<jupyter_start><jupyter_text>Here we have created an instance object "image" of the class BioformatsReader, if you are note familliar with classes in Python you can look [here](https://docs.python.org/3/tutorial/classes.html), codeacademy also has a nice [introduction to classes](https://www.codecademy.com/courses/python-intermediate-en-WL8e4/0/1).<jupyter_code>image<jupyter_output><empty_output><jupyter_text>5 methods can be applied to the object image:  **get_dimension()** will give some information on the dimensionality of your object<jupyter_code>dim = image.get_dimension()
dim<jupyter_output><empty_output><jupyter_text>**get_metadata_raw()** will give you all the metadata associated with your object (similar to image oinfo in imageJ)<jupyter_code>metadata_all = image.get_metadata_raw()
# Here, for clarity, I'm just showing the first 50 keys of the dictionary metadata_all:
list(metadata_all.keys())[0:50]<jupyter_output><empty_output><jupyter_text>**get_stack()** will load into memory all your stack.<jupyter_code>stack = image.get_stack()
stack.shape<jupyter_output><empty_output><jupyter_text>**get_frame_2D(att)** will load a certain frame into memory (att are: t for time point, c for channel and z for z position). Very useful if your stack of image is to bigger than the memory available. It will also create a smaller version of the metadata with some essential.<jupyter_code>metadata, img = image.get_frame_2D(c=1, z=5)
metadata
img.shape<jupyter_output><empty_output>
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{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '618004', 'license': 'MIT', 'n_tokens_mistral': 476, 'n_tokens_neox': 465, 'n_words': 148, 'path': 'Example_usage_bioformats.ipynb', 'repo_name': 'bioimage-analysis/load_image_bioformats', 'size': '1494'}