Bump Numpy version and fix complex type

.github/workflows/tests.yaml

@@ -9,19 +9,21 @@ on:
 
 jobs:
   miniconda:
-    name: Miniconda ${{ matrix.os }}
+    name: Miniconda ${{ matrix.os }} - Python ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
     strategy:
         matrix:
             os: ["ubuntu-latest", "windows-latest"]
+            python-version: [ "3.8", "3.12" ]
     steps:
       - uses: actions/checkout@v2
-      - uses: conda-incubator/setup-miniconda@v2
+      - name: Set up Miniconda (Python ${{ matrix.python-version }})
+        uses: conda-incubator/setup-miniconda@v2
         with:
           activate-environment: test
           channels: conda-forge,defaults
           environment-file: environment.yml
-          python-version: 3.8
+          python-version: ${{ matrix.python-version }}
           auto-activate-base: false
       - shell: bash -l {0}
         run: |
@@ -44,6 +46,7 @@ jobs:
             conda install pytest
             py.test . --cov-report=xml --cov=traja -vvv
       - name: Upload coverage to Codecov
+        if: ${{ matrix.python-version }} == '3.8'
         uses: codecov/codecov-action@v1
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

.gitignore

@@ -127,3 +127,5 @@ docs/source/reference
 
 # Model parameter files
 *.pt
+.python-version
+datasets/

environment.yml

@@ -21,7 +21,6 @@ dependencies:
   - networkx
   - seaborn
   - pytorch
-  - pytest==6.2.2
+  - pytest>=8.0.0
   - numba>=0.50.0
-  - pyDOE2>=1.3.0
   - statsmodels

requirements.txt

@@ -1,5 +1,5 @@
 pandas>=1.2.0
-numpy==1.18.5
+numpy>=1.22.0
 matplotlib
 shapely
 scipy>=1.4.1

requirements/docs.txt

@@ -1,5 +1,5 @@
 pandas>=1.2.0
-numpy==1.18.5
+numpy>=1.22.0
 matplotlib
 shapely
 scipy

requirements/extra.txt

@@ -4,13 +4,7 @@ pytest
 h5py
 ipython
 pre-commit
-shapely
-scipy>=1.4.1
-scikit-learn
-fastdtw
-networkx
-seaborn
-torch
 h5py
-numba>=0.50.0
-pyDOE2>=1.3.0
+numba>=0.50.1
+black
+isort

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.0
+current_version = 23.0.0
 
 [yapf]
 column_limit = 120

traja/__init__.py

@@ -1,15 +1,15 @@
 import logging
 
-from traja import dataset
-from traja import models
+from traja import dataset, models
+
 from .accessor import TrajaAccessor
-from .frame import TrajaDataFrame, TrajaCollection
-from .parsers import read_file, from_df
+from .frame import TrajaCollection, TrajaDataFrame
+from .parsers import from_df, read_file
 from .plotting import *
 from .trajectory import *
 
 __author__ = "justinshenk"
-__version__ = "22.0.0"
+__version__ = "23.0.0"
 
 logging.basicConfig(level=logging.INFO)
 

traja/contrib/rdp.py

@@ -19,8 +19,9 @@
 The above copyright notice and this permission notice shall be included
 in all copies or substantial portions of the Software.
 """
+
 from functools import partial
-from typing import Union, Callable
+from typing import Callable, Union
 
 import numpy as np
 

traja/core.py

@@ -0,0 +1,8 @@
+import pandas as pd
+
+
+# Check whether pandas series is datetime or timedelta
+def is_datetime_or_timedelta_dtype(series: pd.Series) -> bool:
+    return pd.api.types.is_datetime64_dtype(
+        series
+    ) or pd.api.types.is_timedelta64_dtype(series)

traja/dataset/__init__.py

@@ -1,3 +1,3 @@
 from . import example
-from .dataset import TimeSeriesDataset, MultiModalDataLoader
+from .dataset import MultiModalDataLoader, TimeSeriesDataset
 from .pedestrian import load_ped_data, ped_datasets

traja/dataset/dataset.py

@@ -11,6 +11,7 @@
 1. Class distribution in the dataset
 
 """
+
 import logging
 import math
 from collections import defaultdict

traja/dataset/example.py

@@ -6,5 +6,5 @@
 def jaguar(cache_url=default_cache_url):
     # Sample data
     data_url = "https://raw.githubusercontent.com/traja-team/traja-research/dataset_und_notebooks/dataset_analysis/jaguar5.csv"
-    df = pd.read_csv(data_url, error_bad_lines=False)
+    df = pd.read_csv(data_url, on_bad_lines="skip")
     return df

traja/dataset/pedestrian.py

@@ -1,11 +1,12 @@
-import subprocess
 import glob
 import os
+import subprocess
 from typing import List
+
 import pandas as pd
-from traja.dataset import dataset
-import traja
 
+import traja
+from traja.dataset import dataset
 
 """Convenience module for downloading pedestrian-related datasets."""
 

traja/frame.py

@@ -1,6 +1,6 @@
 import logging
-from typing import Optional, Union, Tuple
 import warnings
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -54,13 +54,13 @@ def __init__(self, *args, **kwargs):
             args[0]._copy_attrs(self)
         for name, value in traja_kwargs.items():
             self.__dict__[name] = value
-        
-        # Initialize 
+
+        # Initialize
         self._convex_hull = None
 
         # Initialize metadata like 'fps','spatial_units', etc.
         self._init_metadata()
-    
+
     @property
     def _constructor(self):
         return TrajaDataFrame
@@ -171,15 +171,15 @@ def center(self):
         x = self.x
         y = self.y
         return float(x.mean()), float(y.mean())
-    
+
     @property
     def convex_hull(self):
         """Property of TrajaDataFrame class representing
         bounds for convex area enclosing trajectory points.
 
         """
         # Calculate if it doesn't exist
-        if self._convex_hull is None:            
+        if self._convex_hull is None:
             xy_arr = self.traja.xy
             point_arr = traja.trajectory.calc_convex_hull(xy_arr)
             self._convex_hull = point_arr

traja/models/__init__.py

@@ -2,6 +2,7 @@
 from traja.models.generative_models.vaegan import MultiModelVAEGAN
 from traja.models.predictive_models.ae import MultiModelAE
 from traja.models.predictive_models.lstm import LSTM
+
 from .inference import *
 from .train import HybridTrainer
-from .utils import TimeDistributed, read_hyperparameters, save, load
+from .utils import TimeDistributed, load, read_hyperparameters, save

traja/models/inference.py

@@ -134,6 +134,7 @@ def generate(self, num_steps, classify=True, scaler=None, plot_data=True):
         elif self.model_type == "vaegan" or "custom":
             return NotImplementedError
 
+
 class Predictor:
     def __init__(
         self,

traja/models/losses.py

@@ -2,6 +2,7 @@
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+
 class Criterion:
     """Implements the loss functions of Autoencoders, Variational Autoencoders and LSTM models
     Huber loss is set as default for reconstruction loss, alternative is to use rmse,
@@ -30,7 +31,7 @@ def forecasting_criterion(
         """
 
         if mu is not None and logvar is not None:
-            kld = -0.5 * torch.sum(1 + logvar - mu ** 2 - logvar.exp())
+            kld = -0.5 * torch.sum(1 + logvar - mu**2 - logvar.exp())
         else:
             kld = 0