andrewljohnson

def get_point_photos(request, lat, lon, name):
  '''takes a lat lon pair via an ajax request, and returns a list of photos'''
  #create a Flickr API object
  f = flickrapi.FlickrAPI(api_key) 
  #resolve a (lat,long) to a "Flickr Place" which can be used to search for photos.
  place = f.places_findByLatLon(api_key = api_key, lat=lat, lon = lon)
  #get the id of the Flickr Place returned
  place_id = place.find('places').findall('place')[0].attrib['place_id']
  #returns an XML response that contains data to construct photo URLs
  flickr_search = f.photos_search(api_key = api_key, place_id = place_id, content_type = '1', tags = 'nature, landscape, hiking, outdoors, 

andrewljohnson

    double latitude = self.mapView.mapCenter.latitude;
    metersPerPixel *= cos(latitude * (M_PI / 180));
    double metersPerUnit = 1609.344;
    int numberOfUnits = 1;
    NSString *unitName = MILES_UNIT_STRING;
    if (metersPerPixel < (1000/maxScaleSize)) {
      unitName = METERS_UNIT_STRING;
      metersPerUnit = 1;
    } else {
      unitName = KILOMETERS_UNIT_STRING;

andrewljohnson

on run {input, parameters}
	tell application "Terminal"
		set currentTab to do script ("ps ax | grep jekyll | grep -v grep | awk '{ print $1 }' | xargs kill") in front window
		set currentTab to do script ("jekyll serve --detach;") in front window
	end tell
	
	tell application "Google Chrome"
		activate
		set theUrl to "http://0.0.0.0:4000/"
		

andrewljohnson

#!/usr/local/bin/python

import sys
import urllib

'''
  make a URL that imports a TMS map source into Gaia GPS

  example

andrewljohnson

#!/usr/bin/env python
###############################################################################
# $Id$
#
# Project:  GDAL2Tiles, Google Summer of Code 2007 & 2008
#           Global Map Tiles Classes
# Purpose:  Convert a raster into TMS tiles, create KML SuperOverlay EPSG:4326,
#           generate a simple HTML viewers based on Google Maps and OpenLayers
# Author:   Klokan Petr Pridal, klokan at klokan dot cz
# Web:      http://www.klokan.cz/projects/gdal2tiles/

andrewljohnson

import boto3

def download_naips(self):
    s3_client = boto3.client('s3')
    filename = 'm_3807708_ne_18_1_20130924.tif'
    s3_client.download_file('aws-naip', 'md/2013/1m/rgbir/38077/{}'.format(filename), filename, {'RequestPayer':'requester'})

andrewljohnson

root@8e3b54c9c353:/code# python manage.py shell < real_estate/scripts/generate_neural_net.py 
fetching AnalysisRecords took 0.0015499591827392578
Fetched 15577 records for properties.
annotating years and months took 1.1473767757415771
making value map took 11.682528972625732
Using 15573 properties for analysis.
making model took 0.3301811218261719
Model: "model"
__________________________________________________________________________________________________
 Layer (type)          Output Shape     Param #   Connected to           

andrewljohnson

root@8e3b54c9c353:/code# python manage.py shell < real_estate/scripts/generate_neural_net.py 
fetching AnalysisRecords took 0.0015206336975097656
Fetched 47555 records for properties.
2183530944 - Hashing AnalysisRecords took 3.006469964981079
annotating years and months took 3.25303053855896
making value map took 36.68412470817566
Using 47550 properties for analysis.
4202095867 - data_hash
4036931971 - value_map_hash
making model took 0.8455119132995605

andrewljohnson

import datetime
import math 
import numpy as np
import pandas as pd
import pickle
import time 

from django.db.models.functions import Cast
from django.db.models import IntegerField
from real_estate.models import AnalysisRecord

andrewljohnson

#include <iostream>
using namespace std;


// Current Cards
// Mountain

// Future Cards
// Forest
// Lightning Bolt, Giant Growth, Earthquake