integrations/esri/zipped_toolbox/daily_difference_average.py

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"""-----------------------------------------------------------------------------
  Script Name: NEXUS Daily Difference Graph
  Description: Creates daily difference graph from
               a given dataset
  Created By:  Alice Yepremyan
  Date:        1/28/2020
-----------------------------------------------------------------------------"""

import arcpy
import json
import requests
import datetime
import matplotlib.pyplot as plt
import pandas as pd

arcpy.env.overwriteOutput = True

# Get the input parameters
host_url = arcpy.GetParameterAsText(0)
ds = arcpy.GetParameterAsText(1)
ds2 = arcpy.GetParameterAsText(2)
input_feature = arcpy.GetParameter(3)
start_time = pd.to_datetime(arcpy.GetParameterAsText(4)).strftime('%Y-%m-%dT%H:%M:%SZ')
end_time = pd.to_datetime(arcpy.GetParameterAsText(5)).strftime('%Y-%m-%dT%H:%M:%SZ')

# get coordinates by calculating geometric attributes
arcpy.MakeFeatureLayer_management(input_feature, "layer")
arcpy.AddGeometryAttributes_management("layer", "EXTENT")

rows = arcpy.SearchCursor("layer", fields="EXT_MIN_X;EXT_MIN_Y;EXT_MAX_X;EXT_MAX_Y")
row = next(rows)
min_lon = row.getValue("EXT_MIN_X")
max_lon = row.getValue("EXT_MAX_X")
min_lat = row.getValue("EXT_MIN_Y")
max_lat = row.getValue("EXT_MAX_Y")

# Build the HTTP request
url = 'https://{}/dailydifferenceaverage_spark?dataset={}&climatology={}&b={},{},{},{}&startTime={}&endTime={}'.format(host_url,ds, ds2, min_lat, min_lon, max_lat, max_lon, start_time, end_time)
#url = 'https://{}/dailydifferenceaverage_spark?dataset=SMAP_SSS_L3_MONTHLY_500&climatology=SMAP_SSS_L3_MONTHLY_500_CLIM&b=-150,45,-120,60&startTime=2015-04-16T00:00:00Z&endTime=2018-04-14T00:00:00Z'
arcpy.AddMessage('{}'.format(url))

# Report a success message
arcpy.AddMessage("Url received, getting json")

# Send request to server
response = str(requests.get(url, verify=False).text)
dda_amce = json.loads(response)
arcpy.AddMessage('{}'.format(response))

means = []
dates = []
st_ds = []

# Sampling every 30th data point to reduce plot noise
for i, data in enumerate(dda_amce['data']):
    means.append(data[0]['mean'])
    dates.append(datetime.datetime.fromtimestamp((data[0]['time'])))
    st_ds.append(data[0]['std'])

# Plot the extracted means
plt.figure(figsize=(10, 5), dpi=100)
lines = plt.errorbar(dates, means, st_ds)
plt.xlim(dates[0], dates[-1])
plt.xlabel('Time', fontsize=16)
plt.ylim(min(means)-1, max(means)+1)
plt.ylabel('Temperature Difference (K)', fontsize=16)
plt.title('Temperature Difference from Average (K) with Standard Deviation Error Bars', fontsize=18)
plt.show()