remove unnecessary whitespace

README.md

@@ -63,15 +63,15 @@ To clone the repository and install manually, run the following from a terminal:
 
 The following code runs MAGIC on test data located in the MAGIC repository.
 
-		import magic
-		import pandas as pd
-		import matplotlib.pyplot as plt
-		X = pd.read_csv("MAGIC/data/test_data.csv")
-		magic_operator = magic.MAGIC()
-		X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
-		plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
-		plt.show()
-		magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
+    import magic
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    X = pd.read_csv("MAGIC/data/test_data.csv")
+    magic_operator = magic.MAGIC()
+    X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
+    plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
+    plt.show()
+    magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
 
 #### Tutorials
 
@@ -124,12 +124,12 @@ To clone the repository and install manually, run the following from a terminal:
 
 After installing the package, MAGIC can be run by loading the library and calling `magic()`:
 
-		library(Rmagic)
-		library(ggplot2)
-		data(magic_testdata)
-		MAGIC_data <- magic(magic_testdata, genes=c("VIM", "CDH1", "ZEB1"))
-		ggplot(MAGIC_data) +
-		  geom_point(aes(x=VIM, y=CDH1, color=ZEB1))
+    library(Rmagic)
+    library(ggplot2)
+    data(magic_testdata)
+    MAGIC_data <- magic(magic_testdata, genes=c("VIM", "CDH1", "ZEB1"))
+    ggplot(MAGIC_data) +
+      geom_point(aes(x=VIM, y=CDH1, color=ZEB1))
 
 #### Tutorials
 

python/README.md

@@ -40,15 +40,15 @@ To clone the repository and install manually, run the following from a terminal:
 
 The following code runs MAGIC on test data located in the MAGIC repository.
 
-	import magic
-	import pandas as pd
-	import matplotlib.pyplot as plt
-	X = pd.read_csv("MAGIC/data/test_data.csv")
-	magic_operator = magic.MAGIC()
-	X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
-	plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
-	plt.show()
-	magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
+    import magic
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    X = pd.read_csv("MAGIC/data/test_data.csv")
+    magic_operator = magic.MAGIC()
+    X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
+    plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
+    plt.show()
+    magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
 
 ##### Interactive command line
 We have included two tutorial notebooks on MAGIC usage and results visualization for single cell RNA-seq data.

python/doc/source/index.rst

@@ -55,15 +55,15 @@ Quick Start
 
 To run MAGIC on your dataset, create a MAGIC operator and run `fit_transform`. Here we show an example with a small, artificial dataset located in the MAGIC repository::
 
-        import magic
-        import pandas as pd
-        import matplotlib.pyplot as plt
-        X = pd.read_csv("MAGIC/data/test_data.csv")
-        magic_operator = magic.MAGIC()
-        X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
-        plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
-        plt.show()
-        magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
+    import magic
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    X = pd.read_csv("MAGIC/data/test_data.csv")
+    magic_operator = magic.MAGIC()
+    X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
+    plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
+    plt.show()
+    magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
 
 Help
 ====

python/doc/source/tutorial.rst

@@ -3,15 +3,15 @@ Tutorial
 
 To run MAGIC on your dataset, create a MAGIC operator and run `fit_transform`. Here we show an example with an artificial test dataset located in the MAGIC repository::
 
-        import magic
-        import matplotlib.pyplot as plt
-        import pandas as pd
-        X = pd.read_csv("MAGIC/data/test_data.csv")
-        magic_operator = magic.MAGIC()
-        X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
-        plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
-        plt.show()
-        magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
+    import magic
+    import matplotlib.pyplot as plt
+    import pandas as pd
+    X = pd.read_csv("MAGIC/data/test_data.csv")
+    magic_operator = magic.MAGIC()
+    X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
+    plt.scatter(X_magic['VIM'], X_magic['CDH1'], c=X_magic['ZEB1'], s=1, cmap='inferno')
+    plt.show()
+    magic.plot.animate_magic(X, gene_x='VIM', gene_y='CDH1', gene_color='ZEB1', operator=magic_operator)
 
 A demo on MAGIC usage for single cell RNA-seq data can be found in this notebook_: `http://nbviewer.jupyter.org/github/KrishnaswamyLab/magic/blob/master/python/tutorial_notebooks/emt_tutorial.ipynb`__