Minor testing changes

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# Interval Data

<hr>

``interval_data()`` takes a compressed detections DataFrame, a distance matrix, and a detection radius DataFrame and

creates an interval data DataFrame.

Intervals are lengths of time in which a station detected an animal. Many consecutive detections of an animal are replaced by one interval.

<span style="color:red">Warning:</span>

    Input files must include ``datecollected``, ``catalognumber``, and ``unqdetecid`` as columns.

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``` python

from resonate.filters import get_distance_matrix

from resonate.compress import compress_detections

from resonate.interval_data_tool import interval_data

import pandas as pd

import geopy

input_file = pd.read_csv("/path/to/detections.csv")

compressed = compress_detections(input_file)

matrix = get_distance_matrix(input_file)

```

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Set the station radius for each station name.

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``` python

detection_radius = 400

station_det_radius = pd.DataFrame([(x, geopy.distance.Distance(detection_radius/1000.0))

                                   for x in matrix.columns.tolist()], columns=['station','radius'])

station_det_radius.set_index('station', inplace=True)

station_det_radius

```

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You can modify individual stations if needed by using ``DatraFrame.set_value()`` from Pandas.

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``` python

station_name = 'HFX001'

station_detection_radius = 500

station_det_radius.at[station_name, 'radius'] = geopy.distance.Distance( station_detection_radius/1000.0 )

```

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Create the interval data by passing the compressed detections, the matrix, and the station radii.

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``` python

interval = interval_data(compressed_df=compressed, dist_matrix_df=matrix, station_radius_df=station_det_radius)

interval

```

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You can use the Pandas `DataFrame.to_csv()` function to output the file to a desired location.

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``` python

interval.to_csv('/path/to/output.csv', index=False)

```

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``` python

```