text: import matplotlib.pyplot as plt
  import pyVIA.examples as via#examples as via
  import pandas as pd
  import umap
  import scanpy as sc
  import numpy as np
  import warnings
  
  
  def run_Toy_multi(foldername="Datasets/"):
      via.main_Toy(ncomps=10, knn=30,dataset='Toy3', random_seed=2,foldername=foldername)
  
  
  def run_Toy_discon(foldername="Datasets/"):
      via.main_Toy(ncomps=10, knn=30,dataset='Toy4', random_seed=2,foldername=foldername)
  
  
  def run_EB(foldername="Datasets/"):
      via.main_EB_clean(ncomps=30, knn=20, v0_random_seed=24, foldername=foldername)
  
  
  def run_generic_wrapper(foldername = "Datasets/", knn=20, ncomps = 20):
      # Read the two files:
      # 1) the first file contains 200PCs of the Bcell filtered and normalized data for the first 5000 HVG.
      # 2)The second file contains raw count data for marker genes
  
      data = pd.read_csv(foldername+'Bcell_200PCs.csv')
      data_genes = pd.read_csv(foldername+'Bcell_markergenes.csv')
      data_genes = data_genes.drop(['Unnamed: 0'], axis=1)#cell
      true_label = data['time_hour']
      data = data.drop(['cell', 'time_hour'], axis=1)
      adata = sc.AnnData(data_genes)
      adata.obsm['X_pca'] = data.values
  
      # use UMAP or PHate to obtain embedding that is used for single-cell level visualization
      embedding = umap.UMAP(random_state=42, n_neighbors=15, init='random').fit_transform(data.values[:, 0:5])
      print('finished embedding')
      # list marker genes or genes of interest if known in advance. otherwise marker_genes = []
      marker_genes = ['Igll1', 'Myc', 'Slc7a5', 'Ldha', 'Foxo1', 'Lig4', 'Sp7']  # irf4 down-up
      # call VIA. We identify an early (suitable) start cell root = [42]. Can also set an arbitrary value
      via.via_wrapper(adata, true_label, embedding, knn=knn, ncomps=ncomps, jac_std_global=0.15, root=[42], dataset='',
                  random_seed=1,v0_toobig=0.3, v1_toobig=0.1, marker_genes=marker_genes, piegraph_edgeweight_scalingfactor=1, piegraph_arrow_head_width=.2)
  
  def run_faced_cell_cycle(foldername = '/home/shobi/Trajectory/Datasets/FACED/'):
      #FACED imaging cytometry based biophysical features
      df = pd.read_csv(foldername +'mcf7_38features.csv')
      df = df.drop('Unnamed: 0', 1)
  
      true_label = pd.read_csv(foldername+'mcf7_phases.csv')
      true_label = list(true_label['phase'].values.flatten())
      print('There are ', len(true_label), 'MCF7 cells and ', df.shape[1], 'features')
  
      ad = sc.AnnData(df)
      ad.var_names = df.columns
      # normalize features
      sc.pp.scale(ad)
  
      sc.tl.pca(ad, svd_solver='arpack')
      # Weight the top features (ranked by Mutual Information and Random Forest Classifier)
      X_in = ad.X
      df_X = pd.DataFrame(X_in)
      df_X.columns = df.columns
  
      df_X['Area'] = df_X['Area'] * 3
      df_X['Dry Mass'] = df_X['Dry Mass'] * 3
      df_X['Volume'] = df_X['Volume'] * 20
  
      X_in = df_X.values
      ad = sc.AnnData(df_X)
      # apply PCA
      sc.tl.pca(ad, svd_solver='arpack')
      ad.var_names = df_X.columns
  
      f, ax = plt.subplots(figsize=[20, 10])
  
      embedding = umap.UMAP().fit_transform(ad.obsm['X_pca'][:, 0:20])
      # phate_op = phate.PHATE()
      # embedding = phate_op.fit_transform(X_in)
  
      cell_dict = {'T1_M1': 'yellow', 'T2_M1': 'yellowgreen', 'T1_M2': 'orange', 'T2_M2': 'darkgreen', 'T1_M3': 'red',
                   'T2_M3': 'blue'}
      cell_phase_dict = {'T1_M1': 'G1', 'T2_M1': 'G1', 'T1_M2': 'S', 'T2_M2': 'S', 'T1_M3': 'M/G2', 'T2_M3': 'M/G2'}
  
      for key in list(set(true_label)):  # ['T1_M1', 'T2_M1','T1_M2', 'T2_M2','T1_M3', 'T2_M3']:
          loc = np.where(np.asarray(true_label) == key)[0]
          ax.scatter(embedding[loc, 0], embedding[loc, 1], c=cell_dict[key], alpha=.7, label=cell_phase_dict[key])
      plt.legend(markerscale=1.5, fontsize=14)
      plt.show()
  
      knn = 20
      jac_std_global = 0.5
      random_seed = 1
      root_user = ['T1_M1']
      v0 = via.VIA(X_in, true_label, jac_std_global=jac_std_global, dist_std_local=1, knn=knn,
                   cluster_graph_pruning_std=1.,
                   too_big_factor=0.3, root_user=root_user, dataset='faced', random_seed=random_seed,
                   do_impute_bool=True, is_coarse=True, preserve_disconnected=True,
                   preserve_disconnected_after_pruning=True,
                   pseudotime_threshold_TS=40)
      v0.run_VIA()
  
      tsi_list = via.get_loc_terminal_states(v0, X_in)
  
      v1 = via.VIA(X_in, true_label, jac_std_global=jac_std_global, dist_std_local=1, knn=knn,
                   cluster_graph_pruning_std=1.,
                   too_big_factor=0.05, super_cluster_labels=v0.labels, super_node_degree_list=v0.node_degree_list,
                   super_terminal_cells=tsi_list, root_user=root_user, is_coarse=False,
                   preserve_disconnected=True, dataset='faced',
                   super_terminal_clusters=v0.terminal_clusters, random_seed=random_seed,
                   full_neighbor_array=v0.full_neighbor_array, full_distance_array=v0.full_distance_array,
                   ig_full_graph=v0.ig_full_graph,
                   csr_array_locally_pruned=v0.csr_array_locally_pruned, pseudotime_threshold_TS=40)
      v1.run_VIA()
  
      super_clus_ds_PCA_loc = via.sc_loc_ofsuperCluster_PCAspace(v0, v1, np.arange(0, len(v0.labels)))
      via.draw_trajectory_gams(embedding, super_clus_ds_PCA_loc, v1.labels, v0.labels, v0.edgelist_maxout,
                               v1.x_lazy, v1.alpha_teleport, v1.single_cell_pt_markov, true_label, knn=v0.knn,
                               final_super_terminal=v1.revised_super_terminal_clusters,
                               sub_terminal_clusters=v1.terminal_clusters,
                               title_str='Hitting times: Markov Simulation on biased edges', ncomp=38)
      plt.show()
  
      all_cols = ['Area', 'Volume', 'Dry Mass', 'Circularity', 'Orientation', 'Phase Entropy Skewness',
                  'Phase Fiber Radial Distribution', 'Eccentricity', 'AspectRatio', 'Dry Mass Density', 'Dry Mass var',
                  'Dry Mass Skewness', 'Peak Phase', 'Phase Var', 'Phase Skewness', 'Phase Kurtosis', 'Phase Range',
                  'Phase Min', 'Phase Centroid Displacement', 'Phase STD Mean', 'Phase STD Variance',
                  'Phase STD Skewness', 'Phase STD Kurtosis', 'Phase STD Centroid Displacement',
                  'Phase STD Radial Distribution', 'Phase Entropy Mean', 'Phase Entropy Var', 'Phase Entropy Kurtosis',
                  'Phase Entropy Centroid Displacement', 'Phase Entropy Radial Distribution',
                  'Phase Fiber Centroid Displacement', 'Phase Fiber Pixel >Upper Percentile', 'Phase Fiber Pixel >Median',
                  'Mean Phase Arrangement', 'Phase Arrangement Var', 'Phase Arrangement Skewness',
                  'Phase Orientation Var', 'Phase Orientation Kurtosis']
      plot_n = 7
      fig, axs = plt.subplots(2, plot_n, figsize=[20, 10])  # (2,10)
      for enum_i, pheno_i in enumerate(all_cols[0:14]):  # [0:14]
          subset_ = df[pheno_i].values
  
          if enum_i >= plot_n:
              row = 1
              col = enum_i - plot_n
          else:
              row = 0
              col = enum_i
          ax = axs[row, col]
          v0.get_gene_expression_multi(ax=ax, gene_exp=subset_, title_gene=pheno_i)
  
      fig2, axs2 = plt.subplots(2, plot_n, figsize=[20, 10])
      for enum_i, pheno_i in enumerate(all_cols[2 * plot_n:2 * plot_n + 14]):
          subset_ = df[pheno_i].values
  
          if enum_i >= plot_n:
              row = 1
              col = enum_i - plot_n
          else:
              row = 0
              col = enum_i
  
          ax2 = axs2[row, col]
          v0.get_gene_expression_multi(ax=ax2, gene_exp=subset_, title_gene=pheno_i)
  
      plt.show()
  
  
  def run_scATAC_Buenrostro_Hemato(foldername = '/home/shobi/Trajectory/Datasets/scATAC_Hemato/', knn=20):
  
      df = pd.read_csv(foldername+'scATAC_hemato_Buenrostro.csv', sep=',')
      print('number cells', df.shape[0])
      cell_types = ['GMP', 'HSC', 'MEP', 'CLP', 'CMP', 'LMuPP', 'MPP', 'pDC', 'mono', 'UNK']
      cell_dict = {'UNK': 'gray', 'pDC': 'purple', 'mono': 'gold', 'GMP': 'orange', 'MEP': 'red', 'CLP': 'aqua',
                   'HSC': 'black', 'CMP': 'moccasin', 'MPP': 'darkgreen', 'LMuPP': 'limegreen'}
      cell_annot = df['cellname'].values
  
      true_label = []
      found_annot = False
      #re-formatting labels (abbreviating the original annotations for better visualization on plot labels)
      for annot in cell_annot:
          for cell_type_i in cell_types:
              if cell_type_i in annot:
                  true_label.append(cell_type_i)
                  found_annot = True
  
          if found_annot == False:
              true_label.append('unknown')
          found_annot = False
  
      PCcol = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5']
      embedding = umap.UMAP(n_neighbors=20, random_state=2, repulsion_strength=0.5).fit_transform(df[PCcol])
  
      fig, ax = plt.subplots(figsize=[20, 10])
      for key in cell_dict:
          loc = np.where(np.asarray(true_label) == key)[0]
          ax.scatter(embedding[loc, 0], embedding[loc, 1], c=cell_dict[key], alpha=0.7, label=key, s=90)
  
      plt.legend(fontsize='large', markerscale=1.3)
      plt.title('Original Annotations on UMAP embedding')
      plt.show()
  
      knn = knn
      random_seed = 4
      X_in = df[PCcol].values
  
      start_ncomp = 0
      root = [1200]  # HSC cell
  
      v0 = via.VIA(X_in, true_label, jac_std_global=0.5, dist_std_local=1, knn=knn,
                   cluster_graph_pruning_std=.15,
                   too_big_factor=0.3, root_user=root, dataset='scATAC', random_seed=random_seed,
                   do_impute_bool=True, is_coarse=True, preserve_disconnected=False)
      v0.run_VIA()
  
      tsi_list = via.get_loc_terminal_states(v0, X_in)
  
      v1 = via.VIA(X_in, true_label, jac_std_global=0.15, dist_std_local=1, knn=knn,
                   cluster_graph_pruning_std=.15,
                   too_big_factor=0.1, super_cluster_labels=v0.labels, super_node_degree_list=v0.node_degree_list,
                   super_terminal_cells=tsi_list, root_user=root, is_coarse=False,
                   preserve_disconnected=True, dataset='scATAC',
                   super_terminal_clusters=v0.terminal_clusters, random_seed=random_seed,
                   full_neighbor_array=v0.full_neighbor_array, full_distance_array=v0.full_distance_array,
                   ig_full_graph=v0.ig_full_graph,
                   csr_array_locally_pruned=v0.csr_array_locally_pruned)
      v1.run_VIA()
  
      df['via1'] = v1.labels
      df_mean = df.groupby('via1', as_index=False).mean()
      gene_dict = {'ENSG00000092067_LINE336_CEBPE_D_N1': 'CEBPE Eosophil (GMP/Mono)',
                   'ENSG00000102145_LINE2081_GATA1_D_N7': 'GATA1 (MEP)'}
      for key in gene_dict:
          f, ((ax, ax1)) = plt.subplots(1, 2, sharey=True, figsize=[10, 5])
          v1.draw_piechart_graph(ax, ax1, type_pt='gene', gene_exp=df_mean[key].values, title=gene_dict[key])
          plt.show()
      # get knn-graph and locations of terminal states in the embedded space
      knn_hnsw = via.make_knn_embeddedspace(embedding)
      super_clus_ds_PCA_loc = via.sc_loc_ofsuperCluster_PCAspace(v0, v1, np.arange(0, len(v0.labels)))
      # draw overall pseudotime and main trajectories
      via.draw_trajectory_gams(embedding, super_clus_ds_PCA_loc, v1.labels, v0.labels, v0.edgelist_maxout,
                               v1.x_lazy, v1.alpha_teleport, v1.single_cell_pt_markov, true_label, knn=v0.knn,
                               final_super_terminal=v1.revised_super_terminal_clusters,
                               sub_terminal_clusters=v1.terminal_clusters,
                               title_str='Pseudotime', ncomp=5 )
      plt.show()
      # draw trajectory and evolution probability for each lineage
      via.draw_sc_evolution_trajectory_dijkstra(v1, embedding, knn_hnsw, v0.full_graph_shortpath,
                                                np.arange(0, len(true_label)), X_in)
      plt.show()
  
  def run_generic_discon(foldername ="/home/shobi/Trajectory/Datasets/Toy4/"):
      df_counts = pd.read_csv(foldername + "toy_disconnected_M9_n1000d1000.csv",
                              delimiter=",")
      df_ids = pd.read_csv(foldername + "toy_disconnected_M9_n1000d1000_ids.csv", delimiter=",")
  
  
  
      df_ids['cell_id_num'] = [int(s[1::]) for s in df_ids['cell_id']]
  
      df_counts = df_counts.drop('Unnamed: 0', 1)
      df_ids = df_ids.sort_values(by=['cell_id_num'])
      df_ids = df_ids.reset_index(drop=True)
      #true_label = df_ids['group_id']
      #true_label =['a' for i in true_label] #testing dummy true_label and overwriting the real true_labels
      #true_time = df_ids['true_time']
      adata_counts = sc.AnnData(df_counts, obs=df_ids)
      sc.tl.pca(adata_counts, svd_solver='arpack', n_comps=100)
  
      via.via_wrapper_disconnected(adata_counts, true_label=None, embedding=adata_counts.obsm['X_pca'][:, 0:2], root=[23, 902],
                               preserve_disconnected=True, knn=10, ncomps=30, cluster_graph_pruning_std=1, random_seed=41)
  
  
  if __name__ == '__main__':
      warnings.filterwarnings('ignore')
      run_Toy_multi()
  
  