reduce slug size

app.py

@@ -221,11 +221,7 @@ def explore_regions(country):
 st.title('COVID-19: Situazione in Italia')
 st.text("")
 try:
-    vaccine_repo = import_data.RepoReference(
-        repo_path='covid19-opendata-vaccini',
-        repo_url='https://github.com/italia/covid19-opendata-vaccini.git'
-    )
-    vaccines = import_data.vaccines(vaccine_repo, DATA)
+    vaccines = import_data.vaccines(DATA)
     demography = import_data.demography(vaccines)
 except:
     st.error("L'applicazione è in fase di aggiornamento. Prova a [riaggiornare](/) la pagina tra qualche secondo.")

plot.py

@@ -6,8 +6,8 @@
 import numpy as np
 import plotly
 import plotly.graph_objects as go
-import scipy.optimize
-import scipy.stats
+# import scipy.optimize
+# import scipy.stats
 import streamlit as st
 from matplotlib import cm
 from plotly.subplots import make_subplots
@@ -89,41 +89,41 @@ def get_default_palette(alpha=False):
     return itertools.cycle(rgb_palette)
 
 
-def linear_fit(data, start=None, stop=None, p0=P0):
-    t_0_guess, T_d_guess = p0
-    data_fit = data[start:stop]
-    x_norm = linear(data_fit.index.values, t_0_guess, T_d_guess)
-    y_fit = data_fit.values
+# def linear_fit(data, start=None, stop=None, p0=P0):
+#     t_0_guess, T_d_guess = p0
+#     data_fit = data[start:stop]
+#     x_norm = linear(data_fit.index.values, t_0_guess, T_d_guess)
+#     y_fit = data_fit.values
 
-    x_fit = x_norm[np.isfinite(y_fit)]
+#     x_fit = x_norm[np.isfinite(y_fit)]
 
-    m, y, r2, _, _ = scipy.stats.linregress(x_fit, y_fit)
-    t_0_norm = -y / m
-    T_d_norm = 1 / m
+#     m, y, r2, _, _ = scipy.stats.linregress(x_fit, y_fit)
+#     t_0_norm = -y / m
+#     T_d_norm = 1 / m
 
-    T_d = T_d_norm * T_d_guess
-    t_0 = t_0_guess + t_0_norm * T_d_guess
-    return t_0, T_d, r2
+#     T_d = T_d_norm * T_d_guess
+#     t_0 = t_0_guess + t_0_norm * T_d_guess
+#     return t_0, T_d, r2
 
 
-def fit(data, start=None, stop=None, p0=P0):
-    t_0_guess, T_d_guess = p0
-    data_fit = data[start:stop]
+# def fit(data, start=None, stop=None, p0=P0):
+#     t_0_guess, T_d_guess = p0
+#     data_fit = data[start:stop]
 
-    x_norm = linear(data_fit.index.values, t_0_guess, T_d_guess)
-    log2_y = np.log2(data_fit.values)
+#     x_norm = linear(data_fit.index.values, t_0_guess, T_d_guess)
+#     log2_y = np.log2(data_fit.values)
 
-    t_fit = data_fit.index.values[np.isfinite(log2_y)]
-    x_fit = x_norm[np.isfinite(log2_y)]
-    log2_y_fit = log2_y[np.isfinite(log2_y)]
+#     t_fit = data_fit.index.values[np.isfinite(log2_y)]
+#     x_fit = x_norm[np.isfinite(log2_y)]
+#     log2_y_fit = log2_y[np.isfinite(log2_y)]
 
-    m, y, r2, _, _ = scipy.stats.linregress(x_fit, log2_y_fit)
-    t_0_norm = -y / m
-    T_d_norm = 1 / m
+#     m, y, r2, _, _ = scipy.stats.linregress(x_fit, log2_y_fit)
+#     t_0_norm = -y / m
+#     T_d_norm = 1 / m
 
-    T_d = T_d_norm * T_d_guess
-    t_0 = t_0_guess + t_0_norm * T_d_guess
-    return t_0, T_d, r2
+#     T_d = T_d_norm * T_d_guess
+#     t_0 = t_0_guess + t_0_norm * T_d_guess
+#     return t_0, T_d, r2
 
 
 def plot_fit(data, fig, start=None, stop=None, label=None, shift=5, **kwargs):

requirements.txt

@@ -1,7 +1,7 @@
 matplotlib==3.1.3
 streamlit==0.80.0
 pandas==1.0.1
-scipy==1.4.1
+# scipy==1.4.1
 lxml==4.6.2
 numpy==1.18.1
 plotly==4.12.0