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Stream v1.1 #133

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merged 4 commits into from
Dec 17, 2021
Merged

Stream v1.1 #133

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0844f7b
update stream to v1.1
huidongchen Dec 15, 2021
7278adf
update badge link
huidongchen Dec 17, 2021
29dda7e
fix legend labels
huidongchen Dec 17, 2021
f7fb194
Update README.md
huidongchen Dec 17, 2021
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10 changes: 8 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,18 @@

<!-- [![Build Status](https://travis-ci.org/pinellolab/STREAM.svg)](https://travis-ci.org/pinellolab/STREAM) -->

[![CI](https://github.com/pinellolab/stream/actions/workflows/python-package-conda.yml/badge.svg)](https://github.com/pinellolab/simba/actions/workflows/python-package-conda.yml)
[![CI](https://github.com/pinellolab/STREAM/actions/workflows/python-package-conda.yml/badge.svg)](https://github.com/pinellolab/STREAM/actions/workflows/python-package-conda.yml)

# STREAM (Latest version v1.0)
# STREAM (Latest version v1.1)

Latest News
-----------
> Dec 17, 2021

Version 1.1 is now available.
1) fixed incompatible issues related to the latest version of `pandas`
2) fixed plotting issues related to the latest version of `matplotlib` and `seaborn`

> Jun 1, 2020

Version 1.0 is now available. The v1.0 has added a lot of new functionality:
Expand Down
19 changes: 11 additions & 8 deletions setup.py
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Expand Up @@ -9,23 +9,26 @@
raise RuntimeError('STREAM requires Python 3')


#No dependency packages are specified in setup.py file
#STREAM is built based on bioconda recipe. All the required packages can be found in the recipe file
#https://github.com/bioconda/bioconda-recipes/tree/master/recipes/stream
# No dependency packages are specified in setup.py file
# STREAM is built based on bioconda recipe.
# All the required packages can be found in the recipe file
# https://github.com/bioconda/bioconda-recipes/tree/master/recipes/stream

setup(name='stream',
version=st.__version__,
description='Single-cell Trajectories Reconstruction, Exploration And Mapping of single-cell data',
description=('Single-cell Trajectories Reconstruction,'
' Exploration And Mapping of single-cell data'),
long_description=Path('README.md').read_text('utf-8'),
url='https://github.com/pinellolab/stream',
author='Huidong Chen',
author_email='huidong.chen AT mgh DOT harvard DOT edu',
license='AGPL-3',
packages=['stream'],
package_dir={'stream':'stream'},
package_dir={'stream': 'stream'},
package_data={'stream': ['tests/*']},
include_package_data = True,
include_package_data=True,
install_requires=[''],
entry_points = {'console_scripts': ['stream_run_test=stream.tests.stream_run_test:main']}
entry_points={
'console_scripts': [
'stream_run_test=stream.tests.stream_run_test:main']}
)

2 changes: 1 addition & 1 deletion stream/__init__.py
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@@ -1,3 +1,3 @@
from .core import *

__version__ = "1.0"
__version__ = "1.1"
18 changes: 10 additions & 8 deletions stream/core.py
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Expand Up @@ -539,7 +539,8 @@ def plot_qc(adata,
df_obs['log10_'+ann] = np.log10(df_obs[ann]+1)
ann = 'log10_'+ann
if(i in hist_plot):
sc_i1 = sns.distplot(df_obs[ann],vertical=True)
sc_i1 = sns.histplot(ax=ax_i, data=df_obs, y=ann)
# sc_i1 = sns.distplot(df_obs[ann],vertical=True)
ax_i.set_xticks([])
else:
sc_i1 = sns.violinplot(ax=ax_i,y=ann,data=df_obs,inner=None)
Expand All @@ -551,7 +552,8 @@ def plot_qc(adata,
df_var['log10_'+ann] = np.log10(df_var[ann]+1)
ann = 'log10_'+ann
if(i in hist_plot):
sc_i1 = sns.distplot(df_var[ann],vertical=True)
sc_i1 = sns.histplot(ax=ax_i, data=df_var, y=ann)
# sc_i1 = sns.distplot(df_var[ann],vertical=True)
ax_i.set_xticks([])
else:
sc_i1 = sns.violinplot(ax=ax_i,y=ann,data=df_var,inner=None)
Expand Down Expand Up @@ -1452,7 +1454,7 @@ def plot_dimension_reduction(adata,n_components = None,comp1=0,comp2=1,comp3=2,c
ax_i.scatter(df_plot_shuf['Dim'+str(comp1+1)], df_plot_shuf['Dim'+str(comp2+1)],df_plot_shuf['Dim'+str(comp3+1)],
c=colors_sns,alpha=alpha,linewidth=0,
zorder=-1)
ax_i.legend(legend_sns[1:],labels_sns[1:],bbox_to_anchor=(1.03, 0.5), loc='center left', ncol=fig_legend_ncol,
ax_i.legend(legend_sns,labels_sns,bbox_to_anchor=(1.08, 0.5), loc='center left', ncol=fig_legend_ncol,
frameon=False,)

else:
Expand All @@ -1464,7 +1466,7 @@ def plot_dimension_reduction(adata,n_components = None,comp1=0,comp2=1,comp3=2,c
df_plot_shuf['Dim'+str(comp3+1)],
c=df_plot_shuf[ann],vmin=vmin_i,vmax=vmax_i,alpha=alpha,linewidth=0,
zorder=-1)
cbar = plt.colorbar(sc_i,ax=ax_i, pad=0.04, fraction=0.05, aspect=30)
cbar = plt.colorbar(sc_i,ax=ax_i, pad=0.1, fraction=0.05, aspect=30)
cbar.solids.set_edgecolor("face")
cbar.ax.locator_params(nbins=5)
if(show_graph):
Expand Down Expand Up @@ -1505,7 +1507,7 @@ def plot_dimension_reduction(adata,n_components = None,comp1=0,comp2=1,comp3=2,c
else None
)
legend_handles, legend_labels = ax_i.get_legend_handles_labels()
ax_i.legend(handles=legend_handles[1:], labels=legend_labels[1:],
ax_i.legend(handles=legend_handles, labels=legend_labels,
bbox_to_anchor=(1, 0.5), loc='center left', ncol=fig_legend_ncol,
frameon=False,
)
Expand Down Expand Up @@ -2623,7 +2625,7 @@ def plot_flat_tree(adata,color=None,dist_scale=1,
if (ann+'_color' in adata.uns_keys()) and (set(adata.uns[ann+'_color'].keys()) >= set(np.unique(df_plot_shuf[ann]))) \
else None)
legend_handles, legend_labels = ax_i.get_legend_handles_labels()
ax_i.legend(handles=legend_handles[1:], labels=legend_labels[1:],
ax_i.legend(handles=legend_handles, labels=legend_labels,
bbox_to_anchor=(1, 0.5), loc='center left', ncol=fig_legend_ncol,
frameon=False,
)
Expand Down Expand Up @@ -2818,7 +2820,7 @@ def plot_visualization_2D(adata,method='umap',n_neighbors=50, nb_pct=None,perple
else None
)
legend_handles, legend_labels = ax_i.get_legend_handles_labels()
ax_i.legend(handles=legend_handles[1:], labels=legend_labels[1:],
ax_i.legend(handles=legend_handles, labels=legend_labels,
bbox_to_anchor=(1, 0.5), loc='center left', ncol=fig_legend_ncol,
frameon=False,
)
Expand Down Expand Up @@ -3008,7 +3010,7 @@ def plot_stream_sc(adata,root='S0',color=None,dist_scale=1,dist_pctl=95,preferen
else None
)
legend_handles, legend_labels = ax_i.get_legend_handles_labels()
ax_i.legend(handles=legend_handles[1:], labels=legend_labels[1:],
ax_i.legend(handles=legend_handles, labels=legend_labels,
bbox_to_anchor=(1, 0.5), loc='center left', ncol=fig_legend_ncol,
frameon=False,
)
Expand Down