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chart: Fix various lint issues. #2679

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146 changes: 69 additions & 77 deletions geemap/chart.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@


class DataTable(pd.DataFrame):
"""DataFrame that can be initialized from EE objects."""

def __init__(
self,
Expand Down Expand Up @@ -168,7 +169,7 @@ def array_to_df(
return pd.DataFrame(data, **kwargs)


class Chart:
class Chart: # pylint: disable=too-many-instance-attributes
"""Create and display various types of charts from a data table.

Attributes:
Expand Down Expand Up @@ -466,7 +467,7 @@ def set_options(self, **options: Any) -> None:
setattr(self.figure, key, value)


class BaseChartClass:
class BaseChartClass: # pylint: disable=too-many-instance-attributes
"""This should include everything a chart module requires to plot figures."""

def __init__(
Expand Down Expand Up @@ -525,15 +526,11 @@ def __init__(
for key, value in kwargs.items():
setattr(self, key, value)

@classmethod
def get_data(cls) -> None:
def get_data(self) -> None:
"""Placeholder method to get data for the chart."""
pass

@classmethod
def plot_chart(cls) -> None:
def plot_chart(self) -> None:
"""Placeholder method to plot the chart."""
pass

def __repr__(self) -> str:
"""Returns the string representation of the chart."""
Expand All @@ -551,7 +548,7 @@ def __init__(
features: ee.FeatureCollection | pd.DataFrame,
default_labels: list[str],
name: str,
type: str = "grouped",
type: str = "grouped", # pylint: disable=redefined-builtin
**kwargs: Any,
):
"""A BarChart with the given features, labels, name, and type.
Expand All @@ -565,6 +562,10 @@ def __init__(
"""
super().__init__(features, default_labels, name, **kwargs)
self.type: str = type
self.x_data = None
self.y_data = None
self.yProperty = None
self.bar_chart = None

def generate_tooltip(self) -> None:
"""Generates a tooltip for the bar chart."""
Expand Down Expand Up @@ -651,6 +652,7 @@ def __init__(
**kwargs: Additional keyword arguments to set as attributes.
"""
super().__init__(features, labels, name, **kwargs)
self.line_chart = None

def plot_chart(self) -> None:
"""Plots the line chart."""
Expand Down Expand Up @@ -702,20 +704,14 @@ def __init__(
"""
default_labels = y_properties
super().__init__(features, default_labels, name, **kwargs)
self.x_data, self.y_data = self.get_data(x_property, y_properties)

def get_data(
self, x_property: str, y_properties: list[str]
) -> tuple[list[Any], list[Any]]:
"""Returns the x and y data for the chart.

Args:
x_property: The property to use for the x-axis.
y_properties: The properties to use for the y-axis.
"""
x_data = list(self.df[x_property])
y_data = list(self.df[y_properties].values.T)

self.x_property = x_property
self.y_properties = y_properties
self.x_data, self.y_data = self.get_data()

def get_data(self) -> tuple[list[Any], list[Any]]:
"""Returns the x and y data for the chart."""
x_data = list(self.df[self.x_property])
y_data = list(self.df[self.y_properties].values.T)
return x_data, y_data


Expand All @@ -740,37 +736,33 @@ def __init__(
**kwargs: Additional keyword arguments to set as attributes.

Raises:
Exception: If 'labels' is in kwargs.
ValueError: If 'labels' is in kwargs.
"""
default_labels = None
super().__init__(
features, default_labels, name, **kwargs
) # pytype: disable=wrong-arg-types
if "labels" in kwargs:
raise Exception("Please remove labels in kwargs and try again.")
raise ValueError("Please remove labels in kwargs and try again.")

@schwehr schwehr Feb 28, 2026

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This is a behavior change. I'm okay with that if you are.


self.labels = list(self.df[series_property])
self.x_data, self.y_data = self.get_data(x_properties)
self.x_properties = x_properties
self.x_data, self.y_data = self.get_data()

def get_data(
self, x_properties: list[str] | dict[str, str]
) -> tuple[list[Any], list[Any]]:
def get_data(self) -> tuple[list[Any], list[Any]]:

@schwehr schwehr Feb 28, 2026

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This changes the API. While it seems right to do in the long run, I think we should hold off doing that during the initial linting phase.

"""Returns the x and y data for the chart.

Args:
x_properties: The properties to use for the x-axis.

Raises:
Exception: If x_properties is not a list or dictionary.
TypeError: If x_properties is not a list or dictionary.
"""
if isinstance(x_properties, list):
x_data = x_properties
y_data = self.df[x_properties].values
elif isinstance(x_properties, dict):
x_data = list(x_properties.values())
y_data = self.df[list(x_properties.keys())].values
if isinstance(self.x_properties, list):
x_data = self.x_properties
y_data = self.df[self.x_properties].values
elif isinstance(self.x_properties, dict):
x_data = list(self.x_properties.values())
y_data = self.df[list(self.x_properties.keys())].values
else:
raise Exception("x_properties must be a list or dictionary.")
raise TypeError("x_properties must be a list or dictionary.")

return x_data, y_data

Expand All @@ -785,7 +777,7 @@ def __init__(
y_property: str,
series_property: str,
name: str = "feature.groups",
type: str = "stacked",
type: str = "stacked", # pylint: disable=redefined-builtin
**kwargs: Any,
):
"""Initialize a Feature_Groups.
Expand All @@ -805,8 +797,9 @@ def __init__(
self.yProperty = y_property
super().__init__(features, default_labels, name, type, **kwargs)

self.x_property = x_property
self.new_column_names = self.get_column_names(series_property, y_property)
self.x_data, self.y_data = self.get_data(x_property, self.new_column_names)
self.x_data, self.y_data = self.get_data()

def get_column_names(self, series_property: str, y_property: str) -> list[str]:
"""Returns the new column names for the DataFrame.
Expand All @@ -825,17 +818,10 @@ def get_column_names(self, series_property: str, y_property: str) -> list[str]:

return new_column_names

def get_data(
self, x_property: str, new_column_names: list[str]
) -> tuple[list[Any], list[Any]]:
"""Returns the x and y data for the chart.

Args:
x_property: The property to use for the x-axis.
new_column_names: The new column names for the y-axis.
"""
x_data = list(self.df[x_property])
y_data = [self.df[x] for x in new_column_names]
def get_data(self) -> tuple[list[Any], list[Any]]:

@schwehr schwehr Feb 28, 2026

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Another API change

"""Returns the x and y data for the chart."""
x_data = list(self.df[self.x_property])
y_data = [self.df[x] for x in self.new_column_names]

return x_data, y_data

Expand All @@ -859,11 +845,14 @@ def feature_by_feature(
y_properties: Values of y_properties.
**kwargs: Additional keyword arguments to set as attributes.
"""
bar = Feature_ByFeature(
features=features, x_property=x_property, y_properties=y_properties, **kwargs
chart = Feature_ByFeature(
features=features,
x_property=x_property,
y_properties=y_properties,
**kwargs,
)

bar.plot_chart()
chart.plot_chart()


def feature_by_property(
Expand All @@ -887,14 +876,14 @@ def feature_by_property(
series_property (str): The name of the property used to label each
feature in the legend.
"""
bar = Feature_ByProperty(
chart = Feature_ByProperty(
features=features,
x_properties=x_properties,
series_property=series_property,
**kwargs,
)

bar.plot_chart()
chart.plot_chart()


def feature_groups(
Expand All @@ -919,20 +908,20 @@ def feature_groups(
**kwargs: Additional keyword arguments to set as attributes.
"""

bar = Feature_Groups(
chart = Feature_Groups(
features=features,
x_property=x_property,
y_property=y_property,
series_property=series_property,
**kwargs,
)

bar.plot_chart()
chart.plot_chart()


def feature_histogram(
features: ee.FeatureCollection,
property: str,
property: str, # pylint: disable=redefined-builtin
max_buckets: int | None = None,
min_bucket_width: float | None = None,
show: bool = True,
Expand All @@ -958,20 +947,21 @@ def feature_histogram(
**kwargs: Additional keyword arguments to set as attributes.

Raises:
Exception: If the provided xProperties is not a list or dict.
Exception: If the chart fails to create.
TypeError: If features is not an ee.FeatureCollection.
ValueError: If property is not found in features.

Returns:
The bqplot chart object if show is False, otherwise None.
"""
if not isinstance(features, ee.FeatureCollection):
raise Exception("features must be an ee.FeatureCollection")
raise TypeError("features must be an ee.FeatureCollection")

first = features.first()
props = first.propertyNames().getInfo()
if property not in props:
raise Exception(
f"property {property} not found. Available properties: {', '.join(props)}"
raise ValueError(
f"property {property} not found. Available properties:"
f" {', '.join(props)}"
)

def nextPowerOf2(n) -> float:
Expand Down Expand Up @@ -1225,9 +1215,9 @@ def group_by_doy(collection, start, end, reducer):
)

# Group images by their day of year.
filter = ee.Filter(ee.Filter.equals(leftField="doy", rightField="doy"))
doy_filter = ee.Filter.equals(leftField="doy", rightField="doy")
joined = ee.Join.saveAll("matches").apply(
primary=doys, secondary=collection, condition=filter
primary=doys, secondary=collection, condition=doy_filter
)

# For each DoY, reduce images across years.
Expand Down Expand Up @@ -1360,9 +1350,9 @@ def group_by_doy(collection, start, end, reducer):
)

# Group images by their day of year.
filter = ee.Filter(ee.Filter.equals(leftField="doy", rightField="doy"))
doy_filter = ee.Filter.equals(leftField="doy", rightField="doy")
joined = ee.Join.saveAll("matches").apply(
primary=doys, secondary=collection, condition=filter
primary=doys, secondary=collection, condition=doy_filter
)

# For each DoY, reduce images across years.
Expand Down Expand Up @@ -1510,12 +1500,12 @@ def create_feature(image):
distinct_doy_year = tuples.distinct(["doy", "year"])

# Join the original tuples with the distinct (doy, year) pairs.
filter = ee.Filter.And(
doy_filter = ee.Filter.And(
ee.Filter.equals(leftField="doy", rightField="doy"),
ee.Filter.equals(leftField="year", rightField="year"),
)
joined = ee.Join.saveAll("matches").apply(
primary=distinct_doy_year, secondary=tuples, condition=filter
primary=distinct_doy_year, secondary=tuples, condition=doy_filter
)

# For each (doy, year), reduce the values of the joined features.
Expand Down Expand Up @@ -1552,7 +1542,7 @@ def image_histogram(
min_bucket_width: float,
max_raw: int,
max_pixels: int,
reducer_args: dict[str, Any] = {},
reducer_args: dict[str, Any] | None = None,
**kwargs: dict[str, Any],
) -> bq.Figure:
"""Creates a histogram for each band of the specified image within the given region.
Expand All @@ -1576,6 +1566,8 @@ def image_histogram(
Returns:
The bqplot figure containing the histograms.
"""
if reducer_args is None:
reducer_args = {}
# Calculate the histogram data.
histogram = image.reduceRegion(
reducer=ee.Reducer.histogram(
Expand Down Expand Up @@ -1611,7 +1603,7 @@ def create_histogram(
x_sc = bq.LinearScale()
y_sc = bq.LinearScale()

bar = bq.Bars(
chart = bq.Bars(
x=x_data,
y=y_data,
scales={"x": x_sc, "y": y_sc},
Expand All @@ -1625,7 +1617,7 @@ def create_histogram(
scale=y_sc, orientation="vertical", label="Count", tick_format="0.0f"
)

return bq.Figure(marks=[bar], axes=[ax_x, ax_y])
return bq.Figure(marks=[chart], axes=[ax_x, ax_y])

# Define colors and labels for the bands.
band_colors = kwargs.get("colors", ["#cf513e", "#1d6b99", "#f0af07"])
Expand Down Expand Up @@ -1737,7 +1729,7 @@ def get_stats(image):
for band in band_names:
results[band] = stats.get(band)

if x_property == "system:time_start" or x_property == "system:time_end":
if x_property in ("system:time_start", "system:time_end"):
results["date"] = image.date().format("YYYY-MM-dd")
else:
results[x_property] = image.get(x_property).getInfo()
Expand Down Expand Up @@ -1829,7 +1821,7 @@ def image_series_by_region(
df = df.drop(columns=[series_property]).T
df.columns = headers

if x_property == "system:time_start" or x_property == "system:time_end":
if x_property in ("system:time_start", "system:time_end"):
indexes = common.image_dates(image_collection).getInfo()
df["index"] = pd.to_datetime(indexes)

Expand Down