refactored imports

Author: xoolive

docs/paper/sectflow.rst

@@ -15,7 +15,7 @@ Data preparation
 
     import numpy as np
     from traffic.core import Traffic
-    
+
     t = (
         # trajectories during the opening hours of the sector
         Traffic.from_file("data/LFBBPT_flights_2017.pkl")
@@ -42,14 +42,14 @@ the following `github repository <https://github.com/lbasora/sectflow>`_
 
 .. code:: python
 
-    from traffic.core.projection import Lambert93
-    
+    from cartes.crs import Lambert93
+
     # pip install git+https://github.com/lbasora/sectflow
     from sectflow.clustering import TrajClust
-    
+
     features = ["x", "y", "latitude", "longitude", "altitude", "log_altitude"]
     clustering = TrajClust(features)
-    
+
     # use the clustering API from traffic
     t_cluster = t.clustering(
         nb_samples=2, features=features, projection=Lambert93(), clustering=clustering
@@ -59,7 +59,7 @@ the following `github repository <https://github.com/lbasora/sectflow>`_
 
     # Color distribution by cluster
     from itertools import cycle, islice
-    
+
     n_clusters = 1 + t_cluster.data.cluster.max()
     color_cycle = cycle(
         "#fbbb35 #004cb9 #4cc700 #a50016 #510420 #01bcf5 #999999 #e60085 #ffa9c5".split()
@@ -69,29 +69,29 @@ the following `github repository <https://github.com/lbasora/sectflow>`_
 
     import matplotlib.pyplot as plt
     from random import sample
-    
+
     from traffic.data import airways, aixm_airspaces
     from traffic.drawing.markers import rotate_marker, atc_tower, aircraft
-    
+
     with plt.style.context("traffic"):
         fig, ax = plt.subplots(1, figsize=(15, 10), subplot_kw=dict(projection=Lambert93()))
-    
+
         aixm_airspaces["LFBBPT"].plot(
             ax, linewidth=3, linestyle="dashed", color="steelblue"
         )
         for name in "UN460 UN869 UM728".split():
             airways[name].plot(ax, linestyle="dashed", color="#aaaaaa")
-    
+
         # do not plot outliers
         for cluster in range(n_clusters):
-    
+
             current_cluster = t_cluster.query(f"cluster == {cluster}")
-    
+
             # plot the centroid of each cluster
             centroid = current_cluster.centroid(50, projection=Lambert93())
             centroid.plot(ax, color=colors[cluster], alpha=0.9, linewidth=3)
             centroid_mark = centroid.at_ratio(0.45)
-    
+
             # little aircraft
             centroid_mark.plot(
                 ax,
@@ -100,22 +100,22 @@ the following `github repository <https://github.com/lbasora/sectflow>`_
                 s=500,
                 text_kw=dict(s=""),  # no text associated
             )
-            
+
             # plot some sample flights from each cluster
             sample_size = min(20, len(current_cluster))
             for flight_id in sample(current_cluster.flight_ids, sample_size):
                 current_cluster[flight_id].plot(
                     ax, color=colors[cluster], alpha=0.1, linewidth=2
                 )
-                
+
         # TODO improve this: extent with buffer
         ax.set_extent(
             tuple(
                 x - 0.5 + (0 if i % 2 == 0 else 1)
                 for i, x in enumerate(aixm_airspaces["LFBBPT"].extent)
             )
         )
-        
+
         # Equivalent of Fig. 5
 
 
@@ -134,13 +134,13 @@ The anomaly detection method is based on a stacked autoencoder
     import torch
     from torch import nn, optim, from_numpy, rand
     from torch.autograd import Variable
-    
+
     from sklearn.preprocessing import minmax_scale
     from tqdm.autonotebook import tqdm
-    
-    
+
+
     # Stacked autoencoder
-    
+
     class Autoencoder(nn.Module):
         def __init__(self):
             super().__init__()
@@ -150,54 +150,54 @@ The anomaly detection method is based on a stacked autoencoder
             self.decoder = nn.Sequential(
                 nn.Linear(12, 24), nn.ReLU(), nn.Linear(24, 50), nn.Sigmoid()
             )
-    
+
         def forward(self, x, **kwargs):
             x = x + (rand(50).cuda() - 0.5) * 1e-3  # add some noise
             x = self.encoder(x)
             x = self.decoder(x)
             return x
-    
+
     # Regularisation term introduced in IV.B.2
-    
+
     def regularisation_term(X, n):
         samples = torch.linspace(0, X.max(), 100, requires_grad=True)
         mean = samples.mean()
         return torch.relu(
             (torch.histc(X) / n * 100 - 1 / mean * torch.exp(-samples / mean))
         ).mean()
-    
+
     # ML part
-    
+
     def anomalies(t: Traffic, cluster_id: int, lambda_r: float, nb_it: int = 10000):
-    
+
         t_id = t.query(f"cluster=={cluster_id}")
-    
+
         flight_ids = list(f.flight_id for f in t_id)
         n = len(flight_ids)
         X = minmax_scale(np.vstack(f.data.track[:50] for f in t_id))
-    
+
         model = Autoencoder().cuda()
         criterion = nn.MSELoss()
         optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)
-    
+
         for epoch in tqdm(range(nb_it), leave=False):
-    
+
             v = Variable(from_numpy(X.astype(np.float32))).cuda()
-    
+
             output = model(v)
             distance = nn.MSELoss(reduction="none")(output, v).sum(1).sqrt()
-    
+
             loss = criterion(output, v)
             # regularisation
             loss = (
                 lambda_r * regularisation_term(distance.cpu().detach(), n)
                 + criterion(output, v).cpu()
             )
-    
+
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-    
+
         output = model(v)
         return (
             (nn.MSELoss(reduction="none")(output, v).sum(1)).sqrt().cpu().detach().numpy()
@@ -215,16 +215,16 @@ parameter helps reducing this trend.
 .. code:: python
 
     from scipy.stats import expon
-    
+
     # Equivalent of Fig. 4
-    
+
     with plt.style.context("traffic"):
         fig, ax = plt.subplots(1, figsize=(10, 7))
         hst = ax.hist(output, bins=50, density=True)
         mean = output.mean()
         x = np.arange(0, output.max(), 1e-2)
         e = expon.pdf(x, 0, output.mean())
-    
+
         ax.plot(x, e, color="#e77074")
         ax.fill_between(x, e, zorder=-2, color="#e77074", alpha=0.5)
         ax.axvline(
@@ -234,6 +234,3 @@ parameter helps reducing this trend.
 
 .. image:: images/sectflow_distribution.png
    :align: center
-
-
-

docs/tutorial/generation.rst

@@ -38,7 +38,7 @@ from the north.
 
     import matplotlib.pyplot as plt
     from traffic.data.datasets import landing_zurich_2019
-    from traffic.core.projection import EuroPP
+    from cartes.crs import EuroPP
 
     t = (
         landing_zurich_2019

src/traffic/core/traffic.py

@@ -1492,7 +1492,7 @@ def clustering(
 
         Example usage:
 
-        >>> from traffic.core.projection import EuroPP
+        >>> from cartes.crs import EuroPP
         >>> from sklearn.cluster import DBSCAN
         >>> from sklearn.preprocessing import StandardScaler
         >>>