Add files

.gitattributes

@@ -8,8 +8,6 @@
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mds filter=lfs diff=lfs merge=lfs -text
 *.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
@@ -35,25 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text
-# Video files - compressed
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.webm filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.venv/
+.env

app.py

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+import numpy as np
+import polars as pl
+from bokeh.io import curdoc
+from bokeh.events import Tap
+from bokeh.plotting import figure
+from bokeh.palettes import Viridis256
+from bokeh.layouts import row, column
+from bokeh.models import ColumnDataSource, LinearColorMapper, ColorBar, Div
+
+df = pl.read_parquet("./data/results.parquet")
+freq = np.load("./data/frequency.npy")
+lightcurves = pl.scan_parquet("./data/lightcurves.parquet")
+periodograms = pl.scan_parquet("./data/periodograms.parquet")
+
+def get_lightcurve(tic, sector):
+    lc = (
+        lightcurves
+        .filter((pl.col("tic") == pl.lit(int(tic))) & (pl.col("sector") == pl.lit(int(sector))))
+        .select(["time", "flux"])
+        .collect()
+    )
+    return lc.get_column("time").to_numpy(), lc.get_column("flux").to_numpy()
+
+def get_periodogram(tic, sector):
+    pg = (
+        periodograms
+        .filter((pl.col("tic") == pl.lit(tic)) & (pl.col("sector") == pl.lit(sector)))
+        .select(["power"])
+        .collect()
+    )
+    return freq, pg.get_column("power").to_numpy()
+
+tic_arr = df["tic_id"].to_numpy()
+sec_arr = df["sector"].to_numpy()
+
+source_pca = ColumnDataSource({
+    "tic": tic_arr,
+    "sector": sec_arr,
+    "p_sub": df["p_subalfvenic"].to_numpy(),
+    "x": df["PC1"].to_numpy(),
+    "y": df["PC2"].to_numpy()
+})
+
+source_lc = ColumnDataSource(data=dict(time=[], flux=[]))
+source_flc = ColumnDataSource(data=dict(phase=[], flux=[]))
+source_pg = ColumnDataSource(data=dict(freq=[], power=[]))
+
+info = Div()
+
+cmap = LinearColorMapper(
+    palette=Viridis256,
+    low=min(source_pca.data["p_sub"]),
+    high=max(source_pca.data["p_sub"]),
+)
+
+pca_dim = 600
+fig_pca = figure(width=pca_dim, height=pca_dim)
+fig_lc = figure(width=pca_dim, height=pca_dim//3)
+fig_flc = figure(width=pca_dim, height=pca_dim//3)
+fig_pg = figure(width=pca_dim, height=pca_dim//3)
+
+x, y, c = df["PC1"], df["PC2"], df["p_subalfvenic"]
+fig_pca.scatter(source=source_pca, x="x", y="y", fill_color={"field": "p_sub", "transform": cmap}, line_color=None, size=3)
+cbar = ColorBar(color_mapper=cmap, label_standoff=8, location=(0, 0))
+fig_pca.add_layout(cbar, "right")
+
+# Circle for clicks
+sel_source = ColumnDataSource(dict(x=[], y=[]))
+fig_pca.circle("x", "y", source=sel_source, size=10, fill_color=None, line_color="black", line_width=2)
+
+# Highlight same tic
+source_highlight = ColumnDataSource(dict(x=[], y=[]))
+fig_pca.circle("x", "y", source=source_highlight, size=6, fill_color="red", line_color="black", line_width=1)
+
+fig_lc.scatter(source=source_lc, x="x", y="y", size=3)
+fig_flc.scatter(source=source_flc, x="x", y="y", size=3)
+fig_pg.line(source=source_pg, x="x", y="y", width=1)
+
+def tap_callback(event):
+    x_click, y_click = event.x, event.y
+    if not (fig_pca.x_range.start <= event.x <= fig_pca.x_range.end and fig_pca.y_range.start <= event.y <= fig_pca.y_range.end):
+        source_pg.data = dict(x=[], y=[])
+        source_lc.data = dict(x=[], y=[])
+        source_flc.data = dict(x=[], y=[])
+        info.text = ""
+        source_highlight.data = dict(x=[], y=[])
+        source_pca.selected.indices = []
+        sel_source.data = dict(x=[], y=[])
+        return
+
+    xs = np.array(source_pca.data["x"])
+    ys = np.array(source_pca.data["y"])
+    tics = np.array(source_pca.data["tic"])
+    d2 = (xs - x_click)**2 + (ys - y_click)**2
+    i = int(np.argmin(d2))
+    source_pca.selected.indices = [i]
+    sel_source.data = dict(x=[source_pca.data["x"][i]], y=[source_pca.data["y"][i]])
+
+    tic = source_pca.data["tic"][i]
+    sector = source_pca.data["sector"][i]
+    info.text = f"<strong>Currently selected star:</strong> TIC {tic}, sector {sector}"
+    info.text += f"<br>View on: <a href=\"https://simbad.cfa.harvard.edu/simbad/sim-basic?Ident=TIC+{tic}&submit=SIMBAD+search\">Simbad</a> &bullet; <a href=\"https://exofop.ipac.caltech.edu/tess/target.php?id={tic}\">ExoFOP</a>"
+    mask = (tics == tic)
+    source_highlight.data = dict(x=xs[mask], y=ys[mask])
+
+    freq, power = get_periodogram(tic, sector)
+    source_pg.data = dict(x=freq, y=power)
+    time, flux = get_lightcurve(tic, sector)
+    source_lc.data = dict(x=time, y=flux)
+    P = 1/freq[np.argmax(power)]
+    phase = ((time - time[0]) / np.max(P)) % 1.0
+    order = np.argsort(phase)
+    source_flc.data = dict(x=phase[order], y=flux[order])
+
+
+
+fig_pca.on_event(Tap, tap_callback)
+title = Div(text="<h2>PCA Lightcurve Explorer</h2>")
+blurb = Div(text="""
+<p>Click in the PCA plot to load the corresponding periodogram and lightcurve. Click on the color bar or axes to unselect the point. Red points indicate other observations of the selected star.</p>
+""")
+curdoc().add_root(column(
+    title,
+    row(fig_pca, column(fig_pg, fig_lc, fig_flc)),
+    blurb,
+    info,
+    sizing_mode="fixed",
+    align="center"
+))

bokehserverextension.py

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+from subprocess import Popen
+
+def load_jupyter_server_extension(nbapp):
+    """serve the bokeh-app directory with bokeh server"""
+    Popen(["bokeh", "serve", "app.py", "--allow-websocket-origin=*"])
+
+if __name__ == "__main__":
+    load_jupyter_server_extension(None)

data/.gitignore

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+*
+!.*
+!*.parquet
+!periodograms.h5
+!frequency.npy

data/frequency.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9c65cf0dbf6f8ffa1174f70afce976b24d448e5016f08bb7e1063a642a3d0a6
+size 200128

data/lightcurves.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1fff7c3e7424642882a112f7dd43475735f0a21551a263ed834ebfd9d737436
+size 2122386013

data/periodograms.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d14ac543da1574ab187918e1655d3314e14cd25aeb879c041d4d39a327bce75a
+size 2286727311

data/results.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d801245f1d1e825c365ae6338f2f695e5270f93aa9fe36d2b222dc164b26ca56
+size 3147659

environment.yml

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+dependencies:
+  - python=3.13
+  - pip
+  - pip:
+      - -r requirements.txt
+

pca.py

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+import h5py
+import numpy as np
+import polars as pl
+from scipy.signal import find_peaks
+from sklearn.decomposition import PCA
+
+with h5py.File("data/periodograms.h5", "r") as f:
+    tic = f["meta/tic_id"][:]
+    sec = f["meta/sector"][:]
+    freq = f["frequency"][:]
+    power = f["power"][:]
+
+ids = list(zip(tic, sec))
+
+results = []
+n = 10
+for p in power:
+    peaks, _ = find_peaks(p, distance=500)
+    top_peaks = peaks[np.argsort(p[peaks])[-n:]]
+    
+    peak_powers = (p[top_peaks] - np.mean(p))/np.std(p)
+    peak_freqs = (freq[top_peaks]) / np.max(freq)
+    results.append(np.column_stack((peak_freqs, peak_powers)).ravel())
+
+X = np.array(results)
+pca = PCA(n_components=2)
+X_pca = pca.fit_transform(X)
+
+alfven_df = pl.read_csv("data/targets_unc.csv")
+df = pl.DataFrame({"tic_id": tic, "sector": sec, "PC1": X_pca[:, 0], "PC2": X_pca[:, 1]})
+df = df.join(alfven_df, on=["tic_id", "sector"])
+df.write_csv("data/results.csv")

postBuild

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+# enable jupyter_server_proxy
+jupyter serverextension enable --sys-prefix jupyter_server_proxy
+# install the bokeh server extension so that
+# bokeh launches at startup
+mv bokehserverextension.py ${NB_PYTHON_PREFIX}/lib/python*/site-packages/
+# enable bokeh extension
+jupyter serverextension enable --sys-prefix bokehserverextension

pyproject.toml

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+[project]
+name = "dashboard"
+version = "0.1.0"
+requires-python = "<3.14,>=3.13"
+dependencies = [
+    "bokeh>=3.8.1",
+    "jupyter-server-proxy>=4.4.0",
+    "numpy>=2.3.4",
+    "polars>=1.35.2",
+]
+
+[dependency-groups]
+dev = [
+    "scikit-learn>=1.7.2",
+    "scipy>=1.16.3",
+]