In [21]:
#This notebook can post-process a QUALTX output file
#and plot water quality profiles along all the modeled reaches
#ET 20160514
#Enter the location of the QUALTX output file
#This program can parse both urls and local paths
QUALTXoutfile = r'https://raw.githubusercontent.com/Harefoot/TurboQUALTX/master/RHOTRUNC.OUT'
In [22]:
# ET Space holder for when later I want to implement widgets to turn this python into a dashboard
from ipywidgets import interact
from bokeh.io import push_notebook
#http://bokeh.pydata.org/en/0.10.0/docs/user_guide/notebook.html#integrating-ipython-interactors
In [23]:
#Import python packages
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import os
import sys
#sys.path.insert(0,r'M:\Library\Python\Packages')
import ET_Utils.QUALTX_Utils
import numpy as np
import bokeh
from bokeh.plotting import figure, show
from bokeh.models import HoverTool, BoxSelectTool
from bokeh.io import output_notebook
output_notebook()
In [24]:
#Parse the QUALTX output file
Scenario = QUALTXoutfile
DOstd = 4#[5,4.8]
#WQC_of_interest = ['DO_MG/L','BOD_MG/L','NH3_MG/L','NO3+2_MG/L','PHOS_MG/L']
WQC_of_interest = ['DO_MG/L','BOD_MG/L','NH3_MG/L']#,'NO3+2_MG/L','PHOS_MG/L']
StreamNames, Rdf, Hdf, AllWQdf = ET_Utils.QUALTX_Utils.Process_QUALTX(QUALTXoutfile,
Scenario,DOstd = DOstd,
WQC_of_interest = WQC_of_interest,
loc = 1,plot_pdf = 0)
In [25]:
from bokeh.io import output_file, show
from bokeh.models import GeoJSONDataSource
from bokeh.plotting import figure
import json
#from bokeh.sampledata.sample _geojson import geojson
geojsonfile = r'C:\Users\Ernest\Ernest_Sandbox\TurboQUALTX\TurboQUALTX\GIS\TCEQ Wastewater Outfalls 20160514.geojson'
#geojson=json.load(geojsonfile)
#geo_source = GeoJSONDataSource(geojson= geojson)
#GeoJSONDataSource(geojson=json.load(datafilename)))
#p = figure()
#p.circle(x='x', y='y', alpha=0.9, source=geo_source)
#output_file("geojson.html")
#show(p)
#from bokeh.io import output_file, show
#from bokeh.io import output_file, show
#from bokeh.models import GMapPlot, GMapOptions, ColumnDataSource, Circle, DataRange1d, PanTool, WheelZoomTool, BoxSelectTool
#map_options = GMapOptions(lat=30.29, lng=-97.73, map_type="roadmap", zoom=11)
#plot = GMapPlot(x_range=DataRange1d(), y_range=DataRange1d(), map_options=map_options, title="Austin 1")
#source = ColumnDataSource(data=dict(lat=[30.29, 30.20, 30.29],lon=[-97.70, -97.74, -97.78],))
#circle = Circle(x="lon", y="lat", size=15, fill_color="blue", fill_alpha=0.8, line_color=None)
#plot.add_glyph(source, circle)
#plot.add_tools(PanTool(), WheelZoomTool(), BoxSelectTool())
#output_file("gmap_plot.html")
#show(plot)
In [26]:
#Plot a map
import folium
import numpy as np
from folium.element import IFrame
#bbox = [-87.40, 24.25, -74.70, 36.70]
bbox = [-106.6460, 25.8371, -93.5083, 36.5007]
location = np.array(bbox).reshape(2, 2).mean(axis=0).tolist()[::-1]
tiles = ('http://services.arcgisonline.com/arcgis/rest/'
'services/Ocean/World_Ocean_Base/MapServer/tile/{z}/{y}/{x}')
mapa = folium.Map(location=location, zoom_start=6,
tiles=tiles, attr='ESRI')
geo_path = r'C:\Users\Ernest\Ernest_Sandbox\TurboQUALTX\TurboQUALTX\GIS\TCEQ Wastewater Outfalls 20160514.geojson'
#mapa.geo_json(geo_path=geo_path)
points = folium.features.GeoJson(geo_path)
print points
print dir(points)
print points.data
print dir(points.data)
mapa.add_children(points)
marker = folium.Marker(location=[-106.6460, 25.8371])
#marker.add_to(mapa)
#mapa.geo_json(geo_path=geo_path)
#mapa.simple_marker(points)
mapa.add_children(marker)
#See https://github.com/NelsonMinar/vector-river-map
#mapa.lat_lng_popover()
#mapa.LatLngPopup()
#mapa.add_children(folium.ClickForMarker())
#mapa.add_children(folium.LatLngPopup())
mapa
Out[26]:
In [27]:
#Set plotting parameters
plot_width=900
plot_height=600
y_label = 'Concentration (mg/L)'
x_label = 'Distance (km)'
#Set line width
lws = np.zeros(len(WQC_of_interest))+2
#Set line colors
plot_colors = ['blue','black','green','cyan','orange']
#Set line symbols
plot_symbols = ['-','-','-','-','-']
#Set marker sizes
markersizes = np.zeros(len(WQC_of_interest))+1
#Set label sizes
x_labelsize = 15
y_labelsize = 15
In [28]:
# Loop through each stream and plot the WQ profile plot
TOOLS="hover,box_zoom,wheel_zoom,pan,reset"
for StreamName in StreamNames:
WQdf = AllWQdf[AllWQdf['Stream']==StreamName].copy()
SRdf = Rdf[Rdf['StreamName']==StreamName].reset_index()
x_range = [max(WQdf['ENDING_DIST'])*1.025,min(WQdf['ENDING_DIST'])]
#Determine range of y-axis
maxy = []
for tempWQC in WQC_of_interest:
maxy.append(max(WQdf[tempWQC]))
y_range = [0,round(max(maxy))]#*1.5)]
p = figure(title=StreamName, tools=TOOLS,x_range = x_range,y_range=y_range,plot_width=plot_width, plot_height=plot_height)
if StreamName == StreamNames[0]:
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
# format text in tooltip
("Conc ", "$y mg/L"),
]
p.xaxis.axis_label = x_label
p.yaxis.axis_label = y_label
for k in range(0,len(WQC_of_interest)):
# add a line renderer
p.line(np.asarray(WQdf['ENDING_DIST']),np.asarray(WQdf[WQC_of_interest[k]]),
legend=WQC_of_interest[k], line_width= lws[k],
color = plot_colors[k],name = WQC_of_interest[k])
#Overplot reach start and end points of each reach
if len(SRdf) > 0:
Points1 = list(SRdf['BEGIN NAME'])
Rkms1 = list(SRdf['BEGIN REACH KM'])
if len(SRdf) > 1:
Points2 = [SRdf['END NAME'][SRdf.index[-1]]]
Rkms2 = [SRdf['END REACH KM'][SRdf.index[-1]]]
else:
Points2 = list(SRdf['END NAME'])
Rkms2 = list(SRdf['END REACH KM'])
Points = Points1 + Points2
#Rkms = Rkms1.append(Rkms2)
Rkms = Rkms1+Rkms2
#p1.text([65,65,65],[65,65,65], text=[ str(i) for i in x], alpha=0.5, text_font_size="5pt", text_baseline="middle",
#text_align="center")
for i in range(0,len(Points)):
#print Points[i]
p.line([Rkms[i],Rkms[i]],[-999,999],'..',line_dash = 'dotted',color = 'grey')
p.text([Rkms[i]],[np.mean(y_range)],text = [Points[i]],text_align = 'center',
angle = 3.14159265/2,text_font_size = '9pt',text_color = 'grey')
if DOstd != np.nan:
DOstds = [DOstd,DOstd-0.2]
vas = ['bottom','top']
for i in range(len(DOstds)):
p.line([-999,999],[DOstds[i],DOstds[i]],'..',line_dash = 'dotted',color = 'red')
p.text([np.mean(x_range)],[DOstds[i]],["DO std = "+"{:4.1f}".format(DOstds[i])+" mg/L"],
text_baseline = vas[i],text_font_size = '9pt',text_color = 'red')
pass
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
show(p)
In [29]:
from fastkml.kml import KML
def read_kml(fname='ss.kml'):
kml = KML()
kml.from_string(open(fname).read())
points = dict()
for feature in kml.features():
for placemark in feature.features():
if placemark.styleUrl.startswith('#hf'):
points.update({placemark.name:
(placemark.geometry.y, placemark.geometry.x, )})
return points
In [30]:
fname = r'C:\Users\Ernest\Ernest_Sandbox\TurboQUALTX\TurboQUALTX\GIS\ss.kml'
#fname = r'C:\Users\Ernest\Ernest_Sandbox\TurboQUALTX\TurboQUALTX\GIS\TCEQ Wastewater Outfalls2.kml'
locations = read_kml(fname)
In [31]:
import folium
width, height = 650, 500
#radars = folium.Map(location=[40, -122], zoom_start=5,
# tiles='OpenStreetMap', width=width, height=height)
radars = folium.Map(location=location, zoom_start=6,
tiles=tiles, attr='ESRI')
for name, location in locations.items():
radars.simple_marker(location=location, popup=name)
radars
#inline_map(radars)
Out[31]:
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