def splitfile(file_name, file_size):
num_files = ceil(float(file_size)/float(MAX_FILE_SIZE))
print file_size, MAX_FILE_SIZE, num_files
fsp = FileSplitter()
fsp.parseOptions(["-i", file_name, "-n", num_files, "-s"])
fsp.do_work()
return num_files
class TestFileSplitter(TestCase):
def setUp(self):
self.valid_phrases = ("public", "admin")
self.django_po_file = "%s/django.po" % os.path.dirname(__file__)
self.file_splitter = FileSplitter(self.django_po_file, self.valid_phrases)
def test_that_we_can_get_the_first_line(self):
first_line = self.file_splitter.get_line(0)
self.assertEqual(first_line, "#: first public line\n")
def test_that_first_line_is_a_comment(self):
first_line = self.file_splitter.get_line(0)
self.assertTrue(self.file_splitter.is_comment(first_line))
def test_that_the_third_line_is_not_a_comment(self):
third_line = self.file_splitter.get_line(2)
self.assertFalse(self.file_splitter.is_comment(third_line))
def test_that_comment_is_invalid(self):
comment = "invalid comment"
self.assertFalse(self.file_splitter.is_comment_valid(comment))
def test_that_the_comment_is_valid(self):
comment = "valid comment which contains the word public"
self.assertTrue(self.file_splitter.is_comment_valid(comment))
def test_that_we_can_pick_lines_below_a_given_comment(self):
lines = self.file_splitter.get_lines_below_comment(0)
self.assertEqual(len(lines), 5)
def test_that_we_can_pick_lines_below_a_comment_that_contains_admin(self):
pass
def trajectories_knn(self):
start_pos, end_pos, paths = FileSplitter.points_old()
knn_start = pysal.weights.KNN(start_pos, k=NUM_GROUPS)
knn_end = pysal.weights.KNN(end_pos, k=NUM_GROUPS)
start_groups = []
end_groups = []
for n in knn_start.neighbors:
start_group = []
for i in knn_start.neighbors[n]:
start_group.append(start_pos[i])
start_groups.append(start_group)
for n in knn_end.neighbors:
end_group = []
for i in knn_end.neighbors[n]:
end_group.append(end_pos[i])
end_groups.append(end_group)
bboxs_start = []
bboxs_end = []
for g in start_groups:
c = Chain(g)
bboxs_start.append(c.bounding_box)
for g in end_groups:
c = Chain(g)
bboxs_end.append(c.bounding_box)
self.plot_on_bokeh(start_pos, end_pos, bboxs_start, bboxs_end)
def topic_model(url):
page_id_and_access_token = get_access_token_page_id(url)
page_id = page_id_and_access_token[0]
create_directory_for_topic_model(page_id)
FileSplitter(page_id).split()
lda_train = runR(page_id)
lda_train.convert_to_matrix()
lda_train.train_lda()
def neighbors_plot(self):
import gc
from numpy import histogram
import numpy as np
from sklearn.neighbors import radius_neighbors_graph
start_pos, end_pos, paths = FileSplitter.points()
del start_pos, end_pos
gc.collect()
neighbors = radius_neighbors_graph(paths, radius=0.005)
del paths
gc.collect()
neighbors = neighbors.toarray()
x = np.matrix(neighbors)
x = x.sum(axis=1)
counts = [d[0, 0] for d in x]
hist, edges = histogram(counts, bins=10, density=False)
self.plot_on_bokeh_hist('neighbors_hist.html', '# of Neighbors',
'# of Occurrance', 'Neighbors Within Radius',
hist, edges)
pass
def trajectories_hdbscan(self, min_cluster_size):
def centroids(paths):
# distances = euclidean_distances(paths)
# distances = cdist(paths, paths, 'euclidean')
clusterer = hdbscan.HDBSCAN(min_cluster_size=min_cluster_size)
cluster_labels = clusterer.fit_predict(paths)
num_clusters = len(
set(cluster_labels)) - (1 if -1 in cluster_labels else 0)
unique_labels = set(cluster_labels)
clusters = [[] for n in range(num_clusters)]
logging.info('Number of clusters: %s', num_clusters)
for i, v in enumerate(paths):
if cluster_labels[i] != -1:
clusters[cluster_labels[i]].append(v)
return clusters
start_pos, end_pos, paths = FileSplitter.points()
clusters = centroids(
paths) # Array of [start_lat, start_lon, end_lat, end_lon]
gc = self.createGeometry(clusters)
self.createJsonFile(gc)
import sys
from FileSplitter import FileSplitter
if len(sys.argv) < 2:
print "django.po file is missing"
exit()
django_po_file = sys.argv[1]
valid_phrases = ("admin", "noneadmin")
ACCEPTED_PAGES = ("ureport/home.html",
"ureport_layout.html",
"ureport/partials/viz/",
"ureport/partials/tag_cloud/",
"ureport/about.html",
"ureport/how_to_join.html",
"ureport/national_pulse.html",
"ureport/poll_summary.html")
splitter = FileSplitter(django_po_file, ACCEPTED_PAGES)
splitter.split()
def setUp(self):
self.valid_phrases = ("public", "admin")
self.django_po_file = "%s/django.po" % os.path.dirname(__file__)
self.file_splitter = FileSplitter(self.django_po_file, self.valid_phrases)
training = ConfigMap("Training")
eps = float(training['eps'])
grpsize = int(training['grpsize'])
MAX_LINES = int(training['size'])
sourcedir = training['sourcedir']
sourceregex = training['sourceregex']
secret = ConfigMap("Secrets")
matric = 'euclidean'
NUM_GROUPS = 70
API_KEY = secret['google_maps_api_key']
logging = Logging("trajectory")
system = ConfigMap("System")
cores = int(system['cores'])
filesplitter = FileSplitter()
class Trajectory:
def __init__(self):
logging.info("start")
def get_tree(self, pts):
tree = pysal.cg.kdtree.KDTree(pts,
leafsize=10,
distance_metric='Euclidean',
radius=6371.0)
return tree
def plot_on_bokeh(self, starts, ends, bboxes_start, bboxes_end):
from bokeh.io import output_file, show
import sys
from FileSplitter import FileSplitter
if len(sys.argv) < 2:
print "django.po file is missing"
exit()
django_po_file = sys.argv[1]
valid_phrases = ("admin", "noneadmin")
ACCEPTED_PAGES = ("ureport/home.html", "ureport_layout.html",
"ureport/partials/viz/", "ureport/partials/tag_cloud/",
"ureport/about.html", "ureport/how_to_join.html",
"ureport/national_pulse.html", "ureport/poll_summary.html")
splitter = FileSplitter(django_po_file, ACCEPTED_PAGES)
splitter.split()
