Duplicate from fr1ll/wb-avo-streamlit

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README.md

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+---
+title: Wb Avo Streamlit
+emoji: 🔥
+colorFrom: blue
+colorTo: blue
+sdk: streamlit
+sdk_version: 1.26.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: fr1ll/wb-avo-streamlit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import bruges as b
+from einops import repeat
+import numpy as np
+from bruges.reflection.reflection import zoeppritz_rpp as zrpp
+import pandas as pd
+import altair as alt
+
+
+def vs_from_poisson(vp, poisson):
+    '''compute pressure velocity from
+    shear velocity and poissons ratio'''
+    return np.sqrt((vp**2 - 2*poisson*vp**2)/(2 - 2*poisson))
+
+
+def gardners(vp):
+    '''compute density via gardners relation'''
+    return 1000*0.31*np.power(vp, 0.25)/1.33
+
+
+def cartesian_product(*arrays):
+    '''return cartesion product of several arrays'''
+    ndim = len(arrays)
+    return (np.stack(np.meshgrid(*arrays), axis=-1)
+              .reshape(-1, ndim))
+
+
+def loop_zrpp(vp1,vs1,rho1,vp2,vs2,rho2,theta1):
+    '''compute reflectivity for many values of
+    input parameters.
+    "1" denotes layer one, "2" denotes layer two.
+    outside this function, I tend to use 0-indexing though.'''
+    refl_loop = np.empty(len(vp1), dtype=complex)
+    for i in range(vp1.shape[0]):
+        refl_loop[i] = zrpp(vp1=vp1[i], vs1=vs1[i], rho1=rho1[i],
+                         vp2=vp2[i], vs2=vs2[i], rho2=rho2[i],
+                         theta1=theta1[i])
+    return refl_loop
+
+def wb_ava_fig(vwater=1520., rwater = 1025., poissons=0.49):
+    '''
+    AVO: Amplitude Versus Offset
+    AVA: Amplitude Versus Angle
+    compute AVA at the WB for a range of values,
+    then plot using altair
+    '''
+    ### Set up some variables
+    VP1 = np.arange(1530,1820,50)
+    THE = np.arange(0.0, 88., 1.)
+    POI = np.array([poissons])
+    
+    ### Set variables that depend on those variables
+    params = cartesian_product(VP1, THE, POI)
+    VP1, THE, POI = [a.ravel() for a in np.hsplit(params, 3)]
+    VP0 = np.full(VP1.shape, vwater)
+    # V-RMS for ~200 m water depth
+    VS0 = np.full(VP1.shape, 0.)
+    RH0 = np.full(VP1.shape, rwater)
+    # 1025 kg/m^3 per Inversion of the physical properties of seafloor surface, South China Sea, Zhou et al 2021
+    VP1 = VP1
+    VS1 = vs_from_poisson(VP1,POI)
+    RH1 = gardners(VP1)
+    
+    ### Shove into a dict to pass to zrpp
+    params = {"vp1": VP0, "vs1": VS0, "rho1": RH0,
+          "vp2": VP1, "vs2": VS1, "rho2": RH1,
+          "theta1":THE}
+
+    # Compute zoeppritz equation
+    r = loop_zrpp(**params)
+    
+    # Put the results into a DataFrame
+    df = pd.DataFrame({"Vp sub-WB": VP1, "Poisson_s ratio": POI, "Vs sub-WB": VS1,
+                   "Angle": THE, "Amplitude": np.real(r)})
+    
+    # Select only points pre-critical angle
+    df["Ang_Crit"] = np.degrees(np.arcsin(1500 / df["Vp sub-WB"].values))
+    df = df[df["Angle"] < df["Ang_Crit"]]
+    
+    # Create the altair figure
+    highlight = alt.selection(type='single', on='mouseover',
+                          fields=["Vp sub-WB:Q"], nearest=True)
+
+    base = alt.Chart(df).encode(
+            x="Angle",
+            y="Amplitude",
+            color="Vp sub-WB:Q",
+            tooltip=["Vp sub-WB", "Vs sub-WB", "Angle", "Amplitude"]
+    )
+
+    points = base.mark_circle().encode(
+        opacity=alt.value(0)
+    ).add_selection(
+        highlight
+    ).properties(
+        width=600,
+        height=450
+    )
+
+    lines = base.mark_line().encode(
+        size=alt.condition(~highlight, alt.value(3), alt.value(7))
+    )
+    
+    return points + lines
+
+vwater = st.slider("Select a value for water velocity", min_value=1495, max_value=1545, value=1520)
+rwater = st.slider("Select a value for density of water", min_value=1005, max_value=1045, value=1025)
+poissons = st.slider("Select a value for Poisson's ratio", min_value=0.4, max_value=0.5, value=0.48)
+# st.write("Poisson's ratio is:", p)
+
+
+st.altair_chart(wb_ava_fig(vwater, rwater, poissons))
+
+

requirements.txt

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+altair
+pandas
+numpy
+bruges
+scipy
+matplotlib
+einops