From 8b34ca5ac06c9c8d64654e9329d30976589095e9 Mon Sep 17 00:00:00 2001 From: Zargham Ahmad Date: Mon, 13 Nov 2023 14:09:50 +0100 Subject: [PATCH 1/2] first iteration refactoring --- analysis/Python_scripts/add_zeros.ipynb | 27 +- analysis/Python_scripts/scatterplot.ipynb | 879 +++++++++++++-------- analysis/Python_scripts/utils.py | 77 ++ codes/extract_coords_into_inp_xyz.py | 177 +++-- codes/write_dirs_files_for_gamess_qcxms.py | 243 +++--- 5 files changed, 867 insertions(+), 536 deletions(-) diff --git a/analysis/Python_scripts/add_zeros.ipynb b/analysis/Python_scripts/add_zeros.ipynb index d7d05ee..09a0b49 100644 --- a/analysis/Python_scripts/add_zeros.ipynb +++ b/analysis/Python_scripts/add_zeros.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -10,37 +10,32 @@ "from matchms.importing import load_from_msp\n", "from matchms.logging_functions import set_matchms_logger_level\n", "import itertools\n", + "from utils import load_spectra_metadata\n", "\n", "set_matchms_logger_level(\"ERROR\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "predicted_spectra = list(load_from_msp(\"../data/filtered/simulated_matchms_filter_1%I_all_peaks.msp\"))\n", - "predicted_spectra_metadata= pd.DataFrame.from_dict([x.metadata for x in predicted_spectra])\n", - "predicted_spectra_metadata.rename(columns={'compound_name': 'query'}, inplace=True)\n", - "predicted_spectra_names = predicted_spectra_metadata['query'].to_list()" + "predicted_spectra, predicted_spectra_metadata, predicted_spectra_names = load_spectra_metadata(\"../data/filtered/simulated_matchms_filter_1%I_all_peaks.msp\", 'query')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "experimental_spectra = list(load_from_msp(\"../data/experimental/RECETOX_GC-EI_MS_20201028.msp\"))\n", - "experimental_spectra_metadata= pd.DataFrame.from_dict([x.metadata for x in experimental_spectra])\n", - "experimental_spectra_metadata.rename(columns={'compound_name': 'reference'}, inplace=True)\n", - "experimental_spectra_names = experimental_spectra_metadata['reference'].to_list()" + "experimental_spectra, experimental_spectra_metadata, experimental_spectra_names = load_spectra_metadata(\"../data/experimental/RECETOX_GC-EI_MS_20201028.msp\", 'reference')" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ diff --git a/analysis/Python_scripts/scatterplot.ipynb b/analysis/Python_scripts/scatterplot.ipynb index f2cbe54..4ab3414 100644 --- a/analysis/Python_scripts/scatterplot.ipynb +++ b/analysis/Python_scripts/scatterplot.ipynb @@ -2,46 +2,37 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "import plotly.graph_objs as go\n", "import pandas as pd\n", - "from rdkit import Chem\n", "from matchms.importing import load_from_msp\n", "from matchms.logging_functions import set_matchms_logger_level\n", "\n", - "from utils import is_spectrum_for_compound\n", + "from utils import is_spectrum_for_compound, load_spectra_metadata\n", "\n", "set_matchms_logger_level('ERROR')" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "matchms_scores = pd.read_csv(\"../data/output_matching/matchms/matchms_tol_0.01_1%I_all_peaks_with_0s.tsv\", sep=\"\\t\")\n", - "\n", "matchms_scores = matchms_scores[matchms_scores.apply(lambda x: is_spectrum_for_compound(x['query'], x['reference']), axis=1)]\n", "\n", - "molecules = Chem.SDMolSupplier(\"../../data/RECETOX_GC-EI-MS_20201028.sdf\")\n", - "\n", - "predicted_spectra = list(load_from_msp(\"../data/filtered/simulated_matchms_filter_1%I_all_peaks.msp\"))\n", - "spectra_metadata = pd.DataFrame.from_dict([x.metadata for x in predicted_spectra])\n", - "spectra_metadata.rename(columns={'compound_name': 'query'}, inplace=True)\n", + "_ , spectra_metadata, _ = load_spectra_metadata(\"../data/filtered/simulated_matchms_filter_1%I_all_peaks.msp\", 'query')\n", "\n", - "reference_spectra = list(load_from_msp(\"../data/experimental/RECETOX_GC-EI_MS_20201028.msp\"))\n", - "reference_spectra_metadata = pd.DataFrame.from_dict([x.metadata for x in reference_spectra])\n", - "reference_spectra_metadata.rename(columns={'compound_name': 'reference'}, inplace=True)\n" + "_ , reference_spectra_metadata, _ = load_spectra_metadata(\"../data/experimental/RECETOX_GC-EI_MS_20201028.msp\", 'reference')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -54,13 +45,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "merged['CosineHungarian_0.01_0.0_1.0_matches'] = pd.to_numeric(merged['CosineHungarian_0.01_0.0_1.0_matches'], errors='coerce')\n", - "merged['n_peaks_query'] = pd.to_numeric(merged['n_peaks_query'], errors='coerce')\n", - "merged['n_peaks_reference'] = pd.to_numeric(merged['n_peaks_reference'], errors='coerce')\n", + "numeric_columns = ['CosineHungarian_0.01_0.0_1.0_matches', 'n_peaks_query', 'n_peaks_reference']\n", + "merged[numeric_columns] = merged[numeric_columns].apply(pd.to_numeric, errors='coerce')\n", "\n", "merged['FractionQuery'] = merged['CosineHungarian_0.01_0.0_1.0_matches'] / merged['n_peaks_query']\n", "merged['FractionReference'] = merged['CosineHungarian_0.01_0.0_1.0_matches'] / merged['n_peaks_reference']" @@ -68,9 +58,38 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "application/vnd.plotly.v1+json": { @@ -81,18 +100,42 @@ { "marker": { "color": [ + 0.06060606060606061, + 0.631578947368421, + 0.058823529411764705, + 0.7894736842105263, + 0.7619047619047619, + 0.6842105263157895, 0.7222222222222222, + 0.8823529411764706, + 0.8125, 0.68, 0.7906976744186046, + 0.30434782608695654, 0.47619047619047616, 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