellite view
accesstoken=MAPBOX_TOKEN,
center=dict(lat=mid_lat, lon=mid_lon),
zoom=11,
),
showlegend=False,
height=900,
)
return map_fig
def construct_page(name, data):
# Create TS plot
# ts_fig = plot_timeseries_duration(data)
# Create the cross section plot
crossec_fig = plot_cross_section(data)
# Create a Scattermapbox trace
map_fig = map_sat_image(name, data)
content = html.Div(
[
dbc.Card(
dbc.CardBody(
[
html.H2(name, id="site-label"),
]
)
),
dbc.Card(
dbc.CardBody(
[
dbc.Row(
[
dbc.Col(
[
html.Div(
[
html.Label("Select Duration:"),
dbc.RadioItems(
id="duration-selector",
className="btn-group",
inputClassName="btn-check",
labelClassName="btn btn-outline-primary",
labelCheckedClassName="active",
options=DURATIONS,
value=DURATIONS[-1]["value"],
),
dcc.Graph(
figure=go.Figure(),
id="time-series-plot",
),
],
className="p-5 radio-group",
)
],
width=12,
),
],
align="center",
),
# html.Br(),
dbc.Row(
[
dbc.Col([dcc.Graph(figure=crossec_fig)], width=6),
dbc.Col([dcc.Graph(figure=map_fig)], width=6),
],
align="center",
),
# html.Br(),
# dbc.Row([
# dbc.Col([
# dcc.Graph(figure=dem_fig)
# ], width=6),
# dbc.Col([
# # dcc.Graph(figure=im_fig)
# ], width=6),
# ], align="center"),
]
)
),
]
)
return content
def construct_overview_page(sites):
return dcc.Graph(figure=map_overview(sites), id="map")