def get(self, page=None, walk_id=None):
log.info("Home.get %s" % page)
if not len(page):
return self.render("home.html")
if page == "walk":
if len(walk_id) and walk_id == "c":
walks = model.fetch_walks()
if len(walks):
walk_id = random.choice(walks)['id']
else:
walk_id = None
elif not type(walk_id) == int and not len(walk_id):
walk_id = None
return self.render("walk.html",
sequence=json.dumps(
model.fetch_sequence(walk_id)),
ref_id=walk_id)
if page == "walks":
return self.render("walks.html",
walks=model.fetch_walks(hidden=True))
if page == "choose":
return self.render("choose.html", walks=model.fetch_walks())
if page in [
"prepare", "route", "map", "thanks", "orientation",
"background"
]:
return self.render("%s.html" % page)
return self.not_found()
def get(self, page=None, walk_id=None):
log.info("Home.get %s" % page)
if not len(page):
return self.render("home.html")
if page == "walk":
return self.render("walk.html", sequence=json.dumps(model.fetch_sequence(walk_id)), ref_id=walk_id if len(walk_id) else 0)
if page == "choose":
return self.render("choose.html", walks=model.fetch_walks())
if page in ["prepare", "route", "map", "thanks"]:
return self.render("%s.html" % page)
return self.not_found()
def get(self, page=None, walk_id=None):
log.info("Home.get %s" % page)
if not len(page):
return self.render("home.html")
if page == "walk":
if len(walk_id) and walk_id == "c":
walks = model.fetch_walks()
if len(walks):
walk_id = random.choice(walks)['id']
else:
walk_id = None
elif not type(walk_id) == int and not len(walk_id):
walk_id = None
return self.render("walk.html", sequence=json.dumps(model.fetch_sequence(walk_id)), ref_id=walk_id)
if page == "walks":
return self.render("walks.html", walks=model.fetch_walks(hidden=True))
if page == "choose":
return self.render("choose.html", walks=model.fetch_walks())
if page in ["prepare", "route", "map", "thanks", "orientation", "background"]:
return self.render("%s.html" % page)
return self.not_found()
def get_data(hidden=False):
log.debug("HIDDEN %s" % hidden)
data = {}
try:
walks = model.fetch_walks(hidden=hidden)
notes = []
v = 0
ids = []
for walk in walks:
sequence = model.fetch_sequence(walk['id'])
if len(sequence) < config['min_steps']:
continue
for step in sequence: #[:config['max_steps']]:
notes.append((step[0], v, 0 if step[1] == 'left' else 1))
v += 1
ids.append(walk['id'])
# sort and normalize onsets
notes.sort(key=lambda x: x[0])
onsets = [note[0] for note in notes]
onsets = sp.normalize(onsets)
notes = [(onsets[i], note[1], note[2])
for (i, note) in enumerate(notes)]
log.info("NOTES %s" % len(notes))
data['notes'] = notes
data['walk_ids'] = ids
except Exception as e:
log.error(log.exc(e))
return {}
return json.dumps(data)
def get_data(hidden=False):
log.debug("HIDDEN %s" % hidden)
data = {}
try:
walks = model.fetch_walks(hidden=hidden)
notes = []
v = 0
ids = []
for walk in walks:
sequence = model.fetch_sequence(walk['id'])
if len(sequence) < config['min_steps']:
continue
for step in sequence:#[:config['max_steps']]:
notes.append((step[0], v, 0 if step[1] == 'left' else 1))
v += 1
ids.append(walk['id'])
# sort and normalize onsets
notes.sort(key=lambda x: x[0])
onsets = [note[0] for note in notes]
onsets = sp.normalize(onsets)
notes = [(onsets[i], note[1], note[2]) for (i, note) in enumerate(notes)]
log.info("NOTES %s" % len(notes))
data['notes'] = notes
data['walk_ids'] = ids
except Exception as e:
log.error(log.exc(e))
return {}
return json.dumps(data)
#!/usr/bin/env python3
import os, sys, time
sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
import model
import numpy as np
from housepy import config, log, drawing, geo, util
MIN_STEPS = 50
walk_data = model.fetch_walks(desc=False)
# walk_data = [walk for walk in walks if walk['id'] >= config['walk_id']]
walks = []
for walk in walk_data:
sequence = model.fetch_sequence(walk['id'])
if len(sequence) < MIN_STEPS:
continue
walks.append(walk)
LON = 0
LAT = 1
X = 2
Y = 3
all_points = []
for walk in walks:
points = model.fetch_geo(walk['id'])
points = np.array([(point['lng'], point['lat'], None, None) for point in points])
for point in points:
ctx.line([(float(i) / total_samples, y) for (i, y) in enumerate(ys)],
stroke=(0., 1., 0., 1.0)) #, thickness=3.0)
ctx.line([(float(i) / total_samples, z) for (i, z) in enumerate(zs)],
stroke=(0., 0., 1., 1.0)) #, thickness=3.0)
ctx.line([(float(i) / total_samples, d) for (i, d) in enumerate(ds)],
stroke=(0., 0., 0.),
thickness=3.0)
ctx.line([(float(i) / total_samples, f) for (i, f) in enumerate(fs)],
stroke=(1., 0., 1.),
thickness=5.0)
for peak in peaks:
x, y = peak
x = float(x) / total_samples
ctx.arc(x,
y, (10.0 / ctx.width), (10.0 / ctx.height),
fill=(1., 0., 0.),
thickness=0.0)
ctx.output("charts/steps_%s_%s.png" % (walk_id, int(time.time())))
if __name__ == "__main__":
walk_id = sys.argv[1]
force = True if len(sys.argv) > 2 and sys.argv[2][0] == "f" else False
if walk_id == "all":
walks = model.fetch_walks()
log.info("Total walks: %s" % len(walks))
for walk in walks:
process_walk(walk['id'], force)
else:
process_walk(walk_id, force)
import signal_processing as sp
from braid import *
from braid.voice.swerve import Swerve
MIN_STEPS = 50
steps = [ (C3, D3),
(G3, A3),
(D4, E4),
(A4, B4),
(E5, F5),
(G5, A5)
]
walks = model.fetch_walks(desc=False)
# collect notes
notes = []
v = 0
voices = []
for walk in walks:
sequence = model.fetch_sequence(walk['id'])
if len(sequence) < MIN_STEPS:
continue
voices.append(Swerve(v + 1))
for step in sequence:
notes.append((step[0], v, 0 if step[1] == 'left' else 1))
v += 1
if v == len(steps):
break
log.error("Can't draw")
return
# plot
ctx = drawing.Context(5000, 600, relative=True, flip=True)
ctx.line(200.0 / total_samples, 0.5, 350.0 / total_samples, 0.5, thickness=10.0)
ctx.line([(float(i) / total_samples, x) for (i, x) in enumerate(xs)], stroke=(1., 0., 0., 1.0))# thickness=3.0)
ctx.line([(float(i) / total_samples, y) for (i, y) in enumerate(ys)], stroke=(0., 1., 0., 1.0))#, thickness=3.0)
ctx.line([(float(i) / total_samples, z) for (i, z) in enumerate(zs)], stroke=(0., 0., 1., 1.0))#, thickness=3.0)
ctx.line([(float(i) / total_samples, d) for (i, d) in enumerate(ds)], stroke=(0., 0., 0.), thickness=3.0)
ctx.line([(float(i) / total_samples, f) for (i, f) in enumerate(fs)], stroke=(1., 0., 1.), thickness=5.0)
for peak in peaks:
x, y = peak
x = float(x) / total_samples
ctx.arc(x, y, (10.0 / ctx.width), (10.0 / ctx.height), fill=(1., 0., 0.), thickness=0.0)
ctx.output("charts/steps_%s_%s.png" % (walk_id, int(time.time())))
if __name__ == "__main__":
walk_id = sys.argv[1]
force = True if len(sys.argv) > 2 and sys.argv[2][0] == "f" else False
if walk_id == "all":
walks = model.fetch_walks()
log.info("Total walks: %s" % len(walks))
for walk in walks:
process_walk(walk['id'], force)
else:
process_walk(walk_id, force)
