<reponame>ASRCsoft/skykit
'''
Extensions of xarray Datasets and DataArrays
'''
import copy as cp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import wxprofilers.utils as rasp
import wxprofilers._cape as cape

# rewriting as an xarray module, following the guidelines here:
# http://xarray.pydata.org/en/stable/internals.html#extending-xarray
import xarray as xr

@xr.register_dataset_accessor('rasp')
class ProfileDataset(object):
    def __init__(self, xarray_obj):
        self._obj = xarray_obj

    def estimate_wind_leosphere(self, rws='RWS', filter='Status', los='LOS', sequence='Sequence'):
        if ((filter is not None and self._obj[filter] is None) or self._obj[rws] is None or los not in self._obj.coords.keys()):
            # raise an error
            pass

        # elevation
        el = float(self._obj.attrs['scan_elevation_angle_deg']) * np.pi / 180

        # I want to get NA values for all missing sequences and
        # LOS's. The los_format does that automatically, so using it
        # here
        lidar = self._obj.rasp.los_format(sequence=sequence)

        # now flatten back down-- we will have NA's in the right
        # places now
        lidar = lidar.stack(profile=['scan', los]).reset_index('profile').swap_dims({'profile': 'Time'})
        rws_mat = lidar['RWS'].values.transpose()

        if filter is not None:
            # set filter to False if filter value is NA
            lidar[filter] = (['Time', 'Range'],
                             np.where(pd.isnull(lidar[filter]), False, lidar[filter]).transpose())
            status_mat = lidar[filter]
            # the locations where 5 status=1 measurements were taken
            # in a row
            is_good = np.stack([status_mat[0:-4,:], status_mat[1:-3,:], status_mat[2:-2,:],
                                status_mat[3:-1,:], status_mat[4:,:]]).all(axis=0)
        else:
            is_good = np.full((lidar.dims['Time'] - 4, lidar.dims['Range']), True, bool)

        good_indices = np.where(is_good)
        # add 4 columns for the 4 removed earlier
        good_indices = (good_indices[0] + 4, good_indices[1])
        los_ids = lidar.coords[los].astype(int).values
        row_los = los_ids[good_indices[0]]
        # return row_los
        rws_cols = lidar.coords['Range'].values
        good_cols = rws_cols[good_indices[1]]

        # create a multiIndex data frame to hold the data
        iterables = [['x', 'y', 'z'], rws_cols]
        mult_index = pd.MultiIndex.from_product(iterables, names=['Component', 'Range'])
        time_index = lidar.coords['Time'].to_index()
        winds = pd.DataFrame(index=time_index, columns=mult_index, dtype='float')
        rws_df = pd.DataFrame(rws_mat, index=time_index, columns=rws_cols)

        # get the corresponding LOS values for each wind estimate
        los0 = rws_df.lookup(rws_df.index[good_indices[0] - row_los], good_cols)
        los1 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 4) % 5)], good_cols)
        los2 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 3) % 5)], good_cols)
        los3 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 2) % 5)], good_cols)
        los4 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 1) % 5)], good_cols)
        # # get corresponding elevations-- no, don't do this for now,
        # # Leosphere doesn't seem to be doing this
        # elevations = lidar.coords['Elevation'].values * np.pi / 180
        # l0_el = elevations[good_indices[0] - row_los]
        # l1_el = elevations[good_indices[0] - ((row_los + 4) % 5)]
        # l2_el = elevations[good_indices[0] - ((row_los + 3) % 5)]
        # l3_el = elevations[good_indices[0] - ((row_los + 2) % 5)]
        # l4_el = elevations[good_indices[0] - ((row_los + 1) % 5)]
        
        # got these weights from a maximum likelihood calculation
        weight0to3 = np.sin(el) / (4 * np.sin(el) ** 2 + 1)
        weight4 = 1 / (4 * np.sin(el) ** 2 + 1)
        xs = (los0 - los2) / (2 * np.cos(el))
        ys = (los1 - los3) / (2 * np.cos(el))
        zs = weight0to3 * (los0 + los1 + los2 + los3) + weight4 * los4

        # put the results in the right place
        for col in range(self._obj[rws].shape[1]):
            col_indices = np.where(good_indices[1] == col)
            rows = good_indices[0][col_indices]
            # swapping x/y and negative to get American coordinates!!:
            winds.ix[rows, ('x', rws_cols[col])] = -ys[col_indices]
            winds.ix[rows, ('y', rws_cols[col])] = -xs[col_indices]
            winds.ix[rows, ('z', rws_cols[col])] = -zs[col_indices]

        # only take the time indices that were in the original xarray object
        winds_orig = winds.loc[self._obj.coords['Time']]
        windxr = xr.DataArray(winds_orig).unstack('dim_1')
        return windxr

    def estimate_wind(self, method='Leosphere', **kwargs):
        if method=='Leosphere':
            return self.estimate_wind_leosphere(**kwargs)
        elif method=='discrete':
            self.estimate_wind_discrete(**kwargs)
        else:
            pass

    def _estimate_cape(self, hpascals='hpascals', temp='Temperature'):
        hpascals = self._obj.coords[hpascals].values
        tempK = self._obj[temp].values
        tempC = tempK - 273.15
        rel_hum = self._obj['Relative Humidity'].values
        dewpoints = tempK - ((100 - rel_hum) / 5) - 273.15
        return cape.getcape(nk=self._obj.dims['Range'], p_in=hpascals, t_in=tempC, td_in=dewpoints)[0]

    def estimate_cape(self, hpascals='hpascals', temp='Temperature'):
        ntimes = len(self._obj.coords['Time'].values)
        cape = [ self._obj.isel(**{'Time': t}).rasp._estimate_cape() for t in range(ntimes) ]
        return xr.DataArray(cape, {'Time': self._obj.coords['Time']})
        # self
        # hpascals = self._obj.coords['hpascals'].values
        # tempK = self._obj['Temperature'].values
        # tempC = tempK - 273.15
        # rel_hum = self._obj['Relative Humidity'].values
        # dewpoints = tempK - ((100 - rel_hum) / 5) - 273.15
        # return cape.getcape(nk=self._obj.dims['Range'], p_in=hpascals, t_in=tempC, td_in=dewpoints)[0]

    def write_raob(self, time, filename, timedim='Time', temp='Temperature', rh='Relative Humidity',
                  vap_den=None, liq_wat=None, wind=True, wspeed=True, line_terminator='\n'):

        # set up the header information
        header_lines = []
        #header_lines.append('RAOB/CSV, ' + 'Example Data from microwave radiometer and lidar')
        #time_str = time.strftime('%Y-%m-%d %H:%M:%S')
        time_str = pd.to_datetime(time).strftime('%Y-%m-%d %H:%M:%S')
        header_lines.append('RAOB/CSV, ' + time_str)
        # header_lines.append('INFO:1, ' + 'First line of freeform text')
        # header_lines.append('INFO:2, ' + 'Another freeform text line')
        header_lines.append('DTG, ' + time_str)
        if 'latitude' in self._obj.attrs.keys(): header_lines.append('LAT, ' + str(self._obj.attrs['latitude']) + ', N')
        if 'longitude' in self._obj.attrs.keys(): header_lines.append('LON, ' + str(self._obj.attrs['longitude']) + ', W')
        if 'elevation' in self._obj.attrs.keys(): header_lines.append('ELEV, ' + str(self._obj.attrs['elevation']) + ', M')
        # skip for now
        # header_lines.append('WMO, 12345')
        header_lines.append('TEMPERATURE, K')
        header_lines.append('MOISTURE, RH')
        # skip for now
        if wind: header_lines.append('WIND, m/s U/V')
        # wind height type is 'Above Ground Level' for now
        header_lines.append('GPM, AGL')
        header_lines.append('MISSING, -999')
        # what?
        # header_lines.append('SORT, YES')
        # in case of ozone:
        # header_lines.append('OZONE, mPa')
        # not now:
        # header_lines.append('EXTRA#1, Extra#1Name, Extra#1Units')
        # header_lines.append('EXTRA#2, Extra#2Name, Extra#2Units')
        # header_lines.append('SCALAR#1, Scalar#1Name, Scalar#1Value, Scalar#1Units')
        # header_lines.append('SCALAR#2, Scalar#2Name, Scalar#2Value, Scalar#2Units')
        header_lines.append('RAOB/DATA' + line_terminator)
        header = line_terminator.join(header_lines)

        # set up the data (making sure to round as required)
        levels = self._obj.coords['Range'].values
        if wind:
            columns = ['PRES', 'TEMP', 'RH', 'UU', 'VV', 'GPM']
        else:
            columns = ['PRES', 'TEMP', 'RH', 'WIND', 'SPEED', 'GPM']
        df = pd.DataFrame(index=levels, columns=columns)
        df['TEMP'] = np.round(self._obj[temp].sel(**{timedim: time}), 1)
        df['RH'] = np.round(self._obj[rh].sel(**{timedim: time}), 1)
        # df['WIND'] = np.nan
        # df['SPEED'] = np.nan
        df['GPM'] = list(map(int, np.round(1000 * levels)))
        df['PRES'] = np.round(1013.25 * np.exp(-df['GPM'] / 7000), 1)
        if vap_den is not None: df['VapDen'] = np.round(self._obj[vap_den].sel(**{timedim: time}), 3)
        if liq_wat is not None: df['LiqWat'] = np.round(self._obj[liq_wat].sel(**{timedim: time}), 3)
        #if self.wind is not None:
        if wind:
            df['UU'] = np.round(self._obj['Windspeed'].sel(**{timedim: time, 'Component': 'x'}), 1)
            df['VV'] = np.round(self._obj['Windspeed'].sel(**{timedim: time, 'Component': 'y'}), 1)
        if wspeed:
            df['WSPEED'] = np.round(self._obj['Windspeed'].sel(**{timedim: time, 'Component': 'z'}), 1)

        # print(df)

        roab_file = open(filename, "w")
        roab_file.writelines(header)
        roab_file.close()
        # print(df.head())
        # exit()
        df.to_csv(filename, na_rep=-999, index=False, mode='a', line_terminator=line_terminator)

    def nd_resample(self, rule, coord, dim, **kwargs):
        coords = list(self._obj.coords[coord].dims)
        coords.remove(dim)
        return rasp.recursive_resample(self._obj, rule, coord, dim, coords, **kwargs)

    def split_array(self, array, dim):
        ds = xr.Dataset()
        for value in self._obj.coords[dim].values:
            ds[value] = self._obj[array].sel(**{dim: value}).drop(dim)
        ds = xr.merge([self._obj, ds]).drop(array)
        return ds

    def los_format(self, los='LOS', sequence='Sequence', replace_nat=True):
        # switch to LOS format and print the new xarray object

        lidar2 = self._obj.copy()
        has_sequence = sequence is not None and sequence in lidar2.keys()
        # create 'scan' coordinate based on decreasing LOS ID (and
        # changing sequence ID if available)
        los_diff = (lidar2.coords[los][1:].values -
                    lidar2.coords[los][:-1].values)
        if has_sequence:
            seq_diff = (lidar2.coords[sequence][1:].values -
                        lidar2.coords[sequence][:-1].values)
            scan = np.cumsum((los_diff <= 0) | (seq_diff != 0))
        else:
            scan = np.cumsum(los_diff <= 0)
        # add the first scan, which is obviously zero
        scan = np.insert(scan, 0, 0)
        
        lidar2.coords['scan'] = ('Time', scan)
        lidar2 = lidar2.set_index(profile=['scan', los])
        lidar2.coords['profile'] = ('Time', lidar2.coords['profile'].to_index())
        lidar2.swap_dims({'Time': 'profile'}, inplace=True)
        lidar2 = lidar2.unstack('profile')
        # fill in missing sequences
        # all_sequences = np.arange(lidar2.coords[sequence].min(),
        #                           lidar2.coords[sequence].max())
        # lidar2.reindex({'Sequence': all_sequences})

        # fix the sequence and configuration numbers
        if has_sequence:
            seq2 = lidar2[sequence].sel(LOS=0)[1:]
            seq2 = np.insert(seq2, 0, lidar2[sequence].sel(scan=0, LOS=lidar2.dims[los] - 1))
            lidar2.coords[sequence] = ('scan', seq2.astype(int))
        if 'Configuration' in lidar2.keys():
            conf2 = lidar2['Configuration'].sel(LOS=0)[1:]
            conf2 = np.insert(conf2, 0, lidar2['Configuration'].sel(scan=0, LOS=lidar2.dims[los] - 1))
            lidar2.coords['Configuration'] = ('scan', conf2.astype(int))
        if replace_nat:
            lidar2.rasp.replace_nat()

        return lidar2

    def replace_nat(self, timedim='Time'):
        max_los = self._obj.coords['LOS'].max()
        # get all missing except for the last scan
        missing = np.where(pd.isnull(self._obj.coords[timedim][0:-1, :]))
        # replace missing times with the next time:
        for n in reversed(range(len(missing[0]))):
            # index contains scan, then LOS ID
            index = (missing[0][n], missing[1][n])
            if index[1] == max_los:
                next_index = (index[0] + 1, 0)
            else:
                next_index = (index[0], index[1] + 1)
            next_time = self._obj.coords[timedim].values[next_index[0], next_index[1]]
            if not pd.isnull(next_time):
                self._obj.coords[timedim][index[0], index[1]] = next_time - pd.Timedelta('1us')

        # this will leave some times still missing at the end
        # fix the remaining missing times by replacing them with the previous time:
        missing = np.where(pd.isnull(self._obj.coords[timedim]))
        for n in range(len(missing[0])):
            index = (missing[0][n], missing[1][n])
            if index[1] == 0:
                prev_index = (index[0] - 1, max_los)
            else:
                prev_index = (index[0], index[1] - 1)
            prev_time = self._obj.coords[timedim].values[prev_index[0], prev_index[1]]
            if not pd.isnull(prev_time):
                self._obj.coords[timedim][index[0], index[1]] = prev_time + pd.Timedelta('1us')

    def remove_where(self, arrays, logarr):
        for array in arrays:
            self._obj[array].values = np.where(logarr, np.nan, self._obj[array])

    def skewt(self, ranges='Range', temp='Temperature', dewpoint=None, rel_hum='Relative Humidity',
              temp_units='K', wind=None, **kwargs):
        from metpy.plots import SkewT
        if not 'col' in kwargs.keys() and not 'row' in kwargs.keys():
            # get unused dimensions
            unused = list(self._obj[temp].dims)
            if ranges in unused:
                unused.remove(ranges)
            # convert range (m) to hectopascals
            hpascals = 1013.25 * np.exp(-self._obj.coords[ranges] / 7)
            # return hpascals
            #return hpascals
            # convert temperature from Kelvins to Celsius
            #tempC = self._obj[temp] - 273.15
            if temp_units == 'K':
                tempK = self._obj[temp].drop(unused)
                tempC = tempK - 273.15
            else:
                tempC = self._obj[temp].drop(unused)
                tempK = tempC + 273.15

            if dewpoint is None:
                # estimate dewpoint from relative humidity
                dewpoints = tempK - ((100 - self._obj[rel_hum].drop(unused)) / 5) - 273.15
            else:
                dewpoints = self._obj[dewpoint].drop(unused)

            skew = SkewT()
            #return tempC
            skew.plot(hpascals, tempC, 'r')
            skew.plot(hpascals, dewpoints, 'g')
            skew.plot_dry_adiabats()
            skew.plot_moist_adiabats()
            if not wind is None:
                u = self._obj[wind].sel(Component='x').drop(unused)
                v = self._obj[wind].sel(Component='y').drop(unused)
                skew.plot_barbs(hpascals, u, v, xloc=.9)
            # skew.plot_mixing_lines()
            # skew.ax.set_ylim(1100, 100)
        else:
            if not wind is None:
                skewtdat = xr.concat([self._obj['Temperature'], self._obj['Relative Humidity'],
                                      self._obj[wind].sel(Component='x').drop('Component'),
                                      self._obj[wind].sel(Component='y').drop('Component')],
                                     'measure')
                skewtdat.coords['measure'] = ['Temperature', 'Relative Humidity', 'windx', 'windy']
            else:
                skewtdat = xr.concat([self._obj['Temperature'], self._obj['Relative Humidity']], 'measure')
                skewtdat.coords['measure'] = ['Temperature', 'Relative Humidity']

            # skewtdat
            sk1 = xr.plot.FacetGrid(skewtdat, **kwargs)
            #return sk1
            # need to make the subplot tuples

            for ax in sk1.axes.flat:
                ax.axis('off')

            #return sk1.axes.flat
            #return len(sk1.axes.flat)
            splots = range(len(sk1.axes.flat))
            #return splots
            splot_dims = sk1.axes.shape
            splot_tuples = []
            for i in splots:
                splot_tuples.append((splot_dims[0], splot_dims[1], i + 1))

            if not wind is None:
                sk1.map(rasp.skewt, [0, 1, 2, 3], splots=splot_tuples, ranges=skewtdat.coords['Range'].values)
            else:
                sk1.map(rasp.skewt, [0, 1], splots=splot_tuples, ranges=skewtdat.coords['Range'].values)

    def cf_compliant(self, period='5T', shear=False):
        """Return a dataset in cf compliant netcdf format"""
        # lidar = rasp.lidar_from_csv(radial_file, scan_file, wind=wind_file)
        ds = cp.copy(self._obj)
        # remove status==0 data
        # lidar['CNR'] = lidar['CNR'].where(lidar['Status'])
        # lidar['DRWS'] = lidar['DRWS'].where(lidar['Status'])
        # lidar = lidar.drop(['Status', 'Error', 'Confidence'])
        has_wind = 'Windspeed' in ds.data_vars
        if has_wind:
            # set up the windspeed variables
            ds['xwind'] = ds['Windspeed'].sel(Component='x').drop('Component')
            ds['xwind'].attrs.pop('long_name', None)
            ds['xwind'].attrs['standard_name'] = 'eastward_wind'
            ds['xwind'].attrs['units'] = 'm s-1'
            ds['ywind'] = ds['Windspeed'].sel(Component='y').drop('Component')
            ds['ywind'].attrs.pop('long_name', None)
            ds['ywind'].attrs['standard_name'] = 'northward_wind'
            ds['ywind'].attrs['units'] = 'm s-1'
            ds['vwind'] = ds['Windspeed'].sel(Component='z').drop('Component')
            ds['vwind'].attrs.pop('long_name', None)
            ds['vwind'].attrs['standard_name'] = 'upward_air_velocity'
            ds['vwind'].attrs['units'] = 'm s-1'
        ds = ds.drop('Windspeed').drop('Component')
        ds = ds.resample(period, 'Time', keep_attrs=True)
        # do more stuff after resampling
        if has_wind:
            if shear:
                # wind shear (works better after resampling?)

                # first add horizontal wind
                ds['hwind'] = np.sqrt(ds['xwind'] ** 2 + ds['ywind'] ** 2)
                ds['hwind'].attrs['standard_name'] = 'upward_air_velocity'
                ds['hwind'].attrs['units'] = 'm s-1'
                # now can get the shear
                ds['shear'] = ds['hwind'].copy()
                ds['shear'][:,1:] = ds['hwind'][:, 1:].values / ds['hwind'][:, :-1].values
                ds['shear'][:,0] = np.nan
                ds['shear'].attrs['standard_name'] = 'wind_speed_shear'
                ds['shear'].attrs['units'] = 's-1'
        return ds


@xr.register_dataarray_accessor('rasp')
class RaspAccessor(object):
    def __init__(self, xarray_obj):
        self._obj = xarray_obj

    def nd_resample(self, rule, coord, dim):
        coords = list(self._obj.coords[coord].dims)
        coords.remove(dim)
        return rasp.recursive_resample(self._obj, rule, coord, dim, coords)

    def plot_barbs(self, x=None, y=None, components=('x', 'y'), resample=None, resampley=None, ax=None, **kwargs):
        if x is None:
            x = 'Time'
        if resample is not None:
            l1 = self._obj.resample(resample, x)
        else:
            l1 = self._obj
        if y is None:
            y = 'Range'
        if resampley is not None:
            maxy = l1.coords[y].max()
            nbreaks = int(maxy / resampley) + 2
            bins = np.array(range(nbreaks)) * [resampley]
            labels = (bins[1:] + bins[:-1]) / 2
            l1 = l1.groupby_bins(group=y, bins=bins, labels=labels).mean(dim=y)
            bin_name = y + '_bins'
            l1 = l1.rename({bin_name: y})
        xvals = [mdates.date2num(pd.Timestamp(val)) for val in l1.coords[x].values]
        yvals = l1.coords[y]
        X, Y = np.meshgrid(xvals, yvals)
        U = l1.sel(Component=components[0])
        V = l1.sel(Component=components[1])
        if len(U.dims) > 2:
            U = U.squeeze()
        if len(V.dims) > 2:
            V = V.squeeze()
        # there should only be two dimensions!
        assert len(U.dims) == 2
        assert len(V.dims) == 2
        # y values should be the first dimension
        if U.dims[0] != y:
            U = U.transpose()
        if V.dims[0] != y:
            V = V.transpose()
        new_axis = ax is None
        if new_axis:
            ax = plt.subplot(111)
            xtick_locator = mdates.AutoDateLocator()
            xtick_formatter = mdates.AutoDateFormatter(xtick_locator)
        ax.barbs(X, Y, U, V, **kwargs)
        if new_axis:
            ax.set_ylabel(y)
            ax.xaxis.set_major_locator(xtick_locator)
            ax.xaxis.set_major_formatter(xtick_formatter)

    def plot_profile(self, y=None, ax=None, **kwargs):
        if y is None:
            dimname = self._obj.dims[0]
        else:
            dimname = y
        ys = self._obj[dimname].values
        xs = self._obj#.values#.transpose()
        if self._obj.dims[0] != dimname:
            dims_order = [dimname]
            for d in self._obj.dims:
                if d != dimname:
                    dims_order.append(d)
            xs = xs.transpose(*dims_order)
        if ax is None:
            ax = plt.gca()
        ax.plot(xs.values, ys, **kwargs)
        ax.set_xlabel(self._obj.name)
        ax.set_ylabel(dimname)
        # if len(xs.dims) > 1:
        #     dim2 = xs.dims[1]
        #     labels = [ dim2 + ' = ' + str(x) for x in xs.coords[dim2].values ]
        #     plt.legend(labels, loc=2)
        # if legend:
        #     # plt.legend(lines, self.index)
        #     ax.legend(self.index)

    def _plot_profile(self, ax=None, **kwargs):
        fgr = xr.plot.FacetGrid(skewtdat, **kwargs)

    def remove_where(self, logarr, inplace=False):
        if inplace:
            self._obj.values = xr.DataArray(np.where(logarr, np.nan, self._obj))
        else:
            return xr.DataArray(np.where(logarr, np.nan, self._obj), dims=self._obj.dims)

'''
Extensions of xarray Datasets and DataArrays
'''
import copy as cp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import wxprofilers.utils as rasp
import wxprofilers._cape as cape

# rewriting as an xarray module, following the guidelines here:
# http://xarray.pydata.org/en/stable/internals.html#extending-xarray
import xarray as xr

@xr.register_dataset_accessor('rasp')
class ProfileDataset(object):
    def __init__(self, xarray_obj):
        self._obj = xarray_obj

    def estimate_wind_leosphere(self, rws='RWS', filter='Status', los='LOS', sequence='Sequence'):
        if ((filter is not None and self._obj[filter] is None) or self._obj[rws] is None or los not in self._obj.coords.keys()):
            # raise an error
            pass

        # elevation
        el = float(self._obj.attrs['scan_elevation_angle_deg']) * np.pi / 180

        # I want to get NA values for all missing sequences and
        # LOS's. The los_format does that automatically, so using it
        # here
        lidar = self._obj.rasp.los_format(sequence=sequence)

        # now flatten back down-- we will have NA's in the right
        # places now
        lidar = lidar.stack(profile=['scan', los]).reset_index('profile').swap_dims({'profile': 'Time'})
        rws_mat = lidar['RWS'].values.transpose()

        if filter is not None:
            # set filter to False if filter value is NA
            lidar[filter] = (['Time', 'Range'],
                             np.where(pd.isnull(lidar[filter]), False, lidar[filter]).transpose())
            status_mat = lidar[filter]
            # the locations where 5 status=1 measurements were taken
            # in a row
            is_good = np.stack([status_mat[0:-4,:], status_mat[1:-3,:], status_mat[2:-2,:],
                                status_mat[3:-1,:], status_mat[4:,:]]).all(axis=0)
        else:
            is_good = np.full((lidar.dims['Time'] - 4, lidar.dims['Range']), True, bool)

        good_indices = np.where(is_good)
        # add 4 columns for the 4 removed earlier
        good_indices = (good_indices[0] + 4, good_indices[1])
        los_ids = lidar.coords[los].astype(int).values
        row_los = los_ids[good_indices[0]]
        # return row_los
        rws_cols = lidar.coords['Range'].values
        good_cols = rws_cols[good_indices[1]]

        # create a multiIndex data frame to hold the data
        iterables = [['x', 'y', 'z'], rws_cols]
        mult_index = pd.MultiIndex.from_product(iterables, names=['Component', 'Range'])
        time_index = lidar.coords['Time'].to_index()
        winds = pd.DataFrame(index=time_index, columns=mult_index, dtype='float')
        rws_df = pd.DataFrame(rws_mat, index=time_index, columns=rws_cols)

        # get the corresponding LOS values for each wind estimate
        los0 = rws_df.lookup(rws_df.index[good_indices[0] - row_los], good_cols)
        los1 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 4) % 5)], good_cols)
        los2 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 3) % 5)], good_cols)
        los3 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 2) % 5)], good_cols)
        los4 = rws_df.lookup(rws_df.index[good_indices[0] - ((row_los + 1) % 5)], good_cols)
        # # get corresponding elevations-- no, don't do this for now,
        # # Leosphere doesn't seem to be doing this
        # elevations = lidar.coords['Elevation'].values * np.pi / 180
        # l0_el = elevations[good_indices[0] - row_los]
        # l1_el = elevations[good_indices[0] - ((row_los + 4) % 5)]
        # l2_el = elevations[good_indices[0] - ((row_los + 3) % 5)]
        # l3_el = elevations[good_indices[0] - ((row_los + 2) % 5)]
        # l4_el = elevations[good_indices[0] - ((row_los + 1) % 5)]
        
        # got these weights from a maximum likelihood calculation
        weight0to3 = np.sin(el) / (4 * np.sin(el) ** 2 + 1)
        weight4 = 1 / (4 * np.sin(el) ** 2 + 1)
        xs = (los0 - los2) / (2 * np.cos(el))
        ys = (los1 - los3) / (2 * np.cos(el))
        zs = weight0to3 * (los0 + los1 + los2 + los3) + weight4 * los4

        # put the results in the right place
        for col in range(self._obj[rws].shape[1]):
            col_indices = np.where(good_indices[1] == col)
            rows = good_indices[0][col_indices]
            # swapping x/y and negative to get American coordinates!!:
            winds.ix[rows, ('x', rws_cols[col])] = -ys[col_indices]
            winds.ix[rows, ('y', rws_cols[col])] = -xs[col_indices]
            winds.ix[rows, ('z', rws_cols[col])] = -zs[col_indices]

        # only take the time indices that were in the original xarray object
        winds_orig = winds.loc[self._obj.coords['Time']]
        windxr = xr.DataArray(winds_orig).unstack('dim_1')
        return windxr

    def estimate_wind(self, method='Leosphere', **kwargs):
        if method=='Leosphere':
            return self.estimate_wind_leosphere(**kwargs)
        elif method=='discrete':
            self.estimate_wind_discrete(**kwargs)
        else:
            pass

    def _estimate_cape(self, hpascals='hpascals', temp='Temperature'):
        hpascals = self._obj.coords[hpascals].values
        tempK = self._obj[temp].values
        tempC = tempK - 273.15
        rel_hum = self._obj['Relative Humidity'].values
        dewpoints = tempK - ((100 - rel_hum) / 5) - 273.15
        return cape.getcape(nk=self._obj.dims['Range'], p_in=hpascals, t_in=tempC, td_in=dewpoints)[0]

    def estimate_cape(self, hpascals='hpascals', temp='Temperature'):
        ntimes = len(self._obj.coords['Time'].values)
        cape = [ self._obj.isel(**{'Time': t}).rasp._estimate_cape() for t in range(ntimes) ]
        return xr.DataArray(cape, {'Time': self._obj.coords['Time']})
        # self
        # hpascals = self._obj.coords['hpascals'].values
        # tempK = self._obj['Temperature'].values
        # tempC = tempK - 273.15
        # rel_hum = self._obj['Relative Humidity'].values
        # dewpoints = tempK - ((100 - rel_hum) / 5) - 273.15
        # return cape.getcape(nk=self._obj.dims['Range'], p_in=hpascals, t_in=tempC, td_in=dewpoints)[0]

    def write_raob(self, time, filename, timedim='Time', temp='Temperature', rh='Relative Humidity',
                  vap_den=None, liq_wat=None, wind=True, wspeed=True, line_terminator='\n'):

        # set up the header information
        header_lines = []
        #header_lines.append('RAOB/CSV, ' + 'Example Data from microwave radiometer and lidar')
        #time_str = time.strftime('%Y-%m-%d %H:%M:%S')
        time_str = pd.to_datetime(time).strftime('%Y-%m-%d %H:%M:%S')
        header_lines.append('RAOB/CSV, ' + time_str)
        # header_lines.append('INFO:1, ' + 'First line of freeform text')
        # header_lines.append('INFO:2, ' + 'Another freeform text line')
        header_lines.append('DTG, ' + time_str)
        if 'latitude' in self._obj.attrs.keys(): header_lines.append('LAT, ' + str(self._obj.attrs['latitude']) + ', N')
        if 'longitude' in self._obj.attrs.keys(): header_lines.append('LON, ' + str(self._obj.attrs['longitude']) + ', W')
        if 'elevation' in self._obj.attrs.keys(): header_lines.append('ELEV, ' + str(self._obj.attrs['elevation']) + ', M')
        # skip for now
        # header_lines.append('WMO, 12345')
        header_lines.append('TEMPERATURE, K')
        header_lines.append('MOISTURE, RH')
        # skip for now
        if wind: header_lines.append('WIND, m/s U/V')
        # wind height type is 'Above Ground Level' for now
        header_lines.append('GPM, AGL')
        header_lines.append('MISSING, -999')
        # what?
        # header_lines.append('SORT, YES')
        # in case of ozone:
        # header_lines.append('OZONE, mPa')
        # not now:
        # header_lines.append('EXTRA#1, Extra#1Name, Extra#1Units')
        # header_lines.append('EXTRA#2, Extra#2Name, Extra#2Units')
        # header_lines.append('SCALAR#1, Scalar#1Name, Scalar#1Value, Scalar#1Units')
        # header_lines.append('SCALAR#2, Scalar#2Name, Scalar#2Value, Scalar#2Units')
        header_lines.append('RAOB/DATA' + line_terminator)
        header = line_terminator.join(header_lines)

        # set up the data (making sure to round as required)
        levels = self._obj.coords['Range'].values
        if wind:
            columns = ['PRES', 'TEMP', 'RH', 'UU', 'VV', 'GPM']
        else:
            columns = ['PRES', 'TEMP', 'RH', 'WIND', 'SPEED', 'GPM']
        df = pd.DataFrame(index=levels, columns=columns)
        df['TEMP'] = np.round(self._obj[temp].sel(**{timedim: time}), 1)
        df['RH'] = np.round(self._obj[rh].sel(**{timedim: time}), 1)
        # df['WIND'] = np.nan
        # df['SPEED'] = np.nan
        df['GPM'] = list(map(int, np.round(1000 * levels)))
        df['PRES'] = np.round(1013.25 * np.exp(-df['GPM'] / 7000), 1)
        if vap_den is not None: df['VapDen'] = np.round(self._obj[vap_den].sel(**{timedim: time}), 3)
        if liq_wat is not None: df['LiqWat'] = np.round(self._obj[liq_wat].sel(**{timedim: time}), 3)
        #if self.wind is not None:
        if wind:
            df['UU'] = np.round(self._obj['Windspeed'].sel(**{timedim: time, 'Component': 'x'}), 1)
            df['VV'] = np.round(self._obj['Windspeed'].sel(**{timedim: time, 'Component': 'y'}), 1)
        if wspeed:
            df['WSPEED'] = np.round(self._obj['Windspeed'].sel(**{timedim: time, 'Component': 'z'}), 1)

        # print(df)

        roab_file = open(filename, "w")
        roab_file.writelines(header)
        roab_file.close()
        # print(df.head())
        # exit()
        df.to_csv(filename, na_rep=-999, index=False, mode='a', line_terminator=line_terminator)

    def nd_resample(self, rule, coord, dim, **kwargs):
        coords = list(self._obj.coords[coord].dims)
        coords.remove(dim)
        return rasp.recursive_resample(self._obj, rule, coord, dim, coords, **kwargs)

    def split_array(self, array, dim):
        ds = xr.Dataset()
        for value in self._obj.coords[dim].values:
            ds[value] = self._obj[array].sel(**{dim: value}).drop(dim)
        ds = xr.merge([self._obj, ds]).drop(array)
        return ds

    def los_format(self, los='LOS', sequence='Sequence', replace_nat=True):
        # switch to LOS format and print the new xarray object

        lidar2 = self._obj.copy()
        has_sequence = sequence is not None and sequence in lidar2.keys()
        # create 'scan' coordinate based on decreasing LOS ID (and
        # changing sequence ID if available)
        los_diff = (lidar2.coords[los][1:].values -
                    lidar2.coords[los][:-1].values)
        if has_sequence:
            seq_diff = (lidar2.coords[sequence][1:].values -
                        lidar2.coords[sequence][:-1].values)
            scan = np.cumsum((los_diff <= 0) | (seq_diff != 0))
        else:
            scan = np.cumsum(los_diff <= 0)
        # add the first scan, which is obviously zero
        scan = np.insert(scan, 0, 0)
        
        lidar2.coords['scan'] = ('Time', scan)
        lidar2 = lidar2.set_index(profile=['scan', los])
        lidar2.coords['profile'] = ('Time', lidar2.coords['profile'].to_index())
        lidar2.swap_dims({'Time': 'profile'}, inplace=True)
        lidar2 = lidar2.unstack('profile')
        # fill in missing sequences
        # all_sequences = np.arange(lidar2.coords[sequence].min(),
        #                           lidar2.coords[sequence].max())
        # lidar2.reindex({'Sequence': all_sequences})

        # fix the sequence and configuration numbers
        if has_sequence:
            seq2 = lidar2[sequence].sel(LOS=0)[1:]
            seq2 = np.insert(seq2, 0, lidar2[sequence].sel(scan=0, LOS=lidar2.dims[los] - 1))
            lidar2.coords[sequence] = ('scan', seq2.astype(int))
        if 'Configuration' in lidar2.keys():
            conf2 = lidar2['Configuration'].sel(LOS=0)[1:]
            conf2 = np.insert(conf2, 0, lidar2['Configuration'].sel(scan=0, LOS=lidar2.dims[los] - 1))
            lidar2.coords['Configuration'] = ('scan', conf2.astype(int))
        if replace_nat:
            lidar2.rasp.replace_nat()

        return lidar2

    def replace_nat(self, timedim='Time'):
        max_los = self._obj.coords['LOS'].max()
        # get all missing except for the last scan
        missing = np.where(pd.isnull(self._obj.coords[timedim][0:-1, :]))
        # replace missing times with the next time:
        for n in reversed(range(len(missing[0]))):
            # index contains scan, then LOS ID
            index = (missing[0][n], missing[1][n])
            if index[1] == max_los:
                next_index = (index[0] + 1, 0)
            else:
                next_index = (index[0], index[1] + 1)
            next_time = self._obj.coords[timedim].values[next_index[0], next_index[1]]
            if not pd.isnull(next_time):
                self._obj.coords[timedim][index[0], index[1]] = next_time - pd.Timedelta('1us')

        # this will leave some times still missing at the end
        # fix the remaining missing times by replacing them with the previous time:
        missing = np.where(pd.isnull(self._obj.coords[timedim]))
        for n in range(len(missing[0])):
            index = (missing[0][n], missing[1][n])
            if index[1] == 0:
                prev_index = (index[0] - 1, max_los)
            else:
                prev_index = (index[0], index[1] - 1)
            prev_time = self._obj.coords[timedim].values[prev_index[0], prev_index[1]]
            if not pd.isnull(prev_time):
                self._obj.coords[timedim][index[0], index[1]] = prev_time + pd.Timedelta('1us')

    def remove_where(self, arrays, logarr):
        for array in arrays:
            self._obj[array].values = np.where(logarr, np.nan, self._obj[array])

    def skewt(self, ranges='Range', temp='Temperature', dewpoint=None, rel_hum='Relative Humidity',
              temp_units='K', wind=None, **kwargs):
        from metpy.plots import SkewT
        if not 'col' in kwargs.keys() and not 'row' in kwargs.keys():
            # get unused dimensions
            unused = list(self._obj[temp].dims)
            if ranges in unused:
                unused.remove(ranges)
            # convert range (m) to hectopascals
            hpascals = 1013.25 * np.exp(-self._obj.coords[ranges] / 7)
            # return hpascals
            #return hpascals
            # convert temperature from Kelvins to Celsius
            #tempC = self._obj[temp] - 273.15
            if temp_units == 'K':
                tempK = self._obj[temp].drop(unused)
                tempC = tempK - 273.15
            else:
                tempC = self._obj[temp].drop(unused)
                tempK = tempC + 273.15

            if dewpoint is None:
                # estimate dewpoint from relative humidity
                dewpoints = tempK - ((100 - self._obj[rel_hum].drop(unused)) / 5) - 273.15
            else:
                dewpoints = self._obj[dewpoint].drop(unused)

            skew = SkewT()
            #return tempC
            skew.plot(hpascals, tempC, 'r')
            skew.plot(hpascals, dewpoints, 'g')
            skew.plot_dry_adiabats()
            skew.plot_moist_adiabats()
            if not wind is None:
                u = self._obj[wind].sel(Component='x').drop(unused)
                v = self._obj[wind].sel(Component='y').drop(unused)
                skew.plot_barbs(hpascals, u, v, xloc=.9)
            # skew.plot_mixing_lines()
            # skew.ax.set_ylim(1100, 100)
        else:
            if not wind is None:
                skewtdat = xr.concat([self._obj['Temperature'], self._obj['Relative Humidity'],
                                      self._obj[wind].sel(Component='x').drop('Component'),
                                      self._obj[wind].sel(Component='y').drop('Component')],
                                     'measure')
                skewtdat.coords['measure'] = ['Temperature', 'Relative Humidity', 'windx', 'windy']
            else:
                skewtdat = xr.concat([self._obj['Temperature'], self._obj['Relative Humidity']], 'measure')
                skewtdat.coords['measure'] = ['Temperature', 'Relative Humidity']

            # skewtdat
            sk1 = xr.plot.FacetGrid(skewtdat, **kwargs)
            #return sk1
            # need to make the subplot tuples

            for ax in sk1.axes.flat:
                ax.axis('off')

            #return sk1.axes.flat
            #return len(sk1.axes.flat)
            splots = range(len(sk1.axes.flat))
            #return splots
            splot_dims = sk1.axes.shape
            splot_tuples = []
            for i in splots:
                splot_tuples.append((splot_dims[0], splot_dims[1], i + 1))

            if not wind is None:
                sk1.map(rasp.skewt, [0, 1, 2, 3], splots=splot_tuples, ranges=skewtdat.coords['Range'].values)
            else:
                sk1.map(rasp.skewt, [0, 1], splots=splot_tuples, ranges=skewtdat.coords['Range'].values)

    def cf_compliant(self, period='5T', shear=False):
        """Return a dataset in cf compliant netcdf format"""
        # lidar = rasp.lidar_from_csv(radial_file, scan_file, wind=wind_file)
        ds = cp.copy(self._obj)
        # remove status==0 data
        # lidar['CNR'] = lidar['CNR'].where(lidar['Status'])
        # lidar['DRWS'] = lidar['DRWS'].where(lidar['Status'])
        # lidar = lidar.drop(['Status', 'Error', 'Confidence'])
        has_wind = 'Windspeed' in ds.data_vars
        if has_wind:
            # set up the windspeed variables
            ds['xwind'] = ds['Windspeed'].sel(Component='x').drop('Component')
            ds['xwind'].attrs.pop('long_name', None)
            ds['xwind'].attrs['standard_name'] = 'eastward_wind'
            ds['xwind'].attrs['units'] = 'm s-1'
            ds['ywind'] = ds['Windspeed'].sel(Component='y').drop('Component')
            ds['ywind'].attrs.pop('long_name', None)
            ds['ywind'].attrs['standard_name'] = 'northward_wind'
            ds['ywind'].attrs['units'] = 'm s-1'
            ds['vwind'] = ds['Windspeed'].sel(Component='z').drop('Component')
            ds['vwind'].attrs.pop('long_name', None)
            ds['vwind'].attrs['standard_name'] = 'upward_air_velocity'
            ds['vwind'].attrs['units'] = 'm s-1'
        ds = ds.drop('Windspeed').drop('Component')
        ds = ds.resample(period, 'Time', keep_attrs=True)
        # do more stuff after resampling
        if has_wind:
            if shear:
                # wind shear (works better after resampling?)

                # first add horizontal wind
                ds['hwind'] = np.sqrt(ds['xwind'] ** 2 + ds['ywind'] ** 2)
                ds['hwind'].attrs['standard_name'] = 'upward_air_velocity'
                ds['hwind'].attrs['units'] = 'm s-1'
                # now can get the shear
                ds['shear'] = ds['hwind'].copy()
                ds['shear'][:,1:] = ds['hwind'][:, 1:].values / ds['hwind'][:, :-1].values
                ds['shear'][:,0] = np.nan
                ds['shear'].attrs['standard_name'] = 'wind_speed_shear'
                ds['shear'].attrs['units'] = 's-1'
        return ds


@xr.register_dataarray_accessor('rasp')
class RaspAccessor(object):
    def __init__(self, xarray_obj):
        self._obj = xarray_obj

    def nd_resample(self, rule, coord, dim):
        coords = list(self._obj.coords[coord].dims)
        coords.remove(dim)
        return rasp.recursive_resample(self._obj, rule, coord, dim, coords)

    def plot_barbs(self, x=None, y=None, components=('x', 'y'), resample=None, resampley=None, ax=None, **kwargs):
        if x is None:
            x = 'Time'
        if resample is not None:
            l1 = self._obj.resample(resample, x)
        else:
            l1 = self._obj
        if y is None:
            y = 'Range'
        if resampley is not None:
            maxy = l1.coords[y].max()
            nbreaks = int(maxy / resampley) + 2
            bins = np.array(range(nbreaks)) * [resampley]
            labels = (bins[1:] + bins[:-1]) / 2
            l1 = l1.groupby_bins(group=y, bins=bins, labels=labels).mean(dim=y)
            bin_name = y + '_bins'
            l1 = l1.rename({bin_name: y})
        xvals = [mdates.date2num(pd.Timestamp(val)) for val in l1.coords[x].values]
        yvals = l1.coords[y]
        X, Y = np.meshgrid(xvals, yvals)
        U = l1.sel(Component=components[0])
        V = l1.sel(Component=components[1])
        if len(U.dims) > 2:
            U = U.squeeze()
        if len(V.dims) > 2:
            V = V.squeeze()
        # there should only be two dimensions!
        assert len(U.dims) == 2
        assert len(V.dims) == 2
        # y values should be the first dimension
        if U.dims[0] != y:
            U = U.transpose()
        if V.dims[0] != y:
            V = V.transpose()
        new_axis = ax is None
        if new_axis:
            ax = plt.subplot(111)
            xtick_locator = mdates.AutoDateLocator()
            xtick_formatter = mdates.AutoDateFormatter(xtick_locator)
        ax.barbs(X, Y, U, V, **kwargs)
        if new_axis:
            ax.set_ylabel(y)
            ax.xaxis.set_major_locator(xtick_locator)
            ax.xaxis.set_major_formatter(xtick_formatter)

    def plot_profile(self, y=None, ax=None, **kwargs):
        if y is None:
            dimname = self._obj.dims[0]
        else:
            dimname = y
        ys = self._obj[dimname].values
        xs = self._obj#.values#.transpose()
        if self._obj.dims[0] != dimname:
            dims_order = [dimname]
            for d in self._obj.dims:
                if d != dimname:
                    dims_order.append(d)
            xs = xs.transpose(*dims_order)
        if ax is None:
            ax = plt.gca()
        ax.plot(xs.values, ys, **kwargs)
        ax.set_xlabel(self._obj.name)
        ax.set_ylabel(dimname)
        # if len(xs.dims) > 1:
        #     dim2 = xs.dims[1]
        #     labels = [ dim2 + ' = ' + str(x) for x in xs.coords[dim2].values ]
        #     plt.legend(labels, loc=2)
        # if legend:
        #     # plt.legend(lines, self.index)
        #     ax.legend(self.index)

    def _plot_profile(self, ax=None, **kwargs):
        fgr = xr.plot.FacetGrid(skewtdat, **kwargs)

    def remove_where(self, logarr, inplace=False):
        if inplace:
            self._obj.values = xr.DataArray(np.where(logarr, np.nan, self._obj))
        else:
            return xr.DataArray(np.where(logarr, np.nan, self._obj), dims=self._obj.dims)
Extensions of xarray Datasets and DataArrays # rewriting as an xarray module, following the guidelines here: # http://xarray.pydata.org/en/stable/internals.html#extending-xarray # raise an error # elevation # I want to get NA values for all missing sequences and # LOS's. The los_format does that automatically, so using it # here # now flatten back down-- we will have NA's in the right # places now # set filter to False if filter value is NA # the locations where 5 status=1 measurements were taken # in a row # add 4 columns for the 4 removed earlier # return row_los # create a multiIndex data frame to hold the data # get the corresponding LOS values for each wind estimate # # get corresponding elevations-- no, don't do this for now, # # Leosphere doesn't seem to be doing this # elevations = lidar.coords['Elevation'].values * np.pi / 180 # l0_el = elevations[good_indices[0] - row_los] # l1_el = elevations[good_indices[0] - ((row_los + 4) % 5)] # l2_el = elevations[good_indices[0] - ((row_los + 3) % 5)] # l3_el = elevations[good_indices[0] - ((row_los + 2) % 5)] # l4_el = elevations[good_indices[0] - ((row_los + 1) % 5)] # got these weights from a maximum likelihood calculation # put the results in the right place # swapping x/y and negative to get American coordinates!!: # only take the time indices that were in the original xarray object # self # hpascals = self._obj.coords['hpascals'].values # tempK = self._obj['Temperature'].values # tempC = tempK - 273.15 # rel_hum = self._obj['Relative Humidity'].values # dewpoints = tempK - ((100 - rel_hum) / 5) - 273.15 # return cape.getcape(nk=self._obj.dims['Range'], p_in=hpascals, t_in=tempC, td_in=dewpoints)[0] # set up the header information #header_lines.append('RAOB/CSV, ' + 'Example Data from microwave radiometer and lidar') #time_str = time.strftime('%Y-%m-%d %H:%M:%S') # header_lines.append('INFO:1, ' + 'First line of freeform text') # header_lines.append('INFO:2, ' + 'Another freeform text line') # skip for now # header_lines.append('WMO, 12345') # skip for now # wind height type is 'Above Ground Level' for now # what? # header_lines.append('SORT, YES') # in case of ozone: # header_lines.append('OZONE, mPa') # not now: # header_lines.append('EXTRA#1, Extra#1Name, Extra#1Units') # header_lines.append('EXTRA#2, Extra#2Name, Extra#2Units') # header_lines.append('SCALAR#1, Scalar#1Name, Scalar#1Value, Scalar#1Units') # header_lines.append('SCALAR#2, Scalar#2Name, Scalar#2Value, Scalar#2Units') # set up the data (making sure to round as required) # df['WIND'] = np.nan # df['SPEED'] = np.nan #if self.wind is not None: # print(df) # print(df.head()) # exit() # switch to LOS format and print the new xarray object # create 'scan' coordinate based on decreasing LOS ID (and # changing sequence ID if available) # add the first scan, which is obviously zero # fill in missing sequences # all_sequences = np.arange(lidar2.coords[sequence].min(), #                           lidar2.coords[sequence].max()) # lidar2.reindex({'Sequence': all_sequences}) # fix the sequence and configuration numbers # get all missing except for the last scan # replace missing times with the next time: # index contains scan, then LOS ID # this will leave some times still missing at the end # fix the remaining missing times by replacing them with the previous time: # get unused dimensions # convert range (m) to hectopascals # return hpascals #return hpascals # convert temperature from Kelvins to Celsius #tempC = self._obj[temp] - 273.15 # estimate dewpoint from relative humidity #return tempC # skew.plot_mixing_lines() # skew.ax.set_ylim(1100, 100) # skewtdat #return sk1 # need to make the subplot tuples #return sk1.axes.flat #return len(sk1.axes.flat) #return splots Return a dataset in cf compliant netcdf format # lidar = rasp.lidar_from_csv(radial_file, scan_file, wind=wind_file) # remove status==0 data # lidar['CNR'] = lidar['CNR'].where(lidar['Status']) # lidar['DRWS'] = lidar['DRWS'].where(lidar['Status']) # lidar = lidar.drop(['Status', 'Error', 'Confidence']) # set up the windspeed variables # do more stuff after resampling # wind shear (works better after resampling?) # first add horizontal wind # now can get the shear # there should only be two dimensions! # y values should be the first dimension #.values#.transpose() # if len(xs.dims) > 1: #     dim2 = xs.dims[1] #     labels = [ dim2 + ' = ' + str(x) for x in xs.coords[dim2].values ] #     plt.legend(labels, loc=2) # if legend: #     # plt.legend(lines, self.index) #     ax.legend(self.index)