def prices():
query = 'SELECT DISTINCT driver FROM results WHERE year = 2014'
drivers = db.fetchall(query)
driver_prices = [(d[0], scoring.price(d[0])) for d in drivers]
return render_template('prices.html', prices=driver_prices)
def populateDb():
conn = Connection('localhost', 27017)
db = conn.asterank
coll = db.asteroids
#coll.drop()
coll.ensure_index('full_name', unique=True, background=True)
coll.ensure_index('score', background=True)
coll.ensure_index('profit', background=True)
coll.ensure_index('prov_des', background=True) # necessary for upcoming pass lookups
coll.ensure_index('closeness', background=True)
coll.ensure_index('price', background=True)
# load mass data
print 'Loading mass data...'
f = open(MASS_PATH, 'r')
lines = f.readlines()
f.close()
massd = {}
for line in lines:
parts = line.split(' ')
massidx = len(parts) - 2
mass = float(parts[massidx])
name = ' '.join(parts[:massidx]).strip()
if name not in massd:
massd[name] = []
massd[name].append(mass)
for name, masses in massd.iteritems():
avg = sum(masses) / len(masses)
massd[name] = avg
del massd['']
# load delta v data
f = open(DV_PATH, 'r')
lines = f.readlines()
f.close()
print 'Loading delta-v data...'
deltav_map = {}
for line in lines:
parts = line.split(',')
des = parts[0]
dv = float(parts[1])
deltav_map[des] = dv
print 'Loading small body data...this may take a while'
print DATA_PATH
reader = csv.DictReader(open(DATA_PATH), delimiter=',', quotechar='"')
designation_regex = re.compile('.*\(([^\)]*)\)')
n = 0
for row in reader:
if row['spec_B'] == '':
newspec = THOLEN_MAPPINGS.get(row['spec_T'], None)
if newspec:
# TODO should have our own merged spec row, instead we overwrite spec_B
row['spec_B'] = newspec.strip()
elif row['pdes'] == '2012 DA14':
print 'Adjust 2012 DA14'
row['spec_B'] = 'L'
elif row['class'] in COMET_CLASSES:
row['spec_B'] = 'comet'
else:
continue # TODO temp
#row['spec_B'] = 'S'
"""
elif row['pdes'] == '2008 HU4':
print 'Adjust 2008 HU4'
row['spec_B'] = 'C'
row['GM'] = 3.0268194e-14 # 500 tons
row['diameter'] = 8 # 8 meters
"""
if row['spec_B'] == 'C type':
row['spec_B'] = 'C'
# match it with its delta-v
m = designation_regex.match(row['full_name'])
if 'pdes' in row and 'prov_des' not in row:
row['prov_des'] = row['pdes'] # backwards compatibility for NASA change
if m:
row['prov_des'] = m.groups()[0]
dv = deltav_map.get(row['prov_des'], None)
if dv:
row['dv'] = dv
else:
row['prov_des'] = ''
# Clean up inputs
for key,val in row.items():
try:
fv = float(val)
except ValueError, TypeError:
row[key] = val.strip()
else:
row[key] = fv
row['spec_T'] = row['spec_T'].replace(':', '')
row['spec_B'] = row['spec_B'].replace(':', '')
# match mass
if row['full_name'] in massd:
row['GM'] = massd[row['full_name']] * G
# compute score
row['price'], row['saved'] = scoring.price(row)
# assume the cost of mining a distant asteroid per kg won't be much better
# than cost to launch from earth
# ie., 99.999% of revenue is spent on operations
row['saved'] = row['saved'] * 0.00001
row['closeness'] = scoring.closeness_weight(row)
row['profit'] = scoring.profit(row)
# TODO move this into scoring once I get it right
score = min(row['price'], 1e14) / 5e12
if score < 0.0001:
# It's worthless, so closeness doesn't matter
row['score'] = score
else:
score = score * row['closeness']**3
row['score'] = score
coll.update({'full_name': row['full_name']}, {'$set': row}, True) # upsert
n += 1
if n % 3000 == 0:
print n, '...'
def _run(partial=False):
# Constants and settings
# Fill database
conn = Connection('localhost', 27017)
db = conn.asterank
coll = db.asteroids
print 'Dropping asteroids (SBDB) collection...'
coll.drop()
coll.ensure_index('full_name', unique=True, background=True)
coll.ensure_index('score', background=True)
coll.ensure_index('profit', background=True)
coll.ensure_index('prov_des',
background=True) # necessary for upcoming pass lookups
coll.ensure_index('closeness', background=True)
coll.ensure_index('price', background=True)
# load mass data
print 'Loading mass data...'
f = open(MASS_PATH, 'r')
lines = f.readlines()
f.close()
massd = {}
for line in lines:
parts = line.split(' ')
massidx = len(parts) - 2
mass = float(parts[massidx])
name = ' '.join(parts[:massidx]).strip()
if name not in massd:
massd[name] = []
massd[name].append(mass)
for name, masses in massd.iteritems():
avg = sum(masses) / len(masses)
massd[name] = avg
del massd['']
# load delta v data
print 'Loading delta-v data...'
reader = csv.DictReader(open(DV_PATH, 'r'))
deltav_map = {}
for row in reader:
deltav_map[row['pdes']] = row['dv']
print 'Loading small body data...this may take a while'
print DATA_PATH
reader = csv.DictReader(open(DATA_PATH), delimiter=',', quotechar='"')
designation_regex = re.compile('.*\(([^\)]*)\)')
n = 0
items = []
for row in reader:
row['spec'] = row['spec_B']
row['full_name'] = row['full_name'].strip()
if row['spec'] == '':
newspec = THOLEN_MAPPINGS.get(row['spec_T'], None)
if newspec:
row['spec'] = newspec.strip()
# TODO(@ian) move specific adjustments out into its own file.
elif row['pdes'] == '2012 DA14':
print 'Adjust 2012 DA14'
row['spec'] = 'L'
elif row['full_name'] == '6178 (1986 DA)':
print 'Adjust 1986 DA'
row['spec'] = 'M'
elif row['full_name'] == '436724 (2011 UW158)':
print 'Adjust 2011 UW158'
row['spec'] = 'Xc'
elif row['class'] in COMET_CLASSES:
row['spec'] = 'comet'
else:
if partial:
# don't build the full db of 600k objects
continue
row['spec'] = '?'
if row['spec'] == 'C type':
row['spec'] = 'C'
# match it with its delta-v
# TODO(@ian) don't overwrite prov_des, create some unified name field instead.
m = designation_regex.match(row['full_name'])
if 'pdes' in row and 'prov_des' not in row:
row['prov_des'] = row[
'pdes'] # backwards compatibility for NASA change
if m:
# Set delta-v first
dv = deltav_map.get(row['prov_des'], None)
if dv:
row['dv'] = dv
row['prov_des'] = m.groups()[0]
else:
row['prov_des'] = ''
# Clean up inputs
for key, val in row.items():
try:
fv = float(val)
except ValueError, TypeError:
row[key] = val.strip()
else:
row[key] = fv
row['spec_T'] = row['spec_T'].replace(':', '')
row['spec_B'] = row['spec_B'].replace(':', '')
row['spec'] = row['spec'].replace(':', '')
# match mass
if row['full_name'] in massd:
row['GM'] = massd[row['full_name']] * G
# compute score
row['price'], row['saved'] = scoring.price(row)
# assume the cost of mining a distant asteroid per kg won't be much better
# than cost to launch from earth
# ie., 99.999% of revenue is spent on operations
row['saved'] = row['saved'] * 0.00001
row['closeness'] = scoring.closeness_weight(row)
row['profit'] = scoring.profit(row)
# TODO move this final scoring pass into scoring.py
# cap price influence on score at 10 B
score = min(row['price'], 1e10) / 5e11
if score > 0.0001:
score = score + row['closeness'] / 20
row['score'] = score
items.append(row)
n += 1
if len(items) > 20000:
# insert into mongo
print 'Row #', n, '... inserting/updating %d items into asteroids (SBDB) collection' % (
len(items))
coll.insert(items, continue_on_error=True)
items = []
def _run(partial=False):
# Constants and settings
# TODO this method should be factored out
# Fill database
conn = Connection("localhost", 27017)
db = conn.asterank
coll = db.asteroids
# coll.drop()
coll.ensure_index("full_name", unique=True, background=True)
coll.ensure_index("score", background=True)
coll.ensure_index("profit", background=True)
coll.ensure_index("prov_des", background=True) # necessary for upcoming pass lookups
coll.ensure_index("closeness", background=True)
coll.ensure_index("price", background=True)
# load mass data
print "Loading mass data..."
f = open(MASS_PATH, "r")
lines = f.readlines()
f.close()
massd = {}
for line in lines:
parts = line.split(" ")
massidx = len(parts) - 2
mass = float(parts[massidx])
name = " ".join(parts[:massidx]).strip()
if name not in massd:
massd[name] = []
massd[name].append(mass)
for name, masses in massd.iteritems():
avg = sum(masses) / len(masses)
massd[name] = avg
del massd[""]
# load delta v data
print "Loading delta-v data..."
reader = csv.DictReader(open(DV_PATH, "r"))
deltav_map = {}
for row in reader:
deltav_map[row["pdes"]] = row["dv"]
print "Loading small body data...this may take a while"
print DATA_PATH
reader = csv.DictReader(open(DATA_PATH), delimiter=",", quotechar='"')
designation_regex = re.compile(".*\(([^\)]*)\)")
n = 0
for row in reader:
row["spec"] = row["spec_B"]
if row["spec"] == "":
newspec = THOLEN_MAPPINGS.get(row["spec_T"], None)
if newspec:
row["spec"] = newspec.strip()
elif row["pdes"] == "2012 DA14":
print "Adjust 2012 DA14"
row["spec"] = "L"
elif row["class"] in COMET_CLASSES:
row["spec"] = "comet"
else:
if partial:
continue # don't build the full db of 600k objects
row["spec"] = "?"
if row["spec"] == "C type":
row["spec"] = "C"
# match it with its delta-v
# TODO(@ian) don't overwrite prov_des, create some unified name field instead.
m = designation_regex.match(row["full_name"])
if "pdes" in row and "prov_des" not in row:
row["prov_des"] = row["pdes"] # backwards compatibility for NASA change
if m:
# Set delta-v first
dv = deltav_map.get(row["prov_des"], None)
if dv:
row["dv"] = dv
row["prov_des"] = m.groups()[0]
else:
row["prov_des"] = ""
# Clean up inputs
for key, val in row.items():
try:
fv = float(val)
except ValueError, TypeError:
row[key] = val.strip()
else:
row[key] = fv
row["spec_T"] = row["spec_T"].replace(":", "")
row["spec_B"] = row["spec_B"].replace(":", "")
row["spec"] = row["spec"].replace(":", "")
# match mass
if row["full_name"] in massd:
row["GM"] = massd[row["full_name"]] * G
# compute score
row["price"], row["saved"] = scoring.price(row)
# assume the cost of mining a distant asteroid per kg won't be much better
# than cost to launch from earth
# ie., 99.999% of revenue is spent on operations
row["saved"] = row["saved"] * 0.00001
row["closeness"] = scoring.closeness_weight(row)
row["profit"] = scoring.profit(row)
# TODO move this final scoring pass into scoring.py
score = min(row["price"], 1e14) / 5e13
if score < 0.0001:
# It's worthless, so closeness doesn't matter
row["score"] = score
else:
score = score * row["closeness"]
row["score"] = score
coll.update({"full_name": row["full_name"]}, {"$set": row}, True) # upsert
n += 1
if n % 3000 == 0:
print n, "..."
def _run(partial=False):
# Constants and settings
# Fill database
conn = Connection('localhost', 27017)
db = conn.asterank
coll = db.asteroids
print 'Dropping asteroids (SBDB) collection...'
coll.drop()
coll.ensure_index('full_name', unique=True, background=True)
coll.ensure_index('score', background=True)
coll.ensure_index('profit', background=True)
coll.ensure_index('prov_des', background=True) # necessary for upcoming pass lookups
coll.ensure_index('closeness', background=True)
coll.ensure_index('price', background=True)
# load mass data
print 'Loading mass data...'
f = open(MASS_PATH, 'r')
lines = f.readlines()
f.close()
massd = {}
for line in lines:
parts = line.split(' ')
massidx = len(parts) - 2
mass = float(parts[massidx])
name = ' '.join(parts[:massidx]).strip()
if name not in massd:
massd[name] = []
massd[name].append(mass)
for name, masses in massd.iteritems():
avg = sum(masses) / len(masses)
massd[name] = avg
del massd['']
# load delta v data
print 'Loading delta-v data...'
reader = csv.DictReader(open(DV_PATH, 'r'))
deltav_map = {}
for row in reader:
deltav_map[row['pdes']] = row['dv']
print 'Loading small body data...this may take a while'
print DATA_PATH
reader = csv.DictReader(open(DATA_PATH), delimiter=',', quotechar='"')
designation_regex = re.compile('.*\(([^\)]*)\)')
n = 0
items = []
for row in reader:
row['spec'] = row['spec_B']
row['full_name'] = row['full_name'].strip()
if row['spec'] == '':
newspec = THOLEN_MAPPINGS.get(row['spec_T'], None)
if newspec:
row['spec'] = newspec.strip()
# TODO(@ian) move specific adjustments out into its own file.
elif row['pdes'] == '2012 DA14':
print 'Adjust 2012 DA14'
row['spec'] = 'L'
elif row['full_name'] == '6178 (1986 DA)':
print 'Adjust 1986 DA'
row['spec'] = 'M'
elif row['full_name'] == '436724 (2011 UW158)':
print 'Adjust 2011 UW158'
row['spec'] = 'Xc'
elif row['class'] in COMET_CLASSES:
row['spec'] = 'comet'
else:
if partial:
# don't build the full db of 600k objects
continue
row['spec'] = '?'
if row['spec'] == 'C type':
row['spec'] = 'C'
# match it with its delta-v
# TODO(@ian) don't overwrite prov_des, create some unified name field instead.
m = designation_regex.match(row['full_name'])
if 'pdes' in row and 'prov_des' not in row:
row['prov_des'] = row['pdes'] # backwards compatibility for NASA change
if m:
# Set delta-v first
dv = deltav_map.get(row['prov_des'], None)
if dv:
row['dv'] = dv
row['prov_des'] = m.groups()[0]
else:
row['prov_des'] = ''
# Clean up inputs
for key,val in row.items():
try:
fv = float(val)
except ValueError, TypeError:
row[key] = val.strip()
else:
row[key] = fv
# row['spec_T'] = row['spec_T'].replace(':', '')
# row['spec_B'] = row['spec_B'].replace(':', '')
# row['spec'] = row['spec'].replace(':', '')
# match mass
if row['full_name'] in massd:
row['GM'] = massd[row['full_name']] * G
# compute score
row['price'], row['saved'] = scoring.price(row)
# assume the cost of mining a distant asteroid per kg won't be much better
# than cost to launch from earth
# ie., 99.999% of revenue is spent on operations
row['saved'] = row['saved'] * 0.00001
row['closeness'] = scoring.closeness_weight(row)
row['profit'] = scoring.profit(row)
# TODO move this final scoring pass into scoring.py
# cap price influence on score at 10 B
score = min(row['price'], 1e10) / 5e11
if score > 0.0001:
score = score + row['closeness'] / 20
row['score'] = score
items.append(row)
n += 1
if len(items) > 20000:
# insert into mongo
print 'Row #', n, '... inserting/updating %d items into asteroids (SBDB) collection' % (len(items))
coll.insert(items, continue_on_error=True)
items = []
def _run(partial=False):
# Constants and settings
# TODO this method should be factored out
# Fill database
conn = Connection('localhost', 27017)
db = conn.asterank
coll = db.asteroids
#coll.drop()
coll.ensure_index('full_name', unique=True, background=True)
coll.ensure_index('score', background=True)
coll.ensure_index('profit', background=True)
coll.ensure_index('prov_des', background=True) # necessary for upcoming pass lookups
coll.ensure_index('closeness', background=True)
coll.ensure_index('price', background=True)
# load mass data
print 'Loading mass data...'
f = open(MASS_PATH, 'r')
lines = f.readlines()
f.close()
massd = {}
for line in lines:
parts = line.split(' ')
massidx = len(parts) - 2
mass = float(parts[massidx])
name = ' '.join(parts[:massidx]).strip()
if name not in massd:
massd[name] = []
massd[name].append(mass)
for name, masses in massd.iteritems():
avg = sum(masses) / len(masses)
massd[name] = avg
del massd['']
# load delta v data
print 'Loading delta-v data...'
reader = csv.DictReader(open(DV_PATH, 'r'))
deltav_map = {}
for row in reader:
deltav_map[row['pdes']] = row['dv']
print 'Loading small body data...this may take a while'
print DATA_PATH
reader = csv.DictReader(open(DATA_PATH), delimiter=',', quotechar='"')
designation_regex = re.compile('.*\(([^\)]*)\)')
n = 0
for row in reader:
row['spec'] = row['spec_B']
if row['spec'] == '':
newspec = THOLEN_MAPPINGS.get(row['spec_T'], None)
if newspec:
row['spec'] = newspec.strip()
elif row['pdes'] == '2012 DA14':
print 'Adjust 2012 DA14'
row['spec'] = 'L'
elif row['class'] in COMET_CLASSES:
row['spec'] = 'comet'
else:
if partial:
continue # don't build the full db of 600k objects
row['spec'] = '?'
if row['spec'] == 'C type':
row['spec'] = 'C'
# match it with its delta-v
# TODO(@ian) don't overwrite prov_des, create some unified name field instead.
m = designation_regex.match(row['full_name'])
if 'pdes' in row and 'prov_des' not in row:
row['prov_des'] = row['pdes'] # backwards compatibility for NASA change
if m:
# Set delta-v first
dv = deltav_map.get(row['prov_des'], None)
if dv:
row['dv'] = dv
row['prov_des'] = m.groups()[0]
else:
row['prov_des'] = ''
# Clean up inputs
for key,val in row.items():
try:
fv = float(val)
except ValueError, TypeError:
row[key] = val.strip()
else:
row[key] = fv
row['spec_T'] = row['spec_T'].replace(':', '')
row['spec_B'] = row['spec_B'].replace(':', '')
row['spec'] = row['spec'].replace(':', '')
# match mass
if row['full_name'] in massd:
row['GM'] = massd[row['full_name']] * G
# compute score
row['price'], row['saved'] = scoring.price(row)
# assume the cost of mining a distant asteroid per kg won't be much better
# than cost to launch from earth
# ie., 99.999% of revenue is spent on operations
row['saved'] = row['saved'] * 0.00001
row['closeness'] = scoring.closeness_weight(row)
row['profit'] = scoring.profit(row)
# TODO move this final scoring pass into scoring.py
score = min(row['price'], 1e14) / 5e13
if score < 0.0001:
# It's worthless, so closeness doesn't matter
row['score'] = score
else:
score = score * row['closeness']
row['score'] = score
coll.update({'full_name': row['full_name']}, {'$set': row}, True) # upsert
n += 1
if n % 3000 == 0:
print n, '...'
import api
import db
import requests
import scoring
# Get a driver's price
print("Price for Hamilton:", scoring.price('hamilton'))
# Get a driver's points so far this season
print("Hamilton's points so far:", scoring.points('hamilton'))
def seed_db():
# Fetch all the Ergast data
for i in range(1950, 2016):
resp = requests.get("http://ergast.com/api/f1/%s.json" % (i,)).json()
rounds = int(resp["MRData"]["total"])
for j in range(1, rounds+1):
print(j, i)
db.store_race_result(i, j, api.race_result(i, j))
def _run(partial=False):
# Constants and settings
# TODO this method should be factored out
# Fill database
conn = Connection('localhost', 27017)
db = conn.asterank
coll = db.asteroids
#coll.drop()
coll.ensure_index('full_name', unique=True, background=True)
coll.ensure_index('score', background=True)
coll.ensure_index('profit', background=True)
coll.ensure_index('prov_des',
background=True) # necessary for upcoming pass lookups
coll.ensure_index('closeness', background=True)
coll.ensure_index('price', background=True)
# load mass data
print 'Loading mass data...'
f = open(MASS_PATH, 'r')
lines = f.readlines()
f.close()
massd = {}
for line in lines:
parts = line.split(' ')
massidx = len(parts) - 2
mass = float(parts[massidx])
name = ' '.join(parts[:massidx]).strip()
if name not in massd:
massd[name] = []
massd[name].append(mass)
for name, masses in massd.iteritems():
avg = sum(masses) / len(masses)
massd[name] = avg
del massd['']
# load delta v data
print 'Loading delta-v data...'
reader = csv.DictReader(open(DV_PATH, 'r'))
deltav_map = {}
for row in reader:
deltav_map[row['pdes']] = row['dv']
print 'Loading small body data...this may take a while'
print DATA_PATH
reader = csv.DictReader(open(DATA_PATH), delimiter=',', quotechar='"')
designation_regex = re.compile('.*\(([^\)]*)\)')
n = 0
for row in reader:
row['spec'] = row['spec_B']
if row['spec'] == '':
newspec = THOLEN_MAPPINGS.get(row['spec_T'], None)
if newspec:
row['spec'] = newspec.strip()
elif row['pdes'] == '2012 DA14':
print 'Adjust 2012 DA14'
row['spec'] = 'L'
elif row['class'] in COMET_CLASSES:
row['spec'] = 'comet'
else:
if partial:
continue # don't build the full db of 600k objects
row['spec'] = '?'
if row['spec'] == 'C type':
row['spec'] = 'C'
# match it with its delta-v
# TODO(@ian) don't overwrite prov_des, create some unified name field instead.
m = designation_regex.match(row['full_name'])
if 'pdes' in row and 'prov_des' not in row:
row['prov_des'] = row[
'pdes'] # backwards compatibility for NASA change
if m:
# Set delta-v first
dv = deltav_map.get(row['prov_des'], None)
if dv:
row['dv'] = dv
row['prov_des'] = m.groups()[0]
else:
row['prov_des'] = ''
# Clean up inputs
for key, val in row.items():
try:
fv = float(val)
except ValueError, TypeError:
row[key] = val.strip()
else:
row[key] = fv
row['spec_T'] = row['spec_T'].replace(':', '')
row['spec_B'] = row['spec_B'].replace(':', '')
row['spec'] = row['spec'].replace(':', '')
# match mass
if row['full_name'] in massd:
row['GM'] = massd[row['full_name']] * G
# compute score
row['price'], row['saved'] = scoring.price(row)
# assume the cost of mining a distant asteroid per kg won't be much better
# than cost to launch from earth
# ie., 99.999% of revenue is spent on operations
row['saved'] = row['saved'] * 0.00001
row['closeness'] = scoring.closeness_weight(row)
row['profit'] = scoring.profit(row)
# TODO this belongs in scoring
score = min(row['price'], 1e14) / 5e12
if score < 0.0001:
# It's worthless, so closeness doesn't matter
row['score'] = score
else:
score = score * row['closeness']**3
row['score'] = score
coll.update({'full_name': row['full_name']}, {'$set': row},
True) # upsert
n += 1
if n % 3000 == 0:
print n, '...'