def join_csv2shp(shapefile,
shp_joinfield,
csvfile,
csv_joinfield,
out_shapefile,
how='outer'):
'''
add attribute information to shapefile from csv file
shapefile: shapefile to add attributes to
shp_joinfield: attribute name in shapefile on which to make join
csvfile: csv file with information to be added to shapefile
csv_joinfield: column in csv with entries matching those in shp_joinfield
out_shapefile: output; original shapefile is not modified
type: pandas join type; see http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.join.html
'''
shpdf = GISio.shp2df(shapefile, index=shp_joinfield, geometry=True)
csvdf = pd.read_csv(csvfile, index_col=csv_joinfield)
print('joining to {}...'.format(csvfile))
joined = shpdf.join(csvdf, how='inner', lsuffix='L', rsuffix='R')
# write to shapefile
GISio.df2shp(joined, out_shapefile, 'geometry', shapefile[:-4] + '.prj')
def dissolve(inshp, outshp, dissolve_attribute=None):
df = GISio.shp2df(inshp)
df_out = dissolve_df(df, dissolve_attribute)
# write dissolved polygons to new shapefile
GISio.df2shp(df_out, outshp, prj=inshp[:-4]+'.prj')
def dissolve(inshp, outshp, dissolve_attribute):
df = GISio.shp2df(shp, geometry=True)
df_out = dissolve_df(df, dissolve_attribute)
# write dissolved polygons to new shapefile
GISio.df2shp(df_out, outshp, 'geometry', inshp[:-4]+'.prj')
def dissolve(inshp, outshp, dissolve_attribute=None):
df = GISio.shp2df(inshp)
df_out = dissolve_df(df, dissolve_attribute)
# write dissolved polygons to new shapefile
GISio.df2shp(df_out, outshp, prj=inshp[:-4] + '.prj')
def join_csv2shp(shapefile, shp_joinfield, csvfile, csv_joinfield, out_shapefile, how='outer'):
'''
add attribute information to shapefile from csv file
shapefile: shapefile to add attributes to
shp_joinfield: attribute name in shapefile on which to make join
csvfile: csv file with information to be added to shapefile
csv_joinfield: column in csv with entries matching those in shp_joinfield
out_shapefile: output; original shapefile is not modified
type: pandas join type; see http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.join.html
'''
shpdf = GISio.shp2df(shapefile, index=shp_joinfield, geometry=True)
csvdf = pd.read_csv(csvfile, index_col=csv_joinfield)
print('joining to {}...'.format(csvfile))
joined = shpdf.join(csvdf, how='inner', lsuffix='L', rsuffix='R')
# write to shapefile
GISio.df2shp(joined, out_shapefile, 'geometry', shapefile[:-4]+'.prj')
def write_shp(self, df, shpname='NWIS_export.shp'):
"""Write a shapefile of points from NWIS site file
Parameters
----------
df: dataframe
dataframe of site info, must have dec_long_va and dec_lat_va columns with lon/lat in DD
shpname: string
Name for output shapefile
Notes
-----
NAD83 is assumed for dec_long_va and dec_lat_va.
If some entries are in NAD27, a difference of ~5 to >15m will result for WI
(see http://en.wikipedia.org/wiki/North_American_Datum#/media/File:Datum_Shift_Between_NAD27_and_NAD83.png)
"""
shpdf = df.copy()
shpdf['geometry'] = [Point(r.dec_long_va, r.dec_lat_va) for i, r in shpdf.iterrows()]
GISio.df2shp(shpdf, shpname, epsg=4269)
def write_shp(self, df, shpname='NWIS_export.shp', **kwargs):
"""Write a shapefile of points from NWIS site file
Parameters
----------
df: dataframe
dataframe of site info, must have dec_long_va and dec_lat_va columns with lon/lat in DD
shpname: string
Name for output shapefile
Notes
-----
NAD83 is assumed for dec_long_va and dec_lat_va.
If some entries are in NAD27, a difference of ~5 to >15m will result for WI
(see http://en.wikipedia.org/wiki/North_American_Datum#/media/File:Datum_Shift_Between_NAD27_and_NAD83.png)
"""
shpdf = df.copy()
shpdf['geometry'] = [Point(r.dec_long_va, r.dec_lat_va) for i, r in shpdf.iterrows()]
GISio.df2shp(shpdf, shpname, epsg=4269)
print 'building UZF package IRUNBND array from {}'.format(MFgrid)
MFgrid_joined = GISio.shp2df(os.path.join(os.getcwd(),
'MFgrid_catchments.shp'),
geometry=True)
MFgrid_joined.index = MFgrid_joined.node
nrows, ncols = np.max(MFgrid_joined.row), np.max(MFgrid_joined.column)
# make new column of SFR segment for each grid cell
MFgrid_joined['segment'] = MFgrid_joined.FEATUREID.apply(
segments_dict.get).fillna(0)
print 'writing {}'.format(out_IRUNBND)
# should add code to allow for a dataframe that only includes a subset of model cells
# (could build a DF of zeros for each cellnum, and then merge with DF containing UZF cells, replacing the zeros for those cells
IRUNBND = np.reshape(MFgrid_joined['segment'].sort_index().values,
(nrows, ncols))
np.savetxt(out_IRUNBND, IRUNBND, fmt='%i', delimiter=' ')
print 'writing {}'.format(out_IRUNBND_shp)
#df, shpname, geo_column, prj
GISio.df2shp(MFgrid_joined, os.path.join(os.getcwd(), 'MFgrid_segments.shp'),
'geometry',
os.path.join(os.getcwd(), 'MFgrid_catchments.shp')[:-4] + '.prj')
MFgrid_joined_dissolved = GISops.dissolve_df(MFgrid_joined, 'segment')
GISio.df2shp(MFgrid_joined_dissolved,
os.path.join(os.getcwd(), 'MFgrid_segments_dissolved.shp'),
'geometry',
os.path.join(os.getcwd(), 'MFgrid_catchments.shp')[:-4] + '.prj')
segments_dict[cmt] = segment
# can also use values_count() to get a frequency table for segments (reaches) in each catchment
print 'building UZF package IRUNBND array from {}'.format(MFgrid)
MFgrid_joined = GISio.shp2df(os.path.join(os.getcwd(), 'MFgrid_catchments.shp'), geometry=True)
MFgrid_joined.index = MFgrid_joined.node
nrows, ncols = np.max(MFgrid_joined.row), np.max(MFgrid_joined.column)
# make new column of SFR segment for each grid cell
MFgrid_joined['segment'] = MFgrid_joined.FEATUREID.apply(segments_dict.get).fillna(0)
print 'writing {}'.format(out_IRUNBND)
# should add code to allow for a dataframe that only includes a subset of model cells
# (could build a DF of zeros for each cellnum, and then merge with DF containing UZF cells, replacing the zeros for those cells
IRUNBND = np.reshape(MFgrid_joined['segment'].sort_index().values, (nrows, ncols))
np.savetxt(out_IRUNBND, IRUNBND, fmt='%i', delimiter=' ')
print 'writing {}'.format(out_IRUNBND_shp)
#df, shpname, geo_column, prj
GISio.df2shp(MFgrid_joined,
os.path.join(os.getcwd(), 'MFgrid_segments.shp'),
'geometry',
os.path.join(os.getcwd(), 'MFgrid_catchments.shp')[:-4]+'.prj')
MFgrid_joined_dissolved = GISops.dissolve_df(MFgrid_joined, 'segment')
GISio.df2shp(MFgrid_joined_dissolved,
os.path.join(os.getcwd(), 'MFgrid_segments_dissolved.shp'),
'geometry',
os.path.join(os.getcwd(), 'MFgrid_catchments.shp')[:-4]+'.prj')
break
else:
knt += 1
return columns, knt
# read in NWIS site information and study area boundary
header_text = open(NWIS_site_info_file).readlines()
columns, header_rows = NWIS_header(header_text)
df = pd.read_csv(NWIS_site_info_file, sep='\t', names=columns, skiprows=header_rows)
bounds = GISio.shp2df(model_domain_polygon, geometry=True).geometry[0]
# make geomtries for each station, and drop stations not in the study area
df['geometry'] = df.apply(lambda x: Point(x['dec_long_va'], x['dec_lat_va']), axis=1)
GISio.df2shp(df, 'D:/ATLData/GFL files/Great_Divide/flux_targets/NWIS_sites_all.shp', prj='epsg:4269')
within = [p.within(bounds) for p in df.geometry]
df = df[within]
GISio.df2shp(df, NWIS_site_info_file[:-4]+'.shp', prj='epsg:4269')
# now do spatial join of NWIS locations to NHD comids
arcpy.SpatialJoin_analysis(NWIS_site_info_file[:-4]+'.shp', flowlines_clipped, NWIS_site_info_file[:-4]+'_joined.shp',
"JOIN_ONE_TO_ONE", "KEEP_ALL", '', "WITHIN_A_DISTANCE", .001)
# now read back in and make a csv file for input into flux_targets.py
df = GISio.shp2df(NWIS_site_info_file[:-4]+'_joined.shp')
site_info = df[['site_no', 'COMID']]
# read in NWIS measurements
# read in NWIS site information and study area boundary
header_text = open(NWIS_site_info_file).readlines()
columns, header_rows = NWIS_header(header_text)
df = pd.read_csv(NWIS_site_info_file,
sep='\t',
names=columns,
skiprows=header_rows)
bounds = GISio.shp2df(model_domain_polygon, geometry=True).geometry[0]
# make geomtries for each station, and drop stations not in the study area
df['geometry'] = df.apply(lambda x: Point(x['dec_long_va'], x['dec_lat_va']),
axis=1)
GISio.df2shp(
df,
'D:/ATLData/GFL files/Great_Divide/flux_targets/NWIS_sites_all.shp',
prj='epsg:4269')
within = [p.within(bounds) for p in df.geometry]
df = df[within]
GISio.df2shp(df, NWIS_site_info_file[:-4] + '.shp', prj='epsg:4269')
# now do spatial join of NWIS locations to NHD comids
arcpy.SpatialJoin_analysis(NWIS_site_info_file[:-4] + '.shp',
flowlines_clipped,
NWIS_site_info_file[:-4] + '_joined.shp',
"JOIN_ONE_TO_ONE", "KEEP_ALL", '',
"WITHIN_A_DISTANCE", .001)
# now read back in and make a csv file for input into flux_targets.py
df = GISio.shp2df(NWIS_site_info_file[:-4] + '_joined.shp')
logq = np.log10(q)
qs_1e6 = q * 1e6
logqs_1e6 = np.log10(qs_1e6) # scale specific discharge to easier units for plotting
df = pd.DataFrame({'cellnum': cellnums.flatten(),
'x': xy[0].flatten(),
'y': xy[1].flatten(),
'U': U.flatten(),
'V': V.flatten(),
'Z': Z.flatten(),
'Q': Q.flatten(),
'logQ': logQ.flatten(),
'us': u.flatten(),
'vs': v.flatten(),
'zs': z.flatten(),
'qs_1e6': qs_1e6.flatten(),
'logqs_1e6': logqs_1e6.flatten(),
'rot': rot.flatten()})
df['updown'] = None
df.ix[df['zs'] > 0, 'updown'] = 'down'
df.ix[df['zs'] < 0, 'updown'] = 'up'
df['geometry'] = [Point(df.ix[i, 'x'], df.ix[i, 'y']) for i in df.index]
GISio.df2shp(df, os.path.join(path, 'cbb_arrows{}.shp'.format(l+1)), 'geometry', prj=PRJfile)
