db = []
for rf in tqdm.tqdm_notebook(results_files):
if rf.endswith(".csv"):
loaded = ScmDataFrame(rf)
else:
loaded = ScmDataFrame(rf, sheet_name="your_data")
db.append(loaded)
db = df_append(db).timeseries().reset_index()
db["unit"] = db["unit"].apply(lambda x: x.replace(
"Dimensionless", "dimensionless") if isinstance(x, str) else x)
db = ScmDataFrame(db)
db.head()
# %%
db.filter(climatemodel="*cicero*").head()
# %%
db["climatemodel"].unique()
# %% [markdown]
# ### Minor quick fixes
# %% [markdown]
# We relabel all the ssp370-lowNTCF data to remove ambiguity.
# %%
db = db.timeseries().reset_index()
db["scenario"] = db["scenario"].apply(lambda x: "ssp370-lowNTCF-gidden"
if x == "ssp370-lowNTCF" else x)
db["scenario"] = db["scenario"].apply(lambda x: "esm-ssp370-lowNTCF-gidden"
relevant_files = [str(p) for p in relevant_files if quantile not in p]
print("Number of relevant files: {}".format(len(relevant_files)))
relevant_files
# %% [markdown]
# ### Read in all variables:
# %% jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"}
db = []
for rf in tqdm.tqdm_notebook(relevant_files):
# print(rf.endswith('sf'))
if rf.endswith(".csv"):
loaded = ScmDataFrame(rf)
else:
loaded = ScmDataFrame(rf, sheet_name="your_data")
db.append(loaded.filter(variable=variables_erf,
scenario=scenarios_fl)) # variables_of_interest))
print(db)
db = df_append(db).timeseries().reset_index()
db["unit"] = db["unit"].apply(lambda x: x.replace(
"Dimensionless", "dimensionless") if isinstance(x, str) else x)
clear_output()
db = ScmDataFrame(db)
db.head()
# %% jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"}
db[variable].unique()
# %%
db[climatemodel].unique()
# %%