Merge pull request #109 from AllenNeuralDynamics/han_use_bokeh

Use bokeh to show plots in hover messages

code/Home.py

@@ -20,10 +20,10 @@
 import streamlit as st
 import streamlit_nested_layout
 from aind_auto_train import __version__ as auto_train_version
-from aind_auto_train.auto_train_manager import DynamicForagingAutoTrainManager
-from aind_auto_train.curriculum_manager import CurriculumManager
 from pygwalker.api.streamlit import StreamlitRenderer, init_streamlit_comm
-from util.aws_s3 import (draw_session_plots_quick_preview, load_data,
+from util.aws_s3 import (draw_session_plots_quick_preview, 
+                         load_data,
+                         load_auto_train,
                          show_debug_info,
                          show_session_level_img_by_key_and_prefix)
 from util.fetch_data_docDB import load_data_from_docDB
@@ -287,6 +287,8 @@ def plot_x_y_session():
 def show_curriculums():
     pass
 
+
+
 # ------- Layout starts here -------- #
 def init(if_load_bpod_data_override=None, if_load_docDB_override=None):
 
@@ -323,23 +325,10 @@ def init(if_load_bpod_data_override=None, if_load_docDB_override=None):
     for source in ["dataframe", "plotly"]:
         st.session_state[f'df_selected_from_{source}'] = pd.DataFrame(columns=['h2o', 'session'])
 
-    # Init auto training database
-    st.session_state.curriculum_manager = CurriculumManager(
-        saved_curriculums_on_s3=dict(
-            bucket='aind-behavior-data',
-            root='foraging_auto_training/saved_curriculums/'
-        ),
-        saved_curriculums_local=os.path.expanduser('~/curriculum_manager/'),
-    )
-    st.session_state.auto_train_manager = DynamicForagingAutoTrainManager(
-        manager_name='447_demo',
-        df_behavior_on_s3=dict(bucket='aind-behavior-data',
-                                root='foraging_nwb_bonsai_processed/',
-                                file_name='df_sessions.pkl'),
-        df_manager_root_on_s3=dict(bucket='aind-behavior-data',
-                                root='foraging_auto_training/')
-    )
-
+    # Load autotrain
+    auto_train_manager, curriculum_manager = load_auto_train()
+    st.session_state.auto_train_manager = auto_train_manager
+    st.session_state.curriculum_manager = curriculum_manager
 
     # Some ad-hoc modifications on df_sessions
     _df = st.session_state.df['sessions_bonsai']  # temporary df alias
@@ -544,7 +533,7 @@ def app():
         # with col1:
         # -- 1. unit dataframe --
 
-        cols = st.columns([2, 4, 1])
+        cols = st.columns([4, 4, 4, 1])
         cols[0].markdown(f'### Filter the sessions on the sidebar\n'
                          f'#####  {len(st.session_state.df_session_filtered)} sessions, '
                          f'{len(st.session_state.df_session_filtered.h2o.unique())} mice filtered')
@@ -570,7 +559,7 @@ def app():
                     init()
                     st.rerun()  # Reload the page to apply the changes
 
-        table_height = slider_wrapper_for_url_query(st_prefix=cols[2],
+        table_height = slider_wrapper_for_url_query(st_prefix=cols[-1],
                                                     label='Table height',
                                                     min_value=0,
                                                     max_value=2000,
@@ -583,7 +572,8 @@ def app():
 
 
     if len(st.session_state.df_session_filtered) == 0:
-        st.markdown('## No filtered results!')
+        st.markdown('## No filtered results! :thinking_face:')
+        st.markdown('### :bulb: Try clicking "Reset filters" and add your filters again!')
         return
 
     aggrid_outputs = aggrid_interactive_table_session(
@@ -702,7 +692,7 @@ def add_main_tabs():
             st.markdown("#### Select auto training curriculums")
 
             # Curriculum drop down selector
-            cols = st.columns([0.8, 0.5, 0.8, 4])
+            cols = st.columns([0.8, 0.8, 0.8, 3])
             cols[3].markdown(f"(aind_auto_train lib version = {auto_train_version})")
 
             options = list(df_curriculums['curriculum_name'].unique())

code/__init__.py

@@ -1 +1 @@
-__ver__ = 'v2.6.0'
+__ver__ = 'v3.0.0 beta'

code/util/aws_s3.py

@@ -1,10 +1,14 @@
 import json
-
+import os
+import numpy as np
 import pandas as pd
 import s3fs
 import streamlit as st
 from PIL import Image
 
+from aind_auto_train.auto_train_manager import DynamicForagingAutoTrainManager
+from aind_auto_train.curriculum_manager import CurriculumManager
+
 from .settings import (draw_type_layout_definition,
                        draw_type_mapper_session_level,
                        draw_types_quick_preview)
@@ -44,6 +48,33 @@ def load_raw_sessions_on_VAST():
         raw_sessions_on_VAST = json.load(f)
     return raw_sessions_on_VAST
 
+@st.cache_data(ttl=12*3600)
+def load_auto_train():
+    # Init auto training database
+    curriculum_manager = CurriculumManager(
+        saved_curriculums_on_s3=dict(
+            bucket='aind-behavior-data',
+            root='foraging_auto_training/saved_curriculums/'
+        ),
+        saved_curriculums_local=os.path.expanduser('~/curriculum_manager/'),
+    )
+    auto_train_manager = DynamicForagingAutoTrainManager(
+        manager_name='447_demo',
+        df_behavior_on_s3=dict(bucket='aind-behavior-data',
+                                root='foraging_nwb_bonsai_processed/',
+                                file_name='df_sessions.pkl'),
+        df_manager_root_on_s3=dict(bucket='aind-behavior-data',
+                                root='foraging_auto_training/')
+    )
+
+    _df = auto_train_manager.df_manager.copy()
+    # Remove invalid subject_id
+    _df = _df[(999999 > _df["subject_id"].astype(int)) 
+              & (_df["subject_id"].astype(int) > 300000)]
+    auto_train_manager.df_manager = _df
+
+    return auto_train_manager, curriculum_manager
+
 def draw_session_plots_quick_preview(df_to_draw_session):
 
     container_session_all_in_one = st.container()
@@ -138,4 +169,15 @@ def show_debug_info():
     with st.expander('NWB convertion and upload log', expanded=False):
         with fs.open(s3_nwb_folder['bonsai'] + 'bonsai_pipeline.log') as file:
             log_content = file.read().decode('utf-8')
-        st.text(log_content)
+        st.text(log_content)
+
+
+def get_s3_public_url(
+    subject_id, session_date, nwb_suffix, figure_suffix="choice_history.png",
+    result_path="foraging_nwb_bonsai_processed", bucket_name="aind-behavior-data", 
+):
+    nwb_suffix = "" if np.isnan(nwb_suffix) or int(nwb_suffix) == 0 else f"_{int(nwb_suffix)}"
+    return (
+        f"https://{bucket_name}.s3.us-west-2.amazonaws.com/{result_path}/"
+        f"{subject_id}_{session_date}{nwb_suffix}/{subject_id}_{session_date}{nwb_suffix}_{figure_suffix}"
+    )