chart module¶
Module for creating charts from Earth Engine data.
BarChart (BaseChartClass)
¶
Create Bar Chart. All histogram/bar charts can use this object.
Source code in geemap/chart.py
class BarChart(BaseChartClass):
"""Create Bar Chart. All histogram/bar charts can use this object."""
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
default_labels: List[str],
name: str,
type: str = "grouped",
**kwargs: Any,
):
"""
Initializes the BarChart with the given features, labels, name, and type.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
default_labels (List[str]): The default labels for the chart.
name (str): The name of the chart.
type (str, optional): The type of bar chart ('grouped' or 'stacked').
Defaults to 'grouped'.
**kwargs: Additional keyword arguments to set as attributes.
"""
super().__init__(features, default_labels, name, **kwargs)
self.type: str = type
def generate_tooltip(self) -> None:
"""
Generates a tooltip for the bar chart.
"""
if (self.x_label is not None) and (self.y_label is not None):
self.bar_chart.tooltip = Tooltip(
fields=["x", "y"], labels=[self.x_label, self.y_label]
)
else:
self.bar_chart.tooltip = Tooltip(fields=["x", "y"])
def get_ylim(self) -> Tuple[float, float]:
"""
Gets the y-axis limits for the bar chart.
Returns:
Tuple[float, float]: The minimum and maximum y-axis limits.
"""
if self.ylim:
ylim_min, ylim_max = self.ylim[0], self.ylim[1]
else:
if self.name in ["feature.byFeature", "feature.byProperty"]:
ylim_min = np.min(self.y_data)
ylim_max = np.max(self.y_data) 0.2 * (
np.max(self.y_data) - np.min(self.y_data)
)
if self.name in ["feature.groups"]:
ylim_min = np.min(self.df[self.yProperty])
ylim_max = np.max(self.df[self.yProperty])
ylim_max = ylim_max 0.2 * (ylim_max - ylim_min)
return (ylim_min, ylim_max)
def plot_chart(self) -> None:
"""
Plots the bar chart.
"""
fig = plt.figure(
title=self.title,
legend_location=self.legend_location,
)
self.bar_chart = plt.bar(
self.x_data,
self.y_data,
labels=self.labels,
display_legend=self.display_legend,
)
self.generate_tooltip()
plt.ylim(*self.get_ylim())
if self.x_label:
plt.xlabel(self.x_label)
if self.y_label:
plt.ylabel(self.y_label)
if self.width:
fig.layout.width = self.width
if self.height:
fig.layout.height = self.height
self.bar_chart.colors = self.colors
self.bar_chart.type = self.type
plt.show()
__init__(self, features, default_labels, name, type='grouped', **kwargs)
special
¶
Initializes the BarChart with the given features, labels, name, and type.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection | pd.DataFrame |
The features to plot. |
required |
default_labels |
List[str] |
The default labels for the chart. |
required |
name |
str |
The name of the chart. |
required |
type |
str |
The type of bar chart ('grouped' or 'stacked'). Defaults to 'grouped'. |
'grouped' |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Source code in geemap/chart.py
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
default_labels: List[str],
name: str,
type: str = "grouped",
**kwargs: Any,
):
"""
Initializes the BarChart with the given features, labels, name, and type.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
default_labels (List[str]): The default labels for the chart.
name (str): The name of the chart.
type (str, optional): The type of bar chart ('grouped' or 'stacked').
Defaults to 'grouped'.
**kwargs: Additional keyword arguments to set as attributes.
"""
super().__init__(features, default_labels, name, **kwargs)
self.type: str = type
generate_tooltip(self)
¶
Generates a tooltip for the bar chart.
Source code in geemap/chart.py
def generate_tooltip(self) -> None:
"""
Generates a tooltip for the bar chart.
"""
if (self.x_label is not None) and (self.y_label is not None):
self.bar_chart.tooltip = Tooltip(
fields=["x", "y"], labels=[self.x_label, self.y_label]
)
else:
self.bar_chart.tooltip = Tooltip(fields=["x", "y"])
get_ylim(self)
¶
Gets the y-axis limits for the bar chart.
Returns:
| Type | Description |
|---|---|
Tuple[float, float] |
The minimum and maximum y-axis limits. |
Source code in geemap/chart.py
def get_ylim(self) -> Tuple[float, float]:
"""
Gets the y-axis limits for the bar chart.
Returns:
Tuple[float, float]: The minimum and maximum y-axis limits.
"""
if self.ylim:
ylim_min, ylim_max = self.ylim[0], self.ylim[1]
else:
if self.name in ["feature.byFeature", "feature.byProperty"]:
ylim_min = np.min(self.y_data)
ylim_max = np.max(self.y_data) 0.2 * (
np.max(self.y_data) - np.min(self.y_data)
)
if self.name in ["feature.groups"]:
ylim_min = np.min(self.df[self.yProperty])
ylim_max = np.max(self.df[self.yProperty])
ylim_max = ylim_max 0.2 * (ylim_max - ylim_min)
return (ylim_min, ylim_max)
plot_chart(self)
¶
Plots the bar chart.
Source code in geemap/chart.py
def plot_chart(self) -> None:
"""
Plots the bar chart.
"""
fig = plt.figure(
title=self.title,
legend_location=self.legend_location,
)
self.bar_chart = plt.bar(
self.x_data,
self.y_data,
labels=self.labels,
display_legend=self.display_legend,
)
self.generate_tooltip()
plt.ylim(*self.get_ylim())
if self.x_label:
plt.xlabel(self.x_label)
if self.y_label:
plt.ylabel(self.y_label)
if self.width:
fig.layout.width = self.width
if self.height:
fig.layout.height = self.height
self.bar_chart.colors = self.colors
self.bar_chart.type = self.type
plt.show()
BaseChartClass
¶
This should include everything a chart module requires to plot figures.
Source code in geemap/chart.py
class BaseChartClass:
"""This should include everything a chart module requires to plot figures."""
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
default_labels: List[str],
name: str,
**kwargs: Any,
):
"""
Initializes the BaseChartClass with the given features, labels, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
default_labels (List[str]): The default labels for the chart.
name (str): The name of the chart.
**kwargs: Additional keyword arguments to set as attributes.
"""
self.ylim = None
self.xlim = None
self.title = ""
self.legend_location = "top-left"
self.layout_width = None
self.layout_height = None
self.display_legend = True
self.x_label = None
self.y_label = None
self.labels = default_labels
self.width = None
self.height = None
self.name = name
if isinstance(self.labels, list) and (len(self.labels) > 1):
self.colors = [
"#604791",
"#1d6b99",
"#39a8a7",
"#0f8755",
"#76b349",
"#f0af07",
"#e37d05",
"#cf513e",
"#96356f",
"#724173",
"#9c4f97",
"#696969",
]
else:
self.colors = "black"
if isinstance(features, ee.FeatureCollection):
self.df = ee_to_df(features)
elif isinstance(features, pd.DataFrame):
self.df = features
for key, value in kwargs.items():
setattr(self, key, value)
@classmethod
def get_data(cls) -> None:
"""
Placeholder method to get data for the chart.
"""
pass
@classmethod
def plot_chart(cls) -> None:
"""
Placeholder method to plot the chart.
"""
pass
def __repr__(self) -> str:
"""
Returns the string representation of the chart.
Returns:
str: The name of the chart.
"""
return self.name
__init__(self, features, default_labels, name, **kwargs)
special
¶
Initializes the BaseChartClass with the given features, labels, and name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection | pd.DataFrame |
The features to plot. |
required |
default_labels |
List[str] |
The default labels for the chart. |
required |
name |
str |
The name of the chart. |
required |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Source code in geemap/chart.py
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
default_labels: List[str],
name: str,
**kwargs: Any,
):
"""
Initializes the BaseChartClass with the given features, labels, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
default_labels (List[str]): The default labels for the chart.
name (str): The name of the chart.
**kwargs: Additional keyword arguments to set as attributes.
"""
self.ylim = None
self.xlim = None
self.title = ""
self.legend_location = "top-left"
self.layout_width = None
self.layout_height = None
self.display_legend = True
self.x_label = None
self.y_label = None
self.labels = default_labels
self.width = None
self.height = None
self.name = name
if isinstance(self.labels, list) and (len(self.labels) > 1):
self.colors = [
"#604791",
"#1d6b99",
"#39a8a7",
"#0f8755",
"#76b349",
"#f0af07",
"#e37d05",
"#cf513e",
"#96356f",
"#724173",
"#9c4f97",
"#696969",
]
else:
self.colors = "black"
if isinstance(features, ee.FeatureCollection):
self.df = ee_to_df(features)
elif isinstance(features, pd.DataFrame):
self.df = features
for key, value in kwargs.items():
setattr(self, key, value)
__repr__(self)
special
¶
Returns the string representation of the chart.
Returns:
| Type | Description |
|---|---|
str |
The name of the chart. |
Source code in geemap/chart.py
def __repr__(self) -> str:
"""
Returns the string representation of the chart.
Returns:
str: The name of the chart.
"""
return self.name
get_data()
classmethod
¶
Placeholder method to get data for the chart.
Source code in geemap/chart.py
@classmethod
def get_data(cls) -> None:
"""
Placeholder method to get data for the chart.
"""
pass
plot_chart()
classmethod
¶
Placeholder method to plot the chart.
Source code in geemap/chart.py
@classmethod
def plot_chart(cls) -> None:
"""
Placeholder method to plot the chart.
"""
pass
Chart
¶
A class to create and display various types of charts from a data table.
Attributes:
| Name | Type | Description |
|---|---|---|
data_table |
pd.DataFrame |
The data to be displayed in the charts. |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
chart |
The bqplot Figure object for the chart. |
Source code in geemap/chart.py
class Chart:
"""
A class to create and display various types of charts from a data table.
Attributes:
data_table (pd.DataFrame): The data to be displayed in the charts.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart',
'AreaChart', and 'Table'.
chart: The bqplot Figure object for the chart.
"""
def __init__(
self,
data_table: Union[Dict[str, List[Any]], pd.DataFrame],
chart_type: str = "LineChart",
x_cols: Optional[List[str]] = None,
y_cols: Optional[List[str]] = None,
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> None:
"""
Initializes the Chart with data.
Args:
data_table (Union[Dict[str, List[Any]], pd.DataFrame]): A 2-D array of data.
If it's a dictionary, it will be converted to a DataFrame.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
x_cols (Optional[List[str]]): The columns to use for the x-axis.
Defaults to the first column.
y_cols (Optional[List[str]]): The columns to use for the y-axis.
Defaults to the second column.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
"""
self.data_table = DataTable(data_table)
self.chart_type = chart_type
self.chart = None
self.title = title
self.x_label = x_label
self.y_label = y_label
self.x_cols = x_cols
self.y_cols = y_cols
self.colors = colors
self.xlim = kwargs.pop("xlim", None)
self.ylim = kwargs.pop("ylim", None)
if title is not None:
kwargs["title"] = title
self.figure = plt.figure(**kwargs)
if chart_type is not None:
self.set_chart_type(chart_type, **kwargs)
def display(self) -> None:
"""
Display the chart without toolbar.
"""
self._set_plt_options()
display(self.figure)
def save_png(self, filepath: str = "chart.png", scale: float = 1.0) -> None:
"""
Save the chart as a PNG image.
Args:
filepath (str): The path to save the PNG image. Defaults to 'chart.png'.
scale (float): The scale factor for the image. Defaults to 1.0.
"""
self.figure.save_png(filepath, scale=scale)
def _ipython_display_(self) -> None:
"""
Display the chart with toolbar.
"""
self._set_plt_options()
plt.show()
def _set_plt_options(self) -> None:
"""
Set the title and labels for the chart.
"""
if self.title is not None:
self.figure.title = self.title
if self.x_label is not None:
plt.xlabel(self.x_label)
if self.y_label is not None:
plt.ylabel(self.y_label)
if self.xlim is not None:
plt.xlim(self.xlim[0], self.xlim[1])
if self.ylim is not None:
plt.ylim(self.ylim[0], self.ylim[1])
def set_chart_type(
self,
chart_type: str,
clear: bool = True,
**kwargs: Any,
) -> None:
"""
Sets the chart type and other chart properties.
Args:
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
clear (bool): Whether to clear the current chart before setting a new one.
Defaults to True.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects.
Returns:
Chart: The Chart instance with the chart set.
"""
if clear:
plt.clear()
self.chart_type = chart_type
x_cols = self.x_cols
y_cols = self.y_cols
colors = self.colors
if x_cols is None:
x_cols = [self.data_table.columns[0]]
if y_cols is None:
y_cols = [self.data_table.columns[1]]
if isinstance(x_cols, str):
x_cols = [x_cols]
if isinstance(y_cols, str):
y_cols = [y_cols]
if len(x_cols) == 1 and len(y_cols) > 1:
x_cols = x_cols * len(y_cols)
if "axes_options" not in kwargs:
kwargs["axes_options"] = {
"x": {"label_offset": "30px"},
"y": {"label_offset": "40px"},
}
if chart_type == "PieChart":
if colors is None:
colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf",
] # Default pie chart colors
else:
if colors is None:
colors = [
"blue",
"orange",
"green",
"red",
"purple",
"brown",
] # Default colors
if chart_type == "IntervalChart":
x = self.data_table[x_cols[0]]
y = [self.data_table[y_col] for y_col in y_cols]
if "fill" not in kwargs:
kwargs["fill"] = "between"
self.chart = plt.plot(
x,
y,
colors=colors,
**kwargs,
)
else:
for i, (x_col, y_col) in enumerate(zip(x_cols, y_cols)):
color = colors[i % len(colors)]
if "display_legend" not in kwargs and len(y_cols) > 1:
kwargs["display_legend"] = True
kwargs["labels"] = [y_col]
else:
kwargs["labels"] = [y_col]
x = self.data_table[x_col]
y = self.data_table[y_col]
if isinstance(x, pd.Series) and (
not pd.api.types.is_datetime64_any_dtype(x)
):
x = x.tolist()
if isinstance(y, pd.Series) and (
not pd.api.types.is_datetime64_any_dtype(y)
):
y = y.tolist()
if chart_type == "ScatterChart":
self.chart = plt.scatter(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "LineChart":
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "AreaChart":
if "fill" not in kwargs:
kwargs["fill"] = "bottom"
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "ColumnChart":
self.chart = plt.bar(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "BarChart":
if "orientation" not in kwargs:
kwargs["orientation"] = "horizontal"
self.chart = plt.bar(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "AreaChart":
if "fill" not in kwargs:
kwargs["fill"] = "bottom"
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "PieChart":
kwargs.pop("labels", None)
self.chart = plt.pie(
sizes=y,
labels=x,
colors=colors[: len(x)],
**kwargs,
)
elif chart_type == "Table":
output = widgets.Output(**kwargs)
with output:
display(self.data_table)
output.layout = widgets.Layout(width="50%")
display(output)
else:
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
self._set_plt_options()
def get_chart_type(self) -> Optional[str]:
"""
Get the current chart type.
Returns:
Optional[str]: The current chart type, or None if no chart type is set.
"""
return self.chart_type
def get_data_table(self) -> DataTable:
"""
Get the DataTable used by the chart.
Returns:
DataTable: The DataTable instance containing the chart data.
"""
return self.data_table
def set_data_table(self, data: Union[Dict[str, List[Any]], pd.DataFrame]) -> None:
"""
Set a new DataTable for the chart.
Args:
data (Union[Dict[str, List[Any]], pd.DataFrame]): The new data to be
used for the chart.
"""
self.data_table = DataTable(data)
def set_options(self, **options: Any) -> None:
"""
Set additional options for the chart.
Args:
**options: Additional options to set for the chart.
"""
for key, value in options.items():
setattr(self.figure, key, value)
__init__(self, data_table, chart_type='LineChart', x_cols=None, y_cols=None, colors=None, title=None, x_label=None, y_label=None, **kwargs)
special
¶
Initializes the Chart with data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data_table |
Union[Dict[str, List[Any]], pd.DataFrame] |
A 2-D array of data. If it's a dictionary, it will be converted to a DataFrame. |
required |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
'LineChart' |
x_cols |
Optional[List[str]] |
The columns to use for the x-axis. Defaults to the first column. |
None |
y_cols |
Optional[List[str]] |
The columns to use for the y-axis. Defaults to the second column. |
None |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to a predefined list of colors. |
None |
title |
Optional[str] |
The title of the chart. Defaults to the chart type. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to an empty string. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to an empty string. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. For axes_options, see https://bqplot.github.io/bqplot/api/axes |
{} |
Source code in geemap/chart.py
def __init__(
self,
data_table: Union[Dict[str, List[Any]], pd.DataFrame],
chart_type: str = "LineChart",
x_cols: Optional[List[str]] = None,
y_cols: Optional[List[str]] = None,
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> None:
"""
Initializes the Chart with data.
Args:
data_table (Union[Dict[str, List[Any]], pd.DataFrame]): A 2-D array of data.
If it's a dictionary, it will be converted to a DataFrame.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
x_cols (Optional[List[str]]): The columns to use for the x-axis.
Defaults to the first column.
y_cols (Optional[List[str]]): The columns to use for the y-axis.
Defaults to the second column.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
"""
self.data_table = DataTable(data_table)
self.chart_type = chart_type
self.chart = None
self.title = title
self.x_label = x_label
self.y_label = y_label
self.x_cols = x_cols
self.y_cols = y_cols
self.colors = colors
self.xlim = kwargs.pop("xlim", None)
self.ylim = kwargs.pop("ylim", None)
if title is not None:
kwargs["title"] = title
self.figure = plt.figure(**kwargs)
if chart_type is not None:
self.set_chart_type(chart_type, **kwargs)
display(self)
¶
Display the chart without toolbar.
Source code in geemap/chart.py
def display(self) -> None:
"""
Display the chart without toolbar.
"""
self._set_plt_options()
display(self.figure)
get_chart_type(self)
¶
Get the current chart type.
Returns:
| Type | Description |
|---|---|
Optional[str] |
The current chart type, or None if no chart type is set. |
Source code in geemap/chart.py
def get_chart_type(self) -> Optional[str]:
"""
Get the current chart type.
Returns:
Optional[str]: The current chart type, or None if no chart type is set.
"""
return self.chart_type
get_data_table(self)
¶
Get the DataTable used by the chart.
Returns:
| Type | Description |
|---|---|
DataTable |
The DataTable instance containing the chart data. |
Source code in geemap/chart.py
def get_data_table(self) -> DataTable:
"""
Get the DataTable used by the chart.
Returns:
DataTable: The DataTable instance containing the chart data.
"""
return self.data_table
save_png(self, filepath='chart.png', scale=1.0)
¶
Save the chart as a PNG image.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
filepath |
str |
The path to save the PNG image. Defaults to 'chart.png'. |
'chart.png' |
scale |
float |
The scale factor for the image. Defaults to 1.0. |
1.0 |
Source code in geemap/chart.py
def save_png(self, filepath: str = "chart.png", scale: float = 1.0) -> None:
"""
Save the chart as a PNG image.
Args:
filepath (str): The path to save the PNG image. Defaults to 'chart.png'.
scale (float): The scale factor for the image. Defaults to 1.0.
"""
self.figure.save_png(filepath, scale=scale)
set_chart_type(self, chart_type, clear=True, **kwargs)
¶
Sets the chart type and other chart properties.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
required |
clear |
bool |
Whether to clear the current chart before setting a new one. Defaults to True. |
True |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The Chart instance with the chart set. |
Source code in geemap/chart.py
def set_chart_type(
self,
chart_type: str,
clear: bool = True,
**kwargs: Any,
) -> None:
"""
Sets the chart type and other chart properties.
Args:
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
clear (bool): Whether to clear the current chart before setting a new one.
Defaults to True.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects.
Returns:
Chart: The Chart instance with the chart set.
"""
if clear:
plt.clear()
self.chart_type = chart_type
x_cols = self.x_cols
y_cols = self.y_cols
colors = self.colors
if x_cols is None:
x_cols = [self.data_table.columns[0]]
if y_cols is None:
y_cols = [self.data_table.columns[1]]
if isinstance(x_cols, str):
x_cols = [x_cols]
if isinstance(y_cols, str):
y_cols = [y_cols]
if len(x_cols) == 1 and len(y_cols) > 1:
x_cols = x_cols * len(y_cols)
if "axes_options" not in kwargs:
kwargs["axes_options"] = {
"x": {"label_offset": "30px"},
"y": {"label_offset": "40px"},
}
if chart_type == "PieChart":
if colors is None:
colors = [
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf",
] # Default pie chart colors
else:
if colors is None:
colors = [
"blue",
"orange",
"green",
"red",
"purple",
"brown",
] # Default colors
if chart_type == "IntervalChart":
x = self.data_table[x_cols[0]]
y = [self.data_table[y_col] for y_col in y_cols]
if "fill" not in kwargs:
kwargs["fill"] = "between"
self.chart = plt.plot(
x,
y,
colors=colors,
**kwargs,
)
else:
for i, (x_col, y_col) in enumerate(zip(x_cols, y_cols)):
color = colors[i % len(colors)]
if "display_legend" not in kwargs and len(y_cols) > 1:
kwargs["display_legend"] = True
kwargs["labels"] = [y_col]
else:
kwargs["labels"] = [y_col]
x = self.data_table[x_col]
y = self.data_table[y_col]
if isinstance(x, pd.Series) and (
not pd.api.types.is_datetime64_any_dtype(x)
):
x = x.tolist()
if isinstance(y, pd.Series) and (
not pd.api.types.is_datetime64_any_dtype(y)
):
y = y.tolist()
if chart_type == "ScatterChart":
self.chart = plt.scatter(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "LineChart":
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "AreaChart":
if "fill" not in kwargs:
kwargs["fill"] = "bottom"
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "ColumnChart":
self.chart = plt.bar(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "BarChart":
if "orientation" not in kwargs:
kwargs["orientation"] = "horizontal"
self.chart = plt.bar(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "AreaChart":
if "fill" not in kwargs:
kwargs["fill"] = "bottom"
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
elif chart_type == "PieChart":
kwargs.pop("labels", None)
self.chart = plt.pie(
sizes=y,
labels=x,
colors=colors[: len(x)],
**kwargs,
)
elif chart_type == "Table":
output = widgets.Output(**kwargs)
with output:
display(self.data_table)
output.layout = widgets.Layout(width="50%")
display(output)
else:
self.chart = plt.plot(
x,
y,
colors=[color],
**kwargs,
)
self._set_plt_options()
set_data_table(self, data)
¶
Set a new DataTable for the chart.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
Union[Dict[str, List[Any]], pd.DataFrame] |
The new data to be |
required |
Source code in geemap/chart.py
def set_data_table(self, data: Union[Dict[str, List[Any]], pd.DataFrame]) -> None:
"""
Set a new DataTable for the chart.
Args:
data (Union[Dict[str, List[Any]], pd.DataFrame]): The new data to be
used for the chart.
"""
self.data_table = DataTable(data)
set_options(self, **options)
¶
Set additional options for the chart.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
**options |
Any |
Additional options to set for the chart. |
{} |
Source code in geemap/chart.py
def set_options(self, **options: Any) -> None:
"""
Set additional options for the chart.
Args:
**options: Additional options to set for the chart.
"""
for key, value in options.items():
setattr(self.figure, key, value)
DataTable (DataFrame)
¶
Source code in geemap/chart.py
class DataTable(pd.DataFrame):
def __init__(
self,
data: Union[Dict[str, List[Any]], pd.DataFrame, None] = None,
date_column: Optional[str] = None,
date_format: Optional[str] = None,
**kwargs: Any,
) -> None:
"""
Initializes the DataTable with data.
Args:
data (Union[Dict[str, List[Any]], pd.DataFrame, None]): The input
data. If it's a dictionary, it will be converted to a DataFrame.
date_column (Optional[str]): The date column to convert to a DataFrame.
date_format (Optional[str]): The format of the date column.
**kwargs: Additional keyword arguments to pass to the pd.DataFrame
constructor.
"""
if isinstance(data, ee.FeatureCollection):
data = ee_to_df(data)
elif isinstance(data, ee.List):
data = data.getInfo()
kwargs["columns"] = data[0]
data = data[1:]
super().__init__(data, **kwargs)
if date_column is not None:
self[date_column] = pd.to_datetime(
self[date_column], format=date_format, errors="coerce"
)
__init__(self, data=None, date_column=None, date_format=None, **kwargs)
special
¶
Initializes the DataTable with data.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
data |
Union[Dict[str, List[Any]], pd.DataFrame, None] |
The input data. If it's a dictionary, it will be converted to a DataFrame. |
None |
date_column |
Optional[str] |
The date column to convert to a DataFrame. |
None |
date_format |
Optional[str] |
The format of the date column. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the pd.DataFrame constructor. |
{} |
Source code in geemap/chart.py
def __init__(
self,
data: Union[Dict[str, List[Any]], pd.DataFrame, None] = None,
date_column: Optional[str] = None,
date_format: Optional[str] = None,
**kwargs: Any,
) -> None:
"""
Initializes the DataTable with data.
Args:
data (Union[Dict[str, List[Any]], pd.DataFrame, None]): The input
data. If it's a dictionary, it will be converted to a DataFrame.
date_column (Optional[str]): The date column to convert to a DataFrame.
date_format (Optional[str]): The format of the date column.
**kwargs: Additional keyword arguments to pass to the pd.DataFrame
constructor.
"""
if isinstance(data, ee.FeatureCollection):
data = ee_to_df(data)
elif isinstance(data, ee.List):
data = data.getInfo()
kwargs["columns"] = data[0]
data = data[1:]
super().__init__(data, **kwargs)
if date_column is not None:
self[date_column] = pd.to_datetime(
self[date_column], format=date_format, errors="coerce"
)
Feature_ByFeature (BarChart)
¶
An object to define variables and get_data method for features by feature.
Source code in geemap/chart.py
class Feature_ByFeature(BarChart):
"""An object to define variables and get_data method for features by feature."""
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
x_property: str,
y_properties: List[str],
name: str = "feature.byFeature",
**kwargs: Any,
):
"""
Initializes the Feature_ByFeature with the given features, x_property,
y_properties, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
x_property (str): The property to use for the x-axis.
y_properties (List[str]): The properties to use for the y-axis.
name (str, optional): The name of the chart. Defaults to
'feature.byFeature'.
**kwargs: Additional keyword arguments to set as attributes.
"""
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]]:
"""
Gets the data for the chart.
Args:
x_property (str): The property to use for the x-axis.
y_properties (List[str]): The properties to use for the y-axis.
Returns:
Tuple[List[Any], List[Any]]: The x and y data for the chart.
"""
x_data = list(self.df[x_property])
y_data = list(self.df[y_properties].values.T)
return x_data, y_data
__init__(self, features, x_property, y_properties, name='feature.byFeature', **kwargs)
special
¶
Initializes the Feature_ByFeature with the given features, x_property, y_properties, and name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection | pd.DataFrame |
The features to plot. |
required |
x_property |
str |
The property to use for the x-axis. |
required |
y_properties |
List[str] |
The properties to use for the y-axis. |
required |
name |
str |
The name of the chart. Defaults to 'feature.byFeature'. |
'feature.byFeature' |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Source code in geemap/chart.py
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
x_property: str,
y_properties: List[str],
name: str = "feature.byFeature",
**kwargs: Any,
):
"""
Initializes the Feature_ByFeature with the given features, x_property,
y_properties, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
x_property (str): The property to use for the x-axis.
y_properties (List[str]): The properties to use for the y-axis.
name (str, optional): The name of the chart. Defaults to
'feature.byFeature'.
**kwargs: Additional keyword arguments to set as attributes.
"""
default_labels = y_properties
super().__init__(features, default_labels, name, **kwargs)
self.x_data, self.y_data = self.get_data(x_property, y_properties)
get_data(self, x_property, y_properties)
¶
Gets the data for the chart.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x_property |
str |
The property to use for the x-axis. |
required |
y_properties |
List[str] |
The properties to use for the y-axis. |
required |
Returns:
| Type | Description |
|---|---|
Tuple[List[Any], List[Any]] |
The x and y data for the chart. |
Source code in geemap/chart.py
def get_data(
self, x_property: str, y_properties: List[str]
) -> Tuple[List[Any], List[Any]]:
"""
Gets the data for the chart.
Args:
x_property (str): The property to use for the x-axis.
y_properties (List[str]): The properties to use for the y-axis.
Returns:
Tuple[List[Any], List[Any]]: The x and y data for the chart.
"""
x_data = list(self.df[x_property])
y_data = list(self.df[y_properties].values.T)
return x_data, y_data
Feature_ByProperty (BarChart)
¶
An object to define variables and get_data method for features by property.
Source code in geemap/chart.py
class Feature_ByProperty(BarChart):
"""An object to define variables and get_data method for features by property."""
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
x_properties: Union[List[str], Dict[str, str]],
series_property: str,
name: str = "feature.byProperty",
**kwargs: Any,
):
"""
Initializes the Feature_ByProperty with the given features, x_properties,
series_property, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
x_properties (List[str] | Dict[str, str]): The properties to use for
the x-axis.
series_property (str): The property to use for labeling the series.
name (str, optional): The name of the chart. Defaults to
'feature.byProperty'.
**kwargs: Additional keyword arguments to set as attributes.
Raises:
Exception: If 'labels' is in kwargs.
"""
default_labels = None
super().__init__(features, default_labels, name, **kwargs)
if "labels" in kwargs:
raise Exception("Please remove labels in kwargs and try again.")
self.labels = list(self.df[series_property])
self.x_data, self.y_data = self.get_data(x_properties)
def get_data(
self, x_properties: Union[List[str], Dict[str, str]]
) -> Tuple[List[Any], List[Any]]:
"""
Gets the data for the chart.
Args:
x_properties (List[str] | Dict[str, str]): The properties to use for
the x-axis.
Returns:
Tuple[List[Any], List[Any]]: The x and y data for the chart.
Raises:
Exception: 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
else:
raise Exception("x_properties must be a list or dictionary.")
return x_data, y_data
__init__(self, features, x_properties, series_property, name='feature.byProperty', **kwargs)
special
¶
Initializes the Feature_ByProperty with the given features, x_properties, series_property, and name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection | pd.DataFrame |
The features to plot. |
required |
x_properties |
List[str] | Dict[str, str] |
The properties to use for the x-axis. |
required |
series_property |
str |
The property to use for labeling the series. |
required |
name |
str |
The name of the chart. Defaults to 'feature.byProperty'. |
'feature.byProperty' |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Exceptions:
| Type | Description |
|---|---|
Exception |
If 'labels' is in kwargs. |
Source code in geemap/chart.py
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
x_properties: Union[List[str], Dict[str, str]],
series_property: str,
name: str = "feature.byProperty",
**kwargs: Any,
):
"""
Initializes the Feature_ByProperty with the given features, x_properties,
series_property, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
x_properties (List[str] | Dict[str, str]): The properties to use for
the x-axis.
series_property (str): The property to use for labeling the series.
name (str, optional): The name of the chart. Defaults to
'feature.byProperty'.
**kwargs: Additional keyword arguments to set as attributes.
Raises:
Exception: If 'labels' is in kwargs.
"""
default_labels = None
super().__init__(features, default_labels, name, **kwargs)
if "labels" in kwargs:
raise Exception("Please remove labels in kwargs and try again.")
self.labels = list(self.df[series_property])
self.x_data, self.y_data = self.get_data(x_properties)
get_data(self, x_properties)
¶
Gets the data for the chart.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x_properties |
List[str] | Dict[str, str] |
The properties to use for the x-axis. |
required |
Returns:
| Type | Description |
|---|---|
Tuple[List[Any], List[Any]] |
The x and y data for the chart. |
Exceptions:
| Type | Description |
|---|---|
Exception |
If x_properties is not a list or dictionary. |
Source code in geemap/chart.py
def get_data(
self, x_properties: Union[List[str], Dict[str, str]]
) -> Tuple[List[Any], List[Any]]:
"""
Gets the data for the chart.
Args:
x_properties (List[str] | Dict[str, str]): The properties to use for
the x-axis.
Returns:
Tuple[List[Any], List[Any]]: The x and y data for the chart.
Raises:
Exception: 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
else:
raise Exception("x_properties must be a list or dictionary.")
return x_data, y_data
Feature_Groups (BarChart)
¶
An object to define variables and get_data method for feature groups.
Source code in geemap/chart.py
class Feature_Groups(BarChart):
"""An object to define variables and get_data method for feature groups."""
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
x_property: str,
y_property: str,
series_property: str,
name: str = "feature.groups",
type: str = "stacked",
**kwargs: Any,
):
"""
Initializes the Feature_Groups with the given features, x_property,
y_property, series_property, name, and type.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
x_property (str): The property to use for the x-axis.
y_property (str): The property to use for the y-axis.
series_property (str): The property to use for labeling the series.
name (str, optional): The name of the chart. Defaults to 'feature.groups'.
type (str, optional): The type of bar chart ('grouped' or 'stacked').
Defaults to 'stacked'.
**kwargs: Additional keyword arguments to set as attributes.
"""
df = ee_to_df(features)
self.unique_series_values = df[series_property].unique().tolist()
default_labels = [str(x) for x in self.unique_series_values]
self.yProperty = y_property
super().__init__(features, default_labels, name, type, **kwargs)
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)
def get_column_names(self, series_property: str, y_property: str) -> List[str]:
"""
Gets the new column names for the DataFrame.
Args:
series_property (str): The property to use for labeling the series.
y_property (str): The property to use for the y-axis.
Returns:
List[str]: The new column names.
"""
new_column_names = []
for value in self.unique_series_values:
sample_filter = (self.df[series_property] == value).map({True: 1, False: 0})
column_name = str(y_property) "_" str(value)
self.df[column_name] = self.df[y_property] * sample_filter
new_column_names.append(column_name)
return new_column_names
def get_data(
self, x_property: str, new_column_names: List[str]
) -> Tuple[List[Any], List[Any]]:
"""
Gets the data for the chart.
Args:
x_property (str): The property to use for the x-axis.
new_column_names (List[str]): The new column names for the y-axis.
Returns:
Tuple[List[Any], List[Any]]: The x and y data for the chart.
"""
x_data = list(self.df[x_property])
y_data = [self.df[x] for x in new_column_names]
return x_data, y_data
__init__(self, features, x_property, y_property, series_property, name='feature.groups', type='stacked', **kwargs)
special
¶
Initializes the Feature_Groups with the given features, x_property, y_property, series_property, name, and type.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection | pd.DataFrame |
The features to plot. |
required |
x_property |
str |
The property to use for the x-axis. |
required |
y_property |
str |
The property to use for the y-axis. |
required |
series_property |
str |
The property to use for labeling the series. |
required |
name |
str |
The name of the chart. Defaults to 'feature.groups'. |
'feature.groups' |
type |
str |
The type of bar chart ('grouped' or 'stacked'). Defaults to 'stacked'. |
'stacked' |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Source code in geemap/chart.py
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
x_property: str,
y_property: str,
series_property: str,
name: str = "feature.groups",
type: str = "stacked",
**kwargs: Any,
):
"""
Initializes the Feature_Groups with the given features, x_property,
y_property, series_property, name, and type.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
x_property (str): The property to use for the x-axis.
y_property (str): The property to use for the y-axis.
series_property (str): The property to use for labeling the series.
name (str, optional): The name of the chart. Defaults to 'feature.groups'.
type (str, optional): The type of bar chart ('grouped' or 'stacked').
Defaults to 'stacked'.
**kwargs: Additional keyword arguments to set as attributes.
"""
df = ee_to_df(features)
self.unique_series_values = df[series_property].unique().tolist()
default_labels = [str(x) for x in self.unique_series_values]
self.yProperty = y_property
super().__init__(features, default_labels, name, type, **kwargs)
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)
get_column_names(self, series_property, y_property)
¶
Gets the new column names for the DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
series_property |
str |
The property to use for labeling the series. |
required |
y_property |
str |
The property to use for the y-axis. |
required |
Returns:
| Type | Description |
|---|---|
List[str] |
The new column names. |
Source code in geemap/chart.py
def get_column_names(self, series_property: str, y_property: str) -> List[str]:
"""
Gets the new column names for the DataFrame.
Args:
series_property (str): The property to use for labeling the series.
y_property (str): The property to use for the y-axis.
Returns:
List[str]: The new column names.
"""
new_column_names = []
for value in self.unique_series_values:
sample_filter = (self.df[series_property] == value).map({True: 1, False: 0})
column_name = str(y_property) "_" str(value)
self.df[column_name] = self.df[y_property] * sample_filter
new_column_names.append(column_name)
return new_column_names
get_data(self, x_property, new_column_names)
¶
Gets the data for the chart.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
x_property |
str |
The property to use for the x-axis. |
required |
new_column_names |
List[str] |
The new column names for the y-axis. |
required |
Returns:
| Type | Description |
|---|---|
Tuple[List[Any], List[Any]] |
The x and y data for the chart. |
Source code in geemap/chart.py
def get_data(
self, x_property: str, new_column_names: List[str]
) -> Tuple[List[Any], List[Any]]:
"""
Gets the data for the chart.
Args:
x_property (str): The property to use for the x-axis.
new_column_names (List[str]): The new column names for the y-axis.
Returns:
Tuple[List[Any], List[Any]]: The x and y data for the chart.
"""
x_data = list(self.df[x_property])
y_data = [self.df[x] for x in new_column_names]
return x_data, y_data
LineChart (BarChart)
¶
A class to define variables and get_data method for a line chart.
Source code in geemap/chart.py
class LineChart(BarChart):
"""A class to define variables and get_data method for a line chart."""
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
labels: List[str],
name: str = "line.chart",
**kwargs: Any,
):
"""
Initializes the LineChart with the given features, labels, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
labels (List[str]): The labels for the chart.
name (str, optional): The name of the chart. Defaults to 'line.chart'.
**kwargs: Additional keyword arguments to set as attributes.
"""
super().__init__(features, labels, name, **kwargs)
def plot_chart(self) -> None:
"""
Plots the line chart.
"""
fig = plt.figure(
title=self.title,
legend_location=self.legend_location,
)
self.line_chart = plt.plot(
self.x_data,
self.y_data,
label=self.labels,
)
self.generate_tooltip()
plt.ylim(*self.get_ylim())
if self.x_label:
plt.xlabel(self.x_label)
if self.y_label:
plt.ylabel(self.y_label)
if self.width:
fig.layout.width = self.width
if self.height:
fig.layout.height = self.height
plt.show()
__init__(self, features, labels, name='line.chart', **kwargs)
special
¶
Initializes the LineChart with the given features, labels, and name.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection | pd.DataFrame |
The features to plot. |
required |
labels |
List[str] |
The labels for the chart. |
required |
name |
str |
The name of the chart. Defaults to 'line.chart'. |
'line.chart' |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Source code in geemap/chart.py
def __init__(
self,
features: Union[ee.FeatureCollection, pd.DataFrame],
labels: List[str],
name: str = "line.chart",
**kwargs: Any,
):
"""
Initializes the LineChart with the given features, labels, and name.
Args:
features (ee.FeatureCollection | pd.DataFrame): The features to plot.
labels (List[str]): The labels for the chart.
name (str, optional): The name of the chart. Defaults to 'line.chart'.
**kwargs: Additional keyword arguments to set as attributes.
"""
super().__init__(features, labels, name, **kwargs)
plot_chart(self)
¶
Plots the line chart.
Source code in geemap/chart.py
def plot_chart(self) -> None:
"""
Plots the line chart.
"""
fig = plt.figure(
title=self.title,
legend_location=self.legend_location,
)
self.line_chart = plt.plot(
self.x_data,
self.y_data,
label=self.labels,
)
self.generate_tooltip()
plt.ylim(*self.get_ylim())
if self.x_label:
plt.xlabel(self.x_label)
if self.y_label:
plt.ylabel(self.y_label)
if self.width:
fig.layout.width = self.width
if self.height:
fig.layout.height = self.height
plt.show()
array_to_df(y_values, x_values=None, y_labels=None, x_label='x', axis=1, **kwargs)
¶
Converts arrays or lists of y-values and optional x-values into a pandas DataFrame.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
y_values |
Union[ee.Array, ee.List, List[List[float]]] |
The y-values to convert. |
required |
x_values |
Optional[Union[ee.Array, ee.List, List[float]]] |
The x-values to convert. Defaults to None. |
None |
y_labels |
Optional[List[str]] |
The labels for the y-values. Defaults to None. |
None |
x_label |
str |
The label for the x-values. Defaults to "x". |
'x' |
axis |
int |
The axis along which to transpose the y-values if needed. Defaults to 1. |
1 |
**kwargs |
Any |
Additional keyword arguments to pass to the pandas DataFrame constructor. |
{} |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
The resulting DataFrame. |
Source code in geemap/chart.py
def array_to_df(
y_values: Union[ee.Array, ee.List, List[List[float]]],
x_values: Optional[Union[ee.Array, ee.List, List[float]]] = None,
y_labels: Optional[List[str]] = None,
x_label: str = "x",
axis: int = 1,
**kwargs: Any,
) -> pd.DataFrame:
"""
Converts arrays or lists of y-values and optional x-values into a pandas DataFrame.
Args:
y_values (Union[ee.Array, ee.List, List[List[float]]]): The y-values to convert.
x_values (Optional[Union[ee.Array, ee.List, List[float]]]): The x-values to convert.
Defaults to None.
y_labels (Optional[List[str]]): The labels for the y-values. Defaults to None.
x_label (str): The label for the x-values. Defaults to "x".
axis (int): The axis along which to transpose the y-values if needed. Defaults to 1.
**kwargs: Additional keyword arguments to pass to the pandas DataFrame constructor.
Returns:
pd.DataFrame: The resulting DataFrame.
"""
if isinstance(y_values, ee.Array) or isinstance(y_values, ee.List):
y_values = y_values.getInfo()
if isinstance(x_values, ee.Array) or isinstance(x_values, ee.List):
x_values = x_values.getInfo()
if axis == 0:
y_values = np.transpose(y_values)
if x_values is None:
x_values = list(range(1, len(y_values[0]) 1))
data = {x_label: x_values}
if not isinstance(y_values[0], list):
y_values = [y_values]
if y_labels is None:
y_labels = [
f"y{str(i 1).zfill(len(str(len(y_values))))}" for i in range(len(y_values))
]
if len(y_labels) != len(y_values):
raise ValueError("The length of y_labels must match the length of y_values.")
for i, series in enumerate(y_labels):
data[series] = y_values[i]
df = pd.DataFrame(data, **kwargs)
return df
array_values(array, x_labels=None, axis=1, series_names=None, chart_type='LineChart', colors=None, title=None, x_label=None, y_label=None, **kwargs)
¶
Converts an array to a DataFrame and generates a chart.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
array |
Union[ee.Array, ee.List, List[List[float]]] |
The array to convert. |
required |
x_labels |
Optional[Union[ee.Array, ee.List, List[float]]] |
The labels for the x-axis. Defaults to None. |
None |
axis |
int |
The axis along which to transpose the array if needed. Defaults to 1. |
1 |
series_names |
Optional[List[str]] |
The names of the series. Defaults to None. |
None |
chart_type |
str |
The type of chart to create. Defaults to "LineChart". |
'LineChart' |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to None. |
None |
title |
Optional[str] |
The title of the chart. Defaults to None. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to None. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to None. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the Chart constructor. |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The generated chart. |
Source code in geemap/chart.py
def array_values(
array: Union[ee.Array, ee.List, List[List[float]]],
x_labels: Optional[Union[ee.Array, ee.List, List[float]]] = None,
axis: int = 1,
series_names: Optional[List[str]] = None,
chart_type: str = "LineChart",
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> Chart:
"""
Converts an array to a DataFrame and generates a chart.
Args:
array (Union[ee.Array, ee.List, List[List[float]]]): The array to convert.
x_labels (Optional[Union[ee.Array, ee.List, List[float]]]): The labels
for the x-axis. Defaults to None.
axis (int): The axis along which to transpose the array if needed. Defaults to 1.
series_names (Optional[List[str]]): The names of the series. Defaults to None.
chart_type (str): The type of chart to create. Defaults to "LineChart".
colors (Optional[List[str]]): The colors to use for the chart. Defaults to None.
title (Optional[str]): The title of the chart. Defaults to None.
x_label (Optional[str]): The label for the x-axis. Defaults to None.
y_label (Optional[str]): The label for the y-axis. Defaults to None.
**kwargs: Additional keyword arguments to pass to the Chart constructor.
Returns:
Chart: The generated chart.
"""
df = array_to_df(array, x_values=x_labels, y_labels=series_names, axis=axis)
fig = Chart(
df,
x_cols=["x"],
y_cols=df.columns.tolist()[1:],
chart_type=chart_type,
colors=colors,
title=title,
x_label=x_label,
y_label=y_label,
**kwargs,
)
return fig
doy_series_by_year(image_collection, band_name, region=None, region_reducer=None, scale=None, same_day_reducer=None, start_day=1, end_day=366, chart_type='LineChart', colors=None, title=None, x_label=None, y_label=None, **kwargs)
¶
Generates a time series chart of an image collection for a specific region over multiple years.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_collection |
ee.ImageCollection |
The image collection to analyze. |
required |
band_name |
str |
The name of the band to analyze. |
required |
region |
Optional[Union[ee.Geometry, ee.FeatureCollection]] |
The region to analyze. |
None |
region_reducer |
Optional[Union[str, ee.Reducer]] |
The reducer type for zonal statistics. |
None |
scale |
Optional[int] |
The scale in meters at which to perform the analysis. |
None |
same_day_reducer |
Optional[Union[str, ee.Reducer]] |
The reducer type for daily statistics. |
None |
start_day |
int |
The start day of the year. |
1 |
end_day |
int |
The end day of the year. |
366 |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
'LineChart' |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to a predefined list of colors. |
None |
title |
Optional[str] |
The title of the chart. Defaults to the chart type. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to an empty string. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to an empty string. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. For axes_options, see https://bqplot.github.io/bqplot/api/axes |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The generated chart. |
Source code in geemap/chart.py
def doy_series_by_year(
image_collection: ee.ImageCollection,
band_name: str,
region: Optional[Union[ee.Geometry, ee.FeatureCollection]] = None,
region_reducer: Optional[Union[str, ee.Reducer]] = None,
scale: Optional[int] = None,
same_day_reducer: Optional[Union[str, ee.Reducer]] = None,
start_day: int = 1,
end_day: int = 366,
chart_type: str = "LineChart",
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> Chart:
"""
Generates a time series chart of an image collection for a specific region
over multiple years.
Args:
image_collection (ee.ImageCollection): The image collection to analyze.
band_name (str): The name of the band to analyze.
region (Optional[Union[ee.Geometry, ee.FeatureCollection]]): The region
to analyze.
region_reducer (Optional[Union[str, ee.Reducer]]): The reducer type for
zonal statistics.
scale (Optional[int]): The scale in meters at which to perform the analysis.
same_day_reducer (Optional[Union[str, ee.Reducer]]): The reducer type
for daily statistics.
start_day (int): The start day of the year.
end_day (int): The end day of the year.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
Returns:
Chart: The generated chart.
"""
# Function to add day-of-year ('doy') and year properties to each image.
def set_doys(collection):
def add_doy(img):
date = img.date()
year = date.get("year")
doy = date.getRelative("day", "year").floor().add(1)
return img.set({"doy": doy, "year": year})
return collection.map(add_doy)
# Set default values and filters if parameters are not provided.
region_reducer = region_reducer or ee.Reducer.mean()
same_day_reducer = same_day_reducer or ee.Reducer.mean()
# Optionally filter the image collection by region.
filtered_collection = image_collection
if region:
filtered_collection = filtered_collection.filterBounds(region)
filtered_collection = set_doys(filtered_collection)
# Filter image collection by day of year.
filtered_collection = filtered_collection.filter(
ee.Filter.calendarRange(start_day, end_day, "day_of_year")
)
# Generate a feature for each (doy, value, year) combination.
def create_feature(image):
value = (
image.select(band_name)
.reduceRegion(reducer=region_reducer, geometry=region, scale=scale)
.get(band_name)
) # Get the reduced value for the given band.
return ee.Feature(
None, {"doy": image.get("doy"), "year": image.get("year"), "value": value}
)
tuples = filtered_collection.map(create_feature)
# Group by unique (doy, year) pairs.
distinct_doy_year = tuples.distinct(["doy", "year"])
# Join the original tuples with the distinct (doy, year) pairs.
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
)
# For each (doy, year), reduce the values of the joined features.
def reduce_features(doy_year):
features = ee.FeatureCollection(ee.List(doy_year.get("matches")))
value = features.aggregate_array("value").reduce(same_day_reducer)
return doy_year.set("value", value)
reduced = joined.map(reduce_features)
df = ee_to_df(reduced, columns=["doy", "year", "value"])
df = pivot_df(df, index="doy", columns="year", values="value")
y_cols = df.columns.tolist()[1:]
x_cols = "doy"
fig = Chart(
df,
chart_type,
x_cols,
y_cols,
colors,
title,
x_label,
y_label,
**kwargs,
)
return fig
feature_by_feature(features, x_property, y_properties, **kwargs)
¶
Generates a Chart from a set of features. Plots the value of one or more properties for each feature. Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyfeature
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection |
The feature collection to generate a chart from. |
required |
x_property |
str |
Features labeled by x_property. |
required |
y_properties |
List[str] |
Values of y_properties. |
required |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Exceptions:
| Type | Description |
|---|---|
Exception |
Errors when creating the chart. |
Source code in geemap/chart.py
def feature_by_feature(
features: ee.FeatureCollection,
x_property: str,
y_properties: List[str],
**kwargs: Any,
) -> None:
"""
Generates a Chart from a set of features. Plots the value of one or more
properties for each feature.
Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyfeature
Args:
features (ee.FeatureCollection): The feature collection to generate a chart from.
x_property (str): Features labeled by x_property.
y_properties (List[str]): Values of y_properties.
**kwargs: Additional keyword arguments to set as attributes.
Raises:
Exception: Errors when creating the chart.
"""
bar = Feature_ByFeature(
features=features, x_property=x_property, y_properties=y_properties, **kwargs
)
try:
bar.plot_chart()
except Exception as e:
raise Exception(e)
feature_by_property(features, x_properties, series_property, **kwargs)
¶
Generates a Chart from a set of features. Plots property values of one or more features. Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyproperty
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection |
The features to include in the chart. |
required |
x_properties |
list | dict |
One of (1) a list of properties to be plotted on the x-axis; or (2) a (property, label) dictionary specifying labels for properties to be used as values on the x-axis. |
required |
series_property |
str |
The name of the property used to label each feature in the legend. |
required |
Exceptions:
| Type | Description |
|---|---|
Exception |
If the provided xProperties is not a list or dict. |
Exception |
If the chart fails to create. |
Source code in geemap/chart.py
def feature_by_property(
features: ee.FeatureCollection,
x_properties: Union[list, dict],
series_property: str,
**kwargs,
):
"""Generates a Chart from a set of features. Plots property values of one or
more features.
Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturebyproperty
Args:
features (ee.FeatureCollection): The features to include in the chart.
x_properties (list | dict): One of (1) a list of properties to be
plotted on the x-axis; or (2) a (property, label) dictionary
specifying labels for properties to be used as values on the x-axis.
series_property (str): The name of the property used to label each
feature in the legend.
Raises:
Exception: If the provided xProperties is not a list or dict.
Exception: If the chart fails to create.
"""
bar = Feature_ByProperty(
features=features,
x_properties=x_properties,
series_property=series_property,
**kwargs,
)
try:
bar.plot_chart()
except Exception as e:
raise Exception(e)
feature_groups(features, x_property, y_property, series_property, **kwargs)
¶
Generates a Chart from a set of features. Plots the value of one property for each feature.
Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturegroups
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection |
The feature collection to make a chart from. |
required |
x_property |
str |
Features labeled by xProperty. |
required |
y_property |
str |
Features labeled by yProperty. |
required |
series_property |
str |
The property used to label each feature in the legend. |
required |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Exceptions:
| Type | Description |
|---|---|
Exception |
Errors when creating the chart. |
Source code in geemap/chart.py
def feature_groups(
features: ee.FeatureCollection,
x_property: str,
y_property: str,
series_property: str,
**kwargs: Any,
) -> None:
"""
Generates a Chart from a set of features.
Plots the value of one property for each feature.
Reference:
https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturegroups
Args:
features (ee.FeatureCollection): The feature collection to make a chart from.
x_property (str): Features labeled by xProperty.
y_property (str): Features labeled by yProperty.
series_property (str): The property used to label each feature in the legend.
**kwargs: Additional keyword arguments to set as attributes.
Raises:
Exception: Errors when creating the chart.
"""
bar = Feature_Groups(
features=features,
x_property=x_property,
y_property=y_property,
series_property=series_property,
**kwargs,
)
try:
bar.plot_chart()
except Exception as e:
raise Exception(e)
feature_histogram(features, property, max_buckets=None, min_bucket_width=None, show=True, **kwargs)
¶
Generates a Chart from a set of features. Computes and plots a histogram of the given property. - X-axis = Histogram buckets (of property value). - Y-axis = Frequency
Reference: https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturehistogram
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
features |
ee.FeatureCollection |
The features to include in the chart. |
required |
property |
str |
The name of the property to generate the histogram for. |
required |
max_buckets |
int |
The maximum number of buckets (bins) to use when building a histogram; will be rounded up to a power of 2. |
None |
min_bucket_width |
float |
The minimum histogram bucket width, or null to allow any power of 2. |
None |
show |
bool |
Whether to show the chart. If not, it will return the bqplot chart object, which can be used to retrieve data for the chart. Defaults to True. |
True |
**kwargs |
Any |
Additional keyword arguments to set as attributes. |
{} |
Exceptions:
| Type | Description |
|---|---|
Exception |
If the provided xProperties is not a list or dict. |
Exception |
If the chart fails to create. |
Returns:
| Type | Description |
|---|---|
Optional[Any] |
The bqplot chart object if show is False, otherwise None. |
Source code in geemap/chart.py
def feature_histogram(
features: ee.FeatureCollection,
property: str,
max_buckets: Optional[int] = None,
min_bucket_width: Optional[float] = None,
show: bool = True,
**kwargs: Any,
) -> Optional[Any]:
"""
Generates a Chart from a set of features.
Computes and plots a histogram of the given property.
- X-axis = Histogram buckets (of property value).
- Y-axis = Frequency
Reference:
https://developers.google.com/earth-engine/guides/charts_feature#uichartfeaturehistogram
Args:
features (ee.FeatureCollection): The features to include in the chart.
property (str): The name of the property to generate the histogram for.
max_buckets (int, optional): The maximum number of buckets (bins) to use
when building a histogram; will be rounded up to a power of 2.
min_bucket_width (float, optional): The minimum histogram bucket width,
or null to allow any power of 2.
show (bool, optional): Whether to show the chart. If not, it will return
the bqplot chart object, which can be used to retrieve data for the
chart. Defaults to True.
**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.
Returns:
Optional[Any]: The bqplot chart object if show is False, otherwise None.
"""
import math
if not isinstance(features, ee.FeatureCollection):
raise Exception("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)}"
)
def nextPowerOf2(n):
return pow(2, math.ceil(math.log2(n)))
def grow_bin(bin_size, ref):
while bin_size < ref:
bin_size *= 2
return bin_size
try:
raw_data = pd.to_numeric(
pd.Series(features.aggregate_array(property).getInfo())
)
y_data = raw_data.tolist()
if "ylim" in kwargs:
min_value = kwargs["ylim"][0]
max_value = kwargs["ylim"][1]
else:
min_value = raw_data.min()
max_value = raw_data.max()
data_range = max_value - min_value
if not max_buckets:
initial_bin_size = nextPowerOf2(data_range / pow(2, 8))
if min_bucket_width:
if min_bucket_width < initial_bin_size:
bin_size = grow_bin(min_bucket_width, initial_bin_size)
else:
bin_size = min_bucket_width
else:
bin_size = initial_bin_size
else:
initial_bin_size = math.ceil(data_range / nextPowerOf2(max_buckets))
if min_bucket_width:
if min_bucket_width < initial_bin_size:
bin_size = grow_bin(min_bucket_width, initial_bin_size)
else:
bin_size = min_bucket_width
else:
bin_size = initial_bin_size
start_bins = (math.floor(min_value / bin_size) * bin_size) - (bin_size / 2)
end_bins = (math.ceil(max_value / bin_size) * bin_size) (bin_size / 2)
if start_bins < min_value:
y_data.append(start_bins)
else:
y_data[y_data.index(min_value)] = start_bins
if end_bins > max_value:
y_data.append(end_bins)
else:
y_data[y_data.index(max_value)] = end_bins
num_bins = math.floor((end_bins - start_bins) / bin_size)
if "title" not in kwargs:
title = ""
else:
title = kwargs["title"]
fig = plt.figure(title=title)
if "width" in kwargs:
fig.layout.width = kwargs["width"]
if "height" in kwargs:
fig.layout.height = kwargs["height"]
if "x_label" not in kwargs:
x_label = ""
else:
x_label = kwargs["x_label"]
if "y_label" not in kwargs:
y_label = ""
else:
y_label = kwargs["y_label"]
histogram = plt.hist(
sample=y_data,
bins=num_bins,
axes_options={"count": {"label": y_label}, "sample": {"label": x_label}},
)
if "colors" in kwargs:
histogram.colors = kwargs["colors"]
if "stroke" in kwargs:
histogram.stroke = kwargs["stroke"]
else:
histogram.stroke = "#ffffff00"
if "stroke_width" in kwargs:
histogram.stroke_width = kwargs["stroke_width"]
else:
histogram.stroke_width = 0
if ("x_label" in kwargs) and ("y_label" in kwargs):
histogram.tooltip = Tooltip(
fields=["midpoint", "count"],
labels=[kwargs["x_label"], kwargs["y_label"]],
)
else:
histogram.tooltip = Tooltip(fields=["midpoint", "count"])
if show:
plt.show()
else:
return histogram
except Exception as e:
raise Exception(e)
image_by_class(image, class_band, region, reducer='MEAN', scale=None, class_labels=None, x_labels=None, chart_type='LineChart', **kwargs)
¶
Generates a Chart from an image by class. Extracts and plots band values by class.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
Image to extract band values from. |
required |
class_band |
str |
The band name to use as class labels. |
required |
region |
ee.Geometry | ee.FeatureCollection |
The region(s) to reduce. |
required |
reducer |
str | ee.Reducer |
The reducer type for zonal statistics. Can be one of 'mean', 'median', 'sum', 'min', 'max', etc. Defaults to 'MEAN'. |
'MEAN' |
scale |
int |
The scale in meters at which to perform the analysis. |
None |
class_labels |
List[str] |
List of class labels. |
None |
x_labels |
List[str] |
List of x-axis labels. |
None |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. Defaults to 'LineChart'. |
'LineChart' |
**kwargs |
Any |
Additional keyword arguments. |
{} |
Returns:
| Type | Description |
|---|---|
Any |
The generated chart. |
Source code in geemap/chart.py
def image_by_class(
image: ee.Image,
class_band: str,
region: Union[ee.Geometry, ee.FeatureCollection],
reducer: Union[str, ee.Reducer] = "MEAN",
scale: Optional[int] = None,
class_labels: Optional[List[str]] = None,
x_labels: Optional[List[str]] = None,
chart_type: str = "LineChart",
**kwargs: Any,
) -> Any:
"""
Generates a Chart from an image by class. Extracts and plots band values by class.
Args:
image (ee.Image): Image to extract band values from.
class_band (str): The band name to use as class labels.
region (ee.Geometry | ee.FeatureCollection): The region(s) to reduce.
reducer (str | ee.Reducer, optional): The reducer type for zonal statistics. Can
be one of 'mean', 'median', 'sum', 'min', 'max', etc. Defaults to 'MEAN'.
scale (int, optional): The scale in meters at which to perform the analysis.
class_labels (List[str], optional): List of class labels.
x_labels (List[str], optional): List of x-axis labels.
chart_type (str, optional): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart',
'AreaChart', and 'Table'. Defaults to 'LineChart'.
**kwargs: Additional keyword arguments.
Returns:
Any: The generated chart.
"""
fc = zonal_stats(
image, region, stat_type=reducer, scale=scale, verbose=False, return_fc=True
)
bands = image.bandNames().getInfo()
df = ee_to_df(fc)[bands [class_band]]
df_transposed = df.set_index(class_band).T
if x_labels is not None:
df_transposed["label"] = x_labels
else:
df_transposed["label"] = df_transposed.index
if class_labels is None:
y_cols = df_transposed.columns.tolist()
y_cols.remove("label")
else:
y_cols = class_labels
fig = Chart(
df_transposed, chart_type=chart_type, x_cols="label", y_cols=y_cols, **kwargs
)
return fig
image_by_region(image, regions, reducer, scale, x_property, **kwargs)
¶
Generates a Chart from an image. Extracts and plots band values in one or more regions in the image, with each band in a separate series.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
Image to extract band values from. |
required |
regions |
ee.FeatureCollection | ee.Geometry |
Regions to reduce. Defaults to the image's footprint. |
required |
reducer |
str | ee.Reducer |
The reducer type for zonal statistics. Can be one of 'mean', 'median', 'sum', 'min', 'max', etc. |
required |
scale |
int |
The scale in meters at which to perform the analysis. |
required |
x_property |
str |
The name of the property in the feature collection to use as the x-axis values. |
required |
**kwargs |
Any |
Additional keyword arguments to be passed to the
|
{} |
Returns:
| Type | Description |
|---|---|
None |
None |
Source code in geemap/chart.py
def image_by_region(
image: ee.Image,
regions: Union[ee.FeatureCollection, ee.Geometry],
reducer: Union[str, ee.Reducer],
scale: int,
x_property: str,
**kwargs: Any,
) -> None:
"""
Generates a Chart from an image. Extracts and plots band values in one or more
regions in the image, with each band in a separate series.
Args:
image (ee.Image): Image to extract band values from.
regions (ee.FeatureCollection | ee.Geometry): Regions to reduce.
Defaults to the image's footprint.
reducer (str | ee.Reducer): The reducer type for zonal statistics. Can
be one of 'mean', 'median', 'sum', 'min', 'max', etc.
scale (int): The scale in meters at which to perform the analysis.
x_property (str): The name of the property in the feature collection to
use as the x-axis values.
**kwargs: Additional keyword arguments to be passed to the
`feature_by_feature` function.
Returns:
None
"""
fc = zonal_stats(
image, regions, stat_type=reducer, scale=scale, verbose=False, return_fc=True
)
bands = image.bandNames().getInfo()
df = ee_to_df(fc)[bands [x_property]]
feature_by_feature(df, x_property, bands, **kwargs)
image_doy_series(image_collection, region=None, region_reducer=None, scale=None, year_reducer=None, start_day=1, end_day=366, chart_type='LineChart', colors=None, title=None, x_label=None, y_label=None, **kwargs)
¶
Generates a time series chart of an image collection for a specific region over a range of days of the year.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_collection |
ee.ImageCollection |
The image collection to analyze. |
required |
region |
Optional[Union[ee.Geometry, ee.FeatureCollection]] |
The region to reduce. |
None |
region_reducer |
Optional[Union[str, ee.Reducer]] |
The reducer type for zonal statistics.Can be one of 'mean', 'median', 'sum', 'min', 'max', etc. |
None |
scale |
Optional[int] |
The scale in meters at which to perform the analysis. |
None |
year_reducer |
Optional[Union[str, ee.Reducer]] |
The reducer type for yearly statistics. |
None |
start_day |
int |
The start day of the year. |
1 |
end_day |
int |
The end day of the year. |
366 |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
'LineChart' |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to a predefined list of colors. |
None |
title |
Optional[str] |
The title of the chart. Defaults to the chart type. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to an empty string. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to an empty string. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. For axes_options, see https://bqplot.github.io/bqplot/api/axes |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The generated chart. |
Source code in geemap/chart.py
def image_doy_series(
image_collection: ee.ImageCollection,
region: Optional[Union[ee.Geometry, ee.FeatureCollection]] = None,
region_reducer: Optional[Union[str, ee.Reducer]] = None,
scale: Optional[int] = None,
year_reducer: Optional[Union[str, ee.Reducer]] = None,
start_day: int = 1,
end_day: int = 366,
chart_type: str = "LineChart",
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> Chart:
"""
Generates a time series chart of an image collection for a specific region
over a range of days of the year.
Args:
image_collection (ee.ImageCollection): The image collection to analyze.
region (Optional[Union[ee.Geometry, ee.FeatureCollection]]): The region
to reduce.
region_reducer (Optional[Union[str, ee.Reducer]]): The reducer type for
zonal statistics.Can be one of 'mean', 'median', 'sum', 'min', 'max', etc.
scale (Optional[int]): The scale in meters at which to perform the analysis.
year_reducer (Optional[Union[str, ee.Reducer]]): The reducer type for
yearly statistics.
start_day (int): The start day of the year.
end_day (int): The end day of the year.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
Returns:
Chart: The generated chart.
"""
# Function to add day-of-year ('doy') and year properties to each image.
def set_doys(collection):
def add_doy(img):
date = img.date()
year = date.get("year")
doy = date.getRelative("day", "year").floor().add(1)
return img.set({"doy": doy, "year": year})
return collection.map(add_doy)
# Reduces images with the same day of year.
def group_by_doy(collection, start, end, reducer):
collection = set_doys(collection)
doys = ee.FeatureCollection(
[ee.Feature(None, {"doy": i}) for i in range(start, end 1)]
)
# Group images by their day of year.
filter = ee.Filter(ee.Filter.equals(leftField="doy", rightField="doy"))
joined = ee.Join.saveAll("matches").apply(
primary=doys, secondary=collection, condition=filter
)
# For each DoY, reduce images across years.
def reduce_images(doy):
images = ee.ImageCollection.fromImages(doy.get("matches"))
image = images.reduce(reducer)
return image.set(
{
"doy": doy.get("doy"),
"geo": images.geometry(), # // Retain geometry for future reduceRegion.
}
)
return ee.ImageCollection(joined.map(reduce_images))
# Set default values and filters if parameters are not provided.
region_reducer = region_reducer or ee.Reducer.mean()
year_reducer = year_reducer or ee.Reducer.mean()
# Optionally filter the image collection by region.
filtered_collection = image_collection
if region:
filtered_collection = filtered_collection.filterBounds(region)
filtered_collection = set_doys(filtered_collection)
doy_images = group_by_doy(filtered_collection, start_day, end_day, year_reducer)
# For each DoY, reduce images across years within the region.
def reduce_doy_images(image):
region_for_image = region if region else image.get("geo")
dictionary = image.reduceRegion(
reducer=region_reducer, geometry=region_for_image, scale=scale
)
return ee.Feature(None, {"doy": image.get("doy")}).set(dictionary)
reduced = ee.FeatureCollection(doy_images.map(reduce_doy_images))
df = ee_to_df(reduced)
df.columns = df.columns.str.replace(r"_.*", "", regex=True)
x_cols = "doy"
y_cols = df.columns.tolist()
y_cols.remove("doy")
fig = Chart(
df,
chart_type,
x_cols,
y_cols,
colors,
title,
x_label,
y_label,
**kwargs,
)
return fig
image_doy_series_by_region(image_collection, band_name, regions, region_reducer=None, scale=None, year_reducer=None, series_property=None, start_day=1, end_day=366, chart_type='LineChart', colors=None, title=None, x_label=None, y_label=None, **kwargs)
¶
Generates a time series chart of an image collection for multiple regions over a range of days of the year.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_collection |
ee.ImageCollection |
The image collection to analyze. |
required |
band_name |
str |
The name of the band to analyze. |
required |
regions |
ee.FeatureCollection |
The regions to analyze. |
required |
region_reducer |
Optional[Union[str, ee.Reducer]] |
The reducer type for zonal statistics. |
None |
scale |
Optional[int] |
The scale in meters at which to perform the analysis. |
None |
year_reducer |
Optional[Union[str, ee.Reducer]] |
The reducer type for yearly statistics. |
None |
series_property |
Optional[str] |
The property to use for labeling the series. |
None |
start_day |
int |
The start day of the year. |
1 |
end_day |
int |
The end day of the year. |
366 |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
'LineChart' |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to a predefined list of colors. |
None |
title |
Optional[str] |
The title of the chart. Defaults to the chart type. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to an empty string. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to an empty string. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. For axes_options, see https://bqplot.github.io/bqplot/api/axes |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The generated chart. |
Source code in geemap/chart.py
def image_doy_series_by_region(
image_collection: ee.ImageCollection,
band_name: str,
regions: ee.FeatureCollection,
region_reducer: Optional[Union[str, ee.Reducer]] = None,
scale: Optional[int] = None,
year_reducer: Optional[Union[str, ee.Reducer]] = None,
series_property: Optional[str] = None,
start_day: int = 1,
end_day: int = 366,
chart_type: str = "LineChart",
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> Chart:
"""
Generates a time series chart of an image collection for multiple regions
over a range of days of the year.
Args:
image_collection (ee.ImageCollection): The image collection to analyze.
band_name (str): The name of the band to analyze.
regions (ee.FeatureCollection): The regions to analyze.
region_reducer (Optional[Union[str, ee.Reducer]]): The reducer type for
zonal statistics.
scale (Optional[int]): The scale in meters at which to perform the analysis.
year_reducer (Optional[Union[str, ee.Reducer]]): The reducer type for
yearly statistics.
series_property (Optional[str]): The property to use for labeling the series.
start_day (int): The start day of the year.
end_day (int): The end day of the year.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
Returns:
Chart: The generated chart.
"""
image_collection = image_collection.select(band_name)
# Function to add day-of-year ('doy') and year properties to each image.
def set_doys(collection):
def add_doy(img):
date = img.date()
year = date.get("year")
doy = date.getRelative("day", "year").floor().add(1)
return img.set({"doy": doy, "year": year})
return collection.map(add_doy)
# Reduces images with the same day of year.
def group_by_doy(collection, start, end, reducer):
collection = set_doys(collection)
doys = ee.FeatureCollection(
[ee.Feature(None, {"doy": i}) for i in range(start, end 1)]
)
# Group images by their day of year.
filter = ee.Filter(ee.Filter.equals(leftField="doy", rightField="doy"))
joined = ee.Join.saveAll("matches").apply(
primary=doys, secondary=collection, condition=filter
)
# For each DoY, reduce images across years.
def reduce_images(doy):
images = ee.ImageCollection.fromImages(doy.get("matches"))
image = images.reduce(reducer)
return image.set(
{
"doy": doy.get("doy"),
"geo": images.geometry(), # // Retain geometry for future reduceRegion.
}
)
return ee.ImageCollection(joined.map(reduce_images))
if year_reducer is None:
year_reducer = ee.Reducer.mean()
if region_reducer is None:
region_reducer = ee.Reducer.mean()
doy_images = group_by_doy(image_collection, start_day, end_day, year_reducer)
if series_property is None:
series_property = "system:index"
regions = regions.select([series_property])
fc = zonal_stats(
doy_images.toBands(),
regions,
stat_type=region_reducer,
scale=scale,
verbose=False,
return_fc=True,
)
df = ee_to_df(fc)
df = transpose_df(df, label_col=series_property, index_name="doy")
df["doy"] = df.index.str.split("_").str[0].astype(int)
df.sort_values("doy", inplace=True)
y_cols = df.columns.tolist()
y_cols.remove("doy")
fig = Chart(
df,
chart_type,
"doy",
y_cols,
colors,
title,
x_label,
y_label,
**kwargs,
)
return fig
image_histogram(image, region, scale, max_buckets, min_bucket_width, max_raw, max_pixels, reducer_args={}, **kwargs)
¶
Creates a histogram for each band of the specified image within the given region using bqplot.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
The Earth Engine image for which to create histograms. |
required |
region |
ee.Geometry |
The region over which to calculate the histograms. |
required |
scale |
int |
The scale in meters of the calculation. |
required |
max_buckets |
int |
The maximum number of buckets in the histogram. |
required |
min_bucket_width |
float |
The minimum width of the buckets in the histogram. |
required |
max_raw |
int |
The maximum number of pixels to include in the histogram. |
required |
max_pixels |
int |
The maximum number of pixels to reduce. |
required |
reducer_args |
Dict[str, Any] |
Additional arguments to pass to the image.reduceRegion. |
{} |
Keyword arguments:
| Name | Type | Description |
|---|---|---|
colors |
List[str] |
Colors for the histograms of each band. |
labels |
List[str] |
Labels for the histograms of each band. |
title |
str |
Title of the combined histogram plot. |
legend_location |
str |
Location of the legend in the plot. |
Returns:
| Type | Description |
|---|---|
bq.Figure |
The bqplot figure containing the histograms. |
Source code in geemap/chart.py
def image_histogram(
image: ee.Image,
region: ee.Geometry,
scale: int,
max_buckets: int,
min_bucket_width: float,
max_raw: int,
max_pixels: int,
reducer_args: Dict[str, Any] = {},
**kwargs: Dict[str, Any],
) -> bq.Figure:
"""
Creates a histogram for each band of the specified image within the given
region using bqplot.
Args:
image (ee.Image): The Earth Engine image for which to create histograms.
region (ee.Geometry): The region over which to calculate the histograms.
scale (int): The scale in meters of the calculation.
max_buckets (int): The maximum number of buckets in the histogram.
min_bucket_width (float): The minimum width of the buckets in the histogram.
max_raw (int): The maximum number of pixels to include in the histogram.
max_pixels (int): The maximum number of pixels to reduce.
reducer_args (Dict[str, Any]): Additional arguments to pass to the image.reduceRegion.
Keyword Args:
colors (List[str]): Colors for the histograms of each band.
labels (List[str]): Labels for the histograms of each band.
title (str): Title of the combined histogram plot.
legend_location (str): Location of the legend in the plot.
Returns:
bq.Figure: The bqplot figure containing the histograms.
"""
# Calculate the histogram data.
histogram = image.reduceRegion(
reducer=ee.Reducer.histogram(
maxBuckets=max_buckets, minBucketWidth=min_bucket_width, maxRaw=max_raw
),
geometry=region,
scale=scale,
maxPixels=max_pixels,
**reducer_args,
)
histograms = {
band: histogram.get(band).getInfo() for band in image.bandNames().getInfo()
}
# Create bqplot histograms for each band.
def create_histogram(
hist_data: Dict[str, Any], color: str, label: str
) -> bq.Figure:
"""
Creates a bqplot histogram for the given histogram data.
Args:
hist_data (dict): The histogram data.
color (str): The color of the histogram.
label (str): The label of the histogram.
Returns:
bq.Figure: The bqplot figure for the histogram.
"""
x_data = np.array(hist_data["bucketMeans"])
y_data = np.array(hist_data["histogram"])
x_sc = bq.LinearScale()
y_sc = bq.LinearScale()
bar = bq.Bars(
x=x_data,
y=y_data,
scales={"x": x_sc, "y": y_sc},
colors=[color],
display_legend=True,
labels=[label],
)
ax_x = bq.Axis(scale=x_sc, label="Reflectance (x1e4)", tick_format="0.0f")
ax_y = bq.Axis(
scale=y_sc, orientation="vertical", label="Count", tick_format="0.0f"
)
return bq.Figure(marks=[bar], axes=[ax_x, ax_y])
# Define colors and labels for the bands.
band_colors = kwargs.get("colors", ["#cf513e", "#1d6b99", "#f0af07"])
band_labels = kwargs.get("labels", image.bandNames().getInfo())
# Create and combine histograms for each band.
histograms_fig = []
for band, color, label in zip(histograms.keys(), band_colors, band_labels):
histograms_fig.append(create_histogram(histograms[band], color, label))
combined_fig = bq.Figure(
marks=[fig.marks[0] for fig in histograms_fig],
axes=histograms_fig[0].axes,
**kwargs,
)
for fig, label in zip(histograms_fig, band_labels):
fig.marks[0].labels = [label]
combined_fig.legend_location = kwargs.get("legend_location", "top-right")
return combined_fig
image_regions(image, regions, reducer, scale, series_property, x_labels, **kwargs)
¶
Generates a Chart from an image by regions. Extracts and plots band values in multiple regions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image |
ee.Image |
Image to extract band values from. |
required |
regions |
Union[ee.FeatureCollection, ee.Geometry] |
Regions to reduce. Defaults to the image's footprint. |
required |
reducer |
Union[str, ee.Reducer] |
The reducer type for zonal statistics. Can be one of 'mean', 'median', 'sum', 'min', 'max', etc. |
required |
scale |
int |
The scale in meters at which to perform the analysis. |
required |
series_property |
str |
The property to use for labeling the series. |
required |
x_labels |
List[str] |
List of x-axis labels. |
required |
**kwargs |
Any |
Additional keyword arguments. |
{} |
Returns:
| Type | Description |
|---|---|
bq.Figure |
The bqplot figure. |
Source code in geemap/chart.py
def image_regions(
image: ee.Image,
regions: Union[ee.FeatureCollection, ee.Geometry],
reducer: Union[str, ee.Reducer],
scale: int,
series_property: str,
x_labels: List[str],
**kwargs: Any,
) -> None:
"""
Generates a Chart from an image by regions. Extracts and plots band values
in multiple regions.
Args:
image (ee.Image): Image to extract band values from.
regions (Union[ee.FeatureCollection, ee.Geometry]): Regions to reduce.
Defaults to the image's footprint.
reducer (Union[str, ee.Reducer]): The reducer type for zonal statistics.
Can be one of 'mean', 'median', 'sum', 'min', 'max', etc.
scale (int): The scale in meters at which to perform the analysis.
series_property (str): The property to use for labeling the series.
x_labels (List[str]): List of x-axis labels.
**kwargs: Additional keyword arguments.
Returns:
bq.Figure: The bqplot figure.
"""
fc = zonal_stats(
image, regions, stat_type=reducer, scale=scale, verbose=False, return_fc=True
)
bands = image.bandNames().getInfo()
fc = fc.select(bands [series_property])
return feature_by_property(fc, x_labels, series_property, **kwargs)
image_series(image_collection, region, reducer=None, scale=None, x_property='system:time_start', chart_type='LineChart', x_cols=None, y_cols=None, colors=None, title=None, x_label=None, y_label=None, **kwargs)
¶
Generates a time series chart of an image collection for a specific region.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_collection |
ee.ImageCollection |
The image collection to analyze. |
required |
region |
Union[ee.Geometry, ee.FeatureCollection] |
The region to reduce. |
required |
reducer |
Optional[Union[str, ee.Reducer]] |
The reducer to use. |
None |
scale |
Optional[int] |
The scale in meters at which to perform the analysis. |
None |
x_property |
str |
The name of the property to use as the x-axis values. |
'system:time_start' |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
'LineChart' |
x_cols |
Optional[List[str]] |
The columns to use for the x-axis. Defaults to the first column. |
None |
y_cols |
Optional[List[str]] |
The columns to use for the y-axis. Defaults to the second column. |
None |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to a predefined list of colors. |
None |
title |
Optional[str] |
The title of the chart. Defaults to the chart type. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to an empty string. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to an empty string. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. For axes_options, see https://bqplot.github.io/bqplot/api/axes |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The chart object. |
Source code in geemap/chart.py
def image_series(
image_collection: ee.ImageCollection,
region: Union[ee.Geometry, ee.FeatureCollection],
reducer: Optional[Union[str, ee.Reducer]] = None,
scale: Optional[int] = None,
x_property: str = "system:time_start",
chart_type: str = "LineChart",
x_cols: Optional[List[str]] = None,
y_cols: Optional[List[str]] = None,
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> Chart:
"""
Generates a time series chart of an image collection for a specific region.
Args:
image_collection (ee.ImageCollection): The image collection to analyze.
region (Union[ee.Geometry, ee.FeatureCollection]): The region to reduce.
reducer (Optional[Union[str, ee.Reducer]]): The reducer to use.
scale (Optional[int]): The scale in meters at which to perform the analysis.
x_property (str): The name of the property to use as the x-axis values.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
x_cols (Optional[List[str]]): The columns to use for the x-axis.
Defaults to the first column.
y_cols (Optional[List[str]]): The columns to use for the y-axis.
Defaults to the second column.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
Returns:
Chart: The chart object.
"""
if reducer is None:
reducer = ee.Reducer.mean()
band_names = image_collection.first().bandNames().getInfo()
# Function to reduce the region and get the mean for each image.
def get_stats(image):
stats = image.reduceRegion(reducer=reducer, geometry=region, scale=scale)
results = {}
for band in band_names:
results[band] = stats.get(band)
if x_property == "system:time_start" or x_property == "system:time_end":
results["date"] = image.date().format("YYYY-MM-dd")
else:
results[x_property] = image.get(x_property).getInfo()
return ee.Feature(None, results)
# Apply the function over the image collection.
fc = ee.FeatureCollection(
image_collection.map(get_stats).filter(ee.Filter.notNull(band_names))
)
df = ee_to_df(fc)
if "date" in df.columns:
df["date"] = pd.to_datetime(df["date"])
fig = Chart(
df,
chart_type,
x_cols,
y_cols,
colors,
title,
x_label,
y_label,
**kwargs,
)
return fig
image_series_by_region(image_collection, regions, reducer=None, band=None, scale=None, x_property='system:time_start', series_property='system:index', chart_type='LineChart', x_cols=None, y_cols=None, colors=None, title=None, x_label=None, y_label=None, **kwargs)
¶
Generates a time series chart of an image collection for multiple regions.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
image_collection |
ee.ImageCollection |
The image collection to analyze. |
required |
regions |
ee.FeatureCollection | ee.Geometry |
The regions to reduce. |
required |
reducer |
str | ee.Reducer |
The reducer type for zonal statistics. |
None |
band |
str |
The name of the band to analyze. |
None |
scale |
int |
The scale in meters at which to perform the analysis. |
None |
x_property |
str |
The name of the property to use as the x-axis values. |
'system:time_start' |
series_property |
str |
The property to use for labeling the series. |
'system:index' |
chart_type |
str |
The type of chart to create. Supported types are 'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart', 'PieChart', 'AreaChart', and 'Table'. |
'LineChart' |
x_cols |
Optional[List[str]] |
The columns to use for the x-axis. Defaults to the first column. |
None |
y_cols |
Optional[List[str]] |
The columns to use for the y-axis. Defaults to the second column. |
None |
colors |
Optional[List[str]] |
The colors to use for the chart. Defaults to a predefined list of colors. |
None |
title |
Optional[str] |
The title of the chart. Defaults to the chart type. |
None |
x_label |
Optional[str] |
The label for the x-axis. Defaults to an empty string. |
None |
y_label |
Optional[str] |
The label for the y-axis. Defaults to an empty string. |
None |
**kwargs |
Any |
Additional keyword arguments to pass to the bqplot Figure or mark objects. For axes_options, see https://bqplot.github.io/bqplot/api/axes |
{} |
Returns:
| Type | Description |
|---|---|
Chart |
The chart object. |
Source code in geemap/chart.py
def image_series_by_region(
image_collection: ee.ImageCollection,
regions: Union[ee.FeatureCollection, ee.Geometry],
reducer: Optional[Union[str, ee.Reducer]] = None,
band: Optional[str] = None,
scale: Optional[int] = None,
x_property: str = "system:time_start",
series_property: str = "system:index",
chart_type: str = "LineChart",
x_cols: Optional[List[str]] = None,
y_cols: Optional[List[str]] = None,
colors: Optional[List[str]] = None,
title: Optional[str] = None,
x_label: Optional[str] = None,
y_label: Optional[str] = None,
**kwargs: Any,
) -> Chart:
"""
Generates a time series chart of an image collection for multiple regions.
Args:
image_collection (ee.ImageCollection): The image collection to analyze.
regions (ee.FeatureCollection | ee.Geometry): The regions to reduce.
reducer (str | ee.Reducer): The reducer type for zonal statistics.
band (str): The name of the band to analyze.
scale (int): The scale in meters at which to perform the analysis.
x_property (str): The name of the property to use as the x-axis values.
series_property (str): The property to use for labeling the series.
chart_type (str): The type of chart to create. Supported types are
'ScatterChart', 'LineChart', 'ColumnChart', 'BarChart',
'PieChart', 'AreaChart', and 'Table'.
x_cols (Optional[List[str]]): The columns to use for the x-axis.
Defaults to the first column.
y_cols (Optional[List[str]]): The columns to use for the y-axis.
Defaults to the second column.
colors (Optional[List[str]]): The colors to use for the chart.
Defaults to a predefined list of colors.
title (Optional[str]): The title of the chart. Defaults to the
chart type.
x_label (Optional[str]): The label for the x-axis. Defaults to an
empty string.
y_label (Optional[str]): The label for the y-axis. Defaults to an
empty string.
**kwargs: Additional keyword arguments to pass to the bqplot Figure
or mark objects. For axes_options, see
https://bqplot.github.io/bqplot/api/axes
Returns:
Chart: The chart object.
"""
if reducer is None:
reducer = ee.Reducer.mean()
if band is None:
band = image_collection.first().bandNames().get(0).getInfo()
image = image_collection.select(band).toBands()
fc = zonal_stats(
image, regions, stat_type=reducer, scale=scale, verbose=False, return_fc=True
)
columns = image.bandNames().getInfo() [series_property]
df = ee_to_df(fc, columns=columns)
headers = df[series_property].tolist()
df = df.drop(columns=[series_property]).T
df.columns = headers
if x_property == "system:time_start" or x_property == "system:time_end":
indexes = image_dates(image_collection).getInfo()
df["index"] = pd.to_datetime(indexes)
else:
indexes = image_collection.aggregate_array(x_property).getInfo()
df["index"] = indexes
fig = Chart(
df,
chart_type,
x_cols,
y_cols,
colors,
title,
x_label,
y_label,
**kwargs,
)
return fig
pivot_df(df, index, columns, values)
¶
Pivots a DataFrame using the specified index, columns, and values.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df |
pd.DataFrame |
The DataFrame to pivot. |
required |
index |
str |
The column to use for the index. |
required |
columns |
str |
The column to use for the columns. |
required |
values |
str |
The column to use for the values. |
required |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
The pivoted DataFrame. |
Source code in geemap/chart.py
def pivot_df(df: pd.DataFrame, index: str, columns: str, values: str) -> pd.DataFrame:
"""
Pivots a DataFrame using the specified index, columns, and values.
Args:
df (pd.DataFrame): The DataFrame to pivot.
index (str): The column to use for the index.
columns (str): The column to use for the columns.
values (str): The column to use for the values.
Returns:
pd.DataFrame: The pivoted DataFrame.
"""
df_pivot = df.pivot(index=index, columns=columns, values=values).reset_index()
df_pivot.columns = [index] [f"{col}" for col in df_pivot.columns[1:]]
return df_pivot
transpose_df(df, label_col, index_name=None, indexes=None)
¶
Transposes a pandas DataFrame and optionally sets a new index name and custom indexes.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
df |
pd.DataFrame |
The DataFrame to transpose. |
required |
label_col |
str |
The column to set as the index before transposing. |
required |
index_name |
str |
The name to set for the index after transposing. Defaults to None. |
None |
indexes |
list |
A list of custom indexes to set after transposing. The length of this list must match the number of rows in the transposed DataFrame. Defaults to None. |
None |
Returns:
| Type | Description |
|---|---|
pd.DataFrame |
The transposed DataFrame. |
Exceptions:
| Type | Description |
|---|---|
ValueError |
If |
ValueError |
If the length of |
Source code in geemap/chart.py
def transpose_df(
df: pd.DataFrame,
label_col: str,
index_name: str = None,
indexes: list = None,
) -> pd.DataFrame:
"""
Transposes a pandas DataFrame and optionally sets a new index name and
custom indexes.
Args:
df (pd.DataFrame): The DataFrame to transpose.
label_col (str): The column to set as the index before transposing.
index_name (str, optional): The name to set for the index after
transposing. Defaults to None.
indexes (list, optional): A list of custom indexes to set after
transposing. The length of this list must match the number of rows
in the transposed DataFrame. Defaults to None.
Returns:
pd.DataFrame: The transposed DataFrame.
Raises:
ValueError: If `label_col` is not a column in the DataFrame.
ValueError: If the length of `indexes` does not match the number of
rows in the transposed DataFrame.
"""
# Check if the specified column exists in the DataFrame
if label_col not in df.columns:
raise ValueError(f"Column '{label_col}' not found in DataFrame")
# Set the specified column as the index
transposed_df = df.set_index(label_col).transpose()
# Set the index name if provided
if index_name:
transposed_df.columns.name = index_name
# Set custom indexes if provided
if indexes:
if len(indexes) != len(transposed_df.index):
raise ValueError(
"Length of custom indexes must match the number of rows in the transposed DataFrame"
)
transposed_df.index = indexes
return transposed_df