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Flexbrdf/scripts/configs/trait_estimate_json_generate.py

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+'''Template script for generating trait_estimate configuration JSON files.
+'''
+
+import os
+import json
+import glob
+
+home = os.path.expanduser("~")
+
+#Output path for configuration file
+config_file = "/.json"
+
+config_dict = {}
+config_dict['file_type'] = 'envi'
+config_dict["output_dir"] = './'
+config_dict['bad_bands'] =[[300,400],[1337,1430],[1800,1960],[2450,2600]]
+
+# Input data settings for NEON
+#################################################################
+# config_dict['file_type'] = 'neon'
+# images= glob.glob("*.h5")
+# images.sort()
+# config_dict["input_files"] = images
+
+# Input data settings for ENVI
+#################################################################
+''' Only difference between ENVI and NEON settings is the specification
+of the ancillary datasets (ex. viewing and solar geometry). All hytools
+functions assume that the ancillary data and the image date are the same
+size, spatially, and are ENVI formatted files.
+
+The ancillary parameter is a dictionary with a key per image. Each value
+per image is also a dictionary where the key is the dataset name and the
+value is list consisting of the file path and the band number.
+'''
+
+config_dict['file_type'] = 'envi'
+aviris_anc_names = ['path_length','sensor_az','sensor_zn',
+                    'solar_az', 'solar_zn','phase','slope',
+                    'aspect', 'cosine_i','utc_time']
+images= glob.glob("*img")
+images.sort()
+config_dict["input_files"] = images
+
+config_dict["anc_files"] = {}
+anc_files = glob.glob("*ort")
+anc_files.sort()
+for i,image in enumerate(images):
+    config_dict["anc_files"][image] = dict(zip(aviris_anc_names,
+                                                [[anc_files[i],a] for a in range(len(aviris_anc_names))]))
+
+config_dict['num_cpus'] = len(images)
+
+# Assign correction coefficients
+##########################################################
+''' Specify correction(s) to apply and paths to coefficients.
+'''
+
+config_dict['corrections'] = ['topo','brdf']
+
+topo_files = glob.glob("*topo.json")
+topo_files.sort()
+config_dict["topo"] =  dict(zip(images,topo_files))
+
+brdf_files = glob.glob("*brdf.json")
+brdf_files.sort()
+config_dict["brdf"] =  dict(zip(images,brdf_files))
+
+# Select wavelength resampling type
+##########################################################
+'''Wavelength resampler will only be used if image wavelengths
+and model wavelengths do not match exactly
+
+See image_correct_json_generate.py for options.
+
+'''
+config_dict["resampling"]  = {}
+config_dict["resampling"]['type'] =  'cubic'
+
+# Masks
+##########################################################
+'''Specify list of masking layers to be appended to the
+trait map. Each will be placed in a separate layer.
+
+For no masks provide an empty list: []
+'''
+config_dict["masks"] = [["ndi", {'band_1': 850,'band_2': 660,
+                                  'min': 0.1,'max': 1.0}],
+                        ['neon_edge',{'radius': 30}]]
+
+# Define trait coefficients
+##########################################################
+models = glob.glob('*.json')
+models.sort()
+config_dict["trait_models"]  = models
+
+with open(config_file, 'w') as outfile:
+    json.dump(config_dict,outfile)
+