ruff and black formatting

openosmia · Dec 2, 2024 · df21188 · df21188
1 parent f8011f5
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diff --git a/app/Intro.py b/app/Intro.py
@@ -5,24 +5,22 @@
 st.markdown(
     """
     Snowlaps is an open-source python package to study the albedo reducing
-    effect of red snow algae, mineral dust and black carbon on melting snow 
+    effect of red snow algae, mineral dust and black carbon on melting snow
     surfaces. The package is built on a deep-learning emulator of the radiative
     transfer model [biosnicar](https://biosnicar.vercel.app/) and can be used
     in forward and inverse mode.
-    
+
     In forward mode, the albedo of a snow surface is predicted from the solar
-    zenith angle, snow grain size, liquid water content, and abundance of 
-    algae, mineral dust and black carbon. In inverse mode, the surface 
+    zenith angle, snow grain size, liquid water content, and abundance of
+    algae, mineral dust and black carbon. In inverse mode, the surface
     properties are retrieved from prescribed spectral measurements. Snowlaps
-    also directly calculates the albedo-reduction caused by each type of 
-    surface particles. 
-    
+    also directly calculates the albedo-reduction caused by each type of
+    surface particles.
+
     More details and performance evaluation of the model were presented in a
     [recent scientific publication](https://doi.org/10.5194/egusphere-2024-2583).
-    
+
     **👈 Select a mode from the sidebar** to start working with Snowlaps!
-    
+
 """
 )
-
-
diff --git a/app/pages/1_Snowlaps_forward.py b/app/pages/1_Snowlaps_forward.py
@@ -1,6 +1,7 @@
-import streamlit as st
 import pandas as pd
 import plotly.express as px
+import streamlit as st
+
 from snowlaps.snowlaps import SnowlapsEmulator
 
 # st.set_page_config(
@@ -15,22 +16,21 @@
 # )
 
 
-
 st.markdown(
-    f"""
-    
-    
+    """
+
+
 ### Try snowlaps as a fast two-stream radiative transfer model for snow! :zap:
-    
-    
-    
-:point_left: Feed the sidebar with your desired inputs and check the corresponding albedo 
-spectrum on the graph below, which you can also directly download locally. 
 
-:hourglass_flowing_sand: We are working on directly displaying the BBA 
-reduction associated with each light absorbing particle on the graph! 
 
-*Note that impurities are assumed to exist in the upper 2 cm of the snow 
+
+:point_left: Feed the sidebar with your desired inputs and check the corresponding albedo
+spectrum on the graph below, which you can also directly download locally.
+
+:hourglass_flowing_sand: We are working on directly displaying the BBA
+reduction associated with each light absorbing particle on the graph!
+
+*Note that impurities are assumed to exist in the upper 2 cm of the snow
 only, and that the snow grain shape is set to spherical.*
 """
 )
@@ -50,10 +50,17 @@
 placeholder_button = st.sidebar.empty()
 
 
-default_values = {"SZA": 42.0, "SOR": 500.0, "AC": 110000.0, "BCC": 800.0, "MDC": 78000.0, "LWC": 0.015}
+default_values = {
+    "SZA": 42.0,
+    "SOR": 500.0,
+    "AC": 110000.0,
+    "BCC": 800.0,
+    "MDC": 78000.0,
+    "LWC": 0.015,
+}
 
 
-if placeholder_button.button('Reset'):
+if placeholder_button.button("Reset"):
     st.session_state.SZA = default_values["SZA"]
     st.session_state.SOR = default_values["SOR"]
     st.session_state.AC = default_values["AC"]
@@ -63,42 +70,51 @@
 
 
 with st.sidebar:
-
     placeholder_title_1.header("Solar geometry")
 
-
     SZA = placeholder_num_1.number_input(
-        "Solar Zenith Angle (SZA; degrees)", 0.0, 90.0, value=default_values["SZA"], key="SZA"
+        "Solar Zenith Angle (SZA; degrees)",
+        0.0,
+        90.0,
+        value=default_values["SZA"],
+        key="SZA",
     )
-    
+
     placeholder_title_2.header("Snow structure ")
 
     optical_radius = placeholder_num_2.number_input(
         "Snow optical radius (µm)", 0.0, 1000.0, value=default_values["SOR"], key="SOR"
     )
-    
+
     liquid_water_content = placeholder_num_3.number_input(
         "Liquid water content (%)", 0.0, 0.1, value=default_values["LWC"], key="LWC"
     )
 
     placeholder_title_3.header("Light Absorbing Particles (LAPs)")
 
-
     algae_concentration = placeholder_num_4.number_input(
-        "Algae concentration (cells/mL)", 0.0, 1000000.0, value=default_values["AC"], key="AC"
+        "Algae concentration (cells/mL)",
+        0.0,
+        1000000.0,
+        value=default_values["AC"],
+        key="AC",
     )
     black_carbon_concentration = placeholder_num_5.number_input(
-        "Black carbon concentration (ppb)", 0.0, 10000.0, value=default_values["BCC"], key="BCC"
+        "Black carbon concentration (ppb)",
+        0.0,
+        10000.0,
+        value=default_values["BCC"],
+        key="BCC",
     )
     mineral_dust_concentration = placeholder_num_6.number_input(
-        "Mineral dust concentration (ppb)", 0.0, 780000.0, value=default_values["MDC"], key="MDC"
+        "Mineral dust concentration (ppb)",
+        0.0,
+        780000.0,
+        value=default_values["MDC"],
+        key="MDC",
     )
 
 
-
-
-
-
 def run_snowlaps(
     SZA,
     optical_radius,
@@ -142,6 +158,7 @@ def run_snowlaps(
         ),
     }
 
+
 def plot_albedo(albedo: pd.Series):
     fig = px.line(
         result["albedo"],
@@ -152,6 +169,7 @@ def plot_albedo(albedo: pd.Series):
     fig.update_layout(showlegend=False)
     return fig
 
+
 result = run_snowlaps(
     SZA,
     optical_radius,

diff --git a/app/pages/2_Snowlaps_inverse.py b/app/pages/2_Snowlaps_inverse.py
@@ -1,38 +1,33 @@
-import streamlit as st
 import pandas as pd
-import plotly.express as px
 import plotly.graph_objs as go
-from snowlaps.snowlaps import SnowlapsEmulator
-from importlib import reload
-import matplotlib.pyplot as plt
-import numpy as np
-import copy
+import streamlit as st
 
+from snowlaps.snowlaps import SnowlapsEmulator
 
 st.markdown(
-    f"""
-    
-    
+    """
+
+
 ### Try snowlaps to retrieve snow properties from observations! :zap:
 
 
 #### 1 - Load your files and run the inversion :boom:
 
 First, you must give snowlaps your spectral observations as a csv file
 with the spectral observations in the range 350-2500nm (typically measured with
-a field spectrometer or a hyperspectral satellite). The unique IDs of your 
-spectra are given as columns, and the wavelengths are given as index. Then, 
+a field spectrometer or a hyperspectral satellite). The unique IDs of your
+spectra are given as columns, and the wavelengths are given as index. Then,
 the metadata associated with the spectra must be prescribed, with the spectral
 IDs as index and at minimum 1) the latitude, longitude and time for each of the
-measurements or 2) the SZA corresponding to each measurement. 
+measurements or 2) the SZA corresponding to each measurement.
 
-Once you are done, you can click on 'run inversion' and snowlaps will look for 
+Once you are done, you can click on 'run inversion' and snowlaps will look for
 the surface properties that best explain your measurements.
-    
-:bulb: Check [the example files](https://github.com/openosmia/snowlaps-emulator/tree/main/data/spectra) 
+
+:bulb: Check [the example files](https://github.com/openosmia/snowlaps-emulator/tree/main/data/spectra)
 to see the formatting required.
 
-:hourglass_flowing_sand: We are working on making the back-end code more 
+:hourglass_flowing_sand: We are working on making the back-end code more
 flexible with the input file formatting. Feel free to [share your ideas](https://github.com/openosmia/snowlaps-emulator/discussions)
 with us!
 
@@ -42,7 +37,6 @@
 )
 
 
-
 my_emulator = SnowlapsEmulator()
 
 
@@ -91,13 +85,11 @@ def plot_inversion(spectrum):
     emulator = st.session_state.best_emulator_spectra[spectrum]
     df_m = pd.DataFrame({"measures": st.session_state.albedo_spectra[spectrum]})
     df = pd.concat([df_m, emulator], axis=1)
-    fig = go.Figure(layout=go.Layout(
-        xaxis=dict(
-        title="Wavelengths (nm)"
-    ),
-    yaxis=dict(
-        title="Albedo"
-    ) ) )
+    fig = go.Figure(
+        layout=go.Layout(
+            xaxis=dict(title="Wavelengths (nm)"), yaxis=dict(title="Albedo")
+        )
+    )
     fig.add_trace(
         go.Scatter(
             x=df.index,
@@ -124,13 +116,11 @@ def plot_inversion(spectrum):
 
 def plot_forward(spectrum, parameters):
     df_m = pd.DataFrame({"measures": st.session_state.albedo_spectra[spectrum]})
-    fig1 = go.Figure(layout=go.Layout(
-        xaxis=dict(
-        title="Wavelengths (nm)"
-    ),
-    yaxis=dict(
-        title="Albedo"
-    ) ) )
+    fig1 = go.Figure(
+        layout=go.Layout(
+            xaxis=dict(title="Wavelengths (nm)"), yaxis=dict(title="Albedo")
+        )
+    )
     emulator_results = run_snowlaps(parameters)
     df = pd.concat([df_m, emulator_results], axis=1)
     df = df.rename(columns={df.columns[1]: "emulator"})
@@ -157,7 +147,6 @@ def plot_forward(spectrum, parameters):
     return fig1
 
 
-
 placeholder_title_solar_geometry = st.sidebar.empty()
 placeholder_SZA = st.sidebar.empty()
 
@@ -184,25 +173,24 @@ def change_input():
         with st.spinner("Please wait..."):
             best_optimization_results = run_model()
             st.session_state.inv = True
-            
-            
+
+
 st.markdown(
-    f"""
-    
+    """
+
 
 
 #### 2 - Visually inspect the inversion :female-detective: and download the results
 
 :point_down: The first graph below displays your measurement with the best fit
-found by snowlaps, for any selected measurement. 
+found by snowlaps, for any selected measurement.
 
 
 
 """
 )
 
 
-
 if "inv" in st.session_state:
     spectrum = st.selectbox(
         "Choose spectrum",
@@ -221,7 +209,7 @@ def change_input():
             90.0,
             value=st.session_state.best_optimization_results.loc[spectrum]["sza"],
         )
-        
+
         placeholder_title_snow_structure.header("Snow structure")
 
         optical_radius = placeholder_optical_radius.number_input(
@@ -232,7 +220,7 @@ def change_input():
                 "grain_size"
             ],
         )
-        
+
         liquid_water_content = placeholder_lwc.number_input(
             "Liquid water content (%)",
             0.0,
@@ -261,9 +249,6 @@ def change_input():
             value=st.session_state.best_optimization_results.loc[spectrum]["dust"],
         )
 
-
-
-
     parameters = [
         SZA,
         optical_radius,
@@ -272,13 +257,13 @@ def change_input():
         black_carbon_concentration,
         mineral_dust_concentration,
     ]
-    
+
     st.markdown(
-        f"""
-        
+        """
+
 
 
-    :point_left: A side bar appeared! The second gaph lets you play with the 
+    :point_left: A side bar appeared! The second gaph lets you play with the
     surface parameters retrieved by snowlaps and check how it changes the albedo.
     You can for example check what the albedo would look like if there was no algal
     bloom, or a much bigger grain size? :snowman:

diff --git a/app/pages/3_Get_support.py b/app/pages/3_Get_support.py
@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 """
 
 @authors: Adrien Wehrlé (University of Zurich), Lou-Anne Chevrollier (Aarhus University)
@@ -10,16 +9,15 @@
 
 st.markdown(
     """
-    
-    If you run into an issue using the app and need support, feel free to use 
-    the [discussion forum](https://github.com/openosmia/snowlaps-emulator/discussions) 
+
+    If you run into an issue using the app and need support, feel free to use
+    the [discussion forum](https://github.com/openosmia/snowlaps-emulator/discussions)
     of the snowlaps repository so we can work out the problem :female-detective:
-    
+
     If a new feature would be handy for you or that you identify a bug, please
-    [report it](https://github.com/openosmia/snowlaps-emulator/issues) so we 
+    [report it](https://github.com/openosmia/snowlaps-emulator/issues) so we
     can fix it! :comet:
 
-    
+
 """
 )
-