adjusted tests

ms_mint/chromatogram.py

@@ -6,7 +6,7 @@
 
 from .tools import find_peaks_in_timeseries, gaussian, mz_mean_width_to_min_max
 from .io import ms_file_to_df
-from .filter import Resampler, Smoother, GaussFilter
+from .filters import Resampler, Smoother, GaussFilter
 from .matplotlib_tools import plot_peaks
 
 
@@ -21,10 +21,10 @@ def __init__(
         if intensities is not None:
             self.x = np.append(self.x, intensities)
         self.noise_level = None
-        if filter is None:
-            self.filter = [Resampler(), GaussFilter(), Smoother()]
+        if filters is None:
+            self.filters = [Resampler(), GaussFilter(), Smoother()]
         else:
-            self.filter = filter
+            self.filters = filters
         self.peaks = None
         self.selected_peak_ndxs = None
         if expected_rt is None and scan_times is not None:
@@ -43,8 +43,8 @@ def estimate_noise_level(self, window=20):
         data = pd.Series(index=self.t, data=self.x)
         self.noise_level = data.rolling(window, center=True).std().median()
 
-    def apply_filter(self):
-        for filt in self.filter:
+    def apply_filters(self):
+        for filt in self.filters:
             self.t, self.x = filt.transform(self.t, self.x)
 
     def find_peaks(self, prominence=None, rel_height=0.9):

ms_mint/targets.py

@@ -246,7 +246,7 @@ def rt_min_max(
         minimum_intensity=1e4,
         plot=False,
         sigma=20,
-        filter=None,
+        filters=None,
         post_opt=False,
         post_opt_kwargs=None,
         rel_height=0.9,
@@ -271,8 +271,8 @@ def rt_min_max(
         :type plot: bool, optional
         :param sigma: Sigma value for peak selection, defaults to 20
         :type sigma: float, optional
-        :param filter: Filter instances to apply in respective order, defaults to None
-        :type filter: ms_mint.filter.Filter, optional
+        :param filters: Filter instances to apply in respective order, defaults to None
+        :type filters: ms_mint.filters.Filter, optional
         :param post_opt: Optimize retention times after peak selection, defaults to False
         :type post_opt: bool, optional
         :param post_opt_kwargs: _description_, defaults to 20
@@ -312,7 +312,7 @@ def rt_min_max(
             _slice = extract_chromatogram_from_ms1(ms1, mz).groupby("scan_time").sum()
 
             chrom = Chromatogram(
-                _slice.index, _slice.values, expected_rt=rt, filter=filter
+                _slice.index, _slice.values, expected_rt=rt, filters=filters
             )
 
             if chrom.x.max() < minimum_intensity:
@@ -321,7 +321,7 @@ def rt_min_max(
                 )
                 continue
 
-            chrom.apply_filter()
+            chrom.apply_filters()
             chrom.find_peaks(rel_height=rel_height)
             chrom.select_peak_with_gaussian_weight(rt, sigma)
 

tests/test__chromatogram.py

@@ -12,14 +12,14 @@
 from paths import TEST_MZML
 
 
-def test__Chromatogram_without_filter_identifies_peaks_correctly():
+def test__Chromatogram_without_filters_identifies_peaks_correctly():
     rt_peak_1 = 200
     rt_peak_2 = 500
 
     x = np.arange(1000)
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_2, 100) * 5e5
 
-    chrom = Chromatogram(scan_times=x, intensities=y, filter=None, expected_rt=2)
+    chrom = Chromatogram(scan_times=x, intensities=y, filters=None, expected_rt=2)
     chrom.find_peaks()
 
     rt_peak_1_pred, rt_peak_2_pred = chrom.peaks.rt.values
@@ -30,15 +30,15 @@ def test__Chromatogram_without_filter_identifies_peaks_correctly():
     ]
 
 
-def test__Chromatogram__with_filter_identifies_peaks_correctly():
+def test__Chromatogram__with_filters_identifies_peaks_correctly():
     rt_peak_1 = 200
     rt_peak_2 = 500
 
     x = np.arange(1000)
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_2, 100) * 5e5
 
-    chrom = Chromatogram(scan_times=x, intensities=y, filter=[], expected_rt=2)
-    chrom.apply_filter()
+    chrom = Chromatogram(scan_times=x, intensities=y, filters=[], expected_rt=2)
+    chrom.apply_filters()
     chrom.find_peaks()
     rt_peak_1_pred, rt_peak_2_pred = chrom.peaks.rt.values
 
@@ -47,16 +47,16 @@ def test__Chromatogram__with_filter_identifies_peaks_correctly():
     assert max_diff < 1
 
 
-def test__Chromatogram__with_filter_and_opt_identifies_peaks_correctly():
+def test__Chromatogram__with_filters_and_opt_identifies_peaks_correctly():
     rt_peak_1 = 200
     rt_peak_2 = 500
 
     x = np.arange(1, 1000)
 
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_2, 100) * 5e5
 
-    chrom = Chromatogram(scan_times=x, intensities=y, filter=None, expected_rt=2)
-    chrom.apply_filter()
+    chrom = Chromatogram(scan_times=x, intensities=y, filters=None, expected_rt=2)
+    chrom.apply_filters()
     chrom.find_peaks()
     chrom.optimise_peak_times_with_diff()
     rt_peak_1_pred, rt_peak_2_pred = chrom.peaks.rt.values
@@ -75,7 +75,7 @@ def test__Chromatogram__select_peak_by_rt():
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_2, 100) * 5e5
 
     chrom = Chromatogram(
-        scan_times=x, intensities=y, filter=None, expected_rt=rt_peak_1
+        scan_times=x, intensities=y, filters=None, expected_rt=rt_peak_1
     )
     chrom.find_peaks()
     chrom.select_peak_by_rt()
@@ -95,7 +95,7 @@ def test__Chromatogram__select_peak_by_rt_as_argument():
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_2, 100) * 5e5
 
     chrom = Chromatogram(
-        scan_times=x, intensities=y, filter=None, expected_rt=rt_peak_1
+        scan_times=x, intensities=y, filters=None, expected_rt=rt_peak_1
     )
     chrom.find_peaks()
     chrom.select_peak_by_rt(rt_peak_2)
@@ -115,7 +115,7 @@ def test__Chromatogram__select_highest():
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_1, 100) * 5e5
 
     chrom = Chromatogram(
-        scan_times=x, intensities=y, filter=None, expected_rt=rt_peak_2
+        scan_times=x, intensities=y, filters=None, expected_rt=rt_peak_2
     )
     chrom.find_peaks()
     chrom.select_peak_by_highest_intensity()
@@ -144,7 +144,7 @@ def test__Chromatogram__select_peak_with_gaussian_prefers_higher_peak():
     )
 
     chrom = Chromatogram(
-        scan_times=x, intensities=y, filter=None, expected_rt=expected_rt
+        scan_times=x, intensities=y, filters=None, expected_rt=expected_rt
     )
     chrom.find_peaks()
     chrom.select_peak_with_gaussian_weight()
@@ -163,7 +163,7 @@ def test__Chromatogram__plot_runs_without_error_return_figure():
 
     y = gaussian(x, peak_rt, 200) * peak_in
 
-    chrom = Chromatogram(scan_times=x, intensities=y, filter=None)
+    chrom = Chromatogram(scan_times=x, intensities=y, filters=None)
     chrom.find_peaks()
 
     fig = chrom.plot()
@@ -174,7 +174,7 @@ def test__Chromatogram__plot_runs_without_error_return_figure():
 def test__Chromatogram_from_files_runs_through():
     chrom = Chromatogram()
     chrom.from_file(TEST_MZML, mz_mean=101.024323, mz_width=10)
-    chrom.apply_filter()
+    chrom.apply_filters()
     chrom.find_peaks()
     chrom.select_peak_by_highest_intensity()
     peaks = chrom.selected_peak_ndxs
@@ -194,7 +194,7 @@ def test__Chromatogram__optimise_peak_times_with_diff_with_plot():
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_1, 100) * 5e5
 
     chrom = Chromatogram(
-        scan_times=x, intensities=y, filter=None, expected_rt=rt_peak_2
+        scan_times=x, intensities=y, filters=None, expected_rt=rt_peak_2
     )
     chrom.find_peaks()
     chrom.optimise_peak_times_with_diff(plot=True)
@@ -209,7 +209,7 @@ def test__Chromatogram__data():
     y = gaussian(x, rt_peak_1, 20) * 5e6 + gaussian(x, rt_peak_1, 100) * 5e5
 
     chrom = Chromatogram(
-        scan_times=x, intensities=y, filter=None, expected_rt=rt_peak_2
+        scan_times=x, intensities=y, filters=None, expected_rt=rt_peak_2
     )
 
     data = chrom.data

tests/test__plotly_tools.py

@@ -1,63 +1,28 @@
 import pandas as pd
 import numpy as np
-
+import pytest
 
 from plotly.graph_objs._figure import Figure
-
 from ms_mint.plotly_tools import (
     set_template,
     plotly_heatmap,
 )
 
-
-def test__plotly_heatmap():
-    N = 10
-    data = np.random.uniform(size=(N, N)) + np.arange(N) - N / 2
-    df = pd.DataFrame(data)
-    img = plotly_heatmap(df)
-    assert isinstance(img, Figure), type(img)
-
-
-def test__plotly_heatmap__transposed():
-    N = 10
-    data = np.random.uniform(size=(N, N)) + np.arange(N) - N / 2
-    df = pd.DataFrame(data)
-    img = plotly_heatmap(df, transposed=True)
-    assert isinstance(img, Figure), type(img)
-
-
-def test__plotly_heatmap__normed_by_cols():
-    N = 10
-    data = np.random.uniform(size=(N, N)) + np.arange(N) - N / 2
-    df = pd.DataFrame(data)
-    img = plotly_heatmap(df, normed_by_cols=True)
-    assert isinstance(img, Figure), type(img)
-
-
-def test__plotly_heatmap__correlation():
+@pytest.mark.parametrize("transposed,normed_by_cols,correlation,clustered,add_dendrogram", [
+    (False, False, False, False, False),
+    (True, False, False, False, False),
+    (False, True, False, False, False),
+    (False, False, True, False, False),
+    (False, False, False, True, True),
+    (False, False, True, True, False),
+])
+def test__plotly_heatmap(transposed, normed_by_cols, correlation, clustered, add_dendrogram):
     N = 10
     data = np.random.uniform(size=(N, N)) + np.arange(N) - N / 2
     df = pd.DataFrame(data)
-    img = plotly_heatmap(df, correlation=True)
+    img = plotly_heatmap(df, transposed=transposed, normed_by_cols=normed_by_cols, correlation=correlation, clustered=clustered, add_dendrogram=add_dendrogram)
     assert isinstance(img, Figure), type(img)
 
-
-def test__plotly_heatmap__clustered_with_dendrogram():
-    N = 10
-    data = np.random.uniform(size=(N, N)) + np.arange(N) - N / 2
-    df = pd.DataFrame(data)
-    img = plotly_heatmap(df, clustered=True, add_dendrogram=True)
-    assert isinstance(img, Figure), type(img)
-
-
-def test__plotly_heatmap__clustered_correlation():
-    N = 10
-    data = np.random.uniform(size=(N, N)) + np.arange(N) - N / 2
-    df = pd.DataFrame(data)
-    img = plotly_heatmap(df, clustered=True, add_dendrogram=False, correlation=True)
-    assert isinstance(img, Figure), type(img)
-
-
 def test__set_template():
     set_template()
-    assert True
+    assert True