feat: Add PMD command and include Configs in core

.vscode/settings.json

@@ -1,7 +1,11 @@
 {
     "cSpell.words": [
+        "editdistmax",
+        "editdistmin",
         "fusobacteria",
         "Lactobacillales",
+        "minmapq",
+        "ncbi",
         "taxas"
     ]
 }

pyproject.toml

@@ -19,14 +19,16 @@ PyYAML = "^6.0"
 pandas = "^1.4.1"
 scipy = "^1.8.0"
 pyarrow = "^7.0.0"
+logger-tt = "^1.7.0"
+psutil = "^5.9.0"
+
+
 # scipy = { version = "^1.7.3", python = ">=3.9,<3.11" }
 
 # Fit related packages: poetry install --extras fit
-logger-tt = {version = "^1.7.0", optional = true}
 iminuit = {version = "^2.10.0", optional = true}
 numpyro = {version = "^0.9.1", optional = true}
 joblib = {version = "^1.1.0", optional = true}
-psutil = {version = "^5.9.0", optional = true}
 numba = {version = "^0.55.1", optional = true}
 
 # Viz related packages: poetry install --extras viz

src/metaDMG/cli/cli.py

@@ -270,18 +270,12 @@ def compute(
 
     utils.check_metaDMG_fit()
 
-    from metaDMG.fit import (
-        get_logger_port_and_path,
-        make_configs,
-        run_workflow,
-        setup_logger,
-    )
+    from metaDMG.fit import get_logger_port_and_path, run_workflow, setup_logger
 
     log_port, log_path = get_logger_port_and_path()
-
     setup_logger(log_port=log_port, log_path=log_path)
 
-    configs = make_configs(
+    configs = utils.make_configs(
         config_file=config_file,
         log_port=log_port,
         log_path=log_path,
@@ -576,6 +570,26 @@ def mismatch_to_mapDamage(
     mismatch_to_mapDamage.convert(filename=filename, csv_out=csv_out)
 
 
+#%%
+
+
+@cli_app.command("PMD")
+def compute_PMD(
+    config_file: Path = typer.Argument(
+        ...,
+        file_okay=True,
+        help="Path to the config-file.",
+    ),
+):
+    """Compute the PMD scores for the samples in the config file."""
+
+    from metaDMG.utils import make_configs, run_PMD
+
+    configs = make_configs(config_file)
+    for config in configs:
+        run_PMD(config["bam"], config["path_pmd"])
+
+
 #%%
 
 # needed for sphinx documentation

src/metaDMG/fit/__init__.py

@@ -1,3 +1,2 @@
-from metaDMG.fit.fit_utils import make_configs
 from metaDMG.fit.workflow import run_workflow
 from metaDMG.loggers.loggers import get_logger_port_and_path, setup_logger

src/metaDMG/fit/fit_utils.py

@@ -6,7 +6,6 @@
 
 import numpy as np
 import pandas as pd
-import psutil
 import typer
 import yaml
 from iminuit import describe
@@ -15,8 +14,6 @@
 from scipy.special import erf, erfinv
 from scipy.stats import chi2 as sp_chi2
 
-from metaDMG.utils import _update_old_config
-
 
 #%%
 
@@ -30,192 +27,6 @@
 #%%
 
 
-class Config(dict):
-    """Config contains the parameters related to specific alignment file."""
-
-    pass
-
-
-class Configs(dict):
-    """Configs contains the parameters related to config file.
-    Inherits from dict. Implements iterations.
-    """
-
-    def __iter__(self) -> Iterator[Config]:
-        """Iteration
-
-        Yields
-        ------
-        Iterator[Config]
-            Allow for iteration
-        """
-        dir_lca = self["output_dir"] / "lca"
-        samples = self["samples"].keys()
-        for sample in samples:
-            config = Config(self)
-            config["sample"] = sample
-            config["bam"] = config["samples"][sample]
-
-            config["path_mismatches_txt"] = dir_lca / f"{sample}.mismatches.txt.gz"
-
-            if config["damage_mode"] == "lca":
-                config["path_mismatches_stat"] = (
-                    dir_lca / f"{sample}.mismatches.stat.txt.gz"
-                )
-            else:
-                config["path_mismatches_stat"] = dir_lca / f"{sample}.stat.txt"
-
-            config["path_lca"] = dir_lca / f"{sample}.lca.txt.gz"
-            config["path_lca_log"] = dir_lca / f"{sample}.log.txt"
-            config["path_tmp"] = config["output_dir"] / "tmp" / sample
-            yield config
-
-    def get_nth(self, n: int) -> Config:
-        """Gets the n'th config
-
-        Parameters
-        ----------
-        n
-            The index
-
-        Returns
-        -------
-        Config
-            A single configuration
-        """
-        return next(islice(self, n, None))
-
-    def get_first(self) -> Config:
-        """Get the first config
-
-        Returns
-        -------
-        Config
-            A single configuration
-        """
-        return self.get_nth(n=0)
-
-    def __len__(self) -> int:
-        """The number of configs
-
-        Returns
-        -------
-        int
-            The number of configs
-        """
-        return len(self["samples"].keys())
-
-    def check_number_of_jobs(self) -> None:
-        """Compare the number of configs to the number of parallel_samples used."""
-
-        parallel_samples = min(self["parallel_samples"], len(self["samples"]))
-        cores_per_sample = self["cores_per_sample"]
-        N_jobs = parallel_samples * cores_per_sample
-        max_cores = psutil.cpu_count(logical=True)
-        max_cores_real = psutil.cpu_count(logical=False)
-
-        if N_jobs > max_cores:
-            logger.warning(
-                f"The total number of jobs {N_jobs} are higher "
-                f"than the number of parallel_samples {max_cores}. "
-                f"Do not do this unless you know what you are doing. "
-                f"Try decreasing either 'parallel_samples' or 'parallel_samples-per-sample'."
-            )
-        elif N_jobs > max_cores_real:
-            logger.info(
-                f"The total number of jobs {N_jobs} are higher "
-                f"than the real number of parallel_samples {max_cores_real} (non-logical). "
-                f"This might decrease performance. "
-            )
-
-
-def make_configs(
-    config_file: Optional[Path],
-    log_port: Optional[int] = None,
-    log_path: Optional[str] = None,
-    force: bool = False,
-) -> Configs:
-    """Create an instance of Configs from a config file
-
-    Parameters
-    ----------
-    config_file
-        The config file to load
-    log_port
-        Optional log port, by default None
-    log_path
-        Optional log path, by default None
-    force
-        Whether or not the computations are force, by default False
-
-    Returns
-    -------
-        An instance of Configs
-
-    Raises
-    ------
-    typer.Abort
-        If not a proper config file
-    """
-
-    if config_file is None:
-        config_file = Path("config.yaml")
-
-    if not config_file.exists():
-        logger.error("Error! Please select a proper config file!")
-        raise typer.Abort()
-
-    logger.info(f"Using {config_file} as config file.")
-    with open(config_file, "r") as file:
-        d = yaml.safe_load(file)
-
-    d = update_old_config(d)
-
-    d["log_port"] = log_port
-    d["log_path"] = log_path
-
-    d.setdefault("forward_only", False)
-    d.setdefault("cores_per_sample", 1)
-    d.setdefault("damage_mode", "lca")
-    d["force"] = force
-
-    paths = ["names", "nodes", "acc2tax", "output_dir", "config_file"]
-    for path in paths:
-        d[path] = Path(d[path])
-    for key, val in d["samples"].items():
-        d["samples"][key] = Path(val)
-
-    for key, val in d.items():
-        if isinstance(val, str):
-            if val.isdigit():
-                d[key] = int(key)
-
-    d["custom_database"] = 0 if d["custom_database"] else 1
-
-    return Configs(d)
-
-
-#%%
-
-
-def update_old_config(d: dict) -> dict:
-    if "version" in d:
-        # new version, not changing anything
-        return d
-
-    # updating the old version
-
-    logger.warning(
-        "Using an old version of the config file. Please remake the config file."
-    )
-
-    d_new = _update_old_config(d)
-    return d_new
-
-
-#%%
-
-
 def downcast_dataframe(df, categories=None, fully_automatic=False):
 
     if categories is None:

src/metaDMG/fit/serial.py

@@ -21,8 +21,8 @@
     metadamageError,
 )
 from metaDMG.fit import fits, mismatches, results
-from metaDMG.fit.fit_utils import Config
 from metaDMG.loggers.loggers import setup_logger
+from metaDMG.utils import Config
 
 
 #%%

src/metaDMG/fit/workflow.py

@@ -4,19 +4,20 @@
 
 from logger_tt import logger
 
-from metaDMG.fit import fit_utils, serial
+from metaDMG.fit.serial import run_single_config
+from metaDMG.utils import Configs
 
 
-def run_workflow(configs: fit_utils.Configs):
+def run_workflow(configs: Configs):
 
     parallel_samples = min(configs["parallel_samples"], len(configs))
 
     logger.info(f"Running metaDMG on {len(configs)} files in total.")
 
     if parallel_samples == 1 or len(configs) == 1:
-        logger.info(f"Running in seriel (1 core)")
+        logger.info(f"Running in serial (1 core)")
         for config in configs:
-            dfs = serial.run_single_config(config)
+            dfs = run_single_config(config)
             # df_mismatches, df_fit_results, df_results = dfs
 
     else:
@@ -25,6 +26,6 @@ def run_workflow(configs: fit_utils.Configs):
 
         with Pool(max_workers=parallel_samples) as pool:
             # for dfs in pool.imap_unordered(serial.run_single_config, configs):
-            for dfs in pool.map(serial.run_single_config, configs):
+            for dfs in pool.map(run_single_config, configs):
                 pass
                 # df_mismatches, df_fit_results, df_results = dfs

src/metaDMG/main.py

@@ -26,9 +26,9 @@ def compute_config(
 
     utils.check_metaDMG_fit()
 
-    from metaDMG.fit import make_configs, run_workflow
+    from metaDMG.fit import run_workflow
 
-    configs = make_configs(config_file=config_file, force=force)
+    configs = utils.make_configs(config_file=config_file, force=force)
 
     run_workflow(configs)