def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
arrays = a.create_array(('LANDSAT_5', 'nbar'), ['nir', 'red'], dimensions, 'get_data')
ndvi = a.apply_bandmath(arrays, '((array1 - array2) / (array1 + array2))', 'ndvi')
e.execute_plan(a.plan)
plot(e.cache['ndvi'])
b30_result = e.cache['get_data']['array_result']['red']
b40_result = e.cache['get_data']['array_result']['nir']
ndvi_result = e.cache['ndvi']['array_result']['ndvi']
b30_data = Data(x=b30_result[:, ::-1, :], label='B30')
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
ndvi_data = Data(x=ndvi_result[:, ::-1, :], label='ndvi')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([ndvi_data, b30_data, b40_data, long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
b40 = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions, 'b40')
b30 = a.create_array(('LANDSAT_5', 'nbar'), ['red'], dimensions, 'b30')
ndvi = a.apply_expression([b40, b30], '((array1 - array2) / (array1 + array2))', 'ndvi')
e.execute_plan(a.plan)
plot(e.cache['ndvi'])
b30_result = e.cache['b30']['array_result']['red']
b40_result = e.cache['b40']['array_result']['nir']
ndvi_result = e.cache['ndvi']['array_result']['ndvi']
b30_data = Data(x=b30_result[:, ::-1, :], label='B30')
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
ndvi_data = Data(x=ndvi_result[:, ::-1, :], label='ndvi')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([ndvi_data, b30_data, b40_data, long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
ndvi = a.apply_sensor_specific_bandmath('LANDSAT_5', 'nbar', 'ndvi', dimensions, 'get_data', 'ndvi')
result = e.execute_plan(a.plan)
plot(e.cache['ndvi'])
b30_result = e.cache['get_data']['array_result']['red']
b40_result = e.cache['get_data']['array_result']['nir']
ndvi_result = e.cache['ndvi']['array_result']['ndvi']
b30_data = Data(x=b30_result[:, ::-1, :], label='B30')
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
ndvi_data = Data(x=ndvi_result[:, ::-1, :], label='ndvi')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([ndvi_data, b30_data, b40_data, long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {
'x': {
'range': (149.07, 149.18)
},
'y': {
'range': (-35.32, -35.28)
},
'time': {
'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))
}
}
arrays = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions,
'get_data')
median_xy = a.apply_generic_reduction(arrays, ['y', 'x'], 'median(array1)',
'medianXY')
result = e.execute_plan(a.plan)
plot(e.cache['medianXY'])
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
arrays = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions, 'get_data')
median_t = a.apply_generic_reduction(arrays, ['time'], 'median(array1)', 'medianT')
result = e.execute_plan(a.plan)
plot(e.cache['medianT'])
b40_result = e.cache['get_data']['array_result']['nir']
median_result = e.cache['medianT']['array_result']['medianT']
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
median_data = Data(x=median_result[::-1, :], label='medianT')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([median_data, b40_data, long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
arrays = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions, 'get_data')
median = a.apply_expression(arrays, 'median(array1, 1, 2)', 'medianXY')
e.execute_plan(a.plan)
plot(e.cache['medianXY'])
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {
'x': {
'range': (149.07, 149.18)
},
'y': {
'range': (-35.32, -35.28)
},
'time': {
'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))
}
}
arrays = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions,
'get_data')
median_t = a.apply_generic_reduction(arrays, ['time'], 'median(array1)',
'medianT')
result = e.execute_plan(a.plan)
plot(e.cache['medianT'])
b40_result = e.cache['get_data']['array_result']['nir']
median_result = e.cache['medianT']['array_result']['medianT']
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
median_data = Data(x=median_result[::-1, :], label='medianT')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([
median_data,
b40_data,
long_data,
lat_data,
time_data,
])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
b40 = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions, 'b40')
b30 = a.create_array(('LANDSAT_5', 'nbar'), ['red'], dimensions, 'b30')
pq = a.create_array(('LANDSAT_5', 'pqa'), ['pixelquality'], dimensions, 'pq')
ndvi = a.apply_expression([b40, b30], '((array1 - array2) / (array1 + array2))', 'ndvi')
mask = a.apply_expression([ndvi, pq], 'array1{(array2 == 32767) | (array2 == 16383) | (array2 == 2457)}', 'mask')
median_t = a.apply_expression(mask, 'median(array1, 0)', 'medianT')
result = e.execute_plan(a.plan)
plot(e.cache['medianT'])
b30_result = e.cache['b30']['array_result']['red']
b40_result = e.cache['b40']['array_result']['nir']
ndvi_result = e.cache['ndvi']['array_result']['ndvi']
pq_result = e.cache['pq']['array_result']['pixelquality']
mask_result = e.cache['mask']['array_result']['mask']
median_result = e.cache['medianT']['array_result']['medianT']
b30_data = Data(x=b30_result[:, ::-1, :], label='B30')
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
ndvi_data = Data(x=ndvi_result[:, ::-1, :], label='ndvi')
pq_data = Data(x=pq_result[:, ::-1, :], label='pq')
mask_data = Data(x=mask_result[:, ::-1, :], label='mask')
median_data = Data(x=median_result[::-1, :], label='median')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([median_data, mask_data, pq_data, ndvi_data, b30_data, b40_data,
long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
b40 = a.create_array(('LANDSAT_5', 'nbar'), ['nir'], dimensions, 'b40')
b30 = a.create_array(('LANDSAT_5', 'nbar'), ['red'], dimensions, 'b30')
pq = a.create_array(('LANDSAT_5', 'pqa'), ['pixelquality'], dimensions, 'pq')
ndvi = a.apply_expression([b40, b30], '((array1 - array2) / (array1 + array2))', 'ndvi')
mask = a.apply_expression([ndvi, pq], 'array1{(array2 == 32767) | (array2 == 16383) | (array2 == 2457)}', 'mask')
median_t = a.apply_expression(mask, 'median(array1, 0)', 'medianT')
result = e.execute_plan(a.plan)
plot(e.cache['medianT'])
b30_result = e.cache['b30']['array_result']['red']
b40_result = e.cache['b40']['array_result']['nir']
ndvi_result = e.cache['ndvi']['array_result']['ndvi']
pq_result = e.cache['pq']['array_result']['pixelquality']
mask_result = e.cache['mask']['array_result']['mask']
median_result = e.cache['medianT']['array_result']['medianT']
b30_data = Data(x=b30_result[:, ::-1, :], label='B30')
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
ndvi_data = Data(x=ndvi_result[:, ::-1, :], label='ndvi')
pq_data = Data(x=pq_result[:, ::-1, :], label='pq')
mask_data = Data(x=mask_result[:, ::-1, :], label='mask')
median_data = Data(x=median_result[::-1, :], label='median')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([median_data, mask_data, pq_data, ndvi_data, b30_data, b40_data,
long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def main():
a = AnalyticsEngine()
e = ExecutionEngine()
# Lake Burley Griffin
dimensions = {'x': {'range': (149.07, 149.18)},
'y': {'range': (-35.32, -35.28)},
'time': {'range': (datetime(1990, 1, 1), datetime(1990, 12, 31))}}
arrays = a.create_array(('LANDSAT_5', 'nbar'), ['nir', 'red'], dimensions, 'get_data')
ndvi = a.apply_bandmath(arrays, '((array1 - array2) / (array1 + array2))', 'ndvi')
pq = a.create_array(('LANDSAT_5', 'pqa'), ['pixelquality'], dimensions, 'pq')
mask = a.apply_cloud_mask(ndvi, pq, 'mask')
e.execute_plan(a.plan)
plot(e.cache['mask'])
b30_result = e.cache['get_data']['array_result']['red']
b40_result = e.cache['get_data']['array_result']['nir']
ndvi_result = e.cache['ndvi']['array_result']['ndvi']
pq_result = e.cache['pq']['array_result']['pixelquality']
mask_result = e.cache['mask']['array_result']['mask']
b30_data = Data(x=b30_result[:, ::-1, :], label='B30')
b40_data = Data(x=b40_result[:, ::-1, :], label='B40')
ndvi_data = Data(x=ndvi_result[:, ::-1, :], label='ndvi')
pq_data = Data(x=pq_result[:, ::-1, :], label='pq')
mask_data = Data(x=mask_result[:, ::-1, :], label='mask')
long_data = Data(x=b40_result.coords['x'], label='long')
lat_data = Data(x=b40_result.coords['y'], label='lat')
time_data = Data(x=b40_result.coords['time'], label='time')
collection = DataCollection([mask_data, pq_data, ndvi_data, b30_data, b40_data, long_data, lat_data, time_data, ])
app = GlueApplication(collection)
app.start()
def AJAXHandle(request):
if request.is_ajax():
year = int(request.POST['year'])
layer = request.POST['layer']
epsg = request.POST['epsg']
prod = request.POST['prod']
algo = request.POST['algo']
x1 = float(request.POST['x1'])
x2 = float(request.POST['x2'])
y1 = float(request.POST['y1'])
y2 = float(request.POST['y2'])
utm44 = pyproj.Proj("+init=EPSG:32644")
gcs = pyproj.Proj("+init=EPSG:4326")
x1, y1 = pyproj.transform(gcs, utm44, x1, y1)
x2, y2 = pyproj.transform(gcs, utm44, x2, y2)
print x1, x2, y1, y2
imsource = plot(year,layer,x1,x2,y1,y2,epsg,prod,algo)
response = {'status': 1, 'message': imsource + "?t=" + str(random.randint(1,1000))}
else:
response = {'status': 0, 'message': "error"}
return HttpResponse(json.dumps(response), content_type='application/json')