In [1]:
#%matplotlib inline
import pandas as pd
import platform
import sys
import numpy as np
from datetime import datetime, date, timedelta
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
#from pylab import rcParams
#rcParams['figure.figsize'] = 15, 10
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import wellapplication as wa
In [3]:
print("Operating System " + platform.system() + " " + platform.release())
print("Python Version " + str(sys.version))
print("Pandas Version " + str(pd.__version__))
print("Numpy Version " + str(np.__version__))
print("Matplotlib Version " + str(matplotlib.__version__))
print("Well Application Version " + str(wa.__version__))
Call usgs class of wellapplication
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USGS = wa.usgs()
Set working environment.
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rootlocal = 'E:/GIS/'
Read in data prepared by ArcGIS.
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matchedStations = pd.read_csv(rootlocal + "USGS_WL_WELLS_HUCS.txt")
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matchedStations
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Show columns in imported column.
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matchedStations.columns
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Get list of stations for each groundwater area.
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stationLists = matchedStations.groupby(['GWArea'])['site_no'].unique()
Create dictionary matching HUC names to HUC 8 codes.
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nameit = pd.Series(matchedStations.Name.values, index = matchedStations.HUC8).to_dict()
Generate site and data files from HUC 8 in list.
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sites = {}
data = {}
areaList = list(matchedStations.HUC8.unique())[1:]
for area in areaList:
try:
sites[area] = USGS.getStationInfoFromHUC(area)
data[area] = USGS.getWLfromHUC(area)
print area
except:
print("no data for " + str(area))
pass
Combine dictionaries into a complete data and stations file.
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allsites = pd.concat(sites)
alldata = pd.concat(data)
Save files as csv files and an excel file.
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allsites.to_csv(rootlocal + 'USGSSites.csv')
alldata.to_csv(rootlocal + 'USGSData.csv')
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writer = pd.ExcelWriter(rootlocal + 'USGS_WL_UT.xlsx')
allsites.to_excel(writer,'sites')
alldata.to_excel(writer,'data')
writer.save()
Generate and save plots to a pdf file. Skip error files.
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pdf_pages = PdfPages(rootlocal + 'HydrographSummary.pdf')
for area in areaList:
try:
plots = USGS.HUCplot(sites[area],data[area])
fig = plots[0]
fig.suptitle(str(area) + " = " + nameit[area])
pdf_pages.savefig(fig)
plt.close(fig)
fig2 = plots[1]
fig2.suptitle(str(area) + " = " + nameit[area])
pdf_pages.savefig(fig2)
plt.close(fig2)
print(str(area) + " = " + nameit[area])
except:
print("no plot for " + str(area) + " = " + str(nameit[area]))
pass
# Write the PDF document to the disk
pdf_pages.close()
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pdf = PdfPages(rootlocal + 'monthly.pdf')
grp = "month"
itergroup = 'm'
data = "diff"
title = 'Change in Water Level (ft)'
monthlyPlot(df, grp, data, title)
plt.title('All Wells')
for f, h in df.groupby('MonType'):
n = monthlyPlot(h, grp, data, title)
plt.title(TypeDict[f])
pdf.savefig(n)
plt.close()
data = 'avgDiffWL'
title = 'Deviation from Mean Water Level (ft)'
monthlyPlot(df, grp, data, title)
plt.title('All Wells')
for f, h in df.groupby('MonType'):
n = monthlyPlot(h, grp, data, title)
plt.title(TypeDict[f])
pdf.savefig(n)
plt.close()
data = 'stdWL'
title = 'Standardized Deviation from Mean Water Level (ft)'
monthlyPlot(df, grp, data, title)
plt.title('All Wells')
for f, h in df.groupby('MonType'):
n = monthlyPlot(h, grp, data, title)
plt.title(TypeDict[f])
pdf.savefig(n)
plt.close()
grp = pd.TimeGrouper("M")
data = "diff"
title = 'Change in Water Level (ft)'
monthlyPlot(df, grp, data, title)
plt.title('All Wells')
plt.ylim(-0.1,0.1)
for f, h in df.groupby('MonType'):
n = monthlyPlot(h, grp, data, title)
plt.ylim(-0.1,0.1)
plt.title(TypeDict[f])
pdf.savefig(n)
plt.close()
grp = pd.TimeGrouper("M")
data = "stdWL"
title = 'Standardized Deviation from Mean Water Level'
monthlyPlot(df, grp, data, title)
plt.ylim(-3,3)
plt.title('All Wells')
for f, h in df.groupby('MonType'):
n = monthlyPlot(h, grp, data, title)
plt.title(TypeDict[f])
plt.ylim(-3,3)
pdf.savefig(n)
plt.close()
grp = pd.TimeGrouper("M")
data = 'avgDiffWL'
title = 'Deviation from Mean Water Level (ft)'
monthlyPlot(df, grp, data, title)
plt.title('All Wells')
for f, h in df.groupby('MonType'):
n = monthlyPlot(h, grp, data, title)
plt.title(TypeDict[f])
pdf.savefig(n)
plt.close()
pdf.close()
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df1,df2 = self.WLStatdf(siteinfo, data)
wlLongSt = df1[df1['cnt']>2]
wlLongSt2 = df2[df2['cnt']>2]
self.stand = wlLongSt
self.diffs = wlLongSt2
fig1 = plt.figure()
x = wlLongSt.index
y = wlLongSt['mean']
plt.plot(x,y,label='Average Groundwater Level Variation')
plt.fill_between(wlLongSt.index, wlLongSt['meanpluserr'], wlLongSt['meanminuserr'],
facecolor='blue', alpha=0.4, linewidth=0.5, label= "Std Error")
plt.grid(which='both')
plt.ylabel('Depth to Water z-score')
plt.xticks(rotation=45)
self.zPlot = fig1
fig2 = plt.figure()
x = wlLongSt2.index
y = wlLongSt2['mean']
plt.plot(x,y,label='Average Groundwater Level Changes')
plt.fill_between(wlLongSt.index, wlLongSt2['meanpluserr'], wlLongSt2['meanminuserr'],
facecolor='blue', alpha=0.4, linewidth=0.5, label= "Std Error")
plt.grid(which='both')
plt.ylabel('Change in Average Depth to Water (ft)')
plt.xticks(rotation=45)
self.wlPlot = fig2
return fig1, fig2, wlLongSt, wlLongSt2
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area = 16020302
pdf_pages = PdfPages(rootlocal + 'E:/GIS/HydrographSummarySingle.pdf')
plots = USGS.HUCplot(sites[area],data[area])
fig = plots[0]
fig.suptitle(str(area) + " = " + nameit[area])
pdf_pages.savefig(fig)
plt.close(fig)
fig2 = plots[1]
fig2.suptitle(str(area) + " = " + nameit[area])
pdf_pages.savefig(fig2)
plt.close(fig2)
print(str(area) + " = " + nameit[area])
pdf_pages.close()
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