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minimal_set tests for computations first commit
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@mpecchi
mpecchi committed Mar 26, 2024
1 parent 037c0ca commit 3646b50
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223 changes: 1 addition & 222 deletions example/name_to_properties/example_name_to_properties.py
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Expand Up @@ -11,209 +11,6 @@
from gcms_data_analysis import name_to_properties


# def get_compound_from_pubchempy(comp_name: str) -> pcp.Compound:
# if not isinstance(comp_name, str):
# return None
# if comp_name == " " or comp_name == "":
# return None
# cond = True
# while cond: # to deal with HTML issues on server sides (timeouts)
# try:
# # comp contains all info about the chemical from pubchem
# try:
# comp_inside_list = pcp.get_compounds(comp_name, "name")
# except ValueError:
# print(f"{comp_name = }")
# return None
# if comp_inside_list:
# comp = comp_inside_list[0]
# else:
# print(
# f"WARNING: name_to_properties {comp_name=} does not find an entry in pcp",
# )
# return None
# cond = False
# except pcp.PubChemHTTPError: # timeout error, simply try again
# print("Caught: pcp.PubChemHTTPError (keep trying)")
# return comp


# def _order_columns_in_compounds_properties(
# unsorted_df: pd.DataFrame | None,
# ) -> pd.DataFrame | None:
# if unsorted_df is None:
# return None

# # Define a custom sort key function
# def sort_key(col):
# if col.startswith("el_mf"):
# return (2, col)
# elif col.startswith("el_"):
# return (1, col)
# elif col.startswith("fg_mf_unclassified"):
# return (5, col)
# elif col.startswith("fg_mf"):
# return (4, col)
# elif col.startswith("fg_"):
# return (3, col)
# else:
# return (0, col)

# # Sort columns using the custom key
# sorted_columns = sorted(unsorted_df.columns, key=sort_key)
# sorted_df = unsorted_df.reindex(sorted_columns, axis=1)
# sorted_df.index.name = "comp_name"
# # Reindex the DataFrame with the sorted columns
# return sorted_df


# def name_to_properties2(
# comp_name: str,
# dict_classes_to_codes: dict[str:str],
# dict_classes_to_mass_fractions: dict[str:float],
# df: pd.DataFrame | None = None,
# precision_sum_elements: float = 0.05,
# precision_sum_functional_group: float = 0.05,
# ) -> pd.DataFrame | None:
# """
# used to retrieve chemical properties of the compound indicated by the
# comp_name and to store those properties in the df

# Parameters
# ----------
# GCname : str
# name from GC, used as a unique key.
# search_name : str
# name to be used to search on pubchem.
# df : pd.DataFrame
# that contains all searched compounds.
# df_class_code_frac : pd.DataFrame
# contains the list of functional group names, codes to be searched
# and the weight fraction of each one to automatically calculate the
# mass fraction of each compounds for each functional group.
# Classes are given as smarts and are looked into the smiles of the comp.

# Returns
# -------
# df : pd.DataFrame
# updated dataframe with the searched compound.
# CompNotFound : str
# if GCname did not yield anything CompNotFound=GCname.

# """
# # classes used to split compounds into functional groups
# comp = get_compound_from_pubchempy(comp_name)

# if comp is None:
# if not isinstance(comp_name, str):
# return df
# else:
# if not comp_name or comp_name.isspace():
# return df
# else:
# if df is not None:
# df.loc[comp_name, "iupac_name"] = "unidentified"
# return df
# if df is None:
# df = pd.DataFrame(dtype=float)
# try:
# df.loc[comp_name, "iupac_name"] = comp.iupac_name.lower()
# except AttributeError: # iupac_name not give
# df.loc[comp_name, "iupac_name"] = comp_name.lower()
# df.loc[comp_name, "molecular_formula"] = comp.molecular_formula
# df.loc[comp_name, "canonical_smiles"] = comp.canonical_smiles
# df.loc[comp_name, "molecular_weight"] = float(comp.molecular_weight)

# try:
# df.loc[comp_name, "xlogp"] = float(comp.xlogp)
# except (
# TypeError
# ): # float() argument must be a string or a real number, not 'NoneType'
# df.loc[comp_name, "xlogp"] = np.nan
# elements = set(comp.to_dict()["elements"])
# el_dict = {}
# el_mf_dict = {}

# for el in elements:
# el_count = comp.to_dict()["elements"].count(el)
# el_mass = ele.element_from_symbol(el).mass

# # Using similar logic as in the fg_dict example
# if el not in el_dict:
# el_dict[el] = 0
# el_mf_dict[el] = 0.0

# el_dict[el] += int(el_count)
# el_mf_dict[el] += (
# float(el_count) * float(el_mass) / float(comp.molecular_weight)
# )
# # Now, update the DataFrame in a similar way to the fg_dict example
# for key, value in el_dict.items():
# df.at[comp_name, f"el_{key}"] = int(value)

# for key, value in el_mf_dict.items():
# df.at[comp_name, f"el_{key}"] = float(value)
# cols_el_mf = [col for col in df.columns if col.startswith("el_mf")]
# residual_els = df.loc[comp_name, cols_el_mf].sum() - 1
# # check element sum
# try:
# assert residual_els <= precision_sum_elements
# except AssertionError:
# raise AssertionError(
# f"the total mass fraction of elements in {comp_name =} is > 0.001"
# )
# # apply fragmentation using the Fragmenter class (thanks simonmb)
# frg = Fragmenter(
# dict_classes_to_codes,
# fragmentation_scheme_order=dict_classes_to_codes.keys(),
# algorithm="simple",
# )
# fragmentation, _, _ = frg.fragment(comp.canonical_smiles)
# fg_dict = {}
# fg_mf_dict = {}
# # Iterate over each item in the dictionary
# for key, value in fragmentation.items():
# # Determine the root key (the part before an underscore, if present)
# root_key = key.split("_")[0]
# # if root_key in hetero_atoms:
# # pass
# # Check if the root key is in the sum_dict; if not, initialize it
# if root_key not in fg_dict:
# fg_dict[root_key] = 0
# fg_mf_dict[root_key] = 0
# # Add the value to the corresponding root key in the sum_dict
# fg_dict[root_key] += int(fragmentation[key])
# fg_mf_dict[root_key] += (
# float(fragmentation[key])
# * float(dict_classes_to_mass_fractions[key])
# / df.loc[comp_name, "molecular_weight"].astype(float)
# ) # mass fraction of total

# # Update df with fg_dict
# for key, value in fg_dict.items():
# df.at[comp_name, f"fg_{key}"] = int(value) # Update the cell
# # Update df with fg_mf_dict
# for key, value in fg_mf_dict.items():
# df.at[comp_name, f"fg_mf_{key}"] = float(value) # Update the cell
# cols_fg_mf = [col for col in df.columns if col.startswith("fg_mf")]
# residual_fgs = df.loc[comp_name, cols_fg_mf].sum() - 1
# try:
# assert residual_fgs <= precision_sum_functional_group
# except AssertionError:
# print(f"{df.loc[comp_name, cols_fg_mf].sum()=}")
# raise AssertionError(
# f"the total mass fraction of functional groups in {comp_name =} is > 0.05"
# )
# if residual_fgs < -precision_sum_functional_group:
# df.at[comp_name, f"fg_mf_unclassified"] = abs(residual_fgs)
# df.loc[df["iupac_name"] != "unidentified"] = df.loc[
# df["iupac_name"] != "unidentified"
# ].fillna(0)
# df = _order_columns_in_compounds_properties(df)

# return df


folder_path = plib.Path(
r"C:\Users\mp933\OneDrive - Cornell University\Python\gcms_data_analysis\tests\data_name_to_properties"
)
Expand Down Expand Up @@ -269,18 +66,7 @@
compound, dict_cl_to_codes, dict_cl_to_mass_fractions, None
)
list_of_compound_properties.append(n2p)
if n2p is not None:
to_check = n2p.loc[[compound], :]
to_check = to_check.loc[:, (to_check != 0).any(axis=0)]
checked = checked_compounds_properties.loc[[compound], :]
checked = checked.loc[:, (checked != 0).any(axis=0)]
pd.testing.assert_frame_equal(
to_check,
checked,
check_exact=False,
atol=1e-5,
rtol=1e-5,
)

# %%
to_check = pd.DataFrame()
for compound in compounds:
Expand All @@ -291,12 +77,5 @@
dict_cl_to_mass_fractions,
to_check,
)
pd.testing.assert_frame_equal(
to_check,
checked_compounds_properties,
check_exact=False,
atol=1e-5,
rtol=1e-5,
)

# %%
3 changes: 3 additions & 0 deletions scripts/utils.py
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Expand Up @@ -13,6 +13,9 @@


# %%
from collections.abc import Iterable


def print_checked_df_to_script_text_with_arrays(df):
# Convert the DataFrame to a dictionary with 'list' orientation
df_dict = df.to_dict(orient="list")
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0 tests/test_gcms_data_analysis.py → tests/AAAA_test_gcms_data_analysis.py
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File renamed without changes.
0 tests/test_name_to_properties.py → tests/ZZZ_test_name_to_properties.py
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