def merge_data_products(ddict,
file_out,
merge_data_in=None,
left_on=None,
right_on=None,
suffixes=None,
mode='nearest'):
data = raslib.pd_sample_raster_gdal(ddict, include_nans=False, mode=mode)
# if 'dce' in ddict.keys():
# data.dce = np.rint(data.dce * 10)/10
if merge_data_in is not None:
# nest in list if not already
if isinstance(merge_data_in, str):
merge_data_in = [merge_data_in]
if isinstance(right_on, str):
right_on = [right_on]
if isinstance(left_on, str):
left_on = [left_on]
for ii in range(len(merge_data_in)):
# merge with hemisfer outputs
if suffixes is not None:
suffixes_itter = ("", suffixes[ii])
else:
suffixes_itter = ("_x", "_y")
print('Merging with ' + suffixes_itter[1] + '... ', end='')
sup_data = pd.read_csv(merge_data_in[ii])
data = data.merge(sup_data,
how='left',
left_on=left_on[ii],
right_on=right_on[ii],
suffixes=suffixes_itter)
print('done')
# data = data.drop(columns='id')
# save to file
print('saving to file... ', end='')
data.to_csv(file_out, index=False)
print('done')
return data
# define canopy binary
canopy = np.full([ras.rows, ras.cols], 0)
canopy[ras.data >= canopy_min_elev] = 1
# smoothe
kernel = np.full([kernel_dim, kernel_dim], 1)
convolved = convolve(canopy, kernel) / np.sum(kernel)
# points to search:
ids_in = 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\site_library\\hemi_grid_points\\mb_65_r.25m_snow_off_offset.25\\dem_r.25_point_ids.tif'
ddict = {'chm': ras_in,
'lrs_id': ids_in,
'uf': 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\site_library\\hemi_grid_points\\mb_65_r.25m_snow_off_offset0\\uf_plot_r.25m.tif',
}
df = raslib.pd_sample_raster_gdal(ddict, include_nans=False, mode="nearest")
# filter to uf only... for now....
df = df.loc[df.uf == 1, :]
can_dist = np.full((len(df), angle_count), np.nan)
for ii in range(len(df)):
coords = np.array([df.y_index.iloc[ii], df.x_index.iloc[ii]])
for jj in range(angle_count):
aa = jj * 2 * np.pi / angle_count
displacement = np.array([np.cos(aa), np.sin(aa)])
hit = False
step_count = 0
while ~hit & (step_count < max_step):
### export 1m subset of .25m grid
point_batch_dir = "C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\site_library\\hemi_grid_points\\mb_65_r.25m_snow_off_offset.25\\"
data_25_in = point_batch_dir + 'dem_r.25_points.csv'
data_25 = pd.read_csv(data_25_in)
# ddict = {
# 'uf_15': 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\synthetic_hemis\\hemi_grid_points\\mb_65_1m\\uf_plot_r1.00m.tif',
# 'hemi_id': 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\synthetic_hemis\\hemi_grid_points\\mb_65_1m\\1m_dem_point_ids.tif',
# 'id': 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\synthetic_hemis\\hemi_grid_points\\mb_65_r.25m\\dem_r.25_point_ids.tif'
# }
ddict = {
'uf_15': point_batch_dir + 'uf_plot_r1.00m.tif',
#'hemi_id': point_batch_dir + '1m_dem_point_ids.tif',
'id': point_batch_dir + 'dem_r.25_point_ids.tif'
}
data_1 = raslib.pd_sample_raster_gdal(ddict,
include_nans=False,
mode='nearest')
lrs_id = data_1.id
merged = pd.merge(lrs_id, data_25, on="id", how="left")
merged.id = merged.id.astype(int)
file_out = point_batch_dir + "dem_r.25_point_ids_1m subset.csv"
merged.to_csv(file_out, index=False)
# lala = pd.read_csv('C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\ray_sampling\\batches\\lrs_uf_r.25_px181_snow_off\\outputs\\rshmetalog_footprint_products.csv')
'swe_fcon_19_045':
'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\19_045\\19_045_las_proc\\OUTPUT_FILES\\SWE\\fcon\\interp_2x\\masked\\swe_fcon_19_045_r.05m_interp2x_masked.tif',
'swe_fcon_19_050':
'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\19_050\\19_050_las_proc\\OUTPUT_FILES\\SWE\\fcon\\interp_2x\\masked\\swe_fcon_19_050_r.05m_interp2x_masked.tif',
'swe_fcon_19_052':
'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\19_052\\19_052_las_proc\\OUTPUT_FILES\\SWE\\fcon\\interp_2x\\masked\\swe_fcon_19_052_r.05m_interp2x_masked.tif',
# 'dswe_fnsd_19_045-19_050': 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\products\\mb_65\\dSWE\\fnsd\\interp_2x\\19_045-19_050\\masked\\dswe_fnsd_19_045-19_050_r.05m_interp2x_masked.tif',
# 'dswe_fnsd_19_050-19_052': 'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\products\\mb_65\\dSWE\\fnsd\\interp_2x\\19_050-19_052\\masked\\dswe_fnsd_19_050-19_052_r.05m_interp2x_masked.tif',
'dswe_ucgo_19_045-19_050':
'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\products\\mb_65\\dSWE_bias_corrected\\ucgo\\interp_2x\\19_045-19_050\\masked\\dswe_ucgo_19_045-19_050_r.05m_interp2x_masked.tif',
'dswe_ucgo_19_050-19_052':
'C:\\Users\\Cob\\index\\educational\\usask\\research\\masters\\data\\lidar\\products\\mb_65\\dSWE_bias_corrected\\ucgo\\interp_2x\\19_050-19_052\\masked\\dswe_ucgo_19_050-19_052_r.05m_interp2x_masked.tif',
# 'covariant': var_in
}
var = raslib.pd_sample_raster_gdal(ddict, include_nans=False, mode="median")
# var = var.loc[~np.isnan(var.covariant), :] # drop nans in covariant
# if date == "045-050":
# var.loc[:, "min_pc"] = np.nanmin((var.count_045, var.count_050, var.count_149), axis=0) * (.25 ** 2)
# elif date == "050-052":
# var.loc[:, "min_pc"] = np.nanmin((var.count_050, var.count_052, var.count_149), axis=0) * (.25 ** 2)
# elif date == "19_045":
# var.loc[:, "min_pc"] = np.nanmin((var.count_045, var.count_149), axis=0) * (.25 ** 2)
# elif date == "19_050":
# var.loc[:, "min_pc"] = np.nanmin((var.count_050, var.count_149), axis=0) * (.25 ** 2)
# elif date == "19_052":
# var.loc[:, "min_pc"] = np.nanmin((var.count_052, var.count_149), axis=0) * (.25 ** 2)
# elif date == "19_107":
# var.loc[:, "min_pc"] = np.nanmin((var.count_107, var.count_149), axis=0) * (.25 ** 2)