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counts = pd.read_csv("../SPARK-Analysis/processed_data/sim_MOB_pattern2_fc3_tau35_count_power1.csv", index_col = 0)
counts = counts.T[(counts > 0).sum(0) >= 3].T

x, y = zip(*[pos.split('x') for pos in counts.index])
sample_info = pd.DataFrame({
    'x': np.array(x).astype(float), 
    'y': np.array(y).astype(float), 
    'total_counts': counts.sum(1)
})

norm_expr = NaiveDE.stabilize(counts.T).T
resid_expr = NaiveDE.regress_out(sample_info, norm_expr.T, 'np.log(total_counts)').T

X = sample_info[['x', 'y']]
results = SpatialDE.run(X, resid_expr)

(results.qval < 0.05).sum() # returns 0

countdata <- read.csv("../SPARK-Analysis/processed_data/sim_MOB_pattern2_fc3_tau35_count_power1.csv", row.names = 1)

rn <- row.names(countdata)
info <- cbind.data.frame(x = as.numeric(sapply(strsplit(rn, split = "x"), "[", 1)), 
                         y = as.numeric(sapply(strsplit(rn, split = "x"), "[", 2)))
rownames(info) <- row.names(countdata)

spark <- CreateSPARKObject(counts = t(countdata), location = info[,1:2], 
    percentage = 0.1, min_total_counts = 10)
spark@lib_size <- apply(spark@counts, 2, sum)

spark <- spark.vc(spark, covariates = NULL, lib_size = spark@lib_size, 
    num_core = 1, verbose = T, fit.maxiter = 500)
spark <- spark.test(spark, check_positive = T, verbose = T)

spark <- spark.test(spark, check_positive = T, verbose = F)

sum(spark@res_mtest$adjusted_pvalue < 0.05) # returns 102