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@@ -1,9 +1,11 @@
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#!/usr/bin/env python3
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''' plan_gen main functions '''
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-import time
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-import numpy as np
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import lxml.etree as etree
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+import numpy as np
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+import pandas
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+import time
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+import utm
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# constants
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# ------------------
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@@ -30,7 +32,7 @@ def parse_params(param_str):
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if param_str:
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for key_value_str in param_str.split(','):
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key, value = key_value_str.split('=')
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- if key in ['hc', 'wc']:
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+ if key in ['hc', 'hw', 'wc', 'ww']:
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coords = value.split('|')
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dict_params[key] = [np.fromstring(str(x), dtype=int, sep=':') for x in coords]
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elif key in ['hr', 'wr']:
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@@ -39,6 +41,34 @@ def parse_params(param_str):
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dict_params[key] = parse_value(value)
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return dict_params
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+def parse_latlon(csv):
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+ ''' parse lat and lon from a csv '''
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+ df = pandas.read_csv(csv, sep=';')
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+ lat = df.lat.tolist()
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+ lon = df.lon.tolist()
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+ return lat, lon
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+
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+def parse_weights(csv):
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+ ''' parse weights from a csv '''
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+ df = pandas.read_csv(csv, sep=';')
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+ weights = df.nbpeople.tolist()
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+ return weights
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+
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+def latlon_to_utm(lat, lon):
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+ ''' convert lat lon to utm coordinates '''
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+ utms = [utm.from_latlon(a, b) for a, b in zip(lat, lon)]
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+ xutm = [utm_coords[0] for utm_coords in utms]
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+ yutm = [utm_coords[1] for utm_coords in utms]
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+ return xutm, yutm
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+
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+def xy_to_ij(x, y, dx, dy, nb_clusters):
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+ i, j = (int(x/dx), int(y/dy))
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+ if i >= nb_clusters:
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+ i -= 1
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+ if j >= nb_clusters:
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+ j -= 1
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+ return i, j
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+
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def get_seconds(time_str):
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''' returns seconds from a time string '''
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h, m, s = time_str.split(':')
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@@ -60,29 +90,34 @@ def make_gaussian(size, center=None, radius=10):
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def make_clusters(nb_clusters, nodes):
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''' make a grid of (nb_clusters*nb_clusters) from a nodes list '''
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- xmin, xmax, ymin, ymax = get_extrem_nodes(nodes)
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- dx = (xmax - xmin) / nb_clusters
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- dy = (ymax - ymin) / nb_clusters
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+ xmin, xmax, ymin, ymax, dx, dy = get_min_max_steps(nodes, nb_clusters)
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clusters = np.empty((nb_clusters, nb_clusters), dtype=object)
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for node in nodes:
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x, y = (float(node.get('x')) - xmin, float(node.get('y')) - ymin)
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- i, j = (int(x/dx), int(y/dy))
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- if i >= nb_clusters:
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- i -= 1
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- if j >= nb_clusters:
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- j -= 1
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+ i, j = xy_to_ij(x, y, dx, dy, nb_clusters)
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if clusters[i][j] is None:
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clusters[i][j] = []
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clusters[i][j] += [node]
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return clusters
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-def make_densities(nb_clusters, centers=None, radius=None):
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+def make_centers(csv, nb_clusters, nodes):
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+ ''' make centers from a csv file '''
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+ lat, lon = parse_latlon(csv)
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+ xutm, yutm = latlon_to_utm(lat, lon)
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+ xmin, xmax, ymin, ymax, dx, dy = get_min_max_steps(nodes, nb_clusters)
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+ centers = []
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+ for x, y in zip(xutm, yutm):
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+ i, j = xy_to_ij(x - xmin, y - ymin, dx, dy, nb_clusters)
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+ centers.append([i, j])
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+ return centers
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+
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+def make_densities(nb_clusters, centers=None, radius=None, weights=None):
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''' make a list of gaussian probability densities '''
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if centers is None:
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return make_gaussian(nb_clusters, radius=nb_clusters/2)
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densities = np.zeros((nb_clusters, nb_clusters))
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- for n, c in enumerate(centers):
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- densities += make_gaussian(nb_clusters, center=c, radius=radius[n])
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+ for n, (c, w) in enumerate(zip(centers, weights)):
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+ densities += w * make_gaussian(nb_clusters, center=c, radius=radius[n])
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return densities
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def clean_densities(densities, clusters):
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@@ -160,4 +195,11 @@ def get_extrem_nodes(nodes):
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''' returns extremum coordinates of a nodeslist '''
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x = [float(node.get('x')) for node in nodes]
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y = [float(node.get('y')) for node in nodes]
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- return min(x), max(x), min(y), max(y)
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+ return min(x), max(x), min(y), max(y)
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+
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+def get_min_max_steps(nodes, nb_clusters):
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+ ''' returns min max steps '''
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+ xmin, xmax, ymin, ymax = get_extrem_nodes(nodes)
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+ dx = (xmax - xmin) / nb_clusters
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+ dy = (ymax - ymin) / nb_clusters
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+ return xmin, xmax, ymin, ymax, dx, dy
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