evandiewald

with hashes as 

(select transaction_hash, actor from transaction_actors where 
actor = {{address}} 
and actor_role = 'witness' 
and block > (select max(height) - {{n_blocks}} from blocks)),

target_transactions as 
(select fields, hash from transactions where 
(type = 'poc_receipts_v2' or type = 'poc_receipts_v1') 

evandiewald

def load_map(place: str, graph_only: bool = False) -> (nx.MultiDiGraph, dict):
    """
    Load OSM trail data for a given region. Initially check if the graph has already been cached on disk, otherwise it will be downloaded.
    Args:
        place: The geocode of the region of interest, e.g. 'Shenandoah National Park, Virginia, USA'
        graph_only: If true, return only the NetworkX graph, not the geojson.

    Returns: The dataset as a NetworkX MultiGraph, the nodes geojson, the edges geojson

    """

evandiewald

def get_elevation_profile_of_segment(dataset: rasterio.DatasetReader, coords: list[list]):
    """
    Get the elevation profile (distance vs. altitude) of a path segment from the list of coordinates.
    Args:
        dataset: The opened rasterio dataset for the SRTM global topography data. 
        coords: The path coordinates in [[lon1, lat1], [lon2, lat2], ...] format.

    Returns: The distance (in miles) and elevation (in feet) vectors.
    """
    # coordinates are [lon, lat], flip for rasterio

evandiewald

@callback(
    Output("route-path", "data"),
    Input("map", "click_lat_lng"),
    Input("distance-target", "value"),
    Input("elevation-min", "value"),
    Input("mode-select", "value"),
    State("route-path", "data")
)
def cycles(click_lat_lng, distance_target: Optional[Union[int, float]], elevation_min: Optional[Union[int, float]], mode, path):
    if click_lat_lng is None:

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def find_best_loop(G: nx.Graph, root, target_dist, tol=1.0, min_elev: Optional[Union[int, float]] = None,
                   dataset: Optional[rasterio.DatasetReader] = None):
    if min_elev and not dataset:
        raise ValueError("If asking for elevation data, you must include a rasterio dataset")
    error = 1e8
    best_path = []
    for n in G.nodes():
        if nx.has_path(G, root, n):
            shortest_path = nx.shortest_path(G, root, n)
            paths = nx.all_simple_paths(G, root, n, cutoff=10)

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def layout(place=None):
    if place:
        global G, G2
        G, _, edges = load_map(place)
        G2 = nx.Graph(G)

        polyline = dl.Polyline(id="route-line", positions=[], color="red", weight=10, fillColor="red", fillOpacity=1.0)
        patterns = [dict(repeat='100',
                         arrowHead=dict(pixelSize=15, polygon=False, pathOptions=dict(color="red", stroke=True)),
                         line=dict(pixelSize=10, pathOptions=dict(color='#f00', weight=20)))]

evandiewald

from dotenv import load_dotenv
from sshtunnel import SSHTunnelForwarder
from sqlalchemy.engine import create_engine, Engine
from sqlalchemy.ext.asyncio import create_async_engine, AsyncEngine
from typing import Union
import os
from sqlalchemy.pool import NullPool


load_dotenv()