..

README.md

@@ -256,5 +256,6 @@ The target list can be stored as csv or Excel file.
 
 # Release Notes
 
-## 0.1.9 Milestones
+## 0.2.0 Milestones
     - peak_area_top3 comparable to El-Maven PeakAreaTop values
+

ms_mint/Mint.py

@@ -22,7 +22,7 @@
 )
 from .pca import PrincipalComponentsAnalyser
 from .plotting import MintPlotter
-from .filter import Resampler
+#from .filter import Resampler
 from .chromatogram import Chromatogram, extract_chromatogram_from_ms1
 
 import ms_mint
@@ -497,9 +497,8 @@ def get_chromatograms(self, fns=None, peak_label=None, **kwargs):
         if peak_label is None:
             peak_label = self.peak_labels
 
-        resampler = Resampler()
-
         data = []
+
         for fn in self.tqdm(fns):
             df = ms_file_to_df(fn)
             for label in peak_label:
@@ -515,6 +514,7 @@ def get_chromatograms(self, fns=None, peak_label=None, **kwargs):
                 chrom_data["rt_min"] = rt_min
                 chrom_data["rt_max"] = rt_min
                 data.append(chrom_data)
+
         data = pd.concat(data).reset_index()
 
         data["ms_file"] = data["ms_file"].apply(lambda x: P(x).with_suffix("").name)

ms_mint/pca.py

@@ -110,7 +110,12 @@ def cumulative_variance(self, height=4, aspect=2):
         n_components = self.pca.results["n_components"]
         fig = plt.figure(figsize=(height * aspect, height))
         cum_expl_var = self.pca.results["cum_expl_var"]
-        plt.bar(np.arange(n_components) + 1, cum_expl_var, facecolor="grey", edgecolor="none")
+        plt.bar(
+            np.arange(n_components) + 1,
+            cum_expl_var,
+            facecolor="grey",
+            edgecolor="none",
+        )
         plt.xlabel("Principal Component")
         plt.ylabel("Explained variance [%]")
         plt.title("Cumulative explained variance")

ms_mint/plotting.py

@@ -1,6 +1,5 @@
 import numpy as np
 import seaborn as sns
-import pandas as pd
 
 from warnings import simplefilter
 from scipy.cluster.hierarchy import ClusterWarning
@@ -15,11 +14,8 @@
     plot_metabolomics_hist2d,
 )
 
-from tqdm import tqdm
-
 import plotly.express as px
 
-from .filter import Resampler
 from .tools import scale_dataframe, mz_mean_width_to_min_max
 from .io import ms_file_to_df
 

tests/test__integration.py

@@ -107,7 +107,7 @@ def test__optimize_min_max():
     mint.targets["rt_max"] = None
     print("Before")
     print(mint.targets[["rt", "rt_min", "rt_max"]])
-    result = mint.opt.rt_min_max()
+    mint.opt.rt_min_max()
     print("After")
     print(mint.targets[["rt", "rt_min", "rt_max"]])
     n_missing_values_in_rt_cols = mint.targets[["rt_min", "rt_max"]].isna().sum().sum()

tests/test__targets.py

@@ -19,7 +19,7 @@
 
 from paths import (
     # TEST_TARGETS_FN_V2_CSV_SEC,
-    TEST_TARGETS_FN_V0_XLSX,
+    #TEST_TARGETS_FN_V0_XLSX,
     TEST_TARGETS_FN_V0,
     TEST_TARGETS_FN_V1,
 )