def median_mosaic(dataset):
if sys.argv[6] == 'LANDSAT_7':
# The mask here is based on pixel_qa. It comes bundled in with most Landsat Products.
clear_xarray = ls7_unpack_qa(
dataset.pixel_qa, "clear") # Boolean Xarray indicating landcover
water_xarray = ls7_unpack_qa(
dataset.pixel_qa, "water") # Boolean Xarray indicating watercover
elif sys.argv[6] == 'LANDSAT_8':
clear_xarray = ls8_unpack_qa(
dataset.pixel_qa, "clear") # Boolean Xarray indicating landcover
water_xarray = ls8_unpack_qa(
dataset.pixel_qa, "water") # Boolean Xarray indicating watercover
elif sys.argv[6] == 'LANDSAT_5':
clear_xarray = ls5_unpack_qa(
dataset.pixel_qa, "clear") # Boolean Xarray indicating landcover
water_xarray = ls5_unpack_qa(
dataset.pixel_qa, "water") # Boolean Xarray indicating watercover
cloud_free_boolean_mask = np.logical_or(clear_xarray, water_xarray)
return create_median_mosaic(dataset, clean_mask=cloud_free_boolean_mask)
def plot_pixel_qa_value(dataset, platform, values_to_plot, bands = "pixel_qa", plot_max = False, plot_min = False):
times = dataset.time.values
mpl.style.use('seaborn')
plt.figure(figsize=(20,15))
quarters = []
three_quarters = []
percentiles = []
for i,v in enumerate(values_to_plot):
_xarray = ls7_unpack_qa(dataset.pixel_qa, values_to_plot[i])
y = _xarray.mean(dim= ['latitude', 'longitude'])
times = dataset.time.values.astype(float)
std_dev = np.std(y)
std_dev = std_dev.values
b = gaussian(len(times), std_dev)
ga = filters.convolve1d(y, b/b.sum(),mode="reflect")
ga=interpolate_gaps(ga, limit=3)
plt.plot(times, ga, '-',label="Gaussian ", alpha=1, color='black')
x_smooth = np.linspace(times.min(),times.max(), 200)
y_smooth = spline(times, ga, x_smooth)
plt.plot(x_smooth, y_smooth, '-',label="Gaussian Smoothed", alpha=1, color='cyan')
for i, q in enumerate(_xarray):
quarters.append(np.nanpercentile(_xarray, 25))
three_quarters.append(np.nanpercentile(_xarray, 75))
#print(q.values.mean())
ax = plt.gca()
ax.grid(color='lightgray', linestyle='-', linewidth=1)
fillcolor='gray'
fillalpha=0.4
linecolor='gray'
linealpha=0.6
plt.fill_between(times, y, quarters, interpolate=False, color=fillcolor, alpha=fillalpha)
plt.fill_between(times, y, three_quarters, interpolate=False, color=fillcolor, alpha=fillalpha)
plt.plot(times,quarters,color=linecolor , alpha=linealpha)
plt.plot(times,three_quarters,color=linecolor, alpha=linealpha)
medians = _xarray.median(dim=['latitude','longitude'])
plt.scatter(times,medians,color='mediumpurple', label="medians", marker="D")
m, b = np.polyfit(times, y, 1)
plt.plot(times, m*times + b, '-', color="red",label="linear regression")
plt.style.use('seaborn')
plt.plot(times, y, marker="o")
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.xticks(rotation=90)
# <hr> # # ## <a id="clean_mask">Create and Use Clean Mask</a> [▴](#top) # In[ ]: exec(%matplotlib inline) import matplotlib.pyplot as plt import seaborn as sns from utils.data_cube_utilities.dc_mosaic import ls7_unpack_qa #Make a Clean Mask to remove clouds and scanlines mask = ls7_unpack_qa(dataset.pixel_qa, "clear") #Filter the scenes with that clean mask dataset = dataset.where(mask) # <hr> # # ## <a id="calculate">Calculate the NDVI</a> [▴](#top) # In[ ]: #Calculate NDVI ndvi = (dataset.nir - dataset.red)/(dataset.nir + dataset.red)