chore: Remove Mypy errors from examples.

py/examples/background_progress.py

@@ -26,7 +26,7 @@ def blocking_function(q: Q, loop: asyncio.AbstractEventLoop):
         # If future is not done yet, skip the update to keep the correct order.
         if not future or future.done():
             # Assume you are able to emit some kind of progress.
-            future = asyncio.ensure_future(update_ui(q, count / total), loop=loop)
+            future = asyncio.ensure_future(update_ui(q, round(count / total)), loop=loop)
 
 
 async def show_cancel(q: Q):

py/examples/db_todo.py

@@ -67,7 +67,11 @@ async def show_todos(q: Q):
     rows, err = await db.exec('select id, label, done from todo where user=?', q.auth.subject)
     if err:
         raise RuntimeError(f'Failed fetching todos: {err}')
-    todos = [TodoItem(id, label, done) for id, label, done in rows]
+
+    if rows is not None:
+        todos = [TodoItem(id, label, done) for id, label, done in rows]
+    else:
+        todos = []
 
     # Create done/not-done checkboxes.
     done = [ui.checkbox(name=f'todo_{todo.id}', label=todo.label, value=True, trigger=True) for todo in todos if
@@ -85,6 +89,7 @@ async def show_todos(q: Q):
     await q.page.save()
 
 
+
 async def add_todo(q: Q):
     # Insert a new item
     db: WaveDB = q.app.db

py/examples/form.py

@@ -1,7 +1,7 @@
 # Form
 # Use a #form to collect data or show textual information.
 # ---
-from .synth import FakeCategoricalSeries
+from .synth import FakeCategoricalSeries # type: ignore
 from h2o_wave import main, app, Q, ui, pack, data
 import random
 

py/examples/graphics_spline.py

@@ -1,10 +1,10 @@
-# Graphics / Spline
-# Use the #graphics module to render splines.
-# ---
-
 import random
 from h2o_wave import site, ui, graphics as g
 
+# Define a function to convert a list of values to float
+def to_float(lst): # for example, [1, 2, 3] -> [1.0, 2.0, 3.0]
+    return [float(v) for v in lst]
+
 x = [i * 20 for i in range(50)]
 y = [
     88, 100, 116, 128, 126, 128, 118, 108, 121, 120, 99, 113, 117, 103, 98, 90, 104, 98, 82, 102, 104, 89, 87, 69,
@@ -17,43 +17,41 @@
 
 splines = [
     # Lines
-    g.spline(x=x, y=y, **line_style),  # same as curve='linear'
-    g.spline(x=x, y=y, curve='basis', **line_style),
-    g.spline(x=x, y=y, curve='basis-closed', **line_style),
-    g.spline(x=x, y=y, curve='basis-open', **line_style),
-    g.spline(x=x, y=y, curve='cardinal', **line_style),
-    g.spline(x=x, y=y, curve='cardinal-closed', **line_style),
-    g.spline(x=x, y=y, curve='cardinal-open', **line_style),
-    g.spline(x=x, y=y, curve='smooth', **line_style),
-    g.spline(x=x, y=y, curve='smooth-closed', **line_style),
-    g.spline(x=x, y=y, curve='smooth-open', **line_style),
-    g.spline(x=x, y=y, curve='linear', **line_style),
-    g.spline(x=x, y=y, curve='linear-closed', **line_style),
-    g.spline(x=x, y=y, curve='monotone-x', **line_style),
-    g.spline(x=x, y=y, curve='monotone-y', **line_style),
-    g.spline(x=x, y=y, curve='natural', **line_style),
-    g.spline(x=x, y=y, curve='step', **line_style),
-    g.spline(x=x, y=y, curve='step-after', **line_style),
-    g.spline(x=x, y=y, curve='step-before', **line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='basis', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='basis-closed', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='basis-open', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='cardinal', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='cardinal-closed', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='cardinal-open', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='smooth', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='smooth-closed', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='smooth-open', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='linear-closed', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='monotone-x', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='monotone-y', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='natural', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='step', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='step-after', style=line_style),
+    g.spline(x=to_float(x), y=to_float(y), curve='step-before', style=line_style),
     # Areas
-    g.spline(x=x, y=y, y0=y0, **area_style),  # same as curve='linear'
-    g.spline(x=x, y=y, y0=y0, curve='basis', **area_style),
-    g.spline(x=x, y=y, y0=[], curve='basis', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='basis-open', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='cardinal', **area_style),
-    g.spline(x=x, y=y, y0=[], curve='cardinal', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='cardinal-open', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='smooth', **area_style),
-    g.spline(x=x, y=y, y0=[], curve='smooth', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='smooth-open', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='linear', **area_style),
-    g.spline(x=x, y=y, y0=[], curve='linear', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='monotone-x', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='monotone-y', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='natural', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='step', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='step-after', **area_style),
-    g.spline(x=x, y=y, y0=y0, curve='step-before', **area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='linear', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='basis', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=[], curve='basis', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='basis-open', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='cardinal', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=[], curve='cardinal', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='cardinal-open', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='smooth', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=[], curve='smooth', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='smooth-open', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='linear', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=[], curve='linear', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='monotone-x', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='monotone-y', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='natural', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='step', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='step-after', style=area_style),
+    g.spline(x=to_float(x), y=to_float(y), y0=to_float(y0), curve='step-before', style=area_style),
 ]
 
 page = site['/demo']

py/examples/graphics_turtle.py

@@ -7,7 +7,7 @@
 t = g.turtle().f(100).r(90).pd()
 for _ in range(36):
     t.f(200).l(170)
-spirograph = t.pu(1).path(stroke='red', fill='yellow')
+spirograph = t.pu(close=True).path(stroke='red', fill='yellow')
 
 page = site['/demo']
 page['example'] = ui.graphics_card(

py/examples/image.py

@@ -26,11 +26,11 @@
         0.76648938, 0.89679732, 0.77222302, 0.92717429, 0.61465203, 0.60906377,
         0.68468487, 0.25101297, 0.83783764, 0.11861562, 0.79723474, 0.94900427,
         0.14806288])) ** 2,
-    c=[0.90687198, 0.78837333, 0.76840584, 0.59849648, 0.44214562, 0.72303802,
-       0.41661825, 0.2268104, 0.45422734, 0.84794375, 0.93665595, 0.95603618,
-       0.39209432, 0.70832467, 0.12951583, 0.35379639, 0.40427152, 0.6485339,
-       0.03307097, 0.53800936, 0.13171312, 0.52093493, 0.10248479, 0.15798038,
-       0.92002965],
+    c=[0.90687198, 0.78837333, 0.76840584, 0.59849648, 0.44214562, 0.72303802, # type: ignore
+       0.41661825, 0.2268104, 0.45422734, 0.84794375, 0.93665595, 0.95603618, # type: ignore
+       0.39209432, 0.70832467, 0.12951583, 0.35379639, 0.40427152, 0.6485339, # type: ignore
+       0.03307097, 0.53800936, 0.13171312, 0.52093493, 0.10248479, 0.15798038, # type: ignore
+       0.92002965], # type: ignore
     alpha=0.5,
 )
 

py/examples/plot_altair.py

@@ -1,8 +1,8 @@
 # Plot / Altair
 # Use #Altair to create #plot specifications for the #Vega card.
 # ---
-import altair
-from vega_datasets import data
+import altair # type: ignore
+from vega_datasets import data # type: ignore
 from h2o_wave import site, ui
 
 spec = altair.Chart(data.cars()).mark_circle(size=60).encode(

py/examples/plot_app.py

@@ -1,7 +1,7 @@
 # Plot / App
 # Make a #plot from an app.
 # ---
-from .synth import FakeMultiCategoricalSeries as F
+from .synth import FakeMultiCategoricalSeries as F # type: ignore
 from h2o_wave import main, app, data, Q, ui
 
 n = 10

py/examples/plot_bokeh_callbacks.py

@@ -6,6 +6,8 @@
 
 import json
 from random import random
+
+from bokeh.core.types import ID
 from h2o_wave import main, app, Q, ui
 from bokeh.resources import CDN
 from bokeh.layouts import row
@@ -31,7 +33,7 @@ async def serve(q: Q):
         p2 = figure(width=250, height=300, x_range=(0, 1), y_range=(0, 1), tools="", title="Watch Here")
         p2.circle('x', 'y', source=s2, alpha=0.6)
 
-        s1.selected.js_on_change(
+        s1.selected.js_on_change( # type: ignore
             'indices',
             CustomJS(
                 args=dict(s1=s1, s2=s2),
@@ -68,7 +70,7 @@ async def serve(q: Q):
         # Serialize the plot as JSON.
         # See https://docs.bokeh.org/en/latest/docs/user_guide/embed.html#json-items
         plot_id = 'my_plot'
-        plot_data = json.dumps(json_item(layout, plot_id))
+        plot_data = json.dumps(json_item(layout, ID(plot_id)))
 
         q.page['meta'] = ui.meta_card(
             box='',

py/examples/plot_bokeh_script.py

@@ -3,6 +3,8 @@
 # ---
 
 import json
+
+from bokeh.core.types import ID
 from h2o_wave import site, ui
 from bokeh.resources import CDN
 from bokeh.plotting import figure
@@ -21,7 +23,8 @@
 # Serialize the plot as JSON.
 # See https://docs.bokeh.org/en/latest/docs/user_guide/embed.html#json-items
 plot_id = 'my_plot'
-plot_data = json.dumps(json_item(plot, plot_id))
+plot_data = json.dumps(json_item(plot, ID(plot_id)))
+
 
 page = site['/demo']
 

py/examples/plot_events_routing.py

@@ -2,10 +2,10 @@
 # Handle #events on a #plot card using routing.
 # ---
 from h2o_wave import main, app, on, run_on, Q, ui, data
-
+import typing
 
 @on('pricing.select_marks')
-async def show_selected_marks(q: Q, marks: any):
+async def show_selected_marks(q: Q, marks: typing.Any):
     q.page['details'].content = f'You selected {marks}'
     await q.page.save()
 

py/examples/plot_matplotlib.py

@@ -39,10 +39,12 @@ async def serve(q: Q):
 
     # Render plot
     plt.figure(figsize=(2, 2))
+    # Generate random RGB colors for each point
+    colors = [(float(r), float(g), float(b)) for r, g, b in np.random.rand(n, 3)]
     plt.scatter(
         np.random.rand(n), np.random.rand(n),
         s=(30 * np.random.rand(n)) ** 2,
-        c=np.random.rand(n),
+        c=colors,
         alpha=q.client.alpha / 100.0
     )
     image_filename = f'{str(uuid.uuid4())}.png'

py/examples/plot_plotly.py

@@ -4,7 +4,7 @@
 # ---
 
 import numpy as np
-from plotly import graph_objects as go
+from plotly import graph_objects as go # type: ignore
 from plotly import io as pio
 
 from h2o_wave import ui, main, app, Q

py/examples/plot_theme.py

@@ -3,7 +3,7 @@
 # ---
 import random
 
-from .synth import FakeTimeSeries, FakeMultiTimeSeries, FakeCategoricalSeries, FakeMultiCategoricalSeries, FakeScatter
+from .synth import FakeTimeSeries, FakeMultiTimeSeries, FakeCategoricalSeries, FakeMultiCategoricalSeries, FakeScatter # type: ignore
 from h2o_wave import main, app, data, Q, ui
 
 

py/examples/site_async.py

@@ -2,7 +2,7 @@
 # Update any page on a site from within an app using an `AsyncSite` instance.
 # #site
 # ---
-from .synth import FakePercent
+from .synth import FakePercent # type: ignore
 from h2o_wave import Q, app, main, ui, AsyncSite
 
 site = AsyncSite()

py/examples/synth.py

@@ -1,9 +1,10 @@
 import datetime
 import random
+from typing import Optional
 
 
 class FakeSeries:
-    def __init__(self, min=0.0, max=100.0, variation=10.0, start: int = None):
+    def __init__(self, min=0.0, max=100.0, variation=10.0, start: Optional[int] = None):
         self.min = min
         self.max = max
         self.variation = variation
@@ -21,7 +22,7 @@ def next(self):
 
 
 class FakeTimeSeries:
-    def __init__(self, min=0.0, max=100.0, variation=10.0, start: int = None, delta_days=1):
+    def __init__(self, min=0.0, max=100.0, variation=10.0, start: Optional[int] = None, delta_days=1):
         self.series = FakeSeries(min, max, variation, start)
         self.delta_days = delta_days
         self.date = datetime.datetime.utcnow() - datetime.timedelta(days=10 * 365)
@@ -33,7 +34,7 @@ def next(self):
 
 
 class FakeMultiTimeSeries:
-    def __init__(self, min=0.0, max=100.0, variation=10.0, start: int = None, delta_days=1, groups=5):
+    def __init__(self, min=0.0, max=100.0, variation=10.0, start: Optional[int] = None, delta_days=1, groups=5):
         self.series = [(f'G{c + 1}', FakeTimeSeries(min, max, variation, start, delta_days)) for c in range(groups)]
 
     def next(self):
@@ -45,7 +46,7 @@ def next(self):
 
 
 class FakeCategoricalSeries:
-    def __init__(self, min=0.0, max=100.0, variation=10.0, start: int = None):
+    def __init__(self, min=0.0, max=100.0, variation=10.0, start: Optional[int] = None):
         self.series = FakeSeries(min, max, variation, start)
         self.i = 0
 
@@ -56,7 +57,7 @@ def next(self):
 
 
 class FakeMultiCategoricalSeries:
-    def __init__(self, min=0.0, max=100.0, variation=10.0, start: int = None, groups=5):
+    def __init__(self, min=0.0, max=100.0, variation=10.0, start: Optional[int] = None, groups=5):
         self.series = [(f'G{c + 1}', FakeCategoricalSeries(min, max, variation, start)) for c in range(groups)]
 
     def next(self):
@@ -68,7 +69,7 @@ def next(self):
 
 
 class FakeScatter:
-    def __init__(self, min=0.0, max=100.0, variation=10.0, start: int = None):
+    def __init__(self, min=0.0, max=100.0, variation=10.0, start: Optional[int] = None):
         self.x = FakeSeries(min, max, variation, start)
         self.y = FakeSeries(min, max, variation, start)
 

py/examples/table_filter_backend.py

@@ -28,8 +28,9 @@ def df_to_rows(df: pd.DataFrame):
 
 
 def search_df(df: pd.DataFrame, term: str):
-    str_cols = df.select_dtypes(include=[object])
-    return df[str_cols.apply(lambda column: column.str.contains(term, case=False, na=False)).any(axis=1)]
+    str_cols = df.select_dtypes(include=['object']).columns
+    return df[df[str_cols].apply(lambda column: column.str.contains(term, case=False, na=False)).any(axis=1)]
+
 
 
 @app('/demo')

py/examples/table_markdown_pandas.py

@@ -3,11 +3,13 @@
 # ---
 from h2o_wave import site, ui
 import pandas as pd
+import numpy as np
+
 
 df = pd.DataFrame({'A': 1.,
                    'B': pd.Timestamp('20130102'),
                    'C': pd.Series(1, index=list(range(4)), dtype='float32'),
-                   'D': pd.np.array([3] * 4, dtype='int32'),
+                   'D': np.array([3] * 4, dtype='int32'),
                    'E': pd.Categorical(["test", "train", "test", "train"]),
                    'F': 'foo'})
 

py/examples/table_pagination.py

@@ -4,7 +4,7 @@
 # ---
 
 import os
-from typing import Dict, List
+from typing import Optional, Dict, List
 from h2o_wave import main, app, Q, ui
 from copy import deepcopy
 import csv
@@ -17,12 +17,12 @@ def __init__(self, text: str, status: str):
         self.status = status
 
 
-all_rows = [Issue(text=i + 1, status=('Closed' if i % 2 == 0 else 'Open')) for i in range(100)]
+all_rows = [Issue(text=str(i + 1), status=('Closed' if i % 2 == 0 else 'Open')) for i in range(100)]
 rows_per_page = 10
 total_rows = len(all_rows)
 
 
-def get_rows(base: List, sort: Dict[str, bool] = None, search: Dict = None, filters: Dict[str, List[str]] = None) -> List:
+def get_rows(base: List, sort: Optional[Dict[str, bool]] = None, search: Optional[Dict] = None, filters: Optional[Dict[str, List[str]]] = None) -> List:
     # Make a deep copy in order to not mutate the original `all_issues` which serves as our baseline.
     rows = deepcopy(base)
 
@@ -37,7 +37,7 @@ def get_rows(base: List, sort: Dict[str, bool] = None, search: Dict = None, filt
         rows = [row for row in rows if any(search_val in str(getattr(row, col)).lower() for col in cols)]
     # Filter out rows that do not contain filtered column value.
     if filters:
-        for col, filters in filters.items():
+         for col, filters in filters.items(): # type: ignore
             rows = [row for row in rows if not filters or any(f in getattr(row, col) for f in filters)]
 
     return rows