spacekit.analyzer.explore

class spacekit.analyzer.explore.ImagePreviews(X, labels, name='ImagePreviews', **log_kws)

Bases: object

Base parent class for rendering and displaying images as plots

class spacekit.analyzer.explore.SVMPreviews(X, labels=None, names=None, ndims=3, channels=3, w=128, h=128, figsize=(10, 10), **log_kws)

Bases: ImagePreviews

ImagePreviews subclass for previewing SVM images. Primarily can be used to compare original with augmented versions.

Parameters:

ImagePlots (class) – spacekit.analyzer.explore.ImagePreviews parent class

Instantiates an SVMPreviews class object.

Parameters:

• X (ndarray) – ndimensional array of image pixel values

• labels (ndarray, optional) – target class labels for each image

• ndims (int, optional) – number of dimensions (frames) per image, by default 3

• channels (int, optional) – channels per image frame (rgb color is 3, gray/bw is 1), by default 3

• w (int, optional) – width of images, by default 128

• h (int, optional) – height of images, by default 128

check_dimensions(Xi)

get_synthetic_image(img_name, show=False, dim=3, aug=False)

preview_image(Xi, dim=3, aug=False, show=False)

preview_image_mpl(Xi, dim=3, aug=False, show=False)

preview_og_aug_pair(i=None, dim=3)

Plot frames of both original and augmented versions of n-dimensional images

Parameters:

• i (int, optional) – index of image selected from array X, by default None

• dim (int, optional) – dimensions (number of frames per image), by default 3

preview_og_syn_pair(img_name)

select_image_from_array(i=None)

class spacekit.analyzer.explore.DataPlots(df, width=1300, height=700, show=False, save_html=None, telescope=None, name='DataPlots', **log_kws)

Bases: object

Base class for drawing exploratory data analysis plots from a dataframe.

bar_plots(X, Y, feature, y_err=[None, None], width=700, height=500, cmap=['dodgerblue', 'fuchsia'])

Draws a bar plot for a feature, grouped by the group attribute.

Parameters:

• X (array-like) – X-axis values

• Y (array-like) – Y-axis values

• feature (str) – Feature name

• y_err (list, optional) – Y-axis error values, by default [None, None]

• width (int, optional) – Width of the plot, by default 700

• height (int, optional) – Height of the plot, by default 500

• cmap (list, optional) – List of colors for the plot, by default [“dodgerblue”, “fuchsia”]

Returns:

Plotly Figure object representing the bar plot

Return type:

go.Figure

box_plots(cols=None, outliers=True)

Generates multi-trace box plots for each feature in cols param, with or without outliers

Parameters:

• cols (list, optional) – features to plot from dataframe, by default None (uses self.continuous attribute)

• outliers (bool, optional) – whether to include outliers in the box plots, by default True

Returns:

dictionary of plotly box plot figures for each feature in cols parameter

Return type:

dict

det_keys()

Creates a list of detectors based on self.telescope

Returns:

list of detector keys for the specified telescope

Return type:

list

feature_stats_by_target(feature)

Calculates statistical info (mean and standard deviation) for a feature within each target class.

Parameters:

feature (str) – dataframe column to get statistical calculations on

Returns:

list of means and list of standard deviations for a feature, subdivided for each target class.

Return type:

nested lists

feature_subset()

Create a set of groups from a categorical feature (dataframe column). Used for plotting multiple traces on a figure

Returns:

self.categories attribute containing key-value pairs: groups of observations (values) for each category (keys)

Return type:

dictionary

group_keys()

Generates numerically ordered key-pairs for each unique value of self.group found in the dataframe

Returns:

enumerated dictionary of unique values for each group

Return type:

dict

grouped_barplot(target='label', cmap=None)

Draws a grouped bar plot for a target column, grouped by the group attribute.

Parameters:

• target (str, optional) – target column to plot, by default “label”

• cmap (list, optional) – list of colors for the bars, by default None

Returns:

plotly figure object for the grouped bar plot

Return type:

go.Figure

instr_keys()

Generates a list of intruments based on self.telescope

Returns:

list of instrument keys for the specified telescope

Return type:

list

kde_plots(cols, norm=False, targets=False, hist=True, curve=True, binsize=0.2, width=700, height=500, cmap=['#F66095', '#2BCDC1'])

Generates KDE plots for specified columns in the dataframe.

Parameters:

• cols (list of str) – List of column names to generate KDE plots for

• norm (bool, optional) – Whether to normalize the data, by default False

• targets (bool, optional) – Whether to group data by target classes, by default False

• hist (bool, optional) – Whether to show histogram, by default True

• curve (bool, optional) – Whether to show KDE curve, by default True

• binsize (float, optional) – Bin size for the histogram, by default 0.2

• height (int, optional) – Height of the plot, by default 500

• cmap (list, optional) – List of colors for the plot, by default [“#F66095”, “#2BCDC1”]

Returns:

Plotly Figure object representing the KDE plot

Return type:

go.Figure

make_box_figs(vars: list)

Generates single trace box plots, one plot for each var where vars is a list of columns in df

Parameters:

vars (list) – column names in dataframe to plot

Returns:

list of plotly box plot figures for each variable in vars parameter

Return type:

list

make_feature_scatter_figs(xaxis_name, yaxis_name)

Generates scatterplots for two features in the dataframe, grouped by the group attribute.

Parameters:

• xaxis_name (str) – name of column in dataframe to plot on x-axis

• yaxis_name (str) – name of column in dataframe to plot on y-axis

Returns:

scatterplot figures for each group in self.group attribute

Return type:

list

make_subplots(figtype, xtitle, ytitle, data1, data2, name1, name2)

Generates figure with multiple subplots for two sets of data using previously generated figures.

Parameters:

• figtype (str) – type of figure being generated (used for saving html file)

• xtitle (str) – title for the x-axis

• ytitle (str) – title for the y-axis

• data1 (go.Figure) – figure object for the first set of data

• data2 (go.Figure) – figure object for the second set of data

• name1 (str) – name for the first subplot

• name2 (str) – name for the second subplot

Returns:

figure object containing the subplots

Return type:

go.Figure

make_target_scatter(target=None)

Generates target vs feature scatterplot for a given target (by default self.target) for each feature in self.feature_list.

Parameters:

target (str, optional) – target column name, by default None

Returns:

target-feature scatterplot figures for each feature in self.feature_list

Return type:

list

make_target_scatter_figs(xaxis_name, yaxis_name, marker_size=15, cmap=['cyan', 'fuchsia'], categories=None, target=None)

Generates scatterplots for two features in the dataframe, grouped by target classes.

Parameters:

• xaxis_name (str) – column name in dataframe to plot on x-axis

• yaxis_name (str) – column name in dataframe to plot on y-axis

• marker_size (int, optional) – marker size for scatter plot points, by default 15

• cmap (list, optional) – list of colors for different target classes, by default [“cyan”, “fuchsia”]

• categories (dict, optional) – dictionary of categories to group data by, by default None

• target (str, optional) – name of target column in dataframe, by default None

Returns:

list of scatterplot figures for each category

Return type:

list

map_data()

Instantiates data_map as a dictionary of grouped dataframes and color maps for each category in categories attribute.

map_df_by_group()

Instantiates group_dict as a dictionary of grouped dataframes and color map

remove_outliers(y_data)

Removes outliers from a given pandas Series using the IQR method.

Parameters:

y_data (pd.Series) – The data from which to remove outliers.

Returns:

The data with outliers removed via IQR filtering.

Return type:

pd.Series

scatter3d(x, y, z, mask=None, target=None)

Generates a 3D scatterplot for three features in the dataframe.

Parameters:

• x (str) – feature column name for x-axis

• y (str) – feature column name for y-axis

• z (str) – feature column name for z-axis

• mask (pd.DataFrame, optional) – DataFrame to use as a mask/filter, by default None

• target (str, optional) – target column name, by default None

Returns:

Plotly Figure object representing the 3D scatterplot

Return type:

go.Figure

targ_class_keys()

List of standard astronomical target classification categories

Returns:

standard target classification categories

Return type:

list

class spacekit.analyzer.explore.HstSvmPlots(df, group='det', width=1300, height=700, show=False, save_html=None, **log_kws)

Bases: DataPlots

Instantiates an HstSvmPlots class

Parameters:

DataPlots (class) – spacekit.analyzer.explore.DataPlots parent class

alignment_bars()

alignment_kde()

alignment_scatters()

draw_plots()

class spacekit.analyzer.explore.HstCalPlots(df, group='instr', width=1300, height=700, show=False, save_html=None, **log_kws)

Bases: DataPlots

draw_plots()

make_cal_scatter3d()

make_cal_scatterplots()