Consolidate gscontrol plots

tedana/gscontrol.py

@@ -221,7 +221,7 @@ def minimum_image_regression(
     # Tedana has removed the "ignored" classification,
     # so we must separate "accepted" components based on the classification tag(s).
     ignore_tags = ["low variance", "accept borderline"]
-    if not any(tag in classification_tags for tag in ignore_tags):
+    if not any(tag in [c.lower() for c in classification_tags] for tag in ignore_tags):
         LGR.warning(
             "Decision tree does not contain classification tags indicating low variance "
             f"components ({', '.join(ignore_tags)})."
@@ -232,7 +232,9 @@ def minimum_image_regression(
 
         # Select rows where the 'classification_tags' column contains any of the ignore tags
         ign = component_table[
-            component_table.classification_tags.str.contains(pattern, na=False, regex=True)
+            component_table.classification_tags.str.lower().str.contains(
+                pattern, na=False, regex=True
+            )
         ].index.values
 
     acc = component_table[component_table.classification == "accepted"].index.values

tedana/reporting/data/html/report_body_template.html

@@ -283,33 +283,25 @@ <h2>
 <div class="carpet-plots">
   <h2>Global Signal Control</h2>
     {% if gsr %}
-      <h3>Global Signal Regression Weights</h3>
+      <h3>Global Signal Regression</h3>
       <div class="carpet-plots-image">
         <img
-          id="gsrBrainPlot"
-          src="{{ gsrBrainPlot }}"
+          id="gsrPlot"
+          src="{{ gsrPlot }}"
           style="width: 95%; max-width: 1200px; height: auto"
         />
       </div>
     {% endif %}
     {% if mir %}
-      <h3>Minimum Image Regression Weights</h3>
+      <h3>Minimum Image Regression</h3>
       <div class="carpet-plots-image">
         <img
-          id="mirBrainPlot"
-          src="{{ mirBrainPlot }}"
+          id="mirPlot"
+          src="{{ mirPlot }}"
           style="width: 95%; max-width: 1200px; height: auto"
         />
       </div>
     {% endif %}
-    <h3>Global Signal Timeseries</h3>
-    <div class="carpet-plots-image">
-      <img
-        id="gsrTimeseries"
-        src="{{ gsrTimeseries }}"
-        style="width: 95%; max-width: 1200px; height: auto"
-      />
-    </div>
 </div>
 {% endif %}
 {% if externalRegressorsExist %}

tedana/reporting/html_report.py

@@ -190,14 +190,12 @@ def _update_template_bokeh(
     rmse_timeseries = f"./figures/{rmse_timeseries_filename}"
 
     # Check for gscontrol images
-    gsr_brain_filename = f"{prefix}gsr_boldmap.svg"
-    mir_brain_filename = f"{prefix}T1likeEffect_boldmap.svg"
-    gsr_timeseries_filename = f"{prefix}gscontrol_bold.svg"
-    gsr_brain = f"./figures/{gsr_brain_filename}"
-    mir_brain = f"./figures/{mir_brain_filename}"
-    gsr_timeseries = f"./figures/{gsr_timeseries_filename}"
-    gsr = gsr_brain_filename in files_in_figures and gsr_timeseries_filename in files_in_figures
-    mir = mir_brain_filename in files_in_figures and gsr_timeseries_filename in files_in_figures
+    gsr_filename = f"{prefix}gsr_boldmap.svg"
+    mir_filename = f"{prefix}mir_boldmap.svg"
+    gsr_file = f"./figures/{gsr_filename}"
+    mir_file = f"./figures/{mir_filename}"
+    gsr = gsr_filename in files_in_figures
+    mir = mir_filename in files_in_figures
 
     # Check if each set of images exists
     t2star_exists = (
@@ -256,9 +254,8 @@ def _update_template_bokeh(
         rmseExists=rmse_exists,
         gsr=gsr,
         mir=mir,
-        gsrBrainPlot=gsr_brain,
-        mirBrainPlot=mir_brain,
-        gsrTimeseries=gsr_timeseries,
+        gsrPlot=gsr_file,
+        mirPlot=mir_file,
         externalRegressorsExist=external_regressors_exist,
         externalRegressorCorrelations=external_regressor_correlations,
         references=references,

tedana/reporting/static_figures.py

@@ -9,7 +9,6 @@
 import nibabel as nb
 import numpy as np
 import pandas as pd
-import scipy.stats as sstats
 
 matplotlib.use("AGG")
 import matplotlib.pyplot as plt
@@ -825,6 +824,7 @@ def plot_gscontrol(
     *,
     io_generator: io.OutputGenerator,
     gscontrol: list,
+    png_cmap: str,
 ):
     """Plot the results of the gscontrol steps.
 
@@ -837,78 +837,57 @@ def plot_gscontrol(
     """
     import pandas as pd
 
-    if "gsr" in gscontrol:
-        gsr_img = io_generator.get_name("gs img")
-        gsr_plot = f"{io_generator.prefix}gsr_boldmap.svg"
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                message="A non-diagonal affine.*",
-                category=UserWarning,
-            )
-            plotting.plot_stat_map(
-                gsr_img,
-                bg_img=None,
-                display_mode="mosaic",
-                symmetric_cbar=True,
-                black_bg=True,
-                cmap="coolwarm",
-                annotate=False,
-                output_file=os.path.join(io_generator.out_dir, "figures", gsr_plot),
-                resampling_interpolation="nearest",
-            )
-
-    if "mir" in gscontrol:
-        mir_img = io_generator.get_name("t1 like img")
-        mir_plot = f"{io_generator.prefix}T1likeEffect_boldmap.svg"
-        with warnings.catch_warnings():
-            warnings.filterwarnings(
-                "ignore",
-                message="A non-diagonal affine.*",
-                category=UserWarning,
-            )
-            plotting.plot_stat_map(
-                mir_img,
-                bg_img=None,
-                display_mode="mosaic",
-                symmetric_cbar=True,
-                black_bg=True,
-                cmap="coolwarm",
-                annotate=False,
-                output_file=os.path.join(io_generator.out_dir, "figures", mir_plot),
-                resampling_interpolation="nearest",
-            )
-
     if "gsr" in gscontrol or "mir" in gscontrol:
         confounds_file = io_generator.get_name("confounds tsv")
         confounds_df = pd.read_table(confounds_file)
 
         # Get repetition time from reference image
         tr = io_generator.reference_img.header.get_zooms()[-1]
-        time_arr = np.arange(confounds_df.shape[0]) * tr
 
-        fig, ax = plt.subplots(figsize=(10, 6))
-        if "gsr" in gscontrol:
-            gs = confounds_df["global_signal"].values
-            gs_z = sstats.zscore(gs)
-            ax.plot(time_arr, gs_z, label="GSR", color="red")
-
-        if "mir" in gscontrol:
-            mir = confounds_df["mir_global_signal"].values
-            mir_z = sstats.zscore(mir)
-            ax.plot(time_arr, mir_z, label="MIR", color="blue")
-
-        ax.legend()
-        ax.set_title("Global Signals")
-        ax.set_xlabel("Time (s)")
-        ax.set_ylabel("Signal (z-scored)")
-        ax.set_xlim(0, time_arr[-1])
-        fig.savefig(
-            os.path.join(
-                io_generator.out_dir,
-                "figures",
-                f"{io_generator.prefix}gscontrol_bold.svg",
-            )
+    if "gsr" in gscontrol:
+        gsr_img = nb.load(io_generator.get_name("gs img"))
+
+        # Get fft and freqs for this component
+        # adapted from @dangom
+        timeseries = confounds_df["global_signal"].values
+        spectrum, freqs = utils.get_spectrum(timeseries, tr)
+
+        plot_name = f"{io_generator.prefix}gsr_boldmap.svg"
+        plot_name = os.path.join(io_generator.out_dir, "figures", plot_name)
+
+        plot_component(
+            stat_img=gsr_img,
+            component_timeseries=timeseries,
+            power_spectrum=spectrum,
+            frequencies=freqs,
+            tr=tr,
+            classification_color="red",
+            png_cmap=png_cmap,
+            title="Global Signal Regression",
+            out_file=plot_name,
+        )
+
+    if "mir" in gscontrol:
+        mir_img = nb.load(io_generator.get_name("t1 like img"))
+
+        # Get fft and freqs for this component
+        # adapted from @dangom
+        timeseries = confounds_df["mir_global_signal"].values
+        spectrum, freqs = utils.get_spectrum(timeseries, tr)
+
+        plot_name = f"{io_generator.prefix}mir_boldmap.svg"
+        plot_name = os.path.join(io_generator.out_dir, "figures", plot_name)
+
+        plot_component(
+            stat_img=mir_img,
+            component_timeseries=timeseries,
+            power_spectrum=spectrum,
+            frequencies=freqs,
+            tr=tr,
+            classification_color="red",
+            png_cmap=png_cmap,
+            title="Minimum Image Regression",
+            out_file=plot_name,
         )
 
 

tedana/tests/data/fiu_four_echo_outputs.txt

@@ -70,8 +70,7 @@ figures/sub-01_carpet_rejected.svg
 figures/sub-01_carpet_optcom_nogsr.svg
 figures/sub-01_carpet_denoised_mir.svg
 figures/sub-01_carpet_accepted_mir.svg
-figures/sub-01_T1likeEffect_boldmap.svg
-figures/sub-01_gscontrol_bold.svg
+figures/sub-01_mir_boldmap.svg
 figures/sub-01_gsr_boldmap.svg
 figures/sub-01_comp_000.png
 figures/sub-01_comp_001.png

tedana/workflows/tedana.py

@@ -1211,6 +1211,7 @@ def tedana_workflow(
             reporting.static_figures.plot_gscontrol(
                 io_generator=io_generator,
                 gscontrol=gscontrol,
+                png_cmap=png_cmap,
             )
 
         if external_regressors is not None: