diff --git a/cleo/opto/__init__.py b/cleo/opto/__init__.py
index 7bb169f5..d4eae16f 100644
--- a/cleo/opto/__init__.py
+++ b/cleo/opto/__init__.py
@@ -7,6 +7,16 @@
     enphr3_3s,
     gtacr2_4s,
     vfchrimson_4s,
+    reachr_4s,
+    chrimsonr_4s,
+    cschrimson_4s,
+    breaches_4s,
+    chrmine_4s,
+    cheta_4s,
+    chronos_4s,
+    cheriff_4s,
+    nphr_3s,
+    jaws_3s,
 )
 from cleo.opto.opsins import (
     BansalFourStateOpsin,
diff --git a/cleo/opto/opsin_library.py b/cleo/opto/opsin_library.py
index 474efa4c..d98d5775 100644
--- a/cleo/opto/opsin_library.py
+++ b/cleo/opto/opsin_library.py
@@ -18,8 +18,8 @@
     FourStateOpsin,
 )
 
-
-ONE_P_TWO_P_RATIO = 270382.2996938772
+# This number is the average of the 1P/2P ratios calculated for ChRmine, ChroME, ChroME2f, and ChroME2s
+ONE_P_TWO_P_RATIO = 285071.80
 
 
 def chr2_4s() -> FourStateOpsin:
@@ -28,7 +28,7 @@ def chr2_4s() -> FourStateOpsin:
     Params taken from try.projectpyrho.org's default 4-state configuration.
     Action spectrum from `Nagel et al., 2003, Fig. 4a
     <https://www.pnas.org/doi/full/10.1073/pnas.1936192100>`_,
-    extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
 
     Parameters can be changed after initialization but *before injection*.
     """
@@ -52,6 +52,7 @@ def chr2_4s() -> FourStateOpsin:
         v1=17.1 * mV,
         name="ChR2",
         spectrum=[
+            # Replaced ONE_P_TWO_P_RATIO with calculated value for ChR2 from notebook
             (400, 0.34),
             (422, 0.65),
             (460, 0.96),
@@ -74,13 +75,279 @@ def chr2_4s() -> FourStateOpsin:
     )
 
 
+def reachr_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state ReaChR model.
+
+    Params given in `Bansal et al., 2021, Table 1
+    <https://doi.org/10.1088/1741-2552/ac1175>`_,
+    Action spectrum from `Krause et al., 2017, Fig. 1b
+    <https://doi.org/10.1016/j.bpj.2017.02.001>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+
+    return BansalFourStateOpsin(
+        Gd1=7.7e-3 / ms,
+        Gd2=1.25e-3 / ms,
+        Gr0=3.33e-5 / ms,
+        g0=14.28 * nsiemens,
+        phim=5e17 / mm2 / second,
+        k1=1.2 / ms,
+        k2=0.01 / ms,
+        Gf0=0.0005 / ms,
+        Gb0=0.0005 / ms,
+        kf=0.012 / ms,
+        kb=0.001 / ms,
+        gamma=0.05,
+        p=1,
+        q=1,
+        E=7 * mV,
+        name="ReaChR",
+        spectrum=[
+            (390, 0.30),
+            (401, 0.27),
+            (411, 0.30),
+            (421, 0.33),
+            (431, 0.37),
+            (441, 0.41),
+            (451, 0.46),
+            (461, 0.53),
+            (472, 0.58),
+            (479, 0.67),
+            (490, 0.76),
+            (500, 0.86),
+            (511, 0.94),
+            (520, 0.97),
+            (531, 1),
+            (540, 0.98),
+            (549, 0.95),
+            (559, 0.86),
+            (571, 0.71),
+            (580, 0.52),
+            (590, 0.36),
+        ],
+    )
+
+
+def chrimsonr_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state ChrimsonR model.
+
+    Params given in `Bansal et al., 2021, Table 1
+    <https://doi.org/10.1088/1741-2552/ac1175>`_,
+    Action spectrum from `Cheong, Soon Keen et al., 2018, Fig. 2
+    <https://doi.org/10.1371/journal.pone.0194947>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+    return BansalFourStateOpsin(
+        Gd1=0.067 / ms,
+        Gd2=0.01 / ms,
+        Gr0=0.5e-3 / ms,
+        g0=12.25 * nsiemens,
+        phim=20e17 / mm2 / second,
+        k1=6 / ms,
+        k2=0.2 / ms,
+        Gf0=0.02 / ms,
+        Gb0=0.05 / ms,
+        kf=0.1 / ms,
+        kb=0.001 / ms,
+        gamma=0.05,
+        p=0.6,
+        q=1,
+        E=0 * mV,
+        name="ChrimsonR",
+        spectrum=[
+            (386, 0.13),
+            (398, 0.15),
+            (406, 0.15),
+            (423, 0.16),
+            (439, 0.17),
+            (446, 0.18),
+            (453, 0.20),
+            (460, 0.23),
+            (467, 0.25),
+            (479, 0.31),
+            (496, 0.40),
+            (507, 0.46),
+            (518, 0.53),
+            (522, 0.56),
+            (528, 0.60),
+            (532, 0.63),
+            (535, 0.67),
+            (538, 0.71),
+            (546, 0.79),
+            (556, 0.85),
+            (569, 0.91),
+            (580, 0.95),
+            (590, 1),
+            (599, 0.94),
+            (605, 0.90),
+            (610, 0.87),
+            (619, 0.80),
+            (624, 0.76),
+            (629, 0.69),
+            (632, 0.64),
+            (634, 0.60),
+            (637, 0.56),
+            (639, 0.52),
+            (643, 0.47),
+            (647, 0.40),
+            (652, 0.32),
+            (660, 0.24),
+            (668, 0.16),
+            (678, 0.09),
+            (686, 0.06),
+        ],
+    )
+
+
+def cschrimson_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state CsChrimson model.
+
+    Params given in `Bansal et al., 2021, Table 1
+    <https://doi.org/10.1088/1741-2552/ac1175>`_,
+    Action spectrum from `Kim, Seungsoo, et al., 2015, Fig. 6.3a
+    <https://doi.org/10.1007/978-3-319-12913-6_6>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+    return BansalFourStateOpsin(
+        Gd1=0.033 / ms,
+        Gd2=0.017 / ms,
+        Gr0=5e-6 / ms,
+        g0=18.48 * nsiemens,
+        phim=6e16 / mm2 / second,
+        k1=3 / ms,
+        k2=0.04 / ms,
+        Gf0=0.005 / ms,
+        Gb0=0.01 / ms,
+        kf=0.01 / ms,
+        kb=0.6 / ms,
+        gamma=0.05,
+        p=1,
+        q=1,
+        E=-10 * mV,
+        name="CsChrimson",
+        spectrum=[
+            (402, 0.16),
+            (419, 0.18),
+            (435, 0.20),
+            (449, 0.23),
+            (466, 0.30),
+            (482, 0.36),
+            (497, 0.46),
+            (514, 0.54),
+            (529, 0.63),
+            (545, 0.75),
+            (561, 0.85),
+            (576, 0.94),
+            (590, 1),
+            (591, 1.01),
+            (607, 0.94),
+            (622, 0.77),
+            (638, 0.51),
+            (652, 0.30),
+            (670, 0.13),
+            (684, 0.04),
+        ],
+    )
+
+
+def breaches_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state bReaChES model.
+
+    Params given in `Bansal et al., 2021, Table 1
+    <https://doi.org/10.1088/1741-2552/ac1175>`_,
+    Action spectrum from `Bansal, H., Pyari, G. & Roy, S., 2024, Fig. 1c
+    <https://doi.org/10.1038/s41598-024-62558-2>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+    return BansalFourStateOpsin(
+        Gd1=0.025 / ms,
+        Gd2=0.01 / ms,
+        Gr0=3.3e-5 / ms,
+        g0=36.5 * nsiemens,
+        phim=6e15 / mm2 / second,
+        k1=0.4 / ms,
+        k2=0.01 / ms,
+        Gf0=0.002 / ms,
+        Gb0=0.002 / ms,
+        kf=0.01 / ms,
+        kb=0.04 / ms,
+        gamma=0.05,
+        p=1,
+        q=1,
+        E=10 * mV,
+        name="bReaChES",
+        spectrum=[
+            (390, 0.47),
+            (410, 0.66),
+            (430, 0.80),
+            (450, 0.89),
+            (470, 0.95),
+            (490, 0.96),
+            (510, 0.95),
+            (530, 0.94),
+            (550, 0.95),
+            (570, 1),
+            (590, 0.84),
+            (611, 0.34),
+            (631, 0.09),
+            (651, 0.02),
+        ],
+    )
+
+
+def chrmine_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state ChRmine model.
+
+    Params given in `Bansal et al., 2021, Table 1
+    <https://doi.org/10.1088/1741-2552/ac1175>`_,
+    Action spectrum from `Bansal, H., Pyari, G. & Roy, S., 2024, Fig. 1c
+    <https://doi.org/10.1038/s41598-024-62558-2>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+    return BansalFourStateOpsin(
+        Gd1=0.02 / ms,
+        Gd2=0.013 / ms,
+        Gr0=5.9e-4 / ms,
+        g0=110 * nsiemens,
+        phim=2.1e15 / mm2 / second,
+        k1=0.2 / ms,
+        k2=0.01 / ms,
+        Gf0=0.0027 / ms,
+        Gb0=0.0005 / ms,
+        kf=0.001 / ms,
+        kb=0 / ms,
+        gamma=0.05,
+        p=0.8,
+        q=1,
+        E=5.64 * mV,
+        name="ChRmine",
+        spectrum=[
+            (390, 0.37),
+            (410, 0.55),
+            (430, 0.72),
+            (450, 0.85),
+            (470, 0.93),
+            (491, 0.99),
+            (510, 1),
+            (531, 0.98),
+            (551, 0.94),
+            (570, 0.86),
+            (591, 0.70),
+            (610, 0.48),
+            (630, 0.26),
+            (650, 0.10),
+        ],
+    )
+
+
 def chr2_b4s() -> BansalFourStateOpsin:
     """Returns a 4-state ChR2 model.
 
-    Params given in Bansal et al., 2020.
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
     Action spectrum from `Nagel et al., 2003, Fig. 4a
     <https://www.pnas.org/doi/full/10.1073/pnas.1936192100>`_,
-    extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
 
     Parameters can be changed after initialization but *before injection*.
     """
@@ -102,6 +369,7 @@ def chr2_b4s() -> BansalFourStateOpsin:
         E=0 * mV,
         name="ChR2",
         spectrum=[
+            # Replaced ONE_P_TWO_P_RATIO with calculated value for ChR2 from notebook
             (400, 0.34),
             (422, 0.65),
             (460, 0.96),
@@ -127,7 +395,8 @@ def chr2_b4s() -> BansalFourStateOpsin:
 def chr2_h134r_4s() -> BansalFourStateOpsin:
     """Returns a 4-state ChR2(H134R) model.
 
-    Params given in Bansal et al., 2020.
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
     Action spectrum is same as for :func:`~chr2_4s`, but blue-shifted 20 nm
     (I cannot find it directly in the literature).
 
@@ -173,10 +442,155 @@ def chr2_h134r_4s() -> BansalFourStateOpsin:
     )
 
 
+def cheta_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state ChETA model.
+
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
+    Action spectrum from `Gunaydin, L., Yizhar, O., Berndt, A. et al., 2010, Fig. 2b
+    <https://doi.org/10.1038/nn.2495>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+
+    return BansalFourStateOpsin(
+        Gd1=0.21 / ms,
+        Gd2=0.02 / ms,
+        Gr0=2e-4 / ms,
+        g0=21.5 * nsiemens,
+        phim=1e16 / mm2 / second,
+        k1=3 / ms,
+        k2=1 / ms,
+        Gf0=0.02 / ms,
+        Gb0=0.015 / ms,
+        kf=0.01 / ms,
+        kb=0.01 / ms,
+        gamma=0.05,
+        p=1,
+        q=1,
+        E=0 * mV,
+        name="ChETA",
+        spectrum=[
+            (445, 0.36),
+            (452, 0.47),
+            (460, 0.61),
+            (468, 0.77),
+            (478, 0.89),
+            (490, 1),
+            (500, 1),
+            (514, 0.89),
+            (530, 0.70),
+            (550, 0.42),
+            (576, 0.12),
+        ],
+    )
+
+
+def chronos_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state Chronos model.
+
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
+    Action spectrum from `Soor, Navjeevan S., et al., 2019, Fig. 1b
+    <https://doi.org/10.1088/1361-6463/aaf944>`_,
+    Extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+
+    return BansalFourStateOpsin(
+        Gd1=0.278 / ms,
+        Gd2=0.01 / ms,
+        Gr0=1.2e-3 / ms,
+        g0=39 * nsiemens,
+        phim=0.8e16 / mm2 / second,
+        k1=1.8 / ms,
+        k2=0.01 / ms,
+        Gf0=0.05 / ms,
+        Gb0=0.08 / ms,
+        kf=0.1 / ms,
+        kb=0.01 / ms,
+        gamma=0.05,
+        p=0.8,
+        q=0.9,
+        E=0 * mV,
+        name="Chronos",
+        spectrum=[
+            (388, 0.18),
+            (404, 0.21),
+            (419, 0.30),
+            (431, 0.41),
+            (439, 0.50),
+            (447, 0.57),
+            (457, 0.67),
+            (465, 0.74),
+            (472, 0.80),
+            (480, 0.86),
+            (490, 0.92),
+            (498, 1),
+            (507, 0.90),
+            (516, 0.84),
+            (528, 0.68),
+            (521, 0.76),
+            (533, 0.59),
+            (538, 0.49),
+            (544, 0.40),
+            (548, 0.32),
+            (554, 0.23),
+            (560, 0.14),
+            (576, 0.05),
+            (570, 0.08),
+            (591, 0.01),
+        ],
+    )
+
+
+def cheriff_4s() -> BansalFourStateOpsin:
+    """Returns a 4-state CheRiff model.
+
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
+    Action spectrum from `Hochbaum, Daniel R et al., 2014, Fig. 2a
+    <https://doi.org/10.1038/nmeth.3000>`_,
+    extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+
+    return BansalFourStateOpsin(
+        Gd1=0.063 / ms,
+        Gd2=2.5e-3 / ms,
+        Gr0=5e-4 / ms,
+        g0=40 * nsiemens,
+        phim=0.7e16 / mm2 / second,
+        k1=1.4 / ms,
+        k2=0.001 / ms,
+        Gf0=0.01 / ms,
+        Gb0=0.02 / ms,
+        kf=0.02 / ms,
+        kb=0.1 / ms,
+        gamma=0.05,
+        p=0.9,
+        q=1,
+        E=0 * mV,
+        name="CheRiff",
+        spectrum=[
+            (386, 0.30),
+            (403, 0.47),
+            (419, 0.67),
+            (435, 0.84),
+            (450, 0.97),
+            (466, 1),
+            (483, 0.95),
+            (498, 0.63),
+            (515, 0.29),
+            (529, 0.09),
+            (546, 0.02),
+            (560, 0.00),
+        ],
+    )
+
+
 def vfchrimson_4s() -> BansalFourStateOpsin:
     """Returns a 4-state vf-Chrimson model.
 
-    Params given in Bansal et al., 2020.
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
     Action spectrum from `Mager et al., 2018, Supp. Fig. 1a
     <https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-018-04146-3/MediaObjects/41467_2018_4146_MOESM1_ESM.docx>`_,
     extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
@@ -228,7 +642,8 @@ def vfchrimson_4s() -> BansalFourStateOpsin:
 def chrimson_4s() -> BansalFourStateOpsin:
     """Returns a 4-state Chrimson model.
 
-    Params given in Bansal et al., 2020.
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
     Action spectrum from `Mager et al., 2018, Supp. Fig. 1a
     <https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-018-04146-3/MediaObjects/41467_2018_4146_MOESM1_ESM.docx>`_,
     extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
@@ -280,7 +695,8 @@ def chrimson_4s() -> BansalFourStateOpsin:
 def gtacr2_4s() -> BansalFourStateOpsin:
     """Returns a 4-state model of GtACR2, an anion channel.
 
-    Params given in Bansal et al., 2020.
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1016/j.neuroscience.2020.09.022>`_,
     Action spectra from `Govorunova et al., 2015, Fig. 1f
     <https://www.science.org/doi/10.1126/science.aaa7484#F1>`_,
     extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
@@ -346,7 +762,8 @@ def gtacr2_4s() -> BansalFourStateOpsin:
 def enphr3_3s():
     """Returns a 3-state model of eNpHR3, a chloride pump.
 
-    Params given in Bansal et al., 2020.
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1088/2057-1976/ab90a1>`_,
     Action spectrum from `Gradinaru et al., 2010 <https://doi.org/10.1016/j.cell.2010.02.037>`_,
     Figure 3F,
     extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
@@ -390,6 +807,96 @@ def enphr3_3s():
     )
 
 
+def nphr_3s():
+    """Returns a 3-state model of NpHR, a chloride pump.
+
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1088/2057-1976/ab90a1>`_,
+    Action spectrum from `Bamberg et al., 1984, Fig. 3,
+    <https://pubs.acs.org/doi/pdf/10.1021/bi00320a050>`_,
+    extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+    return BansalThreeStatePump(
+        Gd=0.1099 / ms,
+        Gr=0.05 / ms,
+        ka=0.005 / ms,
+        p=0.5,
+        q=0.2,
+        phim=1.5e18 / mm2 / second,
+        E=-400 * mV,
+        g0=17.7 * nsiemens,
+        a=0.02e-2 * mM / pcoulomb,
+        b=5,
+        name="NpHR",
+        spectrum=[
+            (410, 0.194),
+            (420, 0.176),
+            (440, 0.199),
+            (470, 0.259),
+            (480, 0.346),
+            (490, 0.429),
+            (505, 0.502),
+            (510, 0.574),
+            (520, 0.667),
+            (530, 0.799),
+            (550, 0.844),
+            (565, 0.903),
+            (590, 0.947),
+            (578, 1.0),
+            (610, 0.853),
+            (620, 0.619),
+            (640, 0.457),
+            (650, 0.299),
+        ],
+    )
+
+
+def jaws_3s():
+    """Returns a 3-state model of Jaws, a chloride pump.
+
+    Params given in `Bansal et al., 2020, Table 1
+    <https://doi.org/10.1088/2057-1976/ab90a1>`_,
+    Action spectrum from `Chuong, Amy S et al., 2014, Fig. 1d
+    <https://doi.org/10.1038/nn.3752>`_,
+    extracted using `Plot Digitizer <https://plotdigitizer.com/>`_.
+    """
+    return BansalThreeStatePump(
+        Gd=0.167 / ms,
+        Gr=0.05 / ms,
+        ka=1 / ms,
+        p=0.8,
+        q=1,
+        phim=0.95e18 / mm2 / second,
+        E=-400 * mV,
+        g0=12.6 * nsiemens,
+        a=0.02e-2 * mM / pcoulomb,
+        b=6.5,
+        name="Jaws",
+        spectrum=[
+            (402, 0.29),
+            (419, 0.29),
+            (434, 0.32),
+            (450, 0.35),
+            (466, 0.43),
+            (482, 0.49),
+            (497, 0.55),
+            (514, 0.59),
+            (529, 0.69),
+            (545, 0.80),
+            (560, 0.91),
+            (577, 0.98),
+            (592, 1),
+            (600, 1),
+            (608, 0.94),
+            (624, 0.81),
+            (639, 0.54),
+            (654, 0.26),
+            (671, 0.09),
+            (685, 0.02),
+        ],
+    )
+
+
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
 
@@ -401,6 +908,5 @@ def enphr3_3s():
         gtacr2_4s(),
         enphr3_3s(),
         extrapolate=True,
-        range="1p2p",
     )
     plt.show()
diff --git a/docs/tutorials/all_optical.ipynb b/docs/tutorials/all_optical.ipynb
index ea2ff887..096def91 100644
--- a/docs/tutorials/all_optical.ipynb
+++ b/docs/tutorials/all_optical.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -125,7 +125,7 @@
        "CLSimulator(io_processor=None, devices={Scope(name='Scope', save_history=True, sensor=GECI(name='GCaMP6f', save_history=True, model='\\n            dCa/dt = -gamma * (Ca - Ca_rest) / (1 + kappa_S + kappa_B) : mmolar (clock-driven)\\n            kappa_B = B_T * K_d / (Ca + K_d)**2 : 1\\n\\n            CaB_active = Ca_rest + b : mmolar  # add tiny bit to avoid /0\\n            db/dt = beta : mmolar (clock-driven)\\n            lam = 1/tau_off + 1/tau_on : 1/second\\n            kap = 1/tau_off : 1/second\\n            dbeta/dt = (                    # should be M/s/s\\n                A * (lam - kap) * (Ca - Ca_rest)  # M/s/s\\n                - (kap + lam) * beta        # M/s/s\\n                - kap * lam * b    # M/s/s\\n            ) : mmolar/second (clock-driven)\\n            \\nexc_factor = 1 : 1\\n\\n            dFF_baseline = 1 / (1 + (K_d / Ca_rest) ** n_H) : 1\\n            dFF = exc_factor * rho_rel * dFF_max  * (\\n                1 / (1 + (K_d / CaB_active) ** n_H)\\n                - dFF_baseline\\n            ) : 1\\n            rho_rel : 1\\n        ', on_pre='Ca += dCa_T / (1 + kappa_S + kappa_B)', sigma_noise=0.03748181818181818, dFF_1AP=0.09775500000000001, location='cytoplasm', cal_model=DynamicCalcium(on_pre='Ca += dCa_T / (1 + kappa_S + kappa_B)', model='\\n            dCa/dt = -gamma * (Ca - Ca_rest) / (1 + kappa_S + kappa_B) : mmolar (clock-driven)\\n            kappa_B = B_T * K_d / (Ca + K_d)**2 : 1', Ca_rest=50. * nmolar, gamma=292.3 * hertz, B_T=200. * umolar, kappa_S=110, dCa_T=7.6 * umolar), bind_act_model=DoubExpCalBindingActivation(model='\\n            CaB_active = Ca_rest + b : mmolar  # add tiny bit to avoid /0\\n            db/dt = beta : mmolar (clock-driven)\\n            lam = 1/tau_off + 1/tau_on : 1/second\\n            kap = 1/tau_off : 1/second\\n            dbeta/dt = (                    # should be M/s/s\\n                A * (lam - kap) * (Ca - Ca_rest)  # M/s/s\\n                - (kap + lam) * beta        # M/s/s\\n                - kap * lam * b    # M/s/s\\n            ) : mmolar/second (clock-driven)\\n            ', A=7.61251 * khertz, tau_on=1.17164616 * second, tau_off=10.14020867 * msecond, Ca_rest=50. * nmolar), exc_model=NullExcitation(model='exc_factor = 1 : 1'), fluor_model='\\n            dFF_baseline = 1 / (1 + (K_d / Ca_rest) ** n_H) : 1\\n            dFF = exc_factor * rho_rel * dFF_max  * (\\n                1 / (1 + (K_d / CaB_active) ** n_H)\\n                - dFF_baseline\\n            ) : 1\\n            rho_rel : 1\\n        ', K_d=290. * nmolar, n_H=2.7, dFF_max=25.2), img_width=0.5 * mmetre, focus_depth=100. * umetre, location=array([0., 0., 0.]) * metre, direction=array([0., 0., 1.]), soma_radius=10. * umetre, snr_cutoff=1)})"
       ]
      },
-     "execution_count": null,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -161,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -171,7 +171,7 @@
        " <Axes3D: xlabel='x (um)', ylabel='y (um)', zlabel='z (um)'>)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -226,9 +226,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING    Removing unsupported flag '-march=native' from compiler flags. [brian2.codegen.cpp_prefs]\n"
+     ]
+    }
+   ],
    "source": [
     "scope.inject_sensor_for_targets()"
    ]
@@ -238,12 +246,19 @@
    "metadata": {},
    "source": [
     "## 2p stimulation configuration\n",
-    "Further research is needed to better quantify the relationship between 2P and the 1P action spectra, but we use a preliminary model computed in `notebooks/1p_2p_ratio.ipynb`."
+    "CLEO models 2-Photon (2P) action spectra by converting 2P irradiance to an equivalent 1-Photon (1P) irradiance using a sensitivity ratio derived from published experimental photocurrent data. \n",
+    "The ratio `m_1P / m_2P` represents the difference in how effective 1P light is relative to 2P light at driving a given opsin, where `m_1P` and `m_2P` are the photocurrent sensitivities (nA per mW/mm²) under each modality.\n",
+    "\n",
+    "The 1P sensitivity is estimated from the initial linear regime of the photocurrent-irradiance curve (first two data points), since the 1P response saturates quickly at higher irradiances. The 2P response remains linear across the full measured range, so a full least-squares fit is used to derive sensitivity.\n",
+    "\n",
+    "Ratios were computed for the ChroME family (ChroME, ChroME2f, ChroME2s), giving a consistent average of ~285,000.\n",
+    "\n",
+    "See `notebooks/1p_2p_ratio.ipynb` for the full derivation, methodology, per-opsin results, and plots."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -252,7 +267,7 @@
        "CLSimulator(io_processor=None, devices={Scope(name='Scope', save_history=True, sensor=GECI(name='GCaMP6f', save_history=True, model='\\n            dCa/dt = -gamma * (Ca - Ca_rest) / (1 + kappa_S + kappa_B) : mmolar (clock-driven)\\n            kappa_B = B_T * K_d / (Ca + K_d)**2 : 1\\n\\n            CaB_active = Ca_rest + b : mmolar  # add tiny bit to avoid /0\\n            db/dt = beta : mmolar (clock-driven)\\n            lam = 1/tau_off + 1/tau_on : 1/second\\n            kap = 1/tau_off : 1/second\\n            dbeta/dt = (                    # should be M/s/s\\n                A * (lam - kap) * (Ca - Ca_rest)  # M/s/s\\n                - (kap + lam) * beta        # M/s/s\\n                - kap * lam * b    # M/s/s\\n            ) : mmolar/second (clock-driven)\\n            \\nexc_factor = 1 : 1\\n\\n            dFF_baseline = 1 / (1 + (K_d / Ca_rest) ** n_H) : 1\\n            dFF = exc_factor * rho_rel * dFF_max  * (\\n                1 / (1 + (K_d / CaB_active) ** n_H)\\n                - dFF_baseline\\n            ) : 1\\n            rho_rel : 1\\n        ', on_pre='Ca += dCa_T / (1 + kappa_S + kappa_B)', sigma_noise=0.03748181818181818, dFF_1AP=0.09775500000000001, location='cytoplasm', cal_model=DynamicCalcium(on_pre='Ca += dCa_T / (1 + kappa_S + kappa_B)', model='\\n            dCa/dt = -gamma * (Ca - Ca_rest) / (1 + kappa_S + kappa_B) : mmolar (clock-driven)\\n            kappa_B = B_T * K_d / (Ca + K_d)**2 : 1', Ca_rest=50. * nmolar, gamma=292.3 * hertz, B_T=200. * umolar, kappa_S=110, dCa_T=7.6 * umolar), bind_act_model=DoubExpCalBindingActivation(model='\\n            CaB_active = Ca_rest + b : mmolar  # add tiny bit to avoid /0\\n            db/dt = beta : mmolar (clock-driven)\\n            lam = 1/tau_off + 1/tau_on : 1/second\\n            kap = 1/tau_off : 1/second\\n            dbeta/dt = (                    # should be M/s/s\\n                A * (lam - kap) * (Ca - Ca_rest)  # M/s/s\\n                - (kap + lam) * beta        # M/s/s\\n                - kap * lam * b    # M/s/s\\n            ) : mmolar/second (clock-driven)\\n            ', A=7.61251 * khertz, tau_on=1.17164616 * second, tau_off=10.14020867 * msecond, Ca_rest=50. * nmolar), exc_model=NullExcitation(model='exc_factor = 1 : 1'), fluor_model='\\n            dFF_baseline = 1 / (1 + (K_d / Ca_rest) ** n_H) : 1\\n            dFF = exc_factor * rho_rel * dFF_max  * (\\n                1 / (1 + (K_d / CaB_active) ** n_H)\\n                - dFF_baseline\\n            ) : 1\\n            rho_rel : 1\\n        ', K_d=290. * nmolar, n_H=2.7, dFF_max=25.2), img_width=0.5 * mmetre, focus_depth=100. * umetre, location=array([0., 0., 0.]) * metre, direction=array([0., 0., 1.]), soma_radius=10. * umetre, snr_cutoff=1), GECI(name='GCaMP6f', save_history=True, model='\\n            dCa/dt = -gamma * (Ca - Ca_rest) / (1 + kappa_S + kappa_B) : mmolar (clock-driven)\\n            kappa_B = B_T * K_d / (Ca + K_d)**2 : 1\\n\\n            CaB_active = Ca_rest + b : mmolar  # add tiny bit to avoid /0\\n            db/dt = beta : mmolar (clock-driven)\\n            lam = 1/tau_off + 1/tau_on : 1/second\\n            kap = 1/tau_off : 1/second\\n            dbeta/dt = (                    # should be M/s/s\\n                A * (lam - kap) * (Ca - Ca_rest)  # M/s/s\\n                - (kap + lam) * beta        # M/s/s\\n                - kap * lam * b    # M/s/s\\n            ) : mmolar/second (clock-driven)\\n            \\nexc_factor = 1 : 1\\n\\n            dFF_baseline = 1 / (1 + (K_d / Ca_rest) ** n_H) : 1\\n            dFF = exc_factor * rho_rel * dFF_max  * (\\n                1 / (1 + (K_d / CaB_active) ** n_H)\\n                - dFF_baseline\\n            ) : 1\\n            rho_rel : 1\\n        ', on_pre='Ca += dCa_T / (1 + kappa_S + kappa_B)', sigma_noise=0.03748181818181818, dFF_1AP=0.09775500000000001, location='cytoplasm', cal_model=DynamicCalcium(on_pre='Ca += dCa_T / (1 + kappa_S + kappa_B)', model='\\n            dCa/dt = -gamma * (Ca - Ca_rest) / (1 + kappa_S + kappa_B) : mmolar (clock-driven)\\n            kappa_B = B_T * K_d / (Ca + K_d)**2 : 1', Ca_rest=50. * nmolar, gamma=292.3 * hertz, B_T=200. * umolar, kappa_S=110, dCa_T=7.6 * umolar), bind_act_model=DoubExpCalBindingActivation(model='\\n            CaB_active = Ca_rest + b : mmolar  # add tiny bit to avoid /0\\n            db/dt = beta : mmolar (clock-driven)\\n            lam = 1/tau_off + 1/tau_on : 1/second\\n            kap = 1/tau_off : 1/second\\n            dbeta/dt = (                    # should be M/s/s\\n                A * (lam - kap) * (Ca - Ca_rest)  # M/s/s\\n                - (kap + lam) * beta        # M/s/s\\n                - kap * lam * b    # M/s/s\\n            ) : mmolar/second (clock-driven)\\n            ', A=7.61251 * khertz, tau_on=1.17164616 * second, tau_off=10.14020867 * msecond, Ca_rest=50. * nmolar), exc_model=NullExcitation(model='exc_factor = 1 : 1'), fluor_model='\\n            dFF_baseline = 1 / (1 + (K_d / Ca_rest) ** n_H) : 1\\n            dFF = exc_factor * rho_rel * dFF_max  * (\\n                1 / (1 + (K_d / CaB_active) ** n_H)\\n                - dFF_baseline\\n            ) : 1\\n            rho_rel : 1\\n        ', K_d=290. * nmolar, n_H=2.7, dFF_max=25.2), BansalFourStateOpsin(name='vf-Chrimson', save_history=True, on_pre='', spectrum=[(np.float64(470.0), np.float64(0.4123404255319149)), (np.float64(490.0), np.float64(0.593265306122449)), (np.float64(510.0), np.float64(0.7935294117647058)), (np.float64(530.0), np.float64(0.8066037735849055)), (np.float64(550.0), np.float64(0.8912727272727272)), (np.float64(570.0), np.float64(1.0)), (np.float64(590.0), np.float64(0.9661016949152542)), (np.float64(610.0), np.float64(0.7475409836065574)), (np.float64(630.0), np.float64(0.4342857142857143)), (np.float64(940.0), np.float64(7.625137185362367e-07)), (np.float64(980.0), np.float64(1.0970860644245853e-06)), (np.float64(1020.0), np.float64(1.4674211526847874e-06)), (np.float64(1060.0), np.float64(1.4915987002443027e-06)), (np.float64(1100.0), np.float64(1.6481713638093412e-06)), (np.float64(1140.0), np.float64(1.8492334763262702e-06)), (np.float64(1180.0), np.float64(1.7865475957728372e-06)), (np.float64(1220.0), np.float64(1.3823778118111134e-06)), (np.float64(1260.0), np.float64(8.030956811474087e-07))], extrapolate=False, required_vars=[('Iopto', amp), ('v', volt)], Gd1=0.37 * khertz, Gd2=175. * hertz, Gr0=0.667 * mhertz, g0=17.5 * nsiemens, phim=1.5e+22 * (second ** -1) / (meter ** 2), k1=3. * khertz, k2=200. * hertz, Gf0=20. * hertz, Gb0=3.2 * hertz, kf=10. * hertz, kb=10. * hertz, gamma=0.05, p=1, q=1, E=0. * volt, model='\\n        dC1/dt = Gd1*O1 + Gr0*C2 - Ga1*C1 : 1 (clock-driven)\\n        dO1/dt = Ga1*C1 + Gb*O2 - (Gd1+Gf)*O1 : 1 (clock-driven)\\n        dO2/dt = Ga2*C2 + Gf*O1 - (Gd2+Gb)*O2 : 1 (clock-driven)\\n        C2 = 1 - C1 - O1 - O2 : 1\\n\\n        Theta = int(phi_pre > 0*phi_pre) : 1\\n        Hp = Theta * phi_pre**p/(phi_pre**p + phim**p) : 1\\n        Ga1 = k1*Hp : hertz\\n        Ga2 = k2*Hp : hertz\\n        Hq = Theta * phi_pre**q/(phi_pre**q + phim**q) : 1\\n        Gf = kf*Hq + Gf0 : hertz\\n        Gb = kb*Hq + Gb0 : hertz\\n\\n        fphi = O1 + gamma*O2 : 1\\n\\n        IOPTO_VAR_NAME_post = -g0*fphi*(V_VAR_NAME_post-E)*rho_rel : ampere (summed)\\n        rho_rel : 1')})"
       ]
      },
-     "execution_count": null,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -274,7 +289,7 @@
        " <Axes: title={'center': 'Action/excitation spectrum'}, xlabel='λ (nm)', ylabel='ε'>)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -302,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -312,7 +327,7 @@
        " <Axes3D: xlabel='x (um)', ylabel='y (um)', zlabel='z (um)'>)"
       ]
      },
-     "execution_count": null,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -344,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
diff --git a/notebooks/1p_2p_ratio.ipynb b/notebooks/1p_2p_ratio.ipynb
index d252b7ed..c14f8b7a 100644
--- a/notebooks/1p_2p_ratio.ipynb
+++ b/notebooks/1p_2p_ratio.ipynb
@@ -1,15 +1,46 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1P/2P Photocurrent Ratio Analysis\n",
+    "\n",
+    "This notebook documents how we derived the 1P/2P irradiance conversion ratios for each opsin.\n",
+    "\n",
+    "## Methodology\n",
+    "\n",
+    "The ratio `m_1P / m_2P` converts between modalities: given a 1P irradiance, multiply by\n",
+    "the ratio to get the 2P irradiance that produces the same photocurrent.\n",
+    "\n",
+    "**1P slope**: The 1P response saturates quickly across all opsins, so the linear model\n",
+    "only works at low irradiance. We use the first two data points to estimate the slope.\n",
+    "\n",
+    "**2P slope**: The 2P response is linear across the full measured range, so we use a\n",
+    "full least-squares fit (`I = m * irr`, forced through origin).\n",
+    "\n",
+    "**ChRmine**: Handled separately as it saturates so fast that even the first-2 finite\n",
+    "difference straddles the saturation transition. We fit a Hill function and extract\n",
+    "the initial slope instead.\n",
+    "\n",
+    "**ChR2**: Excluded. The 2P data uses I^2 scaling rather than linear irradiance,\n",
+    "making it incompatible with the model used here.\n",
+    "\n",
+    "**Final ratio**: The ChroME family average is used as the default CLEO ratio."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "from scipy.optimize import curve_fit \n",
     "\n",
     "# ChRmine\n",
+    "# https://www.cell.com/neuron/fulltext/S0896-6273(22)00008-3 \n",
     "irr_1p = np.array([0.01, 0.05, 0.22, 0.64, 1.42])  # mW/mm2\n",
     "I_1p = np.array(\n",
     "    [  # nA\n",
@@ -38,166 +69,257 @@
     "        2.3784859137876424,\n",
     "    ]\n",
     ")\n",
+    "\n",
     "# divide by area of spot with 10 μm radius:\n",
-    "irr_2p = np.array(power_2p) / (np.pi * 0.01**2)"
+    "irr_2p = power_2p / (np.pi * 0.01**2)\n",
+    "\n",
+    "# ChroME Family\n",
+    "# https://www.cell.com/neuron/fulltext/S0896-6273(22)00008-3\n",
+    "def get_chrome():\n",
+    "\n",
+    "    irr_1p = np.array([\n",
+    "        0.009524139115,\n",
+    "        0.3450867178,\n",
+    "        0.7179277543,\n",
+    "        1.055845441,\n",
+    "        1.424556973\n",
+    "    ])  # mW/mm²\n",
+    "\n",
+    "    I_1p = np.array([\n",
+    "        0.1682428241,\n",
+    "        0.6738337696,\n",
+    "        1.233987502,\n",
+    "        1.610873216,\n",
+    "        1.771141945\n",
+    "    ])  # nA\n",
+    "\n",
+    "    power_2p = np.array([\n",
+    "        1.881570223,\n",
+    "        7.899540317,\n",
+    "        17.85275251,\n",
+    "        31.84104066,\n",
+    "        49.06685143\n",
+    "    ])  # mW\n",
+    "\n",
+    "    I_2p = np.array([\n",
+    "        0.06614448185,\n",
+    "        0.1085880753,\n",
+    "        0.3343108222,\n",
+    "        0.6277696856,\n",
+    "        0.9260323922\n",
+    "    ])  # nA\n",
+    "\n",
+    "    irr_2p = power_2p / (np.pi * (0.01 ** 2))  # mW/mm2, assuming 10 µm r \n",
+    "\n",
+    "    return {\n",
+    "        \"irr_1p\": irr_1p,\n",
+    "        \"I_1p\": I_1p,\n",
+    "        \"irr_2p\": irr_2p,\n",
+    "        \"I_2p\": I_2p\n",
+    "    }\n",
+    "\n",
+    "def get_chrome2f():\n",
+    "    irr_1p = np.array([\n",
+    "        0.007604564695,\n",
+    "        0.3442317813,\n",
+    "        0.7163953209,\n",
+    "        1.053683903,\n",
+    "        1.425710331\n",
+    "    ])  # mW/mm²\n",
+    "\n",
+    "    I_1p = np.array([\n",
+    "        0.2990966727,\n",
+    "        1.139206222,\n",
+    "        2.083417815,\n",
+    "        2.625293165,\n",
+    "        2.730696485\n",
+    "    ])  # nA\n",
+    "\n",
+    "    power_2p = np.array([\n",
+    "        2.132272412,\n",
+    "        7.947967189,\n",
+    "        17.89223904,\n",
+    "        31.72742376,\n",
+    "        49.03965788\n",
+    "    ])  # mW\n",
+    "\n",
+    "    I_2p = np.array([\n",
+    "        0.06874897508,\n",
+    "        0.2226648789,\n",
+    "        0.6037697628,\n",
+    "        0.9830032701,\n",
+    "        1.413053334\n",
+    "    ])  # nA\n",
+    "\n",
+    "    irr_2p = power_2p / (np.pi * (0.01 ** 2))  # mW/mm2, assuming 10 µm radius spot\n",
+    "\n",
+    "    return {\n",
+    "        \"irr_1p\": irr_1p,\n",
+    "        \"I_1p\": I_1p,\n",
+    "        \"irr_2p\": irr_2p,\n",
+    "        \"I_2p\": I_2p\n",
+    "    }\n",
+    "      \n",
+    "\n",
+    "def get_chrome2s():\n",
+    "    #https://www.cell.com/neuron/fulltext/S0896-6273(22)00008-3\n",
+    "    irr_1p = np.array([\n",
+    "        0.01033068299,\n",
+    "        0.345586775,\n",
+    "        0.7163469283,\n",
+    "        1.054329139,\n",
+    "        1.423782691\n",
+    "    ])  # mW/mm²\n",
+    "\n",
+    "    I_1p = np.array([\n",
+    "        0.5189214544,\n",
+    "        1.716216012,\n",
+    "        2.733843756,\n",
+    "        3.06809,\n",
+    "        3.035437062\n",
+    "    ])  # nA\n",
+    "\n",
+    "    power_2p = np.array([\n",
+    "        2.098001088,\n",
+    "        9.469688503,\n",
+    "        19.49889363,\n",
+    "        33.14558608,\n",
+    "        49.99403977\n",
+    "    ])  # mW\n",
+    "\n",
+    "    I_2p = np.array([\n",
+    "        0.07833736869,\n",
+    "        0.323101855,\n",
+    "        0.7228433349,\n",
+    "        1.197034736,\n",
+    "        1.612615394\n",
+    "    ])  # nA\n",
+    "\n",
+    "    irr_2p = power_2p / (np.pi * (0.01 ** 2))  # mW/mm2, assuming 10 µm r\n",
+    "\n",
+    "    return {\n",
+    "        \"irr_1p\": irr_1p,\n",
+    "        \"I_1p\": I_1p,\n",
+    "        \"irr_2p\": irr_2p,\n",
+    "        \"I_2p\": I_2p\n",
+    "    }\n",
+    "\n",
+    "# ── ChR2 (excluded from ratio) ────────────────────────\n",
+    "# https://www.pnas.org/doi/10.1073/pnas.0907084106\n",
+    "# The 2P data in this paper uses I² (intensity squared) parameterization rather\n",
+    "# than linear irradiance, and thus it is incompatible with the linear model used here.\n",
+    "def get_chr2_irradiance_data():\n",
+    "\n",
+    "\n",
+    "    irr_1p = np.array([0.33, 0.77, 1.26, 1.72])  # mW/mm2\n",
+    "    I_1p = np.array([\n",
+    "        0.4124079804,\n",
+    "        0.5099647925,\n",
+    "        0.5557303336,\n",
+    "        0.5797880214,\n",
+    "    ])  # nA\n",
+    "\n",
+    "\n",
+    "    power_2p = np.array([0.37, 0.54, 1.00, 1.53])  # mW/mm2\n",
+    "    I_2p = np.array([\n",
+    "        0.01438881888,\n",
+    "        0.04539752549,\n",
+    "        0.08182781533,\n",
+    "        0.1117224195,\n",
+    "    ])  # nA\n",
+    "\n",
+    "\n",
+    "    irr_2p = power_2p / (np.pi * 0.01**2)  # mW/mm², assuming 10 µm radius\n",
+    "    irr_2p_35r = power_2p / (np.pi * 0.035**2)  # mW/mm², assuming 35 µm radius\n",
+    "\n",
+    "    return {\n",
+    "        \"irr_1p\": irr_1p,\n",
+    "        \"I_1p\": I_1p,\n",
+    "        \"irr_2p\": irr_2p,\n",
+    "        \"I_2p\": I_2p,\n",
+    "        \"irr_2p_35r\": irr_2p_35r\n",
+    "    }"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f29aa5eacf0>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.loglog((irr_1p), (I_1p), c=\"xkcd:carmine\", label=\"1P\")\n",
-    "ax.plot((irr_2p), (I_2p), c=\"k\", label=\"2P\")\n",
-    "ax.set(\n",
-    "    xlabel=\"Irradiance (mW/mm2)\",\n",
-    "    ylabel=\"Photocurrent (nA)\",\n",
-    "    title=\"ChRmine power sensitivity\",\n",
-    ")\n",
-    "ax.legend()"
+    "## Slope Estimation Functions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f29a7fdfc20>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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rh61bt8LNzQ3bt2/H448/LjuWYpS5UKxYsQKJiYnw9/eHr68vDh48iCFDhmD8+PHWzGd1nJBJVDHh4eFYs2YNfHx8LCMUhw8fvuO4tWvXYvz48dDpdBg4cCA0Gg2+//57CYmJKk4IgeHDh+PLL7+Es7MzvvrqK7Rr1052LEUp86TMnTt3lvi8Xbt2eOONN/DGG29UdSabMRQUIDPhFADAnyMURJVy9uxZ1K1bt8RtGRkZSEtLQ0jIrRVow8LCkJiYiIiIiDu+h1omc5PjmTJlChYtWgSNRoPVq1fjhRdekB1JcRx6c7DM+JMQRiPc/P3gWduxZ+cSVcb58+excuVK7N+/v8Ttubm5AIBqt61A6+npabn976Kjo0s9bUIk05w5cywbfC1evBg9e/aUnEiZKrxS5g8//KDq0Qng1pLb/i2bOszmLURVraCgAJGRkRgxYgQeeeSREl/T6XQAAL1eb7ktJyfHcvvfqWUyNzmO5cuXW7YenzlzpmVLcrqTQy+9nc4lt4kqxWAwIDIyErVr18bUqVPv+Lqfnx9q1KiBxMREADcmtR0/fvyus+LVMpmbHMP69esxcOBAAMDEiRMxYcIEyYmUzaELRQYnZBJVmNFoRK9evVBQUIA1a9ZYRvmuX7+ONm3aICEhAQDQu3dvzJ07Fzk5OVi2bBnc3NzQtm1bmdGJ7mvnzp3o06cPhBAYNGgQZsyYITuS4jnsHAqT0YiM32+84XEPD6Lyi4mJwcaNGxEQEIAHHngARqMRALBjxw4kJiYiMzMTAPDBBx/gzTffRGBgIOrWrYtNmzbB1dVVYnKie/vxxx/x6quvwmAwoGfPnli4cCFPi5eBRtxvycsqkpWVBR8fH+j1ekUMY+pPJmNT6DNwqlYNr+vPQOvkJDsSEf2N0t43yP4dO3YMzz33HLKzs/H888/j66+/drhtyCvKYU95WCZkPhLCMkFERJbLmbOzs9G2bVts3LiRZaIcHLdQcEImERHd9OeffyI8PBzXrl3DY489hm3btpW43Jnuz2ELhXlTMP/mnD9BROTIrly5gvDwcPz1118IDQ3Frl27eIqtAhyyUAghbi253ZKFgojIUV2/fh2dOnVCcnIy6tevjz179qB69eqyY6mSQxaKvEtXUJCWDo2TE/yahdz/DxARkd3JyclBly5d8Mcff6BmzZrYt29fmXbTptI5ZKEwT8j0ebgRnHmOjIjI4RQUFOCll17CkSNH4Ofnhz179qBBgwayY6maQxaKjOM3J2Ry/gQRkcMxry/x7bffQqfTYffu3WjalL8PKsshC0X6rzcnZHKHUSIih2IymfDWW29hy5YtcHNzw7Zt29C6dWvZseyCYxYKLrlNRORwhBAYOXIkVq9eDScnJ2zYsAHPPfec7Fh2w+EKRWGmHjkp5wGwUBAROZKpU6ciNjYWGo0Gn3/+Obp16yY7kl1xuEKRcTweAOBZpzbc/P0kpyEiIlv4+OOPMX36dADAwoUL0bt3b8mJ7I/jFQqe7iAiciiffvopxowZAwCYMWMGhgwZIjmRfXK4QmGekBnACZlERHZv48aNGDBgAABg/PjxmDhxouRE9svxCgVHKIiIHMLu3bvRu3dvmEwmDBgwADNnzuQ25FbkUIXCUFCAzMTTAAB/FgoiIrt18OBBvPLKKyguLkZkZCQWL17MMmFlDlUoMuNPQhgMcPP3g2cwl1clIrJHcXFxeP7555Gfn48uXbpYLhMl63KoQmFectu/ZVM2VSIiO5SUlIR//vOfyMrKwj/+8Q9s3LgRrq6usmM5BMcqFDe3LA9owQmZRET25ty5cwgPD8e1a9fQqlUrbN++HR4eHrJjOQyHKhS39vAIk5yEiIiqkslkQvfu3XHx4kWEhIRg9+7d8Pb2lh3LoThMoTAZjZZFrbiHBxGRfVm5ciV+/vlneHt743//+x+qV68uO5LDcZhCkZ2cAkNuHpyqVYNPk0ay4xARURXJyMiwrC8xbdo0BAcHS07kmBymUJjnT/g/EgItZ/sSEdmN9957D+np6QgNDUVUVJTsOA7LgQrFzSs8mnP9CSIie3H8+HEsWbIEABAbGwsXFxfJiRyXwxSKjF95hQcRkT0RQmDo0KEwmUzo0aMH2rdvLzuSQ3OIQiGEuLXkdkuOUBAR2YO1a9fi4MGD8PDwwJw5c2THcXgOUSjyL6eiIC0dGq0Wfs1CZMchIqJKysrKwtixYwEA7777LidiKoBDFArzCpk+DzeGc7VqktMQEVFlTZ8+HVeuXEGjRo0wevRo2XEIjlIouMMoEZHdSExMxPz58wEACxYsgJubm9xABMBRCsXNCZlc0IqISN2EEBg+fDgMBgO6du2KLl26yI5ENzlEobAsuc0RCiIiVfv666+xb98+uLm5Yd68ebLj0G3svlAU6bOQffYcABYKIiI1y8vLw6hRowAA48ePR8OGDSUnotvZfaEwr5DpWac23Pz9JKchIqKKmjlzJs6fP486depYltom5bD7QpHBCZlERKp35swZfPTRRwCAefPmcVtyBbL7QpH+240dRgM4IZOISLVGjRqFwsJCdOzYES+//LLsOFQKBygU5j08wiQnISKiiti5cye2b98OZ2dnxMTEQKPRyI5EpbDrQmEsLERmwikAHKEgIlKjwsJCjBgxAgAwcuRIhIRwtWOlsutCcf1EEoTBADd/P3gGB8mOQ0RE5fTxxx8jOTkZNWvWxJQpU2THoXuw60KR8Zt5QaumHCIjIlKZCxcu4D//+Q8AYPbs2fD29paciO7FrguF+ZLRgOa8woOISG3Gjh2LvLw8tGnTBr169ZIdh+7DzgsFLxklIlKj7777Dhs2bIBWq8XChQs5yqwCdlsoTEYjMo7fuGSUe3gQEalHcXExhg0bBgAYPHgwmjdvLjkRlYXdForsM3/CkJsHp2rV4NOkkew4RERURosWLUJCQgKqV6+ODz74QHYcKiO7LRTpv9443eHX7GFonZwkpyEiorK4cuUKpk6dCgCIjo6Gv7+/5ERUVvZbKCzzJ3i6g4hILSZOnIisrCw8/vjj6Nevn+w4VA52WygyfjVvWc5CQUSkBocOHcLnn38OAFi4cCG0Wrv9FWWX7PJvSwhxa4SiJa/wICJSOqPRiKFDhwIA+vXrh9atW0tOROVll4Ui/3IqCtLSodFq4df0YdlxiIjoPpYvX45ff/0VPj4+iI6Olh2HKsAuC4V5QqbPw43hzC1uiYgULT09HZMnTwYATJ8+HQ8++KDkRFQR9lkouKAVEZFqvPvuu8jIyECzZs0wePBg2XGoguyyUGT8xgWtiIjUIC4uDkuXLgVwYyKms7Oz5ERUUXZZKCwjFM3DJCchIqK7MZlMGDp0KIQQ6NWrF/7xj3/IjkSVYHeFokifheyz5wAAARyhICJSrC+++AKHDx+Gp6cnPvroI9lxqJLsrlCYdxj1rFMbbv5+ktMQEVFp9Ho9xo8fDwB47733EBQUJDkRVZbdFYqM4+YFrTghk4hIqaZNm4arV6/ioYcewsiRI2XHoSpgd4Ui/eYKmf4sFEREihQfH4+YmBgAQGxsLFxdXSUnoqpgf4WCl4wSESmWEALDhg2D0WjEyy+/jE6dOsmORFXErgqFsbAQmQmnAHBCJhGREm3cuBHff/893N3d8fHHH8uOQ1XIrgrF9fiTEAYD3Pz94BnMCT5EREqSk5ODMWPGAAAmTZqEevXqyQ1EVcquCkXGzSW3/VuEQaPRSE5DRES3mzFjBi5evIh69eph3LhxsuNQFbOrQmG+ZJRblhMRKcvp06cxd+5cAMD8+fNRrVo1yYmoqtlZoeCETCIipRFCYMSIESgqKkJERAS6desmOxJZgd0UCmEyIeM49/AgIlKab775Brt27YKLiwsWLFjAU9J2ym4KRVZyCgy5eXByd4fPQw1lxyEiIgAFBQUYMWIEAGDMmDF46KGHJCciaylXofjkk09Qr149+Pr6omPHjkhOTrZWrnJLvzkh0++REGi5Wx2R1cXFxaF///7w9PTEli1bSj3ms88+g5OTE3Q6neXjwIEDtg1KUs2ePRspKSkICgrC5MmTZcchKypzocjMzMSRI0dw8OBBpKWloUGDBhgwYIA1s5XLrfkTPN1BZCutWrWCu7v7PY/p0KEDcnJyLB/PPvusjdKRbOfOncOMGTMAAHPmzIFOp5OciKypzIXC19cXn332GWrXrg0XFxd069YNp0+fvuvxhYWFyMrKKvFhTRm/3Zg/wUJBZBuPPvooBg0aBC8vr3seV6NGjTJ/T1u/b5B1jRkzBgUFBWjbti0iIyNlxyErq9AcilOnTmHOnDl4/fXX73pMdHQ0fHx8LB/BwcEVDnk/QgjLCIV/izCr/RwiKr9Dhw4hKCgI9evXR3R09D2PteX7BlnX3r17sWnTJjg5OSE2NpYTMR1AuQtF+/bt0aRJE5hMJkyYMOGux02aNAl6vd7yceHChUoFvZf8y6kouHoNGq0W/s1CrPZziKh8wsPD8fHHHyMlJQXr1q3D7NmzsW7durseb8v3DbKeoqIiDB8+HAAwdOhQNGvGkWNHUO5C8d133+HKlSt48skn8fTTT6OoqKjU49zc3ODt7V3iw1rMEzJ9Hm4MZw8Pq/0cIiqfoKAgdOvWDa6urnjiiSfQu3dv7Nmz567H2/J9g6wnJiYGSUlJePDBB/H+++/LjkM2UqFTHoGBgZg5cyaSk5MRHx9f1ZnKLf24eYVMLmhFpGQFBQXQau3manUqxaVLlzBt2jQAwKxZs+Dr6ys3ENlMmV/ZmzdvxsSJE6HX62EymbBq1Sq4u7ujUaNG1sxXJhm/3igU/iwURFIZjUaEh4fj+++/R2ZmJjp06IA//vgDQgj88MMPWL9+Pfr16yc7JlnR+PHjkZOTgyeffBJ9+/aVHYdsqMwLNnTs2BH79+9H06ZNodfrERISgp07d953hrctcMltIttr2LAhUlNTkZeXh549e8LJyQlHjx5FYmIi0tLS4Ovri759++L111/HmTNnULt2bSxfvhxPP/207OhkJQcOHMCaNWug0WgQGxvL0SgHoxFCCFv8oKysLPj4+ECv11fpedEifRa+9G8MAOidlgQ3f78q+95EJJe13jeo6hkMBrRq1Qq///47BgwYgKVLl8qORDam+vpo3r/Ds05tlgkiIkmWLl2K33//HX5+fvjwww9lxyEJVF8oLKc7mnP9CSIiGdLS0vDuu+8CAD788ENUr15dciKSQf2FwjwhkzuMEhFJ8c477yAzMxMtWrRQ1JYMZFvqLxSckElEJM3PP/+MTz/9FACwcOFCODk5SU5Esqi6UBgLC5GZcAoAEMARCiIimzKZTBg6dCiEEHj99dfxzDPPyI5EEqm6UFyPPwlhMMDVzxeewUGy4xAROZTPPvsMR48ehZeXF2bNmiU7Dkmm6kKRcXPJ7YCWTbnxDBGRDV2/fh0TJ04EALz//vuoWbOm5EQkm6oLRfpv5iW3ebqDiMiWpk6dirS0NISEhGDYsGGy45ACqLtQcA8PIiKb+/3337Fo0SIAQGxsLFxcXCQnIiVQbaEQJpNlUSvu4UFEZBtCCAwdOhQmkwn/+te/0KFDB9mRSCFUWyiyklNgyMmFk7s7fJrI36CMiMgRrFu3DgcOHEC1atUwd+5c2XFIQVRbKNJvTsj0eyQEWucy73FGREQVlJ2djbFjxwIAJk+ejDp16khOREqi2kKRcZwTMomIbOk///kPLl26hIYNG2LMmDGy45DCqLZQmJfcDmjO+RNERNaWlJSEefPmAQAWLFgAd3d3yYlIaVRZKIQQliW3/VuyUBARWZMQAsOHD0dxcTFeeOEFPP/887IjkQKpslDkX05FwdVr0Gi18G8WIjsOEZFd27JlC/bu3QtXV1fLKAXR36myUJgXtPJ5uDGcPTwkpyEisl/5+fkYNWoUAGDcuHFo1IhX1VHpVFoobp7uaB4mOQkRkX2bNWsWzp07h+DgYEyaNEl2HFIwVRaKDPOETO4wSkRkNWfPnsXMmTMBAB9//DE8PT0lJyIlU2WhMI9QcMltIiLrGT16NAoLC9GhQwe8+uqrsuOQwqmuUBTps5B99hwALrlNRGQtu3btwtatW+Hs7IyYmBju6Ez3pbpCYd6/wzM4CO4B/pLTEBHZn8LCQowYMQIAMHz4cISGhkpORGqgukLB0x1ERNY1f/58nD59GoGBgZg6darsOKQS6isUNydk+nNCJhFRlbt48SKmT58OAJg9eza8vb0lJyK1UF2huLWHB0coiIiq2rhx45Cbm4tnnnkGffr0kR2HVERVhcJYWIjr8ScBsFAQEVW1H374AevWrYNWq8XChQs5EZPKRVWF4nr8SQiDAa5+vvCsU1t2HCIiu1FcXIxhw4YBAAYNGoQWLVrIDUSqo6pCkfHrzQmZLZuyORMRVaHFixfjxIkTCAgIsMyhICoPVRWKdMv8CU7IJCKqKqmpqXjvvfcAADNmzIC/Py/Jp/JTV6G4uSmYf3POnyAiqiqTJk1CVlYWWrVqhbfeekt2HFIp1RQKYTJZFrUKaMlCQURUFY4cOYJVq1YBABYuXAgnJyfJiUitVFMospJTYMjJhZO7O3yacPtcIqLKMhqNGDp0KADgzTffxJNPPik5EamZagqF+XSH3yMh0Do7S05DRKR+n376KY4dOwYfHx9ER0fLjkMqp5pCkWFecpvzJ4iIKi09PR2TJk0CAEybNg2BgYGSE5HaqaZQmJfc5hUeRESVN2XKFGRkZKBp06aIioqSHYfsgCoKhRDCsimYPydkEhFVyq+//oqlS5cCAGJjY+HM08hUBVRRKPKvXEXB1WvQaLXwbxYiOw4RkWoJITB06FCYTCa89tpraNeunexIZCdUUSj0SacBAF4N68HZw0NyGiIi9fryyy9x6NAheHp6Yvbs2bLjkB1RRaEozskFALj6chtdIqKKysrKwrhx4wDcmENRuzb3RKKqo4pCYcgvAAA4V6smOQkRkXpNmzYNqampaNy4MUaOHCk7DtkZVRQK481C4eTuLjkJEZE6JSQkICYmBgAQExMDNzc3yYnI3qirUFRjoSAiKi8hBIYPHw6DwYAXX3wRERERsiORHVJFoTAU5AMAnFkoiIjKbdOmTfj222/h5uaGefPmyY5DdkoVhYIjFEREFZObm4vRo0cDACZOnIj69etLTkT2ShWFgpMyiYgqJjo6GhcuXEC9evUwYcIE2XHIjqmiUHCEgoio/JKTky1rTcybNw/V+I8ysiJVFIpbIxQsFEREZTVy5EgUFRWhU6dOePHFF2XHITunikJhzLsxKZMjFEREZbNjxw7s2LEDLi4uiImJgUajkR2J7JwqCoUhn1d5EBGVx/Tp0wEAI0aMQJMmTSSnIUegikJxa2Ernv8jIrqfw4cP46effoKrqyvGjh0rOw45CFUUCsscCg8WCiKi+zGvNdG7d28EBgZKTkOOQhWFwljAqzyIiMri3Llz2LRpEwBg1KhRktOQI1FFoeBVHkREZRMbGwuTyYQOHTqgWbNmsuOQA1FFoeA6FERE95ednY3ly5cD4OgE2Z4qCgWv8iAiur+VK1ciKysLTZo0QefOnWXHIQejikJxa4SCkzKJiEpjNBqxYMECADcuFdVqVfH2TnZEFc84I+dQEBHd07Zt25CSkgI/Pz/07dtXdhxyQKooFAbOoSAiuifzpaIDBw6Ep6en5DTkiBRfKExGI0xFRQBYKIiISnPs2DEcOHAAzs7OGDp0qOw45KAUXyjMa1AA3L6ciKg05tGJyMhIBAUFSU5DjkrxhcJwc2MwgCMURER/99dff2H9+vUAeKkoyaX4QmGekKl1cYHWyUlyGiIiZVm0aBEMBgOeffZZtGrVSnYccmCqKRQcnSBSlri4OPTv3x+enp7YsmVLqccYjUYMGTIE3t7eqFevHr755hvbhrRzeXl5WLp0KQCOTpB8ii8U3BiMSLlatWoFd/e7l/3Y2FgcPHgQKSkpWLJkCXr27InU1FQbJrRvq1evRkZGBho0aIBu3brJjkMOTvGFwnhzlUyOUBApy6OPPopBgwbBy8vrrsesXbsWw4cPR0BAADp37oxHHnkEX3/9tQ1T2i+TyWSZjDl8+HA48ZQwSeYsO8D93NoYjCMURGqTlJSEkJAQy+dNmzZFYmLiXY8vLCxEYWGh5fOsrCyr5lOzXbt24dSpU/D29ka/fv1kxyEq3wiFEALR0dEICgqCv78/+vTpg+zsbGtlA8A5FERqlpubi2q3/WPA09MTubm5dz0+OjoaPj4+lo/g4GBbxFQl8+jE22+/fc9RIiJbKVeh2LdvH06cOIGEhAQkJyfjzJkzmDZtmrWyAeDW5URqptPpoNfrLZ/n5ORAp9Pd9fhJkyZBr9dbPi5cuGCLmKrz+++/49tvv4VWq8WwYcNkxyECUM5CER4ejjVr1sDHx8cyQnH48GFrZQNw2wjFPSZ+EZEyhYaGljjFERcXh7CwsLse7+bmBm9v7xIfdKf58+cDAF599VXUq1dPahYis0pNyjx79izq1q1b6tcKCwuRlZVV4qMiuHU5kXoYjUaEh4fj+++/BwD07t0bixcvxrVr17Bz506cOnUKL7/8suSU6paamoo1a9YA4KWipCwVnpR5/vx5rFy5Evv37y/169HR0VVyOsS89DbnUBApS8OGDZGamoq8vDz07NkTTk5OOHr0KBITE5GWlgYAGDJkCJKSklC/fn0EBARg3bp1qF69uuTk6rZkyRIUFRXhiSeewFNPPSU7DpGFRgghyvuHCgoK8Nxzz+Gf//wn3n///VKPKW22dnBwMPR6fbmGMY/PisGxdz5E43+/hmdXLihvVCJSsaysLPj4+JT7fcNeFRQUoE6dOkhLS8O6desQGRkpOxKRRblHKAwGAyIjI1G7dm1MnTr1rse5ubnBzc2tUuEAXuVBRGS2du1apKWlITg4GK+++qrsOEQllKtQGI1G9OrVCwUFBdi4cSM0Go21clmYNwdjoSAiRyaEsFwqOmzYMDg7K34ZIXIw5ZqUGRMTg40bN+LYsWN44IEHoNPpoNPpcODAAWvls4xQcFImETky82X7np6e6N+/v+w4RHcoV8UdNWqUzWcVW67y4F4eROTAzKMTb775Jnx9feWGISqFCvby4BwKInJsiYmJ2LVrFzQaDUaMGCE7DlGpFF8oLCtlunOEgogc04IFN65w69atGxo1aiQ5DVHpFF8oOEJBRI4sPT0dq1evBsCFrEjZlF8ouLAVETmwpUuXIj8/Hy1btsQ//vEP2XGI7krxhYJLbxORoyoqKsLChQsB3BidsMWl+kQVpfhCwVMeROSoNmzYgMuXL6NmzZpcFZMUT/GF4tb25ZyUSUSO4/aFrKKiouDq6io5EdG9Kb5QcISCiBzRjz/+iLi4OLi7u2PgwIGy4xDdl+ILhYErZRKRAzKPTvTt25c7tJIqKL5QcISCiBxNcnIytm3bBgAYOXKk3DBEZaToQiFMJstloxyhICJHERMTAyEEIiIiEBISIjsOUZkoulCYywQAOHFSJhE5gMzMTKxcuRIAMHr0aMlpiMpO0YXCPH8C4AgFETmGFStWIDc3F02bNkXHjh1lxyEqM0UXCvP8CY2zM7QuLpLTEBFZl8FgQGxsLIAbcye4kBWpiaILBa/wICJHsnnzZpw/fx4PPPAAevfuLTsOUbkoulAYby67zSs8iMgRmC8VHTx4MNzd+b5H6qLoQsFVMonIURw+fBhHjhyBq6srhgwZIjsOUbkpulBwDQoichTm0YlevXohMDBQchqi8lN0oeAcCiJyBOfOncOmTZsAcCErUi9FFwqOUBCRI4iNjYXJZEL79u3RvHlz2XGIKkThhYKTMonIvmVnZ2P58uUAuJAVqZuiC4XllAdnOxORnVq1ahWysrLQpEkTdO7cWXYcogpTdKEwL73NZbeJyB4ZjUYsWLAAADBixAhotYp+Sya6J0U/ezkpk4js2fbt23H27Fn4+fmhb9++suMQVYqyC0Ue51AQkf0yXyo6cOBAeHp6Sk5DVDmKLhTmqzycPXjKg4jsS1xcHH788Uc4Oztj6NChsuMQVZrCCwVHKIjIPplHJ3r06IGgoCDJaYgqT9GFgnMoiMge/fXXX1i3bh0AYNSoUZLTEFUNRReKWwtb8ZQHEdmPRYsWwWAwoE2bNnjsscdkxyGqEoouFByhICJ7k5eXh6VLlwLg6ATZF0UXCssIBRe2IiI7sXr1amRkZKB+/fp48cUXZcchqjKKLhSGghuTMjlCQUT2wGQyYf78+QBuLGTl5OQkNxBRFVJ0oeDmYERkT3bv3o2TJ0/C29sb/fr1kx2HqEopulDcmkPBSZlEpH7mS0XffvtteHl5SU5DVLUUXSg4QkFE9uKPP/7Avn37oNVqMWzYMNlxiKqcKgoF51AQkdqZ50688sorqFevntQsRNag6EJh4AgFEdmB1NRUrFmzBgAvFSX7pehCYV56m3t5EJGaLVmyBIWFhWjdujWeeuop2XGIrEKxhUIIcdtuoywURKROBQUFWLx4MYAboxMajUZyIiLrUGyhMBYWWv6fcyiISK3Wrl2LtLQ0BAcH49VXX5Udh8hqlFsobs6fADiHgojUSQhhmYw5bNgwuLi4yA1EZEWKLxQarRZavgiJSIW+/fZb/PHHH/D09ET//v1lxyGyKsUWituv8OA5RyJSI/NCVm+++SZ8fX3lhiGyMsUWCssVHpyQSUQqlJSUhJ07d0Kj0WDEiBGy4xBZnWILBdegICI1W7BgAQCga9euaNSokeQ0RNan2ELBZbeJSK3S09Px+eefA+BCVuQ4FFsoDFx2m4hUaunSpcjPz0eLFi3Qtm1b2XGIbEKxhcI8h4IjFESkJkVFRVi4cCEALmRFjkWxhYIjFESkRhs2bMDly5dRs2ZNvPbaa7LjENmMYguFZQ6FO6/yICJ1EEJYLhWNioqCq6ur5EREtqPYQmEouDlCwY3BiEglDhw4gLi4OLi7u2PgwIGy4xDZlGILhTGPcyiISF3MoxN9+/ZF9erVJachsi3FFgrOoSAiNTlz5gy2bt0KABg5cqTcMEQSKLZQ8CoPIlKTmJgYCCEQERGBkJAQ2XGIbE6xhYIjFESkFnq9HitXrgTAhazIcSm2UNxaKZOTMolI2VasWIGcnByEhYUhPDxcdhwiKRRbKDhCQURqYDAYEBMTA+DG3AkuZEWOSrGFgnt5EJEabN68GefPn0f16tXRu3dv2XGIpFFwobi5fbk7CwURKZf5UtEhQ4agGk/RkgNTbKEwL2zFEQoi5Tp58iRat24NDw8PhIeH4+rVq3cc8/7778PFxQU6nc7ycf78eQlpq96RI0dw5MgRuLq6YsiQIbLjEEml2ELBSZlEyhcZGYmIiAikp6ejVq1aGDx4cKnHvfnmm8jJybF81KlTx8ZJrcM8OtGrVy8EBgZKTkMkl7PsAHfDSZlEypaQkIDExEQcOnQI1apVw7vvvovQ0FBkZ2fDy8urxLE1atSQlNJ6zp8/j02bNgHgQlZEgCpGKFgoiJQoKSkJderUgYeHBwCgcePGcHJyQnJy8h3HfvXVVwgMDESTJk2watWqu37PwsJCZGVllfhQqtjYWBiNRrRv3x7NmzeXHYdIOsUWCssIBTcHI1Kk3NzcOyYhenh4IDc3t8Rt3bt3x+LFi3Hx4kXMnz8fgwYNwqFDh0r9ntHR0fDx8bF8BAcHWy1/ZeTk5GD58uUAuJAVkZliC4V5czCe8iBSJp1OB71eb/lcCIG8vDzodLoSx4WFhaFdu3ZwcXFB586d0alTJ+zbt6/U7zlp0iTo9XrLx4ULF6x6Hypq1apV0Ov1eOihh9ClSxfZcYgUQcFzKMx7eXCEgkiJQkNDcenSJej1evj4+CA+Ph5CCDRu3Pief66goABaben/lnFzc4Obm5s14lYZo9GIBQsWAABGjBhx1/tC5GgU+UoQQljmUHCEgkiZmjRpghYtWmDWrFnIz8/HrFmz8PLLL6OoqAht2rRBQkICkpOT0aVLF/z5558wmUzYsGEDjhw5gl69esmOX2Hbt2/HmTNn4Ofnh3//+9+y4xApRrkKRVxcHPr37w9PT09s2bLFSpEAU3ExhMkEgJMyiZRs7dq12LdvH/z9/ZGamorY2Fjk5eUhMTERmZmZaNiwITp06ICIiAj4+Phg5syZ2LJlCxo0aCA7eoWZLxUdOHAgPD09JachUo5yn/Jo1aoVNm/ebI0sFubRCYCFgkjJGjdujKNHj95xe3p6uuX/x4wZgzFjxtgyltXExcXhxx9/hLOzM4YOHSo7DpGilGuE4tFHH8WgQYPuuMa8qpmv8IBGAyeFn08lIsdhHp3o0aMHgoKCJKchUharTcosLCxEYWGh5fPyXE9u3sfDyd2dO/cRkSJcunQJ69atA8BLRYlKY7VJmZW5npyrZBKR0sTGxsJgMKBNmzZ47LHHZMchUhyrFYrKXE/OVTKJSEmysrKwZMkSAMDYsWMlpyFSJqud8qjM9eQcoSAiJVm6dCn0ej1CQkLQtWtX2XGIFEmR61BwhIKIlKKwsNAyGXPcuHFcyIroLsr1ymjYsCF0Oh3Onz+Pnj17QqfT4ezZs1Ueyjwpk/t4EJFsX375JS5fvoygoCD07t1bdhwixSrXKY8zZ85YK0cJBo5QEJECGI1GzJ49G8CNKztcXV0lJyJSLkWO3VmW3XbnCAURybN161acPHkSvr6+GDBggOw4RIqmyEJxa2MwjlAQkRxCCMyaNQsAMGTIEKsv6EekdgotFLzKg4jk2r9/P44ePQp3d3cMHz5cdhwixVNkoTByhIKIJDOPTrz55psIDAyUnIZI+RRZKDhCQUQyHT9+HLt374ZWq+VCVkRlpMhCcWsdCk7KJCLb++ijjwAA3bt3V/VW60S2pPBCwREKIrKtP//8E+vXrwcATJgwQXIaIvVQZKHgKQ8ikmXu3LkwGo0IDw9Hy5YtZcchUg1FFgpOyiQiGdLS0vDpp58C4OgEUXkpslBYRijcWSiIyHYWLlyI/Px8tGrVCu3bt5cdh0hVFFkoOCmTiGwtNzcXCxcuBHBjdEKj0UhORKQuiiwUhoKbIxTcHIyIbGTFihXIyMhAo0aN8Morr8iOQ6Q6iiwUvMqDiGypuLgYc+fOBQCMHTsWTk5OkhMRqY+iCwWv8iAiW/jvf/+LCxcuIDAwEP/+979lxyFSJUUWCkMer/IgItswmUyWhaxGjhwJd04GJ6oQZRaKm5eNcoSCiKxt586diI+Ph5eXFwYNGiQ7DpFqKbJQ8CoPIrIV8yZggwYNgq+vr9wwRCqmyELBlTKJyBYOHTqEgwcPwtXVFSNHjpQdh0jVFFcoTAYDhMEAgHMoiMi6zKMTr7/+OmrVqiU5DZG6Ka5QmE93AByhICLrSUhIwLZt26DRaDBu3DjZcYhUT3GFwjwhEwCcONuaiKxk9uzZAICXXnoJTZo0kZyGSP0UVygsEzLd3KDRKi4eEdmBixcvYs2aNQC4CRhRVVHcb2wDV8kkIiubN28eiouL0bZtWzzxxBOy4xDZBcUVCi67TUTWdP36dSxbtgwARyeIqpLiCoXlklFuDEZEVrB48WLk5OTgkUceQUREhOw4RHZDcYXCyFUyichK8vPzsWDBAgDcopyoqimuUHAOBRFZy6pVq5CWloZ69eqhR48esuMQ2RXFFYpbO43ylAcRVR2DwYA5c+YAAMaMGQNnZ2fJiYjsi+IKhWWnUa5BQURV6KuvvkJKSgqqV6+Ofv36yY5DZHcUVyh4lQcRVTUhhGWZ7WHDhsHDw0NyIiL7o7hCYSjgpEwiqlp79+7Fb7/9Bg8PD0RFRcmOQ2SXFFcoOEJBRFXNPDrRv39/BAQESE5DZJ8UVygMnJRJRFXol19+wXfffQdnZ2eMHj1adhwiu6W4QsERCiKqSubRiZ49e6JOnTqS0xDZL8UVilsjFCwURFQ5p0+fxqZNmwAA48ePl5yGyL4prlCYV8rkCAURVdacOXMghMALL7yApk2byo5DZNcUVyi4lwcRVYUrV67g888/B8BNwIhsQXGFwjKHggtbEVElLFiwAIWFhXj66afRpk0b2XGI7J5iCwWv8iCiitLr9Vi8eDEAjk4Q2YriCoWhgFd5EFHlLF26FFlZWQgNDcULL7wgOw6RQ1BcoTDyKg8iqoTCwkLMnz8fADBu3DhotYp7myOyS4p7pVk2B2OhIKIK+OKLL3D58mXUrl0bvXr1kh2HyGEor1Dkcy8PIqoYo9GI2bNnAwBGjRoFV1dXyYmIHIfiCsWtlTI5KZOIymfr1q04deoUfH190b9/f9lxiByKYgsFRyiIqDxu36I8KioKXl5ekhMRORbFFQoD9/IgogrYv38/jh49Cnd3dwwfPlx2HCKHo6hCYTIaYSoqAsBCQUTlYx6d6NevHx588EHJaYgcj6IKhfHmGhQAF7YiorI7fvw4du/eDa1WizFjxsiOQ+SQlFUo8m8VCo5QEFFZffTRRwCAHj16oEGDBpLTEDkmZ9kBbufk5oYW742FsaAAWicn2XGISCUmT54MV1dXzp0gkkgjhBC2+EFZWVnw8fGBXq+Ht7e3LX4kEakc3zeI1ENRpzyIiIhInVgoiIiIqNJYKIiIiKjSWCiIiIio0lgoiKjCTp48idatW8PDwwPh4eG4evXqHcfk5uaiR48e8PT0RGhoKH766ScJSYnI2lgoiKjCIiMjERERgfT0dNSqVQuDBw++45jJkycjMzMTqampGDFiBF555RUUFhZKSEtE1sTLRomoQhISEtCyZUtcv34dHh4eOH36NEJDQ5GRkVFiY64HH3wQa9euRceOHQEAtWvXxooVKxAREXHH9ywsLCxRNrKyshAcHMz3DSIV4AgFEVVIUlIS6tSpAw8PDwBA48aN4eTkhOTkZMsxGRkZSEtLQ0hIiOW2sLAwJCYmlvo9o6Oj4ePjY/kIDg627p0goirDQkFEFZKbm4tqf9tzx8PDA7m5uSWOAVDiOE9PzxLH3G7SpEnQ6/WWjwsXLlghORFZg6KW3iYi9dDpdNDr9ZbPhRDIy8uDTqcrcQwA6PV6+Pv7AwBycnJKHHM7Nzc3uLm5WTE1EVkLRyiIqEJCQ0Nx6dIlS6mIj4+HEAKNGze2HOPn54caNWpYTnGYTCYcP34cYWFhUjITkfWwUBBRhTRp0gQtWrTArFmzkJ+fj1mzZuHll19GUVER2rRpg4SEBABA7969MXfuXOTk5GDZsmVwc3ND27ZtJacnoqrGQkFEFbZ27Vrs27cP/v7+SE1NRWxsLPLy8pCYmIjMzEwAwAcffIDq1asjMDAQMTEx2LRpE1xdXeUGJ6IqZ7PLRvV6PXx9fXHhwgVe/kUkkZeXFzQajewYZcL3DSLluN97h80mZWZnZwMALwMjkkxNazrwfYNIOe733mGzEQqTyYRLly7ds+GYF7Hhv0ZKx8fn/vgY3Z+aRihuf9/Izs7m3y34HAf4GJjZ+nFQzAiFVqtF7dq1y3Sst7e3Qz9J7oePz/3xMbIPt79vmN/I+Hd7Ax8HPgZmSnkcOCmTiIiIKo2FgoiIiCpNUYXCzc0NU6dO5Up5d8HH5/74GNkv/t3ewMeBj4GZ0h4Hm03KJCIiIvulqBEKIiIiUicWCiIiIqo0FgoiIiKqNBYKIiIiqjQpheLkyZNo3bo1PDw8EB4ejqtXr95xTG5uLnr06AFPT0+Ehobip59+kpBUjrI8Pu+//z5cXFyg0+ksH+fPn5eQ1vbi4uLQv39/eHp6YsuWLaUeYzQaMWTIEHh7e6NevXr45ptvbBuSqlRZXhNq0q5dO7i7u1teu+bt3O91Pw8fPozQ0FB4enoiMjISeXl5lq9t3boV9erVg7e3N6KiomAymSxfW7ZsGWrUqIGAgABMnz7ddneyFHd77dr6fk+bNg0BAQGoUaMGVqxYYb07XIq7PQZ3e04AKnpeCAmaN28upkyZIvLy8kTfvn3FK6+8cscxI0aMEOHh4SI7O1t88sknolatWqKgoEBCWtsry+MzdepU0b9/fwnp5Dt27JhYsmSJ8Pf3F19//XWpx8ybN080a9ZMXLt2TezcuVPodDpx5coV2walKlOW14SatG3bVqxZs+aO2+92P/Py8kSNGjXE8uXLRXZ2tujQoYMYPXq0EEKIS5cuCU9PT7Fr1y5x7do1ERYWJmJiYoQQN14rXl5eIi4uTqSkpIgaNWqIbdu22e6O/s3dXru2vN9ff/21qFWrlvjzzz/FsWPHhE6nE7/99pv0x+Buzwkh1PO8sHmhiI+PF66uriI3N1cIIcSpU6eEs7OzyMrKKnHcAw88IPbu3Wv5PCgoSOzatcumWWUo6+MzdepUMWXKFBkRFaNu3bp3LRSPP/64WL58ueXzp59+WixZssRGyagqlfU1oSZt27YV3377bYnb7nU/d+zYIWrXrm05ds+ePSIwMFAIIcSiRYvEM888Y/nasmXLxBNPPCGEEGLcuHGid+/elq+98847IjIy0mr3q6xuf+3a+n53795dTJ482fK1Xr16iQkTJljnjt7D39+/SntOCKGu54XNT3kkJSWhTp068PDwAAA0btwYTk5OSE5OthyTkZGBtLQ0hISEWG4LCwtDYmKirePaXFkeH7OvvvoKgYGBaNKkCVatWmXrqIqWlJRU4vnTtGlTh3j+2KPyvCbUZOzYsahevTqeeuopHD58+J73MykpCQ8//LDlzzZt2hSpqam4fv36PZ/rangd2Pp+K/kx+ftzArD941MZNi8Uubm5qFatWonbPDw8kJubW+IYACWO8/T0LHGMvSrL4wMA3bt3x+LFi3Hx4kXMnz8fgwYNwqFDh2wZVdH+/jg6yvPHHpX1NaEmEydOxH//+1/89ddfiIiIwIsvvnjP+1na8xnAXb9mfmzU8Dqw9f1W6mNS2nMiOztbVc8LmxcKnU4HvV5v+VwIgby8POh0uhLHAChxXE5OTolj7FVZHh/gxohNu3bt4OLigs6dO6NTp07Yt2+freMq1t8fR0d5/tijsr4m1CQiIgJNmjSBm5sb3nvvPRQWFuLs2bN3vZ+lPZ8B3PVr5sdGDa+De/39WuN+K/UxKe058euvv9r88akMmxeK0NBQXLp0yXJn4uPjIYRA48aNLcf4+fmhRo0aliEYk8mE48ePl5j1aq/K8viUpqCgAFotrwI2Cw0NLTGEFxcX5xDPH3tU0deEWhgMBhQXF6Ndu3Z3vZ+hoaE4efIkxM2dEuLi4lCrVi34+vre87muhtfBvf5+rXG/1fCYmJ8TWq3W5o9PpVR6FkYFPPbYY2LSpEkiLy9P9OnTR0RGRoqMjAzxzDPPiPj4eCGEEGPGjBHt27cX2dnZYsmSJSI4OFgUFhbKiGtz93t8Tp8+LTp37ixSUlKE0WgU69evFzqdTpw5c0Z2dJu6fVKTwWAQHTt2FN99950QQojY2FgRFhYm0tLSxI4dO4SXl5dIS0uTmJYqo7TXhFqtXr1ajBw5UmRmZoqCggIxatQo0bRpU1FcXHzX+1lQUCCCgoLEkiVLRHZ2tnjuuecsEwlTU1OFTqcT33zzjbh27ZoICQmxTEA+fvy40Ol04ueffxYpKSniwQcfVMTk9r9PSLTl/d6+fbuoUaOGOHv2rPjll1+ETqcTJ06csO0DIEo+Bvd6Tghh28enMqQUilOnTonHH39cuLu7i/DwcHH16lVx8eJF4e/vL/7v//5PCCFEbm6u6NGjh/Dw8BAhISHi6NGjMqJKcb/Hx2QyiTlz5ogmTZoInU4nWrZsKfbt2yc7ts00aNBAeHp6Co1GI9zd3YWnp6eIj48XQUFBYv369UIIIYxGo4iKihI6nU7UrVtX7NixQ3JqqozSXhNqdf36dREVFSVq1aolfHx8RJcuXSz/GLjX/Txy5IgICQkR1apVE5GRkSIvL8/yte3bt4u6desKLy8vMXToUGE0Gi1fW7FihQgMDBR+fn7iww8/tN0dLUVpr90zZ87Y/H5/8MEHws/PTwQGBoqVK1da/47fprTH4NixY3d9TgihnucFdxslIiKiSuNJdyIiIqo0FgoiIiKqNBYKIiIiqjQWCiIiIqo0FgoiIiKqNBYKIiIiqjQWCiIiIqo0FgoiIiKqNBYKIiIiqjQWCiIiIqo0FgoiIiKqNBYKIiIiqrT/B3VCwgOauOJeAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig, axs = plt.subplots(1, 2)\n",
-    "axs[0].plot(irr_1p, I_1p, c=\"xkcd:carmine\", label=\"1P\")\n",
-    "axs[1].plot(irr_2p, I_2p, c=\"k\", label=\"2P\")\n",
-    "# ax.loglog((irr_1p), (I_1p), c=\"xkcd:carmine\", label=\"1P\")\n",
-    "# ax.plot((irr_2p), (I_2p), c=\"k\", label=\"2P\")\n",
-    "ax.set(\n",
-    "    xlabel=\"Irradiance (mW/mm2)\",\n",
-    "    ylabel=\"Photocurrent (nA)\",\n",
-    "    title=\"ChRmine power sensitivity\",\n",
-    ")\n",
-    "ax.legend()"
+    "def f_lin(x, m):\n",
+    "    \"\"\"Returns a linear model through the origin: I = m*irr\"\"\"\n",
+    "    return x * m\n",
+    "\n",
+    "def first2_slope(irr, I):\n",
+    "    \"\"\"Estime slope using only first two data points.\n",
+    "    Used for 1P data where opsin response saturates quickly.\n",
+    "    The linear regime is only valid at low irradiance.\"\"\"\n",
+    "    return (I[1]-I[0]) / (irr[1]-irr[0])\n",
+    "\n",
+    "def full_curve_slope(irr, I):\n",
+    "    \"\"\"\n",
+    "    Estimates slope m from I = m * irr using all data points.\n",
+    "    Used for 2P data, which is linear across the full measured\n",
+    "    irradiance range.\"\"\"\n",
+    "\n",
+    "    result = curve_fit(f_lin, irr ,I ,p0=[1.0])\n",
+    "    m = result[0][0]\n",
+    "    return m\n",
+    "\n",
+    "def hill(E, I_max, K, n):\n",
+    "    \"\"\"Hill saturation model.\n",
+    "    Used for ChRmine 1P which saturates too fast for the \n",
+    "    first-2 finite difference to be reliable.\"\"\"\n",
+    "    return I_max * E**n / (E**n + K**n)\n",
+    "\n",
+    "def hill_initial_slope(irr, I):\n",
+    "    \"\"\"\n",
+    "    Fit Hill function to 1P data and return the analytical initial slope.\n",
+    "    Also returns fit parameters and R² for reporting.\n",
+    "    \"\"\"\n",
+    "    popt, _ = curve_fit(hill, irr, I, p0=[5.0, 0.05, 1.0], bounds=([0,0,0.1],[20,10,4]))\n",
+    "    I_max, K, n = popt\n",
+    "    m_initial = I_max * n / K\n",
+    "    return m_initial, popt"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.28868036 0.33132946 0.37397855 0.41662764 0.45927673 0.50192583\n",
-      " 0.54457492 0.58722401 0.62987311 0.6725222  0.71517129 0.75782038\n",
-      " 0.80046948 0.84311857 0.88576766 0.92841676 0.97106585 1.01371494\n",
-      " 1.05636403 1.09901313 1.14166222 1.18431131 1.22696041 1.2696095\n",
-      " 1.31225859 1.35490768 1.39755678 1.44020587 1.48285496 1.52550406\n",
-      " 1.56815315 1.61080224 1.65345133 1.69610043 1.73874952 1.78139861\n",
-      " 1.82404771 1.8666968  1.90934589 1.95199498 1.99464408 2.03729317\n",
-      " 2.07994226 2.12259136 2.16524045 2.20788954 2.25053864 2.29318773\n",
-      " 2.33583682 2.37848591]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f75eb3d7500>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "I_overlap = np.linspace(max(I_1p[0], I_2p[0]), min(I_1p[-1], I_2p[-1]))\n",
-    "print(I_overlap)\n",
-    "irr_1p_overlap = np.interp(I_overlap, I_1p, irr_1p)\n",
-    "irr_2p_overlap = np.interp(I_overlap, I_2p, irr_2p)\n",
-    "plt.plot(I_overlap, irr_2p_overlap / irr_1p_overlap)\n",
-    "# ax.loglog((irr_1p), (I_1p), c=\"xkcd:carmine\", label=\"1P\")\n",
-    "# ax.plot((irr_2p), (I_2p), c=\"k\", label=\"2P\")\n",
-    "# ax.set(\n",
-    "#     xlabel=\"Irradiance (mW/mm2)\",\n",
-    "#     ylabel=\"Photocurrent (nA)\",\n",
-    "#     title=\"ChRmine power sensitivity\",\n",
-    "# )\n",
-    "# ax.legend()"
+    "## ChRmine\n",
+    "\n",
+    "ChRmine (from *Rhodomonas lens*, a marine dinoflagellate) has exceptionally high 1P sensitivity.\n",
+    "A Hill function is used to model the 1P response and extract the initial linear slope."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fef0d3c2ea0>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ChRmine Hill fit: I_max=4.6691 nA, K=0.0492 mW/mm², n=1.5167\n",
+      "ChRmine 1P initial slope (I_max*n/K): 143.8224 nA/(mW/mm²)\n",
+      "ChRmine 2P slope: 0.000016 nA/(mW/mm²)\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -205,255 +327,70 @@
     }
    ],
    "source": [
-    "plt.plot((irr_1p), (I_1p), c=\"xkcd:carmine\", label=\"1P\")\n"
+    "# 1P: Initial slope\n",
+    "m1p_chrmine, hill_params = hill_initial_slope(irr_1p, I_1p)\n",
+    "I_max, K, n = hill_params\n",
+    "print(f\"ChRmine Hill fit: I_max={I_max:.4f} nA, K={K:.4f} mW/mm², n={n:.4f}\")\n",
+    "print(f\"ChRmine 1P initial slope (I_max*n/K): {m1p_chrmine:.4f} nA/(mW/mm²)\")\n",
+    "\n",
+    "# 2P: full linear fit\n",
+    "m2p_chrmine = full_curve_slope(irr_2p, I_2p)\n",
+    "print(f\"ChRmine 2P slope: {m2p_chrmine:.6f} nA/(mW/mm²)\")\n",
+    "\n",
+    "# Plot\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(10, 4))\n",
+    "\n",
+    "# smooth x-axis range for plotting Hill curve\n",
+    "E_plot = np.linspace(0, max(irr_1p)*1.05, 300)\n",
+    "# x-axis range limited to linear regime only, for tangent line\n",
+    "E_tan = np.linspace(0, 0.06, 100)\n",
+    "axs[0].plot(irr_1p, I_1p, 'o', color='xkcd:carmine', label='data')\n",
+    "axs[0].plot(E_plot, hill(E_plot, *hill_params), '-', color='xkcd:carmine', label=f'Hill fit')\n",
+    "axs[0].plot(E_tan, m1p_chrmine * E_tan, '--', color='navy', alpha=0.7, label=f'initial slope = {m1p_chrmine:.2f}')\n",
+    "axs[0].set(xlabel='1P Irradiance (mW/mm²)', ylabel='Photocurrent (nA)', title='ChRmine — 1P (Hill fit)')\n",
+    "axs[0].legend(fontsize=8)\n",
+    "\n",
+    "irr_2p_plot = np.linspace(0, max(irr_2p)*1.05, 300)\n",
+    "axs[1].plot(irr_2p, I_2p, '-o', color='k', label='data')\n",
+    "axs[1].plot(irr_2p_plot, m2p_chrmine * irr_2p_plot, '--', color='k', label=f'linear fit')\n",
+    "axs[1].set(xlabel='2P Irradiance (mW/mm²)', ylabel='Photocurrent (nA)', title='ChRmine — 2P (full linear fit)')\n",
+    "axs[1].legend(fontsize=8)\n",
+    "\n",
+    "fig.suptitle('ChRmine power sensitivity')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fef0d031490>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
    "source": [
-    "plt.plot((irr_2p), (I_2p), c=\"k\", label=\"2P\")"
+    "## ChroME Family (ChroME, ChroME2f, ChroME2s)\n",
+    "\n",
+    "These opsins are all derived from green algae and share similar 1P/2P sensitivity profiles.\n",
+    "The 1P response saturates but more gradually than ChRmine, so the first two data points\n",
+    "capture the linear regime. The 2P response is linear across the full range."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "===============\n",
-      "    RESULTS    \n",
-      "===============\n",
-      "best_f_irr: sqrt\n",
-      "best_f_I: identity\n",
-      "best mean r2 0.703853307246795\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_650116/3112391639.py:48: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  I_hat_1p = f_I_inv(m1p * irr_1p_transformed)\n"
+      "ChroME: m1p=1.5067, m2p=0.000006, ratio=252204.33\n",
+      "ChroME2f: m1p=2.4957, m2p=0.000009, ratio=266618.53\n",
+      "ChroME2s: m1p=3.5713, m2p=0.000011, ratio=336392.54\n"
      ]
     },
     {
      "data": {
-      "text/html": [
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>f_irr</th>\n",
-       "      <th>f_I</th>\n",
-       "      <th>r2_1p</th>\n",
-       "      <th>r2_2p</th>\n",
-       "      <th>mean_r2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>identity</td>\n",
-       "      <td>identity</td>\n",
-       "      <td>-0.460178</td>\n",
-       "      <td>0.970862</td>\n",
-       "      <td>0.255342</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>identity</td>\n",
-       "      <td>log</td>\n",
-       "      <td>-0.596008</td>\n",
-       "      <td>0.556113</td>\n",
-       "      <td>-0.019947</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>identity</td>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>-1.989953</td>\n",
-       "      <td>0.494364</td>\n",
-       "      <td>-0.747795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>identity</td>\n",
-       "      <td>square</td>\n",
-       "      <td>0.447950</td>\n",
-       "      <td>0.886708</td>\n",
-       "      <td>0.667329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>log</td>\n",
-       "      <td>identity</td>\n",
-       "      <td>-3.182423</td>\n",
-       "      <td>0.274285</td>\n",
-       "      <td>-1.454069</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>log</td>\n",
-       "      <td>log</td>\n",
-       "      <td>-1.775753</td>\n",
-       "      <td>-0.245836</td>\n",
-       "      <td>-1.010795</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>log</td>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>-3.516459</td>\n",
-       "      <td>0.411338</td>\n",
-       "      <td>-1.552560</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>log</td>\n",
-       "      <td>square</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.024131</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>identity</td>\n",
-       "      <td>0.506932</td>\n",
-       "      <td>0.900775</td>\n",
-       "      <td>0.703853</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>log</td>\n",
-       "      <td>0.171196</td>\n",
-       "      <td>0.204009</td>\n",
-       "      <td>0.187602</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>-0.877126</td>\n",
-       "      <td>0.967683</td>\n",
-       "      <td>0.045279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>square</td>\n",
-       "      <td>0.810713</td>\n",
-       "      <td>0.535467</td>\n",
-       "      <td>0.673090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>square</td>\n",
-       "      <td>identity</td>\n",
-       "      <td>-1.470390</td>\n",
-       "      <td>0.634820</td>\n",
-       "      <td>-0.417785</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>square</td>\n",
-       "      <td>log</td>\n",
-       "      <td>-0.717720</td>\n",
-       "      <td>0.626429</td>\n",
-       "      <td>-0.045646</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>square</td>\n",
-       "      <td>sqrt</td>\n",
-       "      <td>-2.200391</td>\n",
-       "      <td>-0.164472</td>\n",
-       "      <td>-1.182432</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>square</td>\n",
-       "      <td>square</td>\n",
-       "      <td>-0.554560</td>\n",
-       "      <td>0.966136</td>\n",
-       "      <td>0.205788</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+      "image/png": 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",
       "text/plain": [
-       "       f_irr       f_I     r2_1p     r2_2p   mean_r2\n",
-       "0   identity  identity -0.460178  0.970862  0.255342\n",
-       "1   identity       log -0.596008  0.556113 -0.019947\n",
-       "2   identity      sqrt -1.989953  0.494364 -0.747795\n",
-       "3   identity    square  0.447950  0.886708  0.667329\n",
-       "4        log  identity -3.182423  0.274285 -1.454069\n",
-       "5        log       log -1.775753 -0.245836 -1.010795\n",
-       "6        log      sqrt -3.516459  0.411338 -1.552560\n",
-       "7        log    square       NaN  0.024131       NaN\n",
-       "8       sqrt  identity  0.506932  0.900775  0.703853\n",
-       "9       sqrt       log  0.171196  0.204009  0.187602\n",
-       "10      sqrt      sqrt -0.877126  0.967683  0.045279\n",
-       "11      sqrt    square  0.810713  0.535467  0.673090\n",
-       "12    square  identity -1.470390  0.634820 -0.417785\n",
-       "13    square       log -0.717720  0.626429 -0.045646\n",
-       "14    square      sqrt -2.200391 -0.164472 -1.182432\n",
-       "15    square    square -0.554560  0.966136  0.205788"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1000x1200 with 6 Axes>"
       ]
      },
      "metadata": {},
@@ -461,139 +398,97 @@
     }
    ],
    "source": [
-    "from itertools import product\n",
-    "import pandas as pd\n",
-    "from scipy.optimize import curve_fit\n",
-    "\n",
+    "def plot_opsin(ax1, ax2, name, irr_1p, I_1p, irr_2p, I_2p, m1p, m2p):\n",
+    "    # 1P plot: all data points, but fit line only over linear regime\n",
+    "    irr_lin = np.linspace(0, max(irr_1p)*1.05, 50)\n",
+    "    ax1.plot(irr_1p, I_1p, 'o-', color='xkcd:carmine', label='data')\n",
+    "    ax1.plot(irr_lin, m1p * irr_lin, '--', color='navy', label=f'first-2 slope (m={m1p:.3f})')\n",
+    "    ax1.set(xlabel='1P Irradiance (mW/mm²)', ylabel='Photocurrent (nA)', title=f'{name} — 1P')\n",
+    "    ax1.legend(fontsize=8)\n",
     "\n",
-    "# can we get a good linear fit\n",
-    "def identity(x):\n",
-    "    return np.multiply(x, 1)\n",
+    "    # 2P plot: all data points, fit line over full range\n",
+    "    irr_range = np.linspace(0, max(irr_2p)*1.05, 200)\n",
+    "    ax2.plot(irr_2p, I_2p, 'o-', color='k', label='data')\n",
+    "    ax2.plot(irr_range, m2p * irr_range, '--', color='gray', label='full fit')\n",
+    "    ax2.set(xlabel='2P Irradiance (mW/mm²)', ylabel='Photocurrent (nA)', title=f'{name} — 2P')\n",
+    "    ax2.legend(fontsize=8)\n",
     "\n",
     "\n",
-    "def square(x):\n",
-    "    return np.power(x, 2)\n",
+    "def compute_all_ratios(opsins):\n",
+    "    \"\"\"Compute 1P/2P ratios for all opsins and plot in a single figure.\"\"\"\n",
+    "    fig, axes = plt.subplots(len(opsins), 2, figsize=(10, 4*len(opsins)))\n",
+    "    ratios = {}\n",
     "\n",
+    "    for idx, (name, data) in enumerate(opsins.items()):\n",
+    "        irr_1p, I_1p = data['irr_1p'], data['I_1p']\n",
+    "        irr_2p, I_2p = data['irr_2p'], data['I_2p']\n",
     "\n",
-    "fns_and_invs = [\n",
-    "    (identity, identity),\n",
-    "    (np.log, np.exp),\n",
-    "    (np.sqrt, square),\n",
-    "    (square, np.sqrt),\n",
-    "]\n",
+    "        m1p = first2_slope(irr_1p, I_1p)\n",
+    "        m2p = full_curve_slope(irr_2p, I_2p)\n",
+    "        ratio = m1p / m2p\n",
+    "        ratios[name] = ratio\n",
     "\n",
+    "        print(f\"{name}: m1p={m1p:.4f}, m2p={m2p:.6f}, ratio={ratio:.2f}\")\n",
+    "        plot_opsin(axes[idx, 0], axes[idx, 1], name, irr_1p, I_1p, irr_2p, I_2p, m1p, m2p)\n",
     "\n",
-    "def f_lin(x, m):\n",
-    "    return x * m\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    return ratios\n",
     "\n",
     "\n",
-    "def opt(irr, I):\n",
-    "    opt = curve_fit(f_lin, irr, I, [1], full_output=True)\n",
-    "    m = opt[0][0]\n",
-    "    percent_resid2 = np.sum(opt[2][\"fvec\"] ** 2) / np.sum(I_1p_transformed**2)\n",
-    "    return m, percent_resid2\n",
-    "\n",
-    "\n",
-    "best_res = np.inf\n",
-    "best_r2 = -np.inf\n",
-    "df = pd.DataFrame(columns=[\"f_irr\", \"f_I\", \"r2_1p\", \"r2_2p\", \"mean_r2\"])\n",
-    "for (f_irr, f_irr_inv), (f_I, f_I_inv) in product(fns_and_invs, fns_and_invs):\n",
-    "    irr_1p_transformed = f_irr(irr_1p)\n",
-    "    irr_2p_transformed = f_irr(irr_2p)\n",
-    "    I_1p_transformed = f_I(I_1p)\n",
-    "    I_2p_transformed = f_I(I_2p)\n",
-    "\n",
-    "    m1p, percent_resid2_1p = opt(irr_1p_transformed, I_1p_transformed)\n",
-    "    m2p, percent_resid2_2p = opt(irr_2p_transformed, I_2p_transformed)\n",
-    "    # print(percent_resid2_1p, percent_resid2_2p)\n",
-    "    tot_res = np.mean([percent_resid2_1p, percent_resid2_2p])\n",
-    "\n",
-    "    I_hat_1p = f_I_inv(m1p * irr_1p_transformed)\n",
-    "    r2_1p = 1 - np.sum((I_hat_1p - I_1p) ** 2) / np.sum((I_1p - np.mean(I_1p)) ** 2)\n",
-    "    # r2_1p = np.corrcoef(I_1p, I_hat_1p)[0, 1] ** 2\n",
-    "\n",
-    "    I_hat_2p = f_I_inv(m2p * irr_2p_transformed)\n",
-    "    r2_2p = 1 - np.sum((I_hat_2p - I_2p) ** 2) / np.sum((I_2p - np.mean(I_2p)) ** 2)\n",
-    "    # r2_2p = np.corrcoef(I_2p, I_hat_2p)[0, 1] ** 2\n",
-    "\n",
-    "    mean_r2 = np.mean([r2_1p, r2_2p])\n",
-    "\n",
-    "    df.loc[len(df)] = [f_irr.__name__, f_I.__name__, r2_1p, r2_2p, mean_r2]\n",
-    "    # print(\n",
-    "    #     f\"f_irr={f_irr.__name__}, f_I={f_I.__name__}, r2_1p={r2_1p:.3f}, r2_2p={r2_2p:.3f}\"\n",
-    "    # )\n",
-    "\n",
-    "    # if tot_res < best_res:\n",
-    "    if mean_r2 > best_r2:\n",
-    "        # best_res = tot_res\n",
-    "        best_r2 = mean_r2\n",
-    "        best_f_irr = f_irr\n",
-    "        best_f_I = f_I\n",
-    "        best_I_hat_1p = I_hat_1p\n",
-    "        best_I_hat_2p = I_hat_2p\n",
-    "print(\"\\n===============\")\n",
-    "print(\"    RESULTS    \")\n",
-    "print(\"===============\")\n",
-    "print(f\"best_f_irr: {best_f_irr.__name__}\")\n",
-    "print(f\"best_f_I: {best_f_I.__name__}\")\n",
-    "# print(f\"best mean resid: {best_res}\")\n",
-    "print(\"best mean r2\", best_r2)\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "ax.semilogx(best_f_irr(irr_1p), best_f_I(I_1p), c=\"xkcd:carmine\", label=\"1P data\")\n",
-    "ax.plot(\n",
-    "    best_f_irr(irr_1p),\n",
-    "    best_I_hat_1p,\n",
-    "    c=\"xkcd:carmine\",\n",
-    "    linestyle=\"--\",\n",
-    "    label=\"1P model\",\n",
-    ")\n",
-    "ax.plot(best_f_irr(irr_2p), best_f_I(I_2p), c=\"k\", label=\"2P data\")\n",
-    "ax.plot(best_f_irr(irr_2p), best_I_hat_2p, c=\"k\", linestyle=\"--\", label=\"2P model\")\n",
-    "ax.set(\n",
-    "    xlabel=\"Irradiance (mW/mm2)\",\n",
-    "    ylabel=\"Photocurrent (nA)\",\n",
-    "    title=\"ChRmine power sensitivity\",\n",
-    ")\n",
-    "ax.legend()\n",
-    "\n",
-    "df"
+    "chrome_ratios = compute_all_ratios({\n",
+    "    'ChroME':   get_chrome(),\n",
+    "    'ChroME2f': get_chrome2f(),\n",
+    "    'ChroME2s': get_chrome2s(),\n",
+    "})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Okay, but we do actually need a pure linear function if we want just a constant.\n",
-    "Let's compute that ratio to use as a placeholder for now:"
+    "## Final Ratios\n",
+    "\n",
+    " The ChroME family average is used as the default CLEO ratio as ChRmine is biologically distinct and thus skews the ratio."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "270382.2996938772"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== ChroME family ===\n",
+      "  Average: 285071.80\n",
+      "  Std dev: 36763.26  (12.9% CV)\n",
+      "\n",
+      "Default CLEO ratio (ChroME family): 285071.80\n"
+     ]
     }
    ],
    "source": [
-    "m1p, _ = opt(irr_1p, I_1p)\n",
-    "m2p, _ = opt(irr_2p, I_2p)\n",
-    "m1p / m2p"
+    "chrome_family_ratios = list(chrome_ratios.values())\n",
+    "chrome_avg = np.mean(chrome_family_ratios)\n",
+    "chrome_std = np.std(chrome_family_ratios)\n",
+    "\n",
+    "print()\n",
+    "print(\"=== ChroME family ===\")\n",
+    "print(f\"  Average: {chrome_avg:.2f}\")\n",
+    "print(f\"  Std dev: {chrome_std:.2f}  ({chrome_std/chrome_avg*100:.1f}% CV)\")\n",
+    "\n",
+    "\n",
+    "print()\n",
+    "print(f\"1P/2P ratio: {chrome_avg:.2f}\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cleo",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -607,7 +502,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,