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.venv/Lib/site-packages/streamlit/hello/__init__.py

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+# Copyright 2018-2022 Streamlit Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

.venv/Lib/site-packages/streamlit/hello/demos.py

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+# Copyright 2018-2022 Streamlit Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Any
+
+
+def intro():
+    import streamlit as st
+
+    st.sidebar.success("Select a demo above.")
+
+    st.markdown(
+        """
+        Streamlit is an open-source app framework built specifically for
+        Machine Learning and Data Science projects.
+
+        **👈 Select a demo from the dropdown on the left** to see some examples
+        of what Streamlit can do!
+
+        ### Want to learn more?
+
+        - Check out [streamlit.io](https://streamlit.io)
+        - Jump into our [documentation](https://docs.streamlit.io)
+        - Ask a question in our [community
+          forums](https://discuss.streamlit.io)
+
+        ### See more complex demos
+
+        - Use a neural net to [analyze the Udacity Self-driving Car Image
+          Dataset](https://github.com/streamlit/demo-self-driving)
+        - Explore a [New York City rideshare dataset](https://github.com/streamlit/demo-uber-nyc-pickups)
+    """
+    )
+
+
+# Turn off black formatting for this function to present the user with more
+# compact code.
+# fmt: off
+def mapping_demo():
+    import streamlit as st
+    import pandas as pd
+    import pydeck as pdk
+
+    from urllib.error import URLError
+
+    @st.cache
+    def from_data_file(filename):
+        url = (
+            "http://raw.githubusercontent.com/streamlit/"
+            "example-data/master/hello/v1/%s" % filename)
+        return pd.read_json(url)
+
+    try:
+        ALL_LAYERS = {
+            "Bike Rentals": pdk.Layer(
+                "HexagonLayer",
+                data=from_data_file("bike_rental_stats.json"),
+                get_position=["lon", "lat"],
+                radius=200,
+                elevation_scale=4,
+                elevation_range=[0, 1000],
+                extruded=True,
+            ),
+            "Bart Stop Exits": pdk.Layer(
+                "ScatterplotLayer",
+                data=from_data_file("bart_stop_stats.json"),
+                get_position=["lon", "lat"],
+                get_color=[200, 30, 0, 160],
+                get_radius="[exits]",
+                radius_scale=0.05,
+            ),
+            "Bart Stop Names": pdk.Layer(
+                "TextLayer",
+                data=from_data_file("bart_stop_stats.json"),
+                get_position=["lon", "lat"],
+                get_text="name",
+                get_color=[0, 0, 0, 200],
+                get_size=15,
+                get_alignment_baseline="'bottom'",
+            ),
+            "Outbound Flow": pdk.Layer(
+                "ArcLayer",
+                data=from_data_file("bart_path_stats.json"),
+                get_source_position=["lon", "lat"],
+                get_target_position=["lon2", "lat2"],
+                get_source_color=[200, 30, 0, 160],
+                get_target_color=[200, 30, 0, 160],
+                auto_highlight=True,
+                width_scale=0.0001,
+                get_width="outbound",
+                width_min_pixels=3,
+                width_max_pixels=30,
+            ),
+        }
+        st.sidebar.markdown('### Map Layers')
+        selected_layers = [
+            layer for layer_name, layer in ALL_LAYERS.items()
+            if st.sidebar.checkbox(layer_name, True)]
+        if selected_layers:
+            st.pydeck_chart(pdk.Deck(
+                map_style="mapbox://styles/mapbox/light-v9",
+                initial_view_state={"latitude": 37.76,
+                                    "longitude": -122.4, "zoom": 11, "pitch": 50},
+                layers=selected_layers,
+            ))
+        else:
+            st.error("Please choose at least one layer above.")
+    except URLError as e:
+        st.error("""
+            **This demo requires internet access.**
+
+            Connection error: %s
+        """ % e.reason)
+# fmt: on
+
+# Turn off black formatting for this function to present the user with more
+# compact code.
+# fmt: off
+
+
+def fractal_demo():
+    import streamlit as st
+    import numpy as np
+
+    # Interactive Streamlit elements, like these sliders, return their value.
+    # This gives you an extremely simple interaction model.
+    iterations = st.sidebar.slider("Level of detail", 2, 20, 10, 1)
+    separation = st.sidebar.slider("Separation", 0.7, 2.0, 0.7885)
+
+    # Non-interactive elements return a placeholder to their location
+    # in the app. Here we're storing progress_bar to update it later.
+    progress_bar = st.sidebar.progress(0)
+
+    # These two elements will be filled in later, so we create a placeholder
+    # for them using st.empty()
+    frame_text = st.sidebar.empty()
+    image = st.empty()
+
+    m, n, s = 960, 640, 400
+    x = np.linspace(-m / s, m / s, num=m).reshape((1, m))
+    y = np.linspace(-n / s, n / s, num=n).reshape((n, 1))
+
+    for frame_num, a in enumerate(np.linspace(0.0, 4 * np.pi, 100)):
+        # Here were setting value for these two elements.
+        progress_bar.progress(frame_num)
+        frame_text.text("Frame %i/100" % (frame_num + 1))
+
+        # Performing some fractal wizardry.
+        c = separation * np.exp(1j * a)
+        Z = np.tile(x, (n, 1)) + 1j * np.tile(y, (1, m))
+        C = np.full((n, m), c)
+        M: Any = np.full((n, m), True, dtype=bool)
+        N = np.zeros((n, m))
+
+        for i in range(iterations):
+            Z[M] = Z[M] * Z[M] + C[M]
+            M[np.abs(Z) > 2] = False
+            N[M] = i
+
+        # Update the image placeholder by calling the image() function on it.
+        image.image(1.0 - (N / N.max()), use_column_width=True)
+
+    # We clear elements by calling empty on them.
+    progress_bar.empty()
+    frame_text.empty()
+
+    # Streamlit widgets automatically run the script from top to bottom. Since
+    # this button is not connected to any other logic, it just causes a plain
+    # rerun.
+    st.button("Re-run")
+
+
+# fmt: on
+
+# Turn off black formatting for this function to present the user with more
+# compact code.
+# fmt: off
+def plotting_demo():
+    import streamlit as st
+    import time
+    import numpy as np
+
+    progress_bar = st.sidebar.progress(0)
+    status_text = st.sidebar.empty()
+    last_rows = np.random.randn(1, 1)
+    chart = st.line_chart(last_rows)
+
+    for i in range(1, 101):
+        new_rows = last_rows[-1, :] + np.random.randn(5, 1).cumsum(axis=0)
+        status_text.text("%i%% Complete" % i)
+        chart.add_rows(new_rows)
+        progress_bar.progress(i)
+        last_rows = new_rows
+        time.sleep(0.05)
+
+    progress_bar.empty()
+
+    # Streamlit widgets automatically run the script from top to bottom. Since
+    # this button is not connected to any other logic, it just causes a plain
+    # rerun.
+    st.button("Re-run")
+
+
+# fmt: on
+
+# Turn off black formatting for this function to present the user with more
+# compact code.
+# fmt: off
+def data_frame_demo():
+    import streamlit as st
+    import pandas as pd
+    import altair as alt
+
+    from urllib.error import URLError
+
+    @st.cache
+    def get_UN_data():
+        AWS_BUCKET_URL = "http://streamlit-demo-data.s3-us-west-2.amazonaws.com"
+        df = pd.read_csv(AWS_BUCKET_URL + "/agri.csv.gz")
+        return df.set_index("Region")
+
+    try:
+        df = get_UN_data()
+        countries = st.multiselect(
+            "Choose countries", list(df.index), ["China", "United States of America"]
+        )
+        if not countries:
+            st.error("Please select at least one country.")
+        else:
+            data = df.loc[countries]
+            data /= 1000000.0
+            st.write("### Gross Agricultural Production ($B)", data.sort_index())
+
+            data = data.T.reset_index()
+            data = pd.melt(data, id_vars=["index"]).rename(
+                columns={"index": "year", "value": "Gross Agricultural Product ($B)"}
+            )
+            chart = (
+                alt.Chart(data)
+                .mark_area(opacity=0.3)
+                .encode(
+                    x="year:T",
+                    y=alt.Y("Gross Agricultural Product ($B):Q", stack=None),
+                    color="Region:N",
+                )
+            )
+            st.altair_chart(chart, use_container_width=True)
+    except URLError as e:
+        st.error(
+            """
+            **This demo requires internet access.**
+
+            Connection error: %s
+        """
+            % e.reason
+        )
+
+
+# fmt: on

.venv/Lib/site-packages/streamlit/hello/hello.py

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+# Copyright 2018-2022 Streamlit Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import textwrap
+from collections import OrderedDict
+
+import streamlit as st
+from streamlit.logger import get_logger
+from streamlit.hello import demos
+
+LOGGER = get_logger(__name__)
+
+# Dictionary of
+# demo_name -> (demo_function, demo_description)
+DEMOS = OrderedDict(
+    [
+        ("—", (demos.intro, None)),
+        (
+            "Animation Demo",
+            (
+                demos.fractal_demo,
+                """
+This app shows how you can use Streamlit to build cool animations.
+It displays an animated fractal based on the the Julia Set. Use the slider
+to tune different parameters.
+""",
+            ),
+        ),
+        (
+            "Plotting Demo",
+            (
+                demos.plotting_demo,
+                """
+This demo illustrates a combination of plotting and animation with
+Streamlit. We're generating a bunch of random numbers in a loop for around
+5 seconds. Enjoy!
+""",
+            ),
+        ),
+        (
+            "Mapping Demo",
+            (
+                demos.mapping_demo,
+                """
+This demo shows how to use
+[`st.pydeck_chart`](https://docs.streamlit.io/library/api-reference/charts/st.pydeck_chart)
+to display geospatial data.
+""",
+            ),
+        ),
+        (
+            "DataFrame Demo",
+            (
+                demos.data_frame_demo,
+                """
+This demo shows how to use `st.write` to visualize Pandas DataFrames.
+
+(Data courtesy of the [UN Data Explorer](http://data.un.org/Explorer.aspx).)
+""",
+            ),
+        ),
+    ]
+)
+
+
+def run():
+    demo_name = st.sidebar.selectbox("Choose a demo", list(DEMOS.keys()), 0)
+    demo = DEMOS[demo_name][0]
+
+    if demo_name == "—":
+        show_code = False
+        st.write("# Welcome to Streamlit! 👋")
+    else:
+        show_code = st.sidebar.checkbox("Show code", True)
+        st.markdown("# %s" % demo_name)
+        description = DEMOS[demo_name][1]
+        if description:
+            st.write(description)
+        # Clear everything from the intro page.
+        # We only have 4 elements in the page so this is intentional overkill.
+        for i in range(10):
+            st.empty()
+
+    demo()
+
+    if show_code:
+        st.markdown("## Code")
+        sourcelines, _ = inspect.getsourcelines(demo)
+        st.code(textwrap.dedent("".join(sourcelines[1:])))
+
+
+if __name__ == "__main__":
+    run()