HEATMAP
Create a Plotly Heatmap visualization for a given input DataContainer. Inputs
------
default : OrderedPair|OrderedTriple|DataFrame|Vector|Matrix|Grayscale|Surface
the DataContainer to be visualized Params: show_text : bool whether or not to show the text inside the heatmap color blocks histogram : bool whether or not to render a histogram of the image next to the render Returns: out : Plotly the DataContainer containing the Plotly heatmap visualization
Python Code
from flojoy import (
Plotly,
OrderedPair,
flojoy,
Matrix,
Grayscale,
DataFrame,
Vector,
OrderedTriple,
Surface,
)
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import numpy as np
from blocks.DATA.VISUALIZATION.template import plot_layout
@flojoy
def HEATMAP(
default: OrderedPair
| Matrix
| Grayscale
| DataFrame
| Vector
| OrderedTriple
| Surface,
show_text: bool = False,
histogram: bool = False,
) -> Plotly:
"""Create a Plotly Heatmap visualization for a given input DataContainer.
Inputs
------
default : OrderedPair|OrderedTriple|DataFrame|Vector|Matrix|Grayscale|Surface
the DataContainer to be visualized
Parameters
----------
show_text : bool
whether or not to show the text inside the heatmap color blocks
histogram : bool
whether or not to render a histogram of the image next to the render
Returns
-------
Plotly
the DataContainer containing the Plotly heatmap visualization
"""
layout = plot_layout(title="HEATMAP")
if histogram:
layout.sliders = [
{
"steps": [
{
"label": str(v),
"method": "restyle",
"args": [{"zmin": 0, "zmax": v}],
}
for v in range(1, 255, 1)
],
"name": "zmax",
},
]
text_template = "%{text}"
fig = (
go.Figure()
if not histogram
else make_subplots(
rows=1,
cols=2,
column_widths=[0.9, 0.1],
specs=[[{}, {}]],
horizontal_spacing=0.05,
)
)
match default:
case Vector():
z = default.v
if z.ndim < 2:
num_columns = len(z) // 2
z = np.reshape(z, (2, num_columns))
fig.add_trace(
go.Heatmap(
z=z,
text=z if show_text else None,
texttemplate=text_template,
),
row=None if not histogram else 1,
col=None if not histogram else 1,
)
if histogram:
histogram = np.histogram(z, bins="auto")
x_values = histogram[1][:-1] + 0.05 # Center bars on bin edges
histogram_trace = go.Bar(
x=x_values, y=histogram[0], orientation="h", showlegend=False
)
fig.add_trace(histogram_trace, row=1, col=2)
case OrderedPair():
z = default.y
if default.y.ndim < 2:
num_columns = len(default.y) // 2
z = np.reshape(default.y, (2, num_columns))
fig.add_trace(
go.Heatmap(
z=z,
x=default.x,
y=default.y,
text=z if show_text else None,
texttemplate=text_template,
),
row=None if not histogram else 1,
col=None if not histogram else 1,
)
if histogram:
histogram = np.histogram(z, bins="auto")
x_values = histogram[1][:-1] + 0.05 # Center bars on bin edges
histogram_trace = go.Bar(
x=x_values, y=histogram[0], orientation="h", showlegend=False
)
fig.add_trace(histogram_trace, row=1, col=2)
case OrderedTriple():
x = np.unique(default.x)
y = np.unique(default.y)
z_size = len(x) * len(y)
if z_size > len(default.z):
z = np.pad(
default.z, (0, z_size - len(default.z)), mode="constant"
).reshape(len(y), len(x))
else:
z = default.z[:z_size].reshape(len(y), len(x))
if z.ndim < 2:
num_columns = len(z) // 2
z = np.reshape(z, (2, num_columns))
fig.add_trace(
go.Heatmap(
z=z,
x=x,
y=y,
text=z if show_text else None,
texttemplate=text_template,
),
row=None if not histogram else 1,
col=None if not histogram else 1,
)
if histogram:
histogram = np.histogram(z, bins="auto")
x_values = histogram[1][:-1] + 0.05 # Center bars on bin edges
histogram_trace = go.Bar(
x=x_values, y=histogram[0], orientation="h", showlegend=False
)
fig.add_trace(histogram_trace, row=1, col=2)
case Matrix():
m = default.m
if m.ndim < 2:
num_columns = len(m) // 2
m = np.reshape(m, (2, num_columns))
fig.add_trace(
go.Heatmap(
z=m,
text=m if show_text else None,
texttemplate=text_template,
),
row=None if not histogram else 1,
col=None if not histogram else 1,
)
if histogram:
histogram = np.histogram(m, bins="auto")
x_values = histogram[1][:-1] + 0.05 # Center bars on bin edges
histogram_trace = go.Bar(
x=x_values, y=histogram[0], orientation="h", showlegend=False
)
fig.add_trace(histogram_trace, row=1, col=2)
case Grayscale():
m = default.m
fig.add_trace(
go.Heatmap(
z=m,
text=m if show_text else None,
texttemplate=text_template,
),
row=None if not histogram else 1,
col=None if not histogram else 1,
)
if histogram:
histogram = np.histogram(m, bins="auto")
x_values = histogram[1][:-1] + 0.05 # Center bars on bin edges
histogram_trace = go.Bar(
y=x_values, x=histogram[0], orientation="h", showlegend=False
)
fig.add_trace(histogram_trace, row=1, col=2)
case DataFrame():
df = default.m
fig = px.imshow(df, text_auto=show_text)
case Surface():
x = np.unique(default.x)
y = np.unique(default.y)
z = default.z
fig.add_trace(
go.Heatmap(
z=z,
x=x,
y=y,
text=z if show_text else None,
texttemplate=text_template,
colorscale="Electric",
),
row=None if not histogram else 1,
col=None if not histogram else 1,
)
if histogram:
histogram = np.histogram(z, bins="auto")
x_values = histogram[1][:-1] + 0.05 # Center bars on bin edges
histogram_trace = go.Bar(
x=x_values, y=histogram[0], orientation="h", showlegend=False
)
fig.add_trace(histogram_trace, row=1, col=2)
if histogram:
layout.xaxis2 = dict(
tickmode="array",
tickvals=[0, histogram[0].max()],
ticktext=["0", f"{histogram[0].max():.0f}"],
)
layout.yaxis2 = dict(
tickmode="array",
tickvals=[x_values.min(), x_values.max()],
ticktext=["", ""],
)
fig.update_layout(layout)
return Plotly(
fig=fig,
)
Example
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In this example we used -
LINSPACEto simulateVectortype of dataSINEto simulateOrderedPairtype of dataPLOTLY_DATASETto simulateDataFrametype of data, andDF_2_ORDERED_TRIPLEto simulateOrderedTripletype of data
finally we visualized each of data types with Plotly HEATMAP visualizer node.