More linting and formatting

Author: aarmey

pf2rnaseq/figures/commonFuncs/plotGeneral.py

@@ -1,13 +1,12 @@
+import anndata
 import numpy as np
 import pandas as pd
-import seaborn as sns
 import scanpy as sc
-from scipy.stats import ranksums
-import anndata
+import scipy.sparse
+import seaborn as sns
 from matplotlib.axes import Axes
+
 from ...factorization import pf2_pca_r2x
-import matplotlib.pyplot as plt
-import scipy.sparse
 
 
 def plot_r2x(data, rank_vec, ax: Axes):

pf2rnaseq/figures/commonFuncs/plotPaCMAP.py

@@ -1,13 +1,13 @@
+import anndata
+import datashader as ds
+import datashader.transfer_functions as tf
+import matplotlib.colors
 import numpy as np
 import pandas as pd
 import seaborn as sns
-import matplotlib.colors
 from matplotlib import pyplot as plt
 from matplotlib.axes import Axes
-import datashader as ds
-import datashader.transfer_functions as tf
 from matplotlib.patches import Patch
-import anndata
 from scipy.sparse import spmatrix
 
 

pf2rnaseq/figures/figureCytokineGeneSet.py

@@ -3,21 +3,18 @@
 """
 
 import pandas as pd
-from anndata import read_h5ad
-from .common import subplotLabel, getSetup
+
+from ..factorization import correct_conditions, pf2
+from ..imports import import_cytokine
+from .common import getSetup, subplotLabel
 from .commonFuncs.plotFactors import (
-    plot_condition_factors,
-    plot_eigenstate_factors,
     plot_geneSet_factors,
-    plot_factor_weight,
     plot_geneSetScore,
-    plot_geneSetScoreDot
+    plot_geneSetScoreDot,
 )
-from ..factorization import correct_conditions
-from ..imports import import_cytokine,import_pf2Cytokine30 
-from ..factorization import pf2
 
-#plots gene component factors for specifc subset of genes
+
+# plots gene component factors for specifc subset of genes
 def samples_only(X) -> pd.DataFrame:
     """Obtain samples once only with corresponding observations"""
     samples = X.obs
@@ -34,20 +31,29 @@ def makeFigure():
     # Add subplot labels
     subplotLabel(ax)
     X = import_cytokine()
-    
 
-    X = pf2(X, 15,regularize_A=True, regParam=5e-1)
-    
+    X = pf2(X, 15, regularize_A=True, regParam=5e-1)
+
     X.uns["Pf2_A"] = correct_conditions(X)
-    
-    immune_suppressive = ["FOXP3","TIGIT","ICOS","IL2RA","PDCD1","TGFB1","SOCS3","PD1", "LAG3","TNFRSF18","CTLA4"]  
-    immune_activating = ["GZMA","GZMAB", "PRF1"] 
-    plot_geneSet_factors(X,ax[0],immune_suppressive, False )
-    #plot_geneSet_factors(X,ax[1],["GZMA","GZMB","PRF1",], False )
-    plot_geneSetScore(X,ax[1],immune_suppressive, False )
-    plot_geneSetScoreDot(X,ax[2],immune_suppressive, False )
-    plot_geneSetScore(X,ax[3],immune_activating, False )
-
-    
-
-    return f
+
+    immune_suppressive = [
+        "FOXP3",
+        "TIGIT",
+        "ICOS",
+        "IL2RA",
+        "PDCD1",
+        "TGFB1",
+        "SOCS3",
+        "PD1",
+        "LAG3",
+        "TNFRSF18",
+        "CTLA4",
+    ]
+    immune_activating = ["GZMA", "GZMAB", "PRF1"]
+    plot_geneSet_factors(X, ax[0], immune_suppressive, False)
+    # plot_geneSet_factors(X,ax[1],["GZMA","GZMB","PRF1",], False )
+    plot_geneSetScore(X, ax[1], immune_suppressive, False)
+    plot_geneSetScoreDot(X, ax[2], immune_suppressive, False)
+    plot_geneSetScore(X, ax[3], immune_activating, False)
+
+    return f

pf2rnaseq/figures/figureCytokineGenes.py

@@ -1,21 +1,20 @@
 """
 Cytokine: Highly weighted genes per component
 """
-from anndata import read_h5ad
+
+from ..factorization import correct_conditions, pf2
+from ..imports import import_cytokine
 from .common import getSetup
 from .commonFuncs.plotFactors import plot_gene_factors_partial
-from ..imports import import_cytokine,import_pf2Cytokine30
-from ..factorization import correct_conditions
-from ..factorization import pf2
 
 
 def makeFigure():
     """Get a list of the axis objects and create a figure."""
     # Get list of axis objects
     ax, f = getSetup((21, 24), (10, 10))
     X = import_cytokine()
-    #X=read_h5ad("/home/nicoleb/Pf2-scRNAseq-1/pf2rnaseq/Cytokine_Pf2__NB30.h5ad")
-    #X = import_pf2Cytokine30()
+    # X=read_h5ad("/home/nicoleb/Pf2-scRNAseq-1/pf2rnaseq/Cytokine_Pf2__NB30.h5ad")
+    # X = import_pf2Cytokine30()
     X = pf2(X, 30, regParam=1e-5, regularize_A=True)
 
     X.uns["Pf2_A"] = correct_conditions(X)

pf2rnaseq/figures/figureCytokineR2X.py

@@ -2,17 +2,10 @@
 Plotting the R2X
 """
 
-import pandas as pd
-from anndata import read_h5ad
-from .common import subplotLabel, getSetup
-from .commonFuncs.plotGeneral import (
-    plot_r2x
-)
-from ..factorization import correct_conditions
-from ..imports import import_pf2Cytokine30, import_cytokine
 from ..factorization import pf2
-
-
+from ..imports import import_cytokine
+from .common import getSetup, subplotLabel
+from .commonFuncs.plotGeneral import plot_r2x
 
 
 def makeFigure():
@@ -22,11 +15,11 @@ def makeFigure():
 
     # Add subplot labels
     subplotLabel(ax)
-    
+
     X = import_cytokine()
-    
+
     X = pf2(X, 30)
-    ranks=list(range(1, 31))
-    plot_r2x(X,ranks,ax[0])
+    ranks = list(range(1, 31))
+    plot_r2x(X, ranks, ax[0])
 
     return f

pf2rnaseq/figures/figureCytokineRegTest.py

@@ -1,17 +1,17 @@
-
 """FMS score for different regularization parameters"""
+
 import anndata
 import numpy as np
 import pandas as pd
-import scanpy as sc
 import seaborn as sns
+import wandb
 from matplotlib.axes import Axes
 from tensorly.cp_tensor import CPTensor
 from tlviz.factor_tools import factor_match_score as fms
-import wandb
+
 from ..factorization import pf2, pf2_pca_r2x
-from .common import getSetup, subplotLabel
 from ..imports import import_cytokine
+from .common import getSetup, subplotLabel
 
 run = wandb.init(
     # Set the wandb entity where your project will be logged (generally your team name).
@@ -21,23 +21,19 @@
     # Track hyperparameters and run metadata.
     config={
         "rank": 20,
-        
     },
 )
 
 
-
 def makeFigure():
     ax, f = getSetup((6, 3), (1, 1))
     subplotLabel(ax)
 
     X = import_cytokine()
     lambdaList = [5e-6, 1e-5, 5e-5, 1e-4]
-    rank=np.arange(1, 31, 5)
+    rank = np.arange(1, 31, 5)
     plot_fms_diff_reg(X, ax[0], 20, lambdaList, runs=3)
 
-    
-
     return f
 
 
@@ -76,22 +72,23 @@ def plot_fms_diff_reg(
     regsList: list[float],
     runs: int,
 ):
-    #Plots FMS when using different regularization params
+    # Plots FMS when using different regularization params
     fmsLists = []
 
     for j in range(0, runs, 1):
         scores = []
         for r in rank:
             for i in regsList:
-                dataX = pf2(X, rank=r, random_state=j, doEmbedding=False,regParam=i)
+                dataX = pf2(X, rank=r, random_state=j, doEmbedding=False, regParam=i)
 
-                sampledX = pf2(resample(X), rank=r, random_state=j, doEmbedding=False, regParam=i)
+                sampledX = pf2(
+                    resample(X), rank=r, random_state=j, doEmbedding=False, regParam=i
+                )
                 r2xError = pf2_pca_r2x(dataX, r)
                 fmsScore = calculateFMS(dataX, sampledX)
                 scores.append(fmsScore)
-                run.log({"fms": fmsScore,"regParam": i,"rank": r, "R2X": r2xError})
+                run.log({"fms": fmsScore, "regParam": i, "rank": r, "R2X": r2xError})
             fmsLists.append(scores)
-        
 
     runsList_df = []
     for i in range(0, runs):
@@ -114,4 +111,3 @@ def plot_fms_diff_reg(
     run.finish()
     sns.lineplot(data=df, x="Parameter", y="FMS", ax=ax)
     ax.set_ylim(0, 1)
-

pf2rnaseq/figures/figureHeiser1.py

@@ -3,20 +3,16 @@
 """
 
 import pandas as pd
-import numpy as np
-import scipy.sparse as sp
-from anndata import read_h5ad, AnnData
-import anndata as an
-from .common import subplotLabel, getSetup
+
+from ..factorization import correct_conditions, pf2
+from ..imports import import_Heiser
+from .common import getSetup, subplotLabel
 from .commonFuncs.plotFactors import (
     plot_condition_factors_groups,
     plot_eigenstate_factors,
-    plot_gene_factors,
     plot_factor_weight,
+    plot_gene_factors,
 )
-from ..factorization import correct_conditions
-from ..imports import import_Heiser
-from ..factorization import pf2
 
 
 def samples_only(X) -> pd.DataFrame:

pf2rnaseq/figures/figureHeiserCellPac.py

@@ -1,12 +1,9 @@
 "Plot cell lineage pacmap"
 
-import pandas as pd
 from anndata import read_h5ad
-from .common import subplotLabel, getSetup
-from .commonFuncs.plotPaCMAP import plot_labels_pacmap
-from ..factorization import correct_conditions
 
-from ..factorization import pf2
+from .common import getSetup, subplotLabel
+from .commonFuncs.plotPaCMAP import plot_labels_pacmap
 
 
 def makeFigure():

pf2rnaseq/figures/figureHeiserCellTypePer.py

@@ -2,10 +2,10 @@
 Cell type percentage per Leiden cluster per condition
 """
 
-from anndata import read_h5ad
-from ..imports import import_Heiser
-from .common import subplotLabel, getSetup
 import seaborn as sns
+
+from ..imports import import_Heiser
+from .common import getSetup, subplotLabel
 from .commonFuncs.plotGeneral import cell_count_perc_df
 
 
@@ -17,11 +17,8 @@ def makeFigure():
     # Add subplot labels
     subplotLabel(ax)
 
-    
-   
     X = import_Heiser()
 
-    
     df = cell_count_perc_df(X, celltype="cell_type_1", grouping="expBatch")
 
     sns.barplot(

pf2rnaseq/figures/figureHeiserCompPac.py

@@ -2,18 +2,16 @@
 Weighted projections per component in PaCMAP and boxplot of cell types
 """
 
-import pandas as pd
 
 import numpy as np
-from .common import subplotLabel, getSetup
-from anndata import read_h5ad
+
+from ..factorization import correct_conditions, pf2
+from ..imports import import_Heiser
+from .common import getSetup, subplotLabel
 from .commonFuncs.plotPaCMAP import (
-    plot_wp_per_celltype,
     plot_wp_pacmap,
+    plot_wp_per_celltype,
 )
-from ..factorization import correct_conditions
-from ..imports import import_Heiser
-from ..factorization import pf2
 
 
 def makeFigure():