minor

Author: mengaldo

pyspod/__init__.py

@@ -1,10 +1,12 @@
 """PySPOD init"""
 __all__ = ['spod_base', 'spod_low_storage', 'spod_low_ram', 'spod_streaming']
+# __all__ = ['SPOD_base', 'SPOD_low_storage', 'SPOD_low_ram', 'SPOD_streaming']
 
 from .spod_base        import SPOD_base
 from .spod_low_storage import SPOD_low_storage
 from .spod_low_ram     import SPOD_low_ram
 from .spod_streaming   import SPOD_streaming
+# from pyspod import SPOD_low_storage, SPOD_low_ram, SPOD_streaming
 
 import os
 import sys

tutorials/basic/methods_comparison_file/methods_comparison_file.ipynb

@@ -365,7 +365,7 @@
    "source": [
     "# Initialize libraries by using data_handler for the low storage algorithm\n",
     "spod_ls = SPOD_low_storage(\n",
-    "    X=os.path.join(CWD,'data.nc'),\n",
+    "    data=os.path.join(CWD,'data.nc'),\n",
     "    params=params,\n",
     "    data_handler=read_data_netCDF,\n",
     "    variables=variables)\n",
@@ -666,7 +666,7 @@
    "source": [
     "# Let's try the low_ram algorithm\n",
     "spod_ram = SPOD_low_ram(\n",
-    "    X=os.path.join(CWD,'data.nc'),\n",
+    "    data=os.path.join(CWD,'data.nc'),\n",
     "    params=params,\n",
     "    data_handler=read_data_netCDF,\n",
     "    variables=variables)\n",
@@ -872,7 +872,7 @@
    "source": [
     "# Finally, we can try the streaming algorithm\n",
     "spod_st = SPOD_streaming(\n",
-    "    X=os.path.join(CWD,'data.nc'),\n",
+    "    data=os.path.join(CWD,'data.nc'),\n",
     "    params=params,\n",
     "    data_handler=read_data_netCDF,\n",
     "    variables=variables)\n",

tutorials/climate/ERA5_MSLP_2D/ERA5_MSLP_2D.ipynb

@@ -411,7 +411,7 @@
    ],
    "source": [
     "# Perform SPOD analysis using low storage module\n",
-    "SPOD_analysis = SPOD_low_ram(X=ds, params=params, data_handler=read_data, variables=variables)\n",
+    "SPOD_analysis = SPOD_low_ram(data=ds, params=params, data_handler=read_data, variables=variables)\n",
     "spod = SPOD_analysis.fit()"
    ]
   },
@@ -606,8 +606,8 @@
     "The results are stored in the results folder defined in the parameter you specified under `params[savedir]`. We can load the results for both modes and eigenvalues, and use any other postprocessing tool that is more suitable to your application. The files are stored in `numpy` binary format `.npy`. There exists several tools to convert them in `netCDF`, `MATLAB` and several other formats that can be better suited to you specific post-processing pipeline.\n",
     "\n",
     "This tutorial was intended to help you setup your own multivariate case. You can play with the parameters we explored above to gain more insights into the capabilities of the library. You can also run on the same data the other two SPOD algorithms implemented as part of this library by simply calling:\n",
-    "- SPOD_analysis = SPOD_low_storage(X=X, params=params, file_handler=False)\n",
-    "- SPOD_analysis = SPOD_streaming(X=X, params=params, file_handler=False)\n",
+    "- SPOD_analysis = SPOD_low_storage(data=X, params=params, file_handler=False)\n",
+    "- SPOD_analysis = SPOD_streaming(data=X, params=params, file_handler=False)\n",
     "\n",
     "and compare the results.\n",
     "\n",