Merge pull request #9 from pachterlab/dev

bump main to 0260
pachterlab · Apr 17, 2023 · a7a5db3 · a7a5db3
2 parents e7c2430 + d7e3ff5
commit a7a5db3
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Showing 4 changed files with 33 additions and 14 deletions.
diff --git a/setup.cfg b/setup.cfg
@@ -1,6 +1,6 @@
 [metadata]
 name = monod
-version = 0.2.5.0
+version = 0.2.6.0
 author = Gennady Gorin, Lior Pachter
 author_email = [email protected], [email protected]
 description = the Monod package fits CME models to sequencing data.

diff --git a/src/monod/cme_toolbox.py b/src/monod/cme_toolbox.py
@@ -144,24 +144,42 @@ def get_log_name_str(self):
             TeX-formatted log-parameter names for the current transcriptional model.
             Useful for plot labels.
         """
+        param_str = []
         if self.bio_model == "Constitutive":
-            return (r"$\log_{10} \beta$", r"$\log_{10} \gamma$")
+            param_str += [r"$\log_{10} \beta$", r"$\log_{10} \gamma$"]
         elif self.bio_model == "Delay":
-            return (r"$\log_{10} b$", r"$\log_{10} \beta$", r"$\log_{10} \tau^{-1}$")
+            param_str += [
+                r"$\log_{10} b$",
+                r"$\log_{10} \beta$",
+                r"$\log_{10} \tau^{-1}$",
+            ]
         elif self.bio_model == "DelayedSplicing":
-            return (r"$\log_{10} b$", r"$\log_{10} \tau^{-1}$", r"$\log_{10} \gamma$")
+            param_str += [
+                r"$\log_{10} b$",
+                r"$\log_{10} \tau^{-1}$",
+                r"$\log_{10} \gamma$",
+            ]
         elif self.bio_model == "Bursty":
-            return (r"$\log_{10} b$", r"$\log_{10} \beta$", r"$\log_{10} \gamma$")
+            param_str += [r"$\log_{10} b$", r"$\log_{10} \beta$", r"$\log_{10} \gamma$"]
         elif self.bio_model == "Extrinsic":
-            return (r"$\log_{10} \alpha$", r"$\log_{10} \beta$", r"$\log_{10} \gamma$")
+            param_str += [
+                r"$\log_{10} \alpha$",
+                r"$\log_{10} \beta$",
+                r"$\log_{10} \gamma$",
+            ]
         elif self.bio_model == "CIR":
-            return (r"$\log_{10} b$", r"$\log_{10} \beta$", r"$\log_{10} \gamma$")
+            param_str += [r"$\log_{10} b$", r"$\log_{10} \beta$", r"$\log_{10} \gamma$"]
         else:
             raise ValueError(
                 "Please select a biological noise model from {}.".format(
                     self.available_biomodels
                 )
             )
+        if self.amb_model == "Equal":
+            param_str += [r"$\log_{10} p$"]
+        elif self.amb_model == "Unequal":
+            param_str += [r"$\log_{10} p_N$", r"$\log_{10} p_M$"]
+        return param_str
 
     def get_num_params(self):
         """Return the number of parameters for the model instance.

diff --git a/src/monod/extract_data.py b/src/monod/extract_data.py
@@ -281,7 +281,9 @@ def __init__(self, attr_names, *input_data):
         for j in range(len(input_data)):
             setattr(self, attr_names[j], input_data[j])
 
-    def get_noise_decomp(self, sizefactor="pf", lognormalize=True, pcount=0, which_variance_measure='CV2'):
+    def get_noise_decomp(
+        self, sizefactor="pf", lognormalize=True, pcount=0, which_variance_measure="CV2"
+    ):
         """
         This method performs normalization and variance stabilization on the raw data, and
         reports the fractions of normalized variance retained and removed as a result of the process.
@@ -312,14 +314,13 @@ def get_noise_decomp(self, sizefactor="pf", lognormalize=True, pcount=0, which_v
         S = np.copy(self.layers[1])
         U = np.copy(self.layers[0])
 
-        if which_variance_measure == 'CV2':
-            var_fun = lambda X: X.var(1) / (X.mean(1)**2)
-        elif which_variance_measure == 'var':
+        if which_variance_measure == "CV2":
+            var_fun = lambda X: X.var(1) / (X.mean(1) ** 2)
+        elif which_variance_measure == "var":
             var_fun = lambda X: X.var(1)
-        elif which_variance_measure == 'Fano':
+        elif which_variance_measure == "Fano":
             var_fun = lambda X: X.var(1) / (X.mean(1))
 
-
         var_S = var_fun(S)
         var_U = var_fun(U)
         # CV2_S = S.var(1) / S.mean(1) ** 2

diff --git a/src/monod/preprocess.py b/src/monod/preprocess.py
@@ -14,7 +14,7 @@
 import scipy
 from scipy import sparse
 
-code_ver_global = "025"  # bumping up version early Nov
+code_ver_global = "026"  # bumping up version early Nov
 
 ########################
 ## Debug and error logging