MFP - MultiFunctionPlotter

✨ Key Features

Ultra-Lightweight DSL

Gnuplot-style syntax. 85% fewer tokens than matplotlib. Perfect for AI agents.

Multi-Format Support

CSV, TXT, DAT, Excel, JSON. Automatic format detection.

30+ Plot Styles

Lines, markers, error bars, scatter, heatmaps, contours, distributions, and more.

Advanced Formatting

Scientific notation, custom ticks, date formatting, log scales, rotations.

Subplot Mosaics

Flexible multi-panel layouts with custom grid definitions.

Math Functions

Plot mathematical expressions with NumPy. Parameter control included.

Reproducibility

Save/replay configurations as JSON. Perfect for research.

Python API

Use as a library. Full programmatic control over plots.

🚀 Quick Start

Installation

pip3 install multifunctionplotter

Then run anywhere:

mfp --help

MCP Server (for AI Assistants)

pip3 install multifunctionplotter && mfp-mcp

Claude Code:
claude mcp add mfp -- mfp-mcp

opencode — find the path first:

which mfp-mcp

Then open ~/.config/opencode/opencode.json and add:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "mfp": {
      "type": "local",
      "command": [""]
    }
  }
}

Basic Line Plot

mfp data.csv using 1:2 with lines

With Title & Labels

mfp data.csv using 1:2 with lines title "Stock Prices" xlabel "Date" ylabel "Close"

Save to File

mfp data.csv using 1:2 with lines --save output.pdf

Custom Figure Size

mfp data.csv using 1:2 with lines --figsize 10:5

Multiple Series (Overlay)

mfp data.csv using 1:2 with lines, data.csv using 1:3 with dashed

Get Help

mfp --help

CLI vs Python Tool

The MFP CLI command and Python tools work slightly differently:

CLI parses labels but may not save them to plot.json for replay
Python tools (mfp_multi_plot, mfp_plot) handle complex plots more reliably
For subplots with specific labels, prefer Python tools or specify labels per-command

⚙️ Command Reference

Token	Format	Description	Example
`using / u`	X:Y	Column indices (1-based for text, 0-based for CSV)	`using 1:2`
`with / w`	style	Plot style (see Styles section)	`with lines`
`title`	"text"	Plot title	`title "Stock Prices"`
`xlabel`	"text"	X-axis label	`xlabel "Date"`
`ylabel`	"text"	Y-axis label	`ylabel "Price"`
`legend / lg`	label	Legend entry (no spaces)	`legend "Series A"`
`linewidth / lw`	int	Line width in points (default: 2)	`linewidth 3`
`linecolor / lc`	color	Color: tab:red, #3a7ab3, steelblue, etc.	`linecolor tab:blue`
`xrange`	min:max	Force x-axis limits	`xrange 0:100`
`yrange`	min:max	Force y-axis limits	`yrange 0:1000`
`bin`	int	Histogram bin count (default: auto)	`bin 30`
`func:`	"expr"	Math expression (requires xrange)	`func: "f(x) = np.sin(x)"`

Flag	Description	Example
`--xlog`	Set x-axis to logarithmic scale	`--xlog`
`--ylog`	Set y-axis to logarithmic scale	`--ylog`
`--save`	Save figure to file (format from extension)	`--save output.pdf`
`--subplot`	Create subplot mosaic layout	`--subplot AB-CD`
`--figsize`	Set figure size (width:height)	`--figsize 10:5`
`--help`	Display help documentation	`--help`
`--list-styles`	List all available plot styles	`--list-styles`

Discrete Error Bars

Token	Format	Description
`yerr`	<col>	1-based column index holding ±σ values
`capsize`	int	Cap width in points (default: 4)

mfp data.csv using 1:2 with errorbars yerr 3 capsize 5

Shaded Error Band

Same tokens as errorbars; capsize is ignored.

mfp data.csv using 1:2 with errorshade yerr 3

Scatter with Colormap

Token	Format	Description
`cmap`	<col>	1-based column index for colour values (required)
`colormap`	name	Matplotlib colormap (default: viridis)
`cbar_label`	"text"	Colorbar label (optional)

mfp results.csv using 1:2 with scatter cmap 3 colormap plasma cbar_label "Temp (K)"

Popular Colormaps

Perceptually Uniform

viridis
plasma
inferno
magma
cividis

Diverging

coolwarm
RdBu
seismic

Sequential

Blues
Reds
YlOrRd

📌 Important: 2-D styles read the entire file as a numeric matrix (no header). Rows → y-axis, columns → x-axis. The using, xrange, and yrange tokens are ignored.

Style	Description	Example
`heatmap`	2D heatmap (imshow) — best for raster data	`with heatmap`
`contour`	Contour lines only	`with contour levels 15`
`contourf`	Filled contours	`with contourf levels 20`

2D-Specific Tokens

colormap <name> — Colormap (default: viridis)
levels <int> — Number of contour levels (default: 10)
cbar_label "text" — Colorbar label

🎨 Plot Styles

Style	Alias	Description	Example
`lines`	`l`	Solid line	`with lines`
`dashed`		Dashed line	`with dashed`
`dotted`		Dotted line	`with dotted`
`points`	`p`	Circle markers only	`with points`
`linespoints`	`lp`	Line + circle markers	`with linespoints`
`stars`		Star markers	`with stars`
`d`		Diamond markers	`with d`

Style	Description	Example
`hist`	Histogram (seaborn histplot)	`with hist bin 30`
`kde`	Kernel Density Estimation	`with kde`
`box`	Box-and-whisker plot	`with box`
`violin`	Violin plot	`with violin`

Style	Description	Requirements
`errorbars / eb`	Discrete error bars	Requires `yerr <col>`
`errorshade / es`	Shaded ±σ band	Requires `yerr <col>`
`scatter`	Scatter with colormap	Requires `cmap <col>`
`heatmap`	2D heatmap	2D matrix file
`contour`	Contour lines	2D matrix file
`contourf`	Filled contours	2D matrix file

⚡ Advanced Features

Scientific Notation

mfp data.csv using 1:2 with lines sci_notation both

Options: x, y, or both

Custom Tick Positions

mfp angles.csv using 1:2 with points xticks "0,90,180,270"

Tick Rotation

mfp data.csv using 1:2 with lines xtick_rotation 45 ytick_rotation 90

Date Formatting

mfp timeseries.csv using 1:2 with lines date_format "%Y-%m-%d"

Format codes: %Y (year), %m (month), %d (day), %H (hour), %M (min), %S (sec)

Basic Function Plot

mfp func: "f(x) = np.sin(x)" xrange 0:10

Function with Parameters

mfp func: "f(x,a=2,b=0.5) = a*np.exp(-b*x)" xrange 0:10

Common Expressions

np.sin(x), np.cos(x), np.tan(x) — Trigonometric
np.exp(x), np.log(x), np.sqrt(x) — Exponential & Logarithmic
np.where(condition, true_val, false_val) — Conditional
np.linspace(start, stop, num) — Linear spacing

2×2 Grid Layout

mfp --subplot AB-CD data1.csv using 1:2 with lines, data2.csv using 1:2 with hist, data3.csv using 1:2 with kde, data4.csv using 1:2 with scatter cmap 3

Asymmetric Layout

mfp --subplot AA-BC top_spans_two, bottom_left, bottom_right

Layout Format:

Letters represent panels (A, B, C, ...)
Use - to separate rows
Each command separated by comma

Important: Subplot Labels
When using subplots, axis labels must be specified per-command:

mfp "cmd1 xlabel 'X' ylabel 'Y', cmd2 xlabel 'A' ylabel 'B'"

Global xlabel/ylabel may not apply to individual subplots.

Histogram Requirement
The hist style requires both x and y columns:

                    # CORRECT

                    mfp data.csv using 5:0 with hist bin 25

                    # WILL ERROR - missing y column

                    mfp data.csv using 5 with hist bin 25

Logarithmic X-Axis

mfp spectrum.csv using 1:2 with lines --xlog

Logarithmic Y-Axis

mfp spectrum.csv using 1:2 with lines --ylog

Both Axes Logarithmic

mfp spectrum.csv using 1:2 with lines --xlog --ylog

📝 Examples & Recipes

Basic Line Plot

mfp data.csv using 1:2 with lines lc tab:blue legend "Series A"

Multiple Series (Overlay)

mfp data.csv using 1:2 with lines lc green, data.csv using 1:3 with dashed lc red

Error Bars (Discrete)

mfp data.csv using 1:2 with errorbars yerr 3

Error Shaded Band

mfp data.csv using 1:2 with errorshade yerr 3 lc steelblue, data.csv using 1:2 with lines lc steelblue

Colormap Scatter

mfp results.csv using 1:2 with scatter cmap 3 colormap plasma

Heatmap

mfp matrix.dat with heatmap colormap viridis cbar_label "Intensity"

Filled Contour Plot

mfp matrix.dat with contourf levels 20 colormap RdBu

Log-Scale Spectrum

mfp spectrum.csv using 1:2 with lines --xlog --ylog

Histogram

mfp samples.csv using 0:1 with hist bin 30 lc tab:orange

Math Function

mfp func: "f(x,a=1) = a*np.sin(x)" xrange 0:10 lc tab:green

2×2 Subplot Grid

mfp --subplot AB-CD d1.csv using 1:2 with lines, d2.csv using 1:2 with hist, d3.csv using 1:2 with kde, d4.csv using 1:2 with scatter cmap 3

Publication-Quality PDF

mfp data.csv using 1:2 with lines title "Results" xlabel "Time" ylabel "Value" sci_notation y --save figure.pdf

Replay Session

mfp plot.json

MFP saves last plot config to plot.json

🔧 Data Manipulator (DM)

Interactive CLI tool for exploring, cleaning, and transforming tabular data without writing code.

Quick Start

mfp DM

Launches interactive data manipulation REPL.

Supported Formats

.csv — Comma-separated values

.xlsx / .xls — Excel workbooks

.json — JSON arrays

Commands Reference

Inspection

`show`	Print current DataFrame
`head [N]`	First N rows (default 10)
`tail [N]`	Last N rows (default 10)
`properties / props`	Column dtypes, NaN counts, stats
`counts <col>`	Frequency of unique values

Filtering & Sorting

`filter <expr>`	Pandas query expression
`slice start:end`	Keep rows [start, end)
`sort <col> asc\|desc`	Sort by column

Transformation

`rename old:new,...`	Rename columns
`cast <col> type`	Change dtype (int/float/str)
`addcol <name> expr`	Add column from expression
`modify <col> old new`	Replace values
`delete <col>`	Drop column or row

Data Cleaning

`dedup [col1,col2]`	Remove duplicate rows
`fillna <col> value`	Fill NaN cells
`dropna <col>`	Drop rows with NaN

I/O

`load <file>`	Load new file
`save <file>`	Save to file
`append <file>`	Append rows from file
`merge <file> col`	Merge on column
`generate expr`	Generate data from expression

History

`undo`	Revert last operation
`redo`	Re-apply undone operation

Example Workflow

Action> load data.csv
Action> head
Action> properties
Action> filter price > 100
Action> sort volume desc
Action> rename old_name:new_name
Action> addcol profit revenue - cost
Action> dedup
Action> save cleaned_data.csv

Tips

Use help command to see all available actions
Use undo / redo to navigate through changes (up to 20 steps)
Filter expressions use pandas query syntax: price > 100, name == "test"
Add columns using df.eval() expressions: addcol total price * quantity
Chain operations: filter → sort → modify → save

MultiFunctionPlotter (MFP)

✨ Key Features

Ultra-Lightweight DSL

Multi-Format Support

30+ Plot Styles

Advanced Formatting

Subplot Mosaics

Math Functions

Reproducibility

Python API

🚀 Quick Start

Installation

MCP Server (for AI Assistants)

Basic Line Plot

With Title & Labels

Save to File

Custom Figure Size

Multiple Series (Overlay)

Get Help

⚙️ Command Reference

Discrete Error Bars

Shaded Error Band

Scatter with Colormap

Popular Colormaps

2D-Specific Tokens

🎨 Plot Styles

⚡ Advanced Features

Scientific Notation

Custom Tick Positions

Tick Rotation

Date Formatting

Basic Function Plot

Function with Parameters

Common Expressions

2×2 Grid Layout

Asymmetric Layout

Logarithmic X-Axis

Logarithmic Y-Axis

Both Axes Logarithmic

📝 Examples & Recipes

Basic Line Plot

Multiple Series (Overlay)

Error Bars (Discrete)

Error Shaded Band

Colormap Scatter

Heatmap

Filled Contour Plot

Log-Scale Spectrum

Histogram

Math Function

2×2 Subplot Grid

Publication-Quality PDF

Replay Session

🔧 Data Manipulator (DM)

Quick Start

Supported Formats

Commands Reference

Inspection

Filtering & Sorting

Transformation

Data Cleaning

I/O

History

Example Workflow

Tips