Merge branch 'main' into pixie/filter_with_nuclear_mask

ark/analysis/visualize_test.py

@@ -172,7 +172,9 @@ def test_visualize_topic_eda():
     # Run topic EDA
     tops = [3, 4, 5, 6, 7]
     eda = pros.compute_topic_eda(cell_table_features["featurized_fovs"],
-                                 featurization=cell_table_features["featurization"], topics=tops)
+                                 featurization=cell_table_features["featurization"],
+                                 topics=tops,
+                                 num_boots=25)
 
     with pytest.raises(FileNotFoundError):
         # trying to save on a non-existant directory

ark/utils/metacluster_remap_gui/metaclustergui.py

@@ -122,7 +122,7 @@ def make_gui(self):
         self.fig.canvas.mpl_connect('pick_event', self.onpick)
 
         # heatmaps
-        self.normalizer = ZScoreNormalize(-1, 0, 1)
+        self.normalizer = ZScoreNormalize(-3, 0, 3)
 
         def _heatmap(ax, column_count):
             data = np.zeros((self.mcd.marker_count, column_count))
@@ -384,8 +384,6 @@ def update_gui(self):
         def _preplot(df):
             return df.apply(zscore).clip(upper=self.zscore_clamp_slider.value).T
 
-        self.normalizer.calibrate(_preplot(self.mcd.clusters).values)
-
         # clusters heatmap
         self.im_c.set_data(_preplot(self.mcd.clusters))
         self.im_c.set_extent((0, self.mcd.cluster_count, 0, self.mcd.marker_count))

ark/utils/metacluster_remap_gui/zscore_norm.py

@@ -14,7 +14,7 @@ class ZScoreNormalize(Normalize):
         [vcenter,vmax] -> [0.5,1.0]
 
     """
-    def __init__(self, vmin=-1, vcenter=0, vmax=1):
+    def __init__(self, vmin=-3, vcenter=0, vmax=3):
         """Initial ZScoreNormalize
 
         vmin < vcenter < vmax
@@ -30,10 +30,13 @@ def __init__(self, vmin=-1, vcenter=0, vmax=1):
         self.vcenter = vcenter
         super().__init__(vmin, vmax)
 
-    def calibrate(self, values):
-        self.vmin = min([np.min(values), 0])
-        self.vcenter = 0.0
-        self.vmax = np.max(values)
+    def inverse(self, value):
+        result = np.interp(
+            value,
+            [0, 0.5, 1],
+            [self.vmin, self.vcenter, self.vmax],
+        )
+        return result
 
     def __call__(self, value: np.ndarray, clip=None):
         """Map ndarray to the interval [0, 1]. The clip argument is unused."""
@@ -42,6 +45,7 @@ def __call__(self, value: np.ndarray, clip=None):
         normalized_values = np.interp(
             result,
             [self.vmin, self.vcenter, self.vmax],
-            [0, 0.5, 1.])
+            [0, 0.5, 1.]
+        )
 
         return np.ma.masked_array(normalized_values, mask=np.ma.getmask(result))

requirements.txt

@@ -12,7 +12,7 @@ ipython>=8.4.0,<9
 jupyter_contrib_nbextensions>=0.5.1,<1
 jupyterlab>=3.4.3,<4
 jedi>=0.18.1,<0.19
-matplotlib==3.4.3
+matplotlib>=3.6.2,<4
 multiprocess==0.70.13
 natsort>=8.1.0,<9
 numpy>=1.23.4,<2
@@ -31,7 +31,7 @@ spatial-lda>=0.1.3,<1
 statsmodels>=0.13.2,<1
 tables>=3.7.0,<4
 tifffile>=2022.8.12,<2023
-tmi @ git+https://github.com/angelolab/tmi.git@v0.1.0
+tmi @ git+https://github.com/angelolab/tmi.git@v0.1.2
 umap-learn>=0.5.3,<1
 xarray>=2022.6.0,<2023
 tqdm>=4.64.0,<5