How to use the getdist.paramnames.ParamNames function in getdist

def setParamNames(self, names=None):
        """
        Sets the names of the params.

        :param names: Either a :class:`~.paramnames.ParamNames` object, the name of a .paramnames file to load, a list
                      of name strings, otherwise use default names (param1, param2...).
        """
        self.paramNames = None
        if isinstance(names, ParamNames):
            self.paramNames = deepcopy(names)
        elif isinstance(names, six.string_types):
            self.paramNames = ParamNames(names)
        elif names is not None:
            self.paramNames = ParamNames(names=names)
        elif self.samples is not None:
            self.paramNames = ParamNames(default=self.n)
        if self.paramNames:
            self._getParamIndices()
        self.needs_update = True

def setLabelsAndDerivedFromParamNames(self, fname, set_derived=True):
        if isinstance(fname, ParamNames):
            p = fname
        else:
            p = ParamNames(fname)
        for par in p.names:
            param = self.parWithName(par.name)
            if param is not None:
                param.label = par.label
                if set_derived:
                    param.isDerived = par.isDerived

:param label: name for labelling this mixture
        :param labels: list of latex labels for each parameter. If not set, defaults to p_{1}, p_{2}...
        """

        self.means = np.asarray(means)
        self.dim = self.means.shape[1]
        self.covs = [np.array(cov) for cov in covs]
        self.invcovs = [np.linalg.inv(cov) for cov in self.covs]
        if weights is None: weights = [1. / len(means)] * len(means)
        self.weights = np.array(weights, dtype=np.float64)
        if np.sum(self.weights) <= 0:
            raise ValueError('Weight <= 0 in MixtureND')
        self.weights /= np.sum(weights)
        self.norms = (2 * np.pi) ** (0.5 * self.dim) * np.array([np.sqrt(np.linalg.det(cov)) for cov in self.covs])
        self.lims = lims
        self.paramNames = ParamNames(names=names, default=self.dim, labels=labels)
        self.names = self.paramNames.list()
        self.label = label
        self.total_mean = np.atleast_1d(np.dot(self.weights, self.means))
        self.total_cov = np.zeros((self.dim, self.dim))
        for mean, cov, weight, totmean in zip(self.means, self.covs, self.weights, self.total_mean):
            self.total_cov += weight * (cov + np.outer(mean - totmean, mean - totmean))

dim = array_dimension(files_or_samples)
            if dim in [1, 2]:
                self.chains = None
                self.setSamples(slice_or_none(files_or_samples, ignore_lines),
                                slice_or_none(weights, ignore_lines),
                                slice_or_none(loglikes, ignore_lines), self.min_weight_ratio)
                if self.paramNames is None:
                    self.paramNames = ParamNames(default=self.n)
                nchains = 1
            elif dim == 3:
                for i, samples_i in enumerate(files_or_samples):
                    self.chains.append(WeightedSamples(
                        samples=samples_i, loglikes=None if loglikes is None else loglikes[i],
                        weights=None if weights is None else weights[i], **WSkwargs))
                if self.paramNames is None:
                    self.paramNames = ParamNames(default=self.chains[0].n)
                nchains = len(self.chains)
            else:
                raise ValueError('samples or files must be array of samples, or a list of arrays or files')
        self._weightsChanged()
        return nchains > 0

def setLabelsAndDerivedFromParamNames(self, fname, set_derived=True):
        if isinstance(fname, ParamNames):
            p = fname
        else:
            p = ParamNames(fname)
        for par in p.names:
            param = self.parWithName(par.name)
            if param is not None:
                param.label = par.label
                if set_derived:
                    param.isDerived = par.isDerived

def setParamNames(self, names=None):
        """
        Sets the names of the params.

        :param names: Either a :class:`~.paramnames.ParamNames` object, the name of a .paramnames file to load, a list
                      of name strings, otherwise use default names (param1, param2...).
        """
        self.paramNames = None
        if isinstance(names, ParamNames):
            self.paramNames = deepcopy(names)
        elif isinstance(names, six.string_types):
            self.paramNames = ParamNames(names)
        elif names is not None:
            self.paramNames = ParamNames(names=names)
        elif self.samples is not None:
            self.paramNames = ParamNames(default=self.n)
        if self.paramNames:
            self._getParamIndices()
        self.needs_update = True

def setParamNames(self, names=None):
        """
        Sets the names of the params.

        :param names: Either a :class:`~.paramnames.ParamNames` object, the name of a .paramnames file to load, a list
                      of name strings, otherwise use default names (param1, param2...).
        """
        self.paramNames = None
        if isinstance(names, ParamNames):
            self.paramNames = deepcopy(names)
        elif isinstance(names, six.string_types):
            self.paramNames = ParamNames(names)
        elif names is not None:
            self.paramNames = ParamNames(names=names)
        elif self.samples is not None:
            self.paramNames = ParamNames(default=self.n)
        if self.paramNames:
            self._getParamIndices()
        self.needs_update = True

d = 0
                while True:
                    try:
                        a = a[0]
                        d += 1
                    except:
                        return d

            dim = array_dimension(files_or_samples)
            if dim in [1, 2]:
                self.chains = None
                self.setSamples(slice_or_none(files_or_samples, ignore_lines),
                                slice_or_none(weights, ignore_lines),
                                slice_or_none(loglikes, ignore_lines), self.min_weight_ratio)
                if self.paramNames is None:
                    self.paramNames = ParamNames(default=self.n)
                nchains = 1
            elif dim == 3:
                for i, samples_i in enumerate(files_or_samples):
                    self.chains.append(WeightedSamples(
                        samples=samples_i, loglikes=None if loglikes is None else loglikes[i],
                        weights=None if weights is None else weights[i], **WSkwargs))
                if self.paramNames is None:
                    self.paramNames = ParamNames(default=self.chains[0].n)
                nchains = len(self.chains)
            else:
                raise ValueError('samples or files must be array of samples, or a list of arrays or files')
        self._weightsChanged()
        return nchains > 0

def setParamNames(self, names=None):
        """
        Sets the names of the params.

        :param names: Either a :class:`~.paramnames.ParamNames` object, the name of a .paramnames file to load, a list
                      of name strings, otherwise use default names (param1, param2...).
        """
        self.paramNames = None
        if isinstance(names, ParamNames):
            self.paramNames = deepcopy(names)
        elif isinstance(names, six.string_types):
            self.paramNames = ParamNames(names)
        elif names is not None:
            self.paramNames = ParamNames(names=names)
        elif self.samples is not None:
            self.paramNames = ParamNames(default=self.n)
        if self.paramNames:
            self._getParamIndices()
        self.needs_update = True