بنقرة واحدة
plotly-compact
Compact Plotly visualization patterns. Express for quick plots, Graph Objects for control.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Compact Plotly visualization patterns. Express for quick plots, Graph Objects for control.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Write a marimo notebook in a Python file in the right format.
R plotting conventions for Seurat/scRNA-seq: UMAP square plots (aspect.ratio=1, raster=FALSE), polychrome cluster colors, DimPlot/FeaturePlot settings, saving 300 DPI PNG, volcano plots with EnhancedVolcano, statistical comparisons with rstatix/ggpubr.
R Markdown formatting patterns: chunk display strategies (include=FALSE + inline R vs results='asis'), table rendering with knitr::kable, header hierarchy with tabsets, downloadable file embedding, and what to avoid (cat/print in visible chunks).
Sequencing read requirements and bcl2fastq base masks for 10x Genomics libraries: chemistry-specific BasesMaskCR values, ATAC I2 barcode requirement (capital I not lowercase y), Visium FFPE vs frozen R2 length difference.
Python interface to the B-Fabric laboratory information management system. Use when working with B-Fabric API for CRUD operations on samples, datasets, workunits, resources, or building B-Fabric applications and scripts.
Fast, reproducible scientific Python environments with pixi - conda and PyPI unified
| name | plotly-compact |
| description | Compact Plotly visualization patterns. Express for quick plots, Graph Objects for control. |
import plotly.express as px
px.scatter(df, x="x", y="y", color="cat", size="val", hover_data=["name"])
px.line(df, x="date", y="value", color="series")
px.bar(df, x="cat", y="val", color="group", barmode="group") # or "stack"
px.histogram(df, x="val", color="group", barmode="overlay", opacity=0.7)
px.box(df, x="group", y="val", points="all")
px.violin(df, x="group", y="val", box=True)
px.imshow(corr_matrix, text_auto=".2f", color_continuous_scale="RdBu_r")
px.scatter_matrix(df, dimensions=["a", "b", "c"], color="cat")
import plotly.graph_objects as go
from plotly.subplots import make_subplots
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, mode="lines+markers", name="series"))
fig.update_layout(title="Title", template="plotly_white")
fig.update_traces(marker=dict(size=10))
fig = make_subplots(rows=2, cols=2, subplot_titles=["A", "B", "C", "D"])
fig.add_trace(go.Scatter(x=x, y=y), row=1, col=1)
# Secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])
fig.add_trace(go.Scatter(...), secondary_y=False)
fig.add_trace(go.Bar(...), secondary_y=True)
fig.update_layout(
template="plotly_white", # or plotly_dark, ggplot2, seaborn
legend=dict(orientation="h", yanchor="bottom", y=1.02)
)
fig.add_hline(y=threshold, line_dash="dash")
fig.add_annotation(x=2, y=5, text="Note", showarrow=True)
fig.update_xaxes(type="log", title="X Label")
fig.write_html("plot.html")
fig.write_image("plot.png", scale=2) # requires kaleido
fig.write_image("plot.svg")
import networkx as nx
G = nx.karate_club_graph()
pos = nx.spring_layout(G, seed=42)
# Edges
edge_x, edge_y = [], []
for u, v in G.edges():
x0, y0 = pos[u]; x1, y1 = pos[v]
edge_x.extend([x0, x1, None])
edge_y.extend([y0, y1, None])
edge_trace = go.Scatter(x=edge_x, y=edge_y, mode="lines", line=dict(width=0.5, color="#888"))
node_trace = go.Scatter(x=[pos[n][0] for n in G.nodes()], y=[pos[n][1] for n in G.nodes()],
mode="markers", marker=dict(size=10, color=list(dict(G.degree()).values())))
fig = go.Figure([edge_trace, node_trace])
fig.update_layout(showlegend=False, xaxis=dict(showgrid=False, showticklabels=False),
yaxis=dict(showgrid=False, showticklabels=False))
px.scatter(..., render_mode="webgl")color_discrete_sequence=px.colors.qualitative.Safe