In [1]:
#Enter the location of the QUALTX output file
QUALTXoutfile = r'C:\Users\Ernest\Ernest_Sandbox\TurboQUALTX\TurboQUALTX\RB55NOV.OUT'
In [2]:
#Import python packages
import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
import os
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 [3]:
#Parse the QUALTX output file
Scenario = QUALTXoutfile
DOstds = [5,4.8]
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,DOstds = DOstds,
WQC_of_interest = WQC_of_interest,
loc = 1,plot_pdf = 0)
In [4]:
#Perform plotting
y_label = 'Concentration (mg/L)'
x_label = 'Distance (km)'
#Determine range of y-axis
maxy = []
for tempWQC in WQC_of_interest:
maxy.append(max(AllWQdf[tempWQC]))
y_range = [0,round(max(maxy)*1.5)]
#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 [7]:
# Loop through each stream and plot the WQ profile plot
#TOOLS = "pan,wheel_zoom,box_zoom,reset,save,box_select"
#TOOLS = [BoxSelectTool(), HoverTool()]
TOOLS="hover"
#output_file("legend.html", title="legend.py example")
#show(vplot(p1, p2)) # open a browser
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'])]
p = figure(title=StreamName, tools=TOOLS,x_range = x_range,y_range=y_range,plot_width=900, plot_height=450)
if StreamName == StreamNames[0]:
hover = p.select(dict(type=HoverTool))
hover.tooltips = [
# add to this
("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)):
#ls.append(ax.plot(np.asarray(WQdf['ENDING_DIST']),np.asarray(WQdf[WQC_of_interest[k]]),plot_symbols[k],
#lw = lws[k],markersize=markersizes[k],
# color = plot_colors[k],label = WQC_of_interest[k]))
# 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]],y_range,'..',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 len(DOstds) > 0:
for DOstd in DOstds:
p.line(x_range,[DOstd,DOstd],'..',color = 'red')
#print DOstd
p.text([np.mean(x_range)],[DOstd],text = ["DO std = "+"{:4.1f}".format(DOstd)+" mg/L"],
text_font_size = '9pt',text_color = 'red')
#p.text(np.mean(x_range),DOstd,"DO std = Monkey mg/L")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
show(p)
In [6]:
print Points[i]
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