Python Reader.record方法代码示例

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
def merge_shapes(inputfile, 
                 outputfile = None, 
                 overwrite = False, 
                 verbose = True, 
                 vverbose = False,
                 ):
    """
    Merges all the shapes in a shapefile into a single shape.
    """

    if outputfile is None: output = '{}/merged'.format(os.getcwd())

    if os.path.isfile(outputfile + '.shp') and not overwrite:
        if verbose: 
            print('combined watershed shapefile {} exists'.format(outputfile))
        return
   
    if verbose: print('combining shapes from {}\n'.format(inputfile) + 
                      'this may take a while...\n')

    # start by copying the projection files

    shutil.copy(inputfile + '.prj', outputfile + '.prj')

    # load the catchment and flowline shapefiles

    r = Reader(inputfile, shapeType = 5)

    try: 

        combined = combine_shapes(r.shapes(), verbose = vverbose)

    except:

        print('error: unable to combine shapes')
        raise

    # create the new file with the merged shapes

    w = Writer(shapeType = 5)

    w.poly(shapeType = 5, parts = [combined])

    # copy the fields from the original and then the first record; note this
    # can be adapted as needed

    for field in r.fields: w.field(*field)
    w.record(*r.record(0))

    w.save(outputfile)

    if verbose: 

        its = inputfile, outputfile
        print('successfully combined shapes from {} to {}\n'.format(*its))

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
def make_timeseries(directory, HUC8, start, end, evapstations = None, 
                    plot = True):
    """Makes an hourly timeseries of the reference evapotranspiration using
    the ASCE hourly Penman-Monteith Equation."""

    nrcm = '{}/{}/NRCM'.format(directory, HUC8)

    # start and end datetime instances

    s = datetime.datetime(start, 1, 1)
    e = datetime.datetime(end,   1, 1)

    # average the time series together from the NRCM simulation

    average_timeseries(nrcm)

    # open the watershed info to use to make subbasin precipitation

    watershedfile = '{}/{}/watershed'.format(directory, HUC8)

    with open(watershedfile, 'rb') as f: watershed = pickle.load(f)

    make_precipitation(watershed.subbasins, nrcm)

    # convert temperature and humidity to dewpoint

    make_dewpoint('{}/{}/NRCM/averages'.format(directory, HUC8))

    # open the 3-hr temperature, solar, and dewpoint, and daily wind files

    tempfile  = '{}/averages/average_temperature'.format(nrcm)
    solarfile = '{}/averages/average_solar'.format(nrcm)
    dewfile   = '{}/averages/average_dewpoint'.format(nrcm)
    windfile  = '{}/averages/average_wind'.format(nrcm)

    # watershed timeseries

    output = '{}/watershedtimeseries'.format(nrcm)

    if not os.path.isdir(output): os.mkdir(output)

    hourlytemp  = '{}/hourlytemperature'.format(output)
    hourlysolar = '{}/hourlysolar'.format(output)
    dailydew    = '{}/dewpoint'.format(output)
    dailywind   = '{}/wind'.format(output)
    hourlyRET   = '{}/hourlyRET'.format(output)
    hourlyPETs  = '{}/hourlyPETs'.format(output)
   
    if not os.path.isfile(hourlyRET):

        print('calculating an hourly time series for the reference ET...\n')

        # open the bounding box and get the mean lat, lon, and elevation

        f  = '{0}/{1}/{1}boundaries'.format(directory, HUC8)
        sh = Reader(f)

        record = sh.record(0)
        lon, lat, elev = record[-3:]

        with open(windfile,  'rb') as f: ts, Ws   = zip(*pickle.load(f))
        with open(tempfile,  'rb') as f: ts, Ts   = zip(*pickle.load(f))
        with open(solarfile, 'rb') as f: ts, Ss   = zip(*pickle.load(f))
        with open(dewfile,   'rb') as f: ts, dews = zip(*pickle.load(f))

        # dump the daily series

        with open(dailydew,  'wb') as f: 
            pickle.dump((s, 1440, list(dews)), f)
        with open(dailywind, 'wb') as f: 
            pickle.dump((s, 1440, list(Ws)), f)

        # dump all the hourly series and convert the solar radiation 
        # from Watts/m2 to MJ/hour/m2

        temp  = [T for T in Ts for i in range(3)]
        solar = [S for S in Ss for i in range(3)]

        with open(hourlysolar, 'wb') as f: pickle.dump((s, 60, solar), f)
        with open(hourlytemp,  'wb') as f: pickle.dump((s, 60, temp),  f)

        # convert to hourly numpy arrays

        temp     = numpy.array(temp)
        solar    = numpy.array(solar) * 3600 / 10**6
        wind     = numpy.array([w for w in Ws   for i in range(24)])
        dewpoint = numpy.array([T for T in dews for i in range(24)])

        # dates

        dates = [s + i * datetime.timedelta(hours = 1) 
                 for i in range(len(solar))]
 
        RET = penman_hourly(lat, lon, elev, dates, temp, dewpoint, solar, wind,
                            verbose = False)

        # dump the timeseries

        with open(hourlyRET, 'wb')   as f: pickle.dump((s, 60, RET), f)

#.........这里部分代码省略.........

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
    def plot_gage_subbasin(self, hspfmodel, folder):
        """Makes a plot of the subbasin area."""

        subbasinfile  = '{}/subbasins'.format(folder)
        boundaryfile  = '{}/boundary'.format(folder)
        flowfile      = '{}/flowlines'.format(folder)
        combinedfile  = '{}/combined'.format(folder)
        watershedplot = '{}/watershed.png'.format(folder)

        # make a shapefile of the subbasins for the watershed

        f = '{0}/{1}/{1}subbasins'.format(self.directory, self.HUC8)
        for out in (subbasinfile, boundaryfile, flowfile, combinedfile):
            if not os.path.isfile(out + '.prj'):
                shutil.copy(f + '.prj', out + '.prj')

        if not os.path.isfile(subbasinfile + '.shp'):

            subshapes  = []
            subrecords = []
            for subbasin in hspfmodel.subbasins:

                f = '{0}/{1}/{2}/combined'.format(self.directory, self.HUC8, 
                                                  subbasin)
                s = Reader(f, shapeType = 5)

                subshapes.append(s.shape(0).points)
                subrecords.append(s.record(0))

            w = Writer(shapeType = 5)

            for field in s.fields:    w.field(*field)
            for record in subrecords: w.record(*record)
            for shape in subshapes:   w.poly(shapeType = 5, parts = [shape])

            w.save(subbasinfile)

        if not os.path.isfile(combinedfile + '.shp'):

            fshapes    = []
            frecords   = []
            for subbasin in hspfmodel.subbasins:
                f = '{0}/{1}/{2}/combined_flowline'.format(self.directory, 
                                                           self.HUC8, 
                                                           subbasin)
                r = Reader(f, shapeType = 3)

                fshapes.append(r.shape(0).points)
                frecords.append(r.record(0))

            w = Writer(shapeType = 3)

            for field in r.fields:  w.field(*field)
            for record in frecords: w.record(*record)
            for shape in fshapes:   w.poly(shapeType = 3, parts = [shape])

            w.save(combinedfile)

        # merge the shapes into a watershed

        if not os.path.exists(boundaryfile + '.shp'):

            merge_shapes(subbasinfile, outputfile = boundaryfile)

        # make a flowline file for the subbasins for the watershed

        if not os.path.isfile(flowfile + '.shp'):

            shapes  = []
            records = []
            for subbasin in hspfmodel.subbasins:
                f = '{0}/{1}/{2}/flowlines'.format(self.directory, 
                                                   self.HUC8, subbasin)
                r = Reader(f, shapeType = 3)
                for shape  in r.shapes():  shapes.append(shape.points)
                for record in r.records(): records.append(record)

            w = Writer(shapeType = 3)

            for field in r.fields: w.field(*field)
            for record in records: w.record(*record)
            for shape in shapes:   w.poly(shapeType = 3, parts = [shape])

            w.save(flowfile)

        if not os.path.isfile(watershedplot):

            plot_gage_subbasin(folder, self.HUC8, self.gageid, hspfmodel,
                               output = watershedplot)

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
# result is the same as the previous example. as before, the subbasin_catchments
# shapefile will be used that contains the centroid for each aggregation.

sf = Reader(filename)

# index of the comid, latitude, and longitude records

comid_index = [f[0] for f in sf.fields].index('ComID') - 1
lon_index   = [f[0] for f in sf.fields].index('CenX')  - 1
lat_index   = [f[0] for f in sf.fields].index('CenY')  - 1

# iterate through the shapefile records and aggregate the timeseries

for i in range(len(sf.records())):

    record = sf.record(i)
    comid  = record[comid_index]
    lon    = record[lon_index]
    lat    = record[lat_index]

    i = comid, lon, lat
    print('aggregating timeseries for comid {} at {}, {}\n'.format(*i))

    precipitation = processor.aggregate('precip3240', 'precip', start, end,
                                        method = 'IDWA', longitude = lon,
                                        latitude = lat)

    mean = sum(precipitation) / (end - start).days * 365.25

    print('aggregated annual average precipitation: {:.1f} in\n'.format(mean))

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]

#.........这里部分代码省略.........
            ts = s, 60, climateprocessor.aggregate('NSRDB', 'metstat', s, e)
            with open(hsolar, 'wb') as f: pickle.dump(ts, f)
            
        # aggregate the hourly solar to daily

        dsolar = '{}/solar'.format(daily)
        if not os.path.isfile(dsolar):

            with open(hsolar, 'rb') as f: t, tstep, data = pickle.load(f)
            ts = s, 1440, [sum(data[i:i+24]) / 24 
                           for i in range(0, 24 * (e-s).days, 24)]

            with open(dsolar, 'wb') as f: pickle.dump(ts, f)

        # aggregate the hourly precipitation for each subbasin using IDWA

        precip = '{}/hourlyprecipitation'.format(climatedata)
        if not os.path.isdir(precip): os.mkdir(precip)

        # use the subbasin shapefile to get the location of the centroids

        sf = Reader(subbasinfile)

        # index of the comid, latitude, and longitude records

        comid_index = [f[0] for f in sf.fields].index('ComID') - 1
        lon_index   = [f[0] for f in sf.fields].index('CenX')  - 1
        lat_index   = [f[0] for f in sf.fields].index('CenY')  - 1
        elev_index  = [f[0] for f in sf.fields].index('AvgElevM') - 1
        area_index  = [f[0] for f in sf.fields].index('AreaSqKm') - 1

        # iterate through the shapefile records and aggregate the timeseries

        for i in range(len(sf.records())):

            record = sf.record(i)
            comid  = record[comid_index]
            lon    = record[lon_index]
            lat    = record[lat_index]

            # check if the aggregated time series exists or calculate it

            subbasinprecip = '{}/{}'.format(precip, comid)
            if not os.path.isfile(subbasinprecip):

                if verbose:
                    i = comid, lon, lat
                    print('aggregating timeseries for comid ' +
                          '{} at {}, {}\n'.format(*i))

                p = climateprocessor.aggregate('precip3240', 'precip', s, e,
                                               method = 'IDWA', 
                                               longitude = lon,
                                               latitude = lat)

                ts = s, 60, p
                with open(subbasinprecip, 'wb') as f: pickle.dump(ts, f)

        # make a directory for the evapotranspiration time series

        evapotranspiration = '{}/evapotranspiration'.format(climatedata)
        if not os.path.isdir(evapotranspiration): 
            os.mkdir(evapotranspiration)

        # use the ETCalculator to calculate the ET time series

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
    def extract_bbox(self, bbox, output, verbose = True):
        """Extracts the NID dam locations for a watershed from the dam 
        shapefile and the 8-digit hydrologic unit code of interest. 
        """

        self.download_compressed()

        xmin, ymin, xmax, ymax = bbox

        # copy the projection files

        if verbose: print('copying the projections from the NID source\n')

        projection = self.source + '.prj'

        shutil.copy(projection, output + '.prj')

        # get the dams within the watershed

        if verbose: print('reading the dam file\n')

        sf = Reader(self.source, shapeType = 1)

        # work around for issues with pyshp

        damrecords   = []
        for i in range(len(sf.shapes())):
            try: damrecords.append(sf.record(i))
            except: damrecords.append([-100 for i in range(len(sf.fields))])

        name_index  = sf.fields.index(['DAM_NAME',   'C', 65,   0]) - 1
        nid_index   = sf.fields.index(['NIDID',      'C', 7,    0]) - 1
        long_index  = sf.fields.index(['LONGITUDE',  'N', 19,  11]) - 1
        lat_index   = sf.fields.index(['LATITUDE',   'N', 19,  11]) - 1
        river_index = sf.fields.index(['RIVER',      'C', 65,   0]) - 1
        owner_index = sf.fields.index(['OWN_NAME',   'C', 65,   0]) - 1
        type_index  = sf.fields.index(['DAM_TYPE',   'C', 10,   0]) - 1
        purp_index  = sf.fields.index(['PURPOSES',   'C', 254,  0]) - 1
        year_index  = sf.fields.index(['YR_COMPL',   'C', 10,   0]) - 1
        high_index  = sf.fields.index(['NID_HEIGHT', 'N', 19,  11]) - 1
        mstor_index = sf.fields.index(['MAX_STOR',   'N', 19,  11]) - 1
        nstor_index = sf.fields.index(['NORMAL_STO', 'N', 19,  11]) - 1
        area_index  = sf.fields.index(['SURF_AREA',  'N', 19,  11]) - 1

        # iterate through the fields and determine which points are in the box

        if verbose: print('extracting dams into new file\n')

        dam_indices = []

        i = 0
        for record in damrecords:

            lat = record[lat_index]
            lon = record[long_index]

            if self.inside_box([xmin, ymin], [xmax, ymax], [lon, lat]):
                dam_indices.append(i)
            i+=1

        # write the data from the bbox to a new shapefile

        w = Writer(shapeType = 1)

        for field in sf.fields:  w.field(*field)

        for i in dam_indices:
            point = sf.shape(i).points[0]
            w.point(*point)

            values = damrecords[i]

            rs = []

            for value in values:

                if isinstance(value, bytes): value = value.decode('utf-8')
                rs.append(value)

            w.record(*rs)

        w.save(output)

        if verbose: 

            print('successfully extracted NID dam locations to new file\n')

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
    def calculate_landuse(self,
                          rasterfile,
                          shapefile,
                          aggregatefile,
                          attribute,
                          csvfile = None,
                          ):
        """
        Calculates the land use for the given year for the "attribute"
        feature attribute in the polygon shapefile using the aggregate
        mapping provided in the "aggregatefile."
        """

        # make sure the files exist

        for f in rasterfile, shapefile + '.shp', aggregatefile:
            if not os.path.isfile(f):
                print('error, {} does not exist\n'.format(f))
                raise

        # read the aggregate file

        self.read_aggregatefile(aggregatefile)

        # open the shapefile

        sf = Reader(shapefile, shapeType = 5)

        attributes = [f[0] for f in sf.fields]

        try:    index = attributes.index(attribute) - 1
        except:
            print('error: attribute ' +
                  '{} is not in the shapefile fields'.format(attribute))
            raise

        # iterate through the shapes, get the fractions and save them

        for i in range(len(sf.records())):

            points = numpy.array(sf.shape(i).points)
            record = sf.record(i)

            k = record[index]

            # store the results

            self.landuse[k] = {r:0 for r in self.order}

            try:

                values, origin = get_raster_in_poly(rasterfile, points,
                                                    verbose = False)
                values = values.flatten()
                values = values[values.nonzero()]

                tot_pixels = len(values)

                # count the number of pixels of each land use type

                for v in numpy.unique(values):

                    # find all the indices for each pixel value

                    pixels = numpy.argwhere(values == v)

                    # normalize by the total # of pixels

                    f = len(values[pixels]) / tot_pixels

                    # add the landuse to the aggregated value

                    self.landuse[k][self.groups[v]] += f

            # work around for small shapes

            except: self.landuse[k][self.groups[0]] = 1

        if csvfile is not None:  self.make_csv(attribute, csvfile)

        return self.landuse

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
    def plot_landuse(self,
                     landuse,
                     catchments,
                     attribute,
                     categoryfile,
                     output = None,
                     datatype = 'raw',
                     overwrite = False,
                     pixels = 1000,
                     border = 0.02,
                     lw = 0.5,
                     show = False,
                     verbose = True,
                     vverbose = False
                     ):
        """
        Makes a plot of the landuse of a catchment shapefile on top of a
        raster landuse file.
        """

        if self.order is None:
            print('error: no landuse aggregation file information provided\n')
            raise

        self.read_categoryfile(categoryfile)

        if verbose: print('generating a {} land use plot\n'.format(datatype))

        # make the figure

        fig = pyplot.figure()
        subplot = fig.add_subplot(111, aspect = 'equal')
        subplot.tick_params(axis = 'both', which = 'major', labelsize = 11)

        # add the title

        if datatype == 'results': title = 'Land Use Fractions'
        else:                     title = 'Raw Land Use Data'

        subplot.set_title(title, size = 14)

        # open the shapefile and get the bounding box

        s = Reader(catchments, shapeType = 5)

        xmin, ymin, xmax, ymax = s.bbox

        # get the index of the field for the attribute matching

        index = [f[0] for f in s.fields].index(attribute) - 1

        # set up a custom colormap using the rgbs supplied in the aggregate file

        color_table = [(self.reds[g] / 255, self.greens[g] / 255,
                        self.blues[g] / 255) for g in self.order]

        cmap = colors.ListedColormap(color_table)

        # provide the cutoff boundaries for the mapping of values to the table

        bounds = [i-0.5 for i in range(len(self.order)+1)]

        # create a norm to map the bounds to the colors

        norm = colors.BoundaryNorm(bounds, cmap.N)

        # get the pixel width and origin

        w = (xmax - xmin) / pixels

        # calculate the image array height and the height of a pixel

        height = int(numpy.ceil((ymax - ymin) / (xmax - xmin)) * pixels)
        h = (ymax - ymin) / height

        # set up the image array

        image_array = numpy.zeros((height, pixels), dtype = 'uint8')

        # get the land use fraction for each category

        if datatype == 'results':

            # iterate through the shapes and make patches

            for i in range(len(s.records())):
                comid = s.record(i)[index]
                points = numpy.array(s.shape(i).points)

                # convert the shape to pixel coordinates

                pixel_polygon = [(get_pixel(x, xmin, w), get_pixel(y, ymin, h))
                                 for x, y in points]

                # make a PIL image to use as a mask

                rasterpoly = Image.new('L', (pixels, height), 1)
                rasterize  = ImageDraw.Draw(rasterpoly)

                # rasterize the polygon
#.........这里部分代码省略.........

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
contains = [i for i, b in enumerate(bboxes)
            if b[0] <= x and x <= b[2] and b[0] <= y and y <= b[3]]

# find the distances between all the overlapping shapes points and the gage

distances = [min([(x1 - x)**2 + (y1 - y)**2 
                  for x1, y1 in reader.shape(i).points])
             for i in contains]

# find the shape with the point closest to the gage

closest = contains[distances.index(min(distances))]

# read the record for the flowline

record = reader.record(closest)

# find the record indices of the comid and reach length in km in the file

i = [f[0] for f in reader.fields].index('LENGTHKM') - 1
j = [f[0] for f in reader.fields].index('COMID') - 1

# get the reach length and common identifier

length = record[i]
comid  = record[j]

it = comid, length
print('comid {} is closest to the gage and has a length of {} km\n'.format(*it))

# make an instance of the FtableCalculator to use for the data from the file

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
def merge_shapes(inputfile, outputfile = None, overwrite = False, 
                 verbose = True, vverbose = False):
    """Merges all the shapes in a shapefile into a single shape."""

    if outputfile is None: output = '{}/merged'.format(os.getcwd())

    if os.path.isfile(outputfile + '.shp') and not overwrite:
        if verbose: print('combined watershed shapefile %s exists' % outputfile)
        return
   
    if verbose: print('combining shapes from {}\n'.format(inputfile) + 
                      'this may take a while...\n')

    # start by copying the projection files

    shutil.copy(inputfile + '.prj', outputfile + '.prj')

    # load the catchment and flowline shapefiles

    r = Reader(inputfile, shapeType = 5)
    n = len(r.records())

    try: 
        shapes  = []
        records = [] 
        bboxes  = []

        for i in range(n):
            shape = r.shape(i)
            record = r.record(i)

            shape_list = format_shape(shape.points)

            for sh in shape_list:
                shapes.append(sh)
                records.append(record)
                bboxes.append(shape.bbox)

                try: combined = combine_shapes(shapes, bboxes, 
                                               verbose = vverbose)
                except: 
                    if verbose: print('trying alternate trace method')
                    combined = combine_shapes(shapes, bboxes, skip = True, 
                                              verbose = vverbose)

    except:
        if verbose: print('trying alternate trace method')
        shapes  = []
        records = [] 
        bboxes  = []
        for i in range(n):
            shape = r.shape(i)
            record = r.record(i)

            shape_list = format_shape(shape.points, omit = True)

            for sh in shape_list:
                shapes.append(sh)
                records.append(record)
                bboxes.append(shape.bbox)

        try:    combined = combine_shapes(shapes, bboxes, verbose = vverbose)
        except: 
            if verbose: print('trying alternate trace method')
            combined = combine_shapes(shapes, bboxes, skip = True,
                                      verbose = vverbose)

    # create the new file with the merged shapes

    w = Writer(shapeType = 5)

    w.poly(shapeType = 5, parts = [combined])

    # copy the fields from the original and then the first record; note this
    # can be adapted as needed

    for field in r.fields: w.field(*field)
    w.record(*r.record(0))

    w.save(outputfile)

    if verbose: 
        print('successfully combined shapes from %s to %s\n' % 
              (inputfile, outputfile))

# 需要导入模块: from shapefile import Reader [as 别名]
# 或者: from shapefile.Reader import record [as 别名]
def extract_aquifers(directory, HUC8, aquifers, pad = 0.2, verbose = True):
    """Extracts aquifers from the source datafile to the destination using
    the HUC8 boundaries for the query."""

    start = time.time()

    # open up the HUC8 boundary shapefile and use it to get the bounding box

    shapefile = Reader(directory + '/%s/%scatchments' % (HUC8, HUC8))

    xmin, ymin, xmax, ymax = get_boundaries(shapefile.shapes())

    # convert to bounding corners for testing

    p1 = [xmin - pad * (xmax - xmin), ymin - pad * (ymax - ymin)]
    p2 = [xmax + pad * (xmax - xmin), ymax + pad * (ymax - ymin)]

    shapefile = None

    # start by copying the projection files

    if verbose: print('\ncopying the projections\n')

    shutil.copy(directory + '/%s/%scatchments.prj' % (HUC8, HUC8), 
                directory + '/%s/%saquifers.prj' % (HUC8, HUC8))

    # open the flowline file
    
    if verbose: print('reading the aquifer file\n')
    
    shapefile = Reader(aquifers, shapeType = 5)

    # work around for issues with pyshp

    records   = []
    for i in range(len(shapefile.shapes())):
        try: records.append(shapefile.record(i))
        except: records.append('')
     
    # use the bounding boxes to see if the shapes are within the watershed area

    if verbose: print('searching for aquifers in the watershed\n')

    bboxes = [shapefile.shape(i).bbox for i in range(len(records))]

    corners = [[[b[0], b[1]], [b[0], b[3]], [b[2], b[1]], [b[2], b[3]]]
               for b in bboxes]

    indices = [i for i, c in zip(range(len(corners)), corners) if 
               any([inside_box(p1, p2, p) for p in c]) or 
               all([inside_box(c[0], c[3], p1), inside_box(c[0], c[3], p2)])]

    # remove any non aquifers

    indices = [i for i in indices if shapefile.record(i)[4] != 999]

    # find a record for the non aquifer

    i = 0
    while shapefile.record(i)[4] != 999: i+=1

    nonrecord = shapefile.record(i)
    nonrecord[1] = nonrecord[1].decode('utf-8')
    nonrecord[5] = 0
    nonrecord[6] = 0

    if len(indices) == 0:
        if verbose: print('query returned no values, returning\n')
        return
    
    # write the data from the HUC8 to a new shapefile
    
    w = Writer(shapeType = 5)
    
    for field in shapefile.fields:  w.field(*field)
    
    for i in indices:
        shape = shapefile.shape(i)

        # check for multiple parts

        if len(shape.parts) > 1:
            parts = [shape.points[i:j] 
                     for i, j in zip(shape.parts[:-1], shape.parts[1:])]
        else: parts = [shape.points]

        record = records[i]
    
        # little work around for blank binary values
    
        if isinstance(record[1], bytes):
            record[1] = record[1].decode('utf-8')

        w.poly(shapeType = 5, parts = parts)
        w.record(*record)

    # add a shape for the bounding box showing no aquifer locations

    part = [p1, [p1[0], p2[1]], p2, [p2[0], p1[1]]]

#.........这里部分代码省略.........