<jupyter_start><jupyter_text>import_coords_and_indexThis function will take in the following parameters:1) metadata_df= metadata from make_metadata()function above. Currently this must already be read into the notebook2) path_to_coords= path to the folder containing the 96 to 384 coordinate conversation 3) path_to_i7_384 = path to folder containing i7_indexes from 384 well plate 4) path_to_i5_384 = path to folder containing the i5_indexes from 384 well plate 5) i7_384_coord = coordinate from your i7 384 well plate you want to draw from 6) i7_keyname = name of csv for the particular i7 index plate you are using7) i5_384_coord = coordinate from your i5 384 well plate you want to draw from 8) i5_keyname = name of csv for the particular i5 index plate you are using9) well_type = default is "well_id_96"10) path_to_data=path to folder where you will store your final table11) date= date to be appended to your final sheet 12) name= name of the final file, defaul is "metadata_indexes"<jupyter_code>## make classes for accessing paths, metadat and indices

class MetaData:
    RETURNED_DATA="returned_data"
    
    def __init__(self, metadata_csv):
        self.path = "{a}/{b}".format(a=MetaData.RETURNED_DATA, b=metadata_csv)
        self.name = os.path.splitext(metadata_csv)[0]
    def make_df(self):
        return pd.read_csv(self.path).drop("Unnamed: 0", axis=1)
    def col_names(self):
        return pd.read_csv(self.path).columns  
    
class PathTo: 
    COORD_CONVS='coordinate_conversion_maps'
    i7_INDEXES='i7_indexes_plates'
    i5_INDEXES='i5_indexes_plates'
    RETURNED_DATA="returned_data"

class PlateChoices: 
    def __init__(self, i7_plate_name, i7_chosen_coord, i5_plate_name, i5_chosen_coord):
        self.i7_plate_name = i7_plate_name
        self.i5_plate_name = i5_plate_name
        self.i7_chosen_coord = i7_chosen_coord
        self.i5_chosen_coord = i5_chosen_coord
        self.merge_384w = "well_id_384"
        self.merge_96w = "well_id_96"
        
class MergeParams:
    def __init__(self, index_merge_value, plate_merge_value):
        self.index_merge_id = index_merge_value 
        self.plate_merge_id = plate_merge_value
    

## define paths and index selections and get metadata
data_paths_20180607 = PathTo()
chosen_indices_20180607 = IndexPlateChoices("i7_index_384", "coord1_384", "i5_index_384", "coord1_384")
meta_data_20180607 = MetaData("20180529_metadata.csv")
merge_params_20180607 = MergeParams(index_merge_value="well_id_384", plate_merge_value="well_id_96")


def convert_csv_to_df(path_to_data):
    all_data=glob.glob("{}/*.csv".format(path_to_data))
    
    dict_of_all_data={}

    for file in all_data:
        file_name=os.path.splitext(os.path.basename(file))[0]
        df=pd.read_csv(file)
        dict_of_all_data[file_name]=df
    return dict_of_all_data

def import_coords_and_index(metadata_df,
                            data_paths,
                            chosen_indices,
                            plate_merge_well_type, 
                            date, 
                            saved_csv_name="metadata_w_indexes"):
    
    dict_i7_indices = convert_csv_to_df(data_paths.i7_INDEXES) ## could also so PathTo.i7_INDEXES bc all constants ( cool)
    dict_i5_indices = convert_csv_to_df(data_paths.i5_INDEXES)
    dict_of_coords = convert_csv_to_df(data_paths.COORD_CONVS)
    
    
    i7_selected_indices=dict_of_coords[chosen_indices.i7_chosen_coord].merge(dict_i7_indices[chosen_indices.i7_plate_name],left_on=chosen_indices.i7_chosen_coord, right_on=merge_params_20180607.index_merge_id)
    i5_selected_indices=dict_of_coords[chosen_indices.i5_chosen_coord].merge(dict_i5_indices[chosen_indices.i5_plate_name],left_on=chosen_indices.i5_chosen_coord, right_on=merge_params_20180607.index_merge_id)
    i7_metadata_merged=i7_selected_indices.merge(metadata_df, on=plate_merge_well_type)
    i5_i7_metadata_merged=i7_metadata_merged.merge(i5_selected_indices,on=plate_merge_well_type)
    
    i5_i7_metadata_merged.to_csv("{a}/{b}_{c}.csv".format(a=data_paths.RETURNED_DATA, b=date, c=saved_csv_name))
    return i5_i7_metadata_merged
    
experiment=import_coords_and_index(metadata_df = meta_data_20180607.make_df(),
                            data_paths = data_paths_20180607,
                            chosen_indices = chosen_indices_20180607,
                            plate_merge_well_type = merge_params_20180607.plate_merge_id, 
                            date = "20180607", 
                            saved_csv_name="metadata_with_indices")
list(experiment.columns)
guides_ordered=pd.read_csv("guide_seq_tidy.csv")
guides_ordered
experiment_and_guides=experiment.merge(guides, on="well_id_96")
guides_ordered<jupyter_output><empty_output><jupyter_text>Convert your work to a python script. `! jupyter nbconvert --to script [yournotebookname].ipynb`<jupyter_code>! jupyter nbconvert --to script 20180605_Giana_Aaron_labmate_96totidy_addindex.ipynb<jupyter_output>[NbConvertApp] Converting notebook 20180605_Giana_Aaron_labmate_96totidy_addindex.ipynb to script
[NbConvertApp] Writing 4504 bytes to 20180605_Giana_Aaron_labmate_96totidy_addindex.py
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