Helium Analytics

This is a set of python helper functions for generating helium statistics based on regions and individual hotspots. It utlizes the helium.io API.

Setup

Have not tested on windows

1. Install conda python virtual environment manager
2. Open up Terminal, iterm, or whatever you use then type the following
3. git clone https://github.com/grahambryan/helium-analytics.git
4. cd helium-analytics
5. conda env create -f environment.yml
6. conda activate env_hnt
7. ipython
8. import hellium_analytics as ha

Example

get_hotspot_stats

    """
    Get stats for a single hotspot based on bucket size

    Args:
        address (string): address of hotspot
        max_time (string): query up to this date of format mm/dd/yy (default: datetime.now().strftime("%D"))
        min_time (string): start query at this date for format mm/dd/yy (defualt: 12/01/16)
        bucket (string): Sample size ("hour", "day", "month", "year")

    Return:
        data (dict): dictionary of hotspot stats
    """

The current hnt price is pulled directly from the coin gecko API

In [2]: ha.get_hotspot_stats("11zSUKzNbtTaG4LebcNL71VPb4PxgQVwCtRgkepbE94341bPLzG", max_time="12/01/20", min_time="11/01/20")
Out[2]:
{'address': '11zSUKzNbtTaG4LebcNL71VPb4PxgQVwCtRgkepbE94341bPLzG',
 'bucket': 'day',
 'total_hnt': 770.5057999599999,
 'avg_hnt': 25.68352666533333,
 'avg_max_hnt': 45.07513248,
 'avg_min_hnt': 3.44686095,
 'avg_std_hnt': 9.018622928616315,
 'max_of_max': 45.07513248,
 'min_of_min': 3.44686095,
 'from': '2020-11-01T00:00:00Z',
 'to': '2020-12-01T00:00:00',
 'hnt_usd_price': 1.17,
 'total_usd': 901.4917859531998}

get_hotposts

    """
    Make requests to helium.io API to grab pages of hotspot data up to max_cursor_cnt

    arg:
        max_cursor_cnt (int): Max number of pages to request (default: 100)

    return:
        hotspots (pd.DataFrame): dataframe of all hotspots listed at helium.io
    """

In [3]: df_hotspots = ha.get_hotspots()

In [4]: df_hotspots.info
Out[4]:
<bound method DataFrame.info of                    timestamp_added                                              owner  nonce                    name         location  ...  geocode.long_country  geocode.long_city                                 geocode.city_id         lng        lat
0      2020-12-11T20:14:23.000000Z  13NTNgUtMJm8Qht2WJVx2yNFqNoBsUARKBgh4VLCyz1ame...      0       exotic-red-baboon             None  ...                  None               None                                            None         NaN        NaN
1      2020-12-11T19:52:03.000000Z  13NTNgUtMJm8Qht2WJVx2yNFqNoBsUARKBgh4VLCyz1ame...      1        fit-carob-spider  8c196b85cd431ff  ...           Netherlands           Den Haag      ZGVuIGhhYWd6dWlkLWhvbGxhbmRuZXRoZXJsYW5kcw    4.270548  52.091945
2      2020-12-11T19:39:25.000000Z  13NTNgUtMJm8Qht2WJVx2yNFqNoBsUARKBgh4VLCyz1ame...      1   nice-umber-tardigrade  8c196b8427227ff  ...           Netherlands           Den Haag      ZGVuIGhhYWd6dWlkLWhvbGxhbmRuZXRoZXJsYW5kcw    4.276760  52.101151
3      2020-12-11T19:30:11.000000Z  13NTNgUtMJm8Qht2WJVx2yNFqNoBsUARKBgh4VLCyz1ame...      1  breezy-parchment-gecko  8c196bb8a974bff  ...           Netherlands          Maassluis     bWFhc3NsdWlzenVpZC1ob2xsYW5kbmV0aGVybGFuZHM    4.232060  51.930748
4      2020-12-11T19:21:38.000000Z  13NTNgUtMJm8Qht2WJVx2yNFqNoBsUARKBgh4VLCyz1ame...      1       lone-velvet-gecko  8c195180c2c53ff  ...        United Kingdom      Bamber Bridge  YmFtYmVyIGJyaWRnZWVuZ2xhbmR1bml0ZWQga2luZ2RvbQ   -2.656217  53.736434
...                            ...                                                ...    ...                     ...              ...  ...                   ...                ...                                             ...         ...        ...
12995  2019-11-01T03:51:25.000000Z  132CWNPXPtY5eRnAE6G7NW2GrnkStgUmYrwvNrX7RmrX3M...      4   silly-velvet-mandrill  8c44a100839d5ff  ...         United States              Miami              bWlhbWlmbG9yaWRhdW5pdGVkIHN0YXRlcw  -80.125260  25.959672
12996  2019-11-01T03:46:12.000000Z  13bUUxLLDj9cPQ7cKUdqkioY1hSb9ecsWtT9aSya1E8vUs...      4      able-chili-mammoth  8c29a1d086819ff  ...         United States           Alhambra      YWxoYW1icmFjYWxpZm9ybmlhdW5pdGVkIHN0YXRlcw -118.141511  34.080566
12997  2019-11-01T03:40:42.000000Z  13bUUxLLDj9cPQ7cKUdqkioY1hSb9ecsWtT9aSya1E8vUs...      6      sticky-foggy-perch  8c29a0a0578c1ff  ...         United States            Anaheim        YW5haGVpbWNhbGlmb3JuaWF1bml0ZWQgc3RhdGVz -117.774319  33.868570
12998  2019-11-01T02:37:48.000000Z  13mNwP5ecb3gCHWocZFXX8CJDp6XA2Q5vc4LYMrMbb8kVn...      7  clever-juniper-caribou  8c29868c62cc7ff  ...         United States          Henderson          aGVuZGVyc29ubmV2YWRhdW5pdGVkIHN0YXRlcw -114.981678  36.035109
12999  2019-11-01T02:14:40.000000Z  14JzKJVerNbhwsGX3Rh3zywWh5ECeGKXMUsF7H8xAAkwok...      1      wild-black-pelican  8c2a107054261ff  ...         United States        Jersey City  amVyc2V5IGNpdHluZXcgamVyc2V5dW5pdGVkIHN0YXRlcw  -74.071538  40.724913

[13000 rows x 22 columns]>

get_hotspot_by_loc

    """
    get region based hotspots organized by state and city

    Args:
        df_hotspots (pd.DataFrame): DataFrame of hotspots from get_hotspots()
        region (string): the short_country geocode listed in df_hotspots to filet on

    Return:
        get_hotspots_by_loc (dict(pd.DataFrame)): dictionary of region and state dataframes
    """

get_hnt_stats_per_location

    """
    Grab HNT stats for a location

    Args:
        state_filter (string):
        city_filter (string):
        max_time (datetime):
        bucket (string): Day, hour, Week, Month
    """