{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "16d0d565-999c-439b-b898-2ac8e038f874", "metadata": {}, "outputs": [], "source": [ "import os\n", "import glob\n", "import pandas as pd\n", "import numpy as np\n", "from openpyxl import load_workbook\n", "import csv\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import datetime as dt\n", "from datetime import datetime as dt2" ] }, { "cell_type": "code", "execution_count": 2, "id": "b568bf59-ab51-4b88-a1e8-a6be3a3954c5", "metadata": {}, "outputs": [], "source": [ "# paths\n", "v0_path = r\"C:\\Users\\Nora Greenwood\\Desktop\\CONTRASTS\\processing_albedo_data\\v0_CONTRASTS_albedo\"\n", "\n", "# make a list of things in v0_path that 1) are folders and 2) have a kipps file in them\n", "kipps_dirs = [subdir for subdir in os.listdir(v0_path)\n", " if os.path.isdir(os.path.join(v0_path, subdir)) and\n", " any(fname.startswith(\"kipps\") for fname in os.listdir(os.path.join(v0_path, subdir)))]\n", " # and any(fname.endswith(\"transect\") for fname in os.listdir(os.path.join(v0_path, subdir)))]\n", "\n", "notes_path = r\"C:\\Users\\Nora Greenwood\\Desktop\\CONTRASTS\\processing_albedo_data\\v1_CONTRASTS_albedo\\albedonotes\"\n", "\n", "v3_path = r\"C:\\Users\\Nora Greenwood\\Desktop\\CONTRASTS\\processing_albedo_data\\v3_CONTRASTS_albedo\\broadband\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "a71279d8-3247-453a-a3be-02dacb4887d0", "metadata": {}, "outputs": [], "source": [ "# load in GPS data\n", "\n", "gps_path = r\"C:\\Users\\Nora Greenwood\\Desktop\\CONTRASTS\\processing_albedo_data\\PS149_mastertrack.tab\"\n", "# Read in ship track\n", "# data format is list of lists of strings of dateTtime, lat, lon, speed [kn], course [deg]\n", "gps_data = []\n", "with open(gps_path, newline='') as f:\n", " reader = csv.reader(f, delimiter='\\t')\n", " for row in reader:\n", " # Skip empty lines\n", " if not row:\n", " continue\n", " # Check if this line starts the real data\n", " if row[0].startswith('2025'):\n", " # Convert to numbers if appropriate\n", " gps_data.append(row)\n", "\n", "gps_dt = []\n", "lats = []\n", "lons = []\n", "for row in gps_data:\n", " date = row[0]\n", " gps_dt.append(dt.datetime(int(date[0:4]),int(date[5:7]),int(date[8:10]),int(date[11:13]),int(date[14:16])))\n", " lats.append(float(row[1]))\n", " lons.append(float(row[2]))\n", "\n", "gps = {\"datetime\": gps_dt, \"latitude\": lats, \"longitude\": lons}\n", "\n", "# Convert to a dataframe\n", "gps_df = pd.DataFrame(gps)" ] }, { "cell_type": "code", "execution_count": 4, "id": "b6963fdd-7986-4319-8c1f-3670297984ae", "metadata": {}, "outputs": [], "source": [ "# load in event IDs\n", "event_path = r\"C:\\Users\\Nora Greenwood\\Desktop\\CONTRASTS\\processing_albedo_data\\eventIDs.xlsx\"\n", "events = pd.read_excel(event_path,'Kipps')" ] }, { "cell_type": "code", "execution_count": 6, "id": "0ca0c1a1-4e82-4ebf-968e-09700431e11f", "metadata": {}, "outputs": [], "source": [ "def process_kipps(site, out_path, notes_path = notes_path):\n", " \"\"\"\n", " Reads in albedo notes, Kipps files, and ship track data and converts them into processed formatted albedo\n", "\n", " parameters: \n", " -------\n", " in_list: path\n", " \"\"\"\n", " files = os.listdir(v0_path + '\\\\' + site)\n", "\n", " # maybe re-write with file names\n", " for filename in files:\n", " if filename.startswith(\"k\"):\n", " kipps_path = os.path.join(v0_path, site, filename)\n", " notes_path = os.path.join(notes_path, 'albedonotes-contrasts-'+site+'-v1.csv')\n", "\n", "\n", " # read in notes and pull in position data\n", " notes = pd.read_csv(notes_path,header=2)\n", " # filter to just important notes columns\n", " notes_info = notes.loc[:,['Position','Repetition','Snow or Pond (s/p)','S/P Depth (cm)','Notes on footprint','Trampled']]\n", " notes_info = notes_info.rename(columns={'Position': 'position', 'Repetition': 'repetition', 'Snow or Pond (s/p)': 'surface type', 'S/P Depth (cm)': 'surface thickness (cm)', 'Notes on footprint': 'notes', 'Trampled': 'trampled'})\n", " notes_info = notes_info.drop_duplicates()\n", " # notes_info = notes_info.set_index(['position','repetition'])\n", " \n", " # read in Kipps, rename columns\n", " kipps = pd.read_excel(kipps_path,header=2)\n", " kipps = kipps.rename(columns={'Position': 'position', 'Repetition': 'repetition', 'Incoming': 'incoming (milliV)', 'Incoming (corrected)': 'incoming (W m^-2)', \n", " 'Reflected': 'reflected (milliV)', 'Reflected (corrected)': 'reflected (W m^-2)', 'Albedo (corrected)': 'albedo'})\n", " # round data\n", " kipps['albedo'] = kipps['albedo'].round(3)\n", " kipps['incoming (W m^-2)'] = 1000*kipps['incoming (W m^-2)'].round(3)\n", " kipps['reflected (W m^-2)'] = 1000*kipps['reflected (W m^-2)'].round(3)\n", "\n", " if 'Transect' in site:\n", " # add blank positions as needed\n", " if 'R2_main' in site:\n", " pos = pd.DataFrame({'position': range(0, 91, 5)})\n", " elif 'R1_mainB' in site:\n", " pos = pd.DataFrame({'position': range(0, 186, 5)})\n", " elif 'R1_mainA' in site:\n", " pos = pd.DataFrame({'position': range(0, 201, 5)})\n", " elif 'main' in site or 'R1_opticsC' in site or 'R2_tTransect' in site or 'R3_optics' in site:\n", " pos = pd.DataFrame({'position': range(0, 101, 5)})\n", " elif 'raftedIce' in site:\n", " pos = pd.DataFrame({'position': range(15, 101, 5)})\n", " elif 'R2_optics' in site:\n", " pos = pd.DataFrame({'position': range(0, 91, 5)})\n", " elif 'R1_opticsB' in site:\n", " pos = pd.DataFrame({'position': range(5, 66, 5)})\n", " \n", "\n", " # kipps = kipps.set_index(['position','repetition'])\n", " # kipps_notes_merged = kipps.join(notes_info).reset_index()\n", " # return kipps, notes_info\n", "\n", "\n", " if kipps['repetition'].max() > notes_info['repetition'].max():\n", " notes_info = notes_info[notes_info['repetition'] == 1]\n", " notes_info = notes_info.drop('repetition',axis='columns')\n", " notes_info = notes_info.drop_duplicates()\n", " kipps_notes_merged = kipps.merge(notes_info,how='left',on='position')\n", " else:\n", " kipps_notes_merged = kipps.merge(notes_info,how='left',on=['position','repetition'])\n", " \n", " kipps_notes_merged = kipps_notes_merged[['position','repetition', 'surface type','surface thickness (cm)','trampled','notes','incoming (milliV)','incoming (W m^-2)','reflected (milliV)','reflected (W m^-2)','albedo']]\n", "\n", "\n", " ## QC flags\n", " #1. add estimate for error based on resolution of instrument\n", " k_acc = 0.005 #assumed accuracy of kipps measurement \n", " kipps_notes_merged['albedo error'] = ((kipps_notes_merged['reflected (milliV)']+.005)/(kipps_notes_merged['incoming (milliV)']-.005) - (kipps_notes_merged['reflected (milliV)']-.005)/(kipps_notes_merged['incoming (milliV)']+.005)).round(3)\n", " \n", " #2. add percent change of incident compared to before/after\n", " kipps_notes_merged['incoming change (%)'] = -999.9\n", " incoming_diff = kipps_notes_merged['incoming (milliV)'].diff().abs()\n", " for di in range(0,len(kipps_notes_merged)-1):\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", " kipps_notes_merged.loc[len(incoming_diff)-1, 'incoming change (%)'] = (incoming_diff[len(incoming_diff)-1]/kipps_notes_merged['incoming (milliV)'][len(incoming_diff)-1]*100).round(1)\n", " kipps_notes_merged['surface thickness (cm)'] = kipps_notes_merged['surface thickness (cm)'].round(1)\n", "\n", " \n", " #read in met file again to get additional header information\n", " header = pd.read_csv(notes_path,header=None, nrows=2)\n", " header = header.fillna('NaN')\n", " start_time = header.iloc[1,3]\n", " timing = dt2.strptime(start_time, \"%H:%M:%S\").time()\n", " date = dt.datetime(int(site[0:4]),int(site[4:6]),int(site[6:8])).date()\n", " \n", " datetime = dt.datetime.combine(date,timing)\n", " idx = (gps_df['datetime'] - datetime).abs().idxmin()\n", " lat = round(gps_df['latitude'][idx],5)\n", " lon = round(gps_df['longitude'][idx],5)\n", "\n", " event_id = events.loc[events['Site'] == site, 'Event IDs'].squeeze()\n", " \n", " \n", " sky_cond = header.iloc[0,3]\n", " sky_cond = sky_cond.replace(',',';')\n", " print(site, sky_cond)\n", " print('')\n", " other = str(header.iloc[1,5])\n", " other = other.replace(',',';')\n", " line = header.iloc[0,1]\n", " \n", " #write a new csv with the header and data\n", " # write the header\n", "\n", " \n", " header = 'Site, ' + line + '\\n Event ID, ' + str(event_id) + '\\n Date, '+ str(date) + '\\n Start time (UTC), '+ str(start_time) + ' \\n Sky conditions, '+ sky_cond+' \\n Other notes, ' +other+' \\n Ship latitude, '+str(lat)+'\\n Ship longitude, '+str(lon)+'\\n'\n", " header = header.replace(', nan\\n',', NaN\\n')\n", " newname = 'broadband-contrasts-' + site + '-v3.csv'\n", " newfilename = os.path.join(out_path, newname)\n", " \n", " with open(newfilename, 'w') as fp:\n", " fp.write(header)\n", " \n", " # write the rest\n", " kipps_notes_merged.to_csv(newfilename, header=True,index=False, mode='a', na_rep='NaN')\n", "\n", " print('wrote: '+newname)" ] }, { "cell_type": "code", "execution_count": 7, "id": "f80a61ba-b99d-4005-9ee1-fab06e84e883", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "20250710-r1a-R1_mainATransect PC to SDNV\n", "\n", "wrote: broadband-contrasts-20250710-r1a-R1_mainATransect-v3.csv\n", "20250711-r1a-ponds-library SDNV\n", "\n", "wrote: broadband-contrasts-20250711-r1a-ponds-library-v3.csv\n", "20250711-r1a-R1_mainATransect SDNV\n", "\n", "wrote: broadband-contrasts-20250711-r1a-R1_mainATransect-v3.csv\n", "20250712-r1a-ponds-colocated SDV/SDNV\n", "\n", "wrote: broadband-contrasts-20250712-r1a-ponds-colocated-v3.csv\n", "20250712-r1a-ssl-experiment-am1 SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250712-r1a-ssl-experiment-am1-v3.csv\n", "20250712-r1a-ssl-experiment-pm1 SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250712-r1a-ssl-experiment-pm1-v3.csv\n", "20250712-r1a-ssl-experiment-pm2 SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250712-r1a-ssl-experiment-pm2-v3.csv\n", "20250715-r2a-R2_mainTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250715-r2a-R2_mainTransect-v3.csv\n", "20250715-r2a-R2_opticsTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250715-r2a-R2_opticsTransect-v3.csv\n", "20250715-r2a-ssl1-experiment-am1 SDNV\n", "\n", "wrote: broadband-contrasts-20250715-r2a-ssl1-experiment-am1-v3.csv\n", "20250715-r2a-ssl1-experiment-pm1 SDNV\n", "\n", "wrote: broadband-contrasts-20250715-r2a-ssl1-experiment-pm1-v3.csv\n", "20250715-r2a-ssl1-experiment-pm2 SDNV\n", "\n", "wrote: broadband-contrasts-20250715-r2a-ssl1-experiment-pm2-v3.csv\n", "20250716-r2a-ssl1-experiment-am1 SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250716-r2a-ssl1-experiment-am1-v3.csv\n", "20250716-r2a-ssl1-experiment-pm1 SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250716-r2a-ssl1-experiment-pm1-v3.csv\n", "20250717-r2a-ponds-library SDNV\n", "\n", "wrote: broadband-contrasts-20250717-r2a-ponds-library-v3.csv\n", "20250717-r2a-R2_raftedIceTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250717-r2a-R2_raftedIceTransect-v3.csv\n", "20250717-r2a-snow-colocated SDNV\n", "\n", "wrote: broadband-contrasts-20250717-r2a-snow-colocated-v3.csv\n", "20250717-r2a-ssl1-experiment-pm1 SDNV\n", "\n", "wrote: broadband-contrasts-20250717-r2a-ssl1-experiment-pm1-v3.csv\n", "20250717-r2a-ssl1-experiment-pm2 SDNV\n", "\n", "wrote: broadband-contrasts-20250717-r2a-ssl1-experiment-pm2-v3.csv\n", "20250720-r3a-R3_mainTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250720-r3a-R3_mainTransect-v3.csv\n", "20250720-r3a-R3_opticsTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250720-r3a-R3_opticsTransect-v3.csv\n", "20250721-r3a-ssl-experiment-am1 BC\n", "\n", "wrote: broadband-contrasts-20250721-r3a-ssl-experiment-am1-v3.csv\n", "20250721-r3a-ssl-experiment-pm1 BC\n", "\n", "wrote: broadband-contrasts-20250721-r3a-ssl-experiment-pm1-v3.csv\n", "20250721-r3a-ssl-experiment-pm2 BC\n", "\n", "wrote: broadband-contrasts-20250721-r3a-ssl-experiment-pm2-v3.csv\n", "20250722-r3a-ponds-colocated BC\n", "\n", "wrote: broadband-contrasts-20250722-r3a-ponds-colocated-v3.csv\n", "20250722-r3a-ponds-library BC\n", "\n", "wrote: broadband-contrasts-20250722-r3a-ponds-library-v3.csv\n", "20250722-r3a-ssl-experiment-pm1 SDV (pm1); BC (pm2)\n", "\n", "wrote: broadband-contrasts-20250722-r3a-ssl-experiment-pm1-v3.csv\n", "20250722-r3a-ssl-experiment-pm2 SDV (pm1); BC (pm2)\n", "\n", "wrote: broadband-contrasts-20250722-r3a-ssl-experiment-pm2-v3.csv\n", "20250726-r1b-ponds-library SDNV\n", "\n", "wrote: broadband-contrasts-20250726-r1b-ponds-library-v3.csv\n", "20250726-r1b-R1_mainBTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250726-r1b-R1_mainBTransect-v3.csv\n", "20250727-r1b-metcity-colocated SD NV; low stratus\n", "\n", "wrote: broadband-contrasts-20250727-r1b-metcity-colocated-v3.csv\n", "20250727-r1b-ponds-colocated SDNV\n", "\n", "wrote: broadband-contrasts-20250727-r1b-ponds-colocated-v3.csv\n", "20250727-r1b-ponds-library-am SDNV\n", "\n", "wrote: broadband-contrasts-20250727-r1b-ponds-library-am-v3.csv\n", "20250727-r1b-ponds-library-pm SDNV\n", "\n", "wrote: broadband-contrasts-20250727-r1b-ponds-library-pm-v3.csv\n", "20250727-r1b-R1_opticsBTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250727-r1b-R1_opticsBTransect-v3.csv\n", "20250728-r1b-ponds-colocated SDNV\n", "\n", "wrote: broadband-contrasts-20250728-r1b-ponds-colocated-v3.csv\n", "20250730-r2b-R2_opticsTransect SDNV mostly\n", "\n", "wrote: broadband-contrasts-20250730-r2b-R2_opticsTransect-v3.csv\n", "20250731-r2b-metcity-colocated SDV; very variable lighting\n", "\n", "wrote: broadband-contrasts-20250731-r2b-metcity-colocated-v3.csv\n", "20250731-r2b-ponds-colocated SDV/SDNV\n", "\n", "wrote: broadband-contrasts-20250731-r2b-ponds-colocated-v3.csv\n", "20250731-r2b-ponds-library SDNV\n", "\n", "wrote: broadband-contrasts-20250731-r2b-ponds-library-v3.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "20250731-r2b-R2_raftedIceTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250731-r2b-R2_raftedIceTransect-v3.csv\n", "20250731-r2b-ssl2-experiment SDNV\n", "\n", "wrote: broadband-contrasts-20250731-r2b-ssl2-experiment-v3.csv\n", "20250801-r2b-R2_mainTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250801-r2b-R2_mainTransect-v3.csv\n", "20250801-r2b-R2_opticsTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250801-r2b-R2_opticsTransect-v3.csv\n", "20250801-r2b-R2_raftedIceTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250801-r2b-R2_raftedIceTransect-v3.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "20250801-r2b-ssl2-experiment SDNV\n", "\n", "wrote: broadband-contrasts-20250801-r2b-ssl2-experiment-v3.csv\n", "20250804-r3b-ponds-colocated SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250804-r3b-ponds-colocated-v3.csv\n", "20250804-r3b-R3_mainTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250804-r3b-R3_mainTransect-v3.csv\n", "20250804-r3b-ssl-experiment SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250804-r3b-ssl-experiment-v3.csv\n", "20250805-r3b-ponds-colocated SDNV\n", "\n", "wrote: broadband-contrasts-20250805-r3b-ponds-colocated-v3.csv\n", "20250805-r3b-R3_opticsTransect-am SDNV\n", "\n", "wrote: broadband-contrasts-20250805-r3b-R3_opticsTransect-am-v3.csv\n", "20250805-r3b-ssl-experiment-am1 SDNV\n", "\n", "wrote: broadband-contrasts-20250805-r3b-ssl-experiment-am1-v3.csv\n", "20250806-r3b-metcity-colocated SDNV; fog\n", "\n", "wrote: broadband-contrasts-20250806-r3b-metcity-colocated-v3.csv\n", "20250806-r3b-ssl-experiment SDNV\n", "\n", "wrote: broadband-contrasts-20250806-r3b-ssl-experiment-v3.csv\n", "20250809-r1c-R1_mainCTransect BC\n", "\n", "wrote: broadband-contrasts-20250809-r1c-R1_mainCTransect-v3.csv\n", "20250810-r1c-coring-colocated SDV\n", "\n", "wrote: broadband-contrasts-20250810-r1c-coring-colocated-v3.csv\n", "20250810-r1c-ponds-colocated SDV\n", "\n", "wrote: broadband-contrasts-20250810-r1c-ponds-colocated-v3.csv\n", "20250810-r1c-R1_mainCTransect SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250810-r1c-R1_mainCTransect-v3.csv\n", "20250810-r1c-R1_opticsCTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250810-r1c-R1_opticsCTransect-v3.csv\n", "20250812-r2c-R2_raftedIceTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250812-r2c-R2_raftedIceTransect-v3.csv\n", "20250812-r2c-R2_tTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250812-r2c-R2_tTransect-v3.csv\n", "20250812-r2c-ssl3-experiment SDNV\n", "\n", "wrote: broadband-contrasts-20250812-r2c-ssl3-experiment-v3.csv\n", "20250813-r2c-coring-colocated CO-SDV\n", "\n", "wrote: broadband-contrasts-20250813-r2c-coring-colocated-v3.csv\n", "20250813-r2c-ssl3-experiment-am1 SDNV\n", "\n", "wrote: broadband-contrasts-20250813-r2c-ssl3-experiment-am1-v3.csv\n", "20250814-r2c-ssl3-experiment-am1 SDNV\n", "\n", "wrote: broadband-contrasts-20250814-r2c-ssl3-experiment-am1-v3.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "20250817-r3c-R3_mainTransect SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250817-r3c-R3_mainTransect-v3.csv\n", "20250817-r3c-R3_opticsTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250817-r3c-R3_opticsTransect-v3.csv\n", "20250823-r2d-beartracks-library SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250823-r2d-beartracks-library-v3.csv\n", "20250823-r2d-ponds-library SDV\n", "\n", "wrote: broadband-contrasts-20250823-r2d-ponds-library-v3.csv\n", "20250823-r2d-R2_raftedIceTransect SDNV\n", "\n", "wrote: broadband-contrasts-20250823-r2d-R2_raftedIceTransect-v3.csv\n", "20250823-r2d-R2_tTransect SDNV/SDV\n", "\n", "wrote: broadband-contrasts-20250823-r2d-R2_tTransect-v3.csv\n", "20250827-r3d-R3_mainTransect BC\n", "\n", "wrote: broadband-contrasts-20250827-r3d-R3_mainTransect-v3.csv\n", "20250827-r3d-R3_opticsTransect BC\n", "\n", "wrote: broadband-contrasts-20250827-r3d-R3_opticsTransect-v3.csv\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n", "C:\\Users\\Nora Greenwood\\AppData\\Local\\Temp\\ipykernel_25084\\2353823898.py:78: RuntimeWarning: Mean of empty slice\n", " kipps_notes_merged.loc[di, 'incoming change (%)'] = (np.nanmean([incoming_diff[di],incoming_diff[di+1]])/kipps_notes_merged['incoming (milliV)'][di]*100).round(1)\n" ] } ], "source": [ "# convert data\n", "\n", "for i in range(len(kipps_dirs)):\n", " try:\n", " process_kipps(kipps_dirs[i], v3_path)\n", " except Exception as e:\n", " print('')\n", " print(kipps_dirs[i])\n", " print(f\"An unexpected error occurred: {e}\")\n", " print('')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.3" } }, "nbformat": 4, "nbformat_minor": 5 }