Merge branch 'main' of https://github.com/tkchafin/autostreamtree

tkchafin · Feb 23, 2024 · a11c6c1 · a11c6c1
2 parents a2902d6 + 540f9e0
commit a11c6c1
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diff --git a/.github/workflows/conda-docker-update.yml b/.github/workflows/conda-docker-update.yml
@@ -32,11 +32,15 @@ jobs:
           python-version: "3.10"
           miniconda-version: latest
 
+      - name: Install Dependencies
+        shell: bash -l {0} 
+        run: |
+          conda install -y anaconda::conda-build>=24.1.0 anaconda-client conda-verify
+
       - name: Build Conda Package
-        shell: bash -l {0}
+        shell: bash -l {0} 
         run: |
-          conda install conda-build anaconda-client conda-verify
-          conda build conda-recipe --output-folder ./build -c ecoevoinfo -c conda-forge -c bioconda 
+          conda build conda-recipe --output-folder ./build -c ecoevoinfo -c conda-forge -c bioconda || true 
 
       - name: Upload Conda Package to Anaconda Cloud
         shell: bash -l {0}

diff --git a/autostreamtree/cli.py b/autostreamtree/cli.py
@@ -96,7 +96,7 @@ def main():
 
         ast.report_genmats_list(params, genlist, popgenlist, point_coords,
                                 pop_coords)
-
+        ast.report_genmats(params, gen, pop_gen, point_coords, pop_coords)
     # Step 4: Constructing a minimal subgraph
 
     # Extract subgraph if needed
@@ -181,8 +181,8 @@ def main():
             print("Fitting StreamTree distances on per-locus matrices...")
             Rlist = list()
             ast.block_print()
-            for gen in genlist:
-                r = ast.fit_least_squares_distances(gen, inc.astype(int),
+            for locgen in genlist:
+                r = ast.fit_least_squares_distances(locgen, inc.astype(int),
                                                     params.iterative,
                                                     params.out, params.weight)
                 Rlist.append(r)
@@ -300,8 +300,8 @@ def main():
         if params.run == "RUNLOCI":
             geoDF = geoDF.merge(sdD, on='EDGE_ID')
             geoDF.plot(column="stdevD", cmap="RdYlGn_r", legend=True)
-            plt.title("Stream network colored by standard deviation of",
-                      "StreamTree fitted distances")
+            plt.title("Stream network colored by standard deviation of"+
+                      " StreamTree fitted distances")
             plt.savefig((str(params.out)+".streamsBystdevD.pdf"))
         geoDF.plot(column="fittedD", cmap="RdYlGn_r", legend=True)
         plt.title("Stream network colored by StreamTree fitted distances")

diff --git a/autostreamtree/params.py b/autostreamtree/params.py
@@ -155,9 +155,6 @@ def __init__(self):
                 self.overwrite = True
             elif opt == "perm":
                 self.permutations = int(arg)
-            elif opt == "method":
-                print("Sorry: Option --method is not yet implemented.")
-                sys.exit(0)
             elif opt in ("plot", "plots"):
                 self.plots = True
             elif opt == "phased":

diff --git a/paper/paper.md b/paper/paper.md
@@ -61,7 +61,7 @@ Users may also provide pre-computed genetic distance matrices either directly at
 
 ## Empirical case study
 
-To demonstrate autoStreamTree, we employed existing SNP data for Speckled Dace (*Rhinichthys osculus*)[@Mussmann2018]. Data represent 13,219 SNPs from N=762 individuals across 78 localities in the Colorado River ecosystem.
+To demonstrate autoStreamTree, we employed existing SNP data for Speckled Dace (*Rhinichthys osculus*)[@Mussmann2018]. Data represent 5,742 SNPs from N=762 individuals across 78 localities in the Colorado River ecosystem, after removing those with >=50% missing data or minor allele frequency (MAF) < 0.1.
 
 Stream networks were parsed directly as a minimal sub-graph from RiverATLAS, which contains various local-scale environmental/ hydrological features as annotations (i.e., physiography, climate, land-cover, geology, anthropogenic effects) [@Linke2019]. Genetic distances were computed globally and per-locus among sites as linearized *F*~ST~ [@Weir1984] (=*F*~ST~/1-*F*~ST~). To compare with @Kalinowski2008, we used unweighted least-squares, iterative negative distance correction, and replicated analyses using linearized *F*~ST~ independently recalculated in Adegenet [@Jombart2008].