def BoxPlotByCluster(DataDirectory, OutDirectory, fname_prefix, raster_name, stream_order=1):
"""
Make a boxplot of the results of the clustering compared to the raster specified
by raster_name
"""
#df = pd.read_csv(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order))
# read in the raster
raster_ext = '.bil'
this_raster = IO.ReadRasterArrayBlocks(DataDirectory+raster_name)
EPSG_string = IO.GetUTMEPSG(DataDirectory+raster_name)
NDV, xsize, ysize, GeoT, Projection, DataType = IO.GetGeoInfo(DataDirectory+raster_name)
CellSize,XMin,XMax,YMin,YMax = IO.GetUTMMaxMin(DataDirectory+raster_name)
pts = PT.LSDMap_PointData(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order),data_type ='csv')
easting, northing = pts.GetUTMEastingNorthing(EPSG_string=EPSG_string)
cluster_id = pts.QueryData('cluster_id', PANDEX=True)
clusters = list(set(cluster_id))
# dict for the data
data = {k: [] for k in clusters}
for x, (i, j) in enumerate(zip(northing, easting)):
# convert to rows and cols
X_coordinate_shifted_origin = j - XMin;
Y_coordinate_shifted_origin = i - YMin;
col_point = int(X_coordinate_shifted_origin/CellSize);
row_point = (ysize - 1) - int(round(Y_coordinate_shifted_origin/CellSize));
# check for data at this cell
this_value = this_raster[row_point][col_point]
if not np.isnan(this_value):
if this_value < 10:
# get the cluster id
data[cluster_id[x]].append(this_value)
print(data)
# now make a boxplot
labels, these_data = [*zip(*data.items())] # 'transpose' items to parallel key, value lists
plt.boxplot(these_data)
plt.xticks(range(1, len(labels) + 1), labels)
plt.show()
def GetLithologyPercentages(DataDirectory, OutDirectory, fname_prefix, raster_name, stream_order=1):
"""
Get the percentage of the nodes in each cluster that drain each lithology
"""
from collections import Counter
# read in the raster
raster_ext = '.bil'
this_raster = IO.ReadRasterArrayBlocks(DataDirectory+raster_name)
EPSG_string = IO.GetUTMEPSG(DataDirectory+raster_name)
NDV, xsize, ysize, GeoT, Projection, DataType = IO.GetGeoInfo(DataDirectory+raster_name)
CellSize,XMin,XMax,YMin,YMax = IO.GetUTMMaxMin(DataDirectory+raster_name)
pts = PT.LSDMap_PointData(OutDirectory+fname_prefix+'_profiles_clustered_SO{}.csv'.format(stream_order),data_type ='csv')
easting, northing = pts.GetUTMEastingNorthing(EPSG_string=EPSG_string)
cluster_id = pts.QueryData('cluster_id', PANDEX=True)
clusters = list(set(cluster_id))
# dict for the data
data = {k: [] for k in clusters}
for x, (i, j) in enumerate(zip(northing, easting)):
# convert to rows and cols
X_coordinate_shifted_origin = j - XMin;
Y_coordinate_shifted_origin = i - YMin;
col_point = int(X_coordinate_shifted_origin/CellSize);
row_point = (ysize - 1) - int(round(Y_coordinate_shifted_origin/CellSize));
# check for data at this cell
this_value = this_raster[row_point][col_point]
if not np.isnan(this_value):
data[cluster_id[x]].append(this_value)
# you have the values. now what percentage are each?
for key, liths in data.items():
c = Counter(liths)
n_ndv = c[0.0]
print(c)
[print(x,": ",vals/len(liths) * 100) for x, vals in c.items()]