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Files changed (8) hide show
  1. .DS_Store +0 -0
  2. .dockerignore +4 -0
  3. .gitignore +3 -0
  4. Dockerfile +10 -0
  5. main.py +138 -0
  6. pca.py +32 -0
  7. pyproject.toml +17 -0
  8. uv.lock +646 -0
.DS_Store ADDED
Binary file (6.15 kB). View file
 
.dockerignore ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
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+ data/
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+ .venv/
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+ pca.py
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+ .env
.gitignore ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ .venv/
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+ data/
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+ .env
Dockerfile ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
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+ FROM ghcr.io/astral-sh/uv:latest
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+
3
+ RUN useradd -m -u 1000 user
4
+ WORKDIR /app
5
+
6
+ COPY --chown=user ./uv.lock ./pyproject.toml ./
7
+ RUN uv sync --no-cache
8
+
9
+ COPY --chown=user . ./
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+ CMD ["uv", "run", "bokeh", "serve", "--port", "7860"]
main.py ADDED
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+ import dotenv
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+ import numpy as np
3
+ import polars as pl
4
+ import huggingface_hub as hf
5
+ from bokeh.io import curdoc
6
+ from bokeh.events import Tap
7
+ from bokeh.plotting import figure
8
+ from bokeh.palettes import Viridis256
9
+ from bokeh.layouts import row, column
10
+ from bokeh.models import ColumnDataSource, LinearColorMapper, ColorBar, Div
11
+ dotenv.load_dotenv()
12
+
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+ hf.snapshot_download(
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+ repo_id="ggrinberg35/pca-results",
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+ repo_type="dataset",
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+ local_dir="/data/"
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+ )
18
+
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+ df = pl.read_parquet("/data/results.parquet")
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+ freq = np.load("/data/frequency.npy")
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+ lightcurves = pl.scan_parquet("/data/pca-results/lightcurves.parquet")
22
+ periodograms = pl.scan_parquet("/data/periodograms.parquet")
23
+
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+ def get_lightcurve(tic, sector):
25
+ lc = (
26
+ lightcurves
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+ .filter((pl.col("tic") == pl.lit(int(tic))) & (pl.col("sector") == pl.lit(int(sector))))
28
+ .select(["time", "flux"])
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+ # .collect()
30
+ )
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+ return lc.get_column("time").to_numpy(), lc.get_column("flux").to_numpy()
32
+
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+ def get_periodogram(tic, sector):
34
+ pg = (
35
+ periodograms
36
+ .filter((pl.col("tic") == pl.lit(tic)) & (pl.col("sector") == pl.lit(sector)))
37
+ .select(["power"])
38
+ .collect()
39
+ )
40
+ return freq, pg.get_column("power").to_numpy()
41
+
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+ tic_arr = df["tic_id"].to_numpy()
43
+ sec_arr = df["sector"].to_numpy()
44
+
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+ source_pca = ColumnDataSource({
46
+ "tic": tic_arr,
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+ "sector": sec_arr,
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+ "p_sub": df["p_subalfvenic"].to_numpy(),
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+ "x": df["PC1"].to_numpy(),
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+ "y": df["PC2"].to_numpy()
51
+ })
52
+
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+ source_lc = ColumnDataSource(data=dict(time=[], flux=[]))
54
+ source_flc = ColumnDataSource(data=dict(phase=[], flux=[]))
55
+ source_pg = ColumnDataSource(data=dict(freq=[], power=[]))
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+
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+ info = Div()
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+
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+ cmap = LinearColorMapper(
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+ palette=Viridis256,
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+ low=min(source_pca.data["p_sub"]),
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+ high=max(source_pca.data["p_sub"]),
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+ )
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+
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+ pca_dim = 600
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+ fig_pca = figure(width=pca_dim, height=pca_dim)
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+ fig_lc = figure(width=pca_dim, height=pca_dim//3)
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+ fig_flc = figure(width=pca_dim, height=pca_dim//3)
69
+ fig_pg = figure(width=pca_dim, height=pca_dim//3)
70
+
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+ x, y, c = df["PC1"], df["PC2"], df["p_subalfvenic"]
72
+ fig_pca.scatter(source=source_pca, x="x", y="y", fill_color={"field": "p_sub", "transform": cmap}, line_color=None, size=3)
73
+ cbar = ColorBar(color_mapper=cmap, label_standoff=8, location=(0, 0))
74
+ fig_pca.add_layout(cbar, "right")
75
+
76
+ # Circle for clicks
77
+ sel_source = ColumnDataSource(dict(x=[], y=[]))
78
+ fig_pca.circle("x", "y", source=sel_source, size=10, fill_color=None, line_color="black", line_width=2)
79
+
80
+ # Highlight same tic
81
+ source_highlight = ColumnDataSource(dict(x=[], y=[]))
82
+ fig_pca.circle("x", "y", source=source_highlight, size=6, fill_color="red", line_color="black", line_width=1)
83
+
84
+ fig_lc.scatter(source=source_lc, x="x", y="y", size=3)
85
+ fig_flc.scatter(source=source_flc, x="x", y="y", size=3)
86
+ fig_pg.line(source=source_pg, x="x", y="y", width=1)
87
+
88
+ def tap_callback(event):
89
+ x_click, y_click = event.x, event.y
90
+ 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):
91
+ source_pg.data = dict(x=[], y=[])
92
+ source_lc.data = dict(x=[], y=[])
93
+ source_flc.data = dict(x=[], y=[])
94
+ info.text = ""
95
+ source_highlight.data = dict(x=[], y=[])
96
+ source_pca.selected.indices = []
97
+ sel_source.data = dict(x=[], y=[])
98
+ return
99
+
100
+ xs = np.array(source_pca.data["x"])
101
+ ys = np.array(source_pca.data["y"])
102
+ tics = np.array(source_pca.data["tic"])
103
+ d2 = (xs - x_click)**2 + (ys - y_click)**2
104
+ i = int(np.argmin(d2))
105
+ source_pca.selected.indices = [i]
106
+ sel_source.data = dict(x=[source_pca.data["x"][i]], y=[source_pca.data["y"][i]])
107
+
108
+ tic = source_pca.data["tic"][i]
109
+ sector = source_pca.data["sector"][i]
110
+ info.text = f"<strong>Currently selected star:</strong> TIC {tic}, sector {sector}"
111
+ 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>"
112
+ mask = (tics == tic)
113
+ source_highlight.data = dict(x=xs[mask], y=ys[mask])
114
+
115
+ freq, power = get_periodogram(tic, sector)
116
+ source_pg.data = dict(x=freq, y=power)
117
+ time, flux = get_lightcurve(tic, sector)
118
+ source_lc.data = dict(x=time, y=flux)
119
+ P = 1/freq[np.argmax(power)]
120
+ phase = ((time - time[0]) / np.max(P)) % 1.0
121
+ order = np.argsort(phase)
122
+ source_flc.data = dict(x=phase[order], y=flux[order])
123
+
124
+
125
+
126
+ fig_pca.on_event(Tap, tap_callback)
127
+ title = Div(text="<h2>PCA Lightcurve Explorer</h2>")
128
+ blurb = Div(text="""
129
+ <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>
130
+ """)
131
+ curdoc().add_root(column(
132
+ title,
133
+ row(fig_pca, column(fig_pg, fig_lc, fig_flc)),
134
+ blurb,
135
+ info,
136
+ sizing_mode="fixed",
137
+ align="center"
138
+ ))
pca.py ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import h5py
2
+ import numpy as np
3
+ import polars as pl
4
+ from scipy.signal import find_peaks
5
+ from sklearn.decomposition import PCA
6
+
7
+ with h5py.File("data/periodograms.h5", "r") as f:
8
+ tic = f["meta/tic_id"][:]
9
+ sec = f["meta/sector"][:]
10
+ freq = f["frequency"][:]
11
+ power = f["power"][:]
12
+
13
+ ids = list(zip(tic, sec))
14
+
15
+ results = []
16
+ n = 10
17
+ for p in power:
18
+ peaks, _ = find_peaks(p, distance=500)
19
+ top_peaks = peaks[np.argsort(p[peaks])[-n:]]
20
+
21
+ peak_powers = (p[top_peaks] - np.mean(p))/np.std(p)
22
+ peak_freqs = (freq[top_peaks]) / np.max(freq)
23
+ results.append(np.column_stack((peak_freqs, peak_powers)).ravel())
24
+
25
+ X = np.array(results)
26
+ pca = PCA(n_components=2)
27
+ X_pca = pca.fit_transform(X)
28
+
29
+ alfven_df = pl.read_csv("data/targets_unc.csv")
30
+ df = pl.DataFrame({"tic_id": tic, "sector": sec, "PC1": X_pca[:, 0], "PC2": X_pca[:, 1]})
31
+ df = df.join(alfven_df, on=["tic_id", "sector"])
32
+ df.write_csv("data/results.csv")
pyproject.toml ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [project]
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+ name = "dashboard"
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+ version = "0.1.0"
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+ requires-python = "<3.14,>=3.13"
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+ dependencies = [
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+ "bokeh>=3.8.1",
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+ "dotenv>=0.9.9",
8
+ "huggingface-hub>=1.1.2",
9
+ "numpy>=2.3.4",
10
+ "polars>=1.35.2",
11
+ ]
12
+
13
+ [dependency-groups]
14
+ dev = [
15
+ "scikit-learn>=1.7.2",
16
+ "scipy>=1.16.3",
17
+ ]
uv.lock ADDED
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