Adding a simple example

README.md

 # datascout
 
 Simple package to handle data saving and reading with minimum required libraries.
-Mainly used as dependance of pyjapcscout, but it can be used for other purposes as well.
+Mainly used as dependance of `pyjapcscout`, but it can be used for other purposes as well.
 
 ## Purpose of this project
 
-The idea is to provide a few sweet functions go from a nested dict of numpy arrays to parquet (and to pickle, and json) and come back.
+The idea is to provide a few sweet functions to go from a nested `dict` of `numpy` arrays to `parquet` (and to `pickle`, and `json`) and come back **preserving** the data types. The aspect related to data types preservation is important for the roud-trip of meachine parameter reading, saving and settings.
 
 ## Getting started
 
 
+
 ## How to develop it:
 I set up this package as:
 

examples/000_example.py

+import datascout as ds
+import math
+from datetime import timezone
+import datetime
+import numpy as np
+
+def convert_2d_array(my_array):
+    if len(np.shape(my_array))==1: return my_array
+    list=[]
+    for jj in range(np.shape(my_array)[0]):
+        list.append(my_array[jj,:])
+    return list    
+
+
+def old2new(my_dict, verbose=False):
+    for device_name, device_value in my_dict.items():
+        if verbose: print(device_name)
+        device_value['header']['acqStamp'] = \
+            np.int64(device_value['header']['acqStamp'].replace(tzinfo=timezone.utc).timestamp()*1e9)
+        device_value['header']['cycleStamp'] = \
+            np.int64(device_value['header']['cycleStamp'].replace(tzinfo=timezone.utc).timestamp()*1e6)*1e3
+        device_value['header']['setStamp'] = \
+            np.int64(device_value['header']['setStamp'].replace(tzinfo=timezone.utc).timestamp()*1e9)
+        if (not type(device_value['value'])==dict) and (not type(device_value['value'])==list):
+            if math.isnan(device_value['value']):
+                device_value['value']= 'no data'
+            if type(device_value['value'])==np.ndarray:
+                device_value['value']= convert_2d_array(device_value['value'])
+        if type(device_value['value'])==dict:
+            for value_name, value_value in device_value['value'].items():
+                if verbose: print(device_value['value'][value_name])
+                if type(device_value['value'][value_name])==np.ndarray:
+                    device_value['value'][value_name]=convert_2d_array(value_value)
+    return my_dict
+
+#my_parquet = '/eos/project/l/liu/datascout/parquet_file/2021.04.30.01.54.47.454151.parquet'
+my_dict_file = '/eos/project/l/liu/datascout/pickle_files/2021.04.30.01.54.47.454151.pkl'
+
+#ds.parquet_to_dict(my_parquet)
+my_dict = ds.pickle_to_dict(my_dict_file)
+
+a=old2new(my_dict[0])
+
+# ONLINE
+ds.dict_to_awkward(a)
+ds.dict_to_pyarrow(a)
+ds.dict_to_pandas(a)
+
+# OFFLINE
+ds.dict_to_parquet(a, 'test') 
+ds.dict_to_json(a, 'test')
+ds.dict_to_pickle(a, 'test')
+
+# 
+print('parquet_to_pyarrow')
+ds.parquet_to_pyarrow('test.parquet')
+
+print('parquet_to_pandas')
+ds.parquet_to_pandas('test.parquet')
+
+print('parquet_to_awkward')
+ds.parquet_to_awkward('test.parquet')
+
+
+
+
+