feat: app version 1.0

README.md

@@ -1,7 +1,7 @@
 ---
-title: Ts Explorations
-emoji: 🌖
-colorFrom: purple
+title: Time Series Explore
+emoji: 👀
+colorFrom: red
 colorTo: blue
 sdk: streamlit
 sdk_version: 1.15.2

app.py

@@ -0,0 +1,84 @@
+import streamlit as st
+import pathlib
+import json
+import pandas as pd
+
+st.header("Time Series Preprocessing Pipeline")
+st.markdown("Users can load their time-series data and select a set of transformations to prepare a training set for univariate or multivariate time-series classification.\
+ Go ahead and use the sidebar on the left to upload your data files in *.json* format and start exploring and transforming it!")
+col1, col2 = st.columns(2)
+
+file_names, file_bytes = [], []
+with st.sidebar:
+    files = st.file_uploader("Load files", accept_multiple_files = True)
+    if files:
+        file_names = [file.name for file in files]
+        file_bytes = [file.getvalue() for file in files]
+        st.text("\n".join(file_names))
+
+data_dict = dict({'trial_id':[], 'pupil_dilation':[], 'baseline':[], 'rating':[]})
+with st.spinner("building base dictionary..."):
+    for file_data in file_bytes:
+        data = json.loads(file_data)
+        for k in data:
+          for i in data[k]:
+            for k, v in i.items():
+              data_dict[k].append(v)
+
+df_base = pd.DataFrame()  # {'<fields>' : []})
+with col1:
+    if file_bytes:
+        with st.spinner("building base dataframe..."):
+            df_base = pd.DataFrame.from_dict(data_dict)
+            df_base["trial_id"] = df_base.trial_id.map(lambda s: "".join([c for c in s if c.isdigit()]))
+            df_base["len_pupil_dilation"] = df_base.pupil_dilation.map(lambda l: len(l))
+            df_base["len_baseline"] = df_base.baseline.map(lambda l: len(l))
+            st.info(f"number of files: {len(file_names)}")
+            st.markdown("Your original data")
+            st.dataframe(df_base)
+    else:
+        st.caption("Upload your data from the sidebar to start :sunglasses:")
+
+with col2:
+    if not df_base.empty:
+        st.markdown("**Cleaning actions**")
+        detect_blinking = st.button("Detect blinking ('0.0' values)")
+        number_of_blinks = 0
+        if detect_blinking:
+            # Initialization of session_state
+            if 'df' not in st.session_state:
+                st.session_state['df'] = df_base
+            for ser in df_base['pupil_dilation']:
+                for f in ser:
+                    if f == 0.0:
+                        number_of_blinks += 1
+            
+            for ser in df_base['baseline']:
+                for f in ser:
+                    if f == 0.0:
+                        number_of_blinks += 1
+            # Initialization of session_state
+            if 'blinks' not in st.session_state:
+                st.session_state['blinks'] = number_of_blinks
+                        
+        if "blinks" in st.session_state.keys():
+            st.info(f"blinking values (0.0) were found in {number_of_blinks} time-steps in all your data")
+            remove_blinking  = st.button("Remove blinking")
+            # df in column 2
+            if remove_blinking:
+                df_right = st.session_state.df.copy(deep=True)
+                df_right.pupil_dilation = df_right.pupil_dilation.map(lambda ser: [f for f in ser if f != 0.0])
+                df_right.baseline = df_right.baseline.map(lambda ser: [f for f in ser if f != 0.0])
+                st.success("blinking values have been removed!")
+                st.info("after transformation")
+                st.dataframe(df_right)
+        elif detect_blinking and not number_of_blinks:
+            st.caption("no blinking values were found in your data!")
+
+if not df_base.empty:
+    st.warning("consider running outlier detection to clean your data!", icon="⚠️")
+
+# for key, value in st.session_state.items():
+#    st.success(f"{key}: {value}")
+
+# reloading new samples would damage the st-session_state loading, vars are only written once

pages/1_📈_Plotting.py

@@ -0,0 +1,3 @@
+import streamlit as st
+
+st.warning("We are working on this functionality!")

pages/2_📊_Outlier_Detection.py

@@ -0,0 +1,3 @@
+import streamlit as st
+
+st.warning("We are working on this functionality. It will soon be ready!")

pages/3_🏋️_Training.py

@@ -0,0 +1,3 @@
+import streamlit as st
+
+st.warning("We are working on this functionality! 🏋️")