class CitrineDataRetrievalTest(unittest.TestCase):
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
def test_get_data(self):
pifs_lst = self.cdr.get_api_data(formula="W",
data_type='EXPERIMENTAL',
max_results=10)
self.assertEqual(len(pifs_lst), 10)
df = self.cdr.get_dataframe(criteria={
'formula': 'W',
'data_type': 'EXPERIMENTAL',
'max_results': 10
},
print_properties_options=False)
self.assertEqual(df.shape[0], 10)
def test_multiple_items_in_list(self):
df = self.cdr.get_dataframe(criteria={
'data_set_id': 114192,
'max_results': 102
},
print_properties_options=False)
self.assertEqual(df.shape[0], 102)
test_cols = {
"Thermal conductivity_5-conditions", "Condition_1",
"Thermal conductivity_10"
}
self.assertTrue(test_cols < set(df.columns))
def plot_expt_compt_band_gaps(citrine_api_key, limit=0):
"""
Pulls experimental band gaps from Citrine (w/o dataset limitations) and
evaluate the DFT computed band gaps (data from materialsproject.org)
in xy scatter plot. To compare the right values, we pick the computed
band gaps calculated for a chemical formula that has the lowest energy
above hull (the most stable structure).
Args:
citrine_api_key (str): Your Citrine API key for getting data. Don't have
a Citrine account? Visit https://citrine.io/
limit (int): limit the number of entries (0 means no limit)
Returns:
plotly plots in "offline" mode poped in the default browser.
"""
# pull experimental band gaps from Citrine
cdr = CitrineDataRetrieval(api_key=citrine_api_key)
cols = ['chemicalFormula', 'Band gap']
df_ct = cdr.get_dataframe(prop='band gap', data_type='experimental',
show_columns=cols, max_results=limit).rename(
columns={'chemicalFormula': 'Formula', 'Band gap': 'Expt. gap'})
df_ct = df_ct[df_ct['Formula'] != 'In1p1'] # p1 not recognized in Composition
df_ct = df_ct.dropna() # null band gaps cause problem when plotting residuals
df_ct['Formula'] = df_ct['Formula'].transform(
lambda x: Composition(x).get_reduced_formula_and_factor()[0])
# pull computational band gaps from the Materials Project
df = MPDataRetrieval().get_dataframe(
criteria={'pretty_formula': {'$in': list(df_ct['Formula'].values)}},
properties=['pretty_formula', 'material_id', 'band_gap', 'e_above_hull'],
index_mpid=False).rename(
columns={'pretty_formula': 'Formula', 'band_gap': 'MP computed gap',
'material_id': 'mpid'})
# pick the most stable structure
df_mp = df.loc[df.groupby("Formula")["e_above_hull"].idxmin()]
df_final = df_ct.merge(df_mp, on='Formula').drop(
'e_above_hull', axis=1).set_index('mpid')
pf = PlotlyFig(df_final, x_title='Experimental band gap (eV)',
y_title='Computed Band Gap (eV)',
filename='band_gaps')
# computed vs. experimental band gap:
pf.xy([
('Expt. gap', 'MP computed gap'),
([0, 12], [0, 12])
],
lines=[{}, {'color': 'black', 'dash': 'dash'}],
labels=df_final.index, modes=['markers', 'lines'],
names=['Computed vs. expt.', 'Expt. gap'])
# residual:
residuals = df_final['MP computed gap']-df_final['Expt. gap'].astype(float)
pf.set_arguments(x_title='Experimental band gap (eV)',
y_title='Residual (Computed - Expt.) Band Gap (eV)',
filename='band_gap_residuals')
pf.xy(('Expt. gap', residuals), labels = df_final.index)
class CitrineDataRetrievalTest(unittest.TestCase):
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
def test_get_data(self):
df = self.cdr.get_dataframe(formula="W", data_type='EXPERIMENTAL', max_results=10)
def _apply_query(self, sorted: Optional[bool]) -> pd.DataFrame:
cdr = CitrineDataRetrieval(api_key=self.API_KEY)
criteria = {"data_type": "EXPERIMENTAL"}
properties = ['Band gap']
common_fields = [
"uid", "chemicalFormula", "references", "Crystallinity",
"Structure", "Crystal structure", "uid"
]
df = cdr.get_dataframe(criteria=criteria,
properties=properties,
common_fields=common_fields)
LOG.info("Writing to raw data...")
df.to_pickle(self.raw_data_path)
return df
def plot_thermoelectrics(citrine_api_key, limit=0):
"""
Scatter plot of the properties of thermoelectric materials based on the data
available in http://www.mrl.ucsb.edu:8080/datamine/thermoelectric.jsp
The data is extracted via Citrine data retrieval tools. The dataset
id on Citrine is 150557
Args:
citrine_api_key (str): Your Citrine API key for getting data. Don't have
a Citrine account? Visit https://citrine.io/
limit (int): limit the number of entries (0 means no limit)
Returns:
plotly plot in "offline" mode popped in the default browser.
"""
cdr = CitrineDataRetrieval(api_key=citrine_api_key)
cols = [
'Electrical resistivity', 'Seebeck coefficient',
'Thermal conductivity', 'Thermoelectric figure of merit (zT)'
]
df_te = cdr.get_dataframe(
criteria={
'data_type': 'experimental',
'data_set_id': 150557,
'max_results': limit
},
properties=['Seebeck coefficient'],
secondary_fields=True,
)
df_te[cols] = df_te[cols].astype(float)
df_te = df_te[(df_te['Electrical resistivity'] > 5e-4) & \
(df_te['Electrical resistivity'] < 0.1)]
df_te = df_te[abs(df_te['Seebeck coefficient']) < 500].rename(
columns={'Thermoelectric figure of merit (zT)': 'zT'})
print(df_te.head())
pf = PlotlyFig(df_te,
x_scale='log',
fontfamily='Times New Roman',
hovercolor='white',
x_title='Electrical Resistivity (cm/S)',
y_title='Seebeck Coefficient (uV/K)',
colorbar_title='Thermal Conductivity (W/m.K)',
filename='thermoelectrics.html')
pf.xy(('Electrical resistivity', 'Seebeck coefficient'),
labels=['chemicalFormula', 'Preparation method', 'Crystallinity'],
sizes='zT',
colors='Thermal conductivity',
color_range=[0, 5])
class CitrineDataRetrievalTest(unittest.TestCase):
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
def test_get_data(self):
df = self.cdr.get_dataframe(formula="W",
data_type='EXPERIMENTAL',
max_results=10)
def plot_thermoelectrics(citrine_api_key, limit=0):
"""
Scatter plot of the properties of thermoelectric materials based on the data
available in http://www.mrl.ucsb.edu:8080/datamine/thermoelectric.jsp
The data is extracted via Citrine data retrieval tools. The dataset
id on Citrine is 150557
Args:
citrine_api_key (str): Your Citrine API key for getting data. Don't have
a Citrine account? Visit https://citrine.io/
limit (int): limit the number of entries (0 means no limit)
Returns:
plotly plot in "offline" mode popped in the default browser.
"""
cdr = CitrineDataRetrieval(api_key=citrine_api_key)
cols = ['Electrical resistivity', 'Seebeck coefficient',
'Thermal conductivity', 'Thermoelectric figure of merit (zT)']
df_te = cdr.get_dataframe(criteria={'data_type': 'experimental',
'data_set_id': 150557,
'max_results': limit},
properties=['Seebeck coefficient'],
secondary_fields=True,
)
df_te[cols] = df_te[cols].astype(float)
df_te = df_te[(df_te['Electrical resistivity'] > 5e-4) & \
(df_te['Electrical resistivity'] < 0.1)]
df_te = df_te[abs(df_te['Seebeck coefficient']) < 500].rename(
columns={'Thermoelectric figure of merit (zT)': 'zT'})
print(df_te.head())
pf = PlotlyFig(df_te,
x_scale='log',
fontfamily='Times New Roman',
hovercolor='white',
x_title='Electrical Resistivity (cm/S)',
y_title='Seebeck Coefficient (uV/K)',
colorbar_title='Thermal Conductivity (W/m.K)',
filename='thermoelectrics.html')
pf.xy(('Electrical resistivity', 'Seebeck coefficient'),
labels=['chemicalFormula', 'Preparation method', 'Crystallinity'],
sizes='zT',
colors='Thermal conductivity',
color_range=[0, 5])
class CitrineDataRetrievalTest(unittest.TestCase):
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
def test_get_data(self):
pifs_lst = self.cdr.get_api_data(formula="W",
data_type='EXPERIMENTAL',
max_results=10)
df = self.cdr.get_dataframe(pifs_lst)
assert df.shape[0] == 10
def test_mutiple_items_in_list(self):
pifs_lst = self.cdr.get_api_data(data_set_id=114192, max_results=102)
df = self.cdr.get_dataframe(pifs_lst)
assert df.shape[0] == 102
for col in [
"Thermal conductivity_5-conditions", "Condition_1",
"Thermal conductivity_10"
]:
assert col in df.columns
class CitrineDataRetrievalTest(unittest.TestCase):
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
def test_get_data(self):
pifs_lst = self.cdr.get_data(formula="W", data_type='EXPERIMENTAL',
max_results=10)
self.assertEqual(len(pifs_lst), 10)
df = self.cdr.get_dataframe(criteria={'formula':'W',
'data_type':'EXPERIMENTAL',
'max_results':10},
print_properties_options=False)
self.assertEqual(df.shape[0], 10)
def test_multiple_items_in_list(self):
df = self.cdr.get_dataframe(criteria={'data_set_id': 114192,
'max_results':102},
print_properties_options=False)
self.assertEqual(df.shape[0], 102)
test_cols = {"Thermal conductivity_5-conditions", "Condition_1",
"Thermal conductivity_10"}
self.assertTrue(test_cols < set(df.columns))
#这是一个matminer的测试性项目，用于比较不同数据库数据
from matminer.data_retrieval.retrieve_MP import MPDataRetrieval
from matminer.data_retrieval.retrieve_Citrine import CitrineDataRetrieval
import pandas as pd
import numpy as np
#首先设置pandas的显示设置，保证都能显示出来
pd.set_option('display.width', 1000)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
c = CitrineDataRetrieval(api_key='QgqmY9PPathNDgu6gJjnTQtt')
df = c.get_dataframe(criteria={
'data_type': 'EXPERIMENTAL',
'max_results': 100
},
properties=['Band gap', 'Temperature'],
common_fields=['chemicalFormula'])
df.to_csv('duibi.csv')
df.rename(columns={'Band gap': 'Experimnetal band gap'}, inplace=True)
df.head()
#然后针对每种组成，从mp数据库中计算的带隙，找到对应的最稳定结构的值
from pymatgen import MPRester, Composition
mpr = MPRester()
def get_mp_bandgap(formula):
#这个函数的作用是给定一定的化学组成，返回稳定状态的带隙
#而mo数据库需要用到interger的化学式
from matminer.featurizers.composition import ElementProperty
# In[2]:
# Retrieve NIST SCD dataset from Citrine using matminer.
# The data will be stored in the df DataFrame.
first_retrieve = False #change it to indicate first time retrieve dataset or not
from matminer.data_retrieval.retrieve_Citrine import CitrineDataRetrieval
from os import environ
if first_retrieve:
api_key = environ['CITRINATION_API_KEY'] # insert your api key here
c = CitrineDataRetrieval(api_key=api_key)
df = c.get_dataframe(criteria={'data_set_id': '151803'})
# Save downloaded dataset
df.to_csv('NIST_CeramicDataSet.csv')
df.to_pickle('NIST_CeramicDataSet.pkl')
else:
df = pd.read_pickle('NIST_CeramicDataSet.pkl')
# In[3]:
# Get the number of samples and number of features of the dataset
df.shape
# In[4]:
df_sym_col[cmp['symbol']] = cmp['df_name']
# Solve each row of the dataframe
for idx, row in df.iterrows():
eqns_tosolve = eqns[:]
# add equation of symbol and its values from provided df
for col in df_sym_col:
eqns_tosolve.append(sp.Eq(col, row[df_sym_col[col]]))
soln = sp.solve(eqns_tosolve)
if soln:
print idx, eqns_tosolve, soln
df.loc[idx, "Calculated Poisson's ratio"] = round(
soln[0][sp.S('nu')], 2)
return df
if __name__ == '__main__':
pd.set_option('display.width', 1000)
# df1 = pd.read_pickle('39135_BMG.pkl')
df = CitrineDataRetrieval().get_dataframe(data_set_id=150628,
max_results=50)
df = df.groupby(['chemicalFormula'], as_index=False).sum()
print df
new_df = decorate_dataframe(df)
print new_df
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
def setUp(self):
self.cdr = CitrineDataRetrieval(citrine_key)
if cmp['catalog_name'] in mech_props:
eqns.append(sp.Eq(mech_props[cmp['catalog_name']]().equation()))
df_sym_col[cmp['symbol']] = cmp['df_name']
# Solve each row of the dataframe
for idx, row in df.iterrows():
eqns_tosolve = eqns[:]
# add equation of symbol and its values from provided df
for col in df_sym_col:
eqns_tosolve.append(sp.Eq(col, row[df_sym_col[col]]))
soln = sp.solve(eqns_tosolve)
if soln:
print(idx, eqns_tosolve, soln)
df.loc[idx, "Calculated Poisson's ratio"] = round(soln[0][sp.S('nu')], 2)
return df
if __name__ == '__main__':
pd.set_option('display.width', 1000)
# df1 = pd.read_pickle('39135_BMG.pkl')
df = CitrineDataRetrieval().get_dataframe(data_set_id=150628, max_results=50)
df = df.groupby(['chemicalFormula'], as_index=False).sum()
print(df)
new_df = decorate_dataframe(df)
print(new_df)
def plot_expt_compt_band_gaps(citrine_api_key, limit=0):
"""
Pulls experimental band gaps from Citrine (w/o dataset limitations) and
evaluate the DFT computed band gaps (data from materialsproject.org)
in xy scatter plot. To compare the right values, we pick the computed
band gaps calculated for a chemical formula that has the lowest energy
above hull (the most stable structure).
Args:
citrine_api_key (str): Your Citrine API key for getting data. Don't have
a Citrine account? Visit https://citrine.io/
limit (int): limit the number of entries (0 means no limit)
Returns:
plotly plots in "offline" mode popped in the default browser.
"""
# pull experimental band gaps from Citrine
cdr = CitrineDataRetrieval(api_key=citrine_api_key)
cols = ['chemicalFormula', 'Band gap']
df_ct = cdr.get_dataframe(criteria={'data_type':'experimental',
'max_results':limit},
secondary_fields=True,
properties=['Band gap'])
df_ct = df_ct[cols].rename(columns={'chemicalFormula': 'Formula',
'Band gap': 'Expt. gap'})
df_ct = df_ct[df_ct['Formula'] != 'In1p1'] # p1 not recognized in Composition
df_ct = df_ct.dropna() # null band gaps cause problem when plotting residuals
df_ct['Formula'] = df_ct['Formula'].transform(
lambda x: Composition(x).get_reduced_formula_and_factor()[0])
# pull computational band gaps from the Materials Project
df = MPDataRetrieval().get_dataframe(
criteria={'pretty_formula': {'$in': list(df_ct['Formula'].values)}},
properties=['pretty_formula', 'material_id', 'band_gap', 'e_above_hull'],
index_mpid=False).rename(
columns={'pretty_formula': 'Formula', 'band_gap': 'MP computed gap',
'material_id': 'mpid'})
# pick the most stable structure
df_mp = df.loc[df.groupby("Formula")["e_above_hull"].idxmin()]
df_final = df_ct.merge(df_mp, on='Formula').drop(
'e_above_hull', axis=1).set_index('mpid')
pf = PlotlyFig(df_final, x_title='Experimental band gap (eV)',
y_title='Computed Band Gap (eV)',
filename='band_gaps')
# computed vs. experimental band gap:
pf.xy([
('Expt. gap', 'MP computed gap'),
([0, 12], [0, 12])
],
lines=[{}, {'color': 'black', 'dash': 'dash'}],
labels=['Formula', df_final.index],
modes=['markers', 'lines'],
names=['Computed vs. expt.', 'Expt. gap'])
# residual:
residuals = df_final['MP computed gap']-df_final['Expt. gap'].astype(float)
pf.set_arguments(x_title='Experimental band gap (eV)',
y_title='Residual (Computed - Expt.) Band Gap (eV)',
filename='band_gap_residuals')
pf.xy(('Expt. gap', residuals),
labels = ['Formula', df_final.index])
}
}, # to limit the number of hits for the sake of time
properties=[
"elasticity.K_VRH", "elasticity.G_VRH", "pretty_formula",
"e_above_hull", "bandstructure", "dos"
])
print("Pb,Te(K_VRH,G_VRH,pretty_formula,e_above_hull,bandstructure,dos):")
print(df.head())
mpid = 'mp-20740'
idx = df.index[df.index == mpid][0]
plt = BSDOSPlotter().get_plot(bs=df.loc[idx, 'bandstructure'],
dos=df.loc[idx, 'dos'])
plt.savefig('mp-20740.png')
cdr = CitrineDataRetrieval()
df_OH = cdr.get_dataframe(criteria={},
properties=['adsorption energy of OH'],
secondary_fields=True)
df_O = cdr.get_dataframe(criteria={},
properties=['adsorption energy of O'],
secondary_fields=True)
print('adsorption energy of OH\n')
print(df_OH.head())
print('adsorption energy of O\n')
print(df_O.head())
mdf_dr = MDFDataRetrieval(anonymous=True)
df = mdf_dr.get_dataframe(criteria={
