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Added funtion to save uploaded file encoded in bytestring into dataproduct/ local folder, and updated created_loaded_file_figure with instfolder argument to display time series from a particular inst folder

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florez_j
2024-09-20 10:57:04 +02:00
parent 2e9c8cadd8
commit b760b791e9
1 changed files with 18 additions and 6 deletions
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24
data_flagging_utils.py
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@ -1,8 +1,11 @@
import dima.src.hdf5_data_extraction as h5de import dima.src.hdf5_ops as h5de
from plotly.subplots import make_subplots from plotly.subplots import make_subplots
import plotly.graph_objs as go import plotly.graph_objs as go
import base64
import os
UPLOAD_DIRECTORY = 'data_products/'
flags_dict = { flags_dict = {
"000" : {"flag_label": 'V', "flag_description": "Valid measurement"}, "000" : {"flag_label": 'V', "flag_description": "Valid measurement"},
@ -21,14 +24,23 @@ flags_dict = {
dropdown_menu_options = [{'label': flags_dict[key]['flag_description'], 'value': key} for key in flags_dict.keys()] dropdown_menu_options = [{'label': flags_dict[key]['flag_description'], 'value': key} for key in flags_dict.keys()]
def create_loaded_file_figure(file_path): def save_file(name, content):
# Decode the content and save the file
content_type, content_string = content.split(',')
decoded = base64.b64decode(content_string)
file_path = os.path.join(UPLOAD_DIRECTORY, name)
with open(file_path, "wb") as f:
f.write(decoded)
return file_path
def create_loaded_file_figure(file_path, instfolder):
DataOpsAPI = h5de.HDF5DataOpsManager(file_path) DataOpsAPI = h5de.HDF5DataOpsManager(file_path)
target_channels = DataOpsAPI.file_obj.attrs['target_channels']['names'][0].decode().split(',') target_channels = DataOpsAPI.file_obj[instfolder].attrs['target_channels']['names'][0].decode().split(',')
target_loc = DataOpsAPI.file_obj.attrs['target_channels']['location'][0].decode() target_loc = DataOpsAPI.file_obj[instfolder].attrs['target_channels']['location'][0].decode()
diagnostic_channels = DataOpsAPI.file_obj.attrs['diagnostic_channels']['names'][0].decode().split(',') diagnostic_channels = DataOpsAPI.file_obj[instfolder].attrs['diagnostic_channels']['names'][0].decode().split(',')
diagnostic_loc = DataOpsAPI.file_obj.attrs['diagnostic_channels']['location'][0].decode() diagnostic_loc = DataOpsAPI.file_obj[instfolder].attrs['diagnostic_channels']['location'][0].decode()
#fig = make_subplots(rows=(len(target_channels+diagnostic_channels)-2), cols=1, shared_xaxes=True, #fig = make_subplots(rows=(len(target_channels+diagnostic_channels)-2), cols=1, shared_xaxes=True,
# row_heights = [1 for i in range(len(target_channels+diagnostic_channels)-2)]) # row_heights = [1 for i in range(len(target_channels+diagnostic_channels)-2)])