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dash_utils.py
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dash_utils.py
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# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------------------------------------------------------
# Climate information dashboard.
#
# Utilities.
#
# Contributors:
# 1. [email protected]
# (C) 2021-2022 Ouranos Inc., Canada
# ----------------------------------------------------------------------------------------------------------------------
# External libraries.
import numpy as np
import os
import pandas as pd
import warnings
from typing import List, Optional, Union
# Dashboard libraries.
import cl_auth
import cl_gd
from cl_constant import const as c
from cl_context import cntx
warnings.filterwarnings("ignore")
def load_data(
mode: Optional[str] = ""
) -> Union[pd.DataFrame, None]:
"""
--------------------------------------------------------------------------------------------------------------------
Load data.
Parameters
----------
mode: Optional[str]
Mode.
ts|ts_bias: mode = {"rcp", "sim"}
cycle: mode = {"MS", "D"}
taylor: mode = {"REGRID", "QQMAP"}
Returns
-------
pd.DataFrame
Dataframe.
Structure of data directory:
+-- ouranos.png
|
+-- <project_code>
|
+-- cycle_d
| |
| +-- <vi_code> ex: pr
| |
| +-- <hor_code> ex: 2021-2050
| |
| +-- <vi_name>_<RCM>_<domain>_<GCM>_<rcp_code>_<hor_code.start>_<hor_code.end>_daily.csv
| ex: pr_HIRHAM5_AFR-44_ICHEC-EC-EARTH_rcp45_2021_2050_daily.csv
| columns: day,mean,min,max,var
| ex: 1,7.244016214648855e-06,-3.332919823397555e-08,0.00021562011394048503,pr
|
+-- cycle_m
| |
| +-- <vi_code> ex: pr
| |
| +-- <hor_code> ex: 2021-2050
| |
| +-- <vi_name>_<RCM>_<domain>_<GCM>_<rcp_code>_<hor_code.start>_<hor_code.end>_monthly.csv
| ex: pr_HIRHAM5_AFR-44_ICHEC-EC-EARTH_rcp45_2021_2050_monthly.csv
| columns: year,1,2,3,4,5,6,7,8,9,10,11,12
| ex: 2021,0.003668,0.000196,0.014072,1.344051,3.065682,28.971143,...
+-- ts
| |
| +-- <vi_code>_<mode>.csv
| ex: pr_rcp.csv
| columns: year,ref,rcp45_moy,rcp45_min,rcp45_max,rcp85_moy,rcp85_min,rcp85_max
| ex: 1981,545.37,475.40,358.05,602.77,469.10,350.61,601.30
|
+-- ts_bias
| |
| +- <vi_code> ex: pr
| |
| +-- <vi_code>_<mode>*.csv
| ex: pr_rcp.csv
| ex: pr_sim_delta.csv
| columns: year,ref,rcp45_moy,rcp45_min,rcp45_max,rcp85_moy,rcp85_min,rcp85_max
| ex: 1981,545.37,475.40,358.05,602.77,469.10,350.61,601.30
|
+-- tbl
| |
| +-- <vi_code>/<vi_name>.csv
| ex: pr.csv
| columns: stn, var, rcp, hor, stat, centile, val
| ex: era5_land, pr, rcp45, 2021-2050, mean, -1, 486.53
+-- map
|
+-- <vi_code> ex: pr
| |
| +-- <hor_code> ex: 2021-2050
| |
| +-- <vi_name>_<rcp_code>_<hor_code.start>_<hor_code.end>_<stat_code>.csv
| ex: pr_rcp45_2021_2050_mean.csv
| ex: pr_rcp45_2021_2050_c010.csv
| ex: pr_rcp45_2021_2050_c090_delta.csv
| columns: longitude,latitude,pr
| ex: -17.30,14.8,226.06
|
+-- boundaries.geojson
--------------------------------------------------------------------------------------------------------------------
"""
# Load data.
p = ""
project_code = cntx.project.code if cntx.project is not None else ""
view_code = cntx.view.code if cntx.view is not None else ""
if view_code == c.VIEW_CLUSTER:
view_code = c.VIEW_TS
delta_code = cntx.delta.code if cntx.delta is not None else "False"
vi_code = cntx.varidx.code if cntx.varidx is not None else ""
vi_name = cntx.varidx.name if cntx.varidx is not None else ""
vi_precision = cntx.varidx.precision if cntx.varidx is not None else 0
hor_code = cntx.hor.code if cntx.hor is not None else ""
rcp_code = cntx.rcp.code if cntx.rcp is not None else ""
stat_code = cntx.stat.code if cntx.stat is not None else ""
sim_code = cntx.sim.code if cntx.sim is not None else ""
if view_code == c.VIEW_TBL:
p = "<view_code>/<vi_code>/<vi_name>.csv"
p = p.replace("<view_code>", view_code)
p = p.replace("<vi_code>", vi_code)
p = p.replace("<vi_name>", vi_name)
elif view_code in [c.VIEW_TS, c.VIEW_TS_BIAS]:
p = "<view_code>/<vi_code>/<vi_name>_<mode>_<delta>.csv"
p = p.replace("_<mode>", "_" + mode)
p = p.replace("<view_code>", view_code)
p = p.replace("<vi_code>", vi_code)
p = p.replace("<vi_name>", vi_name)
p = p.replace("_<delta>", "" if delta_code == "False" else "_delta")
elif view_code == c.VIEW_MAP:
p = "<view_code>/<vi_code>/<hor_code>/*<rcp_code>*<stat>_<delta>.csv"
p = p.replace("<view_code>", view_code)
p = p.replace("<vi_code>", vi_code)
p = p.replace("<hor_code>", hor_code)
p = p.replace("<rcp_code>", rcp_code)
p = p.replace("<stat>", stat_code)
p = p.replace("_<delta>", "" if delta_code == "False" else "_delta")
elif c.VIEW_CYCLE in view_code:
p = "<view_code>/<vi_code>/<hor_code>/*<sim_code>*<rcp_code>*.csv"
view_code += "_" + mode.lower()
p = p.replace("<view_code>", view_code)
p = p.replace("<vi_code>", vi_code)
p = p.replace("<hor_code>", hor_code)
p = p.replace("<sim_code>", sim_code)
if sim_code != "":
p = p.replace("<rcp_code>", "")
elif rcp_code == "":
p = p.replace("<rcp_code>", "*")
elif view_code == c.VIEW_TAYLOR:
p = "<view_code>/<vi_code>/<vi_name><mode>.csv"
p = p.replace("<view_code>", view_code)
p = p.replace("<vi_code>", vi_code)
p = p.replace("<vi_name>", vi_name)
p = p.replace("<mode>", "_" + mode if mode != "" else "")
# Base directory.
p_base = str(cl_auth.path(project_code))
# Select the first path.
if cntx.project.drive is None:
p = project_code + "/" + p
if (view_code == c.VIEW_MAP) or (c.VIEW_CYCLE in view_code):
p_l = list(cntx.files(p)[cl_gd.PROP_PATH])
if len(p_l) > 0:
p = p_l[0]
p = p_base + "/" + p
else:
df_filter = cntx.files(project_code + "/" + p)
if len(df_filter) > 0:
p = list(df_filter[cl_gd.PROP_ITEM_ID])[0]
# Load file.
df = None
if cntx.project.drive is None:
if os.path.exists(p):
df = pd.read_csv(p)
else:
if p != "":
df = pd.DataFrame(cl_gd.GoogleDrive(cntx.project.drive).load_csv(file_id=p))
# Round values.
if df is not None:
n_dec = vi_precision
if (view_code in [c.VIEW_TS, c.VIEW_TS_BIAS]) or (c.VIEW_CYCLE in view_code):
for col in df.select_dtypes("float64").columns:
df.loc[:, col] = df.copy()[col].round(n_dec).to_numpy()
elif view_code != c.VIEW_TAYLOR:
df["val"] = df["val"].round(decimals=n_dec)
return df
def calc_range(
centile_as_str_l: List[str]
) -> List[float]:
"""
--------------------------------------------------------------------------------------------------------------------
Extract the minimum and maximum values, considering all the maps for a single variable.
Parameters
----------
centile_as_str_l: List[str]
Lower an upper centiles as strings, e.g. ["c010", "c090"].
Returns
-------
List[float]
Minimum and maximum values.
--------------------------------------------------------------------------------------------------------------------
"""
min_val, max_val = np.nan, np.nan
# Codes.
project_code = cntx.project.code if cntx.project is not None else ""
view_code = cntx.view.code if cntx.view is not None else ""
vi_code = cntx.varidx.code if cntx.varidx is not None else ""
vi_name = cntx.varidx.name if cntx.varidx is not None else ""
delta_code = cntx.delta.code if cntx.delta is not None else "False"
# Base directory.
p_base = str(cl_auth.path(project_code))
if view_code == c.VIEW_MAP:
# Get centiles.
centile_lower_as_str, centile_upper_as_str = "", ""
if len(centile_as_str_l) >= 1:
centile_lower_as_str = centile_as_str_l[0]
centile_upper_as_str = centile_as_str_l[len(centile_as_str_l) - 1]
# Determine path or file ID (reference file).
p_ref = "<view>/<vi_code>/*/<vi_name>_ref*_mean.csv"
p_ref = p_ref.replace("<view>", view_code)
p_ref = p_ref.replace("<vi_code>", vi_code)
p_ref = p_ref.replace("<vi_name>", vi_name)
# Determine paths or file IDs (RCP files).
p_rcp = "<view>/<vi_code>/*/<vi_name>_rcp*_<centile>_<delta>.csv"
p_rcp = p_rcp.replace("<view>", view_code)
p_rcp = p_rcp.replace("<vi_code>", vi_code)
p_rcp = p_rcp.replace("<vi_name>", vi_name)
p_rcp = p_rcp.replace("_<delta>", "" if delta_code == "False" else "_delta")
p_rcp_centile_lower = [p_rcp.replace("<centile>", centile_lower_as_str)]
p_rcp_centile_upper = [p_rcp.replace("<centile>", centile_upper_as_str)]
pattern_l = p_rcp_centile_lower + p_rcp_centile_upper
if delta_code == "False":
pattern_l = [p_ref] + pattern_l
# Loop through patterns.
for pattern_i in pattern_l:
# List CSV paths.
df_filter = cntx.files(project_code + "/" + pattern_i)
p_l = list(df_filter[cl_gd.PROP_PATH])\
if cntx.project.drive is None else list(df_filter[cl_gd.PROP_ITEM_ID])
# Loop throught paths.
for p_j in p_l:
# Read CSV file.
df = None
if cntx.project.drive is None:
if os.path.exists(p_base + p_j):
df = pd.read_csv(p_base + p_j)
else:
df = pd.DataFrame(cl_gd.GoogleDrive(cntx.project.drive).load_csv(file_id=p_j))
# Update range.
if len(df) > 0:
min_vals = list(df["val"]) + [min_val]
max_vals = list(df["val"]) + [max_val]
min_val = np.nanmin(min_vals)
max_val = np.nanmax(max_vals)
return [min_val, max_val]
def ref_val(
) -> str:
"""
--------------------------------------------------------------------------------------------------------------------
Get the reference value.
Returns
-------
str
Reference value and unit.
--------------------------------------------------------------------------------------------------------------------
"""
df = None
val = ""
# Extract from table.
if cntx.view.code == c.VIEW_TBL:
df = pd.DataFrame(load_data())
val = df[df["rcp"] == c.REF]["val"][0]
# Extract from time series.
elif cntx.view.code in [c.VIEW_TS, c.VIEW_TS_BIAS]:
df = pd.DataFrame(load_data("rcp"))
val = np.nanmean(df[c.REF])
# Adjust precision and units.
if df is not None:
val = round_values(val, cntx.varidx.precision)
unit = cntx.varidx.unit
if unit != "°C":
val += " "
val += unit
return val
def get_shared_sims(
p_l: Optional[List[str]] = None
) -> List[str]:
"""
--------------------------------------------------------------------------------------------------------------------
Get the simulations that are shared between multiple variables.
Parameters
----------
p_l: Optional[List[str]]
Path.
Returns
-------
Listr[str]
Simulations that are shared between multiple variables.
--------------------------------------------------------------------------------------------------------------------
"""
rcp_code = cntx.rcp.code if cntx.rcp is not None else ""
# List simulations associated with each variable, and put them into an array.
arr_sim_l = []
for i in range(cntx.varidxs.count):
cntx.varidx = cntx.varidxs.items[i]
if cntx.varidx.is_var:
if p_l is None:
sim_l = pd.DataFrame(load_data("sim")).columns[2:]
else:
sim_l = pd.read_csv(p_l[i]).columns[2:]
arr_sim_l.append(sim_l)
# Identify the simulations that are available for all variables.
sim_l = []
for sim in arr_sim_l[0]:
available = True
for i in range(1, len(arr_sim_l)):
if sim not in arr_sim_l[i]:
available = False
break
if available and ((rcp_code == c.RCPXX) or ((rcp_code != c.RCPXX) and (rcp_code in sim))):
sim_l.append(sim)
return sim_l
def round_values(
val_l: Union[float, List[float]],
n_dec: int
) -> Union[str, List[str]]:
"""
--------------------------------------------------------------------------------------------------------------------
Round values.
Parameters
----------
val_l: Union[float, List[float]]
Value or list of values.
n_dec: int
Number of decimals.
Returns
-------
Union[str, List[str]]
Rounded values.
--------------------------------------------------------------------------------------------------------------------
"""
val_str_l = []
# Transform into a list.
if not (isinstance(val_l, List) or isinstance(val_l, pd.Series)):
val_rounded_l = [val_l]
else:
val_rounded_l = val_l
# Round each value in the list.
for i in range(len(val_rounded_l)):
val = val_rounded_l[i]
if np.isnan(float(val)):
val_str_l.append("nan")
elif n_dec == 0:
val_str_l.append(str(round(float(val))))
else:
val_str_l.append(str("{:." + str(n_dec) + "f}").format(float(val)))
# Extract value if the input is not a list.
if not (isinstance(val_l, List) or isinstance(val_l, pd.Series)):
val_str_l = val_str_l[0]
return val_str_l