initial commit

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* 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
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

Dockerfile

@@ -0,0 +1,18 @@
+FROM python:3.12
+COPY --from=ghcr.io/astral-sh/uv:0.4.20 /uv /bin/uv
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+ENV PATH="/home/user/.local/bin:$PATH"
+ENV UV_SYSTEM_PYTHON=1
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN uv pip install -r requirements.txt
+
+COPY --chown=user . /app
+# Switch to the "user" user
+USER user
+
+CMD ["solara", "run", "app.py", "--host", "0.0.0.0", "--port", "7860"]

SecB_data.csv

@@ -0,0 +1,147 @@
+,r_number,SecB tetramer ΔG,dimer ΔΔG,aa_category
+0,10,20503.218908795578,0.04102027344197268,3
+1,11,20503.1987580869,-0.013373425168538233,2
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+6,16,20143.07512639456,0.013463255472743185,4
+7,17,20143.05862687419,-0.03987032729492057,2
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+51,61,,,0
+52,62,29683.323649341943,895.8858358495345,4
+53,63,29820.52673172767,1033.0780610037182,3
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+56,66,32091.361301778325,148.57627289799348,4
+57,67,32091.360835556497,148.5765613044059,3
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+67,77,37636.38655502341,5007.988149225941,1
+68,78,33782.48872642615,5273.721614185131,4
+69,79,35905.071510615024,5480.913130223267,3
+70,80,36677.6038686029,5506.0450257956,3
+71,81,36677.61636593746,5506.07698878599,5
+72,82,36677.60260767371,5506.045433733954,5
+73,83,33378.803348391986,5488.374668323548,4
+74,84,33378.758340559325,5488.3754546840755,2
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+77,87,15851.513354479175,3690.8610620640447,4
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+124,134,,,1
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+138,148,10660.375891339996,0.003966357349781902,5
+139,149,10660.333223836176,-0.023842569449698203,3
+140,150,10660.354481127511,0.034241028002725216,1
+141,151,10660.362373368838,0.006191466125528677,1
+142,152,10660.380600976769,0.006496632580820005,0
+143,153,11197.67105254893,0.011001825150742661,3
+144,154,11197.642798281837,-0.017785698271836736,1
+145,155,1629.4416305017826,5151.96303489735,4

app.py

@@ -0,0 +1,339 @@
+import io
+from pathlib import Path
+from typing import Callable, Optional, cast
+from urllib.parse import parse_qsl
+
+import altair as alt
+import numpy as np
+import pandas as pd
+import reacton.core
+import solara
+import solara.lab
+from cmap import Catalog, Colormap
+from ipyvuetify.extra import FileInput
+
+from make_link import encode_url
+from pycafe_linkgen import make_link
+from viewer import AxisProperties, ColorTransform, ProteinView, RoutedView
+
+DEFAULT_CMAP = "tol:rainbow_PuRd"
+NORM_CATEGORIES = ["linear", "diverging", "categorical"]
+VMIN_DEFAULT = 0.0
+VMAX_DEFAULT = 1.0
+HIGHLIGHT_COLOR = "#e933f8"
+MISSING_DATA_COLOR = "#8c8c8c"
+CMAP_OPTIONS = list(Catalog().namespaced_keys())
+
+# BASE_URL = "http://localhost:8765" # local testing
+BASE_URL = "https://huggingface.co/spaces/Jhsmit/ipymolstar-annotate-colors"
+
+pth = Path(__file__).parent
+
+
+@solara.component
+def FileInputComponent(
+    on_file: Callable[[solara.components.file_drop.FileInfo | None], None],
+):
+    """Adaptation of _FileDrop."""
+
+    file_info, set_file_info = solara.use_state(None)
+    wired_files, set_wired_files = solara.use_state(
+        cast(Optional[list[solara.components.file_drop.FileInfo]], None)
+    )
+
+    file_drop = FileInput.element(on_file_info=set_file_info, multiple=False)  # type: ignore
+
+    def wire_files():
+        if not file_info:
+            set_wired_files([])
+            return
+
+        real = cast(FileInput, solara.get_widget(file_drop))
+
+        # workaround for @observe being cleared
+        real.version += 1
+        real.reset_stats()
+
+        set_wired_files(
+            cast(list[solara.components.file_drop.FileInfo], real.get_files())
+        )
+
+    solara.use_effect(wire_files, [file_info])
+
+    def handle_file():
+        if not wired_files:
+            on_file(None)
+            return
+        if on_file:
+            f = wired_files[0].copy()
+            f["data"] = None
+            on_file(f)
+
+    solara.lab.use_task(handle_file, dependencies=[wired_files])
+
+    return file_drop
+
+
+@solara.component
+def ColorPickerMenuButton(title: str, color: solara.Reactive[str]):
+    local_color = solara.use_reactive(color.value)
+
+    def on_open(value: bool):
+        if not value:
+            color.set(local_color.value)
+
+    btn = solara.Button(title, color=local_color.value)
+    with solara.lab.Menu(
+        activator=btn, close_on_content_click=False, on_open_value=on_open
+    ):
+        solara.v.ColorPicker(
+            v_model=local_color.value,
+            on_v_model=local_color.set,
+        )
+
+
+empty_frame = pd.DataFrame()
+R_DEFAULT = ""
+V_DEFAULT = ""
+
+# %%
+
+
+@solara.component
+def MainApp():
+    dark_effective = solara.lab.use_dark_effective()
+    title = solara.use_reactive("My annotated protein view")
+    description = solara.use_reactive("")
+    molecule_id = solara.use_reactive("1QYN")
+    data = solara.use_reactive(empty_frame)
+    warning_text = solara.use_reactive("")
+
+    residue_column = solara.use_reactive(R_DEFAULT)
+    color_column = solara.use_reactive(V_DEFAULT)
+
+    label = solara.use_reactive("value")
+    unit = solara.use_reactive("au")
+
+    highlight_color = solara.use_reactive(HIGHLIGHT_COLOR)
+    missing_data_color = solara.use_reactive(MISSING_DATA_COLOR)
+    autoscale_y = solara.use_reactive(True)
+
+    cmap_name = solara.use_reactive(DEFAULT_CMAP)
+    reverse = solara.use_reactive(False)
+    full_cmap_name = cmap_name.value + "_r" if reverse.value else cmap_name.value
+    cmap = Colormap(full_cmap_name)
+
+    vmin = solara.use_reactive(VMIN_DEFAULT)
+    vmax = solara.use_reactive(VMAX_DEFAULT)
+    norm_type = solara.use_reactive(NORM_CATEGORIES[0])
+
+    rc = reacton.core.get_render_context()
+
+    def on_file(file_info: solara.components.file_drop.FileInfo | None):
+        if not file_info:
+            data.set(pd.DataFrame())
+            return
+
+        try:
+            df = pd.read_csv(file_info["file_obj"])
+        except Exception as e:
+            warning_text.set(str(e))
+            return
+        if len(df.columns) < 2:
+            warning_text.set(f"Expected at least 2 columns, got {len(df.columns)}")
+            data.set(pd.DataFrame())
+            return
+
+        warning_text.set("")
+
+        # order matters!
+        # with solara.batch_update():
+        # https://github.com/widgetti/solara/issues/637
+        with rc:
+            residue_column.set(df.columns[0])
+            color_column.set(df.columns[1])
+            data.set(df)
+
+    colors = ColorTransform(
+        name=full_cmap_name,
+        norm_type=norm_type.value,
+        vmin=vmin.value,
+        vmax=vmax.value,
+        missing_data_color=missing_data_color.value,
+        highlight_color=highlight_color.value,
+    )
+
+    axis_properties = AxisProperties(
+        label=label.value,
+        unit=unit.value,
+        autoscale_y=autoscale_y.value,
+    )
+
+    if data.value.empty:
+        data_view = pd.DataFrame({"residue_number": [], "value": []})
+    else:
+        data_view = pd.DataFrame(
+            {
+                "residue_number": data.value[residue_column.value],
+                "value": data.value[color_column.value],
+            }
+        )
+
+    def load_example_data():
+        bio = io.BytesIO(Path("example_data.csv").read_bytes())
+        bio.seek(0)
+        file_info = solara.components.file_drop.FileInfo(
+            name="example_data.csv",
+            size=Path("example_data.csv").stat().st_size,
+            file_obj=bio,
+            data=None,
+        )
+
+        # with solara.batch_update():
+        # https://github.com/widgetti/solara/issues/637
+        with rc:
+            molecule_id.set("6GOX")
+            title.set("SecA HDX-MS protein local flexibility")
+            on_file(file_info)
+            vmin.set(4e4)
+            vmax.set(1e4)
+            autoscale_y.set(False)
+            description.set(Path("example_data_description.md").read_text())
+
+    with solara.AppBar():
+        with solara.Tooltip("Load example data and settings"):
+            solara.Button(
+                icon_name="mdi-test-tube",
+                icon=True,
+                on_click=load_example_data,
+            )
+
+    query_string = encode_url(
+        title=title.value,
+        molecule_id=molecule_id.value,
+        colors=colors,
+        axis_properties=axis_properties,
+        data=data_view,
+        description=description.value,
+    )
+
+    ProteinView(
+        title.value,
+        molecule_id=molecule_id.value,
+        data=data_view,
+        colors=colors,
+        axis_properties=axis_properties,
+        dark_effective=dark_effective,
+        description=description.value,
+    )
+
+    with solara.Sidebar():
+        with solara.Card("Settings"):
+            solara.InputText(label="Title", value=title)
+            solara.InputText(label="PDB ID", value=molecule_id)
+            solara.Text("Choose .csv data file:")
+            FileInputComponent(on_file)
+
+            if warning_text.value:
+                solara.Warning(warning_text.value)
+
+            if not data.value.empty:
+                with solara.Row():
+                    solara.Select(
+                        label="Residue Column",
+                        value=residue_column,
+                        values=list(data.value.columns),
+                    )
+                    solara.Select(
+                        label="Color Column",
+                        value=color_column,
+                        values=list(data.value.columns),
+                    )
+
+            with solara.Row():
+                solara.InputText(label="Label", value=label)
+                solara.InputText(label="Unit", value=unit)
+
+            solara.Text("Colors")
+            with solara.Row(gap="10px", justify="space-around"):
+                ColorPickerMenuButton("Highlight", highlight_color)
+                ColorPickerMenuButton("Missing data", missing_data_color)
+
+            # with solara.Row():
+            solara.v.Autocomplete(
+                v_model=cmap_name.value,
+                on_v_model=cmap_name.set,
+                items=CMAP_OPTIONS,
+            )
+
+            solara.Select(
+                label="Normalization type", value=norm_type, values=NORM_CATEGORIES
+            )
+
+            with solara.Row():
+
+                def set_vmin(value: float):
+                    if norm_type.value == "diverging":
+                        vmin.set(value)
+                        vmax.set(-value)
+                    else:
+                        vmin.set(value)
+
+                solara.InputFloat(
+                    label="vmin",
+                    value=vmin.value,
+                    on_value=set_vmin,
+                    disabled=norm_type.value == "categorical",
+                )
+                solara.InputFloat(
+                    label="vmax",
+                    value=vmax,
+                    disabled=norm_type.value in ["diverging", "categorical"],
+                )
+            with solara.GridFixed(columns=2):
+                with solara.Tooltip("Reverses the color map"):
+                    solara.Checkbox(label="Reverse", value=reverse)
+
+                with solara.Tooltip(
+                    "Uncheck to use color range as the scatterplot y scale"
+                ):
+                    solara.Checkbox(label="Autoscale Y", value=autoscale_y)
+
+            solara.Image(cmap._repr_png_(height=24), width="100%")
+            solara.InputTextArea(
+                label="Description", value=description, continuous_update=True
+            )
+            solara.Div(style={"height": "10px"})
+
+            solara.Button(
+                label="Open view in new tab",
+                attributes={"href": BASE_URL + "?" + query_string, "target": "_blank"},
+                block=True,
+            )
+
+
+@solara.component
+def Page():
+    route = solara.use_router()
+    solara.Style(Path("style.css"))
+
+    dark_effective = solara.lab.use_dark_effective()
+    dark_previous = solara.use_previous(dark_effective)
+    if dark_previous != dark_effective:
+        if dark_effective:
+            alt.themes.enable("dark")
+        else:
+            alt.themes.enable("default")
+
+    # todo: aways true, check valid
+    if route.search:
+        query_dict = {k: v for k, v in parse_qsl(route.search)}
+        # needs more keys but if this is there then at least there has been an attempt
+        if "molecule_id" in query_dict:
+            RoutedView()
+
+    else:
+        MainApp()
+
+
+# %%

example_data.csv

@@ -0,0 +1,895 @@
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example_data_description.md

@@ -0,0 +1,13 @@
+## SecA HDX-MS protein flexibility data
+
+
+This is an example dataset of HDX-MS derived protein flexibility, expressed as Linderström-Lang Gibbs free energies (ΔG/ΔΔG).
+
+The ΔG values obtained using PyHDX ([DOI: 10.1021/acs.analchem.1c02155](https://pubs.acs.org/doi/10.1021/acs.analchem.1c02155))
+
+There are three protein states, SecA monomer, SecA dimer and SecA with C-tail deletion, each state as apo (ΔG) and ADP-bound (ΔΔG)
+
+
+More details on SecA dynamics can be found here: 
+[A nexus of intrinsic dynamics underlies translocase priming](https://www.cell.com/structure/fulltext/S0969-2126(21)00113-1)
+

interactive.py

@@ -0,0 +1,166 @@
+# %%
+from collections import defaultdict
+
+import altair as alt
+import matplotlib.pyplot as plt
+import pandas as pd
+from cmap import Colormap
+import polars as pl
+
+#%%
+
+df = pl.read_csv("example_data_bk.csv")
+df.columns
+
+df_crop = df[:, :3]
+df_crop
+
+df_crop.write_csv("example_data.csv")
+#%%
+
+# %%
+kwargs = {"comment": "#", "header": [0, 1], "index_col": 0}
+df = pd.read_csv("fit_result_batch.csv", **kwargs)
+# %%
+df
+# %%
+
+
+df_wt = df["SecB WT apo"].reset_index()
+df_dimer = df["SecB his dimer apo"].reset_index()
+
+AA_categories = {
+    "pos": ["R", "H", "K"],
+    "neg": ["D", "E"],
+    "aromatic": ["F", "W", "Y"],
+    "polar": ["S", "T", "N", "Q"],
+    "nonpolar": ["A", "V", "I", "L", "M"],
+    "other": ["G", "C", "P"],
+}
+cat_list = list(AA_categories)
+AA_lut = {aa: category for category in AA_categories for aa in AA_categories[category]}
+AA_lut
+aa_cat_numbers = [cat_list.index(AA_lut[aa]) for aa in df_wt["sequence"]]
+df_wt["aa_cat"] = aa_cat_numbers
+
+# %%
+cmap = Colormap("colorbrewer:Accent_6")
+sol = defaultdict(list)
+colors = cmap(df_wt["aa_cat"])
+
+nums = range(6)
+colors = cmap(nums)
+
+for n, c in zip(nums, colors):
+    print(n, c)
+# %%
+len(cmap.color_stops)
+
+colors = cmap.to_altair(N=cmap.num_colors)
+domain = range(6)
+altair_scale = alt.Scale(domain=domain, range=colors, clamp=True)
+# %%
+alt.Chart(df_wt).mark_point().encode(
+    x="r_number",
+    y="aa_cat",
+    color=alt.Color("aa_cat:N", scale=altair_scale),
+)
+
+# %%
+import pandas as pd
+
+df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [7, 6, 5, 4], "c": ["a", "b", "b", "c"]})
+
+chart = alt.Chart(df).mark_point().encode(alt.X("a"), alt.Y("b"), alt.Color("c:N"))
+chart
+# %%
+
+
+# %%
+
+
+ddG = df_wt["deltaG"] - df_dimer["deltaG"]
+ddG
+
+
+# %%
+
+fig, ax = plt.subplots()
+ax.scatter(df_wt["r_number"], df_wt["deltaG"])
+
+# %%
+fig, ax = plt.subplots()
+ax.scatter(df_wt["r_number"], ddG)
+
+# %%
+df_wt.columns
+# %%
+
+output = pd.DataFrame(
+    {
+        "r_number": df_wt["r_number"],
+        "SecB tetramer ΔG": df_wt["deltaG"],
+        "dimer ΔΔG": ddG,
+        "aa_category": df_wt["aa_cat"],
+    }
+)
+output = output.set_index("r_number")
+
+output
+# %%
+import numpy as np
+
+N = 150
+fuzzy_sin = 0.5 * (1 + np.sin(np.arange(N) / 10.0)) + np.random.normal(
+    loc=0, scale=0.1, size=N
+)
+df = pd.DataFrame({"fuzzy_sin": fuzzy_sin})
+df
+
+
+
+# add series to output dataframe a a column
+
+output["fuzzy_sin"] = series
+output
+# %%
+
+output.to_csv("SecB_data.csv")
+# %%
+
+dir(cmap)
+cmap.category
+
+# %%
+
+tol_cmap = Colormap("tol:rainbow_discrete_7")
+tol_cmap.category
+tol_cmap.num_colors
+tol_cmap.interpolation
+
+# %%
+
+tol_cmap = Colormap("vispy:hsl")
+tol_cmap.category
+tol_cmap.num_colors
+tol_cmap.interpolation
+
+
+# %%
+tol_cmap = Colormap("yorick:stern")
+tol_cmap.category
+tol_cmap.num_colors
+tol_cmap.interpolation
+# %%
+tol_cmap = Colormap("tol:rainbow_whbr")
+tol_cmap.category
+tol_cmap.num_colors
+tol_cmap.interpolation
+# %%
+tol_cmap = Colormap("glasbey:glasbey")
+tol_cmap.category
+tol_cmap.num_colors
+# tol_cmap.interpolation
+
+
+# %%

make_link.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+import base64
+from io import BytesIO
+from typing import TYPE_CHECKING
+from urllib.parse import urlencode
+
+import pandas as pd
+import zstandard as zstd
+
+if TYPE_CHECKING:
+    from viewer import AxisProperties, ColorTransform
+
+# %%
+
+COMPRESSOR = zstd.ZstdCompressor(level=22)
+DECOMPRESSOR = zstd.ZstdDecompressor()
+
+
+def encode_url(
+    title: str,
+    molecule_id: str,
+    colors: ColorTransform,
+    axis_properties: AxisProperties,
+    data: pd.DataFrame,
+    description: str = "",
+):
+    encode_dict = dict(title=title, molecule_id=molecule_id)
+    encode_dict.update({**colors.model_dump(), **axis_properties.model_dump()})
+
+    csv_str = data.to_csv(float_format="%.4f", index=False)
+    compressed = COMPRESSOR.compress(csv_str.encode())
+    base64_text = base64.b64encode(compressed).decode("utf8")
+    encode_dict["data"] = base64_text
+    if description:
+        encode_dict["description"] = description
+
+    return urlencode(encode_dict)
+
+
+def decode_data(base64_text) -> pd.DataFrame:
+    decoded_bytes = base64.b64decode(base64_text)
+    decompressed = DECOMPRESSOR.decompress(decoded_bytes)
+
+    bio = BytesIO(decompressed)
+    data = pd.read_csv(bio)
+    bio.close()
+
+    return data

requirements.txt

@@ -0,0 +1,8 @@
+solara
+ipymolstar
+pandas
+altair
+anywidget
+cmap
+pydantic
+zstandard

style.css

@@ -0,0 +1,14 @@
+.vega-embed {
+    overflow: visible;
+    width: 100% !important;
+}
+
+/* Hide the ipymolstar drag and drop overlay */
+.msp-drag-drop-overlay {
+    display: none !important;
+}
+
+/* remove solara footer */
+.v-application--wrap > div:nth-child(2) > div:nth-child(2){
+    display: none !important;
+}

viewer.py

@@ -0,0 +1,388 @@
+from __future__ import annotations
+
+import base64
+import gzip
+import json
+from dataclasses import dataclass, fields
+from io import BytesIO
+from pathlib import Path
+from urllib.parse import parse_qsl
+
+import altair as alt
+import ipywidgets as widgets
+import numpy as np
+import pandas as pd
+import solara
+import solara.lab
+from cmap import Colormap
+from ipymolstar.widget import PDBeMolstar
+from pydantic import BaseModel
+
+from make_link import decode_data
+
+base_v = np.vectorize(np.base_repr)
+PAD_SIZE = 0.05  # when not autoscale Y size of padding used
+
+
+def norm(x, vmin, vmax):
+    return (x - vmin) / (vmax - vmin)
+
+
+class ColorTransform(BaseModel):
+    name: str = "tol:rainbow_PuRd"
+    norm_type: str = "linear"
+    vmin: float = 0.0
+    vmax: float = 1.0
+    missing_data_color: str = "#8c8c8c"
+    highlight_color: str = "#e933f8"
+
+    def molstar_colors(self, data: pd.DataFrame) -> dict:
+        if self.norm_type == "categorical":
+            values = data["value"]
+        else:
+            values = norm(data["value"], vmin=self.vmin, vmax=self.vmax)
+
+        rgba_array = self.cmap(values, bytes=True)
+        ints = rgba_array.astype(np.uint8).view(dtype=np.uint32).byteswap()
+        padded = np.char.rjust(base_v(ints // 2**8, 16), 6, "0")
+        hex_colors = np.char.add("#", padded).squeeze()
+
+        color_data = {
+            "data": [
+                {"residue_number": resi, "color": hcolor.lower()}
+                for resi, hcolor in zip(data["residue_number"], hex_colors)
+            ],
+            "nonSelectedColor": self.missing_data_color,
+        }
+
+        return color_data
+
+    @property
+    def cmap(self) -> Colormap:
+        return Colormap(self.name, bad=self.missing_data_color)
+
+    @property
+    def altair_scale(self) -> alt.Scale:
+        if self.norm_type == "categorical":
+            colors = self.cmap.to_altair(N=self.cmap.num_colors)
+            domain = range(self.cmap.num_colors)
+        else:
+            colors = self.cmap.to_altair()
+            domain = np.linspace(self.vmin, self.vmax, 256, endpoint=True)
+
+        scale = alt.Scale(domain=list(domain), range=colors, clamp=True)
+        return scale
+
+
+class AxisProperties(BaseModel):
+    label: str = "x"
+    unit: str = "au"
+    autoscale_y: bool = True
+
+    @property
+    def title(self) -> str:
+        return f"{self.label} ({self.unit})"
+
+
+def make_chart(
+    data: pd.DataFrame, colors: ColorTransform, axis_properties: AxisProperties
+) -> alt.LayerChart:
+    xmin, xmax = data["residue_number"].min(), data["residue_number"].max()
+    xpad = (xmax - xmin) * 0.05
+    xscale = alt.Scale(domain=(xmin - xpad, xmax + xpad))
+
+    if axis_properties.autoscale_y:
+        y_scale = alt.Scale()
+    elif colors.norm_type == "categorical":
+        ypad = colors.cmap.num_colors * 0.05
+        y_scale = alt.Scale(domain=(0 - ypad, colors.cmap.num_colors - 1 + ypad))
+    else:
+        ypad = (colors.vmax - colors.vmin) * 0.05
+        y_scale = alt.Scale(domain=(colors.vmin - ypad, colors.vmax + ypad))
+
+    zoom_x = alt.selection_interval(
+        bind="scales",
+        encodings=["x"],
+        zoom="wheel![!event.shiftKey]",
+    )
+
+    scatter = (
+        alt.Chart(data)
+        .mark_circle(interpolate="basis", size=200)
+        .encode(
+            x=alt.X("residue_number:Q", title="Residue Number", scale=xscale),
+            y=alt.Y(
+                "value:Q",
+                title=axis_properties.title,
+                scale=y_scale,
+            ),
+            color=alt.Color(
+                f"value:{'O' if colors.norm_type == 'categorical' else 'Q'}",
+                scale=colors.altair_scale,
+                title=axis_properties.title,
+            ),
+        )
+        .add_params(zoom_x)
+    )
+
+    # Create a selection that chooses the nearest point & selects based on x-value
+    nearest = alt.selection_point(
+        name="point",
+        nearest=True,
+        on="pointerover",
+        fields=["residue_number"],
+        empty=False,
+        clear="mouseout",
+    )
+
+    select_residue = (
+        alt.Chart(data)
+        .mark_point()
+        .encode(
+            x="residue_number:Q",
+            opacity=alt.value(0),
+        )
+        .add_params(nearest)
+    )
+
+    # Draw a rule at the location of the selection
+    rule = (
+        alt.Chart(data)
+        .mark_rule(color=colors.highlight_color, size=2)
+        .encode(
+            x="residue_number:Q",
+        )
+        .transform_filter(nearest)
+    )
+
+    # vline = (
+    #     alt.Chart(pd.DataFrame({"x": [0]}))
+    #     .mark_rule(color=colors.highlight_color, size=2)
+    #     .encode(x="x:Q")
+    # )
+
+    line_position = alt.param(name="line_position", value=0.0)
+    line_opacity = alt.param(name="line_opacity", value=1)
+    df_line = pd.DataFrame({"x": [1.0]})
+
+    # Create vertical rule with parameter
+    vline = (
+        alt.Chart(df_line)
+        .mark_rule(color=colors.highlight_color, opacity=line_opacity, size=2)
+        .encode(x=alt.X("p", type="quantitative"))
+        .transform_calculate(p=alt.datum.x * line_position)
+        .add_params(line_position, line_opacity)
+    )
+
+    # Put the five layers into a chart and bind the data
+    chart = (
+        alt.layer(scatter, vline, select_residue, rule).properties(
+            width="container",
+            height=480,  # autosize height?
+        )
+        # .configure(autosize="fit")
+    )
+
+    return chart
+
+
+@solara.component
+def ScatterChart(
+    data: pd.DataFrame,
+    colors: ColorTransform,
+    axis_properties: AxisProperties,
+    on_selections,
+    line_value,
+):
+    def mem_chart():
+        chart = make_chart(data, colors, axis_properties)
+        return chart
+
+    chart = solara.use_memo(mem_chart, dependencies=[data, colors, axis_properties])
+
+    if line_value is not None:
+        params = {"line_position": line_value, "line_opacity": 1}
+    else:
+        params = {"line_position": 0.0, "line_opacity": 0}
+    dark_effective = solara.lab.use_dark_effective()
+    if dark_effective:
+        options = {"actions": False, "theme": "dark"}
+    else:
+        options = {"actions": False}
+
+    view = alt.JupyterChart.element(  # type: ignore
+        chart=chart,
+        embed_options=options,
+        _params=params,
+    )
+
+    def bind():
+        real = solara.get_widget(view)
+        real.selections.observe(on_selections, "point")  # type: ignore
+
+    solara.use_effect(bind, [data, colors])
+
+
+@solara.component
+def ProteinView(
+    title: str,
+    molecule_id: str,
+    data: pd.DataFrame,
+    colors: ColorTransform,
+    axis_properties: AxisProperties,
+    dark_effective: bool,
+    description: str = "",
+):
+    about_dialog = solara.use_reactive(False)
+    fullscreen = solara.use_reactive(False)
+
+    # residue number to highlight in altair chart
+    line_number = solara.use_reactive(None)
+
+    # residue number to highlight in protein view
+    highlight_number = solara.use_reactive(None)
+
+    if data.empty:
+        color_data = {}
+    else:
+        color_data = colors.molstar_colors(data)
+    tooltips = {
+        "data": [
+            {
+                "residue_number": resi,
+                "tooltip": f"{axis_properties.label}: {value:.2g} {axis_properties.unit}"
+                if not np.isnan(value)
+                else "No data",
+            }
+            for resi, value in zip(data["residue_number"], data["value"])
+        ]
+    }
+
+    def on_molstar_mouseover(value):
+        r = value.get("residueNumber", None)
+        line_number.set(r)
+
+    def on_molstar_mouseout(value):
+        on_molstar_mouseover({})
+
+    def on_chart_selection(event):
+        try:
+            r = event["new"].value[0]["residue_number"]
+            highlight_number.set(r)
+        except (IndexError, KeyError):
+            highlight_number.set(None)
+
+    with solara.AppBar():
+        solara.AppBarTitle(title)
+        with solara.Tooltip("Fullscreen"):
+            solara.Button(
+                icon_name="mdi-fullscreen",
+                icon=True,
+                on_click=lambda: fullscreen.set(not fullscreen.value),
+            )
+        if description:
+            with solara.Tooltip("About"):
+                solara.Button(
+                    icon_name="mdi-information-outline",
+                    icon=True,
+                    on_click=lambda: about_dialog.set(True),
+                )
+        solara.lab.ThemeToggle()
+
+    with solara.v.Dialog(
+        v_model=about_dialog.value, on_v_model=lambda _ignore: about_dialog.set(False)
+    ):
+        with solara.Card("About", margin=0):
+            solara.Markdown(description)
+
+    with solara.ColumnsResponsive([4, 8]):
+        with solara.Card(style={"height": "550px"}):
+            PDBeMolstar.element(  # type: ignore
+                theme="dark" if dark_effective else "light",
+                molecule_id=molecule_id.lower(),
+                color_data=color_data,
+                hide_water=True,
+                tooltips=tooltips,
+                height="525px",
+                highlight={"data": [{"residue_number": int(highlight_number.value)}]}
+                if highlight_number.value
+                else None,
+                highlight_color=colors.highlight_color,
+                on_mouseover_event=on_molstar_mouseover,
+                on_mouseout_event=on_molstar_mouseout,
+                hide_controls_icon=True,
+                hide_expand_icon=True,
+                hide_settings_icon=True,
+                expanded=fullscreen.value,
+            ).key(f"molstar-{dark_effective}")
+        if not fullscreen.value:
+            with solara.Card(style={"height": "550px"}):
+                if data.empty:
+                    solara.Text("No data")
+                else:
+                    ScatterChart(
+                        data,
+                        colors,
+                        axis_properties,
+                        on_chart_selection,
+                        line_number.value,
+                    )
+
+
+@solara.component
+def RoutedView():
+    route = solara.use_router()
+    dark_effective = solara.lab.use_dark_effective()
+
+    try:
+        query_dict = {k: v for k, v in parse_qsl(route.search)}
+        colors = ColorTransform(**query_dict)  # type: ignore
+        axis_properties = AxisProperties(**query_dict)  # type: ignore
+        data = decode_data(query_dict["data"])
+        ProteinView(
+            query_dict["title"],
+            molecule_id=query_dict["molecule_id"],
+            data=data,
+            colors=colors,
+            axis_properties=axis_properties,
+            dark_effective=dark_effective,
+            description=query_dict.get("description", ""),
+        )
+    except KeyError as err:
+        solara.Warning(f"Error: {err}")
+
+
+@solara.component
+def Page():
+    dark_effective = solara.lab.use_dark_effective()
+    dark_previous = solara.use_previous(dark_effective)
+
+    if dark_previous != dark_effective:
+        if dark_effective:
+            alt.themes.enable("dark")
+        else:
+            alt.themes.enable("default")
+
+    solara.Style(
+        """
+        .vega-embed {
+        overflow: visible;
+        width: 100% !important;
+        }"""
+    )
+
+    settings = json.loads(Path("settings.json").read_text())
+
+    colors = ColorTransform(**settings)
+    axis_properties = AxisProperties(**settings)
+
+    data = pd.read_csv("color_data.csv")
+
+    ProteinView(
+        settings["title"],
+        molecule_id=settings["molecule_id"],
+        data=data,
+        colors=colors,
+        axis_properties=axis_properties,
+        dark_effective=dark_effective,
+    )