import json
from os import path, listdir
import unittest
import decode_fbs
import tempfile
import shutil

import numpy as np
import pandas as pd

from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
from server.app.util.fbs.matrix import encode_matrix_fbs
from server.app.util.data_locator import DataLocator


class WritableAnnotationTest(unittest.TestCase):
    def setUp(self):
        self.tmpDir = tempfile.mkdtemp()
        self.annotations_file = path.join(self.tmpDir, "test_annotations.csv")
        args = {
            "layout": ["umap"],
            "max_category_items": 100,
            "obs_names": None,
            "var_names": None,
            "diffexp_lfc_cutoff": 0.01,
            "annotations": True,
            "annotations_file": self.annotations_file,
            "annotations_output_dir": None,
        }
        self.data = ScanpyEngine(DataLocator("../example-dataset/pbmc3k.h5ad"), args)

    def tearDown(self):
        shutil.rmtree(self.tmpDir)

    def make_fbs(self, data):
        df = pd.DataFrame(data)
        return encode_matrix_fbs(matrix=df, row_idx=None, col_idx=df.columns)

    def test_error_checks(self):
        # verify that the expected errors are generated

        n_rows = self.data.data.obs.shape[0]
        fbs_bad = self.make_fbs({"louvain": pd.Series(["undefined" for l in range(0, n_rows)], dtype="category")})

        # ensure attempt to change VAR annotation
        with self.assertRaises(ValueError):
            self.data.annotation_put_fbs("var", fbs_bad)

        # ensure we catch attempt to overwrite non-writable data
        with self.assertRaises(KeyError):
            self.data.annotation_put_fbs("obs", fbs_bad)

    def test_write_to_file(self):
        # verify the file is written as expected
        n_rows = self.data.data.obs.shape[0]
        fbs = self.make_fbs(
            {
                "cat_A": pd.Series(["label_A" for l in range(0, n_rows)], dtype="category"),
                "cat_B": pd.Series(["label_B" for l in range(0, n_rows)], dtype="category"),
            }
        )
        res = self.data.annotation_put_fbs("obs", fbs)
        self.assertEqual(res, json.dumps({"status": "OK"}))
        self.assertTrue(path.exists(self.annotations_file))
        df = pd.read_csv(self.annotations_file, index_col=0, header=0, comment="#")
        self.assertEqual(df.shape, (n_rows, 2))
        self.assertEqual(set(df.columns), set(["cat_A", "cat_B"]))
        self.assertTrue(self.data.original_obs_index.equals(df.index))
        self.assertTrue(np.all(df["cat_A"] == ["label_A" for l in range(0, n_rows)]))
        self.assertTrue(np.all(df["cat_B"] == ["label_B" for l in range(0, n_rows)]))

        # verify complete overwrite on second attempt, AND rotation occurs
        fbs = self.make_fbs(
            {
                "cat_A": pd.Series(["label_A1" for l in range(0, n_rows)], dtype="category"),
                "cat_C": pd.Series(["label_C" for l in range(0, n_rows)], dtype="category"),
            }
        )
        res = self.data.annotation_put_fbs("obs", fbs)
        self.assertEqual(res, json.dumps({"status": "OK"}))
        self.assertTrue(path.exists(self.annotations_file))
        df = pd.read_csv(self.annotations_file, index_col=0, header=0, comment="#")
        self.assertEqual(set(df.columns), set(["cat_A", "cat_C"]))
        self.assertTrue(np.all(df["cat_A"] == ["label_A1" for l in range(0, n_rows)]))
        self.assertTrue(np.all(df["cat_C"] == ["label_C" for l in range(0, n_rows)]))

        # rotation
        name, ext = path.splitext(self.annotations_file)
        backup_dir = f"{name}-backups"
        self.assertTrue(path.isdir(backup_dir))
        found_files = listdir(backup_dir)
        self.assertEqual(len(found_files), 1)

    def test_file_rotation_to_max_9(self):
        # verify we stop rotation at 9
        n_rows = self.data.data.obs.shape[0]
        fbs = self.make_fbs(
            {
                "cat_A": pd.Series(["label_A" for l in range(0, n_rows)], dtype="category"),
                "cat_B": pd.Series(["label_B" for l in range(0, n_rows)], dtype="category"),
            }
        )
        for i in range(0, 11):
            res = self.data.annotation_put_fbs("obs", fbs)
            self.assertEqual(res, json.dumps({"status": "OK"}))

        name, ext = path.splitext(self.annotations_file)
        backup_dir = f"{name}-backups"
        self.assertTrue(path.isdir(backup_dir))
        found_files = listdir(backup_dir)
        self.assertTrue(len(found_files) <= 9)

    def test_put_get_roundtrip(self):
        # verify that OBS PUTs (annotation_put_fbs) are accessible via
        # GET (annotation_to_fbs_matrix)

        n_rows = self.data.data.obs.shape[0]
        fbs = self.make_fbs(
            {
                "cat_A": pd.Series(["label_A" for l in range(0, n_rows)], dtype="category"),
                "cat_B": pd.Series(["label_B" for l in range(0, n_rows)], dtype="category"),
            }
        )

        # put
        res = self.data.annotation_put_fbs("obs", fbs)
        self.assertEqual(res, json.dumps({"status": "OK"}))

        # get
        fbsAll = self.data.annotation_to_fbs_matrix("obs")
        schema = self.data.get_schema()
        annotations = decode_fbs.decode_matrix_FBS(fbsAll)
        obs_index_col_name = schema["annotations"]["obs"]["index"]
        self.assertEqual(annotations["n_rows"], n_rows)
        self.assertEqual(annotations["n_cols"], 7)
        self.assertIsNone(annotations["row_idx"])
        self.assertEqual(
            annotations["col_idx"],
            [obs_index_col_name, "n_genes", "percent_mito", "n_counts", "louvain", "cat_A", "cat_B"],
        )
        col_idx = annotations["col_idx"]
        self.assertEqual(annotations["columns"][col_idx.index("cat_A")], ["label_A" for l in range(0, n_rows)])
        self.assertEqual(annotations["columns"][col_idx.index("cat_B")], ["label_B" for l in range(0, n_rows)])

        # verify the schema was updated
        all_col_schema = {c["name"]: c for c in schema["annotations"]["obs"]["columns"]}
        self.assertEqual(
            all_col_schema["cat_A"],
            {"name": "cat_A", "type": "categorical", "categories": ["label_A"], "writable": True},
        )
        self.assertEqual(
            all_col_schema["cat_B"],
            {"name": "cat_B", "type": "categorical", "categories": ["label_B"], "writable": True},
        )