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major bug fixes with indexing and transformation methods. Added featu…
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…re to calculate indicators regional totals without having to specify intermediate and final demand extesions separately
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@FDonati
FDonati committed Apr 2, 2020
1 parent 33b40c3 commit 4fe6813
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30 changes: 13 additions & 17 deletions pycirk/fundamental_operations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,7 @@ def A(inter_coef, D):
Total requirement multipliers
A = Z * D
"""
return np.matmul(inter_coef, D)
return inter_coef @ D

def L(A):
"""
Expand Down Expand Up @@ -157,7 +157,7 @@ def Z(inter_coef, D, diag_q):
Intermediates
Z = inter_coef * D * diag(q)
"""
return np.matmul(inter_coef, D) @ diag_q
return inter_coef @ D @ diag_q

class IOT:
"""
Expand All @@ -170,7 +170,7 @@ def x(Z, Y):
"""
total product output s the sum of Si and y
"""
return np.sum(np.array(Z), axis=1) + np.sum(np.array(Y), axis=1)
return np.sum(Z, axis=1) + np.sum(Y, axis=1)

def B(R, inv_diag_x):
"""
Expand All @@ -189,7 +189,7 @@ def x_IAy(L, y):
Total product ouput
x = inv(I - A) * yi
"""
return np.dot(L, y)
return L @ y

def Z(A, diag_x):
"""
Expand Down Expand Up @@ -257,7 +257,7 @@ def IOT(Z, Y, W, E, R, M):
x = Operations.IOT.x_IAy(L, y)

ver_base = Operations.verifyIOT(Z, Y, E)

return {"A": A,
"Z": Z,
"L": L,
Expand All @@ -277,17 +277,13 @@ def IOT(Z, Y, W, E, R, M):

def calculate_characterized(data):

data.Cr_E = data.Cr_E_k @ np.array(data.E)
data.Cr_M = data.Cr_M_k @ np.array(data.M)
data.Cr_R = data.Cr_R_k @ np.array(data.R)
data.Cr_W = data.Cr_W_k @ np.array(data.W)

data.Cr_EY = data.Cr_E_k @ np.array(data.EY)
data.Cr_MY = data.Cr_M_k @ np.array(data.MY)
data.Cr_RY = data.Cr_R_k @ np.array(data.RY)

data.Cr_tot_E = data.Cr_E.sum(axis=1) + data.Cr_EY.sum(axis=1)
data.Cr_tot_M = data.Cr_M.sum(axis=1) + data.Cr_MY.sum(axis=1)
data.Cr_tot_R = data.Cr_R.sum(axis=1) + data.Cr_RY.sum(axis=1)
data.Cr_E = data.Cr_E_k @ data.E
data.Cr_M = data.Cr_M_k @ data.M
data.Cr_R = data.Cr_R_k @ data.R
data.Cr_W = data.Cr_W_k @ data.W

data.Cr_EY = data.Cr_E_k @ data.EY
data.Cr_MY = data.Cr_M_k @ data.MY
data.Cr_RY = data.Cr_R_k @ data.RY

return data
49 changes: 27 additions & 22 deletions pycirk/labels.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,35 +64,40 @@ def get_unique_labels(self, dataframe_of_labels, for_units=True):
else:
organize[keys] = self.list_of_something(labels)

count = max(len(labels) for keys, labels in organize.items())
organize["count"] = count

try:
organize["count"] = len(organize["synonym"])
except KeyError:
organize["count"] = len(organize["characterization"])

return Munch(organize)

def organize_unique_labels(self, directory):

labels = self.load_labels(directory)

for l, v in labels.items():
labels[l] = Munch(v)
lbl = self.load_labels(directory)

labels = Munch(labels)

labels.products = self.get_unique_labels(labels.prod)
labels.industries = self.get_unique_labels(labels.ind)
self.product_labels = self.get_unique_labels(lbl["prod"])
try:
self.industry_labels = self.get_unique_labels(lbl["ind"])
except AttributeError:
pass

try:
self.country_labels = self.product_labels.country_code
except Exception:
pass

labels.primary = self.get_unique_labels(labels.primary)
labels.fin_dem = self.get_unique_labels(labels.fin_dem)
self.region_labels = self.product_labels.region

labels.emis = self.get_unique_labels(labels.emis, False)
labels.res = self.get_unique_labels(labels.res, False)
labels.mat = self.get_unique_labels(labels.mat, False)
labels.car_emis = self.get_unique_labels(labels.car_emis, False)
labels.car_res = self.get_unique_labels(labels.car_res, False)
labels.car_mat = self.get_unique_labels(labels.car_mat, False)
labels.car_prim = self.get_unique_labels(labels.car_prim, False)
self.W_labels = self.get_unique_labels(lbl["primary"])
self.Y_labels = self.get_unique_labels(lbl["fin_dem"])
self.E_labels = self.get_unique_labels(lbl["emis"], False)
self.R_labels = self.get_unique_labels(lbl["res"], False)
self.M_labels = self.get_unique_labels(lbl["mat"], False)
self.Cr_E_labels = self.get_unique_labels(lbl["car_emis"], False)
self.Cr_R_labels = self.get_unique_labels(lbl["car_res"], False)
self.Cr_M_labels = self.get_unique_labels(lbl["car_mat"], False)
self.Cr_W_labels = self.get_unique_labels(lbl["car_prim"], False)

return labels

def load_labels(self, directory):

Expand Down Expand Up @@ -278,7 +283,7 @@ def identify_labels(self, M_name):

attr_name = name + name_2 + "_labels"
row_labels = eval("self." + attr_name)

no_row_labs = row_labels.count
no_reg_labs = len(reg_labels)
no_col_labs = column_labels.count
Expand Down
142 changes: 19 additions & 123 deletions pycirk/make_scenarios.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ def make_counterfactuals(data, scen_no, scen_file, labels):
An object contaning a mofified IO system
"""
# set basic data and variables

print(f"Scenario {scen_no} started")
data = deepcopy(data)

x_ = ops.IOT.x(data.Z, data.Y)
Expand All @@ -65,13 +65,15 @@ def make_counterfactuals(data, scen_no, scen_file, labels):
inv_diag_x_int = np.diag(ops.inv(ops.IOT.x(data.Z, data.Y)))

A = ops.IOT.A(data.Z, inv_diag_x_int)

data.A = counterfactual(scen_file, scen_no, A, "A", labels)

data.Y = counterfactual(scen_file, scen_no, data.Y, "Y", labels)

L = ops.IOT.L(data.A)

x_new = ops.IOT.x_IAy(L, data.Y.sum(1))

diag_x_new = np.diag(x_new)

diag_yj_new = np.diag(data.Y.sum(axis=0))
Expand Down Expand Up @@ -107,13 +109,13 @@ def make_counterfactuals(data, scen_no, scen_file, labels):
data.E = counterfactual(scen_file, scen_no, data.E, "E", labels)
data.R = counterfactual(scen_file, scen_no, data.R, "R", labels)
data.M = counterfactual(scen_file, scen_no, data.M, "M", labels)

# Apply policy to final demand extension coefficient matrices
data.EY = counterfactual(scen_file, scen_no, data.EY, "EY", labels)
data.RY = counterfactual(scen_file, scen_no, data.RY, "RY", labels)
data.MY = counterfactual(scen_file, scen_no, data.MY, "MY", labels)

# print((1-np.sum(x_)/np.sum(x_new))*100)
#print((1-np.sum(x_)/np.sum(x_new))*100)
print(f"Scenario {scen_no} completed")

return data

Expand Down Expand Up @@ -147,8 +149,7 @@ def counterfactual(scen_file, scen_no, M, M_name, labels):
elif scen_no.startswith("scenario_"):
pass
else:
raise KeyError("only integer or explicit name (scenario_x)" +
"are allowed")
raise KeyError("only integer or explicit name (scenario_x) are allowed")


scenario = pd.read_excel(scen_file, sheet_name=scen_no, header=1, index=None)
Expand Down Expand Up @@ -339,7 +340,6 @@ def counterfactual_engine(M, inter_sets, subs=False, copy=False):

int4 = inter_sets["at2"]
int4 = basic_add(int3, int4)

M[np.ix_(i, g)] = int4

if subs is True:
Expand Down Expand Up @@ -422,7 +422,7 @@ def make_new(filtered_changes, M, M_name, labels):
try:
change_type = entry.change_type
ide = entry.identifier # used during debugging

# Collecting the specified coordinates for the intevention
# coordinates for region and category
# Row items (i) => Supplied category or extension category
Expand All @@ -433,11 +433,14 @@ def make_new(filtered_changes, M, M_name, labels):
cat_d = sing_pos(entry.cat_d, column_labels)
# Identify coordinates
orig_coor = coord(cat_o, reg_o, no_reg_labs, no_row_labs)
#print(f"row\n ide: {ide}, row: {entry.reg_o}, {entry.cat_o}, {orig_coor}")
dest_coor = coord(cat_d, reg_d, no_reg_labs, no_col_labs)
#print(f"columns\n ide: {ide}, column: {entry.reg_d}, {entry.cat_d}, {dest_coor}")

# organize main changes
kt1 = {"kt": entry.kt1, "kp": entry.kp1}
kt2 = {"kt": entry.kt2, "kp": entry.kp2}

intervention = {"change_type": change_type,
"ide": ide,
"i": orig_coor,
Expand All @@ -447,32 +450,32 @@ def make_new(filtered_changes, M, M_name, labels):
"at1": entry.at1,
"at2": entry.at2,
}

substitution = False
copy = False

# the following is only relevant for susbtitution
if "x" in [entry.Sub, entry.Copy]:

sub_reg_o = sing_pos(entry.reg_o_sc, reg_labels)
sub_cat_o = sing_pos(entry.cat_o_sc, row_labels)

# Column items => Consumption / manufacturing activity
sub_reg_d = sing_pos(entry.reg_d_sc, reg_labels)
sub_cat_d = sing_pos(entry.cat_d_sc, column_labels)

# Translate coordinates from str to numerical position
sub_orig_coor = coord(sub_cat_o, sub_reg_o, no_reg_labs, no_row_labs)
sub_dest_coor = coord(sub_cat_d, sub_reg_d, no_reg_labs, no_col_labs)

intervention["swk"] = entry.swk
intervention["i1"] = sub_orig_coor
intervention["g1"] = sub_dest_coor
intervention["sk1"] = entry.sk1
intervention["sk2"] = entry.sk2
intervention["sk3"] = entry.sk3
intervention["sk4"] = entry.sk4

if entry.Copy == "x":
copy = True
elif entry.Sub == "x":
Expand All @@ -481,112 +484,5 @@ def make_new(filtered_changes, M, M_name, labels):
raise ValueError(f"Check in this entry for potential coordinate errors in your scenario settings:\n{entry} ")

M = counterfactual_engine(M, intervention, substitution, copy)

return M

# =============================================================================
# =============================================================================
# # Here I put work that I started but I still need to finish
# =============================================================================
# =============================================================================
# =============================================================================
#
#
# def make_counterfactuals_SUT(data, scen_no, scen_file, labels):
# """
# Calculate all the counterfactual SUT matrices
#
# Parameters
# ----------
# data : obj
# An object containing all necessary matrices of the SUT system
#
# scen_no : int
# the identification number of the scenario to reference in scen_file
#
# scen_file : str
# the directory where the scenarios.xlsx file is store
#
# labels : obj
# an object containing all labels for the SUT matrices
#
# Outputs
# -------
# An object contaning a mofified SUT system
# """
#
# met = ops.PxP_ITA_MSC
#
# w = ops.IOT.B(data.W, data.inv_diag_g) # Primary input coef
# e = ops.IOT.B(data.E, data.inv_diag_g) # emissions extension coef
# r = ops.IOT.B(data.R, data.inv_diag_g) # Resources extension coef
# m = ops.IOT.B(data.M, data.inv_diag_g) # Materials extension coef
# S = met.S(data.U, data.inv_diag_g) # industry coefficients for intermediate use table
#
# # Start first from a supply approach
# # Supply matrix counterfactual
# data.V = counterfactual(scen_file, scen_no, data.V, "V", labels)
# # new total industry output
# g1 = np.sum(data.V, axis=0)
# # industry use coefficients counterfactual
# S_ = counterfactual(scen_file, scen_no, S, "S", labels)
#
# data.U = counterfactual(scen_file, scen_no, S_ @ np.diag(g1), "U", labels) # industry use transactions counterfactual
#
# W_ = np.array(ops.IOT.R(w, np.diag(g1)))
#
# g2 = np.array(W_[:9].sum(0)) + data.U.sum(0) # recalculate total industry output
#
# g_dif = np.multiply(g2, ops.inv(g1))*100 # calculate the difference between original and new total industry input
#
# # print([round((1-l)*100,4) for l in g_dif if 1-l>.5e-3 and l!=0])
# q2 = np.sum(data.U, axis=1) + np.sum(data.Y, axis=1)
#
# # updating the supply table to match the new total industry input
# D = met.D(data.V, np.diag(ops.inv(data.V.sum(1))))
# data.V = D @ np.diag(q2)
#
# q1 = np.sum(data.V, axis=0) # total product output
#
# q_dif = np.multiply(q2, ops.inv(q1))
#
# g1 = np.sum(data.V, axis=1)
#
# data.E = met.R(e, np.diag(x))
#
# data.R = met.R(r, np.diag(x))
#
# data.M = met.R(m, np.diag(x))
#
#
# return(IOT)
# def balancing_operation(V, U, Y, W):
# """
# Re-balancing of supply-use tables after data changes
#
# Parameters
# ----------
# V (supply) : numpy.array
#
# U (use) : numpy.array
#
# Y (final_demand) : numpy.array
#
# W (primary_inputs) : numpy.array
#
# Output
# ------
# output : dict
#
# It outputs a dictionary containing a re-balanced supply-use tables system
# where:
# V = supply table
#
# U = use table
#
# Y = final demand
#
# W = primary inputs
#
# """
# =============================================================================
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