diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 8e1e07c..141e6f2 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -28,9 +28,6 @@ jobs:
       - name: lint code with ruff
         run: ruff check .
 
-      - name: lint Jupyter notebooks with ruff
-        run: nbqa ruff .
-
       - name: check code format with black
         run: black --check .
 
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 527ca93..24fad0e 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -6,6 +6,15 @@ All notable changes to this project will be documented in this file.
 
 The format is based on `Keep a Changelog <https://keepachangelog.com>`_.
 
+2.0.0
+-----
+- The curve fitting parameters (top, bottom, slope) can now be constrained to a range in addition to being completely free or fixed. This can help with fitting some curves more sensibly (see [this issue](https://github.com/jbloomlab/neutcurve/issues/53)). Specifically:
+  - ``fixtop`` and ``fixbottom`` parameters to ``HillCurve`` can be 2-tuples of bounds
+  - added ``fixslope`` parameter to ``HillCurve`` and ``CurveFits``
+  - New ``constrain_params_range`` notebook tests and documents this functionality.
+
+- Add ``no_curve_fit_first`` argument to ``HillCurve`` to aid debugging/development.
+
 1.1.2
 -----
 
diff --git a/README.rst b/README.rst
index 549bc77..8364aa4 100644
--- a/README.rst
+++ b/README.rst
@@ -4,13 +4,10 @@ neutcurve
 
 .. image:: https://img.shields.io/pypi/v/neutcurve.svg
         :target: https://pypi.python.org/pypi/neutcurve
-
 .. image:: https://github.com/jbloomlab/neutcurve/actions/workflows/test.yml/badge.svg
         :target: https://github.com/jbloomlab/neutcurve/actions/workflows/test.yml
-
 .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
         :target: https://github.com/psf/black
-
 .. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
         :target: https://github.com/astral-sh/ruff
 
diff --git a/docs/constrain_params_range.nblink b/docs/constrain_params_range.nblink
new file mode 100644
index 0000000..fa2a398
--- /dev/null
+++ b/docs/constrain_params_range.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../notebooks/constrain_params_range.ipynb"
+}
diff --git a/docs/index.rst b/docs/index.rst
index f3f8b11..975c5fb 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -20,6 +20,7 @@ Contents
    installation
    hillcurve_example
    curvefits_example
+   constrain_params_range
    combine_curvefits
    test_curves
    neutcurve
diff --git a/neutcurve/__init__.py b/neutcurve/__init__.py
index a09b828..2e6b372 100644
--- a/neutcurve/__init__.py
+++ b/neutcurve/__init__.py
@@ -16,7 +16,7 @@
 
 __author__ = "Jesse Bloom"
 __email__ = "jbloom@fredhutch.org"
-__version__ = "1.1.2"
+__version__ = "2.0.0"
 __url__ = "https://github.com/jbloomlab/neutcurve"
 
 from neutcurve.curvefits import CurveFits  # noqa: F401
diff --git a/neutcurve/curvefits.py b/neutcurve/curvefits.py
index 2f4c238..99fe78e 100644
--- a/neutcurve/curvefits.py
+++ b/neutcurve/curvefits.py
@@ -36,9 +36,11 @@ class CurveFits:
         `replicate_col` (str`)
             Column in data with name of replicate of this measurement. Replicates can
             **not** be named 'average' as we compute the average from the replicates.
-        `fixbottom` (`False` or float)
+        `fixbottom` (`False` or float or 2-tuple)
             Same meaning as for :class:`neutcurve.hillcurve.HillCurve`.
-        `fixtop` (`False` or float)
+        `fixtop` (`False` or float or 2-tuple)
+            Same meaning as for :class:`neutcurve.hillcurve.HillCurve`.
+        `fixslope` (`False` or float or 2-tuple)
             Same meaning as for :class:`neutcurve.hillcurve.HillCurve`.
         `infectivity_or_neutralized` ({'infectivity', 'neutralized'})
             Same meaning as for :class:`neutcurve.hillcurve.HillCurve`.
@@ -121,6 +123,7 @@ def combineCurveFits(
         attrs_can_differ = [  # attributes that can differ among objects
             "fixbottom",
             "fixtop",
+            "fixslope",
             "df",
             "sera",
             "allviruses",
@@ -142,8 +145,8 @@ def combineCurveFits(
         for attr in attrs_can_differ:
             delattr(combined_fits, attr)
 
-        # fixtop and fixbottom are kept at shared value or None
-        for attr in ["fixtop", "fixbottom"]:
+        # fixtop, fixbottom, fixslope are kept at shared value or None
+        for attr in ["fixtop", "fixbottom", "fixslope"]:
             if any(
                 getattr(curvefits_list[0], attr) != getattr(c, attr)
                 for c in curvefits_list
@@ -293,6 +296,7 @@ def __init__(
         init_slope=1.5,
         fixbottom=0,
         fixtop=1,
+        fixslope=False,
         allow_reps_unequal_conc=False,
     ):
         """See main class docstring."""
@@ -304,6 +308,7 @@ def __init__(
         self.replicate_col = replicate_col
         self.fixbottom = fixbottom
         self.fixtop = fixtop
+        self.fixslope = fixslope
         self._infectivity_or_neutralized = infectivity_or_neutralized
         self._fix_slope_first = fix_slope_first
         self._init_slope = init_slope
@@ -462,6 +467,7 @@ def getCurve(self, *, serum, virus, replicate):
                     fs_stderr=fs_stderr,
                     fixbottom=self.fixbottom,
                     fixtop=self.fixtop,
+                    fixslope=self.fixslope,
                     infectivity_or_neutralized=self._infectivity_or_neutralized,
                     fix_slope_first=self._fix_slope_first,
                     init_slope=self._init_slope,
diff --git a/neutcurve/hillcurve.py b/neutcurve/hillcurve.py
index 78823ad..793ca37 100644
--- a/neutcurve/hillcurve.py
+++ b/neutcurve/hillcurve.py
@@ -64,10 +64,15 @@ class HillCurve:
             to re-fit all parameters including slope.
         `fs_stderr` (`None` or array-like)
             If not `None`, standard errors on `fs`.
-        `fixbottom` (bool or a float)
-            If `True`, fix bottom of curve to to this value; otherwise fit.
-        `fixtop` (`False` or a float)
-            If `True`, fix top of curve to this value; otherwise fit.
+        `fixbottom` (`False`, float, or tuple/list of length 2)
+            If `False`, fit bottom of curves as free parameter. If float, fix bottom of
+            curve to that value. If length-2 array, constrain bottom to be in that range.
+        `fixtop` (`False`, float, or tuple/list of length 2)
+            If `False`, fit top of curves as free parameter. If float, fix top of
+            curve to that value. If length-2 array, constrain top to be in that range.
+        `fixslope` (`False`, float, or tuple/list of length 2)
+            If `False`, fit slope of curves as free parameter. If float, fix slope to
+            that value. If length-2 array, constrain slope to be in that range.
         `fitlogc` (bool)
             Do we do the actual fitting on the concentrations or log
             concentrations? Gives equivalent results in principle, but
@@ -80,7 +85,15 @@ class HillCurve:
             points much more than others in a way that may not be
             justified.
         `init_slope` (float)
-            Initial value of slope used in fitting.
+            Initial value of slope used in fitting. If `fixslope` is set to a single
+            value, it overrides this parameter. If `fixslope` is set to a range,
+            `init_slope` is adjusted up/down to be in that range.
+        `no_curve_fit_first` (bool)
+            Normally the method first tries to do the optimization with `curve_fit` from
+            `scipy`, and if that fails then tries `scipy.optimize.minimize`. If you set
+            this to `True`, it skips trying with `curve_fit`. In general, this option is
+            for debugging by knowledgeable developers, and you should not be using it
+            otherwise.
 
     Attributes:
         `cs` (numpy array)
@@ -146,17 +159,17 @@ class HillCurve:
     .. nbplot::
 
         >>> neut = HillCurve(cs, fs, fixbottom=False)
-        >>> numpy.allclose(neut.midpoint, m)
+        >>> numpy.allclose(neut.midpoint, m, atol=1e-4)
         True
         >>> neut.midpoint_bound == neut.midpoint
         True
         >>> neut.midpoint_bound_type
         'interpolated'
-        >>> numpy.allclose(neut.slope, s, atol=1e-4)
+        >>> numpy.allclose(neut.slope, s, atol=1e-3)
         True
-        >>> numpy.allclose(neut.top, t)
+        >>> numpy.allclose(neut.top, t, atol=1e-4)
         True
-        >>> numpy.allclose(neut.bottom, b)
+        >>> numpy.allclose(neut.bottom, b, atol=1e-3)
         True
         >>> for key, val in neut.params_stdev.items():
         ...     print(f"{key} = {val:.2g}")
@@ -176,7 +189,7 @@ class HillCurve:
 
         >>> neut.ic50() > neut.midpoint
         True
-        >>> numpy.allclose(neut.ic50(), 0.0337385586)
+        >>> numpy.allclose(neut.ic50(), 0.0337, atol=1e-4)
         True
         >>> numpy.allclose(0.5, neut.fracinfectivity(neut.ic50()))
         True
@@ -309,6 +322,37 @@ class HillCurve:
     >>> round(neut.r2, 3)
     1.0
 
+    Now fit with bounds on the parameters. First, we make bounds cover the true values:
+
+    >>> neut_bounds_cover = HillCurve(
+    ...     cs, fs, fixbottom=(0, 0.2), fixtop=(0.9, 1), fixslope=(1, 2),
+    ... )
+    >>> numpy.allclose(neut_bounds_cover.midpoint, m, atol=1e-4)
+    True
+    >>> numpy.allclose(neut_bounds_cover.slope, s, atol=1e-3)
+    True
+    >>> numpy.allclose(neut_bounds_cover.top, t, atol=1e-4)
+    True
+    >>> numpy.allclose(neut_bounds_cover.bottom, b, atol=1e-3)
+    True
+    >>> round(neut_bounds_cover.r2, 3)
+    1.0
+
+    Next fit with bounds that do not cover the true parameters:
+    >>> neut_bounds_nocover = HillCurve(
+    ...     cs, fs, fixbottom=(0, 0.05), fixtop=(0.9, 0.95), fixslope=(1, 1.5),
+    ... )
+    >>> round(neut_bounds_nocover.midpoint, 2)
+    0.04
+    >>> round(neut_bounds_nocover.slope, 2)
+    1.5
+    >>> round(neut_bounds_nocover.top, 2)
+    0.95
+    >>> round(neut_bounds_nocover.bottom, 2)
+    0.05
+    >>> round(neut_bounds_nocover.r2, 2)
+    0.99
+
     Now fit with `infectivity_or_neutralized='neutralized'`, which is useful
     when the signal **increases** rather than decreases with increasing
     concentration (as would be the case if measuring fraction bound rather
@@ -319,9 +363,9 @@ class HillCurve:
        >>> neut_opp = HillCurve(cs, [1 - f for f in fs],
        ...                      fixtop=False, fixbottom=False,
        ...                      infectivity_or_neutralized='neutralized')
-       >>> numpy.allclose(neut_opp.top, 0.9)
+       >>> numpy.allclose(neut_opp.top, 0.9, atol=1e-3)
        True
-       >>> numpy.allclose(neut_opp.bottom, 0)
+       >>> numpy.allclose(neut_opp.bottom, 0, atol=1e-3)
        True
        >>> numpy.allclose(neut_opp.midpoint, m)
        True
@@ -329,6 +373,56 @@ class HillCurve:
        True
        >>> fig, ax = neut_opp.plot(ylabel='fraction neutralized')
 
+    For internal testing purposes, try with `no_curve_fit_first=False`.
+
+    >>> neut_ncf = HillCurve(cs, fs, fixbottom=False, no_curve_fit_first=True)
+    >>> numpy.allclose(neut_ncf.midpoint, m, atol=1e-4)
+    True
+    >>> numpy.allclose(neut_ncf.slope, s, atol=5e-3)
+    True
+    >>> numpy.allclose(neut_ncf.top, t, atol=1e-4)
+    True
+    >>> numpy.allclose(neut_ncf.bottom, b, atol=1e-3)
+    True
+
+    >>> neut_bounds_cover_ncf = HillCurve(
+    ...     cs,
+    ...     fs,
+    ...     fixbottom=(0, 0.2),
+    ...     fixtop=(0.9, 1),
+    ...     fixslope=(1, 2),
+    ...     no_curve_fit_first=True,
+    ... )
+    >>> numpy.allclose(neut_bounds_cover_ncf.midpoint, m, atol=1e-4)
+    True
+    >>> numpy.allclose(neut_bounds_cover_ncf.slope, s, atol=1e-3)
+    True
+    >>> numpy.allclose(neut_bounds_cover_ncf.top, t, atol=1e-4)
+    True
+    >>> numpy.allclose(neut_bounds_cover_ncf.bottom, b, atol=1e-3)
+    True
+    >>> round(neut_bounds_cover_ncf.r2, 3)
+    1.0
+
+    >>> neut_bounds_nocover_ncf = HillCurve(
+    ...     cs,
+    ...     fs,
+    ...     fixbottom=(0, 0.05),
+    ...     fixtop=(0.9, 0.95),
+    ...     fixslope=(1, 1.5),
+    ...     no_curve_fit_first=True,
+    ... )
+    >>> round(neut_bounds_nocover_ncf.midpoint, 2)
+    0.04
+    >>> round(neut_bounds_nocover_ncf.slope, 2)
+    1.5
+    >>> round(neut_bounds_nocover_ncf.top, 2)
+    0.95
+    >>> round(neut_bounds_nocover_ncf.bottom, 2)
+    0.05
+    >>> round(neut_bounds_nocover_ncf.r2, 2)
+    0.99
+
     """
 
     def __init__(
@@ -341,9 +435,11 @@ def __init__(
         fs_stderr=None,
         fixbottom=0,
         fixtop=1,
+        fixslope=False,
         fitlogc=False,
         use_stderr_for_fit=False,
         init_slope=1.5,
+        no_curve_fit_first=False,
     ):
         """See main class docstring."""
         # get data into arrays sorted by concentration
@@ -377,19 +473,38 @@ def __init__(
             raise ValueError(f"concentrations must all be > 0\n{self.cs=}")
 
         # create initial guess of `(midpoint, slope, bottom, top)`
-        # make initial guess for top and bottom
+        # make initial guess for top
         if fixtop is False:
             top = max(1, self.fs.max())
-        else:
-            if not isinstance(fixtop, (int, float)):
-                raise ValueError(f"{fixtop=} is not `False` or a number")
+        elif isinstance(fixtop, (int, float)):
+            fixtop = float(fixtop)
             top = fixtop
+        elif hasattr(fixtop, "__len__") and len(fixtop) == 2:
+            fixtop = (fixtop[0], fixtop[1])
+            if fixtop[1] <= fixtop[0]:
+                raise ValueError(f"invalid {fixtop=}: first element must be < second")
+            top = max(fixtop[0], min(max(1, self.fs.max()), fixtop[1]))
+            assert fixtop[0] <= top <= fixtop[1]
+        else:
+            raise ValueError(f"{fixtop=} is not `False`, a number, or length-2 array")
+        # make initial guess for bottom
         if fixbottom is False:
             bottom = min(0, self.fs.min())
-        else:
-            if not isinstance(fixbottom, (int, float)):
-                raise ValueError(f"{fixbottom=} is not `False` or a number")
+        elif isinstance(fixbottom, (int, float)):
+            fixbottom = float(fixbottom)
             bottom = fixbottom
+        elif hasattr(fixbottom, "__len__") and len(fixbottom) == 2:
+            fixbottom = (fixbottom[0], fixbottom[1])
+            if fixbottom[1] <= fixbottom[0]:
+                raise ValueError(
+                    f"invalid {fixbottom=}: first element must be < second"
+                )
+            bottom = max(fixbottom[0], min(min(0, self.fs.min()), fixbottom[1]))
+            assert fixbottom[0] <= bottom <= fixbottom[1]
+        else:
+            raise ValueError(
+                f"{fixbottom=} is not `False`, a number, or length-2 array"
+            )
         # make initial guess for midpoint
         # if midpoint guess outside range, guess outside range by amount
         # equal to spacing of last two points
@@ -409,10 +524,25 @@ def __init__(
             i = numpy.argmax((self.fs > midval)[:-1] != (self.fs > midval)[1:])
             assert (self.fs[i] > midval) != (self.fs[i + 1] > midval)
             midpoint = (self.cs[i] + self.cs[i + 1]) / 2.0
-        init_tup = (midpoint, init_slope, bottom, top)
+        # adjust initial slope if inconsistent with `fixslope`
+        if fixslope is False:
+            slope = float(init_slope)
+        elif isinstance(fixslope, (int, float)):
+            fixslope = float(fixslope)
+            slope = fixslope
+        elif hasattr(fixslope, "__len__") and len(fixslope) == 2:
+            fixslope = (fixslope[0], fixslope[1])
+            if fixslope[1] <= fixslope[0]:
+                raise ValueError(f"invalid {fixslope=}: first element must be < second")
+            slope = max(fixslope[0], min(init_slope, fixslope[1]))
+            assert fixslope[0] <= slope <= fixslope[1]
+
+        init_tup = (midpoint, slope, bottom, top)
 
         # first try to fit using curve_fit
         try:
+            if no_curve_fit_first:
+                raise RuntimeError("skipping curve_fit")
             if fix_slope_first:
                 fix_first_init_tup, _ = self._fit_curve(
                     fixtop=fixtop,
@@ -420,7 +550,7 @@ def __init__(
                     fitlogc=fitlogc,
                     use_stderr_for_fit=use_stderr_for_fit,
                     init_tup=init_tup,
-                    fix_slope=True,
+                    fixslope=slope,
                 )
             else:
                 fix_first_init_tup = init_tup
@@ -430,7 +560,7 @@ def __init__(
                 fitlogc=fitlogc,
                 use_stderr_for_fit=use_stderr_for_fit,
                 init_tup=fix_first_init_tup,
-                fix_slope=False,
+                fixslope=fixslope,
             )
         # A RuntimeError is raised by scipy if curve_fit fails:
         # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html
@@ -445,7 +575,7 @@ def __init__(
                         use_stderr_for_fit=use_stderr_for_fit,
                         method=method,
                         init_tup=init_tup,
-                        fix_slope=True,
+                        fixslope=slope,
                     )
                     if fix_first_init_tup is False:
                         continue
@@ -458,7 +588,7 @@ def __init__(
                     use_stderr_for_fit=use_stderr_for_fit,
                     method=method,
                     init_tup=fix_first_init_tup,
-                    fix_slope=False,
+                    fixslope=fixslope,
                 )
                 self.params_stdev = None  # can't estimate errors
                 if fit_tup is not False:
@@ -468,6 +598,18 @@ def __init__(
 
         for i, param in enumerate(["midpoint", "slope", "bottom", "top"]):
             setattr(self, param, fit_tup[i])
+        # debugging check
+        for name, fixval, val in [
+            ("slope", fixslope, self.slope),
+            ("top", fixtop, self.top),
+            ("bottom", fixbottom, self.bottom),
+        ]:
+            if not (
+                (fixval is False)
+                or (isinstance(fixval, float) and numpy.allclose(fixval, val))
+                or (isinstance(fixval, tuple) and (fixval[0] <= val <= fixval[1]))
+            ):
+                raise ValueError(f"fix{name}={fixval}, but final {name}={val}")
 
         if self.cs[0] <= self.midpoint <= self.cs[-1]:
             self.midpoint_bound = self.midpoint
@@ -497,7 +639,7 @@ def _fit_curve(
         fitlogc,
         use_stderr_for_fit,
         init_tup,
-        fix_slope,
+        fixslope,
     ):
         """curve_fit, return `(midpoint, slope, bottom, top), params_stdev`."""
 
@@ -512,15 +654,26 @@ def _fit_curve(
             evalfunc = self.evaluate
             xdata = self.cs
 
-        if fix_slope:
-            if fixtop is False and fixbottom is False:
+        top_bounds = (-numpy.inf, numpy.inf) if (fixtop is False) else fixtop
+        bottom_bounds = (-numpy.inf, numpy.inf) if (fixbottom is False) else fixbottom
+        slope_bounds = (-numpy.inf, numpy.inf) if (fixslope is False) else fixslope
+
+        if isinstance(fixslope, float):
+            assert fixslope == slope
+            if (not isinstance(fixtop, float)) and (not isinstance(fixbottom, float)):
                 initguess = [midpoint, bottom, top]
+                # https://stackoverflow.com/a/8081580
+                bounds = tuple(
+                    zip(*[(-numpy.inf, numpy.inf), bottom_bounds, top_bounds])
+                )
 
                 def func(c, m, b, t):
                     return evalfunc(c, m, slope, b, t, self._infectivity_or_neutralized)
 
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
+                assert isinstance(fixbottom, float)
                 initguess = [midpoint, top]
+                bounds = tuple(zip(*[(-numpy.inf, numpy.inf), top_bounds]))
 
                 def func(c, m, t):
                     return evalfunc(
@@ -532,8 +685,10 @@ def func(c, m, t):
                         self._infectivity_or_neutralized,
                     )
 
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
+                assert isinstance(fixtop, float)
                 initguess = [midpoint, bottom]
+                bounds = tuple(zip(*[(-numpy.inf, numpy.inf), bottom_bounds]))
 
                 def func(c, m, b):
                     return evalfunc(
@@ -546,7 +701,9 @@ def func(c, m, b):
                     )
 
             else:
+                assert isinstance(fixtop, float) and isinstance(fixbottom, float)
                 initguess = [midpoint]
+                bounds = ([-numpy.inf], [numpy.inf])
 
                 def func(c, m):
                     return evalfunc(
@@ -559,28 +716,57 @@ def func(c, m):
                     )
 
         else:
-            if fixtop is False and fixbottom is False:
+            assert (fixslope is False) or (
+                isinstance(fixslope, tuple) and len(fixslope) == 2
+            )
+            if (not isinstance(fixtop, float)) and (not isinstance(fixbottom, float)):
                 initguess = [midpoint, slope, bottom, top]
+                bounds = tuple(
+                    zip(
+                        *[
+                            (-numpy.inf, numpy.inf),
+                            slope_bounds,
+                            bottom_bounds,
+                            top_bounds,
+                        ]
+                    )
+                )
 
                 def func(c, m, s, b, t):
                     return evalfunc(c, m, s, b, t, self._infectivity_or_neutralized)
 
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
+                assert isinstance(fixbottom, float)
                 initguess = [midpoint, slope, top]
+                bounds = tuple(
+                    zip(*[(-numpy.inf, numpy.inf), slope_bounds, top_bounds])
+                )
 
                 def func(c, m, s, t):
                     return evalfunc(
                         c, m, s, bottom, t, self._infectivity_or_neutralized
                     )
 
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
+                assert isinstance(fixtop, float)
                 initguess = [midpoint, slope, bottom]
+                bounds = tuple(
+                    zip(
+                        *[
+                            (-numpy.inf, numpy.inf),
+                            slope_bounds,
+                            bottom_bounds,
+                        ]
+                    )
+                )
 
                 def func(c, m, s, b):
                     return evalfunc(c, m, s, b, top, self._infectivity_or_neutralized)
 
             else:
+                assert isinstance(fixtop, float) and isinstance(fixbottom, float)
                 initguess = [midpoint, slope]
+                bounds = tuple(zip(*[(-numpy.inf, numpy.inf), slope_bounds]))
 
                 def func(c, m, s):
                     return evalfunc(
@@ -592,6 +778,8 @@ def func(c, m, s):
                         self._infectivity_or_neutralized,
                     )
 
+        assert len(initguess) == len(bounds[0]) == len(bounds[1])
+        assert all(lb < ub for (lb, ub) in zip(bounds[0], bounds[1])), bounds
         (popt, pcov) = scipy.optimize.curve_fit(
             f=func,
             xdata=xdata,
@@ -599,6 +787,7 @@ def func(c, m, s):
             p0=initguess,
             sigma=self.fs_stderr if use_stderr_for_fit else None,
             absolute_sigma=True,
+            bounds=bounds,
             maxfev=1000,
         )
 
@@ -608,17 +797,17 @@ def func(c, m, s):
             midpoint = numpy.exp(midpoint)
 
         midpoint = popt[0]
-        if fix_slope:
+        if isinstance(fixslope, float):
             params_stderr = {"midpoint": perr[0], "slope": 0, "top": 0, "bottom": 0}
-            if fixbottom is False and fixtop is False:
+            if (not isinstance(fixbottom, float)) and (not isinstance(fixtop, float)):
                 bottom = popt[1]
                 params_stderr["bottom"] = perr[1]
                 top = popt[2]
                 params_stderr["top"] = perr[2]
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
                 bottom = popt[1]
                 params_stderr["bottom"] = perr[1]
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
                 top = popt[1]
                 params_stderr["top"] = perr[1]
         else:
@@ -629,15 +818,15 @@ def func(c, m, s):
                 "top": 0,
                 "bottom": 0,
             }
-            if fixbottom is False and fixtop is False:
+            if (not isinstance(fixbottom, float)) and (not isinstance(fixtop, float)):
                 bottom = popt[2]
                 params_stderr["bottom"] = perr[2]
                 top = popt[3]
                 params_stderr["top"] = perr[3]
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
                 bottom = popt[2]
                 params_stderr["bottom"] = perr[2]
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
                 top = popt[2]
                 params_stderr["top"] = perr[2]
 
@@ -652,7 +841,7 @@ def _minimize_fit(
         use_stderr_for_fit,
         method,
         init_tup,
-        fix_slope,
+        fixslope,
     ):
         """Return `(midpoint, slope, bottom, top)` if succeeds or `False` if fails."""
 
@@ -669,17 +858,22 @@ def _minimize_fit(
             xdata = self.cs
             bounds = [(0, None)]
 
-        if fix_slope:
-            if fixtop is False and fixbottom is False:
+        top_bounds = (None, None) if (fixtop is False) else fixtop
+        bottom_bounds = (None, None) if (fixbottom is False) else fixbottom
+        slope_bounds = (None, None) if (fixslope is False) else fixslope
+
+        if isinstance(fixslope, float):
+            if (not isinstance(fixtop, float)) and (not isinstance(fixbottom, float)):
                 initguess = [midpoint, bottom, top]
-                bounds = bounds + [(None, None), (None, None)]
+                bounds = bounds + [bottom_bounds, top_bounds]
 
                 def func(c, m, b, t):
                     return evalfunc(c, m, slope, b, t, self._infectivity_or_neutralized)
 
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
+                assert isinstance(fixbottom, float)
                 initguess = [midpoint, top]
-                bounds.append((bottom, None))
+                bounds.append(top_bounds)
 
                 def func(c, m, t):
                     return evalfunc(
@@ -691,9 +885,10 @@ def func(c, m, t):
                         self._infectivity_or_neutralized,
                     )
 
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
+                assert isinstance(fixtop, float)
                 initguess = [midpoint, bottom]
-                bounds.append((None, top))
+                bounds.append(bottom_bounds)
 
                 def func(c, m, b):
                     return evalfunc(
@@ -706,6 +901,7 @@ def func(c, m, b):
                     )
 
             else:
+                assert isinstance(fixtop, float) and isinstance(fixbottom, float)
                 initguess = [midpoint]
 
                 def func(c, m):
@@ -719,32 +915,36 @@ def func(c, m):
                     )
 
         else:
-            bounds.append((0, None))
+            assert isinstance(fixslope, tuple) or (fixslope is False)
+            bounds.append(slope_bounds)
 
-            if fixtop is False and fixbottom is False:
+            if (not isinstance(fixtop, float)) and (not isinstance(fixbottom, float)):
                 initguess = [midpoint, slope, bottom, top]
-                bounds = bounds + [(None, None), (None, None)]
+                bounds = bounds + [bottom_bounds, top_bounds]
 
                 def func(c, m, s, b, t):
                     return evalfunc(c, m, s, b, t, self._infectivity_or_neutralized)
 
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
+                assert isinstance(fixbottom, float)
                 initguess = [midpoint, slope, top]
-                bounds.append((bottom, None))
+                bounds.append(top_bounds)
 
                 def func(c, m, s, t):
                     return evalfunc(
                         c, m, s, bottom, t, self._infectivity_or_neutralized
                     )
 
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
+                assert isinstance(fixtop, float)
                 initguess = [midpoint, slope, bottom]
-                bounds.append((None, top))
+                bounds.append(bottom_bounds)
 
                 def func(c, m, s, b):
                     return evalfunc(c, m, s, b, top, self._infectivity_or_neutralized)
 
             else:
+                assert isinstance(fixtop, float) and isinstance(fixbottom, float)
                 initguess = [midpoint, slope]
 
                 def func(c, m, s):
@@ -765,6 +965,7 @@ def min_func(p):
                 return sum((func(xdata, *p) - self.fs / self.fs_stderr) ** 2)
 
         initguess = numpy.array(initguess, dtype="float")
+        assert len(initguess) == len(bounds)
         res = scipy.optimize.minimize(min_func, initguess, bounds=bounds, method=method)
 
         if not res.success:
@@ -774,22 +975,23 @@ def min_func(p):
             midpoint = numpy.exp(midpoint)
 
         midpoint = res.x[0]
-        if fix_slope:
-            if fixbottom is False and fixtop is False:
+        if isinstance(fixslope, float):
+            if (not isinstance(fixbottom, float)) and (not isinstance(fixtop, float)):
                 bottom = res.x[1]
                 top = res.x[2]
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
                 bottom = res.x[1]
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
                 top = res.x[1]
         else:
+            assert (fixslope is False) or isinstance(fixslope, tuple)
             slope = res.x[1]
-            if fixbottom is False and fixtop is False:
+            if (not isinstance(fixbottom, float)) and (not isinstance(fixtop, float)):
                 bottom = res.x[2]
                 top = res.x[3]
-            elif fixbottom is False:
+            elif not isinstance(fixbottom, float):
                 bottom = res.x[2]
-            elif fixtop is False:
+            elif not isinstance(fixtop, float):
                 top = res.x[2]
 
         return (midpoint, slope, bottom, top)
diff --git a/notebooks/combine_curvefits.ipynb b/notebooks/combine_curvefits.ipynb
index 6b4c789..b5edb3e 100644
--- a/notebooks/combine_curvefits.ipynb
+++ b/notebooks/combine_curvefits.ipynb
@@ -17,11 +17,11 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:09.466563Z",
-     "iopub.status.busy": "2023-12-30T01:07:09.465803Z",
-     "iopub.status.idle": "2023-12-30T01:07:11.265562Z",
-     "shell.execute_reply": "2023-12-30T01:07:11.264324Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:09.466530Z"
+     "iopub.execute_input": "2024-03-23T12:50:21.759483Z",
+     "iopub.status.busy": "2024-03-23T12:50:21.758464Z",
+     "iopub.status.idle": "2024-03-23T12:50:33.647118Z",
+     "shell.execute_reply": "2024-03-23T12:50:33.645350Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:21.759449Z"
     }
    },
    "outputs": [],
@@ -43,11 +43,11 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:11.274593Z",
-     "iopub.status.busy": "2023-12-30T01:07:11.274105Z",
-     "iopub.status.idle": "2023-12-30T01:07:11.282684Z",
-     "shell.execute_reply": "2023-12-30T01:07:11.281803Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:11.274562Z"
+     "iopub.execute_input": "2024-03-23T12:50:33.649985Z",
+     "iopub.status.busy": "2024-03-23T12:50:33.648874Z",
+     "iopub.status.idle": "2024-03-23T12:50:33.661552Z",
+     "shell.execute_reply": "2024-03-23T12:50:33.660523Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:33.649938Z"
     }
    },
    "outputs": [],
@@ -68,11 +68,11 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:11.287881Z",
-     "iopub.status.busy": "2023-12-30T01:07:11.287198Z",
-     "iopub.status.idle": "2023-12-30T01:07:11.639897Z",
-     "shell.execute_reply": "2023-12-30T01:07:11.638946Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:11.287826Z"
+     "iopub.execute_input": "2024-03-23T12:50:33.664360Z",
+     "iopub.status.busy": "2024-03-23T12:50:33.663975Z",
+     "iopub.status.idle": "2024-03-23T12:50:34.022519Z",
+     "shell.execute_reply": "2024-03-23T12:50:34.021361Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:33.664331Z"
     },
     "tags": []
    },
@@ -94,11 +94,11 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:11.644225Z",
-     "iopub.status.busy": "2023-12-30T01:07:11.643927Z",
-     "iopub.status.idle": "2023-12-30T01:07:12.239501Z",
-     "shell.execute_reply": "2023-12-30T01:07:12.238414Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:11.644202Z"
+     "iopub.execute_input": "2024-03-23T12:50:34.026980Z",
+     "iopub.status.busy": "2024-03-23T12:50:34.026650Z",
+     "iopub.status.idle": "2024-03-23T12:50:34.581382Z",
+     "shell.execute_reply": "2024-03-23T12:50:34.580282Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:34.026955Z"
     },
     "tags": []
    },
@@ -132,11 +132,11 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:12.242849Z",
-     "iopub.status.busy": "2023-12-30T01:07:12.242677Z",
-     "iopub.status.idle": "2023-12-30T01:07:12.340876Z",
-     "shell.execute_reply": "2023-12-30T01:07:12.340073Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:12.242831Z"
+     "iopub.execute_input": "2024-03-23T12:50:34.582583Z",
+     "iopub.status.busy": "2024-03-23T12:50:34.582359Z",
+     "iopub.status.idle": "2024-03-23T12:50:34.677224Z",
+     "shell.execute_reply": "2024-03-23T12:50:34.676140Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:34.582562Z"
     },
     "tags": []
    },
@@ -162,11 +162,11 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:12.345449Z",
-     "iopub.status.busy": "2023-12-30T01:07:12.345222Z",
-     "iopub.status.idle": "2023-12-30T01:07:12.407918Z",
-     "shell.execute_reply": "2023-12-30T01:07:12.406940Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:12.345426Z"
+     "iopub.execute_input": "2024-03-23T12:50:34.678443Z",
+     "iopub.status.busy": "2024-03-23T12:50:34.678228Z",
+     "iopub.status.idle": "2024-03-23T12:50:34.752574Z",
+     "shell.execute_reply": "2024-03-23T12:50:34.751838Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:34.678421Z"
     },
     "scrolled": true
    },
@@ -222,8 +222,8 @@
        "      <td>0.017</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>2.505</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.996</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -239,8 +239,8 @@
        "      <td>0.019</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>2.513</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -256,8 +256,8 @@
        "      <td>0.015</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>1.878</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -273,8 +273,8 @@
        "      <td>0.017</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>2.279</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.992</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -290,8 +290,8 @@
        "      <td>0.012</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>2.025</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -307,8 +307,8 @@
        "      <td>0.013</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>2.059</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -324,8 +324,8 @@
        "      <td>0.012</td>\n",
        "      <td>interpolated</td>\n",
        "      <td>2.035</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.990</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -343,13 +343,13 @@
        "6  FI6v3  P80D   average            2  0.012  interpolated   0.0125     0.012   \n",
        "\n",
        "   midpoint_bound midpoint_bound_type  slope  top  bottom     r2  \n",
-       "0           0.017        interpolated  2.505    1       0  0.996  \n",
-       "1           0.019        interpolated  2.513    1       0  0.986  \n",
-       "2           0.015        interpolated  1.878    1       0  0.982  \n",
-       "3           0.017        interpolated  2.279    1       0  0.992  \n",
-       "4           0.012        interpolated  2.025    1       0  0.980  \n",
-       "5           0.013        interpolated  2.059    1       0  0.994  \n",
-       "6           0.012        interpolated  2.035    1       0  0.990  "
+       "0           0.017        interpolated  2.505  1.0     0.0  0.996  \n",
+       "1           0.019        interpolated  2.513  1.0     0.0  0.986  \n",
+       "2           0.015        interpolated  1.878  1.0     0.0  0.982  \n",
+       "3           0.017        interpolated  2.279  1.0     0.0  0.992  \n",
+       "4           0.012        interpolated  2.025  1.0     0.0  0.980  \n",
+       "5           0.013        interpolated  2.059  1.0     0.0  0.994  \n",
+       "6           0.012        interpolated  2.035  1.0     0.0  0.990  "
       ]
      },
      "execution_count": 6,
@@ -395,11 +395,11 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-12-30T01:07:12.413613Z",
-     "iopub.status.busy": "2023-12-30T01:07:12.412715Z",
-     "iopub.status.idle": "2023-12-30T01:07:13.154041Z",
-     "shell.execute_reply": "2023-12-30T01:07:13.152800Z",
-     "shell.execute_reply.started": "2023-12-30T01:07:12.413564Z"
+     "iopub.execute_input": "2024-03-23T12:50:34.753685Z",
+     "iopub.status.busy": "2024-03-23T12:50:34.753493Z",
+     "iopub.status.idle": "2024-03-23T12:50:35.875072Z",
+     "shell.execute_reply": "2024-03-23T12:50:35.873283Z",
+     "shell.execute_reply.started": "2024-03-23T12:50:34.753666Z"
     }
    },
    "outputs": [
diff --git a/notebooks/constrain_params_range.ipynb b/notebooks/constrain_params_range.ipynb
new file mode 100644
index 0000000..c3d8850
--- /dev/null
+++ b/notebooks/constrain_params_range.ipynb
@@ -0,0 +1,618 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bf88be3a-7238-4602-a776-2636b29fabe1",
+   "metadata": {},
+   "source": [
+    "# Constrain fit parameters to a range of reasonable values\n",
+    "So far we have looked at allowing the top and bottom to either be free parameters or fixed values, and let the slope of the curve be fit as a free parameter.\n",
+    "\n",
+    "But in some cases, you may want to not let these be entirely free parameters, but also not fix them---but rather constrain them to a reasonable range. For instance, maybe constraining the top to be between 0.75 and 1.\n",
+    "Likewise, sometimes you can end up with the slope being fit to an extremely steep or shallow value, so it can also be useful to constrain the slope.\n",
+    "\n",
+    "Note that when constraining these parameters, especially the slope, you need to make sure you are choosing a reasonable range.\n",
+    "For the slope, what is a reasonable range will depend on the units used for concentration, as well as inherent properties of the serum or antibody in question.\n",
+    "\n",
+    "To constrain these parameters, you can use the *fixtop*, *fixbottom*, and *fixslope* parameters to `HillCurve` or `CurveFits`.\n",
+    "Each of these can be:\n",
+    " - `False`: fit as a free parameter\n",
+    " - A single number: value is constrained to this number\n",
+    " - A 2-tuple giving the min max range (eg, `(0.8, 1)`), in which case the value is constrained to this range.\n",
+    "\n",
+    "Here is an example.\n",
+    "\n",
+    "First, import Python modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "23c10a08-2722-4db9-9617-46863a56afa1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-24T16:37:22.863661Z",
+     "iopub.status.busy": "2024-03-24T16:37:22.863301Z",
+     "iopub.status.idle": "2024-03-24T16:37:24.607780Z",
+     "shell.execute_reply": "2024-03-24T16:37:24.606763Z",
+     "shell.execute_reply.started": "2024-03-24T16:37:22.863625Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import neutcurve\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8d0d4e2-0e60-45c4-aa26-561c000c9ae8",
+   "metadata": {},
+   "source": [
+    "Now we read some example data and fit it with the top and the slope either free (eg, `fixtop=False`) or constrained to a range (eg, `fixtop=(0.8, 1.0)`).\n",
+    "The curves are then plotted for each type of fitting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2bef2888-15c1-4120-915b-8bf45f31ea70",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-24T16:37:24.615995Z",
+     "iopub.status.busy": "2024-03-24T16:37:24.615602Z",
+     "iopub.status.idle": "2024-03-24T16:37:30.485109Z",
+     "shell.execute_reply": "2024-03-24T16:37:30.484284Z",
+     "shell.execute_reply.started": "2024-03-24T16:37:24.615961Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x357.5 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x357.5 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jbloom/neutcurve/neutcurve/hillcurve.py:1157: RuntimeWarning: invalid value encountered in power\n",
+      "  return b + (t - b) / (1 + (c / m) ** s)\n",
+      "/home/jbloom/neutcurve/neutcurve/hillcurve.py:1157: RuntimeWarning: divide by zero encountered in divide\n",
+      "  return b + (t - b) / (1 + (c / m) ** s)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x357.5 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1100x357.5 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# NBVAL_IGNORE_OUTPUT\n",
+    "\n",
+    "data = pd.read_csv(\"constrain_params_range_data.csv\")\n",
+    "\n",
+    "fit_params = []\n",
+    "\n",
+    "for group, group_data in data.groupby(\"serum\"):\n",
+    "    for desc, fixtop, fixslope in [\n",
+    "        (\"free top\", False, False),\n",
+    "        (\"top constrained to [0.8, 1.0], slope to [1, 2]\", (0.8, 1), (1, 2)),\n",
+    "    ]:\n",
+    "        fits = neutcurve.CurveFits(group_data, fixtop=fixtop, fixslope=fixslope)\n",
+    "\n",
+    "        fig, _ = fits.plotReplicates(\n",
+    "            sera=[group],\n",
+    "            attempt_shared_legend=False,\n",
+    "            legendfontsize=8,\n",
+    "            heightscale=1.1,\n",
+    "            widthscale=1.1,\n",
+    "            subplot_titles=\"{virus}\",\n",
+    "        )\n",
+    "        _ = fig.suptitle(\n",
+    "            f\"{group} fit with {desc}\",\n",
+    "            y=0.95,\n",
+    "            fontsize=16,\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "        fig.tight_layout()\n",
+    "        display(fig)\n",
+    "        plt.close(fig)\n",
+    "\n",
+    "        if desc != \"free top\":\n",
+    "            fit_params.append(\n",
+    "                fits.fitParams(average_only=False, no_average=True)\n",
+    "                .assign(fit_method=desc)\n",
+    "                .drop(columns=[\"nreplicates\", \"ic50_str\", \"midpoint_bound\"])\n",
+    "            )\n",
+    "    print(\"\\n\\n\")\n",
+    "\n",
+    "fit_params = pd.concat(fit_params, ignore_index=True)\n",
+    "fit_params = fit_params[\n",
+    "    [\"fit_method\"] + [c for c in fit_params.columns if c != \"fit_method\"]\n",
+    "]\n",
+    "if fit_params[\"fit_method\"].nunique() == 1:\n",
+    "    fit_params = fit_params.drop(columns=\"fit_method\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0fabda8-43b1-42f2-82e4-5f832285d6d6",
+   "metadata": {},
+   "source": [
+    "Tabulate the actual fit parameters for each setting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f23eb9e4-de3d-4e25-ba96-b044159d45d8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-24T16:37:30.489649Z",
+     "iopub.status.busy": "2024-03-24T16:37:30.489322Z",
+     "iopub.status.idle": "2024-03-24T16:37:30.532526Z",
+     "shell.execute_reply": "2024-03-24T16:37:30.531447Z",
+     "shell.execute_reply.started": "2024-03-24T16:37:30.489625Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Fit params for problematic slopes\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>virus</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>ic50</th>\n",
+       "      <th>ic50_bound</th>\n",
+       "      <th>midpoint</th>\n",
+       "      <th>midpoint_bound_type</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>top</th>\n",
+       "      <th>bottom</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A/Bangkok/P3755/2023</td>\n",
+       "      <td>plate11_r32_75k-CGCAAGGGATACTAAC</td>\n",
+       "      <td>0.0009</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A/Bangkok/P3755/2023</td>\n",
+       "      <td>plate11_r32_75k-TAGCTGGGCAAAGGCT</td>\n",
+       "      <td>0.0015</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0022</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A/Bangkok/P3755/2023</td>\n",
+       "      <td>plate11_r32_75k-TATCATTTCATCTACA</td>\n",
+       "      <td>0.00051</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0006</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>A/Chipata/15-NIC-001/2023</td>\n",
+       "      <td>plate11_r32_75k-CAGTGCCATCCATCCA</td>\n",
+       "      <td>0.00085</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0011</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>A/Chipata/15-NIC-001/2023</td>\n",
+       "      <td>plate11_r32_75k-CGTATAACTGACGATT</td>\n",
+       "      <td>0.0005</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.81</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>A/Chipata/15-NIC-001/2023</td>\n",
+       "      <td>plate11_r32_75k-TTTCATATAATTTGAG</td>\n",
+       "      <td>0.00034</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00054</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.82</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>A/Oman/3011/2023</td>\n",
+       "      <td>plate11_r32_75k-AATAGGCCCAAATCCA</td>\n",
+       "      <td>0.00094</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0013</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A/Oman/3011/2023</td>\n",
+       "      <td>plate11_r32_75k-TACGAAAATCAAGAGC</td>\n",
+       "      <td>0.00035</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00059</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>A/Oman/3011/2023</td>\n",
+       "      <td>plate11_r32_75k-TCCTTTAACTAATCGA</td>\n",
+       "      <td>0.00021</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00027</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.87</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       virus                         replicate     ic50  \\\n",
+       "0       A/Bangkok/P3755/2023  plate11_r32_75k-CGCAAGGGATACTAAC   0.0009   \n",
+       "1       A/Bangkok/P3755/2023  plate11_r32_75k-TAGCTGGGCAAAGGCT   0.0015   \n",
+       "2       A/Bangkok/P3755/2023  plate11_r32_75k-TATCATTTCATCTACA  0.00051   \n",
+       "3  A/Chipata/15-NIC-001/2023  plate11_r32_75k-CAGTGCCATCCATCCA  0.00085   \n",
+       "4  A/Chipata/15-NIC-001/2023  plate11_r32_75k-CGTATAACTGACGATT   0.0005   \n",
+       "5  A/Chipata/15-NIC-001/2023  plate11_r32_75k-TTTCATATAATTTGAG  0.00034   \n",
+       "6           A/Oman/3011/2023  plate11_r32_75k-AATAGGCCCAAATCCA  0.00094   \n",
+       "7           A/Oman/3011/2023  plate11_r32_75k-TACGAAAATCAAGAGC  0.00035   \n",
+       "8           A/Oman/3011/2023  plate11_r32_75k-TCCTTTAACTAATCGA  0.00021   \n",
+       "\n",
+       "     ic50_bound midpoint midpoint_bound_type slope   top bottom    r2  \n",
+       "0  interpolated    0.001        interpolated     1  0.94      0  0.86  \n",
+       "1  interpolated   0.0022        interpolated   1.4   0.8      0  0.67  \n",
+       "2  interpolated   0.0006        interpolated     1  0.92      0  0.95  \n",
+       "3  interpolated   0.0011        interpolated   1.1   0.9      0  0.87  \n",
+       "4  interpolated  0.00082        interpolated     1  0.81      0  0.87  \n",
+       "5  interpolated  0.00054        interpolated     1  0.82      0  0.95  \n",
+       "6  interpolated   0.0013        interpolated     1  0.87      0  0.52  \n",
+       "7  interpolated  0.00059        interpolated     1   0.8      0  0.35  \n",
+       "8  interpolated  0.00027        interpolated   1.2  0.87      0  0.97  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Fit params for problematic tops\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>virus</th>\n",
+       "      <th>replicate</th>\n",
+       "      <th>ic50</th>\n",
+       "      <th>ic50_bound</th>\n",
+       "      <th>midpoint</th>\n",
+       "      <th>midpoint_bound_type</th>\n",
+       "      <th>slope</th>\n",
+       "      <th>top</th>\n",
+       "      <th>bottom</th>\n",
+       "      <th>r2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A/Catalonia/NSVH102124476/2023</td>\n",
+       "      <td>plate11_r32_75k-AATATACCGGCACTAC</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0025</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>A/Catalonia/NSVH102124476/2023</td>\n",
+       "      <td>plate11_r32_75k-ACTGAACAGTATAACT</td>\n",
+       "      <td>0.0006</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00097</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>A/Catalonia/NSVH102124476/2023</td>\n",
+       "      <td>plate11_r32_75k-CTAAGGGCCTGTTCTT</td>\n",
+       "      <td>0.0012</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0014</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>A/Hong_Kong/2671/2019</td>\n",
+       "      <td>plate11_r32_75k-CGAAAACATTACAAAT</td>\n",
+       "      <td>0.00022</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00022</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>A/Hong_Kong/2671/2019</td>\n",
+       "      <td>plate11_r32_75k-CGGGAATCTCCCATAC</td>\n",
+       "      <td>0.00011</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.00011</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>A/Hong_Kong/2671/2019</td>\n",
+       "      <td>plate11_r32_75k-TTCATCAAGTTGGTGC</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>interpolated</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>A/Hong_Kong/4801/2014_(15/192)</td>\n",
+       "      <td>plate11_r32_75k-CACAGACAATAAAAAA</td>\n",
+       "      <td>7.8e-05</td>\n",
+       "      <td>upper</td>\n",
+       "      <td>4.8e-05</td>\n",
+       "      <td>upper</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>A/Hong_Kong/4801/2014_(15/192)</td>\n",
+       "      <td>plate11_r32_75k-GGTTAACTTTGGAAGC</td>\n",
+       "      <td>7.8e-05</td>\n",
+       "      <td>upper</td>\n",
+       "      <td>4.4e-05</td>\n",
+       "      <td>upper</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             virus                         replicate     ic50  \\\n",
+       "9   A/Catalonia/NSVH102124476/2023  plate11_r32_75k-AATATACCGGCACTAC    0.002   \n",
+       "10  A/Catalonia/NSVH102124476/2023  plate11_r32_75k-ACTGAACAGTATAACT   0.0006   \n",
+       "11  A/Catalonia/NSVH102124476/2023  plate11_r32_75k-CTAAGGGCCTGTTCTT   0.0012   \n",
+       "12           A/Hong_Kong/2671/2019  plate11_r32_75k-CGAAAACATTACAAAT  0.00022   \n",
+       "13           A/Hong_Kong/2671/2019  plate11_r32_75k-CGGGAATCTCCCATAC  0.00011   \n",
+       "14           A/Hong_Kong/2671/2019  plate11_r32_75k-TTCATCAAGTTGGTGC   0.0001   \n",
+       "15  A/Hong_Kong/4801/2014_(15/192)  plate11_r32_75k-CACAGACAATAAAAAA  7.8e-05   \n",
+       "16  A/Hong_Kong/4801/2014_(15/192)  plate11_r32_75k-GGTTAACTTTGGAAGC  7.8e-05   \n",
+       "\n",
+       "      ic50_bound midpoint midpoint_bound_type slope   top bottom    r2  \n",
+       "9   interpolated   0.0025        interpolated     2  0.83      0  0.83  \n",
+       "10  interpolated  0.00097        interpolated   1.1   0.8      0  0.93  \n",
+       "11  interpolated   0.0014        interpolated   1.5   0.9      0  0.92  \n",
+       "12  interpolated  0.00022        interpolated     2     1      0  0.85  \n",
+       "13  interpolated  0.00011        interpolated     2     1      0  0.77  \n",
+       "14  interpolated   0.0001        interpolated     2     1      0  0.85  \n",
+       "15         upper  4.8e-05               upper     2     1      0  0.99  \n",
+       "16         upper  4.4e-05               upper     2     1      0  0.93  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for serum_group, df in fit_params.groupby(\"serum\"):\n",
+    "    print(f\"\\nFit params for {serum_group}\")\n",
+    "    display(\n",
+    "        df.drop(columns=\"serum\").map(\n",
+    "            lambda x: f\"{x:.2g}\" if isinstance(x, float) else x\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aff9f9f-4fe4-4066-ba90-1c64a5707699",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/constrain_params_range_data.csv b/notebooks/constrain_params_range_data.csv
new file mode 100644
index 0000000..84a540d
--- /dev/null
+++ b/notebooks/constrain_params_range_data.csv
@@ -0,0 +1,137 @@
+,serum,virus,replicate,fraction infectivity,concentration
+0,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.02901,0.01
+1,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.04584,0.005
+2,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.2873,0.0025
+3,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.6296,0.00125
+4,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.5716,0.000625
+5,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.5382,0.0003125
+6,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,0.7672,0.0001563
+7,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-CGCAAGGGATACTAAC,1,7.813e-05
+8,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.02949,0.01
+9,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.1528,0.005
+10,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.275,0.0025
+11,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.8956,0.00125
+12,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.4168,0.000625
+13,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.6226,0.0003125
+14,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,0.6454,0.0001563
+15,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TAGCTGGGCAAAGGCT,1,7.813e-05
+16,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.01561,0.01
+17,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.05086,0.005
+18,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.2517,0.0025
+19,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.3524,0.00125
+20,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.3788,0.000625
+21,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.562,0.0003125
+22,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.826,0.0001563
+23,problematic slopes,A/Bangkok/P3755/2023,plate11_r32_75k-TATCATTTCATCTACA,0.7738,7.813e-05
+24,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.009764,0.01
+25,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.1057,0.005
+26,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.3803,0.0025
+27,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.2956,0.00125
+28,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.7049,0.000625
+29,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.5256,0.0003125
+30,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.9126,0.0001563
+31,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CAGTGCCATCCATCCA,0.8178,7.813e-05
+32,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.0286,0.01
+33,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.135,0.005
+34,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.3505,0.0025
+35,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.211,0.00125
+36,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.5,0.000625
+37,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.4535,0.0003125
+38,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.7904,0.0001563
+39,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-CGTATAACTGACGATT,0.7154,7.813e-05
+40,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.03299,0.01
+41,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.06345,0.005
+42,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.1622,0.0025
+43,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.2741,0.00125
+44,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.4527,0.000625
+45,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.4143,0.0003125
+46,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.5788,0.0001563
+47,problematic slopes,A/Chipata/15-NIC-001/2023,plate11_r32_75k-TTTCATATAATTTGAG,0.7831,7.813e-05
+48,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.4973,0.01
+49,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.04783,0.005
+50,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.2234,0.0025
+51,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.4207,0.00125
+52,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.395,0.000625
+53,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,1,0.0003125
+54,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.7924,0.0001563
+55,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-AATAGGCCCAAATCCA,0.7026,7.813e-05
+56,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.01296,0.01
+57,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.04534,0.005
+58,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.4185,0.0025
+59,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.2996,0.00125
+60,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.5032,0.000625
+61,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.3988,0.0003125
+62,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.4989,0.0001563
+63,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TACGAAAATCAAGAGC,0.4689,7.813e-05
+64,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.03077,0.01
+65,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.04591,0.005
+66,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.09214,0.0025
+67,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.09732,0.00125
+68,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.1832,0.000625
+69,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.4771,0.0003125
+70,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.5141,0.0001563
+71,problematic slopes,A/Oman/3011/2023,plate11_r32_75k-TCCTTTAACTAATCGA,0.7143,7.813e-05
+0,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.1019,0.01
+1,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.05815,0.005
+2,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.375,0.0025
+3,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.8747,0.00125
+4,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.5976,0.000625
+5,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.9301,0.0003125
+6,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.9518,0.0001563
+7,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-AATATACCGGCACTAC,0.6482,7.813e-05
+8,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.01931,0.01
+9,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.08482,0.005
+10,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.2382,0.0025
+11,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.3558,0.00125
+12,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.471,0.000625
+13,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.6786,0.0003125
+14,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.7271,0.0001563
+15,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-ACTGAACAGTATAACT,0.5837,7.813e-05
+16,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.06008,0.01
+17,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.1194,0.005
+18,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.2544,0.0025
+19,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.431,0.00125
+20,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.803,0.000625
+21,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.6356,0.0003125
+22,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,1,0.0001563
+23,problematic tops,A/Catalonia/NSVH102124476/2023,plate11_r32_75k-CTAAGGGCCTGTTCTT,0.8456,7.813e-05
+24,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,0.0001956,0.01
+25,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,0.0001168,0.005
+26,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,0,0.0025
+27,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,0,0.00125
+28,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,0.0003835,0.000625
+29,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,0.02784,0.0003125
+30,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,1,0.0001563
+31,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGAAAACATTACAAAT,1,7.813e-05
+32,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0,0.01
+33,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0,0.005
+34,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0.0002767,0.0025
+35,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0,0.00125
+36,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0,0.000625
+37,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0.01955,0.0003125
+38,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,0.05163,0.0001563
+39,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-CGGGAATCTCCCATAC,1,7.813e-05
+40,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0.0003076,0.01
+41,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0,0.005
+42,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,6.13e-05,0.0025
+43,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0.0002412,0.00125
+44,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0,0.000625
+45,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0.02452,0.0003125
+46,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0.1068,0.0001563
+47,problematic tops,A/Hong_Kong/2671/2019,plate11_r32_75k-TTCATCAAGTTGGTGC,0.85,7.813e-05
+48,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0,0.01
+49,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0,0.005
+50,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0,0.0025
+51,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0,0.00125
+52,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0,0.000625
+53,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0.00971,0.0003125
+54,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0.06713,0.0001563
+55,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-CACAGACAATAAAAAA,0.2836,7.813e-05
+56,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0,0.01
+57,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0.0001514,0.005
+58,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0.000182,0.0025
+59,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0,0.00125
+60,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0,0.000625
+61,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0.0001692,0.0003125
+62,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0.01483,0.0001563
+63,problematic tops,A/Hong_Kong/4801/2014_(15/192),plate11_r32_75k-GGTTAACTTTGGAAGC,0.265,7.813e-05
diff --git a/ruff.toml b/ruff.toml
index 76fac61..d0ecca7 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,4 +1,4 @@
-select = [
+lint.select = [
   "E",   # pycodestyle
   "F",   # pyflakes
   "UP",  # pyupgrade
@@ -10,7 +10,8 @@ extend-exclude = [
     "docs/_build",
     ".*",
 ]
-ignore = [
+extend-include = ["*.ipynb"]
+lint.ignore = [
     "D203",
     "D205",
     "D212",
diff --git a/test_requirements.txt b/test_requirements.txt
index 12b8532..4d74bb1 100644
--- a/test_requirements.txt
+++ b/test_requirements.txt
@@ -1,6 +1,5 @@
 pytest
 nbval
-nbqa
 ruff
 black[jupyter]
 jupyterlab