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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Ready to use test dataset
sktracker provides a data module contains sample dataset in order to test or usefulle during development.
Step1:  Generate brownian or directed trajectories
Step2:  Get sample microscopy stack
Step3:  Get sample H5 file stored by sktacker.io.ObjectsIO
Step4:  See also 
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Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
from sktracker import data
from sktracker.trajectories import Trajectories
from sktracker.io import TiffFile
Explanation: Ready to use test dataset
sktracker provides a data module contains sample dataset in order to test or usefulle during development.
End of explanation
trajs = Trajectories(data.brownian_trajectories_generator())
trajs.show(groupby_args={'by': 'true_label'})
trajs = Trajectories(data.directed_trajectories_generator())
trajs.show(groupby_args={'by': 'true_label'})
Explanation: Generate brownian or directed trajectories
End of explanation
tf = TiffFile(data.CZT_peaks())
arr = tf.asarray()
print("Shape is :", arr.shape)
a = arr[0, 0, 0]
plt.imshow(a, interpolation='none', cmap='gray')
Explanation: Get sample microscopy stack
End of explanation
df = pd.HDFStore(data.sample_h5())
print(df.keys())
print(df['metadata'])
Explanation: Get sample H5 file stored by sktacker.io.ObjectsIO
End of explanation
# Run this cell first.
%load_ext autoreload
%autoreload 2
Explanation: See also :
- `sample_ome()`
- `tubhiswt_4D()`
- `stack_list_dir()`
- `TZ_nucleus`
- `TC_BF_cells()`
- `metadata_json()`
- `sample_h5_temp()`
- `brownian_trajs_df()`
- `directed_motion_trajs_df()`
- `trackmate_xml_temp()`
- `trackmate_xml()`
- `with_gaps_d()`
available in the API references.
End of explanation
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