initial commit

Dockerfile

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+FROM python:3.11
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Try and run pip command after setting the user with `USER user` to avoid permission issues with Python
+RUN pip install --no-cache-dir --upgrade pip
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+COPY --chown=user requirements.txt .
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user app.py .
+
+ENTRYPOINT ["solara", "run", "app.py", "--host=0.0.0.0", "--port", "7860"]

app.py

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+# %%
+
+import io
+import uuid
+from dataclasses import dataclass
+from typing import Optional
+
+import matplotlib.pylab as plt
+import numpy as np
+import pandas as pd
+import solara
+import solara.lab
+from matplotlib.figure import Figure
+from scipy import stats
+from solara.components.file_drop import FileInfo
+
+
+def make_cdf_figure(
+    values_left: np.ndarray, values_right: np.ndarray, stat_loc: float
+) -> Figure:
+    fig = Figure(figsize=(5, 5))
+    ax = fig.subplots()
+
+    ax.axvline(stat_loc, color="grey")
+
+    # Cumulative distributions.
+    ax.ecdf(values_left, label="left")
+    ax.ecdf(values_right, label="right")
+    ax.legend()
+    ax.set_xlabel("Value")
+    ax.set_ylabel("CDF")
+
+    return fig
+
+
+def make_pdf_figure(
+    values_left: np.ndarray, values_right: np.ndarray, stat_loc: float
+) -> Figure:
+    fig = Figure(figsize=(5, 5))
+    ax = fig.subplots()
+
+    ax.axvline(stat_loc, color="grey")
+
+    # Cumulative distributions.
+    ax.hist(values_left, bins="fd", density=True, histtype="step", label="left")
+    ax.hist(values_right, bins="fd", density=True, histtype="step", label="right")
+    ax.legend()
+    plt.show(fig)
+    ax.set_xlabel("Value")
+    ax.set_ylabel("PDF")
+
+    return fig
+
+
+# %%
+
+
+def dropna(values: np.ndarray) -> np.ndarray:
+    return values[~np.isnan(values)]
+
+
+@solara.component
+def KSTestResult(values_left, values_right):
+    values_left = dropna(values_left)
+    values_right = dropna(values_right)
+
+    kstat = stats.ks_2samp(values_left, values_right)
+
+    fig_cdf = make_cdf_figure(values_left, values_right, kstat.statistic_location)
+    fig_pdf = make_pdf_figure(values_left, values_right, kstat.statistic_location)
+
+    with solara.Card("Kolmogorov-Smirnov Test"):
+        with solara.Columns():
+            solara.FigureMatplotlib(fig_cdf)
+            solara.FigureMatplotlib(fig_pdf)
+
+        solara.Markdown("# Test Result")
+
+        solara.Info(
+            f"statistic: {kstat.statistic:.3g}",
+        )
+
+        solara.Info(
+            f"p-value: {kstat.pvalue:.3g}",
+        )
+
+        solara.Info(
+            f"location: {kstat.statistic_location:.3g}",
+        )
+
+
+@dataclass
+class Selection:
+    file: Optional[str] = None
+    column: Optional[str] = None
+
+    @property
+    def is_set(self) -> bool:
+        return self.file is not None and self.column is not None
+
+    @property
+    def columns(self) -> list[str]:
+        if self.file is not None:
+            return list(data_store.value[self.file].columns)
+        return []
+
+    @property
+    def array(self) -> np.ndarray:
+        if self.is_set:
+            return data_store.value[self.file][self.column].to_numpy()
+        return np.array([])
+
+
+def all_set(selections: list[Selection]) -> bool:
+    return all(s.is_set for s in selections)
+
+
+@solara.component
+def Selectors(selection: solara.Reactive[Selection]):
+    solara.Select(
+        label="Select file",
+        values=list(data_store.value.keys()),
+        value=selection.value.file,
+        on_value=lambda x: selection.update(file=x, column=None),
+    )
+
+    solara.Select(
+        label="Select_column",
+        values=selection.value.columns,
+        value=selection.value.column,
+        on_value=lambda x: selection.update(column=x),
+    )
+
+
+file_info: solara.Reactive[list[FileInfo]] = solara.reactive([])
+data_store = solara.Reactive({})
+
+selection_left = solara.reactive(Selection())
+selection_right = solara.reactive(Selection())
+
+
+@solara.component
+def KSApp():
+    def load_data():
+        d = {}
+        for f in file_info.value:
+            b_io = io.BytesIO(f["data"])
+            df = pd.read_csv(b_io)
+            d[f["name"]] = df
+        data_store.set(d)
+
+    _ = solara.use_memo(load_data, dependencies=[file_info.value])
+    upload_key = solara.reactive(uuid.uuid4())
+
+    def clear_all():
+        upload_key.set(uuid.uuid4())
+        selection_left.set(Selection())
+        selection_right.set(Selection())
+        file_info.set([])
+        data_store.set({})
+
+    with solara.ColumnsResponsive([3, 9]):
+        with solara.Card("Input"):
+            solara.FileDropMultiple(
+                label="Upload CSV files",
+                on_file=file_info.set,
+                lazy=False,
+            )  # .key(upload_key.value.hex)
+            solara.Text("Select left:")
+            Selectors(selection_left)
+            solara.Text("Select left:")
+            Selectors(selection_right)
+            solara.Button(label="Clear", on_click=clear_all)
+
+        if not all_set([selection_left.value, selection_right.value]):
+            with solara.Card():
+                solara.Text("Please upload data and select both files and columns")
+
+        else:
+            KSTestResult(selection_left.value.array, selection_right.value.array)
+
+
+page = KSApp()

requirements.txt

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+solara
+matplotlib
+numpy
+scipy
+pandas