Upgrade CSP to Perspective 3.0.3

Author: sinistersnare

csp/adapters/perspective.py

@@ -13,21 +13,20 @@
 except ImportError:
     raise ImportError("perspective adapter requires tornado package")
 
-
 try:
-    from perspective import PerspectiveManager, Table as Table_, View as View_, __version__, set_threadpool_size
+    from perspective import Server, Table as Table_, View as View_, __version__, set_threadpool_size
 
     MAJOR, MINOR, PATCH = map(int, __version__.split("."))
-    if (MAJOR, MINOR, PATCH) < (0, 6, 2):
-        raise ImportError("perspective adapter requires 0.6.2 or greater of the perspective-python package")
+    if (MAJOR, MINOR, PATCH) < (3, 0, 3):
+        raise ImportError("perspective adapter requires version 3.0.3 or greater of the perspective-python package")
 except ImportError:
-    raise ImportError("perspective adapter requires 0.6.2 or greater of the perspective-python package")
+    raise ImportError("perspective adapter requires version 3.0.3 or greater of the perspective-python package")
 
 
 # Run perspective update in a separate tornado loop
-def perspective_thread(manager):
+def perspective_thread(client):
     loop = tornado.ioloop.IOLoop()
-    manager.set_loop_callback(loop.add_callback)
+    client.set_loop_callback(loop.add_callback)
     loop.start()
 
 
@@ -61,12 +60,14 @@ def _launch_application(port: int, manager: object, stub: ts[object]):
         s_iothread = None
 
     with csp.start():
-        from perspective import PerspectiveTornadoHandler
+        from perspective import PerspectiveTornadoHandler, Server
+
+        server = Server()
 
         s_app = tornado.web.Application(
             [
                 # create a websocket endpoint that the client Javascript can access
-                (r"/websocket", PerspectiveTornadoHandler, {"manager": manager, "check_origin": True})
+                (r"/websocket", PerspectiveTornadoHandler, {"perspective_server": server, "check_origin": True})
             ],
             websocket_ping_interval=15,
         )
@@ -197,20 +198,19 @@ def create_table(self, name, limit=None, index=None):
 
     def _instantiate(self):
         set_threadpool_size(self._threadpool_size)
+        server = Server()
+        client = server.new_local_client()
 
-        manager = PerspectiveManager()
-
-        thread = threading.Thread(target=perspective_thread, kwargs=dict(manager=manager))
+        thread = threading.Thread(target=perspective_thread, kwargs=dict(client=client))
         thread.daemon = True
         thread.start()
 
         for table_name, table in self._tables.items():
             schema = {
                 k: v.tstype.typ if not issubclass(v.tstype.typ, csp.Enum) else str for k, v in table.columns.items()
             }
-            ptable = Table(schema, limit=table.limit, index=table.index)
-            manager.host_table(table_name, ptable)
+            ptable = client.table(schema, name=table_name, limit=table.limit, index=table.index)
 
             _apply_updates(ptable, table.columns, self._throttle)
 
-        _launch_application(self._port, manager, csp.const("stub"))
+        _launch_application(self._port, client, csp.const("stub"))

csp/dataframe.py

@@ -1,4 +1,4 @@
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
 from typing import Dict, Optional
 
 import csp.baselib
@@ -198,7 +198,7 @@ def to_pandas_ts(self, trigger, window, tindex=None, wait_all_valid=True):
 
         return make_pandas(trigger, self._data, window, tindex, wait_all_valid)
 
-    def to_perspective(self, starttime: datetime, endtime: datetime = None, realtime: bool = False):
+    def to_perspective(self, client, starttime: datetime, endtime: datetime = None, realtime: bool = False):
         import csp
 
         try:
@@ -240,7 +240,7 @@ def apply_updates(table: object, data: {str: csp.ts[object]}, timecol: str, thro
 
             if csp.ticked(data):
                 s_buffer.append(dict(data.tickeditems()))
-                s_buffer[-1][timecol] = csp.now()
+                s_buffer[-1][timecol] = int(csp.now().timestamp() * 1000)
 
             if csp.ticked(alarm):
                 if len(s_buffer) > 0:
@@ -250,9 +250,17 @@ def apply_updates(table: object, data: {str: csp.ts[object]}, timecol: str, thro
                 csp.schedule_alarm(alarm, throttle, True)
 
         timecol = "time"
-        schema = {k: v.tstype.typ for k, v in self._data.items()}
-        schema[timecol] = datetime
-        table = perspective.Table(schema)
+        perspective_type_map = {
+            str: "string",
+            float: "float",
+            int: "integer",
+            date: "date",
+            datetime: "datetime",
+            bool: "boolean",
+        }
+        schema = {k: perspective_type_map[v.tstype.typ] for k, v in self._data.items()}
+        schema[timecol] = "datetime"
+        table = client.table(schema)
         runner = csp.run_on_thread(
             apply_updates,
             table,

csp/impl/pandas_perspective.py

@@ -1,6 +1,7 @@
 import pandas as pd
+import pyarrow as pa
 import pytz
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta, timezone
 from pandas.compat import set_function_name
 from typing import Optional
 
@@ -40,7 +41,8 @@ def _apply_updates(
         if throttle > timedelta(0):
             csp.schedule_alarm(alarm, throttle, True)
         s_has_time_col = time_col and time_col not in data.keys()
-        s_datetime_cols = set([c for c, t in table.schema().items() if t == datetime])
+        s_datetime_cols = set([c for c, t in table.schema().items() if t == "datetime"])
+        s_date_cols = set([c for c, t in table.schema().items() if t == "date"])
 
     with csp.stop():
         try:
@@ -81,14 +83,22 @@ def _apply_updates(
                     row[index_col] = idx
                 if s_has_time_col:
                     if localize:
-                        row[time_col] = pytz.utc.localize(csp.now())
+                        row[time_col] = int(pytz.utc.localize(csp.now()).timestamp() * 1000)
                     else:
-                        row[time_col] = csp.now()
+                        row[time_col] = int(pytz.utc.localize(csp.now()).timestamp() * 1000)
             else:
                 row = new_rows[idx]
 
-            if localize and col in s_datetime_cols and value.tzinfo is None:
-                row[col] = pytz.utc.localize(value)
+            if col in s_date_cols:
+                row[col] = int(
+                    datetime(year=value.year, month=value.month, day=value.day, tzinfo=timezone.utc).timestamp() * 1000
+                )
+
+            elif localize and col in s_datetime_cols:
+                if value.tzinfo is None:
+                    row[col] = int(pytz.utc.localize(value).timestamp() * 1000)
+                else:
+                    row[col] = int(pytz.utc.localize(value).timestamp() * 1000)
             else:
                 row[col] = value
 
@@ -160,28 +170,41 @@ def __init__(
         self._limit = limit
         self._localize = localize
 
+        # TODO: we do not want 1 server per table, make a Client an optional
+        self._psp_server = perspective.Server()
+        self._psp_client = self._psp_server.new_local_client()
+
         self._basket = _frame_to_basket(data)
         self._static_frame = data.csp.static_frame()
-        self._static_table = perspective.Table(self._static_frame)
+        self._static_table = self._psp_client.table(self._static_frame)
         static_schema = self._static_table.schema()
         # Since the index will be accounted for separately, remove the index from the static table schema,
         # and re-enter it under index_col
         raw_index_name = self._static_frame.index.name or "index"
         index_type = static_schema.pop(raw_index_name)
         schema = {index_col: index_type}
+        perspective_type_map = {
+            str: "string",
+            float: "float",
+            int: "integer",
+            date: "date",
+            datetime: "datetime",
+            bool: "boolean",
+        }
+
         if time_col:
-            schema[time_col] = datetime
+            schema[time_col] = "datetime"
         for col, series in data.items():
             if is_csp_type(series):
-                schema[col] = series.dtype.subtype
+                schema[col] = perspective_type_map[series.dtype.subtype]
             else:
                 schema[col] = static_schema[col]
 
         if self._keep_history:
-            self._table = perspective.Table(schema, index=None, limit=limit)
+            self._table = self._psp_client.table(schema, index=None, limit=limit)
             self._static_records = self._static_frame.to_dict(orient="index")
         else:
-            self._table = perspective.Table(schema, index=self._index_col)
+            self._table = self._psp_client.table(schema, index=self._index_col)
             self._static_frame.index = self._static_frame.index.rename(self._index_col)
             self._table.update(self._static_frame)
             self._static_records = None  # No need to update dynamically
@@ -222,7 +245,7 @@ def run_historical(self, starttime, endtime):
             index = self._index_col
         if self._limit:
             df = df.sort_values(self._time_col).tail(self._limit).reset_index(drop=True)
-        return perspective.Table(df.to_dict("series"), index=index)
+        return self._psp_client.table(df, index=index)
 
     def run(self, starttime=None, endtime=timedelta(seconds=60), realtime=True, clear=False):
         """Run a graph that sends data to the table on the current thread.
@@ -280,7 +303,7 @@ def get_widget(self, **override_kwargs):
                 "sort": [[self._time_col, "desc"]],
             }
         else:
-            kwargs = {"columns": list(self._table.schema())}
+            kwargs = {"columns": list(self._table.columns())}
         kwargs.update(override_kwargs)
         return perspective.PerspectiveWidget(self._table, **kwargs)
 
@@ -294,14 +317,33 @@ def _method(self, **options):
 
     @classmethod
     def _add_view_methods(cls):
-        cls.to_df = cls._create_view_method(perspective.View.to_df)
-        cls.to_dict = cls._create_view_method(perspective.View.to_dict)
+        # cls.to_df = cls._create_view_method(perspective.View.to_df)
+        # cls.to_dict = cls._create_view_method(perspective.View.to_dict)
         cls.to_json = cls._create_view_method(perspective.View.to_json)
         cls.to_csv = cls._create_view_method(perspective.View.to_csv)
-        cls.to_numpy = cls._create_view_method(perspective.View.to_numpy)
+        # cls.to_numpy = cls._create_view_method(perspective.View.to_numpy)
         cls.to_columns = cls._create_view_method(perspective.View.to_columns)
         cls.to_arrow = cls._create_view_method(perspective.View.to_arrow)
 
+    def to_df(self, **kwargs):
+        ipc_bytes = self.to_arrow()
+        table = pa.ipc.open_stream(ipc_bytes).read_all()
+        df = pd.DataFrame(table.to_pandas(**kwargs))
+
+        # DAVIS: `pyarrow` does not force alphabetical order on categories, so
+        # we correct this here to make assertions pass. We can enforce this in
+        # Perspective at a performance hit/API complexity.
+        for column in df:
+            if df[column].dtype == "datetime64[ms]":
+                df[column] = df[column].astype("datetime64[ns]")
+            elif df[column].dtype == "category":
+                df[column] = df[column].cat.reorder_categories(df[column].cat.categories.sort_values())
+
+        if df.index.dtype == "category":
+            df.index = df.index.cat.reorder_categories(df.index.cat.categories.sort_values())
+
+        return df
+
 
 CspPerspectiveTable._add_view_methods()
 

csp/tests/impl/test_dateframe.py csp/tests/impl/test_dataframe.py

@@ -134,10 +134,13 @@ def test_perspective(self):
         starttime = datetime(2021, 4, 26)
         endtime = starttime + timedelta(seconds=10)
 
-        _ = df.to_perspective(starttime, endtime)
+        server = perspective.Server()
+        client = server.new_local_client()
+
+        _ = df.to_perspective(client, starttime, endtime)
 
         # realtime
-        widget = df.to_perspective(datetime.utcnow(), endtime=timedelta(seconds=30), realtime=True)
+        widget = df.to_perspective(client, datetime.utcnow(), endtime=timedelta(seconds=30), realtime=True)
         import time
 
         time.sleep(1)