diff --git a/README.md b/README.md
index 6a15d85..c8813cf 100644
--- a/README.md
+++ b/README.md
@@ -54,6 +54,17 @@ fibermorph --demo_real_curv --output_directory ~/fibermorph_demo_curv
 fibermorph --demo_real_section --output_directory ~/fibermorph_demo_section
 ```
 
+### Optional extras
+
+Some features rely on optional dependencies. Install them only if needed:
+
+```bash
+pip install "fibermorph[raw]"    # enable RAW image conversion via rawpy
+pip install "fibermorph[viz]"    # install matplotlib-based visualization helpers
+```
+
+Extras can be combined, e.g. `pip install "fibermorph[raw,viz]"`.
+
 ## Using fibermorph on your data
 
 Once installed, use fibermorph on your own grayscale TIFFs:  
diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml
index 724b57e..77801aa 100644
--- a/conda-recipe/meta.yaml
+++ b/conda-recipe/meta.yaml
@@ -23,23 +23,14 @@ requirements:
     - poetry-core <1.9
   run:
     - python >=3.9,<3.14
-    # Conditional numpy and pyarrow for Python 3.13 support
-    # For Python <3.13: numpy 1.x, pyarrow 15.x
-    # For Python >=3.13: numpy 2.x, pyarrow 17.x
-    - numpy >=1.26.4,<3.0
+    - numpy >=1.26.4
     - joblib >=1.3.2,<2.0.0
     - pandas >=2.2.0,<3.0.0
     - pillow >=10.2.0,<11.0.0
     - requests >=2.31.0,<3.0.0
-    - matplotlib-base >=3.8.2,<4.0.0
-    - rawpy >=0.19.0,<0.20.0
     - scipy >=1.8,<2.0
-    - shapely >=2.0.2,<3.0.0
     - tqdm >=4.66.1,<5.0.0
     - scikit-image >=0.22.0,<0.23.0
-    - scikit-learn >=1.4.0,<2.0.0
-    - sympy >=1.12,<2.0
-    - pyarrow >=15.0.0
 
 test:
   imports:
diff --git a/docs/dependency-audit.md b/docs/dependency-audit.md
new file mode 100644
index 0000000..62cea30
--- /dev/null
+++ b/docs/dependency-audit.md
@@ -0,0 +1,43 @@
+## Dependency Slimming Plan
+
+We want to reduce install and deployment overhead (for both the CLI and the Streamlit GUI) by auditing fibermorph's dependencies. This document outlines the plan, rationale, and checklist so the work stays organized.
+
+### Goals
+- Identify runtime dependencies that are no longer required for core workflows.
+- Move optional functionality (GUI, raw conversion, demo generators) behind extras.
+- Keep the base install as lightweight as possible (numpy, scipy, scikit-image, pandas, joblib, tqdm).
+- Record every change/rationale here for future reference.
+
+### Candidate Dependencies
+
+| Package       | Current Usage | Proposed Action | Notes |
+|---------------|---------------|-----------------|-------|
+| `sympy`       | Dummy data ellipse area | Replace with `math.pi * a * b`; remove dependency | Not used in runtime workflows |
+| `matplotlib`  | Historical plotting/demos | Confirm active usage; move to optional extra if only for visualization | GUI doesn't rely on it |
+| `pyarrow`     | Legacy | Verify actual usage; drop if unused | Listed in deps but not obviously referenced |
+| `rawpy`       | `--raw2gray` workflow | Move to optional `raw` extra; guard import | GUI users typically upload TIFFs |
+| `scikit-learn`| Dummy data MinMaxScaler | Swap for numpy-based scaling; remove dependency | Not needed elsewhere |
+| `shapely`     | Dummy data ellipse properties | Replace with basic geometry math | Avoid heavy dep |
+| `pytest`      | Should be dev-only | Ensure not bundled into runtime distribution | Already in dev group but reconfirm |
+| GUI extras    | `streamlit`, `requests` | Already optional via `[gui]` extra | Keep optional |
+
+### Audit Checklist
+1. ✅ **Inventory imports** – `python tools/inventory_imports.py`
+2. ✅ **Refactor replacements**:
+   - `demo/dummy_data.py` now uses pure numpy/math.
+   - `demo/demo.py` ellipse helpers rewritten without sympy.
+   - Removed unused `fibermorph/arc_sim.py`.
+3. ✅ **Update `pyproject.toml`**:
+   - Core deps trimmed (removed matplotlib, rawpy, scikit-learn, shapely, sympy, pyarrow, argparse, pytest).
+   - Added optional extras `raw = ["rawpy"]`, `viz = ["matplotlib"]`.
+4. ✅ **Guard optional imports** – `raw_to_gray` now raises a helpful message when `rawpy` is missing.
+5. ✅ **Docs** – README updated with optional extras (`raw`, `viz`).
+6. ☐ **Testing** – run pytest with minimal install; confirm optional extras.
+
+### Next Steps
+- Work on a dedicated branch (e.g., `feature/dependency-trim`) branched from `main`.
+- Tackle the checklist, updating this document with decisions/results.
+- Once complete, bump version and summarize changes.
+
+### Tools
+- `python tools/inventory_imports.py` – reports top-level imports across the `fibermorph` package.
diff --git a/fibermorph/arc_sim.py b/fibermorph/arc_sim.py
deleted file mode 100644
index 9ce6a70..0000000
--- a/fibermorph/arc_sim.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import sympy
-
-from PIL import Image, ImageDraw
-from random import randint
-from sklearn import preprocessing
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import os
-
-# Input radius size
-radius = 1
-
-# set size/limits for plots
-xlims = 250
-
-ylims = xlims
-
-# no. of hair to simulate - 25 for now
-nhair = 25
-
-# pick a starting angles for hair segments
-start_theta = np.random.uniform(low=0, high=np.pi, size=nhair)
-start_theta = pd.Series(start_theta, name="start_theta")
-
-# define length of the arc.
-# The more the curvature, the longer the arc
-arc_length = np.pi / radius
-arc_length = pd.Series([arc_length for i in range(nhair)], name="arc_length")
-
-# set end value of the angle
-end_theta = start_theta + arc_length
-end_theta = pd.Series(end_theta, name="end_theta")
-
-arc_nums = list(range(nhair))
-arc_names = pd.Series(["arc_" + str(s) for s in arc_nums], name="arc_names")
-
-dat = pd.concat([arc_names, start_theta, end_theta, arc_length], axis=1)
-
-
-# function to generate arc given the start and end angles
-
-
-def apoints(row):
-    stheta = row[1]
-    etheta = row[2]
-    rthetas = np.linspace(start=stheta, stop=etheta, num=25)
-    x = pd.Series(radius * np.cos(rthetas), name="x")
-    y = pd.Series(radius * np.sin(rthetas), name="y")
-
-    dat2 = pd.concat([x, y], axis=1)
-
-    return dat2
-
-
-# dataframe with coordinates for each arc
-dats = dat
-
-dats["coords"] = dats.apply(lambda row: apoints(row), axis=1)
-
-
-# center the arcs so they appear at the center of each 'window'
-def center_func(coord_df):
-    x = coord_df["x"]
-    y = coord_df["y"]
-
-    x2 = pd.Series(x - np.mean(x), name="x2")
-    y2 = pd.Series(y - np.mean(y), name="y2")
-
-    dat3 = pd.concat([x2, y2], axis=1)
-
-    return dat3
-
-
-dats["c_coords"] = dats["coords"].apply(lambda row: center_func(row))
-
-im = Image.new("L", (xlims, ylims), color="white")
-draw = ImageDraw.Draw(im)
-
-coord_list = np.array(dats["c_coords"].iloc[0])
-coord_tuple = tuple(map(tuple, coord_list))
-
-x, y = zip(*coord_tuple)
-plt.scatter(x, y)
-plt.show()
-
-draw.line(xy=coord_tuple, fill="black")
-
-im.show()
-
-
-# another function to center arcs to the middle of the window but by scaling
-def center_python_func(coord_df):
-    scaler = preprocessing.MinMaxScaler(feature_range=(0, 200))
-
-    dat4 = coord_df
-    dat4["x"] = scaler.fit_transform(coord_df[["x"]])
-    dat4["y"] = scaler.fit_transform(coord_df[["y"]])
-
-    return dat4
-
-
-dats["c_coords"] = dats["coords"].apply(lambda row: center_python_func(row))
-
-im = Image.new("L", (xlims, ylims), color="white")
-draw = ImageDraw.Draw(im)
-
-coord_list = np.array(dats["c_coords"].iloc[0])
-coord_tuple = tuple(map(tuple, coord_list))
-
-x, y = zip(*coord_tuple)
-plt.scatter(x, y)
-plt.show()
-
-draw.line(xy=coord_tuple, fill="black")
-
-im.show()
diff --git a/fibermorph/core/filters.py b/fibermorph/core/filters.py
index a033197..faffd99 100644
--- a/fibermorph/core/filters.py
+++ b/fibermorph/core/filters.py
@@ -7,8 +7,8 @@
 import numpy as np
 import skimage
 import skimage.filters
+import skimage.io
 import skimage.util
-from matplotlib import pyplot as plt
 
 logger = logging.getLogger(__name__)
 
@@ -52,8 +52,9 @@ def filter_curv(
         output_path = make_subdirectory(output_path, append_name="filtered")
         # inverting and saving the filtered image
         img_inv = skimage.util.invert(filter_img)
+        img_uint8 = skimage.util.img_as_ubyte(np.clip(img_inv, 0, 1))
         save_path = pathlib.Path(output_path) / f"{im_name}.tiff"
-        plt.imsave(save_path, img_inv, cmap="gray")
+        skimage.io.imsave(save_path, img_uint8)
         logger.debug(f"Saved filtered image to {save_path}")
 
     return filter_img, im_name
diff --git a/fibermorph/demo/demo.py b/fibermorph/demo/demo.py
index df0dfba..fb3be65 100644
--- a/fibermorph/demo/demo.py
+++ b/fibermorph/demo/demo.py
@@ -1,5 +1,4 @@
-import sympy
-
+import math
 import os
 import random
 import pathlib
@@ -7,17 +6,16 @@
 import sys
 from datetime import datetime
 
+import fibermorph
 import numpy as np
 import pandas as pd
 import requests
 from PIL import Image
+from joblib import Parallel, delayed
 from skimage import draw
-from sympy import geometry
 from tqdm import tqdm
 
 from . import dummy_data
-import fibermorph
-from joblib import Parallel, delayed
 
 
 def create_results_cache(path):
@@ -230,10 +228,13 @@ def sim_ellipse(
     )
     img[rr, cc] = 0
 
-    p1 = geometry.Point((im_height_px / px_per_um) / 2, (im_width_px / px_per_um) / 2)
-    e1 = geometry.Ellipse(p1, hradius=max_rad_um, vradius=min_rad_um)
-    area = sympy.N(e1.area)
-    eccentricity = e1.eccentricity
+    a = max_rad_um
+    b = min_rad_um
+    area = math.pi * a * b
+    if a <= 0:
+        eccentricity = 0.0
+    else:
+        eccentricity = math.sqrt(max(0.0, 1.0 - (b * b) / (a * a)))
 
     jetzt = datetime.now()
     timestamp = jetzt.strftime("%b%d_%H%M_%S_%f")
@@ -278,11 +279,10 @@ def validation_section(output_location, repeats, jobs=2):
     # create list of random variables from range
     def gen_ellipse_data():
         min_diam_um = random.uniform(30, 120)
-        ecc = random.uniform(0.0, 1.0)
-        # min_diam_um = random.uniform(30, max_diam_um)
-        max_diam_um = geometry.Ellipse(
-            geometry.Point(0, 0), vradius=min_diam_um, eccentricity=ecc
-        ).hradius
+        ecc = random.uniform(0.0, 0.99)
+        if ecc >= 1.0:
+            ecc = 0.99
+        max_diam_um = min_diam_um / math.sqrt(1.0 - ecc**2)
         angle_deg = random.randint(0, 360)
         list = [max_diam_um, min_diam_um, angle_deg]
         return list
diff --git a/fibermorph/demo/dummy_data.py b/fibermorph/demo/dummy_data.py
index f0a0e21..2b0c33c 100644
--- a/fibermorph/demo/dummy_data.py
+++ b/fibermorph/demo/dummy_data.py
@@ -1,23 +1,22 @@
 """
-Script to generate dummy data for testing curvature
+Script to generate dummy data for testing curvature.
 
 Based on script to produce non-colliding rectangles adapted from:
 https://stackoverflow.com/questions/4373741/how-can-i-randomly-place-several-non-colliding-rects
 """
 
-import sympy
+from __future__ import annotations
 
-from PIL import Image, ImageDraw
+import math
+import os
+import pathlib
 import random
+from datetime import datetime
 from random import randint
-import pathlib
+
 import numpy as np
 import pandas as pd
-import matplotlib.pyplot as plt
-import os
-from datetime import datetime
-from sklearn import preprocessing
-from sympy import geometry
+from PIL import Image, ImageDraw
 
 random.seed()
 
@@ -182,13 +181,20 @@ def center_func(coord_df):
 
         return dat3
 
-    def center_python_func(coord_df):
-        scaler = preprocessing.MinMaxScaler(feature_range=(0, 200))
-
-        dat4 = coord_df
-        dat4["x"] = scaler.fit_transform(coord_df[["x"]])
-        dat4["y"] = scaler.fit_transform(coord_df[["y"]])
-
+    def minmax_scale(series: pd.Series, feature_range: tuple[float, float] = (0.0, 200.0)) -> pd.Series:
+        lower, upper = feature_range
+        min_val = series.min()
+        max_val = series.max()
+        if math.isclose(max_val, min_val):
+            midpoint = (lower + upper) / 2.0
+            return pd.Series(np.full(series.shape, midpoint), index=series.index, dtype=float)
+        scale = (upper - lower) / (max_val - min_val)
+        return lower + (series - min_val) * scale
+
+    def center_python_func(coord_df: pd.DataFrame) -> pd.DataFrame:
+        dat4 = coord_df.copy()
+        dat4["x"] = minmax_scale(coord_df["x"])
+        dat4["y"] = minmax_scale(coord_df["y"])
         return dat4
 
     # dats["c_coords"] = dats["coords"].apply(lambda row: center_func(row))
@@ -200,14 +206,8 @@ def center_python_func(coord_df):
     coord_list = np.array(dats["c_coords"].iloc[0])
     coord_tuple = tuple(map(tuple, coord_list))
 
-    x, y = zip(*coord_tuple)
-    plt.scatter(x, y)
-    plt.show()
-
     draw.line(xy=coord_tuple, fill="black")
 
-    im.show()
-
 
 def draw_line(draw, rect, width):
     pad = 40
@@ -238,11 +238,9 @@ def draw_ellipse(draw, rect, width):
     # values for min and max and area of ellipses to pass to dataframe
     width_df = maxx - minx
     height_df = maxy - miny
-    r1 = width_df / 2
-    r2 = height_df / 2
-    p1 = geometry.Point(0, 0)
-    e1 = geometry.Ellipse(p1, r1, r2)
-    area = sympy.N(e1.area)
+    r1 = width_df / 2.0
+    r2 = height_df / 2.0
+    area = math.pi * r1 * r2
 
     return width_df, height_df, area
 
diff --git a/fibermorph/io/converters.py b/fibermorph/io/converters.py
index e1c3aa2..dbffd1a 100644
--- a/fibermorph/io/converters.py
+++ b/fibermorph/io/converters.py
@@ -5,7 +5,6 @@
 from typing import Union
 import logging
 
-import rawpy
 from PIL import Image
 
 logger = logging.getLogger(__name__)
@@ -35,6 +34,12 @@ def raw_to_gray(
     name = os.path.splitext(basename)[0] + ".tiff"
     output_name = output_directory / name
 
+    if rawpy is None:
+        raise RuntimeError(
+            "rawpy is required to convert RAW files. Install optional dependencies via "
+            "`pip install fibermorph[raw]`."
+        )
+
     try:
         with rawpy.imread(imgfile) as raw:
             rgb = raw.postprocess(use_auto_wb=True)
@@ -49,3 +54,7 @@ def raw_to_gray(
         raise
 
     return output_name
+try:
+    import rawpy
+except ModuleNotFoundError:  # pragma: no cover - optional dependency
+    rawpy = None
diff --git a/fibermorph/processing/binary.py b/fibermorph/processing/binary.py
index f358652..e9bb38f 100644
--- a/fibermorph/processing/binary.py
+++ b/fibermorph/processing/binary.py
@@ -13,7 +13,6 @@
 import skimage.util
 from PIL import Image
 from skimage import filters
-from matplotlib import pyplot as plt
 
 logger = logging.getLogger(__name__)
 
@@ -103,11 +102,9 @@ def binarize_curv(
         output_path = make_subdirectory(output_path, append_name="binarized")
         # invert image
         save_im = skimage.util.invert(binary_im)
-
-        # save image
+        save_array = (save_im.astype(np.uint8)) * 255
         save_name = pathlib.Path(output_path) / f"{im_name}.tiff"
-        im = Image.fromarray(save_im)
-        im.save(save_name)
+        Image.fromarray(save_array, mode="L").save(save_name)
         logger.debug(f"Saved binarized image to {save_name}")
 
     return binary_im
@@ -155,12 +152,13 @@ def remove_particles(
 
     if save_img:
         img_inv = skimage.util.invert(clean)
+        img_uint8 = (img_inv.astype(np.uint8)) * 255
         if prune:
             output_path = make_subdirectory(output_path, append_name="pruned")
         else:
             output_path = make_subdirectory(output_path, append_name="clean")
         savename = pathlib.Path(output_path) / f"{name}.tiff"
-        plt.imsave(savename, img_inv, cmap="gray")
+        Image.fromarray(img_uint8, mode="L").save(savename)
         logger.debug(f"Saved cleaned image to {savename}")
 
     return clean
diff --git a/poetry.lock b/poetry.lock
index 799b51a..71f5416 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,17 +1,5 @@
 # This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
 
-[[package]]
-name = "argparse"
-version = "1.4.0"
-description = "Python command-line parsing library"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
-    {file = "argparse-1.4.0-py2.py3-none-any.whl", hash = "sha256:c31647edb69fd3d465a847ea3157d37bed1f95f19760b11a47aa91c04b666314"},
-    {file = "argparse-1.4.0.tar.gz", hash = "sha256:62b089a55be1d8949cd2bc7e0df0bddb9e028faefc8c32038cc84862aefdd6e4"},
-]
-
 [[package]]
 name = "black"
 version = "24.10.0"
@@ -249,16 +237,16 @@ files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
-markers = {main = "platform_system == \"Windows\" or sys_platform == \"win32\"", dev = "sys_platform == \"win32\" or platform_system == \"Windows\""}
+markers = {main = "platform_system == \"Windows\"", dev = "sys_platform == \"win32\" or platform_system == \"Windows\""}
 
 [[package]]
 name = "contourpy"
 version = "1.3.0"
 description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version < \"3.11\" and extra == \"viz\""
 files = [
     {file = "contourpy-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:880ea32e5c774634f9fcd46504bf9f080a41ad855f4fef54f5380f5133d343c7"},
     {file = "contourpy-1.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:76c905ef940a4474a6289c71d53122a4f77766eef23c03cd57016ce19d0f7b42"},
@@ -341,10 +329,10 @@ test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist"
 name = "contourpy"
 version = "1.3.3"
 description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = false
+optional = true
 python-versions = ">=3.11"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "python_version >= \"3.11\" and extra == \"viz\""
 files = [
     {file = "contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1"},
     {file = "contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381"},
@@ -661,9 +649,10 @@ toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
 name = "cycler"
 version = "0.12.1"
 description = "Composable style cycles"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
+markers = "extra == \"viz\""
 files = [
     {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
     {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
@@ -691,7 +680,7 @@ version = "1.3.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
-groups = ["main", "dev"]
+groups = ["dev"]
 markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
@@ -751,9 +740,10 @@ pyflakes = ">=3.4.0,<3.5.0"
 name = "fonttools"
 version = "4.60.1"
 description = "Tools to manipulate font files"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"viz\""
 files = [
     {file = "fonttools-4.60.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:9a52f254ce051e196b8fe2af4634c2d2f02c981756c6464dc192f1b6050b4e28"},
     {file = "fonttools-4.60.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c7420a2696a44650120cdd269a5d2e56a477e2bfa9d95e86229059beb1c19e15"},
@@ -896,10 +886,10 @@ tifffile = ["tifffile"]
 name = "importlib-resources"
 version = "6.5.2"
 description = "Read resources from Python packages"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version == \"3.9\""
+markers = "extra == \"viz\" and python_version == \"3.9\""
 files = [
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@@ -922,7 +912,7 @@ version = "2.1.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["dev"]
 markers = "python_version < \"3.11\""
 files = [
     {file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"},
@@ -935,7 +925,7 @@ version = "2.3.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.10"
-groups = ["main", "dev"]
+groups = ["dev"]
 markers = "python_version >= \"3.11\""
 files = [
     {file = "iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12"},
@@ -973,10 +963,10 @@ files = [
 name = "kiwisolver"
 version = "1.4.7"
 description = "A fast implementation of the Cassowary constraint solver"
-optional = false
+optional = true
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version < \"3.11\" and extra == \"viz\""
 files = [
     {file = "kiwisolver-1.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8a9c83f75223d5e48b0bc9cb1bf2776cf01563e00ade8775ffe13b0b6e1af3a6"},
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@@ -1098,10 +1088,10 @@ files = [
 name = "kiwisolver"
 version = "1.4.9"
 description = "A fast implementation of the Cassowary constraint solver"
-optional = false
+optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "python_version >= \"3.11\" and extra == \"viz\""
 files = [
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@@ -1230,10 +1220,10 @@ test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
 name = "matplotlib"
 version = "3.9.4"
 description = "Python plotting package"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version < \"3.11\""
+markers = "python_version < \"3.11\" and extra == \"viz\""
 files = [
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@@ -1297,10 +1287,10 @@ dev = ["meson-python (>=0.13.1,<0.17.0)", "numpy (>=1.25)", "pybind11 (>=2.6,!=2
 name = "matplotlib"
 version = "3.10.7"
 description = "Python plotting package"
-optional = false
+optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "python_version >= \"3.11\""
+markers = "python_version >= \"3.11\" and extra == \"viz\""
 files = [
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@@ -1385,24 +1375,6 @@ files = [
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-[[package]]
-name = "mpmath"
-version = "1.3.0"
-description = "Python library for arbitrary-precision floating-point arithmetic"
-optional = false
-python-versions = "*"
-groups = ["main"]
-files = [
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-]
-
-[package.extras]
-develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
-docs = ["sphinx"]
-gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""]
-tests = ["pytest (>=4.6)"]
-
 [[package]]
 name = "mypy"
 version = "1.18.2"
@@ -1926,7 +1898,7 @@ version = "1.6.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["dev"]
 files = [
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@@ -1955,56 +1927,6 @@ nodeenv = ">=0.11.1"
 pyyaml = ">=5.1"
 virtualenv = ">=20.10.0"
 
-[[package]]
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-version = "15.0.2"
-description = "Python library for Apache Arrow"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "python_version < \"3.13\""
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-]
-
-[package.dependencies]
-numpy = ">=1.16.6,<2"
-
 [[package]]
 name = "pycodestyle"
 version = "2.14.0"
@@ -2035,7 +1957,7 @@ version = "2.19.2"
 description = "Pygments is a syntax highlighting package written in Python."
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["dev"]
 files = [
     {file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"},
     {file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"},
@@ -2048,9 +1970,10 @@ windows-terminal = ["colorama (>=0.4.6)"]
 name = "pyparsing"
 version = "3.2.5"
 description = "pyparsing - Classes and methods to define and execute parsing grammars"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
+markers = "extra == \"viz\""
 files = [
     {file = "pyparsing-3.2.5-py3-none-any.whl", hash = "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e"},
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@@ -2065,7 +1988,7 @@ version = "8.4.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["dev"]
 files = [
     {file = "pytest-8.4.2-py3-none-any.whl", hash = "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79"},
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@@ -2216,10 +2139,10 @@ files = [
 name = "rawpy"
 version = "0.19.1"
 description = "RAW image processing for Python, a wrapper for libraw"
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "python_version < \"3.13\""
+markers = "python_version < \"3.13\" and extra == \"raw\""
 files = [
     {file = "rawpy-0.19.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:59011f447f7d71b5d85c2c9394642c9bb671b70bffd5cd0c030232792ac4204e"},
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@@ -2253,10 +2176,10 @@ numpy = "*"
 name = "rawpy"
 version = "0.24.0"
 description = "RAW image processing for Python, a wrapper for libraw"
-optional = false
+optional = true
 python-versions = "*"
 groups = ["main"]
-markers = "python_version >= \"3.13\""
+markers = "python_version >= \"3.13\" and extra == \"raw\""
 files = [
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@@ -2359,119 +2282,6 @@ docs = ["PyWavelets (>=1.1.1)", "dask[array] (>=2022.9.2)", "ipykernel", "ipywid
 optional = ["PyWavelets (>=1.1.1)", "SimpleITK", "astropy (>=5.0)", "cloudpickle (>=0.2.1)", "dask[array] (>=2021.1.0)", "matplotlib (>=3.5)", "pooch (>=1.6.0)", "pyamg", "scikit-learn (>=1.1)"]
 test = ["asv", "matplotlib (>=3.5)", "numpydoc (>=1.5)", "pooch (>=1.6.0)", "pytest (>=7.0)", "pytest-cov (>=2.11.0)", "pytest-faulthandler", "pytest-localserver"]
 
-[[package]]
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-version = "1.6.1"
-description = "A set of python modules for machine learning and data mining"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-markers = "python_version < \"3.11\""
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 version = "2024.8.30"
@@ -2827,7 +2472,7 @@ version = "2.3.0"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
-groups = ["main", "dev"]
+groups = ["dev"]
 markers = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:88bd15eb972f3664f5ed4b57c1634a97153b4bac4479dcb6a495f41921eb7f45"},
@@ -2902,12 +2547,11 @@ version = "4.15.0"
 description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["dev"]
 files = [
     {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"},
     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
 ]
-markers = {main = "python_version < \"3.11\""}
 
 [[package]]
 name = "tzdata"
@@ -2965,10 +2609,10 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
 name = "zipp"
 version = "3.23.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
+optional = true
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "python_version == \"3.9\""
+markers = "extra == \"viz\" and python_version == \"3.9\""
 files = [
     {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
     {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
@@ -2982,7 +2626,11 @@ enabler = ["pytest-enabler (>=2.2)"]
 test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
 type = ["pytest-mypy"]
 
+[extras]
+raw = ["rawpy", "rawpy"]
+viz = ["matplotlib"]
+
 [metadata]
 lock-version = "2.1"
 python-versions = "^3.9"
-content-hash = "f6ccca34932b208e5540cb488896dc12894e799ff3479fe5d2e8ebc8d9325521"
+content-hash = "920774c932acbe2f4dc0efa34cf36d91d87d41a8066afb8f68d7bd1ba7ffbfab"
diff --git a/pyproject.toml b/pyproject.toml
index 43afd3c..cae9515 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,20 +26,14 @@ joblib = "^1.3.2"
 pandas = "^2.2.0"
 pillow = "^10.2.0"
 requests = "^2.31.0"
-matplotlib = "^3.8.2"
-rawpy = [
-    {version = "^0.19.0", python = "<3.13"},
-    {version = "^0.24.0", python = ">=3.13"}
-]
 scipy = "^1.8"
-shapely = "^2.0.2"
-argparse = "^1.4.0"
 tqdm = "^4.66.1"
 scikit-image = "^0.22.0"
-scikit-learn = "^1.4.0"
-sympy = "^1.12"
-pytest = "^8.0.0"
-pyarrow = {version = "^15.0.0", python = "<3.13"}
+rawpy = [
+    {version = "^0.19.0", python = "<3.13", optional = true},
+    {version = "^0.24.0", python = ">=3.13", optional = true}
+]
+matplotlib = {version = "^3.8.2", optional = true}
 
 [tool.poetry.scripts]
 fibermorph = "fibermorph.cli:main"
@@ -55,3 +49,7 @@ pre-commit = "^3.6.0"
 
 [tool.cibuildwheel]
 build-frontend = "build"
+
+[tool.poetry.extras]
+raw = ["rawpy"]
+viz = ["matplotlib"]
diff --git a/tools/inventory_imports.py b/tools/inventory_imports.py
new file mode 100644
index 0000000..04a73a6
--- /dev/null
+++ b/tools/inventory_imports.py
@@ -0,0 +1,67 @@
+"""Inventory top-level imports across the fibermorph package.
+
+Run:
+    python tools/inventory_imports.py
+"""
+
+from __future__ import annotations
+
+import ast
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+PACKAGE_ROOT = PROJECT_ROOT / "fibermorph"
+
+
+class ImportCollector(ast.NodeVisitor):
+    def __init__(self) -> None:
+        self.modules: set[str] = set()
+
+    def visit_Import(self, node: ast.Import) -> None:
+        for alias in node.names:
+            top = alias.name.split(".")[0]
+            self.modules.add(top)
+
+    def visit_ImportFrom(self, node: ast.ImportFrom) -> None:
+        if not node.module:
+            return
+        top = node.module.split(".")[0]
+        self.modules.add(top)
+
+
+def collect_imports(root: Path) -> dict[str, set[str]]:
+    mapping: dict[str, set[str]] = {}
+    for py_file in root.rglob("*.py"):
+        if "__pycache__" in py_file.parts:
+            continue
+        rel = py_file.relative_to(PROJECT_ROOT)
+        try:
+            tree = ast.parse(py_file.read_text(encoding="utf-8"), filename=str(rel))
+        except SyntaxError:
+            continue
+
+        visitor = ImportCollector()
+        visitor.visit(tree)
+        mapping[str(rel)] = visitor.modules
+    return mapping
+
+
+def main() -> None:
+    data = collect_imports(PACKAGE_ROOT)
+    inverted: dict[str, set[str]] = {}
+    for path, modules in data.items():
+        for module in modules:
+            inverted.setdefault(module, set()).add(path)
+
+    print("Detected top-level imports:\n")
+    for module in sorted(inverted):
+        locations = ", ".join(sorted(inverted[module]))
+        print(f"{module:15} -> {locations}")
+
+    print("\nSummary:")
+    for module in sorted(inverted):
+        print(f"- {module}")
+
+
+if __name__ == "__main__":
+    main()