diff --git a/src/pymatlib/core/data_handler.py b/src/pymatlib/core/data_handler.py
index 3cfa095350dfcd312cfd31e2b8ce5d1b3d95f873..a525f0acfe9539de9345e3d2bd68cd4ce82e9fa6 100644
--- a/src/pymatlib/core/data_handler.py
+++ b/src/pymatlib/core/data_handler.py
@@ -103,7 +103,7 @@ def read_data_from_excel(file_path: str, temp_col: str, prop_col: str) -> Tuple[
return temp, prop
-def read_data_from_file(file_config: Union[str, Dict], header: bool = True) -> Tuple[np.ndarray, np.ndarray]:
+def read_data_from_file1(file_config: Union[str, Dict], header: bool = True) -> Tuple[np.ndarray, np.ndarray]:
"""
Reads temperature and property data from a file based on configuration.
@@ -118,6 +118,12 @@ def read_data_from_file(file_config: Union[str, Dict], header: bool = True) -> T
Returns:
Tuple[np.ndarray, np.ndarray]: Temperature and property arrays
+
+ Raises:
+ ValueError: If:
+ - For txt files: Data has incorrect number of columns (must be exactly 2)
+ - Data contains NaN values
+ - Data contains duplicate temperature entries
"""
# Handle string (direct path) or dictionary configuration
if isinstance(file_config, str):
@@ -144,33 +150,228 @@ def read_data_from_file(file_config: Union[str, Dict], header: bool = True) -> T
else:
# For txt files, assume columns are space/tab separated
data = np.loadtxt(file_path, dtype=float, skiprows=1 if header else 0)
- if data.ndim != 2:
+
+ # Check for correct dimensions - only for txt files when using direct path
+ if isinstance(file_config, str) and (data.ndim != 2 or data.shape[1] != 2):
+ raise ValueError("Data should have exactly two columns")
+ elif data.ndim != 2:
raise ValueError("Data should be two-dimensional")
# Handle both column name (which would be an index for txt files) and column index
if isinstance(temp_col, int):
+ if temp_col >= data.shape[1]:
+ raise ValueError(f"Temperature column index {temp_col} out of bounds (file has {data.shape[1]} columns)")
temp = data[:, temp_col]
else:
temp = data[:, 0] # Default to first column
if isinstance(prop_col, int):
+ if prop_col >= data.shape[1]:
+ raise ValueError(f"Property column index {prop_col} out of bounds (file has {data.shape[1]} columns)")
prop = data[:, prop_col]
else:
prop = data[:, 1] # Default to second column
- # Skip the pandas processing below
+ # Check for NaN values
+ if np.any(np.isnan(temp)) or np.any(np.isnan(prop)):
+ nan_rows = np.where(np.isnan(temp) | np.isnan(prop))[0] + 1
+ raise ValueError(f"Data contains NaN values in rows: {', '.join(map(str, nan_rows))}")
+
+ # Check for duplicate temperatures
+ unique_temp, counts = np.unique(temp, return_counts=True)
+ duplicates = unique_temp[counts > 1]
+ if len(duplicates) > 0:
+ duplicate_rows = [str(idx + 1) for idx, value in enumerate(temp) if value in duplicates]
+ raise ValueError(f"Duplicate temperature entries found in rows: {', '.join(duplicate_rows)}")
+
return temp, prop
# Process pandas DataFrame (for both Excel and CSV)
# Handle both column name (string) and column index (integer)
if isinstance(temp_col, str):
+ if temp_col not in df.columns:
+ raise ValueError(f"Temperature column '{temp_col}' not found in file")
temp = df[temp_col].to_numpy(dtype=np.float64)
else:
+ if temp_col >= len(df.columns):
+ raise ValueError(f"Temperature column index {temp_col} out of bounds (file has {len(df.columns)} columns)")
temp = df.iloc[:, temp_col].to_numpy(dtype=np.float64)
if isinstance(prop_col, str):
+ if prop_col not in df.columns:
+ raise ValueError(f"Property column '{prop_col}' not found in file")
prop = df[prop_col].to_numpy(dtype=np.float64)
else:
+ if prop_col >= len(df.columns):
+ raise ValueError(f"Property column index {prop_col} out of bounds (file has {len(df.columns)} columns)")
+ prop = df.iloc[:, prop_col].to_numpy(dtype=np.float64)
+
+ # Check for NaN values
+ if np.any(np.isnan(temp)) or np.any(np.isnan(prop)):
+ nan_rows = np.where(np.isnan(temp) | np.isnan(prop))[0] + 1
+ raise ValueError(f"Data contains NaN values in rows: {', '.join(map(str, nan_rows))}")
+
+ # Check for duplicate temperatures
+ unique_temp, counts = np.unique(temp, return_counts=True)
+ duplicates = unique_temp[counts > 1]
+ if len(duplicates) > 0:
+ duplicate_rows = [str(idx + 1) for idx, value in enumerate(temp) if value in duplicates]
+ raise ValueError(f"Duplicate temperature entries found in rows: {', '.join(duplicate_rows)}")
+
+ return temp, prop
+
+
+def read_data_from_file(file_config: Union[str, Dict], header: bool = True) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Reads temperature and property data from a file based on configuration.
+
+ Args:
+ file_config: Either a path string or a dictionary containing file configuration
+ If string (direct path):
+ - File must have exactly 2 columns
+ - First column is temperature, second is property
+ If dictionary:
+ - file: Path to data file
+ - temp_col: Temperature column name/index
+ - prop_col: Property column name/index
+ header (bool): Indicates if the file contains a header row.
+
+ Returns:
+ Tuple[np.ndarray, np.ndarray]: Temperature and property arrays
+
+ Raises:
+ ValueError: If:
+ - For direct path: Data doesn't have exactly two columns
+ - For dictionary config: Specified column names don't match headers
+ - Data contains NaN values
+ - Data contains duplicate temperature entries
+ """
+ # Handle string (direct path) or dictionary configuration
+ if isinstance(file_config, str):
+ file_path = file_config
+ direct_path = True
+ # For direct file paths, assume first two columns are temperature and property
+ temp_col = 0
+ prop_col = 1
+ else:
+ file_path = file_config['file']
+ direct_path = False
+ temp_col = file_config['temp_col']
+ prop_col = file_config['prop_col']
+
+ print(f"Reading data from file: {file_path}")
+
+ if file_path.endswith('.xlsx'):
+ df = pd.read_excel(file_path, header=0 if header else None)
+ elif file_path.endswith('.csv'):
+ # Use pandas read_csv for CSV files
+ df = pd.read_csv(file_path, header=0 if header else None)
+ else:
+ # For txt files
+ if header:
+ # Read the header line to get column names
+ with open(file_path, 'r') as f:
+ header_line = f.readline().strip()
+ column_names = header_line.split()
+
+ # Now read the data
+ data = np.loadtxt(file_path, dtype=float, skiprows=1)
+
+ # Direct path case - check for exactly 2 columns
+ if direct_path:
+ if data.shape[1] != 2:
+ raise ValueError(f"Data should have exactly two columns, but found {data.shape[1]} columns")
+ temp = data[:, 0]
+ prop = data[:, 1]
+ # Dictionary case - match column names
+ else:
+ # Handle temperature column
+ if isinstance(temp_col, str):
+ if temp_col in column_names:
+ temp_idx = column_names.index(temp_col)
+ else:
+ raise ValueError(f"Temperature column '{temp_col}' not found in file. "
+ f"Available columns: {', '.join(column_names)}")
+ else:
+ if temp_col >= data.shape[1]:
+ raise ValueError(f"Temperature column index {temp_col} out of bounds (file has {data.shape[1]} columns)")
+ temp_idx = temp_col
+
+ # Handle property column
+ if isinstance(prop_col, str):
+ if prop_col in column_names:
+ prop_idx = column_names.index(prop_col)
+ else:
+ raise ValueError(f"Property column '{prop_col}' not found in file. "
+ f"Available columns: {', '.join(column_names)}")
+ else:
+ if prop_col >= data.shape[1]:
+ raise ValueError(f"Property column index {prop_col} out of bounds (file has {data.shape[1]} columns)")
+ prop_idx = prop_col
+
+ temp = data[:, temp_idx]
+ prop = data[:, prop_idx]
+ else:
+ # No header
+ data = np.loadtxt(file_path, dtype=float, skiprows=0)
+
+ # Direct path case - check for exactly 2 columns
+ if direct_path:
+ if data.shape[1] != 2:
+ raise ValueError(f"Data should have exactly two columns, but found {data.shape[1]} columns")
+ temp = data[:, 0]
+ prop = data[:, 1]
+ # Dictionary case - use column indices
+ else:
+ if isinstance(temp_col, str):
+ raise ValueError(f"Column name '{temp_col}' specified, but file has no header row")
+ if isinstance(prop_col, str):
+ raise ValueError(f"Column name '{prop_col}' specified, but file has no header row")
+
+ if temp_col >= data.shape[1]:
+ raise ValueError(f"Temperature column index {temp_col} out of bounds (file has {data.shape[1]} columns)")
+ if prop_col >= data.shape[1]:
+ raise ValueError(f"Property column index {prop_col} out of bounds (file has {data.shape[1]} columns)")
+
+ temp = data[:, temp_col]
+ prop = data[:, prop_col]
+
+ # Check for NaN values
+ if np.any(np.isnan(temp)) or np.any(np.isnan(prop)):
+ nan_rows = np.where(np.isnan(temp) | np.isnan(prop))[0] + 1
+ raise ValueError(f"Data contains NaN values in rows: {', '.join(map(str, nan_rows))}")
+
+ # Check for duplicate temperatures
+ unique_temp, counts = np.unique(temp, return_counts=True)
+ duplicates = unique_temp[counts > 1]
+ if len(duplicates) > 0:
+ duplicate_rows = [str(idx + 1) for idx, value in enumerate(temp) if value in duplicates]
+ raise ValueError(f"Duplicate temperature entries found in rows: {', '.join(duplicate_rows)}")
+
+ return temp, prop
+
+ # Process pandas DataFrame (for both Excel and CSV)
+ # Handle both column name (string) and column index (integer)
+ if isinstance(temp_col, str):
+ if temp_col in df.columns:
+ temp = df[temp_col].to_numpy(dtype=np.float64)
+ else:
+ raise ValueError(f"Temperature column '{temp_col}' not found in file. "
+ f"Available columns: {', '.join(df.columns)}")
+ else:
+ if temp_col >= len(df.columns):
+ raise ValueError(f"Temperature column index {temp_col} out of bounds (file has {len(df.columns)} columns)")
+ temp = df.iloc[:, temp_col].to_numpy(dtype=np.float64)
+
+ if isinstance(prop_col, str):
+ if prop_col in df.columns:
+ prop = df[prop_col].to_numpy(dtype=np.float64)
+ else:
+ raise ValueError(f"Property column '{prop_col}' not found in file. "
+ f"Available columns: {', '.join(df.columns)}")
+ else:
+ if prop_col >= len(df.columns):
+ raise ValueError(f"Property column index {prop_col} out of bounds (file has {len(df.columns)} columns)")
prop = df.iloc[:, prop_col].to_numpy(dtype=np.float64)
# Check for NaN values
diff --git a/src/pymatlib/data/element_data.py b/src/pymatlib/data/element_data.py
index c6412d708106cd3194a4f9adedc62aa476928b1a..889b4e073d493d3bc9b7a6e605d53c6b36c38071 100644
--- a/src/pymatlib/data/element_data.py
+++ b/src/pymatlib/data/element_data.py
@@ -1,4 +1,4 @@
-from pymatlib.core.constants import Constants
+from pymatlib.data.constants import Constants
from pymatlib.core.elements import ChemicalElement
# NIST: National Institute of Standards and Technology