tropical = glob('../data/TokunagaData_[1-3]_*.csv')
temperate = glob('../data/TokunagaData_8_*.csv')
temperate += glob('../data/TokunagaData_11_*.csv')
temperate += glob('../data/TokunagaData_14_*.csv')
cold = glob('../data/TokunagaData_17_*.csv')
cold += glob('../data/TokunagaData_21_*.csv')
cold += glob('../data/TokunagaData_25_*.csv')
labels = ['Arid', 'Tropical', 'Temperate', 'Cold']
All_Data = []
for i, data in enumerate([arid, tropical, temperate, cold]):
Ds = []
for filename in data:
_, strahler_data, lengths = toku.read_toku_data(filename)
Rr_mean, Rr_std = toku.calc_Rr(strahler_data, lengths)
Rb_mean, Rb_std = toku.calc_Rb(strahler_data)
if Rr_mean < 1.05:
continue
D = toku.D(Rb_mean, Rr_mean)
Ds.append(D)
All_Data.append(D)
print('{} Mean: {} StdDev: {}'.format(labels[i], np.mean(Ds), np.std(Ds)))
print('All Data Mean: {} StdDev: {}'.format(np.mean(All_Data),
np.std(All_Data)))
import tokunaga_fns as toku
from glob import glob
import json
import sys
# Load our data, into lists of filenames catgorized by climate zone
data = glob('/data/Geog-c2s2/toku/*/TokunagaData_*.csv')
# Make sure no wierd filesystem stuff messes up the order of the file list we are slicing
data.sort()
data_dict = {}
start = int(sys.argv[1])
end = int(sys.argv[2])
for filename in data[start:end]:
toku_id = filename.split('TokunagaData_')[1][:-4]
toku_data, strahler_data, _ = toku.read_toku_data(filename)
r_sq, a, c = toku.fit_a_and_c(toku_data, strahler_data)
data_dict[toku_id] = [r_sq, a, c]
with open('/data/Geog-c2s2/a_c_data/a_c_data_{}_{}.json'.format(start, end),
'w') as fp:
json.dump(data_dict, fp)
print('Done')
basin_data = json.load(js)
with open('../data/ac_data.json') as js:
ac_data = json.load(js)
threshold = float(sys.argv[1])
c_vals = []
chan_density = []
for toku_id, values in ac_data.items():
if values[0] > threshold and toku_id in basin_data:
basin = basin_data[toku_id]
c_vals.append(values[2])
_, _, lengths = read_toku_data('../data/TokunagaData_{}.csv'.format(toku_id))
total_length = np.sum(lengths)
area = basin[3]
unchan_area = basin[0]
chan_density.append(total_length / (area - unchan_area))
# plt.scatter()
plt.hist2d(c_vals, np.log10(chan_density), bins=(50, 50), cmap=plt.cm.viridis, density=False)
plt.show()
