import numpy as np
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os, zarr
from pathlib import Path

#Pillow, pycairo, cairoSVG, scikit-images
#
from io import BytesIO
from PIL import Image
import cairosvg

import l5kit.data

#from l5kit import MapAPI


for dirname, dirs, filenames in os.walk('/kaggle/working/'): ##/input/
    print(dirname[-30:], dirs, len(filenames), " files")
    pass;

#sys.path.append("../input/pysvg/");
#import pySvg as gkit

#!pip list

DATA_ROOT = Path("../input/lyft-motion-prediction-autonomous-vehicles")
zdz = zarr.open(DATA_ROOT.joinpath("scenes/sample.zarr").as_posix(), mode="r")


#

class mapI(l5kit.data.MapAPI):
    
    def lane_delta(self, id):
        if self.is_lane(self.elements[id]):
            lane = self.elements[id].element.lane;
            lane_l = lane.left_boundary;
            delta_l = self.unpackDel_cm(lane_l.vertex_deltas_x_cm, lane_l.vertex_deltas_y_cm, lane_l.vertex_deltas_z_cm, lane.geo_frame);
            lane_r = lane.right_boundary;
            delta_r = self.unpackDel_cm(lane_r.vertex_deltas_x_cm, lane_r.vertex_deltas_y_cm, lane_r.vertex_deltas_z_cm, lane.geo_frame);
            
            return {'dl': delta_l, 'dr': delta_r};
    
    def lane_Mask(self, id):
        return self.is_lane(self.elements[id])
    
    #@no_type_check
    def unpackDel_cm(self, dx, dy, dz, frame):
        #"""              # dx: Sequence[int], dy: Sequence[int], dz: Sequence[int], frame: GeoFrame) -> np.ndarray:
        #Get coords in world reference system (local ENU->ECEF->world).
        #See the protobuf annotations for additional information about how coordinates are stored
        #"""
        x = np.cumsum(np.asarray(dx) / 100)
        y = np.cumsum(np.asarray(dy) / 100)
        z = np.cumsum(np.asarray(dz) / 100)
        frame_lat, frame_lng = self._undo_e7(frame.origin.lat_e7), self._undo_e7(frame.origin.lng_e7)
        #xyz = np.stack(pm.enu2ecef(x, y, z, frame_lat, frame_lng, 0), axis=-1)
        #xyz = transform_points(xyz, self.ecef_to_world)
        return {'dx': x, 'dy': y, 'dz':z, 'gps': [frame_lat, frame_lng]};

    
    
    

mapi = mapI("/kaggle/input/lyft-motion-prediction-autonomous-vehicles/semantic_map/semantic_map.pb", [[1,0],[0,1]])

print(zdz.info);

print(zdz.agents.dtype) ## centroid velocity [track_id] yaw extent
print(zdz.frames.dtype) ## agent_index_interval [timestamp] ego_translation ego_rotation
print(zdz.scenes.dtype) ## frame_index_interval

#zdz.agents[0].["yaw"]

s_sub = "/kaggle/input/lyft-motion-prediction-autonomous-vehicles/single_mode_sample_submission.csv";

subH = pd.read_csv(s_sub, header=0, nrows=0);

print(subH);

nelsa = ""

nelsa = """

import numpy as np # linear algebra
import numba

from io import BytesIO
from PIL import Image
import cairosvg

import l5kit.data#.MapAPI


class Nelsa:
    
    def recordAccess(data, sc):
        rS = data["scenes"][sc];    
        rF = data["frames"][slice(rS[0][0], rS[0][1])];    
        rA = data["agents"][slice(rF[0][1][0], rF[-1][1][1])];
        return [rS, rF, rA];

    def movementFilter(self, rA):
        return self.velzero_v(rA["velocity"][:,0], rA["velocity"][:,1]);
        
    
    @numba.jit("float32(float32, float32, float32, float32)")
    def dist_j(pointax, pointbx, pointay, pointby):
        return np.abs(pointbx - pointax) + np.abs(pointay - pointby);
    
    @numba.jit("boolean(float32, float32)")
    def velzero_j(pointa, pointb):
        return np.greater(np.abs(pointa) + np.abs(pointb), 0.);
    
    def xRaster_f(fname):
        out = BytesIO();
        cairosvg.svg2png(url=fname, write_to=out);
        image = Image.open(out)
        return image;
    
    def xRaster(fdata):
        out = BytesIO();
        cairosvg.svg2png(bytestring=fdata, write_to=out);
        image = Image.open(out)
        return image;
    
    def xStore(im, filename):
        try:
            im.save(filename)
        except OSError:
            print("error?");
        
    

    def __init__(self, targ):
        targ.dist_v = np.vectorize(self.dist_j); 
        targ.velzero_v = np.vectorize(self.velzero_j); 
        
class nX:
    pass;

Nelsa.__init__(Nelsa, Nelsa);


class mapI(l5kit.data.MapAPI):
    
    def lane_delta(self, id):
        if self.is_lane(self.elements[id]):
            lane = self.elements[id].element.lane;
            lane_l = lane.left_boundary;
            delta_l = self.unpackDel_cm(lane_l.vertex_deltas_x_cm, lane_l.vertex_deltas_y_cm, lane_l.vertex_deltas_z_cm, lane.geo_frame);
            lane_r = lane.right_boundary;
            delta_r = self.unpackDel_cm(lane_r.vertex_deltas_x_cm, lane_r.vertex_deltas_y_cm, lane_r.vertex_deltas_z_cm, lane.geo_frame);
            
            return {'dl': delta_l, 'dr': delta_r};
    
    def lane_Mask(self, id):
        return self.is_lane(self.elements[id])
    
    def label_Mask(self, id, label):
        return self.elements[id].element.HasField(label)
    
    
    #@no_type_check
    def unpackDel_cm(self, dx, dy, dz, frame):
        #              # dx: Sequence[int], dy: Sequence[int], dz: Sequence[int], frame: GeoFrame) -> np.ndarray:
        #Get coords in world reference system (local ENU->ECEF->world).
        #See the protobuf annotations for additional information about how coordinates are stored
        #
        x = np.cumsum(np.asarray(dx) / 100)
        y = np.cumsum(np.asarray(dy) / 100)
        z = np.cumsum(np.asarray(dz) / 100)
        frame_lat, frame_lng = self._undo_e7(frame.origin.lat_e7), self._undo_e7(frame.origin.lng_e7)
        #xyz = np.stack(pm.enu2ecef(x, y, z, frame_lat, frame_lng, 0), axis=-1)
        #xyz = transform_points(xyz, self.ecef_to_world)
        return {'dx': x, 'dy': y, 'dz':z, 'gps': [frame_lat, frame_lng]};
    

    
    def gps_to_World(self, coord):
        
        ##gps_Static = [37.25456, 122.09157];
        
        gps_Static = [37.429333, -122.154361];
        
        return [(coord[0] - gps_Static[0]) * 1e5, (coord[1] - gps_Static[1]) * 1e5];



#"""

if(nelsa != ""):
    f = open("Nelsa.py", "w")
    f.write(nelsa);
    f.close();