diff --git a/examples/streamlit/playground.py b/examples/streamlit/playground.py
new file mode 100644
index 0000000000000000000000000000000000000000..a45169681695b77ea524d80056e1f96c99b49565
--- /dev/null
+++ b/examples/streamlit/playground.py
@@ -0,0 +1,226 @@
+import io
+from typing import Optional
+
+import streamlit as st
+import xarray as xr
+import matplotlib.pyplot as plt
+import pandas as pd
+
+import enstools.compression.xr_accessor # noqa
+from enstools.encoding.errors import InvalidCompressionSpecification
+
+options = ["Compression", "Analysis"]
+
+
+class DataContainer:
+ def __init__(self, dataset: Optional[xr.Dataset] = None):
+ self.dataset = dataset
+ self.reference_da = None
+ self.compressed_da = None
+
+ def set_dataset(self, dataset):
+ self.dataset = dataset
+ self.reference_da = None
+ self.compressed_da = None
+
+ def select_variable(self, variable):
+ self.reference_da = self.dataset[variable]
+
+ @classmethod
+ def from_tutorial_data(cls, dataset_name: str = "air_temperature"):
+ return cls(dataset=xr.tutorial.open_dataset(dataset_name))
+
+ @property
+ def time_steps(self):
+ if self.reference_da is not None:
+ if "time" in self.reference_da.dims:
+ try:
+ return pd.to_datetime(self.reference_da.time.values)
+ except TypeError:
+ return self.reference_da.time.values
+
+ print(self.reference_da)
+
+ def compress(self, compression):
+ self.compressed_da = self.reference_da.compression(compression)
+
+
+def sidebar():
+ with st.sidebar:
+ st.title("Data selection")
+ data_source = st.radio(label="Data source", options=["Tutorial Dataset", "Custom Dataset"])
+
+ if data_source == "Tutorial Dataset":
+ tutorial_dataset_options = [
+ "air_temperature",
+ "air_temperature_gradient",
+ # "basin_mask", # Different coordinates
+ # "rasm", # Has nan
+ "ROMS_example",
+ "tiny",
+ # "era5-2mt-2019-03-uk.grib",
+ "eraint_uvz",
+ "ersstv5"
+ ]
+ dataset_name = st.selectbox(label="Dataset", options=tutorial_dataset_options)
+ dataset = xr.tutorial.open_dataset(dataset_name)
+
+ data.set_dataset(dataset)
+
+ elif data_source == "Custom Dataset":
+ my_file = st.file_uploader(label="Your file")
+ data.set_dataset(None)
+
+ if my_file:
+ my_virtual_file = io.BytesIO(my_file.read())
+ my_dataset = xr.open_dataset(my_virtual_file)
+ st.text("Custom dataset loaded!")
+ data.set_dataset(my_dataset)
+
+ if data.dataset is not None:
+ variable = st.selectbox(label="Variable", options=data.dataset.data_vars)
+ if variable:
+ data.select_variable(variable)
+
+ slice_selection = {}
+ if data.reference_da.dims and True:
+ for dimension in data.reference_da.dims:
+ if str(dimension) == "time":
+ if len(data.reference_da.time) > 1:
+ slice_selection[dimension] = st.select_slider(label=dimension,
+ options=data.reference_da[dimension].values,
+ )
+ else:
+ slice_selection[dimension] = data.reference_da.time.values[0]
+ st.markdown(f"- {dimension} -> {slice_selection[dimension]}")
+
+ else:
+ st.markdown(f"- {dimension}")
+ _min = float(data.reference_da[dimension].values[0])
+ _max = float(data.reference_da[dimension].values[-1])
+
+
+ st.markdown(f"- {_min, _max}")
+ if _max - _min < 1000:
+ slice_selection[dimension] = st.slider(label=dimension,
+ min_value=_min,
+ max_value=_max,
+ value=(_min, _max),
+ )
+
+ if st.button("Clear Cache"):
+ st.cache_resource.clear()
+
+ return slice_selection
+
+
+@st.cache_resource
+def create_data():
+ return DataContainer.from_tutorial_data()
+
+
+data = create_data()
+
+
+def setup_main_frame():
+ slice_selection = sidebar()
+ st.title("enstools-compression playground")
+ st.markdown("---")
+ compression, analysis = st.tabs(["Compression", "Analysis"])
+
+ with compression:
+ if data.dataset is not None:
+ st.markdown("# Compression")
+
+ with st.expander("Compression Specifications"):
+ specification_mode = st.radio(label="", options=["String", "Options"], horizontal=True)
+ # specification, options = st.tabs(["String", "Options"])
+ if specification_mode == "String":
+ compression_specification = st.text_input(label="Compression")
+ elif specification_mode == "Options":
+ compressor = st.selectbox(label="Compressor", options=["sz", "zfp"])
+ if compressor == "sz":
+ mode_options = ["abs", "rel", "pw_rel"]
+ elif compressor == "zfp":
+ mode_options = ["accuracy", "rate", "precision"]
+ else:
+ mode_options = []
+ mode = st.selectbox(label="Mode", options=mode_options)
+ parameter = st.text_input(label="Parameter")
+
+ compression_specification = f"lossy,{compressor},{mode},{parameter}"
+ st.markdown(f"**Compression Specification:** {compression_specification}")
+
+ if compression_specification:
+ try:
+ data.compress(compression_specification)
+ except InvalidCompressionSpecification:
+ st.warning("Invalid compression specification!")
+ st.markdown(
+ "Check [the compression specification format](https://enstools-encoding.readthedocs.io/en/latest/CompressionSpecificationFormat.html)")
+ if data.compressed_da is not None:
+ st.markdown(f"**Compression Ratio**: {data.compressed_da.attrs['compression_ratio']}")
+
+ col1, col2, *others = st.columns(2)
+
+ new_slice = {}
+
+ for key, values in slice_selection.items():
+ if isinstance(values, tuple):
+ start, stop = values
+ if start != stop:
+ new_slice[key] = slice(start, stop)
+ else:
+ new_slice[key] = start
+ else:
+ new_slice[key] = values
+
+ slice_selection = new_slice
+
+ if data.reference_da is not None:
+ print(f"{slice_selection=}")
+ slice_selection = {key: (value if key != "lat" else slice(value.stop, value.start)) for key, value in
+ slice_selection.items()}
+
+ only_slices = {key: value for key, value in slice_selection.items() if isinstance(value, slice)}
+ non_slices = {key: value for key, value in slice_selection.items() if not isinstance(value, slice)}
+
+ if only_slices:
+ reference_slice = data.reference_da.sel(**only_slices)
+ else:
+ reference_slice = data.reference_da
+
+ if non_slices:
+ reference_slice = reference_slice.sel(**non_slices, method="nearest")
+ print(reference_slice)
+ try:
+ reference_slice.plot()
+ fig1 = plt.gcf()
+ with col1:
+ st.pyplot(fig1)
+ except TypeError:
+ pass
+
+ if data.compressed_da is not None:
+ plt.figure()
+ if only_slices:
+ compressed_slice = data.compressed_da.sel(**only_slices)
+ else:
+ compressed_slice = data.compressed_da
+ if non_slices:
+ compressed_slice = compressed_slice.sel(**non_slices, method="nearest")
+
+
+ try:
+ compressed_slice.plot()
+ fig2 = plt.gcf()
+ with col2:
+ st.pyplot(fig2)
+ except TypeError:
+ pass
+
+ else:
+ st.text("Compress the data to show the plot!")
+
+
+setup_main_frame()