Update version, docs. Fix whitespace in Figure5

REPRODUCEME.md

@@ -12,7 +12,7 @@ and store as an instance of `src.get_data.RawData`.
 
 The data for 
 each replicate plate 
-possessing single-stranded DNA with $D_k=1\times10^{-6}$ mol/L is
+possessing single-stranded DNA with $B_d=22\times10^{-6}$ mol nucleobases/L is
 input into the following structures
 
     >>> kwargs = dict(t="SS", B_d=22e-6, N=22)
@@ -22,7 +22,7 @@ input into the following structures
 
 The data for 
 each replicate plate 
-possessing single-stranded DNA with $D_k=2\times10^{-6}$ mol/L is
+possessing single-stranded DNA with $B_d=44\times10^{-6}$ mol nucleobases/L is
 input into the following structures
 
     >>> kwargs = dict(t="SS", B_d=44e-6, N=22)
@@ -31,7 +31,7 @@ input into the following structures
 
 The data for 
 each replicate plate 
-possessing double-stranded DNA with $D_k=1\times10^{-6}$ mol/L is
+possessing double-stranded DNA with $B_d=22\times10^{-6}$ mol nucleobases/L is
 input into the following structures
 
     >>> kwargs = dict(t="DS", B_d=22e-6, N=22)
@@ -40,7 +40,7 @@ input into the following structures
 
 The data for 
 each replicate plate 
-possessing double-stranded DNA with $D_k=2\times10^{-6}$ mol/L is
+possessing double-stranded DNA with $B_d=44\times10^{-6}$ mol nucleobases/L is
 input into the following structures
 
     >>> kwargs = dict(t="DS", B_d=44e-6, N=22)
@@ -109,6 +109,7 @@ The value for $F_\mathrm{min}$ is
     >>> F_min
     231432.0
 
+(see Section S1.2 in the Supporting Material),
 while the average fluorescence for low dye concentrations is
 
     >>> F_min_avg
@@ -154,15 +155,15 @@ each combination via
     ...     tuple(F_hats),
     ...     (SS_1.T, SS_2.T, DS_1.T, DS_2.T), 
     ...     "figure4.pdf", 
-    ...     sname=r"$\widehat{\mathbf{F}}_{j,i}^\mathrm{LS}$")
+    ...     sname=r"\widehat{\mathbf{F}}_{j,i}^\mathrm{LS}")
     ...
 
 This is Figure 4 of the main text, which can be found [here](out/figure4.pdf).
 
 Subsequently, Equation (18) is solved
 using `src.noise_removal.predictor_corrector`
 and $V(\mathbf{M})$ and $V(\mathbf{C})$
-are calulated 
+are calulated (see Section S1.3 in the SM)
 
 
     >>> from src.noise_removal import predictor_corrector
@@ -200,7 +201,7 @@ The results are plotted via Figure 5
     ...     (SS_1.Fhat_tls, SS_2.Fhat_tls, DS_1.Fhat_tls, DS_2.Fhat_tls),
     ...     (SS_1.T, SS_2.T, DS_1.T, DS_2.T), 
     ...     "figure5.pdf", 
-    ...     sname=r"$\widehat{\mathbf{F}}_{j,i}^\mathrm{TLS}$")
+    ...     sname=r"\widehat{\mathbf{F}}_{j,i}^\mathrm{TLS}")
     ...
 
 which can be found [here](out/figure5.pdf),
@@ -251,7 +252,6 @@ we can readily plot the Figure 8 as
     ...
 
 which looks like [this](out/figure8.pdf).
-
 The association constant is calculated as
 
     >>> K_a = DS.get_K()/2/C_REF/DS.N*1e-6
@@ -273,22 +273,25 @@ which looks like [this](out/figure9.pdf).
 
 ## Supplementary Figures
 
-Figure S1 and S2 are made via
+Figures [S1](out/figureS1.pdf), [S2](out/figureS2.pdf),  [S3](out/figureS3.pdf), 
+[S4](out/figureS4.pdf), and [S5](out/figureS5.pdf) are made via
 
-    >>> from src.plot_raw_data import make_figure_S1, make_figure_S3
+    >>> from src.plot_raw_data import make_figure_S1
     >>> make_figure_S1()
+
+    >>> from src.plot_params import plot_figure_S2
+    >>> plot_figure_S2(SS_1, SS_2, DS_1, DS_2)
+
+    >>> from src.plot_raw_data import make_figure_S3
     >>> make_figure_S3()
     Maximum change from 6/15 to 6/16: 32841
     Minimum change from 6/15 to 6/16: -4525
     Average change from 6/15 to 6/16: 4085
 
-
-Figure S2, S4, and S5 are made via
-
-    >>> from src.plot_params import plot_figure_S2, plot_figure_S4, \
-    ...     plot_figure_S5
-    >>> plot_figure_S2(SS_1, SS_2, DS_1, DS_2)
+    >>> from src.plot_params import plot_figure_S4
     >>> plot_figure_S4(SS, DS)
+
+    >>> from src.plot_params import plot_figure_S5
     >>> from src.parameter_extraction import calculate_relative_brightness, \
     ...     calculate_relative_brightness_err 
     >>> rb = calculate_relative_brightness(SS.get_f(), DS.get_f())

doc/source/conf.py

@@ -24,9 +24,9 @@
 author = 'Robert F. DeJaco'
 
 # The short X.Y version
-version = 'v2.1.0'
+version = 'v2.2.0'
 # The full version, including alpha/beta/rc tags
-release = 'v2.1.0'
+release = 'v2.2.0'
 
 
 # -- General configuration ---------------------------------------------------

doc/source/modules.rst

@@ -14,8 +14,8 @@ Get Data
 .. automodule:: src.get_data
     :members:
 
-Noise Removal
--------------
+Quantifying Noise and Linearity
+-------------------------------
 
 .. automodule:: src.noise_removal
     :members:
@@ -35,8 +35,8 @@ Raw Data
 .. automodule:: src.plot_raw_data
     :members:
 
-Noise Removal
-.............
+Quantifying Noise and Linearity
+................................
 
 .. automodule:: src.plot_noise_removal
     :members:

src/get_data.py

@@ -16,7 +16,7 @@
 def excel_to_data(f_name: str, channel="GREEN"):
     """
     Convert "Raw Data" sheet of excel file to pandas dataframe.
-    Uses Equation (12) of manuscript 
+    Uses Equation (9) of manuscript
     to calculate temperature associated with each cycle.
 
     Parameters
@@ -84,9 +84,9 @@ class RawData:
     Attributes
     ----------
     F : np.ndarray
-        Fluorescence data, :math:`F_d^{t\\ell}` (see Equation 14 of main text)
+        Fluorescence data, :math:`F_d^{t\\ell}` (see Equation 11 of main text)
     C : np.ndarray
-        Dye concentrations, :math:`C` (see Equation 13 of main text)
+        Dye concentrations, :math:`C` (see Equation 10 of main text)
     B : float
         DNA concentration, :math:`B_d` in mol/L
     t : str

src/noise_removal.py

@@ -7,7 +7,7 @@ def compute_M_LS(F, C):
     Returns
     -------
     np.array
-        :math:`\\mathbf{M}^\\mathrm{LS}`, see Equation (21)
+        :math:`\\mathbf{M}^\\mathrm{LS}`, see Equation (17)
 
     """
     return F@C/np.inner(C, C)
@@ -54,7 +54,7 @@ def compute_M_plus(F, c_plus):
 
 
 def predictor_corrector(F, C, rho_squared, maxiter=100000, print_iter=True):
-    """Solve Equation (22) with predictor--corrector algorithm
+    """Solve Equation (18) with predictor--corrector algorithm
 
     Parameters
     ----------
@@ -63,7 +63,7 @@ def predictor_corrector(F, C, rho_squared, maxiter=100000, print_iter=True):
     C : np.ndarray
         Dye concentration data :math:`\\mathbf{C}`
     rho_squared : float
-        Weighting factor for concentrations, :math:`\\rho^2` in Equation (22a)
+        Weighting factor for concentrations, :math:`\\rho^2` in Equation (18a)
     maxiter : int, optional
         maximum iterations allowed, by default 100000
     print_iter : bool, optional

src/parameter_extraction.py

@@ -4,7 +4,7 @@
 
 
 def calculate_relative_brightness(f_SS, f_DS):
-    """Calculate relative brightness, Equation (28).
+    """Calculate relative brightness, Equation (24).
 
     Parameters
     ----------
@@ -24,7 +24,7 @@ def calculate_relative_brightness(f_SS, f_DS):
 
 
 def calculate_relative_brightness_err(SS_M1, SS_M2, DS_M1, DS_M2, SS_V_M1, SS_V_M2, DS_V_M1, DS_V_M2) -> np.array:
-    """Estimate error in relative brightness, Equation (28).
+    """Estimate error in relative brightness, Equation (24).
 
     Parameters
     ----------
@@ -145,81 +145,8 @@ def __init__(self, cls1: CombinedData, cls2: CombinedData):
         self.N = cls1.N
         assert self.N == cls2.N, "Cannot combine different DNA lengths"
 
-    def get_phi_1(self) -> np.array:
-        """Get :math:`\\varphi_{1}`, vectorized version of Equation (S6a)
-
-        Returns
-        -------
-        np.array
-            
-        """
-        return 2*self.r - 1
-
-    def get_std_phi_1(self) -> np.array:
-        """Get estimate of standard deviation in :math:`\\varphi_{1}`
-
-        Returns
-        -------
-        np.array
-
-            .. math::
-                \\frac{2}{\\mathbf{M}_{2j}}\\sqrt{V(\\mathbf{M}_{1j}) + r_j^2V(\\mathbf{M}_{2j})}
-
-        """
-        return 2*np.sqrt(
-            self.V_M1 + self.r*self.r*self.V_M2
-        )/self.M2
-
-    def get_phi_2(self):
-        """Get :math:`\\varphi_{2}`, vectorized version of Equation (S6b)
-
-        Returns
-        -------
-        np.array
-            
-        """
-        return 2 - 1/self.r
-
-    def get_std_phi_2(self):
-        """Get estimate of standard deviation in :math:`\\varphi_{2}`
-
-        Returns
-        -------
-        np.array
-
-            .. math::
-                \\frac{1}{\\mathbf{M}_{1j}}\\sqrt{V(\\mathbf{M}_{2j}) + r_j^{-2}V(\\mathbf{M}_{1j})}
-
-        """
-        return np.sqrt(
-            self.V_M2 + self.V_M1/self.r/self.r
-        )/self.M1
-
-    def get_theta_b_all_1(self, n):
-        return n*self.C1[-1]*(2*self.r - 1)/self.N
-
-    def get_psi_j1(self, j: int):
-        return self.C1*(2*self.r[j] - 1)
-
-    def get_std_psi_j1(self, j: int):
-        return np.sqrt(
-            (2*self.r[j] - 1)*(2*self.r[j] - 1)*self.V_C1
-            + 4*self.C1*self.C1/self.M2[j]/self.M2[j]*self.V_M1[j]
-            + 4*self.C1*self.C1*self.r[j]*self.r[j]/self.M2[j]/self.M2[j]*self.V_M2[j]
-        )
-
-    def get_psi_j2(self, j: int):
-        return self.C2*(1 - 1/2/self.r[j])
-
-    def get_std_psi_j2(self, j: int):
-        return np.sqrt(
-            (1 - 1/2/self.r[j])*(1 - 1/2/self.r[j])*self.V_C2
-            + (self.C2*self.C2/4/self.M1[j]/self.M1[j])*self.V_M2[j]
-            + self.C2*self.C2/self.r[j]/self.r[j]/self.M1[j]/self.M1[j]/4*self.V_M1[j]
-        )
-
     def get_K(self) -> np.array:
-        """Get :math:`\\mathbf{K}` from vectorized version of Equation (24)
+        """Get :math:`\\mathbf{K}` from vectorized version of Equation (21)
 
         Returns
         -------
@@ -251,7 +178,7 @@ def get_K_std(self) -> np.array:
         return np.sqrt((DM*DM*(4*self.V_M1+ 2*self.V_M2) + DH*DH*(self.V_M1+ self.V_M2))/8/DM/DM/DM/DM)
 
     def get_f(self) -> np.array:
-        """Get :math:`\\mathbf{f}` from vectorized version of Equation (27)
+        """Get :math:`\\mathbf{f}` from vectorized version of Equation (23)
 
         Returns
         -------
@@ -301,7 +228,7 @@ def get_f_std(self) -> np.array:
         )
 
     def get_dg(self) -> np.array:
-        """Get free energy of dye binding, :math:`\\Delta g`, vectorized version of Equation (29).
+        """Get free energy of dye binding, :math:`\\Delta g`, vectorized version of Equation (25).
 
         Returns
         -------
@@ -312,7 +239,7 @@ def get_dg(self) -> np.array:
         return -R_kJ_molK * self.T * np.log(K/C_REF/self.N)
 
     def get_dh(self) -> np.array:
-        """Get differential enthalpy of binding, :math:`\\Delta h` as the vectorized version of Equation (30).
+        """Get differential enthalpy of binding, :math:`\\Delta h` as the vectorized version of Equation (26).
 
         Returns
         -------
@@ -368,4 +295,14 @@ def get_dh_std(self) -> np.array:
             + dhj__M1jm1*dhj__M1jm1*self.V_M1[:-2]
             + dhj__M2jm1*dhj__M2jm1*self.V_M2[:-2]
         )
-
+
+    def get_theta_b_all_1(self, n):
+        """Returns :math:`\\theta_{b,j,1}` in Equation (S6a) pointwise in :math:`j`
+        The index :math:`b` is the total number of wells.
+
+        Returns
+        -------
+        np.array
+
+        """
+        return n * self.C1[-1] * (2 * self.r - 1) / self.N