change files of 1_import_file

streamlit_app/FAIR_MS_Library_Editor.py

@@ -50,23 +50,21 @@
     st.markdown("## Datasets")
     if 'datasets' not in st.session_state or st.session_state['datasets'] == {}:
         st.warning("Please upload a file to begin!")
-    if 'selected_sheets' not in st.session_state or st.session_state['selected_sheets'] == {}:
-        st.warning("Please select a dataset to begin!")
+    #if 'selected_sheets' not in st.session_state or st.session_state['selected_sheets'] == {}:
+        #st.warning("Please select a dataset to begin!")
     # with st.spinner("Loading..."):
     #     time.sleep(5)
     # st.success("Done!")
-    if 'datasets' in st.session_state and st.session_state['datasets'] != {}:
-        for key in st.session_state['selected_sheets']:
-            with st.expander(key):
-                datasets = st.session_state['datasets']
-                rowsMetricColumn, columnsMetricColumn = st.columns(2)
-                with rowsMetricColumn:
-                    st.metric('Rows', datasets[key].shape[0])
-                with columnsMetricColumn:
-                    st.metric('Columns', datasets[key].shape[1])
+    if 'df_spectra' in st.session_state:
+
+        df_spectra = st.session_state['df_spectra']
+        st.metric('Detected how many spectra', len(df_spectra))
                 # if st.button("Edit", key=key):
                 #     selected_sheet = key
                 # if key in datasets_metadata:
                 #     st.write(datasets_metadata[key].keys())
             # st.write(datasets[dataset_key])
         # st.json(datasets)
+
+    if 'spectra' in st.session_state:
+        spectra = st.session_state["spectra"]

streamlit_app/README.md

@@ -11,6 +11,15 @@ Use python venv to use defined dependencies.
     python -m venv venv
     source venv/bin/activate
 
+
+### windows
+	1. cd C:/Users/rfm848
+	2. python -m venv venv
+	3. .\venv\Scripts\activate.bat
+	4. Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser 
+    5. .\venv\Scripts\activate.ps1
+
+
 to activate the virtual environment.
 
 You can then use the provided requirements.txt to populate the required dependencies in your virtual environment.

library_spectra_validation/library_handler.py → streamlit_app/library_spectra_validation/library_handler.py

@@ -161,4 +161,4 @@ def user_rerun_repair(self, spectrum_id, rerun: bool):
             self.modifications[spectrum_id] = self.spectrum_repairer.process_spectrum_store_modifications(self.spectra[spectrum_id])
             self.failed_requirements[spectrum_id] = self.spectrum_validator.process_spectrum_store_failed_filters(self.spectra[spectrum_id])
 
-
+

library_spectra_validation/tests/test_library_handler.py → streamlit_app/library_spectra_validation/tests/test_library_handler.py

@@ -62,9 +62,10 @@ def test_user_metadata_change():
     library_handler.user_metadata_change(spectrum_id=spectrum_id, field_name="smiles", user_input="CCC")
     assert library_handler.spectra[spectrum_id].get("smiles") == "CCC"
 
+
 def test_return_():
     library_handler = LibraryHandler("./examples/test_case_correct.mgf")
     spectrum_id = 0
     modifications, failed_requirements, metadata = library_handler.return_user_validation_info(spectrum_id=spectrum_id)
     # todo add checks that tests that the expected output is given.
-    print(metadata)
+    print(metadata)

library_spectra_validation/validation_pipeline.py → streamlit_app/library_spectra_validation/validation_pipeline.py

@@ -18,7 +18,6 @@
 METADATA_FIELDS_OF_INTEREST = ["parent_mass", "precursor_mz", "adduct", "smiles",
                                 "compound_name", "inchi", "inchikey", "charge", "ionmode"]
 
-
 class Modification:
     def __init__(self, metadata_field, before, after, logging_message, validated_by_user):
         self.metadata_field = metadata_field

streamlit_app/pages/1_File_Import.py

@@ -1,9 +1,15 @@
+from library_spectra_validation.library_handler import LibraryHandler
+import os
+
 import streamlit as st
 import pandas as pd
+from matchms.importing import load_from_mgf
+from tempfile import NamedTemporaryFile
+
 
 st.set_page_config(
     layout="wide", 
-    page_title="File Import - FAIR MS Library Curation Editor", 
+    page_title="File Import (.mgf) - FAIR MS Library Curation Editor", 
     #page_icon="assets/favicon.ico",
     menu_items={
         'Get Help': 'https://github.com/mzmine/biohack23_p15',
@@ -12,10 +18,14 @@
     }
 )
 
-st.markdown("## File Import")
-st.markdown("Please select an Excel file to upload. The file should contain one or more sheets. Each sheet should contain sample columns, detailing factors of each individual sample (rows). Lipid identities are the column headers of the non-sample columns, quantities should be reported in the cells.")
+st.markdown("## File Import (.mgf)")
+st.markdown("Please select an mgf to upload.")
+
+uploaded_file = st.file_uploader("Choose a file", type = ".mgf")
+st.set_option('deprecation.showfileUploaderEncoding', False)
+
+
 
-uploaded_file = st.file_uploader("Choose a file", )
 if uploaded_file is not None:
     print(uploaded_file)
     st.session_state['uploaded_file'] = uploaded_file
@@ -29,35 +39,41 @@
             datasets = st.session_state['datasets']
         else:
             st.session_state['datasets'] = datasets
+
+    mgf_file = os.path.join(st.session_state['working_dir'], uploaded_file.name)
+
+    with open(file=mgf_file, mode="wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # load spectra from mgf, TODO: replace with SpectralLibrary implementation and import
+    # we will receive a list of spectra metadata (each being a dataframe)
+    # TODO: we can retrieve each spectrum from the SpectralLibrary object
+    # TODO: display forward and backward buttons to page through spectra
+
+    lib_handler = LibraryHandler(f.name)
+
+    spectra_temp = load_from_mgf(f.name)
+    spectra = list(spectra_temp)
+    df_spectra = pd.DataFrame({"spectrum": spectra})
 
-        xl = pd.ExcelFile(uploaded_file)
-        sheets = xl.sheet_names
-        for sheet in sheets:
-            if sheet not in datasets:
-                df = pd.read_excel(uploaded_file, sheet_name=sheet)
-                datasets[sheet] = df
-
-        st.markdown("## Preview Sheets")
-        sheet_selector = st.selectbox(
-                "Select a sheet",
-                sheets
-            )
-        if sheet_selector is not None and sheet_selector in datasets:
-            rowsMetricColumn, columnsMetricColumn = st.columns(2)
-            with rowsMetricColumn:
-                st.metric('Rows', datasets[sheet_selector].shape[0])
-            with columnsMetricColumn:
-                st.metric('Columns', datasets[sheet_selector].shape[1])
-            st.write(datasets[sheet_selector])
-
-        st.markdown("## Select Sheets as Datasets")
-        selected_sheets = st.multiselect(
-            'Each selected sheet will be converted to a dataset',
-            sheets,
-            sheets
-        )
-        st.session_state['datasets'] = datasets
-        st.session_state['selected_sheets'] = selected_sheets
-
-        if 'datasets' not in st.session_state:
-            st.session_state['datasets'] = []
+    # make dataframe for metadata
+    def extract_metadata(df, keys):
+        for key in keys:
+            df[key] = df["spectrum"].apply(lambda x: x.get(key))
+
+
+    extract_metadata(df_spectra, df_spectra["spectrum"][0].metadata.keys())
+
+    st.markdown("## Preview Information")
+
+    st.metric('Detected how many spectra', len(df_spectra))
+
+    st.data_editor(df_spectra)# , on_change=user_metadata_change())
+
+
+    st.session_state['spectra'] = spectra
+    st.session_state['df_spectra'] = df_spectra
+    st.session_state['len_spectra'] = len(df_spectra)
+
+    if 'df_spectra' not in st.session_state:
+        st.session_state['df_spectra'] = []

streamlit_app/pages/2_Plot_spectrum.py

@@ -0,0 +1,58 @@
+from matchms.plotting.spectrum_plots import plot_spectra_mirror, plot_spectrum
+import streamlit as st
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import pubchempy
+from rdkit import Chem
+from rdkit.Chem import Draw
+
+
+
+
+st.set_page_config(
+    layout="wide", 
+    page_title="Library Export - FAIR MS Library Curation Editor", 
+    #page_icon="assets/favicon.ico",
+    menu_items={
+        'Get Help': 'https://github.com/mzmine/biohack23_p15',
+        'Report a bug': "https://github.com/mzmine/biohack23_p15/issues/new/choose",
+        'About': "# This is the creation and curation wizard for FAIR MS Libraries."
+    }
+)
+
+
+st.markdown("## Plot selected MS2 spectrum")
+st.markdown("Please select a spectrum based on compound_name")
+
+
+spectra = st.session_state['spectra'] 
+df_spectra = st.session_state['df_spectra']
+
+cmp_list = df_spectra["compound_name"].tolist()
+
+st.markdown("## Select a compound name")
+cmp_selector = st.selectbox(
+    "select a compound name",
+    cmp_list
+)
+
+if cmp_selector in cmp_list:
+    cmp_id = cmp_list.index(cmp_selector)
+    cmp_smile = df_spectra.loc[cmp_id]["smiles"]
+
+
+    plt_spectrum = spectra[cmp_id]
+
+    fig, axs = plt.subplots(1, 2, figsize=(12.8, 4.2), gridspec_kw={'width_ratios': [2, 5]}, sharey=False)
+    cmp_img = Chem.Draw.MolToImage(Chem.MolFromSmiles(cmp_smile), ax=axs[0])
+
+    axs[0].grid(False)
+    axs[0].tick_params(axis='both', bottom=False, labelbottom=False, left=False, labelleft=False)
+    axs[0].set_title(cmp_smile)
+    axs[0].imshow(cmp_img)
+    axs[0].axis("off")
+
+    plot_spectrum(plt_spectrum, axs[1])
+
+    st.pyplot(fig)

streamlit_app/requirements.txt

@@ -5,41 +5,63 @@ cachetools==5.3.2
 certifi==2023.7.22
 charset-normalizer==3.3.1
 click==8.1.7
+contourpy==1.1.1
+cycler==0.12.1
+Deprecated==1.2.14
+fonttools==4.43.1
 gitdb==4.0.11
 GitPython==3.1.40
 idna==3.4
 importlib-metadata==6.8.0
+importlib-resources==6.1.0
 Jinja2==3.1.2
 jsonschema==4.19.2
 jsonschema-specifications==2023.7.1
+kiwisolver==1.4.5
+llvmlite==0.40.1
+lxml==4.9.3
 markdown-it-py==3.0.0
 MarkupSafe==2.1.3
+matchms==0.23.1
+matplotlib==3.8.1
 mdurl==0.1.2
-numpy==1.26.1
+networkx==3.2.1
+numba==0.57.1
+numpy==1.24.4
 packaging==23.2
 pandas==2.1.2
+pickydict==0.4.0
 Pillow==10.1.0
 protobuf==4.24.4
+PubChemPy==1.0.4
 pyarrow==13.0.0
 pydeck==0.8.1b0
 Pygments==2.16.1
+pyparsing==3.1.1
+pyteomics==4.6.2
 python-dateutil==2.8.2
 pytz==2023.3.post1
+PyYAML==6.0.1
+rdkit==2023.9.1
 referencing==0.30.2
 requests==2.31.0
 rich==13.6.0
 rpds-py==0.10.6
+scipy==1.10.1
 six==1.16.0
 smmap==5.0.1
+sparsestack==0.4.1
 streamlit==1.28.0
 tenacity==8.2.3
 toml==0.10.2
 toolz==0.12.0
 tornado==6.3.3
+tqdm==4.66.1
 typing_extensions==4.8.0
 tzdata==2023.3
 tzlocal==5.2
 urllib3==2.0.7
 validators==0.22.0
 watchdog==3.0.0
-zipp==3.17.0
+wrapt==1.15.0
+zipp==3.17.0