Quality of life improvement

src/pleiades/__main__.py

@@ -5,7 +5,8 @@
 
 
 def main():
-    print("Placeholder for PLEIADES main function")
+    # TODO: Implement PLEIADES CLI
+    raise NotImplementedError("PLEIADES CLI not yet implemented")
 
 
 if __name__ == "__main__":

src/pleiades/nuclear/manager.py

@@ -144,7 +144,7 @@ def clear_cache(self, method: Optional[DataRetrievalMethod] = None, library: Opt
                         # Call unlink directly on the file path
                         file.unlink()
                         logger.info(f"Deleted cached file: {file}")
-                    except Exception as e:
+                    except (OSError, PermissionError) as e:
                         logger.error(f"Failed to delete {file}: {str(e)}")
 
     def _get_data_from_direct(

src/pleiades/results/models.py

@@ -18,12 +18,12 @@ class AbundanceInfo(BaseModel):
 
 class BackgroundInfo(BaseModel):
     # TODO: Add attributes and methods for background information
-    pass
+    ...
 
 
 class NormalizationInfo(BaseModel):
     # TODO: Add attributes and methods for normalization information
-    pass
+    ...
 
 
 class PixelInfo(BaseModel):

src/pleiades/sammy/backends/local.py

@@ -1,9 +1,8 @@
 #!/usr/bin/env python
 """Local backend implementation for SAMMY execution."""
 
-import shlex
+import os
 import subprocess
-import textwrap
 from datetime import datetime
 from pathlib import Path
 from typing import List, Union
@@ -22,14 +21,6 @@
 
 logger = loguru_logger.bind(name=__name__)
 
-# Known SAMMY output file patterns
-SAMMY_OUTPUT_FILES = {
-    "SAMMY.LPT",  # Log file
-    "SAMMIE.ODF",  # Output data file
-    "SAMNDF.PAR",  # Updated parameter file
-    "SAMRESOLVED.PAR",  # Additional parameter file
-}
-
 
 class LocalSammyRunner(SammyRunner):
     """Implementation of SAMMY runner for local installation."""
@@ -111,36 +102,34 @@ def execute_sammy(self, files: Union[SammyFiles, SammyFilesMultiMode]) -> SammyE
         logger.debug(f"Working directory: {self.config.working_dir}")
 
         # Generate command based on file type
+        # Prepare input text for SAMMY based on mode
         if isinstance(files, SammyFilesMultiMode):
-            # JSON mode command format
-            sammy_command = textwrap.dedent(f"""\
-                {self.config.sammy_executable} <<EOF
-                {shlex.quote(files.input_file.name)}
-                #file {shlex.quote(files.json_config_file.name)}
-                {shlex.quote(files.data_file.name)}
-
-                EOF""")
-            logger.debug("Using JSON mode command format")
+            # JSON mode input format
+            sammy_input = f"{files.input_file.name}\n#file {files.json_config_file.name}\n{files.data_file.name}\n\n"
+            logger.debug("Using JSON mode input format")
         else:
-            # Traditional mode command format
-            sammy_command = textwrap.dedent(f"""\
-                {self.config.sammy_executable} <<EOF
-                {shlex.quote(files.input_file.name)}
-                {shlex.quote(files.parameter_file.name)}
-                {shlex.quote(files.data_file.name)}
-
-                EOF""")
-            logger.debug("Using traditional mode command format")
+            # Traditional mode input format
+            sammy_input = f"{files.input_file.name}\n{files.parameter_file.name}\n{files.data_file.name}\n\n"
+            logger.debug("Using traditional mode input format")
 
         try:
+            # Ensure libcrypto.so.1.1 is found by adding /usr/lib64 to LD_LIBRARY_PATH
+            env = dict(os.environ)
+            env.update(self.config.env_vars)
+            if "LD_LIBRARY_PATH" in env:
+                env["LD_LIBRARY_PATH"] = f"/usr/lib64:{env['LD_LIBRARY_PATH']}"
+            else:
+                env["LD_LIBRARY_PATH"] = "/usr/lib64"
+
+            # Use safer subprocess call without shell
             process = subprocess.run(
-                sammy_command,
-                shell=True,
-                executable=str(self.config.shell_path),
-                env=self.config.env_vars,
+                [str(self.config.sammy_executable)],
+                input=sammy_input,
+                shell=False,
+                text=True,
+                env=env,
                 cwd=str(self.config.working_dir),
                 capture_output=True,
-                text=True,
             )
 
             end_time = datetime.now()

src/pleiades/sammy/data/options.py

@@ -12,6 +12,10 @@
 
 logger = loguru_logger.bind(name=__name__)
 
+# Plot constants
+RESIDUAL_YLIM = (-1, 1)  # Y-axis limits for residual plots
+HISTOGRAM_BIN_RANGE = (-1, 1, 0.01)  # Range and step for histogram bins
+
 
 class DataTypeOptions(str, Enum):
     TRANSMISSION = "TRANSMISSION"
@@ -108,21 +112,44 @@ def validate_columns(self):
                 if col in self.data.columns:
                     raise ValueError(f"Unexpected transmission column for cross-section data: {col}")
 
-    def plot_transmission(self, show_diff=False, plot_uncertainty=False):
+    def plot_transmission(
+        self,
+        show_diff=False,
+        plot_uncertainty=False,
+        figsize=None,
+        title=None,
+        xscale="linear",
+        yscale="linear",
+        data_color="#433E3F",
+        final_color="#ff6361",
+        show=True,
+    ):
         """
         Plot the transmission data and optionally the residuals.
 
         Args:
             show_diff (bool): If True, plot the residuals.
             plot_uncertainty (bool): (Unused, for compatibility)
+            figsize (tuple): Figure size (width, height) in inches.
+            title (str): Plot title.
+            xscale (str): X-axis scale ('linear' or 'log').
+            yscale (str): Y-axis scale ('linear' or 'log').
+            data_color (str): Color for experimental data points.
+            final_color (str): Color for fitted theoretical curve.
+            show (bool): If True, display the plot. If False, return figure object.
+
+        Returns:
+            matplotlib.figure.Figure: The figure object if show=False, None otherwise.
         """
         if self.data is None:
             raise ValueError("No data loaded to plot.")
 
         data = self.data
-        data_color = "#433E3F"
         initial_color = "#003f5c"
-        final_color = "#ff6361"
+
+        # Use provided figsize or default
+        if figsize is None:
+            figsize = (8, 6)
 
         # Column name mapping for compatibility
         col_exp = "Experimental transmission (dimensionless)"
@@ -135,12 +162,12 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
                 2,
                 2,
                 sharey=False,
-                figsize=(8, 6),
+                figsize=figsize,
                 gridspec_kw={"width_ratios": [5, 1], "height_ratios": [5, 2]},
             )
             ax = np.ravel(ax)
         else:
-            fig, ax = plt.subplots(figsize=(8, 6))
+            fig, ax = plt.subplots(figsize=figsize)
             ax = [ax]
 
         # Plot experimental transmission as scatter with error bars if available
@@ -165,11 +192,20 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
                 color=final_color,
                 lw=1,
             )
+        # Apply scale settings first
+        ax[0].set_xscale(xscale)
+        ax[0].set_yscale(yscale)
+
+        # Then remove x-axis labels and ticks (for show_diff layout)
         ax[0].set_xlabel("")
         ax[0].set_xticks([])
         ax[0].legend(["data", "final fit"])
         ax[0].set_ylabel("transmission")
 
+        # Apply title if provided
+        if title:
+            ax[0].set_title(title)
+
         # Determine y-axis limits
         max_y = data[col_exp].max()
         min_y = data[col_exp].min()
@@ -202,13 +238,15 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
             )
             ax[2].set_ylabel("residuals\n(fit-data)/err [σ]")
             ax[2].set_xlabel("energy [eV]")
-            ax[2].set_ylim(-1, 1)
+            ax[2].set_ylim(*RESIDUAL_YLIM)
+            # Apply same x-scale to residual plot
+            ax[2].set_xscale(xscale)
 
             # Plot histograms of residuals
             if "residual_initial" in data.columns:
                 data.plot.hist(
                     y=["residual_initial"],
-                    bins=np.arange(-1, 1, 0.01),
+                    bins=np.arange(*HISTOGRAM_BIN_RANGE),
                     ax=ax[3],
                     orientation="horizontal",
                     legend=False,
@@ -235,7 +273,12 @@ def plot_transmission(self, show_diff=False, plot_uncertainty=False):
             ax[3].spines["left"].set_visible(False)
 
         plt.subplots_adjust(wspace=0.003, hspace=0.03)
-        plt.show()
+
+        if show:
+            plt.show()
+            return None
+        else:
+            return fig
 
     def plot_cross_section(self, show_diff=False, plot_uncertainty=False):
         """Plot the cross-section data."""

src/pleiades/sammy/fitting/config.py

@@ -48,23 +48,3 @@ def append_isotope_from_string(self, isotope_string: str) -> None:
             self.nuclear_params.isotopes.append(isotope_info)
         else:
             logger.error(f"Could not append Isotope {isotope_string}.")
-
-
-# example usage
-if __name__ == "__main__":
-    example_config = FitConfig(
-        fit_title="Example SAMMY Fit",
-        tolerance=1e-5,
-        max_iterations=100,
-        i_correlation=10,
-        max_cpu_time=3600.0,
-        max_wall_time=7200.0,
-        max_memory=8.0,
-        max_disk=100.0,
-        nuclear_params=nuclearParameters(),
-        physics_params=PhysicsParameters(),
-        data_params=SammyData(),
-        options_and_routines=FitOptions(),
-    )
-
-    print(example_config)

src/pleiades/sammy/interface.py

@@ -1,4 +1,4 @@
-#!/usr/env/bin python
+#!/usr/bin/env python
 """
 Interface definitions for SAMMY execution system.
 
@@ -396,15 +396,6 @@ def cleanup(self, files: SammyFiles) -> None:
         """
         raise NotImplementedError
 
-    async def __aenter__(self) -> "SammyRunner":
-        """Allow usage as async context manager."""
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
-        """Ensure cleanup on context exit."""
-        if hasattr(self, "files"):
-            await self.cleanup(self.files)
-
     @abstractmethod
     def validate_config(self) -> bool:
         """

src/pleiades/sammy/io/data_manager.py

@@ -6,7 +6,6 @@
 experimental transmission data.
 """
 
-import csv
 from pathlib import Path
 from typing import Union
 
@@ -21,7 +20,7 @@ def convert_csv_to_sammy_twenty(csv_file: Union[str, Path], twenty_file: Union[s
     """
     Convert transmission spectra from CSV to SAMMY twenty format.
 
-    This function supports both tab- and comma-separated CSV files, with either two columns
+    This function supports tab-, comma-, and space-separated files, with either two columns
     (energy, transmission) or three columns (energy, transmission, uncertainty).
     If only two columns are present, the uncertainty column will be filled with 0.0.
 
@@ -30,11 +29,13 @@ def convert_csv_to_sammy_twenty(csv_file: Union[str, Path], twenty_file: Union[s
         twenty_file: Path to output SAMMY twenty format file
 
     File Formats:
-        Input CSV (tab or comma separated):
+        Input CSV (tab, comma, or space separated):
             "energy_eV,transmission,uncertainty\n6.673,0.932,0.272\n"
             or
             "energy_eV\ttransmission\tuncertainty\n6.673\t0.932\t0.272\n"
             or
+            "# Energy(eV)  Transmission  Uncertainty\n6.673240e+00 1.003460e+00 7.242967e-03\n"
+            or
             "energy_eV,transmission\n6.673,0.932\n"
         Output twenty:
             "        6.6732397079        0.9323834777        0.2727669477\n"
@@ -51,21 +52,54 @@ def convert_csv_to_sammy_twenty(csv_file: Union[str, Path], twenty_file: Union[s
     """
     logger.info(f"Converting {csv_file} to SAMMY twenty format: {twenty_file}")
 
-    # Use csv.Sniffer to detect delimiter
-    with open(csv_file, "r", newline="") as f:
-        sample = f.read(2048)  # Read a sample of the file for delimiter detection
-        f.seek(0)  # Reset file pointer to start
-
-        sniffer = csv.Sniffer()
-        dialect = sniffer.sniff(sample, delimiters=[",", "\t"])  # Detect comma or tab delimiter
-        delimiter = dialect.delimiter
-
-        # Create a CSV reader with the detected delimiter
-        reader = csv.reader(f, delimiter=delimiter)
-        header = next(reader)  # Skip header row
-
-        # Read the remaining rows, skipping empty lines
-        data = [row for row in reader if row and any(field.strip() for field in row)]
+    data = []
+
+    with open(csv_file, "r") as f:
+        lines = f.readlines()
+
+    # Skip header lines (comments starting with # or containing non-numeric first field)
+    for line in lines:
+        # Strip whitespace and skip empty lines
+        line = line.strip()
+        if not line:
+            continue
+
+        # Skip comment lines
+        if line.startswith("#"):
+            continue
+
+        # Try to parse the line with different delimiters
+        # First try splitting by whitespace (most common for scientific data)
+        fields = line.split()
+
+        # If that doesn't give us 2 or 3 fields, try comma
+        if len(fields) not in [2, 3]:
+            fields = line.split(",")
+
+        # If still not right, try tab
+        if len(fields) not in [2, 3]:
+            fields = line.split("\t")
+
+        # Skip lines that don't have the right number of fields
+        if len(fields) not in [2, 3]:
+            # Check if this might be a header line
+            try:
+                float(fields[0])
+            except (ValueError, IndexError):
+                continue  # Skip header lines
+            logger.warning(f"Skipping line with {len(fields)} fields: {line[:50]}...")
+            continue
+
+        # Try to convert to floats
+        try:
+            numeric_fields = [float(field) for field in fields]
+            data.append(numeric_fields)
+        except ValueError:
+            # This is likely a header line, skip it
+            continue
+
+    if not data:
+        raise ValueError(f"No valid data found in {csv_file}")
 
     # Convert data to numpy array of floats
     data = np.array(data, dtype=float)