def create_vis(self, vis, standalone=True):
"""
Input Vis object and return a visualization specification
Parameters
----------
vis: lux.vis.Vis
Input Vis (with data)
standalone: bool
Flag to determine if outputted code uses user-defined variable names or can be run independently
Returns
-------
chart : altair.Chart
Output Altair Chart Object
"""
# Lazy Evaluation for 2D Binning
if vis.mark == "scatter" and vis._postbin:
vis._mark = "heatmap"
from lux.executor.PandasExecutor import PandasExecutor
PandasExecutor.execute_2D_binning(vis)
# If a column has a Period dtype, or contains Period objects, convert it back to Datetime
if vis.data is not None:
for attr in list(vis.data.columns):
if pd.api.types.is_period_dtype(
vis.data.dtypes[attr]) or isinstance(
vis.data[attr].iloc[0], pd.Period):
dateColumn = vis.data[attr]
vis.data[attr] = pd.PeriodIndex(
dateColumn.values).to_timestamp()
if pd.api.types.is_interval_dtype(
vis.data.dtypes[attr]) or isinstance(
vis.data[attr].iloc[0], pd.Interval):
vis.data[attr] = vis.data[attr].astype(str)
if isinstance(attr, str):
if "." in attr:
attr_clause = vis.get_attr_by_attr_name(attr)[0]
# Suppress special character ".", not displayable in Altair
# attr_clause.attribute = attr_clause.attribute.replace(".", "")
vis._vis_data = vis.data.rename(
columns={attr: attr.replace(".", "")})
if vis.mark == "histogram":
chart = Histogram(vis)
elif vis.mark == "bar":
chart = BarChart(vis)
elif vis.mark == "scatter":
chart = ScatterChart(vis)
elif vis.mark == "line":
chart = LineChart(vis)
elif vis.mark == "heatmap":
chart = Heatmap(vis)
elif vis.mark == "geographical":
chart = Choropleth(vis)
else:
chart = None
if chart:
if lux.config.plotting_style and (
lux.config.plotting_backend == "vegalite"
or lux.config.plotting_backend == "altair"):
chart.chart = lux.config.plotting_style(chart.chart)
if self.output_type == "VegaLite":
chart_dict = chart.chart.to_dict()
# this is a bit of a work around because altair must take a pandas dataframe and we can only generate a luxDataFrame
# chart["data"] = { "values": vis.data.to_dict(orient='records') }
# chart_dict["width"] = 160
# chart_dict["height"] = 150
chart_dict["vislib"] = "vegalite"
return chart_dict
elif self.output_type == "Altair":
import inspect
source = ""
if lux.config.plotting_style:
if "def custom_config(chart):" in lux.config.plotting_style_code:
source = lux.config.plotting_style_code
else:
source = inspect.getsource(lux.config.plotting_style)
default_vis_style_code = "# Default Lux Style \nchart = chart.configure_title(fontWeight=500,fontSize=13,font='Helvetica Neue')\n"
default_vis_style_code += "chart = chart.configure_axis(titleFontWeight=500,titleFontSize=11,titleFont='Helvetica Neue',\n"
default_vis_style_code += "\t\t\t\t\tlabelFontWeight=400,labelFontSize=9,labelFont='Helvetica Neue',labelColor='#505050')\n"
default_vis_style_code += "chart = chart.configure_legend(titleFontWeight=500,titleFontSize=10,titleFont='Helvetica Neue',\n"
default_vis_style_code += (
"\t\t\t\t\tlabelFontWeight=400,labelFontSize=9,labelFont='Helvetica Neue')\n"
)
default_vis_style_code += "chart = chart.properties(width=160,height=150)\n"
vis_style_code = "\n# Custom Style Additions"
# TODO: improve parsing such that it splits based on line of logic instead of line of code
for line in source.split("\n ")[1:-1]:
if line.strip() not in default_vis_style_code:
vis_style_code += "\n" + line
if vis_style_code == "\n# Custom Style Additions":
vis_style_code = default_vis_style_code
else:
vis_style_code = default_vis_style_code + vis_style_code
vis_style_code = vis_style_code.replace("\n\t\t", "\n")
vis_style_code = vis_style_code.replace("\n ", "\n")
lux.config.plotting_style_code = vis_style_code
chart.code = chart.code.replace("\n\t\t", "\n")
chart.code = chart.code.replace("\n ", "\n")
var = vis._source
if var is not None:
all_vars = []
for f_info in inspect.getouterframes(
inspect.currentframe()):
local_vars = f_info.frame.f_back
if local_vars:
callers_local_vars = local_vars.f_locals.items()
possible_vars = [
var_name
for var_name, var_val in callers_local_vars
if var_val is var
]
all_vars.extend(possible_vars)
found_variable = [
possible_var for possible_var in all_vars
if possible_var[0] != "_"
][0]
else: # if vis._source was not set when the Vis was created
found_variable = "df"
if standalone:
chart.code = chart.code.replace(
"placeholder_variable",
f"pd.DataFrame({str(vis.data.to_dict())})",
)
else:
# TODO: Placeholder (need to read dynamically via locals())
chart.code = chart.code.replace("placeholder_variable",
found_variable)
return chart.code
def create_vis(self, vis, standalone=True):
"""
Input Vis object and return a visualization specification
Parameters
----------
vis: lux.vis.Vis
Input Vis (with data)
standalone: bool
Flag to determine if outputted code uses user-defined variable names or can be run independently
Returns
-------
chart : altair.Chart
Output Altair Chart Object
"""
# Lazy Evaluation for 2D Binning
if vis.mark == "scatter" and vis._postbin:
vis._mark = "heatmap"
PandasExecutor.execute_2D_binning(vis)
# If a column has a Period dtype, or contains Period objects, convert it back to Datetime
if vis.data is not None:
for attr in list(vis.data.columns):
if pd.api.types.is_period_dtype(
vis.data.dtypes[attr]) or isinstance(
vis.data[attr].iloc[0], pd.Period):
dateColumn = vis.data[attr]
vis.data[attr] = pd.PeriodIndex(
dateColumn.values).to_timestamp()
if pd.api.types.is_interval_dtype(
vis.data.dtypes[attr]) or isinstance(
vis.data[attr].iloc[0], pd.Interval):
vis.data[attr] = vis.data[attr].astype(str)
fig, ax = matplotlib_setup(4.5, 4)
if vis.mark == "histogram":
chart = Histogram(vis, fig, ax)
elif vis.mark == "bar":
chart = BarChart(vis, fig, ax)
elif vis.mark == "scatter":
chart = ScatterChart(vis, fig, ax)
elif vis.mark == "line":
chart = LineChart(vis, fig, ax)
elif vis.mark == "heatmap":
chart = Heatmap(vis, fig, ax)
elif vis.mark == "geographical":
return AltairRenderer().create_vis(vis, False)
else:
chart = None
return chart
if chart:
plt.tight_layout()
if lux.config.plotting_style and (
lux.config.plotting_backend == "matplotlib"
or lux.config.plotting_backend == "matplotlib_code"):
chart.ax = lux.config.plotting_style(chart.fig, chart.ax)
plt.tight_layout()
tmpfile = BytesIO()
chart.fig.savefig(tmpfile, format="png")
chart.chart = base64.b64encode(tmpfile.getvalue()).decode("utf-8")
plt.clf()
plt.close("all")
if self.output_type == "matplotlib":
return {"config": chart.chart, "vislib": "matplotlib"}
if self.output_type == "matplotlib_code":
if lux.config.plotting_style:
import inspect
chart.code += "\n".join(
inspect.getsource(
lux.config.plotting_style).split("\n ")[1:-1])
chart.code += "\nfig"
chart.code = chart.code.replace("\n\t\t", "\n")
return chart.code
def create_vis(self, vis, standalone=True):
"""
Input Vis object and return a visualization specification
Parameters
----------
vis: lux.vis.Vis
Input Vis (with data)
standalone: bool
Flag to determine if outputted code uses user-defined variable names or can be run independently
Returns
-------
chart : altair.Chart
Output Altair Chart Object
"""
# Lazy Evaluation for 2D Binning
if vis.mark == "scatter" and vis._postbin:
vis._mark = "heatmap"
from lux.executor.PandasExecutor import PandasExecutor
PandasExecutor.execute_2D_binning(vis)
# If a column has a Period dtype, or contains Period objects, convert it back to Datetime
if vis.data is not None:
for attr in list(vis.data.columns):
if pd.api.types.is_period_dtype(
vis.data.dtypes[attr]) or isinstance(
vis.data[attr].iloc[0], pd.Period):
dateColumn = vis.data[attr]
vis.data[attr] = pd.PeriodIndex(
dateColumn.values).to_timestamp()
if pd.api.types.is_interval_dtype(
vis.data.dtypes[attr]) or isinstance(
vis.data[attr].iloc[0], pd.Interval):
vis.data[attr] = vis.data[attr].astype(str)
if vis.mark == "histogram":
chart = Histogram(vis)
elif vis.mark == "bar":
chart = BarChart(vis)
elif vis.mark == "scatter":
chart = ScatterChart(vis)
elif vis.mark == "line":
chart = LineChart(vis)
elif vis.mark == "heatmap":
chart = Heatmap(vis)
else:
chart = None
if chart:
if lux.config.plot_config:
chart.chart = lux.config.plot_config(chart.chart)
if self.output_type == "VegaLite":
chart_dict = chart.chart.to_dict()
# this is a bit of a work around because altair must take a pandas dataframe and we can only generate a luxDataFrame
# chart["data"] = { "values": vis.data.to_dict(orient='records') }
# chart_dict["width"] = 160
# chart_dict["height"] = 150
return chart_dict
elif self.output_type == "Altair":
import inspect
if lux.config.plot_config:
chart.code += "\n".join(
inspect.getsource(
lux.config.plot_config).split("\n ")[1:-1])
chart.code += "\nchart"
chart.code = chart.code.replace("\n\t\t", "\n")
var = vis._source
if var is not None:
all_vars = []
for f_info in inspect.getouterframes(
inspect.currentframe()):
local_vars = f_info.frame.f_back
if local_vars:
callers_local_vars = local_vars.f_locals.items()
possible_vars = [
var_name
for var_name, var_val in callers_local_vars
if var_val is var
]
all_vars.extend(possible_vars)
found_variable = [
possible_var for possible_var in all_vars
if possible_var[0] != "_"
][0]
else: # if vis._source was not set when the Vis was created
found_variable = "df"
if standalone:
chart.code = chart.code.replace(
"placeholder_variable",
f"pd.DataFrame({str(vis.data.to_dict())})",
)
else:
# TODO: Placeholder (need to read dynamically via locals())
chart.code = chart.code.replace("placeholder_variable",
found_variable)
return chart.code