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import time
import pandas as pd
import pydeck as pdk
import streamlit as st
from filter_wrap import FilterWrapper
from distribution_wrap import DistriWrapper
from redux_wrap import ReduxWrapper
from symetry_wrap import SymetryWrapper
from rotate_wrap import RotateWrapper
from sort_wrap import SortWrapper
from team_wrap import TeamWrapper
from reward_wrap import RewardWrapper
from monitor_wrap import MonitorWrapper
from runner import run_episode
from settings import Settings, define_
import param_
from swarmenv import SwarmEnv
def run(with_streamlit=True, blues: int = 4, reds: int = 6, policy_folder: str = 'reds_last'):
# define the policy folder is: where the trained policies are to be found
Settings.policy_folder = policy_folder
# define settings with Streamlit (or use default parameters)
blues, reds = define_(with_streamlit=with_streamlit, blues=blues, reds=reds)
# put in place the map
deck_map, initial_view_state = pre_show(with_streamlit=with_streamlit)
# launch the episode to get the data
steps = int(param_.DURATION / param_.STEP)
monitor_env = MonitorWrapper(SwarmEnv(blues=blues, reds=reds), steps)
env = FilterWrapper(monitor_env)
env = DistriWrapper(env)
env = ReduxWrapper(env)
env = SortWrapper(
SymetryWrapper(
RotateWrapper(env)))
env = RewardWrapper(TeamWrapper(env, is_double=True), is_double=True)
obs = env.reset()
run_episode(env, obs, blues=blues, reds=reds)
print('done')
# display the data with Streamlit
if with_streamlit:
show(monitor_env, deck_map, initial_view_state)
def pre_show(with_streamlit=True):
if with_streamlit:
deck_map = st.empty()
pitch = st.slider('pitch', 0, 100, 50)
lat, lon = Settings.latlon
initial_view_state = pdk.ViewState(
latitude=lat,
longitude=lon,
zoom=13,
pitch=pitch
)
return deck_map, initial_view_state
else:
return 0, 0
def show(monitor_env, deck_map, initial_view_state):
blue_df, red_df, fire_df, blue_path_df, red_path_df = monitor_env.get_df()
step_max = monitor_env.step_
for step in range(step_max):
deck_map.pydeck_chart(pdk.Deck(
map_provider="mapbox",
map_style='mapbox://styles/mapbox/light-v9',
initial_view_state=initial_view_state,
layers=get_layers(blue_df,
red_df,
blue_path_df,
red_path_df,
step)
))
time.sleep(param_.STEP*param_.SIMU_SPEED)
def get_layers(df_blue: pd.DataFrame, df_red: pd.DataFrame,
df_blue_path: [pd.DataFrame], df_red_path: [pd.DataFrame],
step) -> [pdk.Layer]:
lat, lon = Settings.latlon
df_target = pd.DataFrame({'lat': [lat], 'lon': [lon]})
layers_ = get_target_layers(df_target)
for (df, dfp, b) in [(df_blue, df_blue_path, True), (df_red, df_red_path, False)]:
layers_.append(get_current_drone_layers(df, step))
nb_drones = df['d_index'].max() + 1
for drone_index in range(nb_drones):
layers_.append(get_path_layers(dfp[drone_index], step))
return layers_
def get_target_layers(df_target) -> [pdk.Layer]:
return [
# this is the GROUNDZONE
pdk.Layer(
'ScatterplotLayer',
data=df_target,
get_position='[lon, lat]',
get_color='[0, 120, 0]',
get_radius=Settings.groundzone,
get_line_width=50,
lineWidthMinPixels=2,
stroked=True,
filled=False,
),
pdk.Layer(
'ScatterplotLayer',
data=df_target,
get_position='[lon, lat]',
get_color='[0, 0, 200]',
get_radius=30,
),
]
def get_current_drone_layers(df_drone: pd.DataFrame, step: int) -> [pdk.Layer]:
df_current = df_drone[df_drone.step == step]
return [
pdk.Layer(
'ScatterplotLayer',
data=df_current,
get_position='[lon, lat, zed]',
get_color='color',
get_radius=50,
),
pdk.Layer(
'ScatterplotLayer',
data=df_current,
get_position='[lon, lat]',
get_color=[50, 50, 50, 50],
get_radius=50,
),
]
def get_path_layers(df_path: pd.DataFrame, step: int) -> [pdk.Layer]:
df_current = df_path[df_path.step == step]
return [
pdk.Layer(
type="PathLayer",
data=df_current,
pickable=True,
get_color="color",
width_scale=10,
width_min_pixels=1,
get_path="path",
get_width=1,
)
]
# and ... do not forget
run(with_streamlit=True, policy_folder='last')
# run(blues=1, reds=3, with_streamlit=False, policy_folder='0527_14_test')
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