subgrounds.subgraph.fieldpath module

subgrounds.subgraph.fieldpath.typeref_of_binary_op(op, t1, t2)

subgrounds.subgraph.fieldpath.type_ref_of_unary_op(op, t)

class subgrounds.subgraph.fieldpath.FieldOperatorMixin: Bases: object

subgrounds.subgraph.fieldpath.fieldpaths_of_object(subgraph, object_)

Returns generator of FieldPath objects that selects all non-list fields of GraphQL Object of Interface object_.

Parameters

schema (SchemaMeta) -- _description_
object (TypeMeta.ObjectMeta | TypeMeta.InterfaceMeta) -- _description_

Yields

_type_ -- _description_

class subgrounds.subgraph.fieldpath.FieldPath(subgraph: 'Subgraph', root_type: 'TypeRef.T', type_: 'TypeRef.T', path: 'list[Tuple[Optional[dict[str, Any]], TypeMeta.FieldMeta]]') → 'None'

Bases: subgrounds.subgraph.fieldpath.FieldOperatorMixin

class subgrounds.subgraph.fieldpath.SyntheticField(f: 'Callable', type_: 'TypeRef.T', deps: 'list[FieldPath | SyntheticField] | FieldPath | SyntheticField', default: 'Any' = None) → 'None'

Bases: subgrounds.subgraph.fieldpath.FieldOperatorMixin

STRING: ClassVar[TypeRef.Named] = TypeRef.Named(name_='String')

INT: ClassVar[TypeRef.Named] = TypeRef.Named(name_='Int')

FLOAT: ClassVar[TypeRef.Named] = TypeRef.Named(name_='Float')

BOOL: ClassVar[TypeRef.Named] = TypeRef.Named(name_='Boolean')

static default_of_type(type_)

static constant(value)

Returns a constant SyntheticField with value value. Useful for injecting additional static data to a schema or merging entities.

Parameters: value (str | int | float | bool) -- The constant field's value
Returns: The constant SyntheticField
Return type: SyntheticField

Example:

>>> from subgrounds.subgrounds import Subgrounds
>>> from subgrounds.subgraph import SyntheticField
>>> sg = Subgrounds()
>>> univ3 = sg.load_subgraph(
...     'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3'
... )

# Create constant SyntheticFields
>>> univ3.Mint.tx_type = SyntheticField.constant('MINT')
>>> univ3.Burn.tx_type = SyntheticField.constant('BURN')

# Last 10 mints and burns
>>> mints = univ3.Query.mints(
...     first=10,
...     orderBy=univ3.Mint.timestamp,
...     orderDirection='desc'
... )
>>> burns = univ3.Query.burns(
...     first=10,
...     orderBy=univ3.Burn.timestamp,
...     orderDirection='desc'
... )

# Query mints and burns. Notice that we merge the two entity tables by
# setting `concat=True` and overwriting the column names (columns must
# match the `FieldPaths`)
>>> df = sg.query_df([
...     mints.transaction.id,
...     mints.timestamp,
...     mints.tx_type,
...     mints.origin,
...     mints.amountUSD,
...     burns.transaction.id,
...     burns.timestamp,
...     burns.tx_type,
...     burns.origin,
...     burns.amountUSD,
... ], columns=['tx_hash', 'timestamp', 'tx_type', 'origin', 'amount_USD'], concat=True)

# Sort the DataFrame
>>> df.sort_values(by=['timestamp'], ascending=False)
                                              tx_hash   timestamp tx_type                                      origin    amount_USD
0   0xcbe1bacacc1e64fe613ae5eef2063563bd0057d1e3df...  1656016553    MINT  0x3435e7946d40b1a83c0cf154326fc6b3ca908952  7.879784e+03
1   0xdddaaddf59e5a3abff4feadef83b3ceb023a74424ea7...  1656016284    MINT  0xc747962e7e416e2a582813b1d7ad59eb83077fa6  5.110840e+04
10  0xa7671452c34a3b083083ef81e364489c2c9ee801a3b8...  1656016284    BURN  0xd40db77990bbb30514276b5ac17c3ce5cc9c951f  2.804573e+05
2   0xc132e73975e77c2c2c91fcf332018dfb01aac0ca9471...  1656015853    MINT  0xc747962e7e416e2a582813b1d7ad59eb83077fa6  5.122569e+04
3   0x1444744f4021a2046787c1b7b88cd9ac21f071c65acc...  1656015773    MINT  0xd11aa2e3a000275ed12b87515c9ac0d67b32e7b9  8.897983e+03
4   0x3315617d426fc2b0db5d8dbccd549efaa8f1ae2969ca...  1656015693    MINT  0xb7dd4d134b1794ee848e1af1a62b85d7b2ea9301  0.000000e+00
11  0xcc713daa2dc58cd1f2218c8f438e7fcf04d2e9c7c83d...  1656015278    BURN  0xa7c43e2057d89b6946b8865efc8bee3a4ea7d28d  1.254942e+06
5   0x7bbfae86f0c3c983651bd0671557cd851fc301317c06...  1656015111    MINT  0xac56cee8ccd00d0c1d72ce3415140874552e80f4  3.432075e+04
12  0xea21c3a68a8f0c6a2721a3072e0c8b2edc77f4d2f0d9...  1656014785    BURN  0x0709b103d46d71458a71e5d81230dd688809a53d  2.059106e+04
6   0x3bd369bf45c55cab40c62db81bb3e0684fd85fe2b662...  1656014120    MINT  0x509984bfc0fb24e2d1377cfec224d3afec4c341e  2.517578e+03
13  0x1ea59da77c442479af8fb51501a931260d473e249de7...  1656014018    BURN  0x509984bfc0fb24e2d1377cfec224d3afec4c341e  0.000000e+00
7   0xb9d31ef78b8bf786b422d948dd1fba246710078abff8...  1656013998    MINT  0x22dfec183294d257f80c15d3c9cd47495bdc728c  8.365750e+04
14  0xc5e3ec84a2860e3c3a055ccdced435a67b4aff4dd3be...  1656013946    BURN  0xac56cee8ccd00d0c1d72ce3415140874552e80f4  3.363809e+04
8   0x7c736255d9a4ebf4781069a1b2a929ad89100f1af980...  1656013913    MINT  0x4ce6aea89f059915ae5efbf34a2a8adc544ae09e  4.837287e+04
15  0x95cf56b9eb69aa45048a9b7b3e472df5bc3bfad591cd...  1656013728    BURN  0x4ce6aea89f059915ae5efbf34a2a8adc544ae09e  5.110010e+04
9   0x76dd2bbf43485c224471dd823c2992178f031f27194b...  1656013599    MINT  0x234a644868c419ce0dcdd9fd539762eba47f3759  5.363896e+03
16  0x47e595b02fdcb51ff42a5008e53be7ee3bdf8063b580...  1656013580    BURN  0xaf0fdd39e5d92499b0ed9f68693da99c0ec1e92e  0.000000e+00
17  0xe20ec9702f455d74b3cc1f54fe2f3450604ca5037a72...  1656013455    BURN  0xaf0fdd39e5d92499b0ed9f68693da99c0ec1e92e  0.000000e+00
18  0xac3e95666be3a45fdfbbfa513a114136ea6ecffb9de2...  1656013237    BURN  0x665d2d2444f2384fb3d96aaa0ea3536b92984dce  2.254100e+05
19  0x01c3424a48c36104ea388482723347f15c0bc1bb1a80...  1656013034    BURN  0x0084ee6c8893c01e252198b56ec127443dc27464  0.000000e+00

static datetime_of_timestamp(timestamp)

Returns a SyntheticField that will transform the FieldPath timestamp into a human-readable ISO8601 string.

Parameters: timestamp (FieldPath | SyntheticField) -- A FieldPath representing a Unix timestamp field.
Returns: An ISO8601 datetime string SyntheticField.
Return type: SyntheticField

Example:

>>> from subgrounds.subgrounds import Subgrounds
>>> from subgrounds.subgraph import SyntheticField
>>> sg = Subgrounds()
>>> univ3 = sg.load_subgraph(
...     'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3'
... )

# Create datetime SyntheticField
>>> univ3.Swap.datetime = SyntheticField.datetime_of_timestamp(univ3.Swap.timestamp)

# Query 100 swaps
>>> sg.query_df([
...     univ3.Query.swaps.timestamp,
...     univ3.Query.swaps.datetime,
... ])
    swaps_timestamp       swaps_datetime
0        1625105710  2021-06-30 22:15:10
1        1629253724  2021-08-17 22:28:44
2        1647333277  2022-03-15 04:34:37
3        1630801974  2021-09-04 20:32:54
4        1653240241  2022-05-22 13:24:01
..              ...                  ...
95       1646128326  2022-03-01 04:52:06
96       1646128326  2022-03-01 04:52:06
97       1626416555  2021-07-16 02:22:35
98       1626416555  2021-07-16 02:22:35
99       1625837291  2021-07-09 09:28:11

static map(dict, type_, fpath, default=None)

Returns a SyntheticField that will map the values of fpath using the key value pairs in dict. If a value is not in the dictionary, then default will be used instead.

Parameters

dict (dict[Any, Any]) -- The dictionary containing the key value pairs used to map fpath's values
type (TypeRef.T) -- The type of the resulting field
fpath (FieldPath | SyntheticField) -- The FieldPath whose values will be mapped using the dictionary
default (Optional[Any]) -- Default value used when a value is not in the provided dictionary

Returns

A map SyntheticField

Return type

SyntheticField

Example:

>>> from subgrounds.subgrounds import Subgrounds
>>> from subgrounds.subgraph import SyntheticField
>>> sg = Subgrounds()
>>> univ3 = sg.load_subgraph(
...     'https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3'
... )

# Hand-crafted mapping of pool addresses to symbol
>>> pooladdr_symbol_map = {
...     '0x5777d92f208679db4b9778590fa3cab3ac9e2168': 'DAI/USDC-001',
...     '0x6c6bc977e13df9b0de53b251522280bb72383700': 'DAI/USDC-005',
...     '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8': 'USDC/ETH-030',
...     '0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640': 'USDC/ETH-005',
... }

# Create map SyntheticField using our dictionary with 'UNKNOWN' as the
# default value
>>> univ3.Pool.symbol = SyntheticField.map(
...     pooladdr_symbol_map,
...     SyntheticField.STRING,
...     univ3.Pool.id,
...     'UNKNOWN'
... )

# Query top 10 pools by TVL
>>> pools = univ3.Query.pools(
...     first=10,
...     orderBy=univ3.Pool.totalValueLockedUSD,
...     orderDirection='desc'
... )
>>> sg.query_df([
...     pools.id,
...     pools.symbol
... ])
                                     pools_id  pools_symbol
0  0xa850478adaace4c08fc61de44d8cf3b64f359bec       UNKNOWN
1  0x5777d92f208679db4b9778590fa3cab3ac9e2168  DAI/USDC-001
2  0x6c6bc977e13df9b0de53b251522280bb72383700  DAI/USDC-005
3  0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8  USDC/ETH-030
4  0x88e6a0c2ddd26feeb64f039a2c41296fcb3f5640  USDC/ETH-005
5  0x3416cf6c708da44db2624d63ea0aaef7113527c6       UNKNOWN
6  0xcbcdf9626bc03e24f779434178a73a0b4bad62ed       UNKNOWN
7  0xc63b0708e2f7e69cb8a1df0e1389a98c35a76d52       UNKNOWN
8  0x4585fe77225b41b697c938b018e2ac67ac5a20c0       UNKNOWN
9  0x4e68ccd3e89f51c3074ca5072bbac773960dfa36       UNKNOWN