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   "source": [
    "# Basic examples from the trimAl documentation\n",
    "\n",
    "This example shows how to run the basic methods shown in the trimAl manual, but using the `pytrimal` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1f5f0b9-e3e3-413b-8ae6-486069e0705f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytrimal\n",
    "pytrimal.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e55561-0213-47e1-addb-5c4252051850",
   "metadata": {},
   "source": [
    "For this example, we will use one of the example alignments from the trimAl source repository. We use `Alignment.load` to load the alignment from a filename; note that `os.PathLike` objects are supported as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f8d4a1c-eaa0-4d29-85e4-9ec852cb4df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "ali = pytrimal.Alignment.load(pathlib.Path(\"data\").joinpath(\"example.001.AA.clw\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb77186-8f4b-4f7b-a60f-72ddf325828e",
   "metadata": {},
   "source": [
    "Let's see how the original alignment looks before trimming:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4bdda8-0206-4ca9-ba92-dccf1003393b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, seq in zip(ali.names, ali.sequences):\n",
    "    print(name.decode().ljust(10), seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293dddfc-ac4b-444d-9bd5-2b670f5f8e3f",
   "metadata": {},
   "source": [
    "## Example 1\n",
    "\n",
    "Remove all positions in the alignment with gaps in 10% or more of the sequences, unless this leaves less than 60% of original alignment. In such case, print the 60% best (with less gaps) positions. Equivalent to:\n",
    "```\n",
    "$ trimal -in data/example.001.AA.clw -gt 0.9 -cons 60\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86e2b0cc-6cad-48a2-a615-a37ecd48a13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmer = pytrimal.ManualTrimmer(gap_threshold=0.9, conservation_percentage=60)\n",
    "trimmed = trimmer.trim(ali)\n",
    "\n",
    "for name, seq in zip(trimmed.names, trimmed.sequences):\n",
    "    print(name.decode().ljust(10), seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b40760e3-b73a-41d9-9a92-0cc824b09e73",
   "metadata": {},
   "source": [
    "## Example 2\n",
    "\n",
    "Same as *Example 1*, but  the gap score is averaged over a window starting 3 positions before and ending 3 positions after each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "872d8097-f54c-469b-9a5c-dba02da590b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmer = pytrimal.ManualTrimmer(gap_threshold=0.9, conservation_percentage=60, window=3)\n",
    "trimmed = trimmer.trim(ali)\n",
    "\n",
    "for name, seq in zip(trimmed.names, trimmed.sequences):\n",
    "    print(name.decode().ljust(10), seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b66c60-54d8-4ab2-ac19-dfd4dfab7a90",
   "metadata": {},
   "source": [
    "## Example 3\n",
    "\n",
    "Use an automatic method to decide optimal thresholds, based on the gap scores the input alignment. Equivalent to:\n",
    "```\n",
    "$ trimal -in data/example.001.AA.clw -gappyout\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00e658aa-9280-437d-a024-853bdaa3fafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmer = pytrimal.AutomaticTrimmer(method=\"gappyout\")\n",
    "trimmed = trimmer.trim(ali)\n",
    "\n",
    "for name, seq in zip(trimmed.names, trimmed.sequences):\n",
    "    print(name.decode().ljust(10), seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01efceb6-8eec-4548-9b70-7178a7670537",
   "metadata": {},
   "source": [
    "## Example 4\n",
    "\n",
    "Use automatic methods to decide optimal thresholds, based on the combination of gap and similarity scores. Equivalent to:\n",
    "```\n",
    "$ trimal -in data/example.001.AA.clw -strictplus\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2580fe78-64a6-43f5-808a-fd3220804c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmer = pytrimal.AutomaticTrimmer(method=\"strictplus\")\n",
    "trimmed = trimmer.trim(ali)\n",
    "\n",
    "for name, seq in zip(trimmed.names, trimmed.sequences):\n",
    "    print(name.decode().ljust(10), seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664c6392-bb2b-4b21-ae4d-655a97f0d65a",
   "metadata": {},
   "source": [
    "## Example 5\n",
    "\n",
    "Use an heuristic to decide the optimal method for trimming the alignment. Equivalent to:\n",
    "```\n",
    "$ trimal -in data/example.001.AA.clw -automated1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38499837-b1ef-45a0-844d-be36adae2f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "trimmer = pytrimal.AutomaticTrimmer(method=\"automated1\")\n",
    "trimmed = trimmer.trim(ali)\n",
    "\n",
    "for name, seq in zip(trimmed.names, trimmed.sequences):\n",
    "    print(name.decode().ljust(10), seq)"
   ]
  }
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