Test files received

Author: mvdh7

tests/_test_geomar_dec25.py

@@ -0,0 +1,87 @@
+# %%
+import os
+
+import pandas as pd
+
+import calkulate as calk
+
+
+# Set the acid concentration (mol/kg) to use for "uncalibrated" alkalinity
+titrant_molinity_guess = 0.01
+
+# Set the file paths where the data files can be found
+path_to_files = "tests/data/geomar-dec25/"
+metadata_file = "Acidbatch 4.xlsx"
+subpath = "Acidbatch 4 20240712 to 20240725"
+#
+# This assumes a folder structure like the following:
+# .
+# └── tests/data/geomar-dec25/              (<-- defined by `path_to_files`)
+#     ├── Acidbatch 4.xlsx                  (<-- defined by `metadata_file`)
+#     └── Acidbatch 4 20240712 to 20240725/ (<-- defined by `subpath`)
+#         ├── 20240712/
+#         │   ├── Junk-SW-01.old
+#         │   ├── Junk-SW-02.old
+#         │   └── ...
+#         ├── 20240716/
+#         │   └── ...
+#         └── ...
+#
+# The subfolder names (20240712, 20240716, etc.) need to be specified in the
+# `session` column of the metadata spreadsheet.
+#
+# The file names, excluding the ".old", need to be in the `name` column.
+#
+#            ==================================================
+#
+#             You shouldn't need to change anything below here
+#                 (but there is more info at the bottom)
+#
+#            ==================================================
+#
+# Next, we use the information provided above to construct columns containing
+# the correct file paths and file names
+ds = pd.read_excel(os.path.join(path_to_files, metadata_file))
+ds["file_path"] = [
+    os.path.join(path_to_files, subpath, str(int(session)))
+    if pd.notnull(session)
+    else None
+    for session in ds.session
+]
+ds["file_name"] = ds.name + ".old"
+ds["file_good"] = ds.file_name.notnull()
+
+# Define some settings we need to read your data files properly
+kwargs_tiamo = dict(
+    file_type="tiamo_de",
+    solve_mode="pH_gran",  # can try "pH" or "pH_adjust" or "pH_gran"
+)
+
+# First, manually set the acid concentration (`titrant_molinity`) and solve
+ds["titrant_molinity"] = titrant_molinity_guess
+calk.solve(ds, **kwargs_tiamo)
+ds["alkalinity_uncalibrated"] = ds.alkalinity.copy()
+
+# Then, calibrate with CRMs and solve for alkalinity again
+calk.calibrate(ds, **kwargs_tiamo)
+
+# Export results to a new spreadsheet
+ds.to_excel(
+    os.path.join(
+        path_to_files,
+        metadata_file.replace(".xlsx", " processed.xlsx"),
+    ),
+    index=False,
+)
+# Columns of interest
+# -------------------
+#   alkalinity_uncalibrated
+#     TA with the manually set acid concentration (µmol/kg-sw)
+#   alkalinity
+#     Final TA values, calibrated to CRMs (µmol/kg-sw)
+#   alkalinity_offset
+#     Difference between measured and certified CRM values (µmol/kg-sw)
+#   titrant_molinity_here
+#     Best-fitting acid concentration for each CRM (mol/kg)
+#   titrant_molinity
+#     Average of titrant_molinity_here, used for the final alkalinity values