Cleanup unused numba functions

alphadia/numba/numeric.py

@@ -59,39 +59,6 @@ def wrap1(
     return values
 
 
-@nb.njit
-def get_mean0(dense, scan, cycle):
-    """create a fixed window around the peak and extract the mean value"""
-    # window size around peak
-    w = 4
-
-    # extract mz
-    mz_window = dense[
-        max(scan - w, 0) : scan + w, max(cycle - w, 0) : cycle + w
-    ].flatten()
-
-    return np.mean(mz_window)
-
-
-@nb.njit
-def get_mean_sparse0(dense, scan, cycle, threshold):
-    """create a fixed window around the peak and extract the mean value"""
-    # window size around peak
-    w = 4
-
-    # extract mz
-    mz_window = dense[
-        max(scan - w, 0) : scan + w, max(cycle - w, 0) : cycle + w
-    ].flatten()
-
-    mask = mz_window < threshold
-    fraction_nonzero = np.mean(mask.astype("int8"))
-
-    values = np.mean(mz_window[mask]) if fraction_nonzero > 0 else threshold
-
-    return values
-
-
 @nb.njit
 def symetric_limits_1d(
     array_1d,
@@ -376,8 +343,3 @@ def fragment_correlation_different(x: np.ndarray, y: np.ndarray):
         output[i_observations] = correlation_matrix
 
     return output
-
-
-@nb.njit(inline="always")
-def amean(array, axis):
-    return np.sum(array, axis=axis) / array.shape[axis]

alphadia/peakgroup/search.py

@@ -1,6 +1,5 @@
 # native imports
 import logging
-import os
 
 # alpha family imports
 import alphatims
@@ -977,147 +976,3 @@ def assemble_precursor_df(self, precursors_flat):
             precursors_flat[self.precursor_mz_column].values,
             precursors_flat[available_isotope_columns].values.copy(),
         )
-
-    def assemble_candidates(self, elution_group_container):
-        """
-        Candidates are collected from the ElutionGroup objects and assembled into a pandas.DataFrame.
-
-        Parameters
-        ----------
-        elution_group_container : ElutionGroupContainer
-            container object containing a list of ElutionGroup objects
-
-        Returns
-        -------
-        pandas.DataFrame
-            dataframe containing the extracted candidates
-
-        """
-
-        candidates = []
-        for i in range(len(elution_group_container)):
-            for j in range(len(elution_group_container[i].candidates)):
-                candidates.append(elution_group_container[i].candidates[j])
-
-        candidate_attributes = [
-            "elution_group_idx",
-            "score_group_idx",
-            "precursor_idx",
-            "rank",
-            "score",
-            "precursor_mz",
-            "decoy",
-            "channel",
-            "scan_center",
-            "scan_start",
-            "scan_stop",
-            "frame_center",
-            "frame_start",
-            "frame_stop",
-        ]
-        candidate_df = pd.DataFrame(
-            {
-                attr: [getattr(c, attr) for c in candidates]
-                for attr in candidate_attributes
-            }
-        )
-        candidate_df = candidate_df.sort_values(by="precursor_idx")
-
-        # add additiuonal columns for precursor information
-        precursor_attributes = ["mz_calibrated", "mz_library"]
-
-        precursor_pidx = self.precursors_flat["precursor_idx"].values
-        candidate_pidx = candidate_df["precursor_idx"].values
-        precursor_flat_lookup = np.searchsorted(
-            precursor_pidx, candidate_pidx, side="left"
-        )
-
-        for attr in precursor_attributes:
-            if attr in self.precursors_flat.columns:
-                candidate_df[attr] = self.precursors_flat[attr].values[
-                    precursor_flat_lookup
-                ]
-
-        # DEBUG: save features for training if desired.
-        if self.feature_path is not None:
-            feature_matrix = np.zeros(
-                (len(candidates), len(candidates[0].features)), dtype=np.float32
-            )
-            for i in range(len(candidates)):
-                feature_matrix[i, :] = candidates[i].features
-
-            np.save(os.path.join(self.feature_path, "features.npy"), feature_matrix)
-
-            sub_df = candidate_df[
-                [
-                    "elution_group_idx",
-                    "score_group_idx",
-                    "precursor_idx",
-                    "rank",
-                    "score",
-                    "decoy",
-                ]
-            ]
-            sub_df.to_csv(
-                os.path.join(self.feature_path, "candidates.tsv"), index=False, sep="\t"
-            )
-
-        return candidate_df
-
-    def append_precursor_information(self, df):
-        """
-        Append relevant precursor information to the candidates dataframe.
-
-        Parameters
-        ----------
-        df : pandas.DataFrame
-            dataframe containing the extracted candidates
-
-        Returns
-        -------
-        pandas.DataFrame
-            dataframe containing the extracted candidates with precursor information appended
-        """
-
-        # precursor_flat_lookup has an element for every candidate and contains the index of the respective precursor
-        precursor_pidx = self.precursors_flat["precursor_idx"].values
-        candidate_pidx = df["precursor_idx"].values
-        precursor_flat_lookup = np.searchsorted(
-            precursor_pidx, candidate_pidx, side="left"
-        )
-
-        df["decoy"] = self.precursors_flat["decoy"].values[precursor_flat_lookup]
-
-        df["rt_library"] = self.precursors_flat["rt_library"].values[
-            precursor_flat_lookup
-        ]
-        if self.rt_column == "rt_calibrated":
-            df["rt_calibrated"] = self.precursors_flat["rt_calibrated"].values[
-                precursor_flat_lookup
-            ]
-
-        df["mobility_library"] = self.precursors_flat["mobility_library"].values[
-            precursor_flat_lookup
-        ]
-        if self.mobility_column == "mobility_calibrated":
-            df["mobility_calibrated"] = self.precursors_flat[
-                "mobility_calibrated"
-            ].values[precursor_flat_lookup]
-
-        df["flat_frag_start_idx"] = self.precursors_flat["flat_frag_start_idx"].values[
-            precursor_flat_lookup
-        ]
-        df["flat_frag_stop_idx"] = self.precursors_flat["flat_frag_stop_idx"].values[
-            precursor_flat_lookup
-        ]
-        df["charge"] = self.precursors_flat["charge"].values[precursor_flat_lookup]
-        df["proteins"] = self.precursors_flat["proteins"].values[precursor_flat_lookup]
-        df["genes"] = self.precursors_flat["genes"].values[precursor_flat_lookup]
-
-        available_isotopes = utils.get_isotope_columns(self.precursors_flat.columns)
-        available_isotope_columns = [f"i_{i}" for i in available_isotopes]
-
-        for col in available_isotope_columns:
-            df[col] = self.precursors_flat[col].values[precursor_flat_lookup]
-
-        return df

gui/src/main/modules/engine.js

@@ -2,7 +2,7 @@ const { exec, execFile, spawn } = require('child_process');
 const StringDecoder = require('string_decoder').StringDecoder;
 const Transform = require('stream').Transform;
 
-const { app, dialog } = require('electron');
+const { app, dialog, BrowserWindow } = require('electron');
 const writeYamlFile = require('write-yaml-file')
 
 const { workflowToConfig } = require('./workflows');
@@ -287,7 +287,9 @@ class CMDExecutionEngine extends BaseExecutionEngine {
             return run
         }).catch((err) => {
             console.log(err)
-            dialog.showMessageBox({
+            dialog.showMessageBox(
+                BrowserWindow.getFocusedWindow(),
+                {
                 type: 'error',
                 title: 'Error while starting workflow',
                 message: `Could not start workflow. ${err}`,
@@ -358,7 +360,9 @@ class BundledExecutionEngine extends BaseExecutionEngine {
             }).catch((err) => {
                 this.available = false
                 this.error = err
-                dialog.showMessageBox({
+                dialog.showMessageBox(
+                BrowserWindow.getFocusedWindow(),
+                {
                     type: 'error',
                     title: 'Error while checking availability of bundled alphaDIA',
                     message: `Could not start bundled alphaDIA. ${err}`,
@@ -449,7 +453,9 @@ class BundledExecutionEngine extends BaseExecutionEngine {
             return run
         }).catch((err) => {
             console.log(err)
-            dialog.showMessageBox({
+            dialog.showMessageBox(
+            BrowserWindow.getFocusedWindow(),
+            {
                 type: 'error',
                 title: 'Error while starting workflow',
                 message: `Could not start workflow. ${err}`,
@@ -615,7 +621,9 @@ class WSLExecutionEngine extends BaseExecutionEngine {
             return run
         }).catch((err) => {
             console.log(err)
-            dialog.showMessageBox({
+            dialog.showMessageBox(
+            BrowserWindow.getFocusedWindow(),
+                {
                 type: 'error',
                 title: 'Error while starting workflow',
                 message: `Could not start workflow. ${err}`,

gui/src/main/modules/profile.js

@@ -1,6 +1,6 @@
 const fs = require("fs")
 const path = require("path")
-const { app, shell} = require("electron")
+const { app, shell, BrowserWindow} = require("electron")
 const { dialog } = require('electron')
 
 const VERSION = "1.7.1"
@@ -42,10 +42,15 @@ const Profile = class {
     }
 
     loadProfile() {
+        let loaded_config = {}
+
         try {
-            config = JSON.parse(fs.readFileSync(this.getProfilePath()))
+            loaded_config = JSON.parse(fs.readFileSync(this.getProfilePath()))
+            console.log(loaded_config)
         } catch (err) {
-            dialog.showMessageBox({
+            dialog.showMessageBox(
+                BrowserWindow.getFocusedWindow(),
+                {
                 type: 'error',
                 title: 'Error while loading profile',
                 message: `Could not load profile. ${err}`,
@@ -55,8 +60,10 @@ const Profile = class {
             return
         }
 
-        if (config.version !== VERSION) {
-            dialog.showMessageBox({
+        if (loaded_config.version !== VERSION) {
+            dialog.showMessageBox(
+                BrowserWindow.getFocusedWindow(),
+                {
                 type: 'info',
                 title: 'Found old alphaDIA profile',
                 message: `Found old alphaDIA profile. Updating to version ${VERSION}`,
@@ -65,7 +72,7 @@ const Profile = class {
             })
             this.saveProfile()
         } else {
-            this.config = config
+            this.config = loaded_config
         }
     }
 

gui/src/renderer/components/ParameterInput.js

@@ -1,12 +1,45 @@
 import * as React from 'react'
 import styled from '@emotion/styled'
 
-import { Box, Checkbox, FormControl, MenuItem, Select, Stack, Tooltip, Typography, TextField } from '@mui/material'
+import { Box, Chip, Button, Checkbox, FormControl, MenuItem, Select, Stack, Tooltip, Typography, TextField } from '@mui/material'
 
 const StyledCheckbox = styled(Checkbox)(({ theme }) => ({
     padding: 0.5
 }))
 
+const SingleFolderSelection = ({parameter, onChange = () => {}}) => {
+
+    const handleSelect = () => {
+        window.electronAPI.getSingleFolder().then((folder) => {
+            onChange(folder);
+        }).catch((err) => {
+            console.log(err);
+        })
+    }
+
+    const folderName = parameter.replace(/^.*[\\\/]/, '')
+
+    return (
+        <>
+            {folderName == ''? '':
+                <Chip
+                label={folderName}
+                onDelete={() => {onChange("")}}
+                size="small"
+                />
+            }
+            <Button
+                variant="outlined"
+                size="small"
+                sx={{width: "150px"}}
+                onClick={handleSelect}>
+                Select Folder
+            </Button>
+        </>
+
+    )
+}
+
 const ParameterInput = ({
         parameter,
         onChange = () => {},
@@ -132,6 +165,13 @@ const ParameterInput = ({
                     />
                     </Box>)
                 break;
+            case "singleFolderSelection":
+                input = (
+                    <SingleFolderSelection
+                        parameter={parameter.value}
+                        onChange={onChange}
+                    />)
+                break;
 
             default:
                 input = (

gui/src/renderer/pages/Home.js

@@ -108,7 +108,7 @@ const Home = () => (
                         Documentation
                     </Typography>
                     <br/>
-                    <StyledLink onClick={() => window.electronAPI.openLink("http://www.google.com")}>
+                    <StyledLink onClick={() => window.electronAPI.openLink("https://alphadia.readthedocs.io/en/latest/")}>
                         Link
                     </StyledLink>
                 </Box>
@@ -122,7 +122,7 @@ const Home = () => (
                         GitHub
                     </Typography>
                     <br/>
-                    <StyledLink onClick={() => window.electronAPI.openLink("http://www.google.com")}>
+                    <StyledLink onClick={() => window.electronAPI.openLink("https://github.com/MannLabs/alphadia")}>
                         Link
                     </StyledLink>
                 </Box>
@@ -141,7 +141,7 @@ const Home = () => (
                         Universe
                     </Typography>
                     <br/>
-                    <StyledLink onClick={() => window.electronAPI.openLink("http://www.google.com")} sx={{color: "white !important"}}>
+                    <StyledLink onClick={() => window.electronAPI.openLink("https://github.com/MannLabs")} sx={{color: "white !important"}}>
                         Link
                     </StyledLink>
                 </Box>