//  [python]
def cluster_probes(self):
        """Use cd-hit-est to cluster highly similar probes."""

        outdir = (
            Path(self.clustered_probes_fasta).parent
            / f"cd-hit-est-{datetime.datetime.today().isoformat(timespec='seconds', sep='_')}"
        )
        outdir.mkdir()
        log_file = outdir / "cd-hit.log"

        res_dict = {"seq_id_thres": [], "n_probes": []}

        for seq_id_thres in np.arange(0.8, 1, self.identity_step).round(3):
            tmp_fas = outdir / f"clustered_{seq_id_thres}.fas"
            cmd = f"cd-hit-est -i {self.probes_fasta} -o {tmp_fas} -c {seq_id_thres} -n {self.threads}"
            logger.debug(f"Clustering probes with command: {cmd} (log in '{log_file}').")

            with open(log_file, "a") as f:
                subprocess.run(cmd, shell=True, check=True, stdout=f)

            res_dict["seq_id_thres"].append(seq_id_thres)
            res_dict["n_probes"].append(len(FastA(tmp_fas)))

        # Add number of probes without clustering
        res_dict["seq_id_thres"].append(1)
        res_dict["n_probes"].append(self.probes_df.shape[0])

        self.json["results"] = res_dict

        # Dataframe with number of probes for each cdhit identity threshold
        pylab.clf()
        df = pd.DataFrame(res_dict)

        import seaborn as sns  # local import to speed up imports

        p = sns.tsplot(data=df, time="seq_id_thres", value="n_probes", err_style="ci_band", markers=["o"])
        p.axhline(
            self.max_n_probes,
            alpha=0.8,
            linestyle="--",
            color="red",
            label="max number of probes requested",
        )
        pylab.xlabel("Sequence identity", fontsize=16)
        pylab.ylabel("Number of probes", fontsize=16)

        # Extract the best identity threshold
        best_thres = df.query("n_probes <= @self.max_n_probes").seq_id_thres.max()

        if not np.isnan(best_thres):
            n_probes = df.query("seq_id_thres == @best_thres").loc[:, "n_probes"].values[0]

            self.json["n_probes"] = int(n_probes)
            self.json["best_thres"] = best_thres

            logger.info(f"Best clustering threshold: {best_thres}, with {n_probes} probes.")
            shutil.copy(outdir / f"clustered_{best_thres}.fas", self.clustered_probes_fasta)
            kept_probes = [seq.name for seq in FastA(outdir / f"clustered_{best_thres}.fas")]
            self.probes_df["kept_after_clustering"] = self.probes_df.seq_id.isin(kept_probes)
            self.probes_df["bed_color"] = self.probes_df.kept_after_clustering.map(
                {True: "21,128,0", False: "128,64,0"}
            )
            self.probes_df["clustering_thres"] = best_thres
            pylab.plot(best_thres, n_probes, "o", label="Final number of probes")
            pylab.legend()
        else:
            logger.warning(
                f"No identity threshold was found to have as few as {self.max_n_probes} probes. Keep all probes. Set a valid value with --max-n-probes between {df.n_probes.min()} (min) and {df.n_probes.max()} (max)"
            )

        self.clustering_df = df.sort_values("seq_id_thres")

        return self.probes_df.query("kept_after_clustering == True")