Update app.py
Browse files
app.py
CHANGED
@@ -134,6 +134,18 @@ def decompose_cavity(pred, th2=0.7, amin=10):
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return image_decomposed
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# Use wide layout and create columns
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st.set_page_config(page_title="Cavity Detection Tool", layout="wide")
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bordersize = 0.6
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@@ -149,29 +161,29 @@ with col:
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st.markdown("Input images should be centred at the centre of the galaxy and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
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st.markdown("If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
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_, col_1, col_2, col_3, _ = st.columns([bordersize, 2.0, 0.5, 0.5, bordersize])
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with
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# Create file uploader widget
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uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
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with col_2:
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with col_3:
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# if NGC4649:
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#
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# If file is uploaded, read in the data and plot it
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if uploaded_file is not None:
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# Make six columns for buttons
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_, col1, col2, col3, col4, col5, col6, _ = st.columns([bordersize,0.5,0.5,0.5,0.5,0.5,0.5,bordersize])
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col1.subheader("Input image")
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@@ -182,7 +194,7 @@ if uploaded_file is not None:
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with col1:
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st.markdown("""<style>[data-baseweb="select"] {margin-top: -46px;}</style>""", unsafe_allow_html=True)
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max_scale = int(data.shape[0] // 128)
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scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden")
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scale = int(scale.split("x")[0]) // 128
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# Detect button
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@@ -192,7 +204,7 @@ if uploaded_file is not None:
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with col4:
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st.markdown("")
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# st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
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threshold = st.slider("Threshold", 0.0, 1.0, 0.0, 0.05) #, label_visibility="hidden")
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# Decompose button
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with col5: decompose = st.button('Decompose', key="decompose")
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@@ -203,8 +215,8 @@ if uploaded_file is not None:
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image = np.log10(data+1)
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plot_image(image, scale)
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if st.session_state.get("detect", True)
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y_pred, wcs = cut_n_predict(data, wcs, scale)
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y_pred_th = np.where(y_pred > threshold, y_pred, 0)
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return image_decomposed
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@st.cache
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def load_file(fname):
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with fits.open(fname) as hdul:
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data = hdul[0].data
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wcs = WCS(hdul[0].header)
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return data, wcs
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def change_scale():
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del st.session_state["threshold"]
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# Use wide layout and create columns
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st.set_page_config(page_title="Cavity Detection Tool", layout="wide")
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bordersize = 0.6
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st.markdown("Input images should be centred at the centre of the galaxy and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
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st.markdown("If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
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# _, col_1, col_2, col_3, _ = st.columns([bordersize, 2.0, 0.5, 0.5, bordersize])
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# with col:
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uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
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# with col_2:
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# st.markdown("### Examples")
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# NGC4649 = st.button("NGC4649")
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# with col_3:
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# st.markdown("""<style>[data-baseweb="select"] {margin-top: 26px;}</style>""", unsafe_allow_html=True)
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# NGC5813 = st.button("NGC5813")
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# if NGC4649:
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# uploaded_file = "NGC4649_example.fits"
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# elif NGC5813:
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# uploaded_file = "NGC5813_example.fits"
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# If file is uploaded, read in the data and plot it
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if uploaded_file is not None:
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data, wcs = load_file(uploaded_file)
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if "data" in locals():
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# Make six columns for buttons
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_, col1, col2, col3, col4, col5, col6, _ = st.columns([bordersize,0.5,0.5,0.5,0.5,0.5,0.5,bordersize])
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col1.subheader("Input image")
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with col1:
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st.markdown("""<style>[data-baseweb="select"] {margin-top: -46px;}</style>""", unsafe_allow_html=True)
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max_scale = int(data.shape[0] // 128)
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scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden", on_change=change_scale)
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scale = int(scale.split("x")[0]) // 128
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# Detect button
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with col4:
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st.markdown("")
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# st.markdown("""<style>[data-baseweb="select"] {margin-top: -36px;}</style>""", unsafe_allow_html=True)
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threshold = st.slider("Threshold", 0.0, 1.0, 0.0, 0.05, key="threshold") #, label_visibility="hidden")
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# Decompose button
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with col5: decompose = st.button('Decompose', key="decompose")
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image = np.log10(data+1)
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plot_image(image, scale)
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if detect or threshold:
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# if st.session_state.get("detect", True):
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y_pred, wcs = cut_n_predict(data, wcs, scale)
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y_pred_th = np.where(y_pred > threshold, y_pred, 0)
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