#!/usr/bin/env python
# coding: utf-8

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# get the latest version from pypi 
# for other installations approaches, see https://dynamo-release.readthedocs.io/en/latest/ten_minutes_to_dynamo.html#how-to-install
# !pip install dynamo-release --upgrade --quiet


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from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
get_ipython().run_line_magic('matplotlib', 'inline')

import dynamo as dyn
dyn.get_all_dependencies_version()


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# simulate ggplot2 plotting styple with black background

dyn.configuration.set_figure_params('dynamo', background='white')  


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adata = dyn.sample_data.hgForebrainGlutamatergic()
adata.obs.Clusters = adata.obs.Clusters.astype('str') # set Clusters to strings 


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dyn.pl.show_fraction(adata)


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dyn.pp.recipe_monocle(adata, n_top_genes=1000)


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dyn.pl.variance_explained(adata)
dyn.pl.feature_genes(adata)


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dyn.tl.dynamics(adata, model='deterministic') 
# or dyn.tl.dynamics(adata, model='stochastic')
# or dyn.tl.dynamics(adata, model='stochastic', est_method='negbin')


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dyn.tl.reduceDimension(adata)


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dyn.tl.cell_velocities(adata)


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dyn.pl.phase_portraits(adata, genes=['RERE', 'ENO1', 'DHRS3'], ncols=3, figsize=(3, 3), basis='pca', show_quiver=False)


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dyn.pl.cell_wise_vectors(adata, color=['Clusters'], basis='pca', quiver_size=2, quiver_length=1, show_legend='on data') # ['GRIA3', 'LINC00982', 'AFF2']


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dyn.pl.grid_vectors(adata, color=['Clusters'], basis='pca')


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dyn.pl.streamline_plot(adata, color=['Clusters'], basis='pca') 


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dyn.vf.VectorField(adata, basis='pca', dims=[0, 1])


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dyn.pl.topography(adata, color=['Clusters', 'RERE'], basis='pca', ncols=2)


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dyn.configuration.set_figure_params('dynamo', background='white')  


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dyn.pl.phase_portraits(adata, genes=['RERE', 'ENO1', 'DHRS3'], ncols=3, figsize=(3, 3), basis='pca', show_quiver=False)

dyn.pl.cell_wise_vectors(adata, color=['Clusters'], basis='pca', quiver_size=2, quiver_length=1) # ['GRIA3', 'LINC00982', 'AFF2']

dyn.pl.grid_vectors(adata, color=['Clusters'], basis='pca', method='gaussian')

dyn.pl.streamline_plot(adata, color=['Clusters'], basis='pca') 

dyn.pl.topography(adata, color=['Clusters', 'RERE'], basis='pca', ncols=2)



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dyn.vf.VectorField(adata, basis='pca', dims=[0, 1], M=adata.n_obs)


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dyn.pl.streamline_plot(adata, color=['ELAVL4'], basis='pca', show_legend='on data',  background='white', 
                        show_arrowed_spines=False, save_show_or_return='save',
                        pointsize=0.4, ncols=1, alpha = 0.7, 
                       save_kwargs={'prefix': 'hg_streamline', 'ext': 'png',
                                     "bbox_inches": None}, 
                        figsize=(3, 3))


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dyn.pl.phase_portraits(adata, genes=['ELAVL4'], basis='pca',
                       show_quiver=True,
                       background='white',
                        show_arrowed_spines=False, save_show_or_return='save',
                        pointsize=0.4, alpha = 0.7, quiver_length=10, quiver_size=6, 
                       save_kwargs={'prefix': 'hg_phase_portrait', 'ext': 'png',
                                     "bbox_inches": None}, 
                        figsize=(6, 4))


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dyn.pl.topography(adata, color=['ELAVL4'], basis='pca', show_legend='on data',  background='white',
                        show_arrowed_spines=False, save_show_or_return='save',
                        pointsize=0.4, ncols=1, alpha = 0.7, 
                       save_kwargs={'prefix': 'hg_topography', 'ext': 'png',
                                     "bbox_inches": None}, 
                        figsize=(3, 3))


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dyn.tl.cell_velocities(adata, basis='pca')
dyn.vf.VectorField(adata, basis='pca', M=adata.n_obs, pot_curl_div=True)


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adata


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dyn.tl.cell_velocities(adata, basis='pca')
dyn.vf.VectorField(adata, basis='pca')
dyn.vf.speed(adata, basis='pca')
dyn.vf.divergence(adata, basis='pca')
dyn.vf.acceleration(adata, basis='pca')
dyn.vf.VectorField(adata, basis='umap', M=adata.n_obs)
dyn.vf.curl(adata, basis='umap')


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adata


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import matplotlib.pyplot as plt
fig1, f1_axes = plt.subplots(ncols=5, nrows=1, constrained_layout=True, figsize=(15, 3))
f1_axes
f1_axes[0] = dyn.pl.streamline_plot(adata, basis='pca', color='pca_ddhodge_potential', pointsize=0.1, alpha = 0.7, ax=f1_axes[0], quiver_length=6, 
                                    quiver_size=6, save_show_or_return='return', show_arrowed_spines=False)
f1_axes[1] = dyn.pl.streamline_plot(adata, basis='pca', color='speed_pca', ax=f1_axes[1], quiver_length=12, quiver_size=12, save_show_or_return='return', 
                                    show_arrowed_spines=False)
f1_axes[2] = dyn.pl.streamline_plot(adata, basis='pca', color='divergence_pca', ax=f1_axes[2], save_show_or_return='return',
                                   show_arrowed_spines=False, cmap='bwr', s_kwargs_dict= {"sym_c": True})
f1_axes[3] = dyn.pl.streamline_plot(adata, basis='pca', color='acceleration_pca', ax=f1_axes[3], save_show_or_return='return', 
                                    show_arrowed_spines=False)
f1_axes[4] = dyn.pl.streamline_plot(adata, basis='pca', color='curl_umap', ax=f1_axes[4], save_show_or_return='return', 
                                    show_arrowed_spines=False, cmap='bwr', s_kwargs_dict= {"sym_c": True})
plt.savefig(dpi=300, fname='./hg_vector_calculus.png')


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example_genes = ['ELAVL4']
import numpy as np 

dyn.pl.scatters(adata, x=np.repeat('pca_ddhodge_potential', 1),
                     y=example_genes, layer='X_spliced', color='pca_ddhodge_potential', 
                     background='white', figsize=(3, 3), save_show_or_return='save',
                     pointsize=0.4, ncols=1, alpha = 0.7, 
                     save_kwargs={'prefix': 'hg_potential', 'ext': 'png', "bbox_inches": None})


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# run dynamo determinstic 

# run dynamo to get RNA velocity 

dyn.configuration.set_figure_params('dynamo', background='white')

adata_det = dyn.sample_data.hgForebrainGlutamatergic()

dyn.pp.recipe_monocle(adata_det, n_top_genes=1000, fg_kwargs={'shared_count': 20})

dyn.tl.dynamics(adata_det, model='deterministic')

dyn.tl.reduceDimension(adata_det, n_pca_components=30)

dyn.tl.cell_velocities(adata_det, method='pearson', basis='pca', other_kernels_dict={'transform': 'sqrt'})
dyn.pl.streamline_plot(adata_det, color=['Clusters'], basis='pca', show_legend='on data', show_arrowed_spines=True)


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# run dynamo stochastic 

# run dynamo to get RNA velocity 

dyn.configuration.set_figure_params('dynamo', background='white')

adata_sto = dyn.sample_data.hgForebrainGlutamatergic()

dyn.pp.recipe_monocle(adata_sto, n_top_genes=1000, fg_kwargs={'shared_count': 20})

dyn.tl.dynamics(adata_sto, model='stochastic')

dyn.tl.reduceDimension(adata_sto, n_pca_components=30)

dyn.tl.cell_velocities(adata_sto, method='pearson', basis='pca', other_kernels_dict={'transform': 'sqrt'})
dyn.pl.streamline_plot(adata_sto, color=['Clusters'], basis='pca', show_legend='on data', show_arrowed_spines=True)


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# run dynamo nb 


# run dynamo to get RNA velocity 

dyn.configuration.set_figure_params('dynamo', background='white')

adata_nb = dyn.sample_data.hgForebrainGlutamatergic()

dyn.pp.recipe_monocle(adata_nb, n_top_genes=1000, fg_kwargs={'shared_count': 20})

dyn.tl.dynamics(adata_nb, est_method='negbin')

dyn.tl.reduceDimension(adata_nb, n_pca_components=30)

dyn.tl.cell_velocities(adata_nb, method='pearson', basis='pca', other_kernels_dict={'transform': 'sqrt'})
dyn.pl.streamline_plot(adata_nb, color=['Clusters'], basis='pca', show_legend='on data', show_arrowed_spines=True)


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adata_sto.var.use_for_transition.sum(), adata_det.var.use_for_transition.sum(), adata_nb.var.use_for_transition.sum()


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dyn.cleanup(adata_sto)
dyn.cleanup(adata_det)
dyn.cleanup(adata_nb)


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adata_sto.write_h5ad('./data/hgForebrainGlutamatergic_dynamo_stochastic.h5ad')
adata_det.write_h5ad('./data/hgForebrainGlutamatergic_dynamo_det.h5ad')
adata_nb.write_h5ad('./data/hgForebrainGlutamatergic_dynamo_nb.h5ad')


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adata_sto.obs.Clusters = adata_sto.obs.Clusters.astype(str)
adata_det.obs.Clusters = adata.obs.Clusters.astype(str)
adata_nb.obs.Clusters = adata.obs.Clusters.astype(str)


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dyn.pl.streamline_plot(adata, color=['Clusters'], basis='pca', show_legend='on data',  background='white',
                        show_arrowed_spines=False, save_show_or_return='save',
                        pointsize=0.4, ncols=1, alpha = 0.7, 
                       save_kwargs={'prefix': 'hg_dynamo_stochastic', 'ext': 'svg',
                                     "bbox_inches": None}, 
                        figsize=(4, 3))


# In[50]:


dyn.pl.streamline_plot(adata_det, color=['Clusters'], basis='pca', show_legend='on data',  background='white',
                        show_arrowed_spines=False, save_show_or_return='save',
                        pointsize=0.4, ncols=1, alpha = 0.7, 
                       save_kwargs={'prefix': 'hg_dynamo_det', 'ext': 'svg',
                                     "bbox_inches": None}, 
                        figsize=(4, 3))


# In[51]:


dyn.pl.streamline_plot(adata_nb, color=['Clusters'], basis='pca', show_legend='on data',  background='white',
                        show_arrowed_spines=False, save_show_or_return='save',
                        pointsize=0.4, ncols=1, alpha = 0.7, 
                       save_kwargs={'prefix': 'hg_dynamo_nb', 'ext': 'svg',
                                     "bbox_inches": None}, 
                        figsize=(4, 3))


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